esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Microstructural Evolution and Mechanical Property Characterization  
Microstructural analysis was performed using electron microscopy imaging, energy dispersive spectroscopy (EDS), and electron backscatter diffraction (EBSD). Electron microscopy imaging was conducted using Thermo Fisher Scientific (TFS), Inc. (Waltham, MA, USA) PRISMA scanning electron microscope (SEM) and a TFS, Inc., Helios Hydra 5 CX SEM. Image analysis of SEM images procured from the TFS PRISMA was performed using ImageJ v1.53k software to detail the change in phase particle size for 0 NA, 6 NA, and 12 NA. The images were thresholded to eliminate all features except the particles before measurement. From the data, the maximum Feret diameter was determined. For the analysis, at least 10,000 particles were analyzed for each case. Furthermore, the area fraction for each case was sampled. Using an aluminum standard, EDS was performed using a TFS, Inc., previously FEI, Helios NanoLab 600 SEM with an Oxford Aztec EDS detector. EBSD was conducted by JH Analytical using a TFS (Brno, Czech Republic) Apreo S SEM with an Oxford Instruments (High Wycombe, Buckinghamshire, UK) Symmetry S2 EBSD with an accelerating voltage of 30 kV. Using the data from EBSD analysis, grain diameter, hereby referred to as grain size, was evaluated using equivalent circle diameter.  
Tensile testing for this study was conducted using Product Innovation and Engineering, LLC's AMT mini-tensile machine at a positional rate of 250 \(\upmu\)m/min. For this study, the chosen tensile specimens were miniature tensile specimens, shown in Figure 3, as the samples can be strategically located in the deposit, and the smaller interaction volumes inherent with this sample configuration are more sensitive to defects present in the AM fabricated material. Miniature tensile specimens were fabricated from the center of a DED deposit in the XY plane using a Sodick, Inc. (Schaumburg, IL, USA) VL600 QH electrical discharge machine (EDM). Before tensile testing, each tensile specimen was hand polished to a metallurgical grid of 800 and measured such that all samples tested were in tolerance. Five tensile samples were analyzed for each natural aging case to ensure statistically relevant results. The data were combined with the dimensions of each corresponding sample to calculate the tensile characteristics.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen ID** & **Natural Aging (Days)** & **Artificial Aging** \\ \hline
0 NA & 0 & \\
2 NA & 2 & \\
4 NA & 4 & \\
6 NA & 6 & \\
8 NA & 8 & 325 \({}^{\circ}\)C @ 4 h \\
10 NA & 10 & \\
12 NA & 12 & \\
14 NA & 14 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specimen identification for this study corresponding to the natural aging time and subsequent artificial aging.
# Variable Structure PID Controller for Satellite Attitude Control Considering Actuator Failure
## 5 Conclusions  
In this paper, a variable structure fault tolerant PID controller was proposed. The system performance was improved by the variable structure, integral partition algorithm, and fault tolerant algorithm. The effectiveness and superiority were demonstrated by numerical simulation.  
The main contribution of this paper is that a controller combined with PID control, trajectory planning, variable structure, and fault tolerance was proposed; hence, the system has both a good convergence rate and strong robustness. The proposed methods are convenient for engineering practice compared with the methods cited in this paper.  
Based on the work of this paper, it could be concluded that: maintaining a high angular velocity is key to improving the system convergence rate, and by implementing a variable structure PD controller, this goal could be achieved; the integral partition algorithm could improve the system performance around its equilibrium point and avoid the issue of slowing down the system convergence rate; the key to solving the actuator is to detect actuator failure by the system state estimation algorithm first and to implement the control algorithm with global stability; the method proposed in this paper has a simple structure; hence, it is convenient for engineering practice, and the complexity to achieve better performance is transformed to stability analysis.  
In addition, it is worth noting that the failure of actuators is considered only partly in this paper, and for engineering practice, more actuator failures should be taken into consideration in later work.  
Conceptualization, Y.Q.; Data curation, H.J.; Formal analysis, X.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of Shaanxi University of Science and Technology (710021, 2022-04-23).  
This study does not involve any informed consent issues since there all the data used in this paper is simulated.  
