esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### WBHE Model Assumptions  
MATLAB is a software for numerical and statistical calculations that is written in the C programming language. MATLAB R2018b version was used to perform the analysis of WBHEs by implementing the proposed model, with consideration of the following assumptions and approximations.  
The reservoir model was built by assuming a single well-positioned at the centre of a circular reservoir. The temperature profile in the radial direction was assumed to be constant. Therefore, there was no temperature gradient in the annulus or in the inner tube. Due to turbulent flow, enhanced mixing phenomena occurred, decreasing the radial gradient. The temperature changed only in the annulus and in the vertical direction of the inner tube, so the temperature profile was vertically unidirectional.  
The properties of the heat-carrier fluid were assumed to be constant. As the fluid used in this study was water (100 \({}^{\circ}\)C, 2 bar), no variations occurred due to pressure or/and temperature gradients.  
The model was built under steady-state conditions; there were no temperature variations over time, with each point in the tubes (annulus and inner tube) maintaining the same temperature for the lifecycle of the system. For the elaborations, \(r_{c}\) (external radius of the external casing) was considered equal to \(r_{w}\) (radius of outside wellbore).  
In addition, the model considered the resistance associated with tube thickness to be negligible. The tube material had very high conductivity, so its resistance could be considered small compared to the other resistances in the system. For estimation of the resistance associated with the rock (see Equation (6)), the time value used was 3 years after the starting of the system. This assumption made the method a conservative estimate: the system in the years before (1-2 years) turns out in fact to work better. As consequence, the heat exchange phenomena are bigger with the possibility of causing overestimations.  
The proposed model followed the path of the fluid using a step-by-step approach. It considered intervals of length \(dz\) in which the inlet and outlet temperatures were calculated by solving the energy balance equation for each volume \(dv\). For estimating the energy exchange in the radial direction, the mean value of the temperature in each volume \(dv\) was used.
# NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches
## Appendix A Technical Aspects
It consists in the fit of a target peak of the compound by using the signal acquired from the reference compound [150]. Different specific software for NMR, such as TopSpin (Bruker), MNova (Mestrelab Research), Spectrus Processor (ACD/Labs), Delta (JEOL) and Chenomx NMR Suite (Chenomx Inc.) can be used for this goal. Among them, JEOL Delta is the only one completely free of charge, while Chenomx NMR Suite seems to show the best performance because it is based on a sophisticated targeted profiling technology and on reference libraries containing hundreds of metabolite spectral data, allowing a user-friendly deconvolution of complex NMR spectra [151]. The spectral analysis and deconvolution can also be performed with non-specific software, such as Matlab (The MathWorks, Inc.) or R (The R Foundation). Several factors (i.e., pulse sequence changes or variation in the repetition time) influence the deconvolution process and its accuracy, including the variety of standard compounds present in the library and the need to repeat the NMR data acquisition in the same experimental conditions. Changes in the pulse sequence and/or the repetition time result in a less accurate fitting. The performance of the deconvolution is also influenced by the protons' bond to nitrogen atoms, also called labile (for instance, the \(\alpha\)-protons in amino acids) [145]. These protons fast exchange with the solvent, and this not only makesit difficult to detect them, but also provokes changes in the line shape of the close protons peaks. The result is an attenuated resonance, which does not precisely match with the integral corresponding to the other proton peaks in the considered sample. Another error regards the partial peak saturation of the protons, with a resonance close to the presaturation peak of the solvent (commonly water). This was observed for the anomeric protons of carbohydrates, which are frequently resonant close to the water signal, or also for the CH quartet at \(\delta\) 4.11 ppm in the lactate spectrum. Beyond these disadvantages, the deconvolution approach is a great and widely employed tool for the metabolomic quantification studies [145]. Generally, successful NMR metabolomics requires statistical analyses, which have become progressively advanced over the years, and are the focus of this review. Dependent and independent parameters are correlated by means of conventional approaches on the basis of the mathematical relationship and, in turn, on model fitting. On the other hand, machine learning approaches group input data based on a cluster classification without any statistical assumption, while deep learning is devoted to find statistical inferences from a large amount of input data. The future of NMR-based metabolomics is to generalize the learning approaches to optimize predictive ability for specific diseases.
