esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Verification Metrics  
In this study, both deterministic and probabilistic metrics were adopted from the Ensemble Verification System [38] to evaluate the performance of EPFs and ESPs. The Mean Absolute Error (MAE) and Continuous Ranked Probability Skill Score (CRPSS) were selected to assess deterministic and probabilistic performances, respectively.  
(1) The MAE was used to measure the mean absolute difference of the ensemble mean forecasts and the observations as follows:  
\[MAE=\frac{1}{N}\sum\limits_{i=1}^{N}{|F_{i}-O_{i}|} \tag{6}\]  
Figure 2: Flowchart of the XAJ model.  
where \(F_{i}\) and \(O_{i}\) represent the forecasts and observations, respectively. The MAE ranges from 0 to infinity, and a smaller MAE indicates a better performance.  
(2) The CRPSS was used to verify the ensemble information when computing the forecast skill. It is calculated based on the Continuous Ranked Probability Score (CRPS), which quantifies the mean squared difference between the distribution of ensemble forecasts and corresponding distributions of observations. The CRPS and CRPSS are defined as follows:  
\[CRPS=\int\limits_{-\infty}^{+\infty}(P_{F}(x)-Po(x))^{2}dx \tag{7}\]  
\[CRPSS=1-\frac{CRPS}{CRPS^{*}} \tag{8}\]  
where \(P_{F}\), \(P_{O}\), and \(x\) represent the cumulative distribution functions (CDFs) of the forecasts and observations, and the event to be analyzed, respectively. \(CRPS^{*}\) represents the value of the reference forecasts [39]. The CRPSS ranges from minus infinity to 1, and a larger CRPSS indicates a better performance.  
Another popular diagnostic tool in forecast verification, the relative operating characteristic (ROC) curve was used to assess the forecast's ability [40]. The ROC curve plots the hit rate (HR) versus the false alarm rate (FAR) of a precipitation event for incremental decision thresholds. The closer the ROC curve to the upper left corner of the diagram (low false alarms, high hits), the greater the ability of discriminating precipitation events it reflects [41].
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 2 Definition of Algorithm
### Implementation of Information Systems Based on the Inverse Symmetry Algorithm  
The modulation process of the information system based on the inverse symmetry algorithm is shown in Figure 2.  
In Figure 1(a), double-sideband modulation is used, and the upper and lower sidebands are separated by two bandpass filters (BPF).Then the lower sideband is inverted, and finally the upper sideband is superimposed into the system shown in Figure 1(b). In Figure 1(b), a pair of orthogonal pre-carriers causes the lower sideband to fold, while the upper sideband is not affected by low-pass filter (LPF) suppression. A pair of orthogonal carriers are used to move the folded spectrum to the ideal position [14], and then two signals are added or subtracted to complete the modulation process. The demodulation process is the reverse of the above process.  
The modulation and demodulation process of the information system based on the inverse symmetry algorithm shall meet the following requirements:  
\[v_{1}=\sin\omega_{2}t\sin\omega_{1}t=\frac{1}{2}[\cos(\omega_{2}-\omega_{1})t -\cos(\omega_{2}+\omega_{1})t] \tag{11}\]  
\[v_{2}=\cos\omega_{2}t\sin\omega_{1}t=\frac{1}{2}[\sin(\omega_{2}-\omega_{1})t +\sin(\omega_{2}+\omega_{1})t] \tag{12}\]  
\[v_{3}=\frac{1}{2}\cos(\omega_{2}-\omega_{1})t \tag{13}\]  
Figure 2: The modulation process of the information system.  
