esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling a Milling Dynamometer as a 3 DOF Dynamic System by Stiffness Identification
## 4 Model of Dynamometer as a System with Three Degrees of Freedom
### Motion Equations  
To define the motion equations for all three degrees of freedom, the d'Alembert principle was used.  
The motions were considered to occur in the positive directions of the axes \"\(Ox\)\" and \"\(Oy\)\", and the rotation was considered to be counterclockwise (Figure 14). As a result of these considerations of movements, the elastic and damping forces were developed (Figure 14).  
In Figure 15, the following notations are considered: \(F_{exi}\left(i\ =\ \overline{1,4}\right)\) and \(F_{eyi}\left(i\ =\ \overline{1,4}\right)\) are the elastic forces in each connection point for both directions (\"\(Ox\)\" and \"\(Oy\)\"), and \(F_{dxi}\left(i\ =\ \overline{1,4}\right)\) and \(F_{dyi}\left(i\ =\ \overline{1,4}\right)\) are the damping forces in each connection point for both directions (\"\(Ox\)\" and \"\(Oy\)\").  
Figure 14: Scheme of plate motion: in longitudinal direction, “\(Ox\)”; in transverse direction, “\(Oy\)”.  
The matrix form of the motion equations can be rewritten as:  
\[\mathrm{M}\,\bar{\delta}\ \ +\ \mathrm{C}\,\dot{\delta}\ \ +\ \mathrm{K}\,\delta\ \ =\ \ 0 \tag{15}\]  
where \(\mathrm{M}\) is the inertia matrix, \(C\) represents the damping matrix, \(\mathrm{K}\) defines the stiffness matrix, \(\bar{\delta}\) is the acceleration vector, \(\dot{\delta}\) is the velocity vector, \(\delta\) represents the displacement vector, and \(\mathbf{0}\) is the zero vector.
# A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model
###### Abstract  
Accurate prediction of the heat load in district heating systems is challenging due to various influencing factors, substantial transmission lag in the pipe network, frequent fluctuations, and significant peak-to-valley differences. An autoencoder--grey wolf optimization--gated recurrent unit (AE-GWO-GRU)-based heat load prediction method for district heating systems is proposed, employing techniques such as data augmentation, lag feature extraction, and input feature extraction, which contribute to improvements in the model's prediction accuracy and heat load control stability. By using the AE approach to augment the data, the issue of the training model's accuracy being compromised due to a shortage of data is effectively resolved. The study discusses the influencing factors and lag time of heat load, applies the partial autocorrelation function (PACF) principle to downsample the sequence, reduces the interference of lag and instantaneous changes, and improves the stationary characteristics of the heat load time series. To increase prediction accuracy, the GWO algorithm is used to tune the parameters of the GRU prediction model. The prediction error, measured by RMSE and MAPE, dropped from 56.69 and 2.45% to 47.90 and 2.17%, respectively, compared to the single GRU prediction approach. The findings demonstrate greater accuracy and stability in heat load prediction, underscoring the practical value of the proposed method.  
