esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, we have studied the performance of parallel FSO/RF transmissions in SAGIN, where the effect of weather conditions and the QoS of ground users are analyzed. Considering the widely distributed and varying densities of ground users, the FSO and RF links are jointly adopted to provide services for users, in which the RF link provides extensive coverage while the FSO link provides enhanced services. Based on the proposed system, we investigate the system performance in terms of capacity outage probability, weighted average BER, and energy efficiency. Asymptotic capacity outage analysis and diversity order at high SNR regions are also provided to show more engineering insights. Numerical results demonstrate that there exists an optimal power allocation coefficient to minimize capacity outage probability and weighted average BER and maximize the system energy efficiency. Moreover, limited by the GS-satellite and satellite-HAP FSO links, there exists a bottleneck for capacity outage probability. Furthermore, parallel FSO/RF links can outperform only FSO and only RF links in capacity outage probability and system energy efficiency, with acceptable performance loss in weighted average BER. Finally, as prospective future research, the co-channel interference in RF links and the dynamic characteristics of the parallel FSO/RF links can be further investigated.  
**Author Contributions:** Formal analysis, X.S.; data curation, J.L.; writing--original draft preparation, X.L.; writing--review and editing, Y.L. and S.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Shaanxi Province, grant number 2023-JC-QN-0084.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are contained within this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 3 Related Work
At the same time, iForest is further used to identify malicious versus genuine reporters by highlighting outlier patterns. In addition to anomaly detection, classification models such as GMM, XGBoost, and Random Forest further make reputation evaluation more accurate. The authors in [37] proposed a GMM-based classification approach that effectively models uncertain and probabilistic distributions--a valuable tool to differentiate misbehavior patterns in DRAMBR. Moreover, the XGBoost classifier has been widely recognized due to its powerful predictive capabilities, as proven by [38], the author applied XGBoost across multiple datasets for classification and decision-making. In DRAMBR, the XGBoost algorithm is proposed to improve accuracy in misbehavior classification from past reputation data. In [39], Random Forest is integrated with the XGBoost to offer ensemble learning features that make a decision more robust by reducing the bias and variance in classification to ensure a proper evaluation of a misbehavior report. In response to ongoing research efforts to develop a reliable and trustworthy V2V communication system that utilizes reputation from neighboring vehicles, this study addresses the challenge of detecting dishonest vehicles within disconnected environments. We present the relevant literature review for centralized and decentralized reputation systems in the proposed DRAMBR framework. Centralized systems, for instance proposed in [18; 19], lacking scalability and connectivity issues, can implement reliable aggregation with consistent threshold management. In contrast, scalability is better in a decentralized system like [22; 25; 33] but lags in consistency over trust. Recognizing the limitations imposed by network disconnectivity, where existing standards may be insufficient, the proposed DRAMBR framework, designed in alignment with SCMS standards [17], addresses these challenges by integrating dynamic reputation updates and advanced clustering techniques to assess the credibility of received messages before submitting the feedback reports to the centralized server, thereby enhancing the accuracy and reliability of the reporting system.
## 4 Experimentation and Results
### Retailer Characteristics
#### 4.1.4 Average Customer Traffic Volume  
The average number of customers visiting each store daily varies depending on location and seasonality. On average, each store experiences between 200 and 1500 customers per day. Stores in high-traffic urban areas may see a more extensive customer base, while those in less densely populated areas may have a lower volume. The integration of real-time tracking technologies allows for a detailed analysis of customer movement patterns, helping to understand how different product sections attract attention and how customer behavior can be influenced by store layout and merchandising strategies.
# Direct Temperature Measurements of Cardiac Stent during MRI Examinations
## 4 Discussion
### Limitations of This Study  
The major limitation of this study is the material used for the phantom that is nongelled and hence it is not compliant with the specific indicated in the ASTM F2182 standard. Following the ACR magnetic resonance accreditation phantom [34] and other ones used during quality controls [35; 36; 37], we have filled the phantom with saline solution. In general, saline phantoms are easy and safe to handle, but they are influenced by vibrational effects. Some studies in literature have demonstrated that the measurement of the MRI-induced temperature changes is influenced by thermal convection but, if the phantom material has a sufficient viscosity, thermal transport does not influence the measurements. In case of nongelled phantom, such as our phantom, the measured temperature rise may underestimate that which would occur in vivo [38]. On the other hand, in vivo conditions are parameters (i.e., blood flow and perfusion) outside the scope of the standard test method that significantly lessen the RF-induced temperature rise near a medical device [26].  
