esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Trust-Driven Incentive and Aggregation Mechanism
### Robustness Enhancement and Anomaly Detection Mechanisms  
To defend against attackers mimicking trustworthy patterns or frequently switching strategies, we introduce the following two robustness enhancement modules:  
1. The Deviation Penalty Mechanism: Here, the relative deviation of the updates is defined as follows: \[\zeta_{i}^{t}=\frac{\|\Delta w_{i}^{t}-\bar{\Delta w}_{t}\|_{2}}{\|\bar{ \Delta w}_{t}\|_{2}},\] (14)If \(\xi_{i}^{t}>\epsilon\), the trust score is penalized as follows: \[\tau_{i}^{t}\leftarrow\tau_{i}^{t}\cdot\exp(-\beta\cdot\xi_{i}^{t}),\] (15)
2. Sliding Window-Based Trust Correction: A sliding window tracks the fluctuation of client trust scores. If a client exhibits drastic, non-monotonic variations, we slow down the growth of its aggregation weight to prevent short-term strategic speculation from receiving high incentives.  
These mechanisms improve the behavioral sensitivity and anomaly adaptability of the trust model, forming a layered defense framework for the system.
# Enhancing Kazakh Sign Language Recognition with BiLSTM Using YOLO Keypoints and Optical Flow
## 4 Proposed Method
### Training  
The dual nature of sign language, combining static and dynamic gestures and poses, is challenging for recognition systems, particularly in capturing motion over time. Indeed, YOLOv8 effectively detects hand regions and extracts keypoints from individual frames. However, it lacks temporal modeling. To address this, we integrated a bidirectional long short-term memory network to learn temporal dependencies from gesture sequences [21].  
Figure 5: Samples of data used for model training: (**a**) dynamic and static sign samples; (**b**) annotated samples from the dataset.  
Figure 6: Impact of static/dynamic sign ratio on system accuracy.  
Some gestures, such as Name and Sumame, are visually and dynamically similar, increasing the risk of misclassification. To improve sensitivity to motion, optical flow computed via the Farneback algorithm was added as an input feature [22], providing information about motion direction and magnitude between consecutive frames.  
For training, we applied stratified sampling [23] to maintain class balance. Annotation files provided class labels and keypoint coordinates in the format [21; 2] (for 21 hand keypoints). These were defined in a custom configuration file, specifying class names and metadata.  
The YOLOv8-Pose model was trained for 100 epochs with a batch size of 16 on two GPUs to simultaneously perform hand detection and keypoint regression [24]. During training, loss metrics and checkpoints were recorded. After convergence, the model was used to extract features for the Bi-LSTM sequence recognition pipeline.  
For each video, three types of features were extracted. First, hand keypoints were obtained and reshaped from a [21; 2] format into an 84-dimensional vector to serve as spatial descriptors. Second, optical flow vectors were computed using the Farneback algorithm to capture motion dynamics. Third, bounding box coordinates were extracted using the YOLOv8 model to localize hand regions within each frame.  
These were concatenated into a single 176-dimensional feature vector per frame. Sequences of 30 frames formed one input sample. A total of 30 sequences per class were collected for dynamic gestures _Aty_ and _Tegi_. Missing frames were zero-padded. The final dataset had the shape \([N,30,176]\), where \(N\) is the number of sequences. Labels were integer-encoded using a predefined map.  
For the binary classification task involving the dynamic gestures _Aty_ and _Tegi_, we employed a standard bidirectional long short-term memory (Bi-LSTM) network. The model consisted of a single bidirectional LSTM layer with 128 hidden units, followed by a fully connected softmax layer. It was trained for 250 epochs using the Adam optimizer (learning rate = 0.0001, weight decay = 5 \(\times\) 10\({}^{-4}\)), a batch size of 32, with the ReduceLROnPlateau scheduler and gradient clipping to ensure stable convergence (Figure 7).
