esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Configuration
### Measurement and Correction of the Laser Beam Drift  
The drift of the He-Ne laser beam is mainly affected by temperature, environmental vibration, air disturbance, and the structure of the laser itself. The principle of common light path compensation is shown in Figure 6, which can measure the angular drift of the laser beam in real time.  
The incident s-polarized light is reflected by PBS2, RR2, and M2 in turn, and finally received by the CMOS4 camera. The spot displacement on the CMOS4 camera can record the beam drift in real time. The beam angular drift can be expressed as  
\[\begin{split}\Delta\alpha&=\frac{\Delta Y_{CMOS4}} {f_{2}},\\ \Delta\beta&=\frac{\Delta X_{CMOS4}}{f_{2}},\end{split} \tag{8}\]  
w  
Figure 5: Measurement principle of roll.  
where \(\Delta\alpha\) and \(\Delta\beta\) represent the beam angular drift in the y and x directions; \(\Delta Y_{CMOS4}\) and \(\Delta X_{CMOS4}\) represent the position of the spot on CMOS4 camera in the x and y directions, respectively; and \(f_{2}\) is the focal length of L2.  
Therefore, Equation (1) of the pitch and yaw can be modified as  
\[\begin{split}\alpha&=\frac{\Delta Y_{CMOS3}}{2f_{1}} \pm\Delta\alpha,\\ \beta&=\frac{\Delta X_{CMOS3}}{2f_{1}}\pm\Delta\beta. \end{split} \tag{9}\]  
Similarly, the straightness error measurement can be modified as  
\[\begin{split}\Delta x&=\frac{\Delta X_{CMOS1}}{4} \pm l_{1}\Delta\beta\\ &=\frac{\Delta X_{CMOS2}}{4}\pm l_{1}\Delta\beta,\\ \Delta y&=\frac{\Delta Y_{CMOS5}}{2}\pm l_{2}\Delta \alpha,\end{split} \tag{10}\]  
where \(l_{1}\) and \(l_{2}\) are the distances of light transmitted from the light source to CMOS1, CMOS2, and CMOS5, respectively;
# AI-Enhanced Prediction of Peak Rate of Torque Development from Accelerometer Signals
## 1 Introduction
Considering that RTD is measured as torque per unit time (e.g., Nm/s), it can be considered analogous to a velocity measure. During explosive contraction testing, it is expected that the evaluated individual produces the maximum force in an isometric task in the least time, increasing from total rest (relaxed muscle). This rapid increase in force leads to a corresponding sudden increase in limb acceleration. Thus, theoretically, accelerometers can provide an indirect but informative signal about explosive force production. If possible, using accelerometers to indirectly measure RTD offers advantages in terms of accessibility, cost-effectiveness, and ease of use compared to traditional methods that require specialized equipment. The implementation of such a system has the potential to be transformative, enabling more precise monitoring and the optimization of explosive strength training routines. Specifically, the integration of real-time data analysis through portable technology can facilitate immediate feedback and dynamically adjust training parameters to maximize performance gains while minimizing the risk of injury. With the current increase in accessibility to Artificial Intelligence (AI), attributed to the popularization of the technique and the increase in computing power, it has become possible to utilize AI for the development of new performance analysis tools [37]. In this context, an artificial neural network (ANN) prediction model can be employed to predict the RTD from ACC signals. ANNs are computational models inspired by the structure and function of the human brain, capable of learning complex patterns in data [37]. The ANN can be trained on a dataset comprising ACC signals as input and corresponding RTD values as output, using a supervised learning approach. Then, the trained model can be used to predict RTD values from ACC signal inputs. This study explored the use of accelerometer signals as predictors of peak RTD using an ANN prediction model. We chose the peak RTD because it is not affected by the onset determination and reflects the maximum explosive capacity. Specifically, a dataset was used to train an ANN using a supervised learning approach. The prediction model was then used to predict the peak RTD and compare the measured and predicted values. We hypothesized that the accelerometer signal could predict the RTD signals.
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 7 Conclusions  
In 5G networks, DoS and DDoS attacks have become a critical issue due to the increasing number of mobile devices. To ensure the robustness and security of the 5G network, we have proposed a Secure Blockchain-based 5G Authentication and Key Agreement (5 GSBA) protocol in this paper. The 5 GSBA protocol holds many security features that the existing 3 GPP 5G-AKA scheme fails to achieve, including perfect forward secrecy, device anonymity, and most importantly, the resistivity to DoS and DDoS attacks. By the decentralization of authentication functions with blockchain technology, the 5 GSBA delivers the best quality of service (QoS) among all schemes in relation to DoS and DDoS attacks. Our performance evaluation also demonstrates that the overhead added by 5 GSBA is imperceptible compared to other existing solutions. Therefore, we believe that the 5 GSBA protocol is ideal for balancing strong security functionality and high performance.  
