esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Parameters Optimization in Fabricating PES Electrospun Nanofibers
#### 3.1.3 Response Surface Model Analysis for Average Fiber Diameter
We found that the increase of the tip-to-collector distance caused the decrease of the average fiber diameter at a low PES concentration of 28-29% (_w/v_) in this contour plot; however, the tip-to-collector distance exerted little influence on the average fiber diameter at a high PES concentration of 30-32% (_w/v_). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Coefficient** & **Standard Error** & **T-Value** & _p-Value_ & \\ \hline Constant & 301.33 & 8.50 & 35.46 & 0.000 & Significant \\ X\({}_{1}\) (LiCl concentration) & \(-\)51.67 & 4.25 & \(-\)12.16 & 0.000 & Significant \\ X\({}_{2}\) (PES concentration) & 20.92 & 4.25 & 4.92 & 0.000 & Significant \\ X\({}_{3}\) (Feed rate) & 34.33 & 4.25 & 8.08 & 0.000 & Significant \\ X\({}_{4}\) (Tip-to-collector distance) & 2.92 & 4.25 & 0.69 & 0.505 & \\ X\({}_{1}{}^{2}\) & 18.71 & 6.37 & 2.94 & 0.012 & Significant \\ X\({}_{2}{}^{2}\) & 4.83 & 6.37 & 0.76 & 0.463 & \\ X\({}_{3}{}^{2}\) & \(-\)3.29 & 6.37 & \(-\)0.52 & 0.615 & \\ X\({}_{4}{}^{2}\) & \(-\)4.17 & 6.37 & \(-\)0.65 & 0.526 & \\ X\({}_{1}\)X\({}_{2}\) & 4.00 & 7.36 & 0.
# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
## 3 System Model of Cellular V2X Network
### Channel Model  
The propagation loss for the transmission link at each RSU considers pathloss, shadowing, and fast fading. The main propagation loss model for the two proposed RSU types is implemented and used according to (3) [13].  
\[\mathrm{G}=\mathrm{Antenna_{Gain}}-\mathrm{PathLoss}-\mathrm{Shadowing}-\mathrm{Fading} \tag{3}\]  
Table 1 outlines the applied assumptions about the channel model between VUE and RSU, and the parameters are considered by applying the 3 GPP TR SLS methodology accordingly [17; 18; 19].  
The pathloss model used in this study assumes the channel model between BS-type RSU and VUE according to the 3 GPP document [17], and the WINNER model for V2V channel assumption between UE-type RSU and VUE. For the BS-type path loss model, only LOS is applied, and for the WINNER + B1 Manhattan, grid layout urban microcell scenario used for the UE-type RSU, the antenna heights of VUE and RSU are assumed to be lower than the top of surrounding buildings, and LOS and NLOS cases are defined and calculated for all positions leading to the RSU [22]. Additionally, to form the shadowing correlation between RSUs, shadowing was applied to the BS-type RSUs using shadowing correlation coefficient 0.5 for inter-site shadowing and 1.0 for inter-sector shadowing.  
In the case of fading, the 3 GPP TR utilizes the ITU-R Urban Micro-Clustered Delay Line Models (CDL) with large, fixed parameters for NLOS [23] in the propagation model  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **5G-V2X** & **5G-V2X** \\  & **(BS-Type RSU)** & **(UE-Type RSU)** \\ \hline \multirow{2}{*}{Pathloss model} & 128.1 + 37.6 log10(R), & WINNER + B1 \\  & (R in kilometers) & Manhattan grid layout \\ \multirow{2}{*}{Penetration loss} & 0 dB & 0 dB \\  & Log-normal Distribution & Log-normal Distribution \\ \multirow{2}{*}{Shadowing} & Mean: 0 dB & Mean: 0 dB \\  & St. Dev.: 8 dB & St. Dev.: 3 dB \\ \multirow{2}{*}{Decorrelation distance} & 50 m & 10 m \\  & 3 GPP Spatial Channel Model & ITU-R Urban Micro-Clustered \\ \multirow{2}{*}{Fading} & (SCM) NLOS & Delay Line Models (CDL) NLOS \\ \hline \hline \end{tabular}
\end{table}
Table 1: BS/UE-type RSU channel model parameters.  
of the UE-type RSU, while the propagation model for the BS-type RSU utilizes the 3 GPP spatial channel model (SCM) NLOS to calculate the radio gain.
