esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
High-precision three-dimensional geological modeling of mining faces is crucial for intelligent coal mining and disaster prevention. Accurate spatial interpolation is essential for building high-quality models. This study focuses on the 25214 workforce of the Hongliulin coal mine, addressing challenges in interpolating terrain elevation, stratum thickness, and coal seam thickness data. We evaluate eight interpolation methods (four kriging methods, an inverse distance weighting method, and three radial basis function methods) for terrain and stratum thickness, and nine methods (including the Bayesian Maximum Entropy method) for coal seam thickness, using cross-validation to assess their accuracy. Research results indicate that for terrain elevation data with dense and evenly distributed sampling points, linear kriging achieves the highest accuracy (MAE = 1.01 m, RMSE = 1.20 m). For the optimal interpolation methods of five layers of thickness data with sparse sampling points, the results are as follows: Q\({}_{4}\), spherical kriging (MAE = 2.13 m, RMSE = 2.83 m); N\({}_{2}\)_b_, IDW (\(p\) = 2), MAE = 2.08 m, RMSE = 2.44 m; J\({}_{2}\)_y\({}^{5}\)_, RS-RBF (MAE = 0.89 m, RMSE = 1.05 m); J\({}_{2}\)_y\({}^{2}\)_, TPS-RBF (MAE = 1.96 m, RMSE = 2.25 m); J\({}_{2}\)_y\({}^{1}\)_, HS-RBF (MAE = 2.36 m, RMSE = 2.71 m). A method for accurately delineating the zero line of strata thickness by assigning negative values to virtual thickness in areas of missing strata has been proposed. For coal seam thickness data with uncertain data (from channel wave exploration), a soft-hard data fusion interpolation method based on Bayesian Maximum Entropy has been introduced, and its interpolation results (MAE = 0.64 m, RMSE = 0.66 m) significantly outperform those of eight other interpolation algorithms. Using the optimal interpolation methods for terrain, strata, and coal seams, we construct a high-precision three-dimensional geological model of the workforce, which provides reliable support for intelligent coal mining.  
spatial interpolation; Bayesian Maximum Entropy; 3D geological modeling; channel wave exploration; intelligent coal mining 2020 IEEEexample: accepted. 1522020
# Compressive Sensing-Based IoT Applications: A Review
## 4 CS-Based IoT Applications
### Sensing Layer
#### 4.1.3 CS-Based Data Gathering  
Efficient data helps to find suitable solutions for the high energy consumption in large-scale WSNs. [PERSON] et al. [81] proposed a compressive data gathering (CDG) approach to extend the sensors lifetime in multi-hope architecture, where each node performs a CS acquisition and transform its random measurements to the next node. Subsequently, the data at the sink node will be the sum of the different random measurements. CDG approach disperses both communication and computation costs to all sensor nodes, resulting in a natural load balancing. Moreover, based on CDG, [PERSON] et al. [82] presented compressed sparse function (CSF) algorithm for data gathering in WSNs. This approach adopts DCT to sparsify the collected data from each sensor and then transmits the sparse functions to the sink. Then, the reconstruction is performed by means of polynomial interpolation techniques. This approach allows reconstructing the whole ensemble data from an incomplete set of received measurements. Beside CSF and CDG, [PERSON] et al. [83] proposed minimum energy compressed data aggregation (MECDA) approach. The algorithm determines the best routing path from the sensor nodes to the sink node that consumes the minimum power. To determine this optimum path, the algorithm exploits minimum spanning tree and shortest path forest techniques. Nevertheless, this algorithm maintains its optimum performance as long as the network topology remains fixed. In addition, an opportunistic routing approach called compressive data collection (CDC) has been proposed in [84]. In the proposed approach, each sensor selects randomly one of its neighbours by means of opportunistic routing protocols [85] and forward to its compressed reading.
## 2 Overview of the Study Area
### Height Prediction of Water-Flowing Fractured Zone  
Generally, empirical formulas and analogy methods are used to estimate the height of the water-flowing fractured zone of the working face. Based on 3-1 coal geological data, two kinds of representative formulas are selected. One is the calculation formula in \"Regulations of buildings, water, railway and main well lane leaving coal pillar and press coal mining\". Second, the formula summarized by [PERSON] et al. [1] is deemed suitable for high-intensity mining in western China. The specific results are shown in Table 1.  
