esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminary Techniques and System Model
### System Model  
The system model of the proposed scheme is illustrated in Figure 1. The controller communicates with the mobile nodes in an in-band manner, utilizing the same wireless medium for both control and data traffic. Groups with different colors represent distinct cluster levels determined by our unsupervised learning approach. Our system model employs OpenFlow 1.3 to implement the southbound interface, enabling the SDN controller to inspect node metrics and coordinates in real-time and distribute routing decisions accordingly. In Figure 1, the DRL agent makes routing decisions based on several real-time node metrics. Key parameters include each node's residual energy for assessing long-term viability, buffer usage to gauge congestion, RSSI, and the number of neighbors reflecting current connectivity. By referencing these values, the routing strategy can adaptively select next hops according to changing network conditions. The SDN architecture includes a controller that collects the status of each node. Using multiple SDN controllers can improve fault tolerance by distributing control tasks. However, synchronizing network states among multiple controllers in a highly dynamic MANET results in significant overhead and increases management complexity. In contrast, a single controller avoids this extra synchronization cost, providing a unified view of the network and streamlined routing decisions. Hence, we employ one SDN controller to minimize both coordination overhead and potential state inconsistencies in our MANET environment. Connectivity, topology, and forwarding managers within the controller detect link failures and provide forwarding decisions. If a node fails to report its parameters within a specified interval, the controller immediately logs the incident and excludes that node from subsequent route selections. This approach ensures that the routing algorithm can continue operating even when partial node failures or unexpected link disruptions occur. These procedural safeguards prevent such events from halting the DRL process. In the routing component, a reliable path is computed and disseminated to each node.  
The routing process involves two main steps:  
* Node Clustering: Nodes are clustered using UL based on mobility-resilient parameters collected from each node.
* DQN-based Routing: DQN is employed to select the most reliable path, considering the clusters formed in the first step.  
Details of each step and procedure are provided in the following sections.
## 4 Discussion
### Giant Enhancement in THz Emission  
As shown in Figure 3c,d, X-ray intensity, not only THz wave emission, also increases to some extent at the same delay time of 4.7 ns. These enhancements in the both emissions in THz wave and X-ray indicate that the absorption of the main-pulse increases effectively and that the density and the temperature of electrons increases in the self-generating plasma by the main-pulse irradiation. The pre-pulse irradiation induces the droplet/mist formation and its expansion outward with time. Then, the delayed main pulse induces the self-generating plasma with a larger volume in the expanding droplet/mist, which results in the enhanced absorption. Or, due to the flow surface with the structural modulations such as nano-to micro-scale roughness/ripples induced by the pre-pulse irradiation, the effective absorption efficiency of the main pulse increases through multiple scatterings. Such dynamic morphological changes keep growing even after 4.7 ns and become larger in size and inappropriate for THz enhancements. X-ray generation is primarily dependent on the peak temperature of electrons, \(T_{e}\), while THz emission can be dependent on its transient \(\mathrm{d}T_{e}/\mathrm{d}t\)[53; 54]. Both entities are decaying on longer times causing lower emission of X-rays and THz wave, respectively.  
In summary, we tentatively consider that the giant THz wave enhancement at 4.7 ns is attributed to the self-generating plasma with the larger volume in the expanding droplet/mist induced by the pre-pulse irradiation. In addition, the modification of the rear surface induced by the pre-pulse irradiation as shown in Figure 4 may contribute to the enhancement observed in the transmission side at the delay time of 4.7 ns. To reach more quantitative descriptions of the light-matter interaction for the described experiment, a fast optical imaging will be added to reveal the geometry and its changes during the laser ablation of the water flow by the pre-pulse irradiation. For the complete mechanism clarification, plasma dynamics diagnostics and/or simulations on hydrodynamics are also indispensable as future work. Knowledge of formation dynamics of wave guide-like channel with density gradient in the flow (prepared by the pre-pulse irradiation) for efficient coupling between the main pulse and the flow and on extent of nano-tomicro-droplets/mist and swelling is necessary to quantify and further validate separate contributions of discussed mechanisms to the enhancement of THz wave emission.