Conceptualization, M.C.C. and M.M.; data curation, M.C.C.; formal analysis, M.C.C.; investigation, M.C.C.; methodology, M.C.C.; software, M.C.C.; visualization, M.C.C.; writing--original draft, M.C.C.; funding acquisition, M.M.; project administration, M.M.; project administration,  
Figure 11: Successful authentications under DoS attacks.  
M.M.; resources, M.M.; supervision, M.M.; validation, M.M.; writing--review and editing, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Engineering Example
### Data Preprocessing and Model Settings
#### 4.2.5 Division and Normalization of Dataset  
All the data are divided into training set, validation set and test set according to 7:2:1. In order to optimize the model convergence, improve the training speed and prevent the gradient explosion, these 3 datasets are normalized with the mean and standard deviation of the training set, which is calculated as Equation (11).  
\[\hat{x}=\frac{x-mean(X_{\text{train}})}{\text{std}(X_{\text{train}})} \tag{11}\]  
where \(x\) is the original data; \(\hat{x}\) is the normalized data; \(mean(X_{\text{train}})\) denotes the mean of the training set \(X_{\text{train}}\); \(\text{std}(X_{\text{train}})\) denotes the standard deviation of the training set \(X_{\text{train}}\).
# Inter-Satellite Link Prediction with Supervised Learning: An Application in Polar Orbits
## 2 Encounter Anticipation Problem
Under this definition, satellites separated by 3000 km or more are considered unable to establish an ISL communication link, whereas those within 1000 km can always exchange data. The wide range between 1000 km and 3000 km aims to accommodate diverse antenna characteristics in heterogeneous environments. Future research could entail a detailed analysis of actual LEO antenna characteristics to define more precise optimal threshold ranges. Notice that the approach proposed in this work remains applicable across various threshold values. Section 5 reports the model's performance associated with three different distance thresholds, namely \(T\) = 1500 km, \(T\) = 2000, and \(T\) = 2500 km. It is important to note that after a few days, the predicted position of a satellite can significantly deviate from its actual position, particularly for those satellites that perform station-keeping maneuvers [21]. Therefore, it is crucial to periodically update the initial satellite state using the latest TLE set. As long as the initial state remains within the range of values used for training, the same SL model can handle these maneuvers without additional training.
## 3 Results and Discussion
### Evaluation of the Printed Structures  
To evaluate the quality of the printing process, the diameter of the pores was measured using the image analysis software ImageJ (Version 1.44p, National Institutes of Health: Bethesda, MD, USA). The measurements were performed in the dry state after lyophilization. The results are shown in Figure 3. For the Alg/MC samples, the mean edge length was 652.2 \(\pm\) 99.7 \(\upmu\)m. Samples containing glass as fillers showed larger pore sizes, while higher pore sizes were observed in the samples containing 70 Si30 Ca glasses. Furthermore, the pores of the glass composite scaffolds showed good geometrical characteristics, indicating that the setup is suitable for the production of scaffolds using Alg/MC/TMC/glass-based hydrogels. The pore size is a very important parameter since it plays a significant role in cell growth and secretion of the extracellular matrix (ECM). It has been reported that for bone tissue regeneration applications, a size of macropores larger than 100 \(\upmu\)m is favorable for cell proliferation and new mineral formation [30]. Sizes in the range between 250 and 500 \(\upmu\)m are favorable for proliferation and ECM production by chondrocytes [31].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & \begin{tabular}{c} **SSA** \\ **m2/g** \\ \end{tabular} & \begin{tabular}{c} **Total Pore Volume** \\ **cm2/g** \\ \end{tabular} & \
\begin{tabular}{c} **Mean Pore Diameter** \\ **nm** \\ \end{tabular} &
\begin{tabular}{c} **Reference** \\ **Reference** \\ \end{tabular} \\ \hline
100 Si & 377.04 & 0.26 & 2.79 & present work \\
100 Si & 679.8 & 0.43 & 2.55 & [18] \\
70 Si30 Ca & 108.26 & 0.40 & 14.82 & present work \\
70 Si30 Ca & 126.0 & 0.46 & 11.00 & [29] \\
70 Si30 Ca & 137.87 & 0.40 & 14.82 & [18] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specific surface area, pore volume, and pore diameter of the glasses prepared in the present work and the literature.  
Figure 3: Optical microscope images of the lyophilized 3D-printed scaffolds according to formulations shown in Table 1 (**A**), and histogram showing the mean pore sizes of the scaffolds (**B**). Scale bar is equal to 5 mm.
