esa-sceva/satcom-chunk-collection huggingface dataset:
# Watermarking Protocols: A Short Guide for Beginners
## 3 Security Primitives
### Joint Watermarking
However, its use in watermarking protocols has to be accompanied by specific solutions to solve the following problem: the content-dependent keys and personalized decryption LUTs are both created and distributed by content providers, which end up fully controlling the protocols. In fact, this prevents protocols from matching some of the requirements reported in the previous sections. In this regard, the solutions proposed in the literature, such as, for example, those described in [55; 56], implement specific variants in order to enable the generation of the personalized decryption LUTs in such a way that content providers cannot know or access them. Such a result is achieved by applying the following procedure. Firstly, proper watermarking LUTs \(\mathbf{W}_{k}\) are generated by buyers or TTPs in the form \(\mathbf{W}_{k}=\mathbb{G}\mathbf{m}_{k}\), where \(\mathbf{m}_{k}\) is obtained by coding the \(L\)-bit fingerprint identifying the \(k\)-th buyer using a binary antipodal modulation, while \(\mathbb{G}\) represents a generator matrix of a linear block code over the set of real numbers [57; 58]. The personalized watermarking LUTs \(\mathbf{W}_{k}\) are then encrypted using an additively homomorphic cryptosystem, thus obtaining  
\[[|\mathbf{W}_{k}(t)|]=[|\mathbb{G}\mathbf{m}_{k}(t)|]=\prod_{l=0}^{L-1}[| \mathbf{m}_{k,l}|]^{\mathbb{G}(l,l)}\]  
where homomorphic encryption is denoted as \([1\;\;|\;]\). The encrypted LUTs are sent to the content provider, which can generate the personalized decryption LUTs \(\mathbf{D}_{k}\) by exploiting the same homomorphic cryptosystem since all the operations to be performed are linear. Therefore, each entry of the decryption LUT \(\mathbf{D}_{k}\) can be calculated directly into the encrypted domain as follows:  
\[[|\mathbf{D}_{k}(t)|]=[|\mathbf{E}_{k}(t)|]^{-1}\prod_{l=0}^{L-1}[|\mathbf{m} _{k,l}|]^{\mathbb{G}(l,l)}\]  
Once the personalized encrypted LUT \([|\mathbf{D}_{k}|]\) is received, the \(k\)-th buyer can perform the decryption, thus obtaining  
\[\mathbf{D}_{k}=-\mathbf{E}_{k}+\mathbb{G}\mathbf{m}_{k}\]  
The \(k\)-th buyer can therefore use \(\mathbf{D}_{k}\) to operate the joint watermarking on the previously received encrypted version of \(X\) and obtain the personalized, final watermarked copy of \(X\), which is unknown to the content provider.
# Smart Routing for Sustainable Supply Chain Networks: An AI and Knowledge Graph Driven Approach
## 4 Use Case
### Experimental Setup  
The test environment replicates the supply chain network of a multinational automotive components manufacturer specializing in small-sized automotive plastic parts. The starting configuration comprises the following:  
* Four production plants located in Mexico, Italy, Slovakia, and China.
* A total of 77 supply chain entities (suppliers and customers) spread across North America, Europe, Africa, and Asia, as visualized in Figure 6.
* A total of 200 needed routes representing logistical connections required for optimal supply chain operation between plants, suppliers, and customers.
* Two waypoints to give the LLM an idea of how the waypoints should look.
* Two routes to give the LLM an idea of how the routes should look.  
The dataset was prepared utilizing standardized Excel templates and was subsequently imported through the application's GET module interface. The complete data enrichment pipeline, comprising three sequential phases (ANALYZE \(\rightarrow\) ENRICH \(\rightarrow\) STORE), was initiated via the dashboard interface. The import procedure employed a batch processing methodology to ensure systematic data integration. Each processing cycle incorporated 25 newly generated route pairs, with the complete dataset processed across eight discrete execution runs (200 total routes). This batch-based approach was implemented to the facilitate comprehensive monitoring and validation of the enrichment process at manageable intervals. As specified in Section 3.2, the automated evaluation framework was exclusively implemented for the road transportation mode. The primary objective of this implementation focuses on validating the core conceptual framework rather than assessing the precision of individual route metrics generated by the RouteEvaluator component. This approach allows for proof-of-concept validation while maintaining analytical focus on the fundamental application architecture and data processing capabilities.  