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 4 Formulation Analysis of OPMSS
### Scenario III: Packet Delivery Cost  
The packet delivery cost of the Kite solution and the proposed OPMSS can be determined when handoff processes are completed and the first interest packet after the handoff reaches the mobile producer. In the Kite solution, the data path is created through the anchor router or the RS, while in OPMSS the best route is created between the consumer and the mobile producer to avoid triangular routing. Therefore, Equations (11) and (13) are generated for both the intra-domain and inter-domain mobility analysis of the Kite solution. The data path is created along the trace left by _TI_ and _TD_ through CR1 to CR3 via CR2 for intra-domain mobility and through CR1 to CR5 via CR2 and CR3 for inter-domain mobility.  
\[DCost_{Kite}=\mu\times S_{data}\times(2a+2b) \tag{11}\]  
\[DCost_{opmss}=\mu\times S_{data}\times(2a+b) \tag{12}\]  
To obtain OPMSS packet delivery cost after the handoff process, the best route strategy creates an optimal path between the consumer and the producer. The path is created from CR1 to CR3 directly for intra-domain mobility and from CR1 to CR5 via CR4 for inter-domain mobility, which is presented in Equations (12) and (14), respectively.  
\[DCost_{Kite}=\mu\times(S_{data}\times(2a+2b)+S_{data}\times(2a+4b)) \tag{13}\]  
\[DCost_{opmss}=\mu\times(S_{data}\times(2a+b)+S_{data}\times(2a+2b)) \tag{14}\]
## 2 System Model and Problem Formulation
### Feeder Link Model and Capacity  
Compared with the traditional SISO, MIMO communication technology has the advantages of higher transmission rate, spectrum utilization, and power efficiency. Recently, many scholars proposed to use MIMO technology at Ka-band to construct high-speed feeder link between satellites and ESs. However, ka-band propagation is influenced by atmospheric attenuation mechanisms from the troposphere, which will result in a difference  
Figure 2: (**a**) Topology diagram of LEO satellites network. (**b**) Topology diagram of LEO satellites network with the upper half rotated 180\({}^{\circ}\). Satellite i is still in its original position on the topology map, and the position of satellite j is changed on the topology map, but the topological connection relationship does not change.  
in feeder link capacity. In this section, we elaborate MIMO channel model at Ka-band and the impact of rain attenuation on feeder link capacity.  
As shown in Figure 3, a MIMO system can be composed of \(M_{T}\) receiving antennas and \(M_{R}\) transmitting antennas between the satellite and the ES. Let \(\mathbf{H}\) denote the channel matrix between the satellite and the ES. The input-output relation in a MIMO channel for constructing the feeder link can be expressed as [15]:  
\[y[k]=\sqrt{\frac{E_{S}}{M_{T}}}\mathbf{H}s[k]+n[k], \tag{1}\]  
where \(E_{S}\) is the average energy of the symbols. \(n[k]\) is the \(M_{R}\times 1\) noise vector of independent ZMCSCG random variables, \(s[k]\) is the \(M_{T}\times 1\) vector of signals transmitted from each antenna and \(y[k]\) is the \(M_{R}\times 1\) vector of signals received by each antenna.  
Some studies researched by other scholars have led to the \"log-det\" capacity formula in Ref. [16]. In this paper, the channel capacity of a MIMO channel can be obtained from the channel matrix:  
\[C=\max_{T(Q)=M_{T}}\{\log_{2}det(\mathbf{I}_{M_{R}}+\frac{E_{S}}{M_{T}N_{0}} \mathbf{H}\mathbf{Q}\mathbf{H}^{H})\}. \tag{2}\]  
In (2), \(\mathbf{Q}=E[ss^{H}]\) is the covariance matrix of the transmitted signal vector \(s\) and \(\mathbf{I}_{m}\) represent the \(m\times m\) identity matrix. Once the distribution of \(H\) is known, the maximum data rate \(C\) that the channel can transmit can be calculated. The element \(h_{i,j}\) of \(\mathbf{H}\) denotes the channel parameter from the \(j\)th transmitting antenna to the \(i\)th receiving antenna. The propagation and scattering impacts are considered in this paper. For convenience, \(h_{i,j}\) is written as [17].  
\[h_{i,j}=h_{1,i,j}h_{2,i,j} \tag{3}\]  
where \(h_{1,i,j}\) denotes path loss and \(h_{2,i,j}\) is a complex random variable, which denotes the fading coefficient induced by random multipath propagation or the incoherent scattering in the troposphere. When the signal of satellite feeder link passes through the atmosphere, it will be absorbed and scattered by oxygen molecules and water vapor condensates in the troposphere, resulting in atmospheric loss. The extreme rain attenuation will reduce feeder link channel capacity and cause bandwidth imbalance of feeder links.  
