esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Our experimental methodology leverages the CARLA simulator (version 9.15) and the PCLA (Pretrained CARLA Leaderboard Agent) framework [40] to simulate and assess these attacks within photorealistic autonomous driving environments [41]. Building upon recent advances in DVS-based perception [42], we implement realistic attack parameters derived from existing literature and real-world attack scenarios [13; 22]. The CARLA simulator provides validated sensor models and physics simulations that enable rigorous evaluation of attack impacts on autonomous driving performance [36]. Our study systematically examines autonomous agent behavior and sensor responses under various attack conditions, providing quantitative insights into the vulnerabilities and resilience characteristics of modern autonomous driving systems [43]. We implement tailored defense mechanisms for each sensor modality and develop a comprehensive anomaly detection system using the EfficientAD model from the anomalib library [44; 45]. Our evaluation encompasses over 160 CARLA episodes across diverse driving scenarios, providing statistical analysis of attack impacts and defense effectiveness [8]. **Important Note on DVS Integration:** It is crucial to clarify that in our experimental setup, DVS cameras are used exclusively for research purposes and cybersecurity analysis. The DVS sensors are not integrated into the vehicle's driving decision model, which relies primarily on RGB cameras and other traditional sensors. Our DVS security analysis provides insights into potential vulnerabilities and defense mechanisms for future integration scenarios, but it does not claim that DVS technology currently enhances the cybersecurity of the autonomous driving system under test. * We provide a systematic analysis of sensor vulnerabilities in autonomous vehicles, with particular focus on the emerging DVS technology and its unique security characteristics in research contexts. * We implement and evaluate realistic attack scenarios in the CARLA simulator with quantified parameters derived from existing literature, demonstrating measurable impacts on autonomous agent behavior and perception accuracy. * We develop an effective anomaly detection system using the EfficientAD model from the anomalib library that achieves a > 95% detection accuracy with < 5% false positives for identifying sophisticated attacks on both RGB and DVS camera sensors. * We provide quantitative analysis of defense mechanism effectiveness, revealing that while GPS spoofing defenses achieve near-perfect mitigation, RGB and depth sensor defenses show limited effectiveness with 30-45% trajectory drift remaining. * We propose a comprehensive multi-layered security framework that combines sensor-specific defenses, anomaly detection, and graceful degradation strategies for resilient autonomous operation.
# Research on the Application of THz-TDS in Coal-Rock Interface Recognition
## 1 Introduction
[17] from China University of Mining and Technology extracted various THz signal parameters from different kinds of coal and rocks, and proposed a classification method based on the absorption characteristics and refractive indices of different coal and rock materials, demonstrating the potential applications of THz technology in coal and rock identification. Subsequently, [PERSON] et al. [18] used the least squares support vector machine algorithm to analyze and study the THz parameters of the coal-rock mixtures with varying proportions. From previous research on THz applications in coal-rock identification, it is evident that THz time-domain spectroscopy technology can be applied not only to coal-rock identification but also to more complex situations. In this study, THz-TDS technology (THz time-domain spectroscopy) is applied to the identification of coal, rock and coal-rock mixtures for the detection of coal seam thickness. The measurement results of different samples prepared from two kinds of coal (anthracite, lignite) and two kinds of rock (limestone, quartz sandstone), and the extracted optical parameters were used to establish a high-resolution optical database for identification of multiple kinds of coal and rock mixtures. Additionally, by preparing and analyzing a wider variety of coal-rock mixtures, various interface scenarios and an optical database for multiple coal-rock mixtures can be established to provide data support for intelligent identification of multiple coal-rock interfaces by coal mining machines. Based on this, coal seam thickness detection technology based on THz-TDS was successfully proposed. Through analyzing the THz spectrum detection of 10 anthracite flakes with different thicknesses and simulating the experimental process using CST 2021 software, the relationship between the time-domain signal and thickness variation of different coal flakes can be established, thereby verifying the effectiveness and feasibility of this method, which has broader applications in the field of coal-rock interface identification.
