esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Reconstruction Method and Protection Algorithm
### Characteristics of Current Trajectory Images  
#### 3.2.1 External Failure and CT Saturation  
When an external fault occurs, the fault currents on the PV side and system side have equal amplitudes and opposite phases. The reconstructed current trajectory image with \(I_{p}\) as the horizontal axis and \(I_{g}\) as the vertical axis is approximately \(y=-x\) in the second and fourth quadrants [30]. When CT saturation is considered, external faults on the system side are not affected much, but the reconstructed waveforms of external faults on the PV side are obviously distorted, as shown in Figure 9.
# Developing a Novel Design for a Tubular Solid Oxide Fuel Cell Current Collector
## 2 Numerical Model
### Charge Transport  
Inside the cell, there are ions (hydrogen and oxygen ions) and electrons, and their movement must be considered. Electronic and ionic movement happens in the anode and cathode, and only ionic transport occurs inside the electrolyte. Only electronic transport happens inside the current collector, and [PERSON]'s law was used to model the charge balance [37]:  
\[\textbf{Electronic charge balance}\] \[\textbf{{\emph{At anode}}}\ \ \ \ \ \ \
abla\cdot\left(\lambda_{a}^{eff}\ \
abla\ \phi_{el}\right)=-j_{a}A_{V} \tag{7}\]  
\[\textbf{{\emph{At cathode}}}\ \ \ \ \ \ \
abla\cdot\left(\lambda_{c}^{eff}\ \
abla\ \phi_{el}\right)=-j_{c}A_{V} \tag{8}\]  
\[\textbf{{\emph{Ionic charge balance}}}\] \[\textbf{{\emph{At electrolyte}}}\ \ \ \ \ \ \ \
abla\cdot\left(\sigma_{mem}\ \
abla\ \phi_{io}\right)=0 \tag{9}\]  
\[\textbf{{\emph{At anode}}}\ \ \ \ \ \ \
abla\cdot\left(\omega_{a}^{eff}\ \
abla\ \phi_{io}\right)=j_{a}A_{V} \tag{10}\]  
\[\textbf{{\emph{At cathode}}}\ \ \ \ \ \ \
abla\cdot\left(\sigma_{c}^{eff}\ \
abla\ \phi_{io}\right)=j_{c}A_{V} \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Items** & **Anode Inlet** & **Cathode Inlet** & **Collector** & **Cell Surroundings** \\ \hline
**Mass flow inlet (kg/s)** & \(1.334\times 10^{-8}\) & Exposed to air & None & None \\ \hline
**Temperature, K** & \(1073\) & \(1073\) & Adiabatic & Adiabatic \\ \hline \multirow{3}{*}{**Species**} & **H2** & \(97\%\) & None & \multirow{3}{*}{None} & \multirow{3}{*}{None} \\  & **O2** & None & \(21\%\) & & \\ \cline{1-1} \cline{2-6}  & **H2O** & \(3\%\) & None & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cell boundary and operating conditions.  
The values of the volumetric current densities were calculated using the Butler-Volmer equation [37] as follows:  
\[j_{a}=j_{o,ref}^{H_{2}}(\frac{C_{H_{2}}}{C_{H_{2}},ref})^{\gamma_{H_{2}}}\ [exp\ \ exp\ \left(\frac{\beta nF\eta_{a}}{RT}\right)-exp\ \ exp\ \left(-\frac{(1-\beta)nF\eta_{a}}{RT}\right)\ ] \tag{12}\]  
\[j_{c}=j_{o,ref}^{\gamma_{2}}(\frac{C_{o_{2}}}{C_{o_{2}},ref})^{\gamma_{\gamma_{ 2}}}\ [exp\ \ exp\ (\frac{\beta nF\eta_{c}}{RT})-exp\ \ exp\ \left(-\frac{(1-\beta)nF\eta_{c}}{RT}\right)\ ] \tag{13}\]  
where \(\eta_{i}\) is the activation overvoltage and can be expressed as:  
\[\eta_{i}=\phi_{el}-\phi_{io}-\Delta\phi_{eq} \tag{14}\]
## 2 Materials and Methods
### FLS Migration into a Cartilage Defect  
To form a cartilage defect, 1 mm cores were removed from each 4 mm bovine cartilage plug using a trephine biopsy. The obtained \(\sim\)1 cm \(\times\) 1 cm pieces of bovine synovium were stained using a lipophilic membrane dye (Vybrant Dil; Life Technologies, Carlsbad, CA, USA). Synovium samples were oriented in direct opposition to the cartilage explant (Figure 4), covering the 1 mm defect region, prior to PEMF or DC EF stimulation. The cartilage plug with the overlying synovium sheath was cultured in DMEM supplemented with 5% FBS, 1% AA, and 50 ug/mL ascorbic acid-2-phosphate. Explants were separately placed in the PEMF and galvanotaxis chambers (\(n=6\)). Control samples were handled similarly, but without the application of electrical stimulation. PEMF chamber was active for 8 h per day and explants were treated for 48 h [37]. The galvanotaxis EF current was applied for 3 h, generating an applied field strength of E = 6 V/cm (EF) [25].  
