esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[31] indicates that the global GPP is predicted to increase under future climate change in the 21 st century. Despite these global-scale studies, there is a noticeable research gap regarding the Tibetan Plateau's GPP under future climate scenarios. Considering that climate change significantly impacts the vegetation composition and functionality [32], it is critical to address the spatiotemporal variations in the GPP in the Tibetan Plateau under changing climate conditions. Employing ML models to predict these variations is not only essential but also strategically crucial in addressing effective ecosystem management. When selecting an ML model for the prediction of GPP, it is essential to recognize that most existing algorithms are not well suited for the automatic learning of spatial structures and hierarchical features within images. These algorithms often overlook the influence of neighboring grid point variables on GPP [22; 31]. Over the past few decades, convolutional neural networks (CNNs) have achieved unprecedented success in addressing computer vision challenges due to their ability to extract complex features and learn compact representations [33]. As a leading paradigm in deep learning, the CNN has significant potential in predicting the spatiotemporal variation in the future GPP. However, the application of CNNs in modeling and predicting the GPP in the Tibetan Plateau remains largely unexplored. Therefore, predicting the annual and seasonal GPP dynamics in response to future climate change in the Tibetan Plateau using a CNN model is crucial in deepening our understanding of vegetation-climate interactions. To address the challenges posed by climate change on GPP in the 21 st century, this study employed a data-driven approach to predict both annual and seasonal variations in the GPP in the Tibetan Plateau. Specifically, a CNN model was utilized to establish nonlinear relationships between the GPP and various environmental factors. This study utilized the GPP dataset developed by [PERSON] et al. [34] to represent the historical monthly distribution of the GPP, while climate variables, CO\({}_{2}\) concentrations, and terrain attributes were incorporated as key environmental factors. Additionally, future climate scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6), including SSP1-2.6 (ssp126), SSP2-4.5 (ssp245), SSP3-7.0 (ssp370), and SSP5-8.5 (ssp585), were considered to project future GPP changes. The primary objectives of this study were (1) to predict the annual and seasonal GPP up to the year 2100 under different future scenarios using a data-driven approach; (2) to examine the spatiotemporal variations in the future GPP and analyze their implications; and (3) to assess the relative importance of multiple environmental factors in predicting the GPP. This study could enhance the understanding of the complex interactions between vegetation and the climate and provide critical insights for the formulation of effective management practices to enhance the carbon sequestration capabilities and achieve carbon neutrality goals.
## 1 Introduction
These systems present several advantages: (_i_) Ga NPs do not damage the graphene structure [15]; (_ii_) the used pristine graphene is directly chemical vapor deposition (CVD)-grown on Cu and Ni substrates, consequently avoiding its transfer, a process that can introduce defects and impurities into the graphene [16] and lead to imperfect contact between graphene and the metal layer underneath, generating an additional barrier, and increases the contact resistance for electrons when passing through the graphene/metal layer contact interface; (_iii_) the Ga NPs are evaporated directly on graphene under vacuum, without the use of any surfactant or capping/functionalizing agents, allowing the investigation of the bare graphene/NP interface. Therefore, the idea consists of investigating the plasmonic coupling of Ga NPs on pristine CVD-grown graphene/Cu and graphene/Ni, those two systems also being characterized by an asymmetry in work function of the two metals in contact with graphene and by different layers of the grown graphene spacer, monolayer in the former case and multilayer in the latter case. The additional peculiarity of this study is the in-situ real-time probing of the Ga NP/G interaction during the growth of the Ga NPs on the graphene surface. Specifically, the interaction of the Ga NP/G/Cu and Ga NP/G/Ni systems with light and the generation of surface plasmon resonances are studied in real time by spectroscopic ellipsometry and compared with Ga NP/G/glass [15]. We demonstrate that there is no appearance of surface plasmon resonances in the work-function asymmetric graphene-metal sandwich systems, and we theoretically show that this process strongly depends on the contact angle between the metal NPs and the G/metal substrate, a parameter determined by the interfacial free energy between the nanoparticle and the graphene layer. The quenching of the localized surface plasmon resonance (LSPR) of the Ga NPs is also corroborated by evidence based on Raman and X-Ray photoelectron spectroscopies of the electron transfer from the Ga NPs into the graphene and, finally, into the metallic substrate.
