esa-sceva/satcom-chunk-collection huggingface dataset:
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 3 Preliminaries
### Short Signature Scheme (BLS)  
In 2001, a short signature scheme (BLS) based on pairing cryptography was proposed by [PERSON] et al. [31]. The length of the signature is only half of the digital signature algorithm (DSA). Thus, BLS can guarantee the lower communication cost in VANETs. The BLS scheme is expressed as follows:  
Setup. Private key generator (PKG) chooses an cycle addition group \(G_{1}\) and cycle multiplication group \(G_{T}\) generated by prime q, a bilinear mapping \(e:G_{1}\times G_{1}\to G_{T}\), \(P\) is the generator of \(G_{1}\), and hash function: \(H_{0}:\{0,1\}^{*}\to G_{1}\).  
Figure 2: SDN architecture.  
Extract. The signer randomly chooses \(r\in Z_{q}^{*}\) as its long term private key and gets the public key \(P_{pub}=rP\).  
Sign. For message \(M\), the signer calculates the signature as: \(V=rH_{0}(M)\).  
Verify. During verifying \(V\), the verifier checks whether \(e(P_{pub},H_{0}(M))==e(P,V)\) is satisfied. If yes, then the verifier accepts the message \(M\). Otherwise, the message \(M\) is refused.
## 3 Fabrication of Class-E Amplifier and Experiment Verification

Table 4: Measured power results of the WPT system with the MMs. Figure 12: The experiment of the optimal load analysis. Figure 13: Effect of the MMs’ loading position on the system transmission efficiency. Figure 14 compares the measured transfer efficiency of the WPT system with/without the MMs. Obviously, the MMs contributed to the efficiency improvement for the mid-range WPT system. The strength of coupling between the \(Tx\) coil and the \(Rx\) coil was large when the two coils were closer together, so the MMs have little effect on the efficiency. However, the MMs was very useful for efficiency enhancement as the transfer distance became larger. The transfer efficiency improved by 3.26% when the \(Rx\) coil and \(Tx\) coil were 100 cm apart. On the contrary, the MMs enabled the WPT system to remain highly efficient when the two coils were far apart from each other. The transfer efficiency was enhanced by 9.13% when the transfer distance was 200 cm apart. In particular, the transfer efficiency was maximized up to 18.58% at 160 cm.
# Methods for Assessing Human-Machine Performance under Fuzzy Conditions
## 4 The Center of Gravity Defuzzification Technique
### Comparison of the RFAM with the GPA Index  
Keeping the same notation as in Section 4.1, the GPA index [15] (Chapter 6, p.125) is calculated by the formula  
\[\mathrm{GPA}=\frac{0n_{F}+1n_{D}+2n_{C}+3n_{B}+4n_{A}}{n} \tag{10}\]  
In other words, since greater coefficients (weights) are assigned to the higher scores, GPA is a weighted average connected to the group's _quality performance_. In the case of the worst performance (\(n_{F}=n\)), Equation (10) gives that \(\mathrm{GPA}=0\), while, in the case of the ideal performance (\(n_{A}=n\)), it gives that \(\mathrm{GPA}=4\). Therefore, we have in general that \(0\leq\mathrm{GPA}\leq 4\). Consequently, values of \(\mathrm{GPA}\) greater than 2 could be considered as indicating a satisfactory performance.  
Equation (10) can be also written with respect to the frequencies in the form  
\[\mathrm{GPA}=y_{2}+2y_{3}+3y_{4}+4y_{5}. \tag{11}\]  
Therefore, \(x_{c}=\frac{1}{2}\left(y_{1}+3y_{2}+5y_{3}+7y_{4}+9y_{5}\right)=\frac{1}{2}( 2\mathrm{GPA}+1)\), or  
\[x_{c}=\mathrm{GPA}+0.5. \tag{12}\]  
As an immediate consequence of Equation (12) and the first case of the assessment criterion in Section 4.1, if the values of the GPA index are different for two groups, then the GPA index and the RFAM provide the same assessment outcomes for those groups. The following example, due to [PERSON] [32], shows that, in the case of the same GPA values, the application of the GPA index cannot lead to logically-based conclusions. Therefore, in such situations, the criterion in Section 4.1 becomes useful due to its logical nature.  
Figure 4: The triangle where the COG lies.  
