esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Driven Deep-Learning-Based Underwater Acoustic OTFS Channel Estimation
## 4 Numerical Results  
For the evaluation of UWA OTFS system performance, the OTFS frame size is set to \((N,M)\)= (8, 64), signifying that each frame consists of 8 symbols and 64 subcarriers within the TF domain. The carrier frequency is set to 6 kHz. In the sea experiment, the maximum multipath delay is approximately 100 ms, which determines the setting of the subcarrier spacing to be \(\Delta f=10\) Hz. Binary phase-shift keying (BPSK) is utilized to map symbol constellation.
# CS Measures for Nuclear Power Plant Protection: A Systematic Literature Review
## 4 Literature Review
### Critical Digital Assets in Nuclear Power Plants  
Prior to developing a risk-informed CS strategy, an analysis of the system should be conducted to identify significant CDAs. Digital assets are classified and managed as CDAs that have safety, security, and emergency preparedness functions if they are the most sensitive to cyber attacks in terms of the functioning of the plant. On average, CDAs represent 70-80% of all digital assets, and applying and managing the same security control for all assets is inefficient [7]. The identification of these assets is necessary for the development of CS systems, which will be dependent on numerous factors, such as attack paths, methods, and potential target systems [41].  
In the US, RG 5.71 is the regulatory guide that has been put forth to provide a framework to aid in the identification of these CDAs. The goal of this regulatory guide is to harmonize the set of security controls (based on NIST CS standards) that address potential cyber risks to CDAs.  
RG 5.71 defines a level-based defensive architecture and a set of security controls addressing the potential cyber risks of CDAs. More specifically, RG 5.71 defines different levels of protection and divides the security controls into three categories: (1) technology, (2) operation, and (3) management.  
That said, RG 5.71 only offers a general overview of tools and criteria for the identification of these CDAs. For this reason, much research has been conducted to develop and discuss methods in order to aid in the identification of assets that should be considered critical.  
The authors of [7] presented criteria for identifying digital assets, which were classified as Vital Digital Assets (VDAs) by the authors. Their proposed criteria for selecting VDAs followed a step-based procedure. The procedure consisted of the following steps: first, gathering information for selecting VDAs; then, selecting initial events that could be caused by cyber attacks, and finally, selecting and analyzing accident mitigation facilities and selecting VDAs from target sets. The authors found that it is more economically and operationally efficient to manage the application of graded security controls to VDAs than to apply equivalent security controls to a number of CDAs. The authors concluded by indicating that further research should be conducted to understand how to apply and regulate the VDA approach in actual nuclear power plants.  
The authors of [39] suggested a selection of complementary points of CS to be used to understand the requirements of nuclear regulations and to find complementary considerations in nuclear CS. The authors believe that further work in this direction could aid in resolving the nuclear CS issues and could help discover the deficiencies in nuclear CS schemes.
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.4.2 Comparison Experiments of Different Improved Tri-Training Algorithm Models
By comparing the experimental results, the F1 value of Model 4 with the improved Tri-Training algorithm is higher than the other models. Model 1 is a novel multi-neural network fusion entity recognition model proposed in this paper, which has good results for medical entity recognition. We improve the division of sub-datasets, the selection of base classifiers, and the voting rules for ensemble learning on the Tri-Training algorithm. In addition, the base  
Figure 5: The performance statistics of the improved semi-supervised learning algorithm model. Figure 6: Statistics of various entity recognition performances. classifier of Model 4 is selected from the BIBC model of Model 1. Model 4 incorporates a hybrid neural network model with strong entity recognition performance while making various improvements to the Tri-Training algorithm to achieve the best recognition results. Overall, the model proposed in this paper is advanced and novel. In the field of low-resource medicine, semi-supervised learning can improve the performance of the model compared with supervised learning, and the various improvements to the Tri-Training algorithm in this paper further enhance the entity recognition ability of the model.
# A Color Matching Method for Mosaic HY-1 Satellite Images in Antarctica
## 5 Conclusions  
In this paper, images from China's Haiyang-1 satellite Coastal Zone Imager (CZI) were used to study the color uniformity of mosaic images in Antarctica based on MASK illumination simulation theory and image color matching via histogram specification matching. For a single image, a method based on MASK theory combined with an auxiliary mask was used, and the uneven illumination effect of the image was effectively eliminated. In the process of multiple-image color matching, a segmental color-matching method based on the threshold value is proposed for the Antarctic region. Compared with the results of the traditional histogram matching method and the Wallis method, the results obtained by the traditional histogram method and the method in this paper are closest to the histogram of the reference image, but the color distortion of the traditional histogram method is serious, and the results of the Wallis method show that the gray value distribution of the glacier target is lower than that of the reference image. The method in this paper is the best at eliminating the color difference between the images and the overall visual effect. Quantitative evaluation shows that the proposed method achieves good results in eliminating color differences in multiple images, while achieving a good balance between increasing image detail and improving visual effects.  
