esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Ionospheric perturbations affect the propagation of electromagnetic waves. These perturbations, besides being a problem for space communications, satellite navigation, and Earth observation techniques, could also be used as another Earth observation tool. Several recent studies showed correlations with earthquakes with ionospheric anomalies, but almost all of them use ground stations to measure the Total Electron Content (TEC) variations, and, in particular, the ones occurring after an earthquake. Here, a preliminary study is presented on how the ionospheric scintillation measured with GNSS-R instruments over oceanic regions shows a small, but detectable correlation with the occurrence of earthquakes, which in some cases occurs before the earthquakes. This study uses GNSS-R data from NASA CYGNSS Mission to measure the ionospheric amplitude scintillation (\(S_{4}\)) for 6 months from March 2019 to August 2019, applying a statistical analysis based on confusion matrixes, and the Receiver Operating Characteristic (ROC) curves to correlate \(S_{4}\) anomalous variations to earthquakes. A small positive correlation is found between the ionospheric scintillation and the earthquakes during the six previous days. However, the study has some weakness because (a) a small number (\(\sim\)45) of large (M \(>\) 6) earthquakes over oceanic regions are studied, (b) the region studied is close to the geomagnetic equator, where ionospheric scintillations are usual, and (c) the overall correlation is small.  
ionospheric scintillation; GNSS-R; CYGNSS; earthquakes 2022142555.
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 4 Video Compressive Sensing
### Spatial VCS
For this network, increasing the size of the CNN has been tested, but it does not provide any improvement for the reconstruction because the CNN network is unable to capture temporal features. So, the LSTM network is important to improve the PSNR, which shows that the temporal resolution is processed at this level. This proves the importance of LSTM for video reconstruction. Thus, CSVideoNet is a non-iterative algorithm for real-time applications. The main goal of CSVideoNet is to improve the reconstruction quality and the compression ratio. In addition to the SVCS models already mentioned, two famous studies, based on stacked denoising autoencoders [33] or CNN [34] have been proposed for spatial CS to extremely fast reconstruct the frames from the compressively sensed measurements. In conclusion, it is important to say that the SVCS is originally based on single pixel cameras (SPC) to execute spatial multiplexing and enable video reconstruction by accelerating the acquisition process. However, there have been many extensions to the SPC. One of the famous extensions aims to parallelize the SPC architecture by applying many sensors  
Figure 7: CSVideoNet Architecture. to separately sample spatial areas of the moving scene [55; 56]. These prototypes are better than traditional SPC not only in terms of the manufacturing cost but also in terms of the measurement rate and the quality of the captured frames.
# Preparation and Properties of Poly(imide-siloxane)
## 3 Results and Discussion
### Properties of PI Films  
Thermal stability of PIs was investigated by thermogravimetric analysis (TGA) (Table 2 and Figure 2). TGA was conducted to study the effects of the molar ratio of diamines on decomposition temperature (\(T_{5}\) and \(T_{10}\)) and char yield of PIs. \(T_{5}\) and \(T_{10}\) values of the PIs ranged from 373 to 505 and 419 to 528\({}^{\circ}\)C, respectively, and the decomposition temperature decreased with increasing PDMS molar ratio. Nevertheless, PIs' decomposition temperatures were much higher than those of silicone [17]. The char yield at 800 \({}^{\circ}\)C of PI-1 without siloxane moiety was the highest (62.4%) and the other char yields were much lower.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PI Code a** & **Film Quality** & \(T_{5}\) (\({}^{\circ}\)C) b & \(T_{10}\) (\({}^{\circ}\)C) c & **Char Yield (\%) d** \\ \hline PI-1 & Brittle & 505 & 528 & 62.4 \\ PI-2 & Brittle & 446 & 473 & 28.2 \\ PI-3 & Flexible & 438 & 454 & 16.7 \\ PI-4 & Flexible & 428 & 443 & 0.7 \\ PI-5 & Flexible & 423 & 441 & 2.1 \\ PI-6 & Sticky & 403 & 426 & 4.3 \\ PI-7 & Sticky & 373 & 419 & 3.2 \\ \hline \hline \end{tabular}  
* PI-1-PI-7 were prepared from PAA-1-PAA-7, respectively; b The temperature at which a sample exhibits 5 wt% decomposition in a nitrogen atmosphere; c The temperature at which a sample exhibits 10 wt% decomposition in a nitrogen atmosphere; d Char yield at 800 \({}^{\circ}\)C in a nitrogen atmosphere.  
