esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results
### The Results and Discussion
#### 5.2.3 Comparative Analysis at \(C=60\)  
Table 10 presents the results of the calculations at \(C=60\). Table 10 shows the validity of the IHBA, which yielded an overall mean RPD value of 31.55%. The results of the Friedman test in Table 11, the results of the Wilcoxon paired sign test in Table 12, and the mean plot in Figure 3 again show that the proposed IHBA performed best in the comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithms for Comparison** & **R+** & **R –** & **Bonding Value** & _p-Value_ \\ \hline IHBA VS CMA & 671 & 3280 & 99 & 0.00 \\ IHBA VS BBO & 2434 & 220 & 1396 & 0.00 \\ IHBA VS EDA & 3879 & 100 & 71 & 0.00 \\ IHBA VS TDIWO & 1933 & 710 & 1407 & 0.00 \\ IHBA VS GA & 3198 & 804 & 48 & 0.00 \\ IHBA VS PSO & 2149 & 540 & 1361 & 0.00 \\ IHBA VS BA & 3050 & 948 & 52 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The Wilcoxon paired signed test result at \(C=40\) and \(\alpha=0.05\).  
Figure 2: Mean plot and 95% LSD interval of the bat algorithm at \(C=40\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Algorithms for Comparison**} & \multicolumn{1}{c}{**R+**} & \multicolumn{1}{c}{**R\(-\)**} & \multicolumn{1}{c}{**Bonding Value**} & \multicolumn{1}{c}{**\(p\)-Value**} \\ \hline IHBA VS CMA & 639 & 3314 & 98 & 0.00 \\ IHBA VS BBO & 2428 & 205 & 1417 & 0.00 \\ IHBA VS FDA & 3840 & 100 & 110 & 0.00 \\ IHBA VS TDIWO & 1981 & 650 & 1419 & 0.00 \\ IHBA VS GA & 3222 & 755 & 72 & 0.00 \\ IHBA VS PSO & 2181 & 493 & 1376 & 0.00 \\ IHBA VS BA & 3099 & 879 & 72 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 12: The Wilcoxon paired signed test result at C = 60 and \(\alpha=0.05\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Parameters**} & \multicolumn{1}{c}{**Algorithms for Comparison**} \\ \hline \(m\) & \(n\) & \(s\) & \(f\) & **CMA** & **BBO** & **TDIWO** & **EDA** & **GA** & **PSO** & **BA** & **IHBA** \\ \hline  
83 & **36.93** \\ \hline  & average & & 36.68 & 37.88 & 39.01 & 37.08 & 38.54 & 37.28 & 39.54 & **38.60** \\ \hline \hline \end{tabular}
\end{table}
Table 11: The Descriptive Statistics Achieved by the Friedman Test at \(C=60\) and \(\alpha=0.05\). The bold figures indicate the comparison between algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithms** & \(Rank\) & \(n\) & \(Mean\) & \(Std.\) _Deviation_ & \(Min\) & \(Max\) \\ \hline
**CMA** & 4.81 & 4050.00 & 1.17 & 0.92 & 0.00 & 7.05 \\
**BBO** & 4.92 & 4050.00 & 1.47 & 1.30 & 0.00 & 7.70 \\
**EDA** & 7.01 & 4050.00 & 16.89 & 12.80 & 0.00 & 50.37 \\
**TDIWO** & 3.36 & 4050.00 & 0.99 & 0.96 & 0.00 & 4.82 \\
**GA** & 4.39 & 4050.00 & 0.98 & 0.85 & 0.00 & 4.46 \\
**PSO** & 4.44 & 4050.00 & 0.99 & 0.94 & 0.00 & 4.78 \\
**BA** & 4.33 & 4050.00
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 3 Approach  
The proposed RF energy harvesting sensor platform contains a hybrid storage containing two supercapacitors of various sizes. In mobile applications, where a node may have limited time passing through the RF charging station, it is critical that the sensor is able to charge its capacitor fully before it leaves the area. The small capacitor is used to boot quickly and perform at least a minimal task, such as sensing or sending a beacon. The large capacitor accumulates energy in high energy areas for energy-intensive tasks, such as communication with the base station. The proposed platform also incorporates a switching hardware, an energy prediction based on the Kalman filter and a switching algorithm, as described in the following subsections.
