esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The Internet generates large volumes of data at a high rate, in particular, posts on social networks. Although social network data have numerous semantic adulterations and are not intended to be a source of geo-spatial information, in the text of posts we find pieces of important information about how people relate to their environment, which can be used to identify interesting aspects of how human beings interact with portions of land based on their activities. This research proposes a methodology for the identification of land uses using Natural Language Processing (NLP) from the contents of the popular social network Twitter. It will be approached by identifying keywords with linguistic patterns from the text, and the geographical coordinates associated with the publication. Context-specific innovations are introduced to deal with data across South America and, in particular, in the city of Arequipa, Peru. The objective is to identify the five main land uses: residential, commercial, institutional-governmental, industrial-offices and unbuilt land. Within the framework of urban planning and sustainable urban management, the methodology contributes to the optimization of the identification techniques applied for the updating of land use cadastres, since the results achieved an accuracy of about 90%, which motivates its application in the real context. In addition, it would allow the identification of land use categories at a more detailed level, in situations such as a complex/mixed distribution building based on the amount of data collected. Finally, the methodology makes land use information available in a more up-to-date fashion and, above all, avoids the high economic cost of the non-automatic production of land use maps for cities, mostly in developing countries.  
Keywords:land use; social networks data; natural language processing; classification +
Footnote †: journal: Applied Sciences
# Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization
## 3 Proposed Method  
### Framework and Definition  
In this article, we propose a Consensus Guided Multi-view Unsupervised Feature Selection with Hybrid Regularization (CGMvFS), and the conceptual architecture of CGMvFS is shown in Figure 1. CGMvFS mainly consists of two steps, Firstly, consensus clustering is applied independently to each view to generate view-specific co-association matrices, which are then aggregated into a unified co-association matrix that encodes comprehensive pairwise relationships across all views. Then, a hybrid-regularized objective function is formulated to learn view-specific feature projection matrices under the cross-view constraints;by convexly relaxing its upper bound, the optimal projections are obtained and embedded sparsity is enforced to select the most discriminative features.  
To facilitate mathematical exposition, the notation we adopt employs uppercase symbols (e.g., **M**) for matrix representation and bold lowercase notation (e.g., **v**) for vector quantities. Given a multi-view dataset \(\mathbf{X}=[\mathbf{X}^{1},\cdots,\mathbf{X}^{V}]\) containing \(V\) views where each view is denoted by \(\mathbf{X}^{v}\in\mathbb{R}^{n\times d_{v}}\) corresponds to a data matrix containing \(n\) instances characterized by \(d_{v}\) dimensional features. The core objective of unsupervised multi-view feature selection is to identify the most \(m\) discriminative features, so as to eliminate noise and redundancy and improve the performance of learning models.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### System Performance Characteristics
#### 6.2.4 Future Directions  
A. High-Frequency Implementation and OWC Integration  
Future work should aim to extend the demonstrated principles to frequencies suitable for OWC systems by advancing circuit design and system integration. This includes the development and validation of high-frequency VCO circuits for efficient LED modulation in the MHz range, optimization of component selection and circuit performance for high-speed operation, and the integration of LED driver circuits with comprehensive optical channel characterization.  
B. Comprehensive Power Analysis  
Quantitative power consumption analysis is necessary to substantiate the claimed efficiency advantages. This should include comparative power measurements between conventional and proposed PPM generation techniques, incorporating other state-of-the-art methods, as well as an evaluation of power consumption across varying modulation depths and signal frequencies. Furthermore, strategies for optimizing power usage in battery-operated OWC applications should be developed to enhance system efficiency.  
C. Advanced Signal Processing Integration  
Enhancing signal processing capabilities could help overcome existing limitations. This includes implementing adaptive filtering techniques to improve noise resistance, developing automatic gain control systems to mitigate component variations, and exploring digital signal processing methods to enhance accuracy and flexibility.  