\[v_{4}=\frac{1}{2}\sin(\omega_{2}-\omega_{1})t \tag{14}\]  
\[v_{5}=\sin\omega_{3}t\cos(\omega_{2}-\omega_{1})t=\frac{1}{2}[\sin(\omega_{3}+ \omega_{2}+\omega_{1})t+\sin(\omega_{3}-\omega_{2}+\omega_{1})t] \tag{15}\]  
\[v_{6}=\cos\omega_{3}t\sin(\omega_{2}-\omega_{1})t=\frac{1}{2}[\sin(\omega_{3}+ \omega_{2}-\omega_{1})t-\sin(\omega_{3}-\omega_{2}+\omega_{1})t] \tag{16}\]  
\[y_{1}(t)=v_{5}-v_{6}=\sin[(\omega_{3}-\omega_{2})+\omega_{1}]t \tag{17}\]  
\[y_{2}(t)=v_{5}+v_{6}=\sin[(\omega_{3}+\omega_{2})-\omega_{1}]t \tag{18}\]
# Reporting Key Features in Cold-Adapted Bacteria
## 3 Compounds and Mechanisms Involved in Stress Resistance and Cold Adaptation  
### Compatible Solutes and Related Compounds  
The importance of traits related to the metabolism of compatible solutes to cope with cold has been recognized in many cold-adapted bacteria. These compounds play an important role in osmoprotection and cryoprotection but can also function as carbon, nitrogen and energy sources [27]. The main studied compounds, glycine, betaine, glycerol, trehalose, sucrose, mannitol and sorbitol, can reduce the freezing point of the cytoplasm, prevent macromolecule aggregation, scavenge free radicals and stabilize cellular membranes under cold conditions [28].  
Recently, genome comparison and global phenotypic characterization of two Antarctic strains of _Pseudoalteromonas_, _P. haloplanktis_ TAC125 and _Pseudoalteromonas_ sp. TB41, conducted at 4 \({}^{\circ}\)C and 15 \({}^{\circ}\)C revealed differences for cold adaptation. Remarkably, protein S-thiolation, regulated by glutathione and glutathionylspermidine, appeared to be a new possible mechanism for cold adaptation in TAC125 [29]. The study provided evidence of the relevance of glutathione metabolism in this bacterium that showed enhanced uptake (or catabolism) of compounds that may confer cryotolerance such as spermine,glutathione and ornithine [29]. These compounds probably link compatible solutes and oxidative stress resistance. In line with these findings, genome analysis of _Colvellia psychrerythtraca_ showed the presence of genes coding for proteins involved in the synthesis and the degradation of polyamides, nitrogen reserves polymers, considered as a probable unique adaptation to cold environments [27; 30]. Moreover, metabolomic analysis of the Arctic isolate _Mesorhizobium_ sp. Strain N33 revealed an increment in the accumulation of sarcosine, threonine and valine when this bacterium was grown at 4 \({}^{\circ}\)C, probably acting as cryoprotectants [31].
## 3 Seismic Waveform Selection and Working Condition Setting  
### Seismic Waveform Selection  
The time-history response of the structure is quite different due to the input of various seismic acceleration waveforms. Thus, when designing and selecting the acceleration time-history curve, the influential factors should be comprehensively considered. Therefore, in this paper, strong earthquake records, such as EL Centro waves and Taft waves [24; 25], are selected according to the IEEE 693 standard for seismic inspection and verification and a set of artificial seismic waves are used for time-history analysis. The acceleration time-history of the EL Centro waves were recorded at the EL Centro station in the Imperial Valley earthquake (M7.1) in the United States on 18 May 1940. It is a classic seismic record widely used in structural testing and seismic response analysis. The Taft seismic wave was recorded in 1952. The earthquake that occurred in Kerr County, California on 21 July 2005 was recorded at Taft Lincoln School. The artificial standard time-history wave was fitted  
Figure 6: The first six modes of the structure.  
