esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras
## 3 Materials and Methods
### Data Processing  
Video processing begins with the generation of a background model, based on Gaussian mixture models [14; 15], aimed at removing static objects from the image that could generate false positives. For each frame of the video, the background model was upgraded and a pre-trained YOLOv8 model [16] was applied for the object segmentation task. YOLO is an object detection model based on convolutional neural networks that processes entire images in a single stage to identify and locate objects. Specifically, the employed model provides us with information corresponding to both segmentation and object detection. We use the'small' version of this model with a confidence threshold of 0.5. While more recent versions of YOLO are available, preliminary testing indicated that YOLOv8 yields optimal performance for this particular application. An analysis of video processing efficiency reported an average frame rate of 61.74 FPS for YOLOv8, in comparison to YOLOv11, which achieved an average of 59.63 FPS.  
To extract detections of dynamic objects, we compare the areas corresponding to the bounding boxes of all detections in the frame with those areas in the background model. These cutouts are in grayscale and undergo Contrast Limited Adaptive Histogram Equalization (CLAHE) processing [17; 18; 19]. An example can be seen in Figure 1.  
To compare the cutouts, we use the Structural Similarity Index (SSIM) [20; 21], which can take values between \(-1\) and \(1\), with 1 indicating perfect similarity between images, and \(-1\) indicating completely dissimilar images. In our case, we consider a heuristic threshold of 0.8, considering detections valid when their SSIM value is below this threshold. Figure 2 illustrates an example of various comparison outcomes, highlighting the occurrence of both false negatives and false positives. False negatives are defined as objects that, while not part of the background, are misclassified as such due to prolonged inactivity. Conversely, false positives refer to background objects that are mistakenly identified as belonging to distinct object classes.  
Figure 1: Original and background frames shown before and after applying CLAHE processing and grayscale transformation.  
The result of this process is stored in a '.csv' file for each video segment. This file will store information regarding the detection timestamp, the predicted class, prediction confidence, as well as the bounding box position and segmentation mask. No other video information is saved, avoiding any privacy concerns with the data.
## 1 Introduction
Developing efficient fault detection and diagnostic procedures requires a thorough understanding of physical, electrical, and environmental defects in PV systems [13]. Implementing reliable fault detection methods is critical for optimizing energy production, preventing damage to PV panels, and mitigating potential fire hazards. IoT-based approaches using wireless sensor nodes and machine learning algorithms enable real-time fault detection and diagnosis, reducing downtime and maintenance costs. A comprehensive understanding of physical, electrical, and environmental faults in PV systems is crucial for developing effective fault detection techniques. Various methods have been suggested for PV fault diagnostics, focusing on the DC side of the system. Implementing reliable fault detection is essential for optimizing energy production, preventing damage to PV panels, mitigating fire hazards, and extending the operational lifespan of solar energy systems [14; 15]. The purpose of this research is to analyze the energy potential of solar-hydrogen systems within university settings, specifically at the Kangwon National University's Samcheok campus, using advanced machine learning models. Additionally, this study aims to design an efficient fault detection system for solar panels by comparing the performance of Convolutional Neural Networks (CNNs) and ResNet-50. Through this case study, the research seeks to optimize renewable energy systems in academic environments, enhancing their reliability, sustainability, and economic viability.
# Ground Subsidence Analysis in Tianjin (China) Based on Sentinel-1A Data Using MT-InSAR Methods
## 2 Materials and Methods
### Data Description  
To analyze the ground subsidence using MT-InSAR methods, Sentinel-1A single look complex (SLC) SAR images, SRTM DEM and precise orbit determination (POD) ephemerides are necessary in our research.  