D. System-Level Performance Evaluation  
A comprehensive system evaluation should encompass end-to-end testing with LED transmitters and photodetector receivers, comparative analysis of modulation techniques regarding bandwidth efficiency, implementation complexity, and power consumption, as well as bit error rate (BER) assessment under varying channel conditions. Moreover, simulation-based evaluation using MATLAB (MATLAB Version R2024b Update 5 (24.2.0.2871072)) should be conducted to benchmark SNR, jitter tolerance, and signal fidelity relative to conventional carrierless PPM systems.  
E. Practical Implementation Optimization  
Further development should focus on practical deployment considerations, including temperature compensation techniques to enhance stability, circuit miniaturization and integration for improved feasibility, and cost analysis to assess manufacturability for commercial applications.  
These future directions provide a clear pathway for advancing the demonstrated laboratory-scale proof-of-concept toward practical OWC system implementation while addressing the limitations identified in the current work.
# Optimized Analytical-Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use
## 6 The Numerical Model
### Linearization of the Algebraic System  
Linearizing the coupled system, we obtain the following linear system:  
\[\begin{bmatrix}\frac{\partial\mathbf{R}_{T}}{\partial\mathbf{T}}&\frac{ \partial\mathbf{R}_{T}}{\partial\mathbf{W}}\\ \frac{\partial\mathbf{R}_{W}}{\partial\mathbf{T}}&\frac{\partial\mathbf{R}_{W }}{\partial\mathbf{W}}\end{bmatrix}\begin{bmatrix}\Delta\mathbf{T}\\ \Delta\mathbf{W}\end{bmatrix}=-\begin{bmatrix}\mathbf{R}_{T}(\mathbf{T}_{k},\mathbf{W}_{k})\\ \mathbf{R}_{W}(\mathbf{T}_{k},\mathbf{W  
from which the updating of solutions is ensured as follows:  
\[\mathbf{T}_{k+1}=\mathbf{T}_{k}+\Delta\mathbf{T},\quad\mathbf{W}_{k+1}=\mathbf{W} _{k}+\Delta\mathbf{W}. \tag{42}\]  
Obviously, iterations proceed to that some residual, \(\epsilon<0\), satisfies:  
\[\|\mathbf{R}_{T}(\mathbf{T}_{k+1},\mathbf{W}_{k+1})\|+\|\mathbf{R}_{\text{IV}}( \mathbf{T}_{k+1},\mathbf{W}_{k+1})\|<\epsilon. \tag{43}\]  
Then, calculating the matrices \(\mathbf{M}_{T}\), \(\mathbf{M}_{W}\), \(\mathbf{A}_{T}\), and the basic form of \(\mathbf{A}_{\text{IV}}(\mathbf{T})\), for each time step \(n+1\), we initialize \(\mathbf{T}_{0}^{n+1}=\mathbf{T}^{n}\) and \(\mathbf{W}_{0}^{n+1}=\mathbf{W}^{n}\) and perform Newton-Raphson iterations until convergence. The solution \((\mathbf{T}^{n+1},\mathbf{W}^{n+1})\) gives the updated values of the variables at the next time step.  
