esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Evaluation Criterion  
Maximum relevance between the feature subset and the target class is a key criterion for \(F\)S. Let \(D\) be a dataset that has \(N\) features \(F=\{f_{i}\), \(i=1\), \(2\),...,\(N\}\) and \(c\) be the target class. According to the definition of MI, selecting the feature subset with maximum relevance can be formalized as selecting a feature subset \(F_{S}=\{f_{j}\), \(j=1\), \(2\),...,\(n\}\) (\(n<N\)) that satisfies the following equation:  
\[\max MI(F_{S};c) \tag{2}\]  
To determine the feature subset that has the maximum mutual information (MI) with the target class, we introduce the following theorem:  
**Theorem 1**.: _For the original feature set \(F\), the joint mutual information between any feature subset \(F\)s and the target class \(c\) is less than or equal to the joint mutual information between the entire feature set \(F\) and the target class \(c\). The equation form of Theorem 1 is as follows:_  
\[\forall F_{S}\subset F,\ M1(c;F_{S})\leq MI(c;F)\]  
**Proof of Theorem 1.** Let \(F_{S}=\big{\{}f_{j},j=1,2,\ldots,n\big{\}}(n<N)\) be a feature subset of the original feature set \(F\).  
\[\therefore F_{S}\subset F_{s}\therefore F=\big{\{}\underbrace{\hat{f}_{1},\hat{f}_ {2},\ldots,\hat{f}_{N-n}}_{F-F_{s}},\underbrace{\hat{f}_{1},\hat{f}_{2},\ldots,\hat{f}_{n}}_{F_{s}}\big{\}}\]  
where feature \(\hat{f}_{i}\) belongs to \(F-F_{S}\). According to the definition of MI, we have  
\[\begin{array}{c}MI(c;F)=MI\left(c;\hat{f}_{1},\hat{f}_{2},\ldots,\hat{f}_{N -n},f_{1},f_{2},\ldots,f_{n}\right)\\ =\sum_{i=1}^{N-n}MI\left(c;\hat{f}_{i}\Big{|}\hat{f}_{i-1},\ldots,\hat{f}_{1}, f_{1},f_{2},\ldots,f_{n}\right)+MI(c;f_{1},f_{2},\ldots,f_{n})\\ \therefore\sum_{i=1}^{N-n}MI\left(c;\hat{f}_{i}\Big{|}\hat{f}_{i-1},\ldots,\hat{f }_{1},f_{1},f_{2},\ldots,f_{n}\right)\geq 0,\\ \therefore\ M1(c;F_{S})\leq MI(c;F).\end{array} \tag{3}\]  
\(\square\)According to Theorem 1, we demonstrate that the feature subset \(F_{s}\) has the maximum MI with the target class if and only if \(F_{s}\) satisfies the following equation:  
\[MI(c;F_{s})=MI(c;F) \tag{4}\]  
Although Equation (4) can ensure that the selected feature subset has maximum relevance, it cannot guarantee that the selected feature subset has no redundant features. In fact, a redundant feature is one that is typically deemed irrelevant to the target class given the presence of other features in the dataset. Therefore, the definition of a redundant feature for a feature set is as follows:  
**Definition 1**.: _(Redundant feature): For a feature subset \(F_{s}\), a feature \(f_{j}(f_{j}\in F_{s})\) is a redundant feature if and only if_  
\[MI\Big{(}c;f_{j}|F_{s}-\big{\{}f_{j}\big{\}}\Big{)}=0 \tag{5}\]  
_or_  
\[MI\big{(}c;F_{s}-\big{\{}f_{j}\big{\}}\big{)}=MI(c;F_{s}). \tag{6}\]  
By combining Theorem 1 and Definition 1, the FS for the MRNR feature subset can be defined as selecting a feature subset \(F_{s}\) that satisfies the following equations:  
\[\bigg{\{}\forall f_{j}\in F_{s},\;MI\big{(}c;F_{s}-\big{\{}f_{j}\big{\}}\big{)} <MI(c;F_{s})\cdot \tag{7}\]
# Nanomechanical Characterization of Bacterial Polyhydroxyalkanoates Using Atomic Force Microscopy
## 3 Results
### Elastic Modulus Evolution with Time of PHA  
A nanomechanical characterization was also carried out on a PHA fraction obtained by a solvent/non-solvent fractionation of an extracted PHA sample, to guarantee the strict compositional homogeneity of the material. In particular, P(3 HB-co-88% 3 HV) with HV content of 88 mol% and a single well-defined peak at 106 \({}^{\circ}\)C was tested. This analysis allowed the evaluation of the elastic modulus of the pure copolymer P(3 HB-co-88% 3 HV), and not of the native blend of copolymers.  
