esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
For the examination of the ionospheric composition, altitudes, locations and other properties, experts usually make use of satellites equipped with suitable sensors, GPS, sonde, radars, various optical instruments, or balloons, rocket probes, etc. A widely used technique for remote sensing of the Earth's ionosphere exploits radio signals (e.g., [21; 22; 23; 24]), and for altitude range that corresponds to the ionospheric lowest layer, i.e., the D region, VLF radio signal range is preferred (e.g., [25; 26; 27; 28; 29; 30]). Remote sensing of the lower ionosphere by utilisation of radio signals in the VLF range is based on the hop wave theory (see, e.g., [31; 32; 33; 34]). These techniques are successfully used for exploration of Earth's lower ionosphere's response to a number of processes, with their origin from extra-terrestrial to terrestrial environments (e.g., [35; 36; 37; 38; 39; 40; 41]).  
The methodology used and results of mid-latitude lower ionosphere diagnostic obtained by this technique, based on VLF radio signals recorded at the Belgrade receiver site, in cases of regular and irregular solar-terrestrial conditions, can be found in, e.g., [23; 37; 39; 42; 43; 44; 45; 46; 47; 48; 49; 50]. In our study, at the ionosphere altitudes 50-90 km (D region), measurements rely on radio wave propagation technique, i.e., monitoring of phase and amplitude of VLF radio signals. For this reason, the perturbation of VLF phase delay and amplitude was estimated as a difference between values of the perturbed signal induced by external disturbance and signal in the normal unperturbed conditions. The details are described in the next section.
## 3 Proposed Scheme
### Communication Round of the Proposed Scheme  
The communication round in the proposed MAC protocol comprises multiple sessions. Each session starts with a beacon frame, followed by a contention access period, an announcement period, and a data transmission period, as shown in Figure 2.
## 5 Conclusions  
Free amino acid profiles and volatile aroma compounds in watermelon rind were analyzed in this study. Compared to watermelon flesh, watermelon rind contained a higher amount of total amino acids due to its concentrated Cit and Arg content; however, the rind had a lower amount of essential amino acids. The results suggest that watermelon rind could be used to nutritionally supplement food and beverages with Arg and Cit. Watermelon rind had a much lower \({}^{\circ}\)Brix and, consequently, a less sweet taste. Watermelon rind also contained a much lower amount of volatiles, only 15% compared to the flesh. The relatively \"bland\" flavor (taste and aroma) for watermelon rind might have less interference when it is used as an ingredient in processed foods. Also, fewer unique volatile compounds were recovered from watermelon rind compared to flesh. Both watermelon rind and flesh, however, were predominant with nine-carbon aldehydes and alcohols, which were potentially associated with fresh, green, and cucumber-like aromas. These volatiles in the rind might enhance aroma for the rind used in food products. The study provides knowledge that watermelon rind could be a promising supplemental ingredient for food and beverages. Manufacturers of food products can apply this research to develop products using watermelon rind waste for its nutritional value and aroma characteristics.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/molecules27082536/s1](https://www.mdpi.com/article/10.3390/molecules27082536/s1)]([https://www.mdpi.com/article/10.3390/molecules27082536/s1](https://www.mdpi.com/article/10.3390/molecules27082536/s1)). Table S1: Volatile compounds in watermelon rind, flesh, and rind-flesh blends (peak area %).  
**Author Contributions:** [PERSON] quantified free amino acids using GC-MS, performed data analysis, and revised the manuscript. [PERSON] carried out the volatile analysis, performed data analysis, and wrote the manuscript. [PERSON] quantified free amino acids using HPLC-UV, built up calibration curves for volatiles, and performed data analysis. [PERSON] designed and supervised the study, secured funding, aided with data analysis and interpretation, and wrote and finalized the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research project was funded by Texas Woman's University-Chancellor Research Fellow and the National Watermelon Promotion Board with grant ID# 00704263.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article or Supplementary Materials.  
**Acknowledgments:** The authors thank [PERSON] (Horticultural sciences, Texas A&M AgriLife Research & Extension Center, Lubbock, TX, USA) for providing watermelon samples.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.  
**Sample Availability:** Samples of the compounds are not available from the authors.
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 5 Algorithm for the Energy Saved Maximization Problem
### Improved DEEC Protocol  
By considering the energy of the sensor node and the distance to the sink comprehensively, we improve the DEEC from three aspects: the probability of the CH selection, the cluster size, and the communication way of sensor nodes around the sink.
