esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Human Head Reconstruction Using Smartphone-Based Close-Range Video Photogrammetry
## 2 Materials and Methods
### Frames Selection Methods
We will perform experiments to check what changes in the results of image similarity order may be achieved by adding a second pass. The extracted feature vectors are multidimensional. The dimensionality of the feature vector is equal to the number of channels in the feature layer. In order to get image embeddings in 2D or 3D, we must reduce the dimensionality of the feature vectors. A set of dimensionality techniques will be compared in order to select the one that, combined with the selected type of feature vector, will provide the best-ordered images by similarity. The goodness of the image order will be measured by the percentage overlap of the two sets that contain the closest images to the target image. It means that for each image, we find the closest group of images in space (according to known image poses), and we find the closest (most similar) images according to the extracted feature vectors. The percentage overlap of these sets gives the estimate of the goodness of the image order. As the ground truth poses the images, we use the reconstructed poses using _Pipeline_ 2b (Table 1). The following dimensionality-reduction techniques will be experimentally compared for suitability for image ordering by similarity. * t-Distributed Stochastic Neighbor Embedding (t-SNE);
* Stochastic Neighbor Embedding (SNE);
* Classical multidimensional scaling (MDS);
* Principal Component Analysis (PCA);
* Probabilistic PCA;
* Kernel PCA;
* Linear Discriminant Analysis (LDA);
* Factor Analysis (FA);
* Sammon mapping;
* Diffusion maps;
* Stochastic Proximity Embedding (SPE);
* Gaussian Process Latent Variable Model (GPLVM);
* Neighborhood Components Analysis (NCA);
* Large-Margin Nearest Neighbor (LMNN). Implementations of the techniques were used from the Matlab Toolbox for Dimensionality Reduction ([[https://lvdmaaten.github.io/drtoolbox](https://lvdmaaten.github.io/drtoolbox)]([https://lvdmaaten.github.io/drtoolbox](https://lvdmaaten.github.io/drtoolbox)) accessed on 9 August 2021) [83; 84]. The best performing combination of the feature type and dimensionality-reduction technique will be used for frame selection in _Pipeline_ 4. Key algorithm steps for frame selection in _Pipeline_ 4:  
1. Extract feature vectors describing the regions of images that contain the head;
2. Perform dimensionality reduction using the selected technique;
3. Define a grid in the low-dimensional feature space that divides the space into uniform cells. A step size of the grid depends on the total frame number we want to select (in this research, the target was 200 frames);
4. In each cell, if several frames get into the same cell, only the image with the largest sharpness gets kept. Visualization of the frame selection process using gridding is presented in Figure 4. Results of experimental comparison of feature types, dimensionality-reduction techniques, single-pass vs. double-pass, and image embedding in 2D vs. 3D, are presented in Section 3.
## 1 Introduction
Furthermore, the texture analysis can also take place by considering the spatial relationship between pixels with different gray levels using the co-occurrence matrices [3; 17; 18] on original images. It is a solid method based on the regular changes of gray levels between two neighboring pixels and how these changes are statistically and spatially correlated. Most of the state-of-the-art works use these features together with others of different kinds, such as shape features [3; 17; 18; 19], which are often operator dependent because they require the intervention of the radiologist to segment the lesion manually or define appropriate descriptors. In previous works, we developed detection and classification models of the microcalcification clusters on Full-Field Digital Mammograms (FFDMs) [15; 20] as support tools for experts radiologists; indeed, they provided the automated ROIs' segmentation and characterization into normal/abnormal with high performing results. In this work, we propose a texture analysis approach based on a multi-scale decomposition of the ROIs with the aim of characterizing them in benign/malignant tissue. In particular, to make the lesion classification more objective and operator independent, once the radiologist has identified the Regions Of Interest (ROIs), the characteristic features are extracted in an automated manner. The process is a multi-phase approach composed of a feature extraction step, performed by texture analysis methods, a features selection step, and a breast region classification in order to categorize clusters of MCs in digital mammograms. Specifically, as a first step, for each ROI, a multi-scale decomposition based on the Haar wavelet transform [21; 22] is obtained. On such a decomposition, a set of well known statistical features and a number of attributes able to describe the image texture using the Gray-Level Co-occurrence Matrix (GLCM) [23] are extracted. Successively, a feature selection technique is exploited, carried out with embedded methods allowing an optimization between the interaction of the selected features and the classification algorithm used. Finally, a random forest classifier is trained on a subset of features selected by means of an embedded method with the aim of labeling the clustered microcalcifications in benign or malignant ones. The model performance is tested in cross-validation and evaluated in terms of accuracy and sensitivity; then it is compared with respect to the state-of-the-art. Differently from the approaches shown in literature and developed for film scanned/digitized mammograms, the proposed approach was developed and evaluated on FFDMs, since digital images have a higher quality than the film scanned ones. In this way, a breast CAD could be implemented on images obtained from digital devices of various cancer institutes preserving a functional compatibility for future research activities. In particular, digital mammograms randomly selected from the public database Breast Cancer Digital Repository ([[http://bcdr.ceta-ciemat.es](http://bcdr.ceta-ciemat.es)]([http://bcdr.ceta-ciemat.es](http://bcdr.ceta-ciemat.es))) (BCDR) were exploited.