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 2 Reinforcement Learning  
The famous Travelling Salesman Problem (TSP) asks for the shortest path that touches all vertices in a graph. In the worst-case, the answer requires exponentially many operations in the number of vertices. However, computing the distance of any arbitrary path requires at most a polynomial (or linear) number of operations. This property is distinctive of \"NP-hard\" problems (i.e., non-deterministic polynomial); optimal solutions seemingly have exponential complexity, but candidate solutions have polynomial complexity. A long-standing open problem in complexity theory is proving (or disproving) \(\mathrm{P}\
eq\mathrm{NP}\); whether or not \"NP-hard\" problems can be solved in polynomial-time.  
Multi-agent tracking is a more complicated version of the TSP because there are multiple agents (i.e., salesmen), the graph topology is time-varying, and the agent trajectories must remain disjoint at each time-instant. The complexity of any solution is thus worse than exponential in the number of agents. While the TSP remains an important benchmark in theoretical computer science, there is also interest in approximately optimal but low-complexity solutions for engineering applications.  
Reinforcement learning (RL) offers these \"engineering\" solutions to high-complexity path-planning problems. The challenge is stabilizing the learning process within a reliably acceptable time-frame. Apart from some narrowly defined scenarios, no such convergence guarantees exist. The following sections introduce basic concepts in RL and discuss their relevance to multi-agent tracking.
# Improving the Robustness of Model Compression by On-Manifold Adversarial Training
## 6 Discussion  
Our results show that MC-AT(on) can improve the robustness of compressed DNNs against noise-induced perturbations that may exist when collecting real-world data. The results of MC-AT(on) showed better robustness against on-manifold perturbations compared to other model compression methods in on-manifold adversarial tests. In particular for the combined accuracy on both the original test and on-manifold adversarial test, MC-AT(on) showed the highest in all five datasets, meaning that compressing a model with MC-AT(on) can develop an optimal compressed model with robustness against noises that reside on a data manifold that embraces the observed data instances.  
However, our results are based on the two approximations, namely in (5) and in the use of an auxiliary classifier \(\hat{P}(Y|Z)\) to approximate the unknown distribution \(P(Y|Z)\). Although the assumptions are not unlikely, the quality of approximation may vary depending on several factors, such as the dimension and number of data points, the variance of the noise, and the quality of the \(\mathrm{en}/\mathrm{decoders}\). Therefore, it can be dangerous to blindly apply our proposed method, especially when these factors of one's application data are different from our experiment settings.  
On the other hand, our results also indicate that knowledge of the sensor's noise characteristics would help to improve the classification performance when learning small neural networks. Therefore, it will be desirable for the manufacturers to provide such information and the experimenters to perform extended data analysis on the noise component in the collected IoT data.  
It was a rather unexpected discovery in the HAR dataset experiment that MC-AT(on) was able to produce a more robust model against noise when the model is highly compressed. This indicates that model compression, in general, reducing model complexity, may help overcome noise in data. We think this subject deserves proper investigation in the future.  
Another type of research that can be conducted in connection to this work is energy efficiency. In the work of [PERSON] et al. [1], they argue that deep model compression can improve the energy efficiency when computing the compressed deep neural networks. Recently, hardware, such as the graphics processing unit (GPU) or tensor processing unit (TPU), is attracting attention as an essential element for developing and learning artificial intelligence models, which require significant power consumption. Moreover, the robust training of DNNs tends to require more computation. Therefore, further research in this direction will be desired from the sustainability perspective.
