esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Access Selection Algorithm for Large-Scale Satellite Networks Based on Dynamic Domain
## 4 Experimental Verifications
### Simulation Parameters  
The constellation topology used in the simulation is shown in Figure 1. The ESs selected in the simulation are all located in Beijing, China. The ESs parameters are shown in Table 2. The number of satellites that can be accessed by \(ES_{1}\) during one orbital cycle of \(S^{1}\) varies with time, as shown in Figure 8. The orbital period of \(S^{1}\) is \(T\):  
\[T=2\pi\sqrt{(R+H_{1})^{3}/GM} \tag{9}\]  
where \(R\) is the earth's radius, \(H_{1}\) is the height of \(S^{1}\) from the ground, and \(GM\) is the gravitational coefficient of the earth. Based on Equation (9), the orbital period \(T\approx 5739\) s of \(S^{1}\) is obtained by STK. The satellite coverage time in each shell during \(T\) is shown in Figure 9. \(ES_{1}\), \(ES_{2}\), \(ES_{3}\), and \(ES_{4}\) are denoted by \(ES_{1\text{-}4}\). As the Poisson model can well simulate network traffic [30], the \(t_{u}\) values of \(ES_{1\text{-}4}\) are set to obey the Poisson distribution of \(\lambda\) = 500 s, \(\lambda\) = 800 s, \(\lambda\) = 1000 s, and \(\lambda\) = 1500 s, respectively. From the satellite coverage time of each shell in Figure 9, when \(t_{u}\) obeys the Poisson distribution of \(\lambda\) = 500 s, \(ES_{1}\) can complete data upload without switching access satellite in most cases. When \(t_{u}\) obeys the Poisson distribution of \(\lambda\) = 800 s and \(\lambda\) = 1000 s, \(ES_{1}\) must switch once access satellite to complete data upload in most cases. When \(t_{u}\) obeys the Poisson distribution of \(\lambda\) = 1500 s, \(ES_{1}\) must switch twice to access satellite to complete data upload in most cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Latitude** & **Longitude** & **Altitude (Km)** & **Elevation** \\ \hline \(ES_{1}\) & 39.908 & 116.420 & 0.049 & 25\({}^{\circ}\) \\ \(ES_{2}\) & 40.117 & 116.228 & 0.038 & 25\({}^{\circ}\) \\ \(ES_{3}\) & 40.072 & 116.257 & 0.041 & 25\({}^{\circ}\) \\ \(ES_{4}\) & 40.558 & 116.976 & 0.151 & 25\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Earth stations parameters.  
Figure 8: The number of satellites accessible to \(ES_{1}\) varies over time.
## 3 Materials and Methods
### Feature Extraction Using CNNs  
Deep feature extraction was performed using three pre-trained Convolutional Neural Networks (CNNs): DenseNet201, EfficientNetB2, and ResNet50. These models were selected to provide a balance of depth, computational efficiency, and representational diversity.  
DenseNet201 [23], with its densely connected layers, improves feature reuse and captures fine-grained texture and structural patterns. EfficientNetB2 [24] offers a good trade-off between accuracy and computational cost, making it suitable for real-time agricultural applications. ResNet50 [25] uses residual connections to extract deep hierarchical features and is widely adopted for general-purpose image classification.  
All models were initialized with ImageNet weights and used as fixed feature extractors. Preprocessed maize leaf images were fed into each CNN, and high-level feature vectors were obtained from the final convolutional layers.  
Figure 6: Effect of CLAHE contrast enhancement on maize leaf images.  
These deep features were then fused with hand-crafted features to form a comprehensive representation, enhancing classification robustness and accuracy. The fusion process is detailed in Section 3.7.
# Dispersion Curve Interpolation Based on Kriging Method
## 4 Results  
### Test Results  
The first test aimed to verify the feasibility of applying kriging to predict the dispersion curves. In this test, ten dispersion curves were interpolated and one of them did not have an output due to the limitation on the deviation angle. The results are shown in Figures 11-13, which present the comparison between the predicted results and the real data (i.e., the ambient noise tomography results). We can see that there was not much difference, and their deviations are provided in Figures 11, 12, 13, 14, 15, 16, 17, 18 and 19, which indicate the possibility of interpolating dispersion curves using kriging. Moreover, the mean relative error is calculated, which is shown in Figure 5. The RMSE and relative error are both evidence that illustrates that it is feasible to interpolate the dispersion curves using kriging. The relative error is defined as:  
\[R_{e}=\frac{1}{n}\frac{(\hat{y}_{i}-y_{i})}{y_{i}}(i=1,\;2,\;3,\ldots,n), \tag{5}\]  
where \(\hat{y}_{i}\) is the predicted value, \(y_{i}\) is the real value.  
