esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Applications of UWSN Technology
### Detection of Landslides  
A landslide detection system uses a wireless sensor network to detect minute soil movements and changes in several parameters that may happen before or during a landslide. It may be feasible to predict the occurrence of landslides using the data collected well before when they occur [73, 74].
## 3 Materials and Methods
### Interoperability and Scalability of Digital Systems  
Interoperability has four broad categories: operational interoperability, system/ technology interoperability, data/semantics interoperability, and legal interoperability [14]. The interoperability of digital systems/technology in operationalizing the exchange of standardized data formats within the established ethical and legal framework can enhance flexibility among stakeholders in agricultural social networks. Thus, they can collaborate in data and information sharing on farming practices, markets, market prices, and agricultural product distribution channels. Interoperability combined with IoT technology can scale the scope of digital systems applications from multifaceted perspectives, including increased financial access, inclusiveness of underserved agricultural populations, and access to broad digital markets regionally or globally.
## 4 Experiments
### Dataset Introduction  
As shown in Figure 5. We evaluate our method on three diverse open-source datasets: LEVIR-CD, WHU-CD, and CLCD.  
LEVIR-CD is a public dataset for land use change detection, containing 637 pairs of very-high-resolution (VHR, 0.5 m/pixel) bi-temporal image patches from Google Earth. Each patch is 1024 \(\times\) 1024 pixels with a time span of 5-14 years, focusing on key challenges of land use changes such as building growth. It covers diverse building types including villas, high-rise apartments, small garages, and large warehouses. The dataset follows a 7:2:1 ratio division rule, where original images are non-overlappingly cropped into 16 sub-images of 256 \(\times\) 256 pixels, finally forming a training set (7120 pairs), a test set (2048 pairs), and a validation set (1024 pairs).  
WHU-CD is a public dataset for building change detection, containing a pair of high-resolution (HR) bi-temporal aerial images with a resolution of 0.2 m and size of 32,507 \(\times\) 15,354 pixels. It focuses on areas that have experienced earthquake disasters and reconstruction over the years, mainly recording change scenarios such as building renovations. Through non-overlapping cropping, the original images were divided into 256 \(\times\) 256 pixel sub-image patches, finally forming a training set (5947 samples), a validation set (744 samples), and a test set (744 samples).  
The CLCD dataset is a public dataset for farmland change detection, containing 600 pairs of high-resolution bi-temporal remote sensing images taken in Guangdong Province, China, in 2017 and 2019. Each image is 512 \(\times\) 512 pixels with a spatial resolution ranging from 0.5 to 2 m, covering land cover types such as buildings, roads, lakes, and bare land. To adapt to model input, the original images were non-overlappingly cropped into 256 \(\times\) 256 pixel sub-images, finally forming 2400 pairs of samples. The dataset was divided into a training set (1440 pairs), a validation set (480 pairs), and a test set (480 pairs) according to a 6:2:2 ratio.
## 8 Implementation and Performance Evaluation
### Implementation Considerations
#### 8.2.1 Side-Channel Threat Model  
In this work, we explicitly address side-channel leakage stemming from timing variations and Simple Power Analysis (SPA). Our implementation mitigates these vectors by enforcing constant-time field operations and avoiding data-dependent branching or memory access patterns during scalar multiplication. However, we do not claim resistance to more advanced side-channel attacks such as differential power analysis (DPA) or electromagnetic analysis. Countermeasures against these would involve techniques like scalar randomization (blinding), noise introduction, or dedicated hardware-level protections, which are outside the scope of the present study. We consider these as important directions for future work.
# Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques
###### Abstract  
Machine learning (ML) is one of the artificial intelligence tools which uses past data to learn the relationship between input and output and helps to predict future trends. Powder bed fusion additive manufacturing (PBF-AM) is extensively used for a wide range of applications in the industry. The AM process establishment for new material is a crucial task with trial-and-error approaches. In this work, ML techniques have been applied for the prediction of the density of PBF-AM. Density is the most vital property in evaluating the overall quality of the AM building part. The ML techniques, namely, artificial neural network (ANN), K-nearest neighbor (KNN), support vector machine (SVM), and linear regression (LR), are used to develop a model for predicting the density of the stainless steel (SS) 316L build part. These four methods are validated using R-squared values and different error functions to compare the predicted result. The ANN and SVM model performed well with the R-square value of 0.95 and 0.923, respectively, for the density prediction. The ML models would be beneficial for the prediction of the process parameters. Further, the developed ML model would also be helpful for the future application of ML in additive manufacturing.  
