esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Molecular Dynamics Studies
#### 3.6.2 Protein-Ligand Interactions
(**a**) with RMAPep (top); total number of contacts with RMAPep (bottom); (**b**) with RMAMut1 (top); total number of contacts with RMAMut1 (bottom); (**c**) with GLAMut1 (top); total number of contacts with GLAMut1 (bottom); (**d**) with GLAMut2 (top); total number of contacts GLAMut2 (bottom); (**e**) with CCA-Mut4 (top); total number of contacts with CGA-Mut4 (bottom); (**f**) with CSAMut4 (top); total number of contacts with CSAMut4 (bottom). Once again, Asp800 showed a high interaction fraction (1.6), followed by Ser720 and Leu718. The total number of contacts throughout the simulation process was found to be in the range of 18-24 for the first 40 ns, after which the number of contacts was found to be 16 on average. In comparison, RMAMut1 (Figure 14b) showed increased ionic interactions with a higher interaction fraction observed for Asp800 (2.5). Residues such as Pro877, Trp880, Lys913, Phe723, Ala722, Leu718, Ala743 and Leu799 were involved in hydrophobic interactions. The number of interactions throughout the simulation process was found to be 20 on average, with higher interactions observed in the first 40 ns of the simulation. GLAMut1 (Figure 14c) once again showed mostly H-bonds and water bridges with some hydrophobic interactions, while the total number of contacts throughout the simulation was found to be 14 on average, which is lower than other conjugates. This may be due to the way that the conjugate folds up in the receptor binding cleft. For GLAMut2, (Figure 14d) the highest interaction fraction was observed for Asn808. The prominent hydrophobic interactions were found to be with Phe997, Phe795, and Pro794. The overall average number of contacts was 10 throughout the simulation process. CGAMut4 (Figure 14e) showed a number of H-bond and water bridge interactions, though Ile715 showed a relatively high interaction fraction primarily for hydrophobic interaction along with Val716, Leu718, Val726 and Leu1001. The total number of receptor-ligand contacts was found to be 20 on average, with highest number of contacts (28) observed within the first 50 ns. CGAMut4 (Figure 14f) showed slightly different ligand contacts with the receptor. Interestingly, Asp800 was not the dominant interaction, in fact Asp800 showed the lowest interaction with CGAMut4. Instead, Arg889 had the highest interaction fraction. Several other interactions were observed which included mostly H-bonds and water bridges and hydrophobic interactions with Phe 723, Leu718, Val726, Pro877, Trp880 and Arg889. The total number of contacts throughout the simulation was lower, with an average of only 9 contacts over the simulation process. These results indicate that, among the conjugates, RMAMut1 and CGAMut4 were found to have the highest number of contacts throughout the simulation. The individual ligand atom-receptor contacts are also shown in Supplementary Information Figure S13, which further corroborates with the results seen from the histograms of the protein-ligand contacts.
## 2 Testing of Dynamic and Static Elastic Parameters
### Static Elastic Parameter Acquisition Method  
The static elastic modulus can be obtained by using rock samples to conduct the laboratory's uniaxial or triaxial static loading experiments. In the experiment, maintaining a constant confining pressure under formation conditions by applying axial stress until the rock sample breakage and recording the deformation parameters during the experiment, a corresponding stress-strain curve is formed throughout the entire process [34; 35]. Thus, the curve can directly give stress values, longitudinal strain, transverse strain, and deformation parameters. The deformation parameters obtained through compression tests are analyzed using regression analysis. The slope of the straight section on the stress-strain curve is taken as the static elastic modulus, and the static Poisson's ratio is obtained according to the ratio of transverse deformation to longitudinal deformation of the rock sample. The elastic modulus is measured within the elastic limits. When rocks treated as an elastic body undergo elongation/compression deformation, the elastic modulus is the ratio of the tensile/compression stress to the relative elongation/shortening in the same direction, the ratio of applied stress to strain (stress in the same direction). Poisson's ratio is measured when a rock sample is under tensile stress, causing elongation in the direction of the applied force and contraction in the direction perpendicular to the applied force. The ratio of transverse linear strain (contraction) to longitudinal linear strain (elongation) is known as Poisson's ratio [36]. The slope on the stress-strain curve can obtain different types of static elastic modulus. The International Society of Rock Mechanics (ISRM) has proposed three standard measurement methods [37]. They are as follows: tangent elastic modulus (\(E_{\text{tan}}\)) is the slope of a tangent line at any point on the stress-strain curve. This is defined as the tangent slope of the stress-strain curve at a fixed percentage of the ultimate strength, as in Figure 1a; the average elastic modulus (\(E_{\text{ave}}\)) is the slope of a straight line portion of the stress-strain curve, as in Figure 1b; and secant elastic modulus (\(E_{\text{sec}}\)) is the slope of a straight line drawn from the origin (0,0) to the point on the curve that corresponds to some fixed percentage of the ultimate strength slope of the straight line plotted in Figure 1c [38]. This article adopts the tangent elastic modulus.
