esa-sceva/satcom-chunk-collection huggingface dataset:
# Viscoelastic Strains of Palaeozoic Shales under the Burger's Model Description
## 1 Introduction  
All rocks display rheological features, which are emphasized to a larger degree in the case of soft rocks with high porosity rather than high strength rocks [1]. Various rock types have different creep predispositions [2]. Strain caused by creep may take place for several minutes or may last for several years. Thus, the study of creep is not only restricted to oil engineering but is also vital in long-term rock engineering and underground dynamics in mines, tunnels, and underground storage of nuclear waste, CO\({}_{2}\), and natural gas [3]. Salt represents a medium well recognized with regard to its rheology [4; 5; 6; 7]; in addition to its exploitation (as potassium salt), salt deposits are used as magazines for gas or hydrocarbons. In the case of strong rocks, rheological phenomena are analyzed less frequently, butlikewise in this case, due to the source and cause of creep, it is important to determine the time at which the rock is damaged or the moment when progressive creep begins. Gas shales are specific heterogeneous rocks [8; 9; 10], with a larger or smaller anisotropy [11; 12; 13]. Their rheological features have been analyzed with regard to the selection of the creep model [14; 15], the influence of the mineral composition on creep [16; 17], and the possibility of applying micro-indentation [18; 19; 20] to determine the rheological properties in shales. Due to the relatively complex description and complexity of tests, anisotropic creep of shales has been rarely studied [21]. Models taking into account creep effects are not frequently used in the simulations of hydrofracturing in shales or other rocks [15; 22]; however, these phenomena may be observed during hydrofracturing [23; 24]. Some attempts of the usage of rheological models in simulation fracturing may be found in [25; 26], but such reports are rare, and their application is restricted. Rheological phenomena play an important role in the analysis of the initial stress state in shales, an issue that is indispensable in hydrofracturing [27; 28]. An important but rarely studied aspect is the significance of volumetric change in the creep process [18; 27; 29; 30]. This kind of change records information about the creep mechanism. This article analyses volumetric change related to creep, with shear and volumetric creep strains treated separately. Moreover, the significance of volumetric creep description is determined here for Paleozoic shales from the Baltic Basin. After creep character was initially recognized, the [PERSON]'s model was selected for its description. The model was defined for triaxial loading conditions. The investigations and analyses were performed at different loading levels with regard to the triaxial shale strength. The linear limit of the creep process, i.e., the limit of application of the viscoelastic physical model, was determined for the analyzed rocks.
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 7 Conclusions and Future Works
Furthermore, the main goal of this work was to test the behaviour of the EKF when the embedded model was a simplified version of the more accurate one used to generate the measurements. For this reason, future work will also investigate the use of an oversimplified model, such as a dipole approximation, onboard the EKF and its impact on the algorithms's performance. Conceptualization, G.G. and F.C. ; methodology, G.G. ; software, G.G. and M.A.d.G. ; validation, G.G., F.C., and M.A.d.G. ; formal analysis, G.G. ; investigation, G.G. and F.C. ; resources, F.C. ; data curation, G.G. and M.A.d.G. ; writing--original draft preparation, G.G. ; writing--review and editing, G.G. and F.C. ; visualization, G.G. ; supervision, F.C. ; project administration, F.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The datasets presented in this article are not readily available because they are part of an ongoing study on the same topic. Requests to access the datasets should be directed to Dr. [PERSON] at [EMAIL_ADDRESS]. The authors would like to thank [PERSON] from the INGV (Istituto Nazionale di Geofisica e Vulcanologia--Italian Geophysics and Volcanology Institute) for her expertise and assistance throughout the implementation of the CHAOS-7 geomagnetic field model. The authors declare no conflicts of interest.
## 2 Pointing Strategy and Model Establishment
### Dynamic Model  
#### 2.2.1 Structural Model  
To achieve ultra-high stability tracking compensation for the TianQin breathing angle variation, stringent requirements were imposed on the structural design of the telescope pointing mechanism. To ensure the stability of the pointing mechanism, a flexible bearing was chosen for the rotation axis, and high-precision piezoelectric actuators are used to drive the MOSA rotation in a direct-drive manner. Figure 3 provides the preliminary three-dimensional model of the telescope pointing mechanism in the TianQin mission.  