The indentation tests by AFM were performed one, three, and seven days after the deposition of the thin film. Figure 4 shows the elastic moduli extrapolated for P(3 HB-co-88% 3 HV) after one, three and seven days after the deposition of the thin film.  
As shown in Figure 4, P(3 HB-co-88% 3 HV) showed an increasing elastic modulus with time. P(3 HB-co-88% 3 HV) after one day exhibited an elastic modulus of 217 \(\pm\) 31 MPa, which gradually increased to 285 \(\pm\) 30 MPa after three days and 708 \(\pm\) 78 MPa after seven days.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**HAcHPr** & **Elastic Modulus** & **Molar Composition** & **Mw** & **Polydispersity** & **Melting** & **Crystallinity** \\  & **Modulus** & **Composition** & **Index** & **Temperatures** & **Degree** \\ \hline
**[g-COD/Lg-** & **[MPa]** & **[HV mol\%]** & **[10\({}^{\circ}\) Da]** & & [\({}^{\circ}\)C] & **[-]** \\ \hline
100:0 & 1623 \(\pm\) 172 & 2\% & 7.47 \({}^{\star}\) & - & 173 & 48\% \\ \hline
30:70 & 528 \(\pm\) 62 & 12\% & 1.95 & 2.78 & 174 & 38\% \\ \hline
60:40 & 693 \(\pm\) 74 & 15\% & 3.06 & 1.53 & 95/138/170 & 55\% \\ \hline
0:100 & 885 \(\pm\) 176 & 40\% & 2.18 & 1.66 & 97/132/172 & 55\% \\ \hline
50:50 & 700 \(\pm\) 130 & 47\% & 2.45 & 1.33 & 98/140/175 & 55\% \\ \hline \end{tabular} \({}^{\star}\) Mw estimated by dilute solution viscometry.  
\end{table}
Table 1: Extracted PHA molar composition, elastic modulus from AFM force-distance curves on thin film, weight-average molecular weight Mw, polydispersity index and thermal properties.
## 2 Materials and Methods
### Test Field Specification
#### 2.2.5 Coordinates Determined by Adjustment  
The control points were measured using the polygon method with a Leica TS1200 (Leica Geosystems, Heerbrugg, Switzerland) and the measurements were adjusted in the EasyNET software (Version 3.5) using the tied network method. Points 1, 5, and 25 were chosen as fixed points because they are in a position with a good view of the sky. This is crucial because it ensures clear reception of satellite signals used to determine point positions and increases the number of satellites used to calculate the position. As a result, their deviations from the GNSS measurements were minimal and the resulting coordinates are more reliable. At these points, the number of satellites was between 30 and 34. The output values of the adjustment are shown in Table 4. Since the network has been measured repeatedly and many redundant measurements are available, the number of excluded measurements can be considered insignificant.  
