esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
One of the challenges in the Internet of Things systems is the security of the critical data, for example, data used for intrusion detection. The paper research construction of an intrusion detection system that ensures the confidentiality of critical data at a given level of intrusion detection accuracy. For this goal, federated learning is used to train an intrusion detection model. Federated learning is a computational model for distributed machine learning that allows different collaborating entities to train one global model without sharing data. This paper considers the case when entities have data that are different in attributes. Authors believe that it is a common situation for the critical systems constructed using Internet of Things (IoT) technology, when industrial objects are monitored by different sets of sensors. To evaluate the applicability of the federated learning for this case, the authors developed an approach and an architecture of the intrusion detection system for vertically partitioned data that consider the principles of federated learning and conducted the series of experiments. To model vertically partitioned data, the authors used the Secure Water Treatment (SWaT) data set that describes the functioning of the water treatment facility. The conducted experiments demonstrate that the accuracy of the intrusion detection model trained using federated learning is compared with the accuracy of the intrusion detection model trained using the centralized machine learning model. However, the computational efficiency of the learning and inference process is currently extremely low. It is explained by the application of homomorphic encryption for input data protection from different data owners or data sources. This defines the necessity to elaborate techniques for generating attributes that could model horizontally partitioned data even for the cases when the collaborating entities share datasets that differ in their attributes.  
intrusion detection; critical infrastructures; confidential data; federated learning; vertically partitioned data; gradient boosting decision trees; homomorphic encryption +
Footnote †: journal:  
00 2022  
Federated Learning for Intrusion Detection in the Critical Infrastructures: Vertically Partitioned Data Use Case
# Achievable Rate of NOMA-Based Cooperative Spectrum-Sharing CRN over Nakagami-\(m\) Channels
## 4 Achievable Rate Analysis  
For achievable sum rate analyses, we assume that all wireless communication links (i.e., \(h_{p}\), \(h_{s}\), \(h_{s,k}\), \(g_{p}\), and \(g_{s,k}\)) experience Nakagami-\(m\) fading. Here, we derive the average sum rate of the NOMA-based spectrum-sharing network discussed in the previous section. First, we provide a preliminary analysis of the Nakagami-\(m\)-distributed channels in a point-to-point wireless communication system. Then, we present our ergodic achievable rate analyses of the network.
# Dynamic Corrosion Test Using LiNO3 Containing Molten Salt for CSP Applications
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Corrosion Rate (mg/cm\({}^{2}\) year)** & **Corrosion Rate (mm/year)** & **Recommendation** \\ \hline \(>\)1000 & \(>\)2 & Completely destroyed within days \\ \hline
100 to 999 & 0.2–1.99 & Not recommended for service greater than a month \\ \hline
50 to 99 & 0.1–0.19 & Not recommended for service greater than one year \\ \hline
10 to 49 & 0.02–0.09 & Caution recommended, based on the specific application \\ \hline
0.3 to 9.9 & NA & Recommended for long term service \\ \hline \(<\)0.2 & NA & Recommended for long term service; no corrosion, other than as a result of surface cleaning, was evidenced \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Corrosion rate and their cooperativity in industries. Adapted from [17]. Figure 4: Backscattered SEM picture of the corrosion layer obtained in A516 at 1000 h (**left**) and concentration profile of Fe and O (**right**). Figure 5: XRD analysis for A516 after 1000 h of immersion in LiNO\({}_{3}\) ternary molten salt at 390 \({}^{\circ}\)C.  
Hematite (Fe\({}_{2}\)O\({}_{3}\)) was obtained as the main corrosive impurity detected in the steel surface, generating a non-protective scale in the steel surface.
# CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence
## 2 Related Work
### Correspondence with Functional Maps  
Different from point-to-point correspondence, the concept of the functional map was introduced in [32], which represented the mapping between shapes as a small matrix and transmitted information by encoding the relationship between the basic functions of shapes. Inspired by that, some scholars have proposed several learning methods for optimizing functional maps [10; 11; 33]. With the development of deep learning, a neural network was used in the functional map framework to generate more accurate correspondence. [PERSON] et al. [12] proposed a deep neural network architecture named FMNet by providing a dense corresponding representation of linear operators through paradigm transformation and a structured prediction model. SURFMNet introduced in [13] was to use pure geometric scalars to directly establish correspondence in the 3D shape set without any prior information. In [34], Deep Geometric Functional Maps was a feature extraction network based on a functional map representation that learned directly from the shape geometry with a new regularized map extraction layer and loss to enforce the structural properties. [PERSON] et al. [35] generalized the functional map framework to the conformal maps between tangent vector fields, which reflected the complex structure of the surface to maintain orientation-aware results but naturally restricted to differentials of conformal mappings. The key difference between the above approach and our proposed approach is that we embed component constraints in the functional maps framework, focusing on the local area feature information of the mesh.
## 2 Materials and Methods
### Constructal Design Method and Exhaustive Search Technique
This resulted in eight different values of \(h_{s}/t_{s}\) for each plate configuration, leading to a total of 128 distinct stiffened plates geometries being investigated. As mentioned earlier, due the volume conservation, as \(N_{ls}\) and/or \(N_{ls}\) increase, the maximum \(h_{s}\) value that the stiffeners can achieve tends to decrease to respect this premise. Moreover, as \(t_{s}\) increases,  
Figure 4: Steps of constructal design method (adapted from [PERSON] et al. [13]). the \(h_{s}\) value also tends to decrease due to the same principle. This behavior is common to all plate configurations and is illustrated in Figure 5, considering arbitrary \(N_{ls}\), \(N_{ls}\), and \(t_{s}\) values. Furthermore, the Supplementary Materials provides all the values of \(N_{ls}\), \(N_{ls}\), \(h_{s}\), and \(t_{s}\), which were determined through the application of constructal design and a graphical explanation of the influence of \(N_{ls}\), \(N_{ls}\), and \(t_{s}\) over \(h_{s}\). After that, the exhaustive search technique was applied, according to the subsequent steps shown in Figure 6.  
As earlier explained, the number of \(N_{ls}\) and \(N_{ls}\) ranging from 2 to 5, is allowed to generate sixteen different plate arrangements P(\(N_{ls};N_{ls}\)): P(2;2), P(2;3), P(2;4), P(2;5), P(3;2), P(3;3), P(3;4), P(3;5), P(4;2), P(4;3), P(4;4), P(4;5), P(5;2), P(5;3), P(5;4), and P(5;5). By varying \(h_{s}/t_{s}\) for each arrangement, the optimal stiffened plate geometry was determined  
Figure 6: Steps of exhaustive search technique (adapted from [PERSON] et al. [13]). to enhance \(\sigma_{uN}\). All these definitions formed the basis and sequence for the search space used to conduct the exhaustive search technique, as illustrated in Figure 7.  
Through the variation of \(N_{ls}\), \(N_{ts}\), and \(h_{s}/t_{s}\), and calculation of \(\sigma_{uN}\), the \(h_{s}/t_{s}\) values were identified for each plate configuration that maximized the \(\sigma_{uN}\). These values were defined as once optimized height-to-thickness \(\left(h_{s}/t_{s}\right)_{o}\) and once maximized ultimate buckling strength \(\left(\sigma_{uN}\right)_{m}\). From these optimized and maximized values, the higher values of \(\left(\sigma_{uN}\right)_{m}\) and consequently their \(\left(h_{s}/t_{s}\right)_{o}\) and \(N_{ts}\) were identified and defined as twice maximized ultimate buckling strength \(\left(\sigma_{uN}\right)_{mm}\), twice optimized height-to-thickness ratio \(\left(h_{s}/t_{s}\right)_{o\omega}\), and once optimized number of transverse stiffeners \(\left(N_{ts}\right)_{o}\). Finally, from these last optimized and maximized values, the plate configuration with the highest ultimate strength was identified, being defined as the three times maximized ultimate strength \(\left(\sigma_{uN}\right)_{mm}\), three times optimized \(\left(h_{s}/t_{s}\right)_{o\omega}\), twice optimized number of transversal stiffeners \(\left(N_{ts}\right)_{o\omega}\), and once optimized number of longitudinal stiffeners \(\left(N_{ls}\right)_{o}\).
