esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of the Kinematic Accuracy of the Hermetic Harmonic Drive in Space Vehicles
## 2 Impact of Type of Generator on Flexspline Deformation Form
As a result of the rotation of the generator at angle \(\varphi\), the teeth of the flexspline will start to move, and its characteristic point A\({}_{1}\) will move along the trajectory shown in Figure 4:4 When the generator rotates at an angle (\(\varphi=90^{\circ}\)), the teeth of the flexspline will come out of the mesh and position themselves opposite the teeth of circular spline A\({}^{\prime}\)\({}_{1}\). In the right-designed harmonic drive, the moving teeth of the flexspline should bypass the teeth of the circular spline so that there is no interference. This complicated character of the work of harmonic drive causes many construction, material, technology, and exploitation problems. Thus, because of the smooth meshing of successive teeth, harmonic drives are characterized by high smoothness of work and kinematic accuracy. The quality of the gear operation also influences the flexspline form of deformation of the neural layer obtained by the application of different types of generators. The harmonic drives of general applications used in the machine tool industry, robotics, or valve control have, for these applications, sufficiently high precision. When there is a need to use the hermetic harmonic drive in spacecraft control systems, the highest kinematic accuracy is expected from them. Therefore, it is important to perform an analysis to determine for which of the mechanical wave generators the angular error of the hermetic harmonic drive is the lowest.
## 1 Introduction  
Recently, there has been an increased interest in the valorization of agri-food byproducts generated during fruit processing. This approach aligns with the global zero-waste trend and has economic and environmental benefits, reducing environmental pollution and supporting the development of innovative options across different branches of industry [1, 2]. Utilizing materials that are considered as waste enables the effective utilization of available resources, and can result in the creation of new products or food/cosmetic additives with elevated pro-health values [3].  
Strawberries (_Fragair \(\times\) ananassa_ Duch.) are seasonal berries known for their taste and nutritional values which are commonly cultivated all over the world, with a global annual production reaching over 13 million tons [4, 5]. Strawberries contain vitamin C,phenolics including anthocyanin, flavonoidoid, and ellagitannin [6] and the health-promoting properties of the fruits have been extensively studied [7]. Apart from direct consumption, they are intensively used for the manufacture of beverages, juices, and jams. However, the processing of the fruits generates up to even several percent of pomace consisting of seeds, stalks, and pulp, which are considered as by-product [8]. The examination of the chemical composition of pomace showed that it contains plenty of bioactive components from both the group of primary metabolites, including sugars, proteins, and fatty acids, and secondary ones such as phenolics, phytosterols, and tools [9].  
Certain recent articles have concentrated on the potential for valorization of strawberry pomace, waste left during the production of distillate, and seeds [8; 10]. For example, it has been demonstrated that seeds contain a substantial amount of oil, characterized by a high level of \(\alpha\)-linolenic acid, indicating its significant potential for cosmetic and pharmaceutical applications [11; 12]. The seeds and strawberry waste are also characterized by a high content of hydrolyzable ellagitannins, demonstrating beneficial properties in gastrointestinal tract function, as evidenced by both in vitro [8; 13] and in vivo studies [14; 15]. [PERSON] et al. demonstrated that defatted seeds contain significant amounts of protein, dietary fiber, phenolics, vitamins, and minerals [16] and that therefore they are a valuable additive to bread [17].  
In this study, the objective was to establish the phytochemical profile of phenolic compounds from defatted seed residues and develop an effective isolation procedure for recovering low molecular weight phenolics from the residues. To assess the biological potential of the phenolic fraction, the effects on the viability of human skin fibroblasts and cellular metabolism were investigated, along with antioxidant potential and cytoprotective activity in cells with induced oxidative stress. All of these features are essential for the valorization of such material for cosmetic applications.
