esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
## 3 Results
### Evaluation of the Performance of AutoDock and Vina
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Vina**} & \multicolumn{2}{c}{**AutoDock**} \\ \hline
**Polarity** & **Number of Targets** & **EF1\%** & **AUC** & **EF1\%** & **AUC** \\ Poorly Polar (0-25\%) & 25 & \(9.9\pm 8.86\) & \(68.1\pm 12.9\) & \(11.7\pm 12.5\) & \(68.7\pm 15.7\) \\ Moderately Polar (25–35\%) & 36 & \(10.7\pm 9.32\) & \(71.7\pm 11.7\) & \(8.0\pm 9.5\) & \(63.7\pm 16.4\) \\ Very Polar (>35\%) & 40 & \(8.6\pm 10.6\) & \(70.0\pm 13.1\) & \(7.0\pm 9.8\) & \(64.2\pm 16.5\) \\ \hline
**Hydrophobicity** & & & & & \\ Poorly Hydrophobic (0–30\%) & 38 & \(11.1\pm 11.5\) & \(72.4\pm 13.4\) & \(8.5\pm 10.1\) & \(66.7\pm 17.2\) \\ Moderately Hydrophobic (30–40\%) & 25 & \(8.3\pm 8.5\) & \(68.4\pm 13.3\) & \(8.2\pm 13.3\) & \(62.9\pm 18.1\) \\ Very Hydrophobic (>40\%) & 38 & \(9.1\pm 8.4\) & \(69.0\pm 9.3\) & \(8.8\pm 9.3\) & \(65.0\pm 14.2\) \\ \hline
**Charge** & & & & & \\ Poorly Charged (0–15\%) & 40 & \(9.9\pm 9.1\) & \(69.2\pm 11.4\) & \(10.2\pm 10.9\) & \(68.8\pm 15.5\) \\ Moderately Charged (15–20\%) & 24 & \(7.7\pm 8.6\) & \(68.2\pm 11.9\) & \(5.8\pm 8.7\) & \(55.1\pm 17.5\) \\ Very Charged (>20\%) & 37 & \(10.7\pm 10.9\) & \(72.4\pm 12.7\) & \(8.5\pm 10.9\) & \(67.5\pm 13.8\) \\ \hline
**Positive Charge** & & & & & \\ Poorly Positive (0–5\%) & 24 & \(10.2\pm 8.5\) & \(67.9\pm 11.6\) & \(12.6\pm 11.6\) & \(68.8\pm 15.6\) \\ Moderately Positive (5–10\%) & 34 & \(8.5\pm 10.8\) & \(69.6\pm 14.2\) & \(6.9\pm 11.1\) & \(62.1\pm 17.5\) \\ Very Positive (>10\%) & 43 & \(10.5\pm 9.4\) & \(72.6\pm 9.7\) & \(7.9\pm 8.8\) & \(65.4\pm 15.3\) \\ \hline
**Negative Charge** & & & & & \\ Poorly Negative (0–5\%) & 22 & \(11.0\pm 9.5\) & \(70.8\pm 9.8\) & \(11.7\pm 10.7\) & \(73.5\pm 11.9\) \\ Moderately Negative (5–10\%) & 44 & \(8.1\pm 8.3\) & \(70.2\pm 11.8\) & \(7.2\pm 10.2\) & \(62.2\pm 17.4\) \\ Very Negative (>10\%) & 35 & \(10.9\pm 11.2\) & \(69.7\pm 13.8\) & \(8.2\pm 10.7\) & \(63.5\pm 10.7\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of Vina and AutoDock in the discrimination between ligands and decoys for targets with binding pockets with different percentages of polar, charged, and hydrophobic amino acid residues. concerning the presence of a charge at the binding pocket were also observed when particularly looking into positively charged residues or into negatively charged residues. In general, these results showed that AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar, and poorly charged pockets, while Vina exhibited early recognition metrics that did not vary so significantly with the type of amino acid residues at the binding pocket. Vina tended to give better results for polar and charged binding pockets, which was particularly interesting, taking into consideration that the scoring function of Vina did not explicitly include charges, while that of AutoDock had an explicit electrostatic term.