Figure 2: Time variations in the three breathing angles \(\alpha_{1,2,3}\) of the constellation.
## 2 Materials and Methods
### Methodology  
Two experimental toolpaths were used to evaluate the effects of interpolator settings on precision and toolpath quality in corner neighborhoods. The first toolpath consisted of a sequence of angles whose sizes formed a uniform sampling of the interval (\(110^{\circ}\), \(170^{\circ}\)) (see Section 2.2). The data obtained from this toolpath were used to quantify toolpath quality and formulate regression models characterizing the dependence of toolpath quality on the values of selected CNC parameters. The models were then verified on data obtained from the verification toolpath. The verification toolpath consisted of piecewise linear segments in the YZ plane separated by constant offsets along the X-axis (see Section 2.3). Both toolpaths were executed on an MCVL 1000 three-axis machining center as part of a full factorial experiment. During the machining process, the timestamp and position vectors generated by the interpolator were recorded. All subsequent data processing was realized with Matlab software.  
Both toolpaths were specifically designed as simple piecewise linear paths. This was in line with the recommendation given for testing CNC parameter settings in the Heidenhain technical manual ([32], p. 833). This design also simplified the data evaluation process, as the NC code for such paths could be produced analytically and the exact locations and sizes of individual angles were known beforehand.
# Fast-Convergence Reinforcement Learning for Routing in LEO Satellite Networks
## 3 Proposed FRL-SR Algorithm
### Complexity Analysis  
In this paper, we compare FRL-SR with the Dijkstra algorithm in terms of algorithm complexity. The time complexity of the Dijkstra algorithm is \(O(n^{2})\): it has to obtain global state information and then compute the shortest path from the current node to any node through two layers of loops. The FRL-SR algorithm proposed in this paper only queries the Q-table stored in the satellite when making routing decisions, so the time complexity is constant. Therefore, FRL-SR is more suitable for solving routing problems for large satellite constellations. In terms of spatial complexity, both of them store a two-dimensional array. The spatial complexity of the Dijkstra algorithm is O(E +N) when using adjacency tables for data storage, where E is the number of edges in the network diagram. The maximum action space of the FRL-SR algorithm is 4, so the space complexity is O(4N), which does not take up more storage space than the Dijkstra algorithm.  
Another important indicator is the communication overhead during the execution of the algorithm. In the Dijkstra algorithm, each satellite node must send its own status  
Figure 3: Schematic diagram of link state propagation.  
information in a flood so that each satellite node can receive the global information. The communication overhead is high and also has some negative impact on the network load. In the FRL-SR algorithm, each satellite node only needs to send the status information to its neighboring nodes without forwarding it to the whole network. Even if we increase the number of communications between neighbor nodes to improve the convergence speed, it is much lower than the communication overhead of the Dijkstra algorithm.
