esa-sceva/satcom-chunk-collection huggingface dataset:
# Optical Wireless Fronthaul-Enhanced High-Throughput FC-AE-1553 Space Networks
## 4 Network Resource Allocation Algorithm Design for the HSN
### Traffic-Type Classification  
The traffic types in space application systems are typically classified into three categories: periodic traffic (PT), common burst traffic (CBT), and time-constraint burst traffic (TCBT) [9; 10; 11]. Similarly, this paper divides traffic into two categories: periodic traffic and bursty traffic. Bursty traffic is divided into multi-priority bursty traffic and preemptive bursty traffic. For the traffic needs analysis, in addition to considering the frequency of traffic events, this paper also takes into account factors such as the physical rate, time sensitivity, and reliability of the traffic (Table 2).  
\(PT_{i}\) denotes the periodic traffic set of terminal \(i\), \(Per\_Traffic_{j}\in PT_{i}\), and N denotes the number of periodic traffic transmissions. \(j\) denotes the index of periodic traffic. \(BT_{i}\) denotes the bursty traffic set of terminal \(i\), \(Bur\_Traffic_{k}\in BT_{i}\), and M denotes the number of bursty traffic transmissions. \(k\) denotes the index of bursty traffic.  
\[PT_{i}=\{Per\_Traffic_{i,1},Per\_Traffic_{i,2},\cdots,Per\_Traffic_{i,N}\} \tag{7}\]  
\[BT_{i}=\{Bur\_Traffic_{i,1},Bur\_Traffic_{i,2},\cdots,Bur\_Traffic_{i,M}\} \tag{8}\]  
\(W_{i}\) denotes the traffic set of terminal i, and the relationship between them is shown below.  
\[W_{i}=PT_{i}\cup BT_{i} \tag{9}\]  
\[PT_{i}\cap BT_{i}=\emptyset \tag{10}\]  
Network scheduling will be time-triggered. The timelines will be cut into the same small cycle \(SP\), and all traffic will be recognized and allocated periodically. The timeline for the FC-AE-1553 still uses the original periodic and dynamic zones, whereas the Li-Fi network is a fully dynamic zone. Periodic traffic will be allocated in periodic zones of the FC timeline, whereas bursty traffic will be allocated in dynamic zones of both the FC and Li-Fi timelines.
## 1 Introduction
Schemes [5; 6; 7] discuss secure session key establishment between participants but later [8; 9; 10] identified that these plans are vulnerable to man-in-the-middle attack and undetectable online and offline dictionary attacks for guessing passwords. ECC-based schemes are also explored to evaluate the applicability of efficient solutions with desired security strengths using small key sizes as compared to preliminaries. The main problem and common deficiency in these schemes are that user anonymity is not provided because the user's identity is sent without encryption on an open channel. The participants'  
Figure 1: Telecare Medical Information System (TMIS) architecture. credentials are insecure, causing a lack of anonymity and un-tractability [11]. The scheme is also vulnerable to identification-guessing attacks and tracking attacks. Specifically, a participant's identification might get exposed by an intruder when it gets disconnected from the internet. Furthermore, an adversary can tune into a selected user with the information the user provided within the login request message. This paper presents a Secure Participant Authentication and Prescription Safety (SAPS) scheme for TMIS to accomplish participant's anonymity and un-traceability. The process begins when the patient registers with the trusted server that validates the communicating parties and then establishes the session key. We have used ECC in this study; results showed that an anonymous ECC-based presented technique has secure and well-organized authentication protocols with foolproof security along with user confidentiality protection which is practical for TMIS. Formal protocol secure analysis using Rubin Logic was used to evaluate their safety performance and reliability. Using the proposed protocol patient can get a medical prescription from physician securely and interacts with health service provider anonymously using TMIS keeping identities secret. The rest of paper is organized as follows; Section 2 includes the previous work which has some related information regarding the present study and an overview of existing practices. The system model and problem statement are presented in Section 3. Section 4 explores the proposing anonymous SAPS scheme in details with formal steps. Formal analysis using Rubin Logic is presented along with security analysis in Section 5. The performance measures and results have been included in Section 6. Section 7 presents the conclusions of the work and a future roadmap.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 8 Conclusions  
The rapid evolution of malware demands innovative detection techniques that can adapt to increasingly sophisticated threats. This study introduces a vision-based deep learning framework that effectively classifies malware by transforming assembly code into RGB images and leveraging a hybrid CNN architecture. By converting Portable Executable (PE) files into structured visual representations, we enable convolutional networks to extract discriminative features from opcode transitions, achieving 98.45% accuracy on the enhanced MalevisAsm dataset. Our approach demonstrates superior performance compared to existing methods, validating the potential of image-based malware analysis as a robust alternative to traditional static and dynamic techniques.  