**4. Response Analysis of a High-Voltage Casing Lead Structure System under Different Seismic Acceleration Peaks**  
_4.1. Vibration Response Analysis of the Lead System under Different PGAs_  
_4.1.1._ Analysis of the Top Displacement of the Lead System  
Figures 11 and 12 are diagrams showing the variation in the maximum displacement of the top of the high-voltage casing side and the arrester side with the peak seismic acceleration in the high-voltage casing lead system. According to Figures 11 and 12, the maximum top displacement of the high-voltage bushing side and the arrester side under different PGAs both increase with increasing PGA. The lead system has different responses to various input directions of seismic waves. For seismic waves input from the X- and Z-directions, the top displacement of the structure is considerably larger than the Y-direction displacement response when the seismic waves are input in three directions. The displacement response of X-direction is slightly larger than the displacement response of Y-direction and Z-direction when seismic waves are input in three directions. Under the action of the same PGA but different types of seismic waves, the displacement response of the structure is largest for the EL wave, followed by the artificial wave, and the Taft wave has the smallest influence.  
Figure 10. Three-way input schematic.  
Figure 9. Time–history diagram of artificial wave acceleration.  
Figure 11: Top displacement on the side of the high-voltage casing.  
Figure 12: Top displacement on the side of the arrester.
# xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
## 3 Results
### Performance Evaluation  
The primary objective is to assess how accurately and efficiently the model can predict short-term traffic congestion, enabling timely decision-making for police dispatch and traffic governance.  
The forecasting task is designed around 15 to 30 min prediction horizons, which are critical time windows for proactive congestion mitigation. Evaluation focuses not only on predictive accuracy but also on how well the system supports real-time application requirements, such as inference speed and robustness to noisy data. Table 1 presents a comparative analysis between the xLSTM-powered system and other benchmark models commonly used in traffic prediction. All the results in Table 1 were derived from evaluations conducted under uniform experimental conditions [6; 7; 8; 9; 30].  
Figure 3: Congestion forecast map-based dashboard.  
The results indicate that the xLSTM-based system consistently achieves the highest predictive accuracy for congestion levels while maintaining low latency, making it particularly effective for deployment in time-sensitive traffic control environments. Notably, the model was able to predict congestion hotspots with over 87% accuracy, and reliably classify severity levels (normal, moderate, severe) in real time.  
Unlike Transformer- or graph-based models which suffer from high computational costs and longer inference times, the xLSTM model delivers a practical balance between performance and deployability, making it suitable for integration with edge-based systems like police command centers. The predictive output directly informs patrol unit deployment and traffic signal control, demonstrating tangible utility in live urban traffic scenarios [13; 21].  
These results substantiate the core contribution of this study: that xLSTM, when integrated into a real-time traffic prediction and dispatch system, provides an empirically validated foundation for intelligent urban traffic governance and proactive police operations.
## 6 Showcase Results  
### Parameters  
As part of the initial evaluation, we ran the two example applications of our ADAS framework in an urban and highway scenario. These scenarios were generated based on real-world maps. The most crucial free parameter was the V2X penetration, which is the ratio of the traffic-equipped vehicles in the simulated environment. The measurement was performed with 0%, 20%, 50%, and 80% penetrations. The length of the simulation was normally 500 s, but it was limited to 200 s in the 80% penetration cases due to feasibility issues. The highway scenario covered a 10 km long highway section and contained around 350 vehicles during the simulation. The urban scenario covered an approximately 5 km\({}^{2}\) urban area.  
The filter and effect components of the application were introduced in Figures 3 and 5. The Platooning application engaged if the speed of the ego vehicle was between 7 and 80 m s\({}^{-1}\) and if there were remote vehicles that had a similar heading (the deviation was not more than 15 m s\({}^{-1}\)) and if the remote vehicle was in front of the ego vehicle, practically within the 4 m times 120 m bounding box. The Road Works Warning was engaged if the ego speed of the vehicle was between 3 m s\({}^{-1}\) and 80 m s\({}^{-1}\) and the remote vehicle was in front of the ego vehicle (100 m times 200 m bounding box).  
In order to showcase the viability and performance of our system and how we can conclude the application's performance, we collected the three most exciting diagrams from the simulation results.