Anyway, the temperature rise measured in our study for each probe has a trend very similar to that obtained using standard phantom [38].  
Another limitation of our study is relative to the position of the stent in the phantom: since superficial tissue have higher exposure compared to deep tissues, the position of the device plays a very important role in modelling for safety evaluation [33]. In our study, testing is performed with the stent placed in the center of the phantom and in correspondence of the center of the RF coil that is not the worst-case scenario.  
An electromagnetic or thermal simulation approach, as well as an analytical mode, would validate our findings. On the other hand, some previous literature studies used RF heating experiments, very similar to those of the set-up described here, to validate an analytical or computational evaluation and, in most cases, the theoretical and experimental results were very similar [22; 23; 24; 25]. Since the approach described here is based on a standard, well-known and validated method, our findings are consistent.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 6 Applications: XAI's Impact across Diverse Domains
### Finance  
XAI has assumed a pivotal role within the finance sector, driven by imperatives of regulatory compliance and customer transparency. Through its capacity to offer lucid insights into decision-making processes, XAI equips financial institutions and investors to enhance their choices grounded in a comprehensive grasp of underlying variables. Its applications in finance span a wide spectrum, encompassing critical areas like risk management, trading strategies, and fraud detection. Notably, XAI models have found utility in revealing the risk profiles of financial products and investments, illuminating the intricate nuances that influence financial decision landscapes.
# A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
## Preliminaries  
### Tree Decompositions and Treewidth  
Formally, given an undirected graph \(G=(V,E)\), a tree decomposition of \(G\) is a pair \((X,T)\) where \(X\) is a set whose individual elements \(X_{i}\) are subsets of \(V\) called _bags_ and \(T\) is a tree whose nodes are the subsets \(X_{i}\). Additionally, the following three rules hold:  
1. The union of all bags \(X_{i}\) equals \(V\), i.e., every vertex of the original graph \(G\) is in at least one bag in the tree decomposition.
2. For every edge \((u,v)\) in \(G\), there exists a bag \(X\) that contains both \(u\) and \(v\), i.e., both endpoints of every edge in the original graph \(G\) can be found together in at least one bag in the tree decomposition.
3. If two bags \(X_{i}\) and \(X_{j}\) both contain a vertex \(v\), then every bag on the path between \(X_{i}\) and \(X_{j}\) also contains \(v\).  
The treewidth of a tree decomposition \((X,T)\) is the size of the largest bag in the decomposition, minus one and the treewidth of a graph \(G\) is the minimum width ranging over all tree decompositions of \(G\)[2].
# Detecting Cryptojacking Web Threats: An Approach with Autoencoders and Deep Dense Neural Networks
## 6 Results and Discussion
At this point, the proposed SAE+DDNN architecture is initially compared with shallow state-of-the-art algorithms, as described in Section 4. To run the comparative tests, a grid-search cross-validation [45] approach is considered, which is a method of hyper-parameter tuning that seeks to maximize the performance of supervised algorithms by setting a series of values prior to training. The grid combines a cross-validation with \(k\) iterations, to calculate the average ability of the algorithms under test, towards a persistent accuracy. To find the optimal subset, it was formulated the approach described in Equation (8). \[\lambda^{*}=\operatorname*{arg\,min}_{\lambda}\{F(L(A,X_{2 score},\lambda))\}, \tag{8}\]  
