esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Data Imbalance  
### Impact of Data Imbalance on Intrusion Detection Systems (IDS)  
In Intrusion Detection Systems (IDS), data imbalance represents a fundamental challenge that directly impacts the performance and reliability of the model. In network traffic data, malicious traffic is typically much less frequent than normal traffic, resulting in a significant scarcity of attack samples. This severe class imbalance makes it difficult for IDS to detect rare yet crucial attack patterns, thereby reducing the detection accuracy of the model. Since models tend to favor the majority class, they may misclassify malicious data as normal traffic, leading to frequent false negatives and consequently causing issues like missed detections. This problem becomes even more critical when dealing with increasingly complex and evolving network attacks, particularly those that are previously unseen or of low frequency, which may not be effectively detected.
# Numerical Simulation and Parameter Optimization of Air Slide Based on CFD-DEM
## 2 Three-Dimensional Model of the Air Slide  
The air slides comprise upper and lower trough-shaped housings fabricated from thin steel plates, separated by a perforated partition plate. The entire assembly is installed with a slight inclination angle. Material enters the upper trough through the feed inlet while the blower delivers air into the lower trough. To ensure directional control and uniform distribution of airflow into the upper trough, a porous permeable layer is integrated into the partition plate structure. When air pressure in the lower trough reaches a predetermined threshold, gas progressively permeates through the permeable layer into the upper trough. Under the synergistic action of gravitational force and the established airflow field, the powdered material advances toward the discharge outlet. This aerated transport mechanism effectively reduces the friction coefficient, enabling highly efficient conveying operation.  
This study is based on the XZ200 air slide, with modeling completed using SolidWorks 2019 3D modeling software. The model was then imported into ANSYS Fluent 2023 and Rocky DEM 23R1 software for gas-solid two-way coupling simulation, as shown in Figure 1. To reduce computation time for numerical simulation, the air slide model was simplified with the following specifications: total length of 200 mm, width of 28 mm, upper chamber height of 18 mm, and lower chamber height of 5 mm. A perforated plate was installed between the upper and lower chambers to investigate the effect of porosity (\(\varphi\)) on conveying performance, designed with four distinct porosity configurations: (1) staggered 1.0 mm diameter holes (\(\varphi\) = 37.84%), (2) in-line 1.5 mm diameter holes (\(\varphi\) = 47.24%), (3) staggered 1.5 mm diameter holes (\(\varphi\) = 50.60%), and (4) in-line 2.0 mm diameter holes (\(\varphi\) = 58.13%). In actual production equipment, the upper chamber is equipped with air suction and dust accumulation devices to minimize airflow disturbances caused by gas at the top. To simplify this setup, an open design was adopted for the upper chamber.  
An experimental air slide model was constructed using acrylic plates for comparative studies. Based on the XZ200 air slide design, a single-section model was fabricated with the following dimensions: total length of 1000 mm, width of 28 mm, and lower chamber height of 50 mm. To minimize top airflow disturbances, the upper chamber height was adjusted to 450 mm. The perforated baffle plate was designed with 9 mm diameter holes in a staggered arrangement, achieving an open area ratio of approximately 50.60%. A canvas layer was installed between the two baffle plates as a porous permeable medium to ensure uniform gas distribution and directional flow into the upper chamber. The experimental setup is shown in Figure 2 below.  
Figure 1: Three-dimensional model of the air slide. 1. Particle inlet. 2. Fluid outlet. 3. Particle outlet. 4. Fluid inlet.  