Following PEMF and DC EF stimulation, explants were fixed in 4% paraformaldehyde overnight. The synovium was removed from the cartilage construct, leaving behind migrated synovial cells. Cartilage plugs were subsequently stained with DAPI (Sigma-Aldrich, St. Louis, MO, USA) for co-localization of endogenous cartilage cells with the DiI stained synovial cells in the sub-cored defect region. Confocal microscopy ([PERSON], Oberkochen, Germany) was used to visualize synovial cell migration through the depth of the cartilage construct. Z-stacks with a 40 um step-size were obtained in order to track the migration distance from the articular cartilage surface across the defect site (Figure 4). A 40 um step-size was selected to ensure that no FLS were missed or double counted  
Figure 4: Experimental set-up of cartilage-synovium defect-repair model. DiI-labeled synovium (1, yellow) was placed in direct apposition (2) with DAPI-labeled cartilage (3, blue). Synovium was removed for confocal z-stack imaging (4), leaving the DiI-stained FLS on cartilage specimen. Migration of FLS were monitored through the depth of the cartilage defect annulus. Scale bar = 100 μm.  
during image processing and cell counting analysis. Only FLS that had adhered to the periphery of the cartilage construct were counted for migratory behavior, which was assessed by the co-localization of DAPI and DiI stains along the cartilage surface. FLS were classified as \"transferred\" if cells were in contact with the topmost layer of the defect site (visible in first z-stack image). FLS were considered \"migrated\" if cells were visible at any location \(\geq\)40 \(\upmu\)m though the cartilage depth. The number of migrated cells was normalized to the total FLS count across the entire cartilage construct for both PEMF and galvanotaxis treated specimens. For explants exposed to DC EF stimulation, directed FLS velocity was computed assuming constant cell migration throughout the 3 h stimulation period. Transferred or non-migrating cells that remained on the cartilage surface were considered to have a velocity of 0 \(\upmu\)m/h.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 5 Discussion
### Transferability to Other Regions  
The transferability of the CNN-LSTM-SAM-Attention model to regions beyond Kumamoto Prefecture remains a critical area for future exploration. While the model has demonstrated excellent performance on the Kumamoto dataset, its success in other regions would depend on the availability of high-quality data and the similarity of spatial and temporal patterns. Landslide susceptibility is highly context-dependent, and factors such as topography, rainfall patterns, soil composition, and human activities can vary significantly from region to region.  
In order to apply the model to other areas, additional data collection would be required to ensure that the model can adapt to different geographical conditions. It may be necessary to fine-tune the model or retrain it using region-specific datasets to achieve optimal performance. Future work should focus on testing the model in various regions, including regions with differing climatic and geological conditions, to assess its robustness and adaptability. Techniques like transfer learning may also be explored to improve the model's ability to generalize across diverse environments.  
In conclusion, while the CNN-LSTM-SAM-Attention model shows promising results in Kumamoto Prefecture, further validation and adaptation to different regions are essential to fully assess its transferability. A more in-depth analysis of model limitations and its ability to predict landslide susceptibility in diverse regions will help enhance the model's robustness and reliability in real-world applications.
# SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning
## 4 Results and Analysis of Experiment
### Experimental Design
#### 4.1.1 Monitoring and Gathering Data  
Traffic monitoring is vital for improving the comprehensive performance of a network and traffic flow optimization, which is the aim of this study. The SDN technology introduces an innovative concept by making the network infrastructure scalable, usable, programmable, and centralized controllable. Furthermore, the SDN presents a dynamic monitoring scheme for network traffic. The Mininet emulation paradigm was used for setting up and installing a SDN environment in this study. The emulation environment includes a Ryu controller, OpenFlow switches, and hosts. Two scenarios for the DCN topologies were emulated, as presented in Figures 3 and 4, for implementing the proposed  
Figure 4: Topology No. 2 of Network.  
Figure 3: Topology No. 1 of Network.  
method. The used topologies are configured for handling the TCP and UDP packets. These flows are generated using a virtual machine created by the VMware workstation in a Linux environment. The OpenFlow Wireshark and tcpdump tools are used with the Mininet emulator to understand the behavior of the proposed DCN topologies for analyzing the performance of the network.  
