esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Architecture and Training
#### 4.3.4 Reconstruction Examples
In this case, as the signature is somewhat higher in amplitude than expected in the normal behaviour, the main contributing factor to the anomalous classification is the amplitude, with the shape being the secondary contributing factor. By studying the reconstruction of the network over both normal expected behaviours and anomalies, it is straightforward to draw some conclusions about this specific network's operation. Firstly, the network fits well with the normal data exhibited in Figure 9A,B. This positively demonstrates half of the desired operation of the network, which must (a) well-reconstruct unseen data close to their training class and (b) struggle to reconstruct anomalous patterns entirely dissimilar to the normal training class. Regarding Figure 9C, which is a low amplitude anomaly, while there is clearly an increase in error, the network does a surprisingly good job of reconstructing the anomaly. While there is some struggle with the shape of the anomaly during reconstruction, it seems that the network can fit unseen data to a degree. This ability is undesired, with the desired behaviour being a significant struggle with anomalous samples. This specific case serves as a good benchmark for reconstructive ability; as such, it will be further explored in Section 4.3.5. In cases such as Figure 9D,F,H, it is clear there is a maximum learned amplitude that the network can reconstruct, with a complete inability to reconstruct past this point. This was learned passively during training, as the training set peaked at approximately 0.38 A. In cases such as Figure 9C,E,G, the amplitude of the anomaly is significantly below that of the maximum expected normal behaviour, so it can be inferred that the main contributing factor to the error, in these cases, would be the unexpected shape. This is the most desirable behaviour of the network--the purpose of using a CAE is the ability to learn and reconstruct complex expected normal patterns while struggling, as much as possible, with patterns that would be entirely unexpected for the network. Overall, the reconstructions are relatively close to the input. This is not desirable. The best-case scenario is to minimise the normal class error while maximising the abnormal class error. Figure 9 shows a network that fits very well with normal examples while only minimally struggling with lower amplitude anomalies. Of course, anomalies vastly outside normal operating amplitudes are completely reconstructible. This suggests overfitting in the network or at least some similar phenomenon. This can occur when a network is too complex for the task, or the test set is far smaller than the training set. In the case of this network, the former is more likely the issue. Before any changes are made to the network, a specific MSE case is explored in Section 4.3.5. Figure 9: \(Cont.\)
## 7 Using Vehicles to Augment MEC Capabilities  
The quick advancement of communication technologies has changed the conventional vehicular networks in the IoV, which is a pillar of ITSs [181]. The 5G infrastructure is envisaged for vehicular networks to connect vehicles to everything. By increasing the scalability of vehicular communications, the concept of Cellular-V2X (C-V2X) has attracted the interest of industry and academia. To support delay-sensitive and high-computation applications in 5G-assisted vehicular networks, MEC can serve offloaded tasks and optimize resource allocation. However, due to the massive growth in produced data and resource demand, there is a need for better solutions to handle large computation and storage resources. The VEC paradigm expands the resource potential of MEC by offering more resources to improve the services provided by the MEC [182]. A car with available processing resources may offer computing services to adjacent nodes. This kind of car is called a VEC server [183]. Research paper [101] focuses on ensuring timely task completion by adopting a novel strategy that utilizes V2V communications for task offloading. To increase the coverage of MEC services, the authors choose collaborative vehicles to collectively manage tasks that require low latency and significant computation, executing them in parallel. The proposed approach leverages local processing, V2V offloading, and collaborative computing among
# Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications
## 3 Proposed RBF-GAT Framework
### RBF-GAT Architecture
In addition, we chosethe node-wise mean function as an aggregation function to aggregate the output characteristics of the GAT layer and concatenate it into each FC layer by using skip connect. After \(D\) FC layers, the final output vectors, denoted as \(c_{k}^{D}\), are passed to the normalization layer to produce the precoding matrix \(\mathbf{w}\) and phase shift matrix \(\mathbf{\Theta}\) while ensuring the constraint of phase shift and transmit power. As with [20], we input \(c_{k}^{D}\) to the linear layer \(f_{w}(\cdot)\) with \(2 MK\) FC units and the linear layer \(f_{\mathbf{\Theta}}(\cdot)\) with \(2N\) FC units. Then, the normalization layer outputs the real and imaginary components of the optimization variables. Finally, the complex solution can be obtained by combining the real and imaginary components.
