esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results
### CDF of UE Throughput Performance  
We train all the methods under the scenario where HAPS is rotated by 30 degrees, respectively, under four different user distributions. Figure 9 shows the cumulative distribution function (CDF) of the users' throughput under the rotation scenario.  
In all the scenarios, the proposed DRLEA reduces the number of low-throughput users in the throughput range of \([0.0,16.0]\) kbps. In the case of Tokyo, the proposed algorithm achieves the best CDF performance in the throughput range of \([2.5,16.0]\) kbps. In the case of Osaka, the proposed method achieves the best performance in the throughput range of \([5.3,16.0]\) kbps. In the case of Sendai, the proposed method achieves the best performance in the throughput range of \([2.0,11.8]\) kbps. In the case of Nagoya, the proposed method achieves the best performance in the throughput range of \([1.3,5.0]\) kbps and \([6.0,16.0]\) kbps.  
In the \(\mathcal{Q}\)-learning, the Fuzzy \(\mathcal{Q}\)-learning, and the DQN, which use the simple \(\epsilon\)-greedy method for searching the optimal solution, are difficult to escape the local optimal. Even if we use the \(\epsilon\)-greedy method to randomly select an action, the result is still close to the local optimal solution. However, in the proposed method, we consider using the strategy of EA.  
Figure 8: The four user distribution scenarios: (**a**) Tokyo, (**b**) Osaka, (**c**) Sendai, and (**d**) Nagoya.  
This strategy can make the result escape the local optimal by randomly setting the different initial states at the beginning of each epoch.  
Similarly, the PSO method also uses the same strategy. In Figure 9, we can find that in the \([0.0,2.0]\) kbps throughput range, the PSO method has comparable or even better CDF of throughput performance than the other methods. In particular, in Figure 9b, PSO achieves the best CDF performance in the \([0.0,4.0]\) kbps throughput range. However, due to the high-dimensional space, the number of particles we set in our experiments is not enough for PSO to search for the optimal solution. It is still easy to fall into the local optimum.  
Unlike PSO, which only targets the current global optimal solution and local optimal solution, the proposed method trains the neural network to adapt itself to avoid falling into the local optimal in a high-dimensional space.  
Moreover, we evaluate the proposed method under the shifting scenario. Here, we use the DRLEA and DQN methods, which are trained under the rotation case, and the other three methods are trained under the shifting scenario. As shown in Figure 10, we can find that the DQN and the proposed DRLEA without retraining achieve comparable throughput performance to that of \(\mathcal{Q}\)-learning and Fuzzy \(\mathcal{Q}\)-learning with training in the HAPS left shift of the 5 km case. Since throughput is closely related to the user distribution, DNNs trained under the same user distribution know how to adjust the antenna parameters to reduce the number of low throughput users, even if HAPS moves again.  
Figure 9: CDF of UE throughput performance under the HAPS with rotation.
## 1 Introduction
### Contributions and Limitations of the Work  
In this work, we assessed the statistics of data exposures that have affected publicly listed U.S. companies and we used the observed patterns and study cases as the foundation of a discussion regarding the following: (i) compliance with data protection regulations in both the geographical and sectoral scopes; (ii) security, technical, and administrative controls applicable to protecting the data while in use, in transit, and at rest for different attack vectors and throughout their entire life cycle: create, store, use, share, archive, and destroy; (iii) guidelines for adequately responding to this kind of incident; (iv) the consequences of an exfiltration for both the data subject and the data owner. To our knowledge, this is the first study to comprehend these four areas.  
For this analysis, we have also improved the dataset proposed by [6] with the industrial sector of the target companies. As a consequence, the paper evaluates the frequency and impact of data breaches on different economic sectors, providing information regarding the prevalence of these incidents and advancing the discourse on prioritizing resources allocation. This is presented in Section 4. In Section 5, we review the literature on effective security measures to safeguard sensitive information against the studied attack vectors, providing a roadmap for organizations to enhance their resilience against data breaches. The paper also discusses compliance aspects, reviewing the role of data protection regulations in protecting organizations against data breaches.  