# Multiscale Backcast Convolution Neural Network for Traffic Flow Prediction in The Frequency Domain
## 4 Experiments and Analysis
### Compare Experiments  
The experimental platform used in this experiment carries the following specifications: Intel i7-7700 CPU, GTX 1080 graphics card, 32 GB RAM, Ubuntu version 18.04. We used Pytorch 1.10.1 (based on Python 3.8) as our deep learning framework.  
Table 1 details the hyperparameter configuration adopted during model training. We model the experimental configuration of Auttoformer [19], setting both the input and output length to 96. The final experimental results on the test set are shown in Table 2. Results are based on the average of three experiments.  
Compared with Auttoformer, our model showed MSE, MAE, and CORR increased by 3.818%, 8.887%, and 4.627%, respectively. Compared with StemGNN, our model showed these figures were 15.059%, 21.48%, and 7.791%. Compared with LSTM, our model showed these figures were 5.712%, 2.472%, and 0.1515%.  
Part of the prediction results as shown in Figure 5:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Batch Size & 128 \\ Epoch & 40 \\ Early Stop & 3 \\ Learning Rate (Initial value) & 0.0005 \\ Input Timestamp Length & 96 \\ Prediction Length & 96 \\ Model Hidden Dimension & 512 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training Hyperparameter Configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MSE** & **MAE** & **CORR** \\ \hline Ours & 0.6272 & 0.3629 & 0.8592 \\ Auttoformer [19] & 0.6521 & 0.3983 & 0.8212 \\ StemGNN [18] & 0.7384 & 0.4622 & 0.7971 \\ LSTM & 0.6652 & 0.3721 & 0.8579 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Results Different Models.
## 2 System Description
### Problem Formulation
Besides, the ESO and NNs are combined to estimate and compensate for the effects of uncertain parameters so that the tracking errors do not violate the prescribed constraints._  
**3. Adaptive Neural Network Backstepping Control with Prescribed Performance and Extended State Observer**  
In this section, the displacement of sprung mass under road disturbance will be guaranteed within boundary constraints to satisfy the control performance of the active suspension by the proposed control scheme. For this purpose, consider the state space of the chassis dynamic equations by  
\[ \begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=x_{5}+\frac{m_{\rm{s}}}{m_{\rm{s}}\left[-k_{sp}(x_{1}-x_{3})-c_{d }(x_{2}-x_{4})\right]+d_{0}+d(t)}\\ \dot{x}_{5}=\frac{\kappa RT}{m_{\rm{s}}\left(z_{\rm{s}\rm{s}\rm{s}\rm{s}\rm{s} \rm{s}}+x_{1}-x_{3}\right)}\sigma_{pc}q_{ss}u-\frac{\kappa}{(z_{\rm{s}\rm{s}\rm {s}\rm{s}}+x_{1}-x_{3})}x_{5}(x_{2}-x_{4})+p_{0}+p(t)\end{array}  \tag{14}\]  
Four main objectives of the proposed controller  
1. An ESO control scheme can estimate uncertain parameters and unmodeled dynamics. 2. The chassis displacement is limited to specific PPF constraints. 3. Ensure the suspension performance by considering road holding and keeping the minimum of suspension deflection. 4. The control scheme is proposed by using adaptive NNs combined with ESO and the prescribed performance constraint. Two control approaches are analyzed to compare the effectiveness of the developed control, which are traditional backstepping and ESO backstepping.
