The synthetic aperture radar (SAR) mounted on unmanned aerial vehicles (UAVs) can efficiently and rapidly acquire high-resolution geographic remote sensing data. However, the limited payload capacity and power consumption of UAVs present a challenge for the application of traditional SAR real-time imaging processing platforms based on DSP or GPU. In this paper, a real-time processor that utilizes the Xilinx Zynq Ultrascale+ZU9EG MPSoC is proposed and successfully applied to a W-band Mini-SAR system. Several essential technologies, including SAR data preprocessing, high-energy-efficient real-time processing, and lightweight motion compensation, are utilized to develop the real-time processing Mini-SAR for use in small UAVs. The total weight of the entire system is only 724 grams, and its power consumption is 38.3 watts. During the UAV flight test, the system is capable of producing SAR images with a resolution of 0.1 meters at a size of 4K x 4K in just 4.5 seconds. The discrepancy between the imaging results and the ground post-processing is less than 10^-4.

The dual-station Interferometric Synthetic Aperture Radar (InSAR) system can overcome the physical limitations imposed by the baseline of single-station dual-antenna InSAR. It provides greater flexibility and can enhance elevation measurement accuracy through a well-designed baseline configuration.Unmanned aerial vehicles (UAV) equipped with dual-station InSAR, with relatively low cost and high flexibility, are useful for mapping and land resource exploration. However, due to challenges including spatiotemporal synchronization and motion errors, there are limited reports on UAV-borne dual-station InSAR. This paper proposes a comprehensive method for processing data from small UAV-borne dual-station InSAR by integrating two-way synchronization chain signals. The proposed method includes compensation for time and phase synchronization errors, trajectory refinement with synchronized chain and Position and Orientation System (POS) data, high-precision dual-station InSAR imaging and interferometric processing. Height inversion results based on the proposed method is also provided, demonstrating the effectiveness of the proposed method and improving interferometric accuracy at calibration points from 0.66 meters to 0.42 meters.

Mini-drones can be used for a variety of tasks, such as weather monitoring, package delivery, search and rescue, and recreation. Their uses are mostly restricted to outside locations with access to the Global Positioning System (GPS) and/or similar systems since their usefulness, safety, and performance substantially rely on ubiquitously accurate positioning and navigation. Indoor localization is getting better, thanks to technologies like Visual Simultaneous Localization and Mapping (V-SLAM). However, more advancements are still required for mini-drone navigation applications with greater safety standards. In this research, a novel method for enhancing indoor mini-drone localization performance is proposed. By merging Oriented Rotated Brief SLAM (ORB-SLAM2), Semi-Direct Monocular Visual Odometry (SVO), and an Adaptive Complementary Filter, the suggested strategy improves V-SLAM approaches (ACF). The findings demonstrate that, when compared to other widely-used indoor localization algorithms, the suggested methodology performs better at estimating location under various situations (low light, low texture, and dynamic environments).

Due to the advantages such as low power consumption and higher concealment, deceptive jamming against synthetic aperture radar (SAR) receives extensive attention during the past decades. However, the large scene deception jamming is still a challenge because of the huge computing burden. In this paper, we propose a new large scene deceptive jamming algorithm. First, the time-delay and frequency-shift (TDFS) algorithm is introduced to improve the jamming processing speed. The system function of jammer (JSF) for a fake scatter is simplified to the multiplication of the scattering coefficient, a time-delay term in range dimension and a frequency-shift term in azimuth dimension. Then, in order to solve the problem that the effective region of the TDFS algorithm is limited, the scene deceptive jamming template is divided into several blocks according to the SAR parameters and imaging quality control factor. The JSF of each block is calculated by the TDFS algorithm and added together to achieve the large scene jamming. Finally, the correction algorithm in squint mode is derived. The simplification and parallel block processing could improve the calculation efficiency significantly. The simulation results verified the validity of the algorithm.

Keywords: SAR; interferometry; bistatic

COMPASS is a mission concept aiming to determine the recent climate history of planet Mars. Aboard this mission, there would be a dual mode radar instrument capable of acting both as a Synthetic Aperture Radar (SAR) and a Subsurface Sounder for mapping shallow ice. The COMPASS Radar Observer for Mars Exploration (CROME) instrument will help advance science and exploration. First, the COMPASS mission is described. Then, the CROME instrument concept and its implementation are presented. Finally, the procedure used to predict the performance is detailed for both the Side-Looking SAR and the Nadir-looking Sounder modes.

Interferometric synthetic aperture radar (InSAR) and tomographic SAR measurement techniques are commonly used for the three-dimensional (3D) reconstruction of complex areas, while the effectiveness of these methods relies on the interferometric coherence among SAR images with minimal angular disparities. Radargrammetry exploits stereo image matching to determine the spatial coordinates of corresponding points in two SAR images, and acquire their 3D properties. The performance of the image matching process directly impacts the quality of the resulting digital surface model (DSM). However, the presence of speckle noise, along with dissimilar geometric and radiometric distortions, poses considerable challenges to achieve accurate stereo SAR image matching. To address the aforementioned challenges, this paper proposes a radargrammetric method based on composite registration of multi-aspect SAR images. The proposed method combines coarse registration using scale invariant feature transform (SIFT) with precise registration using normalized cross-correlation (NCC) to achieve accurate registration between multi-aspect SAR images with large disparities. Furthermore, the multi-aspect 3D point clouds are merged by the proposed radargrammetric 3D imaging method, resulting in 3D imaging of target scenes based on multi-aspect SAR images. For validation purpose, this paper presents a comprehensive 3D reconstruction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) by using Ka-band airborne SAR images. It does not necessitate prior knowledge of the target and is applicable to the detailed 3D imaging of large-scale areas with complex structures. In comparison to other SAR 3D imaging techniques, it reduces the requirements for orbit control and radar system parameters. To sum up, the proposed 3D imaging method with composite registration guarantees imaging efficiency while enhances the imaging accuracy of crucial areas with limited data.

Objective: This prospective study investigated the feasibility performing laparoscopic pelvic surgery in spinal anesthesia and analyzed their intraoperative side effects like pain and nausea and vomitus of 915 patients. Methods: Implementation and performance of laparoscopic surgery under local anes-thesia of 915 patients out of a total of 3212, who underwent laparoscopic pelvic surgery in spinal anesthesia, were analyzed in relation to BMI (Body Mass Index), obesity, pain during surgery and amount of intraperitoneal mmHg CO2 gas pressure and surgical complications. Results: BMI > 30, intraabdominal adhesions, increased duration of the operation, bleeding and increased intraperitoneal CO2 pressure were statistically significant as the main causes of pain during laparoscopic surgery in spinal anesthesia. Under-weight patients, on the other hand, had less pain when intra-abdominal pressure in-creased compared to those of normal weight. The appearance of pain, nausea and vomitus occurred in 10,3% and was easy to manage and treat. It did not affect the sur-geon's work and the course of the operation. Conclusion: In view of these observations, we are proposing spinal anesthesia for lap-aroscopic surgery as the first choice in those patients, who have no contraindications. In our knowledge this clinical study constitutes the largest clinical observations and dataset that applies spinal anesthesia in laparoscopic pelvic surgery.

Pigs are used as laboratory animals in preclinical studies on medical devices for cardiac surgery. On the one hand, the anatomy of the cardiovascular system of these animals has been well studied and acknowledged suitable for use and testing of new cardiovascular devices developed for humans. On the other hand, there are no morphometric characteristics of the aortic root and other parts of it in pigs with different weights. This can lead to difficulties in the work of researchers and even result in the death of experimental animals due to the discrepancy in the size of the implantable devices. Thus, such information is essential to improve the efficiency of surgical technologies used in the correction of aortic pathologies in its various sections. The purpose of our research is to study the anatomy of the aorta in mini-pigs, and to assess whether the size, age, and sex of animals affect the size of the main structures in their aorta. In addition, we attempted to compare the results obtained by the method of transesophageal echocardiography (TEE) and angiography. We studied 28 laboratory mini-pigs weighing from 40 to 105 kg, and then we compared our results with other studies and did not find any relationship between the external somatometric characteristics of animals and the size of their aorta. Each animal has an individual anatomy, so they are to be examined in terms of preoperative planning by any available method - echocardiography, angiography or multispiral computed tomography (MRI).

Background: Malnutrition is a frequent condition in the elderly and is associated with prolonged hospitalization and increased mortality. However, the impact of malnutrition among elderly patients with acute myocardial infarction has not been clarified yet. Methods and Results: We have enrolled 174 patients aged 65 years and over, admitted with the diagnosis of acute myocardial infarction (AMI) who underwent to the evaluation of nutritional status by Mini Nutritional Assessment (MNA) and of mortality risk by Grace score 2.0. All-cause mortality was the outcome considered for this study. Over a mean follow-up of 24.5 ± 18.2 months, 43 deaths have been registered (24.3%). Non-survivors were more likely to be older, with worse GFR, lower SBP, lower albumin and MNA score, higher prevalence of Killip classification III-IV grade, and higher Troponin I levels. Multivariate Cox proportional analysis revealed that Grace score and MNA showed a significant and independent impact on mortality, (HR = 1.76, 95% CI = 1.34–2.32 and HR = 0.56, 95% CI = 0.42–0.73, respectively). Moreover, the clinical decision curve revealed a higher clinical net benefit when the MNA was included compared to the partial models without MNA. Conclusions: Nutritional status is an independent predictor of long-term mortality among elderly patients with AMI. MNA score in elderly patients with AMI may help prognostic stratification and identification of patients with/at risk of malnutrition in order to apply interventions to improve nutritional status and maybe survival in this population.

The problem of filling the bone cavity remains relevant in maxillofacial and oral surgery. There is a large selection of osteotropic materials of various natures for filling of bone defects of different etiology. The aim of our research was to improve the result of surgical treatment in a patient with a complicated mandibular fracture with the use of collagenic xenograft during osteosynthesis. In this article, we introduce our experience in the treatment of a patient with a complicated mandibular angle fracture in a combination with the follicular cyst. The obligate steps of treatment included stabilization of the bony fragments, a decrease of risk in fracture line malposition along with titan mini- plates, and shortening in the time of bone regeneration due to a filling of bone defect with the osteotropic material. This approach allowed us to reduce the rehabilitation period and further prosthetic treatment after 4-5 months without additional bone grafting manipulations. Though, the use of collagen osteotropic material, possessing osteoconductive properties, can improve the treatment of patients with mandibular fractures.

The feasibility of hydroelectric plants depends on a variety of factors: water resource regime, geo-graphical, geological and environmental context, available technology, construction cost and eco-nomic value of produced energy. Choices about the building or renewal of hydroelectric plants should be based on the forecast of the future trend of these factors at least during the project life of the system. Focusing on the economic value of the produced energy this paper examines its influ-ence on the feasibility of hydroelectric plants. Analysis, referred to Italian case, were based on three different phases: i) the economic sustainability of small-scale hydroelectric plants under a minimum price guaranteed to the hydroelectric operator; ii) the estimate of the incentives to reach the thresholds of "acceptability" and "bankability" of the investment; iii) the analysis of results ob-tained in the previous phases using a model of the evolution of the electricity price in the period 2014-2100.

Mini-bioreactor systems enabling automatized operation of numerous parallel cultivations have been used to accelerate and optimize bioprocess development. As implementation of fed-batch conditions, multiple options of process control and sample analysis are possible, these systems represent valuable screening tools for large-scale production. However, the dynamic behavior of cultivations has not yet been considered regarding data evaluation and decision making during high-throughput screening in mini-bioreactors. In this study, the characterization of Saccharomyces cerevisiae AH22 secreting recombinant endopolygalacturonase is performed in 48 parallel fed-batch cultivations regarding 16 experimental conditions. Automated parallel process control, frequent sampling and analysis were implemented. Data-driven multivariate methods were developed to allow for fast, automated decision making as well as online predictive data analysis regarding endopolygalacturonase production. Using dynamic process information, a cultivation with abnormal behavior could be detected by principal component analysis as well as two clusters of similarly behaving cultivations, later classified according to the feeding rate. By decision tree analysis, cultivation conditions leading to an optimal recombinant product formation could be identified automatically. The developed method is easily adaptable and suitable for automatized process development reducing the experimental times and costs.

Hyper-spectral imaging provides the possibility of describing specific characteristics of materials in air, land and water depending on reflections of each material in a specific zone of the electromagnetic spectrum. Hyper-spectral sensors capture images in continuous and narrow spectral bands in the visible, near-infrared, and mid-infrared spectral regions. These sensors look at objects using a large part of the electromagnetic spectrum. Some objects have unique characteristics in certain electromagnetic spectrums. These spectral signatures, known as "spectral signatures," help to identify materials in images. But the main problem with these sensors is that the images they get in bad weather conditions and in the presence of dust or rain or snow are of very poor quality. Therefore, we go to SAR images, which are able to form a high-resolution image even in bad weather conditions. The method proposed in this paper is to obtain the final elements of hyper-spectral images using the decomposition method into a multilayer negative matrix. The reason for using this method is that, unlike the high-spectral images that form the image in a number of high-traffic zones, we can create the image in 8 different frequencies, so the most important thing is to get all the hidden information at those frequencies. And also, we compare between the RDA, SENMAV, OMP, ISMA and ADMM algorithms.

