esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Formulation
### DoS Attack Model  
For the previously mentioned NCS, we consider attacker launch attacks, when the power system requires the emergency auxiliary control offered by DES. DoS attack blocks communication channels to degrade the control performance, even causing system failures. The blocks of communication channels probably result in the absence of some remote states \(x_{j}\) and \(j\in N_{c}(i)\) of the current time stage \(k\)[25]. When controller cannot obtain the remote state from cyber connected area \(j\), \"zero-control\" strategy is applied to assigning \(x_{j}=0\). \(u_{i}\) becomes  
\[u_{i}=\varphi[\{\alpha_{ji}x_{j}|j\in N_{c}(i)\cup i\}], \tag{3}\]  
where \(\alpha_{ji}\in\{0,1\}\) is distributed according to Bernoulli distribution, which is  
\[\left\{\begin{array}{l}P(\alpha_{ji}(k)=0)=\delta_{ji},\\ P(\alpha_{ji}(k)=1)=1-\delta_{ji},\end{array}\right. \tag{4}\]  
where \(\delta_{ji}\in[0,1]\) is the probability of packet drop in the communication from area \(j\) to \(i\), \(k\in N^{+}\). The probability \(\delta_{ji}\) increases the intensity of DoS attack. DoS attacker has the limited cyber resources, which constrains the intensity of DoS attack at a certain degree. The constraint in our model is subject to  
\[\sum_{i=1}^{n}\sum_{j\in N_{c}(i)}\delta_{ji}\leq C. \tag{5}\]  
Figure 1: Area structure.  
The probability \(\delta_{ji}\) is limited to the intensity of DoS attack. The intensity of DoS attack is under the constraints of cyber cost, which is also limited. Therefore, the sum of probability \(\delta_{ji}\) must be less than a maximal real number \(C\).
# Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
## 2 Materials and Methods
### Deep Neural Network Approaches
#### 2.2.1 Conventional Deep ANN/Multilayer Perceptron (MLP)  
Multilayer perceptron was introduced by [PERSON] in 1958 as the basic type of neural network and consists of a number of perceptron [46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59]. There is an input layer to receive the data and there is an output layer that determines and predicts the output value in multilayer perceptron. Between the input and output layers, there is a selected number of hidden layers, which is the main processing engine of MLP [56; 57; 58; 59; 42; 50; 54; 56; 57; 58; 59].  
As shown in Figure 2, MLP is a simple neural network. Equation (1) is used to calculate the output of a single perceptron or neuron [46; 59].  
\[output=f\Big{(}\sum_{i}^{inputs}(x_{i}.w_{i}+b_{i})\Big{)}, \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Dates**}} & \multicolumn{2}{c}{**P\({}_{1}\)**} & \multicolumn{2}{c}{**P\({}_{2}\)**} & \multicolumn{2}{c}{**P\({}_{3}\)**} & \multicolumn{2}{c}{**Average**} & \multirow{2}{*}{**Radiation**} & \multicolumn{2}{c}{**Panels**} \\
**dd.MM.yyyy** & \multirow{2}{*}{**Amorphous Thin-Film**} & \multirow{2}{*}{**Polycrystalline**} & \multirow{2}{*}{**Minocrystalline**} & \multirow{2}{*}{**Minocrystalline**} & \multirow{2}{*}{**Minocrystalline**} & \multirow{2}{*}{**Temperature**} & **Amounts** & **Temperature** \\
**HHem** & & & **(kWh)** & & **(kWh)** & & **(\&Wh)** & **(\& **(\& ^{*}C)** \\ \hline
01.01.2014 11:50 & 454.81 & 600.56 & 613.59 & 7.40 & 55.00 & 7.70 \\
01.01.2014 11:55 & 454.82 & 600.57 & 613  
where \(x_{i}\) is the input of the neuron, \(w_{i}\) is the weight on each connection to the neuron, \(b_{i}\) is the bias, and \(f(.)\) is the activation function, for instance, the tanh activation function [16; 47; 59].
## 4 Future Research Trends for ML-Based IRS-Assisted Wireless Communication
### Protection Against Eavesdropping  
Security is a great concern in communication systems in recent years and physical layer security (PLS) has attracted considerable attention. PLS has an advantage over the cryptography technique. PLS has less computational complexity and the structure of 6G network is decentralized; thus, key management will be difficult for each device in the network [111]. As IRS creates beamforming, security during data transmission can be achieved. Different environment threats combined with ML techniques can be modeled, and appropriate actions can be taken. This approach can be investigated in future studies.
