esa-sceva/satcom-chunk-collection huggingface dataset:
# Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model
## 3 Methods
### Land Surface Phenotype Model  
The initial identification of the timing of the onset of growing season is estimated by computing the multi-annual mean of the satellite-based phenological timings retrieved for the pixel temporal profile (2000-2016). Among other phenological timings retrieved fitting a double hyperbolic tangent model to upper envelop of the NDVI time series as described in [PERSON] et al. [17], we use the number of growing cycles per solar year (either one or two), the start of growing season (SOS) and the time of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Value** \\ \hline Cab, Leaf chlorophyll content & 30 \(\mu\)g cm\({}^{-2}\) \\ Cw, leaf equivalent water thickness & 0.015 g cm\({}^{-2}\) \\ Ns, leaf structure coefficient & 1.6 (-) \\ Lidf\_a and Lidf\_b describing leaf angle distribution & \(-\)035, \(-\)01.15 (spherical distribution) \\ Hot spot size parameter & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PROSAIL5B fixed parameters values.  
maximum development (TOM). Besides setting the first guess of the simulation start day, this land surface phenology timings are used to set the first guesses of \(c\) of Equation (5) and GDD\({}_{\text{max}}\) of Equation (8) as the multi-annual average GDD accumulated between SOS and TOM.
# Background-Filtering Feature-Enhanced Graph Neural Networks for Few-Shot Learning
## 1 Introduction
The main training phase utilizes the same network update module as the feature gap reinforcement phase to train the classification of the query set, as shown in (**c**). After  
training, the final GNN graph is used to predict the classes of the query set nodes and participate in loss calculation, as shown in (**d**), where the output in (**d**) is the predicted result used to calculate classification accuracy. Refined and condensed, our principal contributions are as follows:  
* Introduction of background-filtering module: During the feature extraction stage, we created a background-filtering module that greatly minimizes the impact of intricate backgrounds on the primary target's classification in photos. This module increases the model's robustness in challenging situations and improves the model's recognition accuracy of target items. * Our idea is for a feature-enhanced training module: We built a feature-enhanced module to boost the reflection of these disparities in feature representation and to better explore the subtle distinctions between images. Prior to formal training, this module performs a feature gap augmentation training with the goal of teaching it to distinguish between different images. It then strengthens these differences throughout the formal training phase to maximize feature representation and achieve more accurate feature extraction and classification. * Building and validating the Tool dataset: We meticulously built the Tool dataset, which includes everyday hardware instruments like hammers and scissors, for model testing, in order to thoroughly assess the generalization capacity of our approach. We further validated the usefulness and efficacy of the BFFE-GNN by conducting comparative studies on the Tool dataset in addition to comprehensive testing on several public datasets. We have shown that the BFFE-GNN algorithm has significant advantages and improvements over current methods, demonstrating its huge potential for few-shot learning in the field through extensive experimental evaluations on both self-built Tool datasets and standard datasets like MiniImagenet and TieredImagenet.
## 4 Kinematic Model  
### Inverse Kinematics of 3(Rc)PU PM  
The inverse kinematics of the mechanism are obtained from the position parameter \(\{X,Y,Z,\alpha,\beta,\gamma\}\) of the moving platform. \(X\), \(Y\), and \(Z\) are the displacement of reference point \(O_{u}\) of the moving platform along the coordinate axis in the fixed coordinate system, and \(\alpha\), \(\beta\), and \(\gamma\) are the angles of the moving platform around the coordinate axis in the fixed coordinate system. On the (Rc)\({}_{1}\)PU limb, one of the outer axes and prismatic pairs can be used as the drive of the reconfigurable PM on this motion mode. The (Rc)\({}_{2}\)PU limb does not provide constraints on the moving platform, so each of the three limbs must provide at least two constraints to completely limit the reconfigurable PM's full degree of freedom. Based on the input selection principle, the angles rotated by the outer axis and the displacement of prismatic pair are selected as the input parameters for inverse kinematic analysis of the (Rc)PU limb, which are  
\[\{\theta_{1},\theta_{2},\theta_{3},l_{1},l_{2},l_{3}\}.\]  