Figure 3: MIMO system.
# BPDM-GCN: Backup Path Design Method Based on Graph Convolutional Neural Network
## 4 Backup Path Design Method Based on BPDM-GCN
### Theoretical and Practical Correlation of GCN and DDPG Fusion Mechanism
#### 4.3.1 Benefits of GCN for Dynamic Network Topology Modeling
(3) Comprehensive experimental verification: Six IEEE benchmark test power system communication networks are used for experiments, and different link load conditions are set as experimental network topologies. Compared with algorithms such as NRLF-RL, LIR-LFR, and FR-VLAN, the method is evaluated by several key indicators such as fault link recovery delay and fault link recovery success rate. The results show that the BPDM-GCN algorithm can effectively reduce the fault link recovery delay and improve the recovery success rate.
# Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
## 2 Positioning Model of BDS Undifferenced NRTK
### Generation of UD Error Correction Information for Multiple Reference Stations  
When a single reference station is used, the carrier phase OSR correction information includes \(N_{r,j}^{s}\), and then the OSR correction value is equivalent to the OMC. When there are multiple reference stations, it is necessary to comprehensively consider the weighting OSR of every reference station for the elimination of user errors. The OSR of the pseudo-range observation is equivalent to the OMC, so no further research will be conducted, and three stations are used as examples to generate carrier phase OSR. By generating UD error correction information for multiple reference stations, independent UD integer ambiguity on each station can be obtained by solving the integer ambiguity of the reference station network, and the OSR correction information on a single station can be separated. If there is a reference station A, the OSR information can be represented as:  
\[OSR\left\{L_{A,j}^{s}\right\} =E\left\{\widetilde{L}_{A,j}^{s}-\lambda_{j}N_{A,j}^{s}\right\} \tag{7}\] \[=T_{A}^{s}+dt_{A}+\lambda_{j}\delta_{A,j}-dt^{s}-\lambda_{j}\delta _{j}^{s}-\mu_{j}\cdot\iota_{A}^{s}\]  
From Equation (7), it can be seen that the UD error correction information includes the ionospheric delay, tropospheric delay, and satellite clock and receiver clock. If there are three reference stations A, B, and C, assuming that the interpolation coefficients of the three stations are \(\alpha_{A}\), \(\alpha_{B}\), and \(\alpha_{C}\), the user observations error interpolation is generally assumed such that the sum of the interpolation coefficients is 1, so the comprehensive error correction value formed by the three stations has the following form:  
\[OSR\left\{L_{r,j}^{s}\right\} =\alpha_{A}\cdot E\left\{\widetilde{L}_{A,j}^{s}-\lambda_{j}N_{A, j}^{s}\right\}+\alpha_{B}\cdot E\left\{\widetilde{L}_{B,j}^{s}-\lambda_{j}N_{B,j}^{s} \right\}+\alpha_{C}\cdot E\left\{\widetilde{L}_{C,j}^{s}-\lambda_{j}N_{C,j}^{ s}\right\} \tag{8}\] \[=\left(\alpha_{A}\cdot T_{A}^{s}+\alpha_{B}\cdot T_{B}^{s}+\alpha _{C}\cdot T_{C}^{s}\right)-\mu_{j}(\alpha_{A}\cdot\iota_{A}^{s}+\alpha_{B} \cdot\iota_{B}^{s}+\alpha_{C}\cdot\iota_{C}^{s})\] \[+(\alpha_{A}\cdot dt_{A}+\alpha_{B}\cdot dt_{B}+\alpha_{C}\cdot dt _{C})+\lambda_{j}\left(\alpha_{A}\cdot\delta_{A,j}+\alpha_{B}\cdot\delta_{B,j }+\alpha_{C}\cdot\delta_{C,j}\right)\] \[-dt^{s}-\lambda_{j}\delta_{j}^{s}\]  
The comprehensive error correction value includes the weighted combination of atmospheric errors, receiver clock error, and receiver hardware delay at every reference station, as well as the complete satellite clock error and satellite hardware delay. Receiver-related errors can be eliminated after satellite differential, while satellite-related errors are directly eliminated during the user error correction process, and the interpolation accuracy for atmospheric delay error determines the quality of error correction.