## 4 Overview of GNSSs
### Quasi-Zenith Satellite System
#### 4.5.4 QZSS User Segment  
Signals that complement the GPS positioning service are designed to minimize the need for modifications to the user's hardware. Receivers can seamlessly receive and track GPS and QZSS signals, decode their navigation messages, and calculate a user's position, speed, and time based on a combination of GPS and QZSS observations. There is a difference in the handling of GPS and QZSS clock information between the systems, requiring the user to take into account the distortion between the signals [92].
## 4 Simulation of Direct Vapor Generation
### Single Pore Scenario  
The problem is investigated using the TLBM approach. In the simulations, the depth of pore is fixed as \(400\Delta x\), and the width of the pore varies according to the pore size. It is assumed that the total surface is \(2\times 10^{4}\Delta x\). Hence, \(\Gamma>0.98\) implies that the pore width is less than \(400\Delta x\). In our simulations, the heat flux density \(q_{\rm in}\) is set to be \(1\times 10^{-4}\). The constant heat flux boundary condition is applied to the solid walls of the pore. By changing the pore width and adopting the corresponding heat flux at the solid walls according to Equation (41), scenarios with various pore width are realized. Since the vapor outlet is connected to the ambient, a constant density and temperature corresponding to the ambient are assumed for the boundary layer of the outlet. At the inlet of the pore, the replenishment boundary condition is considered for the flow field, and a constant temperature boundary condition is employed for the temperature field. We mainly focus on the overall efficiency, which is then compared with the experimental data reported in [48].  
The overall efficiency \(\eta\) is an important performance index for vapor generation devices. It is defined as the ratio of enthalpy change in the generated vapor to the incident solar power, that is  
\[\eta=\frac{nh_{fg}}{Q_{a}}, \tag{42}\]  
Figure 11: Microscale pores for vapor generation: the yellow layer is the absorber that absorbs solar energy; under the absorber is the insulator; liquid is shown as the blue region of the figure; vapor is generated from evaporation.  
where \(\dot{m}\) is the mass flow rate and \(h_{fg}\) is the specific enthalpy change of water (from liquid to vapor). When the dimensionless Van der Waals EOS is considered, the enthalpy \(h\) is expressed as [32]  
\[h=4T\Big{(}\frac{5-\rho}{3-\rho}\Big{)}-6\rho. \tag{43}\]  
And \(h_{fg}\) is obtained via its definition, that is  
\[h_{fg}=h\Big{|}_{\text{vapor}}-h\Big{|}_{\text{liquid}}. \tag{44}\]  
The comparison between the experimental data reported in [48] and simulation results is shown in Figure 12. Note that in experiments, the estimation of efficiency takes into account the conduction and radiation losses of the device, which are ignored in simulations. In other words, TLBM simulation studies the ideal case. It is observed that the efficiency decreases as the thermal concentration keeps increasing. According to Equation (42), efficiency is mainly dominated by the mass flow rate \(\dot{m}\), since the input power \(Q_{a}\) and the enthalpy change \(h_{fg}\) does not vary much. Hence, we have the following relationship  
\[\eta\propto f_{\text{e}}A_{\text{e}}, \tag{45}\]  
where \(f_{\text{e}}\) is the mass flux of vapor, and \(A_{\text{e}}\) is the surface area for evaporation. In our two-dimensional study, \(A_{\text{e}}\) is the channel width. The above relationship is also plotted in Figure 12 as a comparison with both simulations and experimental data.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 2 Materials and Methods
### Uplink Transmission Energy Model
\[E_{fail}=P_{SL}\frac{1}{r_{atempt}}+P_{GPS}t_{GPS}+P_{RX}t_{pass} \tag{7}\]  
The above two cases can be combined into a complete model: \(E_{attempt}=p_{success}E_{success}+(1-p_{success})E_{fail}\), which expands into the following expression:  
\[E_{attempt}=\frac{P_{SL}}{r_{attempt}}+P_{GPS}t_{GPS}+p_{success}( \epsilon_{pass}P_{RX}t_{pass}+\frac{E_{TX}t_{pkt}}{p_{success}r_{atempt}}) \tag{8}\] \[+(1-p_{success})P_{RX}t_{pass}\]  
where \(P_{SL}\), \(P_{GPS}\), and \(P_{RX}\) are all constants and given by Swarm. Similarly, location fix time \(t_{GPS}\) is rather constant, reported to be about 30 s by Swarm. Furthermore, note that \(r_{attempt}\) depends on site conditions, project requirements, and packet scheduling. Similarly, \(p_{success}\) and \(t_{pass}\) depend heavily on site conditions and packet scheduling.
