esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Observers gather increasing amounts of information about the states of systems from their inputs and outputs [1; 2; 3; 4; 5; 6]. The first state observation was made by [PERSON] [7] by observing inputs and outputs, and numerous subsequent versions, including sliding-mode observers [8] and high-gain observers [9], have been proposed since. Unlike its predecessors, the Extended State Observer (ESO) was pioneering in its independence from mathematical models [10]. Active Disturbance Rejection Control (ADRC) was used to estimate the uninformative dynamics of a nonlinear system as well as the model uncertainties that contribute to the generalized disturbance [11]. As reported in [10], ADRC was proposed to address the shortcomings of PID controllers. Several versions of ADRC were based on a nonlinear version of the ESO [12] and were parameterized in [13]. The main components of ADRC are a Tracking Differentiator (TD), a Nonlinear State Error Feedback (NLSEF), and an Extended State Observer (ESO) [14; 15; 16].
## 5 Conclusions  
(1) There are obvious differences in the mechanical properties and failure modes of specimens under different constant loading rates. The higher the loading rate is, the higher the peak strength and elastic modulus are, and the smaller the corresponding peak strain is. With the increase of loading rate, the crack initiation type changes from tensile wing crack to shear crack. The rock bridge area changes from smooth tensile crack to sawtooth shear crack.  
(2) During the process of sudden change-rate loading, the specimen exhibits an approximate elastic deformation. The mutational stress-strain curve is similar to the corresponding stress-strain curve under constant loading rate.  
(3) Combined with acoustic emission detection, it is found that when the rates before mutation have no differences, the stress-strain curves of the specimens were similar with the same rates after mutation.  
At the low-rate loading stage, AE count is generally low. While at the high-rate loading stage, AE count is generally high.  
The change from low-rate loading to high-rate loading easily induces stress drop, resulting in crack generation and an increase in AE count. Therefore, the potential surge of loading rate can be inferred by the stress drop and acoustic emission surge in engineering monitoring applications.  
From high-rate loading to low-rate loading, the AE counting image presents a \"U\" shape or a \"W\" shape. This is because the AE signal was at a high value in the early high-rate loading. When the crack development stage arrives, the rapid crack development could also lead to the rapid increase of AE signal. When the specimen is damaged, the AE signal suddenly increases and then disappears.  
(4) The failure mode of the specimen is determined by the rate of the specimen in the crack development period. In the early period, a certain range of low-rate loading has little influence on the specimen, as shown in Figure 10. When the early-stage stress is low, the AE count of the specimen is high, which will cause certain damage inside the specimen. As a result, the final peak stress and failure mode may be different even if the rate is the same after changing the rate.  
**Author Contributions:** Methodology, Y.Z.; Writing--original draft, X.H.; Writing--review & editing, M.W. and J.L.; Supervision, W.C.; Funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was financially supported by the National Natural Science Foundation of China (No. 52274194), and Hunan Provincial Natural Science Foundation of China (No. 2021 JJ30265).  
**Conflicts of Interest:** The authors declare that they have no competing interest.
# Digital Twin-Based Clamping Sequence Analysis and Optimization for Improved Geometric Quality
## 1 Introduction
### Variation Simulation  
Part, tool, and fixture variations are summarized as part variations and process variations. In modern automotive engineering, to reduce the development time and physical experimentation during the prototyping and verification phase, digital tools are used to simulate the geometric variation in complex assemblies with extensive tolerance chains. Commercial computer-aided tolerancing (CAT) software packages, e.g., RD&T [3] and 3 DCS [4], have been introduced, taking into account the aforementioned tolerances to perform variation simulation based on the defined positioning points and assigned tolerances.  
In sheet metal assemblies, the parts are bent and deformed during the assembly process. Therefore, the positioning system of the assembly, which ideally is reflected precisely in the assembly station fixture, allows for over-constrained conditions [5]. Often, several clamping points are considered as support points during the assembly process, mainly to lock the normal-to-surface direction of the formed sheet metal parts. When dealing with the non-rigid parts, the assembly process is typically composed of four steps: positioning the parts in the fixture; clamping the parts; fastening, i.e., joining the parts together; and releasing the assembly from the fixture. This process is referred to as Position, Clamp, Fasten, Release (PCFR) [6].  
