esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Universal Feature Extraction from Dual- and Compact Polarimetric SAR Data
#### 2.4.2 H/\(\alpha\) Decomposition Parameters  
Polarimetric entropy of CP SAR data can be directly calculated from the eigenvalues of the covariance matrix \(\mathcal{C}_{CP}\):  
\[H=\sum\limits_{i=1}^{2}-P_{i}\log_{2}P_{i} \tag{23}\]  
\[P_{i}=\frac{\lambda_{i}}{\sum\limits_{j}\lambda_{i}} \tag{24}\]  
Additionally, \(\lambda_{i}\) (\(i\) = 1, 2) is the eigenvalue of coherency matrix \(\mathcal{C}_{CP}\). Entropy that is derived directly from CP SAR data has similar performance as that derived from quad-pol SAR data, in describing the complexity of the physical scattering mechanisms of targets.  
Then, the mean scattering angle in CP SAR modes can be approximated by:  
\[\alpha=P_{1}\alpha_{1}+P_{2}\alpha_{2} \tag{25}\]  
where \(\alpha_{i}\) can be derived from the eigenvector of the covariance of CP SAR data, similarly as in Section 2.2.
## 4 Evaluation
### Effect of the Training Data on the Frequency Basis Learning  
To analyze the effect of the training data on the frequency basis matrices in the NMF methods, the performances of the NMF methods were compared with the different  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Electric** & **Speech** & **Dishes** & **Cat** & **Running** & **Dog** & **Frying** & **Blender** & **Alarm** & **Vacuum** \\  & **Shaver** & & & & **Water** & & & **Bell** & **Cleaner** \\ \hline ine & & & & & & & & & & \\ NMF & 32.9 & **46.8** & 18.0 & 39.7 & 22.4 & 21.5 & 28.9 & 27.5 & 36.3 & 41.7 \\ (iterative) & & & & & & & & & & \\ ine & 43.9 & 21.9 & 39.5 & 25.7 & **22.1** & 22.8 & 22.1 & **39.6** & 39.9 \\ (non-iterative) & & & & & & & & & & \\ ine & 35.7 & 45.0 & 18.0 & 39.1 & **30.9** & 17.4 & 24.0 & 32.0 & 32.0 & 49.0 \\ (ine & **38.3** & 37.3 & 14.2 & 36.7 & 27.4 & 13.7 & 23.5 & 23.0 & 35.8 & 49.0 \\ (ine & 29.5 & 34.2 & **24.6** & **41.7** & 28.3 & 19.7 & **31.4** & **33.9** & 39.4 & 48.2 \\ (ine CQT) & 34.6 & 46.7 & 18.4 & 35.9 & 21.7 & 16.5 & 9.1 & 24.8 & 24.1 & **55.6** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Class-wise F1-scores [%] of various features. The boldface means the best performance of each class.  
Figure 7: Performance comparison between the _iterative NMF_, _NMF-EUC_, and _non-iterative NMF_.  
frequency basis matrices from various parts of the database. Table 3 presents the F1-score results of different frequency basis matrices with the event-wise post-processing. STR, WEAK, and WEAK(U) denote the frequency basis matrices trained by the strongly-labeled data, weakly-labeled data, and weakly-labeled unitary label data, which consist of single-class clips, respectively.  
As presented in Table 3, the strongly-labeled data exhibited relatively good overall performance. The weakly-labeled data demonstrated good micro-averaged performance for iterative NMF but degraded performances for the other criteria, including non-iterative NMF. Using both the strongly- and weakly-labeled unitary label data (STR + WEAK(U)) also had good macro-averaged performance for non-iterative NMF, but it also showed degraded performances for other criteria. Conversely, the performance of the weakly-labeled unitary label data (WEAK(U)) did not exceed that of the weakly-labeled data (WEAK) that has interference classes. For instance, Class 2 data interferes with the training of Class 1 frequency basis matrix (Figure 3c). Therefore, we suggest that the class interference problem does not significantly affect the classification performance.
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 4 Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
### Fault Location Case
#### 4.4.4 Test Case 4: Active Distribution Network  
In Figure 12, assume that section \(x_{18}\) in the active distribution network has a fault. SG and DG are the main power sources, respectively, and the corresponding nodes' fault information is marked in red and green boxes in Figures 12a and 12b, respectively.  
