esa-sceva/satcom-chunk-collection huggingface dataset:
# SDN Based Collaborative Scheme for Mitigation of DDoS Attacks
## 3 System Design and Architecture
### Work-Flow of Inter AS Collaborative DDoS Mitigation  
The complete work flow of the Collaborative DDoS mitigation is summarized as follows.  
1. The detection engine communicates with the SDN controller via C-to-C protocol and forwards a list of malicious IP addresses in the form of an attack definition.
2. The SDN controller first validates the communicating server by going through the following steps: 1. A certificate is retrieved from the payload. 2. The Payload validation module validates the certificate via a root certificate of the issuing certification authority present in the trusted store. 3. Once the certificate is authenticated via root chaining, the signature of the message is validated. 4. Upon the successful validation of the signature, the payload is processed further or it is discarded.
3. The IP addresses present in the payload are then written to a policy file and L3-Learning module is informed about the updates in the policies.
4. The L3-Learning module then reads the updated policies from the policy file.
5. The L3-Learning module then installs the new policies on each connected node.
6. As a result of the new policies, malicious flows are blocked. Any previously blocked flows can be allowed depending upon the improved detection.
7. The SDN controller then forwards the policies to the neighboring controllers via Policy Pusher Module.
8. The neighboring SDN controllers performs the same steps starting from step 2 to 7.
## 3 Orphan and Abandoned Wells Distribution in USA  
Orphan wells, typically abandoned due to uneconomical oil or gas production or operator bankruptcy, are significant sources of methane emissions [19]. Several studies have documented methane emissions from these wells, as summarized in Table 1. This table summarizes the findings on methane emissions from abandoned and orphaned wells across various U.S. states, highlighting the environmental risks associated with these wells. The data reveal significant variability in emission rates between plugged and unplugged wells, with unplugged wells generally emitting more methane. For instance, in Colorado, unplugged wells emitted an average of 586 g CH\({}_{4}\) per hour, compared to zero emissions from plugged wells, indicating the effectiveness of proper well plugging in mitigatingemissions. Similar trends are observed in Pennsylvania, West Virginia, and other states, where unplugged wells consistently show higher methane emissions. Thus, it is highly required to know the number of the orphan well to quantify the methane emissions and the root causes of the emission. Table 2 shows the methane emission measurement techniques. To update the number of documented orphan wells, state-level data were explored through each state's commission, revealing an estimated 225,287 orphan wells as shown in Figure 2. This is significantly higher than the EPA's last estimate of 123,000 wells, though still lower than the anticipated total of 3.9 million wells nationwide. Figure 3 shows the distribution of abandoned and orphan wells across the United States with depth. These wells pose significant environmental risks, not only due to methane emissions and potential contamination of subsurface freshwater but also by compromising efforts to store CO2 in the subsurface. These wells also present critical challenges to CO2 storage efforts, as they often lack proper cement barriers, potentially serving as leakage pathways if CO2 storage sites intersect with them. As such, this map is an invaluable tool for identifying optimal storage depths and locations, enabling the avoidance of areas with high concentrations of orphan wells to mitigate the risk of leakage. In scenarios where storage must occur in regions densely populated with orphan wells, the map underscores the necessity of drilling deeper injection wells to bypass these compromised structures, thereby enhancing the security and effectiveness of CO2 storage operations. These findings underscore the importance of addressing orphaned and abandoned wells to reduce methane emissions and ensure the integrity of future carbon capture and storage (CCS) projects. Figure 4 shows the location of the potential CCS project and the surrounding orphan and abandoned wells in the state of Louisiana. Most of the depth of the CCS is located in the range of the orphan and abandoned well depths. This lead also emphasizes the need for strategic well management and plugging practices, particularly in regions with high concentrations of these wells, to mitigate their environmental impact.
## 4 Results and Discussion
### Thermal Performance in Winter Conditions  
The temperature distributions in the hot channel of W1 TW and W2 TW are presented in Figure 6. The right boundary is the wall and the left boundary is the glass in each heat map. The temperature in the hot channel increased obviously along with an increase in the sections of the Y-axis, which had similar regulations to the summer conditions. The temperature difference along the thickness direction was approximately 8.6 K. The results demonstrated that the temperature in the hot channel was much higher in WITW than in W2 TW. However, the temperature difference between the different modes declined with an increase in the ambient temperature.  
Figure 6: The temperature distributions in the winter: (**a**) Ta = 268 K; (**b**) Ta = 273 K; (**c**) Ta = 278 K; and (**d**) Ta = 283 K. (For all heat maps, the left boundary is the glass cover and the right boundary is the wall).  