Figure 6: The geographic distribution of the 77 predefined supply chain entities (plants: orange dots, suppliers: green dots, and customers: blue dots) used in the test scenario.
# Audlet Filter Banks: A Versatile Analysis/Synthesis Framework Using Auditory Frequency Scales
## 3 The Proposed Approach
### Analysis Filter Bank
#### 3.1.2 Construction of \(H_{0}\) and \(H_{k}\)
There are some corner cases in which one or both of the compensation filters \(h_{0},h_{K}\) are unnecessary, namely if \(f_{1}\) is very close to \(0\) (resp. \(f_{K-1}\) to \(f_{N}\)). In that case the maximum \(M\) should be computed over the interval \([f_{1}/f_{s},1/2]\) (resp. \([0,f_{K-1}/f_{s}]\)) and we set \(H_{0}=0\) (\(H_{K}=0\)). A rule of thumb is if \(\min_{\xi\in[0,f_{1}/f_{s}]}\mathcal{H}_{0}^{(r)}(\xi)\geq(1-\epsilon)\min_{ \xi\in[f_{1}/f_{s},f_{K-1}/f_{s}]}\mathcal{H}_{0}^{(r)}(\xi)\), for some \(\epsilon\ll 1\), then the low-pass filter \(H_{0}\) is not required. An analogous argument is valid for \(H_{K}\)._  
The total frequency response of the analysis FB (i.e., including the \(K+1\) filters) is then  
\[\mathcal{H}_{0}(\xi):=\widetilde{\mathcal{H}_{0}}(\xi)+\widetilde{\mathcal{H}_ {0}}(-\xi),\quad\text{with} \tag{11}\]  
\[\widetilde{\mathcal{H}_{0}}(\xi):=\sum_{k=0}^{K}d_{k}^{-1}|H_{k}(e^{2\pi i\xi}) |^{2},\text{ for all }\xi\in(-1/2,1/2].\]  
and the redundancy of the FB is  
\[R=d_{0}^{-1}+2\sum_{k=1}^{K-1}d_{k}^{-1}+d_{K}^{-1}. \tag{12}\]  
The factor of 2 stems from the fact that coefficients in the \(1\)-st to \((K-1)\)-th sub-bands may be complex valued.
# Effect of Thermal Boundary Condition on Tilting Pad Journal Bearing Behavior
## 2 Numerical Methods
### Validation of Numerical Model  
In order to verify the performance of the bearing model presented in this research, the bearing dynamic properties experimentally measured by [PERSON] [36] were compared with the results calculated by the numerical model presented in this study. Table 1 shows the bearing design parameters taken from [PERSON]'s paper [36]. In addition to the factors shown in this table, actual bearings have various factors such as pad thickness, thermal boundary conditions and pivot stiffness, and the resulting change in performance cannot be ignored. The experimental approaches that have been carried out before assumed the negligible influence of these factors. That is why this study attempts to analyze the performance change according to the thermal boundary conditions around the bearing system. In this study, it is assumed that 43 \({}^{\circ}\)C of lubricant is flowing around the bearing pad and 50 \({}^{\circ}\)C of air is present around the spinning shaft. The thickness of the pad in the radial direction was assumed to be 10 mm, and it was modeled as a cylinder-cylinder pivot.  
Figure 6 compares the dynamic coefficients measured experimentally by [PERSON] [36] and the results calculated by the numerical model provided in this paper. The results of this analysis predicted a larger value for both the \(x\) and \(y\)-direction bearing stiffness, and the results of this study showed lower values for the viscosity coefficient. This error is considered to be caused by the addition of the condition assumed in this study in addition to the bearing design factor given in [PERSON]'s paper [36].
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 2 Datasets
### The GF-6 Images Dataset
#### 2.1.3 Dataset Creation  
In the images captured in winter or spring, developed land, water bodies, agricultural buildings, roads, bare fields, and farmland can be directly distinguished by visual interpretation within ENVI. To accurately distinguish winter wheat and woodland, 353 sample points were obtained through ground surveys (118 woodland and 235 winter wheat, Figure 2). Woodland areas had a rough texture, a large color change, and an irregular shape, while winter wheat areas were finer, smoother, and generally regular in shape. Defining these features helped to improve the accuracy of visual interpretation.  