# A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions
## 4 Results and Discussion  
### Impedance Inversion Result  
By adjusting the wavelet length, the seismic wavelet was optimized to have stable waveform characteristics, with energy concentrated in the main lobe, minimal energy in the side lobes that decay rapidly, a broad frequency band, and high dominant frequency. These characteristics best met the requirements for modeling. The wavelet length was adjusted to 100 ms, with a constant-phase wavelet and side-lobe length of 25 ms, within a time window of 200-500 ms for extraction. Figure 7 shows the initially extracted wavelet and its spectrum, while Figure 8 presents the final modified wavelet and spectrum.  
By employing the least squares method for the analysis, impedance was determined. The calculation of reflection coefficients was based on the linear relationship between density and velocity, allowing for the estimation of impedance values. By selecting an appropriate seismic wavelet (Figure 9), a synthetic seismic record was generated, which was then used for horizon calibration, Typical seismic profiles were then transformed and compared with drilling and well-logging data, as depicted in Figure 8.  
The precision of seismic stratigraphic interpretation directly influences the lateral resolution of the initial model, while seismic sampling rate is the primary factor affecting vertical resolution. Utilizing high-frequency information from well-logging enables detailed description of geological thin layers, often requiring densification of seismic data sampling. To meet the high vertical resolution requirements for reservoir identification, the seismic data sampling rate is increased to 0.5 to 1 ms. In constrained inversion, ensuring minimal error between observed seismic data and synthetic records calculated by the model involves employing a hard constraint approach, which bounds the final impedance values within a specified range of variability. The wave impedance values at borehole LB12 (see Figure 10) indicated that the wave impedance of the coal seam was relatively low compared to the  
Figure 8: Final wavelet and spectrum.  
Figure 7: Extraction of wavelet and spectrum for the first time.  
wave impedance of the roof and floor. The inversion profile accurately identified the boundaries of the coal seam; the results at the borehole location aligned perfectly with the well log data. In this parameter testing, a hard constraint of 25% resulted in relatively small overall errors. Throughout this process, the average block size was chosen to match the sampling interval (1 ms).
# Enhanced Path Planning by Repositioning the Starting Point +
## 4 Experimental Evaluation
### Evaluation Results
#### Analysis of Station Variation Effects  
The station-based analysis, presented in Figure 16, reveals different but equally important patterns. SPD improvement shows a clear inverse relationship with station count, achieving the highest gains when fewer stations are present and declining as station numbers increase. The average SPD improvement across all station configurations reaches 22%.  
Distance improvements follow a similar declining trend as station numbers increase, with higher gains observed at lower station counts. The average improvement for total distance across all station configurations is 13%. This behavior aligns with expectations, as additional stations create longer flight paths, making the relative impact of starting point optimization less significant compared to the overall mission distance.  
Interestingly, SPF shows a different behavior pattern, displaying a gradual but modest increase in improvement from 9% to 11%, with an average improvement of 9%. This trend occurs because adding stations to a flight plan does not change the number of drones executing the mission, and drone count is the primary factor influencing SPF values.  
The comprehensive analysis reveals that both SPD and distance follow predictable declining improvement trends as station numbers increase. This behavior stems from the fundamental nature of the optimization approach: since improvements are achieved by optimizing distances between the first and last stations of each drone route while leaving intermediate station sequences unchanged, the relative benefit naturally diminishes as mission complexity grows. These findings provide valuable insights for mission planners seeking to maximize the benefits of geometric median-based starting point optimization in various operational scenarios.  
Figure 16: Centering using geometric median for varying number of stations. (**a**) Improvement by stations. (**b**) SPD analysis. (**c**) Distance analysis. (**d**) SPF analysis.