# ENSeg: A Novel Dataset and Method for the Segmentation of Enteric Neuron Cells on Microscopy Images
## 4 Proposed Approach
### Candidate Selection  
Considering that only a subset of all ENCs are used in the morphometric and quantitative analyses, the first step in our approach would be to generate segmentation candidates to be later selected by the user. This step is conducted by running an instance segmentation model trained on ENSeg. Since not all cells are annotated for the training of such models, our goal would be to identify the highest number of ENCs, considering that their segmentation could not be optimal.  
In our dataset, only ENCs that meet the analysis standards are annotated. Therefore, rather than using a weakly supervised approach, we opted for supervised segmentation models to perform the candidate generation step. This approach was chosen to assess the model's ability to avoid segmenting inadequate cells, such as overlapped ENCs. The generated candidates are only used in the final segmentation mask if their respective areas intersect with user prompts. This way, we enable the method to choose between (or fuse) segmentation masks in the later stages of the pipeline. Also, we naturally eliminate the unwanted segmented cells that may be rejected by users.  
The instance segmentation architectures selected for this study were three of the latest versions from the YOLO family, known for their ability to perform both object detection and segmentation: YOLOv8 [14], YOLOv9 [36], and YOLOv11 [37]. This choice was motivated by their lower latency and superior generalization capabilities, as initially reported by their authors [14; 36] and subsequently validated by independent studies in the literature [34]. These advantages are particularly evident in different applications,  
Figure 4: Visual representation of our proposed approach that is based on candidate generation; prompt fusion (or enhancement); and instance mask fusion.  
including in medical imaging, where YOLO models have demonstrated better performance compared to alternative architectures such as Mask R-CNN [64; 65; 66]. In this work, we fine-tuned 12 different versions of the architectures, differing in their backbone size. Nominally, we shall refer to them as YOLOv8-[n,s,m,l,x], YOLOv9-[c,e] and YOLOv11-[n,s,m,l,x].  
To evaluate the standalone performance of these models, we measure segmentation metrics (presented in detail in Section 5.2) individually for each ENC annotation (ground truth) in the input images. Therefore, for each annotated ENC, we search for the candidate with the greatest Intersection over Union (IoU). In this paper, if a candidate is found for the target annotation, we call it a match. Once matched, the candidate is removed from the list of candidates and the process continues for all other cell annotations.
# Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm
## 7 Genetic Algorithm  
The genetic algorithm (GA) is a type of evolutionary algorithm, which was inspired by biological evolution. In biological evolution, the process involves choosing parents and with the ultimate goal of producing offspring that are biologically superior to their parents through reproduction and mutation. The fundamental idea is to choose the most-exceptional individuals as parents from a group and then extend their lineage by generating new chromosomes through the exchange or blending of genes from healthier parents, a process referred to as crossover. The genes are then mutated, and this process leads to a healthier generation. The performance of chromosomes and fitness is evaluated using a  
Figure 3: Flowchart of weighted user-scheduling algorithm.  
cost function in which the best genes are selected using this function. The new generation is created from parent chromosomes after crossover and mutation, ensuring that the children's chromosomes satisfy the fitness function. The use of GA was motivated by the following characteristics [39]:  
* Versatility: GAs are utilized to tackle complex issues that possess large search spaces. GAs excel at traversing extensive areas and quickly discovering the best-possible solution. Although GAs do not promise the optimal solution, they are beneficial to prevent local optima with a high likelihood.
* Ability to find good building blocks: The GA operates within a population composed of numerous chromosomes, allowing it to form various solutions in order to identify the building blocks. Chromosomes are referred to as building blocks, and in biological terms, the genes are mixed and interchanged to form healthier offspring out of the parent chromosomes. These chromosomes again act as a parent and reproduce new offspring. In this way, a GA can evolve to better solutions through a series of biological events to form good building blocks.
* Support for multi-objective function: The GA optimization problem supports multiple parameters since real-time practical problems in various wireless networks require solving more than one parameter. In practical scenarios involving wireless networks, the objectives in a multi-objective function may potentially contradict each other.
* Parallel nature and scalability: The GA uses evolutionary approaches to test, improve, and produce new solutions using various techniques such as selection, crossover, mutation, and recombination. These parallel approaches in the GA help it become a suitable framework for optimization and solve various scalable problems in wireless networks.
* Support for global optimization: The GA supports global optimization, which involves finding the optimal solution to problems containing local optima and is suited to global optimization because of a number of properties: (1) searching by means of a population of individuals; (2) encoding multiple parameters; (3) using a fitness function to evaluate its merit; (4) probabilistic search.
