esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of GIS and IoT Integration: Applications and Architecture
## 3 GIS and IoT Integration Use Cases
### Agriculture  
Broadly speaking, agriculture can be considered as an inherently geospatial practice [93] which not surprisingly can be pertained to GIS subjects. According to Reference [93], GIS and spatial decision support hold some benefits in agriculture such as Enhancing the efficiency of food production systems [94; 95; 96], Managing pests and diseases [97; 98; 99; 100], Minimizing soil erosion [101; 102; 103], Preserving biodiversity [104; 105], and simulating the effects of climate change [94; 106]. As mentioned in [107], due to a rise in migration to urban areas, agriculture is facing some difficulties. They propose smart agriculture techniques based on IoT which exploit WSN for measuring soil properties and environmental factors continuously. They control the data, gathered by various sensors planted in the farm, through remote devices or internet services. The role of sensors in agricultural applications is so essential so that in Papers [108; 109], besides using humidity and temperature sensors, an image sensing node is also exploited to monitor the changes in crop fields by taking images. Combining GIS with other technologies such as IoT would be a great step toward improving current systems in the mentioned aspects in agriculture since utilizing just mere geospatial data and analysis will no longer perform well in some agricultural issues [110]. Using GIS, IoT, and socket programming, a Detection and Warning System of Rice Disease is proposed in [111] in which statistics such as air humidity, illumination, water temperature, etc. are gathered and finally the polluted regions are displayed on the map as warning massages which help the farmers with preventing the spread of a rice disease. A Precision Agriculture Management System (PAMS) is proposed by [27] based on IoT and WebGIS. WebGIS is used as a tool for providing basic spatial information, positioning, and a mobile calculation platform for the system. Monitoring farmlands remotely in real-time can be accessed using the functions provided by WebGIS to collect, process, and display the appropriate data. In their study, collecting agricultural production data is performed using IoT sensors. As the integration of GIS and IoT will result in an improvement in the production and economical section of agriculture, [112] presents a DC-driven tool which is a power switch to optimally control the appliances used in agriculture. To maximize production and gain more profit of land, GIS is used to obtain the localized satellite data and combine it with on-site IoT sensors.
## 4 Methods
### Cluster Establishment Phase
#### 4.1.2 Clustering the Network  
In this paper, we use Euclidean distance to calculate the distance function. The clustering steps based on K-means clustering algorithm are as follows.  
Step 1: the base station randomly selects K nodes among N nodes as the initial clustering centers.  
Step 2: for other nodes, calculate the distances between them and the K initial clustering centers (_dist_) in step 1 according to the formula in Equation (4) [31].  
\[dist\big{(}t_{i},t_{j}\big{)}=\sqrt{\big{(}x_{i}-x_{j}\big{)}^{2}+\big{(}y_{i} -y_{j}\big{)}^{2}} \tag{4}\]  
where \(d\big{(}t_{i},t_{j}\big{)}\) denotes the Euclidean distance from node \(t_{i}\) to node \(t_{j}\), and \(x_{i}\) and \(y_{i}\) are the horizontal and vertical coordinates of \(t_{i}\), respectively.  
Step 3: assign the nodes, which are close to the cluster center, to the nearest cluster where the corresponding cluster center is located.  
Step 4: use Equation (5) [32] to calculate the mean value of the new cluster after assignment and update the clustering center.  
\[C_{j}=\frac{1}{N_{j}}\underset{i=1}{\overset{N_{j}}{\sum}}x_{i}^{(j)},j=1,2,3 \ldots,K \tag{5}\]  
Step 5: repeat Step 2, Step 3, and Step 4 until the number of iterations exceeds the predefined value, then the process of clustering is ended.  
When the network is initialized, the base station assigns unique IDs to all nodes to identify their respective identities. The base station uses the K-means algorithm to cluster based on the node's location, repeatedly iterates to the maximum time which is given prior to the construction of the network, and finally broadcasts the results of clustering to all nodes in WSN.