## 2 Tension-Shear Coupling Model  
### Geometrical Model  
Figure 1 shows the configuration of a plain-woven representative volume element (RVE). The RVE geometric parameters, \(w_{0}\), \(g_{0}\), and \(N_{y}\), are, respectively, the yarn width, the yarn gap, and the yarn amount in the RVE. \(L_{R}\), the side length of the RVE, is defined as follows:  
\[L_{R}=N_{y}(w_{0}+g_{0}). \tag{1}\]  
In order to focus on the tension-shear coupling, this work only considers the case that the warp and fill yarns have identical geometry. It should be noted that Equation (1) is applicable for fabrics with different weave patterns. The plain weave pattern shown in Figure 1 is only an example for illustration.  
The number of inflection points of a yarn, \(\eta_{y}\) is introduced to characterize different weave patterns. In a woven fabric, a warp yarn passes over and under fill yarns in sequence, and vice versa. The ratio of the under and over areas of warp and fill yarns determines the weave pattern. In this work, when a yarn turns from under to over or from over to under one time, it is counted as one inflection point. The inflection point at the boundary of an RVE is counted as a half point. For example, as shown in Figure 1(a), the inflection point number of a warp yarn is 2. The weave pattern of RVEs with the same yarn amounts can be distinguished by different yarn inflection point numbers. For example, as shown in Figure 1(a), both the twill and plain RVEs have 4 warp and 4 fill yarns, but their inflection point numbers of the warp yarn are 2 and 4, respectively. The number of inflection points in an RVE determines the normal pressure between overlapped yarns induced by the tensile force. Furthermore, normal pressure is closely related to the tension-shear coupling which will be explained in detail hereinafter. Hence, it is believed that the inflection point number is critical to evaluate the effects of different weave patterns on the tension-shear coupling of fabrics.  
Figure 1: Geometry of a plain-woven RVE.  
Figure 2: Illustration of inflection points.
## 4 Data and Evaluation Metrics
* _Recall_ (recall) measures how many Windows PE malware files are correctly classified as malware, given all occurrences of class malware, i.e., \(\mathsf{recall}=\frac{\mathsf{tm}}{\mathsf{tm}+\mathsf{tg}}\). This metric estimates whether the decision model can find all samples of the target class \"malware\". * _Fscore_ (F) measures the harmonic mean of precision and recall, i.e., \(\mathsf{F}=2\frac{\mathsf{prec}-\mathsf{recall}}{\mathsf{prec}+\mathsf{ recall}}\). As precision and recall are equally important, the Fscore is measured to estimate the trade-off between precision and recall. In particular, the higher the Fscore, the better the balance between precision and recall achieved by the evaluated approach. * _The false negative rate_ (fnr) measures the probability that Windows PE malware is wrongly classified as goodware, i.e., \(\mathsf{fm}=\frac{\mathsf{fg}}{\mathsf{tm}+\mathsf{tg}}\). The lower the false negative rate, the lower the number of malware files that are undetected. * _The false positive rate_ (fpr) measures the probability that Windows PE goodware is wrongly classified as malware, i.e., \(\mathsf{fpr}=\frac{\mathsf{fm}}{\mathsf{tg}+\mathsf{fm}}\). The lower the false positive rate, the lower the number of goodware files that are wrongly detected as malware. The higher the values of \(\mathsf{oa}\), \(\mathsf{prec}\), \(\mathsf{recall}\) and \(\mathsf{F}\), the better the decision model. The lower the values of \(\mathsf{fn}\) and \(\mathsf{fpr}\), the better the decision model. As an additional metric to measure the evasion ability of an attack method against a malware detection model, we consider evasion, which is the number of Windows PE malware files that are correctly classified with the model but whose adversarial counterparts, generated via the attack method, are wrongly classified as goodware according to the model. Formally, let \(\mathsf{tm}\) be the number of true malware files recovered on the set of Windows PE malware files and \(\mathsf{tm}^{\mathsf{A}}\) be the number of true malware files recovered on the set of original Windows PE malware files where original malware files were replaced with adversaries whenever adversaries exist. Hence, \(\mathsf{evasion}=\mathsf{tm}-\mathsf{tm}^{\mathsf{A}}\). The higher the evasion value, the higher the number of adversarial Windows PE malware files that evaded the decision model, and consequently, the lower the integrity of the model versus adversarial attacks.