At the same time, based on the proven height of water-flowing fractured zone data of adjacent mining areas, an analogy analysis of this working face was carried out. [PERSON] et al. [9] sorted the ratio data of \(R_{\text{f}}\) of fully mechanized mining faces in the Yushenfu mining area and Shendong mining area, with an average of approximately 26 times. [PERSON] et al. [1] suggested that the development \(H_{\text{f}}\) in the Yushenfu shallow coal seam should be 18-28 times the mining height. A large number of measured results show that the \(H_{\text{f}}\) in the western region is more than 20 times higher, and close to 30 times higher, while the results based on the empirical formula of \"Regulations of buildings, water, railway and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Formula Type**} & \multirow{2}{*}{**Caving Zone Formula**} & **Water-Flowing Fractured Zone Formula** & \multirow{2}{*}{\(H_{\text{c}}\)/(m)} & \multirow{2}{*}{\(H_{\text{f}}\)/(m)} & \multirow{2}{*}{\(R_{\text{c}}\)} & \multirow{2}{*}{\(R_{\text{f}}\)} \\ \cline{1-1} \cline{6-6}  & & & & & & \\ \hline First category & \(100M/(4.7M+19)\pm 2.2\) & \(100M/(1.6M+3.6)\pm 5.6\) & 13.2 & 46.8 & 3.0 & 10.8 \\ Second category & \(4.3665M-4.8462\) & \(100M/(0.095M+7.28)\pm 8.62\) & 14.1 & 65.2 & 3.2 & 15.0 \\ \hline \hline \end{tabular} Note: In the formula, \(M\) is 4.35 m, and the calculated value is the maximum. \(H_{\text{c}}\) is the height of the saving zone, \(H_{\text{f}}\) is the height of the water-flowing fractured zone, \(R_{\text{c}}\) is the ratio between the saving zone and coal seam thickness, and \(R_{\text{f}}\) is the ratio between the water-flowing fractured zone and coal seam thickness. The same applies below.  
\end{table}
Table 1: The predicted result of experience formula of working face fractured zone height.  
Figure 1: Geographical location and geological profile of the study area. (**a**) Geographical location; (**b**) Geologic profile.  
main well lane leaving coal pillar and press coal mining\" are basically less than 15 times higher, which is quite different.
## 6 Conclusions  
BSNs, arranging sensors in, on and around the human body, realizes the detection of human action and physiological information, which has been widely used in the fields of health care, social welfare, sports, entertainment, _etc._ The ubiquitous network is coming with the method of taking human body as a part of the communication network. Therefore, BSN has broad application prospects and market potential.  
Although BSNs have been a hotspot of research with the emergence of many practical applications, many open problems still remain. For example, in the design of sensor nodes, more attention should be paid to node size minimization and energy consumption reduction. Designers should place emphasis especially on avoiding the danger of damage to human tissues caused by the heat generated by sensorsimplanted in the human body. When designing data processing algorithms and communication protocols, the same problem of power consumption should be solved, as well as the network robustness problem resulting from moving nodes. In addition, due to the close relationship between BSNs and human health, there are topics of moral and legal constraints in actual applications. In short, BSNs represent the integration of multi-disciplinary areas, aiming to solve many key issues in several of these areas, which requires researchers to do further exploration.  
This paper describes the concepts, origin, architecture, and research areas of BSNs, with a comprehensive introduction to the current applications and issues of BSNs. We focus especially on the research status, development trends and challenges of sensor technology, data fusion technology, and network communication technology. Besides, there are some more aspects to be further improved and summarized, such as data security and service quality.
## 2 Materials and Methods  
### Dataset  
The dataset used in this study was the publicly available SGCBP (Small Grain Cereals Biomass Prediction) dataset [30], with data sourced from field experiments conducted in 2019 at Yanco, New South Wales, Australia. The experiment involved sowing 26 varieties of small grain cereals (wheat and barley) at two different densities--250 seeds per square meter and 50 seeds per square meter--resulting in a total of 156 plots. Each plot was scanned at two distinct growth stages: the early stage (August, vegetative phase) and the late growth stage (October, flowering phase). A LiDAR system was employed at a height of 2 m to generate point cloud data during the scans. Following the LiDAR scanning, ground samples were collected from each plot. In the early stage, samples were collected from a 1-square-meter area, while in the flowering stage, samples were collected from a 0.5-square-meter area. The samples were dried at 70 \({}^{\circ}\)C for 7 days until a constant weight was achieved, after which the above-ground biomass (AGB) per unit area (g/m\({}^{2}\)) was calculated. The dataset comprised 306 point clouds along with their corresponding manually measured ground-truth AGB data. The dataset was split into 204 training plots and 102 testing plots. A visualization of the SGCBP point cloud data is presented in Figure 1, displaying, from left to right, the top view, side view, and oblique view, respectively.  
