esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Security Analysis
#### 4.1.4 Man-in-the-Middle Attack  
To mitigate the threat of man-in-the-middle (MITM) attacks, this research implements several methods and measures. Public key encryption is utilized to safeguard sensitive information during the authentication process. Encryption and decryption using a pair of public and private keys ensure that attackers cannot access or modify transmitted data. The challenge response verification mechanism is employed, where the smart contract sends a unique challenge to the client, and the client generates a response by encrypting the challenge with their private key. Even if attackers intercept the communication, they cannot replay the same response due to the uniqueness of the challenge.  
Moreover, Public Key Infrastructure (PKI) is employed to verify identities and establish trust. Through digital certificates, signatures, and verification, the system prevents attackers from executing MITM attacks using forged certificates. Additionally, biometric features are stored on the device itself rather than being transmitted to the smart contract for verification. This prevents attackers from intercepting biometric information during transmission. These mechanisms effectively mitigate the risk of MITM attacks.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 4 Results
### The Accuracies of the Methods without a Separate Training Data  
The use of specific training data is not necessarily possible in operational applications due to a need for very rapid or almost real-time localization of the damages. A final goal should be development of a method and a model that predict the locations of the damages without new training data and using only the existing information before the damage and SAR data (1-6 scenes) after the damage.  
This possibility was tentatively tested in a simple way by selecting a set of the Sentinel-1 scenes and another distinct set for prediction. The cover numbers of the scenes for modeling were 2, 3, 4, 6, 7, 11, 12, 15, 16, 17, 18, 24, 25 and 26, and in prediction 1, 5, 8, 9, 10, 13, 14, 19, 20, 21, 22, 23 and 27 (see Table 4). The proportion of correctly predicted stands was 0.73. This simple test could already indicate if it is possible to develop an unsupervised damage prediction method.
# Correlation of Optical Constants and Morphologies with Mueller Matrix for Micro-Rough Surfaces
## 2 Theoretical Background
### NSC Descriptions of Mueller Matrix Elements  
The parameters in the NSC representation are the associated ellipsometry parameters, which can be used to replace the Mueller matrix elements with electric vectors. This makes it possible to analyze the correlation of optical constants and morphological parameters with Mueller matrix elements and explore methods to accurately measure optical constants and deduce the morphologies of rough solid surfaces. The Mueller matrix, \(\mathbf{M_{L}}\), of a sample is given by [33]  
\[\mathbf{M_{L}}=\begin{bmatrix}1&-N-\alpha_{ps}&C_{sp}+\zeta_{1}&S_{sp}+\zeta _{2}\\ -N-\alpha_{sp}&1-\alpha_{sp}-\alpha_{ps}&-C_{sp}+\zeta_{1}&-S_{sp}+\zeta_{2}\\ C_{ps}+\zeta_{1}&-C_{ps}+\zeta_{1}&C+\beta_{1}&S+\beta_{2}\\ -S_{ps}+\zeta_{2}&S_{ps}+\zeta_{2}&-S+\beta_{2}&C-\beta_{1}\end{bmatrix} \tag{4}\]where  
\[N=(1-\gamma^{2}-\gamma_{sp}^{2}-\gamma_{ps}^{2})/D \tag{5}\]  
\[D=(1+\gamma^{2}+\gamma_{sp}^{2}+\gamma_{ps}^{2})=2/(1+N) \tag{6}\]  
\[S=2\gamma\sin(\Delta)/D \tag{7}\]  
\[C=2\gamma\cos(\Delta)/D \tag{8}\]  
where \(\Delta_{\rm xy}\) and \(\gamma_{\rm xy}\) (x, y = \(p,s\)) represent the phase difference and the tangent function of the amplitude ratio (\(\psi\)) of x-polarized light induced by the incident y-polarization, respectively.  