## 4 Discussion
However, they are not ideal for EV battery state calculation applications as they cannot estimate the SOC and SOH with accuracy and their estimation accuracy further extensively decreases as the battery age increases. Compared to other modern filtering methods, it is prone to error accumulation in it when high variations are introduced in the system and its accuracy is \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Battery Charging**} & \multicolumn{4}{c}{**Battery Discharging**} & \multicolumn{4}{c}{**Error Margin**} \\ \hline Cycles & & 1 st & 2 nd & 3 rd & 4 th & 5 th & 1 st & 2 nd & 3 rd & 4 th & 5 th & Charge & Discharge \\ \hline  & Real & 90 & 90 & 90 & 90 & 90 & 30 & 30 & 30 & 30 & 30 & \\ \hline  & CC & 100 & 100 & 100 & 100 & 100 & 40 & 40 & 40 & 40 & 10\% & 10\% \\ \cline{2-11} Initial SOC 40\% & EKF & 97 & 89 & 88.5 & 88.5 & 92.5 & 31 & 28 & 29 & 29.5 & 29.5 & 2.7\% & 1\% \\ \cline{2-11}  & UKF & 97 & 91 & 90.4 & 90 & 90 & 32.5 & 31 & 30 & 30 & 30 & 1.68\% & 0.7\% \\ \hline  & CC & 110 & 110 & 110 & 110 & 110 & 50 & 50 & 50 & 50 & 20\% & 20\% \\ \cline{2-11} Initial SOC 50\% & EKF & 107 & 88 & 89 & 88 & 88 & 31 & 29 & 29 & 30 & 29 & 4.8\% & 0.8\% \\ \cline{2-11}  & UKF & 105 & 95 & 91 & 91 & 90 & 36 & 32 & 31 & 30 & 30 & 2.4\% & 1.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of real and estimated SOC levels with different initial SOC conditions. highly dependent on the accuracy of the given previous SOC value. All these characteristics of the traditional estimation method make it not an ideal option if the user is looking for a highly advanced BMS. While the proposed BMS demonstrates significant advancements in accuracy, adaptability, and integration with the IoT, it is important to recognise certain practical implications and limitations. For instance, the increased computational demands of advanced algorithms may affect system efficiency, and the integration of the IoT and advanced sensors may elevate costs. Additionally, while simulation results validate the system's performance, real-world testing under diverse operational conditions, such as extreme temperatures and high vibrations, remains necessary. These considerations highlight opportunities for future research to further refine and validate the system's capabilities.
## 4 Experimental Results and Discussion
### CLP Modeling Comparison and Results
The RF model was developed using Python language programming and required three parameters, namely the minimum number of terminal nodes for each tree, the number of trees, and the number of randomly selected variables to grow the trees [62]. In this study, these three parameters were set to 5, 145, and 6, respectively. The results of the RF prediction model are listed in Table 3 along with results of the ANN, ANFIS, and ANFIS-GA models for comparison. The results presented in Table 3 indicate that the RF model had the highest accuracy among the four predictive models, with an RMSE of 0.137 and MAE of 0.112 in the testing data set. The second most accurate algorithm was the ANN model, with a testing data set RMSE of 0.165 and MAE of 0.135. The third most accurate algorithm was the combination of ANFIS and GA, \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**ANFIS-GA Model No.**} & \multirow{2}{*}{**Population Size**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{3-4}  & & **Training** & **Testing** \\ \hline
1 & 12 & 0.159 & 0.185 \\
2 & 18 & 0.165 & 0.191 \\
3 & 25 & 0.162 & 0.172 \\
4 & 30 & 0.163 & 0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Selecting the population size in ANFIS-GA modeling. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Training Dataset**} & \multicolumn{2}{c}{**Testing Dataset**} \\ \cline{2-5}  & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline ANN & 0.164 & 0.130 & 0.165 & 0.135 \\ ANFIS & 0.042 & 0.034 & 0.176 & 0.138 \\ ANFIS-GA & 0.162 & 0.096 & 0.172 & 0.129 \\ RF & 0.074 & 0.051 & 0.137 & 0.112 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparing the performance of the four developed models for predicting CLP. with an RMSE of 0.172 and MAE of 0.129 in the testing data set. Finally, testing data set RMSE of 0.176 and MAE of 0.138 indicate the ANFIS model was the least accurate. According to the RMSE value of 0.137 for the RF testing data set, CLP predicted by RF was closer to the actual CLP values than for the other three developed models. In other words, RF was found to be better than ANN, ANFIS, and ANFIS-GA in mapping the relationship between the selected CLP factors and CLP. Moreover, the closeness of the RMSE values for the training and testing data sets indicate that ANN and RF were more stable than ANFIS and ANFIS-GA. Therefore, the RF model was selected to predict CLP in the optimization process for this study. Comparing the results of this study with past studies indicate that the RF predictive model has better performance. For example, [PERSON] et al. [39] obtained an RMSE value of 0.22 for their proposed CLP predictive model, while in this study, using the same data set, the RMSE value of the RF model was 0.137. Therefore, the proposed CLP predictive model achieved better performance accuracy in CLP prediction, compared with [PERSON] et al. [39].