Solution of the Algebraic System  
At each iteration, the linear system can be written in the following form:  
\[\mathbf{K}\begin{bmatrix}\Delta\mathbf{T}\\ \Delta\mathbf{W}\end{bmatrix}=-\begin{bmatrix}\mathbf{R}_{T}(\mathbf{T}_{k}, \mathbf{W}_{k})\\ \mathbf{R}_{\text{W}}(\mathbf{T}_{k},\mathbf{W}_{k})\end{bmatrix}, \tag{44}\]  
where:  
\[\mathbf{K}=\begin{bmatrix}\mathbf{K}_{TT}&\mathbf{K}_{\text{TW}}\\ \mathbf{K}_{\text{WT}}&\mathbf{K}_{\text{WW}}\end{bmatrix}, \tag{45}\]  
with:  
\[\mathbf{K}_{TT}=\frac{\mathbf{M}_{T}}{\Delta t}+\mathbf{A}_{T},\quad\mathbf{K} _{\text{TW}}=-\frac{\lambda\mathbf{M}_{T}}{\Delta t}, \tag{46}\]  
\[\mathbf{K}_{\text{WT}}=-\frac{\partial\mathbf{A}_{W}(\mathbf{T}_{k})}{ \partial\mathbf{T}}\mathbf{W}_{k},\quad\mathbf{K}_{WW}=\frac{\mathbf{M}_{W}}{ \Delta t}-\mathbf{A}_{W}(\mathbf{T}_{k}). \tag{47}\]  
Whose known term is:  
\[\mathbf{b}=-\begin{bmatrix}\mathbf{R}_{T}(\mathbf{T}_{k},\mathbf{W}_{k})\\ \mathbf{R}_{\text{W}}(\mathbf{T}_{k},\mathbf{W}_{k})\end{bmatrix}. \tag{48}\]  
Once the matrices \(\mathbf{M}_{T}\), \(\mathbf{M}_{W}\), \(\mathbf{A}_{T}\), \(\mathbf{A}_{W}(\mathbf{T}_{k})\) are constructed by FEM discretization, \(\mathbf{K}\) is constructed as a sparse block matrix in which \(\mathbf{K}_{TT}\) and \(\mathbf{K}_{WW}\) are dominant diagonal matrices. Finally, the paired \(\mathbf{K}_{\text{TW}}\) and \(\mathbf{K}_{\text{WT}}\) blocks (calculated from the problem under study). Since the linear system is large with sparse matrices, GMRES is employed. For more complex problems, we can introduce a preconditioner based on the diagonal blocks \(\mathbf{K}_{TT}\) and \(\mathbf{K}_{WW}\) to speed up convergence.
## 3. Results and Discussion  
The aim of embedding FOSs into composite structures is to enhance their protection and monitor strains. However, embedded FOSs are \"foreign\" materials whose diameters (e.g., 125 \(\upmu\)m) are much larger than that of carbon fibers (e.g., 5-10 \(\upmu\)m) [19], thereby inevitably resulting in perturbed strain states around them. They could act as internal flaws that might initiate damage (e.g., debonding and delamination) and cause premature failures [1; 2; 3; 4].  
Figure 6. U3 = \(-0.5\) mm applied on the top row of nodes (arrows indicating the loading direction).  
Figure 7. Torsional moment applied to RP-1, linked to YZ-top-end-plane by coupling constraint.  
In view of this, embedment influences on the mechanical behavior of composite structures should be considered first. Then, it is appropriate to discuss what monitoring information can be obtained from novel placement strategies under various boundary conditions. Finally, a balance among mechanical influences, sensing functions, and monitoring coverage should be stricken. These three points will be discussed in the following.
# Enhanced Convolutional-Neural-Network Architecture for Crop Classification
## 7 Conclusions and Future Work  
This work proves the suitability of a 2D-CNN-based methodology to deal with satellite images for purposes of crop classification in tropical regions. Specifically, an enhanced 2D-CNN designed into smaller-scale setting, along with a post-processing (able to properly refine the labeling) based on morphological operators results in a recommendable methodology as it reaches competitive results to those reported by recent studies. A remarkable advantage of the proposed methodology was its ability to deal with both imbalanced classes and low-spatial-resolution images.  
In addition, it is worth noting that the proposed 2D-CNN architecture may be suitable for other applications related to satellite image analysis, such as the classification of urban materials.  
Although the proposed methodology substantially decreases the depth of the 2D-CNN for patch classification purposes and reaches a competitive accuracy, a significant improvement of its overall performance is a matter to be still investigated. Moreover, the exploration of methods such as transfer learning, and generative-adversarial-networks (GANs)-based data augmentation should be an important point in future research to properly address the class imbalance problem.  