\end{table}
Table 2: Film quality and thermal properties of PIs.  
Figure 1: FT-IR spectra of (a) PAA-4, and (b) PI-4.  
Flexible and free-standing PI films (PI-3, PI-4, and PI-5) could be prepared from PAA-3, PAA-4, and PAA-5, respectively (Table 2 and Figure 3). When the films were bent, twisted, rolled-up, or wrapped on the 3-mm diameter bar numerous times, their appearance was almost unchanged (no damage occurred). However, PI-1 and PI-2 films were brittle due to their rigid chemical structures. On the other hand, PI-6 and PI-7 films were sticky due to the very high PDMS group contents.
# Investigation of Oil Extracted from Roasted and Unroasted Oats with Use of Chemometrics
## 3 Results and Discussion
### Chromatography  
Data considering the composition of fatty acids in the investigated samples are presented in Table 1, Figure 3. It is observed that the content of unsaturated fatty acids significantly dominates the saturated fatty acid content of oat oil, with a ratio of 80:20%, respectively. The ratio does not change statistically considerably after 60 min of the roasting process. Among unsaturated fatty acids, monounsaturated acids account for 39-42%, with oleic acid being the most abundant (range of 38-41% of this fraction). Of the polyunsatu  
Figure 2: The calorific value of roasted and unroasted whole oat grains. * Different letters indicate that the samples differ significantly at \(p<0.05\).  
rated fatty acids (38-40% in total fatty acids), linoleic acid is the most abundant (range of 37-39% of this fraction) (see Figure 3). The roasting process did not significantly impact the composition of the unsaturated fatty acids group. The studies [34] discussed the effects of seed roasting on mustard seed oil. The results showed that roasting the seeds did not impact the fatty acid composition of the mustard seed oil extracted after the roasting process.  
High levels of oleic acid in oat grains are a distinctive feature of the fatty acid profile that is of interest from a nutritional perspective. Of particular importance is the fact that this profile remains unchanged even after exposure to a 60 min roasting process.
## 2 Materials and Methods
### Methods
#### 2.3.3 Ridge Regression  
In a multiple linear regression, the collinearity issues among several independent variables may lead to errors of the least-square estimation [44; 55]. Ridge regression is often applied to eliminate collinearity among independent variables. The general estimation of the ridge-regression coefficient is:  
\[\rm\hat{\beta}(k)=\left(X^{T}X+k\times I\right)^{-1}X^{T}Y \tag{8}\]  
where \(X\) is the observation matrix of the independent variables. \(Y\) is the observation matrix of the dependent variable. \(I\) is the unit matrix. \(k\) (\(\geq\)0) is a ridge parameter. The ridge-regression coefficient is the sensitivity coefficient of the independent variables to a dependent variable. According to the ridge-regression coefficient and the linear trend of the independent variables, the contributions of the changes in the independent variables to the trend of dependent variable were calculated as [27; 45]:  
\[\rm C_{i}=\beta_{i}\timesslope_{X\_S}\timesslope_{Y\_S} \tag{9}\]  
where \(\rm\beta_{i}\) is the normalized ridge-regression coefficient of the independent variable i. \(slope_{X\_S}\) is the linear trend of the independent variable i after normalization. \(slope_{Y\_S}\) is the linear trend of the dependent variable. \(slope_{Y\_S}\) is the linear trend of the dependent variable after normalization. \(\rm C_{i}\) denotes the contribution of the changes in the independent variable i to the variation trend of the dependent variable. If \(\rm C_{i}>0\), then the change in the independent variable i has a positive contribution to the trend of the dependent variable; otherwise, the contribution is negative. According to the contributions of all variables, the relative contribution rate can be calculated as:  
\[\rm R_{i}=100\times\frac{C_{i}}{\sum_{1}^{n}|C_{i}|} \tag{10}\]  
where \(\rm R_{i}\) is the relative contribution rate of the changes in the independent variable i to the trend of the dependent variable. The dependent variable with the maximum relative contribution rate was chosen as the dominant factor over the trend of the dependent variable. In this study, the WUE was the dependent variable, whereas the influencing factors, which were chosen according to the grey relational analysis results, were the independent variables.