The outlet air temperature increased with an increase in the ambient temperature for both W1 TW and W2 TW, as plotted in Figure 7. As the inlet temperature was constant with a value of 291 K, the outlet temperature of W1 TW was definite and the temperature rose approximately 8 K. Comparatively, the inlet temperature of W2 TW was the same as the ambient temperature and the temperature difference between the outlet and inlet was 11 K. When the ambient temperature was 268 K and 273 K, the outlet temperatures of W1 TW were 297.9 K and 198.6 K, respectively, which were much higher than that of W2 TW. Therefore, the space heating effect was prominent and applicable in this condition. With an increase in the ambient temperature, the outlet temperature of W2 TW raised and the temperature difference between the different modes ranged from 6-9 \({}^{\circ}\)C. In this case, the heating effect was approximately identical and W2 TW was recommended to preheat fresh air to indoors.
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 6 LoRa Performance: Using Error Correction and Filtering
### LoRa with Forward Error Correction and Filtering
#### 6.2.1 One to Many Mode  
The introduction of IIR and FIR filters to receivers operating in OtM mode is a crucial step in enhancing LoRa's PER performance, whatever the configurations involved. This improvement, however, manifests itself differently depending on the type of filter and its order. The details of these dynamics are illustrated in Figures 22 and 23, which highlight the optimum performance associated with each filter order. In a LOS configuration, the positive impact of these filters is indisputable. Both the IIR and FIR filters deliver an appreciable 4 dB gain in signal-to-noise ratio. This translates into perfect packet reception, with values of \(-7\) dB and 4 dB for spreading factors 7 and 12, respectively. In NLOS mode, on the other hand, the benefits, while present, are slightly attenuated due to fading phenomena and disturbances such as industrial noise. Despite these challenges, a gain of 2 dB is noted, enabling the correct reception of 90% of packets with values of 2 dB and 12 dB for the two spreading factors mentioned. Overall, the incorporation of IIR and FIR filters proves to be a strategic initiative to improve the reliability of LoRa transmission in OtM mode, although the degree of improvement intrinsically depends on the transmission context.
## 2 Related Work
In fact, the above algorithm can be used as a supplement to the LPRA algorithm, using the path calculated in LPRA as the main path in the above algorithm, and switching backup paths according to real-time queue congestion indicators to cope with real-time link load jitter. The IADR [14] algorithm (ISL attributes-based dynamic routing algorithm) evaluates the link utilization by modeling the SNR, link duration and buffer queue of the link. Based on the links that the path passes, the path with the lowest utilization rate is selected from the set of alternative paths as the forwarding path. The SALB (state-aware and load-balanced) routing method [15] integrates load changes and fault conditions of links and nodes, quantitatively estimates link status and dynamically adjusts the weight of queuing delays, improving QoS performance while maintaining low routing overhead. Based on the multi-objective decision theory, [PERSON] et al. [16] calculated paths that meet business QoS requirements based on the actual status of satellite network nodes and links and the specific requirements of the business to achieve multi-objective dynamic routing. The above algorithm can provide high-quality real-time access and has certain load-balancing capabilities. Since the optimal path is calculated independently by each service, when a large number of services need to be rerouted, the algorithm's effect is not ideal. LPRA does not use any models to evaluate link availability, but performs centralized routing calculations based on the real load of links within a limited area. Compared with distributed route calculation, LPRA is more suitable for scenarios in which a large number of services need to be rerouted.
