esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementation and User Scenario
### User Scenario: [PERSON]'s Virtual Shopping Experience  
The CORES VRShop offers a dynamic shopping experience, demonstrated through various specialized stores. In this scenario, [PERSON], a fitness enthusiast, uses his VR headset to enter VRShop, where he finds himself in a lively virtual mall with numerous stores, each dedicated to different product categories.  
[PERSON] chooses to explore a sporting goods store. He browses products such as running shoes, exercise equipment, and sportswear, using the realistic 3D models to closely inspect the items, providing him with a tactile shopping experience. However, after a while of searching, [PERSON] does not find what he wants. He decides to use the system's conversational interface to find a specific product. A user interface prompts him to select a product category and specify his preferences, where he inputs: _\"I'm looking for a new yoga mat and I prefer a thicker mat for extra cushioning, and I like vibrant colors. Eco-friendly materials would be a plus\"_.  
The VRShop assistant immediately processes these preferences and suggests three personalized options: a 6 mm thick eco-friendly yoga mat in teal, a 5 mm thick non-slip mat in purple, and a 7 mm thick high-density mat in green. [PERSON] examines these suggestions in 3D, rotating the mats to assess their texture and thickness. He repeats this with the purple and green mats, as depicted in Figure 9.  
Each item is equipped with four main features: update, insights, comments, and chat. For example, if [PERSON] wants to learn more about the teal yoga mat, he can click the  
Figure 9: Specification of user’s preferences.  
\"Insight\" button to view detailed information about its features, displayed on the user interface as shown in Figure 10. Additionally, he can check real-time reviews from other users by clicking on the product image, providing him with insights from social networks, as illustrated in Figure 11. Appendix A provides more detailed prompts in CORES.  
Another feature allows [PERSON] to click the \"Chat\" button, which opens a dialogue interface for voice and text communication, enabling him to ask more questions or receive further suggestions. If [PERSON] wants to act on a recommendation, he can click on the corresponding comment to view it in real time.  
Figure 12 illustrates the intermediate results of each step in the reconstruction workflow. First, the VR loader receives recommended items from CORES and constructs a digital representation of the 3D scene. Next, Unreal Engine generates virtual 3D models of the recommended items, adding realistic surface details. Finally, the restored VR scene is created by aligning the 3D models of the items and incorporating ex  
Figure 11: Display matching results with relevant comments.  
Figure 10: Selection of virtual shop.
# Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
###### Abstract
This study aims to determine the surface displacement dynamics of a reported landslide event located in the Buyukalan region of Antalya, Turkiye, through multi-temporal satellite radar interferometry (InSAR). Satellite imagery and radar interferometry provide valuable data for detecting mass movements occurring over large areas. [PERSON] and [PERSON] (2024) [16] employed remote sensing (RS) techniques, particularly interferometric synthetic aperture radar (InSAR), to analyze landslides in the Taurus Mountains, revealing the impacts of heavy rainfall on slope stability and mapping topographic changes. Furthermore, national institutions such as the General Directorate of Mineral Research and Exploration (MTA) and the Disaster and Emergency Management Authority (AFAD) conduct landslide inventory studies based on aerial photographs and satellite imagery throughout Turkiye. One of the primary data sources for this study is the landslide inventory map. The landslide data used in this research were obtained from inventory maps published by MTA [17]. Western Antalya holds a significant position within these national inventories; for instance, active and historical landslide areas in the region are clearly marked in the MTA's landslide inventory maps. These types of maps, when integrated with up-to-date satellite data in a geographical information system (GIS) environment, allow for the development of comprehensive landslide hazard maps that can be used in land use planning and early warning systems. It has been observed that the processing steps employed in the utilization of InSAR data for landslide studies in the current literature are largely similar. Despite the similarities in these steps, researchers have opted to utilize diverse InSAR techniques, including PSInSAR, DInSAR, and SBAS, in subsequent steps, owing to the presence of uncertaintiesin the results. The utilization of InSAR techniques in landslide detection and classification studies remains challenging [18]. Although InSAR is widely used in landslide monitoring studies, conventional approaches often evaluate deformation outputs, such as LOS velocity maps from ascending and descending tracks, or vertical and horizontal components in isolation. This practice can hinder a holistic interpretation of surface displacement patterns, particularly in areas with complex ground motion. Moreover, in classification-based analyses, methodological inconsistencies or subjective interpretations may limit the accurate identification of deformation zones. Consequently, the concept of utilizing diverse InSAR-derived data, produced at a specific level, in conjunction has emerged. The objective of this study was to develop and evaluate two novel approaches that utilize multi-dimensional InSAR products to detect surface displacements.
