esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Intervention (EG)  
The experimental group participated in a 4-week neuromuscular training program, conducted three times per week (total of 12 sessions), designed to reduce inter-limb asymmetries and enhance functional force efficiency. Each session (~40 min) was supervised by certified coaches and physiologists and followed a structured progression protocol. Key training components included:  
* Unilateral pyborometric jumps with EMG monitoring. Surface EMG electrodes (Noraxon DTS, 1000 Hz; bandpass 20-500 Hz) were placed bilaterally on the vastus lateralis and medial gastrocnemius to monitor muscle activation patterns during jumps. EMG data were recorded for post-session analysis but were not presented to athletes as real-time feedback. Athletes performed 3 sets of 6 unilateral jumps per leg, focusing on minimizing observed asymmetry based on pre-test movement metrics and verbal cues.
* Asymmetrical depth jumps. Athletes performed single-leg depth jumps from variable heights (20-40 cm), alternating between legs. Ground contact time and RSI were recorded using Optolump (Microgate, Bolzano BZ, Italy). Height progression was based on prior week performance.
* Reactive pyborometric drills. Lateral and anterior hops were performed in response to randomized auditory cues. Flight and contact times were measured per limb via contact mats. Unpredictability was introduced to enhance motor control variability.
* Stabilization-focused single-leg hops. Athletes executed hop-and-hold tasks (3 s hold) for dynamic postural control. Three sets of 5 repetitions per leg were completed with verbal correction cues.
* Isometric force equalization drills. Included unilateral mid-high pulls and wall sits with bilateral force sensors (Loadsol, Pittsburgh, PA, USA). Athletes received instantaneous asymmetry feedback and were instructed to maintain balance < 10%.  
Progressive overload was achieved through weekly adjustment of jump height, repetition volume, or biofeedback sensitivity, based on individualized RSI and asymmetry data. The protocol was developed in accordance with principles of motor adaptation and asymmetry-targeted neuromechanical training.  
Mid-program monitoring (session 6) allowed for interim assessment of adaptation trajectory. All sessions were logged and supervised to ensure protocol fidelity. Asymmetry targeting in the experimental group was achieved using individualized unilateral psychometric tasks, guided by pre-test asymmetry metrics (e.g., jump height, force impulse, and landing rate of force development). The primary emphasis was on correcting asymmetry through task-specific cues and individualized thresholds derived from pre-intervention assessments.
## 2 Materials and Methods
### Stimulation Parameters
* The attenuation of the sound beam through the cover will be minimal and can be assessed using the following formula [11]: \[p(x)=p(0)e^{-\alpha\cdot x}\] (3) where \(\alpha\) = 1.1 dB/(cm MHz) is the attenuation coefficient of PMMA, \(x\) is the distance traveled by the beam through the material, \(p\) (0) the initial pressure. According to the previous considerations, the SATA values were calculated considering the Spatial Peak Intensity (\(\text{I}_{\text{SP}}\)), that represents the energy provided by the beam on the area of 1 cm\({}^{2}\), at the three stimulation frequencies (\(\text{I}_{\text{sp1}}\) = 1 Mhz; \(\text{I}_{\text{sp2}}\) = 2 Mhz; \(\text{I}_{\text{sp3}}\) = 3 Mhz), considering a Gaussian waveform of the beam (whence the Beam Uniformity Ratio) and a duty cycle of 20%, obtaining the following values:  
\[I_{\text{SP1}}=\frac{p^{2}}{2\cdot Z}=17.514\frac{\text{W}}{\text{cm}^{2}} \tag{4}\]  
\[I_{\text{SP2}}=34.133\frac{\text{W}}{\text{cm}^{2}} \tag{5}\]  
Figure 6: Focal distance calculation. At 3 MHz stimulation frequency the selected stimulation point is at a distance of 150 mm from the transducer (green circumference). This value results from the test made with the hydrophone and by a subsequent linear interpolation. \(Z_{R}\) indicates the distance of Rayleigh (dotted line in red), in this case the \(Z_{R}\) is located right after the cut-off region where the pression is very uneven. The orange line in the figure indicates the peak-to-peak pressure trend made by the USCND laboratories. \[I_{SP3}=36.471\frac{\text{W}}{\text{cm}^{2}} \tag{6}\]  
\[SATA_{1}=1.167\frac{\text{W}}{\text{cm}^{2}} \tag{7}\]  
Figure 4:0  
Figure 4:1  
These SATA values fall within the characteristic range of the low intensities 0.07-3 W/cm\({}^{2}\) of ultrasound that involves the temporal average of all propagating pulses that are emitted during several frames and that make significant intensity contributions over a specified scan cross-sectional area.
