esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Development of \(n_{p}\) Estimation Model
#### 2.5.1 Estimation of TSM from \(R_{\rm rs}(\lambda)\)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**X** & **Detailed Band Form** & **Optimal Band Combination** & **R** \\ \hline X1 & \(R_{\text{rs}}(\lambda_{1})\) & \(\lambda_{1}\) = 680 nm & 0.839 \\ \hline X2 & \(\log_{10}(R_{\text{rs}}(\lambda_{1}))\) & \(\lambda_{1}\) = 680 nm & 0.588 \\ \hline X3 & \(R_{\text{rs}}(\lambda_{1})-R_{\text{rs}}(\lambda_{2})\) & \(\lambda_{1}\) = 660 nm, \(\lambda_{2}\) = 680 nm & 0.725 \\ \hline X4 & \(\frac{R_{\text{rs}}(\lambda_{1})}{R_{\text{rs}}(\lambda_{2})}\) & \(\lambda_{1}\) = 660 nm, \(\lambda_{2}\) = 555 nm & 0.606 \\ \hline X5 & \(\frac{\log_{10}R_{\text{rs}}(\lambda_{1})}{\log_{10}R_{\text{rs}}(\lambda_{2})}\) & \(\lambda_{1}\) = 490 nm, \(\lambda_{2}\) = 660 nm & 0.581 \\ \hline X6 & \(\frac{R_{\text{rs}}\lambda_{1}-R_{\text{rs}}(\lambda_{2})}{R_{\text{rs}} \lambda_{1}/R_{\text{rs}}(\lambda_{2})}\) & \(\lambda_{1}\) = 660 nm, \(\lambda_{2}\) = 680 nm & 0.739 \\ \hline X7 & \(\frac{R_{\text{rs}}\lambda_{1}+R_{\text{rs}}(\lambda_{2})}{R_{\text{rs}}( \lambda_{1})/R_{\text{rs}}(\lambda_{2})}\) & \(\lambda_{1}\) = 555 nm, \(\lambda_{2}\) = 660 nm & 0.847 \\ \hline X8 & \(\frac{R_{\text{rs}}\lambda_{1}-R_{\text{rs}}(\lambda_{2})}{R_{\text{rs}}( \lambda_{1})+R_{\text{rs}}(\lambda_{2})}\) & \(\lambda_{1}\) = 660 nm, \(\lambda_{2}\) = 555 nm & 0.555 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlations between TSM and band form X derived from GOCI (Geostationary Ocean Color Imager) six bands. X1 to X8 indicate eight band forms, respectively; \(R\) is the correlation coefficient. Figure 3: Scatter plots of X7 and TSM using different mathematical functions ((**A**) linear, (**B**) power, (**C**) exponential, and (**D**) logarithmic functions), overlaid by model accuracies and function expressions. X7 refers to \((R_{\text{rs}}(555)+R_{\text{rs}}(660))/(R_{\text{rs}}(555)/R_{\text{rs}}(660))\). The solid red lines are the fitted function curves, and the dotted red lines are the 95% confidence bounds. established two strong relationships using the X7 form to model the \(b_{\text{bp}}(488)\). As shown in Figure 4C, the fitted determination coefficient, \(R^{2}\), was 0.820 (\(p<0.001\)), with relatively low predictive errors.