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 4 Experiments and Results
### Comparative Experiments
#### 4.2.1 Data Preprocessing  
First, corresponding label attributes were assigned based on the different operating conditions in the original dataset. To ensure the representativeness and robustness of the training data, we adopted a random splitting strategy, dividing the original data into training and testing sets in a 2:8 ratio. This approach effectively avoids biases that may arise from rule-based splitting, thereby enhancing the model's generalization ability and robustness. During the algorithm evaluation, we paid special attention to avoiding any data leakage between the training and testing sets. To achieve this, we ensured that each sample in the split data remained independent between the training and testing sets, with no shared information.  
To further optimize the model's input data, we employed a sliding window technique to construct the training-validation dataset. Based on an analysis of the original data's temporal characteristics and multiple experimental results, we observed that fault signalsexhibit distinct features within relatively short time windows. Consequently, we set the sliding window step size to 1 and the window size to 10. This parameter configuration effectively balanced the model's ability to capture data features while enhancing its generalization ability and predictive accuracy. Specifically, a window size of 10 meant that for each movement of the sliding window by one time step, 10 consecutive data points were extracted as a sample, enabling the model to fully leverage the temporal dependencies in the data and improve its ability to capture fault characteristics.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 5 Experiment and Results Analysis
### Results Analysis
#### 5.3.2 Embedding Rate  
Since different embedding rates during information steganography will affect the result of steganalysis, we use five different types of embedding rates for 10%, 30%, 50%, 80%, and 100%. The average detection rate (%) of ACD and SCD is shown in Table 4.  
In order to more intuitively compare the performance of the two types of detection, the relationship between the average detection rate of the five data sets and the embedding rate of the speech segment is shown in Figure 8.  
Figure 8 shows that the average detection rate of ACD and SCD is proportional to the embedding rate. The higher the embedding rate, the higher the accuracy. When the embedding rate is lower than 30%, the average detection rate of ACD is significantly lower than SCD. This shows the steganalysis performance of SCD is better. When the embedding rate is higher than 30%, the detection effect of ACD and SCD is equivalent. In the actual analysis, one of them can be used for analysis, which not only guarantees the accuracy but also can improve the detection efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Embedding Rate**} & \multicolumn{6}{c}{**ACD (\%)**} & \multicolumn{6}{c}{**SCD (\%)**} \\ \cline{2-10}  & **CM** & **CW** & **EM** & **EW** & **Hybrid** & **CM** & **CW** & **EM** & **EW** & **Hybrid** \\ \hline
10\% & 43 & 46 & 44.5 & 45 & 44.5 & 49.5 & 52 & 54 & 54 & 55 \\
30\% & 64 & 65 & 63.5 & 65 & 64.5 & 65 & 66 & 65.5 & 64 & 64 \\
50\% & 76 & 77 & 76.5 & 75 & 76.5 & 77 & 79 & 78.5 & 77.5 & 78 \\
80\% & 86 & 87.5 & 87 & 85 & 86 & 87 & 86.5 & 88 & 87.5 & 86 \\
100\% & 95.5 & 93.5 & 94 & 94 & 93 & 94 & 95.5 & 94 & 95.5 & 94.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average detection rates for ACD and SCD of different embedding rates (%).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Steganography Algorithm**} & \multicolumn{2}{c}{**Without PCA**} & \multicolumn{2}{c}{**PCA**} \\ \cline{2-5}  & **FV** & **DR** & **FV** & **DR** \\ \hline CNV-QIM & 256 & 95.83 & 120 & 95.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PCA test results (%). FV denotes feature vector, and DR expresses detection rate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**ACD**} & \multicolumn{2}{c}{**SCD**} \\ \cline{2-7}  & **TPR** & **FPR** & **Pr** & **TPR** & **FPR** & **Pr** \\ \hline CM & 96 & 5 & 95.5 & 95 & 6 & 94 \\ CW & 93 & 6 & 93.5 & 96 & 5 & 95.5 \\ EM & 95 & 7 & 94 & 94 & 6 & 94 \\ EW & 94 & 6 & 94 & 96 & 5 & 95.5 \\ Hybrid & 93 & 7 & 93 & 95 & 6 & 94.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average values of a first-class detection (ACD) and second-class detection (SCD) results (%).