If anyone wants the data availability of this paper, please send e-mail to [EMAIL_ADDRESS].  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Standard PD Controller** & **Finite Time Controller** & **Proposed Method** \\ \hline Convergence time & 120 s & 80 s & 50 s \\ \hline Steady accuracy & \(3\times 10^{-5}\) rad/s (velocity) & \(6\times 10^{-6}\) rad/s (velocity) & \(1\times 10^{-5}\) rad/s (velocity \(\omega\)) \\  & 0.17 degree (Euler angle) & \(10^{-5}\) degree (Euler angle) & 0.01 degree (Euler angle) \\ \hline Robustness & Has high frequency chattering & Has high frequency chattering & Does not rely on accurate model and robustness to disturbance torque \\ \hline Fault tolerant capability & Fault tolerant & None & Fault tolerant \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of the proposed method and existing methods.
# Data Augmentation Method Using Room Transfer Function for Monitoring of Domestic Activities
###### Abstract
Tasks involving MDA are often limited by the ability to obtain high-quality acoustic data, so this work aimed to improve the performances of machine learning models through data augmentation techniques. Specifically, the focus was on addressing the limitations of existing data augmentation methods, which either do not consider acoustic features or require additional data collection efforts (such as using RIRs) or introduce additional computational burden through simulations. In this study, we aimed to augment the dataset by leveraging the acoustic characteristics of sound. Particularly, sounds generated indoors are influenced by room transfer functions (RTFs). Hence, we devised a data augmentation technique that considers the physical characteristics of RTFs. Our proposed method does not require additional training for data augmentation and can be directly applied to the frequency domain, making it suitable for various indoor acoustic applications that use frequency data as a feature.
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 3 Plant State Space Models and Their Properties
### Properties of the Discrete-Time State Space Model
#### 3.3.2 Controllability and Observability of \(\Sigma_{\mathbb{G}}^{d}\)  
We will show that, for \(\mu>0\), the controllability and observability of the continuous-time plant model \((A,B,C,0)\) are preserved for the discrete-time model \((A^{d},B^{d},C^{d},D^{d})\) under the considered transformation (28). In order to prove this statement, we use the Hautus test, which implies that \((A,B)\) is controllable if and only if the following condition holds:  
\[\operatorname{rank}\left[\begin{array}{cc}\mu_{I}(A)I-A&B\end{array}\right] =4\,,\quad l=1,2,3,4\,, \tag{38}\]and \((C,A)\) is observable if and only if  
\[\text{rank}\left[\begin{array}{c}\mu_{I}(A)I-A\\ C\end{array}\right]=4\,,\quad l=1,2,3,4\,. \tag{39}\]  
In order to prove the required equivalence, we start with (31); that is,  
\[\frac{1}{\lambda+1}(\lambda I-A^{d})(\mu I-A)=\mu\frac{\lambda-1}{\lambda+1}I- A\,,\quad\lambda\in\mathbb{C}\setminus\{-1\}\,. \tag{40}\]  
Using (40), we easily obtain the following:  
\[\frac{1}{\lambda+1}\Big{[}\begin{array}{cc}\lambda I-A^{d}&B^{d}\end{array} \Big{]}=\frac{1}{\lambda+1}(\mu I-A)^{-1}\bigg{[}\begin{array}{cc}\mu\frac{ \lambda-1}{\lambda+1}I-A&\sqrt{2\mu}B\end{array}\bigg{]}. \tag{41}\]  
Hence,  
\[\text{rank}\left[\begin{array}{cc}\lambda I-A^{d}&B^{d}\end{array}\right]= \text{rank}\left[\begin{array}{cc}\mu\frac{\lambda-1}{\lambda+1}I-A&\sqrt{2 \mu}B\end{array}\right]=\text{rank}\left[\begin{array}{cc}\mu\frac{\lambda -1}{\lambda+1}I-A&B\end{array}\right]. \tag{42}\]  
This implies, due to (34), that  
\[\text{rank}\left[\begin{array}{cc}\lambda_{I}(A^{d})I-A^{d}&B^{d}\end{array} \right]=\text{rank}\left[\begin{array}{cc}\mu_{I}(A)I-A&B\end{array}\right]= 4,\quad l=1,2,3,4\,; \tag{43}\]  
that is, \((A^{d},B^{d})\) is controllable for all parameters \(I>0\), \(p>0\), \(k>0\), and \(b\geq 0\) (recall (16)). In turn, the observability of \((C^{d},A^{d})\) follows easily from the following relation:  
\[\frac{1}{\lambda+1}\Bigg{[}\begin{array}{c}\lambda I-A^{d}\\ C^{d}\end{array}\Bigg{]}=\frac{1}{\lambda+1}\Bigg{[}\begin{array}{c}\mu\frac{ \lambda-1}{\lambda+1}I-A\\ \sqrt{2\mu}C\end{array}\Bigg{]}(\mu I-A)^{-1}, \tag{44}\]  
which leads to the following condition:  
\[\text{rank}\left[\begin{array}{c}\lambda_{I}(A^{d})I-A^{d}\\ C^{d}\end{array}\right]=\text{rank}\left[\begin{array}{c}\mu_{I}(A)I-A\\ C\end{array}\right]=4,\quad l=1,2,3,4\,. \tag{45}\]  
Although it is not needed in our considerations, the relations (43) and (45), combined with (37), imply that stabilizability and detectability are also preserved under the transformation.
