esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In our paper, we propose a generalized version of the Alternating Projections Digital Hard Successive Interference Cancellation (AP-HSIC) algorithm that is capable of decoding any order of constellation M in an M-Quadrature Amplitude Modulation (QAM) system. Our approach applies to Rayleigh deep-fading Multiple-Input Multiple-Output (MIMO) channels with high-level Additive White Gaussian Noise (AWGN). It can handle various destructive phenomena without restricting the number of antenna arrays in the transmitter/receiver. Importantly, it does not rely on closed-loop MIMO feedback or the need for Channel-State Information Transmission (CSIT). We have demonstrated the effectiveness of our approach and provided a Bit Error Rate (BER) analysis for 16-, 32-, and 64-QAM modulation systems. Real-time simulations showcase the differences and advantages of our proposed algorithm compared to the Multi-Group Space-Time Coding (MGSTC) decoding algorithm and the Lagrange Multipliers Hard Successive Interference Cancellation (LM-HSIC) algorithm, which we have also developed here. Additionally, our paper includes a mathematical analysis of the LM-HSIC algorithm. The AP-HSIC algorithm is not only effective and fast in decoding, including interference cancellation computational feedback, but it can also be integrated with any Linear Processing Complex Orthogonal Design (LPCOD) technique, including Complex Orthogonal Design (COD) schemes such as high-order Orthogonal Space-Time Block Code (OSTBC) with high-order QAM symbols.  
M-QAM; MIMO; Rayleigh deep-fading; interference cancellation; parallel decoding; computational feedback +
Footnote †: journal: _Sensors_**2024**, 24, 5038. [[https://doi.org/10.3390/s24155038](https://doi.org/10.3390/s24155038)]([https://doi.org/10.3390/s24155038](https://doi.org/10.3390/s24155038))
## 2 Dynamic Power Management
### Dynamic Frequency and Voltage Scaling
#### 2.2.4 Dynamic Voltage and Threshold Scaling (DVTS)
Here the power of the logic block is directly measured by monitoring the voltage across the sleep transistor [15]. This voltage is related to the logic block's current consumption as \(I\times R_{sleep,on}\), where the \(R_{sleep,on}\) is the resistance of the power gating transistor when it is ON. An additional multiplication with \(V_{DD}\) is achieved by making the integration time proportional to the supply voltage. This _in-situ_ monitoring approach ensures that the power measurements are accurate across process and temperature conditions and can track the changes in activity easily. The monitoring circuit consists of an integrator, a capacitor to store monitored value of the previous step in an analog form and a comparator. The difference of the measured power in the current control loop step with respect to that from the previous step is quantized into two bits. The difference power information is used by the control system to continually adjust the \(V_{DD}\), \(V_{BB}\) values to drive the system to the point of optimal power. The system can track activity changes as long as they occur at a rate less than the loop bandwidth. Simulated power savings of up to 42% is observed in a 90 nm triple well process node. The main short coming of the DVTS technique is related to the ineffectiveness of body bias control on the threshold voltage in deep sub-micron processes. In order to limit the diode leakage currents through diffusion-body interface, the body bias voltage has to be limited to a value of \(\pm\)500 mV around the nominal. With a body effect coefficient of less than 0.1, this leads to a threshold voltage modulation of less than \(\pm\)50 mV, leaving very little range for adaptation. Figure 8: _In-Situ_ Difference Power Monitor for a DVTS System. Figure 7: Dynamic Voltage and Threshold Scaling System.
## 5 Performance Evaluation
### Performance Matrices
#### 5.2.4 Service Provider Profit  
The profit gained by the service provider is calculated by the difference between the revenue gained and cost required to execute a request as in Equation (4), and then the average is taken for all requests arrived during the simulation period.  
The average profit gained by the service provider for each request can be calculated as:  
\[\overline{\rho}_{r,b}=\frac{1}{|R|}\sum_{r\in R}\left(\rho_{r,b}\right), \tag{20}\]  
where \(\rho_{r,b}\) represents the profit of processing a request gained by the service provider, and \(R\) is the total number of assigned requests. The higher the value of \(\overline{\rho}_{r,b}\), the higher the profit obtained by the service provider for each request.