thus then, \(L\) denotes the loss function, \(F\) is the objective function, \(A\) is the algorithm to be submitted, \(X_{2 score}\) is the normalized training set, where each sample \(x_{i}\in X_{2 score}\) is a fixed vector \(\mathbb{R}^{1\times 18}\), and \(\lambda\) the hyper-parameters to be evaluated, resulting in a subset \(\lambda^{*}\) of optimal parameters. Table 8 lists the authors, algorithms, and hyper-parameters to be compared. Likewise, from the works that present applications of DL algorithms, a base architecture was arranged, to resemble as close as possible the authors scopes. The configurations used for training and comparison are tabulated in Table 9. It should be \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Testing Set** & **Configuration** & **Target Class** & **Precision** & **Recall** & **F1-Score** \\ \hline \(X_{test}\) & DDNN & cryptojacking & 83.61\% & 51.44 \% & 63.69 \% \\ \(X_{test}\) & DDNN & benign site & 27.87\% & 17.15 \% & 21.23 \% \\ Average & DDNN & & 55.74\% & 34.29\% & 42.46\% \\ \hline \(X_{test}\) & DDNN +SAE & cryptojacking & 99.57\% & 12.57 \% & 22.33 \% \\ \(X_{test}\) & DDNN + SAE & benign site & 80.49\% & 36.61 \% & 28.97 \% \\ Average & DDNN + SAE & & 90.03\% & 24.59\% & 25.65\% \\ \hline \(X_{test,}\) & DDNN & cryptojacking & 81.40\% & 99.29 \% & 89.46 \% \\ \(X_{test,}\) & DDNN & benign site & 99.70\% & 91.38\% & 95.36 \% \\ Average & DDNN & & 90.55\% & 95.33\% & 92.41\% \\ \hline \(X_{test,}\) & DDNN + SAE & cryptojacking & 99.41\% & 99.78\% & 99.59 \% \\ \(X_{test,}\) & DDNN + SAE & benign site & 99.42\% & 98.43\% & 98.92\% \\ Average & DDNN + SAE & & 99.41\% & 99.10\% & 99.25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance results obtained after evaluating the DDNN + SAE model. Figure 8: Comparison of loss function curves using MSE, in contrast to raw and normalized samples, subject to SAE reconstruction. mentioned that the classification strategy for the LSTM network employs a many-to-one design, since this work does not consider a sequential network, in this sense, for the CNN a fixed vector was used as with the shallow algorithms, since the samples can hardly be coupled to the dimensions of an image with 2D spatial layers for the convolution of graphs.
## 3 Photocatalytic Materials for Water Treatment
### UV-Responsive (Conventional) Semiconductors  
Titanium dioxide (TiO\({}_{2}\)) is the most widely studied and commercially used photocatalyst due to its strong oxidizing power, chemical stability, low cost, and non-toxicity. It exists mainly in anatase and rutile crystalline forms, with anatase typically exhibiting higher pho tocatalytic activity. However, TiO\({}_{2}\) has a wide band gap (\(\sim\)3.2 eV), limiting its activation to the UV region, which accounts for only a small fraction of the solar spectrum [82; 83; 79; 83].  
Zinc oxide (ZnO) is another UV-active semiconductor with photocatalytic properties comparable to TiO\({}_{2}\). It features a similar band gap (\(\sim\)3.3 eV) but often suffers from photocorrosion and reduced long-term stability in aqueous systems [84; 85; 86].  
Their characteristics are shown in Table 1. This comparison highlights that while both TiO\({}_{2}\) and ZnO are effective photocatalysts, ZnO often demonstrates higher electron mobility and faster photocatalytic disinfection rates [40; 84; 85; 87]. However, ZnO susceptibility to photocorrosion can affect its long-term stability. TiO\({}_{2}\), on the other hand, offers greater chemical stability but may experience limitations due to lower electron mobility and a narrower light absorption range. The choice between these materials should consider the specific requirements of the application, including desired efficiency, durability, and operating conditions.  
In addition to TiO\({}_{2}\) and ZnO, other conventional photocatalysts have been widely studied in earlier photocatalytic research and continue to serve as reference materials. WO\({}_{3}\) (tungsten trioxide), with its visible-light responsiveness and oxidative capability, has been employed in the degradation of organic pollutants and photocatalytic water splitting. Fe\({}_{2}\)O\({}_{3}\) (hematite) is another abundant and stable semiconductor with a narrow bandgap; however, its performance is often constrained by short charge carrier diffusion lengths and rapid recombination. CdS (cadmium sulfide) has drawn attention due to its high visible-light activity and tunable bandgap, though its practical application is limited by photostability and toxicity concerns. Additionally, SrTiO\({}_{3}\) has been explored for photocatalytic hydrogen production and degradation applications, particularly in UV-active systems. The exploration of these materials has laid the foundation for the development of modified and doped photocatalysts with improved performance and environmental compatibility [31; 44; 53; 42].