# MAG Welding Tests of Modern High Strength Steels with Minimum Yield Strength of 700 MPa
## 3 Results and Discussion
### Hardness Tests  
The complete hardness profiles of all three materials are established in Figure 12.  
Figure 11: Charpy-V impact toughness results for all the welds. Specimens having the notch at FL + 1 mm were tested at \(-20\), \(-40\) and \(-60\)\({}^{\circ}\)C. Specimens having the notch at WM, FL + 3 mm and FL + 5 mm were tested only at \(-40\) and \(-60\)\({}^{\circ}\)C.  
Based on comparison of these hardness profiles, the following observation can be made:  
* The domains of the lowest hardness values are located approximately similarly by all materials, when comparing the same heat input.
* At the coarse grained zone, increased hardness can be observed in S700 MC-2 (maximum hardness of about 290 HV5) and especially in quenched and tempered S690 QL with high cooling rates (maximum hardness of about 360 HV5). Hardness peaks are clearly visible in the profiles of the face side and are located similarly in both steels. Hardness of these domains decrease by increasing heat input. On the root side, no excessive hardness peaks occur, due to tempering effect of the second pass [31]. However, in S690 QL, some hardening can be noted at the coarse-grained zone also at the root side.
* S700 MC-1 exhibits increased hardness domain next to the softest region, when moving further from the weld (tempered zone). The effect is visible on both face and root side and the maximum hardness is about 280-290 HV5. This behavior is not pronounced in S700 MC-2 and in S690 QL.
* Generally, the root side is more critical in terms of softening because both of the passes affect the microstructure in a cumulative effect.  
According to the hardness profiles, quenched and tempered S690 QL is more prone to the softening than the two other materials. At the face side, the softening of the S690 QL is more sensitive to the heat input compared to two other materials. At the root side, the minimum hardness value of the S690 QL is affected considerably even when welding with the lowest heat input. Considering the effect of the second weld pass on the first, these hardness results are in line with the results presented by [PERSON] et al. [32], where this decrease can be explained by carbon diffusion and transformation of martensite into a phase with lower strength and higher plasticity. On the other hand, the differences in minimum hardness values between the different materials dissipate by increasing heat input. According to the data hardness profiles, it can be concluded that the material S700 MC-2 has the lowest tendency for extensive softening at the HAZ among these three materials.  
Figure 12: Hardness profiles of the welds of different materials with four different heat inputs.
###### Abstract
Although existing studies propose various methods to identify the spatial boundaries of urban agglomerations, including spatial econometric models based on economic activities, urban expansion models based on land use changes, and dynamic analysis methods based on population distribution, these approaches have notable limitations. Methods relying on a single data source often fail to capture cross-boundary connections and dynamic changes between regions [45]. Moreover, many studies heavily depend on administrative boundaries or resident population distribution data, whose static characteristics make them unsuitable for addressing the complex dynamics of rapid urbanization and population mobility [62]. To address these issues, this study fuses multi-source data (NTL data, LandScan data, and heatmap data) and adopts a dual perspective of economic activity and population mobility. This approach provides a more comprehensive method for identifying the spatial boundaries of urban agglomerations, overcoming the shortcomings of existing methods in terms of dynamism and applicability. This study selects the Pearl River Delta (PRD) urban agglomeration as a case study, integrating NTL, LandScan population distribution data, and heatmap data to identify the spatial range of the urban agglomeration. First, by fusing NTL and LandScan data, an NTL_LandScan dataset is created to delineate the spatial boundaries of the PRD urban agglomeration. Next, an NTL_Heatmap dataset is generated by combining NTL and heatmap data to further refine the identification of the urban agglomeration's spatial range. Finally, the identification results from the NTL_LandScan and NTL_Heatmap datasets are subjected to accuracy validation and comparative analysis. By fusing NTL, LandScan, and heatmap data, we aim to systematically explore the identification of urban agglomeration spatial boundaries from a perspective that combines static and dynamic analyses. This approach addresses the limitations of traditional methods based on a single data source, offering significant advantages, particularly in capturing cross-regional dynamic connections and high mobility characteristics. It also enables a more accurate identification of the spatial boundaries of the Pearl River Delta urban agglomeration from different perspectives. Furthermore, it provides reliable references for resource allocation and regional development in the Pearl River Delta, contributing to its sustainable development.