Sentinel-1A was launched on 3 April 2014 by the Europe Space Agency (ESA), which provides C-band imagery continuity following the retirement of ERS-2. Sentinel-1A includes four exclusive imaging modes with different resolutions and coverage. Its default acquisition pattern, known as terrain observation by progressive scan (TOPS), minimizing image scalloping and reducing noise, is designed for topographic mapping with a 12-day repeat cycle [30]. In this paper, for interferometry analysis to estimate the ground subsidence rate and subsidence time series of the Tianjin area, a dataset consisting of 20 Sentinel-1A SLC images in interferometric wide (IW) mode from 16 March 2017 to 18 March 2019 were selected. The datasets are provided by the ESA via Copernicus Open Access Hub. Specific parameters of the dataset are displayed in Table 1, and a detailed description about the images used in our study is provided in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Description** \\ \hline Product type & Sentinel SLC IW \\ Track number & 69 \\ Azimuth angle & 90\({}^{\circ}\) \\ Mean incidence angle & 33.68\({}^{\circ}\) \\ Resolution (range and azimuth) & 5 m \(\times\) 20 m \\ Orbit direction & Ascending \\ Polarization & VV \\ Wavelength & 5.6 cm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Particular parameters of Sentinel-1A terrain observation by progressive scan (TOPS) data used in the research.  
The 3-arc second (\(\sim\)90 m) SRTM3 version-4 DEM offered by NASA was applied to take out the topographic phases from the interferograms. Besides, the corresponding POD ephemerides released by ESA were adopted to carry out the orbital refinement and phase reflattening procedure.  
In addition, for the purpose of explaining InSAR time series results, Google Earth images and the multi-spectral Sentinel-2A images with a 10 m spatial resolution were utilized. These high-resolution and up-to-date images have an important impact in helping better interpret InSAR outcomes and comprehend the development of the ground subsidence in the study area.  
Figure 1: (**a**) Location of Tianjin city from the Google Earth map; (**b**) the study area outlined by a black rectangle; (**c**) administrative districts of the study area with the background of Sentinel-2A images acquired in 2018.
## 2 Materials and Methods
### Classification
#### 2.6.3 Anfis  
ANFIS is a hybrid intelligent system that includes a TSK (Tagaki-Sugeno-Kang) model with a multi-layer neural network structure. The parameters of the TSK systems are determined by learning methodologies. Learning methodology (back-propagation method) automatically defines the parameters of fuzzy sets, such as membership functions and rules, without an operator's intervention. The difference between ANFIS and neural networks is that the neural networks are created by training while ANFIS is formed by fuzzy rules, and then the learning method optimizes the membership functions and rule parameters [19; 40; 41]. Although ANFIS shows that it has a problem with the curse of dimensionality, it has a significant improvement in classification accuracy [57].  
ANFIS is based on the Sugeno fuzzy model, and the structure can be represented by the following first-order rules:  
\[\begin{array}{l}Rule1:If\ (x\ is\ A_{1})\ and\ (y\ is\ B_{1})\\ Then\ f_{1}=p_{1}x+q_{1}y+r_{1}\\ Rule2:If\ (x\ is\ A_{2})\ and\ (y\ is\ B_{2})\\ Then\ f_{2}=p_{2}x+q_{2}y+r_{2}\end{array} \tag{12}\]  
where \(x\) and \(y\) are the inputs; \(A_{i}\) and \(B_{i}\) are the fuzzy sets; and \(f_{i}\) are the outputs set by the fuzzy rules with parameters \(p_{i}\), \(q_{i}\) and \(r_{i}\).  
The parameters of fuzzy sets and the \(p_{i}\), \(q_{i}\) and \(r_{i}\) are those determined by the learning process. The learning method is hybrid as it uses the least squares technique and back propagation. The process is performed in two passes. The first pass is the forward direction; the fuzzy set parameters are kept constant while the parameters \(p_{i}\), \(q_{i}\) and \(r_{i}\) are determined using the least squares technique. The output \(y\) of ANFIS is extracted by using the new rule values. The desired output with the new output has an error, which is calculated by subtracting them from each other. The next pass is the back propagation, where the parameters of the fuzzy sets are updated. The parameters of the rules remain constant. Figure 8 shows an ANFIS system with three inputs, two fuzzy rules at each input and one output [41; 58]. MATLAB's command \"anfis\" was used for the training process and \"evalfis\" for the testing process [37].  
The general procedure is shown in Figure 9.