# Methodological Considerations of Using Thermoelectrics with Fin Heat Sinks for Cooling Applications
## 2 Materials and Methods
### Experimental Procedure  
An experimental apparatus to determine the thermal performance of a FHS-TE assembly is shown in Figure 5. The 40 mm \(\times\) 40 mm \(\times\) 4 mm thick TE module used is the HT8, 12, F2, 4040 manufactured by Laird Technologies (Penang, Malaysia). The five-fin aluminum FHS measured 45 mm \(\times\) 45 mm, with a 10 mm thick base. The heating element measured 40 mm \(\times\) 40 mm \(\times\) 4 mm, and the aluminum block 40 mm \(\times\) 40 mm \(\times\) 5 mm. The electrical circuit for this is also shown. Thermal insulation was provided at the bottom and sides of the assembly up to, and including, the TE module to minimize heat loss to the ambient environment. The insulation consisted of a composite layer of 10 mm thick cork board (Trusco, Osaka, Japan) and 95 mm thick rockwood (Rockwool, Selangor, Malaysia). Heating was provided from an AC power supply (Shanghai MCP, Shanghai, China). Power to the heat source (\(P_{EH}\)) was determined from the product of AC voltmeter (\(V_{EH}\)) (GW Instek, Taipei, Taiwan) and ammeter (\(I_{EH}\)) readings (GW Instek, Taipei, Taiwan). Power input to the TE (\(P_{te}\)) was determined from the product of DC voltmeter (\(V_{te}\)) (GW Instek, Taipei, Taiwan) and ammeter (\(I_{te}\)) readings (GW Instek, Taipei, Taiwan). Four thermocouples (Tf1-Tf4) (Graphtec, Tokyo, Japan) were inserted into 3-mm diameter holes drilled in a row through the base of the heat sink to measure the FHS/TE interface temperature distribution. TE hot surface temperature (\(T_{h}\)) was obtained from the arithmetic mean of these four temperatures. Thermocouples centrally located in 1.5-mm deep grooves machined on the top and bottom surfaces of the aluminum block measured the mean cold surface temperature at the top of the aluminum block (\(T_{alm}\)) and of the heating surface (\(T_{s}\)). The TE cold junction temperature (\(T_{c}\)) was assumed equal to \(T_{alm}\). The mean operating temperature (\(T_{m}\)) across the TE module was calculated by taking the arithmetic mean temperatures between hot (\(T_{h}\)) and cold surfaces (\(T_{c}\)). Additional thermocouples were employed to measure the external surface temperature of the insulation (\(T_{ins}\)) and ambient temperature (\(T_{a}\)). All thermocouples were connected to a data logger and logged every minute. Experiments were performed with heater power inputs at 10 W and 20 W (nominal). At each power input setting, experiments were carried out with increasing TE voltages from 1 V to 6 V. The results are tabulated in Table 1 for NC (Runs D1-D2) and for FC (Runs D3-D4).  
Figure 4: Flowchart to simulate the TE hot side (\(T_{h}\)) temperature.
## 3 Preliminaries
### Basic Ant-Based Routing (BABR) Algorithm  
In ACO, ants exchange data by pheromones, and according to the positive feedback principle, a path with a high density of pheromones has a higher probability of being selected. Such optimization can be adapted to implement basic ant-based routing for WSNs [9; 23]:  
**Step 1**: At regular intervals, a forward ant \(k\) starts to move from the source node toward the destination. While moving, the identifiers of every visited node are recorded in a list, \(M_{k}\), and each forward ant avoids traversing a node that has been visited previously.  
Figure 1: The energy consumption model.  
**Step 2**: At each node \(r\), a forward ant selects the next hop node in accordance with a certain probability distribution:  
\[P_{k}(r,s)=\left\{\begin{array}{l}\frac{[T(r,s)]^{\mu},[E(s)]^{\
u}}{\sum_{ \sigma M_{k}}[T(r,s)]^{\mu},[E(s)]^{\
u}},\;\mbox{if}\;s\
otin M_{k}\\ 0,\mbox{\emph{otherwise}}\end{array}\right. \tag{4}\]  
where \(P_{k}(r,s)\) is the probability of individual \(k\) that moves from node \(r\) to node \(s\), and \(T\) is the routing table at each node with the amount of pheromone on the link \((r,s)\) stored. \(E\) represents the heuristic information given by \(1/(C-e_{s})\) (\(C\) is the initial energy level of the nodes and \(e_{s}\) is the actual energy level of node \(s\)), and \(\mu\), \(\
u\) are weight parameters that signify the importance of pheromones versus heuristics.  