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Non Orthogonal Multiple Access
#### 3.4.3 Signalling Issues  
With the increase in wireless users in 5G, effective spectrum utilisation plays an important role. NOMA has been proposed to improve spectrum efficiency by allocating multiple users to a single RB. However, the allocation of multiple users to one RB will cause signalling issues such as CSI imperfections, latency, SIC receiver complexity, and
## 3 Results and Discussion
### Partial Cloaking  
Figure 6 illustrates an example of partial cloaking for an extended slab (24 \(\times\) 60 rows). Again, the IFC (see Figure 6a,b) predicts the effective behaviour of the structure to waves impinging at a given angle of incidence and certain centre frequency \(f=3.05\) MHz. More specifically, in the neighbourhood of the coincidence point for shear waves, the group velocity points in the same direction as the group velocity of the incident wave.  
Figure 6: Scattering of a shear-dominated Gaussian beam at centre frequency \(f=3.05\) MHz and an angle of incidence \(\alpha=60^{\circ}\). Panels (**a**,**b**) show the IFC diagram (and its magnification); the red asterisk represents the intersection point of the Gaussian beam propagation direction and the isofrequency contours. Panels (**c**–**e**) show the magnitude of the displacement field at different times in the FE simulation, with the total time being \(T=120\) μs.  
The panels Figure 6c-e display a time evolution of the shear-dominated Gaussian beam. Across all featured time frames, the transmitted beam approximately maintains the incident direction, with limited--yet still clearly visible--refraction and reflection. In this sense, an observer can feel the effect of the original incident beam on the other side of the slab, hence the use of the term \"partial cloaking\". There is also evidence of trapped modes within the slab throughout the simulation time window, as shown in Figures 6d,e, which is predicted by the flat dispersion curves in Figure 2a.
## 2 Methodology
### Interpolation Model
#### 2.1.4 DSM  
Unlike USM, which requires the overall least squares of all satellites in the same system to solve for the atmospheric delay coefficients of each satellite, this paper proposes an SD ionospheric surface model based on the mean value of the IPP of the satellite pair and the center position of the reference station. When the number of reference stations is at least three, the atmospheric delay model coefficient (DSM) of any single-difference satellite pair can be calculated. The mathematical model is as follows:  
\[\Delta\overline{I}_{i}^{p,q}=a_{0}^{p,q}+a_{1}^{p,q}.(\frac{\varphi_{q+} \varphi_{p}}{2}-\varphi_{0})+a_{2}^{p,q}.(\frac{\lambda_{q}+\lambda_{p}}{2}- \lambda_{0}) \tag{5}\]  
where \(a_{0}^{p,q}\), \(a_{1}^{p,q}\), and \(a_{2}^{p,q}\) represent model coefficients of the satellite \(p\) and \(q\). The definitions of other terms in this equation are the same as for the previous Equation (4). After the user receives the atmospheric delay coefficient, the same formula can be used to calculate the atmospheric delay of any single difference. When solving for the USM and DSM coefficients, each system selects a reference satellite. The user side uses the same method to restore the ionospheric delay of any satellite pair when applying coefficients.
# Text Classification Model Based on Graph Attention Networks and Adversarial Training
## 2 Materials and Methods  
### Model Framework  
The framework of the text classification model based on graph attention networks and adversarial training is illustrated in Figure 1. The model consists of four components: the encoding layer, the graph attention layer, the fusion layer, and the attention layer. In the encoding layer, text is vectorized and perturbed with adversarial training to construct adversarial samples. TextCNN is used for feature extraction from the text, after which character embedding matrices and word embedding matrices are concatenated and fed into the graph attention network layer to capture contextual vocabulary information. The fusion layer integrates information from different network structures captured in the text sequences. An attention mechanism is introduced to assign varying weight values to the global sequence. Finally, the resultant feature representation is classified using a softmax function to determine the category with the highest probability label.  
Figure 1: The overall architecture of our method.
# A Comparative Study of On-Body Radio-Frequency Links in the 420 MHz-2.4 GHz Range
## 2 Materials and Methods
### RF Technologies
#### 2.2.3 Bluetooth Nodes at 2.4 GHz  
Bluetooth is a wireless technology that operates between 2400-2485 MHz. The technology is widely used by for short-range communication among personal wireless user devices and for wearable  
Figure 4: (**a**) BCC electrodes (connector part), (**b**) pre-gelled electrodes, (**c**) 450 MHz monopole antenna from the evaluation kit. (**d**) BCC on-body, (**e**) 450 MHz monopole antenna on-body.  
sensor applications. A low-power consumption standard, Bluetooth low-energy (BLE), exists and is used often as a standard competing with IEEE 802.15.6 for wearable devices.  