**Example 3**.: The student grades of two classes with 60 students in each class are depicted in Table 2.  
The GPA index for the two classes is equal to \(\frac{2\times 10_{+}4\times 50}{60}=\frac{3\times 20_{+}4\times 40}{60}\approx 3.67\), which means that the two classes demonstrate the same performance. However, Equation (12) gives that \(x_{c}=4.17\) for both classes. However, \(\sum\limits_{i=1}^{5}{y_{i}}^{2}=\left(\frac{1}{6}\right)^{2}+\left(\frac{5}{ 6}\right)^{2}=\frac{26}{36}\) for the first and \(\sum\limits_{i=1}^{5}{y_{i}}^{2}=\left(\frac{2}{6}\right)^{2}+\left(\frac{4}{ 6}\right)^{2}=\frac{20}{36}\) for the second class. Therefore, according to the second case of the assessment criterion in Section 4.1, the first class demonstrates a better performance in terms of the RFAM.  
Observe now that the ratio of the students receiving B or better to the total number of students is equal to \(\frac{5}{5}\) for the first class and 1 for the second class, which means that Class II demonstrates a better quality performance. However, many educators might prefer the situation in Class I having a greater number of excellent students. Conclusively, in no case is it logical to accept that the two classes demonstrate the same performance, as the equal values of the GPA index indicate.
# Low Delay Inter-Packet Coding in Vehicular Networks
## 2 Preliminaries  
### Wireless Channels  
Throughout the paper, we assume that at the physical layer data are protected by forward error correction. The information packet is organized as a series of codewords and data in the packet are protected by cyclic parity check (CRC). The probability of undetected error is negligible. Consequently, the decoder after computing CRC classifies each packet either as successfully delivered or as erased. Thus, we interpret packet losses as erasures and model packet transmission as a transmission over the binary erasure channel (BEC). In this channel, either all bits of a packet are successfully received or they all are erased. If an erasure correcting code is used, then all encoding and decoding operations are performed in parallel with all symbols of the packet. By default, in the coding theory literature it is assumed that BEC is a memoryless channel. However, in this paper, we consider both memoryless BEC and BEC with memory (M-BEC). One example of M-BEC model is obtained from the wireless fading channel model by applying discrete model constructing technique in [19; 20].
## 3 System Test and Application
### Technical Standards of Measuring Equipment  
#### 3.1.1 Professional A Sound Level Noise Meter  
1. Sampling frequency: 50 Hz, real-time measurement, fast response;
2. A-weighted processing of ambient noise;
3. Resolution: 0.1 dB, accuracy error: \(\pm\)1.5 dB;
4. The measurement range is 30 dB-130 dB, and the frequency response range is 31.5 Hz-8.5 kHz;
5. The time constant is 125 ms, and the time weighting is fast.  
Figure 3: The user interface of the DTRCMS: (**a**) the vibration measurement interface; (**b**) the noise measurement interface.
## 2 Strip Packing Problem (SPP)  
The strip packing problem is an NP-hard optimization problem commonly found in the industrial sector in materials such as paper, fabrics, wood, glass, plastics, and metal. Their purpose is to reduce the amount of waste in a strip, avoiding overlapping and placing objects outside the strip limits. This problem is defined using the following sets:  
\[O=\{o_{1},o_{2},\ldots,o_{n}\} \tag{1}\] \[OW=\{ow_{1},ow_{2},\ldots,ow_{n}\}\] (2) \[OH=\{oh_{1},oh_{2},\ldots,oh_{n}\} \tag{3}\]  
where \(O\) is the set of rectangular objects; \(OW\) is the set of object widths, and \(OH\) is the set of heights.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Differences between Approaches**} \\ \hline GRASPSPP (Our proposal) & Reactive GRASP [20] \\ \hline Simple GRASP & Use of reactive GRASP \\ Use of flags & Use of best fit (BF) \\ Overlap functions & Evaluate and rearrange the solution \\ Height and width measurement functions & Use of floating accommodations \\ Avoid floating objects & - \\ \hline New Branch and Bound [21] & Iterative Search [24] \\ \hline Use of Branch and Bound & Use of first-fit \\ GRASP for improvements & Arrangement in open space \\ Evaluate and rearrange the solution & Change solutions \\ Floating objects & Use of random local search \\ Waste space validation & Combination of 2D-SPP and CVRP \\ \hline Branch and Bound [19] & Two-stage ISA [22] \\ \hline Use of Branch and Bound & Use of search algorithm \\ Available space by two points & GRASP makes improvements \\ Arrangements by constraints & Accommodations in six dynamic \\  & spaces \\ Height and width evaluation & Width measurement \\ Branches reduction by selection & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between approaches.  