The second test was similar to the first, in which the accuracy was compared between UK and OK. The root mean square error (RMSE) is shown in Figure 6. The mean RMSE of OK was slightly larger than UK; in particular periods, however, the RMSE of OK was lower than that of UK. We believe that this is due to the different standards of fitting variogram models in different periods. To avoid contingency in the results, we conducted multiple tests; the results are shown in Table 11. Overall, universal kriging with the 'Spherical' setting had lower RMSE, so was considered more suitable for our data.  
In order to choose an appropriate distance, further tests were conducted. We kept the other parameters the same, only changing the search scope. The RMSE results are shown in Figure 7a-d. To avoid contingency of the results, random tests were conducted to compare the RMSE values of OK and UK. Figures 11-13 show the results, where the parameters and the mean RMSE are detailed in the top right-hand corner. The RMSE values of OK and UK showed little difference, but the RMSE of UK was lower. And the results of sensitivity analysis about \"distance\" and \"angle\" are shown in Table 12. The RMSE is used to assess the sensitivity of the parameters. When the other parameter does not change, the variation of RMSE for different angles is much less than that for different distances. A more detailed discussion will be seen in Section 5.  
Figure 5: Relative error of each interpolation result with different methods: (**a**) Relative error of the results interpolated by OK. The mean is 1.9%; and (**b**) Relative error of the results interpolated by UK. The mean is 2.1%.
## 4 Conclusions  
Poloxamer-_graft_-carboxymethyl pullulan (Plx-\(g\)-CMP) copolymer was successfully synthesized by the coupling reaction between carboxymethyl groups of pullulan and amine groups introduced on poloxamer. The grafted copolymer has the ability to form a gel under simulated physiological conditions (phosphate buffer at pH = 7.4 and 37 \({}^{\circ}\)C). The sol-gel transition temperature of the copolymer is lower (20 \({}^{\circ}\)C) than that of Plx (25 \({}^{\circ}\)C) at the same concentration (18%, \(w/v\)). Moreover, the sol-gel transition of the grafted copolymer occurs until a concentration of 11% (\(w/v\)), while the gelation of poloxamer 407 does not occur at concentrations lower than 18% (\(w/v\)). The rheological tests evidenced a high elasticity and ability of Plx-\(g\)-CMP to recover the structure after the removal of the applied force or other external stimuli (such as temperature).  
The Plx-\(g\)-CMP hydrogel synthesized at two concentrations (13% and 22%, \(w/v\)) proved to be a suitable support for the sustained delivery of amoxicillin (taken as model drug).  
Based on the physico-chemical and rheological properties, the copolymer is recommended as an appropriate material in cartilage tissue engineering. Further studies concerning its cytocompatibility and degradability need to be performed. Moreover, new investigations must be conducted to improve the gelation time and the hydrogel durability by variation of the number of grafted chains or by introducing a supplementary covalent crosslinking.  
Conceptualization, M.C. and M.B.; funding acquisition, G.F.; investigation, M.C., B.C., M.B. and G.-L.A.; methodology, M.C. and M.B.; resources, M.C.; validation, M.B.; writing--original draft, M.C.; writing--review and editing, G.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant from the Romanian Ministry of Research, Innovation and Digitization, CNCS/CCCDI-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0050, within PNCDI III. [PERSON] acknowledges the financial support from the Ministry of Research, Innovation and Digitization, CNCS/CCCDI-UEFISCDI, project number PN-III-P2-21-PED-2019-1780, within PNCDI III.  
Not applicable.  
Data Availability Statement: Data are available on request.  
The authors declare no conflict of interest.
## 3 Result and Discussion
### Production Rates  
Biomethane production rates peaked faster with much higher maximum production rates in samples with cellulase addition (RF-CEL and CF-CEL) than those without addition (Figure 6). Cellulase-added samples showed maximum biomethane production rate in the first 30 h of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specific Methane Yield** & **RF** & **RF-CEL** & **CF** & **CF-CEL** \\ \hline L CH4/g TS & 0.25 & 0.38 & 0.36 & 0.51 \\ L CH4/g VS & 0.4 & 0.6 & 0.7 & 1.0 \\ L CH4/g CODtotal & 0.16 & 0.25 & 0.22 & 0.31 \\ L CH4/g CODsoluble & 0.32 & 0.5 & 0.39 & 0.54 \\ g CODCH4/g CODtotal & 0.44 & 0.69 & 0.61 & 0.86 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Specific methane yield of samples.  