## 3 Program Design
### WD-Based Multiparameter Component Assessment Model
#### 3.1.2 Multiparametric Assessment Models Based on Data Standardization  
Since different types of parameters exhibit significant differences in amplitude and variation, direct calculation of their HDDD and weighted fusion is not feasible. Therefore, there is a need to make improvements to the WD-based HDDD calculation method. First, the benchmark data for the parameters are normalized. Then, the mapping process is performed on the real-time operational data using the extreme values of the benchmark data so that parameters of different variation types are on the same scale. Next, HDDDs of different parameters can then be compared and fused. In this way, the problem of difficulty in reasonably fusing the results of multiple categories of parameters due to different types of amplitude and parameter variation can be solved.  
After the benchmark data have been collected, the benchmark data for the different parameters first need to be normalized in the forward direction. Assume that the data of the parameter \(\xi_{i}\) are \([x_{1},\cdots,x_{m},\cdots,x_{n},\cdots]\) and the corresponding benchmark data are \([x_{1},\cdots,x_{m}]\), where the maximum value is \(\xi_{i}^{\mathrm{h}}(\mathrm{max})\) and the minimum value is \(\xi_{i}^{\mathrm{h}}(\mathrm{min})\). The formula for the forward normalization of the benchmark data is:  
\[y_{i}=\frac{x_{i}-\xi_{i}^{\mathrm{h}}(\mathrm{min})}{\xi_{i}^{\mathrm{h}}( \mathrm{max})-\xi_{i}^{\mathrm{h}}(\mathrm{min})}\ (1\ \leq\ i\ \leq\ m) \tag{3}\]  
Subsequent data are mapped according to the changes in the benchmark data. The mapping method is shown in Equation (4):  
\[y_{n}=\frac{x_{n}-\xi_{i}^{\mathrm{h}}(\mathrm{min})}{\xi_{i}^{\mathrm{h}}( \mathrm{max})-\xi_{i}^{\mathrm{h}}(\mathrm{min})}\ (n\ >\ m) \tag{4}\]  
After the mapping process, the degree of deviation of the different variation types of parameters can be calculated and reasonably fused according to the method presented in this research. Taking the shell temperature data of a solar sail component of a star as an example, the effect after the above operation is shown in Figure 5.  
Figure 4: Comparison of the effectiveness of different single-parameter health state description methods.  
By combining the entropy and CRITIC methods, a comprehensive component assessment model is established. The average value of the weights calculated using the two methods is taken as the final weight of the parameters. This model takes into account multiple parameters and their respective weights, providing a holistic evaluation of the health status of the component. The fusion process ensures that the assessment results are based on the collective information from all relevant parameters, leading to a more accurate and reliable assessment outcome. In Equation (5), \(\xi_{i}\) is the ith health feature parameter of the component, \(w_{i}\) is the weight of the ith health feature parameter, and \(f(\xi_{i})\) is the HDDD of the ith health feature parameter.  
\[f(\xi) = w_{1}f(\xi_{1})+w_{2}f(\xi_{2})+\cdots+w_{n}f(\xi_{n}) \tag{5}\]
# Current Status, Issues and Future Prospects of Personalized Medicine for Each Disease
## 4 Alzheimer's Disease
### Gene Mutation Analysis  
Most of AD is sporadic and occurs in people aged 65 years or older. It is known that sporadic AD accounts for more than half of dementia and caused by a complex interaction of environmental and genetic factors. Genetic factors play a major role in the pathogenesis of AD; it is said to be about 60 to 80%, and it is thought that many genetic factors (polygenic) are related to the pathogenesis and progression of AD. The E4 allele of the Apolipoprotein E gene (APOE) on chromosome 19 (a gene locus with a specific gene polymorphism) is known to be the highest genetic risk factor for sporadic AD [68]. One of the substances involved in the accumulation and aggregation of amyloid \(\beta\) peptide is apolipoprotein E. There are three main types of APOE genes, \(\varepsilon\) (epsilon) 2, \(\varepsilon\)3, and \(\varepsilon\)4, which make up a pair of two. When the relationship between the presence or absence of \(\varepsilon\)4 and the onset of AD is examined, the risk of developing AD for a genotype having one or two \(\varepsilon\)4 is about 3 to 12 times higher than that for a genotype having no \(\varepsilon\)4 [69]. However, many other genetic risk factors are still unknown.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 4 Dataset Validation
### Data Processing and Analysis  
#### 4.1.1 Feature Extraction and Matching  
Frame 1, Frame 2, and Frame 3 were extracted orb feature points, and this setup involved an eight-layer image pyramid on the extraction of the FAST corner points, with a pyramid scale factor of 1.2. In this image, the resolution was 640 * 480, and we chose to extract 1000 corner points, as Figure 5 shows.  
