esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Low-Power Cellular Technologies for IoT
### 5G and LPWAN Integration  
The Fifth Generation Cellular Network (5G) is the most recent cellular IoT standardisation effort. The 5G development agenda was not to replace cellular LTE IoT technology as it has dominated the IoT market but to meet the growing demand for network capacity and coverage. 5G is projected to reach 3.5 billion subscriptions in 2026--exclusive of IoT--according to the 2020 Report by Ericsson [72]. In the same report, the 2026 IoT connection outlook based on NB-IoT and LTE-M was estimated to account for 45% of mobile IoT deployment. Hence, they were left out in the first 5G new radio (NR) release since MTC is already offered in LTE [73]. In 3 GPP release 15, LTE could offer reliability as high as 99.99%. However, to meet the LPWAN needs in terms of 5G standards, 3 GPP added features that allow cellular LPWAN to be seamlessly integrated into 5G with provisions for potential coexistence enhancement to be considered. The NR frequency band, numerology, beamforming, and duplex mode features are research areas of interest when integrating MTC into NR. Network slicing, network sharing, and machine learning are other research areas of interest to determine a secure means of leveraging the public network infrastructure to deploy 5G private networks [30].  
5G is suitable for low latency applications. The ultra-reliable and low-latency (uRLLC) teleprotection and remote access services in utility networks are examples of the benefitsof using 5G to complement other LTE technologies. In the 3 GPP release 16 and above, 5G NR is being evaluated to support the IoT deployment in the unlicensed spectrum and the possibility of achieving integrated access backhaul (IAB) and unified non-terrestrial networks based on satellites to scale network coverage to hard-to-reach locations. The International Telecommunication Union (ITU) has since 2018 established a focus group to drive a vision beyond 5G by formulating the requirements of network 2030 and beyond [74].
## 2 The Generation of 3D Microstructure Models
### Volume Fraction Analysis  
Five groups of DP steel microstructure models were obtained by the method above (Table 1). Figure 2 shows the simulated DP steel microstructures and the initial RCPS, with an increasing volume fraction of ferrite phase, i.e., 10%, 30%, and 50%. In the top of Figure 2, three initial RCPS with different volume fractions of ferrite phase, i.e., 10%, 30%, and 50% are given. In the bottom of Figure 2, three generated DP steel microstructures with a corresponding volume fraction of ferrite phase, i.e., 10%, 30%, and 50% are shown.  
Figure 1: Two-dimensional illustration for constructing dual-phase (DP) steel microstructure with the Voronoi modelling method based on the random close packing of spheres (RCPS-VM): (**a**) DP RCPS; (**b**) computing the Voronoi diagram; (**c**) DP steel microstructure.  
In Table 1, we can see that the mean volume of grains is about 1.2, which is slightly larger than that of its initial sphere (i.e., 1). This is due to the space gap between the spheres in RCPS (Figure 1a), which is filled when the Voronoi diagram in Figure 1b is formed. Nevertheless, because the volumes of both phases increase, the volume fraction does not significantly change, as shown in Figure 3, which means that our approach can precisely control the volume fractions of individual phases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group No.** & **1** & **2** & **3** & **4** & **5** \\ \hline Volume fraction of ferrite phase spheres & 0.1 & 0.2 & 0.3 & 0.4 & 0.5 \\ Volume fraction of ferrite phase grains & 0.1017 & 0.2019 & 0.3072 & 0.4071 & 0.5018 \\ Mean number of grains & 6425 & 6416 & 6423 & 6411 & 6435 \\ Mean number of ferrite phase grains & 1031 & 1287 & 1958 & 2607 & 3230 \\ Mean volume of grains & 1.2362 & 1.2374 & 1.2362 & 1.2405 & 1.2361 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of each group model.  
Figure 3: The relations of volume fraction between ferrite phase grains and corresponding spheres (the reference line represents the relations when these two volume fractions are equal).  