For every \(o_{i}\) their correspondent width and height are \(ow_{i}\) and \(oh_{i}\) respectively. The objective of the strip packing problem is to accommodate \(O\) into a delimited strip with a fixed width and infinite height, avoiding overlapping and aiming to reduce the total strip length used.  
We implement a GRASP meta-heuristic algorithm to the strip packing problem, prioritizing the objects that generate less waste of area in the strip to minimize the height used.  
One interesting element of our proposal is the use of flags. These flags are in the upper left and bottom right corners of an accommodated object. Additionally, they store the existing information in the strip, such as the widths and heights available for the next candidate.  
On the other hand, we analyze the total height, waste area, the algorithm performance, and computational time. The impact of waste functions is also analyzed, and finally, functions that validate overlaps for objects are incorporated to determine if the current flag is disabled or updated.
## 3 Factors Influencing the Genetic Transformation of Cucumber
### Acetosringone (AS) Concentration  
The addition of AS into _Agrobacterium_ suspensions and pre-culture and co-culture media can enhance the genetic transformation of cucumber because acetosringone can induce the activation and expression of virulence genes in _Agrobacterium_, affecting gene transformation efficiency by promoting T-DNA transfer. The use of acetosringone was comprehensively affected by plant species, strains, and pH values, resulting in differences in the optimal concentration of acetosringone found in different studies [43]. The lowest concentration used in cucumber was 20 \(\upmu\)mol/L [25], whereas the highest was 500 \(\upmu\)mol/L [47]. In general, 50-150 \(\upmu\)mol/L concentrations have been used frequently in most reports [3; 59]. In addition, in recent years, the strong antioxidant \(\upalpha\)-lipoic acid has been used as a new inducer in cucumber, and this can synergize with AS to improve transformation efficiency. The commonly used concentration in cucumber is 50-100 \(\upmu\)mol/L [3]. However, because AS and \(\upalpha\)-lipoic acid can be toxic to the explants, and this can be affected by the medium's pH, type of explants, and co-cultivation temperature, the specific addition method and concentration need to be confirmed experimentally [3].
# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
## 4 Experiments and Discussion
### Evaluation Metrics  
Each machine learning problem demands a tailored set of metrics to evaluate its performance accurately. In our research, we applied a variety of recognized metrics for classification tasks to derive essential insights into the effectiveness of algorithms and to conduct a comparative evaluation. The metrics [41; 42] we use in this study are accuracy, confusion matrix and precision, recall, and F1-score.  
AccuracyThis metric is the most commonly used and is often the preferred method for assessing the performance of an algorithm in emotion classification tasks. It is defined as the proportion of emotion classes correctly identified compared to the total number of observations, as shown in Formula (8).  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{8}\]  
Figure 4: Emotion distributions in RAVDESS dataset.  
Figure 3: Emotion distributions in TESS dataset.  
Precision  
Precision is an important metric for evaluating the accuracy of correctly identified actual emotional utterances in a model. It calculates the ratio of accurately predicted positive instances (true positives) to all instances classified as positive, including both true positives (TP) and false positives (FP). For emotion classification, the precision metric is determined as per Equation (9).  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{9}\]  
Recall  
In the context of emotion classification, recall indicates \"how many relevant data items are selected\" by the model. Specifically, it measures how many of the actually positive observations are correctly identified by the algorithm. According to Formula (10), recall is calculated as the number of true positives divided by the total of true positives and false negatives.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{10}\]  
F1-score  
The F-score, or F-measure, is a metric that combines both precision and recall to evaluate an algorithm's performance. It is calculated as the harmonic mean of precision and recall, as detailed in Equation (11).  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{11}\]