# Automatic Hybrid Attack Graph (AHAG) Generation for Complex Engineering Systems +
## 3 Level-of-Resilience Assessment  
Each path in the graph is a single attack scenario and has an associated Level-of-Resilience (LoR) [20]. The following definition can be utilized to identify the worst case Level-of-Resilience of a system with its Attack graph given. A system is the worst resilient to an attack scenario in the graph if it acquires the highest loss of stability, the highest loss of performance, or the highest recovery-time.  
**Definition 1** ([20]).: _Given a system \(M\) and an attack graph \(A_{G}\) comprising a set of attack scenarios \(S\equiv\cup S_{i}\), \(i\in\{1,\,\ldots,\,z\}\), where \(z\) is the number of attack scenarios, we say that LoR(\(M\), \(S_{i}\)) is the worst if:_  
\[[LoS_{R}(M,\,S_{i})>LoS_{R}(M,\,S-S_{i})]\] \[\vee\ [[LoS_{R}(M,\,S_{i})=LoS_{R}(M,\,S-S_{i})]\] \[\wedge\ [LoP_{R}(M,\,S_{i})>LoP_{R}(M,\,S-S_{i})]J]\] \[\vee\ [[LoS_{R}(M,\,S_{i})=LoS_{R}(M,\,S-S_{i})]\] \[\wedge\ [LoP_{R}(M,\,S_{i})=LoP_{R}(M,\,S-S_{i})]\] \[\wedge\ [RT(M,\,S_{i})>RT(M,\,S-S_{i})]J]\]  
The next definition compares the LoR of many systems against an Attack scenario. A system is the most resilient to an attack scenario if this attack acquires a smallest loss of stability, a smallest loss of performance, or smallest recovery-time.  
**Definition 2** ([20]).: _Given a set of systems \(M\equiv\cup M_{j}\), \(j\in\{1,\,\ldots,\,y\}\), where \(y\) is the number of systems, and an attack scenario \(S_{i}\in S\), we say that LoR(\(M_{i}\), \(S_{i}\)) \(>\) LoR(\(M\)-\(M_{i}\), \(S_{i}\)) if:_  
\[[LoS_{R}(M_{i},\,S_{i})<LoS_{R}(M-M_{i},\,S_{i})]\] \[\vee\ [[LoS_{R}(M_{i},\,S_{i})=LoS_{R}(M-M_{i},\,S_{i})]\] \[\wedge\ [LoP_{R}(M_{i},\,S_{i})<LoP_{R}(M-M_{i},\,S_{i})]\] \[\vee\ [[LoS_{R}(M_{i},\,S_{i})=LoS_{R}(M-M_{i},\,S_{i})]\] \[\wedge\ [RT(M_{i},\,S_{i})<RT(M-M_{i},\,S_{i})]J].\]  
Figure 3: Network \(CN_{2}\) Attack Graph.
## 5 Theoretical Implications  
The development of BIS theoretical framework is intended to provide a precise description of potential determinants and clear prediction about the successful adoption of BIS in organizations. It may influence the decisions of practitioners before integrating and adopting the BIS in their enterprises. The proposed framework would also contribute significantly in the development of theory for the BIS acceptance/adoption at same time that is almost non-existent in the existing literature. There have been a handful of developed models and theories proposed for the BIS adoption at organizational level as well as for the BIS acceptance at individual level considering two different categories. This is the first theoretical framework that has integrated the construct for acceptance at individual's level as well as technological, organizational and environmental constructs with the intention to adopt the BIS at firm level. All contexts have been widely addressed and investigated independently in the literature, but limited researchers have touched the importance of user acceptance to complete the BIS adoption at firm level. First, even though BIS adopts at an organizational level, user's acceptance cannot be taken out of the framework. Users' acceptance is critical for the optimal success of BIS [3; 25; 50]. By focusing on this important point, identification of users' acceptance determinants for the BIS adoption is crucial and must be considered carefully prior to the BIS implementation. This framework contributes to the knowledge about BIS research which leads the practitioners to understand the users' significance for successful adoption of BIS. Second, the conceptual model and theoretical framework will not only contribute to the body of knowledge in the BIS field but also open the new horizons of research. Further, existing models or theories can be extended or refined by the proposed conceptual model. Industry experts can gain optimal results to identify the users, technology, organization and environment-related determinants and it will greatly contribute to the success of BIS projects.  
Figure 8: A conceptual model and theoretical framework for BIS adoption.