Introduction: Transesophageal echocardiography (TEE) is a vital diagnostic tool in clinical prac-tice, particularly in transcatheter interventions where it aids in both pre-operative planning and intra-operative guidance. Traditional TEE probes often require general anesthesia due to patient discomfort. However, the development of miniaturized TEE probes presents a promising alter-native, enabling routine examinations and interventions with minimal sedation. This study evaluates the feasibility of performing a complete 2D/4D TEE protocol with the new 4D-mini-TEE probe in echocardiography departments and its application in transcatheter interventions. Methods: This prospective, observational study included 30 consecutive patients from two high-volume European hospitals (Interbalkan Medical Center, Thessaloniki, Greece, and Rennes University, France) undergoing TEE or transcatheter interventions. The new 4D-mini-TEE 9VT-D probe (GE Healthcare) was utilized. The quality of the images and the tolerance of the probe were assessed in the Cath lab during interventions and in the echocardiography department during routine TEE examinations. Results: Direct comparison of the 4D-mini-TEE probe with the standard 6VT-D probe, confirmed the excellent image quality of this new pediatric probe. Most of the patients required minimal sedation or local oropharyngeal anesthesia, with satisfactory tolerance reported. Most of the transcatheter procedures did not require general anesthesia and intubation, resulting in shorter procedural time. Both 2D and 4D imaging modalities offered adequate intra-operative guidance for transcatheter procedures. Conclusions: The 4D-mini-TEE probe delivers exceptional imaging capabilities for routine ex-aminations and transcatheter interventions without needing sedation. Its use reduces esophageal trauma and the need for general anesthesia, enhancing patient comfort and safety.

To realize fast and effective synthetic aperture radar (SAR) deception jamming, a high-quality SAR deception jamming template library can be generated by performing sample augmentation on SAR deception jamming templates. However, current sample augmentation schemes of SAR deception jamming templates face certain problems. First, the authenticity of templates is low due to the lack of speckle noise. Second, the generated templates have low similarity to the target and shadow areas of the input templates. To solve these problems, this study proposes a sample augmentation scheme based on generative adversarial networks, which can generate a high-quality library of SAR deception jamming templates with shadows. The proposed scheme solves the two aforementioned problems from the following aspects. First, the influence of the speckle noise is considered in the network to avoid the problem of reduced authenticity in generated images. Second, a channel attention mechanism module is used to improve the network's learning ability of shadow features, which improves the similarity between the generated template and the shadow area in the input template. Finally, the proposed scheme and the SinGAN scheme are compared regarding the equivalent numbers of looks and the structural similarity between the target and shadow in the sample augmentation results. The comparison results demonstrate that, compared to the templates generated by the SinGAN scheme, those generated by the proposed scheme have targets and shadow features similar to those of the original image, and can incorporate speckle noise characteristics, resulting in higher authenticity, which helps to achieve fast and effective SAR deception jamming.

We describe an efficient approach to radiometrically flatten geocoded stacks of calibrated synthetic aperture radar (SAR) data for terrain-related effects. We use simulation to demonstrate that, for the Sentinel-1 mission, one static radiometric terrain flattening factor derived from actual SAR imaging metadata per imaging geometry is sufficient for flattening interferometrically compliant stacks of SAR data. We quantify the loss of precision due to application of static flattening factors, and show that these are well below stated requirements of change detection algorithms. Finally, we discuss the implications of applying radiometric terrain flattening to geocoded SAR data instead of the traditional approach of flattening data provided in the original SAR image geometry. The proposed approach allows for efficient and consistent generation of five different Committee of Earth Observation Satellites (CEOS) Analysis Ready Dataset (ARD) families - Geocoded Single Look Complex (GSLC), Interferometric Radar (InSAR), Normalized Radar Backscatter (NRB), Polarimetric Radar (POL) and Ocean Radar Backscatter (ORB) from SAR missions in a common framework.

We describe an efficient and cost effective data access mechanism for Sentinel-1 TOPS 1 mode bursts. Our data access mechanism enables burst-based data access and processing, thereby 2 eliminating ESA’s Sentinel-1 SLC data packaging conventions as a bottleneck to large scale processing. 3 Pipeline throughput is now determined by available compute resources and efficiency of the analysis 4 algorithms. For targeted infrastructure monitoring studies, we are able to generate coregistered, 5 geocoded stacks of SLCs for any AOI in the world in a few minutes. In addition, we describe our 6 global scale radar backscatter and interferometric products and associated pipeline design decisions 7 that ensure geolocation consistency across the suite of derived products from Sentinel-1 data. Finally, 8 we discuss the benefits and limitations of working with geocoded SAR SLC data.

Recently, a rising use of wireless internet technologies has been demonstrated. The devices 18 which use these technologies emit in new spectral regions an electromagnetic radiation (EMFs) 19 which could interact with the male reproductive system. The aim of this study was to investigate in 20 vitro influence of electromagnetic fields at 27 GHz on sperm quality in Mytilus galloprovincialis. 21 Sperm samples, were collected from sexually mature males of M. galloprovincialis and placed in sea- 22 water. Once evaluated the number and quality of spermatozoa, sperm cells were exposed to elec- 23 tromagnetic fields radiated by a pyramidal horn antenna. The effect of exposure was evaluated after 24 10, 20, 30, 40 and 60 minutes with light microscope and using Eosin test. Ten replications were per- 25 formed for each time series, and statistical analysis was carried out byt-test. A significative decrease 26 in sperm motility was observed after 10 minutes of exposure and after 30 minutes most of sperms 27 were immobile and not vital. This study provides useful data on potential ecological impact of the 28 5G high-band on animal fertility, whose effect is currently under investigation.

Synthetic Aperture Radar (SAR) is an active type of microwave remote sensing. Using the microwave imaging system, remote sensing monitoring of the land and global ocean can be done in any weather conditions around the clock. Detection of SAR image targets is one of the main needs of radar image interpretation applications. In this paper, an improved two-parameter CFAR algorithm based on Rayleigh distribution and morphological processing is proposed to perform ship detection and recognition in high resolution SAR images. Through simulation experiments, comprehensive study of the two algorithms for high resolution SAR image target detection is achieved. Finally, the results of ship detection experiments are compared and analyzed, and the effects of detection are evaluated according to the Rayleigh distribution model and algorithms.

The Volturno Plain is one of the largest alluvial plain of peninsular Italy. This area is characterized by both natural and human induced subsidence, and is and most susceptible to coastal hazards. The present study is based on post-processing, analysis and mapping of the available Persistent Scatterer interferometry datasets, derived from combination of both ascending and descending orbits of three different SAR satellite systems, during an observation period of almost two decades (June 1992 - September 2010). The main output of the research work is a map of the vertical deformation that provides new insights into the areal variability of ground deformation processes (subsidence/uplift) of Volturno plain over the last decades. Vertical displacement values derived by interferometric data post-processing show that the Volturno river plain is characterized by significant subsidence in the central axial sectors and in the river mouth area, whereas moderate uplift is detected in the eastern part of the plain. Other sectors of the study area are characterized by moderate subsidence and/or stability. We infer that the subsidence recorded in the Volturno plain is mainly a consequence of a natural process related to the compaction of the fluvial deposits that fill up the alluvial plain. Anthropic influence (e.g. water exploitation, urbanization) can be substantially regarded as an additional factor that only locally may enhance subsidence. The uplift imaged in the eastern sector of the plain can be related to tectonic activity. The study of subsidence in the Volturno plain is a valuable tool relevant for river flood analyses and coastal inundation hazard assessment addressed to risk mitigation.

The study objective is to evaluate the reliability of the Göttingen Mini-pig (G-MP) as a large animal model for preclinical studies addressing articular cartilage treatment procedures. Second this work compared the G-MP to the domestic pig (DP) in terms of surgical anatomy, postoperative management, and follow-up period possibilities and challenges. Six G-MP and four DP underwent a two-stage surgical procedure including cartilage harvesting using a superolateral approach followed by cartilage implantation using a medial parapatellar tendon approach. The superolateral approach exposed 11% (SD ± 5) of the entire trochlea in G-MP and 20% in the DP model. The medial parapatellar tendon approach in G-MP exposed 63% (SD ± 4) of the trochlear surface and exposed 34% (SD ± 13) of the medial femoral condyle, allowing 4 trochlear lesions of 6mm of diameter and one to the medial condyle in 4 out of 6 G-MP and in all DPs. Cartilage thickness was under 1mm in G-MP compared to more than 2mm in DP. Weight-progression was + 4 kg/week in DP and + 0.2 kg/week for G-MP. The G-MP is a feasible model for cartilage research, with adequate access to the joint via the dual approach. Thus enabling 4-5 lesions of 6mm, however a thinner cartilage in the G-MP needs to be considered.

Sorghum (Sorghum bicolor L.) ranks fifth as the most crucial cereal crop globally, yet its seed morphology remains relatively unexplored. This study investigated seed morphology in sorghum based on 115 mini core and 130 Senegalese germplasms. Eight seed morphology traits encompassing size, shape, and color parameters were assessed. Statistical analyses explored potential associations between these traits and resistance to three major sorghum diseases: anthracnose, head smut, and downy mildew. Furthermore, genome-wide association studies (GWAS) were conducted using phenotypic data from over 24,000 seeds and over 290,000 publicly available single nucleotide polymorphisms (SNPs) through the Genome Association and Prediction Integrated Tool (GAPIT) R package. Significant SNPs associated with various seed morphology traits were identified and mapped onto the reference sorghum genome to identify novel candidate defense genes.

This study investigated the efficacy of carvone, abscisic acid (ABA), gibberellin (GA3), and variable temperature in managing dormancy and sprouting in aeroponically grown mini-tuber potato seeds. Results showed that carvone treatment effectively reduced weight loss rate by 12.25% and decay rate by 3.33% at day 25, compared to control. ABA treatment significantly enhanced germination rate to 97.33%. GA3 treatment resulted in the longest sprouts of 14.24 mm and reduced MDA content by 23.08% at day 30, indicating its potential in shortening dormancy and maintaining membrane integrity. Variable temperature treatment showcased a balanced performance in reducing weight loss and maintaining a lower relative conductivity, indicating less cellular damage. Enzymatic activities of α-amylase, CAT, and SOD were modulated by the treatments, ensuring a balanced enzymatic environment for seed vitality. These findings provide a robust foundation for refining post-harvest management strategies to optimize germination uniformity and preserve the quality of aeroponic potato seeds during extended dormancy, promising enhanced yield and productivity in potato cultivation.

Diet modification may be a potential contributor in the prevention of age-related cognitive decline. The association between dairy product consumption and cognitive function remains unknown in older people. We investigated whether cheese intake is associated with lower cognitive function (LCF) in community-dwelling older adults. This cross-sectional study included 1503 adults aged over 65 years. Analyzed data were obtained from face-to-face interviews and functional ability measurement. Cognitive function was assessed using mini-mental state examination (MMSE), and a score ≤ 23 was defined as LCF. The prevalence of LCF was 4.6% and this group had smaller calf circumference, slower usual walking speed, and more frequent history of anemia than subjects with MMSE scores ≥ 24. After adjusting for confounding factors, logistic regression analysis revealed cheese intake (odds ratio (OR) = 0.404, 95% confidence interval (CI) = 0.198-0.824), age (OR = 1.170, 95% CI = 1.089-1.256), usual walking speed (OR = 0.171, 95% CI = 0.062-0.472) and calf circumference (OR = 0.823, 95% CI = 0.747-0.908) as significant factors associated with LCF. Although the present study was an analysis of cross-sectional data of Japanese community-dwelling older adults, the results suggest that cheese intake is inversely associated with LCF.

Noise pollution is an environmental problem due to its effect on hearing and other related health issues. The elevated noise levels above standard limits cause hearing loss and other attendant problems. In Nigeria, viewing centers where football matches are watched via satellite are known as ‘Mini Stadium’. In this study, the noise pollution of a viewing center was assessed. To do this, five top teams each from the English Premier League (EPL), La Liga, Bundesliga, League 1, and Serie A were selected for the assessment. A sound level meter (GB: 2266204, made in China) was used for the monitoring. The range of noise levels in dBA showed as follow: EPL (56-108); La Liga (46-106); Bundesliga (54-102); Serie A (49-101); and Ligue 1 (54-101). The results when compared with the standard limits, it was observed that part of the results exceeded the limits, this means that the viewers and others within the viewing center may be prone to the hearing problem. It is recommended that acoustic insulators and hearing aids should be used to reduce the attendant problems associated with noise.

