esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Federated Learning
#### 2.1.3 Federated Learning Lifecycle and FedOps  
Nevertheless, because federated learning does not centralize data or models, the complexity of implementing FL projects increases [19]. Federated learning depends on the collaboration of multiple clients, and administering various aspects of the life cycle, such as model deployment and regulation of client and server states, is necessary [20]. The FL lifecycle describes the state transition from creation to completion of an FL model [3]. This procedure included data collection and preprocessing, model building, global model aggregation and evaluation, and redeployment. Federated learning can be conducted effectively when each phase is managed proficiently.  
FedOps, built on Flower Framework, is a platform designed to address the difficulties in managing heterogeneity and facilitating operations in federated learning systems. It extends MLOps to support the systematic execution of FL by automating and monitoring the entire FL process from integration to evaluation and deployment, thereby providing an optimized operational environment [21]. Although various FL frameworks are available [15; 22; 23; 24; 25], FedOps prioritizes the management of the entire FL lifecycle and is aptly suited for administering the federated learning lifecycle [19]. FedOps aids the efficient execution of FL initiatives through its Web UI and Python libraries [19]. Table 1 illustrates a comparison table between FedOps and other frameworks.  
FedOps is a platform that independently separates and manages the lifecycle of servers and clients in federated learning (FL), addressing the specific requirements of FL and supporting efficient operations. The server is responsible for managing client states, updating the global model, and monitoring tasks, all structured systematically using Apache Airflow. The client, on the other hand, trains the model using local data, analyzes system heterogeneity with the Chunk-Bench tool, and performs real-time monitoring through an ELK stack-based log processing pipeline [19].  
FedOps leverages Kubernetes and container technologies to provide flexibility and scalability in cloud-native environments, supporting multi-GPU environments and load balancing through tools like MicroK8s and MetalLB. This setup allows researchers and engineers to develop and operate FL projects effectively without system constraints. By adopting this design, FedOps significantly improves communication costs and the time-to-accuracy of learning, while simplifying the complexities of FL projects and delivering an optimized environment [19].
## 2 System Architecture
### Software  
#### 2.2.1 ROS  
An ROS (Robot Operating System) [19] is an open-source framework that provides tools, libraries, and conventions for the development of robotic applications. An ROS is middleware designed to help robot designers with different areas, such as perception, control, navigation, planning, and communications. The key features of an ROS are modularity (nodes are used to run small programs that perform a specific task), package management (organized code, libraries, and data), tools (visualization, debugging, etc.), multi-language support (Python and C++), and hardware communication (reading sensor data and sending control commands to actuators).  
One of the main attributes of an ROS is the multi-machine setup, this allows for communication with multiple computers on the same network just by setting some environment variables. There are different versions of ROSs, including ROS2, which is a recent version with new capabilities. In this work, as the underwater robot, (Girona500, IquaRobotics, Girona, Spain) works using the ROS Noetic version and the experiments were implemented with this middleware.
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
## 2 Deployable Mesh Antenna Design
### Antenna Configuration  
Among the different types of reflector configurations, a primary center-fed parabolic type was chosen. Other reflector configurations were considered, but they would either present more complex design or not satisfy the CP-SAR requirements. For instance, having an offset feed could avoid feed blockage, but at the cost of decrease of CP signal purity and increased design complexity of the deployment mechanism; a L-band Cassegrain configuration could significantly improve feed illumination efficiency but would require a too large subreflector, posing a problem in terms of volume constraints and signal purity. Hence, aiming for a simple configuration suitable for the system specifications, a primary-fed parabolic configuration was chosen. Careful feed design to minimize feed blockage achieving desired level of signal purity is then required for optimum performance.  
The antenna system consists of the following elements: feed assembly, comprising of switchable LHCP and RHCP receiving and transmitting antennas, six aluminum support struts, and a parabolic reflector, composed of 24 ribs, a solid aluminum center plate and thin mesh layer covering the rest of the surface. Figure 3a,b illustrates the deployable antenna. The fairing space limited the parabolic focal distance to \(f_{d}=1.3\) m, resulting in f/D parameter of 0.363. The deployable reflector's total weight is 15 kg, with dimensions of 3.6 m (diameter) by 0.65 m (depth) when deployed, and 0.85 m (length) by 0.85 m (width) by 1.3 m (height) when stowed.  