## 3 Results
### UV-Vis Absorption and Bandgap Estimation  
The electronic band structures of the as-prepared samples were analyzed by UV-Vis absorption (Figure 8). The pristine TiO\({}_{2}\) at anatase phase shows an intense absorption in the Ultra-Violet region (around 350 nm). While the rutile sample exhibits at 380 nm, on the other hand the Cu-doped sample indicated a red shift extending up to 600 nm. This is attributed to the characteristic surface plasmon resonance of Cu nanoparticles. The successful doping of Cu is also evident from the change in the color observed in the samples, shifting from pure white to light yellow (as shown in the inset of Figure 8b). The band gap value of the samples was estimated using the Kubelka-Munk equation (Equation (4)) [51]. The band gap values of pristine TiO\({}_{2}\) at anatase and rutile phases were observed to be 3.17 eV and 3.03 eV, respectively. The Cu-doped sample showed a significant dip in the band gap value up to 2.8 eV.
# Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach
## 7 Conclusions  
The great diffusion of embedded and portable communication devices on modern vehicles and smart transport systems enable communication with internal and external devices, networks, applications, and services. As vehicle connectivity becomes common, new security risks emerge, since communication protocols, such as CAN network, are still insecure and vulnerable to attacks. For this reason, there is an increasing interest in automotive cybersecurity for in-vehicle communication systems [56]. In this work we propose a distance-based intrusion detection system based on an unsupervised Kohonen SOM network. The SOM network found general applications in security issues because of its features of high detection rate, short training time, and high versatility. In our work, we introduced the Kohonen SOM network as an intrusion detection system for in-vehicle communication networks aimed at identifying attack messages injected on a CAN bus. In a previous paper we showed the results of the implementation of a hybrid supervised Kohonen SOM network combined with other techniques for clustering in order to improve the performance of the network. In the present work we implemented an unsupervised Kohonen SOM network combined with a k-means algorithm in order to improve the efficiency of the model in detecting attack messages. We proposed a novel distance-based procedure to integrate the unsupervised Kohonen SOM network with the k-means algorithm and compared the proposed method to the traditional procedure. We tested both the methods on open source data containing four different datasets: _DoS_ attack, _spoofing the drive gear_, _spoofing the RPM gauge_, and _fuzzy_ attack. We first used the networks on separate datasets with single kinds of attack, and then merged them all together in a _mixed_ dataset. The _mixed_ dataset presented a highly complex structure to analyze, characterized by large volume of traffic with low number of features and highly imbalanced data distribution with more than 2000 different attack types sent totally randomly. Despite the complex structure of the CAN network dataset, the proposed method showed high performance in detection accuracy--completely succeeding in _DoS_ attack, _spoofing
# Enhancing Route Guidance with Integrated V2X Communication and Transportation Systems: A Review
## 5 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model Name/Year** & **RGS Type** & **Data Collection Unit** & \begin{tabular}{c} **Optimal Route** \\ **Calculation Unit** \\ \end{tabular} & **Sharing Technology** & \
\begin{tabular}{c} **Communication** \\ **Structure** \\ \end{tabular} \\ \hline SOTIS [115] (2003) & Dynamic & Vehicles through GPS & Vehicles & Wireless Ad Hoc/FleetNet & Decentralized V2V \\ \hline Zero Public Infrastructure (ZPD) [116] (2005) & Dynamic & Vehicles & Equiped vehicles & VANET (IEEE 802.11/WiMAX) & Decentralized V2V \\ \hline Novel Wireless Mesh Networks (WWM) [117] (2007) & Dynamic & TMC & TMC & VANET (IEEE 802.11p) & Centralized V2V \\ \hline Vehicle-to-Vehicle Real-Time Routing [118] (2009) & Dynamic & Vehicles & Vehicles & VANET & Decentralized V2V \\ \hline Persian Guth [119] (2011) & Dynamic & Intersection Nodes & Traffic signal controllers & Wireless Sensor Network & Decentralized V2I \\ \hline LTE/NR V2X Communication of ITS [95] (2022) & Dynamic & Vehicles/RSUs & - & LTE and NR & Decentralized V2V, Centralized V2I \\ \hline Flow-Based Capacity-Aware Rerouting (FCR) [116] (2005) & Dynamic & RSUs &
