esa-sceva/satcom-chunk-collection huggingface dataset:
# Internet of Things and Its Applications: A Comprehensive Survey
## 5 IoT Applications
### Infrastructural Applications
The researchers conducted an experiment that verified the recommended solution for user association and resource allocation in the Fog environment. The effectiveness and productivity of the suggested spectrum sharing technique and the constancy of the user involvement of a higher value and efficiency represent the benefits of this research. The work in [154] suggests an IoT-based WSDN architecture directed to operate with the usage-particular requirement and necessities. The architecture has sensors and a managing node, which can support SDN in WSN. Two elements have been suggested to ensure the QoS network: (1) A controller device manager, which is used to manage the device-specific responsibilities, and (2) a topology manager, which is adapted to handle the network topology. The suggested solution focusing on sensors and protocol management was compared to the actual SDN approaches for WSNs. The obtained results show that this solution is valuable for usage-particular service offerings, at the same time as enhancing and boosting the network routines with conventional sensor networking techniques. The suggested solution does not take into consideration the decrease in the network delay and control messages overhead.
# Analysis of Cryptographic Algorithms to Improve Cybersecurity in the Industrial Electrical Sector
## 2 Communication Protocols GOOSE and R-GOOSE and Disadvantages of the IEC61850 Standard
### Disadvantages of the IEC61850 Standard  
Although the IEC61850 standard offers numerous advantages, it does not cover an important aspect: cybersecurity. This aspect is generally not relevant to those working in electrical substations or the power systems field, particularly in Latin America where the majority of substations are still conventional.  
Figure 2: Session PDU field of an R-GOOSE/R-SV frame.  
However, due to the standardization of communication protocols, staff can no longer ignore the threat of cyber attacks. Attacks on electrical systems already exist, as demonstrated by the Stuxnet virus. This virus infected a large number of computers worldwide through pen drives, with the specific objective of infecting Siemens PLCs of a particular model that controlled centrifuges responsible for stirring uranium in the form of gas to enrich it. The ultimate goal was assumed to be the creation of nuclear weapons. It is important to note that this information is presented objectively and without any subjective evaluations. It is estimated that the virus caused damage to over a thousand centrifuges by altering the speed of their motors [7].  
Another example of a cyber attack on the electrical industry is Dragonfly, which specifically targets industrial control systems (ICSs) used in the energy sector. This advanced persistent threat (APT) had a significant impact on European countries in 2014. Dragonfly uses several remote access tool (RAT) components to infect and remotely control the affected computers [16].  
In 2017, Ukraine was hit by another malware called CrashOverride. This malware targeted organizations that use IEC101, IEC104, IEC61850, and OPC protocols. It has modules that are specific to ICS protocol stacks, as well as non-ICS-specific modules, such as a Wiper that removes files and processes from the running system. The malware exploits switches driven by RTUs and IEDs, forcing them into an infinite loop and keeping circuit breakers open, even if operators try to close them [17].  
Figure 3 shows the malware that has been found in different industrial control systems, with emphasis on CrashOverride, which was made especially to attack power networks that use IEC61850 communication protocols [18].
# Effect of Ionic Liquids on the Isobaric Vapor-Liquid Equilibrium Behavior of Acetone-Chloroform
## 5 Conclusions  
The VLE data for the two systems of acetone + chloroform containing [MMIM][DMP] or [EMIM][DEP] were measured at 101.3 kPa. Two ILs have remarkable separation efficiency on the binary acetone-chloroform azeotropic system and can break the azeotrope when the mole fraction of ILs is over 0.15. Compared with [MMIM][DMP], [EMIM][DEP] produces a more significant effect on the VLE of acetone and chloroform. By use of the NRTL model, the experimental data correlated well. Besides, the results of the quantum chemical calculations indicate that chloroform had stronger affinity with ionic liquids than acetone, which is in accordance with the experiment results.  
**Author Contributions:** Data Curation, Q.G.; Funding Acquisition, Z.Z.; Methodology, Z.Z.; Investigation, Q.G. and P.J.; Writing--Original Draft Preparation, Q.G.; Project Administration, Z.Z. and X.Z.; Supervision, Z.Z.  