## 3 Methodology  
The methodology of this study is composed of four key steps. Firstly, the original phenology module of the Biome-BGC was modified by remotely sensed phenology, which was achieved by using the phenological indicators derived from remote sensing to prescribe the phenological states within the Biome-BGC model. Secondly, based on the Biome-BGC model after a phenology module modification, the [PERSON] qualitative sensitivity analysis method was used to screen out those parameters that have negligible effects on the GPP simulation, and then the Sobol' quantitative sensitivity analysis method was used to evaluate the influence of each of the remaining parameters on the simulated GPP and further determine the parameters to be calibrated. Moreover, considering the differences in the physiological characteristics of alpine meadows and alpine steppes, we performed a sensitivity analysis on them separately. Thirdly, we used the simulated annealing optimization algorithm combined with EC-derived GPP data to calibrate those influential parameters selected in the previous step, and set those non-influential parameters to the default values. This calibration process was also performed on alpine meadows and alpine steppes, respectively. In addition, since only two years of EC-derived GPP data was obtained for both alpine meadows and alpine steppes, we used the first year of GPP data to calibrate the influential parameters while using the second year of GPP data to validate the performance of the Biome-BGC model. Finally, we extrapolated the Biome-BGC model after a phenology module modification and parameter calibration to the entire alpine grasslands on the TP to simulate its GPP from 1982 to 2015. Figure 2 shows the organizational flow chart of the methodology of this study, and details will be given in the subsequent sections.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & \begin{tabular}{c} **Grassland** \\ **Subtype** \\ \end{tabular} & **Coordinate** & **Elevation** & \begin{tabular}{c} **Canopy** \\ **Height** \\ \end{tabular} & **Period** \\ \hline Haibei Station & \
\begin{tabular}{c} Alpine \\ Meadow \\ \end{tabular} &
\begin{tabular}{c} 37\({}^{\circ}\)37\({}^{\prime}\)N, \\ \(101^{\circ}\)19\({}^{\prime}\)E \\ \(30^{\circ}\)29\({}^{\prime}\)N, \\ \(91^{\circ}\)04\({}^{\prime}\)E \\ \end{tabular} & 3190 m & 0.2–0.3 m & 2003–2004 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptions of the two flux sites used in this study.
# Validating Precise Orbit Determination from Satellite-Borne GPS Data of Haiyang-2D
## 3 Results and Discussion
### Quality Analysis of Satellite-Borne GPS Data
The multipath error, cycle slips, and data completeness of the 7 days of satellite-borne GPS data were calculated and tabulated in Table 1. The errors of MP1 and MP2 were 0.35 and 0.23 m, respectively, and MP1 was about 1.5 times that of MP2. The HY-2D observation of DOY 265 was missing in the period from 19:49 to 23:27, resulting in a significantly lower than average data completeness and utilization rate.
## 1 Introduction
### Major Contribution and Organization  
In the present study, we propose the hybridisation of the CapsNet and the LSTM network in a novel multi-channel input Autoencoder architecture for use on raw time series data. The contributions of this paper are summarised as follows:  
* A Novel Hybridisation of the LSTM and Capsule layers is proposed using LSTM layers as encoders and Capsules as decoders;
* The hybridisation is implemented in a novel multi-channel input, merged output model architecture for use on raw multivariate time series data;
* The model is tested on a real-world dataset and benchmarked on another real-world dataset against prominent detection methods in the field.  
This article is organised as follows: Background theory relating to the proposed method will first be provided. Following this, the proposed method will be introduced. The experimental design on the first dataset used, a drone dataset, will be briefly explained, then the experimental work completed on the dataset will be presented and analysed. The second dataset, a benchmark motor dataset, will then be introduced, and results from the experiments on the latter will be provided. A discussion as well as the conclusion will follow the experimentation.