Conceptualization, T.Z.; data curation, T.Z. and Y.Z.; funding acquisition, L.S.; methodology, T.Z., L.H. and H.Z.; resources, L.S.; software, T.Z.; validation, L.H., H.Z. and X.Y.; visualization, Y.Z., H.Z. and X.Y.; writing--original draft, T.Z.; writing--review and editing, L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China under Grant 2021 YFC2803300 and the Antarctic Geographic Information Investigation.  
The data presented in this study are available on request from the author.  
The authors declare no conflict of interest.
# Inertial Parameter Identification in Robotics: A Survey
## 1 Introduction
### Motivation and Related Works
The results suggest that IDIDIM, IDIM-IV, CLOE and CLIE are significantly less sensitive to noise than Least-Squares approaches. The authors moreover enhanced the fact that IDIDIM and IDIM-IV require much lower computational effort compared to CLOE and CLIE due to the reduced number of model evaluations. However, it is worth noting that IDID and IDIM-IV were not directly compared to each other. In [13; 14], the authors compared five identification methods, namely the IDIM-OLS, Adaline Neural Networks (AdaNN), Hopfield-Tank Recurrent Neural Networks (HTRNN), EKF, and a genetic algorithm, on a 5-DoF SCARA robot. Among these five methods, only IDIM-OLS and AdaNN gave results that were accurate enough to be exploited. The results also suggest that other methods may lack regularity, as they do not converge for all the considered parameters. Recently, multiple works highlighted the possibility of explicitly enforcing physical consistency by means of constrained optimization techniques, rather than implicitly, through statistical consistency. With some notable exceptions (e.g., [15; 16; 17; 18; 19]), the classic approach to physically consistent parameter identification consists of expressing physicality constraints in the form of a Linear Matrix Inequality (LMI), thereby allowing the reformulation of the whole identification process as a constrained optimization problem, solvable using Semi-Definite Programming (SDP) methods, as for example proposed in [20; 21; 22; 23], or using optimization on manifolds as proposed in [24; 25; 26]. Please note that although several approaches such as [27; 28], use Deep Neural Networks (DNN) to learn the full robot dynamics, these contributions will not be considered in this work, as the mapping between DNN hyper-parameters and robot dynamic parameters is not yet fully understood, thereby making a comparison with conventional identification methods irrelevant.
# Consensus Tracking Control of Multiple Unmanned Aerial Vehicles Subject to Distinct Unknown Delays
## 3 Control Strategy
### Translation Control  
According to Equation (5), the control inputs are computed to determine the desired roll and pitch angles for all agents. These angles are functions of the tracking errors in the \(y_{i}\) and \(x_{i}\) coordinates, respectively. Specifically, in the leader-follower scheme, a disturbance observer-based control is implemented on the leader agent to enable it to track the desired trajectory within the plane. Consequently, an observer-based consensus control is formulated for the follower agents to maintain formation flight, with the leader agent as the reference. Addressing the challenge of an unknown delayed input in Equation (15), control schemes are developed based on estimating the state vectors for all agents.
## Appendix A  
The following procedures may be used to calculate the model \(T_{3}\) for planning trajectories, a multi-objective hierarchical optimization framework coupled with a non-linear planning model.  
Step 1: Determine the range of the time \(t^{\omega}_{pass}\).  
When the passing zone length \(L_{P}\) and variable virtual waiting zone length \(L_{C}\), maximum velocity \(v_{max}\), maximum deceleration \(a^{L}\), and maximum acceleration \(a^{U}\) are given, the lower limit of CAV pass time \(t^{L}_{pass}\) can be determined. Considering that this study attempts to pass through the intersection with minimum delay  
Considering the constraints together, the range of \(t_{pass}^{\omega}\) is as follows:  
\[t_{pass}^{L}\leq t_{pass}^{\omega}\leq t_{pass}^{U} \tag{10}\]  
Step 2: Calculate the optimization target.  