## 4 Materials and Methods  
International benchmark problems with well-known theoretical lower limits (calculated using graph theory) were selected to assess the proposed modified F/DR algorithms. A summary of these scenarios can be found in Table 1. The problems from P1 to P19 are described in [2; 43; 56; 57] and correspond to the Philadelphia problem, which formulates a cellular network comprising 21 cells. The final problem (i.e., P20) corresponds to the requirements of an operational cellular network consisting of 25 cells in the city of Helsinki, Republic of Finland, as described in [58; 59].  
All benchmarks were implemented and solved in MATLAB(r) V.9.11. The conventional F/DR algorithm and the two modified algorithms (as described in the previous section) were employed to solve these scenarios. Additionally, to assess the impact of both the prioritization algorithm and channel assignment strategy in the selected scenarios, all benchmarks were solved by generating a list, \(L\), with a random permutation. That algorithm will be referred to as F/PER in this paper. This ordering strategy is equivalent to not having a specific prioritization algorithm before applying the allocation algorithm F.  
Each heuristic strategy (i.e., F/PER, F/DR-k, and F/DR-D-k) was simulated 100 times, whereas the standard F/DR algorithm was simulated only once due to its deterministic nature. The percentages of better, worse, and equal optimizations were used to assess the merit of the proposed modified algorithms. These percentages were calculated by dividing the number of times the random scenario found a local optimum that was lower, higher, or equal (to the one found by the deterministic conventional F/DR algorithm) by the total number of calculated solutions (i.e., 100).  
Furthermore, the performance assessment of the algorithms was conducted through a rigorous comparison between the local optimum value obtained by the conventional algorithm and the best local optimum value achieved by each novel strategy. To quantify this evaluation, we introduced two essential metrics. Firstly, we defined the Probability of One Better Result (PoOB), representing the percentage of instances in which at least one local optimum value, as computed by the modified algorithm, exhibited superior performance when compared to the conventional algorithm. Secondly, we introduced the Probability of One Better or Equal Result (PoOBE), signifying the percentage of instances whose worsening probability is less than 50%.
## 3 Types of Supplementary Dampers and Their Characteristics
### Friction Dampers  
#### 3.3.1 Operating Principle  
Friction dampers function by dissipating energy through the relative movement of sliding surfaces. These devices act as passive mechanisms for energy dissipation, employing frictional principles to mitigate seismic forces impacting structures [71; 72]. Typically, they consist of two or more surfaces engineered to slide against each other under seismic loads, transforming kinetic energy into thermal energy via frictional interactions. Common types include Pall friction dampers, slotted-bolted connections, and rotational friction dampers. By strategically incorporating these devices into a structural design, engineers can significantly reduce vibrations and displacements caused by seismic events [73; 74]. The attractiveness of friction dampers stems from their simple design, reliability, and consistent performance across various loading conditions. They require minimal maintenance and can be easily inspected and replaced, making them an advantageous option for both new constructions and retrofitting projects [75; 76].  
One of the notable advantages of friction dampers lies in their ability to activate at designated force thresholds, which allows for meticulous regulation of the structural response. This characteristic enables engineers to enhance the performance of structures in response to diverse seismic scenarios [77; 78]. Additionally, friction dampers exhibit rectangular hysteretic behavior, which contributes to increased energy dissipation during each cycle. Recent advancements in materials and surface treatments have led to more consistent and predictable friction coefficients, thus bolstering the long-term dependability of these devices [79; 80]. Furthermore, research into self-centering friction dampers has tackled challenges associated with residual displacements that occur after seismic activities. As performance-based design methodologies evolve, friction dampers continue to play a crucial role in the creation of resilient structures capable of withstanding substantial earthquakes while sustaining minimal damage [81; 82].  
Figure 9: The National Museum of Emerging Science and Innovation (Tokyo) [70].
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 5 Conclusions  
In order to make AES resistant to the threat of the quantum computer environment, this project implemented the key scheduling extension. In addition to software implementation, we hope to speed up the calculation speed of encryption and decryption. Therefore, we also implemented the hardware description language of AES-384 and AES-512 so that they can run smoothly on FPGA and achieve the effect of hardware acceleration.  
In addition to basic hardware acceleration, we have also been working hard to optimize the hardware description language. By increasing the memory and cutting smaller input segments, parallel computing can be more complete, and the amount of computation per unit time can be increased.  