## 2 Measurement of Complete Geometric Errors
### Measurement of Axial Displacement Errors  
As Table 1 shows, \(\delta_{\mathrm{xx}}\), \(\delta_{\mathrm{yy}}\), \(\delta_{\mathrm{zz}}\), \(\delta_{\mathrm{Ax}}\), \(\delta_{\mathrm{By}}\) and \(\delta_{\mathrm{Cz}}\) are the axial geometric errors of the X, Y, Z, A, B and C-axis, respectively. They are measured by a laser interferometer (Renishaw XL-80 laser system, resolution: 0.01 \(\upmu\)m). The measurement of axial geometric displacement errors \(\delta\mathrm{xx}\) is shown in Figure 3. The measurement points on the X-axis and A-axis are shown in Figure 4, where the points on the A-axis are at the end face.  
As is shown in Figure 4, for the translation axes: X, Y and Z-axis, the axial geometric displacement errors of each point are obtained via recording the time difference between the laser interferometer transmitting and receiving infrared rays. The errors between the ideal position and the actual position of the points to be calculated, as in Equation (1):  
\[\delta_{\mathrm{xx}}^{i}=p_{i}(i=1,2,\dots,m). \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **X-Axis** & **Y-Axis** & **Z-Axis** & **A-Axis** & **B-Axis** & **C-Axis** \\ \hline Displacement & \(\delta_{\mathrm{xx}}\) & \(\delta_{\mathrm{yx}}\) & \(\delta_{\mathrm{zx}}\) & \(\delta_{\mathrm{Ax}}\) & \(\delta_{\mathrm{Bx}}\) & \(\delta_{\mathrm{Cx}}\) \\ errors & \(\delta_{\mathrm{xy}}\) & \(\delta_{\mathrm{yy}}\) & \(\delta_{\mathrm{zy}}\) & \(\delta_{\mathrm{Ay}}\) & \(\delta_{\mathrm{By}}\) & \(\delta_{\mathrm{Cy}}\) \\  & \(\delta_{\mathrm{xz}}\) & \(\delta_{\mathrm{yz}}\) & \(\delta_{\mathrm{zz}}\) & \(\delta_{\mathrm{Az}}\) & \(\delta_{\mathrm{Bz}}\) & \(\delta_{\mathrm{Cz}}\) \\ \hline \multirow{2}{*}{Angle errors} & \(\varepsilon_{\mathrm{xx}}\) & \(\varepsilon_{\mathrm{yx}}\) & \(\varepsilon_{\mathrm{zx}}\) & \(\varepsilon_{\mathrm{Ax}}\) & \(\varepsilon_{\mathrm{Bx}}\) & \(\varepsilon_{\mathrm{Cx}}\) \\  & \(\varepsilon_{\mathrm{xy}}\) & \(\varepsilon_{\mathrm{yy}}\) & \(\varepsilon_{\mathrm{zy}}\) & \(\varepsilon_{\mathrm{Ay}}\) & \(\varepsilon_{\mathrm{By}}\) & \(\varepsilon_{\mathrm{Cy}}\) \\  & \(\varepsilon_{\mathrm{xz}}\) & \(\varepsilon_{\mathrm{yz}}\) & \(\varepsilon_{\mathrm{zz}}\) & \(\varepsilon_{\mathrm{Az}}\) & \(\varepsilon_{\mathrm{Bz}}\) & \(\varepsilon_{\mathrm{Cz}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric errors of multi-axis machine tools.  
Figure 4: Measurement points on the X-axis and the A-axis.  
Figure 3: Measurement of axial geometric displacement errors \(\delta_{\mathrm{xx}}\).  