**Step 3**: When a forward ant reaches the destination, a backward ant goes back along the links that the forward ant has visited. Before moving, the amount of pheromones that the ant will drop during the trip is computed:  
\[\Delta T_{k}=\frac{1}{N-Fd_{k}} \tag{5}\]  
where \(N\) is the total number of nodes, and \(Fd_{k}\) is the distance traveled by the forward ant \(k\).  
**Step 4**: Whenever a node \(r\) receives a backward ant from a neighbor node, the routing table is updated:  
\[T_{k}(r,s)=(1-\rho)T_{k}(r,s)+\Delta T_{k} \tag{6}\]  
where \(\rho\) is a coefficient, and then \((1-\rho)\) represents the evaporation of pheromones.  
**Step 5**: Once a backward ant returns to the source node, the next interval is continued.  
After several iterations, each node will find the best neighbors to which to send a data packet. While the ability and robustness of the ACO-based method qualify it to find a good solution, it still has the possibility of getting stuck in slow astringency and early stagnation.
# Edge and Cloud Computing in Smart Cities
## 2 Architectural Models for Edge and Cloud Computing in Smart Cities
### Multi-Tier Hierarchical Architecture
To capture the overall energy consumption at different layers, both active and idle states are considered  
\[E_{total}=\sum_{i=1}^{K}\Bigl{(}P_{D\to E_{i}}T_{D\to E_{i}}+P_{E_{i}}^{\text{ active}}\alpha_{i}T_{E_{i}}+P_{E_{i}}^{\text{idle}}(1-\alpha_{i})T_{E_{i}}+P_{E_{i} \to\text{C}}T_{E_{i}\to\text{C}}+P_{\text{C}}T_{\text{C}}\Bigr{)}, \tag{9}\]  
where \(P_{D\to E_{i}}\) is the power consumed for data transmission from devices to the edge, \(P_{E_{i}}^{\text{active}}\) is the power used when processing at the edge, \(P_{E_{i}}^{\text{idle}}\) accounts for background energy usage when idle, and \(P_{E_{i}\to\text{C}}\) and \(P_{\text{C}}\) represent power usage for cloud communication and processing. This model ensures that idle energy consumption is accounted for, making it more realistic for power-constrained edge devices [24; 25; 26]. This hierarchical architecture efficiently balances latency, computational demand, and energy efficiency, enabling real-time smart-city applications. However, challenges such as network congestion, resource synchronization, and adaptive task migration require advanced AI-based scheduling techniques to enhance system resilience [27].
## 4 Results
### More Challenging Tests: LFTO as Additional Fault
#### 4.3.1 Tests with Four Classification Classes and LFTO  
The data signal with LFTO and those without LFTO were fed into the network separately to see how much the double fault condition \"disturbs\" the classification process. It should be noted that validation accuracy changes greatly from non-decimated to decimated data. Accuracy also varies between the validation dataset and the test dataset. As for the previous tests with the current signal, a maximum number of 24 epochs was set for the process, whilst all other parameters were kept the same as before. The results are shown in Table 5.  
As can be seen from Table 5, the double fault condition introduced more difficulties in the classification process, as expected. The data with a full (non-decimated) frequency spectrum gives better classification results. In particular, with the decimated data, difficulties were especially encountered in recognizing the H12 class (for both the single and double fault conditions) and the H6 class (in the case of double fault), as shown in Figure 15.  
It should be noted that, in the case of LFTO, the electromagnetic signal's images generated with the CWT do not show characteristic patterns easily visible to the naked eye, as is possible to see in some of the vibration spectrogram images presented in [25], so it should also be quite difficult to distinguish the scalograms obtained with the double fault compared to the single fault condition for the neural network. Two sample images from the decimated data are reported in Figure 16.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Decimation** & **Validation Accuracy** & **Test Accuracy** \\ \hline \multirow{2}{*}{Bearing fault + LFTO} & NO & 65\% & 83.3\% \\  & YES & 56.7\% & 29.2\% \\ \hline \multirow{2}{*}{Bearing fault} & NO & 98.33\% & 95.8\% \\  & YES & 75\% & 75\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Validation and test accuracy for the bearing fault and LFTO condition compared with the single fault condition data.