## 1 Introduction
They then transformed each target region into a binary image to locate rebar. [PERSON] et al. [30] improved the Mask Region-based Convolutional Neural Network (R-CNN) by incorporating a novel distance-guided intersection over union (DGIoU) method to detect and segment object signatures in GPR scans. [PERSON] et al. [31] demonstrated the application of a deep semantic segmentation model to separate diffraction signals from the rest of the wavefield in GPR data, effectively delineating complete-shaped diffraction signals and recognizing weaker and incomplete ones. While these deep learning methods on GPR images have improved the efficiency of anomaly detection of underground targets, there is little literature on the application of deep neural networks to GPR image segmentation. Furthermore, GPR models are data-driven and require large amounts of hand-labeled data for training. The lack of different types of typical GPR anomaly labeled data also limits the implementation of image segmentation algorithms in GPR images. This presents an opportunity for researchers to broaden the segmentation method's applicability to GPR images. By utilizing a deep convolutional network framework for image segmentation for GPR, it can partition multiple distinct reflection hyperbola targets in GPR images into binarized ROIs, enhance the stability of the segmentation method across various GPR images, and eliminate reliance on skilled operators in the GPR post-processing phases. The paper proposes an advanced approach utilizing a lightweight deep Res-CNN with skip connections based on the encoder-decoder framework for ROIs hyperbolic segmentation, aiming to simplify GPR profiles and identify significant ROIs. This framework effectively segments reflection hyperbolas in GPR data, obtaining binary features of anomalous reflection hyperbolas. Compared to traditional methods, this approach swiftly separates the anomalous reflection hyperbola from the backgrounds in massive GPR data, enhances the ROIs, and yields better preprocessing result across different target materials. The paper is structured as follows: In Section 2, an introduction to GPR data binarization preprocessing and the proposed methods for binarizing ROIs hyperbolic region is provided, followed by detailed implementation descriptions. In Section 3, the model training dataset is introduced, and experimental testing and analysis are performed on simple models, complex scenarios, and field data. Discussion In Section 4, it focuses on the analysis of research results, the limitations of the methods, and future research directions. Conclusion In Section 5, the key findings and implications of the study are summarized.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 3 Methodology
### Dataset Collection  
#### 3.1.1 Source of Data  
The Roman Urdu dataset is different from English language datasets on many accounts; it features non-standardized spellings (e.g., mujhe vs. mujay), heavy code-mixing with English (e.g., mujhe idea nahi), and grammar inconsistency. These properties come with new difficulties not common in English data, including large lexical variation, informal grammar, and mixed-language sentence patterns. The material for this study was collected from Facebook comments, as social media is a significant source of hate speech due to its open and interactive nature. Facebook was chosen specifically because it has a diverse user base in South Asia, making it a valuable platform for capturing Roman Urdu text. Data were retrieved using web scraping techniques and Facebook's API, ensuring compliance with ethical guidelines and privacy laws. To avoid biases, data were collected from a variety of public pages, posts, and comment sections, ensuring a balanced representation of opinions. Furthermore, all personally identifiable information (PII) was removed to maintain user anonymity and comply with data protection regulations.  
In order to understand the properties of the dataset, we analyzed the text properties in detail. The dataset contains 46,026 Roman Urdu-English code-mixed Facebook post  
Figure 1: Methodology workflow for hate speech detection.  
comments. The mean number of words in a comment is around 18.7 words, with a standard deviation of 6.5 words. The one with the fewest words has 3 words, and the one with the most words has 47 words. For class distribution, 22,314 comments were annotated as \"Hate Speech\", and 23,712 of the comments were labeled as \"Not Hate Speech\", which indicated that it was balanced. These statistics show the variety of words used and the variation in length of comments in the dataset, which increases the difficulty of classification.
# Detecting Out-of-Distribution Samples in Complex IoT Traffic Based on Distance Loss
## 4 Experiments
### Ablation Study  
To assess the contribution of each component in our proposed OOD detection framework, we conduct a stepwise ablation study with the following configurations:  
* Baseline: A standard classifier trained only with cross-entropy loss, without any grouping or distance-based regularization.
* Baseline + Grouping: A class grouping mechanism based on class mean similarity is introduced, encouraging similar classes to form compact groups and enabling more localized decision boundaries.
* Complete ODDL (Baseline + Grouping + Distance Loss): A group-wise distance loss is added to enforce intra-group compactness and inter-group separation in the feature space.  
As shown in Figure 8, the grouping module alone reduces FPR95, indicating improved suppression of high-confidence misclassification on OOD samples. Although AUROC improvement is modest at this stage, adding the distance loss yields substantial gains in both AUROC and FPR95.  
This improvement is attributed to the way each component shapes the feature space. Grouping organizes classes with similar distributions into semantically meaningful regions, while the distance loss tightens intra-group clusters and pushes apart different groups. As a result, in-distribution (ID) samples tend to cluster near group centers, whereas OOD samples are more likely to fall into low-density regions, improving their separability under the Mahalanobis distance metric.  
All experiments were conducted under identical training protocols and data splits to ensure fair comparison. No other variables were changed across configurations, ensuring the observed improvements result solely from the added components. These results validate the design of our framework and explain the significant performance differences reported in Table 3.  