## 5 Conclusions  
This paper presented a deep learning model for strawberry plant real-time detection embedded in a pesticide-spraying robotic platform for greenhouse applications. Comparative studies on YOLO versions for embedded systems were conducted, leading to the selection of YOLOv3 as the suitable model for this application. To train the model, a dataset was locally captured covering different illumination conditions in the greenhouse environment. To perform the detection and ensure a better and more effective implementation, the ONNX representation was needed. The model shows high accuracies, above 97%, which affirm its utility in plant detection tasks. However, it is important to acknowledge that the model has certain limitations, as the accuracies may not consistently exceed 97%. We attribute this to potential issues related to the lack of training data and the quality of annotations. As an extension of this study, a tracking system based on a machine learning model will be added so the robot can track the plants accurately, thereby facilitating tasks such as plant counting and other applications.  
K.E.A.: writing--original draft, data curation, conceptualization, methodology, software, formal analysis, and writing--review and editing. M.E.A. ([PERSON]): validation, formal analysis, and project administration. M.L.: data curation and resources. M.E.A. ([PERSON]): validation and data curation. A.A.: data curation and resources. B.J.: validation and resources. L.M.: project administration, supervision, resources, and funding acquisition. J.V.d.O.: writing--review and editing, validation, resources, and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Strawberry plant real-time detection using the trained model. (**a**) Afternoon test and (**b**) morning test.  
**Funding:** This work was funded by the Ministry of Higher Education, Scientific Research and Innovation of Morocco (MESRSI), the National Centre of Scientific and Technical Research of Morocco (CNRST), and the Digital Development Agency of Morocco (ADD) through the AL-KHAWARIZMI program of Morocco, project Alkhawarizmi/2020/37. This work was also supported by NOVA LINCS ref. UIDB/04516/2020 ([[https://doi.org/10.54499/UIDB/04516/2020](https://doi.org/10.54499/UIDB/04516/2020)]([https://doi.org/10.54499/UIDB/04516/2020](https://doi.org/10.54499/UIDB/04516/2020))) with the financial support of FCT.IP.  
**Data Availability Statement:** Dataset available on request from the authors.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 4 Results--Sea Surface Temperature Comparison  
Only a limited amount of data was made available for the SST analysis with SLSTR-A and SLSTR-B data provided for certain days in September and December 2018. September was when the closest approach occurred so we have concentrated on the three available days of data (3, 10 and 24 September 2018, respectively). September 17 was not used due to a processing error which led to all SLSTR-A SST data being flagged as bad.  
Each SST observation comes with a set of flags which provide information related to quality and status [9] as well as algorithm type (N2,N3,D2,D3) and the probability of cloud. In this analysis, only quality flags 3 to 5 for ocean pixels have been considered.
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 4 System Model: Proposed Framework
### Scenario 3 (Train Ensemble Model at Cloud, and Detect at the Edge)  
In this scenario, the third scenario aims to enhance the MLP model's performance by applying ensemble learning in the cloud and sending the enhanced MLP model to the edge layer for detection. To take advantage of the high power resources at the cloud computing layer, ensemble learning (training) applies to the cloud. The ensemble uses different voting task models to select the suited model to apply at the edge (detection) as shown in Figure 7.
