esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring Within-Field Variability of Corn Yield using Sentinel-2 and Machine Learning Techniques
## 2 Materials and Methods
### Measured Yield Data  
A grain yield monitoring system mounted on a combine harvester was used to record corn yield. This system could record about 1000 yield points for each hectare (about 20,000 points from the whole study field). The harvester width was 6 m and the monitor could record yield every 1 s, which is equivalent to an average speed of the harvester of 1.5 m along the direction of harvesting. Corn yield varied between 9 and 18 ton/ha across the study field for the three seasons. After harvesting and downloading the yield data, maps were processed as follows. Field borders were removed to avoid noise arising from the partial presence of corn in the field margins. Data recorded while the harvesting machine was turning at the end of each line were also removed to avoid zero production from the corresponding field portions. Secondly, two lines were removed from the 2016 yield data where the harvester passed twice during field operations. The measured yield is provided as a dataset with \(-\)20,000 sparse points, whereas the Sentinel-2 data is a raster with a 10-m resolution. Therefore, the sparse points were interpolated to a raster with the same resolution as Sentinel-2 using the Kriging method. Kriging was run using R's Gstat package. The best models and fits (range, partial sill, and nugget) are, respectively, for each of the three seasons from 2016 to 2018: (i) Exponential\(-\)57.29 m, 1.628, 2.78; (ii) Exponential\(-\)46.18 m, 3.087, 5.123; (iii) Spherica\(-\)114.87 m, 3.982, 5.55. The field contains  
Figure 1: Study field in Ferrara, North Italy.  
2006 cells with yield values for each of the three seasons analyzed. Figure 2 shows a color-scaled image of the final corn yield data for the three studied seasons.
## 5 Conclusions  
The objective of the present study is to unravel the possibility of analyzing the effects of soil structure distortions from ground-penetrating radar scanning responses vis-a-vis its characterization, which may inform optimum soil utilization and management. To this effect, normal stress was induced in some parts of the test sites using varying passes of a known-weight tractor while GPR scanning measurements were taken along a pre-established transect both across and along the induced normal stress on the soil. Analyses of the acquired data, coupled with the laboratory estimation of some physical properties from some selected samples, have shown that soil structure alterations and their effects on the general management of the soil can be investigated using the GPR dataset. Soil structure distortion inflicts significant adverse effects on the soil, preventing it from maximizing its potential. For instance, soil structure alteration may hinder appropriate aeration and water infiltration in the soil.  
In the zones of soil structure alterations, the GPR signal amplitude values decrease, which may be attributed to changes in the media constituent properties as confirmed by the analyzed soil samples at these zones. The discrepancy in the mean attenuation of the GPR signals is another characteristic signature that may be attributed to the soil structure distortion, which could be explained by the changes in the media's physical properties and complemented by the soil sample analysis results. The swiftness of the GPR techniques and their simplicity allow for possible repeated measurements for such soil evaluation. Reports of such an evaluation may provide vital information on soil utilization and management if available to stakeholders in the sector, particularly when considering precision agriculture practices, and, importantly, when real-time and temporal variation data are sought. The technique (GPR) utilized in this study is without prejudice to the conventional methods of evaluation, rather, it could be a leverage to them. It is worth noting that the adopted method of study is not without some limitations, particularly the attenuation of GPR signals in highly conductive media. Thus, the method may not be effective in waterlogged areas and soils of very high clay content.  
**Funding:**: Financed by a subsidy from the Ministry of Education and Science for the Hugo Koldataj University of Agriculture in Cracow for 2025.
**Institutional Review Board Statement:**: Not applicable.
**Informed Consent Statement:**: Not applicable.
**Data Availability Statement:**: Data will be made available on request.
**Conflicts of Interest:**: The author declares no conflicts of interest.
## 2 System Model and Problem Formulation
### Performance Metrics
#### 2.5.2 UAV's Computation  
When \(a_{u,n}(t)=1\), tasks \(\pi_{n}(t)\) will be offloaded to UAV \(u\). At each time slot \(t\), the UAV \(u\) needs to process multiple tasks from vehicles within its coverage area simultaneously. However, due to the limited computation resources of UAVs, UAVs must efficiently allocate a portion of their computation resources to each task. Then, the computation delay of task \(\pi_{n}(t)\) processed by UAV \(u\) is expressed as  
\[T_{n,u}^{\text{comp}}(t)=\frac{c_{n}^{d}(t)}{f_{u}}, \tag{23}\]  
where \(f_{u}\) is the total computation resource of UAV \(u\). This delay depends on the UAV's processing capacity [38].  