## 2 Study Area and Materials  
### Study Area  
The portion of the Yangtze River Basin inside Hubei province, China, was selected as the study area (Figure 1). It is located between 30\({}^{\circ}\)13'-30\({}^{\circ}\)50'N and 110\({}^{\circ}\)56'-111\({}^{\circ}\)52'E. The terrain of the study area includes mountains, foothills, and plains, from the northwest to the southeast. The highest elevation, 2,399 m, occurs in the western mountainous area, and the lowest elevation, approximately 36 m, is found in the southeast plain area. The study area has many types of water features, including rivers (e.g., the Yangtze River and the Qingjiang River), lakes, reservoirs (e.g., the Three Gorges Reservoir formed by the Three Gorges Dam and the Gaobazhou Reservoir formed by the Gaobazhou Dam), ponds, and ditches. The land cover types of the study area include vegetation (trees, bushes, fields, and lawns), soil, rock, building (hydraulic structures, roads), and water bodies (rivers, lakes, reservoirs, ponds, wetlands, and ditches) [11]. The terrains and land cover types of the study area can be used to verify the validity of LSW mapping from EO-1 ALI remotely sensed images.  
Figure 1: Location of the study area.  
To find which NDWI model of the ALI data is the best indicator for LSW mapping, three study sites (Figure 1), each sized 300 \(\times\) 240 pixels and having different water features (e.g., rivers, reservoirs, and lakes) and background features (non-water land cover types) were selected as sample areas for detailed analysis. The Three Gorges Reservoir and the Yangtze River were selected as the main water features of Region I. The main background features of Region I are the Three Gorges Dam and the vegetation features. In Region II, the main water bodies are the Baifengxi Reservoir and the Gaobazhou Reservoir, and the background features are vegetation features. In Region III, the main water bodies are the Yangtze River and Dong Lake, and the background features include vegetation, sandbank, and buildings [12].
## 2 Materials and Methods
### Integration  
For LSM monitoring on long time scales, the use of static features alone is inappropriate and real-time modeling and analysis based on dynamic data is time-consuming and expensive. On this basis, a correction method that quickly reflects changes in the LSM is needed. A refined LSM could reduce false positives and false negatives in predicting landslides. Especially in long-term monitoring, a refined LSM could update the LSM over time according to the frequency of dynamic monitoring. This dynamic monitoring method in this study was PS-InSAR landslide deformation monitoring, and the correction method was the correction matrix.  
The correction matrix included the LSM and PS-InSAR data for the landslide cells. As shown in Table 4, the values for each cell were reclassified as five classes: low; low-medium; medium; medium-high, and high. Based on the landslide deformation values, the susceptibility corrections were correspondingly set from 1 to 5. Finally, the corrected susceptibility levels were classified into six classes, 1 = low; 2 = low-moderate; 3 = moderate; 4 = moderate-high, 5 = high and 6 = very high. The principle of the correction matrix was that high deformation increases landslide susceptibility, while low deformation decreases it. For areas of high deformation and high susceptibility, the refined LSM added one level, meaning that 6 represents very high susceptibility. Thus, the refined LSM corrected for landslide susceptibility characterized by ground deformation velocity, thereby reducing the probability of false positives and false negatives. On the other hand, this method is very fast and can be kept up to date over the time series.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 1 Introduction
### Current Challenges in Malware Detection
#### 1.1.1 Evasion and Obfuscation Techniques  
Modern malware employs advanced evasion strategies that significantly complicate detection processes. Code obfuscation techniques, including control flow flattening, instruction substitution, and garbage code insertion, deliberately obscure the true functionality of malicious programs [4]. Polymorphic and metamorphic malware variants dynamically alter their code structure while preserving malicious functionality, creating distinct signatures that bypass signature-based detection systems. Additionally, packing and encryption techniques compress and encrypt malware payloads, making static analysis particularly challenging as the actual malicious code remains hidden until runtime execution.
# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 2 Related Work
### Hardware-Based Approaches  
[PERSON] et al. [9] proposed a single-shot optical mechanism to handle the problem of glare. They positioned a high-frequency filter near the camera sensor to block unwanted  
Figure 1: Taxonomy of state-of-the-art approaches.  
rays in the ray space. Since the authors did not develop a light field camera that encoded the ray space in a reversible manner, this adjustment simplified the design, requiring less precision and minimal calibration. Finally, they demonstrated glare reduction by employing a pinhole array mask with randomly perturbed pinhole locations for non-uniform ray sampling.  
[PERSON] and [PERSON] [23] proposed an innovative approach to enhance the performance of ADAS under adverse lighting conditions by modifying the gain calculation in images from embedded cameras. This method uses physical data such as pavement reflectance, vehicle orientation, sun position, and sky luminance to identify and exclude glare zones during gain evaluation, improving image quality and system reliability.  
[PERSON] et al. [24] presented a vision-based deriving assistance system named \"ShadeVision\". Unlike existing systems, ShadeVision can detect the dazzling effects both during the day (especially at dawn and dusk) and at night. To achieve this, a high-speed camera with a resolution of \(1280\times 500\) pixels and a frame rate of 1000 Hz was installed inside the car to capture the driver's eye region, the areas surrounding the eyes, the vehicle's interior, and the background light outside the window. The collected data were then synthesized and analyzed to determine the dazzling effect.  
[PERSON] et al. [25] presented an anti-glare solution that consisted of already existing technologies in a high-end car such as light intensity sensors, eyes tracking systems, or head-up displays. Also, the authors added a sun-tracking sensor that could detect the sun's position and intensity, automatically adjusting the sun visors to block direct sunlight from hitting the driver's eyes. This reduced the need for manual adjustment and enhanced driver comfort and safety.
###### Abstract
Recently, some of the feature extraction studies have shown some improvement in terms of accuracy [25; 26; 27]. Such studies mainly follow parallel-based and serial-based approaches. The aim of these studies is to enrich the information related to the subject gathered from different sources. Nevertheless, incorporating this technique gives the rise to the number of predictors, which in turn has a negative impact on computational time. Due to this reason, many researchers only focus on introducing techniques that select only the best features. Feature-based techniques are of two types: heuristic-based and meta-heuristic technique. Meta-heuristic techniques are more valuable for the selection process and provide a fewer number of predictors. In this paper, a detailed comparative analysis based on the properties of the Transformers and CNNs is carried out. The following points summarize the contribution of the paper:  
* We discuss the challenges in the skin lesion segmentation. * We provide a detailed comparative analysis of the state-of-the-art methods for the task of skin lesion segmentation. * We evaluate the efficacy of the state-of-the-art methods, and the experiments demonstrate the effectiveness of the U-Net and Transformer-based methods for skin lesion segmentation. Figure 1: Sample of Skin Lesion Images. The rest of the paper is organized as follows: related works to this research are reported in Section 2. In Section 3, the details about the comparative methods are presented. Section 4 shows the experimental details and performance evaluation of the comparative methods and the benchmarking models. Finally, Section 5 concludes the paper.
## 2 Monitoring Principle
### Wavelet Packet-Based Active Sensing Method  
The signal analysis in the structural damage identification technology using the active sensing analysis method mainly includes time domain analysis, frequency domain analysis and wavelet packet analysis [28]. The time domain analysis mainly investigates the change of signal amplitude to identify structural damage. Time domain waveform is intuitive and easy to understand, so time domain analysis is widely applied in engineering. Structural damage will cause the change of signal frequency. The damage can be analyzed according to the change of signal frequency spectrum. The frequency domain signal is more intuitive than the time domain signal in the change of the signal amplitude. Therefore, we transform the time domain signal data into frequency domain signal by Fourier and perform comparative analysis [31]. Wavelet packet analysis can identify damage by comparing the energy difference of the received signal. In recent years, wavelet packet analysis has proven to be an effective SHM method. Wavelet packet analysis has the advantages of time domain and frequency domain analysis and can accurately decompose the low frequency and high frequency. The special characterization of the signal based on wavelet packet analysis can quantitatively describe the severity of structural damage during the loading process. Therefore, this paper can analyze the signal by wavelet packet principle and form the damage index to identify the severity of structural damage.  