The checkpoints were surveyed twice using the polar method from the points of the adjusted grid. **The maximum difference between the two coordinates is 5 mm.**
# A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network
## 4 Functionality Comparisons and Security Analysis
### Efficiency Comparisons
#### 4.5.3 Indistinguishability  
In this definition, we assume the Indistinguishable (Ind for short) game where there exists an attacker \(\mathcal{A}\) in the following simulation, which is controlled by a simulator \(\mathcal{S}\). First, we defined that there is a symmetric encryption/decryption function \(E_{pk_{i}}(\cdot)/D_{sk_{i}}(\cdot)\), where \(i\in\{DS_{j},BS_{j},R\},j=1\sim l\), in which \(DS_{j}\) is one of the \(l\) IoT devices; \(BS\) is the base station, and \(R\) is the receiver of the outside network. The simulator \(\mathcal{S}\) will prepare all  
Figure 2: RSA Signature Simulation on 10 nodes to 50 nodes.  
set-up parameters including key pairs for the above parties. After set-up is complete, \(\mathcal{S}\) will launch the proposed scheme simulation with \(\mathcal{A}\). \(\mathcal{A}\) can perform the encryption/decryption on the chosen message \(m\). \(\mathcal{S}\) also can reply the cipher-text \(C=E_{pk_{i}}(m)\) and the original message \(m\) to \(\mathcal{A}\). After the above game simulation, \(\mathcal{S}\) can replace the encryption/decryption functions to an encryption/decryption oracle (\(\tau\), \(\tau^{-1}\)), which performs the same action as our above symmetric encryption/decryption function. Through the above training phase, \(\mathcal{A}\) sends a chosen target message (\(M_{0}\), \(M_{1}\)) to \(\mathcal{S}\); \(\mathcal{S}\) will perform a coin flip \(b\) on the message (\(M_{b}\), \(M_{1-b}\)). Then, \(\mathcal{S}\) inputs the \(M_{b}\) to the encryption oracle \(E_{pk_{i}}\) to obtain the final result \(C_{b}\). \(\mathcal{S}\) forwards \(C_{b}\) to \(\mathcal{A}\) to guess whether \(M_{b}\) is \(M_{0}\) or \(M_{1}\) on its coin flip \(b^{\prime}\)--that is,  
\[Pr[b^{\prime}\longleftarrow\mathcal{A}^{(E_{pk_{i}}(\cdot),D_{pk_{i}}(\cdot ),\tau,\tau^{-1})}|b=b^{\prime}]<\frac{1}{2}+\epsilon^{\prime}.\]
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 1 Introduction
### Motivation

Table 1: Performance of AF/DF based CR-NOMA with CBSM channel model. Figure 2: Downlink cooperative relay aided NOMA network with CBSM channel. CBSM is basically used for theoretical analysis and has less accuracy for real-world mMIMO systems due to the nonstationary phenomenon and spherical effects [17; 89]. The best we can tell is that authors in [47] have analyzed CR-NOMA performance using GBSM based on Saleh-Valenzuela channel model. For mMIMO systems, there are several typical antenna array configurations. These include linear antenna array, rectangular antenna array, spherical antenna array, distributed antenna array and cylindrical antenna array [17]. With CR-NOMA, only linear antenna array has been examined so far in [61]. Based on the above, the motivation of this work is to address the missing gap in the literature on CR-NOMA concerning the adoption of 3 GPP 3D GBSM channel model with large antenna transmitters such as URA and CA. Our research aims to provide solutions to the following research challenges to fulfill future demands of wireless communication systems: (1) What effect will the large antenna transmitters (CA or URA) modeling have on CR-NOMA system's performance? (2) How will mMIMO CR-NOMA work when the user, the dedicated relay, and transmitter all use the 3D GBSM channel model? (3) How will the coexistence of large antenna transmitters, 3D GBSM channel models, and coding schemes affect performance of CR-NOMA? (4) what coding scheme and large antenna combination will provide the superior performance when using the GBSM channel model for mMIMO CR-NOMA regarding outage probability, achievable rate, and bit-error-rate (BER)?
# A Two-Phase Approach for Predicting Highway Passenger Volume
## 4 Methodology  
The flow diagram of the proposed two-phase approach and associated designed framework is shown in Figure 1. Firstly, the raw dataset, including highway passenger volume and impact factors, was collected. Then, the two-phase approach was proposed. Phase I extracted the significant impact factors with the RF algorithm and Phase II predicted highway passenger volume with the DFNN. Finally, the typical machine learning algorithm, support vector machine (SVM), was also developed for predicting highway passenger volume and compared with the DFNN, because it has a better ability to solve machine learning problems with a small sample size. Moreover, the traditional multiple regression, which is widely used for discerning the relationship between dependent variables and multiple independent variables, served as the benchmark for the prediction of highway passenger volume. All predicted models were evaluated by calculating errors, including mean absolute error (MAE) and root mean squared error (RMSE).  
