esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 5 Discussion
### Why Is a Synergy of S1 SSM with LAI Observed in the Assimilation?

Table 3: Comparison of the performance of S1 SSM with other satellite SSM products over Southern France and the Salamanca region. Average correlation coefficient (\(R\)), unbiased root mean square difference (ubRMSD), and number of locations are indicated. [PERSON] et al. [66] used SMOMANIA stations together with additional soil moisture measurements in the region of Montpellier. When LAI was assimilated alone, changes in LAI were negatively correlated with changes in WG5 (\(R=-0.82\)). This can be explained by the reduced evapotranspiration caused by lower LAI values. The addition of S1 SSM in the assimilation changed this relationship in the agricultural areas, with a greater variety of WG5 difference values, either negative or positive. The largest WG5 differences could be related to increased irrigation during summer 2019 to counteract the drought that occurred. The irrigation map in Figure 10d presents similarities with the increased addition of water. However, detecting irrigation from S1 data is challenging [71; 72] because (1) the C-band signal from the soil is attenuated by well-developed vegetation and (2) the S1 sampling time is not sufficient. Moreover, errors in the land cover map used in the retrieval process may propagate to the SSM values, especially in heterogeneous landscapes.
# TuSeSy: An Intelligent Turntable Servo System for Tracking Aircraft and Parachutes Automatically
## 3 Case Study
### Mechanical Structure of TuSeSy  
The basic principle of motion control in TuSeSy is that when the image processing algorithm gives the position deviation signal of the target, TuSeSy drives the camera to the tracked target according to the position deviation signal. The load, speed, acceleration, and tracking accuracy are considered in the design of TuSeSy. The design goal was to ensure that the turntable servo system could still achieve accurate tracking under large speed conditions and acceleration.  
Through the analysis of TuSeSy's technical requirements, the structure of the turntable is an azimuth-pitch type. Figure 2 shows the material objects of the turntable; the driving side is a coaxial direct drive motor. Coaxial direct drive has the following advantages. First, it can effectively avoid the gap error caused by the reducer and gear transmission. Second, it has a high precision and high dynamic response while ensuring accuracy. Therefore, the coaxial direct motor drive structure is suitable for the high precision and dynamic response of a turntable scene. The turntable is made of aluminum alloy, which is produced by casting. Aluminum alloy not only has high strength and low density, but it can effectively reduce the overall mass of the turntable while ensuring its strength.
# YOLO-PGC: A Tomato Maturity Detection Algorithm Based on Improved YOLOv11
## 2 Related Works
### Tomato Ripeness Detection  
Tomato ripeness detection is a pivotal task in precision agriculture that is essential for improving harvesting efficiency and ensuring the quality of produce. Traditional methods have largely relied on color-based approaches for ripeness classification; however, these techniques often fail in real-world conditions due to varying illumination, occlusion, and complex background. In recent years, deep learning-based methods have significantly improved detection performance by leveraging advanced feature extraction techniques that are more robust to these challenges.
# Fractionation of Lignocellulosic Biomass by Selective Precipitation from Ionic Liquid Dissolution
## 3 Results and Discussion
### NMR Analysis  
The Solid I and Solid II samples were analyzed by \({}^{1}\)H magnetic resonance in DMSO-\(d_{6}\) solution and were compared with commercial reference materials of cellulose, xylans and lignin (Figures 5-9). The spectra of Solid I and Solid II are very different.  
The \({}^{1}\)H spectrum of Solid I (Figure 5), obtained after the first filtration, was compared with the reference \({}^{1}\)H spectra of commercial samples of cellulose and hemicellulose (Figures 6 and 7). A combination of the structural skeletons of both sugars in the range of 5.5-3.5 ppm is observed [64; 65]. This observation indicates that Solid I is mainly a combination of carbohydrates; however, in the spectrum of Solid I, peaks corresponding to aromatic rings at 7.8 and 7.7 ppm and aldehyde at 9.0 ppm, associated with lignin, were observed. These data are consistent with the mass balance, which indicated that the mass recovered in the first filtration was slightly higher than expected. This analysis confirms that a small amount of lignin was present in Solid I.  