where \(l_{1}\), \(l_{2}\), and \(l_{3}\) are respectively the length of line vectors \(\mathbf{A_{1}B_{1}}\), \(\mathbf{A_{2}B_{2}}\), and \(\mathbf{A_{3}B_{3}}\), that is, the length of the prismatic pair where the linear motor is located. \(\theta_{1}\), \(\theta_{2}\), and \(\theta_{3}\) are respectively the angle between the projection of line vectors \(\mathbf{A_{1}B_{1}}\), \(\mathbf{A_{2}B_{2}}\), and \(\mathbf{A_{3}B_{3}}\) on the plane \(A_{1}O_{n}B_{1}\), \(A_{2}O_{n}B_{2}\), and \(A_{3}O_{n}B_{3}\) and the \(XO_{n}Z\) plane. The angle at which the outer axis is rotated is the difference between the values of \(\theta_{1}\), \(\theta_{2}\), and \(\theta_{3}\) at the initial position and the values at the target position. The rotation matrix \(\mathbf{R}\) represented by the Euler angle is  
\[\mathbf{R}=\mathbf{R}(X,\gamma)\mathbf{R}(Y,\beta)\mathbf{R}(Z,\alpha)= \begin{bmatrix}c\gamma c \beta&c\gamma s\beta\kappa-s\gamma c\alpha&c\gamma s\beta c\alpha+s\gamma s \alpha\\ s\gamma c\beta&s\gamma s\beta\kappa+c\gamma c\alpha&s\gamma s\beta c\alpha-c \gamma s\alpha\\ -s\beta&c\beta s\alpha&c\beta c\alpha\end{bmatrix}  \tag{17}\]  
Figure 5: A reconfigurable production center based on the 3(Rc)PU PM. Taking the closed-loop motion chain formed by each limb with the static platform and the moving platform as a loop, the closed-loop vector equation of this loop can be obtained as follows:  
\[A_{i}B_{i}+O_{n}A_{i}-O_{n}B_{i}=0,\ (i=1,\ 2,\ 3) \tag{18}\]  
It should be noted that \(A_{i}B_{i}\), \(O_{n}A_{i}\), and \(O_{n}B_{i}\) are all vectors in the fixed coordinate system \(O_{n}\)-XYZ.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## Appendix B Genetic Algorithm Implementation
### Creating the Initial Population (GA_InitialPopulation)  
The initial population could be generated randomly but if individuals are prone to be fit, the performance of the GA is improved [66]. To do so, our starting individuals are created based on the _k-means_ clusterization method:  
1. First, we apply a two-dimensional _k-means_ algorithm, as illustrated in Figure 11, to define \(D\) clusters so that the average distance from each user to its closest centroid is minimized.
2. Then, we convert the 2D centroids to 3D ones by initializing the height coordinate to the mean altitude of the grid. These centroids will represent the first individual.
3. Finally, the rest of the initial population (\(p-1\) individuals) is created by randomly placing each drone (of each individual) among the spatial regions we created around each centroid. In particular, each region is a 3D cube that represents the _D-th_ part of the grid volume.  
The spatial regions are only used when creating the initial population, so genes carried by individuals from future generations could be at any edge in \(\mathcal{P}\) with no restrictions.
## 2 Evolution of Maritime Communication Networks
### Current Challenges and Limitations in MCNs
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Limitation in MCNs** & **6G-Enabling Technology/Approach** & **Expected Benefit** \\ \hline Limited coverage in deep sea or polar regions & LEO mega-constellations, RIS, UAVs-as-relays & Seamless coverage, dynamic link establishment \\ \hline Complex over-sea propagation & RIS, AI-driven channel modeling & Adaptive link enhancement and environment-aware communication \\ \hline Interference in densified coastal areas & NOMA, Smart Spectrum Management, AI-based resource allocation & Enhanced spectral efficiency, coexistence of multiple networks \\ \hline High mobility and topology dynamics & DRL, Mobility-Aware Routing & Real-time decision-making and adaptive network topology control \\ \hline Inefficient handover and session continuity & AI-assisted beamforming, predictive mobility models & Smoother transitions between network elements (e.g., UAV-to-satellite) \\ \hline Diverse QoS requirements & Network Slicing, Semantic Communications, Multi-objective Scheduling & Service-differentiated resource management for mission-critical tasks \\ \hline Security and trust in distributed platforms & Federated Learning, Blockchain, Zero-Trust Architectures & Secure and privacy-preserving AI at the network edge \\ \hline Regulatory spectrum fragmentation & AI-assisted spectrum sharing, Cognitive Radio, mIAB & Dynamic access to heterogeneous bands across jurisdictions \\ \hline Latency and bandwidth fluctuations & MEC, Digital Twins & Localized processing and predictive optimization of workloads \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mapping of MCN Limitations to 6G-Enabling Solutions. QoS provisioning becomes complex due to varying latency, bandwidth, and reliability requirements across applications (e.g., remote sensing vs. telemedicine) [2; 15]. Security and trust management also remain underdeveloped, especially with the growing reliance on distributed AI and edge computing in vessels and aerial platforms. These limitations highlight the urgent need for intelligent, flexible, and adaptive communication frameworks, where 6G technologies can play a central role in rethinking MCN design and operation [45].