**Funding:** This research was funded by National Natural Science Foundation of China under grant number No. 51376036.  
**Acknowledgments:** The authors want to thanks prof. [PERSON]'s research group in Dalian University of Technology for helping us with the theoretical calculation in Gaussian 0.9 A.01.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**A** & **B** & **\(\Delta E(\text{AB})\)** \\ \hline acetone & chloroform & \(-\)14.290 \\ [MMIM]\({}^{+}\) & [DMP]\({}^{-}\) & \(-\)387.833 \\ [EMIM]\({}^{+}\) & [DEP]\({}^{-}\) & \(-\)381.524 \\ acetone & [MMIM][DMP] & \(-\)24.519 \\ acetone & [EMIM][DEP] & \(-\)14.892 \\ chloroform & [MMIM][DMP] & \(-\)30.939 \\ chloroform & [EMIM][DEP] & \(-\)30.421 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The value \(\Delta E(\text{AB})\) of interaction energy between ionic liquid and solvent. All energies are given in kJ-mol\({}^{-1}\).  
Figure 44: Chloroform-[MMIM][DMP]; red: oxygen atom; green: chlorine atom gray: carbon atom; white: hydrogen atom; blue: nitrogen atom; dotted line: hydrogen bond.  
Figure 45: Chloroform-[EMIM][DEP]; red: oxygen atom; green: chlorine atom gray: carbon atom; white: hydrogen atom; blue: nitrogen atom; dotted line: hydrogen bond.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{x_{3}}\) & T/K & \(\mathbf{x_{1}}^{\prime}\) & \(\mathbf{y_{1}}^{\text{exp}}\) & \(\mathbf{y_{1}}^{\text{cal}}\) & \(\mathbf{\alpha_{12}}\) \\ \hline  
\hline \hline \end{tabular} Standard uncertainties u(\(x_{1}\)) = u (\(y_{1}\)) = 0.002, u(\(T\)) = 0.1 K, ARD =1.7948\(\%\).  
\end{table}
Table 10: _Cont._
## 1 Introduction
#### Contribution  
The main contributions are as follows:  
1. First, symmetric data is collected based on the questionnaire from a specified locality of India.
2. After the collection of data, based on hypothesis, the TAM model is implemented with the help of AMOS.
3. With the help of AMOS, it identifies the significant symmetric path coefficients between the primary factors.
4. A detailed result analysis of the TAM model is performed, giving insight to justification of the hypothesis proposed.
5. For further analysis, various symmetric ML techniques, such as logistic regression, SVM, etc., are applied on the same dataset to analyze the proposed hypothesis for encouraging predictive analysis.
6. The results for the main factor's predictions are compared with various implemented techniques. Last, we conclude our paper based on the objective.  
The rest of the paper is assembled as follows. In Section 2, we define the basic TAM model. In Section 3, a detailed literature reviews is given, along with the hypothesis. In Section 4, we will provide the research methodology, including data analysis. Section 5 gives a brief introduction to machine learning approaches. In Section 6 is the detailed metric and result analysis based on the machine learning technique. Section 7 is focused on the discussion and limitation of the study. In the last section, Section 8, the conclusion of the paper will be given, along with future aspects of the study.
# A Multiprotocol Wireless Sensor Network for High Performance Sport Applications
## 2 Materials and Methods
### Design of the Slave Nodes  
The main components of a slave node (Figure 5b) are the MCU, the nRF24L01P+PA+LNA module, and the signal conditioning circuits for the force sensors (two channels are available): A load cell or a strain gage bridge placed on the paddle shaft or on the footrest (two sensors in the latter case). Since the computational burden of the master node is much lighter, a smaller MCU, namely an Arduino Nano (based on the 16 MHz ATmega328 processor), has been adopted. The slave node is powered by a 3.7 V, 500 mAh LiPo battery and it is equipped with a battery charge circuit.  
In particular, for the e-kayak system, two custom-made boards have been designed, for the paddle nodes and for the footrest nodes, respectively. They only differ in their form factors, the one for the paddle being long and thin enough to be placed inside the shaft (Figure 6b).