# Adaptive Binaural Cue-Based Amplitude Panning in Irregular Loudspeaker Configurations
## 2 Methods  
Amplitude panning methods determine the gains for each loudspeaker based on the positions of both the loudspeakers and the virtual sound source, following a specified amplitude panning function. Each virtual source signal \(s(t)\) is then scaled by the corresponding gain \(g_{s}\) and distributed across the \(S\) loudspeakers. As a result, the listener perceives the virtual sound source at the intended direction or location, as illustrated in Figure 1. The specific amplitude panning method varies depending on the acoustic environment and loudspeaker arrangement. In this section, we introduce a binaural cue-based amplitude panning method designed for irregular loudspeaker configurations within a room. The proposed algorithm comprises two distinct stages. The initial stage involves magnitude compensation, wherein an inverse filter is designed to equalize the magnitude frequency response of each loudspeaker. To ensure robustness, the magnitude is smoothed using one-third octave bands prior to computing the inverse filter. The subsequent stage explores the gain and delay combinations within a specified range. Using the acquired BRIRs, the ITD and IACC of the synthesized virtual sound source are calculated. The optimal gain and delay combination is then selected based on its ability to match the ITD of the real sound source at the intended azimuth. In instances where multiple combinations satisfy the target ITD criterion, the final gain and delay are determined by maximizing the IACC. We have focused our research on a stereo system, as the multichannel surround system can be realized by partitioning the multichannel setup into a combination of several stereo systems, akin to VBAP [13].
## 4 Results and Discussion
### Moduli of PG Amended Line-Stabilized Soil  
The soil elastic modulus is defined as the slope of the stress-to-strain curve. The modulus plays an important role in settlement characteristics. An increase in the modulus value means that the settlement decreases. The moduli of the lime-stabilized soil amended with PG were determined from the stress-strain curves of the individual soil specimens. In this analysis, the initial tangent modulus was determined for the tangent drawn to the initial straight-line portion of the stress-strain curves of the individual specimens. Figure 2 shows the moduli of the stabilized soil composites. The modulus value for L1, L2 and L3 are 35,092.7, 107,199.3 and 145,287.3 kPa. LP1 showed a modulus value of 60,762.3 kPa. LP2 showed a modulus value of 140,337.7 kPa and LP3 gave a modulus value of 157,142.9 kPa. When the combinations LPI, LP2 and LP3 were compared, the contribution of PG toward the modulus in LP1 and LP2 was more when compared to that of LP3 (Figure 2). This indicates that when lime dosage lesser than ICL was used for stabilization, PG contributed a 73.15% increase in the modulus. This reduces to 30.92% and 8.16% for LP2 and LP3. Thus, it can be concluded that the contribution of PG in the augmentation of the modulus of the lime-stabilized soil is significant up to ICL and beyond which its contribution drastically reduces.