# The Impact Assessment of Water Supply DMA Formation on the Monitoring System Sensitivity
## 3 Results and Discussion
### Model Network
#### 3.1.2 Second Phase Analysis  
In the second stage, it was assumed that there were no measurement uncertainties in the sensors used. The simulation tests carried out showed that in each case the tested monitoring system detected a leak. However, it did not always correctly indicate the leak location. Only in 70 out of 144 cases did the location of the leak coincide with the location of the open hydrant. In other cases, the so-called length of the error (the distance between the indication of the leakage location and the open hydrant, calculated along the network pipelines) ranged from 100 to 1100 m. Figure 9 shows the exemplary results of indicating the leakage location represented by opening the H1 hydrant in network \(A\), in the case of using only the sensors: (a) p0 and q0; (b) p0, p1, and q0; (c) p0, p1, p2, and q0; and (d) p0, p1, p2, p3, and q0. The location of the open hydrant is marked with a red dot in Figure 9. The estimation of the leak location is marked with a purple circle. The so-called length of the error is marked with a blue line. In the case of (d), the location of the leak coincided with the location of the open hydrant. Table 2 lists all the error lengths calculated during the simulations in networks \(A\)-\(L\). Figure 10 shows the average error length for each of the analysed network variants, depending on the number of monitoring sensors used. There is a clear tendency to decrease the average error length with the increase in the number of network sensors used. Interestingly, adding the p3 sensor to the system consisting of q0, p0, p1, and p2 sensors did not cause a significant change in the calculated average error length. The calculated average error length was compared with the total length of the pipes (Figure 11), the number of loops (Figure 12), and the fractal dimension (Figure 13) of the analysed networks. In all cases presented in Figures 11-13, there seems to be no clear relationship between the calculated average error length and the total length of the pipes, the number of loops, or the fractal dimension, which may suggest the lack of such relationships. However, it is worth noticing that there is a significant reduction in the average error length after adding the p1 sensor to the analysed networks, supplementing the measurement of the network entrance. Adding more pressure sensors did not cause significant changes in the size of this error, regardless of the complexity of the network. Figure 9: Leakage locations (purple circles) in network \(A\) in reference to the open fire hydrant H1 (red dots) taking into account different quantities of monitoring points: (**a**) monitoring flow meter q0 and pressure sensor p0 at the border of the network; (**b**) including one additional network pressure logger (p1); (**c**) including two network pressure loggers (p1 and p2); and (**d**): including three network pressure loggers (p1, p2, and p3).
## 4 Results and Discussion
### Effect of Different Digestion Methods of USEPA 3050, 3051, and 3051A on Analytical Results  
In order to understand the efficiencies and variations in the recovery of B, P, and Mo achieved using different USEPA digestion procedures (3050, 3051, and 3051A), a comparative study was undertaken using a sewage sludge certified reference material, CRM EnviroMAT(tm) (BE-1), followed by sample analysis using MP-AES. The elements B, P, and Mo were recovered at 89.9%, 90.9%, and 92.2% using Method 3050; 84.8%, 92.2% and 87.5% with Method 3051; and 92.9%, 93.7%, and 90.6% using Method 3051A, respectively. Yields obtained with Method 3051A are much closer to the certified values (Table 5) than those determined using other two methods. Moreover, the precision in the case of Method 3051A was always below 2% RSD, which is substantially better than corresponding values for other two methods, i.e., 3050 and 3051. In fact, a similar study was carried out by [PERSON] and [PERSON] (1998) to compare the effectiveness of four USEPA digestion methods for trace metal analysis using certified Florida soil reference materials [31]. These workers also observed that Method 3051A was more effective as compared to Method 3050 and Method 3051. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Elements**} & \multicolumn{2}{c}{**MPT-AES**} & \multicolumn{2}{c}{**ICP-OES**} & \multicolumn{2}{c}{**Deviation**} \\  & & **Concentation,** & **Precision,** & **Concentation,** & **Precision,** & **MP-AES vs. ICP-OES** & **\(p\)** \\  & & **(mg/kg)** & **\% RSD** & **(mg/kg)** & **\% RSD** & **\% RSD** & **\% RSD** & \\ \hline \multirow{3}{*}{Bosludge-1} & B & \(19.7\pm 1.2\) & 2.4 & \(18.5\pm 0.8\) & 1.8 & 6.5 & 0.40 \\  & P & \(3513\pm 65\) & 1.2 & \(3548\pm 65\) & 0.8 & \(-1.0\) & 0.71 \\  & Mo & \(7.7\pm 0.3\) & 0.8 & \(7.5\pm 0.2\) & 0.6 & 2.7 & 0.06 \\ \hline \multirow{3}{*}{Bosludge-2} & B & \(17.1\pm 0.7\) & 2.2 & \(18\pm 0.7\) & 2.4 & \(-5.0\) & 0.30 \\  & P & \(3423\pm 55\) & 0.6 & \(3476\pm 60\) & 0.8 & \(-1.5\) & 0.42 \\  & Mo & \(7.7\pm 0.5\) & 1.2 & \(7.7\pm 0.3\) & 1 & 0.0 & 0.72 \\ \hline \multirow{3}{*}{Bosludge-3} & B & \(18.6\pm 0.8\) & 2 & \(18.2\pm 0.7\) & 1.4 & 2.2 & 0.65 \\  & P & \(3489\pm 60\) & 0.4 & \(3450\pm 55\) & 1.8 & 1.1 & 0.15 \\  & Mo & \(7.1\pm 0.5\) & 1 & \(7.2\pm 0.2\) & 1.6 & \(-1.4\) & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of B, P and Mo in analytical samples digested by USEPA Method 3051A and analyzed by using MP-AES & ICP-OES.