The NSC representation for anisotropic samples requires seven parameters constrained by the following relation [33]:  
\[N^{2}+S^{2}+C^{2}+S_{ps}^{2}+C_{ps}^{2}+S_{sp}^{2}+C_{sp}^{2}=1 \tag{21}\]
# Spectrum Sharing Opportunities for 6G Terrestrial and Non-Terrestrial Networks +
## 3 Coexistence Analysis Results
### Evaluation of Exclusion Zones Around the FS P-MP CS Receiver  
Here, the exclusion zones around the FS victim receivers have been evaluated considering a flat terrain (red area) and an example of actual terrain elevation (green area). The exclusion areas have been calculated both around the FS TS and around the FS CS considering that the FSS transmitter and the FS receiver were aligned in the azimuthal plane. The following technical characteristics of the FSS and FS systems have been taken into account:  
* FSS: an antenna diameter of 1.8 m, elevation angle of 10 degrees, antenna height of 10 m, Pt = 25.7 W, antenna gain = 52.91 dBi and bandwidth = 320 MHz
* FS CS: an antenna height of 15 m, antenna gain of 15 dBi, elevation angle of 0 degrees and bandwidth of 28 MHz.
* FS TS: an antenna height of 15 m, antenna gain of 42 dBi, elevation angle of 0 degrees and bandwidth of 28 MHz.  
As shown in Figure 4, the effect of the terrain clearly reduces the extension of the exclusion area. In Figure 4a, the geographical area varies from 175 km\({}^{2}\) considering flat terrain to 12.7 km\({}^{2}\) taking into account the terrain elevation; in Figure 4b, the geographical area varies from 292 km\({}^{2}\) considering flat terrain to 27.3 km\({}^{2}\) taking into account the terrain elevation. Comparing the shapes of the exclusion areas around the FS TS and around the FS CS, the effect of the antenna diagram is clear: since the FS TS antenna is more directive, the exclusion area is more lengthened than the one resulting from the coexistence analysis on the FS CS receiver, which implements a sectoral antenna.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Azimuth FS** (\({}^{\circ}\)) & **0** & **10** & **20** & **30** & **40** & **50** & **60** & **70** & **80** & **90** \\ \hline Distance (km) & 44.3 & 32.5 & 17.8 & 11.3 & 8 & 6.5 & 6.5 & 0.3 & 0.3 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Separation distance between FSS Earth station and FS CS (FS CS antenna gain 15 dBi).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Azimuth FS** (\({}^{\circ}\)) & **0** & **10** & **20** & **30** & **40** & **50** & **60** & **70** & **80** & **90** \\ \hline Distance (km) & 44.3 & 32.5 & 17.8 & 11.3 & 8 & 6.5 & 6.5 & 0.3 & 0.3 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Separation distance between FSS Earth station and FS TS.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Azimuth FS** & **0** & **10** & **20** & **30** & **40** & **50** & **60** & **70** & **80** & **90** \\ (\({}^{\circ}\)) & & & & & & & & & & \\ \hline Distance (km) & 38.3 & 36.3 & 33 & 31.8 & 26.8 & 23.5 & 21 & 19 & 17.3 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Separation distance between FSS Earth station and FS CS (FS CS antenna gain 25 dBi).
# Design of Multifunctional Mesosphere-Ionosphere Sounding System and Preliminary Results
The mesospheric parameter inversion algorithms and the wide band requirements for ionospheric sounding are taken into account concurrently. In addition to verify the feasibility of this antenna solution, it also greatly reduces the volume of the system and the difficulty of construction. Through the flexible switch between the working modes of the mesospheric and ionospheric sounding, the wind field of the mesosphere, the electron density distribution of the lower ionosphere, the ionogram and its inversion profile at the same location can be obtained. The acquisition of these parameters may have important value to the study of the interaction and coupling mechanism between the mesosphere and ionosphere.
## 5 Discussion  
### Tectonism  
Detrital zircon U-Pb ages, heavy mineral assemblages, and paleocurrent data demonstrate that the Tianshan Orogenic Belt fundamentally governed sediment supply to the Kuqa Depression [5, 70, 71, 72, 73].  
Early Early Jurassic (Stage I), the Kuqa Depression underwent significant sedimentation characterized by large-scale braided river plain deposits in the LST of SQ1 and SQ2 (Figure 6). This sedimentation reflects the intense uplift of the South Tianshan Mountains, which is manifested by pronounced basin elevation and enhanced sediment flux. This orogenic activity is further evidenced by large-scale incised surfaces and angular unconformities [74, 75], consistent with Late Triassic-Early Jurassic collisional reactivation of Paleozoic structures along the Qiangtang-Eurasia margin [5, 76, 77, 78, 79, 80] that directly drove the South Tianshan uplift [5, 76, 81].  