## 2 Methodology
### Calculation of \(z_{k}^{1}\)  
The vertical equilibrium equation of the isolated pile can be obtained as Equation (5) if the pile can be regarded as a column connected by a series of nonlinear soil springs in the vertical direction. To describe the nonlinear pile-soil interaction along the piles, proper pile-soil interaction relationships in the vertical direction were assumed as follows: the relationships of skin friction vs. vertical pile-soil relative displacement at a depth of \(y\) below the surface (see Equation (6)), and the relationship of end bearing capacity vs. vertical pile-soil relative displacement at the pile tip (see Equation (7)).  
\[E_{p}A_{p}\frac{\mathrm{d}^{2}z(y)}{\mathrm{d}y^{2}}-t(y)=0 \tag{5}\]  
\[t(y)=k_{t}(y)\cdot[z_{s}(y)-z(y)] \tag{6}\]  
\[Q_{b}=k_{b}\cdot(z_{sb}-z_{b}) \tag{7}\]  
where \(t(y)\) and \(Q_{b}\) are the peripheral skin friction and the bearing force at the pile tip, \(k_{t}(y)\) and \(k_{b}\) are the stiffness of skin friction and end bearing, and with nonlinear PSI relationships (\(t-z/Q-z\) curves) taken into consideration, \(k_{t}(y)\) and \(k_{b}\) vary with pile-soil relative displacement, \(z_{s}(y)\) and \(z_{sb}\) are the vertical free-field soil settlements along depth and at the depth of pile tip, and \(z(y)\) and \(z_{b}\) are the pile settlements along the pile shaft and at the pile tip, respectively.  
Given that the PSI relationships vary with depth, Equation (5) is solved by the finite difference method, where the isolated pile with \(L\) in length is first discretized uniformly into \(n\) segments, with each segment length equal to \(\delta\), and two virtual nodes above the top and below the tip of the pile (see Figure 1).  
Equation (5) is then transformed into Equation set (8), where there are \(n\) equations and \(n+2\) unknown variables. Therefore, two additional boundaries are added to solve the equation set. Equations (9) and (10) indicate that a concentrated load \(P\) is applied at the pile head and the pile tip is connected to a nonlinear soil spring obeying the \(Q-z\) relationship, respectively, are adopted to form the \((n+2)\)\(\times\)\((n+2)\) Equation set (8), whose solution is the pile settlement vector \([z]\). The superscripts hereafter also represent the node number of the pile.  
\[z^{i-1}-(2+k_{t}^{i}\delta^{2}/E_{p}A_{p})z^{i}+z^{i+1}+k_{t}^{i}\delta^{2}z_{ s}^{i}/E_{p}A_{p}=0\;i=2,3,\ldots n+2 \tag{8}\]  
\[E_{p}A_{p}(z^{3}-z^{1})=-2\delta P \tag{9}\]  
\[E_{p}A_{p}(z^{n+3}-z^{n+1})=-2\delta k_{b}(z^{n+2}-z_{s}^{n+2}) \tag{10}\]  
The initial tangents of both the \(t-z\) and \(Q-z\) curves can be adopted as \(k_{t}\) and \(k_{b}\) to calculate the pile settlement, which in turn returns new \(k_{t}\) and \(k_{b}\) values according to the \(t-z\) and \(Q-z\) curves, leading to another solution of the pile settlement. Repletion of the above procedure with updated \(k_{t}\) and \(k_{b}\) values will be stopped if the relative error of the pile settlements between two iterations is less than a given tolerance of \(1\)\(\times\)\(10^{-5}\).
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 4 Framework and Formal Definition of the Problem
### Delivery Probability  
In the proposed scheme, we classify nodes into two categories: neighbors of the MS and non-neighbors of the MS. Their delivery probability is given as \(del_{prob}=\frac{pt_{d}}{pkt_{f}}\), where \(pkt_{d}\) are the successfully delivered packets by the node and \(pkt_{f}\) are the packets sent by the node.  