## Materials and Methods
### Characterization Methods  
UV-Vis measurements were carried out with a Shimadzu UV-1800 spectrophotometer (Shimadzu Corporation, Kyoto, Japan). Powder diffraction patterns were collected on an X'Pert Pro MPD automated diffractometer (PANalytical, Almelo, The Netherlands) equipped with a Ge(111) primary monochromator (strictly monochromatic Cu-K\({}_{\alpha}\) radiation) and an X'Celerator detector (PANalytical, Almelo, The Netherlands). Textural parameters were obtained from N\({}_{2}\) adsorption-desorption isotherms with a Micromerities ASAP 2020 (Micromerities Ltd., Bedfordshire, UK). The specific surface area and pore volume of the \(\Phi_{\chi}\)-PPH were determined by the adsorption-desorption of N\({}_{2}\) at \(-196\)\({}^{\circ}\)C. Before analysis, the \(\Phi_{\chi}\)-PPH samples were degassed at 150 \({}^{\circ}\)C up to \(10^{-4}\) Torr. Pore volume and an average pore diameter were determined by the Barrett, Joyner, [PERSON] model. X-ray photoelectron spectra (XPS) were obtained using a Physical Electronics PHI 5700 spectrometer with a non-monochromatic Mg K\({}_{\alpha}\) radiation (300 W, 15 kV, \(h\
u\) = 1256.3 eV) as the excitation source. Spectra were recorded at 45\({}^{\circ}\) take-off angles by a concentric hemispherical analyser (Physical Electronics, Inc., Chanhassen, MN, USA) operating in the constant pass energy mode at 25.9 eV, using a 720 \(\upmu\)m diameter analysis area. Under these conditions the Au 4\(f_{7/2}\) line was recorded with 1.16 eV FWHM at a binding energy of 84.0 eV. The spectrometer energy scale was calibrated using Cu 2\(p_{3/2}\), Ag 3\(d_{5/2}\) and Au 4\(f_{7/2}\) photoelectron lines at 932.7, 368.3 and 84.0 eV, respectively. Charge referencing was done against adventitious carbon (C 1s 284.8 eV). The C 1s signal of some samples was also studied with an Al K\({}_{\alpha}\) radiation due to the presence of sodium to avoid the overlapping of the Na KLL Auger signal at 290.3 eV. Solid surfaces were mounted on a sample holder without adhesive tape and kept overnight in a high vacuum in the preparation chamber before they were transferred to the analysis chamber of the spectrometer. Each region was scanned with several sweeps until a good signal-to-noise ratio was observed. The pressure in the analysis chamber was maintained lower than \(10^{-9}\) Torr. A Shirley-type background was subtracted from the signals. Recorded spectra were always fitted using Gauss-Lorentz curves in order to determinate more accurately the binding energy of the different element core levels. The accuracy of binding energy (BE) values was within \(\pm 0.1\) eV.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 3 New Energy-Aware Routing Protocols
### Discussion  
We have presented three different strategies to support energy awareness for DTN routing protocols. The Threshold strategy is straightforward and efficient; however, it requires the user to provide a predetermined threshold for the node to enter a power-saving mode. Since the threshold is fixed, the Threshold strategy does not perform optimally under different operating conditions such as different energy harvesting, packet generation rates and changed motion patterns.  
The RRE strategy addresses the inflexibility of the Threshold strategy by determining the optimal threshold automatically. A drawback of RRE is that nodes can consume too much energy to forward or sample data in scenarios where they encounter other animals non-uniformly during the day.  