Subsequent tectonic weakening initiated systematic depositional reorganization: Middle-Late Early Jurassic (Stage II) transitioned to meandering deltaic-lacustrine systems (SQ3-SQ5) under persistent fine-grained sedimentation (Figure 6), progressing to Middle Jurassic (Stage III) swamp (SQ6-SQ7) and deltaic deposits (SQ8-SQ9) within dominantly low-energy regimes. Critically, this evolution records post-Early Jurassic orogenic decay and diminished sediment flux [76, 81] that controlled depositional system restructuring. Particularly significant in Stage III, subdued geometric gradients constrained erosional energy, suppressing proximal coarse input while optimizing organic accumulation. Quantifying this trend, zircon fission-track data [82] document declining South Tianshan uplift rates post-Early Jurassic, robustly substantiating the mechanism.  
Progressive sediment maturity increases [70] further reflect northward sedimentary expansion and provenance retreat, thereby establishing tectonic pacing as the primary driver of foreland basin stratigraphic architecture, with direct implications for hydrocarbon resource distribution and basin evolution models.
## 3 Outer Layer Modeling and Optimization
### Steady State Operation Modeling of Integrated Energy System
#### 3.1.1 Electricity Transmission System  
Given a power system described by its electric conductance \(G_{ij}\) and electrical susceptance \(B_{ij}\), and given a subset of voltage, phase angle, and power injections, the power flow determines the other voltage, phase angles, active and reactive power injections [14; 19; 24]. The power distribution system is represented by the power balance equations at all nodes:  
\[\left\{\begin{array}{l}P_{i}=U_{i}\sum\limits_{j=1}^{n}U_{j}(G_{ij}\cos \theta_{ij}+B_{ij}\sin\theta_{ij})\\ Q_{i}=U_{i}\sum\limits_{j=1}^{n}U_{j}(G_{ij}\sin\theta_{ij}-B_{ij}\cos\theta_ {ij})\end{array}\right. \tag{17}\]  
where \(n\) stands for the total number of nodes; \(P_{i}\) and \(Q_{i}\) are the total active and reactive power injections at node \(i\), respectively.
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
###### Abstract  
Remote sensing targets typically exhibit characteristics of gradual scale changes and diverse orientations. Most existing remote sensing detectors adapt to these differences by adding multi-level structures for feature fusion. However, this approach leads to incomplete coverage of the overall target by the extracted local features, resulting in the loss of critical directional information and an increase in computational complexity which affect the detector's performance. To address this issue, this paper proposes a Rotation Target Detection Network based on Multi-kernel Interaction and Hierarchical Expansion (MIHE-Net) as a systematic solution. Specifically, we first refine scale modeling through the Multi-kernel Context Interaction (MCI) module and Hierarchical Expansion Attention (HEA) mechanism, achieving sufficient extraction of local features and global information for targets of different scales. Additionally, the Midpoint Offset Loss Function is employed to mitigate the impact of gradual scale changes on target direction perception, enabling precise regression for targets across various scales. We conducted comparative experiments on three commonly used remote sensing target datasets (DOTA, HRSC2016, and UCAS-AOD), with mean average precision (mAP) as the core evaluation metric. The mAP values of the method in this paper on the three datasets reached 81.72%, 92.43%, and 91.86% respectively, which were 0.65%, 1.93%, and 1.87% higher than those of the optimal method, significantly outperforming existing one-stage and two-stage detectors. Through multi-scale feature interaction and direction-aware optimization, MIHE-Net effectively addresses the challenges posed by scale gradation and direction diversity in remote sensing target detection, providing an efficient and feasible solution for high-precision remote sensing target detection.  
depathwise separable convolution; progressive scale variation; targets with diverse orientations 2025158727  
1
## 1 Introduction  
The number of pistons in an axial piston pump significantly affects the vibrations of the pump housing (Figure 1). More pistons can lead to a more balanced distribution of forces through the pump. This balance helps reduce vibration, as the alternating forces from each piston can cancel out to some degree [1, 2, 3]. The overall design and configuration of the pump, including the number of pistons used, also play a role.  
For example, pumps designed with an even number of pistons may exhibit different vibration patterns than those with an odd number [4, 5].  
