esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Deployment Models
### Inspection Using Cryptographic Functions
#### 3.2.2 Privacy-Preserving Inspection through Searchable Encryption  
While the previous deployment models have been popularized by the industry, the models are based on trusting middleboxes. However, the middleboxes with vulnerabilities can also be used by adversaries to compromise the privacy of the client and server [61]. According to [PERSON] et al. [62], while the levels vary, all tested enterprise-grade TLS interception middleboxes are vulnerable.  
Several studies have been conducted to enable privacy-preserving inspection with the help of searchable encryption techniques (e.g., [58]). For example, BlindBox [63] as shown in Figure 3 is a pioneering work on privacy-preserving deep packet inspection, based on a searchable encryption technique. While two privacy models are implemented, the common idea is for the client to transmit encrypted tokens generated from plaintext of a (unidirectional for simplicity) TLS session, to the middlebox through an out-of-band channel. Next, the middlebox attempts to perform deep packet inspection rule matching for the encrypted tokens, which is enabled by the searchable encryption technique. In addition, as the encrypted tokens could be different from the TLS-encrypted traffic, the receiver (which has the valid decryption key for the TLS session) cooperates with the middlebox (which should not have the key) by checking whether the receiver-decrypted tokens (forwarded from the middlebox) and the recovered plaintext from the TLS session are the same. The authors of BlindBox extend their system to support a wide range of middlebox services such as firewall, network address translation (NAT), HTTP proxy, and deep packet inspection [64]. [PERSON] et al. [65] proposed an architecture to perform private preserving deep packet inspection with a novel rule filter for achieving better performance than BlindBox. [PERSON] et al. [66] utilize a reusable obfuscation mechanism for faster encrypted rule generation.  
Although the idea of privacy-preserving inspection through searchable encryption is interesting and innovative, unfortunately, such approaches are less promising in the current generation of SOCs. At first, as compared with other deployment solutions, BlindBox and the following studies (e.g., [65; 66]) exhibit poor performance. For instance, in BlindBox, given an IDS with typically 3000 rules, the required client-side time is 97 s. Furthermore, BlindBox requires an out-of-band channel with its own protocol in conjunction with TLS, which is an implausible assumption for malware; malware may use other encrypted channels to hide its malicious network behavior. Note that similar weakness can be  
Figure 2: TLS inspection with a private key.  
found in recent inspection methods using cryptographic functions such as BlindIDS [67], IA2-TLS [68], P2 DPI [69], etc., and other methods [70; 71] relying on trusted execution environments, such as Intel SGX [72] in the middlebox, they are beyond the scope of this survey.
## 3 Results and Discussions  
### AE Data Filtering and Selecting  
During the real-time AE monitoring, the real AE signals, grip noises, electromagnetic noises, and friction noises will be generally recorded. The lack of an accurate selection of AE signals results in the unavailability of subsequent AE data analysis. Therefore, several methods like high-pass filters and linear source location filters have been proposed to remove the noise and acquire the accurate selection of AE signals originating from the damage [36; 37; 38].  
[PERSON] et al. [39] proposed an energy-based approach by using two sensors, which can effectively obtain the accurate location and selection of AE signals originating from the damage of the composites. In order to ensure that most AE signals were generated in the gauge section, the AE signals were monitored with two identical sensors located 55 mm apart. From the times of arrival at the top (t\({}_{1}\)) and bottom (t\({}_{2}\)) sensors, the time difference is \(\Delta\)t (\(\textless\)\(\Delta\)t\({}_{\text{max}}\)). It could be concluded that most of the AE signals were generated in the gauge section during the tensile process. In addition, [PERSON] et al. [40] concluded that there is a good correlation between the amplitude and duration of AE signals. Similarly, [PERSON] et al. [41] and [PERSON] et al. [42] used amplitude-duration filtering to remove outliers from the AE signals. Therefore, a statistical pattern recognition technique of the Maxmin Distance algorithm was employed to locate outliers [43]. The Maxmin Distance algorithm is a tentative-based algorithm in the field of pattern recognition. The purpose is to select objects that are located far apart from each other as the clustering center, thus avoiding the situation that the clustering seeds are too close when the initial value of the k-means algorithm is selected. The typical AE waveforms are shown in Figure 5a. The high amplitude and low duration (in the range of 0.00008 s) waveform at the top of Figure 5a corresponds to AE events (points) on the bottom right side of Figure 5b. The low-amplitude (near the threshold) and high duration (reaches up to 0.0003 s) waveform at the bottom of Figure 5a corresponds to AE events (points) on the upper left side of the Figure 5b.