We ignore water currents in our scheme and assume that nodes present in the network are static.
# The Suitability of Short Rotation Coppice Crops for Phytoremediation of Urban Soils
## 3 Results and Discussion  
### Soil Characteristics  
The analysis performed at the beginning of the study revealed very homogeneous chemicophysical properties at the experimental site, with all the blocks showing a moderately alkaline pH and a silty sand texture, with 40% skeleton. In Table 2 the average values with standard deviations are reported.  
The soil had moderate organic matter content, due to compost addition, and low carbonates. The available P (Olsen P) was low, as were the total nitrogen content and the CEC. The limited fertility of the soil was probably due to the fact that it was excavated from deep layers in the adjacent areas, as a result of urban redevelopment plans.  
The pseudo-total concentrations of Co, Cr, Ni, Pb, and Zn in the soil exceeded the legislative limit in all the blocks. However, for Co, Cr, and Ni, the bioavailable concentrations were very low or below the detection limit. This has been often observed in the soils of Turin [33] as the metals are considered to be incorporated in the crystalline lattices of the soil minerals and can scarcely be absorbed by plants. This was in line with the local background values calculated according to the ISO 19258/2005 norm by ARPA Piemonte [34], arising from the parent rock material and from the diffuse historical contamination of the urban area.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **pH** & **TN** & **TC** & **Org. C** & **CaCO\({}_{3}\)** & **Sand** & **Silt** & **Clay** & **Skeleton** & **Olsen P** & **CEC** \\ \hline  & & \% & \% & \% & g/kg & \% & \% & \% & \% & mg/kg & cmol/kg \\ Mean & 8.4 & 0.11 & 3.4 & 1.93 & 123.5 & 65.3 & 29.1 & 5.6 & 38.2 & 13.1 & 5.2 & \\ S.D. & 0.1 & 0.01 & 0.14 & & 3.5 & 3 & 2.9 & 1 & 4.4 & 1.8 & 0.4 & \\ \hline  & **Co** & **Cr** & & **Cu** & **Ni** & **Pb** & **Zn** & & & \\ (mg/kg) & Total & DTPA & Total & DTPA & Total & DTPA & Total & DTPA & Total & DTPA & Total & DTPA \\ \hline Mean & 28 & \textless{}0.12 & 191 & \textless{}0.5 & 97 & 8.3 & 185 & 1.3 & 215 & 27.7 & 447 & 12.6 \\ S.D. & 0.6 & & 14 & & 35 & 1.5 & 4 & 0.2 & 34 & 2.5 & 82 & 12.2 \\ Limit & 20 & & 150 & & 120 & & 120 & & 100 & & 150 & \\ \hline \hline \end{tabular}  
* TN: Total Nitrogen; TC: Total Carbon; Org. C: Organic Carbon; Olsen P: bioavailable Phosphorus; CEC: Cation Exchange Capacity.  
\end{table}
Table 2: Chemico-physical characterization of the soil (n = 32). Average values for the 8 blocks with Standard Deviation (S.D.). Total and diethylenetriaminepenta-acetic acid (DTPA)-extractable (bioavailable) concentrations of metals (in mg/kg) reported along with the Italian legislative limit for green areas.  
Conversely, the concentrations of Pb and Zn suggested an anthropic contribution, with Zn posing the major threats due to its higher concentration. The contamination was probably of historic nature, as the bioavailable fraction of both metals was low [35; 36].
# Advancing the Solar Radiation Pressure Model for BeiDou-3 IGSO Satellites
## 2 Materials and Methods
### BeiDou-3 IGSOs' Structure  
Based on the above description, the knowledge of satellite structure information is the prerequisite to conducting the ABW solution. From the BeiDou-3 metadata released by CSNO [17], the box-shaped body of BeiDou-3 IGSOs has, essentially, a rectangular cross-section of \(2.098\times 2.358\) m, and a length of \(3.602\) m. Moreover, BeiDou-3 IGSOs carry a regular hexagon and two circular communication payloads on the \(\pm X\) surfaces, as shown in Figure 1. These payloads will not only increase the illuminated area of \(\pm Z\) surfaces but also may shadow the \(+X\) surfaces. Unfortunately, the installation information (angle, location and distance relative to the spacecraft body) and dimensions for these payloads are not disclosed.  