The mathematical model relates pressure pulsations to vibration frequencies through coupled differential equations [6]. An analysis of the system's response to pressure changes reveals how increased pulsation leads to higher vibration frequencies, especially under resonant conditions [7]. Using numerical methods or simulations can provide further insight into pump dynamics under different operating conditions.  
A higher number of pistons can result in reduced torque fluctuations in the drive mechanism, which contributes to smoother operation and less vibration [8; 9]. Fewer pistons can create larger torque spikes, increasing the potential for vibration [10].  
The arrangement and number of pistons can affect how the pump absorbs and dissipates energy. More pistons can improve the damping characteristics of the system, reducing the amplitude of vibrations transmitted to the case [11; 12; 13].  
Researchers are actively investigating the effect of piston numbers on pressure pulsations and vibrations in axial piston pumps. The dominant frequency components of the pressure pulsations are identified. These frequencies are analyzed with varying piston numbers. The effects of pulsations on system stability and component fatigue are investigated. The cylinder block geometry is optimized (e.g., cylinder bore size and cylinder spacing) to reduce pressure pulsations and improve vibration characteristics. The fluid flow inside the pump is simulated to predict pressure pulsations and their effects on the pump housing. Experimental tests are conducted on pumps with different piston numbers to validate the simulation results and gain insight into real-world behavior.  
Advanced measurement techniques (e.g., high-speed pressure sensors and laser vibrometers) are used to obtain detailed data for analysis. Key research objectives are focused on reducing noise and vibration, minimizing the noise and vibration levels generated by the pump to improve operator comfort, reducing noise pollution, and improving system stability. Design guidelines and optimization techniques have been developed to minimize pulsations and vibrations in axial piston pumps. By following these research paths, researchers aim to develop more efficient, quieter, and more reliable axial piston pumps for a wide range of applications
# Monitoring Cropland Abandonment in Hilly Areas with Sentinel-1 and Sentinel-2 Timeseries
## 2 Materials and Methods
### Method
#### 2.3.6 Accuracy Assessment  
The verification samples were divided into two categories; one was obtained using our method (Sections 2.3.3 and 2.3.4), and the other constituted field samples (Section 2.2.3). On the basis of the validation samples, confusion matrices were created, and the max, min, and mean of producer accuracy (PA), user accuracy (UA), and overall accuracy (OA), as well as the kappa coefficient, were calculated for the 3 years. The F1 score (F1 = 2 \(\times\) UA \(\times\) PA (UA + PA)), a harmonic mean of user and producer accuracy, was calculated. It ranges from 0 to 1 with a higher score indicating better classification performance, and it is advantageous when learning from imbalanced data [74]. The classification results were compared with existing classification products, highlighting the classification accuracy and spatial detail in the study area.
## 3 Experimental Validation
### Investigation of Temperature Variation Effects  
To validate the assumption that DIs' at multiple sensing regions are simultaneously changed due to temperature variation, the temperature variation effects on multi-region DIs' are investigated. The test setup for investigating temperature variation effects is shown in Figure 3. AWG (NI PXI-5412) generates the chirp input waveform with a broad frequency ranged from 100 Hz to 150 Hz, and digitizer (NI PXI-5122) simultaneously measures the corresponding responses with a sampling rate of 1 MHz. The responses are 5 times measured and averaged in the time domain to avoid undesired noise components. The reference capacitor of 10 nF is used for the self-sensing circuit. For the temperature variation effect tests, the temperature data sets are collected from the intact condition of the FRCCs at \(-10\)\({}^{\circ}\)C, \(0\)\({}^{\circ}\)C, \(10\)\({}^{\circ}\)C, \(20\)\({}^{\circ}\)C and \(30\)\({}^{\circ}\)C with a relative humidity 60% using a temperature chamber. The representative temperature variation test results are shown in Figure 4.  
The multiple sensing regions of the FRCCs specimens are designed as Regions A, B, C and D as described in Figure 1. The Nyquist plots overall move to the left as the temperature increases. It can be observed that the temperature increase leads to simultaneously affect to the all sensing regions. The physical reason is speculated that the temperature changes cause the change of the ion mobility along the internal electrical bridge of FRCCs, although the absolute values might be different in each region because of FRCCs' inhomogeneity.  