Figure 8: Ablation study results on different OOD test sets. Each bar group represents the performance of three configurations: (1) Baseline (only cross-entropy loss), (2) Baseline + Grouping, and (3) Baseline + Grouping + Distance Loss (ODDL). (**a**) shows the AUROC results, and (**b**) shows the FPR95 results across the different OOD test sets. The figure illustrates the incremental effect of the grouping and distance loss modules in improving OOD detection performance.
## 3 Approach
### Rail Track Topology Reconstruction
#### 3.2.3 Fitting of the Track Area  
Firstly, for each railway track, we divide them into 10 equal parts based on the perception range from nearest to farthest. Under normal circumstances, each part is fitted with a quadratic polynomial curve, as shown in equation 9.  
\[L_{ji}(z_{j})=a_{i}z_{j}^{2}+b_{i}z_{j}+c_{i}(0\leq j<10) \tag{9}\]  
where \(L_{j}\) represents the equation of the \(j\)-th track, \(i\) denotes the \(i\)-th segment, \(a\), \(b\), and \(c\) are the parameters to be fitted, and \(z\) is the independent variable distance.  
We artificially impose some constraints on the formula shown in Equation (9). Firstly, we match each pair of tracks and utilize the parallelism between the two tracks, as well as the fixed distance between them, to constrain the parameter fitting. Specifically, after matching a pair of railway tracks, based on prior information that the left and right tracksare parallel and separated by a fixed distance \(d\), we can utilize this characteristic to correct any outliers in the tracks and fill in missing points accordingly. Furthermore, based on prior Characteristic 1 and Characteristic 2, we limit the size of \(a_{i}\) as a constraint on curvature. The process of railway topological reconstruction is shown in Figure 8.  
Additionally, we devise a contingency plan to handle extreme cases. We set a threshold \(n\) and if the number of key points for a particular pair of tracks is less than the threshold \(n\), we abandon the segmented polynomial fitting for that track and instead opt for linear fitting. Figure 9 illustrates the qualitative results of the aforementioned process. Once the interpretation of the railway track is obtained, we set a fixed distance. When the distance of the detected key points is less than the preset distance, we extend the track appropriately based on the first and last segments of the segmented curve. The curve parameter fitting methods all use the least squares method [41].  
Figure 8: The clustered candidate points are subjected to piecewise polynomial curve fitting and matching to obtain the track area. During curve fitting, constraints are added to improve the reliability of the results.  
Figure 9: The qualitative results of the railway correction and analysis process.
# On Data Leakage Prevention Maturity: Adapting the C2M2 Framework
## 2 Materials and Methods
### Input Knowledge
#### 2.2.4 Impact of Large Language Models on DLP and Future Trends  
The limitations of traditional DLP methods in large corporate settings are recognized and understood [1, 22, 34, 35]. Extensive research has addressed specific aspects of DLP (among others [36, 37, 38, 28, 39]), yet a comprehensive solution to its complex practical challenges remains elusive. This gap underscores a significant opportunity for future exploration into the potential of large language models (LLMs) to revolutionize DLP strategies in dynamic business contexts. While recent studies have explored the use of LLMs in detecting phishing attacks [40], there is a notable absence of research on applying LLMs to DLP.  
The prospective utility of LLMs in DLP remains speculative, yet their emerging capabilities suggest significant untapped potential. The current absence of LLMs from established DLP maturity frameworks is defensible, considering the technology's incipient stage of development and application in this domain. Nevertheless, the prospective integration of LLMs promises to be transformative, potentially streamlining DLP operationsand reducing costs. In the preliminary phase, organizations might explore the support of LLMs to augment DLP analysts' capabilities in sophisticated data interpretation, which could lower the financial and logistical thresholds for attaining advanced DLP maturity. Close observation of these developments is imperative, as they may assist organizations in attaining elevated DLP maturity levels with diminished financial outlay.
## 4 Results
### Performance of the Proposed Method with Different Frequency Bands  
As mentioned in Section 3.3, subject 27 had the highest number of accurate subtractions (count: 34), and subjects 10 and 21 had the lowest number (count: 1). Therefore, subjects 27 and 21 were selected to compare the frequency band activations and their corresponding connectivity. Figure 6 displays topmaps of subjects 27 and 21 in theta, alpha, beta, and gamma frequency bands (Figure 6a) and their corresponding functional connectivity (Figure 6b). For ease of comparison, the topmaps and corresponding functional connectivity are plotted at 6.4 s, when the subjects received the input numbers and started to perform the mental arithmetic task (subtraction). The red-to-blue gradient indicates the activity intensity, with red areas showing stronger activity and the blue regions showing less activity.  