heat load prediction; data augmentation; GRU neural network; grey wolf optimization algorithm; parameter optimization +
Footnote †: journal: _sciences_
# Mass Processing of Sentinel-1 Images for
## 3 Methods
### Platform for Earth Observation Data Storage and Processing: the JEODPP
Another essential component of the JEODPP is its workload manager that efficiently distributes the requested computing jobs to the processing servers by taking into account the resources required by each job, their priority, and the current load of each node. This is achieved with the HTCondor workload manager given its suitability to manage massive amounts of parallel jobs with little need for inter-job communication [36]. For instance, the ship detection with SUMO boils down to the independent detection performed on each input Sentinel-1 image followed by a subsequent analysis restricted to the detected ships. This can be optimally addressed by switching off any multithread computation and launching as many Docker containers as the number \(n\) of available cores. In this case, each container processes sequentially a number of Sentinel 1 images equal to the total number of files to process divided by \(n\). The results of this approach are presented in Section 4.2. Finally, besides accommodating scientific workflows written in a variety of languages over time (legacy software), the JEODPP also offers interactive visualisation and analysis capabilities through a web interface. This was not used in the present study given that the analysis was fully automatic and that modules for digesting the detection performed at scene level were already developed prior to the platform. This will be addressed in the future given that it would further ease collaboration and knowledge sharing with other projects running on the JEODPP.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 9 Performance Analysis
### Land-Based UBRD System with Early Rail Defect Detection Capability: Performance Analysis  
Various experimental results referring to the land-based UBRD system with early defect detection capability are analyzed in Ref. [65]. As already mentioned in Section 7.4, the reordered set of pre-processed data has been processed by three algorithms (namely, SVD, ICA, and dimensionally-reduced ICA), whose performance in defect detection has been evaluated. Since the outcomes generated by these algorithms are highly dependent on the input dataset, we do not illustrate here an accurate analysis of the achieved results. We comment briefly on the performance achieved by such algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Speed** & **Recording Time Window** &
\begin{tabular}{c} **SNR of Reconstructed** \\ **Transfer Function** \\ \end{tabular} \\ \hline
30 mph (48.28 km/h) & \(\sim\)15.25 ms & \(\sim\)12 \\
50 mph (80.46 km/h) & \(\sim\)9.15 ms & \(\sim\)9 \\
80 mph (128.75 km/h) & \(\sim\)5.7 ms & \(\sim\)4.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compromise values between test speeds, recording time window and SNR of the passively reconstructed transfer function achieved in field tests.  
It is expected that, when a monotonically growing defect is found, its weight in ICA or SVD also grows monotonically. To establish if a weight exhibits a monotonic increase, the _Mann-Kendall_ (M-K) test can be exploited, as suggested in Ref. [76]. This test is based on computing the difference between the number of increments and that of decrements between each pair of data points forming the input sequence; normalizing this difference with respect to the overall number of data points produces the normalized test statistic, denoted \(Z_{mk}\). If the value of \(Z_{mk}\) is greater than 1.96, the probability of a monotonic trend is equal to 0.95; larger values of \(Z_{mk}\) indicate that this probability is even higher. The M-K test has been performed on the weights computed by all the considered algorithms.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 9 Conclusions  
Here, we present the recent advances in high-speed visible light communication: transmitter, modulation technology, receiver standard, and application. Micro-LED/laser-diode-based VLC are promising approaches towards next-generation wireless data communication networks, which have caught the attention of both academia and industry. VLC technology provides many advantages over RF and has become an alternative to RF communication, despite the disadvantage of blind spots due to light spacing and line-of-sight blocking. The successful development of the VLC system addresses the immediate multi-billion-dollar markets in beyond-5G optical wireless communication. Empowering the Internet of Underwater and Underground things (IoUZT), VLC will unfold many new ideas and enable future applications related to energy, connectivity, and the environment.  
**Author Contributions:** Supervision, W.-B.L., S.-W.C., C.-W.C. and H.-C.K.; writing--original draft, T.-C.Y. and W.-T.H.; Writing--Review & editing, C.-W.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Ministry of Science and Technology, Taiwan, grant number 110-2124-M-A49-003-, 108-2221-E-009-113-MY3.  
**Conflicts of Interest:** The authors declare no conflict of interest.
### Reinforcement Learning for Portfolio Optimization  
In the context of portfolio optimization, RL has emerged as a promising alternative to traditional optimization methods by allowing dynamic asset allocation based on market conditions. Unlike traditional approaches that assume static models or rely on historical data, RL adapts dynamically to evolving market conditions by adjusting asset weights in response to volatility, correlation shifts, and other real-time market signals [2; 5]. In RL-based portfolio optimization, the agent typically learns to allocate weights to different assets in a way that optimizes a predefined objective, such as maximizing the cumulative returns or minimizing risk-adjusted returns over a specified investment horizon. The reward function, which guides the agent's learning, can be designed to reflect various financial performance measures, such as the Sharpe or Sortino ratios [18]. For a detailed discussion on RL methodologies and their applications in portfolio optimization, refer to Section 2.3 and Appendix A.  