###### Abstract  
A new control estimator to maximize the power generated with a maximum power point estimator is introduced. The power mapping characteristics from the double-stator generator are modeled as a mathematical equation which is used to develop the estimator for maximum power tracking to maximize the generated power. The proposed estimator automatically traces the instantaneous maximum power at various load conditions. However, to stabilize the output voltage, a boost converter is used from the inverter side. The developed double-stator generator is tested with the new estimator for the maximizing power generation capability under laboratory conditions. The experimental results confirm that with the new estimator, the average power generation capability is increased by 12% and the peak value is increase by 22%.  
maximum power point estimator; double stator generator; power mapping; palm oil mill
# Dynamic Analysis of Tethered Satellites with a Payload Moving Along a Flexible Tether
## 1 Introduction
[24] examine control mechanisms for tethered satellite systems using elastic tethers, highlighting the importance of accurate modeling to capture the elastic behavior of the tether, which directly impacts the overall dynamics of the system. [PERSON] et al. [25] take a complementary approach by investigating optimal control strategies tailored for tethered satellite systems, emphasizing the interaction between propulsion and tether deployment. Their research indicates that effective control necessitates a careful balance between propulsion forces and tether dynamics to optimize system performance. [PERSON] et al. [26] contribute further to this area by focusing on adaptive control in tethered satellite systems, particularly under conditions of uncertainty and external disturbances. Their findings stress the need for robust control mechanisms that can respond dynamically to real-time changes, ensuring both system stability and performance. Together, these studies highlight the role of advanced control strategies in tethered satellite systems. They advocate for models that not only incorporate the elastic characteristics of tethers but also emphasize the necessity of adaptive responses to uncertainties, paving the way for enhanced control frameworks in future research. To the best of our knowledge, no prior research has investigated the dynamic behavior of a three-body tethered satellite system while considering the flexibility of the tethers. Previous studies predominantly modeled three-body tethered satellite systems as a two-piece dumbbell, treating the tethers as rigid rods. Although dynamic behaviors such as deformation, slack, and rebound have been explored in the context of two-body satellite systems with flexible tethers, similar considerations have not been extended to the three-body configurations. In systems where a payload is transferred between two satellites, the deformation of the tethers caused by the moving payload cannot be accurately analyzed using a rigid two-piece dumbbell model. Therefore, studying the dynamic behavior of three-body tethered satellite systems requires modeling the tethers as flexible bodies. In this study, we analyzed the dynamic behavior of a three-body tethered satellite system comprising a payload transported along a flexible tether connecting two satellites. The remainder of this paper is organized as follows. Section 2 models the two satellites and payload as point masses, with the tether modeled using the ANCF to account for its flexibility. Section 3 derives and expresses the discretized nonlinear equations as matrix-vector equations using [PERSON]'s equation. The Newmark time integration method [27] is applied to these equations to calculate the dynamic response. Section 4 analyzes the dynamic behavior of the tethered satellite system as the payload moves along the flexible tether connecting the two satellites. Finally, Section 5 summarizes the findings and presents the conclusions.
## 4 Test Results
### Output Current Distortion During Steady-State Operation at MPP  
For these tests, static MPPT efficiency test specifications and conditions according to EN 50530 are utilized to assess PV inverters' current distortion during steady-state operations at MPP [15]. Measurements were carried out for MPP normalized at 100%, 75%, 50%, 30%, 25%, 20%, 10%, and 5% of the specified nominal DC power, with assessments conducted at maximum MPP voltage (\(V_{\text{MPP,max}}\)), rated input voltage (\(V_{\text{DC,r}}\)), and minimum MPP voltage (\(V_{\text{MPP,min}}\)).
# Routing Protocol for Heterogeneous Wireless Sensor Networks Based on a Modified Grey Wolf Optimizer
## 5 Simulation Results
### Network Lifetime  
Figure 2 shows that the HMG was superior to other protocols in terms of its network lifespan for different proportions of advanced nodes obtained from the simulation. When the ratio of advanced nodes is \(\alpha=0.1\), it can be seen in Figure 2a that the network lifespan of the HMG increased by 55.7%, 31.9%, 46.3%, and 27.0%, respectively, relative to SEP, DEEC, M-SEP, and FIGWO. When the ratio of advanced nodes is \(\alpha=0.2\), it can be seen in Figure 2b that the network lifespan of the proposed algorithm is increased by 51.2%, 32.0%, 24.6%, and 32.8%, respectively, relative to SEP, DEEC, M-SEP, and FIGWO. The main reason for the above results is that the nodes with low residual energies have a low probability of becoming the CH, which avoids the phenomenon of rapid death of the node with low residual energy, thus extending the life cycle of the network.  