Employing the dynamic voltage and frequency scaling technique at the UAV, the energy consumption for computation at the u-th UAV at the time slot \(t\) is  
\[E_{u}^{\text{comp}}(t)=\sum_{n=1}^{N}\beta_{u}f_{u}^{3}T_{n,u}^{\text{comp}}(t), \tag{24}\]  
where \(\beta_{u}\) represents the computation energy efficiency coefficient related to the processor's chip of the \(u\)-th UAV [51].  
In addition to minimizing task latency and energy consumption, we also want to achieve fair task allocation among multiple UAVs to avoid situations where some UAVs are overloaded while others are underloaded. Jain's Fairness Index (JFI) is commonly used to measure the fairness of resource allocation, so we include it in our optimization objective. JFI is a standard metric for evaluating fairness in resource allocation problems [52]. \(P_{Fair}(t)\) is Jain's fairness index, which is calculated as follows:  
\[P_{\text{Fair}}(t)=\frac{\left(\sum_{n=1}^{U}\sum_{n=1}^{N}a_{u,n}(t)\right)^{ 2}}{U\sum_{n=1}^{U}(\sum_{n=1}^{N}a_{u,n}(t))^{2}}, \tag{25}\]
## 3 Survey Results
### Privacy  
* _Do you know the patient population characteristics used for training and validating?_ A considerable proportion of respondents (40%) is unaware of patient population characteristics used for training and validating AI systems they use on a regular basis or are unable to access such information.
* _Do you know what equipment, protocols, and data processing workflows were used to build the training and validation dataset?_ Answers to this question were even: around half of the participants manifest to know or have access to such information, and the remaining half that they do not.
* _Do you know who annotated or labelled the datasets used for training and validating the AI system (credentials, expertise, and company)?_ Around 58% of participants are not aware of the team in charge of this process.
* _Are both training and validation datasets available to the general public or via request?_ Only a fourth of the participants are aware of AI systems whose training and validation datasets are available to the general public; the remainder were evenly split between not knowing whether such data were available (37.5%) and knowing such data were either partially or completely unavailable (37.5%).
* _In general, are datasets typically available in your area of expertise?_ More than half of participants stated datasets tend to be unavailable to the general public (80%), although a few private ones can be accessed after approval (20%).
* _In general, are your needs regarding privacy addressed adequately by AI systems?_ According to survey respondents, their privacy demands are properly met by the AI system they typically employ (yes: 90%; no: 10%). Nonetheless, some expressed their concerns about data availability, the lack of specifics about how AI systems were built and evaluated, and the loss of control when employing cloud-based technology owing to privacy regulations.
# Spectral Data-Driven Prediction of Soil Properties Using LSTM-CNN-Attention Model
## 2 Materials and Methods  
### LUCAS Soil Database  
The Land Use and Coverage Area frame Survey (LUCAS), a project conducted by Eurostat, periodically surveys land use, land cover, and their changes over time across the European Union (EU) [33]. LUCAS encompasses 27 EU member states, along with the UK, and is carried out every 3 years. This project provides real-time data on soil physical and chemical properties, along with Global Positioning System (GPS) coordinates, across the EU region. The observation points are mapped, as illustrated in Figure 1. Soil samples were collected using a composite sampling process, with five subsamples taken at each site. The central subsample was obtained from the LUCAS point, while the other four were collected 2 m away in a cross pattern [34]. At each site, approximately 0.5 kg of topsoil samples (0-20 cm) were collected following standard protocols. The samples were subsequently sent to the laboratory for analysis, which included measurements of organic carbon (g\(\cdot\)kg\({}^{-1}\)), pH (measured in H\({}_{2}\)O), calcium carbonate (g\(\cdot\)kg\({}^{-1}\)), total nitrogen (g\(\cdot\)kg\({}^{-1}\)), and spectral data.