## 3 Results  
Four patients were treated using the CAD/CAM and 3D printing technology as the described protocol. The sex was equally distributed, the average age was 79 years (range 61-88 years). The operating times were very short, with an average surgical time of 45 min. No complications were observed during surgery or at the immediate post-surgical care. The average hospitalization time after surgery was four days (ranging from two to six days). At the six-monthly follow-up, we observed good clinical outcomes in our samples in terms of facial symmetry restoration, absence of sensory disturbances of the inferior alveolar nerve, no facial nerve weakness, satisfying joint and masticatory function, and mean mouth opening of 37 mm (ranging from 33 to 40 mm).  
Regarding the digital workflow, the virtual planning design took around 2 h for each case while about 6 h for each case for the printing process and sterilization. On average, about 45 \(\pm\) 2 mL of resin was required to print the models. An estimated cost of about 6.3 Euros is estimated for each case. The discrepancy values deriving from the overlapping analysis of the models were shown in Table 1 (Table 1). In the examined samples, all the obtained discrepancies' values were less than 1.5 mm. The mean value of the discrepancies  
Figure 3: Digital analysis. Distance color map of the four cases. On the right the color legend bar.  
of the whole overlapping cloud point was 0.69 \(\pm\) 0.33 mm. Regarding the discrepancies of the cephalometric examined points in the sample, the higher mean value was obtained in the condylar site (Right: 0.80 \(\pm\) 0.26 mm--Left: 0.79 \(\pm\) 0.32 mm), while the lower mean value was obtained in the B Point (0.55 \(\pm\) 0.41 mm).
## 3 Preparation and Testing of Specimens
### Corrosion Tests  
Concrete specimens with dimensions of \(280\times 70\times 70\) mm\({}^{3}\) were manufactured for electrochemical tests. Each specimen was reinforced with a bare or galvanized steel plate (\(210\times 40\times 1\) mm\({}^{3}\)), embedded within a 3 cm concrete cover. Steel plates were used instead of common bars because they can allow specimen cracking without splitting and they offer a higher anodic area at the crack apex. The galvanized steel plates, obtained by molten zinc immersion, were covered by a 100 \(\upmu\)m thick zinc layer, with an outer pure zinc layer (\(\upeta\) phase) about 20 \(\upmu\)m thick. The galvanized reinforcements, just before being embedded in the fresh concrete, were submerged for 5 s in a 15% sodium hydroxide solution to dissolve the ZnCO\({}_{3}\) layer formed during air exposure. The electric contacts between the metallic plates and the measurement equipment were arranged as reported in [14].  
After 1 month of air curing at T = 20 \(\pm\) 3 \({}^{\circ}\)C and RH = 50 \(\pm\) 5%, a crack width of 1 mm was produced in a pre-formed notch area of the specimens by applying a flexural stress with the apex crack reaching the metallic plates. Then, the specimens were exposed to weekly wet-dry cycles (2 days dry and 5 days wet) in a 10% NaCl solution.  
The corrosion risk of the reinforcement in the concrete specimens exposed to the chloride environment was evaluated by corrosion potential measurements by using a saturated calomel electrode (SCE) as a reference. The kinetics of the corrosion process was followed by polarization resistance measurements through the galvanodynamic method, where an external graphite bar was used as a counter-electrode. The polarization resistance was calculated as the average value between the anode and the cathode branch.  
In the following graphs, the reported electrochemical values are averages of the measurements carried out on 3 specimens of each type during the full immersion period.  
In order to validate the electrochemical tests, after 7 wet-dry cycles in the chloride solution, the concrete specimens were saw-cut and all the metallic plates were removed after splitting the concrete specimens, to evaluate the reinforcement corrosion by visual observation. The surface of the corroded area on bare steel plates was evaluated after pickling, whereas metallographic analysis was carried out on the cross-section of the galvanized steel plates to evaluate the coating thickness decrease due to the corrosive attack.