## 4 Materials and Methods
### Detection of Transposition Loci  
To detect transposition loci, we used primer sets encompassing our four streptomycin resistance-related target genes, namely, _dgeo_1873_ (_rpsL_), _dgeo_2335_ (_rsmG_), _dgeo_0776_, and _dgeo_0447_ (_mthA_). Additional target genes related to the antibiotic transporter and ribosomal component were selected based on previous review articles [3; 49], namely, _dgeo_1583_ (_trkH_) for potassium uptake, _dgeo_0915_ (_nuG_, a gene of the _nuo_ operon) for antibiotic uptake, _dgeo_0534_ (_zmuA_, encodes a high-affinity zinc ABC transporter), and _dgeo_1841_ (_rpsD_, encodes ribosomal protein S4). Transposition detection was performed using PCR with the target gene primer sets, followed by agarose gel electrophoresis to identify larger PCR products (Table S1). After PCR fragments were sequenced, the detected IS element was determined using ISFinder ([[http://isfinder.biotoul.fr](http://isfinder.biotoul.fr)]([http://isfinder.biotoul.fr](http://isfinder.biotoul.fr))) to search the bacterial IS distribution database [50].
## 3 Experiment and Results
### Simulation Experiment
#### 3.1.2 Statistical Testing of Simulation Data  
To ensure the robustness and validity of the dataset, a comprehensive statistical verification protocol was implemented. This process aimed to (1) confirm the integrity of the data by identifying anomalies, (2) validate distributional assumptions, and (3) quantify the compliance with predefined quality thresholds. The analysis was conducted using Python (SciPy and Pandas libraries) to ensure computational efficiency and reproducibility.  
(1) Data Cleaning  
Outliers were detected using [PERSON]'s interquartile range (IQR) method:  
\[\textit{Lower Bound}=Q1-1.5\times IQR,\textit{Upper Bound}=Q3+1.5\times IQR \tag{11}\]  
Extreme values were replaced via median imputation to preserve central tendency.  
(2) Distribution Analysis  
Normality was tested using the Shapiro-Wilk test (W-statistic), while skewness (\(\gamma_{1}\)) and kurtosis (\(\gamma_{2}\)) were calculated as follows:  
\[\gamma_{1}=\frac{\frac{1}{n}\underline{\sum(x_{i}-\overline{x})}^{3}}{\sigma^{ 3}},\gamma_{2}=\frac{\frac{1}{n}\underline{\sum(x_{i}-\overline{x})}^{4}}{ \sigma^{4}}-3 \tag{12}\]  
(3) Evaluation Metrics  
As presented in Table 2, the dataset demonstrates high reliability for downstream applications, as supported by rigorous statistical validation. The near-symmetric distribution, evidenced by a skewness of \(\gamma_{1}=0.15\), minimizes the systematic bias risks, while the platykurtic profile (\(\gamma_{2}=-0.82\)) indicates lighter tails compared to a normal distribution, reducing sensitivity to extreme values. Normality assumptions remained statistically tenable, as the Shapiro-Wilk test yields a marginal non-rejection of the null hypothesis (\(p=0.062>0.05\)), supporting the use of parametric methods. Furthermore, outlier contamination was well-controlled, with only 3.2% of data points exceeding [PERSON]'s IQR-based thresholds--well within the 5% acceptability limit. These findings collectively confirm the dataset's compliance with stringent quality standards, including a balanced central tendency (mean \(\approx\) median: 2048.2 vs. 2047), controllable dispersion (\(\sigma=318.7\)), and adherence to all critical evaluation metrics. Consequently, the validated dataset is suitable for high-precision applications such as signal filtering, machine learning model training, and operational diagnostics, ensuring robust analytical outcomes.  
Figure 8: Schematic diagram of phase unwrapping.
# Density Self-Adaptive Hybrid Clustering Routing Protocol for Wireless Sensor Networks
## 5 Conclusions  
In this paper, we propose a new method to select CHs, which is not a single usage of distributed or centralized control, but the combination of both of them, both of which have advantages. In the process of the implementation of the algorithm, the centralized control method is used to select the optimal CH position and quantity in an epoch. The election of the distributed method is based on the information of the previous round, so as to save the communication cost with the BS. In addition, the location and number of the CHs are reasonable. In order to balance the energy load of the network, we propose a method for adaptive cluster density. When the cluster density is greater than the upper bound or less than the lower bound, the corresponding adjusting operation is performed. The DAHC protocol achieves better performance in energy efficiency, the balanced load of the cluster, and extends the network lifetime. The experimental results show that our method is effective.  