Since the sensor has no electronic beam steering, the whole satellite must be rolled in the necessary position for pointing to the desired target.  
\begin{table}
\begin{tabular}{c c} \hline
**Mission Parameter** & **Specification** \\ \hline Altitude & 500–600 km \\ Orbit & sun-synchronous polar \\  & inclination of 97.6\({}^{\circ}\) \\ SAR Modes & stripmap, spotlight \\ Coverage & Japan, Indonesia \\ Frequency & L-band (1.275 GHz \\  & 85 MHz bandwidth) \\ Polarization & transmit LHCP/RHCP \\  & receive LHCP+RHCP \\ Spatial resolution & 10–30 m \\ Swath width & 15–30 km \\ Look angle & 20\({}^{\circ}\)–35\({}^{\circ}\) \\ Bus Size & 80 cm \(\times\) 80 cm \(\times\) 85 cm \\ Total mass & \textless{150 kg} \\ Transmitting power & 800–1500 W \\ Duty cycle & 10 \% \\ PRF & 4.2–5 kHz \\ NESZ & \textless{-20 dB} \\ ASR & \textless{-18 dB} \\ Chirp Pulse & 10–20 \(\upmu\)s \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline
**Antenna Parameter** & **Specification** \\ \hline Gain & \textgreater{}30 dBic \\ Polarization & LHCP and RHCP \\ Surface accuracy & \textless{}4.22 mm RMS \\ Axial ratio & \textgreater{}3 dB \\ Side lobe level & \textgreater{}20 dBic \\ Stowed size & 0.85 m \(\times\) 0.85 m \(\times\) 1.3 m \\ Deployed size & 3.6 m \(\times\) 0.65 m (depth) \\ Total mass & \textless{15 kg} \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 4 Results and Discussion
### ML Classification
#### 4.3.1 Receiver Operating Characteristic Curve (ROC) and Area under Curve (AUC)
Overall, by comparing the precision scores and recall rates, using micro-averaging does provide a higher sensitivity and thus better performance. However, it is needful to state that the application of the micro-averaging technique should be approached with care because unlike macro-averaging, it may not be suitable when dealing with an imbalanced class distribution given it does not average over larger group or class instances. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **CV** & \multirow{2}{*}{**Set**} & \multirow{2}{*}{**No. of Folds**} & \multicolumn{3}{c}{**AUC**} \\ \cline{4-7}  & **Method** & & & **Micro** & **Macro** & **Weighted** \\ \hline \multirow{5}{*}{SE} & TVS & 20 & - & 0.957 & 0.869 & 0.975 \\  & TVS & 10 & - & 1.000 & 1.000 & 1.000 \\  & KFCV & - & 5 & **0.989** & **0.982** & **0.992** \\  & KFCV & - & 10 & **0.982** & **0.986** & **0.992** \\  & MCCV & 10 & 5 & 0.861 & 0.813 & 0.833 \\  & MCCV & 20 & 5 & 0.890 & 0.843 & 0.911 \\  & MCCV & 10 & 10 & 0.923 & 0.887 & 0.904 \\  & MCCV & 20 & 10 & 0.946 & 0.929 & 0.946 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: AUC metrics for SE model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **CV** & \multirow{2}{*}{**Set**} & \multirow{2}{*}{**No. of Folds**} & \multicolumn{3}{c}{**AUC**} \\ \cline{4-7}  & & & & **Micro** & **Macro** & **Weighted** \\ \hline \multirow{5}{*}{VE} & TVS & 20 & - & 0.986 & 0.986 & 0.996 \\  & TVS & 10 & - & 1.000 & 1.000 & 1.000 \\  & KFCV & - & 5 & **0.992** & **0.984** & **0.993** \\  & KFCV & - & 10 & **0.991** & **0.996** & **0.998** \\  & MCCV & 10 & 5 & 0.950 & 0.964 & 0.973 \\  & MCCV & 20 & 5 & 0.974 & 0.969 & 0.984 \\  & MCCV & 10 & 10 & 0.966 & 0.976 & 0.979 \\  & MCCV & 20 & 10 & 0.983 & 0.982 & 0.990 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: AUC metrics for VE model. Generally, within the method of model aggregation used, it could also be observed that the voting method (VE) seems to generally perform better than its stacking counterpart across the validation methods used.