The ENVI software was used to fuse the images into a complete mosaic, covering the whole study area. This image was then segmented into patches of \(480\times 360\) pixels, from which 587 images were  
\begin{table}
\begin{tabular}{c c c c} \hline
**Band** & **Range (\(\mu\)m)** & **Spatial Resolution (m)** & **Width (km)** \\ \hline Panchromatic & 0.45–0.90 & 2 & \(>\)90 \\ B1 & 0.45–0.52 & 8 & \(>\)90 \\ B2 & 0.52–0.60 & 8 & \(>\)90 \\ B3 & 0.43–0.69 & 8 & \(>\)90 \\ B4 & 0.76–0.90 & 8 & \(>\)90 \\ \hline \end{tabular}
\end{table}
Table 1: Specifications for Gaofen 6 satellite imagery used in this study.  
Figure 2: Geographic location of ground survey sample points used to distinguish winter wheat (green) and woodland (yellow).  
selected for manual classification based on the eight land-use types defined above. Numerical codes were then assigned to these land-use types; an example is provided in Figure 3. All labeled image patches and their labeled files formed data sets for training and testing the WFCNN model.
# REERP: A Region-Based Energy-Efficient Routing Protocol for IoT Wireless Sensor Networks
## 3 Proposed System Model  
The SN which comprise of the network topology are designated as \(n_{1},n_{2},\ldots,n_{SN}\). Nodes in a WSN are randomly dispersed within a region of S. In this paper, S and SN are set to 120. Each node seems to have a sensor radius of \(r_{0}\) meters and a finite radio range. There are three different kinds of nodes included in this framework: normal nodes, CH nodes, and sink nodes.  
A clustering strategy is used in communication networks, where a small number of nodes are chosen as CH to collect data from other nodes and transfer it to the sink node. In contrast to the SN, which has minimum energy, the sink node in this paper is believed to be in the region's center and endowed with a limitless energy source. All nodes are believed to have fixed positions and the same initial energy. Furthermore, all nodes have a GPS and their positions have always been known [31; 32; 33; 34].
## 2 Materials and Methods
### Left/Right Judgment Test (LRJT)  
A series of 20 and 50 images representing left hands or right hands and a series of 20 and 50 images representing left feet and right feet, with different rotations and seen from palmar or dorsal views, were shown on the screen of an electronic device using software with an installed application. The order of appearance and the sequential images displayed were random. The images were shown for a maximum duration of 10 s or until the answer was made. Patients were instructed to indicate hand or foot side as quickly and accurately as possible by pressing the appropriate key on the screen of the electronic device: \"Left\" for the left and \"Right\" for the right. Participants saw an example image to familiarize themselves with the test and were instructed not to move their limbs during the test. The reaction time (s) and the correct hits (%) were recorded as result measurements. Application software collected the reaction time and the hit percentage data. The reliability of the Recognise online application has previously been established in populations with and without pain [42]. The intraclass correlation coefficient (ICC) response time for \"feet\" was ICC = 0.63-0.75, and for \"trunk\" ICC = 0.51-0.91. The accuracy of the laterality recognition answers was ICC = 0.61-0.77 for \"feet\" and ICC = 0.69-0.71 for \"trunk\" [49]. The internal and external validity were established before the application was online. Trials were conducted using a cluster of images tested with the letters \"L\" for \"left\" and \"R\" for \"right\". The application was tested three times, and the internal validity was 100% [45].  
Figure 1: Study design. The following flowchart shows how the participants are distributed in the FMS study group and in the control group of healthy individuals. The discarded cases also shown, as they either did not meet the selection criteria or abandoned the investigation.  
\"Left\" and \"right\" were included as factors in the analysis because the study conducted by [PERSON] et al. [58] performed a similar separation in their research on hands.