# Passive Indoor People Counting by Bluetooth Signal Deformation Analysis with Deep Learning
## 3 BLE-Based Passive Human Sensing
### The Proposed Deep Learning Model
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Architecture** & **Advantages** & **Disadvantages** & **References** \\ \hline DenseNN & Simple architecture, fast training. & Poor at capturing temporal dependencies. Limited feature extraction from structured data. & [36] \\ \hline CNN & Excellent at extracting spatial features. Efficient with structured input like RSSI matrices. & Limited temporal modeling. May require large datasets for generalization. & [37; 38] \\ \hline LSTM & Effective for modeling sequential dependencies. Captures temporal dynamics in RSSI variations. & Slower training. May overfit on small datasets. & [39] \\ \hline CNN+LSTM & Combines spatial and temporal modeling. High accuracy and robustness. Best performance in BLE signal deformation tasks. & Slightly higher computational cost. Requires careful tuning. & [40; 41] \\ \hline Transformer & Captures long-range dependencies. Scalable and parallelizable. & High computational demand. Needs large datasets to perform well. & [42; 43] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of deep learning architectures. training the model on data collected in the same environment and setup as the test data. Here, instead, we pre-train each model on a large, heterogeneous dataset and then fine-tune it on a much smaller dataset, gathered in the same setup and environment as the test dataset. We show in Section 5 that with such a simpler and faster procedure, the model outperforms all other approaches in the literature. Comparing classification and regression models on the same task is not straightforward. In [25, 44], the performance of the regression models is evaluated in terms of the percentage of predictions that fall within \([\theta-1,\theta+1]\) where \(\theta\) is the true value, i.e., the correct value that the model must predict. Such a large interval corresponds to a resolution coarser than 1 and is questionable, as shown by the following simple example. If the correct value is, say, 5 and the predicted value is 4, the prediction will still be considered correct according to the chosen interval, but it is indeed not correct because the model is predicting a value that corresponds to a wrong occupancy. Therefore, in our experiments, we consider the narrower interval \([\theta-0.5,\theta+0.5]\), which corresponds to the most common approximation of a real-valued number to an integer. As a consequence, to evaluate the performance of a regressive model in terms of accuracy-like metrics, we measure the percentage of predictions that, when approximated to an integer, exactly match the true value. This metric is also known as the Almost-Correct Prediction Rate (ACPR), and we use it, setting \(Th=0.5\), together with the mean absolute error to evaluate the performance of the regressive models.
# GPS-Induced Disparity Correction for Accurate Object Placement in Augmented Reality
## 2 Materials and Methods
### Framework for Object Rendering in Augmented Reality  
As depicted in Figure 1, the proposed system introduces an advanced framework for acquiring and refining AR imagery and location data. The system's cornerstone, the \"Digital Twin Server\", hosts a comprehensive repository of 3D spatial information, including latitude, longitude, and a versatile virtual coordinate system for object management and registration. This server not only stores detailed building information but also integrates digital elevation model (DEM) and digital terrain model (DTM) updates provided by the \"Spatial Information Acquisition via UAV (Unmanned Aerial Vehicle)\" component. The DSM and DTM are instrumental in depicting the Earth's surface and terrain morphology, respectively, thereby enhancing the precision of digital twins for AR applications. The \"Spatial Information Acquisition via UAV\" segment deploys drones to capture imagery, which includes crucial GPS coordinate information embedded in TIFF files. These files are processed by the \"Building Semantic Segmentation Detection\" module, where semantic segmentation extracts geolocation data as building polygons, enriching the digital twin database. This process benefits from cross-referencing public building records, ensuring that the building height data of the DT server remain updated. To facilitate dynamic interactions with AR objects, the \"Object Management System\" oversees the registration and management of items within a virtual coordinate framework. End-users interact with these AR objects through the \"User Application\", which communicates with the digital twin server via the \"API (web)\". This interaction enables the server to retrieve and display object information based on user location data, ensuring a seamless and interactive AR experience.