# Urban Geological 3D Modeling Based on Papery Borehole Log
## 4 Processing of Papery Borehole Data Based on Machine Learning
### Characteristics of Papery Borehole Logs  
The accuracy of urban 3D geological modeling depends on the reliability of the modeling data. Based on the papery borehole log, the usability of the three-dimensional geological model mainly depends on the quality of data extraction. Compared with other kinds of data, the papery borehole log has its own characteristics, which lead to many difficulties [12].  
(1) Difficulty in extracting information. Papery borehole logs have high requirements for the temperature and humidity of the archive room, and most paper-based geological data have not been well protected during the preservation process, resulting in many of these drawings becoming brittle and yellow. There is also the phenomenon of flipping, which brings great difficulties to the use of papery borehole logs. Therefore, in the process of papery table recognition, this paper adds a preprocessing process to the image data [13,14].  
(2) Missing coordinates. For the different survey units of urban geological data, the local coordinate systems used are also different, which makes the coordinate system of papery borehole data inconsistent. Some engineering surveys are only to meet the needs of a certain project, and the borehole coordinates are not specified. The information related to its location is only the name of the project, which makes it  
Figure 3: 3D geological modeling flow based on a papery borehole log.  
difficult to spatialize the borehole data. Therefore, there is a large amount of interpolation processing in the subsequent intelligent 3D modeling [15].  
(3) Different data standards. For the different accuracy of engineering surveys, the stratigraphic division standards of borehole logs are inconsistent, and the standards of different surveying units are also inconsistent. Only independent local geological models can be formed by local data within a limited range in practice. At the city scale, many sources of data cannot be converted, and a larger-scale urban geological information model cannot be built uniformly. Therefore, this paper focuses on the standardization of stratigraphic data, so that all types of data can be integrated and unified in advance for software to generate models.  
(4) Low data quality. Limited to the survey level contemporarily, the errors of the borehole data are large, especially the reliability of the sampling data and the experimental data in the boreholes are low. Due to the lack of surveying methods, these borehole data related to urban spatial layout, resource development, environment protection, and disaster prevention, are insufficient, and the integration of data models and geological knowledge is required to handle unreasonable spatial data.
# Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM
## 5 Conclusions  
This study proposed EECP-CBL model for electric energy consumption prediction that utilizes the combination of CNN and Bi-LSTM on the IHEPC dataset. The proposed model consists of three modules including CNN, Bi-LSTM and FC modules for predicting the energy consumption. Firstly, two CNN layers in the first module were used to extract information from several variables in IHEPC dataset. Next, the results of the previous module as well as the trends of time series in two directions will be passed to the next module namely Bi-LSTM module that consists of the two Bi-LSTM layers. In the last module, EECP-CBL used two fully connected layers to finally predict the values of energy consumption. The experiments were conducted to indicate that EECP-CBL outperforms CNN-LSTM, LSTM and Linear Regression, which are the state-of-the-art models for electric energy consumption prediction, in terms of several performance metrics such as MSE, RMSE, MAE and MAPE as well as processing time in different time frame settings.  
In the future, we will improve the performance of electric energy consumption prediction model by applying several techniques such as evolutionary algorithms, optimized EECP-CBL model. In addition, we will try to collect more datasets regarding to electric energy consumption prediction to verify our proposed model.  
**Author Contributions:** [PERSON] proposed the topic and obtained funding; [PERSON] and [PERSON] proposed and implemented the framework. [PERSON] wrote the paper. [PERSON], [PERSON], [PERSON] and [PERSON] improved the quality of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**\#No**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**MSE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**MAPE**} & \multirow{2}{*}{\
\begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Predicting** \\ **Time (s)** \\ \end{tabular} } \\ \hline
1 &
\begin{tabular}{c} Linear \\ Regression \\ \end{tabular} & 0.148 & 0.385 & 0.320 & 41.33 & 11.23 & 1.48 \\
2 & LSTM & 0.105 & 0.324 & 0.244 & 35.78 & 24.42 & 3.66 \\
3 & CNN-LSTM & 0.095 & 0.309 & 0.238 & 31.84 & 14.12 & 2.06 \\
4 & EECP-CBL & 0.049 & 0.220 & 0.177 & 21.28 & 20.7 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performances of the experimental methods for weekly dataset.  