## 4 Signal Processing Algorithms for RIS Channel Estimation
### Compressed Sensing-Based Algorithms for RIS Channel Estimation
In the improved BiG AMP algorithm, a pilot was used to eliminate the effect of quantization and the paper used a quantization-aware approach to estimate the CSI. The performance was further improved relative to Ref. [93]. During numerical processing, the row sparse matrix elements were coupled to each other causing a certain degree of error in the conventional estimation scheme. A hybrid approximate message passing framework was proposed in [96] to decompose the derivative frequency matrix, and recover the row sparse representation in the model. The hybrid architecture compensates for the problem that GAMP cannot operate in the non-Gaussian distribution dictionary. In this section, we give an overview of the idea and use of CS algorithms in the RIS channel estimation process, mainly describing the shortcomings of the classical OMP algorithm, such as high computational complexity and fixed sparsity leading to large application limitations. The extension literature improves on the shortcomings, how to reduce the computational complexity, and the joint adaptive sparsity AMP algorithm. The compressed sensing-based channel estimation algorithms and performance comparison are described in Table 7. Table 7 describes the methods from four aspects: antenna/RIS architecture, channel model, major algorithm, and performance analysis. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Year** & \begin{tabular}{c} **Antenna/RIS** \\ **Architecture** \\ \end{tabular} & \begin{tabular}{c} **Channel** \\ **Model** \\ \end{tabular} & \
\begin{tabular}{c} **Major Algorithm** \\ \end{tabular} &
\begin{tabular}{c} **Performance Analysis** \\ \end{tabular} \\ \hline [PERSON] et al. [68] & 2020 & MIMO/Passive RIS & Time-varying channels & Sparse matrix factorization and completion & Improved channel estimation accuracy over the baseline methods \\ [PERSON] et al. [93] & 2021 & MIMO/Passive RIS & Static channel & Bilinear generalized AMP & 50\% reduction in transmit power compared with random algorithm when achievable rate was 3 \(\mathrm{bps/Hz}\) \\ [PERSON] et al. [94] & 2022 & MIMO/Passive RIS & Static channel & Multidimensional OMP & The error smaller than 20 cm for 80\% of the user positions Improve the performance with faster convergence 1/6 reduction in training pilot symbols compared with baseline scheme (\(\epsilon_{\mathrm{NMSE}}=10^{-2}\)) \\ [PERSON] et al. [97] & 2022 & MIMO/Passive RIS & Static channel & Three-step OMP & 50\% reduction in pilot overhead compared with based scheme (\(\epsilon_{\mathrm{NMSE}}=-16\) dB) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of compressed sensing-based algorithms for RIS channel estimation.
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 7 Conclusions  
During the process of mobile data collection, the protection of data collectors' location data has become an increasingly critical concern. To prevent exposing real staff location information to potential attackers, certain documents, such as [41; 42; 43; 44], have employed differential privacy technology to obfuscate location information on the local client before uploading the perturbed data to the platform. Building upon this prior work, this paper presents implementation algorithms for different privacy protection levels within specific grid cells in the second-level grid. Finally, the task allocation strategy of minimum reward allocation is proposed. We compare the algorithm proposed in this paper with the algorithm proposed by the author of Reference [35] through experimental simulation. The purpose of the comparison is to analyze the effectiveness and practicability of the personalized privacy protection algorithm proposed in this paper in data protection. By analyzing the results, we can conclude that the task allocation model proposed in this paper has a positive impact on improving performance indicators such as the task completion rate in the task allocation process.  
While the algorithm proposed in this paper yields favorable results in terms of data privacy protection and task allocation, it is not without its limitations. One prominent issue is that the algorithm sacrifices a portion of the communication cost for lower remuneration and a flexible privacy protection level. Our forthcoming area of improvement is to find a more suitable algorithm that minimizes the number of propagation hops during communication, consequently reducing the cost associated with it.  
