esa-sceva/satcom-chunk-collection huggingface dataset:
# Design Optimization of Underground Mining Vehicles Based on Regenerative Braking Energy Recovery
## 2 Design of Regenerative Braking System for Underground Mining Vehicles
### Coupling Design of Regenerative Braking System for Underground Mining Vehicles  
This study focuses on the CTYS/6 GB explosion-proof electric locomotive manufactured by the Xiangtan Yutong Company. A model of the cooperative regenerative braking system is developed, with the battery serving as the energy storage component and integrated with the vehicle's auxiliary system. The system layout is depicted in Figure 6. In this system, the electric motor supplies power to the traction locomotive through the drive shaft. During vehicle startup, the battery provides power to the electric motor, ensuring quick and efficient acceleration. During braking, the electric motor transitions into a generator state, recuperating energy generated by the braking process and storing it in the battery. This feature enables energy recovery and storage, effectively enhancing the locomotive's energy efficiency. Furthermore, in situations such as long downhill slopes where forced braking is not required, the electric motor operates in generator mode. This allows the conversion of the vehicle's inertia energy into electricity, which is then stored in the battery. This approach not only reduces energy consumption but also prolongs the battery's lifespan.
## 5 Discussion, Conclusion and Recommendations
### Discussion
Closer analysis shows that different scales and perspectives come with their own limitations: elevated oblique views at a regional scale, including forest canopy of the new 3D trees in Google Earth version 6, provide a rather realistic visual impression due to the \"defocusing\" and edge detection, _i.e._, the phenomenon of being better able to detect object edges when an image with the same resolution is reduced in one area [55]. However, the oblique view tends to \"underwhelm\" impacts, as one stakeholder put it, confirming the concerns by [PERSON] and [PERSON] [1] and by [PERSON] and [PERSON] [11] that \"when vegetation growth is maximized and visually prominent [here, vegetation growth was not included in form of 3D models but the linear layout of the town required a distant oblique view that minimized the visibility of the buildings. ], the result often obscures the built environment, and thus diminishes the presence of people in the landscape\" (2009: 498). In comparison to earlier virtual globe versions, views from a ground viewpoint have improved although they usually don't reach the realism of photorealistic visualizations in entertainment or architectural visualization, which can focus on high resolution foliage and objects in small limited views. On the other hand, the zoom feature of virtual globes provides users with a sense of \"gestalt\" for the context of the surrounding environment. The findings on interest and representativeness, _i.e._, that the use of virtual globes sometimes may overemphasize, and sometimes may obscure, visual impacts, depending on scale and the nature of the development, lead to the biggest issue, the risk of misinterpretation. This risk is particularly acute if complex issues shown in virtual globes are not mediated. [PERSON] and [PERSON] [11] identify: \"the genuine difficulties in properly seeing with satellite imagery, the need for specialized skills of interpreting features and reading off patterns to gain meaning of the situation on the ground. The key problem-you can see it clearly, but what is it that you are seeing?-is not easily solved\" (2009: 499). The KCAP shows that embedding and framing the virtual globe model into a facilitated collaborative process [5], using multiple media such as posters, presentations and virtual globes, can ensure better levels of understanding [16,24]. It seems difficult to provide such framing and assistance for interpretation online, but [PERSON] _et al._[3] suggest that a great deal could be learned from the multimedia design disciplines.