Combining the signal intersection signal phase and the derivation of step 1, the range of \(t_{pass}^{\omega}\) can be expressed by the following equation:  
\[\max\left(t_{p,j}^{\mathcal{S}},\,t_{pass}^{L}\right)\leq t_{pass}^{\omega} \leq\min\left(t_{p,j}^{\mathcal{S}}+g_{p,j}\Delta t,t_{pass}^{U}\right) \tag{11}\]  
Given that the objective function minimizes \(t_{pass}^{\omega}\), two cases can be identified as follows:  
Case 1: If \(t_{p,j}^{\mathcal{S}}\leq t_{pass}^{L}\leq t_{p,j}^{\mathcal{S}}+g_{p,j}\Delta t\), then the objective function \(t_{pass}^{\omega}\) exists at the minimum value of \(t_{pass}^{L}\).  
Case 2: If \(t_{pass}^{L}<t_{p,j}^{\mathcal{S}}\), then the minimum value of the objective function \(t_{pass}^{\omega}\) exists as \(t_{p,j}^{\mathcal{S}}\).  
Step 3: Determine the solution.  
Stituation 1: \(t_{p,j}^{\mathcal{S}}\leq t_{pass}^{L}\leq t_{p,j}^{\mathcal{S}}+g_{p,j}\Delta t\)  
When \(t_{pass}^{\omega}=t_{pass}^{L}\), the motion trajectory is shown in Figure 1(a), first accelerating to the highest speed and then exercising at the highest speed at a uniform rate until passing the variable virtual waiting zone stop line and entering inside the intersection. \(t_{pass}^{\omega}\) can be calculated directly by the constraints.  
Stituation 2: \(t_{pass}^{L}<t_{p,j}^{\mathcal{S}}\)  
When \(t_{pass}^{\omega}=t_{p,j}^{\mathcal{S}}\), there will be three different trajectory segments, each with acceleration, constant speed, and deceleration processes. Therefore, there are various ways of collocation. However, the optimal solution must be the one with the maximum final CAV velocity.
# Mobile Network Performance and Technical Feasibility of LTE-Powered Unmanned Aerial Vehicle
## 2 Methodology
### Measurement Area  
A 1.6 km route located at the National University of Malaysia (UKM) was chosen as the research area. The campus can be considered as a sub-urban metropolitan area with a geographic terrain of undulating hills. It is fully covered with an excellent mobile network infrastructure. The locations of available eNodeB towers were extracted from some open-based websites such as www.cellmapper.net (accessed on 20 May 2019) [38] and www.opencellid.org (accessed on 20 May 2019) [39]. The tower locations were then plotted into the map provided by Google Earth Pro software. Figure 6 shows the research area considered.  
Figure 4: Hardware configuration for communication module of the drone prototype.  
Figure 5: The developed drone prototype with an LTE-based control system.
## Abbreviations  
The following abbreviations are used in this manuscript:  
5G Fifth Generation  
3 GPP Third Generation Partnership Project  
AI Artificial Intelligence  
B5G Beyond 5G  
BS Base Station  
CP Control Plane  
DL Deep Learning  
DRL  
Deep Reinforcement Learning  
DNN  
Deep Neural Network  
D2D  
Device to Device  
DP  
Data Plane  
eMBB  
Enhanced Mobile Broadband  
gNB  
gNodeB  
HO Hand Over  
HetNet  
Heterogeneous Network  
IoT Internet of Things  
LTE  
Long Term Evolution  
MAB  
Multi-arm Bandit  
MDP  
Markov Decision Process  
ML  
Machine Learning  
mMIMO  
Massive Multiple input Multiple Output  
mMTC  
Massive Machine type communication  
mmWave  
Millimeter wave  
M2M  
Machine to Machine  
NFV  
Network FunctionVirtual  
NGWN  
Next Generation Wireless Network  
NOMA  
Non-Orthogonal Multiple Access  
NR  
New Radio  
OFDM  
Orthogonal Frequency Division Multiplexing  
QoE  
Quality of Experience  
QoS  
Quality of Service  
RAT  
Radio Access Technology  
RAN
Radio Access Network  
RL  
Reinforcement Learning  
RSRP  
Reference Signal Received Power  
RSRQ  
Reference Signal Received Quality  
RSSI  
Reference Signal Strength Indicator  
SC  
Small Cell  
SDN  
Software Defined Networks  
SON  
Self Organized Network  
UAV  
Unmanned Ariel Vehicle  
UAV-BS  
UAV- Base Station  
UAV-UET Equipment  
UDN  
Ultra-Dense Network  
uRLLC  
Ultra-Reliable low-latency communications
## 4 Results
### Scientific Analysis of Safety Verification Topology  
The grid-based pressure(Figure 5a) wave intensity map (X/Y coordinates: \(\pm\)4 range) quantifies safety-critical thresholds through normalized intensity values (\(-\)0.9 to 0.9). Red zones (intensity > 0.6) correlate with 97.3% true-positive emergency braking detections in ISO 13264 trials (n = 1204 samples), while blue regions (intensity < \(-\)0.5) correspond to 0.08% false-negative rate boundaries. The checkerboard pattern reveals 22.7% spatial variance reduction (Levine's test, \(p\) < 0.01) compared to single-sensor baselines, confirming dual-modality fusion efficacy under multipath interference.  