Previous studies have focused on part variation representation and its contribution to final assembly variation [7]. Representing the part variation in digital models through skin model shapes has also been considered [8]. Assembly process variation simulation has also been introduced, analyzing the impact of the fixture design [9; 10; 11], clamping [12; 13; 14], and welding distortion [15; 16; 17; 18]. Similarly, the optimization perspectives on each step during the assembly process have been studied [7; 19; 20]. In the following, the optimization perspective regarding the assembly process, more specifically concerning the clamping step, is presented.
## 4 Results and Discussion
### Morphology and Porosity Structure of Porous Materials
#### 4.1.2 Morphology and Pore Size Distribution of Composite Porous Materials  
As previously mentioned, for materials with the same density, smaller fiber diameters result in better sound absorption performance. Therefore, in the optimization experiments, glass fibers with a diameter of 5 \(\upmu\)m were chosen as the subject for optimization. By adding zeolite particles (200 mesh, 74 \(\upmu\)m) to alter the pore structure of the fibrous porous material, composite porous materials were prepared to further optimize the sound absorption performance of the porous material.  
Taking the density of 100 kg/m\({}^{3}\) as an example, the pore morphology of the composite porous materials is shown in Figure 5, which can be categorized into three distinct cases: zeolite particles uniformly dispersed within the fiber pores (10% addition), partial blockage of pores by zeolite particles (20% addition), and extensive blockage of fiber pores by zeolite particles (30% addition).  
Figure 6 shows the median pore sizes and pore size distribution of porous materials with different zeolite addition levels across three densities (the median pore sizes for the remaining densities are provided in Table S2). It can be observed that at the same density, the median pore size of the porous material first increases and then decreases with the increase in zeolite replacement content. This phenomenon can be attributed to the fine particle characteristics of zeolite. At low replacement levels, zeolite particles partially substitute glass fibers, reducing the number of fibers required to form pores in the same volume. This results in a slight increase in the median pore size of the porous material. However, as the zeolite replacement level continues to increase, the excessive addition of zeolite particles leads to particle agglomeration and pore blockage within the material, as shown in Figure 5c. This causes the median pore size to decrease. These results clearly demonstrate that the addition of particles has a significant impact on the pore structure of fiber porous materials.  
Figure 5: SEM images of different zeolite additions at 100 kg/m\({}^{3}\): (a) 10%; (b) 20%; (c) 30%.  
Figure 6: \(Cont.\)
## 1 Introduction
In this paper, we propose a novel approach to compute the absolute depth of target objects (it is given beforehand which objects are target objects). It only needs a monocular camera and no calibrating. Firstly, the image \(A\) is captured, and then the image \(B\) is captured after moving the camera a distance \(d\) along the optical axis. The camera's intrinsic parameters are kept constant during the whole process. Secondly, we get the objects by segmenting the two images. Simultaneously, the SIFT points are detected in the two images. The image of the same object in the two images should be matched after completing the SIFT point matching. Finally, the absolute depth of an object can be computed by using the corresponding lengths of two straight line segments in the two images. The first straight line segment is composed of two SIFT feature points in image \(A\), and the second one is composed of two corresponding matching SIFT feature points in image \(B\).
###### Abstract  
The rapid expansion of 5G-enabled Internet of Things (IoT) devices in smart homes has heightened the need for robust, privacy-preserving, and real-time cybersecurity mechanisms. Traditional cloud-based security systems often face latency and privacy bottlenecks, making them unsuitable for edge-constrained environments. In this work, we propose Edge-FLGuard+, a federated and lightweight anomaly detection framework specifically designed for 5G-enabled smart home ecosystems. The framework integrates edge AI with federated learning to detect network and device anomalies while preserving user privacy and reducing cloud dependency. A lightweight autoencoder-based model is trained across distributed edge nodes using privacy-preserving federated averaging. We evaluate our framework using the TON_IoT and CIC-IDS2018 datasets under realistic smart home attack scenarios. Experimental results show that Edge-FLGuard+ achieves high detection accuracy (\(\geq\)95%) with minimal communication and computational overhead, outperforming traditional centralized and local-only baselines. Our results demonstrate the viability of federated AI models for real-time security in next-generation smart home networks.  