The call algorithm determines the result of fault location with each power supply as the main power supply, and the algorithm's divide process and combine process are shown in Figure 13.  
The execution process of the algorithm in an active distribution network. (**a**) Divide process with SG as the main power source; (**b**) combine process with SG as the main power source; (**c**) divide process with DG as the main power source; (**d**) combine process with DG as the main power source.  
Figure 12: Multiple faults in the multi-branch active distribution network. (**a**) Fault information of SG as the main power source; (**b**) fault information of DG as the main power source.  
Figure 13: The execution process of the algorithm in an active distribution network. (**a**) Divide process with SG as the main power source; (**b**) combine process with SG as the main power source; (**c**) divide process with DG as the main power source; (**d**) combine process with DG as the main power source.  
In Figure 13, it can be seen that the result of fault location with SG and DG as the main power source are \(\mathbf{X}_{1}=~{}\{x_{18}\},~{}\mathbf{X}_{11}=~{}\{x_{18}\}\), respectively. The final location result is obtained by taking the two together: \(\mathbf{X}=\mathbf{X}_{1}\cup\mathbf{X}_{11}=~{}\{x_{18}\}\). After the algorithm is executed, \(x_{18}\) is determined to be the fault section.
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 5 Evaluation Scenario
### Study Area and Data  
The proposed digital twin was evaluated on a 13 km segment of the C-32 highway in Spain, considering different traffic scenarios. The input data was provided by the highway's traffic operator through a dataset from January to December 2022. Figure 3 illustrates the traffic data at a specific point on the highway, showing the number of vehicles detected at the main toll in each direction. The top plot represents northbound traffic, while the bottom plot shows southbound traffic. Weekdays are shown in blue and weekends in gray.  
This visual distinction highlights typical weekly patterns as well as seasonal variations, particularly the increase in southbound volume during summer months.  
A key limitation in this study is the absence of recorded data corresponding to real-world incidents involving extreme congestion, as the highway operator does not maintain such records. This lack of empirical data restricts the ability to analyze system behavior under high-demand conditions. To overcome this limitation, we apply scaling factors to the available traffic data. This approach maintains the original temporal and spatial characteristics of observed traffic patterns while systematically increasing traffic intensity. In doing so, it provides a controlled means of simulating congested scenarios without resorting to entirely synthetic flows or hypothetical demand profiles.  
The 13 km of the highway are divided into sections, represented in Figure 4 with red lines. These sections are defined between entry and exit points, where sensors are placed to detect vehicles entering and exiting the highway. Additionally, to be able to test scenarios where the congestion has been caused by incidents, a specific type of incident has been defined and placed at various locations along the highway. This incident represents a scenario where the highway has two lanes available, but one lane is fully blocked for the entire simulation duration, either due to an accident, roadwork, or a disabled vehicle. Driver behavior in response to the incident is modeled using the two corner cases defined in this study: full compliance (where all vehicles follow the recommended strategy) and full non-compliance (baseline, where no vehicle responds). Figure 4 shows the locations of the tested incident A.  
Figure 3: Daily vehicle counts on the C-32 highway during 2022, based on sensor data. The top plot shows northbound traffic volumes; the bottom plot shows southbound volumes.
## 3 The Proposed MOA-CV Method
### Demonstration of MOA-CV Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Term** & **PRS** & **RBF-MQ** & **RBF-TPS** & **KRG** \\ \hline \(R^{2}\) & 0.18 & 0.66 & 0.53 & 0.69 \\ Order by \(R^{2}\) & 4 & 2 & 3 & 1 \\ LOO-CVerror & 44.262 & 64.356 & 473.491 & 44.263 \\ Order by LOO-CV & 1 & 3 & 4 & 2 \\
**MOA-CVerror** & 22.948 & 3.438 & 4.293 & 3.127 \\
**Order by MOA-CV** & 4 & 2 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ranking results of surrogate models by MOA-CV and LOO-CV methods. Figure 2: Demonstration of the move-one-away cross validation (MOA-CV) method. (**a**) virtual training points; (**b**) 1 st iteration; (**c**) 2 nd iteration; (**d**) 3 rd iteration; (**e**) 4 th iteration; (**f**) 5 th iteration.