Two Bluetooth nodes, one custom-made transmitter [43] and a commercial receiver, were placed on the on-body locations shown in Figure 1. The RX node registered RSSI values in dBm over time, which are treated as a proxy for received power in this study. 100 samples are retained per TX-RX separation distance and fed into Equations (1) and (2) in order to fit the path loss models. Table 1 shows the transmitter and receiver specifications for these measurements. Figure 5 shows the used nodes with indicated antenna locations. Fully operational Bluetooth nodes were used instead of antennas at this frequency in order to study a complete wireless system.  
The boards were placed on the subject's arm in the measurement configuration illustrated in Figure 5b. The boards were placed parallel to the skin, with the antennas facing each other. Once worn on the body, the RX antenna was at a distance of 3.9 mm from the skin, while the transmitter was at a distance of 6.9 mm from the skin.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Transmitter** & **Receiver** \\ \hline
**Board** & Custom-Made & Nordic nRF51 Development Kit \\
**Radio Chip** & nRF51822 (Nordic Semiconductor, & nRF51422 (Nordic Semiconductor, \\  & Oslo, Norway) & Oslo, Norway) \\
**Center Frequency** & & 2400 MHz \\
**Transmission Power** & +4 dBm & - \\
**Receiver Sensitivity** & - & \(-85\) dBm \\
**Bandwidth** & & 2 Mbps \\  & ANT-2.45-CHP-T & \\
**Antenna Type** & (Linux Technologies Inc., & Quarter-Wavelength Monopole PCB \\  & Merlin, OR, USA) SMD & \\
**Sample Rate** & & 1 ksps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical Specifications of the used Bluetooth nodes.  
Figure 5: (a) Bluetooth nodes with the transmitter node shown on the left and the receiver on the right. (b) Bluetooth nodes measurement setup.
## 3 Results and Discussion
### Data Processing using Machine Learning Methods
#### 3.4.2 Baseline Characterization Method  
We chose a strategy based on statistical surface values as a basic characterization method. These values are: mean roughness, mean square roughness Rq, kurtosis Rku, and depth depression R3z. Following the calculation of these values for each sample, they were tested for clustering quality. Clustering was provided by the K-means method [51]. Basic characteristics were used as the features to study for an unsupervised algorithm (K-means). As the result every AFM array became an object with four features. Elbow method [52] was used to identify the optimal number of clusters for the algorithm. In Elbow method the clusterization is given for a few amounts of clusters (N) and for each N we calculated the total divergence of clusters. The optimal number of clusters is the amount when there is a sharp decrease in the rate of fall of the total dispersion. Clustering was provided in the initial 4D space. As a result, five following clusters were obtained. Then, the dimension was reduced to 2 using T-distributed stochastic neighbor embedding method (T-SNE) [53] for the purpose of visualization  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Type of SERS** & **Rq,** & **R3z,** & **Rku** & **Wq,** & **aa,** & **Av. Defect size,** & **S def.,** \\
**Structure** & **nm** & **nm** & **Rku** & **nm** & **nm** & **nm** & **\%** \\ \hline I & 0.27 & 2.8 & 1.23 & 0.23 & 55 & 12 & 5 \\ II & 0.23 & 0.9 & 3.2 & 0.3 & 890 & 37 & 3.3 \\ III & 1.5 & 3.5 & 8.2 & 0.6 & 286 & 250 & 7.9 \\ IV & 1.9 & 10 & 8 & 1.5 & 196 & 62 & 1 \\ V & 5 & 13.2 & 16.5 & 57.5 & 590 & 127 & 12.5 \\ VI & 7.4 & 29 & 3.9 & 81.7 & 680 & 253 & 32 \\ VII & 26.2 & 84.9 & 2.6 & 15.4 & 418 & 183 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of structures.  
Figure 6: Myoglobin spectrum on type VI sample.  
(Figure 7). The clusters obtained were tested for compliance with the groups of substrates with surface enhancement of Raman spectra to assess the representativeness of the features used for characterization. Such type of substrates is red X marked.  
The results showed that 70% of SERS-active substrates distribution is concentrated in clusters 3 and 4. Due to high variation of AFM-features during manufacturing, that is not enough for building of a robust engineering process. That is why it was decided to use more sophisticated methods, such as convolutional neural networks (CNN).