# Flaw Size Quantification for Cable Flaw Inspection System with Inductive Search Coil Sensor
## 4 Experimental Result Analysis
### Analysis of Flaw Length Based on Flaw Cross-Sectional Area  
This section focuses on comparing the effects of different defect cross-sectional areas on peak-valley distances. Figure 12a,b depict the distributions of flaws with varying cross-sectional areas, identified as \(D_{2-3}\) and \(D_{2-6}\) respectively, where the hatched sections illustrate the presence of flaws. Figure 13a,b show the corresponding peak-valley distance comparisons for each situation. It can be observed that both \(D_{2-3}\) and \(D_{2-6}\) exhibit a similar variation trend and follow the same sequence rule compared to the response under \(D_{2-1}\). Furthermore, they demonstrate low standard deviations. It suggests that increasing the flaw cross-sectional area significantly enhances detection accuracy and repeatability.  
Figure 14a compares the peak-valley distances of \(D_{2-1}\), \(D_{2-3}\), and \(D_{2-6}\) at the anchor points. It is evident that clear differentiation exists among the peak-valley distances for various flaw lengths. Additionally, the peak-valley distances show a substantial degree of overlap under situations with different flaw cross-sectional area, indicating that the cross-sectional area has little effect on the response of flaw axial length. By weighing the effect of the excitation frequency on the peak-valley distance dispersion degree and the response amplitude, the follow-up analysis was deployed at 2500 Hz. Combined with the above conclusion, the self-inductance coil response of the flaw axial length is minimally affected by the flaw position and the flaw cross-sectional area on the scanning plane. Therefore, flaw length information can be quantified without considering the influence of the above two  
Figure 11: Peak–valley distance comparison of different length flaws on different layers.  
Figure 12: Distribution of the flaws (**a**) \(D_{2-3}\)\(situation\) on Layer 2 (**b**) \(D_{2-6}\)\(situation\) on Layer 2.  
factors. Figure 14b illustrates a linear relationship between the actual length of a flaw in the axial direction and its corresponding peak-valley distance, where the peak-valley distance is averaged from the result obtained from the various cross-sectional area situations shown in Figure 14a.
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 5 Experiments and Results
### Research Questions and Findings
#### 5.3.3 RQ3: Benefits  
There are several benefits of utilizing DL for processing static analysis alarms. DL models have the ability to directly learn from raw code and original warning data, taking advantage of the hierarchical structure inherent in the data. This enables the models to capture intricate relationships and patterns within the alarms. Additionally, DL models excel in automatically learning relevant features from static analysis alarms, eliminating the need for manual feature engineering. This characteristic of DL significantly reduces the dependence on domain expertise and minimizes the effort required to extract meaningful information from static analysis alarms. Furthermore, DL models demonstrate adaptability by accommodating various types of static analysis alarms. This flexibility allows the models to scale well to large datasets and efficiently process a substantial number of alarms, making them suitable for real-world software development scenarios. Once DL models are trained on a diverse dataset, they exhibit robust generalization capabilities across various programming languages and types of static analysis alarms. This scalability means that they can effectively handle new alarms without extensive manual adjustments, further contributing to their scalability and applicability in practice. Finally, DL has the ability to reduce false positives by distinguishing between genuine actionable alerts and benign alarms. These advantages collectively contribute to the effectiveness of DL in handling static analysis alarms. However, the effectiveness of DL models depends on various factors, including the quality and representativeness of the training data, the chosen model architecture, and the tuning of hyperparameters. Proper consideration of these factors is crucial in order to attain optimal performance.
## 3 Methodology
### Proposed LSTM-Attention-BiTCN Model
#### 3.3.3 Attention Layer  
The concept of the attention mechanism was initially introduced to enhance the performance of machine translation tasks [19], and it serves as a pivotal component in our LSTM-Attention-BiTCN model. This mechanism operates by emulating human attention processes, integrating a deep learning layer designed to emphasize the more salient pieces of information through data weighting. An attention layer can be essentially conceptualized as computing the weighted average of the input data, thereby reducing its dimensionality or rank.  
In our model, we strategically positioned a self-attention layer subsequent to the TCN layers. The role of this self-attention layer was to meticulously extract and accentuate the essential elements within the posts fed into the model. This is crucial for ensuring that the model focuses on the most informative parts of the text, thereby enhancing the accuracy and relevance of its output. The computational process of the self-attention layer in our model is encapsulated in Equations (2)-(4):  
\[U=\tanh(XW_{t}+b_{t}) \tag{2}\]  
\[a=softmax(U) \tag{3}\]  
\[output=X\times U \tag{4}\]