Rural electrification in remote areas of developing countries has several challenges which hinders energy access to the population. For instance the extension of the national grid to provide electricity in these areas is largely not viable. The Kenyan government has put a target to achieve universal energy access by the year 2020. In order to realize this objective, focus is being shifted to establishing off-grid power stations in rural areas.Among rural areas to be electrified, Habaswein is a settlement in Kenya's North Eastern region without connection to the National Power Grid where Kenya Power installed a stand alone hybrid mini-grid.Based on field observations, power generation data analysis, evaluation of the potential energy resource and simulations, this research intends to evaluate the performance of the Habaswein mini-grid and optimize the existing hybrid generation system to enhance its reliability and reduce the operation costs.The result will be a suggestion of how Kenyan rural areas could be sustainably electrified by using renewable energy based off-grid power stations. It will contribute to bridge the research gap currently existing on that area, and it will be a vital tool to researchers, implementers and the policy makers in energy sector.

We introduce a novel method for fabricating perovskite solar modules using selective spin-coating on various Au/ITO patterned substrates. These patterns were engineered for twofold purposes: (1) to enhance selectivity of monolayers primarily self-assembling on the Au electrode, and (2) to enable seamless interconnection between cells through direct contact of the top electrode and the hydrophobic Au connection electrode. Utilizing SAMs-treated Au/ITO, we achieved sequential selective deposition of the electron transport layer (ETL) and the perovskite layer on the hydrophilic amino-terminated ITO, while the hole transport layer (HTL) was deposited on the hydrophobic CH3-terminated Au connection electrodes. Importantly, our approach had negligible impact on the series resistance of the solar cells, as evidenced by the measured specific contact resistivity of the multilayers. A significant outcome was the production of a 6-cell series-connected solar module with a notable average PCE of 8.32%, providing a viable alternative to the conventional laser scribing technique.

This paper introduces a systematic approach to the design of Direct Current-to-Digital Converter (DCDC) specifically engineered to overcome the limitations of traditional current measurement methodologies in System-on-Chip (SoC) designs. The proposed DCDC addresses critical challenges such as high power consumption, large area requirements, and the need for intermediate analog signals. By incorporating a current mirror in a cascode topology and managing the current across multiple binary-sized branches with the Successive Approximation Register (SAR) logic, the design achieves precise current measurement. A simple comparator, coupled with an isolation circuit, ensures accurate and reliable sensing. Fabricated using the TSMC 180 nm process, the DCDC achieves 8-bit precision without the need for nonlinearity calibration, showcasing remarkable energy efficiency with an energy per conversion of 1.52 pJ, power consumption of 117 µW, and a compact area of 0.016 mm². This innovative approach not only reduces power consumption and area, but also provides a scalable and efficient solution for next-generation semiconductor technologies. The ability to conduct online measurements during both standard operations and in-field conditions significantly enhances the performance and reliability of SoCs, making this DCDC a promising advancement in the field.

The increasing demand for large-scale, high-frequency environmental monitoring has driven the adoption of satellite-based technologies for effective forest management, especially in the context of climate change. This study explores the potential of SAR for estimating the mass of harvesting residues, a significant component of forest ecosystems that impacts nutrient cycling, fire risk, and bioenergy production. The research hypothesizes that while the spatial distribution of residues remains stable, changes in moisture content—reflected in variations in the dielectric properties of the woody material—can be detected through SAR techniques. Two models, generalized linear model (GLM) and random forest (RF), were used to predict the mass of residues using interfer-ometric variables (phase, amplitude and coherence) as well as the backscatter signal from several acquisition pairs. The models provided encouraging results (R2 of 0.48 for GLM and 0.13 for RF), with acceptable bias and RMSE. We concluded that SAR data are feasible to predict the mass of harvesting residues and the findings could lead to improved monitoring and management of forest residues, contributing to sustainable forestry practices and enhanced bioenergy resource utilization.

Our world is increasingly challenged by managing the impacts of natural disasters, particularly floods, which are frequent, dangerous, and costly. Traditional flood mapping methods, reliant on Optical satellites (MSI), struggle under cloud cover which is typical during such events. Synthetic Aperture Radar (SAR) offers a promising alternative with its cloud-penetrating capability, though its use has been limited due to complexity and data labeling challenges. This project aims to develop a SAR-based flood segmentation model that can rapidly and accurately map floods globally, with high adaptability to different flood types and regions. By utilizing deep learning and a novel transfer-learning technique that combines the strengths of Optical/MSI and SAR data, the model seeks to bypass the challenges of manual labeling and improve mapping accuracy. Initial results show the model's effective generalization across various flood events, with superior performance indicated by an Intersection over Union (IoU) of 0.72, outperforming existing methods and demonstrating promising capabilities in precise flood mapping.

The integration of deep neural networks into sparse Synthetic Aperture Radar (SAR) imaging has been explored to enhance SAR imaging performance and reduce the system’s sampling rate. However, the scarcity of training samples and mismatches between the training data and the SAR system pose significant challenges to the method’s further development. In this paper, we propose a novel SAR imaging approach based on Deep Image Prior Powered by RED (DeepRED), enabling unsupervised SAR imaging without the need for additional training data. Initially, DeepRED is introduced as the regularization technique within the sparse SAR imaging model. Subsequently, variable splitting and the Alternating Direction Method of Multipliers (ADMM) are employed to solve the imaging model, alternately updating the magnitude and phase of the SAR image. Additionally, the SAR echo simulation operator is utilized as an observation model to enhance computational efficiency. Through simulations and real data experiments, we demonstrate that our method maintains imaging quality and system downsampling rate on par with deep neural network-based sparse SAR imaging, but without the requirement for training data.

Knowledge of the spatio-temporal occurrence of avalanche activity is critical for avalanche forecasting and hazard mapping. We present a near-real time automatic avalanche monitoring system that outputs detected avalanche polygons within roughly 10 min after Sentinel- 1 SAR data download. Our avalanche detection algorithm has an average probability of detection of 67.2 % with a false alarm rate averaging 45.9, with maximum POD's over 85 % and minimum FAR's of 24.9 % compared to manual detection of avalanches. The high variability in performance stems from the dynamic nature of snow in the Sentinel-1 data. After tuning parameters of the detection algorithm, we processed five years of Sentinel-1 images acquired over a 150 x 100 km large area in Northern Norway, with the best setup. Compared to a dataset of field-observed avalanches, 77.3 % were manually detectable. Using these manual detections as benchmark, the avalanche detection algorithm achieved an accuracy of 79 % with high POD's in cases of medium to large wet snow avalanches. For the first time, we can present a dataset of spatiotemporal avalanche activity over several winters from a large region. This unique dataset allows for research into the relationship between avalanche activity and triggering meteorological factors, mapping of avalanche prone areas and near-real time avalanche activity monitoring to assist public avalanche forecasting. Currently, the Norwegian Avalanche Warning Service is using our processing system for pre-operational use in three regions in Norway.

Lake ice thickness (LIT) is a sensitive indicator of climate change identified as a thematic variable of Lakes as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). Here, we present a novel and efficient analytically based retracking approach for estimating LIT from high-resolution Ku-band (13.6 GHz) synthetic aperture radar (SAR) altimetry data. The retracker method is based on the analytical modeling of the SAR radar echoes over ice-covered lakes that show a characteristic double-peak feature attributed to the reflection of the Ku-band radar waves at the snow-ice and ice-water interfaces. The method is applied to Sentinel-6 Unfocused SAR (UFSAR) and Fully Focused SAR (FFSAR) data, with their corresponding tailored waveform model, referred to as the SAR_LITand FFSAR_LITretracker, respectively. LIT retrievals from Sentinel-6 SAR data at different posting rates are evaluated against those obtained from thermodynamic lake ice model simulations and Low Resolution Mode (LRM) Sentinel-6 and Jason-3 data over two ice seasons during the tandem phase of the two satellites, allowing precise assessment of the continuity between LRM and SAR LIT retrievals. Consistency checks of the Sentinel-6 SAR LIT estimates are also performed using optical/radar images that provide information on the snow/ice conditions on the same dates. The analysis is performed on Great Slave Lake and Baker Lake (Canada) that differ in terms of lake size, bathymetry, snow/ice properties, and seasonal evolution of LIT. The accuracy of the LIT estimates with the SAR LIT retrackers is on the order of 5 cm once the ice is well established on the lakes, meeting the GCOS requirements of LIT measurement uncertainty, which is a factor of 2 to 3 times better than that of LIT obtained with LRM data, bringing a further improvement compared to previous analyses and methods. The SAR LIT retrackers presented are promising tools for monitoring the inter-annual variability and trends in LIT from current and future SAR altimetry missions.

Ageing is the strongest known risk factor for many of the neurodegenerative diseases causing cognitive decline and dementia. Identification of cognitive impairment may be a prelude to appropriate treatment, hopefully disease-modifying. Use of cognitive screening instruments may be an equitable way to identify cognitive impairment. This study examined the use of two such instruments, Free-Cog and Mini-Addenbrooke’s Cognitive Examination (MACE), in patient cohorts referred to a dedicated cognitive disorders clinic based at a tertiary neurosciences centre. Results showed that: (1) specificity and positive predictive value increased with patient age for both tests with some loss of sensitivity and negative predictive value. (2) In the oldest age groups (≥75 and ≥70 years respectively) where specificity was at maximum, a positive test result (i.e. below the specified test cut-off) rules in the diagnosis of cognitive impairment. (3) Values of an “Efficiency Index” for each test indicated qualitatively a moderate change in the probability of correct diagnosis and quantitatively an approximately 15-25% increase in the probability of correct diagnosis. These findings show that both Free-Cog and MACE may be used with confidence for the identification of cognitive impairment and dementia in older patient cohorts. These findings may have implications for public health policies directed to case-finding in clinical practice as opposed to population-based screening.

High-accuracy geolocation is crucial for high-resolution spaceborne SAR images. Most advanced SAR satellites have better theoretical geolocation accuracy than 1 meter but maybe unrealizable with less accurate external data, including the atmospheric parameters and the ground elevations. To investigate the actual SAR geolocation accuracy in common applications, we analyze the properties of different geolocation errors, propose a set of geolocation procedures, and conduct experiments on TerraSAR-X images and a pair of Tianhui-2 images. The results show that based on GNSS elevations, the geolocation accuracy is better than 1 meter for TerraSAR-X and 2 meters / 4 meters for the Tianhui-2 reference / secondary satellite. Based on the WorldDEM and the SRTM, an extra geolocation error of 2 and 4 meters will be introduced, respectively. By comparing the effectiveness of different tropospheric correction methods, we find that the GACOS mapping method has advantages in terms of resolution and computational efficiency. We conclude that the tropospheric error and the ground elevation error are the primary factors influencing the geolocation accuracy, and the key to improving accuracy is to use higher-accuracy DEMs. Additionally, we propose and validate a geolocation model for the Tianhui-2 secondary satellite.

The development of non-destructive testing (NDT) methods for infrastructure facilities and residential buildings that may affect human life is urgent to prevent collapse due to aging or damage in the event of earthquakes and other events. In this study, we developed a single-input and multiple-output (SIMO) synthetic aperture radar (SAR) imaging system using a high-sensitivity millimeter-wave (MM-wave) array antenna module with high directivity and absolute gains of 13.5 dB and 14.5 dB for the transmit and receive antennas, respectively. After various performance evaluations, we successfully demonstrated the usefulness of this system in NDT. Specifically, we observed cracks hidden by fiberglass tape and black acrylic paint during measurements on concrete materials. In addition, we demonstrated the transmission performance of the MM-wave against a thin concrete plate and obtained the attenuation distance Λ_exp=6.0 mm. Furthermore, in the experiment using composite materials consisting of ceramic tiles and a refractory board bonded together, and another experiment using composite plywood which is used in ordinary residential construction materials in Japan, we were able to observe defects in the internal structure that are invisible to the human eye through a perspective view.

This paper presents a statistical approach to identify the underlying roughness characteristics in synthetic aperture radar (SAR) intensity data. The physical modeling of this kind of data allows the use of the Gamma distribution in the presence of fully-developed speckle, i.e., when there are infinitely many independent backscatterers per resolution cell, and none dominates the return. Such areas are often called ``homogeneous'' or ``textureless'' regions. The GI0 distribution is also a widely accepted law for heterogeneous and extremely heterogeneous regions, i.e., areas where the fully-developed speckle hypotheses do not hold. We propose three test statistics to distinguish between homogeneous and inhomogeneous regions, i.e., between gamma and GI0 distributed data, both with a known number of looks. The first test statistic uses a bootstrapped non-parametric estimator of Shannon entropy, providing a robust assessment in uncertain distributional assumptions. The second test uses the classical coefficient of variation (CV). The third test uses an alternative form of estimating the CV based on the ratio of the mean absolute deviation from the median to the median. We apply our test statistic to create maps of \(p\)-values for the homogeneity hypothesis. Finally, we show that our proposal, the entropy-based test, outperforms existing methods, such as the classical CV and its alternative variant, in identifying heterogeneity when applied to both simulated and actual data.

