esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Task Dynamic Weight Optimization Framework Based on Deep Reinforcement Learning
## 3 Methodology  
The core idea of Multi-Task Learning (MTL) is to improve learning efficiency and prediction accuracy by sharing representations among related tasks. Consider a classification neural network model containing \(T\) tasks, where the \(t\)-th task has the corresponding data set \(\mathcal{D}_{t}=\{(\mathbf{x}_{i}^{t},y_{i}^{t})\}_{i=1}^{N_{t}}\), \(\mathbf{x}_{i}^{t}\in\mathbb{R}^{d}\) is the input feature, and \(y_{i}^{t}\) is its corresponding label. The model f contains shared parameters \(\theta_{s}\) with task-specific parameters \(\theta_{t}\) to optimize the model performance by minimizing the weighted total loss function for all tasks:  
\[\min_{\theta_{s}\{\theta_{s}\}_{t=1}^{T}}\sum_{t=1}^{T}\alpha_{t}\mathcal{L}_ {t}\big{(}f\big{(}\mathbf{x}_{i}^{t};\theta_{s},\theta_{t}\big{)},y_{i}^{t} \big{)} \tag{1}\]where \(\alpha_{t}\geq 0\) is the weight of the task \(t\) and \(\sum\limits_{t=1}^{T}\alpha_{t}=1\). In multi-task learning, determining the optimal task weights \(\{\alpha_{t}\}\) is a key challenge. If there are gradient conflicts or loss scale differences between tasks, fixing or manually adjusting the task weights often fails to achieve balanced optimization between multiple tasks, which may lead to some tasks being neglected or some tasks over-dominating the overall training process.  
Some methods assign new weights to the task at the end of each training cycle via genetic algorithms, but such methods are usually computationally expensive (requiring a large number of functions to be evaluated) and place high demands on the design of the genetic algorithm. To this end, we propose a dynamic task weight adjustment method based on Deep Reinforcement Learning (DRL). Through the DRL agent of Actor-Critic architecture, the weights are automatically adjusted according to the current task performance \(\{\alpha_{t}\}\), achieving dynamic balance and collaborative optimization among tasks and thus alleviating the problems of gradient conflict and loss scale inconsistency. Figure 2 shows the framework flow of the method. More details of the network can be found in the Figure A1.
# Physics-Based Data Augmentation Enables Accurate Machine Learning Prediction of Melt Pool Geometry
###### Abstract  
Accurate melt pool geometry prediction is essential for ensuring quality and reliability in Laser Powder Bed Fusion (L-PBF). However, small experimental datasets and limited physical interpretability often restrict the effectiveness of traditional machine learning (ML) models. This study proposes a hybrid framework that integrates an explicit thermal model with ML algorithms to improve prediction under sparse data conditions. The explicit model--calibrated for variable penetration depth and absorptivity--generates synthetic melt pool data, augmenting 36 experimental samples across conduction, transition, and keyhole regimes for 316 L stainless steel. Three ML methods--Multilayer Perceptron (MLP), Random Forest, and XGBoost--are trained using fivefold cross-validation. The hybrid approach significantly improves prediction accuracy, especially in unstable transition regions (D/W \(\approx\) 0.5-1.2), where morphological fluctuations hinder experimental sampling. The best-performing model (MLP) achieves R\({}^{2}\) > 0.98, with notable reductions in MAE and RMSE. The results highlight the benefit of incorporating physically consistent, nonlinearly distributed synthetic data to enhance generalization and robustness. This physics-augmented learning strategy not only demonstrates scientific novelty by integrating mechanistic modeling into data-driven learning, but also provides a scalable solution for intelligent process optimization, in situ monitoring, and digital twin development in metal additive manufacturing.  