Figure 2: Simulated DP steel microstructure (**d–f**) and initial RCPS (**a–c**), with an increasing volume fraction of ferrite phase, i.e., 10%, 30%, and 50%.
## 5 Conclusions  
1. A reinforced UHPFRC beam-column joint has a higher initial cracking strength and shear carrying capacity. Furthermore, the integrity of UHPFRC joint specimens is superior to NC joints when they eventually fail.
2. A suitable configuration of the transverse closed stirrup can enhance the shear carrying capacity owing to restraining occurrences and development of cracks. The shear bearing capacity of exterior joints is smaller than that of interior joints with the same sectional area and reinforcement. The shear strengths of exterior joints are approximately 80-90% of those of interior joints.
3. The use of HRB600 grade high-strength steel bars with relatively high yield strain and low elastic modulus can improve the ductility and strength degradation of UHPFRC joints.
4. The shear resistance mechanism of the reinforced UHPFRC beam-column joint consists of the diagonal strut and truss mechanisms. Steel fibers, such as the dispersive reinforcement bars, constitute the horizontal and vertical ties in the truss mechanism.
5. Based on test results, a formula (Equation (14)) was proposed, which can predict the shear bearing capacity of reinforced UHPFRC and steel fiber high-strength concrete joints. In the formula, the contribution of steel fibers to shear carrying capacity of joints was determined based on the test bond strength between steel fibers and UHPFRC matrix.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
The work described in this paper was supported by the National Natural Science Foundation of China (Project No. 51378095) and the Science and Technology Development Plan of Jilin Province (Project No. 20180101064]C).  
The authors declare that they have no conflict of interest regarding the publication of this paper.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
###### Abstract  
With the rapid development of information technology and the increasing application of UAV in various fields, the security problems of unmanned aerial vehicle (UAV) communication network have become increasingly prominent. It has become an important scientific challenge to design a routing protocol that can provide efficient and reliable node to node packet transmission. In this paper, an efficient Digital Signature algorithm based on the elliptic curve cryptosystem is applied to routing protocol, and an improved security method suitable for on-demand routing protocol is proposed. The UAV communication network was simulated through the NS2 simulation platform, and the execution efficiency and safety of the improved routing protocol were analyzed. In the simulation experiment, the routing protocols of ad-hoc on demand distance vector (AODV), security ad-hoc on demand distance vector (SAODV), and improved security ad-hoc on demand distance vector (ISAODV) are compared in terms of the performance indicators of packet delivery rate, throughput, and end-to-end delay under normal conditions and when attacked by malicious nodes. The simulation results show that the improved routing protocol can effectively improve the security of the UAV communication network.  
U  
A _et electronics_
# Dynamic Rating of Three-Core XLPE Submarine Cables for Offshore Wind Farms
## 2 Dynamic Cable Rating
### Determination of Cable Parameters  
Thermal and electrical parameters are used to perform a temperature analysis and are calculated according to IEC 60287-1-1 [10] and IEC 60287-2-1 [11]. The thermal parameters represent the heat flow through the materials of the cable, and the electrical parameters represent the losses that contribute to heating.
# Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
## 1 Introduction
The main contribution of this paper is as follows:  
* Initially, we use a dataset named CSE-CIC-IDS2018 [29] on Amazon Web Service (AWS) for training and testing, which are performed once per iteration. The same training and test data are considered for all models to provide a fair comparison between them. Eighty percent of the dataset is assigned to training set, and 20% is assigned to the test set. * Model creation can be defined as feature selection, implementation, refinement, and comparison. We propose a 5G satellite-based UAV model. We implemented security based on ML algorithms in gateways. To increase the accuracy of our system model as well as implementing it in the real world, we consider the features that we mention here. Some of the most important features include flow duration, total packets in the forward and backward direction, maximum and minimum size of the packet in the forward and backward direction, average and total size of the packet in forward direction, standard deviation packet size in forward and backward direction, etc. We consider zero (0) for normal and one (1) for attack records. * In order to provide security in this paper, data packets are encrypted initially, and then ML algorithms are used to increase the level of accuracy of packets to identify which one is the correct packet and which one is fake or attack packet. The ML algorithms such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), KNN, Decision Tree (DT), Gaussian Naive Bayes (GNB), Stochastic Gradient Descent (SGD), and K-mean are used. * Finally, we compare the output of the above ML algorithms for above-mentioned attacks based on their precision, recall, F1-score parameters, accuracy rate, false-negative rate, correctly classified records, and incorrectly classified records, which will be explained in Section 4. The remainder of the paper is organized as follows. Section 2 describes the background of satellite backhaul connectivity based on the 5G system. Section 3 presents the system model and IDS for satellite-based UAV security. Section 4 is related to an intrusion detection system based on ML approaches to detect various types of attacks. Section 5 provides the experimental results using ML techniques for various attacks. Section 6 provides discussion on the experimental results obtained using ML, while Section 7 presents the future works, and finally, Section 8 provides the conclusion.
## 1 Introduction
### Critical Drilling Operations
#### 1.2.1 Crisis Detection  
The most important type of decision support is the just-in-time detection of an upcoming critical situation [2]. Note that in contrast to crisis prediction, described later, missing the detection of a critical situation leads to inevitable damages at physical entities, injury for humans, and last but not least financial losses.
## 2 Problem Formulation and Framework
### Framework  
The present work is based upon two main fields of research, the construction of SHI and failure prognostics. Consequently, the complete framework is depicted by Figure 1.  
Step 1: The system operation parameters were measured by various sensors. These sensors usually included temperature, pressure, flow, liquid level, viscosity, etc. They were conventional sensors installed in ship systems. The measurement data needed to be processed before being used for failure prediction. The data processing technology included outlier filtering and data imputation, and those processes were completed by the alarm and monitoring system (AMS) at the ship's end. The dataset cited in this paper came from the AMS, meaning it was processed data. To meet the needs of green and economic navigation, the ship system usually includes a variety of working modes. Using the fuel supply system  
Figure 1: Schematic diagram of the proposed method for failure prognosis framework.  
as an example, it usually includes two working modes. Heavy oil mode is adopted for normal navigation in the ocean. When sailing in ports or special areas, the ship needs to switch to light oil mode in order to meet the demand for low emissions. The change of working modes leads to a great change of system state parameters and significantly affects the prediction results. This article studied failure prediction under one working mode, so the data obtained need to be classified and stored according to working modes.  
Step 2: To further characterize degradation characteristics and improve prediction accuracy, for multivariate feature data, the DKPCA method was adapted to reduce the dimensions of the data, and the SAE method was used to fuse the processed data to obtain the SHI. The DKPCA implemented dynamic dimension analysis according to the change of the system state parameters, and the SAE could map multiple features to a single HI.  
Step 3: The VMD method was used to decompose the SHI. According to the characteristics of the system degradation, the SHI sequence was decomposed into global degradation, local self-healing, and local interference signals.  
Step 4: According to the characteristics of different decomposed signals, different prediction methods were selected to achieve hybrid prediction. According to the global degradation characteristics, considering the influence of the uncertainty, the RVM method was used to achieve the prediction. Compared with RVM, the LSTM prediction method has greater advantages in processing strong periodic and nonlinear signals. Therefore, LSTM is used to analyze local self-healing and interference signals.  
Step 5: The health prediction was achieved by fusing the prediction results. The decision was made according to the change of the health condition.
## 3 Results and Discussion
### Discussion of the Method Choice  
We chose the biophysical (also called indicator-based [114]) method to avoid underestimation, to which the use of sociocultural methods (according to ref. [20]) could lead. Studies comparing biophysical and sociocultural values to assess local climate change mitigation services are rare (e.g., ref. [115]). Biophysical values quantify biotic and abiotic configurations related to the provision of ESs [18]. For our three selected ESs, we sought suitable indicators that have been recommended in official reports or used in a number of other studies (see Section 1.3). From the range of suitable indicators, we selected those suitable for the scale of the entire urban administrative territory and in the lookup table approach, which we preferred because of its ease of use in practice. The methods and data used for their quantification were based on the results of previous projects of the Czech Ministry of Life Environment (Czech Carbo, Czech Terra, habitat valuation of the Czech Republic, and valuation of ecosystem functions and services of the Czech Republic) and the Interreg project Bidelin, in which the authors of this paper participated.  