## 5 Conclusions  
In conclusion, our results indicate that, in the vast majority of cases, we observed compliance with the Greek established reference levels for RF exposure. The median values of the exposure ratio were found less than 0.035 in all cases examined, whereas the maximum values of the exposure ratio lay below 0.28 except for a very few cases, where the maximum value of the E-field strength exceeded the reference level. Most recordings of the electric field strength (i.e., 90%) were found lower than 1 V/m, while the majority of the total exposure ratio values (i.e., almost 94%) were below 0.2. The median values of the E-field strength were found to be statistically significantly different in urban and suburban environments, except for the 27-879 MHz band where broadcast applications dominate. Readings were slightly higher in urban areas than in the suburbs and the latter exhibited a narrower interquartile range than the former due to their more uniform construction. We also found that mobile communication frequencies and broadcast were the sources that contributed most to the total exposure.  
Our future plans include EMF measurements not only around but also inside schools and classrooms, in the frequency range of 27 MHz-6 GHz, especially with the prospect of the rapidly upcoming 5G mobile services everywhere. A comparison of the electric field strength at the same measurement locations, before and after the advent of 5G, will be interesting and revealing.  
Conceptualization, T.N.K. and I.O.V.; methodology, T.N.K. and I.O.V.; software, T.N.K. and I.O.V.; validation, T.N.K., M.P.I., A.T.B., C.D.N. and I.O.V.; formal analysis, T.N.K., M.P.I., A.T.B., C.D.N. and I.O.V.; investigation, T.N.K., M.P.I., A.T.B., C.D.N. and I.O.V.; resources, T.N.K., A.T.B., C.D.N. and I.O.V.; data curation, T.N.K., A.T.B., C.D.N., E.S.S., A.J.K. and I.O.V.; writing--original draft preparation, T.N.K., M.P.I. and I.O.V.; writing--review and editing, M.P.I. and I.O.V.; visualization, T.N.K., M.P.I., A.T.B., C.D.N., E.S.S., A.J.K. and I.O.V.; supervision, I.O.V.; project administration, I.O.V.; funding acquisition, C.D.N. and I.O.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Methodology
### Machine Learning Approach  
In this section, we perform sentiment analysis on Arabic consumer reviews using various classifiers from the Python scikit-learn library. The classifiers include Linear Support Vector Machines (LSVMs), Passive Aggressive Classifiers (PACs), Multinomial Naive Bayes (MNB), K-Nearest Neighbors (KNNs), Decision Trees (DTs), Random Forests (RFs), and Logistic Regression (LR). We employ GridSearchCV for hyperparameter tuning to ensure optimal performance. This approach aims to identify the most effective model for analyzing Arabic sentiments, as illustrated in Figure 1.
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
###### Abstract  
The rapid proliferation of handheld intelligent devices and the advent of 5G technology have brought about convenient and fast services for people. In perception-oriented application services, participating users will upload sensitive mobile data in order to obtain benefits. While devising privacy protection strategies to ensure data security, it is crucial to accomplish task perception related to data collection to the fullest extent possible. To address this challenge, this paper proposes a personalized data privacy protection algorithm based on an adaptive dynamic adjustment grid and the minimum wage task allocation strategy. According to the different levels of users' needs for privacy protection, combined with the privacy budget allocation strategy, we design a different-level differential privacy protection mechanism and consider the reward mechanism in task allocation to balance the effectiveness and security of the location data uploaded by users. Experiments show that the strategy proposed in this paper can not only protect the data but also enable users to freely choose the level of privacy protection.  
differential privacy; personalized privacy protection; task assignment; mobile crowdsensing Article  
Personalized Privacy Protection Mobile Crowdsensing
# Simulation of Parameters of Plasma Dynamics of a Magneto Plasma Compressor
## 1 Introduction  
Recently, theoretical and experimental studies of non-stationary processes in pulsed radiation magneto gas dynamic systems have been of great interest. Such systems include plasma sources of radiation and shock-wave generators, thermonuclear systems, systems for preliminary electromagnetic acceleration of targets, magneto plasma compressors and plasma accelerators, and pulsed plasma dynamic systems for controlling high-speed gas flows, as well as systems for plasma-stimulated ignition and combustion of fuel mixtures.  