The energy of loading process is calculated based on wavelet packet theory and the damage index is determined by root mean square deviation (RMSD) to evaluate the damage extent of the specimen. In order to eliminate the initial error of different sensors, the damage index is normalized. The energy loss of stress wave propagation is caused by structural damage (such as cracking, debonding and bond slip) at different loading stages. The structure is in the state of no debonding and cracking before the loading test, indicating that the damage index value is zero. The damage index reaches a certain value, indicating that the structure will exhibit debonding/cracking during the loading process. When the damage index is closer to 1, the structural state indicates that the damage extent is serious.  
The wave components are generated by the wavelet packet decomposition of the wave signals obtained from the experimental study. The \"Daubechies (db2)\" wavelet packet provided by Matlab is selected in the process of wavelet packet analysis. The original signal measured by SA is decomposed into three layers of wavelet packets. Three level decomposition diagram of wavelet packet, as shown in Figure 2.
## 3 Method
### Semantic Learner
#### 3.2.2 BERT Model Training  
BERT employs a Transformer architecture that enables the model to capture context from both directions in a sequence. To enhance the ability of BERT to learn code semantics, GMN+ utilizes two key pre-training tasks: Masked Language Modeling and Instruction Type Prediction tasks.  
Masked Language Modeling (MLM): In this task, 15% of the tokens in the instruction sequence along with their corresponding instruction types are randomly masked. The model then predicts these masked tokens based on their surrounding context, which enables BERT to learn semantic relationships and interactions within the instruction sequences. This task helps the model develop a comprehensive understanding of binary function semantics. The MLM process is formalized as follows:  
\[O=Transformer(E_{mask}) \tag{1}\]  
\[p(w\mid E_{mask})=Softmax(MLP_{token}(O)) \tag{2}\]  
where \(E_{mask}\) represents input embedding after masking, and \(O\) represents the output from the Transformer encoder. The \(MLP_{token}\) is a multilayer perceptron and \(w\) denotes the input token sequence of the model. The cross-entropy loss \(L_{MLM}\) for the MLM task is computed by comparing the predicted probability distribution of the masked tokens with their ground truth, as shown below, where \(M\) represents the index set of the masked tokens.  
\[L_{MLM}=-\frac{1}{|M|}\sum_{j\in M}\text{log}(p(w_{j}\mid E_{mask})) \tag{3}\]  
Instruction Type Prediction (ITP): Similarly, 15% of the input tokens and their types are masked, and the model predicts the types based on the context. This task facilitates learning  
Figure 3: BERT input embedding.  
the semantics inherent to various instruction types and allows the model to develop deeper semantic representations of the code. The formalization of the ITP process is as follows:  
\[p(t\mid E_{mask})=Softmax(MLP_{type}(O)) \tag{4}\]  
where \(MLP_{type}\) is a multilayer perceptron, and \(t\) is the type sequence of the input token sequence. The cross-entropy loss \(L_{TYPE}\) for the ITP task is computed based on the predicted probability distribution of the masked token types compared to their ground truth, as shown below.  
\[L_{ITP}=-\frac{1}{|M|}\sum_{j\in M}\log(p(t_{j}\mid E_{mask})) \tag{5}\]  
GMN+ employs the above two pre-training tasks to pre-train the BERT model. The pre-trained BERT model is subsequently utilized to generate semantic embeddings for basic blocks of binary functions. Specifically, the normalized instruction sequence and instruction type sequence of basic blocks in a function are fed into the BERT model. The semantic embedding of the basic block is obtained by averaging the outputs of the last four layers of the Transformer encoder.