## 5 Analysis and Discussion
### Evaluating the System Usability and Presence of the Proposed System Group  
The proposed system group also responded to a questionnaire on the sense of presence and system usability. This survey was conducted with the post-test and a total of 20 people participated. The Cronbach's alpha values for the SUS and IPQ are 0.71 and 0.79, indicating acceptable reliability.  
The result of the SUS test, which was conducted to assess the usability of the system, is 77.6 points. This was higher than the SUS average of 68 points. When converted according to the adjective rating scale, this result corresponds to \"Good\" [52]. Therefore, the proposed system is considered suitable for practical use.  
Figure 8 shows the IPQ results. SP exhibits the highest result at 5.08 points (SD = 0.417). G also has a high score, which was close to 5 points (M = 4.95, SD = 0.759). INV and REAL were 4.25 (SD = 0.693) and 4.11 (SD = 0.763), respectively, which are both above average scores. Considering that the survey was conducted on a Likert scale from 0 to 6, all values are higher than the average, which shows that a sense of presence was felt in the proposed system.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **Group** & **Mean** & **SD** & **N** \\ \hline \multirow{2}{*}{Post-test} & Proposed system & 75.00 & 20.455 & 20 \\  & Video & 65.50 & 22.705 & 20 \\ \hline \multirow{2}{*}{Retention-test} & Proposed system & 68.75 & 19.390 & 20 \\  & Video & 53.25 & 18.516 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Post-test and retention-test average results for each group.  
Figure 8: Average result of response for each IPQ item.
## 3 Results
### ANN Model
#### 3.1.2 Models' Accuracy Evaluation  
Having selected the best set of input features for both the heating and the cooling periods, the following step is to evaluate ANN models' accuracy by comparing their resulting forecasted values with heating and cooling energy data obtained from the case study building's dynamic simulation model, used as reference. Figure 7 depicts a load profile comparison between ANN model C2 and the reference values, for a complete working day, for the test subset in the cooling period.  
As it was depicted in Figure 7, ANN models are very accurate in forecasting cooling energy demand throughout the day, with the trend of predicted values being located below the reference.  
Figure 8 presents a comparison between the load profile for ANN model H4 and the reference values, for a complete working day of the test subset, during the heating period.  
Comparing forecasted values from the ANN model to the reference ones for the same day in the test subset, it is patent that the ANN model provides a fair approximation to the reference data.  
Figure 6: Error metrics for heating energy demand using ANN.
# Evaluating the Efficacy of Segment Anything Model for
## 6 Conclusions  
By assessing the segmentation accuracy across various data types to evaluate SAM's utility in processing ARSI and UGS images, we have uncovered substantial potential within SAM for such applications. Nevertheless, SAM does exhibit certain limitations, including challenges in segmenting agricultural regions within large-scale images, areas with subtle edges, and agricultural and forestry regions with minimal color differentiation. These limitations could be attributed to the lack of samples and an algorithm model structure or hyperparameters setting that places excessive emphasis on object-oriented segmentation in the processing hierarchy. Therefore, we propose that the SAM model can be further specialized based on agricultural and UGS remote sensing big data, with an emphasis on expanding training datasets. Meanwhile, SAM based on unsupervised segmentation exhibits poor performance and high instability. Nevertheless, unsupervised segmentation in specific scenarios may become a primary tool in the future for urban green space segmentation and agriculture. After all, even adding a small number of interactive samples on top of a large volume of raw data can result in a significant workload. Therefore, the further expansion and targeted improvement of unsupervised segmentation for extraction may represent the future direction of development. Additionally, model adjustments tailored to the specific demands of ARSI, including greater focus on individual pixels and heightened multispectral model training to augment feature diversity, can pave the way for SAM to achieve even more robust results in the realm of ARSI and UGS image segmentation.  