Figure 4: Multi-dimensional algorithm performance validation for safety-critical systems. (**a**) Confusion matrix for gesture classification accuracy; (**b**) latency–accuracy optimization profile; (**c**) Hilbert–Huang feature space topology; (**d**) failure mode severity analysis and mitigation.  
Concentric feature distribution(Figure 5b) (radius: 0-2.1 units) demonstrates 89.4% class separation (Silhouette score = 0.71) between intentional commands (inner rings) and ambient motions (outer rings). Critical safety radius at 1.2 units (annotated threshold line) achieves 99.1% recall for emergency gestures (F1-score = 0.983), validated through 15,000 trial iterations. Radial symmetry (angular deviation < 3.2\({}^{\circ}\)) confirms algorithmic robustness to orientation variations (\(p\) < 0.001, Kruskal-Wallis test).  
The bimodal X-Y distribution(Figure 5c) identifies high-risk zones (red clusters: \(\mathrm{x}\in[-0.5,0.5]\), \(\mathrm{y}\in[-1.0,\,-0.8]\)) with 6.8\(\sigma\) deviation from non-critical regions. Kernel density estimation (bandwidth = 0.15) reveals 93.7% of non-critical false positives (n = 147 errors) occur in low-probability blue regions (\(p\) < 0.001, \(\chi^{2}\) test, Cohen's d = 1.82), meeting SIL-2 safety integrity requirements. Contour spacing (\(\Delta\) = 0.3 intervals) corresponds to 80 km/h vibration-induced feature drift compensation thresholds.  
Cylindrical coordinate trajectories(Figure 5d) (State 1: \(-1.0\)-1.0, State 2: 0-20) exhibit 98.4% temporal consistency (Dynamic Time Warping distance < 0.25) across 32-subject trials. Critical braking states (red contours at \(Z\) = 5-15) demonstrate 48.3 ms average transition latency (\(\sigma\) = 3.1 ms), with helical path structures confirming 99.2% high-dimensional feature stability under EMI interference (10 V/m RF exposure tests).  
This multi-layered visualization framework provides quantifiable evidence (\(\alpha\) = 0.01 confidence) for system safety certification, correlating topological features with ISO 26262/61508 validation metrics through statistically rigorous spatial-temporal analysis.
## 2 Theoretical Basis of Time-Varying Target Formation
### Multi-Agent System Control Law Design Based on Graph Theory and Consistency Algorithm
For a group of multi-agents, each agent using the double integrator model can be expressed as follows  
\[\left\{ \begin{array}{l}\dot{p}_{si}=v_{si}\\ \dot{v}_{si}=u_{si}\end{array} \right. \tag{14}\]  
\(v_{si}\) can be expressed as the velocity of the agent and \(u_{si}\) indicates the control input that needs to be designed. For the purpose of driving all agents to reach the formation control, the following distributed control laws are proposed as follow:  
\[ \begin{array}{l}\dot{p}_{si}=v_{si}\\ \dot{v}_{si}=-\frac{1}{\gamma_{si}}\sum\limits_{j\in N_{i}}w_{s_{ji}}[k_{p}(p_{ si}-p_{sj})+k_{v}(v_{si}-v_{sj})-\dot{v}_{sj}]\end{array}  \tag{15}\]  
where the non-singularity of \(\gamma_{si}\) is \(\gamma_{si}=\sum\limits_{j\in N_{i}}w_{s_{ij}}\). The adjacency matrix element \(w_{s_{ij}}\) is time-varying for different formation topologies. The control law (15) is designed according to the consensus protocols for tracking time-varying references in [25]. **Proposition 1** (Non-singularity of \(\gamma_{si}\)). : _Under Assumptions 1-3, \(\gamma_{si}>0\) for all \(i\in v_{sf}\)._In this article, \(\overline{\Omega}\) are used to represent \(\Omega\otimes I_{d}\). Based on the partition of leaders and followers, the associated stress matrix \(\overline{\Omega}_{s}\) can be denoted as  
\[\overline{\Omega}_{s}= \begin{bmatrix}\overline{\Omega}_{s_{ll}}&\overline{ \Omega}_{s_{lf}}\\ \overline{\Omega}_{s_{fl}}&\overline{\Omega}_{s_{ff}}\end{bmatrix}  \tag{16}\]  
where \(\overline{\Omega}_{s_{fl}}\in\mathbb{R}^{(\mathrm{dn}_{d})\times(\mathrm{dn}_ {d})}\), \(\overline{\Omega}_{s_{fl}}\in\mathbb{R}^{(\mathrm{dn}_{d})\times(\mathrm{dn}_ {d})}\). It is assumed that \(\overline{\Omega}_{s_{ff}}\) is positive definite, thus we can obtain \(p_{sf}^{*}(t)=-\overline{\Omega}_{s_{ff}}^{-1}\overline{\Omega}_{s_{fl}}p_{sl }^{*}(t)\) and \(v_{sf}^{*}(t)=\overline{\Omega}_{s_{ff}}^{-1}\overline{\Omega}_{s_{fl}}p_{sl }^{*}(t)\), respectively. For \(\underset{l\to\infty}{\lim}v_{s_{l}}(t)=v_{s_{l}}^{*}(t)\) and \(\underset{l\to\infty}{\lim}p_{s_{l}}(t)=p_{s_{l}}^{*}(t)\), the position tracking error and the speed tracking error of the follower are expressed as  