\begin{tabular}{c} Cloud-based central \\ server \\ \end{tabular} & Cellular Communications & Centralized V2I \\ \hline Drone-Assisted Cooperative Routing (DACR) [108] (2024) & Dynamic & Vehicles & Vehicles & VANET and Drone-Based FANET & Decentralized V2V \\ \hline Autonomous Vehicles’ Impact on Accessibility [79] (2024) & Dynamic & Centralized Unit & Centralized Unit & Cellular/VANET & Centralized V2V, Centralized V2I \\ \hline Smart Road Management System [106] (2024) & Dynamic & Vehicles & TMC & - & Centralized V2V \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of vehicular communication models. such as SOTIS, ZPI, and WMN, are tailored for general TIS and fail to address RGS-specific requirements. Others, like DACR and FCR, focus heavily on data routing or modeling, with limited attention to integrating comprehensive RGS frameworks. Additionally, centralized systems often depend on reliable connectivity, which can be challenging in low-coverage areas. The lack of detailed communication architecture for models like Persian Gulf and Autonomous Vehicles' Impact of Accessibility further restricts their practical implementation. These findings underscore the need for future research to bridge these gaps by integrating robust RGS frameworks, enhancing communication architectures, and leveraging advanced technologies like 5G and hybrid systems to support real-time, scalable, and efficient transportation solutions.
# Evaluation of HY-2 Series Satellites Mapping Capability on Mesoscale Eddies
## 2 Materials and Methods
### Materials
#### 2.1.3 DUACS DT2018 Data  
To test the accuracy of our interpolation algorithm, we compared the interpolated data with the most popular gridded altimetric product. At present, the best altimetric data are the Data Unification and Altimeter Combination System (DUACS) product, which was developed by Collect Localization Satellites (CLS) with the support of CNES and distributed by CMEMS ([[https://marine.copernicus.eu/](https://marine.copernicus.eu/)]([https://marine.copernicus.eu/](https://marine.copernicus.eu/)) (accessed on 8 November 2021)) [26]. The newly released DUACS DT2018 product provides interpolated global sea level anomaly (SLA) maps from 1993 to 2017 with a resolution of \(1/4^{\circ}\times 1/4^{\circ}\)[2].
## Case Study: Data Analysis and Results
### Plausibility Check with Classical Statistics  
As a means of verification, the result of the previous section can be reproduced approximately with classical (frequentist) statistics. One way of handling the hierarchical data structure is to derive one transponder ERCS per overpass, and then to combine the resulting eight ERCS values through averaging into an overall transponder ERCS. The disadvantage of this simplified approach (in comparison to the approach using hierarchical Bayesian data analysis as shown before) is that information about the uncertainty of each of the eight transponder ERCS values is lost and does not contribute to the combined uncertainty.  
After averaging, an estimated standard deviation of the mean can be derived from the eight estimated ERCS values, resulting in a standard uncertainty for the estimated transponder ERCS [7]. The third step is then to derive a combined standard uncertainty by incorporating the ERCS knowledge uncertainty of the 1.5 corners of 0.2 dB through the method of \"root-sum-square\". Lastly, the expanded uncertainty [7] with a coverage factor of \(k=2\) is derived.  
This approach results in an estimated transponder ERCS of 60.80 dBm\({}^{2}\) with a standard uncertainty of 0.20 dB or an expanded standard uncertainty of 0.41 dB at a confidence level of 95% (\(k=2\)). The result confirms the findings of the previous section.  
Note that once again the combined uncertainty is dominated by the assumed ERCS knowledge uncertainty of the 1.5 corner reflectors. The transponder's combined backscatter uncertainty is sufficiently low to permit the calibration of SAR instruments like Sentinel-1 to an absolute radiometric uncertainty of 1.00 dB at a confidence level of 99%, provided that the SAR instrument is otherwise sufficiently precise [23].  