## 2 PCM and STT-MRAM Technologies
On the other hand, if the two layers are aligned in the opposite direction, the MTJ resistance is high, which represents the logical value of one. To read data in an STT-MRAM's cell, we should apply a small voltage between the sense and bit lines, and detect the current flow. To write data in an STT-RAM's cell, we should push a large current through MTJ in order to modify the magnetic orientation. According to the current's direction, the two ferromagnetic layers' magnetic fields are aligned in the same or the opposite direction. The level of the current necessary for storing in MTJ is larger than that required for reading from it. Table 1 lists the characteristics of STT-MRAM and PCM in comparison with DRAM and NAND flash memory [9; 17; 21]. As listed in the table, the performance and the endurance characteristics of STT-MRAM are very similar to those of DRAM, but STT-MRAM consumes less power than DRAM. Similar to NAND flash, PCM has asymmetric read/write latencies. However, PCM does not have erase operations, and its access latency and endurance limit are all better than NAND flash.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
###### Abstract  
The underwater acoustic (UWA) communication system faces challenges due to environmental factors, extensive multipath spread, and rapidly changing propagation conditions. Deep learning based solutions, especially for orthogonal frequency division multiplexing (OFDM) receivers, have been shown to improve performance. However, the UWA channel characteristics are highly dynamic and depend on the specific underwater conditions. Therefore, these models suffer from model mismatch when deployed in environments different from those used for training, leading to performance degradation and requiring costly, time-consuming retraining. To address these issues, we propose a transfer learning (TL)-based pre-trained model for OFDM based UWA communication. Rather than training separate models for each underwater channel, we aggregate received signals from five distinct WATEMARK channels, across varying signal to noise ratios (SNRs), into a unified dataset. This diverse training set enables the model to generalize across various underwater conditions, ensuring robust performance without extensive retraining. We evaluate the pre-trained model using real-world data from Qingdao Lake in Hangzhou, China, which serves as the target environment. Our experiments show that the model adapts well to these challenging environment, overcoming model mismatch and minimizing computational costs. The proposed TL-based OFDM receiver outperforms traditional methods in terms of bit error rate (BER) and other evaluation metrics. It demonstrates strong adaptability to varying channel conditions. This includes scenarios where training and testing occur on the same channel, under channel mismatch, and with or without fine-tuning on target data. At 10 dB SNR, it achieves an approximately 80% improvement in BER compared to other methods.  
O +
Footnote †: journal: Marine Science and Engineering  
_Article_  
_Article_  
A Novel Transfer Learning-Based OFDM Receiver  
Enhanced Underwater Acoustic Communication  
[PERSON]  
11 National Key Laboratory of Underwater Acoustic Technology, Harbin 150001, China; [EMAIL_ADDRESS] (M.A.); [EMAIL_ADDRESS] (S.L.); [EMAIL_ADDRESS] (H.Y.); [EMAIL_ADDRESS] (M.M.)  
* Correspondence: [EMAIL_ADDRESS]
# A B5G Non-Terrestrial-Network (NTN) and Hybrid Constellation Based Data Collection System (DCS)
## 6 DCS Equipment Design
### Operation Control  
Based on the flow designs, SDR-based DCS equipment can be constructed. A duty planning strategy is necessary for resource allocation. The orbit period is equally divided into time slots, as shown in Figure 10a. Each slot is engaged by a duty. And the \"break in\" task and the \"forbid\" task are responsible for urgent task and fault task. Each task can include \"duty\", \"break in\", and \"forbid\".  
The specific schedule is configured by a duty planning strategy, as shown in Figure 10b. All tasks in the schedule can be determined by the tele-command (TC) from ground stations. The \"forbid\" task can also be determined by the on-board computer. A restart function is introduced to avoid infinite loops. The periodical loop between the \"operation begin\" function and the \"operation delay\" function is the main operation in this strategy.  
A cloud-server-based ground station network [30] is introduced as a gateway network. There are six ground gateway equipment, which work together to increase data transmission efficiency. They are No. 1, Haerbin (128\({}^{\circ}\)4', 45'3'); No. 2, Shanghai (121\({}^{\circ}\)3', 31\({}^{\circ}\)0'); No. 3, Xi'an (108\({}^{\circ}\)4', 33'8'); No. 4, Yunnan (103\({}^{\circ}\)4', 25\({}^{\circ}\)3'); No. 5, Qinghai (95\({}^{\circ}\)0', 36\({}^{\circ}\)2'); and No. 6, Xin-jiang (80\({}^{\circ}\)6', 39\({}^{\circ}\)4'), as shown in Figure 11. Task schedules are uploaded to CubeSats from the ground station network. All CubeSats will accomplish the tasks according to the arrangements.  
Figure 9: Different DCS flow design.  
The ground gateway equipment is composed of USRP B201 and Raspberry Pi 4B, as shown in Figure 12. After data packets have been collected by DCS equipment from ground sensors, they will be transmitted to ground gateway equipment. Each piece of gateway equipment is connected to the terrestrial 5G network via an Ethernet interface. All collected data packets are sent to the terrestrial 5G core network through the terrestrial 5G bearer network. GMSK and BPSK are the modulation methods of the downlink channel, which back up each other.  