## 2 Operation Principle of Proposed KNN Based Scheme
In the receivers of the noted OOFDM systems, fast Fourier transform (FFT) processing is first performed on the received signal to obtain the complex signal data, and then the features of the data can be abstracted from the complex signal data. The AH is introduced in the proposed scheme in which only the real part of the received signals is involved to abstract features. In this study, 30 samples are selected as a training data set, of which every sample is a subcarrier transmission data of OFDM signals. Subsequently, for every subcarrier, 3000 data points are selected from whole 20,400 data points to obtain features. Accordingly, for the 4-QAM, 16-QAM, 32-QAM, 64-QAM and 128-QAM, 2, 4, 6, 8 and 12 peaks should be observed in corresponding AHs. The clustering degree of peaks represents different OSNR. Obviously, when the peak is prominent, the value of OSNR is large. To simplify the computation complexity and improve the robustness of the algorithm, a data preprocessing scheme is first implemented. The real part of the complex data is divided into 100 intervals from -1.5 to 1.5, after taking real part operations for each data point. The number of points falling in different intervals is used as input feature vector data for KNN. To reduce the effect that computational overhead increases with the increase in data size, each interval is processed as follow:  
1. If the number of points in an interval \(\geq\)256, the value of this interval will be set to 32. 2. If the number of points in an interval \(\leq\)32, the value of this interval will be set to 0. 3. If the number of points in an interval is between 32 and 256, the value of this interval will be set to an integer obtained by using the number divided by 8, which can be conveniently performed by a commercial processor in the future. Then, the final features of each interval can be obtained.
# Simulation of a Low Concentrator Photovoltaic System Using COMSOL
## 3 Results and Discussion
### Concentration Ratio
This can be attributed to the fact that the direction of solar radiation on the December solstice falls at an oblique angle at noon. Therefore, the same portion of the reflected rays reaches the PV panel for the 13-positions and 3-positions. In contrast for the longest day, the \(\mathrm{B_{PV}}\) for 3-positions slightly differs from 13-positions, as the direction of the solar radiation is perpendicular to the surface of the system at noon. Hence, \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Double Coverage**} & \multicolumn{2}{c}{**Partial Coverage**} \\ \hline  & \(\mathbf{\theta_{1}}\) & \(\mathbf{\theta_{2}}\) & \(\mathbf{\theta_{1}}\) & \(\mathbf{\theta_{2}}\) \\ \hline Morning & \(20^{\circ}\) & \(115^{\circ}\) & \(25^{\circ}\) & \(115^{\circ}\) \\ Noon & \(70^{\circ}\) & \(70^{\circ}\) & \(60^{\circ}\) & \(60^{\circ}\) \\ Afternoon & \(115^{\circ}\) & \(20^{\circ}\) & \(115^{\circ}\) & \(25^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The mirror inclination angles when fixed at three positions for double and partial coverage (for the city of Jeddah). Figure 8: The irradiance falling on the system for the city of Jeddah for the cases of C1 (double coverage) and C2 (partial coverage) with three different operation schemes. For (**a**) the longest and (**b**) shortest days of daylight in the year. tilting the mirror at the optimum angle for each hour ensures that more incident radiation reflects inside the V-trough cavity. The same pattern was observed when the simulation was performed for different geographical locations, with differences in the maximum value of \(\mathrm{B_{PV}}\) reached. To visualize the values of CR when the system is placed in different geographical locations, Figure 9 illustrates the results of the configurations for the highest irradiances reached. That is, the case of double coverage, with the three operation schemes of changing the mirrors' inclinations every hour or limiting it to three moves during the day or fixing it at one position throughout the day. It can be seen from Figure 9 that the effect of limiting the number of movements of the mirrors to three movements did not affect significantly the CR, although this restriction can help in cutting down operation costs. The reduction in CR values was as low as 1.2% for Sharurah and as high as 14% for Dhahran, while other cities varied between these two values. However, with fixed mirrors, the CR was reduced, approximately, to half its original value for all of the cities under study. Hence, simulating the different operational schemes can assist in balancing between maintaining the high efficiency of the LCPV system and reducing the cost and effort. The results in this work indicated that the optimum performance came when moving the mirrors to three positions per day.
