esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
The European Space Agency's (ESA) PANGAEA-X event highlighted the potential of swarm intelligence to achieve greater autonomy and scalability in operations [19]. Looking ahead, the roadmap proposed by [PERSON] [20] envisions fully autonomous mining operations without human operators on-site. This roadmap focuses on key areas such as production, construction, and demolition of infrastructure, monitoring of equipment and the mine environment, and logistics and support systems as the first steps toward this future [21]. The shades from grey to white represent the progression from manual mining to mechanized, remote production, robotized mining and finally autonomous mining, indicating increasing levels of automation with autonomous mining requiring minimal to no human intervention, as shown in Figure 1.  
This study presents simulations of nature-inspired swarm robot models, developed by the author, based on the behaviors of ants, honeybees, and fireflies using the Robot Operating System (ROS). These models have been specifically designed to investigate their applications in mining operations, such as ore detection, extraction, and transportation. Furthermore, a case study on Pilbara iron ore mine has been conducted to assess the real-world applicability of these models in an actual mining environment. Rio Tinto's Pilbara iron ore mine has been selected for the real-world model, because it has successfully implemented automated systems and is one of the most technologically advanced mining sites in the world renowned for leading smart mines. These make it the best-suited mine site for testing the swarm robot's applications in automated large-scale mining operations. To provide a clear roadmap on the research conducted, the overall logic diagram, illustrating the progression from model development through to simulation, testing, and analysis, is presented in Figure 2.  
The subsequent sections of this paper are organized as follows: Section 2 presents case studies on applying the considered swarm robotics models to the Pilbara iron ore mine. Section 3 discusses the development of the swarm models, and Section 4 evaluates the performances of the proposed swarm robotics systems. Section 5 focuses on results and discussions, Section 6 discusses future directions and real-world implementations, and Section 7 concludes the paper.
## 3 Results
### Oxidation With Ageing
#### 3.3.2 Urine  
For urine, in contrast to semen and saliva samples, the average maximum fluorescence intensity of the protein emission spectra was found at 371.7 (SD 22.6) nm. The maximum intensity of the FOX peak was at approximately the same wavelength (431.4 (SD 8.5) nm) as the semen and saliva samples. Therefore, the ratio of the fluorescence intensities at wavelength 370 over 430 was plotted as a function of time. Although the median fluorescent ratios of urine decreased over time, the results were not found to be statistically significant (Figure 5A), (F(5,43) = 0.876, \(p\) = 0.505). This was unexpected, as a previous study observed a decline in the fluorescence ratios of urine over time using the area under the curve (AUC) of the protein fluorescence at excitation 285 nm/emission 330-400 nm and the FOX fluorescence at excitation 365 nm/emission 400-500 nm [31]. In addition, the protein carbonyl levels seem to remain stable upon ageing and no statistically significant differences were observed over the course of time (Figure 5B), as determined by ANOVA (F(5,54) = 0.01, \(p\) = 0.999). Again, both figures show large error bars, which indicate that the ratio and protein carbonyl levels vary between different donors.  
Figure 4: Overview of oxidation parameters in semen during ageing. (**A**) Median protein/FOX ratio over time, with whiskers indicating the range from the smallest to the largest ratio. A significant decrease in the fluorescence ratios of semen over time is observed. (**B**) Mean protein carbonyl concentrations (nmol/mg) over time with error bars representing standard deviation. There was no statistically significant increase in protein carbonyl levels over time.  
Figure 5: Overview of oxidation parameters in urine during ageing. (**A**) Median protein/FOX ratio over time, with whiskers indicating the range from the smallest to the largest ratio. The median protein/FOX ratio decreased over time, but this change was not found to be statistically significant. (**B**) Mean protein carbonyl concentrations (nmol/mg) over time with error bars representing standard deviation. Note that the mean protein carbonyl concentration remains stable with ageing.