Figure 5: Average biomethane production for raw feed (RF) and centrifuged feed (CF), with and without cellulase, and two blank cases.  
experiment while the cases without enzyme addition had much lower maximum production and it was distributed over a longer time span, peaking after \(\sim\)150 h. A brief peak during startup in all cases is assumed irrelevant (methane release from methane saturated inoculum due to temperature increase). RF-CEL reached a maximum of 34 mL/h at 17 h and CF-CEL reached 19 mL/h at 19 h followed by a decrease to \(\sim\)3 mL/h at 250 h. The BLANK-CEL sample with cellulase but without added substrates reached a methane production rate of 48 mL/h at 28 h, showing that the cellulase itself has a significant BMP and can be degraded quickly. Fortunately, the cellulase was degraded after the main degradation peaks for the feeds, implying that it can carry out the intended enzymatic attack on the feed particulates before it is itself degraded and converted to methane. Cellulose can, therefore, be added to anaerobic digesters to enhance biomethane production from cellulose containing feeds but it is not analyzed here whether this is a sustainable solution.
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 4 Resource Allocation Algorithms for MWSC
### Greedy Suboptimal Algorithm
In the resource allocation process with the resource guarantee factor \(Q\), it requires at most \(\mathcal{O}((Q+2N_{b})N_{slot})\) operations to update the candidate sets \(\Omega_{s}\) and \(\Omega_{t}\) based on \(d_{pro}^{st}\) and \(d_{th}^{sat}\) when \(N_{slot}\) time slots are allocated. Therefore, the worst-case complexity is \(\mathcal{O}((N_{ter}+1)(N_{ter}/2+2N_{b})N_{slot})\). In practice, the computational complexity is much less than this because the algorithm terminates if the minimum required capacity of all the terrestrial systems is met.
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 3 Results
### General Conclusions from the Meta-Analysis  
This meta-analysis comprehensively examined the relationship between muscle activity, biomechanics, and sprinting speed, integrating electromyographic (EMG) data, kinematic and kinetic variables, fatigue effects, and statistical modeling. The findings provide robust evidence for key neuromuscular adaptations and biomechanical changes across different sprinting phases. Below are the conclusions derived from the review.
## 4 Results
### Facies Analysis
#### 4.1.1 Massive Sandstone Facies (f1)  
Description: This is a dominant rock layer with coarse to fine sand grains, silt, or mudsized matrix (<20 percent) and is occasionally found with coarse to very coarse sand grains (<5 percent). These facies are present in single layers and amalgamations with a thickness starting from 10 cm to more than 1 m (Figure 2). The bottom border is sharp and flat, with an eroded and irregular border. Sorting is moderate to good with mud matrix support clasts. The presence of sedimentary structures, such as parallel laminations, fluted casts, load casts, flame structures, tool marks, and rip-up mud clasts was observed. _Cruziana_ sp., _Nereites_ sp., and _Chondrites_ sp. are examples of bioturbation structures (Figures 3 and 4).  
Figure 3: Examples of sedimentary structures in the Crocker Formation. (**A**) Flame structure found in L1. (**B**) Complete Bouma sequence from Ta to Tf found in L3. (**C**) Rip-up mud clasts in L2. (**D**) _Cruziana_ sp. of trace fossils in L2.  
Figure 2: Characteristic examples of lithofacies encountered in the study area. (**A**) Outcrop section showing the sandstone and mudstone with incline bedded at L1. (**B**) Outcrop section showing the thickness of the interbedded sandstone and mudstone at L1. (**C**) Interbedded sandstone and mudstone at L1. (**D**) Syncline structure in the L1. (**E**) Outcrops of interbedded sandstone and mudstone in L2. (**F**) Massive sandstone in L2.  
Interpretation: The massive sandstone facies can be formed through two depositional methods, namely, high-density turbidity currents or debris flows [26; 43].
# Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar
###### Abstract  
Real-time measurement of atmospheric wind field parameters plays an important role in weather analysis and forecasting, including improving the efficiency of wind energy, particle tracking, boundary layer measurements, and airport security. In this study, a wind profile coherent wind Light Detection and Ranging (Lidar) measurement with a wavelength of 1.55 \(\upmu\)m was developed and demonstrated based on the principle of eight-beam velocimetry. The wind speed information was retrieved, and vertical and horizontal profiles were calculated via power spectrum estimation of sampled echo signals through the measurement of the atmospheric wind field in Hefei for several consecutive days. The experimental results show that the wind profiles produced using different techniques are quite consistent and the standard error is less than 0.42 m/s compared with three-beam and five-beam wind measurements.  
eight-beam; wind profile Lidar; range gate; atmospheric wind speed; carrier-to-noise ratio 2021
## 3 Results
### Mechanism of Action at Gene and Protein Level

Table 2: Expression levels of cell death-related genes. B-cell lymphoma-2 (Bcl-2) and Bcl-2-associated X protein (Bax) expression were also evaluated to better understand the mechanism of action induced. These two proteins are involved in the early stage of apoptotic processes [38,39]. Considering that Pp_a_ treatment was reported to be associated with lipid peroxidation [40], the level of glutathione peroxidase 4 (GPX4) and nuclear receptor coactivator 4 (NCOA4) was also analyzed at the protein level. GPX4 is known to protect membrane lipids against peroxidation; indeed, GPX4 reduction has been associated with lipid peroxide accumulation [41]. NCOA4 is responsible for ferritin transport to lysosomes and degradation [42], impairing iron trafficking [43]. Our results showed that Pp_a_ treatment induced a significant reduction in Bcl-2, GPX4, and NCOA4 protein levels (Figures 2a-d and S2a), and it did not affect Bax protein levels (Figures 2a and S2b). _Cc_ Fraction E treatment induced a reduction in Bcl-2 and NCOA4 (Figures 2a,b,d and S2a) and increased the level of Bax protein Figures 2a and S2b). Combination treatment by testing Pp_a_, Cc fraction E plus photo-activation reduced Bcl-2, GPX4, and NCOA4 protein levels (Figures 2a-d and S2a), and increased Bax protein level (Figures 2a,b and S2b). The reduction in the Bcl-2/Bax ratio triggered the mitochondrial-dependent apoptotic pathway [38,44]. Loss of NCOA4 led to ferritin accumulation [42] and tumor growth suppression in melanoma cell models [45]. \(\beta\)-tubulin, initially selected as a possible reference gene for Western blotting, was also found to be differentially expressed \begin{table}
\begin{tabular}{c c c c c c} \hline
**UniGene** & **RefSeq** & **Symbol** & **Description** & **Fold** & **SD** \\ \hline Hs.189782 & NM\_018202 & TMEM57 & Transmembrane protein 57 & \(-\)4.24 & 0.00277 \\ Hs.591834 & NM\_003844 & TNFRF10A & Tumor necrosis factor receptor superfamily, member & \(-\)3.72 & 0.00042 \\ Hs.171844 & NM\_006505 & PVR & Poliovirus receptor & \(-\)3.70 & 0.01497 \\ Hs.643120 & NM\_000875 & IGFR1 & Insulin-like growth factor 1 receptor & \(-\)2.58 & 0.02103 \\ Hs.713833 & NM\_001065 & TNFRF1A & Tumor necrosis factor receptor superfamily, member & \(-\)2.54 & 0.01441 \\ Hs.181301 & NM\_004079 & CISS & Cathepsin S & \(-\)2.46 & 0.00208 \\ Hs.520898 & NM\_001908 & CTSB & Cathepsin B & \(-\)2.17 & 0.01346 \\ \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 2: Western blotting analyses. (**a**) Immunodetection of Bcl-2, Bax, GPX4, NCOA4 and beta-tubulin in treated cells with Pp_a_, Cc fraction E or both, followed by photo-activation; (**b**) Bcl-2/Bax ratio; (**c**) band intensity for GPX4, (**d**) NCOA4, (**e**) \(\beta\)-tubulin normalized with glyceraldehyde 3-phosphate dehydrogenase (GAPDH) (* \(p\) \(<\) 0.05; **p_ \(<\) 0.01).