## 2 Materials and Methods
### In Situ Density Estimation  
The density of the wood elements was estimated using the previous correlation established after the laboratory-phase density determination and needle penetration tests (Section 3.1). Since the moisture content of the structural elements in the buildings were different from 12%, a moisture correlation of the NPR results was required. To this end, a recent moisture correlation [31] suggested for _P. syvestris_ wood was used (Equation (1)).  
\[NP_{corrected}=\frac{NP_{measured}}{1+0.015\times\Delta u} \tag{1}\]  
In this equation, \(NP_{measured}\) is the needle penetration test result (mm) and \(\Delta u\) (%) is the moisture content difference (%) between the in situ and reference (12%) moisture contents.  
After obtaining the corrected NPR values, the correlation between the air-dry density and needle penetration (NP) was used to estimate density values. These values were later compared to the real density values calculated from core drilling measurements after air-conditioning of the cores as a sensitivity analysis. A total of 18 extracted cores were used for the density determination, with four, five, or seven readings were taken, depending on the uniformity of the wood surface and the disparity of resulting values. Core drilling  
Figure 4: (**a**) Visual inspection of timber members; (**b**) needle penetration test; and (**c**) moisture content determination.  
Figure 5: (**a**) In situ core extraction; (**b**) determination of the real (measured) wood density from wood cores; and (**c**) structure of wood cores.  
was also used to assess the decay in the tested samples because NP tests are not suitable to assess the decay in thick wood samples.  
Since the wood density was measured on defect-free and small wood samples (20 \(\times\) 20 \(\times\) 30 mm) and reported as oven-dry values, the estimated density results were validated by comparing them with the previously reported oven-dry density values using the following equation (Equation (2)) for transformation [37]:  
\[\rho_{u}=\rho_{0}\times\frac{1+u}{1+0.84\times\rho_{0}\times u} \tag{2}\]  
where \(\rho_{u}\) is the density at moisture content \(u\), and \(\rho_{0}\) (kg/m\({}^{3}\)) is the oven-dry density.
# Bonded-Particle Model with Nonlinear Elastic Tensile Stiffness for Rock-Like Materials
## 2 Method Description
### Specim Preparation
The critical time step \(\Delta t_{c}=2.5\times 10^{-8}\) s is obtained with the method introduced in [34] if the safety factor is set to 0.1. Thus, the time step \(\Delta t=10^{-8}\) s is proper in our model. The loading speed \(V=0.1\) m/s in both compression and tension tests. Damping constant \(\alpha^{t}\) and \(\alpha^{r}\) are set to be 0.02 which are proved to be large enough to guarantee the quasi-static state of equilibrium of the particles. All five specimens presented in Figure 5 are used for simulations with the micro parameters in Table 2.  
A simulation case costs about two hours on a machine with an Intel Core 5 3.2 GHz Processor. The compressive loads and tensile loads of specimen A at different displacements are presented in Figure 7. The crack patterns of specimen A under compression and tension are shown in Figure 8. The \(t_{cm}\) and \(t_{tm}\) are the time when the loads reach their peak values in compression and tension, respectively. The color of the particle varies from black to red gradually according to the ratio of the present number to the original number of the bonded contacts on the particle, which varies from 1 to 0. Tensile failure is the dominant failure pattern of the bonded contacts in both uniaxial compression tests and direct tension tests. The number of the failed bonded contacts was 420 by the time \(t=t_{cm}\), which increased sharply to 931 after 10,000 time steps in compression test. The number of the failed bonded contacts was 7 by the time \(t=t_{tm}\), which increased sharply to 88 after 4000 time steps in tension test. Only one main crack appeared in tension test, which is almost perpendicular to the tensile direction, but several oblique cracks appeared in compression tests. All cracks observed in our simulation are similar to those observed in experiments presented in [47; 48]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\mathbf{k_{x}}\) (\(\mathbf{10^{6}}\) N) & \(\mathbf{k_{x}}\)/\(\mathbf{k_{m}}\) & \(\mathbf{k_{m}}\)/\(\mathbf{k_{m}}\)R) & \(\epsilon_{0}^{a}\) (\(\mathbf{10^{-3}}\)) & \(\epsilon_{0}^{a}\)/\(\epsilon_{0}^{a}\) & \(\epsilon_{m\text{ax}}^{a}\)/\(\epsilon_{0}^{a}\) & \(\mathbf{\varphi_{f}}\)(\(\mathbf{{}^{c}}\)) & \(\mathbf{w}\) & \(\mathbf{b}\) \\ \hline
30.0 & 0.5 & 0.256 & 4.0 & 3.5 & 10 & 45 & 39 & 0.8 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Values of the micro parameters in the model. Figure 6: Models for uniaxial compression tests and direct tension tests. The properties of specimen A-E from the simulations are listed in Table 3. It shows that the difference of the properties between different specimens is small. The maximum ratio of the stand deviation value to the average value is 0.15. This indicates that the specimens created by the method in [44] with the same \(R_{max}\) and \(R_{min}\) share almost the same properties.