Figure 2: Experimental setup diagram of the air slide.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
###### Abstract  
One of the major challenges on large-scale Multi-Terminal High Voltage Direct Current (MT-HVDC) systems is the steady-state interaction of the hybrid AC/DC grids to achieve an accurate Power Flow (PF) solution. In PF control of MT-HVDC systems, different operational constraints, such as the voltage range, voltage operating region, Total Transfer Capability (TTC), transmission reliability margin, converter station power rating, etc. should be considered. Moreover, due to the nonlinear behavior of MT-HVDC systems, any changes (contingencies and/or faults) in the operating conditions lead to a significant change in the stability margin of the entire or several areas of the hybrid AC/DC grids. As a result, the system should continue operating within the acceptable limits and deliver power to the non-fauulted sections. In order to analyze the steady-state interaction of the large-scale MT-HVDC systems, an improved mixed AC/DC PF algorithm for hybrid AC/DC grids with MT-HVDC systems considering the operational constraints is developed in this paper. To demonstrate the performance of the mixed AC/DC PF algorithm, a five-bus AC grid with a three-bus MT-HVDC system and the modified IEEE 39-bus test system with two four-bus MT-HVDC systems (in two different areas) are simulated in MATLAB software and different cases are investigated. The obtained results show the accuracy, robustness, and effectiveness of the improved mixed AC/DC PF algorithm for operation and planning studies of the hybrid A/DC grids.  
Keywords:improved mixed AC/DC power flow; multi-terminal high voltage direct current (MT-HVDC) systems; operational constraints; voltage-sourced converter (VSC)-high voltage direct current (HVDC) station +
Footnote †: journal: Applied
## 4 Proposed Architecture
### Classification Module  
After extracting the sequence of frames exhibiting substantial motion from the input videos, the respective sequence comprising a subset of key points is fed into the classification module, aiming to discern the occurrence of a fall event. As depicted in Figure 4, the classification module encompasses various vital components, including the convolution layer, MaxPool layer, Flatten layer, and dense units. The computational pipeline commences with applying the convolution layer, effectively capturing essential spatiotemporal features from the input key points. Subsequently, the MaxPool layer is employed to reduce the dimensionality of the extracted features while preserving crucial information. As a subsequent step, the Flatten layer is introduced to transform the output of the MaxPool layer into a one-dimensional vector representation. Finally, the compressed and structured representation is fed into a fully connected layer comprising dense units to facilitate the detection and identification of instances of fall events. The network structure and parameters of the classification module are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Parameters** \\ \hline InputLayer & (None, \(50,8\)) & 0 \\ Conv1D & (None, \(48,32\)) & 800 \\ MaxPooling1D & (None, \(24,32\)) & 0 \\ Conv1D & (None, \(22,64\)) & 6208 \\ MaxPooling1D & (None, \(11,64\)) & 0 \\ Flatten & \(704\) & 0 \\ Dense & \(64\) & 45120 \\ Dense & \(1\) & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification module network architecture and parameters.  
Figure 4: Detailed architecture of the classification module. The sequence of 50 frames, each containing key point data extracted from the STFT module, serves as the input to the CNN. This sequence is processed through a series of layers, including convolution, MaxPool, Flatten, and dense units, to classify each sequence as ‘Fall’ or ‘Not Fall’.
## 2 Extended Scaled Boundary Finite Element Method
### Force Balance and Displacement Coordination  
Figure 4 shows a typical method simulating the crack with the coupling of the XFEM and the SBFEM [28] domain. In the process of XFEM and X-SBFEM coupling, there is a problem of the balance of virtual and real freedom, continuous displacement, and force. To solve the problem, the SBFEM simulates the crack surface using the boundary of the element, while the XFEM introduces an additional degree-of-freedom based on the use of the step function to describe the discontinuous displacement field. To coordinate the displacement of the two types of elements along the boundary, a transition matrix T is used to match the nodal displacements in SBFEM with those in XFEM. A transformation matrix, which can be derived from the previous equations that translates the unknown SBFEM nodal displacements (\(\mathbf{u}_{E}\), \(\mathbf{u}_{F}\), \(\mathbf{u}_{A}\), and \(\mathbf{u}_{B}\)) to the unknown XFEM nodal displacements (\(\mathbf{q}_{2}\), \(\mathbf{q}_{3}\), \(\mathbf{a}_{2}\), and \(\mathbf{a}_{3}\)) can be described as  