# A New Modified Firefly Algorithm for Optimizing a Supply Chain Network Problem
## 3 Results and Discussion
### Problem Description
It should be noted that the shipping costs considered as proportional to the traveling distances. \(\bullet\)Constraints  
\(i.\)Capacity Constraints  
\[\sum\limits_{j=1}^{I}X_{\text{ijpt}}\leq Ca_{\text{ipt}}\qquad\forall i,p,t \tag{6}\]  
\[\sum\limits_{p=1}^{P}I_{\text{jpt}}\leq V_{\bar{\mu}}\qquad\forall j,t \tag{7}\]  
\[\sum\limits_{p=1}^{P}I_{\text{kpt}}\leq U_{\bar{\mu}k}\qquad\forall k,t \tag{8}\]  
Constraints (6)-(8) are the capacity constraints and ensure the maximum allowable to be distributed or kept. More precisely, Constraint (6) takes into account that total items shipped from each manufacturing plant to all the DCs in every period do not exceed the supply capacity of that manufacturing plant. Constraint (7) denotes the capacity storage of DCs and Constraint (8) indicates the retailers' storage capacity. Delivery Capacity Constraints \[\sum_{k=1}^{K}\sum_{p=1}^{p}Y_{jkpt}\leq Ca^{\prime}_{jt}?\qquad\forall j,t\] (9)  
Constraint (9) shows the restrictions of delivery capacity for the DCs. This constraint ensures the number of products that are distributed from each DC, do not exceed the maximum capacity of each distributor. _iii. Flow Conservation Constraints_  
\[I_{jp(t-1)}+\sum_{i=1}^{I}X_{ijpt}=\sum_{k=1}^{K}Y_{jkpt}+I_{jpt}\qquad\forall j,p,t \tag{10}\]  
\[\sum_{j=1}^{J}Y_{jkpt}+J_{kp(t-1)}-B_{kp(t-1)}=D_{kpt}+J_{kpt}-B_{kpt}\qquad \forall k,p,t \tag{11}\]  
Constraints (10) and (11) formulate the flow conservation at DCs and retailers' echelon respectively. These constraints mathematically ensure that the sum of the flow through a supply chain echelon toward another stage plus that stage's supply and available inventories minus shortages, if any, equals the sum of the flow through a supply chain echelon directed away from that stage plus that node's demand plus available inventories and minus shortages, if any. _iv. Non-negativity Constraint_  
\[X_{ijpt},Y_{jkpt},I_{jpt},I_{kpt},B_{kpt}\geq 0,\quad Integer\qquad\forall i,j,k,p,t \tag{12}\]  
Constraint (12) guarantees non-negativity values of the decision variables, since all the decision variables must be positive-integer numbers.
## 1 Introduction
The URL traversing task has as objective to define the order in which URLs are analyzed. Techniques like bread-first, depth-first, and best-first are representative traversing strategies for this task [15]. In an SFWC, an ontology is commonly used to determine if a web page is related to the domain, comparing its text content with the ontology structure through similarity measures such as the cosine similarity [20] or the semantic relevance [12]. The use of a domain specific ontology helps to face  
Figure 1: General focused web crawler architecture. problems like heterogeneity, ubiquity, and ambiguity [21] since a domain ontology defines classes and their relationships, limiting its scope to predefined elements. The main limitation of any SFWC is its dependency to the domain ontology being used, with particularly two main issues [22]: (i) an ontology is designed by domain experts, limiting their representation to the experts' understanding on the domain and (ii) data are dynamic and constantly evolving. As an alternative to classic SFWC designs that use ontologies, this work presents a novel SFWC based on a generic knowledge representation schema (KRS) to model a target domain. The KRS analyzes the content of a document to identify and extract concepts, i.e., it maps the content of a document, from an input corpus, to an SW representation. The KRS, generated from each document, is stored in a knowledge base (KB) [23] to provide access to their content. The KRS is less expressive than a domain ontology (it does not define any rule or restriction over the data), but it is domain independent. Ontology-based approaches are structures whose concepts and relations are predefined by domain experts. Additionally, a similarity measure is proposed based on the inverse document frequency (IDF) measure and statistical measures such as arithmetic mean and standard deviation to compute the similarity between a web page content against the KRS. Our proposed SFWC is simple to build without the complexity and cost of constructing a more formal knowledge representation such as a domain ontology, but keeps the advantage of using SW technologies like RDFS (Resource Description Framework Schema). In summary, the main contributions of this work are:  
* A new SFWC based on a KRS,
* A generic KRS based on SW technologies to model any domain,
* A methodology to build a KRS from an input corpus,
* A similarity measure based on IDF and the statistical measures of arithmetic mean and standard deviation to determine the relevance of a web page for a given topic. The proposed KRS builds a KB from an input corpus without an expert intervention, i.e., the KRS is based on content, representing entities as the most important element in a domain. The rest of this paper is organized as follows. Section 2 discusses the related work. Section 3 presents the methodology for the construction of the SFWC and the similarity measure. Section 4 presents the results from the experiments. Finally, Section 5 concludes this work.
# Projection onto the Set of Rank-Constrained Structured Matrices for Reduced-Order Controller Design
## 2 Reduced-Order Controller Design Problems
### Reduced-Order Stabilizing Controllers  
Let us consider the following linear time-invariant system:  
\[\dot{x}(t)=Ax(t)+Bu(t),\quad y(t)=Cx(t),\quad t\geq 0, \tag{5}\]  
where \(x(t)\in\mathbb{R}^{n}\), \(u(t)\in\mathbb{R}^{m}\), \(y(t)\in\mathbb{R}^{p}\), \(A\in\mathbb{R}^{n\times n}\), \(B\in\mathbb{R}^{n\times m}\), and \(C\in\mathbb{R}^{p\times n}\). For this system, we consider an output-feedback controller \(u=Ky\), whose order is assumed to be \(n_{c}<n\). Then, the reduced-order output-feedback controller design is described as the following feasibility problem [10].  