# The Influence of Feature Representation of Text on the Performance of Document Classification
## 4 Results
### F1-Score Rankings  
Table 7 summarizes the rankings of the document representation models according to the F1-score. The average ranks suggest that bow-TF and doc2 vec-25 variants outperformed the other representation models. The TFIDF variant of BOW and word2 vec exhibited lower ranks, while the high-dimensional variants of doc2 vec and all GOW-model variants were (again) the worst performers. This time, gow-host was the top-ranked variant among the GOW models. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Brown2** & **Brown4** & **Brown10** & **Brown15** & **Reuters8** & **WebKB** & **20 News** & **Avg. Rank** \\ \hline bow-TF & **1** & 2 & **1** & 3 & 2 & 6 & **1** & **2.29** \\ bow-TFIDF & 5 & 6.5 & 5 & 6 & **1** & 5 & 6 & 4.93 \\ \hline word2 vec-25 & 8.5 & 10 & 11 & 9.5 & 6 & 4 & 13 & 8.86 \\ word2 vec-50 & 2 & 8 & 8.5 & 7.5 & 5 & 2 & 10 & 6.14 \\ word2 vec-75 & 5 & 15 & 10 & 12 & 3 & **1** & 9 & 7.86 \\ word2 vec-100 & 5 & 10 & 12.5 & 9.5 & 4 & 3 & 8 & 7.43 \\ \hline doc2 vec-25 & 3 & 6.5 & 2.5 & 5 & 7 & 7 & 3 & 4.86 \\ doc2 vec-50 & 11.5 & 3 & 2.5 & 3 & 8 & 8 & 2 & 5.43 \\ doc2 vec-75 & 8.5 & **1** & 4 & **1** & 9 & 9 & 4 & 5.21 \\ doc2 vec-100 & 13 & 4 & 6 & 3 & 10 & 10 & 5 & 7.29 \\ doc2 vec-200 & 14.5 & 5 & 7 & 7.5 & 12 & 11 & 7 & 9.14 \\ doc2 vec-500 & 16 & 10 & 8.5 & 13 & 13 & 15 & 12 & 12.50 \\ doc2 vec-1000 & 14.5 & 16 & 14 & 14 & 11 & 16 & 11 & 13.79 \\ \hline gow-avg & 8.5 & 12.5 & 12.5 & 11 & 16 & 14 & 16 & 12.93 \\ gow-qrt & 11.5 & 14 & 15 & 15.5 & 15 & 13 & 14 & 14.00 \\ gow-hist & 8.5 & 12.5 & 16 & 15.5 & 14 & 12 & 15 & 13.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ranking of the 16 variants of the three document representation models by the document classification task with respect to classification accuracy. The last column reports the average rankings over the seven tasks. The top-ranked models in each column are shown in bold. Figure 4: Radar-chart comparison of the rankings by task (**left**) and their averages (**right**) for the four top-performing variants of the document representation models in each of the four groups with respect to classification accuracy. The graphical representation of the top-ranked variants in each group in Figure 5 reveals that for longer documents, the top-performing doc2 vec and BOW variants exhibited comparable rankings. For medium and small sized documents, the TFIDF variant of BOW (and word2 vec in the case of WebKB) had a slight edge over doc2 vec, although the differences were negligible. Furthermore, the same descendant ordering of ranked representation models BOW and doc2 vec on top, word2 vec in the middle, and GOW at the bottom, was preserved regardless of the size of the vocabulary or the number of class labels. Note that, for the small number of class labels, the GOW variants' performance became comparable to some of the lower-ranked variants of doc2 vec and word2 vec.
# A Comparative Study on the Schedulability of the EDZL Scheduling Algorithm on Multiprocessors
## 2 Background
### Real-Time Scheduling on Multiprocessors
Small laxity indicates that there is little time to idle or wait for execution. If the laxity of a job is zero, the job should be executed immediately. Otherwise, it would miss its deadline. A ZL (Zero Laxity) policy raises the priority of a job if its laxity becomes zero, so that it can be executed immediately. ZL policy can improve the performance of fixed priority scheduling algorithms dramatically. ZL policy has been analyzed and adapted to many real-time scheduling schemes [12; 32; 33; 34; 35; 36]. EDZL [7] is a global scheduling algorithm for multiprocessor real-time systems that firstly employs ZL policy. In essence, EDZL gives priority to a job based on EDF. If a job's laxity becomes zero, ZL policy changes the priority of the job to the highest one. Note that the laxity is a non-increasing function. Once a job is given the highest priority, it retains the priority until it completes execution. The schedulability of EDZL is discussed in detail in Section 2.3. The first optimal scheduling algorithm in the global approach is Pfair [1]. Pfair splits every job into sub-jobs and assigns a pseudo release time and deadline for the sub-jobs such that every job can progress at the same proportion. Pfair can successfully schedule any task set whose total utilization is less than or equal to \(m\). The drawbacks of Pfair is the very high scheduling overhead. The number of context switches (or preemptions) and migrations may be very high. To mitigate these drawbacks, other optimal algorithms have been developed, like ERfair [37], EKG [2], LLREF [3], DP-Wrap [38], U-EDF [39], and RUN [40]. Some of them employ a semi-partitioned approach where some tasks are allowed to migrate but some are not. However, these algorithms require complex computation for scheduling decisions and still incur more preemptions and migrations than EDZL and EDF\({}^{(k)}\). In this work, we compare the schedulability of EDZL and EDF\({}^{(k)}\). EDZL and EDF\({}^{(k)}\) are not optimal, but we choose these algorithms because they are practical in the sense that the scheduler implementation cost is low, the scheduling overhead is bounded, and they can achieve high processor utilization. They do not require task partitioning, job splitting, frequent priority change or complex scheduling decisions. The implementation of the schedulers is relatively simple. In addition, since the priority is given at job release or the zero-laxity point (in case of EDZL), the number of preemptions in a time interval is bounded by the number of jobs or two times the number of jobs (in case of EDZL) released in the interval. The number of migrations is bounded by the number of preemptions.