\[\left\{\begin{array}{c}\mathbf{u}_{B}\\ \mathbf{u}_{E}\\ \mathbf{u}_{A}\\ \mathbf{u}_{F}\end{array}\right\}=\left[\begin{array}{cccc}I&0&0&0\\ 0&I&0&0\\ \mathbf{N}_{2}(x_{A})&\mathbf{N}_{3}(x_{A})&0&-2\mathbf{N}_{3}(x_{A})\\ \mathbf{N}_{2}(x_{F})&\mathbf{N}_{3}(x_{F})&2\mathbf{N}_{2}(x_{F})&0\end{array}\right] \left\{\begin{array}{c}\mathbf{q}_{2}\\ \mathbf{q}_{3}\\ \mathbf{a}_{2}\\ \mathbf{a}_{3}\end{array}\right\} \tag{17}\]  
where \(I\) is a unit matrix. In order to ensure the compatibility of the displacement and to integrate the element stiffness matrix, it is necessary to rearrange the column displacement vectors of the SBFEM and the stiffness matrix according to whether the nodes are on the common boundary,  
\[\left[\begin{array}{cc}\mathbf{K}_{aa}&\mathbf{K}_{ab}\\ \mathbf{T}^{T}\mathbf{K}_{ba}&\mathbf{T}^{T}\mathbf{K}_{bb}T\end{array}\right]\left\{\begin{array} []{c}\mathbf{u}_{qS}\\ \mathbf{u}_{xF}\end{array}\right\}=\left\{\begin{array}{c}\mathbf{F}_{a}\\ \mathbf{T}^{T}\mathbf{F}_{b}\end{array}\right\} \tag{18}\]  
where the subscript \(a\) represents the nodes on the common boundary of SBFEM and XFEM and subscript \(b\) represents the nodal degree-of-freedom of the noncommon boundary. The transformation matrix \(\mathbf{T}\) is only related to the interpolation shape function at the crack opening at the common boundary of the SBFEM and the XFEM.
# On the Potential of Enhancing Delay-Tolerant Routing Protocols via Age of Information
## 2 Related Work
### Delay Tolerant Networks
* **Time-based routing:** The corresponding protocols determine the next hop node based on any time-related metric, such as time interval, contact duration, time between encounters, contact time, etc. Protocols which fall into the above category are Contact Duration Based Routing (CDBR) [24; 25], SEDUM [26] and Seek-and-Focus Protocol [27]. * **Infrastructure-based routing:** These protocols exploit infrastructure information to determine the next hop. Typically they use node location, determined routes, map information, or node traffic direction, to make the forwarding decisions. Protocols which fall into the above category are MOvement of VEhicle (MOVE) Protocol [28], Distance Aware Epidemic Routing (DAER) [29] and Source Routing [30]. * **Hybrid and other types of routing:** This category includes routing protocols that may combine the approaches of the above categories (hybrid), or other protocols following approaches that cannot be classified in the above. Such protocols are Neighborhood contact history routing (NECTAR) [31], Erasure Coding [32] and Minimizing Relay node and Hop count (MRH) [33].
## 2 Methods and Tools
### Radar Interferometry  
Synthetic Aperture Radar (SAR) technology has become indispensable for monitoring ground deformation and surface changes in the tectonically active Dead Sea basin. Operating independently of weather and lighting conditions, SAR provides high-resolution, all-weather imaging capabilities. Over recent decades, SAR satellite technology has advanced significantly, with spatial resolutions improving from approximately 30 m in the 1990s, as seen with the European Remote Sensing satellites ERS-1 and ERS-2, to about 25 cm in current commercial satellites constellations such as Umbra, Capella Space, and Iceye [29]. These enhancements have enabled near real-time monitoring of geological phenomena, including subsidence and sinkhole formation [30].  
A key application of SAR data is InSAR, which identifies ground deformation with millimeter-level precision by analyzing radar signals captured at different times over the same area [31; 32; 33; 34]. Advanced InSAR methodologies, including Persistent Scatterer (PS) and Small Baseline Subset (SBAS) techniques, have further enhanced the utility of SAR data. PS InSAR focuses on identifying and analyzing coherent targets, known as persistent scatterers, which remain stable over time. This method is highly effective in areas with abundant man-made structures or natural reflectors, allowing for the detection of very small deformations over long periods. SBAS InSAR involves generating interferograms from pairs of SAR images with small temporal and spatial baselines. This technique is particularly suited to areas with distributed scatterers and can effectively monitor gradual ground deformations over large zones [32; 33; 34].  