federated learning; edge computing; anomaly detection; smart home security; 5G-enabled IoT [table]capposition=top
# Dynamic Modeling and Characteristic Analysis of Articulated Steering Vehicles
## 2 Dynamic Model of ASVs
### Dynamic Model of Frames
#### 2.1.1 Model of Frames  
The motion of the frames can be described with the two sets of frames: the ISO (the longitudinal forward direction of the vehicle is defined as the X axis, the vertical downward direction is defined as the Z axis, and the Y axis is in the right-hand coordinate system) front frame (X\({}_{1}\)Y\({}_{1}\)Z\({}_{1}\)) and the ISO rear frame (X\({}_{2}\)Y\({}_{2}\)Z\({}_{2}\)) [24]. The motion in the XY plane is focused on considering the target in this paper. According to [PERSON]'s second law, the dynamic model of ASVs in the XY plane can be expressed as follows:  
\[\left\{\begin{array}{ll}m_{1}\big{(}\dot{v}_{x1}-v_{y1}\omega_{z1}\big{)}=F_{ txf}+F_{x}+F_{sxf}\\ m_{1}v_{x1}\big{(}\dot{\beta}_{1}+\omega_{z1}\big{)}=F_{tyf}-F_{y}+F_{ssf}\\ m_{2}\big{(}\dot{v}_{x2}-v_{y2}\omega_{z2}\big{)}=F_{txt}-F_{x}\cos\varphi-F_{ y}\sin\varphi+F_{sxr}\\ m_{2}v_{x2}\big{(}\dot{\beta}_{2}+\omega_{z2}\big{)}=F_{tyr}+F_{y}\cos\varphi-F_{ x}\sin\varphi+F_{syr}\end{array}\right. \tag{1}\]  
\[\left\{\begin{array}{ll}I_{z1}\dot{\omega}_{z1}=B\Big{(}F_{txf1}-F_{txf} \Big{)}-\\ \Big{(}F_{tyf1}+F_{tyf}\Big{)}L_{f1}+M_{szf}+F_{y}L_{f2}\\ I_{z2}\dot{\omega}_{z2}=B(F_{txt}-F_{txrr})-\Big{(}F_{tyf1}+F_{tyrr}\Big{)}L_{ r1}-\\ M_{szr}+\big{(}F_{y}\cos\varphi-F_{x}\sin\varphi\big{)}L_{r2}\end{array}\right. \tag{2}\]  
where subscripts 1 and 2 denote the front and rear frames, respectively; \(\varphi\) is the articulation angle; \(B\) is half of the wheel track; \(m_{1}\) and \(m_{2}\) are the masses of the front and rear frames, respectively; \(I_{z1}\) and \(I_{z2}\) are the moments of inertia in the \(z\) direction; \(F_{x}\) and \(F_{y}\) are internal forces at the central point; \(F_{ssf}\), \(F_{ssf}\), \(F_{ssf}\), \(F_{swr}\), \(M_{ssf}\), and \(M_{szr}\) are the forces and moments caused by the steering cylinder; \(F_{txf}\), \(F_{txf}\), \(F_{txf}\), \(F_{txf}\), \(F_{txf}\), \(F_{txf}\), \(F_{txf}\), and \(F_{txrr}\) denote the front and rear tire forces in the \(x\) and \(y\) directions; \(L_{f1}\) and \(L_{r1}\) are the distances between the mass center of the front and the rear frames (as well as the corresponding axles), respectively; and \(L_{f2}\) and \(L_{r2}\) are the distances between the mass center of the front and the rear frames (as well as the central articulated point), respectively.
# A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud-Fog Computing
## 6 Service Management Framework
### Problem Formulation: Minimum Cost of QoS and Energy Penalty
#### 6.3.1 Penalty Cost \(\mathcal{C}\) of QoS across the Cloud-Fog Environment  
The total penalty cost of scheduling the stream \(\ell\) across the cloud-fog computing environment is given by \(\mathcal{C}\), which formulates the performance of:  
* The communication penalty cost \(\rho_{i}^{\Gamma}\) of bandwidth \(\Gamma\) allocated to transmit a fog job \(J_{i}\);
* The service penalty cost \(\rho_{i}^{\mathcal{E}}\) to execute a time unit of \(\mathcal{E}_{i}\) for a fog job \(J_{i}\) in a cloud resource \(R_{m}\);
* The waiting penalty cost \(\rho_{i}^{\omega}\) for each time unit of waiting \(t\omega_{i}^{\beta}\) to queue a fog job \(J_{i}\) in resource queues \(\mathbf{Q}\) of the cloud tier;
* The violation penalty cost \(\rho_{i}^{\alpha}\) of not fulfilling SLA of a fog job \(J_{i}\).  