## 4 Experiment and Results  
### Data and Experiment Environment  
In this experiment, data from CNC Machine, a production facility of Synswin Co., Ltd., located in Changwon, Korea, were used. The data used in the experiment is vibration data. The vibration data was collected from the MSENS-AC sensor via RS485 communication, and the data was collected by the IIoT installed in the field. The sampling rate of the applied sensor was 100 Hz and supported a 115,200 baudrate. Three-axis data was collected according to the characteristics of the gyro sensor. The data used in the double experiment  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Output Size** & **Parameter** \\ \hline Conv2D & \(14,14,128\) & 1280 \\ MaxPooling2D & \(7,7,128\) & 0 \\ Conv2D & \(7,7,128\) & 147,584 \\ UpSampling2D & \(14,14,128\) & 0 \\ Conv2D & \(14,14,128\) & 147,584 \\ Conv2D & \(14,14,1\) & 1153 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Architecture of Convolutional Autoencoder.  
was X-axis data. Data was collected on a 1000 ms basis, and examples of used data shown in Figure 7.  
The data was collected during three days, from 5 October to 7 October 2021. The data matched to the manufactured product consists of an average of 552 data sets per day, with a total of 1700 data sets, and has 160 data per set. Therefore, experiment was conducted with 272,000 data. The data were all collected through sensors on the same CNC.  
The computer used in this experiment included Intel Corei7-8700k, 3.7 Ghz, six-core twelve threads, 16 GB of CPU, and a GPU of Geforce RTX2080 Ti. A Jetson nano from Nvidia was used as the EID used as a data collection, data reproduction, and as a classifier. Tensorflow 2.0 version and Python 3.7 were used in the software environment. The hardware and software specifications used for the experiments are listed in Table 2.
###### Abstract
This combined mechanism facilitates the targeted delivery of the nanoparticles to the tumor and their subsequent removal after radiotherapy, contributing to the potential success of this therapeutic approach. Since the utilization of contrast agents in MRI and radiosentizers is still in the research phase and not yet commonly implemented in clinical practice, limited information is available regarding the effects of combining these agents. However, both radiosentizers and contrast agents play crucial roles in cancer research. With the emergence of IGRT and the use of gold nanoparticles, investigating the DNA damage resulting from the combination of these agents is becoming a vital aspect of study. Two types of DNA damage can occur during radiotherapy: single-strand breaks (SSBs) and double-strand breaks (DSBs). SSBs happen when one strand of the DNA molecule breaks, while DSBs occur when both strands of the DNA molecule break simultaneously. In an SSB, a single nucleotide sequence is broken, while a DSB results in two nucleotide sequences being broken, leading to disruption or breakage of the sugar-phosphate backbone. SSBs are more frequent than DSBs. However, DSBs are more challenging to repair compared to SSBs due to the severity of the damage. In the case of SSBs, the intact strand serves as a template for repairing the broken DNA strand. In radiotherapy, DSBs are more desired due to their potential to induce mutations and cell death in cancer cells [15]. As IGRT gains prominence in hospital settings and becomes a focal point in radiotherapy, it becomes crucial to investigate the potential of delivering contrast agents alongside gold nanoparticles to consistently enhance DNA damage [16]. Specifically, there is a need to explore whether the inclusion of iron within gold nanoparticles can produce comparable levels of DNA damage. It is worth noting that iron nanoparticles are generally not considered optimal radiosentizers [17]. Therefore, understanding whether gold nanoparticles with embedded iron (i.e., iron-gold nanoparticle heterojunction) can elicit similar DNA damage is of significant importance in this context. The aim of this study is to evaluate the dependence of DNA damage on the proportion of iron and gold in the nanoparticle heterojunction. Monte Carlo simulation was used to determine the DNA strand breaks when the iron-gold nanoparticle heterojunction was irradiated by the kV photon beams.
# Optimization of Multi-Mission CubeSat Constellations with a Multi-Objective Genetic Algorithm
## 1 Introduction
Global and regional missions share conflicting requirements that need optimization to be satisfied by a single system. A computational approach using a modified non-dominated sorting genetic algorithm II (NSGA-II) algorithm is implemented using MATLAB(r). Therefore, the main contribution of this paper is the design of a CubeSat constellation capable of performing multiple missions. Additionally, the optimization of the CubeSat constellation design to efficiently perform missions by minimizing the number of satellites and average revisit time (ART) while maximizing total coverage.