# Simulink Model of a Thermoelectric Generator for Vehicle Waste Heat Recovery
## 4 TEG Model for Exhaust Gas Waste Heat Recovery  
For the current study, the system is analyzed under steady-state conditions. The domain is discretized along the flow direction in control volumes (CVs) having the same length as a TEM (Figure 6). Initially, only one control volume is considered for the calculation; however, a greater domain can be built by adding more control volumes with each taking input data from the previous CV. The changes in fluid properties with temperature are considered separately for each control volume and are defined at the mean CV temperature (average of inlet and outlet temperatures). For simplicity, thermoelectric parameters are considered constant, but the model can be readily updated with equations for these parameters, either from the literature or from the manufacturer.  
Thermoelectric modules are often combined with heat exchangers (HX) to form the one-electric generators (TEG), which are used to recover otherwise wasted thermal energy from the exhaust gases of ICEs. The fins of the HX allow for an increased heat transfer surface area, subsequently enhancing the amount of heat extracted through convection from the hot gases. This heat is then transferred through conduction to the TEMs. An  
Figure 5: Manufacturer data compared to calculated data at matched load resistance conditions for TGM-127-1.4-2.5: (a) Module power; (b) module current; (c) module voltage; (d) module power and voltage with respect to current; and (e) module efficiency.  
example of such a TEG with all relevant dimensions of the finned heat exchanger (L\({}_{\text{HX}}\) [m]--heat exchanger length, W\({}_{\text{HX}}\) [m]--heat exchanger width, H\({}_{\text{HX}}\) [m]--heat exchanger height, z\({}_{\text{HX,fin-spacing}}\) [m]--heat exchanger fin spacing, t\({}_{\text{HX,fin}}\) [m]--heat exchanger fin thickness), as well as the positioning of the TEMs is presented in Figure 6. If the heat exchanger is covered with TEMs on both sides, the CV must be modified accordingly (Figure 7).
## 4 Numerical Simulations and Discussion
### Comparison with Other AOA Methods
#### 4.3.1 Number of Snapshots  
Typically, resorting to a larger number of snapshots is not always practical in wireless environments since the sources of signals are changing rapidly. This simulation compares the estimation accuracy of the proposed method with other AOA methods for obtaining a reliable estimation of DOAs signals using a limited number of snapshots. A ULA with \(M=8\) elements with half wave spacing between elements is selected. Two BPSK signals (\(D=2\)) are incident on this array from different directions--these signals are corrupted with AWGN and the SNR is 10 dB. Five different numbers of snapshots of the received signals are collected in this experiment namely: \(N=1,2,3,4\) and \(5\). A thousand trials of two AOAs are randomly generated, each set being applied to all AOA methods. The ARMSE of every \(N\) is calculated and plotted for all techniques as shown in Figure 7. Clearly, the PDDA method gives the best resolution compared with other AOA methods for a single snapshot. With \(N=2\) and \(3\), the performance estimation of PDDA and OGBSI are convergent and they are the best compared with the other AOA methods. At \(N=4\), PDDA, OGBSI and MUSIC have roughly the same estimation accuracy and are better than the other algorithms. At \(N=5\), the estimation resolution of each method can be sorted in descending order as follows: MUSIC, OGSBI, PDDA, ESPRTI, Root-SBL, Min-Norm and Capon. So, the performance of the proposed method outperforms all methods with a single snapshot and is comparable to MUSIC and OGSBI for other numbers of snapshots. The reason for poor accuracy for the Capon method is that when \(N\) is less than \(M\), the Capon method suffers from singularities due to matrix inversion operations.
# Occlusion Vehicle Target Recognition Method Based on Component Model
## 3 Experiment and Result Analysis
### Ablation Experiment  
To verify the effectiveness of the vehicle recognition method in this paper, the ablation experiments were carried out on the two units of the component perception enhancement unit and the efficient attention unit in the vehicle component feature extraction and classification module designed in Section 2.2 and the experimental verification was carried out on the PASCAL VOC2012 data set. Table 2 shows the changes in vehicle recognition performance after the addition of two units, among which \"\(\times\)\" indicates that the Vehicle Component Feature Mask module does not introduce this unit, and \"\(\surd\)\" indicates that the Vehicle Component Feature Mask module introduces this unit.  
The real-time analysis of these modules shows that the CPE unit has the greatest impact on the success rate, which makes the extraction accuracy rate increase by 1.6% compared with the original feature extraction net, and the EA unit has the greatest impact on the accuracy rate compared with the original feature extraction net of 3.2%. When the RFE unit and EA unit are applied to the module at the same time, the module obtains a higher mAP value, reaching 92.9%. It shows that the two improvements can improve the module performance at the same time. Through ablation experiments, it is proved that the extraction algorithm in this paper has better performance. The mAP reaches 92.9%, which is 4.1% higher than the original module, and the accuracy is improved compared with the original module. In summary, the recognition method in this paper has improved the performance of feature extraction.