## 2 Materials and Methods
### New GaN HEMT Small-Signal Equivalent Circuit Considering Source via Effects  
The well-known small-signal equivalent circuit of III-V-based HEMTs including GaN HEMTs is shown in Figure 5a. For better understanding, the port numbers are also indicated when measuring S-parameters. The source port is usually connected to ground. It consists of a pad model composed of pad parasitic capacitances (C\({}_{\text{pg}}\), C\({}_{\text{pd}}\)) and pad parasitic inductances (L\({}_{\text{pg}}\), L\({}_{\text{pd}}\)), an extrinsic part depending on the layout structure, and an intrinsic part whose value changes according to the channel variation of the transistor by applied biases [27; 28; 29]. Usually, the C\({}_{\text{ds}}\) is classified as an intrinsic model parameter, but empirically, the difference in values extracted according to biases is not large, so in some cases it is set as a constant [27; 28; 29]. In this work, it is assumed that both the extrinsic part and the intrinsic part of C\({}_{\text{ds}}\) exist.  
Figure 5b shows the modified small-signal equivalent circuit including the source via effect discussed in the previous section. As shown in Figure 5b, the \"C\({}_{\text{ds}}\)\" part is modified to reflect the coupling effect between the drain and source via feeding lines. For convenience, we divide the C\({}_{\text{ds}}\) into C\({}_{\text{ds1}}\) and C\({}_{\text{ds2}}\), one including the source via inductance indicating that the coupling signal path is long and the other part directly falling to the ground, and the intrinsic C\({}_{\text{ds}}\) will be included in the latter.  
Figure 4: Comparison of S11 (**a**) on the Smith chart and (**b**) on the phase between EM data and equivalent circuits.  
Figure 5: _Cont._
## 2 Materials and Methods
### Training and Testing Datasets  
The dataset was generated from the equations obtained in the forward kinematics of the Quetzal manipulator. The variables involved in the proposed dataset were the orientation vector \([n\ o\ a]=\{n_{x},n_{y},n_{z},o_{x},o_{y},o_{z},a_{x},a_{y},a_{z}\}\), the position vector \([p]=\{p_{x},p_{y},p_{z}\}\), and the vector of joint angles \([\theta]=\{\theta_{1},\theta_{2},\theta_{3},\theta_{4},\theta_{5},\theta_{6}\}\), for a total of 18 variables.  
Table 2 shows the ranges of movement established for each of the joints in the workspace of the manipulator. Dataset \(X\) was generated with a spatial resolution of \(25\times 25\times 25\times 25\times 25\times 25\times 25\) in a six-dimensional matrix with 18 variables involved. The total size of the dataset was 4,394,531,250 data, occupying an approximate physical memory space of 32.74 Gb due to the data class of type \(Single-precision\) used in the study, where the data are stored as 4-byte (32-bit) floating point values.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Description** & \(\theta_{1}\) & \(\theta_{2}\) & \(\theta_{3}\) & \(\theta_{4}\) & \(\theta_{5}\) & \(\theta_{6}\) \\ \hline Min & 0 & 0 & \(2\pi\) & 0 & \(2\pi\) & 0 \\ \  
Figure 3 shows a graphical representation of the workspace using a 3D data scatter plot corresponding to the position vector \([p]\) based only on the joints \(\theta_{1},\theta_{2},\theta_{4},\theta_{5}\) and \(\theta_{6}\), where it is possible to appreciate the workspace of the robotic manipulator without taking into account joint \(\theta_{3}\). The illustrated workspace was generated from the forward kinematic equations as a function of the six joints.  
In order to process the enormous volume of data in a conventional processor, a data reduction filter (DRF) based on linear systematic sampling (LSS) was applied to reduce the set to a size of 190.7 Kb in memory [45]. The data were processed on an 8-core AMD Ryzen 7 5000 series processor with a base clock of 1.8 GHz, 16 GB of RAM, and integrated Radeon 16-thread graphics with a maximum clock of 4.3 GHz [46]. The dataset and code are available in Supplementary Materials.  
Figure 4 shows the scatter matrices corresponding to the position dataset before and after applying the FRD filter, where a reduction of 99.99% of the data was obtained with a size of 24,410 data. In Figure 4b, it is shown that the data maintain a constant and uniform distribution with respect to the dataset of Figure 4a.  