s _Article_  
18 June 2025  
30 July 2025  
31 July 2025  
Multilayer Perceptron Published 2 August 2025  
202515887  
additive manufacturing; explicit model; machine learning; melt pool
## 4 Proposed Solution and Algorithm
### The RSP as an Instance of the CHA Problem  
In our problem, the set of residents coincides with the ED set \(\mathcal{D}\), and the number of houses is equal to the number of possible communication modes, in our case two sets \(\mathcal{S}^{\prime}\) and \(\mathcal{S}^{\prime\prime}\). More in depth, \(\mathcal{S}^{\prime}\) and \(\mathcal{S}^{\prime\prime}\) represent the SN \(\mathcal{S}\) distinguishing between the direct link and the relay communication mode, respectively. One crucial difference between the CHA problem and the RSP, is that in the CHA each house has a maximum number of residents that can accept, while in the RSP \(r\), i.e., the number of selected relays is dynamic and changes based on the \(\mathcal{G}_{d}\) topology that changes, as detailed later, during the relay selection process. In particular, as introduced in Section 3 with the constraint (4), at the end of the proposed strategy each ED in \(\mathcal{D}\) has received the packet \(l\) from one and only one sender (SN \(\mathcal{S}\) or a nearby ED). In order to guarantee such an objective, how many and which relays have to be selected to cover all the vertices both depend on the connectivity of \(\mathcal{G}_{d}\). The proposed matching game acts based on the ED \(\mathcal{D}\) preference lists that are built as follows.
# Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management
## 2 Multi-Robot Formation Control
### System Stability Analysis  
Taking the differential car as an example, this study assumes that the wheeled robot is a rigid body that can rotate freely. Its simplified motion model is shown in Figure 2.  
Set \(C(\text{x},\text{y})\) as the coordinates of the wheeled robot, and \(\theta\) as the forward direction angle. The state information of the robot is represented by \(P=(x,y,\theta)^{\text{T}}\) and \(v\) and \(w\) are the linear velocity and angular velocity of the robot, while \(2L\) is the length between two wheels and \(2r\) is the diameter of the wheel. The kinematic state-space equation can be obtained as shown in Equation (17):  
\[\left\{\begin{array}{l}\dot{x}=v\cos(\theta)\\ \dot{y}=v\sin(\theta)\\ \dot{\theta}=\frac{v\tan(\varphi)}{2L}\end{array}\right. \tag{17}\]  
According to the robot kinematics model and the control law, it is necessary to maintain zero dynamic control input, \(\dot{p}_{i}=0\), that is, \(u_{i}=0\). The ideal output result is that the robot reaches the centroid position of CVT convergence. Considering that the problem is a tracking problem from the robot position to the CVT centroid position, the Lyapunov function was used to prove the convergence of the system.  
In the proof process, the integral part of the control law is separated to obtain Equation (18):  
\[\dot{p}_{i}=u_{i}=k_{p}(p_{i}-p_{i}^{*})-k_{d}\dot{p}_{i} \tag{18}\]  
The Lyapunov function is defined as in Equation (19):  
\[V(p_{i})=\frac{1}{2}k_{p}\|p_{i}-p_{i}^{*}\|+\frac{1}{2}p_{i}^{2} \tag{19}\]  
Figure 2: Robot kinematics model.  
The Lyapunov function is derived to obtain Equation (20):  
\[\dot{V}(p_{i})=k_{p}(p_{i}-p_{i}^{*})\dot{p}_{i}+\dot{p}_{i}\ddot{p}_{i} \tag{20}\]  
Replacing the parameter \(\ddot{p}_{i}=0\) in Equation (20) with \(\dot{p}_{i}\) to simplify, gives Equation (21):  
\[\dot{V}(p_{i})=-k_{d}\ \dot{p}_{i}^{2}\leq 0 \tag{21}\]  
Therefore, it can be concluded that the Lyapunov function is a semi-negative definite function. As shown in Equation (21), when the time tends to infinity the system is asymptotically stable over a large range, if and only if the input is 0, i.e., \(\dot{V}(p_{i})=0\). According to the LaSalle principle, if the surface fine diversity is limited the robot will gradually converge to the centroid of the specified Voronoi cell.  
\[u_{i}=0=k_{p}(p_{i}-p_{i}^{*}) \tag{22}\]
# SVDC: Synchronous Update with the Division and Combination of SRv6 Policy
## 2 Related Works
### Ordered Update  
[PERSON] and [PERSON] [19] have proposed an ordered update scheme to protect the network from transient loops by ordering routing table updates, which ensures packet-level consistency in the network.  