## 4 Closed-Form Solutions and Convergence Deformation of the Weilai Tunnel
### Assessment of Convergence Deformation in the Weilai Tunnel  
Forecasting the convergence deformation of any deep-buried tunnel is of particular importance in ensuring its long-term stability. It is even more indispensable when the geological and hydrological conditions of the surrounding rocks are unfavorable. In fact, the convergence deformation is a key factor related to the safety and stability of deep tunnels. To evaluate the convergence deformation of the Weilai Tunnel, the proposed closed-form solutions are utilized, and the results are compared with the on-site monitoring data. Total station and convergence gauges are conventionally used in the monitoring of critical points (roof and sidewalls) in the tunnel. One considers 27 days of monitoring data. The size of the tunnel cross-section and the monitoring points are illustrated in Figure 29.  
From the cross-sectional area (S) of the tunnel, the equivalent radius (\(R_{e}\)) of the tunnel is calculated as below [47]:  
\[R_{e}=\sqrt{\frac{S}{\pi}} \tag{60}\]  
Using the two variants of the closed-form solutions, the convergence deformations of the tunnel are evaluated, and are compared to the on-site monitoring data. In Figure 30, the results are presented.  
Figure 29: Illustration of tunnel cross-sectional size and arrangement of monitoring points.  
Figure 30 clearly shows that good agreements are found between the calculated convergence deformation and the on-site monitoring data. It can be observed that the convergence deformation in the tunnel roof is very close to the new model. Although they have lower numerical values, the sidewall convergences (A-E) and (B-D), in terms of behavior, are also close to the new model. The proposed closed-form solutions can therefore well forecast the time-dependent deformation of the Weilai Tunnel.  
The Weilai Tunnel exhibits large creep deformation, which surpassed 400 mm only 27 days after excavation. Such a rapid large deformation poses a severe instability risk of the Weilai Tunnel, and can cause several types of damage including human and economic losses. In general, large deformations represent a serious issue in deep-buried tunnels [56]. Markedly, as related by [PERSON] et al. [57], the long-term stability of these tunnels is exceedingly endangered by such deformations. Indeed, such deformations can persist for a long time. Accordingly, they must be addressed adequately and rapidly in order to avoid unforeseen failure. Suitable and strong engineering techniques are therefore involved to effectively solve the problem of large deformation in such tunnels. However, how to continually guarantee the long-term stability of deep tunnels built in complex soft rocky media requires special consideration.  
Figure 30: Calculated convergence deformation compared with on-site monitoring data.
## 4 Security Attacks
### Data Layer
#### 4.1.2 Quantum Attack  
Blockchain systems currently depend on cryptographic schemes--such as RSA and elliptic curve (EC) cryptography--to secure transactions and generate private-public key pairs. However, advances in quantum computing have raised concerns about the future security of these systems. Quantum attacks [42] refer to the theoretical capability of quantum computers to break conventional cryptographic algorithms, potentially rendering these security measures ineffective. Although practical quantum attacks have not yet materialized, the substantial computational power of quantum systems suggests that no cryptography-based system can be considered entirely secure in the long term.  
For instance, researchers estimate that a classical computer with a 5 GHz CPU would require approximately 13.7 billion years [43] to break a 2048-bit RSA cipher--the current standard for secure encryption. In stark contrast, a quantum computer operating at 10 MHz could potentially break the same cipher in roughly 42 min. This dramatic difference underscores the significant threat that quantum computing poses to blockchain security.  
To address this issue, the development and implementation of quantum-resistant key generation techniques have become a priority [44]. These techniques are designed to withstand attacks from both classical and quantum computers, thereby ensuring the long-term security and integrity of blockchain systems.
# CSEM Optimization Using the Correspondence Principle
## 3 Numerical Experiments
### Inversion Results
#### 3.2.1 Model with Two Resistors  
The model with two resistors has the same background as in Section 3.1, with the same resistivity and water layer thickness. The difference is that there are two resistors (not just one) located at depths of 1.8 km and 2.1 km (Figure 7a). The data correspond to the simulated horizontal electric field measured between 3 and 15 km, with sources every 200 m, in the interval of 1.1 km to 16.9 km. The data from each receiver were restricted to offsets between 1 and 6 km. Figure 7b illustrates the gradient at the frequency of 0.25 Hz, relative to the vertical conductivity, computed in a homogeneous medium with a resistivity of 1 ohm.m. As we can see, there is consistency in the regions where the gradient is prominent, considering the position of the resistors in the model used to generate the observed data. The higher value in the left portion is a consequence of the shallower depth of the resistor located in that region.  