## 3 Intrusion Detection Systems Based on the Federated Learning
As each facility supports a different process, the collected technological data are different for each process facility, and the technological data are vertically partitioned. In the case of vertically partitioned data, the aggregation server initiates the training process. All agent-hubs actively participate in the training by calculating local models and then they send obtained results to the aggregation server. It constructs the global analysis model by selecting appropriate local models and sends updates to each intelligent agent-hub after each aggregation round. Figure 3: The intrusion detection system for the cyber-physical system with vertically partitioned data. When constructed in such a way, the intrusion detection system does not have direct access to data, only to the model parameters, and each process facility is isolated from others and does not share data with other participating facilities. It should be noticed that the process of training and logical inference in the case of vertically partitioned data differs from the training process in the case of horizontally partitioned data. In the case of the horizontally partitioned data, as all clients have the same set of attributes, the global model is delivered to each client, and they could analyze new data samples independently from each other. In the case of the vertically partitioned data, when clients have different sets of attributes, the global model is distributed across the clients, and each client has a part of the model that relates to the data attributes the given client has. Thus, in order to infer about a new input sample, all clients need to cooperate and be available during the inference process. This fact means that, in order to preserve privacy of the input and training data, additional privacy preserving mechanisms are needed, and existing solutions of the federated learning for vertically partitioned data assume application of the privacy enhancing techniques based either on encryption [40,41] or differential privacy [42,43]. In the case of intrusion detection systems, these peculiarities of federated learning require that the hubs of all process facilities are to be available when making inference about a sample. To detect the anomalies, the agent-hub calculates the local results when a new sample is received and sends the results to the aggregation server to make the final decision.
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 4 AI for UAV Security
### UAV Cryptography  
Cryptography is one of the key technologies for securing UAV communication and protecting the confidentiality, integrity, and authenticity of communication data. However,  
Figure 15: Identity authentication procedure for UAV security.  
with the rapid development of computing technology, traditional cryptography algorithms face the risk of cracking. To address this challenge, researchers have continued to explore new cryptographic techniques and methods.  
[PERSON] et al. reviewed the application of lightweight cryptographic algorithms in IoT systems, including UAV communication [180]. In 2018, [PERSON] et al. proposed Dronecrypt, an efficient encryption framework designed for small UAVs [181] that significantly reduces energy consumption while maintaining high security. In 2024, [PERSON] et al. proposed a novel method that employs an autoencoder neural network trained using the MNIST dataset to generate unique cryptographic keys [182]. These keys are integrated with traditional encryption algorithms to enhance data security during transmission. [PERSON] et al. proposed a DL-based hybrid arithmetic optimization algorithm with min-max normalization, feature selection, and threat detection for hyperparameter tuning to increase security and performance [183].  
However, traditional AI models have limited generalizability, making it difficult for them to handle new cryptoanalysis attacks [184]. Additionally, compatibility issues in integrating traditional AI and asymmetric encryption systems and the lack of unified AI-assisted encryption system evaluation standards [185] are two major problems. The dynamic changes in IoUAV and the impact of equipment resource constraints on algorithmic selection significantly increase the complexity of encryption algorithms, as shown in Figure 16.  
LLMs possess powerful data analysis capabilities that allow them to thoroughly evaluate existing asymmetric encryption algorithms, identify potential loopholes and weaknesses, and optimize the selection of encryption algorithm parameters. Additionally, LLMs can develop specialized benchmark tests for cryptographic fundamentals, encrypted traffic detection, and so on. These benchmark tests can provide a unified evaluation standard for integrating AI and cryptography methods. LLMs can also provide a natural language interface to lower the threshold of using complex cryptosystems [182], assisting in the design of lightweight and highly secure cryptographic solutions.  
Figure 16: Encryption for UAV data integrity.
## 3 Materials and Proposed Method
### Image Data Augmentation  
An image is depicted as an array of pixels that utilize grey scales or RGB values during image preprocessing. To maximize the pace of learning, the picture data should be scaled from the lowest to the highest normalization. One of the most essential image preparation approaches is image data augmentation, which may be performed both offline and online. Small datasets benefit from offline augmentation approaches, but large datasets benefit from online augmentation techniques. Because computing the model gradients using the complete huge dataset takes a long time, the data is cycled over in mini-batches during each optimization iteration with the batch size determined.  