The fundamentals of the two primary methods used in this study are briefly discussed as follows, including the RF algorithm and the DFNN. Moreover, the evaluating indicators, MAE and RMSE, are introduced as well.
## 2 Materials and Methods
### Functional Parameters  
For the performance analysis of the PTs, the set of parameters presented by [PERSON] et al. [26] was applied. Table 1 shows the adopted parameters and their definitions: total time \(TT\), smoothness \(J\), fluency \(Fl\), movement velocity \(RMS_{G}\), jerk root mean square \(RMSj\), and maximum jerk variation \(\Delta j\). In particular, the parameters smoothness \(J\), fluency \(Fl\), and movement velocity \(RMS_{G}\) were designed according to a modified version of the formulas proposed by [PERSON] et al. (2018) in terms of their set of indicators, time \(TT\) maintained its traditional definition, whereas the last two parameters were additionally evaluated. In the method proposed by [PERSON] et al. (2018), an additional parameter was also presented, defined as rotational speed and evaluated based on the jerk signal. Since the definition of this parameter depends on a condition of trunk tilt, which could not occur in some of the PTs under investigation, this parameter was excluded from the current set of parameters.  
In more detail, the considered set of parameters is composed of the following:  
* Total time TT: this parameter quantifies the time actually elapsed from the beginning to the end of the movement, detected according to a repeatable strategy. TT also represents the normalizing factor for all the other parameters.
* Smoothness J: This parameter investigates how much the movement is disrupted, assessing the evolution of the acceleration in time in terms of jerk.
* Fluency Fl: This parameter quantifies the acceleration due to the bodily motion during the movement without considering the acceleration contributions generated by the actual focal movement, described by \(a_{g}\).
* Movement velocity \(RMS_{G}\): This parameter combines the RMS values of the three components of the angular velocity measured during the movement.
* Jerk root mean square RMSj: This parameter computes the RMS value of the jerk signal between the start and stop instants of the movement.
* Maximum jerk variation \(\Delta j\): This parameter measures the difference between the maximum and minimum values assumed by the jerk signal during the movement.
# Universal Local Attractors on Graphs
## 5 Experiments
### Compared Architectures  
We select architectures that are conceptually similar to ours in that they employ graph diffusion mechanisms. All architectures used for experiments are MPNNs, which means that they exhibit at most WL-1 expressive power. Therefore, we may safely attribute differences in predictive efficacy as the hardness of optimizing them.  
**MLP.** A two-layer perceptron that does not leverage any graph information. We use this as a baseline to measure the success of graph learning.  
**GCN**[12]. The architecture described by Equation (2). It comprises two linear layers, the first of which is followed by a _relu_ activation.  
**GAT**[13]. A gated attention network that learns to construct edge weights based on the representations at each edge's ends, and uses the weights to aggregate neighbor representations.  
**GCNII**[34]. An attempt to create a deeper variant of GCN that is explicitly designed to be able to replicate the diffusion processes of graph filters if needed. We employ the variation with 64 layers.  
**APPNP**[8]. The predict-then-propagate architecture with ppr base filter. Switching to different filters lets this approach maintain roughly similar performance, and we opt for its usage due to it being a standard player in GNN evaluation methodologies.  
**ULA [this work].** Our universal local attractor presented in this work. Motivated by the success of APPNP on node classification tasks, we also adopt the first 10 iterations of personalized PageRank as the filter of choice, and leave further improvements by tuning the filter to future work.  
Figure 6: Distributions (in that order) of degrees, triangle counts, existence of 4-cliques, and longest shortest path distances in synthetic graphs.  