## 5 Optimization Techniques for Routing in Wireless Sensor Networks
### Addressing Scheme  
Wireless sensor networks are formed by a significant number of nodes so the manual assignation of unique identifiers is infeasible. The use of the MAC address or the GPS coordinates is not recommended as it introduces a significant payload [3]. However, network-wide unique addresses are not needed to identify the destination node of a specific packet in wireless sensor networks. In fact, attribute-based addressing fits better with the specificities of wireless sensor networks. In this case, an attribute such as node location and sensor type is used to identify the final destination. Concerning these identifiers, two different approaches have been proposed [3]. Firstly, the ID reuse scheme allows identifiers to be repeated in the network but keeping their uniqueness in close areas. In this way, a node knows that its identifier is unique in a \(k\)-hop neighborhood, being \(k\) a parameter to configure. On the other hand, the field-wide unique ID schemes guarantee that the identifiers are unique in the whole application. With this assumption, other protocols such as routing, MAC or network configurations can be simultaneously used.
## 5 Underwater Data Muling Simulator (UDMSim)  
Performing experiments underwater is expensive and complex in terms of logistics. Thus, it is important to be able to predict the performance of the data muling solution when different parameters are varied, such as the number of DMUs, the distance between the CSU and the SU, and the amount of data to be fetched from the SU. UDMSim is a simulation platform for underwater data muling oriented systems that combines the AUV Motion and Localization (AML) simulator with ns-3 and goes beyond the simple mathematical model presented in Section 4. In what follows, we describe each of these components. The UDMSim block diagram is shown in Figure 6. UDMSim is made available to the community [6] to support the evaluation of underwater data muling oriented communications solutions.
# FedRDS: Federated Learning on Non-IID Data via Regularization and Data Sharing
## 3 Proposed Method
### Problem Formulation  
Figure 1 displays a summarized outline of the FedRDS algorithm. At the beginning, the server chooses specific clients to join the FedRDS round and issues work request information (Step 1). Upon accepting the request, the client sends an acknowledgment to the server (Step 2). Subsequently, the server transmits the pre-trained model, the global model, and a shared data fraction \(\alpha\) to the client (Step 3). Then, the client uses its private data and shared data fraction \(\alpha\) for training (Step 4). Following the completion of local training, after the model is trained on the client, it will be uploaded to the server (Step 5). Finally, utilizing the FedRDS algorithm, the server aggregates the local models uploaded by the client to form a global model (Step 6). The server saves the model and the next FL round is opened.  
Given the compact space \(\mathcal{X}\), the class space \(\mathcal{Y}=\{i|i=1:I\}\). The data points \(\{x,y\}\) follow the distribution p over \(\mathcal{X}\times\mathcal{Y}\). The function \(f:\mathcal{X}\rightarrow\mathcal{S}\) maps x to the probabilistic simplex \(\mathcal{S}\), in which \(\mathcal{S}=\{z|\sum\limits_{i=1}^{L}z_{i}=1,z_{i}\geq 0,\forall i\in[I]\}\) with \(f_{i}\) denoting the probability of  
Figure 1: The proposed framework of FedRDS.  
the \(i\)th class. \(f\) parameterized on the hypothesis category \(\Theta\). In FL, our goal is usually to optimize the following functions:  
\[\Theta^{*}=\operatorname*{argmin}_{\Theta}\mathcal{L}(\Theta)=\sum_{k=1}^{K} \frac{n_{k}}{n}l_{k}(\Theta), \tag{1}\]  
where \(K\) is the number of total clients, \(n_{k}\) is the number of samples on client \(k\), \(n\) is the total number of samples on all clients, also known as \(\sum_{k}\frac{n_{k}}{n}=1\), where \(l_{k}\) is the loss function of client \(k\), and \(\Theta\) is the set of global model parameters. In this paper, we consider \(\mathcal{L}(\Theta)\) as non-convex.  
In traditional FL algorithms, all participating clients perform the same amount of local work. This approach would be appropriate if the data were evenly distributed across each client. In many cases, however, each client may also differ in storage, computation, and communication capabilities due to differences in hardware, power, and network connectivity. At this point, the client training by FL creates the client drift problem (shown in Figure 2, which leads to the client subproblem optimal solution being inconsistent with the global objective [42]. Although many previous studies solved the non-IID data problem by adding regularization, they did not discuss regularization terms when the data distribution gap is enormous.