## 6 Results and Discussion
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Dataset** & **Algorithm** & **MSE** & **RMSE** & **MAE** & \(\mathbf{R}^{2}\) \\ \hline Binance & RF & 0.0001 & 0.0110 & 0.0062 & 0.9998 \\  & GB & 0.0001 & 0.0112 & 0.0070 & 0.9998 \\  & DL & 0.0002 & 0.0144 & 0.0125 & 0.9996 \\ \hline Ethereum & RF & 0.0002 & 0.0167 & 0.0067 & 0.9995 \\  & GB & 0.0004 & 0.0201 & 0.0098 & 0.9993 \\  & DL & 0.0042 & 0.0648 & 0.0364 & 0.9937 \\ \hline Litecoin & RF & 0.0025 & 0.0501 & 0.0172 & 0.9972 \\  & GB & 0.0032 & 0.0574 & 0.0252 & 0.9963 \\  & DL & 0.0158 & 0.1258 & 0.0799 & 0.9825 \\ \hline Bitcoin & RF & 0.000084 & 0.0091 & 0.0041 & 0.9998 \\  & GB & 0.000097 & 0.0098 & 0.0045 & 0.9998 \\  & DL & 0.0087 & 0.0936 & 0.0413 & 0.9879 \\ \hline \end{tabular}
\end{table} 
Table 1: Performance metrics for Random Forest (RF), Gradient Boosting (GB), and Deep Learning (DL) models on all the datasets. GB follows closely with a slightly higher MSE of 0.0004 and an \(R^{2}\) of 0.9993, demonstrating strong performance but falling just short of RF. Conversely, DL shows an increase in MSE to 0.0042 and a lower \(R^{2}\) of 0.9937; nonetheless, these results remain highly accurate. For Litecoin, RF delivers the strongest performance, achieving an MSE of 0.0025 and an \(R^{2}\) of 0.9972. This signifies highly accurate predictions and an excellent fit to the data. Following closely is GB, which records an MSE of 0.0032 and an \(R^{2}\) of 0.9963, slightly trailing RF yet still performing admirably. DL, on the other hand, exhibits lower performance with a higher MSE of 0.0158 and a reduced \(R^{2}\) of 0.9825. Nevertheless, the error remains below zero, demonstrating the effective performance of the DL. On the Bitcoin dataset, RF and GB record the best MSEs at 0.000084 and 0.000097, respectively, surpassing DL, which has an MSE of 0.0087. This signifies that RF and GB offer more accurate predictions than DL based on the MSE. In addition, RF and GB also reach an impressive near-perfect R\({}^{2}\) value of 0.9998, indicating that they closely align with the overall data trends. The DL's R\({}^{2}\) value is 0.9879, slightly lower than those of RF and GB. The Random Forest model exhibits outstanding efficacy in predicting abnormal closing prices from normal closing prices over different rolling window periods (7, 15, and 30 days), attaining an accuracy of 100% across all four cryptocurrency test datasets, utilizing metrics such as precision, recall, F1-score, and overall accuracy [42]. This remarkable performance signifies that the model accurately classified all instances within the test dataset, where the total number of normal cases in the Binance test data was 126, whereas there were 167 abnormal cases. In the Litecoin dataset, 301 normal cases were recorded, while the abnormal cases amounted to 292. In the Ethereum dataset, there were 194 normal cases compared to 233 abnormal cases. Lastly, in the Bitcoin dataset, 296 abnormal cases were recorded, while the normal cases were counted to be 300.
## 2 The Quick Preliminary Design Model
### Payload, BoP and Auxiliary Equipment  
The payload of an AUV consists of a set of sensors and their associated equipment that provide the AUV with the capability of carrying out different missions. Their definition data is stored in a database where each device is defined by a unique identifier, its nominal power, hydrostatic pressure it can withstand, its dry weight and buoyancy, its shape and dimensions, and its position in the vehicle. There are two feasible shapes: cylindrical or parallelepiped. In the first case, the dimensions of an item are its length and diameter, and in the second case, the dimensions are its length, width, and height. There are five eligible positions: bow cone, bow pressure hull, central pressure hull, stern pressure hull, or stern cone. This configuration allows the user to configure the arrangement of the vehicle and decide on the most suitable position of the sensors, thereby avoiding interference among them.  
The BoP combines all the equipment needed for the fuel cells and batteries to operate on such a manner that they form a power plant. This group includes valves, pipes, pumps, power converters, etc. The definition of these items is equivalent to the definition of payload equipment, and they are stored in the same database. Fuel cell stacks and batteries are not included in this database as they constitute the core of the power plant and are sized by DMFC-AUVQpDM for each vehicle depending on the defined operative profile.  
The auxiliary equipment consists of the remaining elements needed for the operation of the AUV, such as control surfaces actuators, an electric propulsion electric motor, communication equipment, etc. As with the payload and BoP, the characteristics of these devices are stored in the cited database.