# Prediction Interval Adjustment for Load-Forecasting using Machine Learning
## 4 Experiments and Results
### Prediction Intervals Evaluation Metrics
#### 4.1.3 DSS (Dawid-Sebastiani Score)  
The Dawid-Sebastiani Score (DSS) helps us to understand the quality of the prediction interval. The DSS is estimated as indicated in the following equation:  
\[DSS=\left(\frac{\epsilon_{k}-E[\epsilon(p,h)]}{\sigma_{\epsilon(p,h)}}\right)^ {2}+2\cdot log(\sigma_{\epsilon(p,h)}) \tag{14}\]  
where \(\epsilon_{k}\) and \(\sigma_{\epsilon(p,h)}\) are the \(k\)th error and the standard deviation from the error distribution \(\epsilon(p,h)\) respectively. Equation (14) is modified to estimate the DDS of the Adjusted prediction intervals based on the support of the rules. The following equation describes the modified version of the DSS:  
\[DSS=\left(\frac{\epsilon_{k}-E[\epsilon(p,h)]}{\sigma_{\epsilon(p,h)}\cdot(1 -E[supp(p,h)])}\right)^{2}+2\cdot log(\sigma_{\epsilon(p,h)}\cdot(1-E[supp(p, h)])) \tag{15}\]  
where \(supp(p,h)\) is the set of support values used to adjust prediction intervals in period \(p\) and horizon \(h\). It is worth mentioning that if no interval were modified, then \(supp(p,h)\) will be filled with \(0^{\prime}s\) and Equation (15) will become (14).
## 2 Research Method  
### Stress Intensity Factor from API 579-1/asme FFS-1  
For a homogeneous and linear-elastic engineering component, \(K_{\rm I}\) depends on the geometry of the component (i.e., the geometry, size and location of the crack) and the applied mechanical action (i.e., type and magnitude of applied load) [4]. As an example, \(K_{\rm I}\) for an infinite plate with a single-edge crack can be written as follows:  
\[K_{\rm I}=\sigma\sqrt{\pi a}f(\alpha), \tag{1}\]  
\[f(\alpha)=0.265(1-\alpha)^{4}+\frac{0.857+0.265\alpha}{\left(1-\alpha\right)^ {3/2}}, \tag{2}\]  
\[\alpha=\frac{a}{W}, \tag{3}\]  
where \(\sigma\) is the applied stress, \(a\) is the crack size, \(W\) is the plate width, and \(f(a/W)\) is the geometry function.  
For the pressurized pipe with an infinitely long internal surface crack in the longitudinal direction (Figure 1), \(K_{\rm I}\) can be calculated using the formula provided in API 579-1/ASME FFS-1:  
\[K_{\rm I}=\frac{PR_{\rm o}^{2}}{R_{\rm o}^{2}-R_{\rm i}^{2}}\sqrt{\pi a}\left[ 2G_{0}-2G_{\rm I}\left(\frac{a}{R_{\rm i}}\right)+3G_{\rm 2}\left(\frac{a}{R_{ \rm i}}\right)^{2}-4G_{\rm 3}\left(\frac{a}{R_{\rm i}}\right)^{3}+5G_{\rm 4} \left(\frac{a}{R_{\rm i}}\right)^{4}\right], \tag{4}\]  
where \(P\) is the internal pressure, \(a\) is the crack size, \(R_{\rm i}\) is the internal radius, \(R_{\rm o}\) is the external radius, and \(G_{0}\) to \(G_{\rm 4}\) are the influence coefficients. API 579-1/ASME FFS-1 provides influence coefficients that cover the range of \(0.01\leq a/t\leq 0.8\) and \(0.001\leq t/R_{\rm i}\leq 0.3333\). As an example of the nonlinear behavior of the influence coefficient, the variation of \(G_{0}\) with respect to \(a/t\) and \(t/R_{\rm i}\) is shown in Figure 1a,b, respectively.  