## 5 Results and Discussion
### Spatial Heterogeneity of Local Influence Factor Coefficients in GWR Model
Starting from the eastern part of Futian District, radiating to the northeast towards the Luohu, Longgang and Longhua Districts, the positive correlation is enhanced, indicating that the proportion of residences in these districts rises and the residents rely on the rail transit due to the distance from the city center, the increase in industrial facilities, and the inadequacy of the transportation facilities. Radiating southwestward to the Nanshan and Bao'an Districts, the negative correlation is enhanced due to the concentration of high-tech enterprises and schools in the Nanshan District and the high residential density and inadequate transportation facilities in the Bao'an District, leading to the possibility that residents may choose other modes  
Figure 10: Spatial distribution of local coefficients of accessibility. of travel. The northern section of Bao'an is close to Dongguan, with a high number of cross-city commuters, and the negative correlation is weakened. From the analysis in Figure 9, it can be seen that the positive effect of floor area ratio on the morning peak passenger flow of Shenzhen rail transit has a coefficient that is relatively consistent across stations. This reflects the fact that a higher floor area ratio in Shenzhen implies denser land use, which attracts more rail transit passenger flows. This effect shows a slow decreasing trend from the eastern to the western administrative regions. The extension of Shenzhen from east to west and the shift from industrial to residential and commercial structures are related to the evolution of the urban spatial structure of Shenzhen, which is \"led by industry, followed by the construction of residential and commercial facilities\" [52]. This spatial change may have an impact on traffic flow and the structure of passenger trips. The analysis in Figure 10 shows the impact of rail transit station accessibility on the morning peak passenger flow in Shenzhen. The accessibility coefficients of the stations are generally negative, indicating that the accessibility of the stations is inversely proportional to the passenger flow, which shows that improving the accessibility of the stations can increase the passenger flow. The impact coefficient is higher in the western part of Futian District due to its proximity to the CBD business district, while it gradually decreases to the east and west and north to the Longhua District. This reflects that the transportation facilities in the center of Shenzhen are perfect, while the peripheral areas need to be upgraded. The impact coefficient of accessibility to the Longhua District rises, probably due to the large number of migrant workers, the relative lag of rail transportation facilities, and the outstanding traffic congestion problem [53]. It shows that the attractiveness and efficiency of rail transit can be improved by optimizing the transportation network and station connections.
# Secure Aviation Control through a Streamlined ADS-B Perception System
## 4 Methodology  
A machine learning-based detection model is adopted to achieve an efficient detection model of malicious ADS-B messages. The machine learning approach would require a dataset to train on, and then a testing dataset that the model has never seen before would be used to assess the model's performance. As was previously mentioned, the dataset by [PERSON] et al. was published in 2022 but has yet to be used by academics. Therefore, several machine learning techniques will be used to preprocess, balance, and test the unique dataset to verify that the detection model performs at its best [33]. This section covers the methodology of the proposed model and will be divided into three subsections. Section 4.1 will provide a detailed view of the dataset, followed by Section 4.2, which highlights data preprocessing steps. Section 4.3 outlines the various machine learning classifiers used and tested in this study. Figure 2 presents an overview of the proposed model for this study. Our main focus using this model is to detect and classify path modification, ghost aircraft injection, and velocity drift attacks.
## 5 Results
### The Proposed Mechanisms
#### 5.3.3 The Impact of the Number of Consumers  
After varying the content request rate, we conducted experiments to evaluate the impact of the number of consumers on the network. The reason for this evaluation was to analyze the performance of the proposed mechanisms with different network saturation levels caused only by the increasing number of consumers. We set the content request rate at 50 int/s for each consumer. The goal was to observe whether the network would experience a high saturation level, even for a low number of consumers.  
Figure 9 presents the results. It can be observed that ReCIF and ReCIF02 provided higher delivery rates as the number of consumer nodes increased. This fact, discussed before, is related to the less restrictive criteria of these mechanisms which therefore, enables them to take advantage of in-network caching by retrieving the most popular contents from their near neighbors. In this scenario, the PIF02 restrictive strategy also produced a lower level of broadcast of interest packets. Therefore, it was not the best mechanism regarding content delivery but provided less overhead.  
The performance of the LFBL degraded as more consumers were added to the network. This is a consequence of the multiple flows created by LFBL between consumers and producers. After the request phase, consumers retrieve contents directly from producers. Thus, LFBL sets multiple flows per consumer, creating multiple flows between consumers and producers. Hence, LFBL presented the worst performance in terms of delivery rate and average delay for 30 consumers.  
Figure 8: Content packets delivered, average delivery delay, and overhead for 30 consumers requesting popular contents at different rates.  
For two consumers requesting contents by popularity, LFBL presented a similar performance to our mechanisms concerning the content delivery rate. The reason for this is because with two consumers, there would be only two flows from consumer to producers. However, as the number of consumers increases, our mechanisms provided higher levels of performance. This performance gain occurred because our mechanisms were able to use the shared cache efficiently.  
In this scenario, PIF02 presented the best performance when compared to our other mechanisms. The reason for this is the same as that described for the previous scenario: PIF02 delivers contents at a slightly lower rate than ReCIF with less overhead and less delay.