# Functionality Investigation of the UAV Arranged FMCW Solid-State Marine Radar
## 2 Concept of the Experiment  
Using a UAV to elevate the marine radar to high altitudes provides a wider field of view and consequently a longer detection range. However, higher altitudes could lead to higher radio interference levels. Practical estimation of radio interferences affecting the solid-state FMCW marine radar at altitudes up to 120 m was the main objective of our experiment. An RTL SDR AIS (automatic identification system) receiver based on software-defined radio was arranged on our UAV for the implementation of the concept of both primary (marine radar) and secondary (AIS) radars on the UAV. Usage of both technologies can provide additional possibilities for marine traffic, anti-piracy, and border protection control.  
The Simrad 4G(tm) FMCW marine radar was chosen due to its low weight, domed antenna, low power consumption, readiness, safety of usage, possibility to use open software, and good ability to detect RCS targets both at low altitudes and at short ranges. The last property may be useful for applications such as detection of other UAVs. Experiments on detection of small UAVs using this radar from the ground are presented in [20].  
It is important to mention that, according to the directivity diagram data of the Simrad 4G(tm) FMCW marine radar's antenna, which is presented in the radar's brochure, the sidelobe level of the radar's Tx and Rx antenna is below -18 dB when within \(\pm\)10\({}^{\circ}\) and below -24 dB outside \(\pm\)10\({}^{\circ}\), the vertical beamwidth (-3 dB) is 25\({}^{\circ}\)\(\pm\) 20% and the horizontal beamwidth (-3 dB) is 5.2\({}^{\circ}\)\(\pm\) 10% [21].
## 3 Experimental Verification  
An experimental study has to be performed to validate the numerical model before the simulation results can be used to analyze the thermodynamic characteristics of the hyper-compressor.  
As shown in Figure 5 and Table 2, a two-stage, eight-cylinder, opposed-balanced, four-row, large-scale hyper-compressor system was modified for the test. The hyper-compressor was equipped with a data acquisition system along with several sensors that recorded the signals of the piston rod, pipe strain, and piston position. Then, these signals were reconstructed based on the non-destructive test method (NDT) to obtain the \(p-\theta\) diagram and pipe dynamic pressure.
# Multipoint Thermal Sensing System for Power Semiconductor Devices Utilizing Fiber Bragg Gratings
## 3 Sensors' Calibration and Installation
### Calibration of FBG Sensors  
Firstly, an FBG sensor (FBG1) with a central Bragg wavelength of 1530 nm was affixed to a stainless-steel plate and placed within a digitally controlled heater. A broadband light source (BBS) was utilized to transmit light through the optical fiber sensor placed on the pre-heater, as depicted in Figure 4, for calibration purposes. The optical light generated at 100 mW is transmitted via the optical circulator to the FBG sensor positioned on the heater, and the reflected signal is then redirected through the circulator to the optical spectrum analyzer for visual display of wavelength. To minimize the impact of fluctuating room temperatures, the setup was isolated within a transparent glass enclosure.  
The digital control heater regulated the temperature within a range of 30 to 100 \({}^{\circ}\)C, maintaining a constant 10 \({}^{\circ}\)C increment. The FBG sensors used in this study had a head length of approximately 5 mm. The choice of 5 mm FBG sensors was based on a balance between spatial resolution and ease of installation. Shorter sensors (e.g., 1 mm) can provide higher resolution but are more sensitive to placement and external disturbances. However, longer sensors (e.g., 5 mm) offer better robustness and easier installation, making them suitable for larger areas with fewer critical spatial resolution requirements [23]. Considering the IGBT baseplate's surface area and the need to capture significant temperature variations, 5 mm FBG sensors were deemed to be the optimal choice in this work. The Bragg wavelength at each temperature level was measured using a spectrum analyzer with a resolution of 0.01 nm, and the resulting temperature-wavelength curve is illustrated in Figure 5. The sensitivity of FBG1 was roughly 8.94 pm/\({}^{\circ}\)C, with a correlation coefficient R\({}^{2}\) of 0.999 using a second-order polynomial fit, and the measurement was conducted at three different times for the sake of repeatability. This process is replicated with two other sensors, i.e., FBG2 and FBG3, at the center wavelengths of 1540 nm and 1555 nm, and the resulting sensitivities were approximately 9.09 pm/\({}^{\circ}\)C and 9.89 pm/\({}^{\circ}\)C, respectively.  