## 5 Experiment Results
### Effectiveness Evaluation against the Known Optimal Solutions
However, considering the coupling relationship between the two types of helicopters during rescue missions, we took the total travel distance of the two types of helicopters as the optimization objective. Comparing the second and fourth columns in Table 6, the IIG algorithm still achieved six optimal values on transport helicopters, while the differences between the values achieved on the other fifty instances and the optimal values were all less than 12%. These results demonstrate that, although the IIG was originally designed for the R-VRPTWLST, it could also perform effectively on R-VRPTWLST-ILS, and even the traditional VRPTW problems. Comparing columns 4-6 and 8-10 in Table 6, respectively, after adding the constraint of life strength, the travel distances of the R-VRPTWLST are greater than those of the R-VRPTWLST-ILS. These results further verify that the traditional VRPTW scheme is not suitable for rescue tasks that integrate life strength, that is, rescue route planning problems that put human life first. In the face of R-VRPTWLST, we need to improve algorithms based on problem characteristics and solve them.
# Gender Identification of Chinese Mitten Crab Juveniles Based on Improved Faster R-CNN
## 2 Materials and Methods
### Methods
#### 2.3.1 Swin Transformer  
The Swin Transformer is a potent and versatile deep-learning model, rooted in the Transformer network architecture. It adeptly captures global context information through a self-attention mechanism. It has demonstrated excellent performance and model versatility in tasks such as speech recognition, image classification, and object detection. Especially in the field of visual recognition, its superiority in handling targets with numerous small variations outshines traditional network structures [26]. Unlike models restricted by a fixed kernel size, it adopts a fully adaptive approach, efficiently dealing with sparse and irregularly shaped targets. For instance, in gender identification of Chinese mitten crab juveniles, it can precisely analyze and distinguish subtle edge details of different specimens.  
\begin{table}
\begin{tabular}{c c c} \hline
**Data** & **Female** & **Male** \\ \hline Original data & 280 & 260 \\ First data augmentation & 840 & 780 \\ Second data augmentation & 3360 & 3120 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of crab juveniles data sets before data augmentation.  
Figure 3: Overall process flow of the proposed method.  
Faster R-CNN, on the other hand, optimizes calculations within each candidate region of a regional convolutional neural network. It passes features from the Region Proposal Network (RPN) to the last layer of the convolutional feature map and employs a dedicated ROI pooling layer. This approach allows simultaneous training of classification and boundary regression. While it addresses limitations in candidate box numbers found in RCNN and Fast RCNN, its commonly used backbone networks like ResNet and VGGNet fall short in identifying detailed features of crab juveniles' abdomens, impacting classification results. In contrast, the Transformer model, with its focus on pixel correlations, captures more subtle visual information, improving recognition rates for male and female crab characteristics. In addition, its self-attention mechanism has excellent global modeling capabilities, allowing the model to understand the image globally and understand the relationship between various parts of the image through the self-attention mechanism, thereby capturing the subtle differences in characteristics of male and female crabs.  
Figure 4: C-SwinFaster Network structure diagram.  
In conclusion, the Swin Transformer not only demonstrates robust performance but also offers unique advantages in crab gender identification, making it an ideal choice as the backbone network for this study.