additive manufacturing; mechanical engineering; machine learning; density prediction; artificial neural network; support vector machine; K-nearest neighbor +
Footnote †: journal: Applied Sciences
## 3 Acsa-Olta
### Adaptive Correlation Space Adjusted Method
In this way, as the satellite signal power is continuously varying in the real application area due to the sheltering, scattering, etc, the proposed ACSA-OLTA can greatly improve the code phase measurement accuracy of open loop tracking. The performance of ACSA-OLTA is evaluated by tests in the next section.
## 2 Exploring SDN, NFV, Policies, ML, Network Slicing and Telecom Security
### Network Slicing
#### 2.3.2 Conflicting Requirements  
These diverse use cases present conflicting requirements that traditional networks struggle to reconcile. For instance, eMBB applications require high bandwidth and data rates, potentially congesting the network and impacting the reliability of URLLC services. Conversely, the sheer number of devices in an mMTC network can introduce additional latency and potentially disrupt the time-sensitive nature of URLLC applications. Works by [48] support this argument, highlighting the limitations of traditional 5G networks in addressing the needs of such diverse applications due to their inherent inflexibility and lack of scalability.
# Enhanced and Combined Representations in Extended Reality through Creative Industries
## 4 Results and Discussion  
### Results  
Among the questions of the survey [49], for the assessment of the virtual museum for the Antikythera Mechanism, question Q15 explored the extent to which the integration of the specific functionality for the combined representation of the fragments was informative. Accordingly, question Q18 investigated the degree of ease of use when handling the X-ray function of studying the interior of fragments. Using a Likert scale from 1 (not informative at all) to 4 (very informative), we received feedback from the end users regarding the usefulness of the X-ray functionality. According to the results, 90.3% (N = 56) of the users expressed their positive opinion toward the combined representations, while 9.7% (N = 6) expressed their doubts (RQ1, RQ2, RQ3). Similarly, we received users' feedback regarding the usability of the proposed functionality. According to the results, 83.9% (N = 52) of the users found the functionality comfortable to use, while 16.1% (N = 10) expressed their doubts [49] (RQ1, RQ2, RQ3).  
In the present research, we analyze further the negative feedback in the specific questions. However, it should be noted that of the total participants, 19.4% (N = 12) had no previous experience with a virtual reality application, while 12.9% (N = 8) had no previous experience with video games.  
We further analyzed the profile of the participants who expressed a negative opinion in question Q15 (N = 6). Five of them mentioned that they had never played any computer game before their testing in the virtual museum for the Antikythera Mechanism, while the sixth participant used to play once per year (Q5). Moreover, regarding their previous experience in virtual reality applications, two of them had no experience at all, while one participant had at least one experience in the last five years, and three of them had tested at least one VR application in the last year (Q7).  
Furthermore, we analyzed the profile of the participants who expressed a negative opinion in question Q18 (N = 10, female: 9, male: 1) regarding the ease of use when handling the X-ray. Three of them had no previous experience in computer games, while five of them had played at least one in the previous year, and only one had played in the last month (Q5). In addition, two of them had no previous experience in VR applications. Two of them had at least one experience in the last five years. Five participants had at least one VR experience during the last year, while one had tested a VR application during the previous month (Q7).