# Using Fluorescence Spectroscopy to Detect Rot in Fruit and Vegetable Crops
## 2 Materials and Methods
### Preparation and Measurement of Vegetables and Fruits in Experiments Pulsed Laser Mode  
Two apple cultivars and potato cultivars \"Udacha\" (healthy) and \"Zhukovsky\" (rotten) were used in the experiments with the pulsed laser. All apple and potato samples were provided by the Federal Scientific Agroengineering Center VIM (Moscow) (Figure 4).  
Initially, the fluorescence spectra of unwashed potatoes were recorded. The spectrum was averaged from the entire potato surface. Next, the potatoes were thoroughly washed from dust and soil residues without damaging the peel. The fluorescence spectra of washed and unwashed potatoes were compared to assess the contribution of dust and soil to the overall spectrum. Moreover, the fluorescence spectra for different zones of the rotten potatoes were recorded. Samples of potatoes were cut in half to examine the internal parts of the potatoes. Spectra were recorded in various tuber regions, the area in the center of the potato (clearly visible to the eye), and the main volume of the potato.  
The fluorescence spectra of outer parts of healthy and rotting apples were registered. Apples were cut and the spectra of the spoiled pulp and the healthy samples were compared.
## 3 Inversion Algorithm of Atmospheric Temperature in MLT  
Lots of atmospheric composition information can be retrieved from the observed O\({}_{2}\)\(A\)-band night glow spectra, such as the atomic oxygen profile and temperature profile in MLT. The inversion is realized by inverting the radiative transfer equation, that is, the forward model. Nevertheless, the inverse problem is generally non-linear and ill-posed, so it is impossible to perform the inverse operation for the non-linear model directly. In this paper, the optimal estimation method, the least squares method, and the peak relative intensity ratio method commonly used in the inversion of atmospheric temperature are, respectively, adopted to match the optimal algorithm for the limb observed radiance profile of \(A\)-band night glow.
# Reconfigurable Intelligent Surface in Wireless-Powered Interference-Limited Communication Networks
## 6 Conclusions  
In this paper, the performance of the RIS in a wireless powered limited interference communication network is analysed. The exact closed-form expressions of the system outage probability, average throughput and average bit error rate are derived. The results depicted that the simulation results perfectly agreed with the analytical results. The results also show that the system performance significantly improves with an increase in the number of RIS reflecting elements, number of antennas at the PB and fading parameter on the PB and interferer links. Moreover, the system performance deteriorates as the number of interferes and \(P_{i}\) increase. Further, the system average throughput increases with the increase in energy harvesting time. The results also demonstrated that the proposed system with the RIS offers better performance compared with the conventional dual-hop system without the RIS. The further research interest of this work can be directed towards the impact of RIS surafcase area on the performance of system. This is owing to the fact that the smaller RIS reflecting elements offer better robustness, but at higher design complexity. However, RIS with large element structures aid better reflection, but at the expense of higher power consumption. Moreover, the system can be made multi-user and the effect of hardware impairment with co-channel interference can be studied.  
Conceptualization, K.O. and P.O.; methodology, K.O.; software, K.O. and O.O.; validation, K.O. P.O. and O.O.; formal analysis, K.O.; writing--original draft preparation, K.O.; writing--review and editing, K.O. P.O. and O.O.; supervision, P.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 9: Effect of number of reflecting elements in the RIS and the modulation order on the system average BER under M-QAM coherent modulation scheme.