Thus, the schedule penalty cost \(\mathcal{C}\) across the cloud-fog computing environment is modeled by:  
\[\mathcal{C}=\sum_{i=1}^{l}(\chi_{i}^{\Gamma}\;\rho_{i}^{\Gamma}+\chi_{i}^{ \omega}\;\rho_{i}^{\omega}+\chi_{i}^{\mathcal{E}}\;\rho_{i}^{\mathcal{E}}+ \chi_{i}^{\alpha}\;\rho_{i}^{\alpha}) \tag{21}\]  
\[\sum_{i}(\chi_{i}^{\Gamma}+\chi_{i}^{\omega}+\chi_{i}^{\mathcal{E}}+\chi_{i}^ {\alpha})=1,\quad\forall i\in[\mathcal{U}\,l] \tag{22}\]  
where \(\chi_{i}^{\Gamma}\), \(\chi_{i}^{\omega}\), \(\chi_{i}^{\mathcal{E}}\), and \(\chi_{i}^{\alpha}\) are scaling factors for communication, service, waiting, and SLA-violation penalty costs, respectively.  
The objective is to formalize the performance penalty cost by allocating a stream \(\ell\) of fog jobs \(\mathbf{J}\) in the cloud tier with a scheduling order \(\beta\), such that the QoS penalty cost \(\mathcal{C}\) is minimized at the level of the cloud-fog computing environment, and thus the schedule performance is optimized, as follows:  
\[\underset{\beta}{\text{minimize}}\;(\mathcal{C})\equiv\underset{\beta}{ \text{minimize}}\;\sum_{i=1}^{l}(\Lambda_{i}\;\Gamma+\psi_{i}\;t\omega_{i}^{ \beta}+\xi_{i}\;\mathcal{E}_{i}+\zeta_{i}\;\alpha_{i}^{\beta}) \tag{23}\]  
Each cloud resource \(R_{m}\) can only execute one fog job \(J_{i}\) at a time. The service execution discipline of a fog job \(J_{i}\) is non-preemptive; a fog job \(J_{i}\) cannot be interrupted once it starts the execution on a cloud resource \(R_{m}\). Cloud resources \(\mathbf{R}\) are homogeneous, and hence the cost of servicing any fog job \(J_{i}\) on any cloud resource \(R_{m}\) is similar.
## 3 Results  
### Quantitative Analysis  
Table 1 shows the RMS \(\pm\) standard deviation (SD) (\(\upmu\)m), +AVG deviation \(\pm\) SD (\(\upmu\)m), \(-\)AVG deviation \(\pm\) SD (\(\upmu\)m), and in TOL (\(\%\)) \(\pm\) SD of the NA, PSM, and SA groups according to the occlusal surface, functional cusp area, central groove area, and non-functional cusp area, respectively. On the occlusal surface, the RMS values of the NA, PSM, and SA groups were \(168.2\pm 43.3\)\(\upmu\)m, \(152.1\pm 54.2\)\(\upmu\)m, and \(160.6\pm 84.6\)\(\upmu\)m, respectively. The RMS value of the PSM group was lower than that of the other groups, but there was no significant difference in the one-way ANOVA (\(p\) = 0.797). The +AVG deviation values of the NA, PSM, and SA groups were \(124.5\pm 45.3\)\(\upmu\)m, \(112.3\pm 46.3\)\(\upmu\)m, and \(135.8\pm 52.7\)\(\upmu\)m, respectively. The \(-\)AVG deviation values of the NA, PSM, and SA groups were \(-87.7\pm 73.1\)\(\upmu\)m, \(-93.1\pm 61.5\)\(\upmu\)m, and \(-91.5\pm 83.8\)\(\upmu\)m, respectively. The in TOL (\(\%\)) values of the NA, PSM, and SA groups were \(90.5\pm 6.7\)\(\%\), \(92.9\pm 5.9\)\(\%\), and \(91.5\pm 8.3\)\(\%\), respectively. The one-way ANOVA showed no statistically significant differences in the RMS value, +AVG deviation, \(-\)AVG deviation, or in TOL between the three groups (\(p\) = 0.443, 0.980, and 0.658, respectively). An analogous tendency was indicated in the split areas according to functional properties (Figure 5).  