# Virtual Reality and Digital Twins for Catastrophic Failure Prevention in Industry 4.0
## 1 Introduction  
The advance of Industry 4.0 has included new paradigms, constantly modifying industrial processes through the integration of digital technologies that optimize production, improve operational efficiency and strengthen maintenance strategies [1]. In this context, the monitoring and advanced diagnosis of faults in industrial equipment has become vitally important within production processes, not only to ensure operational continuity and reliability of machinery, but also to improve the safety of workers and minimize costs associated with unplanned shutdowns. In parallel, the integration of immersive simulation tools such as virtual reality and digital twins also opens new opportunities for technical training and capacity building in industrial environments [2].  
Within this framework, condition-based and preventive maintenance approaches play a fundamental role in industrial asset management. Preventive maintenance is based on the scheduled execution of inspections and the replacement of components before a fault occurs, ensuring optimal performance and extending the useful life of equipment. However, with the advancement of digitalization, predictive maintenance has emerged, which, in conjunction with preventive maintenance, employs advanced technologies such as IoTsensors and digital tools to monitor the condition of assets in real time and predict faults before they occur. This approach enables more efficient maintenance planning, avoiding unnecessary interventions and reducing operating costs.  
The complexity of new industrial systems and their automation, which is taking an increasingly dominant role, together with the need to increase maintenance accuracy, have led to the adoption of costly innovative tools such as virtual reality (VR), augmented reality (AR) and digital twins (DTs) [3]. These technologies, in combination with IoT sensors, cloud storage and real-time data analytics, enable the more efficient lifecycle management of industrial assets, optimizing decision making and reducing downtime.  
As these improvements continue to evolve, their implementation becomes increasingly necessary in all types of industries to keep organizations competitive. The market's growing demand for more rigorous safety standards that seek to protect worker integrity also makes it possible to manage the health and availability of industrial assets, ensuring a more efficient and reliable production environment.
# Retrofitting Masonry Walls against Out-Of-Plane Loading with Timber Based Panels
## 3 Experimental Study
### Material Characterisation  
#### 3.1.1 Experimental Test on Brick Unit, Mortar and Masonry Cubic Specimens  
For the material characterisation tests (Figure 3), an experimental program was developed based on the components (brick unit and mortar) and assemblage (brick masonry cubic specimen), as shown in stage 1 of Table 2.  
Each test on the component was carried out according to the relevant British standards, and allowed for the definition of the dimension, dry density, water absorption, compressive strength, modulus of elasticity and Poisson's ratio of the brick units.  
After testing the brick unit and mortar independently, six masonry cubic (MC) specimens of 215 \(\times\) 215 \(\times\) 215 mm were tested to understand how the selected brick units and the adopted mortar mix ratio work together. The MC specimens were constructed using English bond consisting of alternate rows of headers and stretchers, which is the oldest form of brick bond popular until the late 17 th century [27]. After the construction, each sample was wrapped with polythene sheet for 14 days and thereafter opened and cured further for 14 days in the laboratory to allow the samples to achieve its standard strength. An attempt to measure the deformation of the MC was made by attaching four LVDTs to the MC before testing (Figure 3c). The specimens were carefully aligned with the centre of the ball-seated platen, under the compression testing machine, with 2 mm-thick plywood placed on the top and the bottom of the MC. A uniformly distributed load was applied gradually in equal increments continuously at 4 kN/s rate up to failure.
# Publish/Subscribe-Middleware-Based Intelligent Transportation Systems: Applications and Challenges
## 4 Application of Publish/Subscribe Middleware in ITS
### Infrastructure  
Middleware is essential for ITS, enabling real-time traffic monitoring, vehicle communication, and smart mobility applications. This section explores key ITS infrastructure components, including IoT-based traffic surveillance, Vehicular Ad hoc Networks (VANETs), and road sensor networks (RSNs), and how middleware enhances them. IoT infrastructure utilizes smart cameras and sensors for event-driven traffic management, while VANETs support Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication to improve road safety. Also, RSNs enhance ITS by providing real-time road condition data, supporting autonomous vehicles, and Smart City integration.