Aside from RL, there are alternative formulations for the portfolio optimization problem, such as Genetic Algorithms (GAs) and Quadratic Programming (QP), both of which are commonly found in financial optimization. Genetic Algorithms, inspired by natural selection, have been applied to portfolio optimization by evolving portfolios over time to maximize a fitness function [19; 20]. These algorithms are advantageous in handling non-linear optimization problems but may suffer from slow convergence.  
Quadratic Programming [4] is another well-known method, as was previously mentioned in the context of mean-variance optimization, where the problem is solved as a constrained optimization of quadratic objective functions. While QP offers efficient solutions for convex problems, it is limited by its inability to handle the complexities and non-linearities of real-world financial markets, where assumptions about normal distributions and fixed covariances often do not hold. These traditional methods, although effective in some scenarios, lack the adaptability and dynamic decision-making capabilities that RL introduces into the portfolio management space.
# Tutorial Review on Space Manipulators for Space Debris Mitigation
## 1 Introduction  
Space-based manipulators have traditionally been tasked with robotic on-orbit servicing functions, but despite several decades of development since the 1980s, this has yet to come to pass. A new application of space manipulators has emerged--active debris removal--and this need has become urgent. Much of the technological development in space robotics over this period is directly applicable to this new task and indeed, given that the more challenging aspects of on-orbit servicing are not required (namely, servicing tasks), the prospect of active debris removal can be met. All the kinematic, dynamic and control issues are identical--this includes the requirement for grappling the target and passivating it. This will require robotic clamps or grippers. Indeed, once a grapple hold has been attained on capture of the target, it may be necessary to adjust the grip configuration to secure it for transport. Relocating the grip may require robotic fingers for stable grip-to-grip transitions, but securing the grip will require robotic latching mechanisms. Servicing tasks typically have involved the deployment of power tools for bolt manipulation and the use of specialised tools for more challenging tasks such as cutting, taping and resealing thermal blankets. For the most part, this will not be required for active debris removal if the robot is latched to the target. However, the attachment of a propulsive stage to the target for disposal will require basic servicing capability including power tooling for bolt handling (ensuring that said bolts remain secure and not become further sources of debris). It is envisaged that the more difficult manipulations involving invasive servicing procedures will not be necessary.  
We first consider a brief schematic of recent on-orbit space manipulators employed by the International Space Station (ISS), and thence proceed to describe the rise and fall of robotic on-orbit servicing missions. We then provide a comprehensive review of the growing space debris crisis andproposed solutions. The freeflyer sporting two or more manipulators is the solution of choice to remove large debris pieces. To that end, the bulk of the paper reviews the kinematics, dynamics and control of space manipulators. We pay particular attention to the grappling task. Hence, our interest here is to provide a schematic of on-orbit servicing robotics that can be applied to space debris removal. Since the comprehensive review in [1], there have been many modern developments in space manipulator robotics. Some excellent more modern reviews include [2] which highlights challenges in servicing non-cooperative satellites and [3; 4] which emphasise kinematic and dynamic issues. Here, we are more selective and present a tutorial approach. We take an unusual stance in viewing space manipulators from the perspective of their deployment for space debris mitigation of which on-orbit servicing is in fact a part. Rather than aiming at comprehension, our tutorial review should increase the appeal of the paper to a wider audience.
# DD-FoG: Intelligent Distributed Dynamic FoG Computing Framework
## 3 Proposed System  
At the beginning of 2020, one can already observe the gradual introduction of new technologies of networks and computing infrastructure, as well as new services. At the same time, the concept of IMT-2020 is being replaced by the concept of generation 2030 networks.  