Additionally, we compare our approach with one of greater expressive power:  
**GCNNRI.** This is the GCN-like MPNN introduced by [PERSON] et al. [24]; it employs random node representations to admit greater (universal approximation) expressive power and serves as a state-of-the-art baseline. We adopt its publicly available implementation, which first transforms features through two GCN layers of Equation (2) and concatenates them with a feature matrix of equal dimensions sampled anew in each forward pass. Afterwards, GCNNRI applies three more GCN layers and then another three-layered MLP. The original architecture max-pooled node-level predictions before the last MLP to create an invariant output (i.e., one prediction for each graph), but we do not perform this to accommodate our experiment settings by retaining equivariance, and therefore, create a separate prediction for each node. We employ the variation that replaces half of the node feature dimensions with random representations.
## 3 Results and Analysis
### Analysis of Failure Samples  
In addition to the aforementioned correct diverging samples, this study collected 88 failure samples, comprising 61 cases that resulted in crashes and 27 instances of vehicles failing to enter the ramp. Figure 18 showcases the relative proportions of error samples under varied CD and IV conditions, and also includes a statistical examination of the crash reasons and location distribution.  
As demonstrated in Figure 18a, both collision frequency and misdirection rates amplify with decreasing CD, corroborating our earlier analysis. Larger spacing provides drivers with ample time to read signs and choose cut-in gaps, which helps improve LC stability and diminish speed fluctuations. However, the benefits of additional safety margins diminish once the CD extends beyond a certain threshold (about 700 m).  
Figure 18b displays how increased signage IV corresponds to a rise in collision instances and misdirection rates. The results indicate that when information volume exceeds nine units, there is a marked surge in direction misjudgments. Excessive information can result in cognitive overload and prevent drivers from identifying the expected direction, thus hampering the successful execution of lane changes.  
Of all crash samples, 45% can be attributed to improper lane changing and 31% to driver distraction. The term \"improper lane changing\" includes inadequate gap selection and untimely lane changes, both factors escalating traffic conflicts and driving risks. Furthermore, over 77% of crashes occur within 200 m of the transition point (dot line in Figure 18d), correlating with the majority of vehicles' MLC location (Figure 13) and maximum deceleration rate area (Figure 8). It means that drivers have to make correct decisions and accurately operate vehicles within this challenging range, which heightens task difficulty and driving risk [37].
## 4 Results
### Training and Evaluating the Performance of the Models
#### 4.3.2 Predicting First-Year Students' Performance  
The findings from the correlation map show that higher scores in APS, mathematics, and physical sciences show a slightly positive association with module marks at the university level, leading us to investigate the effect of these three features on student performance. Figure 6 presents the performance metrics for different classification models to predict first-year students' performance. The algorithms used APS, mathematics and physical sciences as input parameters and the result grouping as the target. The first-year data consisted of 705 student records.  
When considering accuracy, four models achieved high accuracy rates, with an accuracy of 98.3% for the decision tree, KNN, random forest, and XGB models. LGBM has a slightly lower accuracy of 96.4%. This implies that these models can accurately predict first-year students' performance. Regarding precision, which measures the ability to avoid false positives, all models perform consistently. Each model achieved 96.6% and 98.3% precision scores, indicating that they can minimise false positives. Similarly, the models demonstrate high recall scores, ranging from 96.4% to 98.3%. Regarding the F1 score, LGBM has a slightly lower score of 96.5%. The other models all achieved a score of 98.3%. The values in the table indicate that the models perform consistently. The RMSE values are between 0.130 and 0.221, and MAE values range from 0.017 to 0.040 between predicted and actual student performance.  
When comparing Figure 5 with Figure 6, notable differences emerge in the performance of the models. Figure 6 exhibits higher accuracy values, ranging from 96.4% to 98.3%, compared to Figure 5's 82% to 83.8% range. Additionally, the models in Figure 5 consistently demonstrate slightly higher precision, recall, and F1 scores, indicating better overall performance in correctly identifying the data and striking a balance between precision and recall. Moreover, Figure 6's models display significantly lower RMSE and MAE values, reflecting higher accuracy and more minor average deviations in their predictions. These results suggest that using APS, mathematics, and physical sciences as input parameters to predict student performance yields better results for first-year students than overall student performance.  