The ORRE strategy aims to improve the packet delivery rates of RRE by introducing a reserved energy budget for a certain minimum number of data samples collected during the day. This approach is capable of achieving an appropriate balance between forwarding and sampling data, while conserving sufficient energy for direct delivery to the base station at later stages.  
In all of these algorithms, the nodes that have never met the base station, are not informed about the packets that are delivered to the base station. Consequently, the energy they expend on packets that were already delivered to the base station through other nodes are wasted. This reduces the chance of delivering packets to the base station. To account for this effect, we consider a global buffer, in which the nodes track the packets that were delivered to the base station.
# Soft Iterative Decoding Algorithms for Rateless Codes in Satellite Systems
## 1 Introduction
Motivated by the previous works, this paper presents two proposals to apply rateless codes for HARQ in satellite systems in order to provide efficient data broadcasting services. We especially focus on compensating rain fading in satellite systems by utilizing efficient soft iterative decoding algorithms for the proposed rateless coded schemes. The first proposal is to use rateless codes in the PHY layer to accomplish Type-II HARQ, by applying an efficient parallel soft iterative decoding algorithm to the rateless LDPC coded scheme proposed in [6]. We present the detailed equations and flowchart for the proposed algorithm and demonstrate the performance over a rain fading channel in the satellite system. The second one is to use rateless codes in the MAC layer for a cross-layer coding scheme. For this, we also present the detailed equations and flowchart for the soft iterative decoding algorithm based on the Tanner graph of the cross-layer code. Simulation results are demonstrated over an additive Gaussian noise channel and a rain fading channel. The remaining part of this paper is organized as follows. After this introduction, we first review the concept of HARQ schemes with rateless codes. Section 3 proposes two HARQ schemes with rateless codes, and for both of the schemes we propose soft iterative decoding algorithms. Section 4 presents performance simulation results over satellite channels and demonstrates the excellence of the proposed schemes. Finally, Section 5 concludes the paper.
# Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
## 4 Experiment
### The Experiment's Models' Training and Evaluation
#### 4.2.1 Evaluation of Machine Learning with a Random Label Flipping Attack  
Comparing these results with the previous analysis, it can be observed that the performance metrics of all models have decreased significantly when trained on the poisoned dataset generated using the label flipping attack, as shown in Table 3. This indicates that the attack successfully reduces the models' performance on the original dataset. Specifically, all models' F1 Scores, precision, recall, and accuracy have decreased compared to their original values. The extent of the decrease varies depending on the model, with some models being more resilient to the attack than others, as shown in Figure 8a-d and Table 3; for example, the logistic regression model appears to be the most resilient, with the smallest decrease in performance, as shown in Figure 8a and Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **LR** & **Random Forest** & **SVC** & **XGB** \\ \hline Acc & 0.8698988644428672 & 0.8089513839602555 & 0.8475869410929737 & 0.8622693399574166 \\ F1 & 0.8806219219341447 & 0.8075771791091453 & 0.8513583664993943 & 0.8655203776690199 \\ Prec & 0.9217791411042945 & 0.9463874345549739 & 0.9569191870076826 & 0.9742589703588144 \\ Recall & 0.8429829346216785 & 0.7042780331956674 & 0.7667731629392971 & 0.7786176264318554 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model evaluation metrics with random label flipping attack.  
Figure 7: (a) Logistic regression; (b) random forest; (c) SVC; and (d) XGB.
## 4 Evaluation Methodology  
This section discusses the evaluation methodology we propose to adapt and optimize the deployment of FastSLAM 2.0 onto the hardware architecture in use. Firstly, we analyze the algorithm's dependencies. Then, we divide the algorithm pipeline into functional blocks with the same dependencies. Subsequently, each parameter is thresholded to limit the processing time. The parallelized design of each functional block contemplates one or more parallelization modalities, depending on which parameters affect its complexity (i.e., number of particles \(N_{p}\), number of existing landmarks \(N_{l}\), number of observations \(N_{o}\), and number of matched observations \(N_{c}\)), as further described in Section 5.  