In order to perform a flow background load on the network, the experiment started with generating UDP packets with different sizes and rates during 300 s of simulation, as shown in Table 1.  
Following that, many TCP connections were initialized with different time flow between two hosts. The behavior of the first DCN topology using the throughput parameter is demonstrated in Figure 5.  
Wireshark was run in the background to capture the OpenFlow packets, which are a TCP packet type. Then, the tcpdump tool was utilized for the loopback interface. The collected data were stored as pcap files for the two proposed DCN topologies. To undertake a comprehensive analysis, the tcptrace tool was employed to produce all complete flows characterized based on the performance parameters and to store them in a CSV files.
# A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
## 4 Fusion Algorithms  
Sensory fusion or data fusion aims to improve the measurement of two or more sources of data from sensors, beyond the individual measurement of each of them. Sensory fusion is especially indicated when large amounts of disparate sensor data are produced. Sensorial fusion applied to the measurement of redundant data reduces the uncertainty of the measurement, increases the accuracy and improves the integrity of the system, improving fault tolerance.  
Obtaining a classification of the algorithms or fusion techniques is an arduous and difficult task due to multidisplinarity and the large number of case studies reported in the literature. In a review carried out by [30], it is possible to find an extensive classification of fusion methods according to different criteria such as: (1) relations between the input data sources; (2) input/output data types and their nature; (3) abstraction level of the employed data; (4) different data fusion levels, and (5) architecture types. In this review, we will stablish three categories: (1) estimation methods based on Gaussian filters (e.g., Kalman filter (KF) or particle filters (PF)), (2) probabilistic inference methods (i.e., Bayes theorem), and (3) artificial intelligence methods based on machine learning algorithms. Three categorizations are applied to both perception systems and location systems in autonomous vehicles. Following are examples that combine the merging of data from the different categorizations, and different sensors of the perception and location systems.
# Quantitative Assessment Methods of Early Enamel Caries with Optical Coherence Tomography: A Review
## 4 Quantitative Assessment Methods of Early Enamel Caries with OCT
### Quantitative Assessment Based on Refractive Index  
Demineralization causes a change in the refractive index of enamel, and accurate measurement of this change can assist in the identification of early caries [32]. The refractive index of teeth can be determined with the optical path-length matching method using OCT [78]. Samples are put onto a metal plate to acquire OCT images. The depth position of the reflection surface of the metal plate before the sample is placed is \(Z_{0}\). After adding samples, the depth positions of the sample surface and the metal plate surface are \(Z_{1}\) and \(Z_{0}^{\prime}\), respectively. The thickness of the sample is \(Z_{1}-Z_{0}\). Then, the refractive index of teeth is determined by [79]:  
\[n=\frac{Z_{1}-Z_{0}^{\prime}}{Z_{1}-Z_{0}} \tag{4}\]  
[PERSON] et al. [80] measured refractive index of sound bovine enamel, demineralized for 2 months, and remineralized for 2 months, by 1310-nm SS-OCT with an axial/lateral resolution 11/17 um, and analyzed mineral content by TMR. The results showed that at an n range between 1.52 and 1.63, the mineral content ranged between 50 and 87 (vol.%). This indicates there were strong positive linear correlations between \(n\) and mineral content in both demineralized enamel (\(R^{2}=0.89\)) and remineralized enamel (\(R^{2}=0.86\)). However, this method required sectioning of the sample to measure the refractive index, which is destructive and cannot be applied in clinical practice.
## 3 Methodology
### AEGCN Layer  
In order to use the syntactic information of the sentence when predicting the sentiment polarity of the sentence, we constructed the L-layer AEGCN to capture the syntactic information. First, we use spaCy toolkit to construct a dependency tree for each sentence, and then we use these dependency trees to get the corresponding adjacency matrix \(A\in\mathbb{R}^{n\times n}\). Among them, \(n\) represents the length of the sentence. Each element in the adjacency matrix represents whether the two word-nodes at the corresponding position are adjacent in the sentence structure. If they are adjacent, the value is 1, and if they are not adjacent, the value is set to 0. For example, element \(A_{ij}\) in the \(i\)-th row and \(j\)-th column of the matrix represents whether the i-th word and the j-th word in the sentence are adjacent in the dependency tree. If it is 1, it means adjacent, and if it is 0, it means not adjacent. In particular, the diagonal elements of the adjacency matrix are all 1, that is, each word is adjacent to itself. After getting the adjacency matrix \(A\), we can use it to capture the syntactic information of the sentence.  