In addition to networking (SDN/NFV), approaches to building a computing infrastructure are also replacing, which will eliminate some components of the round-trip delay introduced by the network in the process of transmitting packets with information to the data center. These technologies are MEC (multi-access edge computing) and fog computing. These concepts for organizing cloud computing structures will allow data processing to be moved to the edge of the network and even to distributed Fog structures (in fact, devices).  
To achieve a positive synergistic effect from the joint use of the above technologies, the following solution is proposed for combining them (Intelligent Framework of Distributed Dynamic fog Computing System). This solution will allow us to achieve new opportunities for the implementation of custom services based on the Internet of Things with microservices architectural support, within the framework of stringent requirements for them.
## Appendix A Computational Complexity  
### Conventional Scheme A  
Recall that the conventional scheme A is based on three operations: the averaged correlation \(Y_{l}(k)\) in (6), the CFO estimation in (10) and the SFO estimation in (11). For each CP subcarrier, it follows from (6) that \(Y_{l}(k)\) requires \(N_{b}-1\) complex multiplications and \(N_{b}-2\) complex additions, which is converted to \(8N_{b}-10\) real flops. To estimate the CFO \(\varepsilon\) in (10), the summation over \(k\in\mathcal{S}_{f}\) requires \(N_{f}-1\) real additions, and the calculation of \(\angle Y_{l}(k)\) consumes \(N_{f}(8N_{b}-10)\) real flops for \(N_{f}\) subcarriers. Thus, (10) requires \(N_{f}(8N_{b}-9)\) real flops. For the estimation of SFO \(\xi\) in (11), the summation over \(k\in\mathcal{S}_{f}\), denoted by \(\sum_{k\in\mathcal{S}_{f}}k\angle Y_{l}(k)\), demands \(N_{f}\) real multiplications and \(N_{f}-1\) real additions. With those calculations in mind, we conclude that the conventional Scheme A uses \(N_{f}(8N_{b}-7)-1\) real flops.  
Figure 5: Performance of the conventional and proposed WLSE methods in Echo Channels 1 and 2.
## 2 Principle and Structure of the In-Situ Simulation Device for Deep Coring Tools  
### The Structure and Design Principle of the Device  
A high-temperature and ultrahigh-pressure simulation test for deep coring tools must have a strictly controlled working environment. The simulation test device is a comprehensive test system that comprises two parts: a high-temperature and ultrahigh-pressure simulation test cabin and a driving module for simulating coring tests, as shown in Figure 1. High-temperature and ultrahigh-pressure simulation test cabins can be used to simulate the in-situ occurrence environment of underground rock. The driving module for simulating coring tests provides a power source for the coring tools and allows it to realize rotation and downward vertical action and ultrahigh-pressure dynamic sealing. The key design specifications of the in-situ simulation device were as follows:  
1. Maximum working pressure of the test cabin: 140 MPa;
2. Maximum operating temperature of the test cabin: 150 \({}^{\circ}\)C;
3. Maximum working pressure of the driving module: 140 MPa;
4. Maximum rotation speed of the driving module: 150 r/min;
5. Inner diameter of the rock cabin: 500 mm;
6. Inner diameter of the drill pipe cabin: 150 mm.  
In addition to meeting the requirements for the temperature, pressure, and size parameters described above, the simulation device should follow these design principles [22; 23; 24]: (1) safety and reliability; (2) economy (the costs of design, materials, processing, and  
Figure 1: Schematic diagram of the in-situ simulation device.  
manufacturing should be reduced as much as possible, as long as the components meet the use requirements); (3) easy maintenance; and (4) simple application (the design and manufacture of the whole test chamber system should adopt existing structural forms and processing technology as much as possible, with simple application as the goal).