Figure 4: Schematic of the calibration setup.  
Figure 5: Temperature–wavelength correlation of FBG sensors.
# Incrementally Mining Column Constant Biclusters with FVSP Tree
###### Abstract  
Bicluster mining has been frequently studied in the data mining field. Because column constant biclusters (CCB) can be transformed to be discriminative rules, they have been widely applied in various fields. However, no research on incrementally mining CCB has been reported in the literature. In real situations, due to the limitation of computation resources (such as memory), it is impossible to mine biclusters from very large datasets. Therefore, in this study, we propose an incremental mining CCB method. CCB can be deemed as a special case of frequent pattern (FP). Currently the most frequently used method for incrementally mining frequent patterns is FP tree based method. In this study, we innovatively propose an incremental mining CCB method with modified FP tree data structure. The technical contributions lie in two aspects. The first aspect is that we propose a modified FP tree data structure, namely Feature Value Sorting Frequent Pattern (FVSFP) tree that can be easily maintained. The second aspect is that we innovatively design a method for mining CCB from FVSFP tree. To verify the performance of the proposed method, it is tested on several datasets. Experimental results demonstrated that the proposed method has good performance for incrementally handling a newly added dataset.  
feature value sorting frequent pattern tree; incremental mining; column constant bicluster
## 3 Methodology  
The objective of this study is to develop an automated methodology for the classification and precise localization of internal rail defects, leveraging advanced object detection algorithms applied to B-scan image datasets. The architecture of the object detection network is tripartite, encompassing the backbone, neck, and head components.  
The backbone is a fundamental feature extractor, processing input images or videos to produce pertinent feature maps [32]. The backbone selection is pivotal, emphasizing optimizing a balance among accuracy, computational speed, and operational efficiency. Sophisticated, highly interconnected backbones such as ResNet and DenseNet offer enhanced accuracy but at the expense of increased processing time. Conversely, streamlined architectures like MobileNet and EfficientNet are preferred for their efficiency and swifter processing capabilities, providing a pragmatic trade-off between speed and performance accuracy [33]. The neck component of the network amalgamates diverse feature maps, with prominent examples including the Spatial Pyramid Pooling (SPP), Path Aggregation Network (PAN), and Feature Pyramid Network (FPN). The head component is responsible for the prediction phase, comprising two distinct types of detectors: the one-stage and the two-stage detectors. The two-stage detector incorporates a Regional Proposal Network (RPN) and a Region of Interest (RoI) pooling network. In this setup, the RPN layer forwards region proposals to a classifier and regressor for classification and bounding box regression. The one-stage detector, in contrast, eschews the region proposal phase, directly predicting bounding boxes from the input images and conflating location regression and classification into a singular, streamlined process. While two-stage networks such as Fast R-CNN and Faster R-CNN have achieved notable accuracy in localization and recognition, their application in real-time scenarios like pedestrian detection and video analysis is often limited by prolonged inference times [34]. To address these limitations, one-stage networks like the YOLO series and SSD have been developed, characterized by their high inference speeds and aimed at establishing efficacious real-time object detection systems [9].
## 4 Experimental Results
### Comparison with State-of-the-Art Based on NEU Dataset

Table 2: Comparison of results with the state-of-the-art based on the NEU dataset (%). Figure 6: The training and testing losses of the models. (**a**) Training progress of the level 0 model and (**b**) training progress of the level 1 model. among crazing, pitted surface, and rolled-in scale, which is challenging to detect. However, the result demonstrates that the MRFFN gets confused by the inclusion and pitted surface defects, in which 0.72% of inclusion samples are misclassified as the pitted surface, and 0.20% of the pitted surface samples are misclassified as inclusion.