## 3 Design and Construction
### 3-D Simulations  
We use 3-D simulations to model more completely the physical situation (still assuming z-axis rotational symmetry) therefore providing more accurate values and both axial (z-axis) and transverse (x-axis) profiles. EMS [13], a SolidWorks add-in by EM Works, and COMSOL (along with the Bio-Savart summations and experimental readings) were used to produce the longitudinal (z-axis) and transverse plots (x-axis) shown in Figure 11.  
The simple axial/longitudinal Bio-Savart Mathematica-based discrete current loop model compares favorably with EMS and COMSOL simulations. Similarly, EMS and COMSOL fit well with transverse experimental measurements in most of the gap region. However, some separation is noted near the ID edge (0.125\") and across the annular rims that end at 0.1875\". EMS tracks closer. EMS operates on a hysteresis curve model while COMSOL was pinned to constant magnetization in the magnet bulk. One may hypothesize that some small mutual coercive effects are present in the annular rim. In addition, some aperture effect occurred for the Lakeshore 40 mil diameter active area probe. The 50 G central dip is apparent in the transverse plot (x-axis).
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 2 Background and Related Work
### Big Data Challenges, Internet of Things, and TinyML  
The rapid expansion of the Internet of Things (IoT) marks a significant shift in the digital landscape, marked by an extensive network of devices and sensors continuously collecting and sending data. This fusion of environments rich in data greatly increases the challenges related to Big Data, particularly concerning its large volume, high speed, and varied complexity.  
The Big Data Dilemma in IoT  
As IoT systems evolve, they inherently generate data that challenge conventional processing and storage infrastructures. Key challenges arising from this scenario include:  
* **Storage Capacity and Scalability**: Traditional storage systems grapple with the ever-growing influx of data from IoT sources, necessitating the development of more scalable and adaptive solutions.
* **Data Processing and Analysis**: The heterogeneity of IoT data requires sophisticated adaptable algorithms and infrastructures to derive meaningful insights efficiently.
* **Data Transfer and Network Load**: Ensuring efficient and timely data transmission across a myriad of devices without overburdening the network infrastructure remains a paramount concern.
* **Data Integrity and Security**: As data become increasingly decentralized across devices, ensuring their authenticity and safeguarding them from potential threats are critical.  
These challenges, which highlight the wider complexities that IoT introduces to Big Data management, are outlined in Table 1.
## 3 Results and Discussion
### Effective Activation of PMS with Catalysts for Contaminant Removal
Materials with elemental cobalt as the main catalytic site often show strong leaching in peroxymonosulfate systems due to the self-generated acidic environment of PMS [47], and the excess H\({}^{+}\) under acidic conditions scavenges -OH in the environment to reduce the degradation rate. In contrast, the concentrations of Fe and Co ions in the leachate were lower than 0.2 mg/L in the five cycling experiments. These values were much lower than the permissible threshold (1 mg/L) specified by the Chinese national standard (GB 25467-2010) [48], which further illustrated the adaptability of the material to different environments. Figure 5: (**a**) Degradation of SMX under various systems (Catalyst concentration: 0.1 g/L, SMX concentration: 10 mg/L, PMS concentration: 0.3 mmol/L, pH \(\approx\) 5.6, Temperature = 25 \({}^{\circ}\)C); (**b**) Effect of different initial pH values on SMX degradation using the FeCo-LDH + PMS system (Catalyst concentration: 0.1 g/L, SMX concentration: 10 mg/L, PMS concentration: 0.3 mmol/L, pH values: 3.0, 5.0, 7.0, 9.0, and 11.0, Temperature = 25 \({}^{\circ}\)C); (**c**) pH variation trends throughout the process under different initial pH conditions; (**d**) Buffer solutions and pH changes during the reaction (Catalyst concentration: 0.1 g/L, SMX concentration: 10 mg/L, PMS concentration: 0.3 mmol/L, pH values: 4.0, 6.86, and 9.18, Temperature = 25 \({}^{\circ}\)C).
# Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System+
###### Abstract  
Malaria, as one of the most serious infectious diseases causing public health problems in the world, affects about two-thirds of the world population, with estimated resultant deaths close to a million annually. The effects of this disease are much more profound in third world countries, which have very limited medical resources. When an intense outbreak occurs, most of these countries cannot cope with the high number of patients due to the lack of medicine, equipment and hospital facilities. The prevention or reduction of the risk factor of this disease is very challenging, especially in third world countries, due to poverty and economic insatiability. Technology can offer alternative solutions by providing early detection mechanisms that help to control the spread of the disease and allow the management of treatment facilities in advance to ensure a more timely health service, which can save thousands of lives. In this study, we have deployed an _intelligent malaria outbreak early warning system_, which is a mobile application that predicts malaria outbreak based on climatic factors using machine learning algorithms. The system will help hospitals, healthcare providers, and health organizations take precautions in time and utilize their resources in case of emergency. To our best knowledge, the system developed in this paper is the first publicly available application. Since confounding effects of climatic factors have a greater influence on the incidence of malaria, we have also conducted extensive research on exploring a new ecosystem model for the assessment of hidden ecological factors and identified three confounding factors that significantly influence the malaria incidence. Additionally, we deploy a smart healthcare application; this paper also makes a significant contribution by identifying hidden ecological factors of malaria.  
malaria; climatic factors; machine learning; prediction; mobile application; structural equation modelling; partial least squares model +
Footnote †: _Appl. Sci._**2017**, 7, 836; doi:10.3390/app7080836
# Psychoeducation on Stress and Anxiety Using Virtual Reality: A Mixed-Methods Study
### Virtual Reality for Psyoeducation  
Psychoeducation refers to interventions aimed at providing information about a mental disorder clearly and articulately [66; 67]. The goal is to educate individuals about their condition and how to cope. Psyoeducation is a fundamental part of CBT [43] and can also be a stand-alone intervention [68; 69]. Usually, it is carried out through face-to-face meetings or audiovisual aids (e.g., booklets or videos) [37].  
VR can be beneficial for delivering psychoeducation [70; 71; 72]. Indeed, compared with traditional teaching methods (e.g., lecture, slideshow, or textbook), this technology allows people to \"be at the center of the lesson.\" Due to VR's unique features in terms of immersion, presence, and engagement, the learning material can be gained deeply [73].  
This technology can help stimulate individuals in all the so-called \"soft skills\" (e.g., creativity and problem-solving) [74] and in teaching specific subjects such as marine science [75], geology [76], and medical topics such as neuroanatomy and clinical anatomy (e.g., ref. [77]). Furthermore, VR is considered an effective and innovative tool for promoting scientific and medical knowledge among the general population. For example, recently, this technology was used to increase public understanding and knowledge of the COVID-19 pandemic [78; 79].  
Although VR represents one of the key technologies to change the way education is delivered, to date, few studies have tested the use of VR within psychoeducation programs [30; 32; 80]. For example, a psychoeducational experience (i.e., VRight) increased depressive symptoms awareness in individuals suffering from depression [30]. Psyoeducational material on Post-Traumatic Stress Disorder (PTSD) was created also as part of the Stress Resilience In Virtual Environments (STRIVE) project, which aimed to made a set of multi-episode interactive narrative experiences for delivering PTSD exposure therapy for military personnel [40]. Furthermore, within Second Life, an online multimedia platform launched in 2003 and accessible from PCs that allows people to interact in an online virtual world using avatars, psychoeducational content on social anxiety (i.e., Drexel Island) [38], and for stress management (i.e., Learning Island) has been successfully tested [39].