The \({}^{1}\)H spectra of Solid II (Figure 8) and commercial lignin (Figure 9) are quite similar. In both spectra, signals appear at 9.2 ppm, corresponding to the strong shielding among guaiacyl groups, although a greater intensity is observed in the \({}^{1}\)H spectrum of Solid II than that of the reference lignin. Methoxyl group protons are in the region of 4.0-3.4 ppm, very close to the water protons of aldehyde groups. signals in the region of 8.5-8.0 ppm correspond to the phenolic hydroxyl protons of lignin, and in the range of 8.0-6.0, the observed proton signals belong to the aromatic rings and vinyl protons of syringl and guaiacyl. In the range of 6.0-4.0 ppm, signals of protons belonging to \(\alpha\), \(\beta\), and \(\gamma\) saturated carbons of \(\beta\)-O-4, \(\beta\)-5 and \(\beta\)-\(\beta\) linkages appear because the lignin and Solid II are not completely dry and the DMSO-d6 is not 100% pure [66; 67]. Finally, in the range of 2.0-1.0 ppm, the signals correspond to CH\({}_{2}\), the aliphatic protons belonging to methyl or methylene groups, while the signals between 1.0 and 0.8 ppm were attributed to the protons in saturated aliphatic chains in lignin [66]. In summary, Solid II is essentially lignin. These observations are in agreement with the IR characterization: Solid I is a carbohydrate-rich solid with a small amount of lignin, and Solid II is essentially lignin.  
Figure 5: \({}^{1}\)H NMR spectrum of Solid I.  
Figure 6: \({}^{1}\)H nuclear magnetic resonance (NMR) spectrum of commercial cellulose.  
Figure 7: \({}^{1}\)H NMR spectrum of commercial xylans.
## 3 Results and Discussion
#### 3.1.3 Alkaloids  
Aside from previous studies that reported the presence of alkaloids in cucumber at moderate levels [3; 16; 17], peak 16 at [M+H]\({}^{+}\) 247.1446, C\({}_{14}\)H\({}_{19}\)N\({}_{2}\)O\({}_{2}\)\({}^{+}\) appeared as a singleton in positive MN (Figure S1) at Rt. 11.4 min. (Table 1) and was identified as N,N,N-trimethyl tryptophan betaine, known as lenticin or hypaphorin alkaloid, which is reported herein for the first time in cucumber fruit. Identification was based on the presence of diagnostic daughter ions at \(m/z\) 188, C\({}_{11}\)H\({}_{10}\)NO\({}_{2}\)\({}^{+}\), post the loss of N-trimethyl moiety, followed by decarboxylation at \(m/z\) 144 C\({}_{10}\)H\({}_{10}\)N\({}^{+}\) or dehydration at \(m/z\) 170, C\({}_{11}\)H\({}_{8}\)NO\({}^{+}\). The latter proceeded into ring cleavage and demethylation at \(m/z\) 146, C\({}_{9}\)H\({}_{8}\)NO\({}^{+}\) and \(m/z\) 122 C\({}_{7}\)H\({}_{8}\)NO\({}^{+}\), as explained in Figure S2, matching the reported literature [18] and HMDB spectrum ([[https://hmdb.ca/spectra/ms_ms/2947783](https://hmdb.ca/spectra/ms_ms/2947783)]([https://hmdb.ca/spectra/ms_ms/2947783](https://hmdb.ca/spectra/ms_ms/2947783)), accessed on 11 January 2023). Lenticin is distributed in various vegetables and known to exhibit cardioprotective and neurological effects, and thus could be correlated with cucumbers' cardioprotective reported effect for the first time using such a metabolomics approach [19]. It is believed that cucumber may encompass several other alkaloids, but a special extract targeting method with solvents of lower polarity is needed to enhance their detection.
# Dynamic Allocation of SDN Controllers in NFV-Based MEC for the Internet of Vehicles
## 5 Experimental Analysis
### Load Balancing  
We considered the expression \(\Delta_{Dif}=\Delta_{max}-\Delta_{min}\)[54] to verify the load balancing of the proposed solution, where \(\Delta=\left(\frac{capacity-load}{capacity}\right)\), \(\Delta_{max}\) is the biggest difference, and \(\Delta_{min}\) the smallest one. Results vary in the range [0, 1], the closer to 0, the better the result, while the opposite, that is, the value 1, corresponds to the worst load balancing case. Table 6 shows that the heuristic becomes more efficient regarding both resource allocation and load balancing as vehicle density increases regardless of the value adopted for the \(\beta\) parameter in the first scenario.  