Given the final experimental results shown in Table 12, our proposed method successfully integrates the original AES-128, AES-192, and AES-256 architectures with the newly arranged AES-384 and AES-512 in both hardware and software. This integration allows for the utilization of the complete set of advanced encryption algorithms. The standard still maintains the Level III and Level V levels in the secure quantum computer attack environment, and there is no need to change the original encryption architecture of Rijndael, as [10] did, which successfully achieves the purpose of this research project.  
Conceptualization, H.-J.S.; Methodology, H.-J.S.; Software, Y.-R.T. and W.-C.L.; Validation, H.-J.S., C.-T.Y. and W.-C.L.; Formal analysis, C.-T.Y.; Investigation, C.-M.L.; Data curation, Y.-R.T.; Writing--original draft, H.-J.S. and Y.-R.T.; Writing--review & editing, C.-T.Y.; Supervision, C.-M.L.; Project administration, C.-M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.  
Not Available.  
Data Availability StatementData sharing is not applicable to this article as no new data were created or analyzed in this study.  
The authors declare no conflict of interest.
# An Overview of Tools and Technologies for Anxiety and Depression Management Using AI
## 4 Results of Overview
### AI Methodologies and Tools Utilized for Anxiety and Depression Management
#### 4.3.3 Wearables and Biosignals  
Technology has become a powerful ally in dealing with anxiety and managing the negative emotions associated with depression. Wearables, also known as portable devices, are an example of wearable computational models used by people to monitor their health and well-being [68]. These devices are worn on various parts of the body, such as the wrist, hand, and neck, and have sensors that continuously measure physiological signals, such as heartbeat, temperature, and galvanic skin response [69].  
The main features of wearables are the ability to connect to the internet for transmitting, recording, or analyzing data, as well as the ability to connect to other electronic devices to expand their functions. Today, wearables are widely used for health management, as they have intelligent detection and communication capabilities [70]. In addition, research focuses on improving the detection capabilities of wearables using machine learning algorithms, as well as designing interventions such as deep breathing guidance. Among the wearable devices is BioBase, which measures heart rate variability and other physiological parameters to assess stress, mood, and emotional well-being [71]. It provides real-time feedback and personalized interventions to help users manage symptoms of depression and anxiety. On the other hand, the \"Spire Health Tag\" helps monitor the symptoms of depression. By monitoring changes in breathing and activity levels, it can detect periods of increased stress or agitation, which may be associated with depressive episodes. Finally, \"Moodmetric\" is a portable ring that measures electrodermal activity to assess stress and emotional arousal [72]. It provides information about everyday stressors and helps users identify triggers for depressive symptoms, allowing them to take preventative measures to manage their mental health [73].  
Smartwatches have also emerged as popular wearable devices, known for their stress reduction capabilities [54]. These devices usually monitor the user's heart rate throughout the day, providing information on stress levels, since stress and anxiety are often associated with increased heart rate. Some smartwatches even provide built-in relaxation or breathing exercises to further aid users in stress management. Alongside smartwatches, Fisher Wallace Labs has released portable brain stimulation computing, which is reported to help reduce depressive symptoms. The innovative devices aim to treat depression, anxiety, and other neuropsychiatric and cognitive disorders, highlighting the potential of wearable computing in addressing mental health problems [74].
# Joint Invariants of Linear Symplectic Actions
## 2 Recollection: Invariants
### Polynomial Invariants  
If \(G\) is semi-simple and \(V\) is linear, then by the Hilbert-Mumford theorem generic orbits can be separated by polynomial invariants \(I\in(S\,V^{*})^{G}\), where \(S\,V^{*}=\oplus_{k=0}^{\infty}S^{k}V^{*}\) is the algebra of homogeneous polynomials on \(V\). With a choice of linear coordinates \(\boldsymbol{x}=(x_{1},...,x_{n})\) on \(V\) we identify \(S\,V^{*}=\mathbb{R}[\boldsymbol{x}]\).  
Moreover, by the Hilbert basis theorem, the algebra of polynomial invariants \(\mathcal{A}_{G}=(S\,V^{*})^{G}\) is Noetherian, i.e., finitely generated by some \(\boldsymbol{a}=(a_{1},\ldots,a_{s})\), \(a_{j}=a_{j}(\boldsymbol{x})\in\mathcal{A}_{G}\).  