Figure 3 shows an example of an AEGCN layer. We denote the output of each layer in AEGCN as \(H^{l}=\left\{h_{1}^{l},h_{2}^{l},\cdots,h_{n}^{l}\right\},\ l\in[1,L]\). If all adjacent nodes of node \(i\) are represented as \(\mathcal{N}_{i}\), the output of the \(i\)-th node in l-th layer AEGCN can be expressed as:  
\[h_{i}^{l}=\text{ReLU}\left(\sum_{j=1}^{n}A_{ij}g_{j}^{l\Sigma 1}W^{l}+b^{l}\right) \tag{5}\]  
\[g_{j}^{l-1}=e_{ij}^{l}h_{j}^{l-1} \tag{6}\]  
\[e_{ij}^{l}=attention\left(h_{i}^{l-1},h_{j}^{l-1}\right),je\mathcal{N}_{i} \tag{7}\]  
\[attention\left(h_{i}^{l-1},h_{j}^{l-1}\right)=softmax\left(h_{i}^{l-1}h_{j}^{l-1 }\right) \tag{8}\]  
Among them, the weight \(W^{l}\) and the bias \(b^{l}\) are parameters that need to be learned, \(A_{ij}\) represents the adjacency coefficient, and \(e_{ij}^{l}\) represents the normalized attention coefficient of node \(i\) and \(j\) in the \(l\)-th AEGCN. The output of the last layer of AEGCN is \(H^{L}=\left\{h_{1}^{L},h_{2}^{L},\cdots,h_{n}^{L}\right\}\in\mathbb{R}^{d_{hid }\times n}\).
# Experimental Testing of Passive Linear TMD for Postural Tremor Attenuation
## 5 Conclusions  
An experimental arm is modeled to reflect the hand of a patient. It is excited by the ECR signal of a patient with PT. The mechanical shaker was able to precisely simulate the data of the PT signal. The TMD is modeled as a cantilever beam with a screw that can be fixed at different positions along the beam. Measurements are carried out to determine the new operation frequency of the TMD resulting from each screw position. The modal damping ratio for each position is determined using the half-power bandwidth method and the continuous wavelets method. The half-power bandwidth method could not provide a good estimation for the modal damping ratio of the designed TMD.  
The effect of the designed TMDs on the response of the experimental arm is obtained. The passive TMD shows its effectiveness in reducing the vibration of the system excited by  
Figure 10: PSD obtained from vibrometer measurements for the system with one TMD adjusted to the optimal frequency compared with that obtained with three TMDs.  
the PT signal of a real patient. Three TMDs with a total mass of 41.23 g, representing a mass ratio of 15.7%, reduced the angular displacement amplitude of the flexion-extension motion by 82%, measured using the IMU. The beam's material used in the design of the TMD needs to be modified to provide a relatively higher level of damping. A 3D printer will be used to manufacture a TMD of precise dimension and less expensive material. Therefore, the stainless steel beam will be replaced by Acrylonitrile Butadiene Styrene (ABS) material.  
The beam composing the TMD in this study has a circular cross-section to allow multi-directional operation of the TMD to be tested in further experiments. An experimental arm is prepared in the form of a tube to position the TMD(s) in orientations different from the excitation direction and thus test the ability of the TMD system for multidirectional excitations, which will occur in experimental tests to be applied for the patient. To anticipate the effect of the skin, an elastomer, representing the hand segment, will be placed between the tube and the TMD fixation to take into account the effect of the patient's epidermis, dermis, and subcutaneous tissues. A passive lightweight bracket will also be designed to keep the TMDs attached in different directions around the tube to attenuate the essential tremor.  
Conceptualization, S.G. and G.C.; methodology, all authors; software, S.G. and G.C.; validation, S.G., G.C., M.H. and G.F.; formal analysis, all authors; investigation, S.G.; data curation, S.G. and G.C.; writing--original draft preparation, S.G.; writing--review and editing, S.G., G.C., M.H. and G.F.; visualization, E.R. and E.H.; supervision, G.C., M.H. and G.F. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the Lebanese International University LIU.  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.
# Efficient and Secured Mechanisms for Data Link in IoT WSNs: A Literature Review
## 6 IOT Architecture and Its Layers  
This section describes the IoT infrastructure layers. These layers build up the IoT infrastructure, and the IoT frameworks would not seem to exist without them [31]. Each layer performs a vital task as well as process-specific operations. Figure 7 depicts the IoT layers from the bottom up (application layer, service layer, network layer, and physical layer) along with the responsibilities of each layer [32].  