# Polar Cap Patches Scaling Properties: Insights from Swarm Data
## 2 Data and Methods
### Swarm Data
#### 2.1.1 Electron Density Measurements and RODI  
Electron density measurements represent a Swarm Level 1b product. Although they are provided by Langmuir Probes (LP) at a rate of 2 Hz [25; 26], we use the 1 Hz dataset distributed through the EFIXPLI_1B product.  
RODI, which provides a measure of electron density variability along the satellite track was first used to characterize ionospheric plasma irregularities using data from CHAMP by [PERSON] and [PERSON] [27] and is now calculated from Swarm satellites and used to investigate a wide range of ionospheric phenomena (see, e.g., [9; 24; 28; 29]). Indeed, because of the way RODI is calculated, it is well suited to study ionospheric irregularities.  
RODI is defined as the root mean square error of the first derivative with respect to the time of the electron density, calculated for a specific moving time window (e.g., [24]).  
In this work, we calculated RODI values according to the procedure implemented in the tool TITIPy (Topside Ionosphere Turbulence Indices with Python) recently developed by [PERSON] [30], setting the width of the moving time window to 10 s. This allows for investigating irregularities with spatial scales down to \(\sim\)70 km.
## 4 Benchmark Techniques
### Clipping-Based Techniques  
#### 4.2.1 Iterative Clipping and Filtering  
The ICF algorithm is outlined in Figure 1. The signal amplitude is kept below a preconfigured limiting threshold by repeatedly clipping amplitudes exceeding the threshold and filtering the resulting OoB radiations. The process therefore zigzags back and forth between time and frequency domain representations through the iterative computation of IFFT and FFT operations for a preset number of times, \(L\). Fortunately, amplitude regrowth decays with the increasing number of iterations, thereby enabling the technique to effectively reduce the PAPR level as more iterations are applied.
# Expansive Soil Stabilization with Time, Cement, and Silica Fume
## 1 Introduction
The adequate water and F1 ionic soil stabilizer mixture was determined at 0.5 L/m\({}^{3}\). According to the findings, adding F1 ionic stabilized to expansive clay soil raised the plastic limit by around 46%, increased the maximum dry unit weight by around 6%, decreased the liquid limit by around 10% and increased both the ideal moisture content and plastic limit. In terms of cohesiveness and the angle of internal friction, the shear strength parameters were both raised by around 64 and 30%, respectively. According to [PERSON] et al. [18] who suggested a combined seepage-erosion water inrush model, a grouting thickness of 6 m is appropriate for various types of soils. These studies illustrate why the amount of water surge should be used as an evaluation metric for grouting effectiveness. For the grouting materials' slurry to permeate the soil, they must have a particular level of grout ability [19]. Technical problems including poor hole formation and challenging slurry diffusion must be addressed by the grouting procedure. For different flowing water circumstances that may arise during grouting, [PERSON] et al. [18] recommended the selection criteria for injection materials and grouting volume per meter. [PERSON] et al. [20] studied the effect of grouting for a tunnel in a case study. The findings demonstrated that minimizing the amount of water surge is mostly achieved by the displacement of the support structure and the thickness of the curtain grouting. The tunnel's ability to stop water rapidly is essentially unaffected by a grouting thicknesses greater than 5 m.  
The objective of this study is to stabilize an expansive soil with (5, 7 and 9%) lime, cement, and silica fume to reduce the free swelling and swell pressure. A grouting technique is used through a small-scale model to improve the bearing carrying capacity, as a technique that can be used in the field. The methodology adopted in this paper presents a practical method of utilizing cement, lime, and silica fume as grout to swell soil, so as to enhance its properties. Thus, the suggested method of applying a stabilizer through grouting can be considered as a new method.
## 2 Materials and Methods  
### Data Sets and Data Processing  
The LUCAS 2009 dataset [32], a flagship outcome of the EU-led Land Use/Cover Area frame Survey, is recognized as one of the most representative continental-scale environmental databases in Europe due to its stringent soil sampling and analytical standards. Implementing a systematic 2 km \(\times\) 2 km grid design, the survey spans the 25 EU Member States and adjacent regions, encompassing approximately 19,000 locations where topsoil (0-20 cm) was sampled with fine granularity. At each site, composite sampling was rigorously applied: five sub-samples were collected in a cross pattern within a 2 m radius centered on the geo-referenced point and subsequently pooled to form a 0.5 kg topsoil sample, thereby objectively representing the soil properties of roughly 4 m\({}^{2}\) of land [33]. The sampling network covers diverse land-use categories, including arable land, grassland, and forest, with a particularly high proportion of agricultural sites.  