Figure 12: Classification Pavia image by 2D-CNN 1.  
**Author Contributions:** M.Y.M.-R.: conceptualization, methodology, validation, formal analysis, investigation, resources, writing--original draft preparation, writing--review and editing; L.G.-G.: conceptualization, methodology, investigation, writing--review and editing, visualization, supervision; [PERSON]-M.: conceptualization, methodology, software, validation, investigation, resources, writing--review and editing, visualization, supervision, project administration; J.R.-F.: investigation, resources, writing--review and editing, supervision, project administration, funding acquisition; D.H.P.-O.: Methodology, investigation, resources, writing--review and editing, visualization, supervision, project administration, funding acquisition. All authors have read and agreed to the published version of the manuscript\({}^{\prime}\).  
**Funding:** This work is supported by the project \"Desarrollo de una metodologia de visualizacion interactiva y eficaz de informacion en Big Data\" stated by the Agreement No. 180 1 November 2016 by VIPRI from Universidad de Narifio.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset used in this work can be found publicly available in Internet and is properly cited throughout the manuscript.  
**Acknowledgments:** Authors thank to \"La fundacion Centro de Estudios Interdisciplinarios Basicos y Aplicados (CEIBA)\", as well as to SDAS Research Group (www.sdas-group.com).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Application and Evaluation of the Gaofen-3 Satellite on a Terrain Survey with InSAR Technology
## 4 Discussion
### Comparison with Yaogan-29 Satellite  
For comparison with other SAR sensors and further analysis of the interferometric measurement capability of Chinese SAR satellites, we processed the dataset of the same area acquired by the Chinese non-civilian Yaogan-29 satellite with the same methods. The basic information of the SAR pair acquired by Yaogan-29 sensors is shown in Table 5.  
In this case, the vertical baseline of the interferometric pair was 1935.54 m, the slant range was 755 km, the viewing angle was 34.91\({}^{\circ}\), and the radar operating wavelength was 3.1 cm. The long vertical baseline means a very small ambiguity as follows:  
\[\Delta h_{2\pi}=\frac{\lambda R_{1}\sin\theta}{2B_{\perp}}\simeq 3.48m. \tag{2}\]  
The DEM calculated with control points is shown in Figure 15a. We further calculated the difference between the obtained DEM and 1:2000-scale DEM to measure the elevation accuracy (Figure 15b).  
Figure 14: Distribution maps of evaluation difference between the 1:2000-scale DEM and the obtained DEM with GCPs (**a**) and without GCPs (**b**).  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Master Image** & **Slave Image** \\ \hline Date & 20 December 2015 & 18 November 2016 \\ Mode & Strip & \\ Resolution & 1 m & \\ Wave length & 3.1 cm (X band) & \\ Path direction & Descending & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Yaogan-29 satellite data of the experimental area (Dengfeng area, China).  
When ignoring the absence of local elevation, the elevation accuracy of the DEM in Dengfeng acquired through Yaogan-29 reached an accuracy of better than 2 m on level ground and better than 5 m in mountainous areas. In this case, benefiting from the large vertical baseline, the elevation accuracy of Yaogan-29 was better than that of Gaofen-3. However, the geometrical decorrelation of the former is more severe than that of the latter. Figure 16 shows the comparison of the effective elevation distributions obtained by these two sensors.
# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 3 Results
### Result Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **MMDL-Net** & **ResNet50** & **ResNet101** & **ResNet152** \\ \hline Urban buildings & 78.43 & 79.31 & 76.12 & 78.21 \\ Commercial and industrial units & 65.89 & 63.36 & 60.51 & 63.17 \\ Arabic land & 85.65 & 80.81 & 87.80 & 85.07 \\ Permanent crops & 72.61 & 68.53 & 69.62 & 68.13 \\ Pastures & 78.56 & 83.67 & 79.56 & 80.29 \\ Complex farming systems & 73.71 & 68.96 & 66.58 & 69.60 \\ Agricultural and vegetation land & 71.13 & 66.20 & 67.30 & 67.99 \\ Agroforestry areas & 76.82 & 79.27 & 71.12 & 75.95 \\ Broadleaf forests & 78.51 & 74.51 & 78.25 & 76.85 \\ Coniferous forests & 86.42 & 76.21 & 84.72 & 86.54 \\ Mixed forests & 81.38 & 71.44 & 81.76 & 79.56 \\ Grasslands and sparse vegetation & 63.54 & 65.47 & 66.87 & 61.98 \\ Swamps and wasstelands & 65.92 & 71.35 & 65.66 & 64.92 \\ Transitional woodland and shrub & 65.03 & 68.33 & 64.59 & 64.98 \\ Beaches, dunes, sandy areas & 57.20 & 57.07 & 55.28 & 49.96 \\ Inland wetlands & 73.00 & 71.16 & 74.94 & 77.54 \\ Coastal wetlands & 67.48 & 55.75 & 72.48 & 62.56 \\ Inland water bodies & 87.85 & 60.32 & 73.14 & 72.84 \\ Marine water bodies & 96.41 & 96.98 & 98.32 & 98.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Precision metrics for 19 categories of MMDL-Net and three variants of ResNet. Figure 7: Comparison of evaluation results between MMDL-Net and ResNet with three different depths. The results above confirm the effectiveness of the MMDL-Net model in the task of remote sensing image classification. Whether compared with the experiments conducted by [PERSON] et al. [32], who proposed the BigEarthNet dataset, or the recent benchmark tests conducted by [PERSON] et al. [33] on 62 DL models, the MMDL-Net model has demonstrated good performance. However, we also note that the recognition performance of the MMDL-Net model is not very good for certain categories. This may be due to the model's inability to adapt to individual classifications, resulting in the loss or inadequate extraction of features for such categories. Nevertheless, the introduction of the MMDL-Net model still provides an effective approach for handling remote sensing image data and offers valuable insights for the optimization and improvement of deep learning models for remote sensing images.
_Article_  
**A New Cost Function Combining Deep Neural Networks (DNNs) and \(I2\),1-Norm with Extraction of Robust Facial and Superpixels Features in Age Estimation**  
**[PERSON] 1,+, [PERSON] 2,+ and [PERSON] 3,* 1 Computer Science Department, Manhattan College, Riverdale, NY 10471 USA; [EMAIL_ADDRESS]**  
Department of Computing Sciences, The College at Brockport State University of New York, Brockport, NY 14420, USA; [EMAIL_ADDRESS]**  
Electrical Engineering Department; University of Bridgeport, Bridgeport, CT 06604, USA * Correspondence: [EMAIL_ADDRESS]; Tel.: +1-203-576-4577 These authors contributed equally to this work.  
Received: 21 August 2018; Accepted: 25 September 2018; Published: 16 October 2018  
**Featured Application: Facial images provide variety of information about a person such as personal identity, age, gender, color, ethnicity, head pose, eye gaze, and emotion. Age estimation can be used to build real-world applications in biometrics, law enforcement, surveillance, and human-computer interaction (HCI).**  
**Abstract:** Automatic age estimation from unconstrained facial images is a challenging task and it recently has gained much attention due to its wide range of applications. In this paper, we propose a new model based on convolutional neural networks (CNNs) and \(I2\),1-norm to select age-related features for the age estimation task. A new cost function is proposed. To learn and train the new model, we provide the analysis and the proof for the convergence of the new cost function to solve minimization problem of deep neural networks (DNNs) and the \(I2\),1-norm. High-level features are extracted from the facial images by using transfer learning, since there are currently not enough large age databases that can be used to train a deep learning network. Then, the extracted features are fed to the proposed model to select the most efficient age-related features. In addition, a new system that is based on DNN to jointly fine-tune two different DNNs with two different feature sets is developed. Experimental results show the effectiveness of the proposed methods and achieved the state-of-art performance on a public database.  