## 6 Presentation and Analysis of Results
### Discussion
#### 6.5.1 Rain (At Different Rain Rates)  
Radar at 10 GHz (X-band) is sensitive to precipitation scattering and attenuation, which affect both signal strength and clarity of vibration features. Light Rain (<2.5 mm/h) induces minor attenuation of the radar signal. Backscattering from raindrops may add low-level noise but usually does not obscure vehicle vibration signatures. Vegetation wetness can slightly increase radar reflectivity but typically has minimal impact. Moderate rain (2.5-7.6 mm/h) will cause noticeable attenuation and increased clutter from rain scatter and micro-Doppler signatures from small vibrations may be masked by returns from falling raindrops. In addition, water on the surface of vegetation increases reflectivity, possibly generating confusing returns if vegetation is moving in the wind. On the other hand, heavy rain (>7.6 mm/h) will cause severe attenuation of the radar signal wherein Doppler shifts from falling raindrops (~5-9 m/s fall speed) may overlap with low-frequency vehicle vibrations (~1-20 Hz), especially if converted to velocity domain. This significantly reduces the signal-to-noise ratio (SNR), potentially masking subtle vibration features like glass panel flexing or body shimmy.
## Appendix A Scattered Pilot Subcarrier Detection  
The detection algorithm proposed in [17] is used to identify the location of the current SPSs.  
This algorithm makes use of the signal property that an OFDM symbol has the same scattered pilot subcarriers as the OFDM symbol located four symbols earlier. The correlation of these two OFDM symbols is computed, obtaining:  
\[S(m)=\sum_{p\in p_{s}(m)}d_{p}^{k}(d_{p}^{k-4})^{*},\ m\in\{3,6,9,12\} \tag{27}\]  
where \(p_{s}(m)\) is the set of the index of the \(m\)-th pattern of the scattered pilot subcarriers and \((\cdot)^{*}\) denotes the complex conjugate.  
The maximum value of the four correlations gives the location of the scattered pilot subcarriers of the \(k\)-th OFDM symbol, which is:  
\[\hat{m}=\arg\max_{\bar{\mathbf{m}}}|S(\bar{\mathbf{m}})| \tag{28}\]  
where \(\hat{m}\in\{3,6,9,12\}\) denote different scattered subcarrier sets with different first scattered pilot subcarrier locations. For example, \(\hat{m}=3\) indicates that the location of the first scattered pilot subcarrier is three. The scattered pilot subcarriers of the other OFDM symbols can be obtained by using the rule described in [1].  
(c 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ([[http://creativecommons.org/licenses/by/3.0/](http://creativecommons.org/licenses/by/3.0/)]([http://creativecommons.org/licenses/by/3.0/](http://creativecommons.org/licenses/by/3.0/))).
## 1 Introduction
Figure 1 shows the virtual CityEnviron model scenario with a virtual AirSim drone from the Microsoft AirSim v1.2 version. The inspiration and idea for our proposed model came from deep reinforcement learning for human-level control [23], which uses a specific architecture in conjunction with a deep convolutional network [24] with hierarchical layers of tiled convolutional filters integrated alongside artificial neural networks in order to learn concepts such as object categories directly from raw sensory data. Our proposed deep reinforcement learning and tracking technique is thought of as a sequential feature learning/prediction and decision-making technique for drone agents to monitor actions' rewards through environment sequence and to acquire a tracking output of the recommended methodology. Traditionally, we estimated the adaptive action-value function using a deep sequential neural network [25] such as  
\[Q^{\pi}(s,a)\doteq\mathbb{E}_{\pi}[G_{t}|s,a]=\mathbb{E}_{\pi}\left[\sum_{k=0 }^{\infty}\gamma^{k}r_{t+k+1}|s,a\right] \tag{1}\]  
where the equation estimates the expected total returns, also known as the sum of rewards \(G_{t}\) beginning from a fixed location state \(s\) and performing an action \(a\) in accordance with some policy \(\pi\). Reinforcement Learning (RL) algorithms are mostly based on the above-mentioned Equation (1), which estimates the best value function from sufficient experience to obtain the excellent value estimation. Our proposal utilizes a Q-learning algorithm that uses an action-value function with particular parameters to learn and obtain the best action-value by repeating the learning process. Basically, the reinforcement learning method is known to be unstable as a nonlinear function approximator in a neural network to represent the action-value (known as \(Q\)) function given above. The \(Q^{\pi}(s,a)\) equation function above estimates the expected total returns by taking some policy \(\pi\). \(\pi(a|s)\) is the policy that maps from state observation \(s\) to action \(a\). Typically, stochastic policies are used; however, deterministic policies can also be specified. Most of the useful RL algorithms heavily rely on the above value function and will be obtained from sufficient experience, the optimal policy \(\pi^{*}\) that can be found in every state, and taking the greedy action that leads to the state with the highest value estimations. The process will be applied continuously for every state environment as an iterative update. The rest of this article is organized as follows. Section 2 will give an overview of deep network-based object tracking descriptions and related work. In Section 3, we describe the deep reinforcement learning-based DQN agent drone tracking algorithm integrated with the Virtual CityEnviron (VCE) simulation platform. Experimental simulation results for verifying our algorithm are presented in Section 4 and our conclusion follows in Section 5.