Conceptualization, B.G., A.B. and L.S.; methodology, B.G., A.B. and L.S.; software, B.G.; validation, B.G.; formal analysis, B.G., A.B. and L.S.; writing--original draft preparation, B.G.; writing--review and editing, A.B. and L.S.; supervision, A.B. and L.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The remote sensing data used for this study are publicly available, and the download web links have been provided. This was an evaluation study, and no new data have been created.  
The authors declare no conflicts of interest.
## 5 Photovoltaic Power Prediction Based on Objective Optimization IHO-ELM
### Further Analysis  
To further validate the performance and generalization ability of the proposed model in improving prediction accuracy. Select other PV power data from Yulara 3 mono-Si Roof 2016 Sails in the Desert--31 DKA Solar Centre, on 1 July 00:00-11 July 9:55 in 2016. Sampling was conducted every 15 min, resulting in a total of 3000 sampling points. Select the top 80% of the data as the training set and the bottom 20% of the data as the testing set. The decomposition steps and experimental environment are the same as above, and the results obtained are shown in Tables 5 and 6. The predicted results can be more intuitively presented in Figures 12-15.  
Figure 11: Curve plots of 8 models’ PV Power training and prediction sets.  
Figure 10: Scatter plot of evaluation indicators for eight prediction models.  
According to Table 5, the proposed models MAE, MAPE, MSE, and RMSE are 0.041205, 0.69879, 0.0044853, and 0.066973, respectively, which are still superior to other benchmark models. Compared to the HO-ELM, they have increased by 19.9%, 4.1%, 25%, and 13.4%, respectively. The IHO algorithm proposed in this article can improve the prediction accuracy and has good optimization accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Model** & **MAE** & **MAPE** & **MSE** & **RMSE** \\ \hline IHO-ELM & 0.04121 & 0.69879 & 0.00449 & 0.06697 \\ HO-ELM & 0.05150 & 0.72837 & 0.00598 & 0.07733 \\ PSO-ELM & 0.06093 & 1.00370 & 0.00759 & 0.08713 \\ ELM & 0.26328 & 6.52800 & 0.10918 & 0.33043 \\ LSTM & 0.48613 & 1.28880 & 0.70688 & 0.84076 \\ TCN & 0.79034 & 6.85980 & 2.58130 & 1.60660 \\ SVM & 0.99275 & 26.0436 & 1.71340 & 1.30900 \\ BP & 0.92858 & 19.1984 & 1.79300 & 1.33900 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The two optimal models’ PV predict the error results.  
Figure 12: Radar chart of PV prediction evaluation index results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Prediction Model** & **MAE** & **MAPE** & **MSE** & **RMSE** \\ \hline IHO-ELM & 0.04121 & 0.69879 & 0.00449 & 0.06697 \\ HO-ELM & 0.05150 & 0.72837 & 0.00598 & 0.07733 \\ PSO-ELM & 0.06093 & 1.00370 & 0.00759 & 0.08713 \\ ELM & 0.26328 & 6.52800 & 0.10918 & 0.33043 \\ LSTM & 0.48613 & 1.28880 & 0.70688 & 0.84076 \\ TCN & 0.79034 & 6.85980 & 2.58130 & 1.60660 \\ SVM & 0.99275 & 26.0436 & 1.71340 & 1.30900 \\ BP & 0.92858 & 19.1984 & 1.79300 & 1.33900 \\ \hline \hline \end{tabular}
\end{table}
Table 5: 8 models’ prediction error results.  
Figure 13: Bar chart of PV prediction evaluation index results.
# A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
## 3 Methods
### Character Segmentation and Recognition  
To recognize alphanumeric characters from the segmented image, the candidate number plate is segmented further into individual characters before the optical character recognition process occurs, as shown in Figure 2g. This process is summarized in Algorithm 2. First, we draw a bounding box over each character region using the CCA method. Then, we generate a feature vector from each extracted character image. This feature vector is fed into our KNN classier to recognize the plate's actual characters.  