Conceptualization, H.G. and H.Z.; methodology, H.G. and Z.Z.; formal analysis, H.G. and Z.Z.; investigation, H.G. and Z.Z.; project administration, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 3 Impedance Model of Converters with Improved Virtual Impedance Control Strategy
### DC Side Impedance Modeling of the Power-Controlled Converter  
The linearized dynamic equations of the power-controlled converter are the same as those of the DC-voltage-controlled converter, which are expressed as (4)-(6), and its outer loop controller linearized equation is expressed as  
\[\left[\begin{array}{c}\Delta i_{sd,ref}\\ \Delta i_{sq,ref}\end{array}\right] = -H_{p}\left(\overbrace{\left[\begin{array}{cc}1.5u_{sd0}&1.5u_{ sq0}\\ 0&0\end{array}\right]}^{G_{pp}}\left[\begin{array}{cc}\Delta^{r}_{sd}\\ \Delta^{r}_{sq}\end{array}\right]+\overbrace{\left[\begin{array}{cc}1.5i_{ sd0}&1.5i_{sq0}\\ 0&0\end{array}\right]}^{G_{ip}}\left[\begin{array}{cc}\Delta u^{c}_{sd}\\ \Delta u^{c}_{sq}\end{array}\right]\right) \tag{43}\]  
where \(H_{p}~{}=~{}k_{pp}+k_{ip}/s\) is the active power compensator and \(k_{pp}\) and \(k_{ip}\) are the proportional and integral gains of the compensator. Its current compensator and modulator is same as (11). Inserting (16)-(18) into (42) gives  
\[\left[\begin{array}{c}\Delta i_{sd,ref}\\ \Delta i_{sq,ref}\end{array}\right] = -H_{p}G_{cp}\left[\begin{array}{c}\Delta^{s}_{sd}\\ \Delta^{s}_{sq}\end{array}\right]-(H_{p}G_{cp}G^{i}_{PLL}+H_{p}G_{ip}G^{i}_{PLL}) \left[\begin{array}{c}\Delta u^{s}_{sd}\\ \Delta u^{s}_{sq}\end{array}\right]. \tag{44}\]  
Inserting (16)-(18) and (28) into (11), the relation between the modulation index and AC current can be expressed as  
\[\left[\begin{array}{c}\Delta m^{s}_{d}\\ \Delta m^{s}_{q}\end{array}\right]_{\forall d0} = \left(G_{pum}G_{cc}H_{p}G_{cp}+G_{pum}(G_{cc}+Z_{dd})\right) \left[\begin{array}{c}\Delta^{s}_{sd}\\ \Delta^{s}_{sq}\end{array}\right]+\] \[\left(G_{pum}G_{cc}H_{p}(G_{cp}G^{i}_{PLL}+G_{ip}G^{i}_{PLL})+G^{i }_{\rm C}\right)\left[\begin{array}{c}\Delta u^{s}_{sd}\\ \Delta u^{s}_{sq}\end{array}\right]\]  
where  
\[G^{i}_{\rm C} = G^{d}_{PLL}+G_{pum}G^{p}_{PLL}+G_{pum}(G_{cc}+Z_{dd})G^{i}_{PLL}. \tag{46}\]  
Equation (45) can be rewritten as  
\[\left[\begin{array}{c}\Delta m^{s}_{d}\\ \Delta m^{s}_{q}\end{array}\right]_{\forall d0} = G^{p}_{\rm Z}\left[\begin{array}{c}\Delta^{s}_{sd}\\ \Delta^{s}_{sq}\end{array}\right]. \tag{47}\]  
Inserting (47) and (28) into (4) yields  
\[\overbrace{\left(Z_{s}(s)-Z_{0}(s)-G^{p}_{\rm Z}(s)\right)^{-1}\left[\begin{array} []{c}m_{d0}\\ m_{q0}\end{array}\right]}^{Y^{p}_{AC}}\Delta v_{dc}~{}=~{}\left[\begin{array}[] {c}\Delta^{s}_{sd}\\ \Delta^{s}_{sq}\end{array}\right]. \tag{48}\]  
The DC-side impedance of the converter can be obtained by inserting (47) and (48) into (21):  
\[Z_{dcA}~{}=~{}\frac{1}{1.5(\left[\begin{array}{cc}m_{d0}&m_{q0}\end{array} \right]+\left[\begin{array}{cc}i_{sd0}&i_{sq0}\end{array}\right]G^{p}_{\rm Z })Y^{p}_{AC}}. \tag{49}\]  
Consider the DC-side capacitor,  
\[Z_{dcB}~{}=~{}\frac{Z_{dcA}}{1+sC_{dc}Z_{dcA}} \tag{50}\]  
Adding the DC cable impedance to the DC-side impedance of the power-controlled converter yields  
\[Z_{dc2}(s)~{}=~{}(Z_{dcB}(s)\left\|\frac{2}{sC_{d}}+sL_{d}+R_{d}\right)\left\| \frac{2}{sC_{d}}. \tag{51}\]
## 2 Problem Formulation and the Convergence of the Quantized ILC Law
: _From the derivative process of Theorem 1, for the given system (1) or (2) with \(\mathbf{CB}\
eq 0\), the convergence condition \(\rho_{1}<1\) depends upon the system's input matrix \(\mathbf{B}\), output matrix \(\mathbf{C}\) and the derivative learning gain \(\Gamma\) and the quantized density \(\eta_{e}\). When the quantized density \(\eta_{e}\) equals one, then the quantized error \(\sigma_{e}\) decreases to zero. This situation is consistent with the system not having data quantization and the algorithm is the derivative-type ILC. Then, we can get \(\rho_{1}=|1-\Gamma g_{1}(1)|=|1-\Gamma\mathbf{CB}|<1\), so \(\lim_{k\to\infty}\left|\left|\epsilon_{k+1}\right|\right|=0\). It has coincided with the algorithms of the D-type ILC._  