The data were normalized with a mean of zero in the range from \(-1\) to \(1\) using Equation (8), where \(data\) is the data to normalize, \(min\) is the minimum value of the dataset, \(range\) is a value established by the difference between the maximum value and the minimum value of the dataset, \(h\) and \(l\) are the maximum and the minimum desired values for normalization.  
\[DataNorm=\left(\frac{data-min}{range}\times(h-l)\right)+l \tag{8}\]
# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 4 Methodology of the Novel Deepfake Detection Framework
### Data Preparation
* Z-Score Normalization (Standardization) follows the formula \(z=\frac{x-\mu}{\sigma}\), where \(\mu\) is the mean and \(\sigma\) is the standard deviation. This technique transforms data to have a mean of 0 and a standard deviation of 1, which is particularly suitable for features that follow a Gaussian distribution. It is commonly used in algorithms such as Gaussian Mixture Models (GMMs), Support Vector Machines (SVMs), and Principal Component Analysis (PCA), all of which are sensitive to the magnitude of the data.
## 4 Implementation and Results  
Figure 2 shows the implementation of the Digital Twin. The different components were created individually and then assembled with Unity using Blender software. An S7-1200 PLC, an embedded system, and a switch were used to establish the bidirectional communication between the physical and the virtual part. The system operates manually or automatically and provides a digital interface containing all process components. Sensors and actuators are connected to a remote I/O interface for sending and receiving process data to the digital environment, following the OPC UA protocol for communication.
## 2 Data Collection and Processing
### Data Processing
#### 2.2.1 Outlier Handling
The function \(s(x,n)\) assigns a higher value to data points that are more likely to be anomalies, and the anomaly data are determined based on a predefined threshold. 1. Anomaly analysis. The key parameters of the blast furnace are comprehensively analyzed, and abnormal values are studied and judged in combination with the operation mechanism of the blast furnace. 2. Abnormal value rejection and retention. Abnormal values caused by failures of blast furnace test equipment are rejected; abnormal values resulting from irregular blast furnace operations are retained and considered as part of the analysis.
## 5 Simulations
### Simulation Setup
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline
**Simulation Area** & 10524 \(\times\) 6924 m\({}^{2}\) area in urban Taipei, Taiwan, with \\  & 25 junctions and 40 road segments \\
**MAC layer** & 802.11p \\
**Propagation model** & Nakagami m = 3 \\
**Data communication range (CR)** & 250 m \\
**Energy detection range (ER)** & 700 m \\
**Base rate in the control channel** & 3 Mbps \\
**Data rate in the service channel** & 12 Mbps \\
**The number of APs** & 4 \\
**Generation size** & 32 pieces \\
**TTL of a SLNC-coded content piece** & 60 s \\
**The size of a SLNC-coded content piece** & 1 KBytes \\
**Output queue length** & 100 pieces \\
**\(t_{max}\)**, \(T_{j}\)** & 2 ms, 100 \(\upmu\)s \\
**Initial credit \(\gamma\)** & 50 \\ \(\alpha\), \(\beta\), \(\delta\), \(\omega\)** & 0.2, 0.04, 0.01, 0.5 \\
**File size** & 10, 6, 2 MBytes \\
**The number of vehicular nodes** & 3468, 867, 216 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation parameters and their values. Figure 11: _Cont_. Figure 10: Average downloading delay vs. file size (**a**) 10 MB; (**b**) 6 MB; (**c**) 2 MB. Figure 9: Average downloading percentage vs. file size (**a**) 10 MB; (**b**) 6 MB; (**c**) 2 MB. Figure 11: Average credit vs. file size (a) 10 MB; (b) 6 MB; (c) 2 MB. Figure 12: Average downloading percentage vs. the number of nodes (a) 3468 nodes; (b) 867 nodes; (c) 216 nodes. Figure 13: Average downloading delay vs. the number of nodes (a) 3468 nodes; (b) 867 nodes; (c) 216 nodes.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 4 Machine Learning and AI for Network Slicing Beyond 5G Systems
### Supervised Learning for Network Slicing
#### 4.1.3 Deep Learning  
Deep learning (DL) is a supervised classification learning technique, it perceives the functioning of the human nerve system in understanding the data representation methods and establishing the communication patterns between the input and output layer. DL can be applied to classify the applications according to the requirement such as mMTC, eMBB, and URLLC [29].