# Inelastic X-ray Scattering as a Probe of Terahertz Phonon Propagation in Nanoparticle Suspensions
## 2 The Pivotal Role of Bayesian Analysis
### The Bayes Theorem  
In the past, a few studies used Bayesian methods to extract accurate structure factor amplitudes from powder diffraction patterns with strongly overlapping Bragg peaks [20]. More recently, these methods have also been employed by my group to determine the number of excitations present in inelastic neutron or X-ray scattering spectra [21; 22; 23; 24], or the number of diffusive processes in the frequency [25; 26] or time-dependent [27] response of a system. The conceptual pillar of this approach is the Bayes theorem, whose enunciation also comes in handy here because it permits us to introduce the key distributions one deals with in Bayesian inference to model the measured spectral shape.  
Suppose a spectral measurement is consistently described by a model identified by the set \(\Theta=(\theta_{1},\theta_{2},\cdots,\theta_{m})\), where the \(\theta_{m}\) component of the vector \(\Theta\) is the \(m\)th parameter defining the spectral profile, as, e.g., the amplitude, inelastic shift, or damping of a mode, the background, and similar.  
If we then represent the measurement outcome with the vector \((y_{1},y_{2},\cdots,y_{n})\), where \(n\) is the number of sampled values, we can assume that the latter provide a sampling \(y_{i}\) (\(i=1,\cdots n\)) of a random variable \(y\). We can also formally represent our a priori information on the physical problem under scrutiny with the symbol \(I\). With this notation, the Bayes theorem [19] reads as:  
\[P(\Theta|y,I)=\frac{P(y|\Theta,I)P(\Theta|I)}{P(y|I)}, \tag{1}\]  
where \(P(\Theta|y,I)\) is the posterior distribution of the model parameters conditional on the achieved measurement and the available information. Our a priori knowledge--or lack thereof--about the model parameters is reflected by the prior distribution \(P(\Theta,|I)\). The distribution \(P(y|\Theta,I)\) is the likelihood of the data and measures the plausibility of a given outcome conditional on the truth of the information and the model. Finally, \(P(y|I)\) is the marginal probability, whose primary role is enabling the probability in Equation (1) to be correctly normalized, i.e., to have the unit integral over the integration variable \(\Theta\). Let us now comment in further detail on these distributions, starting with the prior distribution (or simply the prior).
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 4 Functional Blocks of the Proposed System  
The aim of this design is a low-latency hardware implementation suitable for real-time applications, such as high-efficiency numerical transmission systems and signal processing applications.  
The diagram of functional blocks composing our structure is shown in Figure 2. Memory devices (such as PISO registers) produce possible series to parallel conversion of inputs.  
It is a parallel architecture whose inputs and outputs are the bit slices and the DFT of the \(N\)-samples-long sequence, respectively. Each bit slice is processed with a BFT block. Therefore, all the \(N\) samples of the input sequence need to be collected and arranged in bit slices for the proper operation of the BFT block. Given that the authors seek to design a low-latency arrangement, an improved solution has been synthesized (Figure 3). In this circuit, the data processing is performed as soon as samples are provided in input. It follows that the collection of all the \(N\) samples composing the sequence before starting data processing is not required. As a result, the DFT is obtained a few clock pulses after the last sample of the input sequence has been collected.  
The proposed DFT architecture is composed of four functional modules: the DEMUX block, the BFT block, the left-shift block, and the adder block.  
Figure 1: Example of the complexity-reduction step for each 8 point BFT slice.
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 6 Experimental Evaluation
### Rationale Behind the DNS Simulation Design  
To evaluate the core functionalities and performance of the proposed consensus roots system, our current experimental setup models a single DNS domain with one node per sub-chain. This simplified configuration allows us to focus on key aspects, such as cross-chain synchronization, relay coordination, and transaction efficiency, without introducing extraneous network complexity. Such an approach is consistent with prior research on DNS security frameworks, including DNSSEC [27], HARD-DNS [28], and DepenDNS [29], which also adopt simplified testbeds or emulated zones to isolate and evaluate protocol-level behaviors before scaling to full DNS hierarchies. Furthermore, studies such as [30] use a representative subset of DNS zones and nodes to evaluate latency and resilience, acknowledging that full-scale global DNS replication is impractical for prototype validation. Several studies have demonstrated the rationale for using a single DNS domain setup. The D3 NS scheme emphasizes that verifying DHT routing efficiency within a single domain is essential before optimizing distributed DNS structures [31]. FI-DNS limits its initial experiments to replica differences within a single domain to simplify the scenario and validate its core mechanism [32]. The master-slave chain scheme also treats single-domain resolution as the  
Figure 6: The results of the performance evaluation: (**a**) Latency. (**b**) Throughput.  
foundation for multi-domain expansion [33]. These works show that configuring a single DNS domain is a necessary prerequisite for more complex system designs.