## 3 ITSCF Model and Feature
### The Design of ITSCF Model
=i_{qd}+i_{qd-ITSC}, \tag{18}\]  
and  
\[A=-\sqrt{\frac{3}{2}}\mu r_{s}\text{cos}(\theta_{r})+2\omega_{r}\sqrt{\frac{3} {2}}\mu L_{2}\text{sin}(\theta_{r})+\sqrt{\frac{3}{2}}\mu\omega_{e}L_{q}\text {cos}(\theta_{r}), \tag{19}\]  
\[B=-\sqrt{\frac{3}{2}}\mu r_{s}\text{sin}(\theta_{r})+2\omega_{r}\sqrt{\frac{3} {2}}\mu L_{2}\text{cos}(\theta_{r})+\sqrt{\frac{3}{2}}\mu\omega_{e}L_{d}\text {sin}(\theta_{r}), \tag{20}\]  
where \(L_{q}=L_{0}+L_{2}\), \(L_{d}=L_{0}-L_{2}\). By combining Equation (14) with Equation (18), the following result is obtained:  
\[v_{qd-ITSC}= \begin{bmatrix}\text{v}_{q-ITSC}\\ \text{v}_{q-ITSC}\end{bmatrix} = \begin{bmatrix}C\\ D\end{bmatrix} , \tag{21}\]  
and  
\[ \begin{array}{l}C=\mu\omega_{e}L_{2}SI(r_{s}(\text{cos}(2\theta+\delta)+ \text{cos}(\delta))-3L_{2}(\omega_{e}\text{sin}(2\theta-\delta)+\omega_{e} \text{sin}(\delta))+L_{d}\big{(}\frac{3}{2}\text{cos}(2\theta-\delta)+\frac{ 3}{2}\text{cos}(\delta)-\frac{3}{2}\omega_{e}\text{sin}(2\theta-\delta)+\frac {3}{2}\omega_{e}\text{sin}(\delta))\big{)}\\ +\mu\omega_{e}S\lambda\Big{(}r_{s}\Big{(}-\sqrt{\frac{1}{6}}\text{cos}(2\theta )-\sqrt{\frac{1}{6}}\Big{)}+\sqrt{\frac{3}{2}}L_{2}\omega_{e}\text{sin}(2 \theta)+L_{q}\Big{(}-\sqrt{\frac{3}{2}}\text{cos}(2\theta)-\sqrt{\frac{3}{2} }+\sqrt{\frac{3}{8}}\omega_{e}\text{sin}(2\theta)\Big{)}\Big{)},\end{array}  \tag{22}\]  
\[D=\mu\omega_{e}L_{2}SI(r_{s}(\omega_{e}\text{sin}(2\theta+\delta)+\text{sin}( \delta))+L_{2}(-3\omega_{e}\text{sin}(2\theta-\delta)-3\omega_{e}\text{sin}( \delta))+L_{d}\big{(}\frac{3}{2}\text{sin}(2\theta-\delta)+\frac{3}{2}\text{ sin}(\delta)+\frac{3}{2}\omega_{e}\text{cos}(2\theta-\delta)-\frac{3}{2}\omega_{e} \text{cos}(\delta))\big{)}\]  
\[+\mu\omega_{e}S\lambda\Big{(}-\sqrt{\frac{1}{6}}r_{s}\text{sin}(2\theta)+\sqrt {\frac{3}{2}}L_{2}\omega_{e}\text{sin}(2\theta)+L_{d}\Big{(}-\sqrt{\frac{3}{ 6}}\text{sin}(2\theta)-\sqrt{\frac{3}{6}}\omega_{e}\text{cos}(2\theta)+\sqrt {\frac{3}{6}}\omega_{e}\Big{)}\Big{)}, \tag{23}\]  
By deriving the voltage characteristics from the above model, it can be observed from the previous equations that the voltage exhibits a second harmonic component characteristic, which can be used for ITSC fault detection. This model derives the fault characteristics of ITSC under ideal conditions, assuming a steady-state motor operating at a fixed speed with constant motor parameter and physical properties. Under non-stationary operating conditions, the error characteristic frequency will experience drift. In practice, motor operation generates losses, particularly in the short-circuited coil where the current \(i_{j}\) is substantial. This leads to increased resistance due to heating and potential core saturation, causing deviations from the theoretical results. These factors may hinder the effective observation of ITSCF. Figure 2: ITSC structure of IPMSM.
