esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Machine Learning  
#### 3.4.1 Support Vector Machines (SVMs)  
SVMs are robust ML algorithms employed for both regression and classification tasks. Considering a dataset of training examples divided into two distinct categories, the SVM training process builds a model capable of assigning new examples to one of the two groups, resulting in a non-probabilistic binary linear classifier. SVMs accomplish this by dividing the data across a hyperplane \(f(x)=0\). Effective classification in SVM models depends on constructing a hyperplane that maintains the maximum margin from the nearest data points, identified as support vectors. There are separate objects with labels that fall into the positive and negative classifications in the input dataset \(x_{i}\) (\(i=1,2\ldots,N\)). The hyperplane that splits the provided data is produced by Equation (9) for linear data [48].  
\[y=f(x)=W^{T}x+b=\sum_{i=1}^{N}W_{i}x_{i}+b=0 \tag{9}\]  
The vector \(W\) has \(N\) dimensions, and the scalar \(b\) represents a bias term, where \(N\) denotes the number of samples in the dataset. The separating hyperplane \(f(x)=0\) is defined by the scalar \(b\) and the N-dimensional vector \(W\).  
Successful classification with SVMs relies on choosing the hyperplane that maximizes the distance between the hyperplane and the closest data points. A broader margin implies reduced generalization error, where the data points nearest to the hyperplane are instrumental in establishing this margin. Non-linear classification issues can be tackled by applying a kernel function [49]. Kernel functions serve to project data into a higher dimensional feature space, enabling the achievement of linear separability. Frequently utilized kernel functions consist of the linear kernel, polynomial kernel, Radial Basis Function (RBF), and the sigmoid kernel, each designed to handle particular data structures effectively. The kernel trick allows SVMs to compute the separation in this transformed space without explicitly performing the transformation, making the process computationally efficient. For regression tasks, the SVM algorithm is extended as a Support Vector Regression (SVR), which fits data within a margin of tolerance to predict continuous values. Although the SVM algorithm is primarily a binary classifier, multi-class classification can be achieved.
# Analysis and Design of Integrated Blocks for a 6.25 GHz Spacefibre PLL
## 5 Test Chip  
To prototype the designed PLL, a block view of a possible test chip has been developed and is shown in Figure 22. As can be seen, it consists of a whole PLL, which can be recognized at the bottom, and a duplicate of the PFD and CP. The main idea is to test every block separately: there is the possibility of giving the inputs from an external source, and all the outputs are outputs of the test chip. Since the PFD/CP's area consumption is low, duplicating these blocks gives the possibility of testing them separately, without adding multiplexer and buffer inside the loop, disturbing the PLL's operation. The FD is not duplicated because it consists of three stages that need bias currents. This means that at least 4 pads are necessary for it and, therefore, duplicating it would lead to a too high requirement in terms of pads. Moreover, in Figure 23, a draft of the floor plan is shown: the chip's area to be fabricated using a Europractice Multi Project Wafer is 1 mm\({}^{2}\). As can be seen from the figure, the whole PLL occupies an area of about 0.09 mm\({}^{2}\) (the remaining area and pads are used for other designs not  
Figure 21: Post-layout phase noise.  
Figure 20: Post-layout locking process.  
discussed in this paper). It is to be noted that the design of the pads is inherited from a previous design, where they were tested also with respect to resistance to radiation effects [24].  
Although the figures and tables proposed in this paper refer to post-layout simulation results, the experience of our research group from previous designs in the same 65 nm TSMC technology, using the CAD (Computer-Aided Design) design environment and targeting similar operating frequencies, has proven that there is a coherent alignment between post-layout simulations and experimental measurements. In the next step, when the measures of the prototyped chip will be performed, a complete assessment of the results accuracy will be done.