In the high-density scenario (Table 7), the result was 0.02 in the worst-case and 0.00 in the best-case, the same behavior of the first scenario. This result shows that the proposed  
Figure 6: Capacity utilization.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Solution** & \multicolumn{3}{c}{**Number of RSUs**} \\ \hline  & **5000** & **10,000** & **15,000** \\ \hline Alloc-1 & 99.34 & 99.42 & 99.46 \\ \hline Alloc-10 & 99.98 & 99.99 & 99.99 \\ \hline Alloc-50 & 99.95 & 99.98 & 99.99 \\ \hline Alloc-100 & 99.95 & 99.97 & 99.98 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average capacity utilization in high density scenario.  
solutions are not only efficient in allocating resources, but also promote load balancing between allocated SDN controllers.
## 6 Long-Short-Term Memory Neural Network (LSTM)  
RNN is among the popular neural networks used with sequence data. An RNN comprises several hidden layers distributed across the previous time-steps. Such sequence models are capable of storing previous information to predict a future output [18]. Due to the sequence inputs, RNNs are considered computationally very complex and intensive models.  
In their applications within different domains, such models have not been able to handle long-term dependencies due to forgetting the long-term effects in back-propagation of current error [19]. This limitation of RNN is called 'the vanishing gradient problem'. This vanishing gradient problem occurs due to lack of (\(h_{t-1}\)) in Equation (1), where the long-term effects of derivatives of \(h_{t-1}\) with respect to \(h_{t}\) vanish. Here, \(x_{t}\), \(h_{t}\), and \(y_{t}\) represent input, hidden state, and output at time step \(t\). Moreover, tanh and sigmoid activation functions are applied for nonlinearity:  
\[\left\{\begin{array}{c}h_{t}=tanh(W_{h}x_{t}+w_{hy}y_{t-1})\\ y_{t}=\sigma(Wh_{t})\end{array}\right\} \tag{1}\]  
In order to cope with this challenge, we have employed an updated version of RNN widely known as LSTM. It includes an additional and complex block of components, called gates, which store the necessary information from previous time-steps [19; 20]. These gates significantly eliminate the vanishing gradient problem by including long-term gradient dependencies.  
Recently, LSTM has gained remarkable attention in scientific communities associated with different domains [21]. In addition to the explicit memory element, the model comprises an input, output, and forget gate. Each gate in the cell (see Figure 5) receives inputs from preceding time-steps such as \(x_{t}\) and \(h_{t-1}\) as inputs from current state and from the hidden states, respectively. Moreover, the state information \(C_{t-1}\) is in the cell's internal memory to retain the necessary information from preceding time-steps. The output at time-step t1 is determined by applying the tanh activation function at the hidden unit of the previous time-step, i.e., \(h_{t-1}\). This provides nonlinearity to the model, which significantly improves learning capabilities of the LSTM architecture.  
The overall processing of one time-step in LSTM is  
\[c_{t}=f_{t}*c_{t-1}+i_{t}*\hat{c}_{t} \tag{4}\]  
Finally, the output of cell state is passed through a sigmoid activation function which uses part of the information, from a previous stage:  
\[o_{t}=\sigma[W_{o}(h_{t-1},x_{t})+b_{t}] \tag{5}\]  
\[h_{t}=o_{t}*\tanh(c_{t}) \tag{6}\]  
In this study, we exploited the capabilities of the LSTM architecture shown in Table 2. In addition to this, we have addressed potential over fitting by employing two regularization techniques, namely by including dropout with 0.2 probability at the dropout layer, and, secondly, by using the learning rate 0.0003 to avoid overshoot problem in the gradient.
# Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
## 5 Networking Protocol Based on Digital Twins
However, the physical testing method also faces the following three main problems: (1) high costs--deploying actual hardware in a real-world scenario for testing and verification, especially for large-scale network testing and verification, incurs extremely high costs to build and configure a test network environment that is identical or highly similar to the actual scenario; (2) low testing/integration efficiency--during system testing and performance evaluation experiments, different experimental conditions and environmental parameters need to be selected and set to obtain test results under different scenarios; however, due to the use of actual physical hardware in physical testing, it is usually not as convenient, fast, or flexible as software simulation methods in configuring the experimental scenarios, resulting in lower testing efficiency; (3) difficulties in scenario simulation--in the simulation of LEO constellation scenarios, there are characteristics such as a large number of network nodes and high-dynamic inter-node topology; it is challenging to simulate these characteristics based on physical environments. However, the requirements for digital twins in the context of low Earth orbit (LEO) satellite constellation networks are as follows: (1) a low-cost digital twin with the ability to simulate a large number of network nodes (hundreds/thousands/millions of satellites); (2) a high-fidelity topology simulation that accurately reproduces the dynamic interconnections between satellites and ground stations; (3) high maturity in simulating network node functionalities, where the digital twin scenario should align with the code of the physical devices and network protocols being tested. Only by meeting these requirements can we construct a LEO satellite constellation network digital twin platform that is highly realistic, cost-effective, and flexible, aligning with the future operational LEO satellite systems and fulfilling the needs of twin verification. Therefore, by integrating the advantages and disadvantages of software simulation and physical testing methods, this study introduces mature concepts from academia and industry, such as cloud platform resource management, host and network virtualization, and software-defined networking, into the field of simulation testing. A digital network simulation approach is adopted, and research is conducted on the methods for the con struction of a digital twin platform for LEO satellite constellation networks, taking into account the network's specific scenarios and application requirements.
## 2 Materials and Methods
### Welding Process and Data Acquisition  
The implemented and tested frameworks have been validated on a dataset acquired with an industrial system designed and developed by [PERSON](r). The materials to be welded consist of a nickel layer, i.e., the cell electrode, having a thickness equal to 0.3 mm on a copper busbar with a thickness of 3 mm. The length of the weld joint is equal to 40 mm. The welding system is composed of the following subsystems (see Figure 1A-C):  
* A laser welding system (the Comau(r) LYTHE) that focuses the beam through a 200 mm collimator, creating a spot size of 0.15 mm with a continuous laser power equal to 2000 W;
* A 6-axes industrial robot (the Comau(r) NJ 220) that is used to move the laser head (fixed to the robot flange) at a constant speed of 200 mm/s;
* Two 256 \(\times\) 320 px thermal cameras based on microbolometer technology Teledyne(r) FLIR A35 (each camera can record only one weld with a frame rate equal to 30 Hz) that are mounted 100 mm far away from the welding surface in a fixed setup and oriented over the clamping unit in such a way they can frame the entire 40 mm length linear weld stitch with a spatial resolution equal to 0.08 mm/px.  
The thermal camera acquisition lasts 3 s and its start is triggered by the robot controller one second before the beginning of the welding. The acquisition timing has been designed in order to capture the thermal state just before the welding, the welding process lasts 200 ms, and the cooling dynamic lasts less than 1.5 s.  
Just after each thermal video acquisition, an expert technician manually analyzed the electrical properties of the weld in order to annotate it either as _'defective'_ or _'good'_. This manual process consisted of (a) performing three different electrical resistance measurements at the top, middle, and bottom sections along the weld (see Figure 1F) by using an high-precision milliohmmeter supporting the four-wire method, (b) considering the maximum value among the three electrical resistance measurements, and (c) annotating the weld as _defective_ if the maximum value is above a certain threshold and _good_ otherwise. Such electrical resistance threshold clearly depends on the specific application and the characteristics of the welded parts. In the presented study, considering the desired performances of the entire battery pack and after a careful testing campaign with battery pack manufactures, the threshold value has been set to 20 u\(\Omega\).  
The collected industrial dataset is composed of 2305 thermal image sequences, out of which 2194 belong to negative class (non-defective) and the remaining 111 sequences to the positive class (i.e., defective class). Having available a large amount of defected weld image sequences is essential for ML and for DL models to properly capture the underlying patterns in the data; however, generally, there are significantly fewer samples of defects than those of non-defects, which account for merely 5% of the total data, bringing an unbalanced dataset [30].