# Multi-Region OpenFOAM Solver Development for Compact Toroid Transport in Drift Tube
## 2 Models  
### Physical Model  
The mhdF solver addresses the non-ideal MHD equations, incorporating two primary non-ideal effects in the plasma: electrical resistivity and viscosity. In CT plasmas, the temperature variation is relatively small, and within this temperature range, the electrical conductivity, thermal conductivity, and viscosity do not change significantly. To reduce the complexity of the governing equations, these parameters are therefore treated as constants. This approach has also been commonly adopted in previous magnetohydrodynamic numerical simulations of CT [21]. The governing equations are as follows:  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\textbf{{U}})=0 \tag{1}\]  
\[\frac{\partial(\rho\textbf{{U}})}{\partial t}+\
abla\cdot(\rho\textbf{{U}} \textbf{{U}})=-\
abla p+\
abla\cdot\textbf{{\tau}}+\textbf{{J}}\times\textbf{ {B}} \tag{2}\]  
\[\frac{\partial}{\partial t}(\rho e)+\
abla\cdot(\rho e\textbf{{U}})+\frac{ \partial}{\partial t}(\rho k)+\
abla\cdot(\rho k\textbf{{U}})=\
abla\cdot( \alpha\
abla e)-\
abla\cdot(p\textbf{{U}})\]  
\[+\textbf{{U}}\cdot\left(\frac{1}{\mu_{0}}(\
abla\times\textbf{{B}})\times \textbf{{B}}\right)+\frac{1}{\mu_{0}\sigma_{p}}(\
abla\times\textbf{{B}})^{2} \tag{3}\]  
\[\frac{\partial\textbf{{B}}}{\partial t}=\
abla\times(\textbf{{U}}\times \textbf{{B}})+\frac{1}{\mu_{0}\sigma_{p}}\
abla^{2}\textbf{{B}} \tag{4}\]  
\[\
abla\times\textbf{{B}}=\mu_{0}\textbf{{J}} \tag{5}\]  
\[p=\rho RT \tag{6}\]  
Here, \(\rho\), \(\textbf{{U}}\), \(\textbf{{B}}\), \(\textbf{{J}}\), \(\textbf{{T}}\), and \(p\) denote the density, velocity, magnetic flux density, current, temperature, and pressure, respectively; \(\tau\) is the stress tensor; \(\rho e=\rho c_{p}T\) and \(\rho k=1/2\rho\textbf{{U}}^{2}\) represent internal energy density and kinetic energy density, respectively; \(c_{p}\) is the specific heat capacity at constant volume; \(\alpha=\kappa/c_{p}\) represents the thermal diffusivity (with \(\kappa\) being the thermal conductivity); \(\mu_{0}\) is the vacuum permeability; and \(\sigma_{p}\) is the plasma conductivity. The model is based on the equations derived by [PERSON], but the form of the energy equation has been modified [24].  
The finite-resistivity wall region includes only the resistive [PERSON]'s law:  
\[\frac{\partial\textbf{{B}}}{\partial t}=-\
abla\times(\frac{1}{\mu_{0}\sigma _{w}}\
abla\times\textbf{{B}}) \tag{7}\]  
The wall conductivity \(\sigma_{w}\) is assumed to be spatially uniform and constant, and \(\
abla\cdot\textbf{{B}}=0\), so the equation can be simplified to  
\[\frac{\partial\textbf{{B}}}{\partial t}=\frac{1}{\mu_{0}\sigma_{w}}\
abla^{2} \textbf{{B}} \tag{8}\]
# A New Compact Octagonal Shape Perfect Metamaterial Absorber for Microwave Applications
## 3 Results and Discussions
### Analysis of Design Structure with Polarization Angle  
The numerical results of compact octagonal shape PMA have been presented. The absorption A (\(\omega\)) of the design structure has been illustrated in Figure 6.  
The effect of polarization on the design structure of PMA has been analysed and the results of the absorption is little bit changed for polarization angle. The absorption values for different polarization angle, such as \(\Phi\) = 0\({}^{\circ}\), 10\({}^{\circ}\), 20\({}^{\circ}\), and 30\({}^{\circ}\) are demonstrated in Figure 6. It is seen from Figure 6 that the maximum absorption values of design structure are 99.64% at 8.08 GHz, and 99.95% at 11.41 GHz, for 0\({}^{\circ}\) polarization; the highest peaks of the absorption are 98.93% at 8.05 GHz, and 97.21% at 10.89 GHz, for 10\({}^{\circ}\); the maximum absorption values of structure are 98.22% at 7.87 GHz, and 99.47% at 10.97 GHz, for 20\({}^{\circ}\); and, the highest peaks of the absorption are 97.54% at 7.89 GHz, and 98.28% at 11.04 GHz, for 30\({}^{\circ}\), respectively. At 0\({}^{\circ}\) polarization, the design structure has exhibited highest peak of the absorption. The absorption value is high while the transmittance value is near zero. However, the reflectance value is also less at that point. The polarization of electromagnetic wave has marginally changed the resonance frequencies and absorption values due to altering the material properties.