The image augmentation technique was introduced during the preprocessing procedure in this study, which generates more training data from the original data. The procedure does not require extra memory for storage, and the generated images are small batches that are discarded after the training of the model. The image augmentation techniques used are: (1) Horizontal RandomFlip; (2) RandomRotation (0.1); (3) RandomZoom (0.1); (4) Resizing (h = 32, w = 32); (5) RandomContrast (0.1)  
It was discovered that the image augmentation technique introduced assisted in preventing model overfitting and enhanced the learning capacity thereby decreasing model training time complexity. The framework of the proposed classification transfer learning models with the image data augmentation method is shown in Figure 2.  
Figure 2: The framework of the proposed classification transfer learning models with the image data augmentation method.
# Galileo: The Added Value for Integrity in
## 3 Integrity Algorithm  
In hostile environments, the limited number of observables and the presence of multiple large outliers make the design of the quality monitoring algorithm a challenge. The effectiveness of RAIM algorithms is usually assessed considering reliability and separability. The first one is related to the detection capability of the algorithm, whereas the second one is related to the capability to properly identify an outlier and represents the risk of incorrectly flagging a \"good\" measurement as an outlier. In the RAIM standard [18], for example, the integrity requirements are quite demanding, since both the required detection probability and false alarm rate have to be met for any location and time.
## 4 Conclusions  
The influence of the SPI and EPI on the band gap energy \(E_{g}\) and the phonon energy \(\omega\) and damping \(\gamma\) of Eu and U chalcogenides is discussed for different temperatures. For \(I>0,R>0\)\(E_{g}\) decreases with increasing SPI and EPI, whereas for \(I<0,R>0\), there is a competition; \(E_{g}\) increases with raising the EPI and decreases for enhanced SPI. The magnetic field dependence of \(E_{g}\) for ferro- and antiferromagnetic semiconductors and different signs of \(R\) is observed.  
The temperature dependence of the phonon energy and damping is calculated for the phonon modes \(\omega\) = 445 cm\({}^{-1}\) in EuO (\(R<0\)) and \(\omega\) = 151 cm\({}^{-1}\) in EuSe (\(R>0\)). By softening the phonon mode, the SPI and EPI lead to reducing \(\omega\) in EuO, whereas by hardening \(\omega\), EuSe is enhanced with increasing EPI and reduced with SPI. Both the SPI and EPI lead to the increasing of the phonon damping \(\gamma\) in EuO and EuSe.  
It should be noted that, recently, two-dimensional magnetic semiconductors such as iron and ruthenium trihalides were determined to be important for application in spin-based devices.The effects of SPI and EPI on the properties of these 2D systems will be considered in a future paper.  
Conceptualization, J.M.W.; Software, I.N.A.; Formal analysis, A.T.A.; Investigation, A.T.A., I.N.A. and J.M.W.; Writing--original draft, J.M.W. All authors have read and agreed to the published version of the manuscript.  
A.T.A. and I.N.A. acknowledge financial support by the Bulgarian National Science Fund (contract number KP-06 PN68/17/BG-175467353-2022-04-0232).  
Not applicable.  
Derived data supporting the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors have no conflicts to disclose.
## 5 Results  
To validate the results, the outcomes provided by SIMONE are compared with the results obtained using MaritimeEXODUS, as it is recognized as a validation tool by current regulations.  
The simulations executed in both simulators for the same scenario revealed aspects that gave us insight and a better understanding of the evacuation problem. They are listed below.  
1. Contrary to what we initially expected, the evacuation process is not influenced very much by the size of the simulated population or its relocation throughout the simulation scenario.
2. Congestion due to a large influx of agents to a specific space at the same time (see Figure 3) greatly affects the total evacuation time, slowing it down or even causing a massive deadlock. In the case of MaritimeEXODUS, this paralyzes the execution of the process.
3. The method implemented in SIMONE to address optimal evacuation is based on the distribution of agents (load balancing) along the optimal routes identified (see Figure 3), thus ensuring that their estimated cost (time) is equalized.
4. The optimal distribution of agents throughout the optimal evacuation routes results in less congestion and, thus, a decrease in waiting times, also reducing evacuation times and the length of the evacuation.