Figure 14: Posterior predictive checking for predicted (modeled) transponder data \(y_{t,d}^{\text{rep}}\) and four different test statistics. Especially the most important aspect of the predicted data, its mean, is well modeled.
# A Low Cost BLE Transceiver with RX Matching Network Reusing PA Load Inductor for WSNs Applications
###### Abstract  
In this work, a low cost Bluetooth Low Energy (BLE) transceiver for wireless sensor network (WSN) applications, with a receiver (RX) matching network reusing power amplifier (PA) load inductor, is presented. In order to decrease the die area, only two inductors were used in this work. Besides the one used in the voltage control oscillator (VCO), the PA load inductor was reused as the RX impedance matching component in the front-end. Proper controls have been applied to achieve high transmitter (TX) input impedance when the transceiver is in the receiving mode, and vice versa. This allows the TRX-switch/matching network integration without significant performance degradation. The RX adopted a low-IF structure and integrated a single-ended low noise amplifier (LNA), a current bleeding mixer, a 4 th complex filter and a delta-sigma continuous time (CT) analog-to-digital converter (ADC). The TX employed a two-point PLL-based architecture with a non-linear PA. The RX achieved a sensitivity of \(-93\) dBm and consumes \(9.7\) mW, while the TX achieved a \(2.97\%\) error vector magnitude (EVM) with \(9.4\) mW at \(0\) dBm output power. This design was fabricated in a \(0.11\)\(\upmu\)m complementary metal oxide semiconductor (CMOS) technology and the front-end circuit only occupies \(0.24\) mm\({}^{2}\). The measurement results verify the effectiveness and applicability of the proposed BLE transceiver for WSN applications.  
Bluetooth low power (BLE); wireless sensor networks (WSNs); TRX-switch; two-point modulation; single-ended LNA; ISM; transceiver +
Footnote †: journal: Sensors
## 3 Results
### CLS, OLS, and RLS Adjustment
Static sessions' data processing yielded four independent sets of estimated coordinates of the GNSS receivers. Coordinates were averaged with standard deviations and are presented in Table 1.  
Standard deviations of both the initial (unadjusted) and OLS-estimated coordinates yielded a maximum value of 10.9 mm and a mean value of 5.5 mm. The maximum and mean values of standard deviations were reduced to 5.0 mm and 6.7 mm (RLS) and 4.7 mm and 5.0 mm (CLS). However, it should be noted that obtained smaller values do not necessarily satisfy, in all cases, the condition of nominal geometrical relationships of the survey frame.
# Botnet Detection Based On Machine Learning Techniques Using DNS Query Data
## 3 Machine Learning Based Botnet Detection
### Proposed Botnet Detection Model Based on Machine Learning  
Figure 1 describes the proposed botnet detection model based on machine learning using DNS query data. The model is built on the analysis in Section 1 that bots of botnets routinely send lookup queries to the DNS system to find IP addresses of C & C servers using automatically generated domain names. The detection model is implemented in two phases: (a) the training phase and (b) the detection phase. During the training phase, the DNS query data is collected, and then domain names in DNS queries are extracted. Next, the set of domain names is pre-processed to extract the features for the training. In the training phase, machine learning algorithms are used to learn the classifiers. Through the evaluation process, the machine learning algorithm that gives the highest overall classification accuracy will be selected for use in the proposed detection model. During the detection phase of the model, the DNS queries are monitored and passed through the process of extracting the domain names, pre-processing, and classifying using the classifier produced from the training phase to determine if a domain name is legitimate, or a botnet domain name. The pre-processing step for each domain name in the training and detection phase is the same. However, this step is done in the offline mode for all the domain names of the training dataset in the training phase while it is done for each domain name extracted from the DNS query on the fly in the detection phase.  
Figure 1: Proposed botnet detection model based on machine learning using Domain Name Service (DNS) query data: (**a**) training phase and (**b**) detection phase.
# Experimental Evaluation of Load Distribution between Piles in Case of Block Failure
## 2 Test system and Properties of the Materials Used in Tests
### Model Piles  
The main purpose of this study was to determine the load distribution ratio between the piles. In this study, the interaction between pile material and soil was not investigated. In the literature, St37 class steel was used as the material for modeling piles and raft plates in the experiments. The diameter of the piles used in the experiments is D = 8 mm and their length was L = 280 mm.