Figure 3: The average percentages of experimental methods over four datasets.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019M3F2A1073179).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Multiple UAVs Control Design
### Formation Control  
#### 3.2.1 Formation Objective  
Consider a multi-agent system consisting of \(N\) agents, described as follows:  
\[x_{i}(k+1)=A_{i}x_{i}(k)+B_{i}u_{i}(k)\] \[y_{i}(k)=C_{i}x_{i}(k)\ \ i=1,2\ldots N \tag{17}\]where  
\[A_{i}=diagonal\big{(}\begin{bmatrix}0&1\\ a_{21}^{1}&a_{22}^{1}\end{bmatrix}\ldots\begin{bmatrix}0&1\\ a_{21}^{n}&a_{22}^{n}\end{bmatrix}\big{)},\] \[B_{i}=I_{n}\otimes\begin{bmatrix}0\\ 1\end{bmatrix},x_{i}(k)=\begin{bmatrix}x_{p_{i}}\\ x_{v_{i}}\end{bmatrix}\text{and }x_{i}(k+1)=\begin{bmatrix}x_{v_{i}}\\ x_{a_{i}}\end{bmatrix}\]  
**Definition 1** ([30]).: _A formation is a vector \(h=h_{p}\otimes\begin{bmatrix}1\\ 0\end{bmatrix}\in R^{2N}\). The N agents are in formation \(h\) if there are vectors \(q,w\in R^{N}\) such that \((x_{p})_{i}-(h_{p})_{i}=q,(x_{v})_{i}=w,i=1,\ldots,N\) where subscript \(p\) (position) and subscript \(v\) (velocity) are components of \(x_{i}\)._  
Therefore, the error between vehicles is calculated as follows:  
\[e_{i}=-\frac{1}{|\mathbf{j}_{i}|}\sum_{j\in\mathbf{j}_{i}}(x_{j}-h_{j})+(x_{i} -h_{i})\ \ \ i=1,\ldots,N. \tag{18}\]  
We seek a feedback control \(K_{i}\) that guides the agent \(i\) into a formation \(h\) using the Kronecker product of graph Laplacian matrix \(L=L_{g}\otimes I_{N}\), where the graph Laplacian matrix \(L_{g}=D_{dir}-A_{dir}\), where \(D_{dir}\) and \(A_{dir}\) indicate the degree matrix and the adjacency matrix, respectively, such that the following objective is achieved:  
\[\lim_{t\rightarrow\infty}||x_{i}-h_{i}||=q,\forall i\in N \tag{19}\]  
Then, by substituting \(u_{i}\) into Equation (17), we obtain  
\[x_{i}(k+1) =A_{1_{i}}x_{i}(k)+B_{1_{i}}K_{i}L(x_{i}-h_{i})\] \[y_{i}(k) =C_{1_{i}}x_{i}(k)\ \ \ \ i=1,2...N \tag{20}\]  
with \(A_{1_{i}}=I_{N}\otimes A_{i}\), \(B_{1_{i}}=I_{N}\otimes B_{i}\), _and_\(C_{1_{i}}=I_{N}\otimes C_{i}\).
# Data Analysis for Information Discovery
## 4 Clustering  
The term \"clustering\" refers to a wide family of learning algorithms that comprise unsupervised, supervised [34], and semi-supervised [35] learning techniques. These algorithms implement different approaches (hierarchical, partitional, grid, density, and model based [34]) to solve the same classification problem, namely, grouping objects according to a given similarity criterion.  