5. The total time of an evacuation process is determined by the evacuation time employed by the last agent to evacuate the simulation scenario.
6. The process of executing an evacuation on a platform that dispenses a complete network of routes has a longer execution time (95 \(\pm\) 10 s) than the same process executed on the same network (75 \(\pm\) 10 s) as a result of a failure. The configuration of damage is emulated by disabling those vertices of the dynamic graph that correspond to the spaces of the scenario affected or inoperative due to the breakdown.
7. Disabling vertices simplifies the dynamic graph that emulates the scenario and, in view of the results, the reduction of the dynamic graph is greater when the damage caused by a failure is more significant. Thus, the corresponding simulation presents a lower computational load and less processing time.
## 3 Methods
### Task 7--Linear Regression for the Estimation of Tair from MODIS Satellite Data  
Using the created dataset mentioned above, containing both MODIS surface temperatures (LST) and ARPA air temperature (Tair) data, we have performed a linear regression analysis to better understand the correlation between the two sources of temperature measurement [1].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sensor ID**} & \multirow{2}{*}{**Lat [DD]**} & \multirow{2}{*}{**Lon [DD]**} & \multirow{2}{*}{**MODIS**} & **Tday** & **Tnight** & **Tday** & **Tnight** & **Tair** \\  & & & & **MODIS** & **MODIS** & **MODIS** & **MODIS** & **MODIS** \\  & & & **Date** & **Aqua** & **Aqua** & **Terra** & **Terra** & **Avg** \\  & & & & [°C] & [°C] & [°C] & [°C] & [°C] & [°C] \\ \hline
8007 & 44.9125 & 9.1950 & 30/03/2018 & 20.86 & 4.68 & 20.43 & 6.25 & 10.27 \\
8007 & 44.9125 & 9.1950 & 07/04/2018 & 19.57 & 6.11 & 17.46 & 5.11 & 10.39 \\
8007 & 44.9125 & 9.1950 & 15/04/2018 & 26.75 & 10.86 & 25.34 & 11.52 & 17.53 \\
8007 & 44.9125 & 9.1950 & 23/04/2018 & 24.61 & 10.63 & 24.46 & 12.92 & 17.59 \\
8007 & 44.9125 & 9.1950 & 01/05/2018 & 24.99 & 8.47 & 22.46 & 12.42 & 14.49 \\
8007 & 44.9125 & 9.1950 & 09/05/2018 & 20.90 & 8.48 & 20.20 & 10.13 & 14.44 \\
8007 & 44.9125 & 9.1950 & 17/05/2018 & 23.25 & 8.69 & 21.69 & 11.84 & 15.74 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Table containing LST MODIS and ARPA Tair (8-day average).  
Figure 12: Time-series of the 8-day average air temperature of the ARPA Monitoring Station in Fortunago, and of the LST detected by the MODIS sensors, interpolated at the location coordinates.  
It should be noted that the linear regression is not performed individually for each monitoring station, but rather as an areal regression considering all stations within the study area, with some exceptions explained below. We assumed as explanatory variables the temperatures from ARPA, which represent the \"true\" air temperatures. To evaluate the linear regression coefficients, we decided to exclude two monitoring stations: the one with ID 8002 since the dataset is not of great interest as it presents values for a small part of the dates considered (i.e., from January 2018 to September 2018) and the one with ID 8007, excluded in order to use its data for the validation of the model with the aim to test its the predictive capacity.  
The linear regression has a dual objective: firstly, to evaluate the agreement between MODIS LST and ARPA Tair, and secondly, to estimate in a spatially continuous way the air temperature from available MODIS data.  
The process of regression involves minimizing the residuals, which are the differences between MODIS-estimated temperatures and the ones actually recorded by ARPA stations. By building a linear regression model, we establish a mathematical equation that allows us to predict air temperature from MODIS surface temperature. The entire analysis was carried out thanks to a customized MATLAB version 24.2 (R2024b) script we specifically designed for this purpose.
## 7 Conclusions  
After several decades of extensive research, great progress has been made in the study of APS. The extraction methods and extraction rate of APS have been continuously improved. It has been found that water, microwave-assisted, ultrasonic wave, enzymatic hydrolysis, and other extraction methods can be combined to improve the extraction rate of APS. Depending on the extraction method and the degree of purification, the chemical composition and structure of APS can be confirmed by HPLC, GC, MS, and NMR. The chemical composition of APS mainly includes glucose, rhamn, galactose, arabinose, xylose, mannose, glucuronic acid, and galacturonic acid. However, the monosaccharide composition and the structure of sugar chains of APS, obtained by different extraction and purification methods, are different. Therefore, the extraction and purification methods of APS need to be improved continuously.  