# A Coupling System for Prediction of Physiological Parameters in an Immersed Condition
## 3 Results
### Core Temperature  
The coupling system can predict the human core temperature more accurately in both air exposure and water immersion environments, and all the predicted mean values are within SD of the measured values, according to a comparison of the core temperature prediction results of the thermal manikin and human thermal model coupling system with the experimental test results when wearing two life preservers, LP1 and LP2 (see Figure 6). No significant differences were measured between the predicted and measured core temperatures according to paired-sample t-tests (\(p>0.05\)). Additionally, when wearing LP1 under conditions of air exposure and water immersion, and when wearing LP2 under conditions of air exposure and water immersion, respectively, the maximum deviation between measured and predicted values did not exceed 0.13 \({}^{\circ}\)C and 0.29 \({}^{\circ}\)C. The results demonstrate that all differences between the coupling system's projected and actual core temperatures were less than 0.5 \({}^{\circ}\)C. In fact, a discrepancy of 0.5 \({}^{\circ}\)C between the predicted and measured values for the core temperature was still regarded as having good agreement [31]. Moreover, the \(R^{2}\) for wearing LP1 and LP2 was 0.984 and 0.993; it can be shown that the predicted and measured values are highly correlated. The RMSD and SD for wearing LP1 and LP2 were 0.12 \({}^{\circ}\)C and 0.22 \({}^{\circ}\)C, and 0.13 \({}^{\circ}\)C and 0.24 \({}^{\circ}\)C, respectively. It is clear that RMSD is smaller than SD, indicating that the coupled system predicts the core temperature with high accuracy.
## 5 Vector Tracking Loop Architectures  
VT can be implemented in three different approaches, as shown in Figure 3. The first architecture is a vector delay lock loop (VDLL) in which code phases are tracked through a centralized approach, while carrier tracking loops work independently. The second architecture is the vector frequency lock  
Figure 2: Scalar tracking receiver. Scalar tracking architecture (**top**) and scalar tracking loop (**bottom**).  
loop (VFLL), which handles carrier tracking in a centralized approach, while ST loops handle code phases. Finally, the vector delay frequency lock loop (VDFLL) utilizes a central filter, usually a Kalman filter (KF), to track both the code phase and the carrier frequency of all channels. In this paper, VDFLL is adopted to assess its performance in degraded signal environments.
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 1 Introduction
A multihead attention mechanism within Transformer neural networks was developed in [28] to learn sequential dependencies between wind turbines to qualify spatial information. [PERSON] et al. [29] combined LSTM with a vision Transformer model to better utilize the relationship between extracted characteristics and the desired output for accurate prediction. While the above Transformer-based models represent improvements from traditional LSTM and GRU architectures, their high computational cost for large quantities of wind power data is a concern. The input to the prediction model is enormous, and while performing a time series analysis, further data are produced that must also be stored and remembered. Mechanisms, e.g., GRUs [30] and empirical mode decomposition [16], permit previous information to be discarded and decomposited, and while the LSTM architecture does not permit the complete discarding of past information, losses do occur, which affects the quality of the prediction. To improve the performance of LSTM, particularly for memory and data storage concerns, further modification of the LSTM model is required, which introduces the need for extended LSTM (xLSTM). xLSTM improves upon these models by incorporating additional layers and mechanisms, such as attention mechanisms, to capture more intricate temporal dependencies and better handle non-linear relationships in the data. This results in improved accuracy, particularly for long-range predictions in energy systems. The proposed xLSTM architecture further extends this paradigm by implementing a dual-pathway approach that combines scalar and matrix memory structures, thereby addressing the inherent limitations of conventional recurrent models in capturing multiscale temporal patterns in wind energy data. Moreover, the empirical validation conducted across diverse seasonal conditions demonstrates the xLSTM model's robustness and adaptability to varying meteorological phenomena, establishing a foundation for more reliable integration of intermittent renewable energy sources into existing power grid infrastructures. The remainder of this paper is organized as follows: Section 2 presents the xLSTM architecture, including detailed formulations of both sLSTM and mLSTM components. Section 3 outlines the SCADA data processing methodology for wind power forecasting, encompassing data preprocessing, quality control procedures, and hyperparameter optimization strategies. Section 4 presents the empirical results of the xLSTM model implementation, with detailed analyses of prediction accuracy across various wind speed regimes and seasonal patterns. Finally, Section 5 summarizes the major findings, discusses the implications for wind energy forecasting applications, and proposes directions for future research development.