Figure 5 shows the DIs' computed from the temperature variation test results. The baseline data set is assumed as the data at \(-10\)\({}^{\circ}\)C in each region. Although the maximum DIs' at each region are different as expected, the similar changing patterns are observed at all sensing regions.  
Figure 2: Overall procedure of a crack localization algorithm.  
Figure 4: Temperature variation test results at **(a)** Region A; **(b)** Region B; **(c)** Region C and **(d)** Region D.  
Figure 5: Damage index of temperature variation test results at **(a)** Region A; **(b)** Region B; **(c)** Region C and **(d)** Region D.  
Figure 3: Test setup for temperature variation effects.
# An Energy-Efficient Fail Recovery Routing in TDMA MAC Protocol-Based Wireless Sensor Network
## 2 Network Topology and Energy Model
### Network Topology with Time Division Multiplexing
The authors of [27] proposed a binary linear programming method to  
Figure 2: Example, TDMA based data forwarding in WSN: (**a**) presents the data flow in the network; (**b**) illustrates the TDMA scheduling table. solve the time slot allocation problem and implemented a heuristic algorithm distributed method in each node. Since, the method uses in [27] is a more practical method that can be applied to a network of large scale, we have employed it as the routing and scheduling method. The resulting network topology of the above routing [5] and scheduling process [27] restricts every node to have only a single egress edge while allowing multiple ingress edges. When the WSN wakes up, all nodes wake up at the same time and measure their sensing data at the same time. Then each node starting from the leaf nodes forwards its data to its parent node. Each parent node waits until all sensing data are received from its child nodes. Then, the parent node aggregates all the received sensing data into one data packet and forwards the aggregated data to its parent node in the next hop. Our system model uses the routing algorithm in [5] and the scheduling algorithm in [27] during the network initialization stage.
## 3 Materials and Methods
### Specim Design  
To evaluate the performance of FDM and SLA across multiple metrics, three distinct geometries were designed in SolidWorks\({}^{a}\) 2020 and exported as STL files for slicing:  
* Dimensional Accuracy: A simple cube (50 mm per side) was used to assess dimensional deviations along the X, Y, and Z axes. This geometry allows for straightforward measurements and comparison of tolerances.
* Surface Quality: A flat plate (100 mm \(\times\) 100 mm \(\times\) 5 mm) was designed to facilitate repeatable surface roughness measurements. The large flat surface ensures consistent profilometry scans.
* Mechanical Properties: Tensile bars conforming to ASTM D638 Type IV were used to evaluate ultimate tensile strength (UTS), elongation at break, and Young's modulus. This standardized geometry ensures compatibility with the existing literature and testing protocols.  
All parts were oriented so that their largest face was parallel to the build platform, ensuring consistent support strategies and allowing direct dimensional comparisons in X, Y, and Z.
## 1 Introduction
The creation of a knowledge base should provide a higher level of information security, since countless examples of attack trees already exist, with varying levels of detail and complexity, targeted against different information systems and platforms. Utilizing an expert system for this process would also ensure accuracy, reliability, and repeatability for the risk analyses conducted. By converting attack trees to formats usable by expert systems, the benefits of attack trees and expert systems can be combined and multiplied  
risk is already systematized. While performing transformations, information on attack directions, probabilities, impacts, and possible countermeasures is imported, thereby providing the possibility to use an expert system for decision making and making recommendations on risk management. The research aims to improve the methods for the creation of a knowledge base for the eventual purpose of assisting with conducting risk analyses on an information system or set of systems. Automating the conversion of attack trees to a format that expert systems can use is relevant and essential because it will allow small companies and individuals to perform risk assessments at a level comparable to an assessment conducted by someone with the resources of large companies [18]. Findings on attack tree data transformation and import into expert risk analysis systems can be applied to significantly reduce the amount of time, effort, and the monetary investment required for the risk analysis of a given company or agency. Additionally, this will allow organizations with limited security financing to prioritize the defense of the most likely places at which an attack could occur. This article is organized as follows. Section 1, the current section, is an introduction. Section 2 presents related work on attack trees and expert systems. Section 3 presents an approach of automating the conversion of attack trees into a format that can be accepted by an expert system. Section 4 details the experiments, and the weaknesses and strengths of the methodology. Finally, Section 5 concludes the paper.