As an important bioactive component of Astragali Radix, APS shows important pharmacological activities, including immunoregulatory, antitumor, anti-inflammatory, antiviral, and other activities. Currently, studies on the modification of APS mainly employ chemical methods, including sulfation, phosphorylation, selenation, and carboxymethyl, and suggest that structural modification can change the pharmacological activity of APS. However, studies on pharmacological activities of APS usually use crude polysaccharides, which does not allow the establishment of the structure-activity relationship. In addition, the molecular mechanisms of pharmacological activities of APS are still unclear, which also limits its further development and application. Therefore, separation and purification of APS should be improved. Subsequently, the structure-activity relationship of APS should be elucidated at the primary and secondary structure levels. Finally, the pharmacological activities and molecular mechanisms of APS should be studied with homogeneously purified APS.  
Conceptualization, J.W., J.J., and M.L.; investigation, J.W., L.S., and X.G.; writing--original draft preparation, J.W., X.G., and J.X.; writing--review and editing, J.W., M.Y., and M.L.  
This research was funded by the Science and Technology Innovation Guidance Project, Inner Mongolia (No. KCBJ2018040); National Nonprofit Industry Research (No. 201507002); the Fourth National Traditional Chinese Medicine Resources Survey Project (No. Caishe [2017] 66); the China Agriculture Research System (No. CARS-21); the Science and Technology Planning Project of the Inner Mongolia Autonomous Region, China (No. 201701040); the Standardization Project of Mongolian Medicine in Inner Mongolia (No. 2018-[008]); the Inner Mongolia Natural Science Foundation Project (No. 2018 LH03028); the Baotou Science and Technology Project (No. CX-2016-17); and the Scientific Research Foundation Project of Baotou Medical College (No. BYJJ-DF 2017-01).  
The authors are thankful to [PERSON] from Baotou Medical College, who taught us how to use the ChemDraw software.  
The authors declare no conflicts of interest.
# Improving Spaceborne GNSS-R Algal Bloom Meteorological Data
## 5 Conclusions
\begin{tabular}{l l} AUC & Area Under Curve \\ CYGNSS & Cyclone Global Navigation Satellite System \\ DDM & Delay-Doppler Map \\ GNSS & Global Navigation Satellite System \\ GNSS-R & Global Navigation Satellite System-Reflectometry \\ ML & Machine Learning \\ OA & Overall Accuracy \\ P & Pressure \\ PDF & Probability Density Function \\ PN & Pixel Number \\ ROC & Receiver Operating Characteristic Curve \\ RUS & Random Under Sampling \\ SNR & Signal-to-Noise Ratio \\ SP & Specular Point \\ SR & Surface Reflectivity \\ SRD & Solar Radiation Downwards \\ T & Temperature \\ TN & True Negative \\ ToP & Total Precipitation \\ TP & True Positive \\ WD & Wind Direction \\ WS & Wind Speed \\ \end{tabular}
# Progress on Single-Feed Quality Wideband Linear Wire Array
## 3 Driven Element
### Radiating Characteristics  
The WHEMS antenna is a bidirectional radiation antenna that can achieve an effective bidirectional gain of over 2 dBi within a bandwidth exceeding 158%, making it the widest-known bidirectional effective gain bandwidth [19]. The WHEMS antenna element can be represented by three electrical dipoles within a wideband of low frequencies (over twice the frequency bandwidth) [25, 26, 27, 28], as illustrated in Figure 5. These three dipoles are aligned  
Figure 4: (**a**) Real and imaginary parts of the calculated and simulated input impedances of the WHEMS antenna. (**b**) Input impedance of wire WHEMS antenna.  
to function as a small in-phase antenna array, which can achieve up to 7 dBi of antenna gain in the broadside direction [25]. This bidirectional feature, as shown in Figure 5c, provides better antenna gain and lower sidelobe potential as a driven element. The equivalent dipole array also allows designers to incorporate reflectors and directors to form a linear array, further improving the antenna gain.