Figure 1: Visualization of a representative SGCBP point cloud plot: (**a**) top view showing canopy coverage; (**b**) side view illustrating vertical crop structure; and (**c**) oblique view providing a 3D perspective. These views highlight the complex geometry of crop data used for biomass prediction.
# A Multi-Service Adaptive Semi-Persistent LTE Uplink Scheduler for Low Power M2M Devices
## 3 Multi-Service Adaptive Semi-Persistent Scheduling Algorithm
### Scheduling of H2H Traffic  
So far, we described the allocation of PRBs to adaptive SPS users which is intended for M2M subscribers. However, this does not come at an expense of jeopardizing human users. As expressed in Equation (3), the maximum number of PRBs available for the adaptive SPS users in an uplink subframe, \(R_{S,max}\) must not exceed the total uplink PRBs, \(R\) in a subframe. For fairness between M2M and H2H users, we reserve \(R_{H,res}\) PRBs for H2H users per uplink subframe. Hence it follows:  
\[R=R_{S,max}+R_{H,res} \tag{13}\]  
The actual consumption of PRBs by the adaptive SPS users is often less than the allowed maximum (as the buffered data and is often very small or zero in case of M2M traffic).  
Suppose, in an arbitrary uplink subframe, \(n_{S}\) users of traffic class \(S\) instantaneously consume \(f_{S_{1}}\), \(f_{S_{2}}\),..., \(f_{S_{n_{S}}}\)PRBs respectively based on their buffered data. Then for the particular subframe, the actual number of total PRBs consumed by the adaptive SPS users, \(\hat{R}_{S}\) is given by Equation (14) as follows:  
\[\hat{R}_{S}=\sum_{i=1}^{n_{S}}\hat{r}_{S_{i}} \tag{14}\]  
Then, it follows that the rest of the PRBs unused from the maximum PRB limits of the adaptive SPS ranges i.e., \((R_{S,max}-\hat{R}_{S})\) are also available for the H2H users and can be scheduled dynamically on an on-demand basis. So the total number of PRBs available for human users in one subframe would be given by \(R_{H}\) as expressed by Equation (15).  
\[R_{H}=R_{H,res}+R_{S,max}-\hat{R}_{S} \tag{15}\]  
By replacing \(R_{S,max}\) from Equation (13), we get the expression for \(R_{H}\) as given by Equation (16).  
\[R_{H}=R-\hat{R}_{S} \tag{16}\]  
For any scheduling instance, the actual requirement of PRBs by the adaptive SPS users, \(\hat{R}_{S}\) is determined first by the scheduler (based on the mutual knowledge of the eNodeB and the devices regarding the buffered data and using Equation (1)) and then the remaining \(R_{H}\) PRBs available for the H2H users are scheduled dynamically according to the queued requests.
# Pretreatments Applied to Wheat Straw to Obtain Bioethanol
## 3 Pretreatments Applied to Wheat Straw
### Chemical Pretreatments Applied to Wheat Straw
#### 3.2.3 Pretreatment with Acid  
By pre-treating wheat straw with acid, the degradation of hemicellulose is monitored. The following acids are used: acetic, sulfuric, phosphoric acid [57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67].  
Table 4 shows the results of bioethanol production obtained from wheat straw treated with sulfuric acid and sodium sulphide [40].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Raw Material** & **Alkaline Type** & **Pretreatment** & **Reducing** & **Delignification** & **Fermentation** & **Bioethanol** & **R** \\  & **Condition** & **Sugars** & **Rate (\%)** & **Condition** & **Yield (g/L)** & **R** \\ \hline Wheat straw & Na\({}_{2}\)CO\({}_{3}\) (11\%) & 75 \({}^{\circ}\)C; 10–85 min & Xylose: & 70.4 & 30 \({}^{\circ}\)C; 96 h & 65 & [51] \\ Wheat straw & NaOH/H\({}_{2}\)O\({}_{2}\) & 50 \({}^{\circ}\)C, 3–15 h & 61.9 g/L & 60 & 37 \({}^{\circ}\)C; 96 h & 31.1 & [53] \\ \hline \end{tabular} R-references.  