We have recently reported that a series of 1,3,4-oxadiazol-2-ylbenzenesulfonamides acts as potent and specific monoamine oxidase B (MAO-B) inhibitors with some compounds possessing potencies in the nanomolar range. While sulfonamide compounds (e.g., zonisamide) have been reported to inhibit the MAO enzymes, nanomolar MAO inhibition potencies for this class are not frequently observed. The present study expanded on these findings by replacing the 1,3,4-oxazole moiety with a 1,3-thiazole heterocycle. A series of 23 primary sulfonamides were synthesized by reaction of an α-haloketone with a substituted carbothioamide. The results of the MAO inhibition studies showed that the 1,3-thiazolylbenzenesulfonamides were specific inhibitors of human MAO-B. The most potent MAO-B inhibitor exhibited an in vitro IC50 value of 0.103 µM (3j). The derivatives were, however, weaker inhibitors of human MAO-A with the most potent compound exhibiting an IC50 value of 11.9 µM (3a). Examination of the structure-activity relationships (SARs) showed that the most potent MAO-B inhibition was obtained with substitution of the sulfonamide on the meta position of the phenyl rather than the para position. This study concludes that 1,3-thiazolylbenzenesulfonamides represent a class of potent and specific MAO-B inhibitors, which may serve as leads for the development of new treatments for disease states such as Parkinson’s disease.

Based on the high-precision quantitative measurement ability, the S-band subsystem of China's high- resolution aviation system carried out the agricultural application test for the first time in Jilin elm, China. The experiment is mainly carried out in combination with vegetation growth, soil moisture content inversion, wheat straw returning and ground feature classification. Through the design of quantitative measurement scheme, the results of different straw coverage and observation at different times are compared, which shows the prospect of S-band full polarization synthetic aperture radar system in the field of agricultural application.

Crop yield data is critical for managing sustainable agriculture and assessing national food security. Current study aims to increase Peanut productivity from current levels by analyzing the yield gap of production potential between theoretical yield and actual farmers’ yields. The spatial yield gap of Peanut for Thiruvannamalai district of Tamil Nadu is examined in this paper by integrating the products of microwave remote sensing (SAR Sentinel-1A) with DSSAT CROPGRO peanut simulation model. CROPGRO Peanut model was calibrated and validated by conducting field experiment at Oilseeds Research Station, Tindivanam during Rabi 2019 for predominant cultivars viz. TMV 7, TMV 13, VRI 2 and G 7. Actual attainable yield was recorded by organizing CCE with help of Department of Agriculture Economics and Statistics in the respective monitoring Villages. Regression analysis between maximum recorded DSSAT Leaf Area Index (LAI) at peak flowering stage of peanut and yield recorded by Crop Cutting Experiment (CCE) for spatial yield estimation of Peanut in Thiruvannamalai district of Tamil Nadu during Rabi 2021 was carried out using ArcGIS 10.6 software. The results showed that the simulated potential yield ranged from 3194 to 4843 kg/ha, whereas actual yield ranged from 1228 to 3106 kg/ha, with a considerable disparity between the actual and potential yield levels (1217 to 2346 kg/ha) of the monitored locations. The minimum, maximum and average yield gaps in Peanut for Thiruvannamalai district was assessed as 1890, 2324 and 2134 kg/ha, respectively. To reduce the production difference (Yield gap) of Peanut cultivation, farmers should focus more on management issues such as time of sowing, irrigation or water management, quantity and sources of nutrients, cultivar selection and availability of quality seeds tailored to each region.

The classification of vessel types in SAR imagery is of crucial importance for maritime applications. However, the ability to use real SAR imagery for deep learning classification is limited, due to the general lack of such data and/or the labor-intensive nature of labeling them. Simulating SAR images can overcome these limitations, allowing the generation of an infinite number of datasets. In this contribution, we present a synthetic SAR imagery dataset with ship wakes, which comprises 46080 images for ten different real vessel models. The variety of simulation parameters includes 16 ship heading directions, 6 ship velocities, 8 wind directions, 2 wind velocities, and 3 incidence angles. In addition, we extensively investigate classification performance for noise-free, noisy, and denoised ship wake scenes. We utilize the standard AlexNet architecture and employ training from scratch. To achieve the best classification performance, we conduct Bayesian optimization to determine hyperparameters. Results demonstrate that the classification of vessel types based on their SAR signatures is highly efficient, with maximum accuracies of 96.16%, 92.7%, and 93.59%, when training using noise-free, noisy, and denoised datasets respectively. Thus, we conclude that the best strategy in practical applications should be to train convolutional neural networks on denoised SAR datasets. The results show that the versatility of the SAR simulator can open up new horizons in the application of machine learning to a variety of SAR platforms.

5G technology is evolving to satisfy several service requirements favoring high data-rate connections and lower latency times than current ones (< 1ms). 5G systems use different frequency bands of the radio wave spectrum, taking advantage of higher frequencies than previous mobile radio generations. In order to guarantee a capillary coverage of the territory for high reliability applications, it will be necessary to install a large number of repeaters because higher frequencies waves have a lower capacity to propagate in free space. Following the introduction of this new technology, there has been a growing concern about possible harmful effects on human health. The aim of this study is investigating possible short term effects induced by 5G-millimeter waves on embryonic development of Danio rerio. We have exposed fertilized eggs to 27 GHz frequency, 9.7 mW/cm2 incident power density, 23 dbm and have measured several endpoints every 24 hours. The exposure to electromagnetic fields at 27 GHz (5G) caused no significant impacts on mortality nor on morphology because the exposed larvae showed a normal detachment of the tail, presence of heart-beat and well-organised somites. A weak positivity on exposed larvae has been highlighted by immunohistochemical analysis.

Environmental effects and climate change are lately representing an increasing strain of the coastal areas which topography strongly depends on these conditions. However, the processes by which weather and environmental phenomena influence the highly variable beach morphology are still unknown. A continuous monitoring of the beach environment is necessary to implement protection strategies. This paper presents the results of an innovative study performed on a coastal area using satellite remote sensing data with the aim of understanding how environmental phenomena affect beaches. Two-years of synthetic aperture radar (SAR) Sentinel-1 images are used over a test area in Noordwijk, the Netherlands. At the same time as the SAR acquisitions, information on tidal and weather conditions are collected and integrated from nearby meteorological stations. Dedicated codes are implemented in order to understand the relationship between the SAR amplitude and the considered phenomena: wind, precipitation, tidal conditions. Surface roughness is taken into account. The results indicate a strong correlation between the amplitude and the wind. No particular correlation or trend could be noticed in the relation with the precipitation. The analysis of the amplitude also shows a decreasing trend moving from the dry area of the beach towards the sea and the correlation coefficient between the amplitude and the tide level gets negative with the increase of the water content.

This paper focuses on processing low oversampling echo data of staggered synthetic aperture radar (SAR). In staggered mode, the non-uniformly sampling and irregular loss of echo data cause azimuth ambiguity which severely degrades the imaging quality. To solve this problem, we propose a compressed sensing (CS) method in which the non-uniform fast Fourier transform (NUFFT) technique is adopted to obtain uniform azimuth spectrum, and the fast iterative shrinkage thresholding algorithm (FISTA) is utilized to efficiently reconstruct the ambiguity-free image from in-complete echo data. Simulation results demonstrate the proposed method can effectively suppress the azimuth ambiguity in the vicinity of targets.

This paper presents a novel statistical model i.e. the Laplace-Rician distribution, for the characterisation of synthetic aperture radar (SAR) images. Since accurate statistical models lead to better results in applications such as target tracking, classification, or despeckling, characterising SAR images of various scenes including urban, sea surface, or agricultural, is essential. The proposed Laplace-Rician model is investigated for SAR images of several frequency bands and various scenes in comparison to state-of-the-art statistical models that include K, Weibull, and Lognormal. The results demonstrate the superior performance and flexibility of the proposed model for all frequency bands and scenes.

This dataset consists of integral sea state parameters of significant wave height (SWH) and mean wave period (zero-upcrossing mean wave period, MWP) data derived from the advanced synthetic aperture radar (ASAR) onboard the ENVISAT satellite over its full life cycle (2002-2012) covering the global ocean. Both parameters are calibrated and validated against buoy data. A cross-validation between the ASAR SWH and radar altimeter (RA) data is also performed to ensure that the SAR-derived wave height data are of the same quality as the RA data. These data are stored in the standard NetCDF format, which are produced for each ASAR wave mode Level1B data provided by the European Space Agency. This is the first time that a full sea state product in terms of both the SWH and MWP has been derived from spaceborne SAR data over the global ocean for a decadal temporal scale.

Satellite altimeters have been used to monitor river and reservoir water levels, from which water storage estimates can be derived. Inland water altimetry can therefore play an important role in continental water resource management. Traditionally, satellite altimeters were designed to monitor homogeneous surfaces such as oceans or ice sheets, resulting in a poor performance over small inland water bodies due to the contribution from land contamination in the returned waveforms. The advent of synthetic aperture radar (SAR) altimetry (with its improved along-track spatial resolution) has enabled the measurement of inland water levels with a better accuracy and an increased spatial resolution. This paper presents three specialized algorithms or retrackers to retrieve water levels from SAR altimeter data over inland water bodies dedicated to minimizing land contamination from the waveforms. The performances of the proposed waveform portion selection method with three retrackers, namely, the threshold retracker, Offset Centre of Gravity (OCOG) retracker and 2-step physical-based retracker, are compared. Time series of water levels are retrieved for water bodies in the Ebro River basin (Spain). The results show good agreement with in situ measurements from the Ebro Reservoir (width is approximately 1.8 km) and Ribarroja Reservoir (width is approximately 400 m) with un-biased root-mean-square errors (RMSEs) of approximately 0.28 m and 0.16 m, respectively. The performances of all three retrackers are also compared with the European Space Agency’s ocean retracker in the Sentinel-3 Level-2 product.

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SAR) and Ground Penetrating Radars (GPR) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency Modulated Continuous Wave operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device.

Sub-Saharan Africa, especially its rural areas, faces significant challenges in achieving universal electrification despite its abundant renewable energy resources. The region has the highest population without access to electricity, largely due to economic, infrastructural, and geographical barriers. Energy poverty is a critical issue that hinders sustainable development and exacerbates inequalities. Namibia's sustainable energy policy aligns with the global Sustainable Development Goals (SDGs), particularly SDG 7, which aims to provide affordable and reliable modern energy access for all. The policy emphasizes mini-grids and decentralized power systems as key strategies for rural electrification. However, despite increased deployment of mini-grids, these solutions often struggle with long-term sustainability. This research explores cost-effective electrification strategies through scenario-based modeling to reduce energy poverty and expand energy access in Namibia's rural communities, focusing on the existing mini-grids in Tsumkwe and Gam. Using a comprehensive methodology that incorporates HOMER Pro for mini-grid capacity expansion and MS Excel for evaluating main-grid extensions, the study aims to identify the most feasible and economical electrification solutions. The analysis compares electricity supply, total net present cost, and the levelized cost of electricity across these systems. The findings will offer insights into addressing energy poverty in Namibia and provide recommendations for sustainable and scalable rural electrification across Sub-Saharan Africa.

DNA barcoding has emerged as a transformative tool in the realm of biodiversity assessment, facilitating rapid and precise species identification. This review article delves into the evolving landscape of DNA barcoding, with a particular focus on the emergence of mini-barcodes and alternative genes as innovative approaches. Traditional DNA barcoding, anchored in the COI gene, has significantly advanced our understanding of species diversity; however, inherent challenges persist, especially when dealing with degraded samples or taxonomic groups where COI falls short. The introduction of mini-barcodes, characterized by their reduced sequence length, has revolutionized DNA barcoding by expanding its applicability. We explore the utility of mini-barcodes in various scenarios, ranging from DNA extraction from ancient or fragmented specimens to in-field identification using portable sequencing technologies. Case studies and practical insights into their effectiveness are presented, emphasizing their role in overcoming traditional limitations. Alternative genes, a complementary facet of DNA barcoding, offer a novel avenue for enhancing taxonomic resolution and accuracy. This review showcases instances where researchers have harnessed alternative genes to overcome COI's constraints, particularly in diverse taxonomic groups, such as fungi, plants, and microorganisms. The selection of these genes is discussed, along with their potential to uncover hidden biodiversity and improve species delimitation. Examining the broader implications of mini-barcodes and alternative genes, we unveil their pivotal roles in advancing conservation efforts, forensic science, and citizen science initiatives. The adaptable nature of these approaches empowers a diverse range of stakeholders, from scientists conducting critical research to concerned citizens contributing to our collective understanding of the natural world. Looking ahead, we envision an exciting future for DNA barcoding, as these innovations continue to evolve. Integration with cutting-edge sequencing technologies, data analysis methods, and the harnessing of big data hold immense potential. Nonetheless, challenges related to standardization, scalability, and data sharing persist.

The Sentinel-1 SAR Extra Wide swath mode with its 40 meters pixel size allows accurate lead mapping in Arctic sea ice areas. We present an improved winter lead detection algorithm based on a modified U-Net convolutional neural network. We introduce a preprocessing procedure that balances normalized radar cross section (NRCS) values between sub-swaths. This results in more consistent Sentinel-1 SAR images as input for the classifier, which is especially important for the cross-polarization HV channel. In turn, image consistency is essential for automatic image semantic segmentation. We develop a new lead detection algorithm and compare it with a previously published version. We produce pan-Arctic, 40 meters resolution lead maps and present pan-Arctic lead area fraction on a 12 km grid for January 2019. Within different Arctic regions, the lead area fraction is stable and varies only within the expected natural variability during the one month studied here. The average Arctic-wide lead area fraction is 2.44 % with 0.25 % standard deviation during January 2019. The improved lead detection algorithm leads to a better lead discrimination and extends the applicability area to the entire Arctic.