**Keywords:** age estimation; face images; DNNs; facial and superpixel features; \(I2\),1-norm
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 3 System Model and Problem Formulation
### Task Offloading Model  
The wireless channel between satellites is predominantly Line-of-Sight (LOS). The channel gain from the \(k\)-th SS to the MS in the \(i\)-th time slot is denoted by \(h_{k,i}\), given as follows:  
\[h_{k,i}=\mu_{0}d_{k,i}^{-2}\quad k\in\mathcal{K},i\in\mathcal{T}, \tag{6}\]  
where \(\mu_{0}\) is the channel power gain at a reference distance \(d_{0}=1m\), and the distance between the \(k\)-th SS with the MS at the \(i\)-th time slot is denoted by \(d_{k,i}\).  
We define \(\alpha_{k,i}\) as the ratio of data offloaded to satellite \(k\) in time slot \(i\),  
\[0\leq\alpha_{k,i}\leq 1,\quad k\in\mathcal{K},i\in\mathcal{T}. \tag{7}\]  
The transmission power of the MS to the \(k\)-th SS in the \(i\)-th time slot is denoted as \(P_{k,i}\): \(R_{k,i}\) is denoted as the normalized transmission data rate from the MS to the \(k\)-th SS in the time slot \(i\):  
\[R_{k,i}=\log_{2}(1+\frac{P_{k,i}h_{k,i}}{\sigma^{2}}), \tag{8}\]  
where \(\sigma^{2}\) denotes the Additive White Gaussian Noise (AWGN) power, which we assume to be a constant.  
We assume the task in the \(i\)-th time slot generates \(D_{i}\) bits of data. The computation task remaining in the local can be expressed as \(\alpha_{0,i}D_{i}\).  
\(C_{k}\) denotes the main frequency of the MEC server in the \(k\)-th SS, which is the number of CPU cycles per second, \(C_{0}\) denotes the main frequency of the MEC server in the MS, and \(s\) denotes the number of cycles of CPU to execute one bit of data. Thus, the local computation time, \(t_{0,i}\), is denoted as  
\[t_{0,i}=\frac{\alpha_{0,i}D_{i}s}{C_{0}}. \tag{9}\]  
The computing time of slave satellite \(k\) in the \(i\)-th time slot can be expressed as \(\alpha_{k,i}D_{i}s/C_{k}\). The transmission delay of slave satellite \(k\) in the \(i\)-th time slot can be expressed as \(\alpha_{k,i}D_{i}/R_{k,i}B+2d_{k,i}/c\), where \(B\) represents the bandwidth of the channel and \(c\) represents the speed of free-space light.  
Combining the above expressions, we can obtain the total task completion time, \(t_{k,i}\), as:  
\[t_{k,i}=\frac{\alpha_{k,i}D_{i}s}{C_{0}}+\frac{\alpha_{k,i}D_{i}}{R_{k,i}B}+2 \frac{d_{k,i}}{c}. \tag{10}\]Energy Consumption Model  
The energy consumption of the MS can be expressed as [28]:  
\[E_{i}^{master}=r_{m}\alpha_{0,j}D_{k,i}C_{0}^{2}, \tag{11}\]  
where \(r_{m}\) is the effective capacitance coefficient of the chip equipped in the MEC server of the MS.  
The transmission consumption in the offloading procedure from the MS to the \(k\)-th slave satellite can be presented as  
\[E_{i}^{trans}=\frac{P_{k,i}\alpha_{k,i}D_{k,i}}{R_{k,i}B} \tag{12}\]  
The energy consumption of the \(k\)-th SS can be expressed as  
\[E_{k,i}^{slave}=r_{k}\alpha_{k,i}D_{k,i}C_{0}^{2}, \tag{13}\]  
in the same way, \(r_{s}\) is the effective capacitance coefficient of the chip of the \(k\)-th slave satellite.