# Data Exfiltration through Electromagnetic Covert Channel of Wired Industrial Control Systems
## 2 Related Work
### Visual Based Side Channels  
Most electronic devices have built-in LEDs to display the status of the devices. _xLED_[9] is malware that controls the LEDs' status in routers and LAN switches. The flickering of the LEDs can be used to encode and modulate sensitive data. The produced signal can be recorded by distant cameras, surveillance cameras, smartphone cameras, or other optical sensors. The malware can use both amplitude and frequency-based encoding techniques, achieving from 10 bps to 1 kbps of data rate over the LED covert channel.
## 4 Numerical Results
### Optimal Maintenance Plan  
In this sub-section, we try to find the optimal preventive maintenance plan for the disassembly-assembly system. We adopt the sequential strategy whose objective is to integrate the system plan obtained from the optimal dates calculated above (Table 8). This preventive maintenance plan is determined by the disassembly-assembly plan. Based on dates founded in Table 8, we built the disassembly-assembly system plan as shown in Figure 7. According to the different levels of the system solicitation presented in Tables 1 and 2 for different types, we suppose that our disassembly and assembly operations types vary between type 1 and type 5. In addition, depending on the different disassembly and assembly process and types of our example, Figure 7 present levels of system solicitation on a time scale with the disassembly-assembly schedule planning. We suppose that the used/end-of-life disassembly operation solicits the machine at \(U_{PR}=1.7\) and the solicitation rate of the finished product assembly at \(U_{PF}=1.8\).  
The mathematical function \(C_{M}\) takes into consideration the influence of the variation of system solicitation on the disassembly-assembly system. These solicitation rates presented in Figure 7 represents the vector \(A=\{u(1),\ \ldots,\ u(k),\ \ldots,\ u(H)\}\), \(k\in\{1\ \ldots\ H\}\). This vector is used to calculate the average number of failures \(\varphi(A,N)\).  
To find the optimal value of preventive maintenance number, we implement the maintenance cost function \(C_{M}\) within the \"Wolfram Mathematica\" tool. We try to vary the variable \(N\) from 0 to \(H\)-1 and we calculate, at each value of \(N\), the total maintenance cost using \(C_{M}\). As shown in Figure 8,  
\begin{table}
\begin{tabular}{c c} \hline \hline \(X^{*}=\{X^{*}_{j2}\}\) & \{8, 8, 5, 8, 4, 5, 8, 7, 7, 7\} \\ \hline \(EC^{*}\) & 2996.63 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimal release dates and cost in the case of assembly system (finished product is composed only of new complements).  
\begin{table}
\begin{tabular}{c c} \hline \hline \(X^{*}=\{X^{*}_{j2}\}\) & \{21, 21, 19, 21, 21\} \\ \hline \(T^{*}_{PR}\) & 5 \\ \(EC^{*}\) & 2600.8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimal release dates, optimal ordered date of used/end-of-life product and expected cost in the case of disassembly, remanufacturing, assembly system (finished product is composed of new and remanufactured complements).  
Figure 7: Disassembly-assembly schedule planning with the representation of levels of machine solicitation on the time scale.  
the curve has a convex form. The value \(N^{*}=7\) represents the number of preventive maintenance with minimum cost 7109.4 mu.
## 3 Results and Discussion
### Analysis of CIM's Potential for the Calculation of the Indicators
#### 3.2.26 The Indicator to Measure Air Quality  
In accordance with this indicator, outdoor air quality can be assessed in order to reduce pollutants in outdoor spaces. This is developed by SBTool\({}^{\text{PT}}\) Urban, SNTool, and BREEAM C, and addressed by Levels, ISO 37120, and SDGs. Therefore, this indicator is recognized as a critical environmental concern. The SBTool\({}^{\text{PT}}\) Urban Guide establishes a verification checklist to calculate this indicator. The CIM model cannot evaluate all of the criteria on the list, and the indicator is classified as \"NO\" based on the findings of the study.