# A Study on Detection of Malicious Behavior Based on Host Process Data Using Machine Learning
## 2 Related Works
### Machine Learning Algorithms
#### 2.2.4 Autoencoder (AE)  
AE is one of the Unsupervised Learning algorithms used to extract the features of input data. Autoencoder is composed of an encoder that compresses the input data and a decoder that reconstructs the compressed data, as shown in Figure 2. The encoder maps the input data to the latent space, and the decoder maps this low-dimensional representation back to the original dimension to reconstruct the input data. AE is trained by minimizing the reconstruction error between the input data and the reconstructed data. In this case, AE is used to extract the features of the input data using the learned encoder. AE maps the input data \(X\) to a low-dimensional vector \(h\) as shown in Equation (6).  
\[h=f(X)=\sigma(WX+b) \tag{6}\]  
In this equation, \(W\) is the weight matrix, \(b\) is the bias vector, and \(\sigma\) is the activation function. The sigmoid function or hyperbolic tangent function are commonly used as activation functions. The output of the encoder, \(h\), is a vector that maps the input data \(X\) to  
Figure 2: Autoencoder structure.  
a low-dimensional space. The decoder maps the low-dimensional vector \(h\) back to the input data \(X\) as shown in Equation (7).  
\[r=g(h)=\sigma\big{(}W^{\prime}h+b^{\prime}\big{)} \tag{7}\]  
In this equation, \(W^{\prime}\) is the weight matrix of the decoder and \(b^{\prime}\) is the bias vector of the decoder. The output of the decoder, \(r\), is the reconstructed data from the input data \(X\). AE is trained to minimize the reconstruction error between the input data and the reconstructed data. The reconstruction error is defined as Equation (8).  
\[L(X,r)=||X-r||_{2}^{2} \tag{8}\]  
In this equation, \(||\cdot||\)\({}_{2}\) represents the \(L_{2}\) norm. Therefore, the objective function of AE is defined as Equation (9).  
\[\mathcal{L}(X,r)=L(X,r)=||X-r||_{2}^{2} \tag{9}\]  
The AE can compress the dimension of input data into a lower dimension to extract features, which can be used for various problems such as classification, clustering, and dimensionality reduction.
# Will Zero Vulnerability Computing (ZVC) Ever Be Possible? Testing the Hypothesis
## 6 Experiment: Supporting the ZVC Hypotheses
### Experiment Result  
All five KPIs returned a \"Yes\" outcome in all five repeats of the Case1 and Case2 implementations of the experiments. In the Case1 implementation, the mock malware script infected the control device on the target node and executed the script on the control device. This was evidenced by the transmission of a copy of the \".config\" folder from the target node to the hacker node. This can be visualized in Figure 14, where the UI of both the target node and hacker node shows the presence of the.config folder on both selected nodes (marked in blue). On the other hand, in the Case2 implementation, the ZVC-powered hardware device remained uninfected by the malware script as the SOS software component of ZVC obliterates the attack surface completely by banning all third-party permissions from installing or running any program except the native wallet application. This was observed in the absence of a.config folder (highlighted yellow) on the hacker node UI.  
Even after the continuous attempts to infect the ZVC hardware wallet device, the malware failed to find an attack surface to install and run malicious scripts on the ZVC hardware wallet device. Moreover, the ZVC device remained online only during the wallet transaction activity and switched offline despite being mounted on the USB port 24/7. The ability of the malicious program to copy files from the USB location of the target node to the hacker node when the control device was mounted can be attributed to the absence of the ZVC ecosystem, whereas the inability of the same mock malware program to perform any action on the ZVC device connected to the USB port of the same target node can be  
Figure 14: Screenshots of the command line interface showing the status of the.config file in the control and test devices.  
attributed to the unhackable environment created by the obliteration of the attack surface resulting from the denial of third-party permissions by the ZVC approach.  