**Problem 1** (Stabilizing controller).: _Find \(X_{1},X_{2}\in\mathcal{S}_{n}\) such that the rank constraint_  
\[\operatorname{rank}\begin{bmatrix}X_{1}&I\\ I&X_{2}\end{bmatrix}\leq n+n_{c}, \tag{6}\]and LMIs_  
\[-\begin{bmatrix}X_{1}&I\\ I&X_{2}\end{bmatrix}\preceq 0, \tag{7}\] \[B^{\perp}(AX_{1}+X_{1}A^{\top})B^{\perp\top}\preceq-\epsilon I,\] (8) \[C^{\top\perp}(X_{2}A+A^{\top}X_{2})C^{\top\perp\top}\preceq-\epsilon I \tag{9}\]  
_hold for some \(\epsilon>0\)_  
We note that the inequality \"\(\preceq-\epsilon I\)\" can be \"\(\prec 0\),\" however for the projection-based algorithm described in Section 3, we introduce small \(\epsilon>0\) to make the subsets closed.
# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
###### Abstract  
Recently, with the widespread application of deep learning networks, appearance-based gaze estimation has made breakthrough progress. However, most methods focus on feature extraction from the facial region while neglecting the critical role of the eye region in gaze estimation, leading to insufficient eye detail representation. To address this issue, this paper proposes a multi-stream multi-input network architecture (MSMI-Net) based on appearance. The model consists of two independent streams designed to extract high-dimensional eye features and low-dimensional features, integrating both eye and facial information. A parallel channel and spatial attention mechanism is employed to fuse low-dimensional eye and facial features, while an adaptive weight adjustment mechanism (AWAM) dynamically determines the contribution ratio of eye and facial features. The concatenated high-dimensional and fused low-dimensional features are processed through fully connected layers to predict the final gaze direction. Extensive experiments on the CYEDIAP, MPIIFaceGaze, and Gaze360 datasets validate the superiority of the proposed method.  
Keywords:gaze estimation; multi-stream network; adaptive feature fusion mechanism; deep learning +
Footnote †: journal: Applied sciences
## 5 Robust Optimization
### Optimization Settings  
The robust optimization parameter range and optimization algorithm setting are the same as normal optimization. The main differences are the uncertainties and the number of optimization targets. Robust optimization needs to optimize the mean and variance of the drag coefficient when the lift coefficient is 1.0, the maximum camber is set to less than 5% times the chord length, and the maximum thickness is not less than 15% times chord length, as well as ensure that the friction band distribution satisfies the setting probability  
Figure 13: Monte Carlo sample points and aerodynamic results. (**a**) Monte Carlo sample points’ distribution; (**b**) Distribution of drag coefficient.  
Further aerodynamic analysis of the airfoil is shown in Figure 16, which shows that the aerodynamic performance of the robust optimized airfoil is inferior to that of original airfoil in a clean configuration, but after setting the friction band located at 0.4 times the chord length, the results shows that the robust optimized airfoil is better than the original airfoil in terms of both the range of the low drag region and the magnitude of the drag coefficient. This is because the trend of robust optimization considering the surface contamination is to increase the aerodynamic efficiency at the more forward transition point. Actually, the upper and lower airfoil transition after robust optimization is at 0.45 and 0.6 times the chord length, respectively, at a lift coefficient of 1.0, which is closer to the leading edge than the original airfoil.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Airfoils** & **Mean** & **Standard Deviation** & **Maximum Value** & **Minimum Value** \\ \hline Original & 0.0086 & 0.0019 & 0.0136 & 0.0064 \\ Normal optimization & 0.0090 & 0.0022 & 0.0155 & 0.0055 \\ Robust optimization & 0.0085 & 0.0010 & 0.0125 & 0.0078 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of optimization results.  
Figure 16: Comparison of aerodynamic performance after robust optimization based on triangular probability distribution.  
Figure 15: Comparison of airfoils before and after robust optimization based on triangular probability distribution.