# GPU@SAT DevKit: Empowering Edge Computing Development Onboard Satellites in the Space-IoT Era
## 4 Results
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Name** & **Input Size [\# Pixels]** & **Duration [\(\mu\)s]** \\ \hline \multirow{3}{*}{Conv\({}_{3\times 3}\)1  } & 64 \(\times\) 64 & 1125 \\  & 128 \(\times\) 128 & 2243 \\  & 256 \(\times\) 256 & 6172 \\ \hline \multirow{3}{*}{Conv\({}_{5\times 5}\)2  } & 64 \(\times\) 64 & 2804 \\  & 128 \(\times\) 128 & 5051 \\  & 256 \(\times\) 256 & 12,892 \\ \hline \multirow{3}{*}{Conv\({}_{7\times 7}\)3  } & 64 \(\times\) 64 & 3378 \\  & 128 \(\times\) 128 & 6172 \\  & 256 \(\times\) 256 & 23,374 \\ \hline \multirow{3}{*}{Average pool} & 64 \(\times\) 64 & 1686 \\  & 128 \(\times\) 128 & 3367 \\  & 256 \(\times\) 256 & 11,209 \\ \hline \multirow{3}{*}{Max pool} & 64 \(\times\) 64 & 1681 \\  & 128 \(\times\) 128 & 3364 \\  & 256 \(\times\) 256 & 11,212 \\ \hline \multirow{3}{*}{Matrix Multiplication} & 64 \(\times\) 64 & 2803 \\  & 128 \(\times\) 128 & 15131 \\  & 256 \(\times\) 256 & 118,420 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Execution times for various kernels on the GPU@SAT DevKit, measured in microseconds using the clock of the embedded processor. In these implementations, each output image pixel is consistently mapped to a single WI of the OpenCL kernel. coding effort--was greatly simplified, with notable reductions in both setup time and the need for low-level firmware adjustments. This does not even account for the significant time savings in debugging, which was particularly time-consuming in the old workflow due to necessary changes to the embedded processor firmware. Such a time-consuming nature of the previous process was largely due to the need for extensive manual coding and debugging, which often involved precise and detailed modifications to the embedded software. Additionally, the use of the _EnableTimeRecording_ tag streamlines the collection of execution times, a process that previously, again, had to be manually embedded in the firmware code.
# Total Ionizing Dose Effects on a Delay-Based Physical Unclonable Function Implemented in FPGAs
## 3 Experimental Setup  
### Device Under Test  
The radiation experiments have been carried out on two Xilinx XC3S500E FPGA, manufactured on 90 nm CMOS technology. In the remainder of the paper, these Devices Under Test (DUTs) are referred to as FPGA1 and FPGA2. The clock is set using the on-board 50 MHz crystal oscillator. Two high precision voltage sources (programmable HP 66103A DC power modules) have been used in order to set and monitor the core voltage (1.2 V) and the I/O voltage (3.3 V).  
A conventional RO-PUF consisting of 512 identically laid-out ROs has been implemented. Each of the ROs consists of four inverters and a NAND gate, the latter being able to enable/disable the oscillation. A hard macro that occupies one Configurable Logic Block (CLB) has been created in order to guarantee the same placement and routing for all the ROs. This hard macro has been replicated in the middle of the FPGA creating a 16 \(\times\) 32 array. During the experiment, each RO is activated at a time during 13,000 clock cycles using its enable signal. The rest of the RO-PUF logic (multiplexers, counters, decoders, etc.) have been implemented in other FPGA zones in order to limit the impact of the surrounding logic on RO frequencies. An RS232 communication protocol has been used in order to transfer the measured RO frequencies to the host computer. A CRC has been implemented in order to ensure the integrity of the communication.  
The operating conditions of the room (temperature, pressure and humidity) have been controlled in order to guarantee that these conditions do not affect the RO frequencies.
# Different Object Functions for SWIPT Optimization by SADDE and APSO
## 2 System Model
### Swipt  
Figure 1 shows that the power-splitter block is used for the SWIPT system. The received signals are divided into two streams for the RF energy harvester and the information receiver with various power levels.  
Figure 1: The splitter architecture for the SWIPT system.  