For the \(a/t\) and \(t/R_{\rm i}\) of cracked pipes, which do not exactly correspond to those provide in API 579-1/ASME FFS-1, linear interpolation is usually applied to estimate the required influence coefficients. The linear interpolation for the unknown function \(f(x)\) can be expressed as follows:  
\[f(x)=f(x_{0})+(x-x_{0})\frac{f(x_{1})-f(x_{0})}{x_{1}-x_{0}}, \tag{5}\]  
where \(x,x_{0}\), and \(x_{1}\) are the input parameters (i.e., \(x\) is between \(x_{0}\) and \(x_{1}\)) and \(f(x_{0})\) and \(f(x_{1})\) are the known functions. According to the nonlinear behavior of the influence coefficients, it is likely that the interpolated influence coefficients and subsequently estimated \(K_{\rm I}\) could be inaccurate.
## 3 Results and Discussion
### Calibration of the RF Setup  
The main objective of this study was to achieve RF treatments with repeatable temperature evolutions and stable oscillations around the target frequency of 27.12 MHz. For this purpose, the differences in temperature profiles and RF frequency between repetitions were aimed not to exceed \(\pm\)5 \({}^{\circ}\)C and \(\pm\)0.5 MHz, respectively. A sample volume of 3 mL was selected based on preliminary experiments to (i) allow fundamental microbiological research in small sample volumes, (ii) provide enough sample volume for a variety of analytical techniques, and (iii) acquire stable electromagnetic oscillations.  
The initial parameters of the RF setup were calibrated based on the load of TSB and BHI broths without bacteria during a number of trials (data not shown). An overview of the resulting initial parameter values (i.e., when no sample is present in the setup) is provided in Table 1. It should be noted that the calibration parameters can differ significantly among different laboratory media and/or food products, meaning that these parameters should be revised when using the RF setup for other types of samples.
## 4 Results and Discussion  
### Model's Performance  
This section presents the study's results, focusing on ground displacement analysis using InSAR and deep learning model performance. Using the SBAS technique, interferometric pairs were generated, revealing subsidence patterns linked to groundwater withdrawal. Deep learning models, including LSTM and CNN, were evaluated for spatiotemporal groundwater level prediction, with metrics like R\({}^{2}\) and RMSE analyzed. Temporal groundwater trends highlighted significant depletion and localized subsidence near key archaeological sites, emphasizing the need for sustainable management to preserve these heritage areas.  
Figure 6 provides an integrated evaluation of environmental variable importance, model performance, and training stability across multiple deep learning models for groundwater level prediction. Velocity, NDVI, GPP, and aspect emerge as dominant covariates, collectively explaining 51.2% of predictive power, while secondary variables including DEM, slope, and deformation contribute an additional ~24.9%. The ConvTransformer clearly leads the pack, posting an R\({}^{2}\) of \(\approx\)0.83, an MAE of \(\approx\)3 m, an RMSE of \(\approx\)6 m, and a MAPE below 2%. It outperforms the vanilla Transformer (R\({}^{2}\approx\) 0.69, MAE \(\approx\) 6 m) and decisively eclipses Informer, whose accuracy drops to R\({}^{2}\approx\) 0.35 and MAE \(\approx\) 9 m. The recurrent baselines--LSTM, BiLSTM, and CNN-LSTM--occupy the middle ground, with R\({}^{2}\) values of roughly 0.45-0.60 and MAEs in the 5-8 m range.  
Training dynamics highlight the efficiency of attention-based models, with Informer, Transformer, and ConvTrans achieving stable and low validation losses around 0.08-0.09 after approximately 120 epochs, whereas recurrent models exhibit higher loss (\(\sim\)0.18) with noticeable overfitting. Error distributions underscore these differences, with Informer and Transformer capturing approximately 90% of predictions within \(\pm\)20 m, compared to only ~60% for CNN-LSTM. Overall, attention-based neural networks indicate substantial accuracy improvements by efficiently leveraging critical environmental features.
## 4 Results, Discussions and Achievements
### Histogram Analysis of RMSE  
Histogram analysis shows how much error occurred against the different Monte Carlo simulation runs. By this analysis, we evaluate the performance of optimization schemes. In histogram analysis, bars are in the form of continuous grouping. In the case of six sources estimation, RMSE of CS and PSO is very good at 0 dB, \(-5\) dB and \(-10\) dB SNR over the 500 iterations. Figure 16 explored the analysis for nine targets in terms of RMSE and compared the outcomes among proposed CS and PSO, where the performance of CS is more efficient than PSO. Furthermore, the spread of RMSE at 0, \(-5\) and 10 dB is analyzed for PSO using 500 iterations as discussed in Figures 17 and 18.  
Figure 16: Correlation of proposed CS and PSO models based on RMSE using six targets.  
Figure 15: RMSE investigation in terms of CDF using twelve targets for various values of SNR.