Although it did not interfere with the compliance, mitigation, and response discussion, the dataset used was restricted to companies in the U.S., and the statistics presented may not represent the data breach trends worldwide. Additionally, the study's focus on publicly listed companies may not fully represent the broader corporate landscape, as privately held firms may face distinct cybersecurity challenges. Another limitation of this work was the potential under-reporting of data breaches, as not all incidents may have been publicly disclosed.
## 2 Standards, PV Module and Inverter Requirements
### VDE-AR-N 4105 and VDE 0126-1-1 Standards  
At present, specific standards regulate the generation systems connected to the grid. The code VDE-AR-N 4105 governs the implementation of the European Network Code Requirements for generators for the low-voltage (LV) grid in Germany. In general, the code describes the grid connection requirements for generators at all voltage levels [38]. The regulations include capabilities for frequency stabilization, provision of reactive power to the point of black start capacity of large installations.  
According to VDE 0126-1-1 [39], an independent switching system is required capable of controlling the contribution to the 230/400 V grid from the photovoltaic installations. It must be possible to disconnect small power generators from the grid within 200 ms when the consumer or the protection device wishes to interrupt the connection. The voltage, frequency, and recognition controls of the service operations are the most demanded requirements at the automatic switching points. For this, a control and frequency relay is used that complies with the standard above at the automatic switching points between the power generators and the national low-voltage grid. Table 1 summarizes the main characteristics of both regulations and highlights the differences between them.  
Figure 4 shows a general connection scheme for grid-connected PV systems. In this scheme, two crucial issues are raised, the minimum voltage control and the frequency at the grid connection point. Generally, there are protections systems that disconnect the system if its voltage supply is reduced by 35% of its nominal value.  
Figure 4: General connection scheme for grid connected photovoltaic (PV) systems.
# Retained Austenite Destabilization during Tempering of Low-Temperature Bainite
## 2 Materials and Methods
It is known that changes in the volume fraction of individual phases during phase transformations can be determined through careful analysis of contractions and/or expansions in the dilatometric signal as a function of time or temperature [12; 24]. On the other hand, the change in carbon content leads to a change in the atomic volume of austenite, which makes the austenite dilatometric curve deviate from linearity. As will be described later in this manuscript, during tempering, different scenarios can be simulated in which the initial bainitic microstructure, which is composed of bainitic ferrite (\(\alpha_{b}\)) and retained austenite (\(\gamma_{r}\)), might evolve toward its equilibrium, ferrite (\(\alpha\)) and cementite (\(\theta\)). The procedure used for the theoretical calculations is based on the determination of the volume variation (\(\Delta V\)) between the initial (\(V_{ini}\)) and final (\(V_{fin}\)) state of the microstructure, taking into account the unit cell volume of the different phases (\(V_{i}\)) and their corresponding fraction (\(f_{i}\)), where subscript i stands for the phase. All calculations were performed in this work under the assumption of isotropic dilatation behavior, where the relative change in volume (\(\Delta V/V_{ini}\)) is directly related to \(\Delta L/L_{ini}\) by  
\[\Delta V/V_{ini}\ =\ (1\ +\ \Delta L/L_{0})3-1\ \approx\ 3\Delta L/L_{ini}.\]  
The atomic volumes of the phases involved in these calculations are given in Table 2 as a function of their lattice parameter, while Table 3 gathers the composition dependence at room temperature of the different lattice parameters (note that in the case of cementite, the lattice parameters are considered unique). As the mentioned calculations are intended at a given tempering temperature,  
Figure 1: Scheme of the tempering heat treatments applied in this work. it is imperative to correlate the room temperature lattice parameters, \(\text{a}_{0}\), with those expected at the tempering temperature, \(\text{a}_{\text{i}}\)\((T)\). Therefore, it was necessary to use the thermal expansion coefficients (\(\beta_{\text{i}}\)) shown in Table 2, in expressions of the type \(\text{a}_{\text{i}}\)\((T)\)\(=\)\(\text{a}_{0}(1+\)\(\beta_{\text{i}}\)\((T-25))\)[25; 26], where T is the tempering temperature. In addition, Vickers hardness measurements of the different microstructures obtained with isothermal treatments applied were performed according to ASTM E92 [32] standard using a 30 kg load; reported values correspond to an average of at least three measurements. Finally, the experimental results on the evolution of the microstructure during tempering were compared with the theoretical calculations carried out using MTDATA (National Physical Laboratory, Teddington, UK) [33] commercial software together with the SGTE database for steels to determine the equilibrium phase distribution at different tempering temperatures.