Figure 7: Urban–rural gradients of three EFs (biodiversity, evapotranspiration, and carbon production) and their gradient related to the distance from the center of urban core area and main watercourse visualized for Liberec and Dééin administrative territories.  
Because the urban administrative territory consists of clearly definable segments to which the values and approaches apply, the performance of ecosystem functions can be calculated for each segment. Although most lookup table methods use land-cover data as a basis [116], in most cases, these data are not detailed enough to assess EFs at the urban administrative territory level [32]. In this case, the European Commission suggests using maps created by combining land-cover maps with habitat databases [20]; we used this principle to create a detailed habitat layer and augmented it with other data sources, some of which were results from the Czech case study involved in the ESMERALDA project [117] to make it more precise. To capture the influence of a higher hierarchical level of landscape structure and its complexity, we calculated the connectivity indicator, which indicates the distance (within the urban area structure) to the nearest natural habitat within which the EFs are performed without negative human influence. Ecosystem function values are assigned to individual habitat types or their groups, which makes the method easily applicable and allows transferring the values to other European countries with similar climatic conditions (and, thus, habitat types).
# Design of the CIEMAT Corrosion Loop for Liquid Metal Experiments
## 3 CiCLo-C Loop: Requirements and Design  
There are still several technological issues of paramount importance for the practical design and implementation of liquid metal loops in a fusion device, mostly related to material compatibility, component integration, and engineering tool validation [34]. For all these reasons, the setting-up of new corrosion loops is extremely welcomed in the field of fusion materials.  
The facility consists of a closed isothermal loop placed in a vertical arrangement to facilitate emergency draining by gravity. The liquid metal flows through a permanent magnets pump (PMP) and one exchangeable part where a test section (TS) is installed for material compatibility experiments. One of the most important parameters is the mass flow, which should ideally cover a wide range. The possibility of working at different velocities thanks to the PMP will not only ensure the coverage of different BB cases but also the study of PbLi compatibility behavior in different reactor areas. Moreover, the design of this test section provides the possibility of studying the compatibility with other materials (coatings, functional materials, irradiated materials, etc.) and testing new experimental devices [26].  
For safety reasons, the loop is located in a collecting trap made of stainless steel (3 mm width) with the main dimensions of the loop rack (1 m \(\times\) 3 m) and a board of 100 mm. To guarantee safe drainage by gravity, the entire system has an inclination of 4 degrees toward one of the loop corners connected to a dedicated vessel. The tube material is stainless steel, 316 L, with well-known behavior [13; 18], and the thickness of the tube is 3 mm. The total PbLi inventory in the CiCLo loop is 7 L. The loop is connected over a flange joint, which is sealed by a stainless-steel spiral wound.  
All connections are executed by a Swagelok connection or a flange joint.
# Performance and Requirements of GEO SAR Systems in the Presence of Radio Frequency Interferences
## 3 Bistatic Scattering RFI Impact on GEO SAR
### Impact of Bistatic Scattering RFI on GEO SAR
#### 3.1.2 Specular Bistatic Scattering Case  
At times, if \(\theta_{1}=\theta_{2}\) and \(\psi_{s}=0\), a specular reflection happens. In this case, the power of the RFI will concentrate in the scattering direction of GEO SAR and the received RFI signal from the spaceborne system is very strong. In the specular scattering case, like a mirror reflection [32; 39], \(\sigma_{B}^{0}\) can be expressed as  
\[\sigma_{B}^{0}=\frac{4\pi A_{gs}}{\lambda^{2}}, \tag{13}\]  
where \(A_{gs}\) is the specular scattering area on the ground. Here and in the following part, we also use \(\lambda\) as the wavelength of the spaceborne radio system for simplicity. As the targets within the same range resolution cannot be distinguished by the range delay difference, ideally, the maximum area of \(A_{gs}\) can be approximately determined within the range resolution bin \(\rho_{r}\) by the cross-section area between the ellipsoid formed by GEO SAR and the spaceborne system and the Earth ellipsoid, which has been shown in Figure 5.  