## 3 Results and Discussion
### Chemical Investigation
#### 3.3.2 Characterization of Volatile Organic Compounds (VOCs) by SPME-GC/MS
\begin{table}
\begin{tabular}{c c c c c c c|c c} \hline \hline  & \multicolumn{6}{c}{**Ote 1 F4 P10**} & \multicolumn{2}{c}{**Ote 2 F7 P2**} & \multicolumn{2}{c}{**Grato 1**} \\ \hline
**Component 1** & **LR12** & **LR13** & **Stem** & **SFH** & **Stem** & **SFH** & **Stem** & **SFH** \\ \hline
1-butanol, 3-methyl- & 699 & 700 & 24.5 \(\pm\) 1.15 & 17.3 \(\pm\) 0.85 & 30.2 \(\pm\) 2.14 & 17.4 \(\pm\) 0.23 & 13.2 \(\pm\) 0.18 & 1.2 \(\pm\) 0.09 \\
1-butanol, 2-methyl- & 748 & 744 & 16.3 \(\pm\) 0.25 & 5.9 \(\pm\) 0.05 & 14.2 \(\pm\) 0.30 & 19.0 \(\pm\) 0.86 & 5.0 \(\pm\) 0.09 & 0.3 \(\pm\) 0.02 \\
1-hexanol & 847 & 841 & nd & 26.3 \(\pm\) 1.01 & nd & 5.9 \(\pm\) 0.04 & nd & 33.8 \(\pm\) 1.02 \\
1-pentanol, & 1415 & 1412 & 7.0 \(\pm\) 0.08 & nd & 6.0 \(\pm\) 0.09 & nd & nd & nd \\
3-ethyl-4- & & & & & & & & \\ methylpentan-1-ol & 1029 & 1023 & 50.1 \(\pm\) 3.20 & 7.6 \(\pm\) 0.09 & 48.2 \(\pm\) 1.85 & 11.4 \(\pm\) 0.10 & 8.4 \(\pm\) 0.12 & nd \\ \(\it{rms}\)-\(\beta\)-ocimene & 1051 & 1048 & 2.1 \(\pm\) 0.02 & 4.7 \(\pm\) 0.05 & nd & nd & 24.1 \(\pm\) 1.45 & 0.9 \(\pm\) 0.08 \\ \(\beta\)-caryophyllene & 1414 & 1418 & nd & nd & 5.2 \(\pm\) 0.35 & 0.7 \(\pm\) 0.06 & 6.7 \(\pm\) 0.35 \\ \(\beta\)-eudesmene & 1488 & 1481 & 2.1 \(\pm\) 0.03 & 38.1 \(\pm\) 2.05 & 1.4 \(\pm\) 0.09 & 41.1 \(\pm\) 2.15 & 14.8 \(\pm\) 0.86 & 90.8 \(\pm\) 4.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Chemical volatile composition (percentage mean value \(\pm\) SD) of stem and SFH tissues of the three artichoke genotypes analyzed. 1 The components are reported according to their elution order on apolar column; 2 Linear Retention Indices measured on apolar column; 3 Linear Retention indices from literature; nd: not detected.
# Design of a Computed Tomography Automation Architecture
## 5 Industrial Applications
### Industrial Robots  
Industrial robots are used in many applications due to the improved safety of the operators, increased task speed, productivity, and high repeatability. For CT applications, robots can be used to load and unload parts into a CT scanner, re-orient the part between or during scans, or even can be used directly in robot-based CT systems for heavy objects where they either hold the source, detector, or both during the entirety of a scan. Calibration of these robots is necessary to ensure the geometry of the CT scan is set up well for reconstruction.
# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 3 Pattern and Fuzzy Approaches for Imbalance Problems
### Data- Level Approaches
[72] proposed a hybrid data level that overcomes some difficulties presented in different types of data impurities. They define data impurities as data that present noise, different data distributions, or class imbalance. Their approach has three different phases; in the first phase, they clean the data from noise and applied a radial basis kernel for clustering minority and majority classes. In the second phase, the clusters are artificially balanced; they used radial basis kernel fuzzy membership to reduce the majority class and firefly-based SMOTE [73] to increase the minority class. In the last step, they use a DT [52] as their classifier. They tested their method in three synthetic databases and 44 UCI [54] real-world databases. They used AUC to evaluate the performance of the method. They compared their results against ASS [74], ADOMS [74], AHC [75], B_SMOTE [76], ROS [77], SL_SMOTE [78], SMOTE [35], SMOTE_ENN [77], SMOTE_TL [77], SPIDER [79],SPIDER2, CNN [80], CNNTL [81], CPM [82], NCL [83], OSS [84], RUS [85], SBC [86], TL [81], and SMOTE_RSB, which has a fuzzy approach [87]. Finally, their results outperform the other methods especially in databases with a high imbalanced data ratio.