```
1:procedureIdentifyingPlateCharacters
2:Input:PlateImage (plateImage)
3:Output:PlateCharacters (plateCharList)
4:/*CharacterSegmentation*/
5:charImageList = charSegmentation(plateImage);
6:/*CharacterRecognition*/
7:for eachcharImage img in charImageList do
8:/*extractfeaturevectorfromeverycharacterimage*/
9:char_box = drawBoundingBox(img);
10:vs. = getFeatureVector(char_box);
11:/*SelectKnearestcharacters tothefeaturevectorbycomparingtheirEuclideandistances*/
12:charCandidateList = getNearestChar(v);
13:/*votebetweenKcharacters*/
14:plateCharList.add(vote(charCandidateList));
15:endfor
16:returnplateCharList;
```  
**Algorithm 2** Character Segmentation and Recognition Algorithm.  
The KNN model is trained using \(20\times 20\) pixel images for all alphanumeric characters. We validated the KNN model using the cross-validation strategy that assesses how our model's prediction results will generalize to an independent dataset. To recognize an individual character using KNN, we select the K nearest characters to the feature vector by comparing their Euclidean distances. The K value is set to be 7, which gave us a better classification accuracy. The character recognition module prints the output number plate on the image, as illustrated in Figure 2h.  
The getNearestChar function is implemented based on the following similarity equation between the two feature vectors (\(x_{1},y_{2}\)):  
\[S(x_{1},y_{2})=1-d(x_{1},y_{2}) \tag{3}\]  
where \(d(x_{1},y_{2})\in[0,1]\) is the Euclidean distance between \(x_{1}\) and \(y_{2}\), and \(d\) is calculated as:  
\[d(x,y)=\sqrt{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}} \tag{4}\]  
where \(x_{i}\) and \(y_{i}\) are the Euclidean feature vectors, and \(n\) is the n-space dimension of \(x_{i}\) and \(y_{i}\).
## 4 Results
### Cluster Analysis of Small- and Medium-Sized Enterprises
#### 4.1.6 Validation of Cluster Analysis  
The validity of the cluster analysis results was verified by one-way ANOVA F-tests for each clustered factor (VPi4 Level 1, Level 2, and Level 3). The results of this analysis should verify whether there are statistically significant differences between the clusters. In the one-way ANOVA test, we used a \(p\)-value significance level 0.05, but some of the cluster means are different. If the \(p\)-value for all four variables is greater than 0.05 (labeled by \"*\" in Table 5), excluding them from the analysis should be  
Figure 6: Clusters characteristics: (**a**) Cluster 3—I4 starting enterprises; (**b**) Cluster 4—I4 noobs enterprises. considered. The results of the factor ANOVA are shown in Table 5. The results show that, for all variables, there are some differences among the clusters. However, the ANOVA results cannot be interpreted unambiguously, as it is not clear which pairs of clusters are different. It is advisable to use the Tukey Honest Significant Difference (HSD) test (for multiple pairwise comparison) to find out if there is a statistically significant difference between the means of certain cluster pairs. The results of this analysis are summarized in Table 6. In most cases, the differences between the clusters are statistically significant. However, there were no differences between Cluster 3 and Cluster 1 for VPi4 Level 1; there were no the differences between Clusters 4 and 2 for VPi4 level 2, and three differences for VPi4 level 3. This means that there are some overlaps between the clusters, especially for VPi4 Level 3 factor variables. Such lack of differences in the clusters may partly be due to the fact that all factors are considered follow-up levels of the VPi4 index. In the case of the small- and medium-sized enterprises, it is typical that most of these enterprises do not reach the high third level of the index. Therefore, their values are rather lower, and this causes the similarity of the clusters. Therefore, the differences in clusters of VPi4 Level 3 are not so significant. However, such conclusion should not diminish the significance of the cluster analysis and the fact that the clusters are different. The differences are also reported by Table 3 (see within sum of squares, diameter, average distance, separation, etc.). Another method of cluster analysis validation is related to the abovementioned methods, to determining the optimal number of clusters, the Silhouette and Elbow method. The methods were used to determine the parameters of the cluster analysis in order to achieve the most accurate results. However, it should be noted that the average value of Silhouette coefficient is only 0.32. The reason for