The simulation results of the network life cycle for when the first node died, 50% of the nodes died and all nodes died are shown in Figure 3 at different proportions of advanced nodes. As can be seen from Figure 3, the death time of the first node was later than that of other protocols. When the number of death nodes reached 50%, the network lifetime of HGWO was significantly extended compared to that of the SEP, FIGWO, DEEC, and M-SEP protocols. However, as can be seen in Figure 3a,b, when all nodes died, the lifetimes of SEP and FIGWO were greater than that of the algorithm proposed in this paper. It can be clearly seen in Figure 3 that the network instability period of the HMG was shorter than that of other protocols. The main reason for this is that the HMGO considers the heterogeneity of nodes and the global optimization ability, such that the energy consumption between nodes is relatively balanced, and the network instability period of the HMGO is shorter. The specific reasons are discussed in detail in Section 6.
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
## 3 Configuration Optimization  
### Transferring Configuration Design  
Compared to in-plane maneuvering, out-of-plane maneuvering requires more energy consumption, so maneuvers out of the orbital plane are avoided. And the intersection point is designed as the intersection between the non-cooperative target's orbit and the orbital plane of the tracking satellites. The tracking satellites 1, 2, and 3 are on different parking orbits, with the same inclination and right ascension of the ascending node. At maneuver time, satellites 1 and 2 start transferring, while satellite 3 maintains its original orbit, and chasers 1 and 2 approach the intercept point to intercept the non-coplanar target, as illustrated in Figure 3. To optimize the configuration, the orbital altitudes of chasers 1 and 2 and the true anomaly when the maneuver begins are set as decision variables.
# Tool Wear Monitoring for Complex Part Milling Based on Deep Learning
## 2 Method  
### Overall Monitoring Method  
Tool wear monitoring usually uses force, vibration, acoustic emission, current, power and temperature signals. In this study, to accord with the situation of few sensors in real machining environment, cutting force is selected as the single signal used for tool wear monitoring. In complex part milling, the cutting depth usually varies with the profile of the part. In this case, the cutting force and stability are time-varying, and it is difficult to design features for accurate tool wear estimation. To address this problem, a feature extractor based on pre-selection and deep learning is developed. The hand-designed features from the cutting force signal are further extracted with a deep autoencoder and then input to the deep multi-layer perceptron to estimate the tool wear value. The framework of the proposed tool wear monitoring method is illustrated in Figure 1.
## 7 Discussion  
In addition to the curves and the performance indexes for each scenario, an aggregated table with all the index values for all the scenarios enables further comprehension of the controllers' performance. For the sake of clarity, Table 6 is replicated as Table 10, recalling  
of a turbine operator. However, Fuzzy C achieves better scores than Fuzzy A in some scenarios, although its potential for escalation is lower due to the differences in their topologies. Furthermore, these two controllers are the result of the application of all the knowledge acquired during the development of previous controllers for each of the topologies. Specifically, topologies or techniques that achieved worse scores, such as Fuzzy B or the improved PI, and the previous versions of A and C allowed a better understanding of the problem. However, it should be noted that Fuzzy B acts as a single controller of the system, substituting the PI, which is remarkable because the other four controllers involve a PI in some manner. Thus, this controller could be a candidate to substitute the now standard PI controller. This fuzzy controller features the most stable pitch control behavior but at the cost of worse performance, so it is not attractive for an actual deployment in a commercial turbine. On the other hand, the improved PI enhances the baseline PI by considering the dynamic behavior when the wind speed varies rapidly, i.e., a turbulence scenario. However, as it is based only on the speed tracking error, there is lack of faster adjustments when turbulence occurs, which is completed by fuzzy controllers.  