Because there is no translation on the rotation axes: A, B, C-axis, their axial geometric errors are obtained as Equation (2):  
\[\delta_{Ax}=\sum_{i=1}^{n}p_{i}/n(i=1,2,\dots,n). \tag{2}\]
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Playing Positions** & **All Players Combined** & **Backcount Players** & **Wing Players** & **Privots** & & & & \\  & **(for \(n=23\))** & **(for \(n=10\))** & **(in \(n=7\))** & **(for \(n=6\))** & & & & \\ Variables & Mean \(\pm\) SD & Mean \(\pm\) SD & Mean \(\pm\) SD & Mean \(\pm\) SD & F & Range & \(\eta^{2}\) & \(P\) & \((1-\beta)\) \\  & (95\% CI) & (95\% CI) & (95\% CI) & (95\% CI) & (95\% CI) & & & & \\ \hline
**Team handball game-based performance test** & & & & & & & & \\ VO2-peak (ml O2-min-1.kg-1) & & 62.9 \(\pm\) 6.2 & 62.2 \(\pm\) 4.6 & 60.7 \(\pm\) 4.7 & 66.9 \(\pm\) 8.1 & 1.90 & 55.0-79.7 & 0.16 & 0.18 & 0.51 \\ VO2-peak (ml O2-cm-1.kg-1) & & 60.2 \(\pm\) 6.0 & 58.9 \(\pm\) 6.5 & 10.5 \(\pm\) 2.8 & & & & \\ Blood lactate concentration (mM) & & 9.6 \(\pm\) 2.8 & 9.1 \(\pm\) 3.0 & 9.5 \(\pm\) 2.6 & 10.5 \(\pm\) 2.8 & & & & \\  & (8.4-10.8) & (7.0-11.3) & (7.1-12.0) & (7.2-13.7) & 0.40 & 3.6–14.2 & 0.04 & 0.68 & 0.26 \\ Heart rate (beats-min-1) & & 188 \(\pm\) 6 & 180 \(\pm\) 6 & 190 \(\pm\) 6 & 190 \(\pm\) 4 & 2.48 & 177–201 & 0.21 & 0.11 & 0.49 \\  & (185-190) & (181–189) & (185–195) & (186–194) & 2.48 & 177–201 & 0.21 & 0.11 & 0.49 \\ \hline Jump height (m) & & 0.32 \(\pm\) 0.05 & 0.29 \(\pm\) 0.04 & 0.35 \(\pm\) 0.05 & 0.34 \(\pm\) 0.06 & 3.28 & 0.22-0.42 & 0.25 & 0.06 & 0.66 \\  & (3.0-3.05) & (2.0-2.03) & (0.30-3.09) & (0.28-0.41) & & & & & \\ Throwing velocity (m s\({}^{-1}\)) & & 23.0 \(\pm\) 1.7 & 23.3 \(\pm\) 1.6 & 22.3 \(\pm\) 1.8 & 23.1 \(\pm\) 1.9 & & & & \\ Therefore time (s) & & 22.3-23.7) & (22.2–24.4) & (20.9–25.4) & & & & & & \\ Defence time (s) & & 6.08 \(\pm\) 0.24 & 6.06 \(\pm\) 0.26 & 6.11 \(\pm\) 0.28 & 6.09 \(\pm\) 0.16 & & & & \\  & (5.98-6.19) & (5.87-6.29) & (5.86-6.37) & (5.91-6.27) & 0.10 & 5.61-6.53 & 0.01 & 0.90 & 0.11 \\ \hline Offence time (s) & & 6.13 \(\pm\) 0.35 & 6.07 \(\pm\) 0.29 & 6.30 \(\pm\) 0.34 & 6.04 \(\pm\) 0.25 & & & & \\  & (5.98-6.29) & (5.86-6.27) & (5.87-6.73) & (5.76-6.32) & 1.22 & 5.63-7.08 & 0.11 & 0.32 & 0.41 \\
10-m fast break time (s) & & 2.02 \(\pm\) 0.11 & 2.00 \(\pm\) 0.10 & 2.06 \(\pm\) 0.14 & 2.00 \(\pm\) 0.08 & & & & \\  & (1.97-2.07) & (1.92-2.07) & (1.93-2.19) & (1.91-2.09) & & & & & \\
20-m fast break time (s) & & 4.14 \(\pm\) 0.22 & 4.09 \(\pm\) 0.17 & 4.28 \(\pm\) 0.23 & 4.05 \(\pm\) 0.20 & & & & \\  & (4.05-4.24) & (3.97-4.22) & (4.06-4.49) & (3.77-4.33) & 2.30 & 3.78-4.62 & 0.20 & 0.13 & 0.56 \\ Fast retreat time (s) & & 2.24 \(\pm\) 0.18 & 2.28 \(\pm\) 0.13 & 2.26 \(\pm\) 0.23 & 2.16 \(\pm\) 0.16 & & & & \\  & (2.17-3.23) & (2.19-2.37) & (2.05-4.28) & (1.97-2.34) & 1.04 & 1.88-2.74 & 0.09 & 0.37 & 0.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results from the team handball game-based performance test for female adult top-elite field team handball players. Group means \(\pm\) standard deviations (SD), 95% confidence intervals (CI), F-statistics, range (for all players combined), effect sizes (\(\eta^{5}\)), \(p\)-values and statistical power \((1-\beta)\) for the main effect of measurements are specified.