# Practical Demonstration of 5G NR Transport Over-Fiber System with Convolutional Neural Network
## 4 Experimental Setup

Table 2: Optical Link Parameters. Figure 4: Experimental block diagram for analog multiband 5G NR system. The functions: A: Choose between VSA 1 and VSA 2. B: Choose between a performance post-processing block and a synchronisation block. C: Synch is connected. The synchronisation block is followed by training. D: DPD disables the training DPD disables the training DPD disables the training DPD disables the training DPD disable E: Time synchronisation (TS) technique is required. F: Required for validating DPD inputs before sending them to the VSGs. For the DPD training CNN has been chosen.
## 4 Performance Evaluation Results
### Confusion Matrices for the Combined Scenario
In summary, the performance results, on the one hand, reveal realistic values achievable from real-life experimental datasets as against the performance with simulated datasets. On the other hand, the performance results point in the direction that position-only data may not be sufficient for high accuracies (typically more than 95%) needed to support the ambitious demands of 5G and beyond networks, particularly for high mobility vehicular use cases. The position data can therefore be combined with other sensor data (such as LiDAR, radar, and camera) to improve the beam accuracies and the overall network performance.
## 5 Classification of Smart Grid Cyberattack
### Data Integrity Attacks
#### 5.3.2 Tampering, SM Tampering  
Malicious tampering and the alteration of vital data in sensors, meters, and command centers might be extremely dangerous to smart grids and energy infrastructures. Tampering refers to an illegal change or obliteration of information or a procedure. This attack may fall under data injection attacks, in which attackers take advantage of flaws in devices and communication lines to be able to manipulate them and alter their data. It is possible to intentionally alter the data measured by SCADA field units, such as PMU and RTU devices, to produce incorrect control signals and irregular grid schedules [57].  
The tampering of a smart meter can occur when the utility meters' readings are altered and inaccurate. One instance of this is when the readings from the meters do not accurately reflect the consumption. An example of an tampering attack against smart meters can be based on ping flood attack. In this type of attack, the smart meter is continuously pingged with traffic, and when the smart meter replies, the traffic on the smart meter is overloaded. As a result of this, low power consumption data are read, and this causes utility income loss. Smart grids and energy systems can be damaged by intercepting or altering the data; hence all system data must be properly secured against alteration. To ensure security and confidentiality, proper mechanisms should be used to protect against tampering [104].
# Radar Target Localization with Multipath Exploitation in Dense Clutter Environments
## 3 Method
### Approximation of Likelihood
\right], \tag{36}\]  
where the left part denotes the associations with the target multipath, and the right part denotes the associations with clutter. A valid assignment hypothesis under the extended association matrix expression is defined as a \(N_{m}\times(N_{p}+N_{m})\) matrix \(\mathbf{A}\) with the elements satisfying  
\[ \begin{cases}A_{jk}\in\{0,1\},&\forall j,k,\\ \sum_{j}A_{jk}=1,&\forall k,\\ \sum_{k}A_{jk}\in\{0,1\},&\forall j.\end{cases}  \tag{37}\]  