**Acknowledgments: This work is supported by the NSFC (National Science Foundation of China) (U1536206, 61232016, U1405254, 61373133, 61502242, 61173136), BK20150925, CICAEET (Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology) and PAPD (Priority Academic Program Development of Jiangsu Higher Education Institutions) fund.**  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Releasing Differentially Private Trajectories with Optimized Data Utility
## 5 Experimental Evaluations
### Comparative Evaluations
#### 5.5.2 Evaluation on Geographic Utility  
We compare UDPT's geographic utility with AdaTrace and MLCE in terms of two metrics, namely, negative Hausdorff distance and negative query error.  
As shown in Figure 4, (i) in general, UDPT has better geographical utility than its competitors in terms of negative Hausdorff distance. (ii) UDPT shows a poorer performance in terms of negative query error compared with AdaTrace. (iii) All location privacy protection algorithms perform better on dense datasets, such as GeoLife, than on the sparse datasets, such as Gowalla and Brightkite. The analysis of the experimental results is as follows.  
(i) UDPT's outperformance, in terms of negative Hausdorff distance, is due to its preference of selecting the obfuscated locations nearby the actual locations. Although AdaTrace can also well preserve the density features, i.e., preferring to choosing nearby obfuscated locations, it still performs a little poorer than UDPT in terms of negative Hausdorff distance. The reason mainly lies in AdaTrace's randomness during selecting the obfuscated locations from the grid. In the last phase of AdaTrace, an obfuscated trajectory would be randomly synthesized based on a grid trajectory, where each obfuscated location would be uniformly chosen from a grid at random. However, if the grid was larger, AdaTrace might choose an outlier that is distributed nearby the boundary of the map as the obfuscated location. In this case, the Hausdorff distance would be larger. Consequently, AdaTrace shows a worse performance in terms of negative Hausdorff distance than UDPT. In contrast, the MLCE errors stem from the independent noise injected into the obfuscated trajectory. Compared with UDPT and AdaTrace, the locations generated by MLCE will be more widely and randomly distributed in the map, which leads to a larger distance discrepancy (or lower negative Hausdorff distance) between the actual dataset and the obfuscated one.  
(ii) AdaTrace's outperformance, in terms of the negative query error, can be attributed to its good preservation of mobility features of the population. The query error implies to what extent the density features over the whole dataset are preserved. AdaTrace's density-adaptive grid can preserve the density features of the actual trajectory dataset as much as possible. In contrast, UDPT pays more attention to the utility preservation of each individual than to the population. Consequently, we observe that UDPT shows a little poorer performance in terms of negative query error than AdaTrace.  
(iii) A denser trajectory dataset indicates that more places nearby the actual locations can be chosen as the obfuscated locations, resulting in better preservation of geographical utility. In addition, the map of GeoLife is smaller than those of Gowalla and Brightkite, which further increases the impact of data density on the geographical utility.