In this work, the authors used the fuzzy C-means (FCM) algorithm [36; 37]. This clustering algorithm generates fuzzy partitions and prototypes for any set of numerical data. It works by optimizing a generalized least squares objective function:  
\[Q=\sum_{i=1}^{c}\sum_{k=1}^{N}u_{ik}^{m}\parallel x_{k}-v_{i}\parallel\ ^{2} \tag{3}\]  
where  
* \(\parallel\).\(\parallel\) is a distance function such as: Euclidean, Mahalanobis, etc.;
* \(v_{1}\), \(v_{2}\),..., \(v_{c}\) are the centroids of the clusters, also called prototypes;
* \(\mathbf{X}=\{x_{1}\), \(x_{2}\),..., \(x_{N}\}\) is the set of points to be clustered;
* \(\mathbf{U}=[u_{ik}]\) is the partition matrix;
* \(c\) is the number of clusters;
* \(N\) is the number of points to be clustered;
* \(i\) is an index that varies from \(1\) to \(c\);
* \(k\) is an index that varies from \(1\) to \(N\);
* \"m\" is a coefficient called the \"fuzzification coefficient\". It is greater than \(1\), and it is responsible for the level of \"fuzziness\" of the partition matrix. In other words, it controls the level of fuzziness with which each point belongs to the various clusters.  
This algorithm allows for the discovery of the inner structure of a given dataset \(\mathbf{X}\), minimizing the objective function \(\mathbf{Q}\) according to the given prototypes and the partition matrix \(\mathbf{U}\). The minimization process is iterative. It works by updating the values of the partition matrix and the prototypes until a given criterion of stop is reached.  
For example, considering two partition matrices, U and U', obtained in two consecutive iterations, the procedure may stop when the quantity:  
\[\mid\mid\mathrm{U}-\mathrm{U}^{\prime}\mid\mid\ =\max_{\mathrm{i,k}}\mid\mathrm{u }_{\mathrm{ik}}-\mathrm{u}^{\prime}_{\mathrm{ik}}\mid \tag{4}\]  
gets smaller than some predefined positive threshold \(\varepsilon\).  
It should be noticed that the nature of the optimization function leads to a solution that somehow reflects the geometry of the original dataset.  
On the other hand, the hidden structure of the dataset, discovered by the clustering algorithm, can be used to classify other elements added to the set after the initial clustering. This task can be accomplished applying the following rules:  
* The \"anchor points\" of the classifier are the prototypes of the clusters;
* Each cluster defines a class;
* A point \(x\) belongs to a class defined by the cluster with prototype \(v_{j}\) if: \[j=\arg\Bigl{(}min_{i}||x-v_{i}||^{2}\Bigr{)}\] (5)
# Beam-Hopping-Based Resource Allocation in Integrated Satellite-Terrestrial Networks
## 2 System Model
### Downlink Transmission Model
Therefore, the worst-case transmission capacity of the \(j\)-th terrestrial system in each time slot can be expressed as  
\[c_{j}^{ter}=WT_{slot}\log_{2}\left(1+\frac{\beta_{j}^{ter}P_{j}^{ter}}{N_{0}^{ ter}}\right), \tag{8}\]  
where \(\beta_{j}^{ter}\) denotes the worst-case channel gain between the terrestrial BS and the terrestrial terminals in the \(j\)-th terrestrial system, \(P_{j}^{ter}\) denotes the transmit power of the \(j\)-th terrestrial BS, and \(N_{0}^{ter}\) denotes the noise power. It is assumed that the channel gain remains unchanged in a BH time window. \(\beta_{j}^{ter}\) can be expressed as  
\[\beta_{j}^{ter}=G_{T}^{ter}G_{R}^{ter}/(L_{j}^{ter}\eta_{j}^{ter}), \tag{9}\]  
where \(G_{T}^{ter}\) is the transmitting antenna gain of the terrestrial BS, \(G_{R}^{ter}\) is the receiving antenna gain of the terrestrial terminals, \(L_{j}^{ter}\) is the path loss between the terrestrial BS and the terrestrial terminal at the cell edge, and \(\eta_{j}^{ter}\) is the shadowing loss. The path loss models and the shadowing standard deviations in different scenarios are provided in [32]. On this basis, the worst-case transmission capacity of the \(j\)-th terrestrial system in a BH time window is  
\[C_{j}^{ter}=\sum_{t=1}^{N_{slot}}y_{j,t}c_{j}^{ter}. \tag{10}\]