The airborne and satellite-based synthetic aperture radar enables the acquisition of high-resolution SAR oceanographic images in which even the outlines of ships can be identified. The detection of ship targets from SAR images has a wide range of applications, such as the military, where the dynamic grasp of enemy targets can help improve the early warning capability of naval defence, and the civilian detection of illegal fishing vessels can help improve the level of maritime management. Due to the density of ships in SAR images, the extreme imbalance between foreground and background clutter, and the diversity of target sizes, achieving lightweight and highly accurate multi-scale ship target detection remains a great challenge. To this end, this paper proposes an attention mechanism for multiscale receptive fields convolution block (AMMRF). AMMRF not only makes full use of the location information of the feature map to accurately capture the regions in the feature map that are useful for detection results, but also effectively captures the relationship between the feature map channels, so as to better learn the relationship between the ship and the background. Based on this, a new YOLOv7-based ship target detection method, You Only Look Once SAR Ship Identification (YOLO-SARSI), is proposed, which acquires the abstract semantic information extracted from the high-level convolution while retaining the detailed semantic information extracted from the low-level convolution. Compared to the deep learning detection methods proposed by previous authors, our model is more lightweight, only 18.43M. We examined the effectiveness of our method on two SAR image public datasets: the High-Resolution SAR Images Dataset (HRSID) and the Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-V1.0). The results show that the average accuracy AP50 of the detection method YOLO-SARSI proposed in this paper on the HRSID and LS-SSDD-V1.0 datasets is 4.9% and 5% higher than that of YOLOv7, respectively.

Abstract: In the last five years, the urban development of Limassol City has rapidly increased in the sectors of industry, trade, real estate, and many others. This exponentially increased urban development introduces several concerns about the aggravation of the land subsidence in the Limassol coastal front. Fifty Copernicus Sentinel-1 data from 2017-2021 have been processed and analyzed using the Sentinel Application Platform (SNAP) and the Stanford Method for Persistent Scatters (StaMPS). A case study for the identification and analysis of the elements (PS) in pixels in a series of interferograms, and then, the quantity of the land displacements in the Line of Sight, in the Limassol coastal front, is presented in this research, with the subsidence rates up to about (-5 to 4 mm / year). For the validation of the detected deformation, accurate ground-based geodetic measurements along the coastal area were used. Concordantly, taking into account that there are a significant number of skyscrapers planned to be built, this study attempts a preliminary assessment of the impact these structures will pose on the coastal front of the area of Limassol.

Autofocus is an essential part of the SAR imaging process. Multi-subaperture autofocus algorithm is a commonly used autofocus algorithm for processing SAR stripmap mode data. The multi-subaperture autofocus algorithm has two main steps, the first is to estimate the phase error gradient within the subaperture, the second is to splice the phase error gradient, that is, to remove the shift amount between the estimated adjacent subapertures’ error gradients. Previous gradient-splicing algorithms assume that the estimation of subaperture error is accurate, but when the estimation of subaperture phase error gradients is not accurate enough, these algorithm performance will be degraded. A new phase error gradient splicing algorithm is proposed in this paper. It roughly estimates the shift amount first, and then finely estimates the shift amount based on the minimum-entropy criterion, which can improve the robustness of splicing especially when the estimation of the phase error gradients of the subaperture is not accurate enough. To speed up the algorithm, a variable-step-size search method is used. Simulation and experimental results show that the algorithm has enough accuracy and still has good performance when other splicing algorithms doesn’t perform well.

Recent neuroscience research results show that the nerve information in the brain is not only encoded by the spatial information. Spiking neural network based on pulse frequency coding plays a very important role in dealing with the problem of brain signal, especially complicated space-time information. In this paper, an unsupervised learning algorithm for bilayer feedforward spiking neural networks based on spike-timing dependent plasticity (STDP) competitiveness is proposed and applied to SAR image classification on MSTAR for the first time. The SNN learns autonomously from the input value without any labeled signal and the overall classification accuracy of SAR targets reached 80.8%. The experimental results show that the algorithm adopts the synaptic neurons and network structure with stronger biological rationality, and has the ability to classify targets on SAR image. Meanwhile, the feature map extraction ability of neurons is visualized by the generative property of SNN, which is a beneficial attempt to apply the brain-like neural network into SAR image interpretation.

New class of triazinoindole bearing thiosemicarbazide (1-25) was synthesized and evaluated for α-glucosidase inhibitory potential. All synthesized analogues exhibited excellent inhibitory potential having IC50 values ranging from 1.30 ± 0.01 to 35.80 ± 0.80 µM when compared with the standard acarbose having IC50 value 38.60 ± 0.20 µM. Among series the analogues 1 and 23 was found the most potent having IC50 values 1.30 ± 0.05 and 1.30 ± 0.01 µM respectively. Structure activity relationship (SAR) was mainly based upon by bring about difference of substituents on phenyl rings. To confirm the binding interactions, molecular docking study was performed. Synthesized analogues were characterized through HREI-MS, 1H and 13C-NMR analysis.

In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The goal of this combination is to reduce the SIFT keypoints and their time computing time by maintaining only those located in the target area (salient region). Then, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to construct the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of our method to predict adequately the aircraft and the ground targets.

BackgroundIndex level disc herniation following lumbar spinal decompression is somewhat overlooked as postoperative complication, resulting in return of symptoms. It is proposed that the combination of local instability and degenerated disc is more likely to result in disc herniation. There is no literature comparing post-decompression disc herniation rates between different techniques. This study compares the postoperative disc herniation rates of the "mini-open" laminectomy versus the tubular system decompression.MethodsIn a retrospective comparative study, 563 patient files who underwent primary lumbar spinal decompression, were reviewed and divided into two groups, distinguished by surgical technique. Demographic, clinical, and postoperative data were collected and compared using independent two-tailed t-test and Fisher's exact test with significance set at p<0.05. Primary outcome: comparison of post-decompression surgery, index level, disc herniation occurrence between two surgical techniques.ResultsPostoperative index level disc herniation was significantly lower in the minimally invasive surgery group with 2 cases (0.8%) versus 19 cases (5.8%) in the "mini-open" group [p=0.002]. Disc herniation following single level and multi-level operations were also significantly lower in the minimally invasive group compared with the "mini-open" group (2 versus 11 and 0 versus 8 respectively; p<0.05).ConclusionPostoperative index level disc herniation following lumbar spinal decompression occurs in 3.7% of surgeries. Minimally invasive surgery is found to result in less herniations then "mini-open".

Today electricity is distributed to the individual users via physical Copper connections. From the power stations, the energy is transported through transmission lines where the electricity, sinusoidally modulated at a fixed frequency of about 50 Hz, is raised up to a voltage in a range approximately variating between 30, and 110 kV. When electrons, aligned at this voltage, pass through the transmission lines, they cause coherent vibrations, sometimes even audible in the acoustic frequencies. In the spectrum of movements to which electric cables are subjected, there are also unexpected and sporadic movements caused by winds and rain. The problem of carrying out persistent, precise and spatially distributed monitoring of transmission line health is to use a large number of sensors physically distributed everywhere and somehow transmit the results to a control station. The use of synthetic aperture radar (SAR) data could be a viable solution of persistent and wide-area high-voltage vibration monitoring. In this paper we produce a comprehensive survey on how to monitor both movements in terms of vibrational displacement, due to weathering, and those due to electricity transmission, normally fixed at about 50 Hz. The results demonstrate the technical feasibility of an operational use of SAR for these purposes in the very short term. This would allow the possibility of monitoring malfunctions such as black-outs or other frequency anomalies.

Remote sensing plays a central role in the assessment of environmental phenomena and has increasingly become a powerful tool for monitoring shorelines, rivers morphology, flood waves delineation and floods assessment. Optical based monitoring and characterization of river evolution at long time scales is a key tool in fluvial geomorphology. However, the evolution occurring during extreme events is crucial for the understanding of the river dynamics under severe flow conditions and requires the processing of data from active sensors to overcome cloud obstructions. This work proposes a cloud-based unsupervised algorithm for the intra-event monitoring of river dynamics during extreme flow conditions based on time series of Sentinel-1 SAR data. The method allows the extraction of multi-temporal series of spatially explicit geometric parameters at high time and spatial resolutions, linking them to the hydrometric levels acquired by reference gauge stations. Intra-event reconstruction of inundation dynamics has led to the estimation of the relationship between hydrometric level and wet area extension and the assessment of bank erosion phenomena. Time series of SAR acquisitions, provided by Copernicus Sentinel-1 satellites, were analyzed to quantify changes in the wet area of a reach of the Tagliamento river under different flow conditions. The algorithm, developed within the Python-API of GEE, first empowers the Sentinel-1 images with the hydrometric level, then involves radiometric slope correction and speckle noise filtering. The Otsu method is then used for image segmentation leading to a water and dry land binary classification. Results support many types of analysis about river dynamics, including morphological changes, floods monitoring and relief efforts and bio-physical habitat dynamics. The results encourage future advancements and applications of the algorithm, specifically exploring SAR data from ICEYE and Capella Space constellations, which offer significantly higher spatial and temporal resolutions compared to Sentinel-1 data.

The province of Jaén (Andalusia, Spain), despite being declared the European territory with the largest number of defensive constructions (castles and fortifications), has few conservation plans, with many remains included on the Red List of Spanish Heritage lying abandoned. This presents a problem for the conservation of the landscape and the optimal use of the province’s tourism potential. Through the study case of Magaña hamlet two actions are proposed to alleviate this situation: The creation of an archaeological and environmental risk chart with which to answer such questions as “How have climate change, anthropic alterations and environmental charac-teristics affected the state of conservation of certain heritage sites?” and to put forward proposals for improving their protection using as a basis digital and technological tools, such as remote sensing (SAR), taking advantage of data from the Sentinel 2A and 2B satellites, HBIM, RPAS and GIS. To foster the promotion of smart tourism by digitalising and virtualising tourist routes and archaeological remains by building a Smart Tourism App for mobile devices. Finally, public administrations will be apprised of the need to implement a conservation policy for cultural as-sets and their surroundings in a simple, quick and cost-effective manner.

In the past years, our knowledge of coastal environments has been enriched by remotely sensed data. However, to successfully extract information from a combination of different sensors systems, it should be understood how these interact with the common coastal environment. In this research we co-analyze two sensor systems: Terrestrial Laser Scanning (TLS) and satellite based Synthetic Aperture Radar (SAR). TLS shows large potential for examining coastal processes thanks to the possibility to retrieve repeated, accurate and dense topographic information in a rapid and non-invasive manner. However, TLS presents some limits due to its high economic costs and limited field of view. SAR systems are among the most used active remote sensor system for Earth Observation. Despite their relatively low resolution, SAR systems provide the ability to monitor and map coastal areas with complete, repeated and frequent coverage, penetrating through clouds and providing all weather monitoring. Moreover, Sentinel-1 SAR images are freely available. The availability of a permanently installed TLS system (PLS, Permanent Laser Scanner) allows us, to extensively compare Sentinel-1 SAR data and topographic laser scans during different conditions on a sandy beach. PLS data are compared with simultaneous Sentinel-1 SAR images in order to investigate the combined use of PLS and SAR in coastal environments. The purpose of this comparison is the investigation of a possible relation between PLS and SAR data: knowing their relation, SAR dataset could be correlated to beaches characteristics. Meteorological and surface roughness have also been taken into consideration in the evaluation of the correlation between PLS and SAR data. The permanently installed laser scanner for the present study is located in Noordwijk (the Netherlands). A generally positive but low correlation exists between the two variables. When considering weather phenomena, their correlation increases and shows a dependence on wind directions and speed. The correlation with the surface roughness, evaluated in terms of root-mean squared height, also depends on specific wind speed and directions.

Space-borne Synthetic Aperture Radar (SAR) Interferometry (InSAR) is now a key geophysical tool for surface deformation studies. The European Commission’s Sentinel-1 Constellation began acquiring data systematically in late 2014. The data, which are free and open access, have global coverage at moderate resolution with a 6 or 12-day revisit, enabling researchers to investigate large-scale surface deformation systematically through time. However, full exploitation of the potential of Sentinel-1 requires specific processing approaches as well as the efficient use of modern computing and data storage facilities. Here we present LiCSAR, an operational system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR is designed to automatically produce geocoded wrapped and unwrapped interferograms and coherence estimates, for large regions, at 0.001° resolution (WGS-84 system). The products are continuously updated in a frequency depending on prioritised regions (monthly, weekly or live update strategy). The products are open and freely accessible and downloadable through an online portal. We describe the algorithms, processing, and storage solutions implemented in LiCSAR, and show several case studies that use LiCSAR products to measure tectonic and volcanic deformation. We aim to accelerate the uptake of InSAR data by researchers as well as non-expert users by mass producing interferograms and derived products.