###### Abstract  
Smart logistics, combining the capabilities of logistics with methods and techniques of the Internet of Things, Information and Communication Technologies, and the highest levels of automation are key to addressing the challenges of the 21 st century and minimizing emissions while maximizing logistic performance. High-performance cellular networks are a prerequisite to fully using and leveraging their possibilities. These communication networks were developed based on the need for voice communication and streaming services. While the upcoming requirements are included in the latest versions of cellular networks, the existing infrastructure requires significant improvements and will have to adapt significantly. This study evaluates the performance of the current state of implementation of cellular networks on the German highway experimentally and analytically. The known indicators RSRP, RSSI, and RSRQ are analyzed spatially, over time, and for different driving conditions. The results indicate a high level of spatial correlation and a sufficient level of confidence, which are needed to ensure consistency and repeatability of these measurements. The procedure and the results can be used to assess the suitability of cellular networks for smart logistics applications and continuously monitor their improvement. The results indicate the status of the cellular network on the German highway which is worse compared to the network operator's self-assessment.  
cellular networks; ICT; ITS; automation; smart logistics; IoT; RSSP; RSSI; RSRQ; highway address: 2022  
of 5G networks on German highways with strong logistic relevance lags behind. The path from specification to real-life implementation requires means of assessment and control. Currently, network operators offer LTE-based services along the route under investigation in this study, which are, according to their websites, delivering consistent LTE coverage and \"best in class\" performance [5; 6]. As this study shows, this is not the case. Thus, there is a need to consistently and systematically assess the performance of the current state of mobile networks with respect to logistic applications and provide means to continuously measure the status with significant reproducibility.
## Materials and Methods  
### Sample Preparation  
#### 2.1.1 Kinked p-n Junctions for Measurements of Single SiNWs  
SiNWs with an embedded p-n junction for single SiNW dissolution experiments were grown from 50 nm gold nanoparticles (AuNPs; BBInternational, Crumlin, UK) on single-crystalline (100) Si wafer pieces covered by a thermal oxide layer (500 nm). After cleaning with acetone and 2-propanol in an ultrasonic bath for 5 min each, the wafer pieces were subjected to O\({}_{2}\)-plasma treatment (\(\mu\)-etch; 5 min, 0.1 torr, 100 Watt). The AuNPs were adsorbed to the surface using poly-l-lysine, which was removed prior to growth through O\({}_{2}\)-plasma treatment as described above. In the SiNW chemical vapor deposition (CVD) set-up, the samples were heated in Ar atmosphere to 475 \({}^{\circ}\)C, after which vapor-liquid-solid (VLS) growth was initiated by introducing 100 sccm SiH\({}_{4}\)/He and 10 sccm H\({}_{2}\) at 75 mbar. The p-n junctions were implemented by admixing the dopant gas and changing it from 20 sccm B\({}_{2}\)H\({}_{6}\) for the p-type branch to PH\({}_{3}\) for the n-type branch during growth. To introduce the kink, this dopant change was accompanied by a sudden drop in pressure [12]. SiNWs grown in this manner have been shown to grow along the \(<\)111\(>\) axis, thereby exposing [211] and [110] faces on the sides [13; 14; 15]. The SiNWs were successively transferred from the growth substrate to a chip by releasing them into ethanol (p.a., Merck, Darmstadt, Germany) in an ultrasonic bath and dropwise addition of the resulting dispersion to the respective chip. Then, 100 nm thick Au electrical contacts with 5 nm Ti as adhesive layer were placed by thermal evaporation using electron beam (e-beam) lithography in a lift-off process with polymethyl methacrylate e-beam resist. Previous experiments have shown that thus integrated nanowires with an equivalent level of doping typically show ohmic contact behavior [16].
# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
## 3 Methodology
### VLSI Architecture  
Figure 5 shows the VLSI architecture of the proposed lossless EEG compression VLSI design. It illustrates its components which are composed of a dynamic voting predictor, a two-stage Huffman encoder, a Golomb-Rice encoder, pipeline registers, and their interconnections.  
Figure 4: Sample data for one voting session: (**a**) result for each function, and (**b**) result for function 2.  