While the addition of the Dumpware10 dataset demonstrates the model's generalizability beyond MaleVis, we acknowledge that a large-scale benchmark across multiple, diverse malware datasets (e.g., BIG 2015, Malimg) would provide a more comprehensive evaluation. Such an extensive study is planned as a direct follow-up to the current work, aiming to establish the robustness of our architecture in a wider variety of contexts.  
The success of this methodology lies in its ability to capture semantic patterns in assembly code while avoiding the computational overhead of dynamic execution. Unlike signature-based systems, our model generalizes obfuscated and polymorphic variants by interpreting malware behavior through spatial representations. However, challenges remain, including dependency on disassembly tools and the need for further optimization in real-time deployment. Future research could explore adversarial training to enhance resilience against evasion techniques and extend this framework to other binary analysis tasks. Ultimately, this work bridges the gap between low-level code analysis and computer vision, offering a scalable and automated solution for next-generation malware detection. Future research directions include evaluation against emerging architectures with novel training paradigms and exploration of synergistic combinations between our feature fusion strategy and advanced parameterization techniques.  
Conceptualization, Methodology, Software, Formal analysis, Writing--original draft, E.E.D.; Project administration, Validation, Supervision, Writing--review and editing, M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors extend their sincere appreciation to all contributors who provided valuable insights and support throughout this research.  
The authors declare no conflicts of interest related to this research.
## 2 Requirements of the Robot Patient
### Simulation of STS Movement by Robot Patient
#### 2.2.2 Correct and Incorrect Applications of Skills for Assisting STS Movement  
Biomechanics is concerned with the human body's structure and function, as well as the mechanical motion of the components of biological systems [42]. Accordingly, the STS movement of a patient should obey the principles of human biomechanics. If the robot does not obey the principles of biomechanics during STS transfer, the nurses may learn improper methods for providing standing assistance.  
According to previous studies and an experiment conducted with a simulated patient [5; 43], STS movements were categorized into two types. In Figure 1a, the movement assisted with the correct STS transfer skill is depicted. At the start of the movement, the trunk (i.e., hip joint) along with the knee and ankle joints is bent. This constitutes phase I of the STS movement and is followed by extension of the hip and knee to 0\({}^{\circ}\), while the ankle joints return to 90\({}^{\circ}\). This constitutes phase II of the STS transfer. In contrast, the incorrect application of the skill is depicted in Figure 1b, where the STS movement is shown without bending of the trunk, but with extension of the hip and knee joints from 90\({}^{\circ}\) to 0\({}^{\circ}\) in both STS phases I and II.  
Other standing configurations do exist for specific patients, such as quadriplegic patients who have lost all their muscle functions. However, this study aims to simulate those patients who suffer only from weakness in the lower limbs, rather than complete paralysis. Thus, other configurations of STS movement were not included in the scope of this study.
# Dual-Branch Multimodal Fusion Network for Driver Facial Emotion Recognition
## 1 Introduction
As shown in Figure 1a, we propose a reconstruction module for RGB images, which focuses on capturing the contour detail features of emotional changes in RGB facial images, thereby achieving the reconstruction of facial details under normal lighting situations. The module iteratively refines inter-modal and intra-modal feature representations to enhance the robustness and generalization ability of the model. This method effectively improves the model's adaptability in diverse scenarios, ensuring high-quality facial detail reconstruction under varying lighting situations. We extract features from visible-infrared (RGB-IR) image pairs using a backbone network and subsequently integrate the RGB and IR feature maps through a fusion module. This method employs dual feature interaction to learn global information and achieve information complementation, thereby enhancing the discriminative power of the feature information. Consequently, the model is capable of capturing the facial features and microexpression changes of drivers under varying lighting situations. By fusing these diverse data sources, we can maintain efficient facial recognition performance in complex and dynamic driving environments. In short, the contributions of this paper are as follows:  
1. We propose a driver emotion recognition network based on Dual-branch Multimodal Fusion Network (DMFNet). This architecture performs multimodal feature learning to extract features under different conditions, improving the scene applicability and model generalization ability for driver facial features. 2. To address the detail deficiencies in RGB images, we propose the U-Shape Reconstruction Network (URNet) module for reconstructing the emotional and facial features of drivers in RGB images. This module comprises three components: Spatial Domain Decomposition (SDD), Detail Enhancement Block (DEB), and Reconstruction (RC). The SDD decomposes RGB images from the spatial domain to the frequency domain for multiscale analysis; the DEB enhances details and filters out high-frequencynoise in multiscale image features; finally, the RC module reconstructs the multiscale image features. 3. Finally, the DEFE++ dataset related to driving in natural environments was processed, and 2160 images (1080 RGB images and 1080 IR images) were obtained for experimentation. The results show that the accuracy of the emotion recognition model proposed in this paper reaches 70.62%, which is 18.77% higher than existing driver emotion recognition models. The remainder of this article is organized as follows: Section 2 summarizes related work. Section 3 details the method, providing a detailed description of the specific methods used. Section 4 summarizes the experiment, focusing on the main implementation of this study. The final section concludes the article.