## 5 Hybrid Protocols
### Binary Tree Slotted Aloha
#### 5.2.2 Adaptive Binary Tree Slotted Aloha  
Adaptive Binary Tree Slotted Aloha (Adaptive BTSA) offers an improvement to the Q protocol [65]. This protocol adjusts the frame size based on the tags' responses in a current slot. Adaptive BTSA first uses features from the Q protocol. If there are numerous collisions in a frame, the reader ends the frame earlier and transmits a new command with a new frame length. If there are excessive idle slots, the reader once again ends the frame earlier and sends a new command with a smaller frame length.  
The reader uses the parameters \(B\) and \(Q_{fp}\) in order to calculate the frame length. The initial frame length is to \(L=2^{Q}\) and \(Q=4\). The Q algorithm can adjust the frame length by adjusting \(Q\). The value of \(Q\). is the rounded value of \(Q_{fp}\), which is a floating representation of \(Q\). In the following process, the reader dynamically adjusts each slot using the presented values \(c\)[34]. In the first slot, if a collision occurs, the reader will calculate \(Q_{fp}\) by increasing it by \(c\). In the case of an idle slot, the reader decreases \(Q_{fp}\) by \(c\). When the reader identifies a tag, it will not change \(Q_{fp}\). The flow chart of this protocol is shown in Figure 24.  
The function framesize(Q) shown in Figure 24 denotes that a new frame has started and its length is \(2^{Q}\). Adaptive BTSA combines the Q algorithm and the BinTree strategy. The main difference between Adaptive BTSA and the Q protocol is that when a collision occurs in the slot, the collided tags will be resolved by BinTree.
# Parallel Eclipse-Aware Routing on FPGA for SpaceWire-Based OBC in LEO Satellite Networks
## 5 Conclusions  
This paper proposed an FPGA-based parallel EAR method for LEO satellite networks, which achieves a 3.83\(\times\) execution-time reduction over the conventional CPU-based method. We employed an FPGA to overcome the resource limitations of OBCs and to manage energy consumption effectively using the EAR algorithm. The proposed method first generates multiple routes from the source to the destination using KSP, then selects the optimal path based on energy consumption rate and eclipse duration, and estimates transmission load as evaluated by EAR. Also, the proposed method was parallelized with pipelining methods to reduce the computation time. Finally, the applicability of the proposed method to real space systems was validated by performing remote routing-table updates on SpaceWire Brick MK4 devices.  
Conceptualization, H.L. and M.H.; methodology and software, J.H.P.; validation, formal analysis and investigation, H.L. and M.H.; writing--original draft preparation, J.H.P.; writing--review and editing, H.L. and M.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Agency for Defense Development, funded by the Korean government (Defense Acquisition Program Administration) (UI237032 TG).  
The datasets presented in this article are not readily available because the data are part of an ongoing study.  
The authors declare no conflicts of interest.
## 1 Introduction
Our results show that even without any post-equalization, the 28 Gbaud EDB and the 14 Gbaud PAM-4 formats can be used for the downstream transmission allowing us to achieve the optical power budget of 26-27 dB. With the equalization, the corresponding numbers are 27-30 dB. A strong digital post-equalization, using the equalizer with 43-FF and 21-FB taps, enables the 28 Gbaud PAM-4 alternative. It ensures a bit-error-rate (BER) below a 7% hard-decision forward error correction (HD-FEC) threshold of \(3.8\times 10^{-3}\) and a 23 dB optical power budget. The rest of the paper is organized as follows. Section 2 describes a configuration of the TDM-PON experimental setup used for a fair comparison of the selected IM/DD alternatives. Section 3 discusses the obtained results revealing the impact that signal equalization has on the achievable optical power budget in such PON systems. Finally, Section 4 briefly summarizes the research findings.
## 2 Materials and Methods
### Analysis Method  
First, we statistically analyzed the spatiotemporal dynamics and overall trends of the ESs. Next, we analyzed the trade-offs and synergies among the ESs at three scales: the microscopic spatial distribution, the regional scale, and the temporal scale [7; 20]. A positive correlation coefficient indicated a synergistic relationship between two ESs; a negative correlation coefficient indicated a trade-off relationship. The analyses were mainly performed using the \"scipy\" and \"geopandas\" packages in Python for correlation and spatial analyses.  