Thus, our experiments answered both hypothetical questions that we set out to support and test affirmatively.  
**Hypothesis 1:**_Can Supra OS software (SOS) completely obliterate the primary attack surface by denying third-party permissions to all non-native applications? **Yes**_  
**Hypothesis 2:**_Can a hardware module isolate critical data that require sporadic access to offer in-computer offline storage (ICOS) within the connected device itself without compromising its functionality? **Yes**_
# Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network
## 4 Results  
### Datasets  
The effectiveness of the proposed visual descriptors for crowd anomaly detection in challenging crowded environments is evaluated using two state-of-the-art crowd datasets. Two widely recognised public datasets, the University of Minnesota (UMN) dataset [16] and the violence in crowds dataset [42], are employed for this evaluation. The UMN dataset is highly regarded in the field. This dataset offers a comprehensive collection of videos that capture various crowd behaviours, including both normal and abnormal instances. It consists of eleven videos recorded in diverse indoor and outdoor settings. The videos are categorised into three scenes: Scene 1 (videos 1 to 2), Scene 2 (videos 3 to 8), and Scene 3 (videos 9 to 11). These videos depict scenarios such as crowds running in a single direction or dispersing from a centralised location, providing a wide range of typical and out-of-the-ordinary segments. Researchers can extract valuable insights and evaluate the performance of crowd anomaly detection algorithms using this dataset.  
The violence in crowds dataset is sourced from YouTube. This dataset offers a diverse collection of challenging real-world viewing conditions. It includes footage compiled from various settings and surveillance operations, specifically focusing on crime and violence. The dataset has been meticulously curated, following established standards for violence classification. It consists of videos divided into distinct groups, with an equal number of violent and peaceful videos, ensuring a balanced representation of different scenarios. Researchers can thoroughly evaluate the proposed visual descriptors for crowd anomaly detection by utilising these state-of-the-art datasets.  
The UMN dataset comprehensively assesses various crowd behaviours, while the violence in crowds dataset offers realistic and challenging real-world scenarios related explicitly to crime and violence. Leveraging these datasets enables the analysis and comparison of different approaches, facilitating advancements in the field of crowd anomaly detection.
# A Low-Cost Soft Robotic Hand Exoskeleton for Use in Therapy of Limited Hand-Motor Function
## 2 Materials and Methods  
The main contribution of this work is to present a low-cost soft robotic hand exoskeleton platform built entirely of widely available components and 3D-printable parts. Our hardware development objective was to create a device to integrate the desirable qualities seen in previously developed robotic hand exoskeletons while integrating the key aspects into our design detailed in Table 1. While the exoskeleton and control system could be improved by adding additional modalities, such as more complex controllers and force and electromyography sensors, our design purposely excludes complex and expensive components for ease of use. Our design represents a platform which could be augmented by end-users with additional modalities to allow for enhanced and more complex control using sensor fusion. Development of a robotic exoskeleton designed to be assembled by the end-user involves simplifying the mechanical design and construction process, while minimizing costs. The construction process assumes access to a basic electronics kit and a 3D printer, which was chosen to be the main method of manufacture as this technology is rapidly becoming ubiquitous in schools, libraries, fab-labs, hospitals and homes. The tendons and the soft glove are off-the-shelf components, while everything else involved in the mechanical construction of the soft robotic hand exoskeleton is 3D-printed. This includes: the customizable rings which hold the tendons, the ergonomic wrist support structure with palmar tendon guides, the spur gear, the spool with the spur gear, the housing and enclosure for the controller and motors. In this iteration, a U.S. $200 budget was imposed to make the materials as widely affordable as possible. We also required the glove to be lightweight (\(<\)1 kg) and mobile enough to be used by the patient as they moved about completing daily tasks. This requirement dictated that the device be battery powered and communicate to a network through a wireless connection. The desired run time on battery power was initially established to be 1 h, approximately the same length as the average physical therapy session.