###### Abstract  
High Friction Surface Treatments (HFSTs) are often utilized as a spot treatment to enhance selected areas with high friction demand rather than extended pavement sections and are helpful in increasing skid resistance and minimizing road accidents. A laboratory design approach was created to assess the fundamental ideas behind the international friction index (IFI) concept and update the present IFI model parameters for HFST applications based on test findings to gain a better understanding of HFST performance. Two aggregate types in three sizes were tested under controlled polishing cycles. Friction and texture were measured using the Dynamic Friction Tester (DFT) and Circular Track Meter (CTM). Three physics-informed empirical models, including logarithmic, power law, and polynomial models, were selected to better represent texture effects, nonlinear scaling, and complex interactions between COF and MPD. Results show that friction performance varies with aggregate type, gradation, and polishing, and that traditional IFI parameters may not fully capture HFST behavior. Model refinements are suggested to better represent HFST surface characteristics with the lowest testing Root Mean Squared Error (RMSE) (0.049) and the highest predictive accuracy R\({}^{2}\) (0.821); the logarithmic model was found to be the best. Sensitivity analysis revealed that IFI predictions are more sensitive to COF (\(\Delta\)IFI: 14.3-17.7%) than MPD (\(\Delta\)IFI: 1.5-6.0%) across all models. These results demonstrate how these models can improve HFST design and performance assessment while providing useful information for enhancing road safety. This process is a useful tool for evaluating HFST friction resistance in a lab setting since it calculates HFST skid resistance using results measured in the lab.  
high friction surface treatment (HFST); international friction index (IFI); dynamic friction tester (DFT); skid resistance of the HFST surfaces +
Footnote †: journal: Applied Sciences
## 2 Routing Protocols for UAV Communication  
Currently, UAV communication uses the MANET routing protocol. A large number of routing protocols have been proposed for ad hoc networks. Due to the different characteristics of UANET, the MANET routing protocol is not directly applicable. Therefore, the routing protocol for UANET is still a research issue. The UANET routing protocol can be divided in two ways: single-hop and multihop [24]. In the case of single-hop, there is no intermediate node. The sender UAV carries the data packet to the destination [25]. In multihop routing, data packet transfer from source to destination is performed hop by hop. Therefore, based on hop selection strategies, multihop routing is further classified into two categories: topology-based and position-based. GPS is used in a position-based routing protocol for finding the real-time position of the next hop. Topology-based routing protocols are divided into three categories: proactive routing protocols, reactive routing protocols, and hybrid routing protocols. OLSR is a proactive routing protocol, also called table-driven or active routing protocol; thus, the change in the network is immediately available due to its proactive behavior. On the other hand, AODV and DSR are reactive or on-demand routing protocols. In the on-demand protocol, routing information is exchanged only after the demand. This paper analyzes the performance comparison between routing protocols such as DSR, AODV, GRP, and OLSR, along with E-OLSR. The following is a concise outline of these protocols.
## 3 System Hardware and Software Structure
### Hardware and Software for In-Vehicle Driver Behavior Recognition
#### 3.1.2 Driver's Action Recognition  
Due to the constrained space and numerous obstructions within the vehicle cabin, the vision-based recognition of driver operations is considerably intricate; therefore, the system analyzes the vehicle's internal network signals to indirectly denote the actions of drivers and find out the status of the ego.  
The specific approach involves developing a chassis signal parsing program based on the provided on-board diagnostics (OBD) signal matrix and encoding methods from the vehicle's chassis controller area network (CAN) bus. This program is designed to extract information such as throttle position, steering wheel angle, brake pedal status, handbrake status, and lighting status, as well as the status of various internal vehicle switches. Based on the binary encoding of the original CAN signals, we identify corresponding signal names and states. The encoding is then transformed into boolean status signals and floating-point numerical signals, making it more amenable for subsequent tasks. To ensure signal stability across multiple consecutive frames, a data redundancy approach is applied for signal filtering. Ultimately, the ROS (Robot Operating System) topic subscription and publication mechanism is utilized within the ROS domain of the computing unit for broadcasting. These parsed signals serve as reference values for the driver's vehicle operations, aiding in the recognition of the driver's in-vehicle control actions such as accelerating or braking, and providing the information for the real-time confidence calculation.