# Electro-Thermal and Aging Lithium-Ion Cell Modelling with Application to Optimal Battery Charging
## 6 Conclusions  
A solution for lithium-ion battery optimal fast charging was proposed in this paper. Optimality here relates to battery aging minimization, which is achieved by tracking specific charging and side reaction current reference trajectories (optimal trajectories) using a feedforward-feedback control loop. The optimal trajectories are computed using an algorithm involving an electro-thermal battery aging model recalled in this paper and developed in [6] by the same authors. This electro-thermal battery aging model also permits the display of dynamic behaviors quite different from a lithium-ion cell in relation to age and temperature. This sensitivity problem is taken into account in the charging strategy proposed using a robust CRONE feedback controller. The performance of the closed loop approach proposed is highlighted by improved side reaction current trajectories tracking, thus leading to reduced aging during fast charging. To the best knowledge of the authors, this is the first time that a closed-loop strategy, coupled with an electro-thermal battery aging model for fast charging while taking into account battery health, has been proposed in the literature.  
Most of the proposed algorithms and methods in this thesis were validated by means of simulations. The evaluation of the internal battery states is not possible using real cells and measurements, but simulation provides this possibility. However, the importance of experimental validations cannot be ignored, and thus should be pursued in a future work. Upon the successful validation of the simulation results presented in this work, the electro-thermal aging battery model and the intelligent charging strategy can be implemented as a part of a BMS.  
[PERSON] contributed to the work conceptualization, investigation, development, and validation. She also contributed to paper writing (draft and review). [PERSON] contributed to the work conceptualization, investigation, development, supervision and validation mainly on the modelling part. He also contributed to paper writing (draft, review and editing). [PERSON] contributed to the work conceptualization, investigation, development, supervision and validation mainly on the control part. He also contributed to paper writing (draft and review). [PERSON], contributed to the problem definition, to the work supervision and to funding Acquisition. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Wideband Metal-Only Patch Antenna for CubeSat
## 4 Conclusions  
A metal-only wideband folded patch antenna for CubeSat applications is demonstrated. A wide bandwidth is achieved by using the folded ramp shaped patch approach to feed the arms of the upper radiating patch. Moreover, to reduce the antenna size, shorting pins are used to connect the upper patch to the ground plane, and hence, increase the patch's effective electrical length and reduce its physical size. To increase the efficiency and improve the performance, the proposed antenna uses air substrate. An antenna re  
Figure 16: Measured and simulated efficiency of the proposed antenna.  
Figure 15: Measured and simulated gains of the proposed antenna.  
alization with an overall size of 100 mm \(\times\) 100 mm is presented with a bandwidth of 44.9% (1.6-2.7 GHz) for \(\mid\)S\({}_{11}\)\(\mid\)\(<\) -10 dB and total gain of 8.5 dBi at 2.45 GHz. Moreover, the proposed antenna provides a high efficiency, of 85%, and a 3 dB gain bandwidth of 61.22%. The measurement results prove the wide bandwidth of the antenna and shows good agreement of the radiation pattern with simulation results.  
As a recommendation for future work, to increase the robustness of our proposed antenna for CubeSat applications where the size of the CubeSat platform exceeds the 3 U standard, the air substrate of the proposed antenna could be filled with foam material (dielectric constant) without sacrificing the performance in terms of gain or impedance bandwidth. To finally assess the suitability and durability of our antenna during the CubeSat launch, vibration and shock testing must be performed.  
Design and concept, F.T. and S.A.; methodology, F.T., S.A. and P.I.T.; Investigation, R.R., P.I.T. and S.L.; validation, F.T., S.A., P.I.T. and M.U.A.K.; investigation, F.T. and P.I.T.; resources, F.T., R.R. and P.I.T.; writing--original draft preparation, F.T. and S.A.; writing--review and editing, R.R., P.I.T., S.L. and M.U.A.K.; supervision, R.R. and F.T.; project administration, F.T., R.R., S.A. and P.I.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.