# An Identification Method of Feature Interpretation for Melanoma Using Machine Learning
## 3 Results and Discussion
### Comparison of Melanoma Classification Performance  
Table 1 displays the performance evaluation of classifiers, utilizing the original feature set (TCS) as the training input. The average classification accuracy (using the 100-fold randomization method) ranked as follows: LR (ACC = 74.71%, AUC = 0.767) = SVM linear (ACC = 75.70%, AUC = 0.756) > GaussianNB (ACC = 68.96%, AUC = 0.703) > SVM poly (ACC = 65.92%, AUC = 0.664) > SVM sigmoid (ACC = 62.93%, AUC = 0.566) > SVM rbf (ACC = 61.96%, AUC = 0.67). The findings underscore the LR classifier's supremacy over its counterparts, emerging as the frontrunner in terms of performance. Boasting an accuracy of 74.71% (ACC) and an AUC of 0.767, the logistic regression classifier excels in its capacity to discriminate between classes. Furthermore, this classifier shows enhancements in both sensitivity and specificity.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Feature Set** & **ACC (Average) (\%)** & **Sensitivity** & **Specificity** & **AUC** \\ \hline SVM poly & TCS & 65.92 & 0.568 & 0.760 & 0.664 \\ SVM rbf & TCS & 61.96 & 0.732 & 0.608 & 0.670 \\ SVM linear & TCS & 74.71 & 0.72 & 0.8 & 0.756 \\ SVM sigmoid & TCS & 62.93 & 0.664 & 0.567 & 0.566 \\ Logistic & TCS & 74.71 & 0.735 & 0.799 & 0.767 \\ Regression & TCS & 68.96 & 0.616 & 0.788 & 0.703 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of original features in different classifiers.  
Table 2 displays the performance appraisal shifts its focus to the classifier's efficacy when leveraging the selected feature set (RFE-TCS) as the training input. The average classification accuracy (using the 100-fold randomization method) ranked as SVM rbf (ACC = 77.27%, AUC = 0.766) > SVM linear (ACC = 74.88%, AUC = 0.748) > LR (ACC = 74.80%, AUC = 0.749) > SVM poly (ACC = 74.73%, AUC = 0.766) > SVM sigmoid (ACC = 73.68%, AUC = 0.745) > GaussianNB (ACC = 69.13%, AUC = 0.691), the SVM classifier with the rbf kernel function distinguished itself by surpassing alternative classifiers, thereby attaining the best performance with an accuracy of 76.47% and AUC of 0.766.  
Table 3 shows the classification results on PH2 using the filtered feature set (RFE-TCS). From the table, it can be seen that the SVM classifier with the linear kernel function achieved an accuracy of 82.42%.
# Arm Posture Changes and Influences on Hand Controller Interaction Evaluation in Virtual Reality
###### Abstract  
In virtual reality (VR) applications, hand-controller interaction is largely limited by the biomechanical structure of the arm and its kinematical features. Earlier research revealed that different arm postures generate distinct arm fatigue levels in mid-air operational tasks; however, how they impact interaction performance, e.g., accuracy of target grasp and manipulation, has been less investigated. To fill this gap in knowledge, we conducted an empirical experiment in which thirty participants were recruited to complete a series of target acquisition tasks in a specifically designed VR application. Results show that (1) a bent arm posture resulted in a higher interaction accuracy than a stretched arm posture; (2) a downward arm posture interacted more accurately than an upraised arm posture; since two arms are bilaterally symmetric, (3) either selected arm interacted more accurately on the corresponding side than on the opposite side; and (4) the user-preferred or dominant arm interacted more persistently than the non-dominant one, though two arms generated little difference in interaction accuracy. Implications and suggestions are discussed for designing more efficient and user-satisfying interactive spaces in VR.  
hand-controller interaction; target acquisition; arm posture; interaction accuracy; arm fatigue