[PERSON] et al. [20] generated an optimized sequence for updating the weights of links. The incremental modification of link weights protects the network from transient loops when routers update their flow tables.  
[PERSON] et al. [16] utilized a system for consistent network updates called Dionysus. Dionysus first generates a global dependency graph describing the effective ordering, and then schedules updates based on the dependency graph.  
[PERSON] et al. [17] improves on the work of [PERSON] et al. [16]. They divided the global dependencies into local dependencies between key nodes to speed up the update, but it can lead to congestion in some cases.  
[PERSON] et al. [21] proposed a four-phase ordered update scheme, which includes dividing traffic pairs, generating dependency graphs, generating non-deadlock rounds, and resolving deadlocks. This scheme ensures that black holes, loops, congestion, and deadlocks will not occur during the four phases of the update process.  
It can be seen from the above schemes that the ordered update schemes divide the entire update process into multiple phases. At the start of each phase, we need to wait for the completion of the previous phase. Therefore, the ordered update has a long update cycle. In addition, the ordered update requires additional storage space to store the legacy rules.
## 4 Investigation of the Influence of Major Parameters on the Pipeline Damping Performance
### Effect of Particle Impact Damper Structure on Damping Performance of the Pipeline System  
A test pipeline with a diameter of 16 mm was selected and three kinds of particle impact dampers were designed according to the pipeline diameter, as shown in Figure 14. To investigate the effect of the different number of cavities on the vibrational damping performance, three types of particle impact dampers with two, four, and six cavities were selected.  
Figure 15a shows the comparison frequency responses with different cavities with 1 mm steel balls. The resonant amplitude of the pipeline was reduced by 47.5% with the two-cavity damper, while the corresponding amplitudes in the other cases were cut down by 70.1 and 57.8%, respectively. When filled with 1 mm steel balls, the four-cavity damper has an excellent damping effect at the 20% filling rate. A similar damping effect occurs at the 80% filling rate, as shown in Figure 15b.  
Figure 16 shows the RMS values of pipelines with two-, four-, and six-cavity dampers under two filling rates. When the number of damper cavities is small, the space of the damper cavity is larger, and the free motion stroke between particles becomes larger. As the number of particle-particle and particle-cavity collisions is limited, the energy dissipated by collision and friction is lower, so the damping effect is not obvious. With the increase in damper cavities, the space of the damper cavity becomes smaller, which means that the movement stroke of the particles will be shorter; the more contact between the particles and the particle impact damper, the more sufficient the momentum exchange between the particles and the particle impact damper will be. Therefore, with the increase in particle-particle and particle-cavity collision and friction, the vibration damping effect will improve. However, when the number of damper parts is large, the space of the damper cavity is small, and the movement space of particles is limited, which means that the collision and friction energy dissipation capacity of particle-particle and particle-cavity interactions decreases. Therefore, there must be a reasonable number of damper cavities.  
Figure 14: Three particle impact damper structures. (**a**) Two cavities; (**b**) Four cavities; (**c**) Six cavities.  
Figure 13: Comparison of frequency response with three particle sizes at the (**a**) 20%, (**b**) 80% filling rate.
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 6 Conclusions  
In this work, an attitude tracking robust control for VTOL-UAVs was presented. The control approach shows robustness to endogenous and exogenous disturbances, in addition to displaying a response free of oscillations, which makes it attractive for solving the problem of attitude tracking and stabilization even with mechanisms for aerial manipulation such as grippers. The observer tuning is vital to estimating the disturbances. The performance was compared in simulation with an existing quaternion tracking controller.  
Due to its simplicity, the proposed control schema can be implemented into an embedded system with limited computational resources. Then, as future work, the ADRC will be implemented in real-time together with a position control loop. The implementation will be carried out on the Crazyflie to ease the performance and the robustness towards external disturbances. Furthermore, the tests will consider the kinds of mechanisms for aerial manipulation, such as grippers, cables, and a load delivery and retrieving application. An event-based extension, in the spirit of [59, 60], will also be considered with the aim of reducing the computing and communication costs.  