## 5 Results and Analysis
### The Most Important Landslide Conditioning Factors in the Study Area  
Each conditioning factor contributes differently to landslide occurrence. Therefore, their predictive power must be assessed. According to the values of the AM of IGR (AMIGR) with 10-fold cross-validation, only 10 factors of the 18 conditioning factors have predictive power (Table 3). We, therefore, used only those 10 factors when modeling. Distance to road (AMIGR = 0.1434) is the most significant factor, followed by NDVI (AMIGR = 0.0725), land use (AMIGR = 0.0187), slope aspect (AMIGR = 0.0139), lithology (AMIGR = 0.0097), slope angle (AMIGR = 0.0091), rainfall (AMIGR = 0.0090), distance to fault (AMIGR = 0.0087), elevation (AMIGR = 0.0078), and TWI (AMIGR = 0.0040) (Table 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **TOL** & **VIF** \\ \hline Slope angle & 0.520 & 1.587 \\ Aspect & 0.322 & 2.184 \\ Elevation & 0.212 & 1.135 \\ Profile curvature & 0.825 & 2.381 \\ Plan curvature & 0.705 & 1.843 \\ Distance to road & 0.553 & 2.529 \\ NDVI & 0.498 & 1.814 \\ Land use & 0.340 & 2.321 \\ Lithology & 0.263 & 1.311 \\ LS & 0.541 & 1.849 \\ Rainfall & 0.887 & 1.552 \\ Solar radiation & 0.670 & 2.698 \\ TWI & 0.776 & 1.541 \\ SPI & 0.732 & 1.873 \\ Distance to river & 0.820 & 2.987 \\ River density & 0.922 & 1.784 \\ Distance to fault & 0.712 & 2.835 \\ Fault density & 0.825 & 2.781 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The multicollinearity tests of the factors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Conditioning Factor** & **Rank** & **AMIGR** \\ \hline Distance to road & 1 & 0.1434 \\ NDVI & 2 & 0.0725 \\ Land use & 3 & 0.0187 \\ Aspect & 4 & 0.0139 \\ Lithology & 5 & 0.0097 \\ Slope angle & 6 & 0.0091 \\ Rainfall & 7 & 0.0090 \\ Distance to fault & 8 & 0.0087 \\ Elevation & 9 & 0.0078 \\ TWI & 10 & 0.0040 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ranks of significant landslide conditioning factors based on the IGR technique and the training dataset.
# Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
## 5 Conclusions  
In this paper, we presented tracking failure detection and re-tracking algorithms to improve the KCF-based tracker. The goal of this work was to develop a real-time tracking algorithm with improving the tracking accuracy. Performance of the tracking failure detection algorithm has a decisive effects on total performance of the proposed method. More specifically, the proposed method restarts the tracking algorithm when tracking failure is first detected in a simple, effective manner. The proposed algorithm can predict a tracking failure by analyzing the peak and neighboring correlation values with a little extra computational load. Under the tests using the Visual Tracker Benchmark dataset [29] and our own video sets, we precisely found the threshold value for the tracking failure point. Although the re-tracking process requires an additional computational load for multiple search windows, we minimized the load by giving priority to search windows. At this step, appearance of the target can be preserved by skipping update. As a result, the proposed method could compensate for the defect of the original KCF in challenging scenes including occlusions and blurrings. Although many tracking methods exhibit high accuracy under an offline condition, there were few online tracking methods that give a sufficiently high performance. In this context, the proposed method is suitable for a low-cost, real-time tracking application without using a special hardware accelerator such as a GPU. In the future, we will investigate more tracking failure cases, and then develop a corresponding re-tracking algorithm.  
Conceptualization, H.K. and J.P.; methodology, software, validation, J.S.; data curation, D.K.; writing--Original draft preparation, [PERSON]; writing--Review and editing, H.K.; supervision, project administration, J.P. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (2017-0-00250, Intelligent Defense Boundary Surveillance Technology Using Collaborative Reinforced Learning of Embedded Edge Camera and Image Analysis), and by the ICT R&D program of MSIP/IITP [2014-0-00077, development of global multi-target tracking and event prediction techniques based on real-time large-scale video analysis].  
The authors declare no conflict of interest.
# SARDIM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
## 4 Evaluation
### SARDIMM without RCMC  
To begin, we measured the RDA execution time on images of different sizes on the baseline system. Figure 9 presents the execution time ratios of different RDA operations, including range compression, azimuth FFT, RCMC, azimuth reference signal multiplication, and azimuth IFFT. Notably, RCMC exhibited a significantly lower percentage, averaging 0.8%, compared with the other operations. In contrast, range compression averaged 49.4%, and azimuth compression (excluding RCMC) averaged 49.8%, collectively constituting the majority of the execution time. This implies that when a module to accelerate the RCMC is inserted into the SARDIMM, the overhead due to the increase in area is greater than  
Figure 8: NMP operation flow of the SARDIM system.  
the gain in performance. Consequently, the SARDIMM accelerates all RDA operations except RCMC.