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 4 System Model
### Energy Consumption Model  
In our study, we focused on the power consumption at the transceiver. We assume that all nodes use the same LoRa transceiver, SX1272 [23]. At each period \(T\), we consider four transceiver modes: transmission, standby, receive, and idle. After transmitting an uplink message, one of the following situations will occur:  
* \(S_{1}\): The node successfully receives a downlink message at \(RX1\) with probability \(d_{1}\); then, it will switch to idle mode.
* \(S_{2}\): The node does not receive a downlink message during \(RX1\) with probability \(d_{2}=1-d_{1}\), as shown in Figure 6.  
The current consumptions \(I_{st}\), \(I_{rx}\), and \(I_{id}\) in the standby, receive, and idle modes, respectively, are obtained from [23]. The consumed current at transmission \(I_{tx}(l)\) depends on the power level \(l\). The amount of energy consumed by the nodes using SF \(s\) during the active period \(T_{\textit{active}}\), which is the time before the transceiver enters idle mode, can then be expressed as follows:  
\[\begin{split} E_{\textit{active}}(s)&=d_{1}.p(s).N_{ l}.V.[\textit{ToA}(s).I_{tx}(l)+R\_D1.I_{st}+T_{rx1}(s).I_{rx}]\\ &\quad+d_{2}.p(s).N_{l}.V.[\textit{ToA}(s).I_{tx}(l)+(R\_D2-T_{ rx1}(s)).I_{st}\\ &\quad+(T_{rx1}(s)+T_{rx2}(s)).I_{rx}]\end{split} \tag{11}\]  
The energy consumed by nodes using SF \(s\) during idle period \(T_{\textit{idle}}\) is defined by:  
\[\begin{split} E_{\textit{idle}}(s)&=d_{1}.p(s).N_{ l}.V.[(T-(ToA(s)+T_{rx1}(s)+R\_D1)).I_{id}]\\ &\quad+d_{2}.p(s).N_{l}.V.[(T-(ToA(s)+R\_D2+T_{rx2}(s))).I_{id}] \end{split} \tag{12}\]  
Figure 6: Transceiver modes during a period of time T.  