## 2 Results and Discussion
### Muscle Atrophy
#### 2.1.5 Overall Satisfaction with Treatment  
Fourteen and 9 animals from the aMSCs and PRGF groups, respectively, were treated with NSAIDs and nutraceuticalals. Regarding treatment efficacy after the intra-articular injection of aMSCs or PRGF, satisfaction was high in both groups.  
According to the dog owners, quality of life improved for patients in both groups between baseline and 6 months in aMSCs (\(p=0.004\)) and in PRGF (\(p=0.001\)) groups. However, statistically significant differences between groups were shown at 6 months, where the aMSCs group scored higher than the PRGF group (\(p=0.028\)).  
Figure 3: Evolution of functional limitation (range: 0–23) in dogs after aMSCs or PRGF treatment at the 6-months follow up period. The changes which differed significantly with baseline (\(p<0.05\)) are marked as *. Statistically significant differences between groups are marked as \(\dagger\). Circles, both green and blue, correspond to outlier included in the statistical study.  
In all cases, at the end of the treatment, owners were asked if they would use the same treatment again for their pets, and all of them responded positively.  
**Figure 4.** Evolution of joint mobility (range: 0-7) in dogs after aMSCs or PRGF treatment at the 6-months follow up period. The changes which differed significantly with baseline (\(p<0.05\)) are marked as *. Statistically significant differences between groups are marked as \(\dagger\). The green circle is an outlier included in the statistics study.
## 2 Materials and Methods
### DEM Extraction  
The IMAGINE Photogrammetry toolbox available in ERDAS IMAGINE 2014 (Version 14.00, 0 1990-2013 Intergraph Corporation)) was used to process the Pleiades-1 tri-stereo data. As discussed in [13; 17; 28], in order to obtain the final DEM, point clouds were extracted from each image pair (F-N, F-B, and N-B), and from the merging of the resulting datasets. The application of a semi-global matching (SGM), and the use of the RPC files delivered together with the original scene, allow the orientation and georectification for the imagery. Tie points (TP) for the exterior orientation were automatically identified and improved by adding 12 ground control points (GCP). These points represent 6 sinkholes: for each one, the point of acquisition along the border and the point of maximum depth were identified in each Pleiades image. These points were added to the TPs to input the real field measures of the depth to the dataset. An overall root mean square error of less than 0.1 pixels shift between images at each TP was obtained.  
The resulting merged point cloud was then gridded to a spatial resolution of \(5\times 5\) m/pixel size. The elevation value for each cell was calculated as the average elevation of all the points within one cell as proposed in [13].
## 2 Data and Methods
### Elman Neural Network  
The Elman neural network was established by [PERSON] and has four layers of network structure, that is the input layer, hidden layer, context layer, and output layer (Kip  
predicting time series values. The structure of the Elman neural network is shown in Figure 4.  
The calculation process of the model can be expressed as follows:  
\[y_{q}^{(t)}=f\left(\sum_{j=1}^{L}x_{j}^{(t)}\omega_{qj}\right) \tag{16}\]  
\[x_{j}^{(t)}=f\left(\sum_{i=1}^{L}u_{i}^{(t)}w_{j,i}+\sum_{k=1}^{L}c_{k}^{(t)}w _{j,k}\right) \tag{17}\]  
\[c_{k}^{(t)}=x_{j}^{(t-1)} \tag{18}\]  
where \(y_{q}^{(t)}\) is the output at time \(t\) of the output layer, \(x_{j}^{(t)}\) is the output at time \(t\) of the hidden layer, \(c_{k}^{(t)}\) is the output at time \(t\) of the context layer, \(w_{q,j}\) is the connection weight function of the output layer, \(w_{j,i}\) is the connection weight function of the hidden layer, \(w_{j,k}\) is the connection weight function of the context layer.  
Multi-Model Mean Model  
The principle of MMM is that the results are obtained by averaging the results of multiple model predictions. The MMM models are widely used because of the ease of operation and the ability to balance data differences among models. The MMM model is a simple way to reduce biases in individual model outputs [28]. MMM models have a wide range of applications in climate modeling. In [PERSON]'s study, Nash-Sutcliffe efficiency coefficients indicated that MMM outperformed individual models in 92.3% of cases [29].