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 4 Discussion
### Kinematic Adjustments and Efficiency at Maximal Sprint Speed  
This study also confirmed that step length increased by \(\sim\)15-20% from submaximal to maximal sprinting, consistent with prior research indicating that elite sprinters optimize their stride mechanics to maximize propulsion [30]. This increase in step length is primarily driven by improved horizontal force application and optimized limb positioning, rather than simply greater muscular power output. Efficient sprinters display enhanced hip extension during push-off and better control over swing phase mechanics, which minimizes braking forces and maximizes forward acceleration.  
While stride frequency showed moderate increases, the greatest adaptations were observed in swing phase mechanics, reinforcing the idea that effective sprinting relies on rapid limb repositioning rather than just increasing step rate [14; 40]. The ability to rapidly recover and reposition the swing leg before ground contact is a critical determinant of sprinting success. This phase requires coordinated activation of the hip flexors, hamstrings, and ankle dorsiflexors, highlighting the importance of drills that enhance neuromuscular control and limb stiffness, such as high-speed turnover drills and resisted sprinting with light loads.  
These results highlight the trade-off between step length and frequency, suggesting that individualized sprint training should focus on optimizing both components rather than emphasizing one over the other [31; 32; 41; 42]. A balanced approach to training should incorporate both stride length and frequency-specific drills, as excessive emphasis on either may lead to inefficiencies. Athletes who rely too heavily on increased step length may exhibit prolonged ground contact times and excessive vertical oscillation, whereas those focusing solely on stride frequency may struggle with adequate force application per step. Therefore, optimizing both variables based on an athlete's unique biomechanics and strength profile is crucial for maximizing sprint performance.  
Additionally, training interventions should consider the role of reactive strength and ground contact efficiency. Pyometric exercises, such as drop jumps and bounding drills, have been shown to improve leg stiffness and the ability to generate high forces in minimal ground contact time, which is essential for maintaining top sprinting speeds [11; 14; 15].
## 5 Architecture Comparison  
In the following section, we calculate \(\hat{\mathcal{C}}R\) of the four architectures based on the component performances found in the literature. Today's best results are presented in Table 4.  
Comparison between the four topologies shows a significant impact of waveguide crossings on the overall performance of the network. S&S is the best to reduce first-order and second-order crosstalk powers generated by MZIs. Nevertheless, it does not scale well because its central interconnection contains a high number of waveguide crossings, as shown in Figure 7a. The waveguide crossing count in the dilated Benes is also important; this has a strong impact when its insertion loss is not optimized. Indeed, in Figure 7b and for \(Loss_{X}=0.21\) dB [19], the dilated Benes \(\hat{\mathcal{C}}R\) (solid red line) decreases more rapidly and becomes lower than Benes \(\hat{\mathcal{C}}R\) for \(N=32\). In general, each architecture can be badly affected if the waveguide crossing performances are not sufficiently optimized, as seen in Figure 7c, with \(Xtalk_{X}=~{}-20\) dB [20]. The four architectures have similar performances with a \(\hat{\mathcal{C}}R\) lower than 17 dB for \(N\geq 4\). DLN seems to be the most favorable architecture, whose \(\hat{\mathcal{C}}R\) value decreases more slowly. Its major challenge is the overall number of MZIs, which is relatively large.  
\begin{table}
\begin{tabular}{c c c} \hline  & \(K_{\text{MZI}}\)(N,\(m\)) & \(K_{X}\)(N,\(x\)) \\ \hline Benes & \((2k-1)m+\left(2k^{2}-3k+1\right)m^{2}\) & \(2(N-k-1)x\) \\ Dilated Benes & \(k(2k-1)m^{2}\) & \(2(N-2)x^{*}\) \\ DLN & \(m+(k-1)m^{2}\) & \(3(N/2-1)x\) \\ S\&S & \(km^{2}\) & \((N-1)x\) \\ \hline \end{tabular}  
* Valid for \(N>2\), for \(N=2\): \(K_{X}(2,x)=x\).  
\end{table}
Table 3: Topology comparison in terms of cumulative crosstalk generated by MZIs and waveguide crossings for each topology.
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 3 Results and Discussion  
### Dataset Introduction  
We have measured the fault data of rolling bearing wheel sets in a factory in Wuhan. The operating status of rolling bearings in rotating machinery includes normal, inner ring faults and outer ring faults. The inner ring fault and the outer ring fault are recorded as type A and type B faults, respectively. Consider the FFT transformation, choose 2048 as the sample sampling length, and make sample sets in an overlapping manner. In the training set, the normal data contain 200 samples, and each fault dataset of class A and class B contains 100 samples.