## 3 ADRC Design and Parameterization
### ESO Design
#### 3.2.1 \(\alpha\) and \(\beta\) Parameterization  
The following transformation is applied to the idempotent function in Equation (30):  
\[\left\{\begin{array}{l}\lambda(e)=\frac{fal(e,\alpha,\delta)}{e}\\ fe(t)=\frac{fal(e,\alpha,\delta)}{e}e=\lambda_{1}(e)e\\ fe_{1}(t)=\frac{fal(e(t),\alpha_{2},\delta)}{e}e=\lambda_{2}(e)e\end{array}\right. \tag{32}\]  
From the above equation, it can be seen that parameters \(\alpha\) and \(\delta\) are both important and interrelated to the system performance, respectively.  
When \(\alpha\) is a constant, the linear interval--that is, the range in which the output of function \(\lambda(e)\) is constant--gets smaller as \(\delta\) decreases. The smaller \(\delta\) is, the larger the output value of function \(\lambda(e)\) is at small errors, but when \(\delta\) is too small, the gain is too large, which is likely to cause system instability. In contrast, as \(\delta\) increases, it fails to highlight the advantages of nonlinearity. Therefore, in practice, it is necessary to determine a suitable linear interval for the system, which is generally taken as \(0.01<\delta<0.1\), here \(\delta=0.01\).  
When \(\delta\) is a fixed value, the power \(\alpha\) value is smaller, the constant value of the function \(\lambda(e)\) is larger, and the nonlinear \"large error, small gain; small error, large gain\" characteristics are more obvious. The control volume could oscillate at high frequencies if \(\alpha\) is set too low, and if it is set too high, the fast error decay and strong disturbance immunity will not be utilized. Statistically, \(\alpha_{1}=0.95\) and \(\alpha_{2}=0.5\).
# Dual-Band RF Wireless Power Transfer System with a Shared-Aperture Dual-Band Tx Array Antenna
## 3 Experimental Results of RF Wireless Power Transmission
### Single-Band RF WPT Experiment
#### 3.1.2 WPT Experiment (2): Varying the Rx Position and the Direction of Beam-Forming
It can be seen that higher transmission efficiency is measured when applying beam-focusing rather than beam-forming. From the measured results of the 5.73 GHz received power with beam-focusing, the WPT efficiencies at 1 m and 2 m are distributed as 0.841-0.060% and 0.211-0.072%, respectively. In other words, the overall result for 5.73 GHz received power (Figure 18) was lower than that for 2.482 GHz received power (Figure 17) because the higher frequency causes greater losses in free space. According to the results in Figures 17 and 18, the error between the calculated and measured results increased slightly when the position of the Rx deviated from the center. This is considered to be a measurement error (difficulties in accurately setting the location  
Figure 16: RF WPT experimental set-up (2): according to the Rx position. Figure 17: Distribution of the 2.482 GHz received power according to the Rx position: (**a**) distance = 1 m; (**b**) distance = 2 m.  
of the Rx when fixing the Rx antenna due to cable tension, among others issues), which arose during the experiment in the indoor environment used in this study.
## 3 Results
### Correlation between GPP and mTVWI

Table 2: Spearman rank-order correlation (rs) for the correlation between gross primary production (GPP) and the modified temperature vegetation witness index (mTVWI). Bold indicates significanceSpearman rank-order correlation coefficients between GPP and mTVWI (Table 2) were in agreement with partial correlation analysis results. The GPP and mTVWI relationships were positive and statistically significant (\(p<0.05\)) in conifers and broad-leaf evergreen species and negative in broad-leaf deciduous species. We found positive correlation coefficients in _L. decidua, P. nigra, P. pinaster_, and _Q. ilex_ (the same species highlighted in the partial correlation analysis except for _B. pendula_) and negative coefficients in most of the analyzed species (e.g., _P. sylvestris_, _A. pseudoplatanus_, _B. pubescens_, _F. excelsior_).