## 5 Discussion
Nevertheless, the present research did not stop with a simple assessment of the flash-flood potential and was further developed with the analysis of the correlation between the changes in the land use/land cover and flash-flood potential between 1989 and 2019. To our best knowledge, the results of the present research represent a first for the international literature because a study regarding the correlation between the land-use/land-cover changes and the bi-temporal evolution of flash-flood potential index (FFPI) has not been made before. Our research highlights that the land-use/land-cover changes between 1989 and 2019 determined the increase in the flash-flood potential across the Zabala catchment. Numerous studies in the literature have analyzed the quantitative influence of land-use/land-cover changes in the runoff characteristics using the SWAT model [122; 123; 124; 125; 126]. The vast majority of these studies, in which the influence of land-use/land-cover changes on the runoff characteristics is explored, indicate that during the last few decades, the aggressiveness of run-off and flash-flood frequency significantly increased, mainly due to deforestation activities [127; 128; 129].
# Radiochromic Films for the Two-Dimensional Dose Distribution Assessment
## 2 Basics of Radiochromic Films
### Radiochromic Film Types  
The RCF types differ from each other for their own structural characteristics, such as active layer's or substrate's composition and size, resulting in different sensitivity to the radiation dose, type, and energy. Gafchromic(tm) films are designed to be used in specific applications of medical radiology and radiotherapy. However, thanks to their versatility, they are commonly used in other domains of the radiation physics. The Gafchromic(tm) XR models are sensitive in the dose range 0.1-20 cGy and are designed for Quality Assurance (QA) radiology tests (XR-QA2), for measurements on CT scanners (XR-CT2), for mammography QA tests (XR-M2), and for surface dose measurements (XR-RV3) in procedures guided by fluoroscopy [54; 55; 56]. Gafchromic(tm) EBT models are particularly suited for measurements in high energy photon fields and therefore in Intensity Modulated Radiation Therapy (IMRT), Volumetric Modulated Arc Therapy (VMAT), Stereotactic radiosurgery (STR), and Stereotactic body radiation therapy (SBRT) applications [24; 57]. The structure and dynamic range of these RCF types and of the one mentioned below is shown in Figure 1.  
The EBT models available today are the EBT-3, sensitive in the dynamic range 0.1-20 Gy and EBT-XD, featuring extended dynamics up to 60 Gy. Gafchromic(tm) MD-V3 (dynamic range 1-100 Gy) and HD-V2 (dynamic range 10-1000 Gy) are available for higher doses. According to the manufacturer, these films have a spatial resolution of less than 25 \(\upmu\)m. They are made of one or more polyester substrates, an adhesive layer (present only in some models), lithium pentacosa-10,12-diynoate (LiPCDA) as active layer, and other elements in fraction of the order of 1% [16]. Thin film dosimeters are available for high dose applications, such as radiation processing. Sex Corporation trades B3 RCFs for application in the high dose range from a few hundred Gy to more than 150 kGy [58]. B3 RCFs feature 18 \(\upmu\)m (nominal thickness) polyvinyl butyral (PVB) resin with the addition of pararosaniline as a radiochromic dye. FWT-60 RCFs are traded by Far West Technology, Inc. Goleta, CA, United States. They feature the hexa(hydroxyethyl) aminotriphenylacetonitrile (HHEVC) as a dye and are sensitive in the range 0.5-200 kGy [59]. The information on the  
Figure 1: Structure and dynamic range of some radiochromic film (RCF) types.  
structure and the dynamic range of these RCFs was found in the specification guidelines reported on the manufacturer's website [60]. It should be noted that RCF models of a specified dynamic range could be used beyond the mentioned range at the expense of a reduced measurement sensitivity. This is consequence of the fact the RCF response is not linear with the dose; moreover, the dynamic range and sensitivity depends on the data analysis technique.
# Fisher Discriminative Sparse Representation Based on DBN for Fault Diagnosis of Complex System
## 4 Case Study
### Comprehensive Comparison of Classifier Performance  
To illustrate the discrimination ability of our algorithm, DBN [22], backpropagation (BP) [39], SVM with RBF kernel [28], and sparse representation (SR) classifiers [49] were compared with the FDSR proposed in this study. The neural numbers of DBN were set to 52 \(\times\) 20 \(\times\) 16 \(\times\) 22 and kept the same in all tests. The parameters of SVM were optimized and the optimal \(C=0.10\) and \(\gamma=10.53\) were acquired. The parameters of backpropagation were set to 52 \(\times\) 30 \(\times\) 22. More detail about these methods can been seen in the corresponding references. The classification test was repeated 30 times, and the average classification accuracy is summarized in Table 4. Fault 14 and fault 18 had the lowest accuracy in every classifier, proving the challenge posed in diagnosing unknown faults and sticking faults. The FDSR results were considerably better compared with other methods, verifying the superior clustering and separating ability of this methodology.  