Dependency analysis and parameter thresholding are widely employed in the parallelization process of different types of SLAM algorithms. In [66, 67], such a methodology is leveraged to study the performance of graph-based SLAM on embedded systems; in [68], it was used to evaluate an efficient implementation of EKF-SLAM, and in [39], it was used for FastSLAM 2.0.  
Our design takes a comprehensive approach by exploring multiple parallelization methodologies to achieve the best performance across different contexts. We leverage a dependency analysis to define multiple possible grid configurations, which are later deployed on the hardware architecture of interest. The evaluation is carried out by comparing each parallelization modality to the sequential implementation according to the performance indexes described in Section 4.4. The performance analysis under different test cases (i.e., contexts) is carried out in the form of a sensitivity analysis, which means that the variation of the index of interest is observed as an effect of the variation of a single parameter, while the others are kept constant.  
In the state-of-the-art, the parallelization modality is not always disclosed, making it rather impractical to perform our validation methodology on such proposals with the available information. However, whenever such information is available, the parallelization usually happens particle-wise (i.e., the number of concurrent operations is equal to \(N_{p}\)). Due to the adaptability of our design, we can test multiple modalities, including, among others, particle-wise parallelization. Furthermore, we wish to remark that, unlike the state-of-the-art proposals, it is not within our scope to prove the optimality of a single implementation under the widest variety of scenarios but to provide a flexible design that can be easily adapted to reach optimality under various conditions and with the support of various hardware architectures.
## 3 Results and Discussion
### Temperature Field and Velocity Field
#### 3.1.2 Velocity Field (w)  
When the hydrothermal plume receives vertical momentum, its velocity increases rapidly from the initial velocity to the maximum velocity. Due to the fluid's gravity, viscous effect, and entrainment, the buoyancy effect diminishes gradually. Its velocity begins to decrease until it becomes zero at the maximum plume rising height. Figure 9 shows the velocity distribution simulated by different turbulence models in a quasi-steady state. The velocity vectors of the two models are obviously different in the near-wall region. Compared with the rke model, the sst model carries out too much calculation on the boundary layer of the wall. The upwelling process of hydrothermal plumes is mainly turbulent entrainment, mixing and diffusion, but it is unlikely to have too much contact with the seafloor boundary layer.  
Figure 8: Temperature profile distribution of the center line of the quasi-steady hydrothermal plume.  
Figure 7: Temperature distribution of the quasi-steady hydrothermal plume.  
Since the plume velocity was not measured in this observation, the plume velocity simulated by [PERSON] et al. in the Daxi hydrothermal field of the Carlsberg Ridge in a similar environment was selected as the comparison standard [35]. On this basis, the plume velocity calculated by the rke and sst models is analyzed. Figure 10 shows that the vertical velocity of the hydrothermal plume increases from the initial velocity of 0.2 m s\({}^{-1}\) to 1 m s\({}^{-1}\) or even 2 m s\({}^{-1}\) in the area near the vent. This trend is consistent with the movement of hydrothermal plumes. According to the simulation results of environmental conditions (case2), the RMS of velocity of the rke model is 0.5081 and that of the sst model is 0.5122. The comparison of data again shows that the numerical simulation of hydrothermal plume in this paper is reasonable.  
In the same working conditions, compared with the reference value, the calculation velocity of the rke model is larger than that of the sst model, and the rke model has a smaller RMS error than the sst model. In addition, the sst model has a transitional prediction of the seabed velocity. To sum up, the rke model is better at predicting the velocity of hydrothermal plume.  
Figure 10: Center line velocity profile distribution of the quasi-steady hydrothermal plume ([PERSON] et al. 2020 [35]). The stars represent two special values of velocity, the initial velocity and the maximum velocity.  
Figure 9: Velocity vector distribution of quasi-steady hydrothermal plume.