Figure 6: Predicting result grouping (students’ performance) using first-year students’ data for features—APS, mathematics and physical sciences.
## 2 Theoretical Analysis of Cavity-Based WPT Systems
### WPT Model Decomposition
In this case, matrix \(\mathbf{S}^{\text{PLATE}}\) is a diagonal matrix whose \(n\)-th diagonal entry can be written as \((\mathbf{S}^{\text{PLATE}})_{n,n}=(Z_{\text{S}}-Z_{n})/(Z_{\text{S}}+Z_{n})\), where \(Z_{\text{S}}=(1+\text{i})\sqrt{\eta_{0}\mu_{\text{r}}f\pi/\sigma}\), \(Z_{n}\) is the characteristic impedance of the \(n\)-th waveguide mode given by \(Z_{n}=\text{j}\omega\eta_{0}/\gamma_{n}\) and \(Z_{n}=\gamma_{n}/\text{j}\omega\varepsilon_{0}\) for the TE and TM modes, respectively, \(f\) is the operating frequency, and \(\mu_{\text{r}}\) and \(\sigma\) are the relative permeability and conductivity of the closet material, respectively. It should be noted that the same boundary conditions are imposed on the side walls of the waveguide sections during the full-wave analysis. Also, the effect of the side walls of the empty waveguide sections joining different sub-models is incorporated into \(\gamma_{n}\), calculated with Ansys HFSS 2023 R1. Now, having found the generalized S matrix for the Tx dipole antenna considering the presence of the conducting plate and using the S matrix for the Yagi-like antenna director obtained by analyzing the sub-model involving the director, the generalized scattering matrix for a waveguide section containing both the dipole and the director sub-models separated by a distance \(d_{\text{dip-dir}}\) can be found as follows. For convenience, the expression for each of the four submatrices of \(\mathbf{S}^{\text{YAG}}\) is deduced separately. The generalized reflection matrix \(\mathbf{S}^{\text{YAG}}_{11}\) can be calculated as follows:  
\[\mathbf{S}^{\text{YAG}}_{11}=\mathbf{S}^{\text{TX-DP}}_{11}+\mathbf{S}^{\text {TX-DP}}_{12}\mathbf{T}^{\text{DIP-DIR}}\left(\mathbf{I}^{\text{DR}}-\mathbf{ S}^{\text{DIR}}_{11}\mathbf{T}^{\text{DIP-DIR}}\mathbf{S}^{\text{TX-DP}}_{22} \mathbf{T}^{\text{DIP-DIR}}\right)^{-1}\mathbf{S}^{\text{DIR}}_{11}\mathbf{T}^ {\text{DIP-DIR}}\mathbf{S}^{\text{TX-DP}}_{21}, \tag{4}\]  
where \(\mathbf{S}^{\text{TX-DP}}_{11}\) and \(\mathbf{S}^{\text{TX-DP}}_{22}\) are the generalized reflection matrices for Ports 1 and 2, respectively, \(\mathbf{S}^{\text{TX-DP}}_{21}\) and \(\mathbf{S}^{\text{TX-DP}}_{12}\) are the generalized transmission matrices for waves traveling from Port 1 (the lumped port) towards Port 2 (the front end of the section with the dipole), and vice versa, \(\mathbf{S}^{\text{DIR}}_{11}\) is the reflection matrix for Port 1 of the director sub-model, \(\mathbf{T}^{\text{DIP-DIR}}\) is a diagonal matrix whose order is equal to the number of waveguide modes and the \(n\)-th diagonal entry is \((\mathbf{T}^{\text{DIP-DIR}})_{n,n}=\text{e}^{-\gamma_{n}d_{\text{dip-dir}}}\), and \(d_{\text{Tx-to-Rx}}\) is the distance between the dipole end and the director. The above expression for combining the generalized matrices can be derived either by enforcing the continuity of the mode amplitudes and phases in waveguide cross-sections away from a discontinuity or by utilizing the fact that the multiple reflection method also applies to time-harmonic processes (sine waves).