# Development of a Novel Deployable Solar Panel and Mechanism for 6U CubeSat of STEP Cube Lab-II
## 6 Conclusions  
In this study, a 6U sized highly damped PCB-based deployable solar panel module combined with an optimized pogo pin-based burn HRM was proposed and tested for use in the STEP Cube Lab-II CubeSat. The proposed solar panel ensures the structural safety of solar cells under severe launch environments by reducing the solar panel's dynamic acceleration and deflection owing to the high damping characteristics achieved by thin stiffeners and adhesive tapes. The novel three-pogo pin-based HRM has several advantages, including high loading capability in launch configuration, simpler electrical system, and guaranteed solar panel deployment in a space environment. In addition, the radiation test results of the electrical interface PCB confirmed the radiation hardness of the P-HRM to deploy the solar panel. Further TID testing of the mechanism will be performed at a system level of P-HRM under the estimated one-year total ionizing dose of 10 krad for STEP Cube Lab-II to assure radiation hardness over the mission life. Compared to a typical PCB solar panel, the relative dynamic displacement at the center of the VMLSA under random vibration loads was decreased by a factor of 4.3. The solar panel deployment tests conducted after the vibration and TV tests validated the reliability of the mechanism. In the near future, the solar panel module flight model will be manufactured and validated again by carrying out launch vibration and TV tests at the acceptance level. This design strategy could also be advantageous for rapid attenuation of in-orbit vibration on the panel caused by the satellite during the slew maneuver, which could significantly reduce the performance degradation of satellites where the rapid acquisition of the target point is required.  
Figure 17: Summary of release time of the mechanism at each test event.  
**Author Contributions:** Conceptualization, S.B., H.K. and H.-U.O.; methodology, S.B., J.-S.G., and H.-U.O.; software, S.B. and J.-S.G.; formal analysis, S.B. and J.-S.G.; validation, S.B., J.-S.G. and H.-U.O.; writing--original draft preparation, S.B.; writing--review and editing, H.-U.O.; supervision, H.K. and H.-U.O.; funding acquisition, H.-U.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Science and ICT (MSIT).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Acknowledgments:** This research was supported by the Korea Aerospace Research Institute (KARI) and funded by the Ministry of Science and ICT (MSIT).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# University Student Dropout Prediction Using Pretrained Language Models
## 3 Methodology
### Modeling
#### 3.2.2 Recasting the Task into NLI  
Following the results of previous studies [15; 16; 17], we leveraged the knowledge of our PLM by recasting the prediction of dropout probability as an NLI task. Traditionally, NLI determines whether the given \"premise\" and \"hypothesis\" logically correspond (entailment), contradict (contradiction), or are undecided (neutral). Because the student dropout prediction task is a binary classification task (drop out or not), we recast it into a binary NLI task, comprising only \"entailment\" and \"contradiction\" labels. In general, the NLI task mainly uses a hypothesis consisting of discrete words, as follows:  
\[S=\{[CLS],\hat{s}_{1},\hat{s}_{2},\ldots,\hat{s}_{m},[SEP],\hat{d}_{1},\hat{d} _{2}\ldots,\hat{d}_{k},\hat{a}_{1},\hat{a}_{2},\ldots,\hat{a}_{l}\}, \tag{2}\]  
where \([SEP]\) is a separate token, \(\hat{s}\in V\) denote tokens of the discrete hypothesis, \(V\) is the vocabulary of the PLM, and \(m\) denotes the length of the hypothesis. However, as shown in a previous study, using a discrete hypothesis such as \"This student will drop out\" may not be optimal. Therefore, we propose a novel approach to enhance the NLI task by attaching continuous vector representations to the hypothesis to predict whether a student will drop out. We used the pseudotoken \(\hat{p}\) as the hypothesis, which was not included in the vocabulary of existing language models, as follows:  
\[S=\{[CLS],\hat{p}_{1},\hat{p}_{2},\ldots,\hat{p}_{m},[SEP],\hat{d}_{1},\hat{d}_{ 2},\ldots,\hat{d}_{k},\hat{a}_{1},\hat{a}_{2},\ldots,\hat{a}_{l}\}. \tag{3}\]  
Then, \(\hat{p}\) was embedded through a trainable embedder consisting of a single multilayer perceptron (MLP). Training the trainable embedder with the downstream task made it possible to find a better continuous hypothesis than the discrete hypothesis that the PLM can represent. Additionally, inspired by [38], we proceeded by attaching a continuous hypothesis to the key and value of each layer to make the hypothesis have a deeper impact on the output.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 3 Related Work
While this solution allows the sender and receiver to use the same spreading factor, it significantly increases the time required to send a message due to the need to have a preamble. Clearly, with an increase in sending times also comes an increase in power consumption. Additionally, it is also possible for the receiver to miss the preamble, thus losing the message. [PERSON] et al. constructed a pipeline with multi-hop capability in order to create a LoRa network capable of going further than a single-hop scenario, especially in extreme environments [24]. Furthermore, they developed a system that uses multiple spreading factors simultaneously in order to achieve multiple access in a robust and efficient way. Here the choice of the different spreading factors used was made because they are orthogonal and, as such, do not interfere with each other, not due to some benefit that choosing one over another might have. [PERSON] et al. showed that it is not always possible to have independent LoRa transmission that uses different spreading factors simultaneously due to imperfect orthogonality [25]. Their testing showed that transmissions, particularly the higher spreading factors, can interfere with each other more due to the higher transmission times, resulting in data loss and the need to retransmit non-delivered messages. Knowing this, it might be more appropriate to adopt a solution that uses a single spreading factor for the whole network instead of multiple different ones at the same time, as proposed in [23]. [PERSON] et al. propose an algorithm that tries to identify the best possible spreading factor to use for each node of the network in order to maximize efficiency and reduce data loss [26]. They, however, fall into the same belief that simultaneous transmission that uses different spreading factors does not interfere with each other; thus, their algorithm could result in unexpected errors occurring during message exchange. Additionally, their view of an environment that is static and where nodes have no degree of movement limited the possible uses of their work.