The pulse plasma accelerator of the erosion type (magneto plasma compressor (MPC) [1; 2; 3]) in a vacuum, operating in self-focusing mode, is an effective device for generating flows of emitting plasma with a density of up to \(10^{24}\div 10^{26}\) m\({}^{-3}\) and a temperature of up to 100 kK and higher. On this basis, technical devices can be created that allow efficient conversion of the electric energy of the storage unit into thermal radiation of the ultraviolet (UV) and vacuum ultraviolet (VUV) range [4; 5; 6]. However, since the share of kinetic energy in the total energy balance of open vacuum magneto plasma compressor discharges is large, its thermalization methods are of particular interest: collision of plasma flow with a solid obstacle, counter interaction of high-speed plasma jets, etc. Magneto plasma compressor discharges in air, argon, and other inert gases with initial pressures in the range of \(P_{0}=10^{3}\div 3\times 10^{5}\) Pa at normal temperature \(T_{0}=300\) K have been experimentally and theoretically studied in a series of works [7; 8; 9].  
The MPC parameters: the length of the channel (<1 cm), the main electrode assembly \(2R_{1}=(0.8\div 1.5)\times 10^{-2}\) m and external electrode \(2R_{2}=(3\div 10)\times 10^{-2}\) m, the capacity C \(\sim 900\) uF, stored energy \(W_{0}=\mathrm{CU}_{0}^{2}/2=0.3\div 500\) kJ, the discharge current 5-100 us, and the amplitude of the current \(J_{m}\approx 100\div 2000\) kA.  
Theoretical and numerical modelling of magneto plasma compressor discharges is a necessary stage of research which allows quantification of the parameters and internal structure of discharge plasma, giving a correct interpretation of the available experimental data, allowing optimization of such multi-parameter systems, and determining the peculiarities of plasma modes and parameters in the areas of energy-power and the structural characteristics of different systems not yet covered by experiments [10; 11; 12; 13; 14].  
The earlier work of the authors is devoted to the calculation, description, and justification of the mechanisms of energy transfer in the plasma formation of magneto plasma compressor discharges. The main purpose is to study the features of the structure and the spectral brightness characteristics of pulsed radiating discharges of the MPC type in a dense gas over a wide range of energy-power parameters. It should be noted that no optimization of plasma dynamic processes in the magneto plasma compressor discharge was carried out in this research.
## 4 Simulation Analysis
### Multi-Load  
Figure 10 illustrates that the apex of the vortex tooth experiences the highest deformation, measuring 13 \(\upmu\)m, under multiple loads. At present, the maximum stress concentration is observed at the base of the vortex tooth, peaking at 58 MPa. This deformation and stress distribution pattern closely resembles that observed when subjected solely to thermal loading. This congruence can be attributed mainly to the relatively lower rotational speed  
Figure 8: Deformation and stress distribution of moving vortex disc under temperature loading.  
Figure 9: Deformation and stress distribution of moving vortex disc under gas force loading.  
of the scroll disc and the reduced exhaust pressure, where thermal stress emerges as the dominant factor governing the deformation and stress distribution of the scroll teeth.  
The deformation of the scroll disc mainly occurs in the upper part of the scroll tooth, while the stress mainly accumulates at the root of the scroll tooth. Subsequent analysis of the deformation and stress distribution of the scroll tooth focuses primarily on these two distinct regions: the root and the apex of the tooth. This analysis is further elaborated in Figure 11.  