During laboratory analysis, each soil sample underwent standardized pre-treatments, including air-drying, homogenization, and quality control, before being systematically characterized for fifteen core parameters: texture fractions (clay, silt, sand), chemical properties (pH, organic carbon, carbonates, total nitrogen, available phosphorus, and potassium), physical structure (coarse fragment content), and functional attributes (cation exchange capacity). Multispectral reflectance spectra were additionally acquired for a subset of samples, providing multi-dimensional inputs for subsequent soil-health assessments and carbon-stock modeling. Although the dataset's sampling density (one point per 4 km\({}^{2}\)) and its emphasis on agricultural land impose constraints on fine-scale ecological studies and analyses of non-agricultural soils, its rigorous stratified sampling scheme, harmonized analytical protocols, and open-access policy render it an indispensable benchmark for evaluating EU agricultural policies, investigating the soil-degradation-climate interactions, and validating remote-sensing inversion models. To date, it continues to play an irreplaceable role in environmental science, agricultural management, and carbon-cycle research.  
The rigorous hierarchical sampling framework, standardized analytical methods, and open-access nature of the LUCAS dataset provide a robust foundation for this study. This methodological integrity ensures the reliability of our model's performance evaluation while minimizing the propagation of errors originating from data inaccuracies. A critical consideration, however, is the dataset's pronounced skew toward agricultural land cover. Accordingly, the generalizability of our findings to extensive non-agricultural ecosystems warrants further investigation, representing a clear and compelling avenue for subsequent research efforts.
# Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression
## 3 Results and Discussion
### Limitations  
The results show that the proposed algorithm can realize better results than can the existing methods; however, there remain several limitations. Firstly, if there are errors in orthorectification or image registration, incorrect extraction can be performed during selection of the training pixels in the preprocessing step. Although RF regression is robust to training data, some color relationships can be established incorrectly. Secondly, our method retrieves more color values than do the other methods, but, if the structure is complex, it contains somewhat turbid colors. The histogram specification for the reference color image is performed by post-processing, but there are still limitations. Thirdly, in this study, aerial images three years apart are used. However, further verification is needed to determine the extent of the period in which the colorization can properly be performed. Finally, our method is established by directly correlating color relationships between the input grayscale image and the reference color image, making it dependent on the availability of reference color aerial imagery of the same input area with matching seasonal characteristics. Consequently, when suitable color aerial images are unavailable, colorization may fail.
## 6 Comparison and Verification of Numerical and Measured Data  
The on-site temperature sensor selects a copper-constant thermocouple. The thermocouple is connected to the DATATAKER DT85G, and the DT85G is connected to the computer to form an automatic temperature data detection and acquisition system during the freezing construction process. The temperature acquisition system is shown in Figure 20.  
Because the model of COMSOL numerical simulation is established basically according to the size of field construction, and the arrangement of temperature measuring points is consistent completely with the field measurement, the soil parameters and thermal physical parameters are designed strictly according to the similarity theory, so the numerical simulation results can be compared with the field measured data. Of course, there will be some errors in the comparative analysis of the two due to the complexity of field construction, but this error is acceptable completely within a certain range.  
In order to verify the validity of the numerical analysis, the temperature comparison curves between numerical results and field measured results of T1-6, T2-3, and T2-4 points are drawn are drawn in Figure 21. And the detailed datas are shown in Table 6.  
Figure 21: Numerical simulation and actual measurement verification of T1 and T2: **(a)** T1-6; **(b)** T2-3; **(c)** T2-4.  
Figure 20: Temperature acquisition system.  