## 4 Experiments and Discussion
### Experiment Setting  
In this study, the deep learning framework PyTorch1.7.1 was utilized to implement all of the detectors in this study. The experimental environment was ubuntu18.04, CUDA11.1, CUDNN8.0.5 and NVIDIA GeForce RTX 3080. In order to ensure enough training samples and to make the test set reflect the characteristics of each dataset well, training and test sets in DIOR were split by 1:1, whereas those in RSOD were split by 4:1 randomly. A total of 90% of the training set was utilized for training detectors, and 10% was used for monitoring to avoid overfitting. The input size and batch size of detectors was set to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indicator Class** & **Indicator** & **Description** \\ \hline \multirow{4}{*}{Accuracy} & mAP\({}^{0.5}\) (\%) & Average precision when IOU = 0.5. It is the most used \\  & & indicator in remote sensing object detection. \\  & mAP\({}^{0.50,95}\) (\%) & Mean values of mAPs under each IOU, which are taken \\  & mAP\({}^{0.50,95}\) (\%) & at an interval of 0.05 between 0.5 and 0.95. \\  & mAP\({}^{0.50,95}\) & The mAP\({}^{0.50,95}\) of small, middle and large-sized object \\  & mAP\({}^{1}\) (\%) & defined in MS COCO. \\ \hline \multirow{2}{*}{Deployability} & Params & Number of detector parameters. \\  & Flops & Floating point operations. \\ \hline Speed & FPS (img/s) & Frames transmitted per second. \\ \hline \hline \end{tabular}
\end{table}
Table 1: The evaluation indicators.  
Figure 11: Each category of objects in RSOD.  
416 \(\times\) 416 and 7, respectively. Adam optimizer was employed to update the parameters, with a weight decay of 2 \(\times\) 10\({}^{-4}\). The relationship between learning rate and epoch is shown in Figure 12. For anchor-based detectors, K-means was utilized to optimize the size of anchors before training.
## 5 Solving Pareto Optimal Solution Set and Analysis Decision
### Pareto Optimal Solution Set and Analysis of Running 26 Pumping Wells Schemes
(In the figure (**a**), the red dot indicates the relationship between the total water inflow and the settlement influence coefficient, the green dot indicates the relationship between the central water level drawdown and the total water inflow, the blue dot indicates the relationship between the central water level drawdown and the settlement influence coefficient). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Pareto Solution No.**} & **Score for** & **Score for** & **Score of** & **Weighted Scores of Three Objectives** \\  & **Objective I** & **Objective II** & **Objective III** & **of Dewatering Optimization** \\ \hline
1 & 95 & 90 & 90 & 90.545 \\
2 & 85 & 90 & 90 & 89.455 \\
3 & 85 & 80 & 90 & 86.365 \\
4 & 75 & 80 & 90 & 85.275 \\
5 & 95 & 90 & 80 & 84.725 \\
6 & 75 & 70 & 90 & 82.185 \\
7 & 65 & 70 & 90 & 81.095 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation table for optimal scheme of foundation pit dewatering for running 26 pumping wells. It can be seen from Table 6 that the total water inflow of objective I under the first optimization working condition is about 1.45 \(\times\) 10\({}^{5}\) m\({}^{3}\)/d, the settlement value of objective II is 7.8 mm, which is less than the allowable settlement value of 15 mm, and the foundation pit central water level of objective III is 0.39 m lower than the foundation pit bottom plate, meeting the design requirements for construction of more than 0 m. In this scheme, if the construction and installation of the pumping well have been completed, the score of the three objectives is the best, but the disadvantage is that the number of the pumping wells is the largest, and it is not necessarily the best scheme in the area with a high cost of construction and installation of the pumping well.