# Simulation Framework for Detection and Localization in Integrated Sensing and Communication Systems
## 3 Main Elements of Abstraction: Channel and SNR Generation
Assuming that the target behaving as a UE has a single antenna, the received signal at this UE is  
\[\mathrm{y}_{l}^{\mathrm{com}}(k)=\mathrm{H}_{\mathrm{com}}(k)\mathrm{s}_{l}(k )+\mathrm{n}_{l}(k), \tag{2}\]  
where \(\mathrm{H}_{\mathrm{com}}(k)\in\mathbb{C}^{1\times M}\) is the communication channel matrix, and \(\mathrm{n}_{l}(k)\in\mathbb{C}\) is the noise vector for the \(l\)-th OFDM symbol and the \(k\)-th sub-carrier. The received signal in (2) is considered to compute the SNR for the PLA method. Since all the MPCs associated with the received communication signal interact with the target, the resulting SNR serves as a reliable indicator for sensing applications. Assuming that the elements of \(\mathrm{s}_{l}(k)\) are independent and identically distributed (i.i.d.) from a zero-mean and unit-variance normal distribution (i.e., the transmitted signal has a covariance matrix equal to the identity), the power of the signal in \(\mathrm{y}_{l}^{\mathrm{com}}(k)\) can be obtained as \(\mathrm{tr}(\mathrm{H}_{\mathrm{com}}(k)\mathrm{H}_{\mathrm{com}}^{\mathrm{ r}}(k))=\|\mathrm{H}_{\mathrm{com}}(k)\|^{2}\). Moreover, assuming that the noise is i.i.d. from a zero-mean normal distribution with variance \(\sigma^{2}\), the noise power in \(\mathrm{y}_{l}^{\mathrm{com}}(k)\) is directly \(\sigma^{2}\). Therefore, the communications SNR denoted for all the \(K\) sub-carriers of an OFDM symbol can be calculated as  
\[\mathrm{SNR}_{\mathrm{com}}=\frac{\sum_{k=1}^{K}\|\mathrm{H}_{\mathrm{com}}( k)\|^{2}}{K\sigma^{2}}. \tag{3}\]
## 2 Materials and Methods
### Adsorption Experiments  
The effect of pH on MB adsorption onto A/\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)/f-CNT was studied varying the initial pH values from 3 to 10. The volume used was of 50 mL, operating at room temperature, \(\pm\)25 \({}^{\circ}\)C. Kinetic batch adsorption tests were accomplished using a MB initial concentration of 230 mg L\({}^{-1}\). For this, 50 mg of A/\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)/f-CNT was dispersed in MB solution (50 mL) at natural pH (5.2) and room temperature. At regular time intervals, a sample was magnetic separated. The equilibrium adsorption experiments were accomplished at different initial MB concentrations, ranging from 5 to 270 mg L\({}^{-1}\), mixed with 50 mg of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)/f-CNT. The dispersions were maintained at a constant temperature of 25\({}^{\circ}\)C, at a natural pH of 5.2. All the adsorption experiments were carried out without stirring. When the equilibrium was reached, the magnetic adsorbent could be separated from the aqueous medium by using a magnet. The effect of temperature on the adsorption was studied at different temperatures, i.e., 283, 293, 303 and 313 K using a thermostatic chamber.  
MB concentration was determined on a Shimadzu UV-1700 spectrophotometer at a wavelength of 664 nm. The amount of adsorbed dye at equilibrium time (q\({}_{\rm e}\), mg g\({}^{-1}\)) was determined using Equation (1):  
\[\mathrm{q}_{\rm e}=\frac{(\mathrm{C}_{0}-\mathrm{C}_{\rm e})\cdot\mathrm{V}}{ \mathrm{m}} \tag{1}\]  
where, C\({}_{0}\) and Ce (mg L\({}^{-1}\)) are MB concentration at initial and equilibrium time, respectively, m (g) is the weight of adsorbent and V (L) is the solution volume.