## 1 Introduction
The VR group showed better recall success and delayed reaction times, suggesting that VR experiences become part of the brain's autobiographical memory network. This was supported by a second analysis that used EEG and found that VR led to effortless recall and access to memories, as well as a sense of presence that aided recall [29]. This method is known as virtual reality exposure-based therapy (VR-EBT). VR-EBT, which involves immersion in a computer-generated virtual environment, facilitates emotional processing and has been considered an alternative to traditional exposure-based therapies for specific phobias and post-traumatic stress disorder (PTSD) for more than 10 years [28]. Another significant application of VR-EBT is the treatment of moderate to mild depressive symptoms by focusing on positive autobiographical memories. One of the hallmarks of depression is a tendency for negative recall and overgeneralization/lack of specificity when recalling neutral memories. [PERSON] et al. [18] used VR-EBT to treat depression using image-triggered autobiographical memories. The procedure involved using Google Earth VR to visit a location where a positive event had occurred in the past. The findings indicated that the treatment had a short-term positive impact on their condition, but the effect was not long-lasting, and it should be used as an auxiliary tool. More recently, the effect of exposure to VR on reminiscences was also demonstrated by [PERSON] et al. [29] in a pilot study conducted on patients with the onset of dementia. Continuing the line of studies carried out by us previously [30; 31], in this paper, we present a VR system together with an odor device for autobiographical memory retrieval, but, this time, using a qualitative analysis. To the best of our knowledge, we are the first from this series of experiments to add olfactory stimulation (thus representing a mixed-reality procedure) and offer the participants the opportunity to interact with the virtual environment by manipulating virtual objects with their virtual hands. This is an advancement to what exists currently and provides original research elements for our paper. Also, considering that we use a qualitative analysis of the results, this study can surprise, to a greater extent, the emotional experiences of the participants. This we consider another novelty of the research reported in this paper, as most of the published research for such systems is quantitative in nature. So, the objectives of this exploratory study are (1) to build a prototype of a multisensory VR device capable of triggering autobiographical memories and (2) to test its effects in a qualitative study, from the point of view of positive emotions triggered, of reducing the momentary level of stress, and of improving the quality of autobiographical memories. This research was performed under [PERSON] Grant No. 27/09/10/2023, \"Intelligent VR system for treating autobiographical/episodic memory deficits\".
## 3 Experiments and Analysis
### Evaluation Indicators  
For model verification defect detection, Precision (P), recall rate, Average Precision (AP), and mAP are introduced for this analysis. In the following equations, TP stands for True Positives, which refers to the number of positive instances that are correctly predicted by the model as belonging to the positive class. FP stands for False Positives, which refers to the number of negative instances that are incorrectly predicted by the model as belonging to the positive class. TN stands for True Negatives, which refers to the number of negative instances that are correctly predicted by the model as belonging to the negative class. FN stands for False Negatives, which refers to the number of positive instances that are incorrectly predicted by the model as belonging to the negative class.  
1. Precision: The Precision metric is used to measure the proportion of samples predicted by the model as belonging to the positive class (which is typically the class of interest) that actually belong to the positive class. Specifically, Precision gauges the \"precision\" of the model, which is the extent to which the samples predicted as positive by the model are truly positive. In other words, Precision quantifies how many of the samples predicted as positive by the model are indeed correctly classified, as shown in Equation (5). \[P=\frac{TP}{TP+FP}\] (5) A higher rate indicates a higher recognition rate of the model.
2. Recall rate: It measures the proportion of target objects that the model is able to correctly identify out of all the actual target objects. Specifically, Recall gauges the  
Figure 11: PCB defect image.  
\"recall\" of the model, which is the extent to which the samples that are actually positive are predicted as positive by the model, and is calculated as in Equation (6).  
\[R=\frac{TP}{TP+FN} \tag{6}\]  
A positive recall rate implies low likelihood of producing a missed detection.
3. _AP_: It measures the average of the model's detection precision across different levels of recall. This metric is obtained by plotting the precision-recall curve for each category and calculating the area under this curve. It is calculated as in Equation (7).  
\[AP=\int_{0}^{1}P(r)dr \tag{7}\]
4. _mAP_: it measures the average detection performance of a model across all categories by averaging the AP values of each individual category, resulting in a single metric that reflects the overall detection performance of the model, as in Equation (8).  
\[mAP=\frac{\sum_{i=1}^{C}AP_{i}}{C} \tag{8}\]  
where \(C\) represents the number of AP values taken. \(C\) represents the total number of categories, and \(AP_{e}\) represents the \(AP\) value for the \(i\)-th category.