Denote by \(\mathcal{R}=\mathbb{R}[\boldsymbol{a}]\) the free commutative \(\mathbb{R}\)-algebra generated by \(\boldsymbol{a}\). It forms a free module \(F_{0}\) over itself. \(\mathcal{A}_{G}\) is also an \(\mathcal{R}\)-module with surjective \(\mathcal{R}\)-homomorphism \(\phi_{0}:F_{0}\to\mathcal{A}_{G}\), \(\phi_{0}(a_{j})=a_{j}(\boldsymbol{x})\). The first syzygy module \(S_{1}=\mathrm{Ker}(\phi_{0})\) fits the exact sequence  
\[0\to S_{1}\to F_{0}\to\mathcal{A}_{G}\to 0.\]  
A _syzygy_ is an element of \(S_{1}\), i.e., a relation \(r=r(\boldsymbol{a})\) between the generators of \(\mathcal{A}_{G}\) of the form \(\sum_{p=1}^{k}r_{ip}a_{jp}=0,r_{ip}\in\mathcal{R}\).  
The module \(S_{1}\) is Noetherian, i.e., finitely generated by some \(\boldsymbol{b}=(b_{1},\ldots,b_{t})\). Denote the free \(\mathcal{R}\)-module generated by \(\boldsymbol{b}\) by \(F_{1}=\mathcal{R}[\boldsymbol{b}]\). The natural homomorphism \(\phi_{1}:F_{1}\to S_{1}\subset F_{0}\), \(\phi_{1}(b_{j})=b_{j}(\boldsymbol{a})\), defines the second syzygy module \(S_{2}=\mathrm{Ker}(\phi_{1})\), and we can continue obtaining \(S_{2}\subset F_{2}=\mathcal{R}[\boldsymbol{c}]\), etc. This yields the exact sequence of \(\mathcal{R}\)-modules:  
\[\ldots\xrightarrow{\phi_{3}}F_{2}\xrightarrow{\phi_{2}}F_{1}\xrightarrow{ \phi_{1}}F_{0}\xrightarrow{\phi_{0}}\mathcal{A}_{G}\to 0.\]  
The Hilbert syzygy theorem states that \(q\)-th module of syzygies \(S_{q}\) is free for \(q\geq s=\#\boldsymbol{a}\). In particular, the minimal free resolution exists and has length \(\leq s\), see [13].  
To emphasize the generating sets, we depict free resolutions as follows:  
\[\mathbb{R}[\mathbf{x}]\supset\mathcal{A}_{G}\leftarrow\mathbb{R}[\mathbf{a}]\leftarrow \mathcal{R}[\mathbf{b}]\leftarrow\mathcal{R}[\mathbf{c}]\leftarrow\cdots\gets 0.\]
# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 3 Methodology  
### General Framework  
The framework in this study was developed using vision-based approaches instead of sensor-based approaches. Several proposed models were designed, developed, and evaluated to obtain the optimum possible results with ArSL recognition and classification. In particular, this work aims to investigate the following:  
Problem: Few research works have investigated ArSL vision-based methods which rely on AI techniques. The use of transfer learning with recently built large-sized deep learning models and vision transformers remains a relatively unexplored area of research with ArSL. Solution: To address the abovementioned problem, this work utilized two methodological parts for the classification of Arabic signs to alphabets. First was the transfer learning approach using pretrained deep learning models including VGG, ResNet, MobileNet, Xception, Inception, InceptionResNet, DenseNet, and BiT, and vision transformers including ViT and Swin. Second was the deep learning approach using CNN architectures, namely, CNN-1, CNN-2, and CNN-3 with data augmentation and batch normalization techniques. The aim of the latter was to be compared with the pretrained models and vision transformers for the ArSL classification task. The general framework of the methodology is shown in Figure 1. The input dataset at a glance was split into training and test splits to ensure that our study did not suffer from data leakage and inflation of accuracy results problems [66]. Different pretrained models using transfer learning were used to classify images in the training and test splits. Each model has its own hyperparameters that we initialized, then, fine-tuning was performed on each model separately using an added