The harvested wireless powered is modeled as  
\[HP=\eta\int_{-\infty}^{\infty}h(t)^{2}dt \tag{3}\]  
where \(\eta\) is the portion of RF signals utilized for power gathering. The information decoder quality BER is used to evaluate the inter-symbol-interference for the SWIPT system and can be computed as the following formula [26]:  
\[BER=\sum_{s=1}^{2^{s}}p\left(\overset{\rightharpoonup}{d_{s}}\right)\cdot\frac{ 1}{2}erfc\left[\frac{V(t=sT_{d})}{\sqrt{2}\sigma}\cdot(d_{s})\right] \tag{4}\]  
where \(erfc(x)=\frac{2}{\sqrt{\pi}}\int_{x}^{\infty}e^{-y^{2}}dy\) is the complementary error function and \(\left\{\overset{\rightharpoonup}{d_{s}}\right\}=\{d_{1},d_{2},\ldots d_{5}\}\) is the binary sequence.  
\[\textbf{SNR}_{\textbf{R}}=\textbf{SNR}_{\textbf{T}}\times\textbf{V}\]
# A Novel Alternating \(\mu\)-Law Companding Algorithm for PAPR Reduction in OFDM Systems
## 5 Simulation Results and Complexity Analysis
### Experimental Scenarios and Results Discussion
When the number of subcarriers, \(N\), increases to 1024, as shown in Figure 10b, they maintain the same relationship characteristics. Therefore, from Figures 9 and 10, we know that our proposed methods are suitable for future communication systems with high subcarriers compared to others from both PAPR and BER performance perspectives. We further observe the impact of different order QAMs on the PAPR and BER performance for various methods, as shown in Figures 11 and 12, respectively. In Figure 11, we increase the QAM order to 512 from 256 compared to Figure 9. The PAPR performance of our proposed schemes is still the best among all methods. In terms of the BER, in Figure 12, the order of QAM is increased to 512 from 256 compared to Figure 10, which can verify that the BER performance of the proposed schemes will not be affected. Figure 10: BER performance of different schemes, at QAM = 256 (**a**) \(N=512\) and (**b**) \(N=1024\). clarity, the detailed PAPR data are enumerated in Table 2. Consequently, it is confirmed that the proposed methods remain applicable in B5G systems with elevated requirements for modulation order and the number of subcarriers. Summarizing the results above, our proposed schemes can effectively improve the PAPR and maintain the BER before improvement by selecting an appropriate \(\mu\) value. Also, \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Scheme Name**} & \multicolumn{2}{c}{**256-QAM**} & \multicolumn{2}{c}{**512-QAM**} \\ \cline{2-5}  & \(N=512\) & \(N=1024\) & \(N=512\) & \(N=1024\) \\ \hline SULC & 6.16 dB & 6.16 dB & 6.14 dB & 6.14 dB \\ AULC & 6.14 dB & 6.15 dB & 6.12 dB & 6.14 dB \\ \hline \end{tabular}
\end{table} 
Table 2: Enumerate the PAPR numerical of SULC and proposed AULC at QAM = 256 and 512, \(N=512\) and 1024 when CCDF is \(10^{-4}\). Figure 12: BER performance of different schemes, at QAM = 512 (**a**) \(N=512\) and (**b**) \(N=1024\). without sacrificing PAPR and BER performance, our proposed schemes can still be held under more subcarriers and a higher modulation order environment. Thus, they can keep up with B5G.
## 3 Metrics for Evaluating Prediction Performance
### Non-Dimensional Metrics  
The Pearson correlation coefficient serves to quantify the degree of linear correlation between two variables. A larger absolute value of the correlation coefficient indicates a stronger correlation. The coefficient approaches 1 or \(-1\) for a stronger positive or negative correlation, respectively, while nearing 0 signifies a weaker correlation, as depicted in Equation (25):  
\[\rho_{\mathbf{g},\,\mathbf{y}}\ =\ \frac{\text{cov}(\hat{\mathbf{y}},\,\mathbf{y})}{\sigma_{ \mathbf{g}}\sigma_{\mathbf{y}}}\ =\ \frac{E(\hat{\mathbf{y}}\,\mathbf{y}^{\mathsf{T}})-E(\hat{\mathbf{y}})E(\mathbf{y})}{\sqrt{E \left(\hat{\mathbf{y}}^{2}\right)-\left(E(\hat{\mathbf{y}})\right)^{2}}\sqrt{E\left( \mathbf{y}^{2}\right)-\left(E(\mathbf{y})\right)^{2}}} \tag{25}\]  
where \(\text{cov}(\hat{\mathbf{y}},\,\mathbf{y})\) represents the covariance between the predicted value and the true value and \(E(\cdot)\) denotes the mean value. Generally, the strength of correlation between variables can be assessed based on the magnitude of the correlation coefficient.  