In the coastal zones, retreating shoreline has led to widespread land subsidence and sinkholes. InSAR techniques provided invaluable observations [2; 12; 22; 23]. They have been instrumental in identifying precursory subsidence preceding sinkhole collapse, contributing to early warning systems and hazard mitigation strategies [12].
## 2 Theory
### Pareto Front Selection  
[PERSON] et al. [6] proposed a method called Pareto-Q-learning to solve MORL problems. The algorithm uses scalarization and weight vectors to transform a multi-objective problem to a single objective problem. In our research, we adhere to the method used by [PERSON] et al. Other options for Pareto front selection include the Analytical Hierarchy Process (AHP) suggested by [PERSON] [32], and the Tabular method proposed by [PERSON] [33].  
The AHP breaks down the problem into a hierarchical structure. The solution is then synthesized by a series of comparisons and rankings. The implementation of AHP involves defining the decision criteria in a hierarchical form, constructing comparison matrices, and using a hierarchy synthesis function. The advantage of AHP is that it considers both qualitative and quantitative aspects. A verbal scale is used to specify the DM's preference. It is intuitive to address complex multi-criteria problems by simplifying them into smaller sub-problems and solving those by comparison. Since AHP requires a qualitative process of redefining sub-goals, a process which cannot be easily done by a machine, it is not suitable for our scenario. Even if we are able to create more fine-grained goals, pair-wise comparisons would be computationally expensive due to the large state space.  
The Tabular method specifically focuses on quickly selecting a solution from a range of initial alternatives. The method works by iteratively eliminating the non-Pareto optimum from a table of solutions. The table consists of a column for each criterion. The entries in each column contain the alternatives arranged in preferred order. Constraints are imposed on each column to eliminate non-optimal solutions. The remaining solutions are presented to the DM. If no solution is found, the DM should reduce the constraints. This method is efficient for small sets of solutions. However, similar to AHP, the iterative nature of the algorithm makes it computationally expensive.  
The advantages of using scalarization, over other methods, for selecting the Pareto front are its simplicity and computational efficiency. As discussed in the Related Work section, researchers have studied several approaches for scalarization. The scalarization function provides a single score that represents the quality of the combined objective. The following section details the scalarization techniques used in this study.
## 1 Introduction  
As people age, their skin undergoes changes that result in reduced dermal hydration, elasticity, thickness, and density. These changes can be caused by various factors, including genetics and lifestyle, as well as environmental factors such as sun exposure, pollution, and smoking. As a result, there is a growing interest in the use of natural products to improve skin health [1].  
Colostrum is the first milk produced by humans and other mammals after giving birth. It is rich in various bioactive components, such as growth factors, hormones, immunoglobulins, cytokines, and enzymes, which provide numerous health benefits to newborns. Colostrum contains high levels of insulin-like growth factor-1 (IGF-1), which stimulates cell growth and differentiation, and epidermal growth factor (EGF), which promotes the growth and repair of skin tissue. Additionally, colostrum contains several other growth factors that have been shown to play a crucial role in skin regeneration and repair, including transforming growth factor-beta (TGF-\(\beta\)), platelet-derived growth factor (PDGF), and fibroblast growth factor (FGF) [2, 3]. In addition to its growth factor content, colostrum also contains several other bioactive components that provide various health benefits. For example, it contains lactoferrin, which has antibacterial, antiviral, and anti-inflammatory properties, and has been shown to promote wound healing. Colostrum also contains proline-richpolypeptides (PRPs), which modulate the immune system and have been shown to improve skin barrier function [4].  
Most of the studies concerning dairy foods have focused on bovine milk, the most common milk used in human nutrition. However, sheep colostrum contains higher levels of various bioactive molecules, such as immunoglobulins and growth factors, compared to bovine colostrum [5; 6]. For example, sheep colostrum has been shown to have higher levels of insulin-like growth factor 1 (IGF-1), which plays a crucial role in tissue repair and regeneration [7].  