###### Abstract  
The strong correlation between evoked potentials (EPs) and American Spinal Injury Association (ASIA) scores in individuals with spinal cord injury (SCI) suggests that EPs may serve as reliable predictive markers for rehabilitation progress. Numerous studies have confirmed a relationship between variations in somatosensory evoked potentials (SSEPs) and ASIA scores, especially in the early stages of SCI. Machine learning's (ML's) increasing importance in medicine is driven by the growing availability of health data and improved algorithms. It enables the creation of predictive models for disease diagnosis, progression prediction, personalized treatment, and improved healthcare efficiency. Data-driven approaches can significantly improve patient care, reduce costs, and facilitate personalized medicine. The meticulous analysis of medical data is crucial for timely disease identification, leading to effective symptom management and appropriate treatment. This study applies artificial intelligence to identify predictors of SCI progression, as measured by the disability index, ASIA impairment scale (AIS), and final motor recovery. We aim to clarify the prognostic role of electrophysiological testing (SSEPs, MEPs, and nerve conduction studies (NCSs)) in SCI. We analyzed data from a medical database of 123 records. We developed an ML-based intelligent system, utilizing ensemble algorithms combining decision trees and neural network approaches, to predict SCI recovery. Our evaluation showed SEP accuracies of 90% for motor recovery prediction and 80% for AIS scale determination, comparable to full electrophysiology evaluation accuracies of 93% and 89%, respectively, and generally superior results compared to MEP and NCS results. EPs emerged as the best predictors, comparable to a comprehensive electrophysiology assessment, significantly improving accuracy compared to clinical findings alone. An electrophysiological assessment, when available, increased overall accuracy for final motor recovery prediction to 93% (from a maximum of 75%) and, for ASIA score determination, to 89% (from a maximum of 66%). Further validation is needed with a larger dataset. Future research should validate that sensory electrophysiology assessment is a less expensive, portable, and simpler alternative to other prognostic tests and more effective than clinical assessments, like the AIS, biomarker for SCI, and personalized rehabilitation planning.  
machine learning; spinal cord injury; ASIA score; recovery; prediction +
Footnote †: journal: Applied Sciences
# Small-Scale Foreign Object Debris Detection Using Deep Learning and Dual Light Modes
## 3 Experiment
### Experimental Configuration  
All experiments were conducted based on the Pytorch1.12.1, with hardware consisting of an NVIDIA RTX-3060 Ti GPU and a 4.9 GHz (Inter Core i7-12700F) CPU. The program was run on the Windows 11 operating system, with Python version 3.8.16, and accelerated model training using CUDA11.1 and cuDNN8.0.4 during the experiment.  
Throughout the experiments, we unified the input image size to 640 \(\times\) 640 and added batch size set to 2. In the training phase, the number of iterations is set to 200 epochs, and the initial learning rate is set to 0.01. In the first three epochs, the learning rate of each iteration is updated using warm-up learning rate preheating to improve the convergence speed of model loss. After the three epochs, the learning rate is attenuated using the cosine annealing method to ensure the stability of model loss convergence, and the weight attenuation rate is set to 0.0005. Finally, after a comprehensive comparison of the accuracy and inference speed of the training model, the optimal model is selected.
# An Improved Product Code-Based Data Hiding Scheme
## 6 Experimental Results  
In the experiment, each embedding scheme was performed for a total of 1 million times. At each time, 49 (or 56) LSB slicing bits, 49 SLSB slicing bits, and 33 (or 40) secret message bits were pseudo-randomly generated.  
Let \(R\) be the embedding rate (\(R=33/49=0.673\)) defined by the number of embedding bits per pixel (bpp) and \(M_{a}\) be the expected number of changed bits per embedding. Hence, the average modification for each bit denoted as \(D\) equals \(M_{a}/49\). Then, the embedding efficiency \(\alpha\) defined as the expected hidden bits per embedding modification equals \(R/\mathrm{D}\), and the PSNR (Peak Signal to Noise Ratio) equals \(10\times\log_{10}(255^{2}/D)\).
# Comparative Vulnerability Analysis of Thai and Non-Thai Mobile Banking Applications
## 3 Materials and Methods
### Data Analysis and Vulnerability Classification  
Following the vulnerability identification process mentioned above, a systematic approach to data analysis was performed. This analysis involved two key steps: vulnerability classification and data comparison.  
First, we aimed to classify the found vulnerabilities in mobile banking applications using the OWASP Mobile Top 10 framework. This industry-standard framework categorises the ten most commonly found mobile application security risks. Utilising the OWASP Mobile Top 10, we believe, provided a standardised reference point for classifying the identified vulnerabilities. Each vulnerability was carefully examined and mapped to the most relevant category within the OWASP framework. The vulnerability mapping process involved the following steps. First, we independently reviewed the vulnerability reports from the assessment tools. Second, group discussions were held to compare our individual interpretations and reach a consensus on the appropriate OWASP Mobile Top 10 category for each vulnerability. This categorisation process ensured consistency and facilitated meaningful comparisons between the vulnerabilities discovered in the Thai and non-Thai mobile banking applications. By applying this established framework, the research aimed to contribute to a broader understanding of the frequently found security weaknesses within mobile banking applications and to identify any potential trends in our sample.  