## 2 Materials and Methods
### Measurement Method
In addition, the measurements were at a frequency range between 50-400 Hz to evaluate the frequency-dependent behavior of the dielectric material. This approach provides a more comprehensive understanding of how the dissipation factor varies with frequency, which is crucial for assessing the suitability of the material for applications involving different operating frequencies. The breakdown experiments were performed under a 50 Hz AC voltage following the rapid-rise test method specified in IEC 60243-1:2013 [39]. The voltage was gradually increased until breakdown, and the corresponding breakdown voltages were recorded.
## 7 Compliance with Maritime Regulations
### Emission Control and Sustainability  
Reducing greenhouse gas emissions is a top priority for the maritime sector, driven by international regulations such as the IMO's GHG Strategy, the Carbon Intensity Indicator (CII), and the EU Emissions Trading System (ETS) [123; 124]. AI agents are at the heart of this transition, enabling autonomous vessels to operate with greater environmental awareness, precision, and efficiency.  
Using real-time weather forecasts, sea current data, and historical performance logs, AI agents dynamically optimize speed, route, and engine load, ensuring that the vessel follows the most energy-efficient trajectory at any given moment.  
For example, while crossing the North Atlantic, the AI agent detects a favorable current shift 30 nautical miles south. It recalculates the route, projects fuel savings of 3.5%, and issues a course correction, all without human intervention.  
AI agents also support predictive maintenance, identifying inefficiencies (e.g., injector wear, hull fouling) before they impact fuel consumption. They can trigger adjustments in engine calibration or recommend service actions aligned with emission targets [121].  
Additionally, agents manage hybrid energy flows, coordinating diesel, battery, and even renewable inputs (e.g., wind-assisted propulsion or solar power) [125; 126; 127]. This orchestration ensures not only reduced CO2 emissions but also the improved integration of sustainable technologies onboard.  
Figure 5: Regulatory compliance framework for autonomous maritime systems (own elaboration).  
Unlike traditional fuel management systems, AI agents learn continuously--fine-tuning optimization algorithms over time, adapting to seasonal sea states, vessel wear, or regulatory changes.  
By actively managing fuel use and emissions while producing automated compliance reports, AI agents help autonomous ships align with both sustainability goals and binding regulatory benchmarks, turning environmental stewardship into an embedded feature of maritime autonomy.
# Eff-PCNet: An Efficient Pure CNN Network for Medical Image Classification
## 4 Experiments and Analysis
### Evaluation Indicators  
For the medical image classification direction, when the number of samples of the categories in the dataset is not balanced, a single evaluation metric does not reflect the performance of the model very comprehensively, so in this paper we use four metrics--accuracy (Acc), precision, recall, and \(F1\) score--to measure the performance of the proposed model. All these evaluations are calculated based on the confusion matrix. The definitions of the symbols in the confusion matrix are as follows: true-positive example (\(TP\)), true-negative example (\(TN\)), false-positive example (\(FP\)), and false-negative example (\(FN\)).  
The equation for accuracy (\(Acc\)) is as is shown as follows:  
\[\text{Accuracy}(Acc)=\frac{TP+TN}{TP+FP+TN+FN}, \tag{7}\]  
The equation for precision is as is shown as follows:  
\[\text{precision}=\frac{TP}{TP+FP}, \tag{8}\]  
The equation for recall is as is shown as follows:  
\[\text{recall}=\frac{TP}{TP+FN}, \tag{9}\]  
The equation for \(F1\) Score is as is shown as follows:  
\[F1\text{ Score}=\frac{2\times\text{ precision }\times\text{ recall}}{\text{ precision }+\text{ recall}}, \tag{10}\]  
In addition, we use the area under the receiver ROC curve (\(AUC\)) as an evaluation metric to assess the classification performance of different models, and the \(AUC\) is calculated as follows:  
\[AUC=\frac{\sum_{i\in\text{ positiveClass}}rank_{i}-M(1+M)/2}{M*N}, \tag{11}\]  
where \(M\) is the number of positive samples, \(N\) is the number of negative samples, and \(rank_{i}\) is the model's ranking of the samplei ranking of the predicted probability.