* **Application Layer** The application layer characterizes all the applications where it is implemented. It serves as the link or medium between user-end devices and the IoT network. There are numerous IoT applications, and the application layer can aid the applications. Because of administrations based on sensor data, the administrations may be unique for each application. It is implemented by a dedicated program at the device end. The software uses the application layer on a computer [33] and supports application-layer protocols such as HTTPS, HTTP, FTP, and SMTP. The application layer uses a variety of protocols, including the restricted application protocol (CoAP), message queue telemetry transport (MQTT), the advanced message queuing protocol (AMQP), and an extensible messaging and presence protocol (XMPP) [34].
* **Data Processing Layer** The service layer comprises functionalities that manage gathered data and link it to the component layer's data. This layer serves as a conduit between different IoT devices and provides advanced techniques for communicating between them [35]. The sensors are also connected to the application layer through the service layer, which sits on top of the network layer. It has two responsibilities: First, it verifies that information is submitted by legitimate clients while preventing all dangers and threats. Second, it demonstrates that information is sent by legitimate clients while avoiding all risks and threats [36].
* **Network Layer**  
Figure 7: IoT architecture [32].  
The Internet of things necessitates adaptability in operating a vast range of computers. Over a billion smartphones will be added to the framework every year. As a result, IPv6 will play a critical role in preserving network layer flexibility. This layer comprises network interchange programming as well as actual segments. Its fundamental object is to send information among gadgets and devices to the recipients [37; 38]. The data is transmitted via the network layer, whether wired or wirelessly, using existing advanced methods.
* **Sensing Layer** The sensing layer comprises the essential equipment, the gadget layer, hubs, and sensors, such as RFID, standardized tag names, actuators, and insightful identification gadgets [39]. Gadgets assemble and pass data to the network layer either directly or by implication. To discern the nodes, sensors are used to transport the gathered information into the next layer. It is predicted that all devices will be IPv6-competent in a few years' time [40; 41].
# Safety-Aware Optimal Attitude Pointing for Low-Thrust Satellites
## 2 Methods
### Bright Spot Avoidance  
Figure 1 shows the local vertical and local horizontal (LVLH) reference frame, which has its origin at the satellite center of mass and has orthogonal axes (denoted by \(x,y,z\)), with the \(z\)-axis from the origin of the frame towards the Earth center, the \(x\)-axis in the orbital plane in the direction of orbital motion and perpendicular to the \(z\)-axis, and the \(y\)-axis completing the frame by the right-hand rule. A second, body-fixed, set of axes (denoted by \(X,Y,Z\)) and also centered at the satellite center of mass move with the body, and the spacecraft attitude is then defined as the relative angle from the local-level coordinates to the body frame.  
The line-of-sight unit vector \(\ell_{0}\) is along the initial direction of the boresight, and \(\ell_{f}\) denotes its final desired direction. The figure depicts a bright object to be avoided by the telescope. The centroid of the bright point, \(\mathbf{s}\) (from the Sun), is specified by an angle (azimuth) \(\alpha^{*}\) in the slew-plane \(\ell_{0}-\ell_{f}\) and an elevation angle \(\epsilon^{*}\) that measures its angle from the Y-axis. Although the Sun is moving with respect to the Earth-centered frame, we  
Figure 1: The inertial LVLH frame (X,Y,Z) and the body fixed frame ((x,y,z) shown in grey) with the telescope boresight initial and final directions \(\ell_{0}\) and \(\ell_{f}\).  
may assume for our purposes that since the maneuver time is significantly shorter than a solar day, the Sun's position is fixed in this frame. The celestial object is avoided by a minimum specified angle denoted \(\epsilon_{spec}\); that is, the dot product of the Sun's position vector \(\mathbf{s}\) with the boresight axis position \(\mathbf{a}\) must fall within the set:  
\[\mathcal{S}_{1}=\{\mathbf{v}\in\mathbb{R}^{3}\mid\mathbf{s}\bullet\mathbf{v} \leq\sin\epsilon_{spec},\ \|\mathbf{v}\|=1\}. \tag{4}\]  
This set describes a region bounded by a cone in \(R^{3}\), and the intersection of this cone with the sphere \(\mathbb{S}^{1}\) describes the boundary of the forbidden region of the orientation of the boresight axis. In this paper, the boresight axis is denoted by \(\mathbf{a}=[2,2,1]\), and the trajectory traced by the boresight is then be given by:  
\[\mathbf{a}(t)=R(t)\cdot\mathbf{a}. \tag{5}\]  
This trajectory \(\mathbf{a}(t)\) must lie within the set \(\mathcal{S}_{1}\) at all times.