Figure 11 shows that the deformation and stress distribution of the scroll teeth show similarities under both multiple loads and temperature alone. Particularly noteworthy is that within the profile spread angle range of 900\({}^{\circ}\) to 990\({}^{\circ}\), the maximum deformation experienced under multi-loading conditions amounts to 12.7 \(\upmu\)m, whereas under the sole influence of temperature load, it reaches 10.6 \(\upmu\)m. This discrepancy is considerable. Additionally, the maximum deformation induced solely by gas force stands at 4.5 \(\upmu\)m. Notably, the effect of gas force on scroll tooth deformation is pronounced primarily in instances where the pressure disparity between the internal and external environments of the scroll tooth is substantial near the exhaust orifice. Conversely, at other locations, the effect of the gas force on the stress and strain of the scroll tooth appears to be relatively minimal.
## 3 A Security-Enhanced Authenticated Key-Agreement Protocol: SE-UAVAKA  
### Related Theories and Definitions  
#### 3.1.1 Security Attribute Definition  
Reference [22] shows that the key-agreement protocol is secure enough to determine whether it meets the typical security properties. When conducting security analysis on security protocols, the following typical security properties are mainly considered:  
* **Impersonation attack resistance**: If the attacker cannot obtain the long-term key of protocol entity A, they will not be able to impersonate A and communicate with other protocol entities.
* **Replay attack resistance**: An attacker cannot deceive the target host by sending data packets that the target host has already received.
* **Known session-specific temporary information attack resistance**: The disclosure of temporary intermediate results does not affect the security of the session key.
* **Perfect forward security**: The disclosure of long-term keys of all protocol units has no influence on the security of the previously created session keys.
* **Known-key security**: Even if an attacker gets hold of some previous session keys, they cannot obtain the current session key.
* **User anonymity**: During the execution of the protocol, attackers cannot find out the identity of the protocol instance.
* **Mutual authentication**: Protocol units can authenticate each other, and the authentication process cannot be falsified by attackers.
# Optimization of Multimodal Discrete Network Design Problems Based on Super Networks
## 2 Multimodal Super Network Topology  
### Multimodal Network Representation  
There are 5 kinds of transportation modes considered in this paper: car, bus, rail transit, bicycle, and walking. Multimodal transportation weighted directed graph \(G=(V,\ A,\ W,\ K)\). is presented, in which some notations and definitions are shown in Table 1.  
The relationship among the elements in the multimodal transportation super network studied in this paper is shown in Figure 1.  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline
**Notations** & \multicolumn{1}{c}{**Definitions**} \\ \hline \(K\) & Set of transportation modes. It includes car, rail transit, and bus, which are indexed by \(c\), \(r\), and \(p\) respectively. Thus for any mode \(\ k\in K=\{c,r,p\}\). \\ \hline \(V\) & Set of nodes, node \(\ i\in V\). In a multimodal transportation network, nodes have multiple attributes, which can represent bus stations, rail transit stations, road intersections, and interchange hubs. \\ \hline \(A\) & Set of links, link \((i,j)\in A\). In this paper, the links in topological networks are divided into four types: \(A^{E}\) ( the set of entering links), \(A^{L}\) (the set of leaving links), \(A^{D}\) (the set of driving links), \(A^{T}\) (the set of transfer links). It is concluded that \(A=A^{E}\cup A^{L}\cup A^{D}\cup A^{T}\). \\ \hline \(A^{E,k}\) & Set of the entering links of transportation mode \(k\). It means that passengers begin to enter the network. \\ \hline \(A^{L,k}\) & Set of leaving links of transportation mode \(k\). It indicates that passengers’ leave behavior from the network. \\ \hline \(A^{D,k}\) & Set of driving links of transportation mode \(k\). It expresses that transportation mode \(k\) is driving in the network. \\ \hline \(A^{T,k}\) & Set of links for transferring to transportation mode \(k\). It includes the transfers between the same or different transportation modes. \\ \hline \(W\) & Set of weights, weight \(w_{ij}\in W\). The weight of links in the multimodal network has a variety of attributes, which can be represented as a generalized cost formula. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The notations and definitions of the multimodal transportation network.