After specifying the spatiotemporal dynamics of the ESs, we employed structural equation modeling (SEM) to analyze the driving factors of the different ESs. SEM is a method used to establish, estimate, and test causal relationship models [31]. Compared to other statistical methods, SEM can simultaneously analyze the relationships among multiple factors, capturing more complex causal network structures. It is currently widely applied in the fields of ecological and environmental sciences [32]. Using the SEM model to establish association pathways allowed for a comprehensive analysis of the driving factors behind the ESs by integrating the direct and indirect effects among multiple variables. The analysis was conducted using the \"lavaan\" package in R version 4.2.3. Considering the regional attributes of the SHMB and the data availability, indicators representing geographic factors, socioeconomic factors, climatic factors, and vegetation were selected to explore their impacts on CS, HQ, FP, SC, and WY. The selected indicators included the DEM, slope, aspect, GDP, nightlight, population, temperature, precipitation, runoff, evapotranspiration, and NDVI, among others. We integrated 23 years of data and adjusted the model structure through several iterations to obtain a path structure that passed the goodness-of-fit test.  
Finally, the ES bundles were identified through clustering algorithms to analyze the ES characteristics and their potential management and policy implications. The self-organizing map (SOM) is an unsupervised learning algorithm for clustering data based on machine learning [33], and it has been widely adopted by researchers. The ES bundles were identified and visualized by mapping high-dimensional data onto a two-dimensional grid, allowing regions with similar types of ESs to be categorized together. The data underwent normalization to facilitate the mapping of the input data to grid nodes [34]. The ESs of the SHMB were classified into service clusters based on a raster scale, and geospatially distinct areas were grouped into four bundles of groups belonging to similar ES types (characterized by different colors) based on their clustering characteristics. The analysis was performed using the \"kohonen\" package in R 4.2.3.
# A Cross-Layer Media Access Control Protocol for WBANs
## 4 The Proposed Cross-Layer MAC Protocol  
The PACR-MAC protocol is designed to prolong the network lifetime while maintaining the QoS of the network. In this section, we provide a detailed explanation of the operational processes, which include base transmission power determination, priority-based transmission power adjustment, and point-to-point cooperative relay.
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ4--Edge Intelligence Application Domains
#### 4.4.3 Security  
The increase in the number and diversity of smart objects has raised substantial concerns about the vulnerability of IoT systems. The new and emerging IoT applications, and even further the concept of the smart city, require novel cybersecurity controls, models, and decisions distributed at the edge of the network to tackle challenges such as attack [71], intrusion [29] and anomaly detection [147], trust management [9], data privacy and others.  
The cloud can provide a set of desirable features such as sufficient computing power, memory, energy, etc. However, in a distributed system, some security applications might require a very short response time, and some might produce a large quantity of data (big data) which causes a burden for the network. As a result, cloud computing may not be efficient enough to support these applications [29].  
Motivated by the advent of edge computing, a great number of works have been proposed to improve the existing intelligent security applications in this new paradigm of distributed systems. In general, these frameworks can provide better results in comparison to non-distributed ones as the data can be processed at the edge for a shorter response time, helping to reduce the workload for the central server and the delay.
###### Abstract  
To increase the spatial resolution of Soil Moisture Active Passive (SMAP), this study modifies the downscaling factor model based on the Temperature Vegetation Drought Index (_TVDI_) using data from the Project for On-Board Autonomy (_PROBA-V_). In the modified model, _TVDI_ parameters were derived from the temperature-vegetation space and the Enhanced Vegetation Index (EVI). This study was conducted in the north China region using SMAP, _PROBA-V_, and Moderate Resolution Imaging Spectroradiometer satellite images. The 9-km spatial resolution SMAP data was downscaled to 0.3-km spatial resolution soil moisture using a modified downscaling method. Downscaling accuracies from the original and modified downscaling factor models were compared based on field observations. The results show that both methods generated similar spatial distributions in which soil moisture estimates increased as vegetation coverage increased from built-up areas to forest. However, based on the root mean square error between observations and estimations, the modified model demonstrated an increased estimation accuracy of 4.2% for soil moisture compared to the original method. This study also implies that downscaled soil moisture shows promise as a data source for subsequent watershed scale studies.  
soil moisture; temperature vegetation drought index; downscaling; SMAP; _PROBA-V_