The theta band displays higher activations in the frontal region in both subject 27 and subject 21. However, the cortical activations decreased in subject 21 in the central, parietal, and occipital regions compared to subject 27. The alpha band had higher activations in the left hemisphere of the brain in subject 27, whereas the right hemisphere was highly activated in subject 21. The beta band has higher activation throughout the brain in subject 27, whereas it is limited to frontal and right parieto-temporal regions in subject 21. Similar activations are observed in the gamma band in subject 27, emphasizing higher activations in the left hemisphere than in the right hemisphere in subject 21. For ease of comparison, the functional-connectivity network is plotted at 6.4 s, corresponding to four frequency bands in Figure 6a, and displayed in Figure 6b. The edge weight (\(\epsilon\)) > 1.5 is considered to optimize the connectivity plots across all frequency bands. The lines represent connections (edges) with varying strengths: the thicker the line, the stronger the connectivity. The sidebars next to each map may indicate the overall connectivity strength for each frequency band, with green likely representing a standard or baseline level of connectivity and the pink-to-purple scale indicating deviations from this baseline. The connectivity plots in  
Figure 5: Subtraction counts of good vs. bad performers.  
Figure 6(b) align with the brain activations in Figure 6(a). Higher connectivity is observed in the frontal, central, and parietal regions in the left hemisphere in the good-performance subject; this is compared to right hemisphere connectivity in the performance subject.
###### Abstract  
This study aims to improve firestop application standards by experimentally analyzing changes in fire-resistance performance due to variations in opening size, penetrant diameter, and firestop material volume. Fire-resistance tests were conducted for 120 min in accordance with KS F ISO 10295-1, using eight specimens with systematically varied cross-sectional areas of openings and penetrants. For nonmetallic pipes, which soften or melt at elevated temperatures, the results show that the completeness of the opening seal and the amount of firestop material are the primary factors governing fire resistance. When the firestop-to-opening area ratio decreased from 95-91%, the maximum temperature on the unexposed surface was, on average, 15-30 \({}^{\circ}\)C lower, confirming that modest reductions in firestop material volume for small openings do not compromise performance. In contrast, metallic pipes retained structural integrity and acted as direct heat-transfer paths; fire-resistance performance was more strongly influenced by penetrant diameter and cross-sectional area than by opening size. These findings provide quantitative evidence to support flexible design criteria for firestop systems and offer a practical basis for transitioning toward performance-based approval standards.  
fire resistance; firestop material; penetration systems; firestop volume effect; pipe +
Footnote †: journal: Applied sciences
# Localization of GNSS Spoofing Interference Source Based on a Moving Array Antenna
## 4 Simulations and Results
### Experiment 3: Spoofing Localization Effects under Different Antenna Positioning Errors  
This experiment tests the spoofing interference localization effects of our method under different antenna localization errors and carrier phase measurement errors and selects the Two-step Method 1 with better localization effect as the comparison method. The spoofing interference source setup, carrier phase measurement error setup, and localization accuracy statistics for the spoofing interference source in the experiment are consistent with those in Section 4.2.  
Figure 11: Statistical results for the normalized cost function under different number of iterations.  
Figure 10: Test results from Experiment 2. (**a**) Comparison of localization effects of various methods under Path 1; (**b**) Comparison of localization effects of various methods under Path 2; (**c**) Comparison of localization effects of various methods under Path 3; (**d**) Comparison of localization effects of various methods under Path 4; (**e**) Comparison of localization effects of various methods under Path 5; (**f**) Comparison of our method under five monitoring paths.  
The mean values of the antenna positioning errors in Experiment 3 are all set to 0, and the standard deviation values are set to 1, 5, 15, 20, 30, and 50, respectively, in meters. Here, the standard deviation settings of 1 and 5 correspond to the positioning accuracy that can be achieved by a typical 7-element null antenna under static and low-speed motion, the settings of 15 and 20 correspond to the positioning accuracy of the array antenna under conventional motion, and the settings of 30 and 50 correspond to the positioning accuracy of the array antenna under high-speed motion.  
The experimental results are shown in Figure 12, where Figure 12a,b shows the spoofing interference localization effects of the proposed method and the Two-step Method 1 under different antenna positioning error settings, respectively. The horizontal and vertical coordinate settings in Figure 12 are consistent with those in Figure 10. The simulation results show that the proposed method has stronger adaptability to antenna positioning errors compared to the two-step method, making it more suitable for deception interference monitoring applications in dynamic scenarios in this paper. As the antenna array positioning error increases, the main factor affecting the spoofing location effect of the method in this paper changes from carrier phase measurement error to antenna positioning error.