In this study, the following assumptions were made for the ABW solution. The radius of the two circular payloads is \(0.5\) m, and the side length of the regular hexagon payload is the same as that of the \(Y\) side of the spacecraft's body. These payloads are mounted vertically on \(\pm X\) surfaces, and the resulting SRP perturbations are treated in the same way as for the \(\pm Z\) surfaces by simply increasing the area. Considering that the specific distance and installation locations for these payloads, relative to the spacecraft body, are not available, the potential shading effects are neglected. The coarse reference values for BeiDou-3 IGSO geometrical dimensions and optical properties are given in Table 1. Since the diffuse and specular reflection coefficients are not yet released, their initial values are assumed to be \(0\) and \(1-\alpha\), respectively. Optical parameters of satellite surfaces are adjusted following the same procedures as [PERSON] et al. [16].
###### Abstract  
Uyghur text recognition faces several challenges in the field due to the scarcity of publicly available datasets and the intricate nature of the script characterized by strong ligatures and unique attributes. In this study, we propose a unified three-stage model for Uyghur language recognition. The model is developed using a self-constructed Uyghur text dataset, enabling evaluation of previous Uyghur text recognition modules as well as exploration of novel module combinations previously unapplied to Uyghur text recognition, including Convolutional Recurrent Neural Networks (CRNNs), Gated Recurrent Convolutional Neural Networks (GRCNNs), ConvNeXt, and attention mechanisms. Through a comprehensive analysis of the accuracy, time, normalized edit distance, and memory requirements of different module combinations on a consistent training and evaluation dataset, we identify the most suitable text recognition structure for Uyghur text. Subsequently, utilizing the proposed approach, we train the model weights and achieve optimal recognition of Uyghur text using the ConvNeXt+Bidirectional LSTM+attention mechanism structure, achieving a notable accuracy of 90.21%. These findings demonstrate the strong generalization and high precision exhibited by Uyghur text recognition based on the proposed model, thus establishing its potential practical applications in Uyghur text recognition.  
text recognition; CRNN; GRCNN; ConvNeXt; BiLSTM; attention mechanism; Uyghur
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 3 Low Power Wide Area Network
### DASH7 Alliance  
DASH7 is an industry alliance that employs narrow band modulation schemes domiciled in the sub-GHz bands [13]. It proposes a tree topology as against the star topology of the LoRa alliance. The DASH7 alliance embraces an always ON base station set-up to ensure constant network connectivity, which differentiates it from other standards [13].
## 3 Results and Discussion
In Figure 9, \(\eta\) is at 6, and 200 had the lowest \(PD_{average}\) ranging between 0.75 and 0.82%, respectively. The \(PD_{average}\) approaching 0% indicates that the predicted value of the total settlements is approximately similar to the measured values. Figure 6: Model performance with \(R^{2}\). Figure 8: Model performance with the _PDaverage_. Figure 7: \(R^{2}\) for Model 1 with different locations. Figure 9: \(PD_{average}\) for Model 1 with different locations. The settlement performance with \(\eta=6\) is reported in Figure 10, and it is observed that the analysis resembles the settlement curve well. As previously mentioned, the location distance of the left and right corners from the centre of the embankment were 2.5 and 5 m, respectively. According to Figure 10, 0.03 m of the settlement was observed after 177 days of consolidation on the left of the embankment, while on the right of the embankment, there was a reduction of 0.007 m in the consolidation settlement after the same elapsed time, indicating that the location differences could influence the consolidation rate. In addition, the settlement profile displays that a high total of settlement occurs in the location near the centre of the embankment and decreases at the embankment toe. This finding supports previous research, in which the amount of settlement at the centre of the embankment gradually decreases towards the embankment toe [26]. On the other hand, the process of consolidation settlement can be seen in a rapid manner in the initial stages of the embankment filling, which displays significant differences in the magnitude of settlement. This may be attributed to the compaction energy imposed on the top layer of the soft ground during sand filling, which results in high settlement rates. The comparative results in this study indicate that back-analysis is valuable in decisive parameters related to drainage behaviour. According to the literature, laboratory tests have limitations in determining the permeability values in smear zones. Therefore, it is recommended to back-evaluate the embankment stabilised with PVDs for the existing case history. There are several recommendations for predicting the behaviour improvement of vertical drainage on soft ground. They are as follows:  
1. Evaluate the smear effect parameters via existing case history by verification from predicted and measured comparison values;
2. Determine the permeability in the smear zone proposed by [PERSON] et al. as in Equation (1) using the value of \(\eta\) with 6;
3. Simulate the behaviour of vertical drains improved on soft ground by numerical analysis to predict the settlement.