## 2 Results and Discussion
### Representation of Cases
#### 2.3.2 Series-based Representation  
In the series-based representation, there are several structures which improve case performances in certain application domains such as _hierarchies_ and _chains_ or sequences. In this approach, _chains_ are used to represent changes in values. This representation solves _incompleteness of data_ in a year by considering the following year to compute changes over time.  
In addition, in contrast with other domains, it is not possible to focus on a single case feature because rehabilitation is a _multidimensional_ and holistic process. Effective and high quality rehabilitation of neurological diseases should cater for the physical, cognitive, psychological, social and cultural dimensions of the personality and lifestyle of patients and their families alike.  
Figure 2 shows the symbol taxonomy used to encode changes in ICF values of barriers, facilitators, difficulties, capacity or magnitude of impairment and performance. The three **granularity levels** studied are:  
* fine granularity, which contains all possible changes in ICF values: \(D_{-n}\) means decreasing \(n\) levels; \(I_{+n}\) means increasing \(n\) levels; and \(S\) means remaining stationary;
* coarse granularity, which summarizes changes as decreasing (D), stationary (S) and increasing (I); and
* medium granularity, which is an intermediate representation between coarse and fine granularity.  
Unspecified (U) and inapplicable (N) symbols correspond respectively to ICF values 8 and 9. It is important to consider that when an indicator decreases it improves according to the ICF standard because the lower the level, the less the difficulty, deficiency or barrier. Likewise, an increase indicates a deterioration of health status.  
In a series-based representation, changes are considered instead of values. In order to also include values in series-based representations, the **first point** of the series is included. The similarity measure used is the Euclidean distance. The number of cases increases in fine granularities, especially inlarge-time series and series with high rates of change, and sequences often only partially match. Therefore, finer granularities are the most suitable option for ICF categories with low rates of change and short time-series.  
In the rehabilitation domain, there are no significantly **high rates of change** in the progression of most attributes (peaks are not frequent). The **frequency of time series** is _lower_ than in other medical domains: usually it is yearly. Consequently, in physical rehabilitation, finer granularities are generally the most suitable option. Regarding the optimal length of time-series, it is not studied here as there are only four data points available per patient and they are all used.
## 3 Results and Discussion
### Characterization of FTIR  
Characterization using FTIR is shown in Figure 9. The FTIR results show that by using acid or alkaline solvents, there is a sharp absorption at a wavelength of about 1600 cm\({}^{-1}\) which indicates the C = C skeletal vibration of collagen compounds found in bone [28]. The test results obtained indicate that there is a decrease in the absorbance value at the wave number, which qualitatively indicates a decrease in the collagen content along with the increase in the concentration of eosin solution as a result of the increasing number of OH-which causes swelling and dissolution of collagen by the solvent. At a wavelength of about 2800-2900 cm\({}^{-1}\) is a C-H vibration of the methyl group. At 1500-1600 cm\({}^{-1}\) wavelength, there are C-H vibrations that indicate the absorption characteristics of gelatin [29].  
\begin{table}
\begin{tabular}{c c c} \hline
**No.** & **Solvent** & **Result** \\ \hline
1. & NaOH & <3 APM/gr \\
2. & HCl & <3 APM/gr \\
3. & H\({}_{2}\)SO\({}_{4}\) & <3 APM/gr \\
4. & CH\({}_{3}\)COOH & <3 APM/gr \\
5. & NaHCO\({}_{2}\) & <3 APM/gr \\ \hline \end{tabular}
\end{table}
Table 2: Gelatin Microbial Test Results.  
Figure 8: Point of Isoelectric.  
At the stage of the gelatin fixation process with chitosan and also a crosslink agent, the results of the FTIR and SEM-EDX test are shown in Figure 10 and Table 3. This cross-linking process occurs due to the formation of Schiff bases [30]. This event causes the replacement of the C = O groups from ketones or aldehydes from gelatin to be replaced by C = N-R groups from chitosan catalyzed by acid [31].  
Figure 10: FTIR for Crosslinking Gelatin With Chitosan, Glukosa, and Glutaraldehyde.  
Figure 9: FTIR for Gelatin in Various Solvents.