## 4 Security Assumptions  
### Secure Digital Signature  
In this scheme, we define a secure digital signature. In the beginning, we have that \(\mathit{Sig}(\cdot)\) is a signature generation function that inputs a message \(m\) with a signer's secret key \(sk_{i}\) and outputs a signature \(S_{i}\). We also assert this signature function is based on the RSA factoring hard problem or the discrete logarithm problem. We can then input a signature such as \(S_{i}\) with the signer's public key \(pk_{i}\) into the verification function \(\mathit{Ver}(\cdot)\) and see what the output is. If the output is 1, then we can confirm the signature \(S_{i}\) is valid and signed by the signer \(i\). In this scheme, we also assumed that the signature building block is under the RSA problem. If there is an attacker, we assume it as \(\mathcal{F}^{*}\). If \(\mathcal{F}^{*}\) can make a forged \(l+1\) signature called \(S^{\prime}_{i,j+1}\) of some user \(i\in\{U,V,HCA\}\) in at most \(l\)signature queries, and this signature can pass the verification \(\mathit{Ver}(pk_{i},S^{\prime}_{i,j+1})\) successfully with non-negligible probability \(\varepsilon\), then \(\mathcal{F}^{*}\) can be used to break the RSA factoring problem. Thus,  
\[\mathit{Pr}[S^{\prime}_{i,j+1}\longleftarrow\mathcal{F}^{*}(\mathit{Sig}( sk_{i},\cdot),\mathit{Ver}(pk_{i},\cdot),i\in\{U,V,HCA\})|\mathit{Ver}(S^{ \prime}_{i,j+1})=1]\geq\varepsilon. \tag{1}\]
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
## 3 Encoding-Based Test Stimulus Compression Methods
### Variable-Length to Fixed-Length  
This class of methods divides the original dataset with variable lengths, but the encoded code words have fixed lengths. The fixed length of the code words is relative, meaning within a certain range, the length of the code words does not change with the variation of the data blocks. Representative technologies of this class include traditional run-length encoding, but with the advancement of technology and the increasing demands of testing, this technology has been gradually replaced by variable run-length techniques. In addition, [PERSON] [61] and [PERSON] [62] used the LZ77 algorithm from the data compression field to process test stimuli, successively proposing test stimulus compression algorithms based on LZ77 and LZW, achieving variable-to-fixed-length encoding compression.
## 2 Human-Seat Coupling Modelling  
This type of seat is designed for an Airbus H135 helicopter, mainly composed of several parts such as the outer frame, headrest, seat cushion, safety belt, side rails, seat basin holder (it will be referred to as the key energy-absorbing structure of the seat in the rest of this paper), and shear pin. The 3D model of the seat is shown in Figure 1a. What is shown in Figure 1b is the simplified FEM model of a Hybrid III 50 th Percentile ATD. In general, it can be effectively employed in the performance evaluation of the crashworthiness of an automotive seat. Since the margin of error between the results of the simulation, which was completed by using this FEM model, and the results of the test do not exceed 10%, it was deemed acceptable to use this model for simulation in this paper. See Section 3.3 for details. Its main components are as follows: head, neck, upper torso, lower torso, upper arm, fore arm, hands, thigh, shank and feet. The springs, which are in the spine, were used to simulate human elasticity. There is also a triaxial accelerometer in the head and a force transducer in the femur [25]. The human-seat coupling system under the restraint of the four-point seat belt systems is shown in Figure 1c.  