Figure 5: VLSI architecture of the proposed lossless EEG compression algorithm.
# Blind Spot Detection Radar System Design for Safe Driving of Smart Vehicles
###### Abstract
To verify the signal forms in the time domain and the frequency domain for interpreting the equations and signals presented in the basic principles for designing the system, a simulation was performed using MATLAB TOOL with reference to [16,17], and the basic principles were verified. Reference [18] presents a detection method based on 2D FFT, which is similar to the technique used in this paper. However, this technology is for pedestrian detection, not for automotive applications. Nonetheless, the detection principle is similar. References [19,20,21] present multidimensional FFT for signal acquisition in radar sensors and provide performance evaluations for FFT usage. The utilization of both 2D FFT and multidimensional FFT techniques are referenced in this paper. References [22,23] serve as standardization documents for the design of a BSD radar system in this paper. They provide guidance for designing the system, outlining the requirements for performance evaluation and test scenarios. The BSD system presented in this paper was designed to meet all the requirements specified in [22,23]. The BSD radar system using FMCW continuously emits signals while driving, and the emitted signals bounce off nearby objects and return to the vehicle. This technology can detect the distance, angle, direction, and speed of the target vehicle, calculate the risk of collision with the vehicle, evaluate the driving situation, and inform the driver to ensure the safety of driving a car. The key technologies involved in designing the BSD radar system are signal processing technology, which detects the surrounding situation by analyzing the reflected signals after transmitting a signal [10], tracking algorithms for detecting the surrounding situation of the vehicle [11], and procedures for tracking target vehicles in the surrounding environment. This paper presents a tracking algorithm for designing a BSD radar system, while explaining the principles of FMCW radar technology and signal types. Additionally, this paper presents the target tracking procedure and target filter to design an accurate tracking system and explains the system design. Finally, the radar system that was developed is installed in a vehicle to evaluate the system's performance in a driving environment.
# The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Standley Model
## 5 Conclusions  
In order to cater for the variation factors of the path tracking algorithm for autonomous agriculture machinery, the PSO-enhanced fuzzy Stanley model (PSO-FSM) is developed and verified. The internal factors of the path tracking control system are handled by using fuzzy algorithm design, and the time-varying external factors, such as actuator saturation and varying speed, are taken into consideration by PSO. Experiments based on numerical simulation and autonomous navigation vehicle are firstly performed, and the result demonstrates that PSO-FSM outperforms FSM and SM in terms of guiding distance under large initial error and path tracking precision under automatic turning. The field test of autonomous navigation vehicle indicates that the full-field tracking errors of PSO-FSM  
Figure 15: Path tracking result of the combine harvester. (**a**) Tracking trajectory of the combine harvester; (**b**) Lateral error of path tracking for the combine harvester.  
including straight line and curve path tracking are less than 3 cm. Then, a preliminary experiment on harsh farmland soil is performed, and the PSO-FSM achieves maximum tracking error at 0.63 m.  
In the future, the dual-antenna RTK positioning system should be enhanced by fusing inertial navigation system, which not only provides the position and attitude at high update frequency, but also enables precise lever-arm identification and reliable seamless navigation. In addition, the calculation of PSO optimalization is based on the fitness functions (8) and (9), if more tracking deviations are provided, the optimal result would be better, especially when the curvature and environment disturbances change randomly. With the increase in the number of control periods, the computational complexity is increased too, and thus running the optimization on a FPGA chip to speed up the calculation of PSO is also part of our future work.  
Conceptualization, Y.S. and B.C.; methodology, B.C.; software, Y.S.; validation, Y.S., F.J. and B.C.; formal analysis, Y.Z.; investigation, B.C. and X.W.; resources, B.C.; writing--original draft preparation, Y.S.; writing--review and editing, B.C.; supervision, B.C.; funding acquisition, B.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under grant number 31901416, in part by the Primary Research and Development Plan of Jiangsu Province under grant number BE2021313, in part by the Postdoctoral Science Foundation of China under grant 2019M651745, in part by the Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment and the APC was funded by 31901416.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy of the subjects involved in the study.  
The authors declare no conflict of interest.