The coefficient of determination, denoted as R\({}^{2}\), serves as a statistic for assessing the goodness of fit. The fundamental concept underlying R\({}^{2}\) involves employing the least squares method for parameter estimation, calculated as the ratio of the sum of regression squares to the sum of total deviation squares. The maximum value achievable for R\({}^{2}\) is\(1\), with a value closer to \(1\) indicating a superior fitting effect. Typically, a model with a goodness of fit exceeding 0.8 is considered relatively high, as expressed in Equation (26):  
\[\mathrm{R}^{2}(\hat{\mathbf{y}},\ \mathbf{y})\ =\ 1-\frac{\sum_{i\ =\ 1}^{N}(\hat{y}_{i}-y_{i} )^{2}}{\sum_{i\ =\ 1}^{N}(\overline{y}-y_{i})^{2}} \tag{26}\]  
where \(\overline{\mathbf{y}}\ =\ E(\mathbf{y})\) is the mean value of the actual value of the data, which represents the reference value of the model.  
The APVE is usually applied to evaluate prediction accuracy of the peak value, as shown in Equation (27):  
\[\mathrm{APVE}\ =\ \frac{|\max(|\mathbf{y}|)-\max(|\hat{\mathbf{y}}|)|}{|\max(|\mathbf{y}|)|} \tag{27}\]  
where \(\hat{y}_{\mathrm{peak}}\) represents the response prediction value of the time step corresponding to the peak of the absolute value of the real value of the response data. Because of its normalized nature, the non-dimensional metrics are better suited for comparing prediction effects or accuracy differences across different types of responses.
# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 3 Methodology
### Dataset Building  
#### 3.1.1 Command Choice  
In order to collect only significant commands suitable for car driving, we surveyed 12 participants (eight male, four female) with at least 6 years of driving experience, posing the following questions:  
* Q1: Do you think a voice-controlled option will improve the driving experience?
* Q2: If you would use voice control in your car, what commands feel safe to use?  
Seven participants (n = 7) agreed that this would make the driving experience easier by offering an alternative to button options that could cause distraction while driving. Contrastingly, five participants (n = 5) opposed the idea, expressing concerns about potential accidents if primary functions like controlling lights or adjusting speed were misunderstood. However, they acknowledged that voice commands for secondary functions could enhance the driving experience. In addition, the 12 surveyed participants were tasked with selecting from a comprehensive list of 14 commands the ones they deemed safe for use. This inclusive list encompassed essential primary functions, including speed and brake control, as well as auxiliary functions such as utilizing voice commands for navigation system control or adjusting the media system to alter radio stations and adjust sound volume levels. As illustrated in Figure 2, the acceptance rate of commands can be classified into three distinct categories:  
* High Acceptance Rate: Navigation, Phone, and Media commands were highly accepted, receiving 100% acceptance rates, while climate control commands had an 83.33% acceptance rate and interior lights received a slightly higher rate of 91.67%. Additionally, the acceptance rate for window commands was 75%.
* Low Acceptance Rate: Commands concerning seat positions, exterior lights, mirror adjustments, and gear shifting received low acceptance rates.
* Zero Acceptance Rate: Speed, Brakes, and Clutch controlling commands received 0% acceptance rates.  
Based on the findings shown in Figure 2, the highly accepted commands predominantly comprised secondary functions aimed at activating various in-car features unrelated to driving maneuvers. These commands typically enhance driver comfort and luxury, adding value to the driving experience. Commands with low and zero acceptance rates are designed to execute actions that may interrupt the driving process. This outcome is anticipated as such. These actions are critical for safe driving and require the driver's full attention and control. Thus, they should not be controlled through external commands.  
Figure 1: Schematic diagram of our proposed system.  
Combining the results of our survey with well-established commands used in cars, such as in [15; 16; 17], we have selected 20 commands designed to perform simple tasks making driving comfortable and easier without compromising the driver's safety. Table 1 lists the 20 commands included in our paper with their translation into English and pronunciation in the international phonetic alphabets.