**Remark 2**. : _While the matrix (10) is a lower triangular Toeplitz as shown in Theorem 1, the spectral radius of this matrix satisfies the condition when \(0<\rho_{1}=|1-\Gamma\mathbf{CB}|+\sigma_{e}|\Gamma\mathbf{CB}|<1\). By benefiting from the property of the matrix, the convergence is directly made in terms of the tracking error vector. This indicates that the tracking error measured using any type of norm is convergent when the iteration tends to infinity._  
**Remark 3**. : _In the above-mentioned analysis of convergence, the systems' information, learning gain, and quantized density are all factors in the convergence condition of the ILC law with quantized signals. Therefore, the performance of the tracking behavior is affected by selecting an appropriate learning gain, but the selection of learning gain in the control mechanism above is given by experience. Thus, the learning mechanism needs to be improved. Additionally, even though the tracking error converges as the number of iterations increases, we still want to select an effective optimization strategy to accelerate the speed of convergence. As a result, the optimization of the learning gain is worthwhile research in the systems with data quantization._  
**Remark 4**. : _The paper considers the ILC (7) and the derivation for the SISO system (3). When benefiting the framework by adopting an appropriate super-vector and super-matrix presentation, it is not difficult to generalize issues to multiple input-multiple output systems. In this circumstance, the derivation would be more complex._
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 7 Steganographic Capacity Analysis  
Next, we evaluated the relationship between encrypted image sizes and video sizes. The detailed calculation results are presented in Table 12.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **Video1** & **Video2** & **Video3** \\ \hline \multirow{2}{*}{Peppers} & 196,608 & 196,608 & 196,608 \\  & 48,129,272 & 50,788,096 & 45,524,344 \\ \hline \multirow{2}{*}{House} & 393,216 & 393,216 & 393,216 \\  & 48,129,272 & 50,788,096 & 45,524,344 \\ \hline \multirow{2}{*}{Baboon} & 589,824 & 589,824 & 589,824 \\  & 48,129,272 & 50,788,096 & 45,524,344 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Ratios Between Encrypted Image Size and Video Size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Stage-Video** & **Hiding Encrypted Image** & **Decryption Image** \\ \hline \multirow{2}{*}{Peppers} & 196,608 & 196,608 \\  & 48,129,272 & 50,788,096 \\ \hline \multirow{2}{*}{House} & 393,216 & 393,216 \\  & 48,129,272 & 50,788,096 \\ \hline \multirow{2}{*}{Baboon} & 589,824 & 589,824 \\  & 48,129,272 & 50,788,096 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Ratios Between Encrypted Image Size and Video Size.  
The result were as follows: Peppers: Video1 = 0.0041, Video2 = 0.0039, Video3 = 0.0043; House: Video1 = 0.0081, Video2 = 0.0077, Video3 = 0.0086; Baboon: Video1 = 0.0123, Video2 = 0.0116, Video3 = 0.0129.  
The results reveal low ratios ranging from 0.0041 to 0.0129, meaning masking was very efficient without much impact on video size. The results are suitable for practical application. The purposefully low embedding ratio guarantees imperceptibility and makes it challenging for steganalysis tools to identify the existence of hidden data. It optimizes the capacity vs. security trade-off to maintain robustness against detection assaults.  
The outcomes attest to the suggested MCC/AES/Stego system's resilience in protecting picture transmission within video frames. The concluding section covers the general steganographic capability and usefulness of our method.