## 6 Conclusions  
Considering the inaccuracy of traditional marine photovoltaic power calculation models, this paper has proposed an improved mathematical model of PV power calculation via considering the effect of ship movements leading to the change of PV panel inclination. According to a rotation matrix, the roll and pitch are reduced to one angle and then introduced into the radiation calculation formula, where the radiation is corrected according to the latitude, longitude, and season. The proposed mathematical model provides a more realistic model and an appropriate calculation method for shipborne PV systems under different sea conditions, also this could be used as a reference for a series of stability control strategies for the output power of PV panels. Secondly, the MHPSO algorithm was improved for the prediction of PV panel inclination change. By reforming the structure and speed update equation of a PSO algorithm, the capacity of convergence was improved; thereby, increasing the solution speed of the system. Meanwhile, the MHPSO and LSTM models were combined to predict the motion of a PV panel. This structure optimizes the initial weights and thresholds of LSTM in order to overcome the problem of inaccurate parameter selection. In addition, we have used five different models, including MHPSO-LSTM, Kalman filter, LSTM network, BP neural net structure, and a PSO-LSTM algorithm to forecast the movement data of photovoltaic panels under the fifth- and third-level sea states.  
Our cases show that the proposed method has higher learning accuracy and better performance prediction than the existing methods. The comparison of grid economics and carbon dioxide emissions also shows the necessity of the proposed model. Finally, the ultrashort-term power prediction of a photovoltaic system has been carried out via predicting the fluctuation of the photovoltaic panel inclination.  
In future work, reformation for the MHPSO model will be studied to further raise the prediction precision of shipborne PV panels, and this will be combined with other heuristic algorithms to further study the model performance.  
Conceptualization, L.Z.; methodology, L.Z.; software, B.Z. and L.Z.; validation, X.P.; formal analysis, L.Z.; investigation, L.Z.; resources, L.Z. and B.W.; data curation, L.Z.; writing--original draft preparation, L.Z.; writing--review and editing, X.P.; visualization, B.Z.; supervision, X.P.; project administration, X.P.; and funding acquisition, X.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Industry of People's Republic of China (25B01-02) and supported by the Fundamental Research Funds for the Central Universities (3072020 CFT2403).  
The authors would like to acknowledge the project \"Energy Management System of MVDC Integrated Electric Propulsion System\" of the Basic Product Innovation Research Program for supplying equipment used for experiments  
The authors declare no conflict of interest.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 4 Self-Mixing Signal Processing
### Single Target Case  
Starting with a single receive antenna and a single target, the received signal model can be described in Equation (7), given by  
\[s_{Rx}(t)=A_{dir}\text{cos}(\omega_{0}t)+A_{ref}\text{sin}\left((\omega_{0}+ \omega_{D})t\right) \tag{7}\]  
where \(A_{dir}\) is the direct signal amplitude, \(A_{ref}\) is the signal amplitude reflected from the target, \(\omega_{0}\) is the operational frequency, and \(\omega_{D}\) is the Doppler shift due to the target's motion. The SLD applies a transfer function responsible for squaring the input signal, resulting in Equation (8), given by  
\[\begin{array}{rl}(s_{Rx}(t))^{2}=A_{dir}^{2}\text{cos}^{2}(\omega_{0}t)+2A_{ dir}A_{ref}\text{cos}(\omega_{0}t)\text{sin}((\omega_{0}+\omega_{D})t)\\ \hskip 14.226378 pt+A_{ref}^{2}\text{sin}^{2}((\omega_{0}+\omega_{D})t)\end{array} \tag{8}\]  
For simplification, the trigonometric power reduction and double-angle formulae can be applied to result in Equation (9).  