As previously stated, fuzzy controllers A and C merge the fuzzy logic control and the improved PI, partially removing the flaws of both and creating synergies that allow for further improvement in the behavior of the controlled system. Both fuzzy controllers keep a PI in the architecture, which can help their adoption in commercial systems. The performances of Fuzzy A and C are improved with respect to the rest of the controllers included in the comparison. Additionally, the proposed fuzzy controller can be enhanced incrementally by the turbine operator by adding new rules or changing its topology without modifying the improved PI.
# The Influence of Processing on the Bioactive Compounds of Small Berries
## 2 Bioactive Compounds from Small Berries  
Antioxidants are present in many natural products and provide important benefits for human health [6; 7; 8]. The phytochemical profile and antioxidant properties of berries are highly influenced by the cultivar, growing and environmental factors, harvesting season, and postharvest storage or processing conditions [5].  
Phenolic compounds are the most important contributors to the antioxidant activity of berries. Phenolic acids (cinnamic and benzoic acid derivatives, as well as low amounts of free phenolic acids), flavonoids (anthocyanins, flavonoids, and flavanols), tannins (condensed and hydrolyzable tannins), and stilbenes (low amounts of resveratrol, pterostilbene, and piceatannol) are the most important classes of phenolics found in berries (Figure 1) [5]. The ratio of these phenolic antioxidants can vary significantly among different berry types. It should also be noted that the antioxidant properties depend on the chemical structure of the phenolics. Higher antioxidant activity is generally reported for phenolic compounds with a higher hydroxylation degree of the aromatic rings, especially in o-diphenyl structures [5; 9]. In particular, phenolic acids with bulky or meta-positioned electron-donating substituents, meta-ortho-positioned hydrophobic groups, or ortho-positioned hydrogen- or electron-donating groups on the aromatic rings possess high antioxidant activity [10]. The antioxidant activity of anthocyanins increases with the number of hydroxyl groups in the B ring, and decreases in the case of methoxylation at C3' [5]. Although glycosylation was reported to improve the stability and solubility of anthocyanins, it may reduce their radical-scavenging capacity [5; 11]. In a similar manner, hydroxylation, methoxylation, and glycosylation reactions affect the antioxidant activity of flavonoids. The powerful antioxidant capacity of flavonoids was assigned to the electron localization in the B ring as a result of conjunction between the 2,3-double bond and the 4-oxo group in the C ring [5; 9].  
Figure 1: Main phenolic compounds with antioxidant activity found in small berries [3; 4; 5; 11].
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## Abbreviations  
\begin{tabular}{l l} AI & Artificial Intelligence \\ ALOS & Advanced Land Observing Satellite \\ ASAR & Advanced Synthetic Aperture Radar \\ COTS & Commercial Off-The-Shelf \\ CNN & Convolutional Neural Network \\ DInSAR & Differential Interferometric Synthetic Aperture Radar \\ EO & Earth Observation \\ ESA & European Space Agency \\ GNSS & Global Navigation Satellite System \\ GPS & Global Positioning System \\ IoT & Internet of Things \\ InSAR & Interferometric Synthetic Aperture Radar \\ LiDAR & Light Detection and Ranging \\ LOS & Line of Sight \\ LST & Land Surface Temperature \\ MODIS & Moderate Resolution Imaging Spectroradiometer \\ MSG & Meteosat Second Generation \\ MT-InSAR & Multi-Temporal Interferometric Synthetic Aperture Radar \\ NDMI & Normalized Difference Moisture Index \\ NDVI & Normalized Difference Vegetation Index \\ PS-InSAR & Persistent Scatterer Interferometric Synthetic Aperture Radar \\ PSI & Persistent Scatterer Interferometry \\ RCM & Radarsat Constellation Mission \\ SAR & Synthetic Aperture Radar \\ TZ & Transition Zones \\ UAS & Unmanned Aerial System \\ VTIR & Vegetation Threat Index for Railways \\ \end{tabular}