## 3 Results and Discussion
### Polyphenols Content and Antioxidant Activity
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**TPC**} & \multicolumn{2}{c}{**Antioxidant Capacity (DPPH\(\bullet\))**} \\  & \multicolumn{2}{c}{**[mg GAE/100 mL]**} & \multicolumn{2}{c}{**[μM/100 mL]**} \\ \hline
**FJ** & \multicolumn{2}{c}{**32.5 \(\pm\) 0.9 h**} & \multicolumn{2}{c}{**51.9 \(\pm\) 1.4 d**} \\ \hline
**Storage Time (Month)** & **TP** & **MFP** & **TP** & **MFP** \\ \hline
0 & 38.8 \(\pm\) 1.2 e\({}^{\text{f}}\) & 73.8 \(\pm\) 2.3 a & 65.9 \(\pm\) 2.3 c & 176.8 \(\pm\) 2.7 a \\
2 & 38.1 \(\pm\) 0.4 e\({}^{\text{f}}\) & 67.3 \(\pm\) 0.6 b & 66.9 \(\pm\) 3.9 c & 172.3 \(\pm\) 3.2 a \\
4 & 36.1 \(\pm\) 1.1 g & 59.2 \(\pm\) 2.1 c & 64.6 \(\pm\) 2.0 c & 168.7 \(\pm\) 6.1 a \\
6 & 34.6 \(\pm\) 0.5 gh & 43.2 \(\pm\) 1.3 d & 63.2 \(\pm\) 1.6 c & 143.1 \(\pm\) 0.7 b \\
8 & 33.1 \(\pm\) 0.4 h & 45.0 \(\pm\) 1.7 d & 62.1 \(\pm\) 3.0 c & 146.2 \(\pm\) 2.6 b \\
10 & 33.7 \(\pm\) 0.6 gh & 43.5 \(\pm\) 0.7 d & 63.4 \(\pm\) 1.6 c & 143.4 \(\pm\) 0.7 b \\
12 & 32.0 \(\pm\) 0.6 h & 32.0 \(\pm\) 0.6 h & 63.1 \(\pm\) 0.8 c & 141.5 \(\pm\) 3.1 b \\ \hline \hline \end{tabular}  
* GAE—gallic acid equivalent, FJ—fresh juice, TP—thermal pasteurization, MFP—microwave flow pasteurization. Mean values marked with the same letters within the parameter do not differ statistically significantly in the same factor, \(p\leq\) 0.05.  
\end{table} 
Table 1: Total polyphenol content and antioxidant capacity (DPPH\(\bullet\)) in FJ, TP and MFP-preserved apple juices. increase in antioxidant activity, induced by microwave radiation on the juice tissue, can result in tissue damage and greater extraction of components into the juice serum [47]. A similar effect was observed in the study of tomato puree fixed with microwave energy [61]. [PERSON] et al. (2019) [62] demonstrated that the microwave power had an impact on the antioxidant activity measured with the DPPH free radical assay in camu camu juice. In the tested conditions, a certain dependence was observed, with lower power resulting in higher antioxidant activity. The study by [PERSON] et al. (2018) [63] showed that the use of microwave treatment before pressing had a positive effect, not only on pressing efficiency but also on increasing the polyphenol content and antioxidant activity in blackberry juice compared to traditionally pressed juice.
###### Abstract  
A comprehensive evaluation of the variations in carbon use efficiency (CUE) and water use efficiency (WUE) in the Nanling Mountains Region (NMR) is crucial for gaining insights into the intricate relationships between climate change and ecosystem processes. This study evaluates the spatiotemporal rates of dynamics in CUE, WUE, gross primary productivity (GPP), net primary productivity (NPP), and evapotranspiration (ET) over the period from 2001 to 2023, using remote sensing data and linear regression analysis. Trend analysis, Hurst exponent analysis, and stability analysis were applied to examine the long-term patterns of CUE and WUE, while partial correlation analysis was employed to explore the spatial relationships between these efficiencies and climatic factors. The main findings of the study are as follows: (1) The CUE and WUE of the NMR decreased geographically from 2001 to 2023, and both the CUE and WUE of NMR showed a significant declining trend (\(p<0.05\)) with the CUE decreasing at a rate of 0.0014/a (a: year) and the WUE falling at a rate of 0.0022/a. (2) The average values of the CUE and WUE of the NMR from 2001 to 2023 were 0.47 and 0.82 g C\(\cdot\)m\({}^{-2}\cdot\)mm\({}^{-1}\), respectively, with a clear geographical difference. (3) The CUE and WUE in the NMR showed widespread degradation trends with some localized improvements, yet sustainability analysis indicates a likely continued decline across most areas, particularly for forests, while grasslands exhibit the greatest resilience. (4) Precipitation had a significantly stronger impact on WUE, while temperature appeared to exert a more substantial effect on CUE, with vegetation types responding differently; notably, shrubland displayed a direct association between CUE and temperature. In summary, multi-source data were employed to comprehensively analyze the spatiotemporal dynamics of CUE and WUE in the NMR over the past 23 years. We also examined the features of their responses to global warming, offering valuable theoretical insights into the carbon and water dynamics within the terrestrial ecosystems of the NMR.  
N