###### Abstract
The main advantage of these materials lies in their non-toxicity, biodegradability and in the fact that they can be obtained from renewable sources, e.g., from the exoskeletons of crustaceans (chitosan, a chitin derivative), brown algae (algate) or plants (cellulose, and consequently its derivatives, are known as main constituents of plants) and even agro-industrial wastes such as wheat straw and sugarcane bagasse, among others [20; 21]. Sodium carboxymethylcellulose (CMC-Na) is an anionic biopolymer obtained by introducing carboxymethyl groups along the cellulose chain [22]. CMC-Na is a polysaccharide hydrogel, a well-established material for the encapsulation of several compounds [23; 24; 25]. It is well known that the delivery mechanism of hydrogel reservoirs is based on erosion caused by the adsorption of water through a swelling process. That is, the trigger-release mechanism occurs due to the swelling of CMC-Na capsules in the presence of water molecules, progressively causing the diffusion of the encapsulated component out of the biopolymer network [24; 26]. Greener processes and materials are aspects of great importance, especially in research targeting industrial applications. In a previous work by our group, the potential of CMC-Na microspheres containing benzotriazole (BTA), as delivery systems for the development of smart coatings, was evidenced by electrochemical impedance studies in a corrosion-prone environment [16]. The present study aims to obtain insights on the influence of different inlet temperatures and spray flow rates on the physicochemical properties of CMC-Na microreservoirs, using benzotriazole as a model of corrosion inhibitors. For this purpose, in this work, the different spray drying parameters' influence on CMC-Na microspheres was studied beyond morphology and size distribution. Microspheres' moisture content, encapsulation efficiency and process yield were assessed to evaluate whether different inlet temperatures and spray flow rates would impact the microspheres produced. Moreover, BTA loading and release from CMC-Na microspheres were also performed and discussed. These results may provide crucial information on the behavior of the prepared material for corrosion-inhibiting applications. Considering the aforementioned points, the combination of the spray drying encapsulation technique, together with a CMC-Na encapsulating material, may contribute to the development of smart coatings by using environmentally friendly microreservoirs produced by a green aqueous process.
# Research on the Performance of an End-to-End Intelligent Receiver with Reduced Transmitter Data
## 1 Introduction
Although a variety of different semantic communication models have been designed [5; 6], this improves the communication efficiency and significantly reduces the transmitter data. Unlike the improvement of source coding in semantic communication, there is, however, less research on whether an end-to-end intelligent receiver has the ability of accurate decoding after reducing the data at the transmitting end after source coding. In other words, there is little research on whether an end-to-end intelligent receiver has a similar semantic communication ability to reduce the data of the transmitter. Therefore, this paper will investigate whether the IR model under data reduction at the transmitter side of the multiple input multiple output (MIMO) communication system has the capability of accurate decoding. In this study, we designed two different data clipping methods to reduce the data at the transmitter. At the same time, we built the intelligent receiver network as seen in the literature [18] to explore the performance of intelligent receivers under different scenarios of reducing transmitter data. Our contributions are as follows:  
* To the best of our knowledge, this is a preliminary investigation of whether the intelligent receiver has an accurate decoding capability with reduced data at the transmitter side. * We demonstrate that the intelligent receiver has the ability to decode accurately even with reduced data at the transmitter, and that the BER of the intelligent receiver decoding is lower than that of conventional receivers. * By verifying the effects of two data reduction methods, namely, data end cropping and data equal interval cropping, on the decoding capability of the intelligent receiver, we find that data equal interval cropping has little effect on the decoding performance of the intelligent receiver, and the intelligent receiver can achieve accurate decoding. The rest of this paper is organized as follows. Section 2 introduces the composition of the MIMO and the intelligent receiver network model. Section 3 describes the idea of data clipping at the transmitter. Section 4 explores the decoding performance of the intelligent receiver under different clipping data, and analyzes different clipping methods. Finally, Section 5 concludes this paper and briefly describes future work.
# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
## 3 Common Algorithms for Machine Learning
### Gene Expression Programming (GEP)  
Gene Expression Programming (GEP) is an evolutionary algorithm that combines the features of genetic algorithms (GAs) and Genetic Programming (GP) to automatically discover a model or function for solving a problem by simulating the process of biological evolution and gradually optimizing it through selection, crossover, and mutation operations starting from a randomly generated initial population of solution. GEP (Figure 10) has significant advantages in terms of structural flexibility, efficiency of genetic operations, interpretability, adaptability, and robustness. These advantages enable GEP to provide efficient, interpretable, and robust solutions when dealing with complex problems. However, GEP also has its limitations, such as parameter selection, overfitting problems, local optimal solution problems, and computational complexity, which need to be carefully considered and adapted in practical applications [29; 50; 51].  
Figure 9: SVM schematic diagram.  
In practical applications, Gene Expression Programming (GEP) faces several challenges that may affect its performance, including the sensitivity to parameter settings, the risk of overfitting, and the computational demands associated with its evolutionary process. To address these issues, we adopted the following strategies.  
First, since GEP performance is closely tied to its parameter configuration--such as head length, the number of genes, and mutation rates--we employed a combination of empirical tuning and automated search techniques to identify suitable parameter values. Cross-validation was also used to ensure the selected parameters generalized well beyond the training data.  
Second, to mitigate overfitting, we introduced complexity control during the evolution process. This included penalizing overly long or overly complex expressions in the fitness function, as well as setting limits on the depth and size of the evolved trees. Additionally, we monitored validation performance and applied early stopping when necessary to prevent the model from adapting too closely to the training data.  
Lastly, considering the computational complexity of GEP--especially when dealing with larger datasets or a high number of generations--we optimized the runtime by reducing the dimensionality of the input space and limiting the number of chromosomes per generation. Where possible, we parallelized the evolution process to accelerate convergence without compromising model quality.  
These strategies helped improve the stability, generalization, and efficiency of the GEP model in our study.
# Porous Doped Carbons from Anthracite for High-Performance Supercapacitors
## 1 Introduction
Among various chemical activators, potassium hydroxide (KOH) is one of the efficient activation agents for fabricating porous carbons with fine-tuned textural properties [19]. More interestingly, activation by KOH can introduce a high amount of the oxygen/nitrogen-containing functional groups, thereby fostering the electronic conductivity and imparting the additional pseudocapacitances [12]. In this article, the porous carbon samples were successfully derived from anthracite through the chemical activation method with a KOH activator and then assessed for the supercapacitor application. Impressively, the optimized material featured the high SSA (2814 m\({}^{2}\) g\({}^{-1}\)), uniform PSD (1-4 nm), and proper nitrogen content (2.77 at%). Consequently, the as-resultant porous carbons manifested remarkable electrochemical performance, such as a superior specific capacitance of 325 F g\({}^{-1}\) at the current density of 0.5 A g\({}^{-1}\), excellent cycling stability (92% of capacitance retention after 5000 cycles) at a large current density of 10 A g\({}^{-1}\) in a 6 M KOH solution. More significantly, the as-fabricated symmetric supercapacitor device delivers a high energy density of 23.5 Wh kg\({}^{-1}\) with a power density of 450 W kg\({}^{-1}\) at the current density of 0.5 A g\({}^{-1}\) in 1 M Na\({}_{2}\)SO\({}_{4}\) aqueous solution, transcending the most hitherto reported carbon materials. These exceptional performances of the porous carbon electrode can be attributed to its well-defined structural hallmarks of the high SSA, uniform PSD, and proper nitrogen content. Our current study ushers in a compelling initiative to turn up the functional carbon materials for the energy storage fields.