Indigenous forests cover 24% of New Zealand and provide valuable ecosystem services. However, a national map of forest types, that is, physiognomic types, which would benefit conservation management, does not currently exist at an appropriate level of detail. While traditional forest classification approaches from remote sensing data are based on spectral information alone, the joint use of space-based optical imagery and structural information from synthetic aperture radar (SAR) and canopy metrics from air-borne Light Detection and Ranging (LiDAR) facilitates more detailed and accurate classifications of forest structure. We present a support vector machine (SVM) classification using data from ESA’s Sentinel-1 and 2 missions, ALOS PALSAR, and airborne LiDAR to produce a regional map of physiognomic types of indigenous forest in New Zealand. A five-fold cross-validation of ground data showed that the highest classification accuracy of 80.9% is achieved for bands 2, 3, 4, 5, 8, 11, and 12 from Sentinel-2, the ratio of bands VH and VV from Sentinel-1, HH from PALSAR, and mean canopy height and 97th percentile canopy height from LiDAR. The classification based on the optical bands alone was 73.1% accurate and the addition of structural metrics from SAR and LiDAR increased accuracy by 7.8%. The classification accuracy is sufficient for many management applications for indigenous forest in New Zealand, including biodiversity management, carbon inventory, pest control, ungulate management, and disease management. National application of the method will be possible in several years, once national LiDAR coverage is achieved, and a national canopy height model is available.

In this paper, sea surface wind speed (SSWS) retrieval from Gaofen-3 (GF-3) quad-polarization stripmap (QPS) data in vertical-vertical (VV), horizontal-horizontal (HH) and vertical-horizontal (VH) polarizations is investigated in detail based on 3,170 scenes acquired from October 2016 to May 2018. The radiometric calibration factor of the VV polarization data is examined first. This calibration factor generally meets the requirement of SSWS retrieval accuracy with an absolute bias of less than 0.5 m/s but shows highly dispersed characteristics. These results lead to SSWS retrievals with a small bias of 0.18 m/s but a rather high root mean square error (RMSE) of 2.36 m/s compared with the ERA-Interim reanalysis model data. Two refitted polarization ratio (PR) models for the QPS HH polarization data are presented. Based on a combination of the incidence angle- and azimuth angle-dependent PR model and CMOD5.N, the SSWS derived from the QPS HH data shows a bias of 0.07 m/s and an RMSE of 2.26 m/s relative to the ERA-Interim reanalysis model wind speed. A linear function relating SSWS and the normalized radar cross section (NRCS) of QPS VH data is derived. The SSWS data retrieved from the QPS VH data show good agreement with the WindSat SSWS data, with a bias of 0.1 m/s and an RMSE of 2.02 m/s. We also apply the linear function to the GF-3 Wide ScanSAR data acquired for the typhoon SOULIK, which surprisingly yields a very good agreement with the model results. A comparison of SSWS retrievals among three different polarization datasets is also presented. The current study and our previous work demonstrate that the general accuracy of the SSWS retrieval based on GF-3 QPS data has an absolute bias of less than 0.3 m/s and an RMSE of 2.0 ±0.2 m/s relative to various datasets. Further improvement will depend on dedicated radiometric calibration efforts.

As human activities of the countries in the East Asia have been remarkably expanding over recent decades, various problems in relation to ships, such as oil spill and many other coastal marine pollution, are continuously occurring in the coastal region. In order to conserve marine resources and prepare for possible ship accidents in advance, the need for efficient ship management is increasing over time. Multi-satellite, multi-sensor, multi-wavelength or multi-frequency observations make it possible to monitor a variety of vessels in the coastal region. This study presents the results of ship detection methodology applied to multi-spectral satellite images in the seas around Korean Peninsula based on optical, hyperspectral, and microwave remote sensing. To detect ships from hyperspectral images with a few hundreds of spectral channels, spectral matching algorithms are used to investigate similarity between the spectra and in-situ measurements. In the case of SAR (Synthetic Aperture Radar) images, the Constant False Alarm Rate (CFAR) algorithm is used to discriminate the vessels from backscattering coefficients of Sentinel-1 SAR and ALOS-2 PALSAR2 images. The present ship detection methods can be extensively utilized for optical, hyperspectral, and SAR images for comprehensive coastal management purposes toward perpetual sustainability in the future.

The aim of this study is to investigate, by means of experimental measurements and full-wave simulations, the dominant factors for the very high-resolution (VHR) radar image speckles from exponential correlated rough surfaces. A Ka-band radar system was used to collect the return signal from such a surface sample fabricated by 3D printing, and that signal was further processed into images at different resolution scales, where the image samples were obtained by horizontally turning around the surface sample. To cross-validate the results and to further discuss the VHR speckle properties, full wave simulations by full 3D Finite Difference Time Domain (FDTD) method were conducted with 1600 realizations for the speckle analysis. At the considered very high resolution, speckle statistics show divergence from the fully developed Rayleigh distribution. The factors that impact on the high-resolution speckle properties from exponential correlated rough surface, are analyzed in views of the equivalent number of scatterers theory and scattering scales, respectively. From the data results and extended discussions, it is evident that both of the above factors matter for VHR speckle of backscattering, from the exponential correlated rough surface as a good representative for the ground surface.

This paper presents a unified reconfigurable coordinate rotation digital computer (CORDIC) processor for floating-point arithmetic. It can be configured to operate in multi-mode to achieve a variety of operations and replaces multiple single-mode CORDIC processors. A reconfigurable pipeline-parallel mixed architecture is proposed to adapt different operations, which maximizes the sharing of common hardware circuit and achieves the area-delay-efficiency. Compared with previous unified floating-point CORDIC processors, the consumption of hardware resources is greatly reduced. As a proof of concept, we apply it to 1638416384 points target Synthetic Aperture Radar (SAR) imaging system, which is implemented on Xilinx XC7VX690T FPGA platform. The maximum relative error of each phase function between hardware and software computation and the corresponding SAR imaging result can meet the accuracy index requirements.

The information of seismic damage of buildings in SAR images of different time phase, especially in SAR images after earthquake, is easily disturbed by other factors, which affects the accuracy of information discrimination. In order to identify and evaluate the distribution information of the seismic damage accurately and make full use of the abundant texture features in the SAR image. The conventional method of change detection based on texture features usually takes the pixel as the calculating unit. In this paper, a method of texture feature change detection of SAR images based on watershed segmentation algorithm is proposed. Based on the optimization of texture feature parameters, the feature parameters are segmented by the watershed segmentation algorithm, and the feature object image is obtained. This method introduces the idea of object oriented, and carries out the calculation of the difference map at the object level, Finally, the classification threshold value of different types of seismic damage types is selected, and the recognition of building damage is achieved. Taking the ALOS data before and after the earthquake in Yushu as an example to verify the effectiveness of the method, the overall accuracy of the building extraction is 88.9%, Compared with pixel-based methods, it is proved that the proposed method is effective.

The emerging concept of planetary health needs to be discussed in a more organized and sustainable way within the global public health and healthcare disciplines. Therefore, planetary health should be considered a cardinal component of the global academic framework for healthcare professionals. The availability of related curricula and courses is crucial to equip health professionals in this relatively new discipline of planetary health. In this review article, we aimed to explore published articles and online databases of courses to summarize the available planetary health education opportunities and discussions for health professionals, to identify the gaps in resource allocation and to suggest future recommendations. We observed a visible re-source inequity in global south with the lack of a universal planetary health module for healthcare professionals. Additionally, there is minimal inclusion of allied health disciplines in this learning process. We, therefore, recommend a dedicated network of motivated healthcare professionals and regional hubs with an agenda to ensure a comprehensive, uniform, and inclusive planetary health education curriculum and practice.

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in-situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service that was co-developed with in-region partners to provide year-round, low-latency weather-hazards information across the HKH. The paper describes the end-user focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which is providing capability to scale automatically with event size. A performance assessment of our quantitative algorithms is described, demonstrating capabilities to map flood extent and water depth at an accuracy of >90% and

Ship detection from synthetic aperture radar (SAR) imagery is crucial for various fields in real-world applications. Numerous deep learning-based detectors have been investigated for SAR ship detection, which require a substantial amount of labeled data for training. However, SAR data annotation is time-consuming and demands specialized expertise, resulting in that deep learning-based SAR ship detectors struggle due to a lack of annotations. With limited labeled data, semi-supervised learning is a popular approach for boosting detection performance by excavating valuable information from unlabeled data. In this paper, a semi-supervised SAR ship detection network is proposed, termed as Multi-Teacher Dempster-Shafer Evidence Fusion Net-work (MTDSEFN). The MTDSEFN is an enhanced framework based on the basic teacher-student skeleton frame, comprising two branches: the Teacher Group (TG) and the Agency Teacher (AT). The TG utilizes multiple teachers to generate pseudo-labels for different augmentation versions of unlabeled samples, which are then refined to obtain high-quality pseudo-labels by using Dempster-Shafer (D-S) fusion. The AT not only serves to deliver weights of its own teacher to the TG at the end of each epoch, but also updates its own weights after each iteration, enabling the model to effectively learn rich information from unlabeled data. The combination of TG and AT guarantees both reliable pseudo-label generation and learning comprehensive diversity information from numerous unlabeled samples. Extensive experiments were performed on two public SAR ship datasets, and the results demonstrated the effectiveness and superiority of the proposed approach.

Marine oil spills seriously threaten the coastal environment, challenge the safety of marine eco-system, and cause huge economic losses to marine fishery, aquaculture, and tourism industries. Timely and accurate oil spill monitoring and early warning can not only make the oil spill acci-dent be handled timely and effectively but also provide key information for investigating the re-sponsibility of the polluters. Polarimetric synthetic aperture radar (SAR) has shown its great po-tential in oil spill classification for its capacity to distinguish mineral oil and biogenic look-alikes. Traditional oil spill classification approaches rely on the scattering matrix elements or pre-designed scattering mechanism-based polarimetric SAR features, which limits its robustness and generalization in large-scale marine surveillance tasks. The recent development of deep learning frameworks, especially the convolutional neural network (CNN) provides a powerful tool for remote sensing image classification. However, conventional CNN only accepts re-al-valued input, which cannot efficiently analyze the polarimetric information carried by com-plex-valued SAR data. In this study, a new framework that employs the complex-valued neural network was proposed for marine oil spill detection. Sophisticated studies were designed to evaluate the contribution of the physical scattering mechanism-based features, complex-valued framework, and convolutional feature extraction structures respectively. Comparative studies were conducted on four study cases using Radarsat-2 and SIR-C fully polarimetric SAR data. The outcome of the proposed study may shed light on the development of an end-to-end marine spill classification system and enlarge its application in practical marine surveillance operations.

Target detection and segmentation in synthetic aperture radar (SAR) images are vital steps for many remote sensing applications. In the era of data-driven deep learning, this task is extremely challenging due to the limited labeled data. Few-shot Learning has the ability to learn quickly from few samples with supervised information. Inspired by this, a few-shot learning framework named MSG-FN is proposed to solve the segmentation of ship targets in heterologous SAR images with few annotated samples. The proposed MSG-FN adopts a dual-branch network consisting of a support branch and a query branch. The support branch is used to extract features with an encoder, and the query branch uses a U-shaped encoder-decoder structure to segment the target in the query image. The encoder of each branch is composed of well-designed residual blocks combined with filter response normalization to capture robust and domain-independent features. A multi-scale similarity guidance module is proposed to improve the scale adaptability of detection by applying hand-on-hand guidance of support features to query features of various scales. In addition, a SAR dataset named SARShip-4i is built to evaluate the proposed MSG-FN and the experimental results show that the proposed method achieves superior segmentation results compared with the state-of-the-arts.

The province of Jaén (Andalusia, Spain), despite being declared the European territory with the largest number of defensive constructions (castles, fortifications, etc.), has few conservation plans, with many remains included on the Red List of Spanish Heritage ly-ing abandoned. This poses a challenge for landscape conservation and the optimal use of the province’s tourism potential. In this paper we present the early implementation of an ongoing methodological approach designed to alleviate this situation. It takes advantage of data from the Sentinel 2A and 2B satellites, HBIM, RPAS, GIS and fieldwork that will be applied across five archaeological case studies. It has been tested for the first time in the village of Magaña. Three main objectives and phases of this methodological approach have been designed: 1. Preparation of an Environmental Risk Chart with which to answer such ques-tions as “How have climate change, anthropic alterations and environmental characteris-tics affected the state of conservation of certain heritage sites?” 2. Preparation of HBIM models to foster monitorisation and conservation policies for the main archaeological remains and improving their protection based on digital and technological tools. 3. Building a Smart Tourism App for mobile devices aimed at promoting smart tourism by digitalising and virtualising tourist itineraries and archaeological remains. Finally, public administrations will be apprised of the need to implement a conservation policy for cultural assets and their surroundings in a simple, quick and cost-effective man-ner.