The inversions used data at a frequency of 0.25 Hz on a mesh with a grid interval of 100 m (along the vertical) and 200 m (along the horizontal) and a homogeneous initial model with a resistivity of 1 ohm.m. Below, we present the best results obtained with each tested method. In inversions with the steepest descent and momentum, we used preconditioning 1 (\(\delta=10^{-4}\)), and in inversion with L-BFGS-B, we used preconditioning 2 (\(k=1.0\)). Figure 8 shows the curves of the objective function's evolution during the inversions, highlighting the greater efficiency of the steepest descent method, which takes just over 20 iterations to reach a value comparable to that obtained by the momentum inversion after 60 iterations. The L-BFGS-B method is similar to the steepest descent, although it takes more iterations to reach the minimum of the objective function value.  
Figure 8: Curves showing the evolution of the objective function during the inversions for the model with two resistors, using the steepest descent method (black curve), momentum (blue curve), and the L-BFGS-B (red curve).  
Figure 7: (**a**) Resistivity model formed by two 25 ohm.m bodies at depths of 1.8 and 2.1 km in a homogeneous medium with 1 ohm.m resistivity. Triangles represent the receivers, and stars represent the sources used in the simulation. Not all sources are represented, and their height relative to the seafloor is illustrative. (**b**) Gradient relative to the vertical conductivity obtained from 0.25 Hz data in a homogeneous medium with a resistivity of 1 ohm.m.  
The models obtained from the inversions are similar (Figure 9). They are indicated by the two resistive bodies at different depths, consistent with the resistors' positions in the model used to produce the observed data. In the cases of the steepest descent and the momentum method, we observe that the deeper resistor is slightly above the desired position, unlike the model obtained with the L-BFGS-B method, in which this resistor is better positioned.
## 3 Results and Discussion
### Far Field Measurement on S and RBD Proteins  
Raman lines observed by SERS can be affected by the hybrid localized surface plasmon resonance of heterogeneous metal nanostructures such as Au/Ag [25], the latter leads to a shift and broadening in the Raman lines. That is why, before performing SERS measurement, it is prominent to measure the far-field in order to assure the protein's vibrational modes by comparing them to the existing databases.  
A 10 \(\upmu\)L sample of the stock of S and RBD with a concentration of 10\({}^{-6}\) M and 10\({}^{-5}\) M, respectively, was dropped firstly on a commercial CaF\({}_{2}\) substrate. Far-field Raman spectra of S and RBD were measured during the 150 s and by using a 633 nm laser wavelength, an objective of 50\(\times\), and power of 0.5 mW. Figure 4a,b show the appearance of different Raman bands assigned to amino acids [26; 27; 28; 29; 30; 31; 32]. We can observe a good resemblance between the two spectra. In addition, we observe more bands in the S protein, which is expected because the S protein is much larger and contains more amino acids. In both spectra, we can observe clearly, Raman bands located at 1460 cm\({}^{-1}\), 1370 cm\({}^{-1}\), 1250 cm\({}^{-1}\), 1050 cm\({}^{-1}\), 1005 cm\({}^{-1}\), 1033 cm\({}^{-1}\), 876 cm\({}^{-1}\), 640 cm\({}^{-1}\), and 526 cm\({}^{-1}\). The highest peak is attributed to C-N stretching (876) and belongs to glutamic acid and tyrosine [33]. The peak at 1460 is related to C-H stretching of glycoproteins, while the peak at 1370 is related to C-H rocking in lipids [34; 35; 36]. The peak at 1250 is attributed to the secondary bands of amide III from both the in-plane NH group bending vibration, and the C-N stretching vibration [35]. The second highest Raman intensity peak at 1050 is attributed to C-N and C-C protein stretching [36; 37]. The two peaks at 1033 and 1005 belong to phenylalanine [37]. The last two peaks at 640 and 526 are attributed to tyrosine, cysteine, and histidine, respectively [33; 38]. In conclusion, the structural features of the studied protein were verified and compared to the existing database.  
Figure 4: \(Cont.\)
# An Overview of GEO Satellite Communication Simulation Systems
## 4 Comparative Evaluation of Simulation Tools
### Physical-Layer and Channel Modeling  
MATLAB and GNU Radio are dominant in physical-layer and channel modeling due to their extensive libraries and flexibility. MATLAB supports comprehensive signal processing toolboxes that enable detailed modeling of modulation schemes, channel effects, and link-budget calculations, commonly used by universities, research labs, and industries for algorithm validation. GNU Radio offers real-time signal processing capabilities ideal for prototyping and hardware-in-the-loop scenarios, heavily utilized in software-defined radio experiments and by organizations such as amateur satellite communication groups and defense contractors [35]. NS-3 and OMNeT++ provide more abstracted or simplified physical-layer representations suitable for network-level simulations, while OPNET offers a primarily abstracted modeling approach used extensively in network planning and analysis by telecom providers [36].