## 1 Introduction
### Energy Management Techniques for Extending WSN Lifetime
Energy-efficient data acquisition schemes, on the other hand, are involved in reducing the energy spent by the sensing subsystem by typically reducing the number of samples generated by the sensors. The data reduction data-driven schemes are performed via in-network processing within the WSN. In-network processing involves distributed computing, requiring the processing of data as it travels via the WSN to the sink. It involves actions such as fusion and aggregation on the data as it moves within the WSN from one sensor node to another. This reduces the number of redundant data that needs to transmitted. Popular in-network processing techniques in the literature include techniques such as data aggregation, data compression, and data prediction [12; 13; 24; 25]. Data aggregation techniques increase the network lifetime by merging data in an efficient manner as it traverses the network from one node to the another until it gets to the sink [26; 27]. Likewise, data compression involves encoding information at the sensor nodes and decoding it at the sink, and it can be applied to reduce the amount of information sent by source nodes [12]. This reduction in the amount of data transmitted and received also reduces the radio module's active time, which also decreases the sensor node's energy consumption. Similarly, data prediction techniques aim at reducing the node's energy consumption by minimizing the communication cost. They achieve this by building an abstraction of a sensed phenomenon, i.e., a model describing data evolution. The model can predict the values sensed by sensor nodes within certain error bounds, and resides both at the sensor nodes and at the sink [12]. Transmissions between the nodes and the sink occur only when sensor nodes measure values outside the threshold of the prediction models [13]. This reduces the frequency of transmission and the energy needed for communication as well. The energy-efficient data acquisition data-driven schemes include adaptive sensing techniques such as hierarchical sensing, adaptive sampling, and model-based active sensing [22]. By reducing the number of samples generated by the sensors, an efficient sensing strategy also reduces the amount of data to be processed and possibly transmitted by sensor nodes, and thus generate further energy savings [22]. In hierarchical sensing, a sensor node has multiple sensing devices monitoring the same physical parameter, but with each having a different sensing accuracy and power consumption. Accuracy can be traded off for energy efficiency by using the low-power sensors to get a rough estimate of the monitored parameter [12]. Once an event has been detected, the accurate power-hungry sensors can be activated to give more accurate readings of the physical property at the cost of greater energy consumption [22].
# Optimized Weighted Ensemble Approach for Enhancing Gold Mineralization Prediction
## 2 Method
### Predication Approaches  
Estimating a gold grade can be carried out in various ways, the most common of which fall into one of three broad categories: geostatistical, traditional, or hybrid. This proposed study aims to develop a hybrid system for gold-grade prediction. The suggested hybrid method, dubbed RFKNN-MPA, combines the Random Forest algorithm with the K-nearest neighbor strategy and MPA optimization. The experiments were carried out using the commercial program MATLAB, while the geostatistical methods were performed on the Surpac (6.6.2) software.  
In light of this, a novel hybrid method known as RFKNN-MPA was evaluated and compared to the following techniques for geostatistics strategies (OK and IK) and machine learning approaches (RF, KNN, GPR, DT, and FCN). The underlying principles are briefly discussed in this part.
# Active Data Selection and Information Seeking
## 5 Dynamic Processes
Note that because this is a dynamic process--with a degree of smoothness both in space and time--predictions based upon the current data can be used to inform predictions about nearby spatial locations and to both predict and postdict the values of the function at different points in time. In this and the previous section, we have demonstrated the way in which smart or optimal sampling may be used to select data in a manner that balances the cost of sampling or of performing further experiments against the information gained from those samples or experiments. Each of these examples has relied upon relatively simple, and analytically comfortable, linear Gaussian systems. Next, we address active sampling in a situation where analytical solutions are no longer possible. Figure 7: Dynamic information seeking. This figure deals with the situation in which the function we are trying to map out changes with time. The generative model is summarised in the upper part of this figure as the product of a matrix of coefficients with two matrices of basis functions. The data generated now depend both upon our choice and upon time. The plot of sample data shows data we could have sampled from the function at the final time point by taking the final row of the plot in the lower left and generating samples from this for each possible sampling location. This is to afford a comparison between a relatively exhaustive sampling of the data and the inferred shape of the function in the prediction plot based upon only one sample from this time point. The prediction plot shows the predicted data at this time point. The plot of posterior modes shows the estimated coefficients with 90% credible intervals at the final time point. The plot of choices is like that shown in previous figures. However, each choice occurs eight time steps after the previous choice. The lower two plots show both the time evolution of the underlying function generating the data and the inferred (expected) values of this function at each time point. We see that the shape of this function at different time points, despite only the sparse sampling of the data, is well captured by this scheme.