To explore a new set of anticancer agents, a novel series of pyrazolo[4,3-e]pyrido[1,2-a]pyrimidine derivatives 7a-l have been designed and synthesized via cyclocondensation reactions of pyrazolo-enaminone 5 with a series of arylidene malononitriles; compound 5 was obtained from 5-amino-4-cyanopyrazole (3). The structures of the target compounds 7a-l were investigated by spectral techniques and elemental analysis (IR, UV-Vis, 1H NMR, 13C NMR and ESI-MS). All compounds were evaluated for their in vitro cytotoxicity employing a panel of different human tumor cell lines, A375, HT29, MCF7, A2780, FaDu as well as non-malignant NIH 3T3 and HEK293 cells. It has been found that the conjugate 7e was the most active towards many cell lines with EC50 values ranging between 9.1 and 13.5 µM, respectively. Moreover, in silico docking studies of 7e with six anticancer drug targets, i.e. DHFR, VEGFR2, HER-2/neu, hCA-IX, CDK6 and LOX also was performed, in order to gain some insights into their putative mode of binding interaction and to estimate the free binding energy of this bioactive molecule.

Satellite remote sensing is a powerful tool to map flooded areas. In the last years, the availability of free satellite data sensibly increased in terms of type and frequency, allowing producing flood maps at low cost around the World. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. As case studies, we selected three flood events recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, Sentinel-2 and SAR data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., MNDWI; SAR backscattering variation; Supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data like DEM based water depth model and available ground truth data. For the areas affected by major floods, we also validated and compared the produced flood maps with official maps made by river authorities. We calculated flood detection performance (flood ratio) for the different datasets we used. The results show that it is necessary to take into account different factors for the choice of best satellite data, among these, the time of satellite pass with respect to the flood peak is the most important one. SAR data showed good results only for co-flood acquisitions, whereas multispectral images allowed detecting flooded areas also with the post-flood acquisition. With the support of ancillary data, it was possible to produce reliable geomorphological based flood maps in the study areas.

Secondary forests (SF) are important carbon sinks, removing CO₂ from the atmosphere through the photosynthesis process and storing photosynthates in their aboveground live biomass (AGB). This process occurring at large-scales partially counteracts C emissions from land-use change, playing, hence, an important role in the global carbon cycle. The absorption rates of carbon in these forests depend on forest physiology, controlled by environmental and climatic conditions as well as on the past land use, which is rarely considered for retrieving AGB from remotely sensed data. In this context, the main goal of this study is to evaluate the potential of full polarimetric ALOS-2 PALSAR-2 data for estimating AGB by taking into account the past-land use of SF areas in the Brazilian Amazon. We surveyed a chronosequence of 42 SF plots (20 ha) near the Tapajós National Forest in Pará state to quantifying AGB growth rates. We explored the full polarimetric data testing three regression models including non-linear (NL), multiple linear regressions models (MLR), and the semi-empirical extended water cloud model (EWCM). The results showed that the intensity of previous use has affected the structure of SF by reducing the AGB accumulation and being noticeable by several polarimetric attributes. The combination of multiple prediction variables with MLR improved the AGB estimation by 70% comparing amongst other models (R² adj. = 0.51; RMSE = 13.2 Mg ha^-1) bias = 2.1 ± 37.9 Mg ha^-1. The error propagation of the MLR model was estimated to be 15%.

This paper considers target characterization and recognition in radar images with keypoint-based local descriptor. Most of the preceding works rely on the global features or raw intensity values, and hence produce the limited recognition performance. Moreover, the global features are sensitive to the real-world sources of variability, such as aspect view, configu-ration, and incidence angle changes, clutter, articulation, and occlusion. Keypoint-based local descriptor was developed as a powerful strategy to address invariance to contrast change and geometric distortion. This property inspires us to investigate whether the family of local features are relevant for radar target recognition. Most of the preceding works typically devote to finding the correspondences between a collected image and a reference one. The representative applications include image register and change detection. Little work was pursued to target recognition in SAR images. This is because the huge number of local descriptors resulting from radar images make the computational cost and memory consumption unacceptable. To handle the problems, this paper develops two families of methods. The proposed methods are used to achieve target recognition by means of local descriptors. Our first solver refers to building multiple linear regression models, and addresses the problem by the theory of sparse representation. The second scheme rebuilds a new feature by the feature quantization skill, from which the inference can be drawn. Multiple comparative studies are pursued to verify the performance of detectors and descriptors popularly used. The source code was publicly released on https://ganggangdong.github.io/homepage/.

Adeno-Associated Viruses (AAVs, genus Dependoparvovirus) are the leading gene therapy vector. Until recently, efforts to enhance their capacity for gene delivery had focused on their capsid. However, efforts are increasingly shifting towards improving the viral replicase, Rep78. We discovered that Rep78 and its shorter isoform Rep52 contain a strictly C-terminal sequence motif, DDx3EQ, conserved in most dependoparvoviruses. The motif is highly negatively charged and devoid of prolines. Its wide conservation suggests that it is required for the life cycle of dependoparvoviruses. Despite its short length, the motif’s strictly C-terminal position has the potential to endow it with a high recognition specificity. A candidate target of the DDx3EQ motif might be the DNA-binding interface of the origin-binding domain of Rep78, which is highly positively charged. Published studies suggest that the motif is not required for recombinant AAV production, but that substitutions within it might improve production.

Background. Minimally invasive surgery via right mini-thoracotomy has become the standard of care for the treatment of mitral valve disease worldwide, particularly at high volume centers. In recent years, the spectrum of indications has progressively shifted and extended to fragile and higher risk patients, also addressing more complex mitral valve disease and ultimately including patients with native or prosthetic infective endocarditis. The rationale for the adoption of the minimally invasive approach is to minimize the surgical trauma, promote an earlier postoperative recovery and reduce the incidence of surgical wound infection and other nosocomial infections. The aim of this retrospective observational study is to evaluate the effectiveness and the early and late outcome in patients undergoing minimally invasive surgery for mitral valve infective endocarditis. Methods. Prospectically collected data regarding minimally invasive surgery in patients with mitral valve infective endocarditis were entered into a dedicated database for the period between January 2007 and December 2022 and retrospectively analyzed. All comers during the study period underwent a preoperative evaluation based upon clinical history and anatomy for the allocation to the most appropriate surgical strategy. Selection for the mini-thoracotomy approach was primarily driven by a thorough transthoracic and especially transesophageal echocardiographic evaluation, coupled with total body and vascular imaging. Results. During the study period, 92 patients underwent right mini-thoracotomy to treat native (80/92, 87%) or prosthetic (12/92, 13%) mitral valve endocarditis at our institution, representing 5% of the patients undergoing minimally invasive mitral surgery. Twenty-six (28%) patients had undergone previous cardiac operations, whereas 18 (20%) presented preoperatively with complications related to endocarditis, most commonly systemic embolization. Sixty-nine and 23 patients, respectively, underwent early surgery (75%) or were operated on after the completion of targeted antibiotic treatment (25%). A conservative procedure was feasible in 16/80 (20%) patients with native valve endocarditis. Conversion to standard sternotomy was necessary in a single case (1.1%). No cases of intraoperative iatrogenic aortic dissection were reported. Four patients died perioperatively, accounting for a thirty-day mortality of 4.4%. Causes of death were refractory heart or multiorgan failure and/or septic shock. New onset stroke was observed postoperatively in one case (1.1%). Overall actuarial survival rate at 1 and 5 years after operation was 90.8% and 80.4%, whereas freedom from mitral valve reoperation at 1 and 5 years was 96.3% and 93.2%, respectively. Conclusions. The present study shows good early and long-term results in higher-risk patients undergoing minimally invasive surgery for mitral valve infective endocarditis. Total body, vascular and echocardiographic screening represent the key points to select the optimal approach and allow the extension of indications for minimally invasive surgery to sicker patients, including active endocarditis and sepsis.

Large animal experiments are important for preclinical studies of regenerative stem cell transplantation therapy. Therefore, we investigated the differentiation capacity of pig skeletal muscle-derived stem cells (Sk-MSCs) as an intermediate model between mice and humans for nerve muscle regenerative therapy. Enzymatically extracted cells were obtained from green-fluorescence transgenic micro-mini pigs (GFP-Tg MMP) and sorted as CD34+/45- (Sk-34) and CD34-/45-/29+ (Sk-DN) fractions. The ability to differentiate into skeletal muscle, peripheral nerve, and vascular cell lineages was examined by in vitro cell culture and in vivo cell transplantation into the damaged tibialis anterior muscle and sciatic nerves of nude mice and rats. Protein and mRNA levels were analyzed using RT-PCR, immunohistochemistry, and immunoelectron microscopy. The myogenic potential, which was tested by Pax7 and MyoD expression and the formation of muscle fibers, was higher in Sk-DN cells than in Sk-34 cells but remained weak in the latter. In contrast, the capacity to differentiate into peripheral nerve and vascular cell lineages was totally stronger in Sk-34 cells. In particular, Sk-DN cells did not engraft to the damaged nerve, whereas Sk-34 cells showed active engraftment and differentiation into perineurial/endoneurial cells, endothelial cells, and vascular smooth muscle cells, similar to the human case, as previously reported. Therefore, we concluded that Sk-34 and Sk-DN cells in pigs were closer to those in humans than to those in mice.

High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers and synthetical aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations with the goal of enhancing the accuracy of ocean wind datasets during tropical cyclones (TCs). In 10°longitude×10°latitude blocks, each containing a TC, Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) winds are 6.5% and 4.8% higher than CCMP, while Advanced Scatterometer (ASCATB) is 0.8% lower. For extratropical cyclones, AMSR2 and SMAP also show stronger winds with a 5% difference, and ASCATB is about 0.3% weaker compared to CCMP. The comparison between SAR and CCMP for TC winds, sampled at the locations and time frames of SAR tiles, indicates that SAR winds around TCs are about 9% higher than CCMP winds. Using empirically defined TC structural indices, we find that the TCs observed by CCMP are shifted in locations and lack a compact core region. A Random Forest (RF) regressor was applied to TCs in CCMP with corresponding SAR observations, nearly correcting the full magnitude of low bias in CCMP statistically, with a 15m/s correction in the core region. The hierarchy of importance among the predictors is as follows: CCMP wind speed (62%), distance of SAR pixels to the eye region (21%) and eye center (7%), and distance of CCMP pixels to the eye region (5%) and eye center.

Synthetic Aperture Radar (SAR) is an active sensor that uses microwave for sense, it is unrestricted by weather and illumination conditions, and it can observe targets all day and weather. Aircraft targets are important monitoring objects in military and civilian fields, and how to efficiently detect and recognize aircraft targets is an important topic in the field of SAR image interpretation. Based on the features of SAR images, such as complex background, high resolution, and multi-scale, we proposed an improved method based on YOLOv5s. Firstly, this paper proposed the structure of the multi-scale receptive field and channel attention fusion, which is applied to the shallow layer of the backbone of YOLOv5s, it can adjust the weights of the multi-scale receptive field during the training process to enhance the extraction ability of feature information. Secondly, we proposed four de-coupled detection heads to replace the original part in YOLOv5s, which can improve the efficiency and accuracy of SAR image interpretation for small targets. Thirdly, in the case of the limited amount of SAR images, this paper proposed multi methods of data augmentation, which can enhance the diversity and generalization of the network. Fourthly, this paper proposed the K-means++ to re-place the original K-means to improve the network convergence speed and detection accuracy. Finally, Experiments demonstrate that the improved YOLOv5s can enhance the accuracy of SAR image interpretation by 9.3%, and the accuracy of small targets is improved more obviously, reaching 13.1%.

The application of deep learning in the detection of Synthetic Aperture Radar (SAR) targets has been primarily limited to large objects such as ships and airplanes, with much less popularity in detecting SAR vehicles. The complexities of SAR imaging make it difficult to distinguish small vehicles from the background clutter, creating a barrier to data interpretation and the development of Automatic Target Recognition (ATR) in SAR vehicles. The scarcity of datasets has inhibited progress in SAR vehicle detection in the data-driven era. To address this, we introduce a new synthetic dataset called Mix MSTAR, which mixes target chips and clutter backgrounds with original radar data at the pixel level. Mix MSTAR contains 5,392 objects of 20 fine-grained categories in 100 high-resolution images, predominantly 1478x1784 pixels. The dataset includes various landscapes such as woods, grasslands, urban buildings, lakes, and tightly arranged vehicles, each labeled with Oriented Bounding Box (OBB). Notably, Mix MSTAR presents fine-grained object detection challenges by using the Extended Operating Condition (EOC) as a basis for dividing the dataset. Furthermore, we evaluate 9 benchmark rotated detectors on Mix MSTAR and demonstrate the fidelity and effectiveness of the synthetic dataset. To the best of our knowledge, Mix MSTAR represents the first public multi-class SAR vehicle dataset designed for rotated object detection in large-scale scenes with complex background. Mix MSTAR is available at: https://github.com/TheGreatTreatsby/Mix-MSTAR-mmrotate.