\end{table}
Table 2: Obtaining bioethanol by alkaline pretreatment of wheat straw (data from [51; 53]).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Raw Material** & **Pretreatment Type** & **Pretreatment** & **Reducing** & **Delignification** & **Fermentation** & **Bioethanol** & **R** \\  & **Condition** & **Sugars** & **Rate (\%)** & **Condition** & **Yield (g/L)** & **R** \\ \hline Wheat straw & Organosolv & 190 \({}^{\circ}\)C, 1 h & Glucose: 99 & 84.5 & 50 \({}^{\circ}\)C; 72 h. & NA & [51] \\ Wheat straw & Eutectic solvents & 70 \({}^{\circ}\)C, 9 h & Glucose: 79.7 & 71.4 & 50 \({}^{\circ}\)C; 72 h & 89.8\% & [53] \\ \hline \end{tabular} NA: not available. R-references  
\end{table}
Table 3: Obtaining bioethanol by pretreatment of wheat straw with organic solvent.
# Enhanced Phase Retrieval Method Based on Random Phase Modulation
## 1 Introduction
Since an acceptable initial guess is obtained from a few iterations of the serial framework, the needed iteration number of the whole reconstruction process is reduced, such that the convergence rate is improved. The noise immunity and stability of the method are enhanced by the subsequent iterations of the parallel algorithm. Accordingly, the serial-parallel concatenation framework can realize a rapid convergence rate and quality imaging results in noisy environments. In this work, the validity of the proposed method is confirmed both theoretically and experimentally. Then, we analyze the impact of different allocation schemes with iteration numbers of serial processes on the reconstruction performance.
## 4 Case Study and Results
### Energy Consumption Results
#### 4.2.4 Scenario 4--Early Return Strategy  
Scenario 4 imposes an early return constraint to preserve the bus schedule, resulting in the highest energy consumption across all configurations (Figure 6).  
These results confirm the linear increase in energy with payload mass and highlight the additive effect of each operational constraint. Scenario 4 and Table 8 shows up to **16% more energy consumption** compared to the baseline Scenario 1, justifying the need for scenario-aware planning.  
\begin{table}
\begin{tabular}{c c c} \hline
**Line** & **Min Energy (Wh)** & **Max Energy (Wh)** \\ \hline Line 1 & 33.03 & 74.56 \\ Line 2 & 54.19 & 135.20 \\ Line 3 & 63.31 & 132.51 \\ Line 4 & 37.40 & 90.69 \\ Line 5 & 71.02 & 136.63 \\ \hline \end{tabular}
\end{table}
Table 6: Scenario 2—Energy Consumption Range per Line.  
Figure 5: Energy consumption with aerodynamic corrections (Scenario 3)
# Deep Learning in Food Image Recognition: A Comprehensive Review
## 3 Food Image Recognition Methods
### Convolutional Neural Network (CNN)-Based Methods  
The paradigm shift in food image recognition began with [PERSON] et al. [6], who pioneered the application of convolutional neural networks (CNNs) to this domain. While early implementations directly employed off-the-shelf CNN architectures for feature extraction, these approaches overlooked domain-specific characteristics of food imagery, resulting in suboptimal recognition accuracy. Subsequent research has focused on architectural adaptations of baseline CNNs to enhance model performance through task-specific optimizations. Recent advancements demonstrate three distinct optimization strategies:  
1. Attention-Augmented Architectures: [PERSON] et al. [22] introduced the FRCNNSAM framework, integrating self-attention mechanisms with deep CNNs through weight-sharing and data compression. Ensemble predictions from multiple FRCNNSAM variants via averaging achieved state-of-the-art accuracies of 96.40% on Food-101 and 95.11% on MA Food-121, outperforming transfer learning baselines by 8.12% (Figure 3).
2. Transfer Learning Ensembles: [PERSON] et al. [23] developed an ensemble model leveraging ImageNet-pretrained networks (VGG19, ResNet50, MobileNetV2, AlexNet) for generic feature extraction. Fine-tuning and strategic combination of base learners through feature-space fusion yielded 96.88% accuracy on FOOD-11, demonstrating superior generalization over individual models (Figure 4).