# A Deep Convolutional Neural Network for Detecting Volcanic Thermal Anomalies from Satellite Images
## 6 Conclusions
Ultimately, the SqueezeNet model could be trained and used to analyze every image captured by these satellites over any volcano around the world, to automatically retrieve an effective and accurate extraction of thermal features and to alert an operator in cases of the presence of active flows and erupting vents. The results demonstrate the potential applicability of the proposed approach to the development of automated thermal analysis systems at the global scale using future data coming from planned future missions, or by also directly processing the data on board the satellites. The dataset used in this work for the Transfer Learning approach is freely available at the link: [[https://zenodo.org/record/794434#ZGSp8](https://zenodo.org/record/794434#ZGSp8) HZBy5c]([https://zenodo.org/record/794434#ZGSp8](https://zenodo.org/record/794434#ZGSp8) HZBy5c), with the doi: 10.5281/zenodo.7944343. Conceptualization, E.A. and C.C. ; methodology, E.A. and C.C. ; software, E.A., C.C. and F.T. ; validation, F.T. and C.D.N. ; formal analysis, C.D.N. ; investigation, E.A. ; resources, E.A. ; data curation, E.A. and C.C. ; writing--original draft preparation, E.A. and C.D.N. ; writing--review and editing, C.C. ; visualization, C.D.N. ; supervision, C.D.N. ; project administration, C.D.N. ; funding acquisition, C.D.N. All authors have read and agreed to the published version of the manuscript. This research was funded by [ATHOS Research Programme] (OB.FU. 0867.010) and by [Project FIRST--Forecasting eRuptive activity at Stromboli volcano: timing, eruptive style, size, intensity, and duration, INGV--Progetto Strategico Dipartimento Vulcani 2019] (Delibera n. 144/2020). The dataset used in this work for the Transfer Learning approach is freely available at the Supplementary Materials link: [[https://zenodo.org/record/794434#ZGSp8](https://zenodo.org/record/794434#ZGSp8) HZBy5c]([https://zenodo.org/record/794434#ZGSp8](https://zenodo.org/record/794434#ZGSp8) HZBy5c), with the doi: 10.5281/zenodo.7944343. This work was developed within the framework of the Laboratory of Technologies for Volcanology (TechnoLab) at the INGV in Catania (Italy). We are grateful to the European Space Agency (ESA) for Sentinel-2 data and to the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS) for Landsat 8 data. The SqueezeNet model pretrained is downloadable from the PyTorch website: [[https://pytorch.org/hub/pytorch_vision_squeezenet/](https://pytorch.org/hub/pytorch_vision_squeezenet/)]([https://pytorch.org/hub/pytorch_vision_squeezenet/](https://pytorch.org/hub/pytorch_vision_squeezenet/)). The methodology for the detection of volcanic thermal anomalies is based on Google Colaboratory (Colab). Despite heavily relying on Google products, we have no commercial or financial interest in promoting and using them. In particular, we did not receive any compensation in any form from Google for this work. The authors declare no competing interests. The authors declare no conflict of interest.
## 5 Performance Evaluation Indices  
To evaluate the quality of the PSO-XGBoost, XGBoost, SVM, RF, GP, and CART models, five indices of performance were used, including mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R\({}^{2}\)). The calculation of RMSE, R\({}^{2}\), MAE, VAF, and MAPE was described in Equations (13)-(17):  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y_{i}})}^{2}}} \tag{13}\]  
\[\text{R}^{2}=1-\frac{\sum\limits_{i}{(y_{i}-\hat{y_{i}})^{2}}}{\sum\limits_{i }{(y_{i}-\overline{y})}^{2}} \tag{14}\]  
\[\text{MAE}=\frac{1}{n}\sum\limits_{i=1}^{n}{|y_{i}-\hat{y_{i}}|} \tag{15}\]  
\[\text{VAF}=\left(1-\frac{\text{var }{(y_{i}-\hat{y_{i}})}}{\text{var }{(y_{i})}}\right)\times 100 \tag{16}\]  
\[MAPE=\frac{100\%}{n}{\sum\limits_{i=1}^{n}{\left|\frac{y_{i}-\hat{y_{i}}}{y_ {i}}\right|}} \tag{17}\]  
where \(n\) stands for the number of instances; \(\overline{y}\), \(y_{i}\), and \(\hat{y}_{i}\) consider as average, calculated, and modeled amounts of the response variable.