Based on the above analysis, the considerably different behaviour of the two cases derives from the different bistatic scattering coefficients which are shown in (11) and (13). In the specular scattering case, the bistatic scattering coefficient is large, while it will be very small in the non-specular scattering case.
# Robust Space Time Adaptive Processing Methods for Synthetic Aperture Radar
## 2 SAR Simulation Models
### Basic STAP Method  
The covariance matrix of the array received signal is estimated through finite snapshots [25], assuming the snapshot number is \(L\), then the estimator of the covariance matrix from the received signal:  
\[\hat{R}=\frac{1}{L}\sum_{i=1}^{L}X(i)X^{H}(i) \tag{7}\]  
Here, \(H\) denotes the Hermitian transpose operator. Then the adaptive filtering is based on the minimum variance distortionless response (MVDR) criterion as follows:  
\[\left\{\begin{array}{rl}&\min(w^{H}R_{1}w)\\ &s.t\quad w^{H}a(\theta_{0})=1\end{array}\right. \tag{8}\]  
The equation aims to get the adaptive weight vector w for the minimum output power of the array under the constraint of keeping the output constant and can be solved using Lagrange multipliers [25]. The entire signal process flow of the multi-channel SAR is shown in Figure 2.
## 3 Method and Model
### Dataset
#### 3.1.1 IEMOCAP Dataset Audio  
IEMOCAP (interactive emotional dyadic motion capture) is a specialized multimodal emotion dataset designed to provide rich resources for research on human emotion expression and recognition. This study focuses on the dialogue portion of the dataset, which includes natural conversations recorded with ten professional actors. These dialogues take place in various contexts and aim to simulate emotional communication in real-life situations. The data includes scripted dialogues as well as spontaneous, emotionally rich interactions, covering a wide range of emotions such as anger, sadness, excitement, surprise, and more. The content of these dialogues holds significant research value and is essential for gaining a deeper understanding of human emotion expression and recognition.  
In the data preprocessing phase of this research, 3106 speech samples were selected from the dataset. These samples not only include the speech content but also record the duration of the speech and emotional evaluations. The emotional evaluation part was completed by 3-4 expert evaluators who categorized the emotion of each speech sample into six types: frustration, sadness, anger, neutral, happiness, and excited.
## 3 Possibilities for the Efficient Design of FEA for the Calculation of the Load-Notch-Strain Curve
### Reduction in the Number of Load Steps to Be Simulated  
Another way to reduce the simulation time is to decrease the number of load steps to determine the load-notch-strain curve for the hysteresis branch. If the number of simulated load steps is reduced, not every class a value for the load-notch-strain curve is calculated. In order to still be able to use the same algorithm for the fatigue life calculation with the same number of discrete load classes, the missing intermediate class values must be determined by interpolation or regression of the calculated load steps. For the interpolation or regression, three procedures are presented in the following:  
1. Interpolation by polygonal chain (polynomials of 1 st degree);
2. Regression with power function following the approach of [PERSON] and [PERSON] (Equation (3));
3. Interpolation with spline (polynomials of 3 rd degree).  
All three methods have in common the relationship \(\Delta\varepsilon(\Delta\sigma_{\text{el}})\) between the elastic-plastic strain range and the elastic stress range results, which is used to describe the load-notch-strain curve. The discretised load-notch-strain curve for the hysteresis branch is obtained by inserting the elastic stress ranges at the class limits in this relationship.