\[ \begin{split}\delta_{s_{pf}}(t)&=p_{sf}(t)-p_{sf}^{* }(t)=p_{sf}(t)+\overline{\Omega}_{s_{ff}}^{-1}\overline{\Omega}_{s_{fl}}p_{sl }^{*}(t)\\ \delta_{s_{sf}}(t)&=v_{sf}(t)-v_{sf}^{*}(t)=v_{sf}(t )+\overline{\Omega}_{s_{ff}}^{-1}\overline{\Omega}_{s_{fl}}p_{sl}^{*}(t)\end{split}  \tag{17}\]
# Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
###### Abstract  
Unmanned aerial vehicle (UAV) images that can provide thematic information at much higher spatial and temporal resolutions than satellite images have great potential in crop classification. Due to the ultra-high spatial resolution of UAV images, spatial contextual information such as texture is often used for crop classification. From a data availability viewpoint, it is not always possible to acquire time-series UAV images due to limited accessibility to the study area. Thus, it is necessary to improve classification performance for situations when a single or minimum number of UAV images are available for crop classification. In this study, we investigate the potential of gray-level co-occurrence matrix (GLCM)-based texture information for crop classification with time-series UAV images and machine learning classifiers including random forest and support vector machine. In particular, the impact of combining texture and spectral information on the classification performance is evaluated for cases that use only one UAV image or multi-temporal images as input. A case study of crop classification in Anbandegi of Korea was conducted for the above comparisons. The best classification accuracy was achieved when multi-temporal UAV images which can fully account for the growth cycles of crops were combined with GLCM-based texture features. However, the impact of the utilization of texture information was not significant. In contrast, when one August UAV image was used for crop classification, the utilization of texture information significantly affected the classification performance. Classification using texture features extracted from GLCM with larger kernel size significantly improved classification accuracy, an improvement of 7.72%p in overall accuracy for the support vector machine classifier, compared with classification based solely on spectral information. These results indicate the usefulness of texture information for classification of ultra-high-spatial-resolution UAV images, particularly when acquisition of time-series UAV images is difficult and only one UAV image is used for crop classification.  
unmanned aerial vehicle; texture; gray-level co-occurrence matrix; machine learning; crop 28 January 2019; Accepted: 12 February 2019; Published: 14 February 2019
# Development of an Online Detection Setup for Dissolved Gas in Transformer Insulating Oil
## 3 Experimental Setup Design
### Oil-Gas Separation System  
The degassing rate is dependent on the diffusion coefficient \(D_{i}\), the volume of the oil sample \(V_{o}\), and the volume of the gas chamber \(V_{g}\). \(V_{o}\) and \(V_{g}\) are determined by the volume of the customized optical path cell (400 mL). \(D_{i}\) is a function of temperature, pressure, stirring frequency, and the oil sample cross-section. The temperature, pressure, and stirring rate are used as improvement conditions in the oil-gas separation system. As a result, a constant-temperature and low-pressure dynamic headspace degassing chamber was constructed. The structure of the degassing chamber is shown in Figure 7.  
Figure 5: The definition of the second harmonic amplitude.  
Figure 6: The schematic diagram of the setup.  
The low-pressure and constant-temperature environment in the degassing chamber was created as follows: First, the pressure in the chamber was reduced to a stable 28 kPa. Second, the insulating oil was pumped into the chamber. Finally, the oil sample was evenly heated via thermal resistance and low-frequency stirring. The main parameters of the degassing chamber are shown in Table 1.