The first constraint denotes that the \(j\)-th measurement and the \(k\)-th target path are assigned. The second constraint denotes that each measurement must be assigned to a target path or clutter. The third constraint denotes that each target path or clutter is assigned at most once. Considering that \(\mathbf{G}^{\prime}\) is a sparse matrix, it is feasible to enumerate all possible assignment matrices \(\mathbf{A}\) by a recursive enumeration method. Assume that \(m\) nonzero values are found in the first row of \(\mathbf{G}^{\prime}\). We have \(m\) valid assignment matrices initialized with \(A_{1K_{1}}=1\)separately, where \(K_{1}\) is the corresponding column of the nonzero value. We then remove the first row and the \(K_{1}\)-th column in \(\mathbf{G}^{\prime}\), and \(\mathbf{G}^{\prime}\) is reduced to a \((N_{m}-1)\times(N_{p}+N_{m}-1)\) association matrix. The procedure is repeated with the search and remove steps in the reduced association matrix until it is empty. The column of the nonzero value in the \(j\)-th recursion is recorded as \(K_{j}\), therefore \(A_{jK_{j}}\) is set to 1 recursively. The result of all valid assignment matrices for the example association matrix in (36) is given by  
\[\mathbf{A}^{1} =\left[ \begin{array}{ccccc}1&0&0&0&0\\ 0&1&0&0&0\\ 0&0&0&0&1\end{array} \right],\] \[\mathbf{A}^{2} =\left[ \begin{array}{ccccc}1&0&0&0&0\\ 0&0&0&1&0\\ 0&0&0&0&1\end{array} \right],\] \[\mathbf{A}^{3} =\left[ \begin{array}{ccccc}0&1&0&0&0\\ 0&0&0&1&0\\ 0&0&0&0&1\end{array} \right], \tag{38}\] \[\mathbf{A}^{4} =\left[ \begin{array}{ccccc}0&0&1&0&0\\ 0&1&0&0&0\\ 0&0&0&0&1\end{array} \right],\] \[\mathbf{A}^{5} =\left[ \begin{array}{ccccc}0&0&1&0&0\\ 0&0&0&1&0\\ 0&0&0&0&1\end{array} \right],\]  
where each assignment matrix can be converted into an association vector as  
\[\theta_{k}= \begin{cases}j,&\exists A_{jk}=1,k=[1,\cdots,N_{p}],\\ 0,&\text{otherwise}.\end{cases}  \tag{39}\]  
Therefore, the corresponding valid association vectors are given as  
\[\theta^{1} =[1,2],\] \[\theta^{2} =[1,0],\] \[\theta^{3} =[2,0], \tag{40}\] \[\theta^{4} =[0,2],\] \[\theta^{5} =[0,0].\]
# Vibration Control with Reinforcement Learning Based on Multi-Reward Lightweight Networks
## 4 Experimental Results and Discussion
### Relevant Comparative Experiments
#### 4.6.4 Ablation Experiments  
In this section, ablation experiments were conducted on a narrowband white-noise excitation dataset ranging from 0 to 100 Hz to verify the effectiveness of the proposed modules. Table 5 illustrates the various methods studied alongside average convergence episodes, parameters, and reduction levels. We noticed that compared to the baseline, the introduction of the DRSN not only reduced the parameters by approximately 6.5 times but also increased the damping effect by 2.6 dB. Furthermore, the inclusion of the RMSv+RMS reward function resulted in an additional damping improvement of 0.882 dB. Subsequently, the introduction of prioritized experience replay reduced the average convergence rounds to 634 times. Lastly, the integration of the TD3 framework, although it increased the parameters and rounds slightly, did not affect the overall performance, yet achieved the peak control effectiveness of 20.240 dB.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**V, MSV, RMS, OM** & **Reduction (dB)** \\ \hline V & 10.458 \\ MSV & 11.990 \\ RMS & 13.506 \\ OM & 9.156 \\ V, MSV & 1.938 \\ V, RMS & 13.386 \\ V, OM & 6.504 \\ MSV, RMS & 13.524 \\ MSV, OM & 2.256 \\ RMS, OM & 5.43 \\ V, MSV, RMS & 13.096 \\ V, MSV, OM & 11.508 \\ V, RMS, OM & 1.284 \\ MSV, RMS, OM & 10.526 \\ V, MSV, RMS, OM & 9.318 \\ \hline \hline \end{tabular}
\end{table}
Table 4: In the cases of DDPG and FC\({}_{256}\), with combinations of multiple reward functions, we denote the variance, mean square value, root mean square, and original method as V, MSV, RMS, and OM, respectively. We assign a rating to the reward functions, utilizing measurements before and after the control process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** &
\begin{tabular}{c} **Average** \\ **Convergence** \\ **Episodes** \\ \end{tabular} & **Parameter** & **Reduction (dB)** \\ \hline Baseline DDPG (FC + R\({}_{\text{OM}}\)) & 2031 & 338437 & 12.728 \\ DDPG (DRSN + R\({}_{\text{OM}}\)) & 1562 & 45069 & 15.328 \\ \hline \hline \end{tabular}
\end{table}
Table 5: In the ablation experiments, OM, MSV, RMS, and PER, respectively, represent the original method, mean square value, root mean square, and prioritized experience replay. The ablation experiments demonstrated the efficiency of DRSN lightweighting and the MSV + RMS combination function, with a faster convergence speed for PER.
# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 3 Experimental Data and Parameters
### Experimental Data
#### 3.1.2 Test Components Dataset  
Alongside the training dataset, the test dataset plays a crucial role in evaluating the model's generalization to unseen data. Fifty reconstructed images of real components with natural defects were used as a primary test set. All annotations in this set were based on the corresponding ground images obtained through physical grinding, ensuring high accuracy. This dataset is thus suitable for quantitatively evaluating model performance. However, due to its limited size, test metrics on this set may be affected by sampling bias. To further assess model performance, additional test components were prepared.  
As illustrated in Figure 8, four rectangular graphite components with a 2:1 aspect ratio were fabricated, each containing hole defects of four distinct sizes: 4 mm, 2 mm, 1 mm, and 3 mm. These defects were precisely machined, allowing accurate dimension measurements based on their reconstructed CT images. The positioning of these components can be adjusted to create greater variation in the test dataset. The reconstructed resolution of these images is 1360 \(\times\) 760 pixels, with a pixel size of 0.5 mm. These images are used only for testing purposes, ensuring that training and testing samples come from different component sets, which allows for a more robust evaluation of the model's generalization ability.  
Figure 7: Defects in reconstructed images of actual graphite components.
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
## 3 Preliminaries
### Complexity Assumption  
We review two complexity assumptions related to bilinear map named Decision \(n\)-Bilinear Diffie-Hellman Inversion (D-n-BDHI) assumption proposed in Reference [30] and computational Diffie-Hellman (CDH) assumption introduced in Reference [31]. The security proof of our scheme is based on these two assumptions.  
**Decision n-Bilinear Diffie-Hellman Inversion Assumption [30]**: An algorithm \(\mathbb{C}\) which outputs \(b\in\{0,1\}\) has advantage \(\varepsilon\) in solving the D-n-BDHI assumption in \(G_{1}\) if:  
\[Adv_{\mathbb{C}}^{D-n-BDHI}=|Pr[\mathbb{C}(P,xP,x^{2}P,\ldots,x^{n}P,\hat{e}(p, p)^{\frac{1}{n}})=1]-Pr[\mathbb{C}(P,xP,x^{2}P,\ldots,x^{n}P,R)=1]|\geq\varepsilon\]  
where the probability is over the random choice of \(x\in Z_{q}^{*}\),the random choice of a generator \(P\in G_{1}^{*}\), the random choice \(R\in G_{2}^{*}\) and random bits used by \(\mathbb{C}\).  
**Definition 3**: _It can be said that the decision(t,n,\(\varepsilon\)) \(n\)-Bilinear Diffie-Hellman Inversion assumption holds in \(G_{1}\), if no \(t\)-time algorithm has advantage at least \(\varepsilon\) in solving the decision n-Bilinear Diffie-Hellman Inversion problem in \(G_{1}\)._  
**Computational Diffie-Hellman (CDH) Assumption [31]**: Consider a cyclic group \(G\) of a prime order \(q\). The \(CDH\) assumption states that, for a randomly chosen generator \(g\) and the random integers \(a,b\in Z_{q}^{*}\), given a tuple \((g,g^{a},g^{b})\), it is computationally intractable to compute the value \(g^{(ab)}\).
## 2 Data Sources and Methods
### Data Sources
#### 2.1.3 Remote Sensing Data  
We selected 39 GF-2 remote sensing image, size of each image is 7300 \(\times\) 6900. Of these images, 15 were captured on 17 February 2017, 11 were captured on 21 March 2018 and 13 were captured on 12 April 2018. We select images from different periods to increase the anti-interference abilities of the HSCNN. These remote sensing data cover Feicheng county, Ningyang county and Zhangqiu county, and are matched with ground investigation time. At the same time, the selected remote sensing data have fewer clouds and better clarity.  
The Environment for Visualizing Images (ENVI) software was used for preprocessing the tasks, including fusion of panchromatic spectrum and multispectral band to obtain 1-m spatial resolution multispectral data, and the contrast stretch to generate a color-enhanced color composite image.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 2 Performance Evaluation  
We have classified the parameters used for performance evaluation of radenv-ADCs into three categories. Firstly, we recall the standard parameters used to measure electrical performance. Secondly, we collect conventional parameters used to evaluate radiation performance. Unfortunately, the power efficiency is not considered within the parameters of this second category, which is the reason why we propose new figures of merit in the third category to evaluate the efficiency of radenv-ADCs.