Figure 11: Ground gateway distribution.  
Figure 10: Operation functions: (**a**) Multiple duty operation control; (**b**) Schedule strategy.
## 2 Methods
### Battery Cell Model  
The \"lumped battery model\" provided by Comsol was used to simulate the cell performance as well as the heating up of the cell pack (heat source). Key input parameters of the battery model are summed up in Appendix A. Cell parameters were determined by performing a parameter estimation study to fit experimental charge and discharge curves at different C rates. The parameter estimation was supported by interpretations of impedance measurements (EIS), pulsed discharge tests, and microstructural investigations (see Appendix A for further details), which were used as starting points for the parameter estimation study. A reasonable agreement between simulation and experimental data was achieved in terms of electrochemical and thermal behavior. An event interface was used to control battery mode (charging, discharging) and charging mode (CC, CV) with respect to state of charge. To link battery and thermal model, an electrochemical-thermal coupling model was used. Further documentation of the Comsol lumped battery model and the equations used can be found in Appendix B and in the Comsol documentation [48]. At the beginning of the use/charge cycle, the cells are in fully charged (SOC 1) state.  
Electrochemical cell characterizations were carried out using a BaSyTec XCTS system (BaSyTec GmbH. Ollinger Weg 17, 89176 Asselfingen, Germany) and a Biologic VSP system  
Figure 1: Use/charge cycle: While using the power tool, the battery pack is quickly discharged and heats up ((**A**): Use phase). After being totally discharged, it is placed in the charging unit. However, before the charging can start, the cell pack has to cool down (rest) below the upper safety temperature limit (stop temperature) to prevent accelerated aging of the cells ((**B**): cool down phase I). This is controlled by the charging management system in the charging unit. At a certain temperature (start temperature, usually a few degrees Celsius below stop temperature) set by the manufacturer, the actual charging starts ((**C**): charging phase). During charging, the battery pack can heat up. If the upper safety temperature limit (stop temperature) is reached, the charging has to stop and cool down to the start temperature. When the pack is fully charged it cools down until it reaches room temperature ((**D**): Cool down phase II).  
(Biologic, Rue de Vaucanson 4, 38170 Seyssinet-Pariset, France) at controlled ambient temperature (climate chamber).
## 2 Experimental Development
### Mechanical Characterization of Specimens  
The methodology from ASTM D412-16 [48] was followed to determine the maximum tensile stress. Specimens were secured in an Instron universal tensile testing machine at the PUJB laboratories, equipped with a 50 kN load cell (Figure 2a). UN-FREI specimens were tested at 500 mm/min, while UR-FREI specimens were tested at 50 mm/min to allow greater elongation before failure [16].  
Specimens for compressive stress determination were prepared according to ASTM D575-91 [49] and tested in an Instron universal compression testing machine at PUJB (Figure 2b). Three successive loading-unloading cycles were performed at 12 mm/min. The first two cycles reached 50% vertical deformation for conditioning, and the third cycle recorded the load required to reach 70% deformation.  
Figure 1: Exposure to environmental conditions: (**a**) RH, (**b**) UV radiation, (**c**) drying-salt saltwater, and (**d**) CO\({}_{2}\).  
ASTM D395-18 [50] was used to evaluate deformation under constant compressive stress (Figure 2c). Before testing, the specimens' heights were measured and then placed in an oven at 70 \({}^{\circ}\)C for 22 h, limiting deformation to 25% of their initial height. Thirty minutes after removal, height measurements were taken again. The residual compression (CB) was calculated (Equation (2)), where CB is expressed as a percentage of the original deflection, t\({}_{0}\) is the original thickness, t\({}_{\text{i}}\) is the final thickness, and t\({}_{\text{n}}\) is the spacer bar thickness.  
\[\text{CB}=\frac{(\text{t}_{0}-\text{t}_{\text{i}})}{(\text{t}_{0}-\text{t}_{ \text{n}})}\times 100 \tag{2}\]  
Two assembly types were used to determine shear modulus and damping ratio through cyclic shear tests. UN-FREI specimens followed the setup in Figure 3a, with an 8.6 mm thickness and a rectangular cross-section of 29.5 mm \(\times\) 38.4 mm. UR-FREI specimens (Figure 3b) were cylindrical, 28.6 mm in diameter, and 12.5 mm in height, and were placed under compressive stress to prevent slipping.  