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 3 Conceptual Design of a Ramjet Nozzle
### Nozzle Geometric Configurations
#### 3.3.1 Conical Nozzle  
Conical nozzles work on the principle of converting the exhausted gases into axial thrust, and perform best when the axial thrust is uniform throughout the nozzle axis [8]. Hence, smaller divergence angles (\(\alpha\)) produce greater axial thrust because they maximise the axial component of the exhaust exit velocity, producing high specific impulse in the process (Figure 6a). However, this does result in longer and heavier nozzle divergent sections, which leads to problems with installation. Shortening the nozzles with large divergence angles leads to divergence losses. In a conical nozzle with a semi-angle (\(\alpha\)) divergent nozzle, by the time the exhaust has travelled past the exit, the streamlines have diverged away from the axis of the jet, producing radial thrust instead [7]. Larger divergence angles reduce low altitude performance as high pressures lead to over-expansion, which in turn may lead to flow separation. A divergence loss factor (\(\lambda\)) can be given (Equation (2)) to characterise this phenomenon (Figure 6b):  
\[\lambda=\frac{(1+cos\alpha)}{2} \tag{2}\]
# Validation of Drifting Buoy Data for Ocean Wave Observation
## 3 Results
### Intercomparison of Wave Heights for Various Cases  
The agreement between \(H_{g}\) and \(H_{e}\) was not good in Figure 2c. This reason was explored. GPS buoy-measured wave heights and ERA5 wave heights were compared in the case of winds from the sea and the other case. The case of winds from the sea means \(u_{w}>0\) for the buoy C, \(u_{w}<0\) for buoys D, E, F, G, and H, and \(v_{w}>0\) for buoys J, K, P, and Q (Figure 1), where \((u_{w},v_{w})\) are ERA5 wind vectors at the JMA drifting buoy positions.  
The skill metrics are summarized in Table 3. The fetch length was short in the case of Table 3b. It is expected that the impact of the difference between true fetch length and ERA5 fetch length on wave height difference is larger in the case of Table 3b than in the case of Table 3a. However, the agreement between \(H_{g}\) and \(H_{e}\) in the case of Table 3a was better than that in the case of Table 3b.  
The wave heights by the GPS buoys near the Kuroshio (buoys J, K, P, and Q) and other buoys were compared with ERA5 wave heights. The agreement between \(H_{g}\) and \(H_{e}\) near the Kuroshio was poor (Table 3d). Most of the GPS buoy-measured wave data was from the buoy P and Q in Table 3d (Table 1), where the current speed is large by the Kuroshio (Figure 1). The effects of current are not taken into account in producing ERA wave data [26]. The main reason of the disagreement between \(H_{g}\) and \(H_{e}\) in Figure 2c is due to the strong current of the Kuroshio. On the other hand, the agreement between \(H_{g}\) and \(H_{b}\) was good in all of the cases in Table 3. The agreement was better as shorter \(R_{a}\).
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 4 Test Procedure
### Experiment 1--Static Repeatability  
The first experiment was designed to provide a measure of the accuracy performance of a static commercial GPS receiver. This accuracy will be used as a baseline for the other experiments to determine the drift associated with GPS measurement in different conditions.  
A EM-406A SiRF III GPS receiver was interfaced with a microcontroller which was programmed to periodically log the GPS data to flash memory. The receiver was placed outside in a static position with clear line of sight of the sky for a period of approximately 44 h, logging a total of 162,676 measurements through different weather and atmospheric conditions. The sampled results for Latitude and Longitude are presented in Section 5.2.
## 5 Results and Discussion
### Predictive Models
Figure 5: Correlation plots of the average amount of normal discharge pulses per time unit (\(F_{p}\)), average tool feed rate (\(V_{f}\)), and average gap voltage (\(U\)) with the material removal rate (MRR) and tool wear rate (TWR). In each graph, the data points are the average values computed at the end of each drilling experiments and the dashed line is the linear correlation line. The coefficient of determination (R\({}^{2}\)) is also shown.