Performance of these various classification methods were evaluated based on Accuracy (_Acc_), Sensitivity (_Sen_), and Specificity (_Spe_). _TP_ is the number of healthy data correctly recognized, _TN_ is the number of faulty data correctly recognized, _FN_ is the number of faulty data detected as healthy, and _FP_ is the number of healthy data detected as faulty. _Acc_ is the proportion of true decisions in  
Figure 7: Visualization of weight vectors of RBM1 and RBM2.  
all tests, and _Sen_ and _Spe_ represent the proportion of positive and negative cases that were detected correctly, respectively. The formulation is shown in Equations (19)-(21). We used _Sen_ to evaluate the classification accuracy of these methods.  
\[Acc=\frac{TP+TN}{TP+FP+TN+FN} \tag{19}\]  
\[Sen=\frac{TP}{TP+FN} \tag{20}\]  
\[Spe=\frac{TN}{TN+FP} \tag{21}\]  
The comparison of the various classifiers and their accuracies are illustrated in Table 4. The proposed FDSR was superior, having the highest accuracy in all scenarios. The FDSR performs quite better than the traditional sparse representation classifier (SRC) [49], which could be attributed to the added constraints that limit the scattering of the samples within a class and maximize the scatter of samples between classes. Meanwhile, FDSR had higher reconstruction accuracy than SRC, due to the introduction of minimizing the reconstruction error restriction via L2 norm. The results demonstrate that FDSR is the optimal method for diagnosing faults compared with the other available methodologies.
# Distributed Action-Rule Discovery Based on Attribute Correlation and Vertical Data Partitioning
## 3 Related Works  
The efficient extraction of action rules from large datasets has posed significant challenges to conventional models that analyze data in a non-distributed manner. Recognizing this limitation, there have been efforts to adopt a distributed approach to better handle the intricacies associated with larger volumes of data. This section provides an overview of such distributed approaches to action rule discovery, establishing the context for our proposed improvements.  
[PERSON] et al. [38] introduces a distributed approach to extract action rules from large datasets. Their method emphasizes the data distribution phase, suggesting partitioning data into smaller granules both horizontally (across rows) and vertically (across attributes). Rules extracted from these vertical pairwise disjoint granules, which cover distinct attributes, are concatenated, thereby capturing some of the knowledge from the initial dataset. Similarly, [PERSON] et al. [35] propose another distributed method for action rule discovery using both horizontal and vertical partitioning. They employ Sparkfor horizontal partitioning, while their vertical approach randomly separates attributes. This method notably reduces the time required to discover action rules. However, a limitation lies in their combined study of both partitioning methods without isolation, making it unclear whether the benefits arise from horizontal partitioning, vertical partitioning, or a synergy of both.  
While the above approaches offer a foundation for distributed action rule discovery methods, our work seeks to advance this further. We identify a potential limitation in the random nature of vertical data partitioning present in existing methods. Our contribution focuses on introducing a more logical and structured approach to vertical data partitioning. We propose using feature correlations as a distance measure in data partitioning, specifically for action rule discovery. This is a technique that, to the best of our knowledge, has not yet been explored. By doing so, we aim to enhance both the efficiency and the quality of the actionable patterns extracted from distributed datasets, distinguishing our work from existing methods.
# A Poisson Process-Based Random Access Channel for 5G and Beyond Networks +
## 5 Performance Evaluation
### Discussion on the Substantial Impact  
As we described above in the problem statement section, neither of the related research works propose to replace the traditional uniform distribution-based channel access procedure due to its ease of use and backward compatibility. Therefore, the limitations and challenges posed by the uniform distribution are always ignored. Our proposed 2 PRACH protocol expands the current RACH capabilities by implying parametric channel access, which is a more dynamic and robust technique. It opens new ways for researchers and industrialists to think beyond the conventional RACH mechanism and overcome the channel scarcity challenges. As shown in the results, the achieved higher reliability, and the reduced latency proves that the shift of the RACH mechanism from a uniform distribution to a Poisson process-based mechanism has potentials for the next-generation 5G and beyond networks.