Once the vulnerabilities were classified using the OWASP Mobile Top 10, statistical methods were applied to analyse the collected data. This analysis focused on comparingthe occurrence and types of vulnerabilities between the Thai and non-Thai mobile banking applications. Statistical metrics such as frequency counts were used to determine the distribution of vulnerabilities across the OWASP Mobile Top 10 categories within the chosen applications. This comparative analysis aimed to identify potential regional variations in the security landscape of mobile banking applications. By analysing these trends, the research could explore potential explanations for observed differences and contribute to a more comprehensive understanding of the mobile banking security landscape across different regions.  
Figure 1 summarises the research methodology. In other words, the process began with mobile banking application selection. Next, SAST analyses were performed using MobSF, QARK, and AndroBugs, which output initial vulnerability reports. All identified vulnerabilities were then classified by the OWASP Mobile Top 10. Finally, data analysis and comparison between Thai and non-Thai mobile banking applications were conducted.
## 2 Methods
### Principal Component Analysis (PCA)
#### 2.1.1 The Basic Idea and Theoretical Foundation of Principal Component Analysis  
Principal component analysis (PCA) is a useful technique for reducing the number of dimensions in a dataset prior to modeling. Mathematically, PCA combines original multiple indices into a linear combination of variables. \(F_{i}=a_{1i}X_{1}+a_{2i}X_{2}+\cdots+a_{Pi}X_{P}\). Without any restrictions, there could be many linear combinations. Hence, there are specific requirements placed on this linear combination to ensure that it is useful [14; 15; 16].  
(1) \(F_{1},F_{2},\cdots,F_{p}\) reflects the information for the original indexes. This is determined by the values of variance sorted in descending order. The greater the variance explained by a principal component, the more information it contains from the original indices. \(F_{1}\) is called the first principal component, \(F_{2}\) is called the second principal component, and \(F_{i}\) is called the ith principal component.  
(2) The second requirement for PCA linear combination is that each principal component should exclude the information contained in the preceding principal components. This means that the principal components should be independent of each other, ensuring that subsequent components do not contain information from previous ones.
## Low-Cost Spin Pit Design
### Test Specim and Operating Costs  
Initially, a complex mill-turned part (Figure 10 (left)) was used as test flywheel. The design allowed precise adjustment of burst speed due to a weakening groove and pre-defined fragment geometry by milling a radial notch. In order to reduce operating costs and allow for a high number of tests, a low-cost alternative was found by using off-the-shelf hand wheels made from grey cast iron (Figure 10 (right)), which served the purpose surprisingly well. When operating a rotor on a flexible, cantilevered shaft, close attention must be paid to the ratio of the moments of inertia (\(J_{1}\)--around the main axes of rotation and \(J_{2}\)--perpendicular to it). According to the authors' experience a ratio of \(J_{1}\)/\(J_{2}\) between 0.8 and 1.2 must be avoided, or else instabilities might occur. The properties of the test flywheels are compared in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sub-System** & **Machined Parts** & **Cost** \\ \hline \multirow{2}{*}{Test flywheel + intermediate shaft} & Test flywheel, intermediate shaft, & \multirow{2}{*}{610 \(\epsilon\)} \\  & tailored collet, bearing periphery & \\ \hline \multirow{2}{*}{Vacuum chamber} & Housing, intermediate flange, & \multirow{2}{*}{2860 \(\epsilon\)} \\  & motor mount interface, base plate & \\ \hline \multirow{2}{*}{Drive unit} & Magnetic coupling membrane, & \multirow{2}{*}{420 \(\epsilon\)} \\  & motor mount & \\ \hline \multirow{2}{*}{Burst chamber} & Safety cover plate, containment & \multirow{2}{*}{650 \(\epsilon\)} \\  & support, and fitted key & \\ \hline \multicolumn{3}{c}{Total cost of custom components} & \multirow{2}{*}{4540 \(\epsilon\)} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Custom components used in the test rig.  
Figure 10: Burst rotors, (**left**) mill-turned part; (**right**) low-cost alternative (cast iron hand wheel).  
Determination of inherent kinetic energy during burst is possible in both cases (post mortem analysis) as described in more detail in Section 5.  
Refplacement Parts  
After every burst the test flywheel, the mechanical coupling, and the flexible quill shaft need to be replaced (in addition to the burst containment under investigation). To reduce running costs, the KTR131 coupling (which connects the flexible quill shaft to the rest flywheel and is shown in shown in Figure 11a was replaced by a new custom clamping design shown in Figure 11b. The robust design ensures longevity and reduces operating costs to 19 \(\upxi\) per burst, corresponding to a reduction of 57%.