For the orb feature descriptor for the Frame 1 and Frame 2 feature point matching, a total of 253 pairs of matching points were obtained, as Figure 6
## 5 Damage Detection in Wavefield Images Based on NCA-RPCA Algorithm  
### Establishment of Lamb Wave Damage Model and Parameter Selection  
#### 5.1.1 Model Establishment  
In this paper, a 3D plate model is used; the material chosen is aluminum, and the direction of study is transient. The structure of the 3D aluminum plate is shown in Figure 8. Firstly, a square with a side length of 200 mm is constructed, and another square with a side length of 180 mm is constructed in the square, and then these two squares are overlapped, and the overlapped area is set as a damping layer, which can simulate the energy dissipation mechanism existed in the structure, to more realistically reflect the propagation state of the Lamb wave in the actual situation; and the boundary is set as a low-reflection boundary; next, the combination is stretched by 1 mm and the edges are set as stick supports, which can ensure that the boundary will not shake in the process of Lamb propagation. The specific parameters of the 3D aluminum plate material are shown in Table 2.  
Figure 7: NCA-RPCA algorithm flowchart.  
During the COMSOL finite element simulation experiments, to excite and propagate Lamb waves within the plate structure for obtaining a dataset, it is necessary to establish an excitation. The expression for the excitation signal is as follows.  
\[F(x)=\frac{1}{2}-\frac{1}{2}\cos(\frac{2\pi f_{0}}{n})\sin(2\pi f_{0}x) \tag{35}\]  
where, \(f_{0}\) is the center frequency of the Hanning window, and \(n\) is the number of wave cycles. This study confides the excitation as a sine wave modulated with a Hanning window containing five cycles.  
The center frequency is generally preferred to be set between 150 kHz and 500 kHz. If the excitation signal's center frequency is set too low, it may not excite the desired modes, especially the \(S_{0}\) mode, as the \(S_{0}\) mode corresponds to relatively higher frequencies. Low-frequency excitation might result in significant energy loss, preventing effective propagation and focusing of the wavefield energy. On the other hand, setting the excitation frequency too high can excite more complex modes, causing greater interference in the signal. High-frequency signals also correspond to very short wavelengths, potentially leading to simulation results that do not align with physical reality. In this study, to compare different frequencies, as shown in Figure 9, the selected center frequency is 300 kHz. This selection aims to control the excitation's duration and concentrate the signal, reducing harmonic generation. It also enhances the visibility of the wave packet associated with the \(S_{0}\) mode, which is essential for structural damage detection. The excitation signal is shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Structure** & **Density/kg.m\({}^{-3}\)** & **Young’s Modulus/GPa** & **Poisson Ratio** & **Thickness/mm** & **Shear Modulus/GPa** \\ \hline
3D aluminum plate & 2778 & 71.7 & 0.33 & 1 & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Structural material parameters.  
Figure 8: Three-Dimensional aluminum plate structure.
# Energy Transition on Sifnos: An Approach to Economic and Social Transition and Development
## 3 The Hybrid Power Plant of Sifnos  
### Essential Features  
The hybrid power plant on Sifnos was designed with the essential objective to fully cover the electricity consumption on the island, as is expected to be formulated in the future with the achievement of energy transition for all final energy needs in all sectors (residential, tertiary, primary, municipal facilities, and transportation on the island). Aiming at the construction of a project with the minimum possible set-up and operation cost, adequate storage capacity and technical operational features appropriate to support the insular grid's requirements regarding stability, dynamic security and power quality, a wind park and a PHS system were selected.  
PHS was selected as the optimum storage technology, given the fact that it is the only mature technology with which storage capacities in the range of hundreds of MWhs can be achieved [49; 50; 51]. Additionally, the PHS system, under proper maintenance, exhibits a life period of multiple decades, unlike the short life period of electrochemical storage devices [49]. Given the lack of adequate potable water for the needs of the PHS, the use of seawater was selected as the only available alternative [52; 53; 54].  