For the area outside of the tolerance level, which was regarded as occlusal errors in this study, the results are described in Table 1 and Figure 6. There were significant differences in the RMS values of the NA, PSM, and SA groups (\(p\) = 0.028). The RMS values calculated from all points outside of the tolerance area were \(257.0\pm 73.9\)\(\upmu\)m in the NA group, \(202.3\pm 39.3\)\(\upmu\)m in the PSM group, and \(222.9\pm 41.9\)\(\upmu\)m in the SA group. From the post hoc analysis, the RMS of the PSM group was statistically lower than the NA group (Figure 6). The +AVG values were \(210.9\pm 48.6\)\(\upmu\)m in the NA group, \(173.1\pm 31.31\)\(\upmu\)m in the PSM group, and \(194.7\pm 36.4\)\(\upmu\)m in the SA group. There was also a significant difference between the NA and PSM groups in the post hoc analysis (\(p\) = 0.040).
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 2 The _WeakHEO_ Receiver Architecture
### Description of the Mission Scenario  
The reference trajectory for our study is a direct Earth-Moon Transfer Orbit (MTO). The initial position and velocity with the respect to the Earth Centered Inertial frame (ECI) are reported in Table 1, as well as some characteristics assumed for the host spacecraft. The full trajectory is propagated from the initial conditions by the PosApp software of our Spirent GSS8000 simulator, taking into account gravitational effects from the Earth, Sun and Moon, atmospheric drag and the solar radiation pressure. The reference trajectory is represented by the blue portion of the MTO shown in Figure 2. The reference orbit has been validated using the System Tool Kit (STK) [20]. Figure 3 shows the relation between altitude and time during the considered MTO and also the GPS satellites altitude.  
Figure 1: Architectural components of the _WeakHEO_ receiver.
# Interference Avoidance Using TDMA-Beamforming in Location Aware Small Cell Systems
###### Abstract  
It is ascertained that the current communication systems will not be able to support the future network demands due to the increasing traffic, limitation of frequency resources, and high level of interference. Recently, beamforming techniques have been introduced to reduce the interference by redirecting the transmission towards the desired users only. While such beamforming enables better interference mitigation and improved network performance, the concerns on its effectiveness in dense deployment environments are arising. In this paper, the prospect of interference avoidance in location aware small cell environments using time division multiple access (TDMA) and beamforming is studied. The interference is reduced by identifying the aggressor small cell and transmit the beams towards the desired users at different times. Simulation results show that the proposed scheme is able to enhance the signal to interference plus noise ratio (SINR) by approximately 18 dB, enhance the user throughput by about 10 Mbps in comparison to small cell on/off control scheme with a discovery signal (SCon/off-DS), and improve the fairness index to about 95% in comparison to the baseline scheme. It is believed that the presented results promote the proposed scheme as an efficient interference management paradigm for the fifth generation (5G) communication systems.  
Keywords:beamforming; interference management; throughput; fairness index, small cell networks +
Footnote †: journal: Applied Sciences
## 4 VR-PEKS Scheme and Security Definition
### Security Model  
Secure cryptographic schemes generally have keyword ciphertext indistinguishability under adaptive selection attacks, i.e., an adversary cannot determine with more than 1/2 probability, given any two plaintexts and the ciphertext of any one of them, which plaintext the given ciphertext is generated from. There are two main types of adversaries in the PEKS scheme, namely external adversary \(\mathcal{A}_{1}\) and internal adversary \(\mathcal{A}_{2}\). \(\mathcal{A}_{1}\) can intercept the trapdoor from the public channel between CS and DR, and then perform KGA using DR's public key. \(\mathcal{A}_{2}\) is a more aggressive adversary than \(\mathcal{A}_{1}\), which possesses the ciphertext of files and keyword trapdoors, and is able to execute search algorithms. An adversary can obtain the keyword trapdoor by some means and can use the data user's public key to generate the ciphertext of his guessed plaintext keyword, thus conducting a keyword guessing attack and causing the violation of the user's privacy. Therefore, in the PEKS scheme, it is also necessary to ensure the indistinguishability of having keyword trapdoors. It follows that a PEKS scheme that is secure and resistant to external and internal  
Figure 1: System model for the proposed VR-PEKS scheme.  
keyword guessing attacks should satisfy keyword ciphertext indistinguishability under an adaptive selection attack (KC-IND-CKA) and keyword trapdoor indistinguishability under an adaptive selection attack (KT-IND-CKA). KC-IND-CKA and KT-IND-CKA security is defined by the following two games.