Tests were conducted using an MTS universal hydraulic testing machine at PUJB and an Instron universal testing machine at PUJC, applying 5 s and 9 s load periods, respectively. Since the shear modulus and damping ratio of UN-FREI are independent of the load period [1], both periods were also applied to UR-FREI for comparison. The procedures from ATC-17 [51] and ASTM D4014-03 [52] were followed.  
Figure 3: Specimens for cyclic shear test: (**a**) UN-FREI, and (**b**) UR-FREI.  
Figure 2: Specimens for: (**a**) tension, (**b**) monotonic compression, and (**c**) residual compression tests.  
The material deformations tested were 10%, 20%, 30%, 40%, 50%, 75%, 100%, and 150%, with ten cycles applied at each deformation level. However, although the full protocol was executed, results are presented for a strain of 30% because the UR-FREI specimens were damaged at higher strain levels.
## 5 Discussion
### Synthetic Dataset Evaluation  
The quality of the reconstructed results for the synthetic datasets was evaluated via a comparison between the results and ground-truth data. This evaluation contained two steps: floor map evaluation and wall evaluation.
## 4 Discussion
### Limitations and Strengths  
Admittedly, these findings should be interpreted with caution, as the current research has certain limitations. Perhaps the most important limitation is the cross-sectional design, which prevents the establishment of causal associations. It should also be noted that the size of the sample, although large, did not allow for a balanced and equal distribution of the sexes. Another limitation was conducting the evaluations between December and January. In this regard, we had a minority of primary school students who were not yet 8 years old in December 2023, while others were already 8 years old in January 2023. By 2024, they were already 12 years old. However, and according to the Spanish educational system, they were all primary school students from the first and second cycles of primary school (3 rd, 4 th, 5 th, and 6 th grades). Despite the limitations mentioned above, this research has several strengths. Cognitive function was assessed in multiple dimensions using validated questionnaires such as the Stroop Test and the Word List Recall Test. The physical tests used to assess physical function have been validated and are appropriate for the population studied. In relation to the previous paragraph, we found a review [37] in which the existing relationship between cognitive function and academic performance in children between 5 and 13 years old was investigated. The findings of the study support and strengthen our results, proving, although with limited evidence, that favorable results in terms of cognitive performance in this population lead to improvements in the academic performance of children.
# Hybrid Simulation of Seismic Responses of a Typical Station with a Reinforced Concrete Column
###### Abstract  
During the 1995 Kobe earthquake, damages were observed in the Daikai subway station and adjacent tunnels. It was the first large-scale underground structure that failed under the earthquake excitation. Numerical and experimental analyses have been conducted to study the failure process of the Daikai station. However, the issue of the scale ratio still exists in the shaking table tests of underground structures. In order to tackle this issue, a hybrid simulation technique is developed here to study the seismic performance of a typical subway station. Based on the previous research, it is found that the central column is the critical component of the structure. Therefore, a reinforced concrete central column is physically tested in the hybrid simulation process. On the other hand, the remaining parts of the structure and soil domain are numerically modeled at the same time. Four hybrid simulation cases are conducted with peak ground accelerations of 0.01 g, 0.1 g, 0.22 g, and 0.58 g. The test results of displacement and shear force are compared with the analytical results. Moreover, the good agreement between the test results and numerical results validate the accuracy of the proposed hybrid test method. After the hybrid simulation process, a quasi-static test is conducted to illustrate the mechanical properties of the central column after the earthquake excitation.  
hybrid simulation; subway station; central column; OpenSees; OpenFresco +
Footnote †: journal: Applied Sciences