# User Identity Linkage across Social Networks by Heterogeneous Graph Attention Network Modeling
## 4 Methods
### Multiple Attention Layers
#### 4.2.1 Definition of the Attention Layer  
To aggregate multiple types of nodes in the latent space, we need to obtain the neighbor information between nodes. To achieve this goal, we introduce the attention layer, which works in a graph. In the attention layer, we aggregate the 1-hop neighbor nodes by the attention mechanism. Figure 4 is an example of an attention layer, \(v_{a},v_{b},v_{c},v_{d}\) are 1-hop neighborhoods of node \(v_{i}\), \(h_{a},h_{b},h_{c},h_{d},h_{i}\) are the embedding vectors or the attention layer aggregated vectors of these nodes in the latent space, respectively. The goal of the attention layer is to aggregate \(h_{a},h_{b},h_{c},h_{d}\) and \(h_{i}\). Then, we mathematically explain how the attention layer works.  
The heterogeneous social network graph has a node \(v_{i}\) and its neighbor node \(v_{j}\in N(v_{i},t)\), where \(N(v_{i},t)\) is a set of neighbor nodes whose type is \(t\) of node \(v_{i}\). \(h_{i},h_{j}\) are the embedding vectors or the attention layer aggregated vectors of \(v_{i},v_{j}\) in the latent space. The calculation of the attention coefficient \(\phi:\mathbb{R}^{d}\times\mathbb{R}^{d}\rightarrow\mathbb{R}\) by self-attention [27] is shown as follow:  
\[\phi_{v_{i}v_{j}}=\phi(h_{i},h_{j})=\mathrm{a}^{T}\cdot[h_{i}\|h_{j}], \tag{7}\]  
where \(\mathrm{a}\in\mathbb{R}^{2d}\) is the learnable weight vector, and \(\|\) is the concatenate function. The attention coefficient \(\phi(v_{i}v_{j})\) indicates the importance between \(v_{i}\) and \(v_{j}\). To make the different attention coefficients easier to compare, we calculate all the attention coefficients of neighbor nodes whose type is \(t\) and normalized by a softmax function as follow:  
\[\alpha_{v_{i}v_{j}}=\frac{\exp\left(\phi_{v_{i}v_{j}}\right)}{\sum_{v_{k}\in N (v_{i},t)}\exp\left(\phi_{v_{i}v_{k}}\right)}. \tag{8}\]  
Finally, we aggregate the embedding vectors, or the attention layer aggregated vectors of all neighbor nodes whose type is \(t\) of node \(v_{i}\):  
\[h^{\prime}=\tanh\left(\sum_{v_{j}\in N(v_{i},t)}\alpha_{v_{i}v_{j}}h_{j}\right) \text{ or} \tag{9}\]  
\[h^{\prime}=\tanh\left(\big{\|}_{v_{j}\in N(v_{i},t)}\alpha_{v_{1}v_{j}}h_{j} \right), \tag{10}\]  
where \(\tanh\) is the hyperbolic tangent function, \(\|\) is the concatenate function, and \(h^{\prime}\) is the current attention layer aggregated vector of node \(v_{i}\), which is used to input the next attention layer or a multi-layer perceptron. The calculation of \(h^{\prime}\) will not update the embedding vector \(h_{i}\) of node \(v_{i}\) in the latent space. Therefore, an attention layer can be expressed as:  
\[h^{\prime}=\mathrm{AttLayer}(v_{i},t,\Sigma)\text{ or} \tag{11}\]  
\[h^{\prime}=\mathrm{AttLayer}(v_{i},t,\|). \tag{12}\]
## 3 Results And Discussion  
The summarized experimental outcomes of the conceived classification pipelines in terms of mean accuracy, precision, recall, and f1 score, both for positive and negative classes, are listed in Tables 1-3. The precision, recall, and f1 score for the positive class are also pictorially depicted in the boxplots in Figures 4-6.  
It can be observed from Tables 1-3 that the negative class is always well interpreted among classifiers across the workflows due to large amount of available data for negative class, leading to a global accuracy range between 96% to 99%.