It can be seen from Figure 21 that the temperature difference between the simulated and measured points is small, and most of the time errors do not exceed 2.5 \({}^{\circ}\)C. A certain deviation occurs in 25-35 days, and the maximum error is about 3 \({}^{\circ}\)C. The soil temperature is maintained near the freezing temperature for a period of time, while the latent heat of phase change occurs when the soil temperature drops to 0 \({}^{\circ}\)C in the freezing stage. However, the numerical simulation model in this paper does not consider the latent heat of phase change, which causes it to not be maintained near the freezing temperature of the soil and continues to slowly cool down. Therefore, there is a certain error between the temperature value and the measured value in this stage. At the end of the active freezing period of about 150 days, the measured temperature has a small amount of recovery because the preparation of the opening construction at this time has an impact on the frozen wall. There is a slight deviation between the simulation results and the measured temperature values, but the overall cooling trend is consistent with the measured values. The influence of other external factors such as seepage of groundwater, formation pressure, the stress state of soil under the same temperature, project construction, and so on. Some errors between the measured data and the simulation results are acceptable. It can be considered that the numerical model can describe the development law of the temperature field accurately.
# Plant Exosomal Vesicles: Perspective Information Nanocarriers in Biomedicine
## 4 Biochemical Characterization of Nanovesicles
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plant Species** & **Name of Protein** & **Protein Function** & **References** \\ \hline Thale cress (_A. thaliana_), Kenya violet (_C. plantagineum_), orange (_Cirus sinensis_) & Patellin 1 and 3 (PATL1, PATL3) & Cell division cycle & [35,43,45] \\ grapefruit (_C. parraluse_), bitter orange (_C. aurantium_) & & & \\ \hline Thale cress (_A. thaliana_), orange (_C. aurantium_) & & Defense responses, exocytosis, intracellular protein transport, vesicle fusion & [35] \\ \hline Thale cress (_A. thaliana_), orange (_Cirus sinensis_) & H(+)-ATPase 1 and 10 lemma (_C. limon_) & In transmembare transport, regulation of intracellular pH & [35,45] \\ grapefruit (_C. parraluse_), bitter orange (_C. aurantium_) & & & \\ \hline Thale cress (_A. thaliana_), orange (_Cirus sinensis_), Lemon (_C. limon_), grapefruit (_C. parraluse_), bitter orange (_C. aurantium_) & Phospholipase D alpha and delta (PLD\(\alpha\), PLD\(\delta\)) & Regulation of abscisic acid-activated signaling pathway, programmed cell death & [35,45] \\ \hline Thale cress (_A. thaliana_) & GDSL esterase/lipase ESM1 (ESM1) & Glucosinolate and lipid catabolism & [35] \\ \hline Thale cress (_A. thaliana_) & Protein NRT1/PTR (NPF2.10) & Glucosinolate transport & [35] \\ \hline Thale cress (_A. thaliana_), Kenya violet (_C. plantagineum_), orange (_Cirus sinensis_), Lemon (_C. limon_), grapefruit (_C. parraluse_), bitter orange (_C. aurantium_) & Heat shock 70 kDa protein 3 (HSP70-3) & Chaperone cofactor-dependent protein refolding, stress responses & [35,43,45] \\ \hline Thale cress (_A. thaliana_) & L-ascorbate peroxidase 1 (APX1) & Responses to oxidative stress, lignin and phenylpropanol biosynthesis & [35] \\ \hline Kenya violet (_C. plantagineum_) & Beta-galactosidase (BGaI3) & Carbohydrate metabolism in plants & [43] \\ \hline Kenya violet (_C. plantagineum_) & Peptidylproylyl isomerase (PPIase) & Chaperone-dependent protein refolding & [43] \\ \hline Kenya violet (_C. plantagineum_), orange (_Cirus sinensis_) & Coatomer protein complex (COP) subunits & Membrane transport in the Golgi apparatus & [43,45] \\ grapefruit (_C. parraluse_), bitter orange (_C. aurantium_) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)overestimated. Moreover, the plant exosomes are enriched in the \"tiny\" RNAs of 10 to 17 nucleotides in the length, but their origin and functions still remain unknown [49]. The composition of secondary metabolites in the plant nanovesicles is still not well understood, but it is assumed that their presence may be related to the hydrophobicity of the vesicles themselves [61]. In dependence on the biochemical characteristics of the source, they contain the flavonoids, such as naringin and naringin, ascorbic acid, and other compounds [62]. In addition, several proteins whose function is associated with the