## 4 Results and Discussion
### Indication of Received Signal Strength (RSSI) vs. Signal-to-Noise Ratio (SNR)
#### 4.1.2 Network Performance Relative to the Geographical Side of SMs Relative to DAP  
The same analysis done previously was also performed for the different geographical sides of the SMs in relation to the DAP or Gateway that will receive their packages. In the Figure 29 the average of RSSI and SNR per side are represented. At first, it is important to point out that each side has different geographic specifications, and on the front side it is a scenario full of houses and business buildings of at most \(2\) to \(3\) floors, on the right side it is a cleaner scenario full only of houses and land, and on the left side it is a main avenue full of several houses and the presence of some posts, pharmacies and commercial buildings.  
In this context, it is possible to observe in the Figure 29a that RSSI is better for the right side, because even with longer distances the devices do not have so many barriers  
Figure 28: Average RSSI and SNR per SF LoRaMESH. (**a**) RSSI. (**b**) SNR.  
Figure 29: Average RSSI and SNR per side LoRaWAN. (**a**) RSSI. (**b**) SNR.  
ahead, however the difference for the other sides is only 5 dBm, which also does not make this difference significant. In the Figure 29b it is possible to observe that the presence of barriers interferes with the SNR, thus making the front and left sides have an average SNR of \(-3\) to \(-11\) dBm while on the right side the SNR is around 5 dBm and this is due to the terrain and pressure of fewer barriers.  
After understanding how the RSSI and SNR are being effected for each side, these metrics were analyzed considering each SF configuration and the results are presented in the Figure 30. Where the Figure 30a presents the RSSI for each geographic side in each SF configuration, showing that the smaller SFs have a difference of only 4 dBm regardless of which side the SM has. In larger configurations, the difference can reach almost 15 dBm depending on the side, i.e., depending on how the distribution of the SMs is in relation to the existing barriers in the middle of the communication infrastructure to the gateway.  
In the Figure 30b the SNR clearly presents how larger configured SFs are significantly affected when scenarios are full of barriers. In this case, the larger SFs were SNRed between \(-5\) and \(-15\), while the smaller SFs range from 5 to almost \(-5\), showing that for smaller configurations the side will not be a relevant factor, but rather the barriers present in the middle. In this case, the LoRaMESH was not analyzed by side, limiting this analysis only to the LoRaWAN Gateway, since the LoRaMESH operates on a regular basis regardless of the position it is in, which is important to know to what extent the barriers interfere with this protocol and this analysis was done next.
# Removal of Sodium Dodecylbenzenesulfonate by Macroporous Adsorbent Resins
## 3 Results  
### Characterization of the MARs  
HP20 is composed of a copolymer of St and divinylbenzene (DVB), and HP2 MGL of MMA and ethylene glycol dimethacrylate (EGDMA), in which DVB and EGDMA act as crosslinking agents (Scheme 1). To verify the chemical structure, both crosslinked resins were characterized by FT-IR spectroscopy (Figure 1). For HP20, the characteristic band of the aromatic C=C stretching vibration was shown at 1603 cm\({}^{-1}\). The characteristic band located at 902 cm\({}^{-1}\) was ascribed to the aromatic C-H deformation vibration. The characteristic band of the C-H stretching vibration of HP2 MLG appeared at 3020 cm\({}^{-1}\) and the characteristic bands located at 1730 and 1260 cm\({}^{-1}\) were ascribed to the C=O and C-O-C stretching vibrations, respectively. These features clearly indicated the chemical structures of both MARs, which are based on St and MMA. The surface morphology of the resins was investigated with the SEM, and both resins had macroporous structures (Figure 2). The macroporosity was evaluated by the BET method, and both MARs showed a similar surface area, pore volume, and pore radius (Table 1). The MARs used in this study had different chemical structures but similar morphologies, implying that the chemical structure was a major factor in the adsorption of SDBS, and that the physisorption was dominant in the adsorption process.