## 3 Results and Discussion
### Emulsion Properties  
The emulsifying activity index (EAI) and emulsion stability index (ESI) of the modified and unmodified whey proteins are shown in Figure 5. Modification of the whey protein with oxidized phenolic compounds most likely affected the emulsion properties of the resulting whey protein, depending on the type and concentration of oxidized phenolic compounds used. For whey protein modified with OFA, concentrations of 0.5 and 1.0% OFA increased both the EAI and ESI of modified whey protein when compared with the control (\(p<0.05\)). This result may be related to the increase of the surface hydrophobicity of the modified whey protein with OFA. [PERSON] and [PERSON] [28] reported that protein surface hydrophobicity is often related with improved surface activity, resulting in a decrease in interfacial tension and an increase in emulsifying activity. The whey protein modified with OFA with higher surface hydrophobicity could localize and accumulate at the interface between oil and water, reducing the interfacial tension during homogenization and preventing flocculation and coalescence during storage, resulting in an increase of EAI and ESI. However, whey protein modified with OFA at concentrations of OFA above 1.0% decreased both EAI and ESI. The presence of excess surface hydrophobicity in the whey protein modified with OFA at a high level might cause a loss in water solubility, affecting the surface activity of the modified whey protein and resulting in a loss in emulsion properties as well as foam properties.  
For the whey protein modified with OFA and OTA, the resulting whey protein had lower EAI and ESI when compared with the control (\(p>0.05\)), especially for the whey protein modified with OTA. EAI and ESI of the whey protein modified with OFA and OTA decreased with the use of increasing concentrations. This result might be due to a decrease in surface hydrophobicity of the resulting whey protein causing a loss in ability of whey protein to localize to the oil-water interface, resulting in a decrease of both EAI and ESI. Moreover, interactions of OTA with the whey protein might lead to protein aggregation. Therefore, the whey protein may not move to the oil-water interface and form a film around the oil droplet effectively.  
Figure 5: Emulsifying activity index (**a**) and emulsion stability index (**b**) of whey protein modified with different oxidized phenolic compounds at various concentrations.
## 3 The Proposed Framework for Testing
### Simulator Architecture
#### 3.1.2 Sync Bug Generator  
Loss of packet or data during inter-processor communication in embedded systems takes place due to timing and synchronization issues, i.e., \"breaking-up\" of voice and video communication, extensive buffering, and skipped videos. When scenario like data  
Figure 4: Multiple instances of simulator across PEs.  
Figure 3: Simulator architecture.  
loss, or when it is not received in order, occur during communications then lost data will need to be retransmitted or reordered during a particular communication.  
The sync bug generator generated all possible synchronization bugs that occur during inter-processor communications that are shown in Table 1. These bugs are further reported in [40] and were simulated through a simulator and stored in the knowledge base. These are primarily not functional bugs, but rather bugs created by the interfaces' timing and synchronization.  
The explanation of each bug is as follows:  
1. Data received at Rx with delay: delay occurs in receiving data at Rx. Delay can occur at various points during communication, such as at the beginning, in the middle (anywhere between communication), or both.
2. Data transmission from Tx with delay: delay occurs in transmitting data from Tx.
3. No data received at Rx: Tx sends the data constantly but Rx does not receive it.
4. No data transmitted from Tx: Tx does not send the data to Rx.
5. Data overriding during communication: Rx overrides some packets of data during communication.
6. Out of synchronization: data are not received in a sequence (out of order).
## 2 Materials and Methods
### Fatty Acid Analysis  
The fatty acid analysis was conducted from the oil samples after converting FAs to fatty acid methyl esters (FAMEs) using the sodium methoxide catalyzed method [(22)] with a slight modification: the reaction was stopped with glacial acetic acid instead of oxalic acid. FAMEs dissolved in hexane were analysed with a gas chromatograph (Shimadzu GC-2010 equipped with AOC-20i auto injector, flame ionization detector, Shimadzu corporation, Kyoto, Japan). The injection (0.5 \(\mu\)L) was operated in a splitless mode with a sampling time of 1 min. Helium was used as the carrier gas. The column was a DB-23 (60 m \(\times\) 0.25 mm i.d., liquid film 0.25 \(\mu\)m, Agilent Technologies, J.W. Scientific Santa Clara, CA, USA). The following temperatures were used: inlet temperature 270 \({}^{\circ}\)C; oven temperature 130 \({}^{\circ}\)C for 1 min, followed by increases of 6.5 \({}^{\circ}\)C/min to 170 \({}^{\circ}\)C, 2.75 \({}^{\circ}\)C/min to 205 \({}^{\circ}\)C (held 18 min) and 30 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C (held 2 min); detector 280 \({}^{\circ}\)C. The enzymatically extracted samples included triheptadecanin and the solvent extracted samples included triheneicosanoin as internal standards. FAs were identified using external standards Supelco 37 Component FAME mix (Supelco, St. Louis, MO, USA) and 68D and GLC-490 (Nu-Check-Prep, Elysian, MN, USA). Correction factors were calculated using the external standards. The quantification of the FAs was conducted based on the internal standard area, concentration and correction factors. Each sample was analyzed in triplicate.