We are going to implement the ADRC in real-time together with a position control loop. The implementation will be carried out on the Crazyflie to ease the performance and the robustness towards external disturbances.  
Figure 8: ISE index of the angular velocity error.  
**Author Contributions:** Conceptualization, J.F.G.-C. and J.L.-F.; methodology, J.F.G.-C. and G.A.M.-H.; software, L.L.G.R.; validation, J.F.G.-C. and G.M.-A.; formal analysis, J.F.G.-C., S.D. and N.M.; investigation, [PERSON]-S.; resources, [PERSON]-[PERSON]; data curation, [PERSON]-C.; writing--original draft preparation, [PERSON]-C.; writing--review and editing, G.A.M.-H. and S.D.; visualization, L.L.G.R.; supervision, [PERSON]-C.; project administration, [PERSON]-C.; funding acquisition, S.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is part of the e-VISER project funded by the French National Research Agency (ANR-17-CE33-0008) and the French-Mexican TOBACCO project funded by the FORDECYT-PRONACES through the joint SEP-CONACYT-ANUIES-ECOS Nord program (MX-296702 & FR-M18M02).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Designed Circularly Polarized Two-Port Microstrip MIMO Antenna for WLAN Applications
## 6 MIMO Antenna System Parameters  
In this section, the presentation of the suggested MIMO-CP antenna is calculated by some essential parameters, which characterize MIMO antenna systems, such as DG and ECC. These parameters are illustrated in detail in this section.
# The YouGovern Secure Blockchain-Based Self-Sovereign Identity (SSI) Management and Access Control
## 4 Implementation
### User Interface and Interaction  
The YouGovern user interface is designed to be intuitive and accessible, allowing users to manage their identities without requiring deep technical expertise.  
* MetaMask Integration: A significant component of the YouGovern user interface is its integration with MetaMask (Figure 8), a widely used Ethereum wallet that facilitates secure and direct user interactions with the blockchain via a browser extension. MetaMask acts as a bridge between the browser and the BSC, allowing users to securely manage their identities and perform transactions without needing to trust third-party web servers. The interface guides users through the process of setting up MetaMask, ensuring they can securely sign transactions and authenticate their actions on the blockchain, thereby maintaining privacy and control over their data.
* InterPlanetary File System (IPFS): To enhance the security and decentralization aspects of identity management, YouGovern incorporates the IPFS, a peer-to-peer protocol for storing and sharing data in a distributed file system (Figure 9). IPFS is used to store identity documents and other user data in a way that ensures data permanence and reduces reliance on centralized storage solutions. When a user uploads a document, it is broken into blocks and distributed across the network, making it resilient to node failures and censorship. The integration of IPFS with YouGovern allows for a decentralized, owner-controlled storage solution, aligning with the principles of self-sovereign identity by giving users full control over their personal information.  
Figure 8: MetaMask.  
While IPFS enables decentralized content-addressed storage, it does not inherently ensure long-term data persistence. To address this, YouGovern integrates with Web3. Storage [48], which combines IPFS with Filecoin-based incentives to keep files pinned over time. This integration ensures that user-uploaded identity data remains reliably available across the network. Additionally, the system optionally supports encrypted backup storage through institutional nodes, such as public sector servers, providing a fallback in cases of widespread node unavailability. This hybrid model preserves user control and data sovereignty while enhancing resilience against data loss.
## 4 Results
### RQ5: Network Security Attack in IoT and WSN Layers
#### 4.5.5 Sink Hole Attacks  
This is a network layer attack where attackers attract all network traffic from nearby nodes to a compromised node and appear as attractive and trusted nodes. This attack is also used to initiate other attacks such as spoofing attacks, DoS, and modification of routing information in WSN [34]. When combined with selective routing and worm attacks, sinkhole attacks become even more dangerous. A sinkhole attack is initiated in two ways, either by hacking a node within the network or by a malicious node impersonating itself as the shortest path to the base station [35; 36]. A sinkhole attack impacts the routing configuration/protocols of the forwarding node. Due to this behavior, it is considered as an error or malicious node by the neighboring nodes, which affects the network performance. This leads to mis-routing and incorrect displays of the routing protocol.