Several studies have confirmed that colostrum influences skin health. Topical application of bovine colostrum improved skin regeneration and accelerated cutaneous wound healing [8]. Bovine colostrum promotes wound healing and reduces scar formation in rats [9]. Subsequent research suggests that exosomes sourced from bovine colostrum possess remarkable structural and functional stability, making them a promising natural remedy for mending age-related and sun-induced skin injuries [10].  
One study found a cosmetic preparation containing a mixture of horse colostrum and horse milk to demonstrate a number of benefits for healthy and diseased skin. The effects included various anti-aging, moisturizing, protective, and smoothing effects, as well as pain relief, greater skin tension, and epithelial regeneration. It was found to be suitable for both healthy and diseased skin [11].  
However, little is known about the effect of sheep colostrum on the skin. Therefore, the aim of the present study was to examine the effects of a cosmetic preparation containing 15% sheep colostrum lyophilize on skin with signs of aging.
## 3 The S-Box Generation Algorithm Design and Performance Analysis
### The S-Box Generation Algorithm Design  
In this section, the S-box production algorithm is designed to be used in the encryption algorithm. Three different S-box generations are realized with the S-box production algorithm developed in the study. In the algorithm design, a chaotic system with rich dynamic characteristics introduced to the model is used. The pseudo code block for the algorithm design is shown in Algorithm 1. Firstly, the system parameters \((a,b,c,d)\) and initial conditions \((x(0),y(0),z(0))\) of the chaotic system are entered. Then, in order to obtain a more random output, the appropriate sampling step range is determined. This value is set at 0.001. The chaotic system RK-4 (Runge-Kutta 4) algorithm is solved with the specified sampling step interval. The float values from each phase are obtained from the RK-4 algorithm. The generated float values are taken from the first three digits after the decimal point, and mod-256 operation is applied. Remain and round operations have also been applied to obtain the first three steps after the decimal point. As a result of this process, 0-256 values are obtained from each phase. The values obtained from the \(x\) and \(y\) phases for the proposed S-box1, \(x\), \(y\) and \(z\) phases for  
Figure 5: Experimental attractors displayed by using an oscilloscope in (a) \(X-Y\) plane, (b) \(X-Z\) plane, and (c) \(Y-Z\) plane.  
S-box2, \(y\) and \(z\) phases for S-box3 bitvor operation are applied. It is then checked whether the values produced are on the S-box, in other words whether it has been produced before. If produced, this value is discarded, otherwise it is added to the S-box. In this way a 256 element S-box is produced. Subsequently, S-box performance tests are conducted to test the produced S-boxes.  
```
1:Start
2:i = 1; SBox=[];
3: Entering system parameters \((a,b,c,d)\) and initial conditions \((x(0),y(0),z(0))\) of chaotic system
4: Determination of the appropriate value of \(\Delta\)h (0.001)
5: Sampling with determination \(\Delta\)h value
6: Solving the chaotic system using RK4 algorithm and obtaining output time series (ys)
7:ys \(\rightarrow\) Calculated current values \((ys(1),ys(2),ys(3))\) from RK4 algorithm
8:while (i\(<\)257) do
9:\(x=mod(round2 int(remain(ys(1),1)*10^{3}),256)\);
10:\(y=mod(round2 int(remain(ys(2),1)*10^{3}),256)\)
11:\(z=mod(round2 int(remain(ys(3),1)*10^{3}),256)\)
12:\(xorvalue=bitvor(x,y)\;\;for\;SBox1\)
13:\(xorvalue=bitvor(x,y,z)\;\;for\;SBox2\)
14:\(xorvalue=bitvor(y,z)\;\;for\;SBox3\)
15:if (Is there xorvalue in SBox = yes) then
16: Go step 8.
17:else [Is there xorvalue in SBox = no]
18: SBox[i] \(\leftarrow\) xorvalue
19: i++;
20:endif
21:endwhile
22:sbox \(\leftarrow\) reshape(SBox,16,16) (outputs: SBox1, SBox2, SBox3)
23: Implementation of SBox Performance Tests (outputs: Nonlinearity, BIC-SAC, BIC-Nonlinearity, SAC, DP)
24: End
```  
**Algorithm 1****S-box generation algorithm pseudo code.**