Background and Aims: Liver cirrhosis leads to clinically significant portal hypertension. Transjugular intrahepatic portosystemic shunt (TIPS) has been shown to effectively reduce the degree of portal hypertension and treat its complications. However, poor nutritional status has been shown to be associated with hepatic encephalopathy, acute on chronic liver failure, and mortality following TIPS placement. The purpose of this systematic review is to create another perspective and evaluate the effect of TIPS placement on the nutritional status of patients with liver cirrhosis. Methods: A comprehensive search of four major electronic databases was conducted to identify studies that assessed the nutritional status of cirrhotic patients before and after TIPS placement. The risk of bias was evaluated using ROBINS-I guidelines. Results: Fifteen studies were analyzed in this review. The results indicate that among the 11 studies that evaluated changes in ascites-free weight and body mass index or body cell mass, 10 reported an improvement in one or more measures. Furthermore, all 7 studies that evaluated changes in muscle mass demonstrated an increase in muscle mass. Among the 4 studies that evaluated subcutaneous fat tissue, 3 showed a significant expansion, while 2 out of 3 studies evaluating visceral fat tissue reported a significant reduction. Conclusion: The results of this systematic review suggest that TIPS placement is associated with improvement in the nutritional status of cirrhotic patients, indicated by an increase in ascites-free weight, body mass index, and muscle mass. Additionally, TIPS placement leads to a shift in the distribution of fat mass, with a preference for subcutaneous over visceral adipose tissue. Notably, sarcopenic patients seem to benefit the most from TIPS placement in terms of the nutritional status.

Due to the increasing importance of mangroves in climate change mitigation projects, more accurate and cost-effective aboveground biomass (AGB) monitoring methods are required. However, field measurement of AGB may be a challenge because of its remote location and the difficulty to walk in these areas. This study is based on the Livelihoods Fund’ Oceanium project of 10,000 hectare mangrove plantations monitoring. In a first step, the possibility of replacing traditional field measurements of sample plots in a young mangrove plantation by a semiautomatic processing of UAV-based photogrammetric point clouds was assessed. In a second step, Sentinel-1 radar and Sentinel-2 optical imagery were used as auxiliary information to estimate AGB and its variance for the entire study area under a model-assisted framework. AGB was measured using UAV imagery in a total of 95 sample plots. UAV plot data was used in combination with non-parametric Support Vector Regression (SVR) models for the estimation of the study area AGB using model-assisted estimators. Purely UAV-based AGB estimates and their associated standard error (SE) were compared with model-assisted estimates using (1) Sentinel-1, (2) Sentinel-2 and (3) a combination of Sentinel-1 and Sentinel-2 data as auxiliary information. The validation of the UAV-based individual tree height and crown diameter measurements showed a root mean square error (RMSE) of 0.21 m and 0.32 m respectively. Relative efficiency of the three model-assisted scenarios ranged between 1.61 and 2.15. Although all SVR models improved the efficiency of the monitoring over UAV-based estimates, the best results were achieved when a combination of Sentinel-1 and Sentinel-2 data was used. Results indicated that the methodology used in this research can provide accurate and cost-effective estimates of AGB in mangrove young plantations.

In this paper, we analyze the measurements of the normalized radar cross-section(NRCS) in Wave Mode for Chinese C-band Gaofen-3(GF-3) synthetic aperture radar (SAR). Based on 2779 images from GF-3 quad-polarization SAR in Wave Mode and collocated wind vectors from ERA-Interim, we verify the feasibility of using ocean surface wind fields and VV-polarized NRCS to perform normalized calibration. The method uses well-validated empirical C-band geophysical model function (CMOD4) to estimate the calibration constant for each beam. The Amazon rainforest experiment results show that the accuracy of obtained calibration constant meets the requirements. In addition, the relationship between cross-pol NRCS and wind vectors is discussed. The cross-pol NRCS increases linearly with wind speed and it has an approximate cosine modulation with the wind direction when the wind speed is greater than 8m/s. The cross-polarized system noise floor is low enough to ignore it in wind retrieval. Furthermore, we also investigate the properties of the polarization ratio, denoted PR, and show that it is dependent on incidence angle and azimuth angle. Two empirical models of the PR are fitted, one as a function of incidence angle only, the other with additional dependence on azimuth angle. Assessments show that the σ_VV^0 retrieved from new PR models as well as σ_HH^0 is in good agreement with σ_VV^0 extracted from SAR images directly. And it is also shown that considering the azimuth angle can improve polarization conversion accuracy.

Targets detection in synthetic aperture radar (SAR) remote sensing images, which is a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. Besides, the ability of human visual system to detect visual saliency is extraordinarily fast and reliable. However, computational modeling of SAR image scene still remains a challenge. This paper analyzes the defects and shortcomings of traditional visual models applied to SAR images. Then a visual attention model designed for SAR images is proposed. The model draws the basic framework of classical ITTI model; selects and extracts the texture features and other features that can describe the SAR image better. We proposes a new algorithm for computing the local texture saliency of the input image, then the model constructs the corresponding saliency maps of features; Next, a new mechanism of feature fusion is adopted to replace the linear additive mechanism of classical models to obtain the overall saliency map; Finally, the gray-scale characteristics of focus of attention (FOA) in saliency map of all features are taken into account, our model choose the best saliency representation, Through the multi-scale competition strategy, the filter and threshold segmentation of the saliency maps can be used to select the salient regions accurately, thereby completing this operation for the visual saliency detection in SAR images. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X), Radarsat-2, are used to evaluate the performance of visual models. The results show that our model provides superior performance compared with classical visual models. By further contrasting with the classical visual models, Our model reduce the false alarm caused by speckle noise, and its detection speed is greatly improved, and it is increased by 25% to 45%.

The formula of the Generalized Ambiguity Function (GAF) of passive bistatic SAR system using the non-cooperative illuminators which transmit PSK modulating signals is derived to analyze the spatial resolution of the system. The average GAF is introduced to remove the effect of particular sequence of symbols on resolution because the particular sequence of symbols is usually unpredictable before being received. The influence of the waveform parameters of the PSK modulating signals, such as length of the symbol sequence and roll-off factor, on spatial resolution is investigated by numerical simulation. It is confirmed that the influence of the length of the symbol sequence and roll-off factor is very slight but still exists.

Glacier dynamics play an important role in the mass balance of many glaciers, ice caps and ice sheets. In this study we exploit Radarsat-2 (RS-2) Wide Fine (WF) data to determine the surface speed of Svalbard glaciers in the winters of 2012/2013 and 2013/2014 using Synthetic Aperture RADAR (SAR) offset and speckle tracking. The RS-2 WF mode combines the advantages of the large spatial coverage of the Wide mode (150 x 150 km) and the high pixel resolution (9m) of the Fine mode and thus has a major potential for glacier velocity monitoring from space through offset and speckle tracking. Faster flowing glaciers (1.95 m d^-1 - 2.55 m d^-1) which are studied in detail are Nathorstbreen, Kronebreen, Kongsbreen and Monacobreen. Using our Radarsat-2 WF dataset, we compare the performance of two SAR tracking algorithms, namely the GAMMA Remote Sensing Software and a custom written MATLAB script (GRAY method) that has primarily been used in the Canadian Arctic. Both algorithms provide comparable results, especially for the faster flowing glaciers and the termini of slower tidewater glaciers. A comparison of the WF data to RS-2 Ultrafine and Wide mode data reveals the superiority of RS-2 WF data over the Wide mode data.

This paper provides a study on the development of solar energy, its prospects, and its impacts in Africa. Although the main focus is on technologies adopted for different uses (solar farms, mini-grids, off-grid, solar houses, irrigation, lighting, etc.), we also consider some aspects that increase the adoption of solar energy, such as legislation and business models. Finally, we will examine current and future solar projects managed by NGOs and international companies and how they benefit to leveraging social and economic standards, quality of life as well as capacity building in Africa. This study shows the plethoric interest of institutions outside Africa in the market of solar energy in the continent and that there is no African strategy to increase its independency toward a sustainable development particularly through energy transition.

In recent years, Synthetic Aperture Radar (SAR) has received attention in the Automatic Target Recognition (ATR) field due to its ability to acquire images both day and night, combined with the superior resolution offered by the latest sensors. However, despite advances, there remains a deficiency in dataset quality and quantity. This has necessitated the use of simulated data to overcome challenges associated with obtaining large quantities of high-quality images of targets and their subsequent labeling. In this study, we trained a Convolutional Neural Network (CNN) using a simulated dataset, subsequently employing it to classify real images from the MSTAR dataset. Traditional training methodologies often struggle with generalization when confronted with domain disparities between simulated and real data. To mitigate this, we exploited Margin Disparity Discrepancy (MDD), a domain adaptation technique not previously explored in SAR context. Findings from this study showcased a 13% increase in classification accuracy and improved generalization using MDD. Through Explainable AI (XAI) techniques such as t-SNE and other metrics like Kullback Leibler Divergence (KLD), we revealed a more distinct alignment of feature spaces between the two domains after domain adaptation, emphasizing MDD’s role in enhancing model accuracy and generalization.

The present review explores the critical role of oxime and oxime ether moieties in enhancing the physicochemical and anticancer properties of structurally diverse molecular frameworks. Specific examples are carefully selected to illustrate the distinct contributions of these functional groups to general strategies for molecular design, modulation of biological activities, computational modeling, and structure-activity relationship studies. An extensive literature search was conducted across three databases, including Pub-Med, Google Scholar, and Scifinder, enabling us to create one of the most comprehensive overviews of how oximes and oxime ethers impact antitumor activities within a wide range of structural frameworks. This search focused on various combinations of keywords or their synonyms, related to the anticancer activity of oximes and oxime ethers, structure-activity relationships, mechanism of action, as well as molecular dynamics and docking studies. Each article was evaluated based on its scientific merit and the depth of the study, resulting in 268 cited references and more than 336 illustrative chemical structures carefully selected to support this analysis. As many previous reviews focus on one subclass of this extensive family of compounds, this report represents one of the rare and fully comprehensive assessments of the anti-cancer potential of this group of molecules across diverse molecular scaffolds.

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multi-scene and multi-size, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.

Land Surface Temperature (LST) is significant for climatological and environmental studies. LST products from satellites, however, suffer from the tradeoff between spatial and temporal resolution. Spatial downscaling has emerged as a well explored field aiming to overcome limitations arising from this tradeoff. Previous research on regression based LST downscaling models focused on utilizing predictors derived from optical imagery. Weather-dependency of optical imagery data, however, can influence downscaling models by the weather conditions. To cope this issue, in this study, we involve predictors derived from the weather-independent Sentinel-1 Synthetic Aperture Radar (SAR) imagery to downscale Landsat-8 LST data. In this context, we propose to use machine learning techniques, namely Random Forest (RF) and Convolutional Neural Networks (CNN). To demonstrate the applicability and performance of the proposed method, extensive experimental analyses were conducted over Zuid-Holland in the Netherlands. From the experiments, we found that the results obtained with radar predictors were comparable to those achieved using optical predictors. This confirms that the proposed method indeed paves a new way for mapping land surface temperature using SAR images.

Observing cultivated crops and other forms of land use is an important environmental and economic concern for agricultural land management and crop classification. Crop categorization offers significant crop management data, ensuring food security, and developing agricultural policies. Remote sensing data, especially publicly available Sentinel 1 and 2 data, has effectively been used in crop mapping and classification in cloudy places because of their high spatial and temporal resolution. This study aimed to improve crop type classification by combining Sentinel-1 (Synthetic Aperture Rader (SAR)) data and the Sentinel-2 Multispectral Instrument (MSI) data. In the study, Random Forest (RF) and Classification and Regression Trees (CART) classier were used to classify grain crops (Barley and Wheat). The classification results based on the combination of Sentinel-2 and Sentinel-1 data indicated an overall accuracy (OA) of 93 % and a kappa coefficient (K) of 0.896 for RF and (89.15%, 0.84) for the CART classifier. It is suggested to employ a mix of radar and optical data to attain the highest level of classification accuracy since doing so improves the likelihood that the details will be observed in comparison to the single-sensor classification technique and yields more accurate results.

We scrutinize the reliability of multilooked interferograms for deformation analysis. Designing a simple approach in the evaluation of the accuracy of the estimated deformation signals, we reveal a prominent bias in the deformation velocity maps. The bias is the result of propagation of small phase error of multilooked interferograms through the time series and can sum up to 6.5 mm/yr in case of using the error prone short temporal baseline interferograms. We further discuss the role of the phase estimation algorithms in reduction of the bias and put recommend a unified intermediate InSAR product for achieving high-precision deformation monitoring.

The calculation of echo intensity involved in the SAR image simulation is usually based on the electromagnetic formula or approximate formula derived under certain assumptions. However, the parameters used in these formulas are often difficult to obtain, and the formulas have errors with the actual situation. In this paper, a method of embedding deep neural network (DNN) into the simulation process based on ray tracing is proposed, so that the DNN model can be directly used to fit the calculation formula of echo intensity from real SAR images. Simulation results show that this method can obtain SAR images with high similarity.