# Sparse Envelope Spectra for Feature Extraction of Bearing Faults Based on NMF
###### Abstract  
Periodic impulses and the oscillation response signal are the vital feature indicators of rolling bearing faults. However, finding the suitable feature frequency band is usually difficult due to the interferences of other components and multiple resonance regions. According to the characteristics of non-negative matrix factorization (NMF) on a spectrogram, the feature extraction method from a sparse envelope spectrum for rolling bearing faults is proposed in this paper. On the basis of the time-frequency distribution (TFD) of the periodic transient oscillations, the basic matrix can be interpreted as the spectral bases, and the time weight matrix corresponding to spectral bases can be extracted by NMF. Because the bases and the weights have a one-to-one correspondence, the frequency band filtering with the basic component and the time domain envelope of the weight vector are calculated respectively. Then, the sparse envelope spectrum can be derived by the inner product of the above results. The effectiveness of the proposed method is verified by simulations and experiments. Compared with band-pass filtering and spectral kurtosis methods, and considering the time weights and corresponding the spectral bases for the periodic transient oscillations, the weak fault-rated feature can be enhanced in the sparse spectrum, while other components and noise are weakened. Therefore, the proposed method can reduce the requirement of selecting frequency band filtering.  
feature extraction; time-frequency distribution; non-negative matrix factorization; rolling bearing fault +
Footnote †: journal: Applied Sciences
# A Fast Acquisition Algorithm for Hybrid Signals of 5G and BeiDou B1
## 3 Results and Discussion
### Receiver Hybrid Coherent Acquisition Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \begin{tabular}{c} **Coherent** \\ **Integration** \\ **Time (ms)** \\ \end{tabular} & \begin{tabular}{c} **Search Data** \\ **Phase Point** \\ \end{tabular} & \begin{tabular}{c} **SC\_PMF\_FFT\_X** \\ \end{tabular} & \begin{tabular}{c} **SC\_PMF\_FFT\_D** \\ \end{tabular} & \begin{tabular}{c} **SC\_PMF\_FFT\_N** \\ \end{tabular} & \
\begin{tabular}{c} **Doppler** \\ **Calculation** \\ **Resolution** \\ **Resolution** \\ \end{tabular} &
\begin{tabular}{c} **Code Phase** \\ **Resolution** \\ **(Hz)** \\ \end{tabular} \\ \hline Configuration 1 & 1 & 12,000 & 600 & 20 & 128 & 78.125 & 0.1705 \\ Configuration 2 & 2 & 24,000 & 600 & 100 & 128 & 156.25 & 0.3410 \\ Configuration 3 & 5 & 60,000 & 600 & 500 & 128 & 312.50 & 0.8525 \\ \hline \end{tabular}
\end{table} 
Table 12: SC-PMF-FFT algorithm parameter configuration in the acquisition sensitivity test. The dwell time of each cell in the receiver is related to the processing speed of the receiver. Here, we first test the dwell time by connecting the receiver directly to the source. The time required to search for different code phases is obtained by adjusting the difference between the correct and code phase at the start of the acquisition. The results of the tests are shown in Table 13. The phase search time output by the receiver is subject to a particular deviation due to the lower accuracy of the processor processing time, and this deviation is more apparent when the number of phases is small. As seen from the above table, the search time increases simultaneously with the increase in the number of phase searches, and the two are close to a linear relationship. The time required for receiver initialization, start-up, and receiving and storing the required data length is 10 ms. A coherent integration of 1 ms was used in the test, and based on the subsequent correspondence between the number of phases and the search time, we can conclude that the average search for each phase takes about 0.01 ms. The capture module of the receiver is kept working and returns after each completed search round to re-search for the uncaptured signals. Therefore, when a signal is weak, it usually takes several rounds of repeated searches before capturing it. The success of signal capture is also based on whether the signal can be tracked steadily, and if the signal loses lock quickly, it needs to return to re-acquisition. Figure 11: (a) B1I signal tracking status for 6 baseband channels. (b) B1C signal tracking status for 6 baseband channels. (c) Successful acquisition of B1I signal from satellite 25. (d) Successful acquisition of B1C signal from satellite 25.