\[\begin{array}{rl}(s_{Rx}(t))^{2}&=\frac{A_{dir}^{2}}{2}+\frac{A_{dir}^{2}}{2 }\text{cos}(2\omega_{0}t)+\frac{A_{ref}^{2}}{2}-\frac{A_{ref}^{2}}{2}\text{ cos}(2\omega_{0}t+2\omega_{D}t)\\ &\hskip 14.226378 pt+2A_{dir}A_{ref}\text{cos}(\omega_{0}t)\text{sin}(\omega_{0 }t)\text{cos}(\omega_{D}t)+A_{dir}A_{ref}\text{sin}(\omega_{D}t)\\ &\hskip 14.226378 pt+A_{dir}A_{ref}(\text{cos}2\omega_{0}t)\text{sin}(\omega_{D }t)\end{array} \tag{9}\]  
In a practical application, Equation (9) may be scaled by some SLD factor [23]. For this work, we shall assume this value to be 1. Equation (10) contains the resulting expression after the signal is passed through the LPF:  
\[(s_{Rx}(t))^{2}=\frac{A_{dir}^{2}}{2}+\frac{A_{ref}^{2}}{2}+A_{dir}A_{ref} \text{sin}(\omega_{D}t) \tag{10}\]  
We implemented an LPF at this stage as opposed to an HPF because it is expected that \(\omega_{0}\gg\omega_{D}\). It has been shown that even with extremely high velocities, we can expect debris targets to produce Doppler shifts in the range of \(\pm 14\) kHz [38]. Therefore, the LPF removes the terms containing \(\omega_{0}\). The first two terms contain no frequency. These are the DC terms that can be removed either by an HPF or a DC-blocker. Equation (11) displays the resultant expression after the filters have been applied.  
\[S(t)=(s_{Rx}(t))^{2}=A_{dir}A_{ref}\text{sin}(\omega_{D}t) \tag{11}\]  
where \(S(t)\) represents the Doppler expression after the processing stages have been completed. The Doppler frequency has been extracted and scaled by the product of the direct signal and reflected signal amplitudes.
## 2 Materials and Methods
### Case Studies and Simulations  
To evaluate the performance of the proposed PINN-based state estimation approach, a series of simulation studies were conducted on standard IEEE test feeders [27], spanning a wide range of network sizes and levels of complexity. The selected systems include the IEEE 13-bus, 34-bus, 37-bus, and 123-bus feeders. These networks represent increasingly large and unbalanced low-voltage and medium-voltage distribution systems, offering a robust benchmark for assessing the accuracy, scalability, and robustness of the proposed method.  
For each test system, synthetic datasets were generated using OpenDSS simulations accessed through its Python interface (OpenDSSDirect 0.9.1). A custom simulation script was developed to introduce realistic variability by randomly perturbing the real and reactive power demands of all loads. Specifically, uniformly distributed scaling factors in the range [0.8, 1.2] were applied independently to \(P\) and \(Q\) values, emulating diverse operating conditions. For each scenario, the resulting complex nodal voltages \(\mathbf{V}\), current injections \(\mathbf{I}\), and apparent power injections \(\mathbf{S}\) were extracted and stored. This process was repeated for 10,000 randomized load configurations per network, yielding rich datasets suitable for supervised training and evaluation of the PINN. The complex nodal admittance matrix \(\mathbf{Y}\) was also extracted once at the beginning of the simulation process and reused for all scenarios.  
To thoroughly assess the robustness and generalization ability of the model, the case studies cover the following scenarios:  
1. Normal Operation: Base-case operation with nominal loading and full measurement availability, used to benchmark accuracy under ideal conditions.
2. Limited Observability: Scenarios with sparse sensor deployment, where voltage and power measurements are only available at a subset of buses, testing the model's extrapolation capability.  
These comprehensive simulations provide insight into the behavior of the PINN across practical operating conditions and potential real-world challenges encountered in distribution system monitoring.