3. Spatiotemporal Fusion Networks: [PERSON] et al. [24] proposed ASTFF-Net, a novel architecture combining the following (Figure 5): * Spatial encoder: ResNet50 with parameter reduction for robust feature extraction. * Temporal processor: LSTM-enhanced Conv1D layers for sequential pattern learning. * Adaptive fusion module: Softmax-optimized spatiotemporal feature integration. This framework addresses key challenges in food recognition-including occlusions, image blur, and inter-class similarity while maintaining computational efficiency. Benchmark evaluations across Food11, UEC FOOD-100/256, and ETH Food-101 confirm its competitive performance.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
## 8 Future Research Directions  
In summary, the following directions for future research can be identified:  
* The main challenge is decreasing the computational complexity of the RA methods by defining the graph in such a way as to decrease the number of vertices and avoid unnecessary connections between them.
* Using the proposed graph-based algorithm design model, the most prominent challenges for a specific network scenario and user terminal capabilities can be identified and focused upon.
* Incorporation of different functionalities that enable satisfactory communications and low power consumption.
* Integration of novel communication networks and services alongside existing ones through cognitive capabilities.
# A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression
## 2 The Basic Principle of Self-Organizing Map  
SOM is an unsupervised self-organizing neural network. It has been adopted to solve problems in a wide range of applications. Usually, SOM can compress information while preserving the most important topological and metric relationships of the input data [15]. A SOM is normally constructed by the components named neurons, which are arranged in the form of a two-dimension regular grid. Each neuron contains one \(d\)-dimension vector, \(W_{i}\), called weight vector, where  
\[W_{i}=\{w_{i1},w_{i2},\ldots,w_{id}\}. \tag{1}\]  
During the learning step, neurons are adapted to the input vector by the criterion of similarity matching between the input vector and the weight vectors. In this way, the neurons in SOM can become more representative of the input data. Specifically, the input vectors can be expressed as  
\[X=\{x_{1},x_{2},\ldots,x_{d}\}\in R^{d}. \tag{2}\]  
The neuron whose weight vector is the closest one to the input vector is regarded as the winner-neuron. The Euclidean distance is one of the most popular ways to measure the distance. It is defined as Equation (3), where \(R\) is the number of neurons and thus the winner-neuron can be expressed as Equation (4).  
\[D_{Ei}=\sqrt{\sum_{j=1}^{d}\bigl{(}x_{j}-w_{ij}\bigr{)}^{2}},\text{ for }i\in\{1,2,\ldots,R\}, \tag{3}\]  
\[W_{s}=argmin\{D_{Ei}\},\text{ for }i\in\{1,2,\ldots,R\}. \tag{4}\]In general, the squared Euclidean distance (SED), (\(D_{E}^{2}\)), is preferred over \(D_{E}\) in hardware implementation since the root operation has no influence on the comparison result but will cause additional computational costs. Once the winner-neuron is determined, the weight vector of winner-neuron will be updated according to Equation (5).  
\[W_{s}(t+1)=W_{s}(t)+\alpha(t)[X(t)-W_{s}(t)]. \tag{5}\]  
In Equation (5), \(t\) is the discrete-time coordinate, and \(\alpha(t)\) is the learning rate. \(W_{s}(t)\) and \(W_{s}(t+1)\) represent the weight vectors of the winner-neuron before and after the updating process, respectively. As shown in Equation (5), the update procedure can be regarded as a gradual reduction of the component-wise difference between the input vector and weight vector of the winner-neuron. Geometrically speaking, the weight vector of the winner-neuron is moved towards the input vector and the step size of weight vector movement is affected by \(\alpha(t)\). In order to drastically train the SOM, the winner-neuron searching process in Equation (4) and the weight vector updating process in Equation (5) repeat alternately to facilitate convergence after multiple iterations.
## Abbreviations  
Acronym  
ADCAnalog-to-digital converter  
AFEAnalog front-end  
AIArtificial intelligence  
CFRPComposite fiber reinforce polymer  
CWTContinuous wavelet transform  
DTfoADifferential time of arrival  
IDTInterdigital transducer  
NDTNon-destructive testing  
PVDFPolyvinylidene fluoride  
PZTLead zirconate titanate  
ROIRegion of interest  
SHMStructural health monitoring  
SNRSignal-to-noise ratio  
STFTShort-time Fourier transform