According to CCAR-27-R2 (Airworthiness Regulations for Normal Class Rotocraft in the Chinese Civil Aviation Regulations) [26] and SAE AS 8049B (Performance Standard for Seats in Civil Rotocraft, Transport Aircraft, and General Aviation Aircraft) [27], the defini  
Figure 1: Three-dimensional models. (**a**) The seat. (**b**) The simplified FEM model of Hybrid III 50 th Percentile ATD. (**c**) The human–seat coupling system.  
tion of seat dynamic performance standards intends to reflect the survival limits during aviation accidents. The dynamic testing of the seat was used to simulate circumstances wherein the human-seat coupling system is under the impact from the forced landing of the helicopter. Compared to dynamic testing, static testing is limited to verifying the structural strength and the functionality of the seat and the restraint systems, ignoring the physical tolerance of the human. In addition to the structural strength, vertical dynamic testing and horizontal dynamic testing can also evaluate the occupant trajectory, the floor reaction loads, the HIC (head injury criteria), the spine load and the femur compressive load. Therefore, dynamic testing is irreplaceable in the process of completing the safety assessment of the human-seat coupling system.  
In the vertical dynamic testing of the rear-facing seat, the seat is connected to the floor by rails and they are installed together on a sled which can only move horizontally. Velocity and acceleration are applied to the seat by means of a thrust device, which pushes the sliding table, and then the seat collides with the 77 kg ATD. The angle between the velocity direction and the helicopter floor is 60\({}^{\circ}\) and the region enclosed by acceleration and time in the coordinate system is an isosceles triangle. For details, see Section 3 on Simulation and Crashworthiness Testing.
## 3 Design of a CMG for SWT
### Parameter Determination
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Values** \\ \hline Number of magnet pole pairs on the outer rotor & \(p_{o}\) & 22 \\ Number of magnet pole pairs on the inner rotor & \(p_{i}\) & 4 \\ Number of pole-pieces on the modulation ring & \(N_{c}\) & 26 \\ Outer diameter of the outer rotor (mm) & \(D_{o}\) & 140 \\ Inner diameter of the inner rotor (mm) & \(D_{i}\) & 76 \\ Remanence (T) & \(B_{r}\) & 1.2 \\ Leakage factor in the outer air-gap & \(K_{I1}\) & 0.8 \\ Leakage factor in the inner air-gap & \(K_{I2}\) & 0.8 \\ Reluctance factor & \(K_{r}\) & 1.1 \\ Permeability of free space (H/m) & \(\mu_{0}\) & \(1.26\times 10^{-6}\) \\ Relative permeability & \(\mu_{r}\) & 1.05 \\ Axial length (mm) & \(L_{a}\) & 30 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Value of geometry parameters. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**No.** & **R1** & **R2** & **R3** & **R4** & **R5** & **R6** & **\(B_{g1}\)** & **\(B_{g2}\)** & **\(f(X)\)** \\ \hline
1. & 64 & 54 & 53 & 48 & 47 & 43 & 1.0008 & 0.9018 & 1.9026 \\
2. & 65 & 56 & 55 & 50 & 49 & 43 & 1.0220 & 0.9167 & 1.9387 \\
3. & 64 & 54 & 53 & 49 & 48 & 43 & 1.0032 & 0.8853 & 1.8886 \\
4. & 64 & 55 & 54 & 49 & 48 & 43 & 0.9969 & 0.8962 & 1.8931 \\
5. & 65 & 55 & 54 & 48 & 47 & 43 & 1.0051 & 0.9226 & 1.9277 \\
6. & 65 & 56 & 55 & 48 & 47 & 43 & 0.9684 & 0.9052 & 1.8736 \\
7. & 64 & 53 & 52 & 48 & 47 & 43 & 1.0097 & 0.8927 & 1.9024 \\
8. & 65 & 55 & 54 & 50 & 49 & 43 & 1.0246 & 0.9024 & 1.9270 \\
9. & 65 & 57 & 56 & 50 & 49 & 43 & 1.0012 & 0.9142 & 1.9154 \\
10. & 63 & 54 & 53 & 48 & 47 & 43 & 0.9689 & 0.8730 & 1.8419 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The analytical calculation results. The computational analysis in this study is limited to the magnetic gear design only. The proposed magnetic gear design was analyzed using ANSYS/Maxwell software to depict the magnetostatic field, air-gap flux density, torque, and rotational speed.