A wind park was selected instead of a photovoltaic park, given the capacity factors of wind parks in Greek islands being almost double those of photovoltaics. Additionally, the large storage capacity of the PHS system ensures the long autonomy operation period of the storage plant, without intermediate storage from the RES plant (e.g., in case of a long period with low wind), making the parallel production of an alternative RES technology unnecessary (e.g., photovoltaics).  
The hybrid power plant's sizing was established through the computational simulation of its annual operation, executed with a relevant application designed and developed by the project's scientific team [10]. The hybrid plant's operation algorithm is presented in the following section.
# Multi-Sensor Face Registration Based on Global and Local Structures
## 2 The Proposed Registration Method for Visible and Infrared Face Images
### Inner-Distance Shape Context for 2D Feature Descriptors  
In order to establish the initial correspondences between the two feature point sets of visible and infrared face images, we first use IDSC as the local feature descriptors, and solve the point sets matching problem through a bipartite graph method [32]. Compared to the SC feature matching method, the IDSC feature matching method uses inner-distance to replace the Euclidean distance, which is robust to articulation shape and part structure. For example, Figure 2 shows the schematic illustration of face silhouette feature descriptors based on IDSC, in the IDSC feature histograms from Figure 2c, the horizontal axis \(n_{\theta}=12\) denotes the numbers of orientation bins and the vertical axis \(n_{d}=5\) denotes the numbers of logarithm distance bins.  
Figure 1: Visible and infrared face images fusion basic process for software-based registration.  
As can be seen from Figure 2c, the IDSC feature histograms for all marked points are quite different, and the excellent discriminative performance demonstrates the inner-distance's ability to capture local structures in the point sets. Consider a point \(p_{i}\) in the point set \(\mathbf{X}\) and a point \(q_{j}\) in the point set \(\mathbf{Y}\). Let \(\mathbf{C}_{i,j}\) denote the cost of matching \(p_{i}\) to \(q_{j}\), \(\mathbf{h}_{\mathbf{X},p_{i}}(k)\) and \(\mathbf{h}_{\mathbf{Y},q_{j}}(k)\) denote the normalized histogram of the relative coordinates of the remaining points, respectively, and \(K\) be the number of histogram bins. As the IDSC is represented based on the histogram, it can be measured by the \(\chi^{2}\) test statistic:  
\[\mathbf{C}_{i,j}=\mathbf{C}\Big{(}p_{i},q_{j}\Big{)}=\frac{1}{2}\!\!\sum_{k=1}^{K} \frac{\Big{[}\mathbf{h}_{\mathbf{X},p_{i}}(k)-\mathbf{h}_{\mathbf{Y},q_{j}}(k)\Big{]}^{2}}\mathbf{{h}_{\mathbf{X},p_{i}}(k)+\mathbf{h}_{\mathbf{Y},q_{j}}(k)}, \tag{1}\]  
In general, the smaller the matching cost \(\mathbf{C}_{i,j}\) is, the more similar the local appearance at points \(p_{i}\) and \(q_{j}\) are. Once the cost matrix \(\mathbf{C}\) for all correspondence points in the point set \(\mathbf{X}\) and \(\mathbf{Y}\) is obtained, the correspondences \(\mathbf{\Omega}\) between the two point sets are an instance of an assignment problem, which can be solved by the Hungarian method.
## 3 Results  
### Performance Metrics  
Performance metrics (Table 1) demonstrated systematic adaptations across increasing difficulty levels, with significant changes in temporal, spatial, and velocity parameters. Takeoff time and flight time progressively increased (\(\eta_{\text{p}}^{2}\) = 0.73 and 0.87, respectively), with the greatest percentage increment observed in takeoff time (44.3%). Flight height and takeoff angle also increased significantly (\(\eta_{\text{p}}^{2}\) = 0.77 and 0.53), while departure combined velocity and vertical velocity showed substantial gains (\(\eta_{\text{p}}^{2}\) = 0.83 and 0.91), with the latter exhibiting the strongest effect and a 25.8% increment. Notably, all these effect sizes are considered large, highlighting the practical relevance of these adaptations for snowboard performance. The intermediate difficulty transitions (1080-1440) were particularly crucial, as most parameters exhibited significant adaptations during this phase, highlighting the critical roles of takeoff time and departure vertical velocity in difficulty progression.