Thus, the energy consumption on each SF \(s\) is obtained by:  
\[E(s)=E_{active}(s)+E_{idle}(s) \tag{13}\]  
The total consumed energy (\(\mathcal{E}\)) of the LoRaWAN network during period \(T\) is hence computed as the sum of this consumed energy on each SF:  
\[\mathcal{E}=\sum_{s=S_{min}}^{S_{max}}E(s) \tag{14}\]  
We define (\(\mathcal{E}_{active}\)) as the total consumed energy during active period \(T_{active}\) and it is calculated as the sum of \(E_{active}\) on each SF:  
\[\mathcal{E}_{active}=\sum_{s=S_{min}}^{S_{max}}E_{active}(s) \tag{15}\]
## 3 Proposed Scheme
### GTS Allocation Procedure
#### 3.3.1 Topsis
```
Data: Decision matrix \(X\) representing GTS allocation performance on each criterion, Weights \(w_{j}\) for each criterion (\(w_{t}\), \(w_{e}\), \(w_{c}\), \(w_{f}\)) Result: Ranked list of alternatives based on relative closeness values for GTS allocation
1 Normalization:
2 for each alternative i and criterion jdo
3 Normalized Value\({}_{ij}=\frac{\text{Value}_{ij}}{\sqrt{\sum_{i=1}^{n}(\text{Value}_{ij})^{2}}}\)
4 end for
5 Weighted Normalized Decision Matrix:
6 for each alternative ido
7 Weighted Normalized Value\({}_{i}=w_{t}\cdot\text{Normalized Value}_{ii}+w_{e}\cdot\text{Normalized Value}_{ie}+w_{c}\cdot\text{Normalized Value}_{ic}+w_{f}\cdot\text{Normalized Value}_{if}\)
8 end for
9 Ideal and Negative-Ideal Solutions:
10 for each criterion jdo
11\(A_{j}^{+}=\text{max}(\text{Weighted Normalized Value}_{i})\)// foreach i
12\(A_{j}^{-}=\text{min}(\text{Weighted Normalized Value}_{i})\)// foreach i
13 end for
14 Calculate Euclidean Distances:
15 for each alternative ido
16 Calculate \(d_{i}^{+}\) and \(d_{i}^{-}\) for Trust Value, Emergency Data, Channel Capacity and Data Request Failure
17\(d_{i}^{+}=\sqrt{\sum_{j=1}^{m}(\text{Weighted Normalized Value}_{ij}-A_{j}^{+})^{2}}\)
18\(d_{i}^{-}=\sqrt{\sum_{j=1}^{m}(\text{Weighted Normalized Value}_{ij}-A_{j}^{-})^{2}}\)
19 end for
20 Calculate Relative Closeness:
21 for each node ido
22 Calculate \(C_{i}(\text{TV})=\frac{d_{i}^{-}}{d_{i}^{+}+d_{i}^{-}}\)
23 Calculate \(C_{i}(\text{ED})=\frac{d_{i}^{-}}{d_{i}^{+}+d_{i}^{-}}\)
24 Calculate \(C_{i}(\text{CC})=\frac{d_{i}^{-}}{d_{i}^{+}+d_{i}^{-}}\)
25 Calculate \(C_{i}(\text{DRF})=\frac{d_{i}^{-}}{d_{i}^{+}+d_{i}^{-}}\)
26 end for
27 Overall TOPSIS Value:
28 for each node ido
29 Calculate overall TOPSIS value \(V_{i}=\frac{1}{4}(C_{i}(\text{TV})+C_{i}(\text{ED})+C_{i}(\text{CC})+C_{i}( \text{DRF}))\)
30 end for
31 Ranking:
32 Rank nodes based on their overall TOPSIS values in descending order. ```  
**Algorithm 2**TOPSIS algorithm for GTS allocation. After that, the relative closeness values are calculated for each alternative by following the criteria described in TOPSIS algorithm [28], as shown in Equation (11). \[C_{i}(Criterion)=\frac{d_{i}^{-}}{d_{i}^{+}+d_{i}^{-}} \tag{11}\]Here, \(d_{i}^{+}\) and \(d_{i}^{-}\) are the distances calculated from the positive and negative ideals, respectively. After that, the relative closeness values for all criteria are combined into an overall TOPSIS value for each alternative by following Equation (12). \[V_{i}=\frac{1}{4}(C_{i}(TV)+C_{i}(ED)+C_{i}(CC)+C_{i}(DRF)) \tag{12}\]  
This step ensures a balanced consideration of all criteria. Finally, alternatives are ranked based on their overall TOPSIS values in descending order. The higher the TOPSIS value, the better the alternative is considered in terms of GTS allocation. The algorithm provides a systematic and quantitative way to evaluate and rank alternatives considering multiple criteria. It is particularly useful in decision-making scenarios where a trade-off between different criteria needs to be made.