# 3D-Printed Concrete Bridges: Material, Design, Construction, and Reinforcement
###### Abstract  
3D Concrete Printing (3 DCP) technology is rapidly gaining popularity in the construction industry, particularly for transportation infrastructure such as bridges. Unlike traditional construction methods, this innovative approach eliminates the need for formwork and enhances both economic efficiency and sustainability by lowering resource consumption and waste generation associated with formwork. This paper examines current research on 3D-printed concrete bridges, highlighting key areas such as concrete mixtures, design processes, construction techniques, and reinforcement strategies. It delves into computational methods like topology optimization and iterative \"design by testing\" approaches, which are crucial for developing structurally efficient and architecturally innovative bridges. Additionally, it reviews specific admixtures or additives within the concrete mix, assessing how they improve essential properties of printable concrete, including extrudability, buildability, and interlayer bonding. Moreover, it shows that the primary construction approach for 3 DCP bridges involves prefabrication and on-site assembly, with robotic arm printers leading to scalability and precision. Reinforcement continues to be challenging, with the most commonly used strategies being post-tensioning, hybrid techniques, and fiber reinforcement. This paper offers insights into the advancements and challenges in 3D-printed concrete bridge construction, providing valuable guidance for future research and development in this field.  
3D concrete printing (3 DCP); 3D-printed bridges; 3 DCP bridge design; 3 DCP bridge construction; 3 DCP bridge reinforcement
###### Abstract  
In the process of lithology discrimination from a conventional well logging dataset, the imbalance in sample distribution restricts the accuracy of log identification, especially in the fine-scale reservoir intervals. Enhanced sampling balances the distribution of well logging samples of multiple lithologies, which is of great significance to precise fine-scale reservoir characterization. This study employed data over-sampling and under-sampling algorithms represented by the synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN) to process well logging dataset. To achieve automatic and precise lithology discrimination on enhanced sampled well logging dataset, support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT) models were trained using cross-validation and grid search methods. Aimed to objectively evaluate the performance of different models on different sampling results from multiple perspectives, the lithology discrimination results were evaluated and compared based on the Jaccard index and F1 score. By comparing the predictions of eighteen lithology discrimination workflows, a new discrimination process containing ADASYN, ENN, and RF has the most precise lithology discrimination result. This process improves the discrimination accuracy of fine-scale reservoir interval lithology, has great generalization ability, and is feasible in a variety of different geological environments.  
well logging; lithology discrimination; adaptive synthetic sampling; edited nearest neighbors; random forest
# Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review
## 7 Discussion
### Generalization  
Engagement is a phenomenon that varies significantly among individuals. Researchers have noted a higher risk of road accidents for those with ADHD [126] and evaluated the impact of ADHD on drivers who obviously exhibit different patterns of engagement and can benefit from bespoke estimators. Similarly, it is found adult learners usually cannot maintain an attention span exceeding 45 min. This varies by age, well-being, and individual circumstances. Having an adaptive and individualized engagement estimator is appealing in many applications. A driver assistance system that prompts felicitously is more acceptable to ADHD drivers. Designing social robots that adapt to individual ways of engagement has always been a prime goal [82]. Allowing calibration and algorithmic adaptability could be the next thing for engagement estimation. Despite successes of data-driven methods, few have accomplished this in current applications.  
Engagement is task-oriented. In some situations, having one's eyes glued to a task is critical. Otherwise, this is usually unnecessary. Besides individualization, an emerging focus is the generalization of engagement estimation. The generalization of an engagement estimator is crucial in education, industry, and social settings because it allows for plug and play across diverse tasks and among different users, improving usability of engagement estimation systems and reducing development costs.  
Thanks to a fast-growing amount of data in the human engagement research community, learning-based approaches are well poised to thrive. A well-generalized engagement estimator should be able to accommodate versatile tasks, varying attention spans, learning, driving, and working styles, as well as environmental factors that impact the sensing from a noisy manufacturing shop to darkness when driving at night. This ensures that businesses and designers can deploy engagement estimators that are broadly effective and reliable. This is not in conflict with more personalized solutions. Generalization and individualization can be reciprocal because the former leads to technological maturity fostering widespread deployment [82] and the latter promotes user acceptance [163]. Ultimately, individualization will deliver usefulness, satisfaction, and profiting end users with different needs.