esa-sceva/satcom-chunk-collection huggingface dataset:
# Perceived Usefulness of a Mandatory Information System
## 3 Hypotheses  
The structural hypothesized equation model (SEM) presented in this study aims to investigate the relationships between perceived ease of use, perceived usefulness, symbolic adoption, and supervisor influence. The model integrates five hypotheses into a structural framework to provide a comprehensive understanding of how these constructs interact to influence technology adoption in a mandatory setting.  
The SEM is grounded in the technology acceptance model (TAM) and its extensions, which have been widely validated in prior research [5; 29]. TAM posits that perceived ease of use and perceived usefulness are primary determinants of user acceptance of technology. In our model, we extend TAM by incorporating symbolic adoption and supervisor influence to better capture the dynamics of mandatory technology use in organizational settings.  
It is hypothesized that:  
**H1:**: _Perceived ease of use positively predicts perceived usefulness. This is grounded in the technology acceptance model (TAM), which has been extensively validated in prior studies [5; 29]._
**H2:**: _Perceived usefulness, in turn, positively predicts symbolic adoption. The influence of perceived usefulness on behavioral intentions is well-documented in the literature [5; 8; 29]._
**H3:**: _Supervisor influence positively influences perceived usefulness. Previous research highlights the role of social influence and management support in shaping technology perceptions and usage [19; 47]._
**H4:**: _The relationship between perceived ease of use and symbolic adoption is fully mediated by perceived usefulness. This mediation effect has been supported by various studies that integrate TAM with other frameworks [8; 29]._
**H5:**: _Similarly, the relationship between supervisory influence and symbolic adoption is also fully mediated by perceived usefulness. The mediating role of perceived usefulness in technology acceptance models has been explored in the context of various mandatory environments [2; 19]._  
Figure 1 illustrates the structural hypothesized equation model.  
Innovative Ideas Behind the Model:  
1. Incorporation of symbolic adoption: Unlike traditional TAM models that focus primarily on behavioral intention and actual use, this study introduces symbolic adoption as a construct. symbolic adoption captures the deeper psychological acceptance and internalization of the system by users, which is particularly relevant in mandatory settings where mere compliance does not reflect true acceptance.
2. Supervisor influence: The inclusion of supervisor influence acknowledges the organizational context in which technology adoption occurs. Supervisors can significantly impact employees' perceptions and attitudes towards the system, making this an important factor in understanding technology acceptance in a workplace setting.
3. Methodological rigor: The model was tested using SEM with bootstrapping techniques to enhance the robustness of the findings. Additionally, contemporary data normalization methods, such as the 'bestNormalize' algorithm, were employed to ensure the reliability and validity of the data.
# SCM-IoT: An Approach for Internet of Things Services Integration and Coordination
## 5 Discussion  
Regarding the computational models described, even though both models, CPN and BPEL, are widely accepted and used to design and construct interoperable distributed systems, there are still severe problems to be solved to satisfy the needs demanded by IoT systems. Among the most critical problems that have been identified in the development of IoT systems are the lack of dynamism, limited expressiveness, and the lack of continuity in the development process.
# An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
## 2 Modeling the Stroke Possibility
### Procedure and Experimental Methodology
#### 2.3.4 Data Preprocessing  
Data preprocessing is an important step in building accurate machine learning models. It involves cleaning, transforming, and enhancing the raw data to make it suitable for analysis. In our study, we implemented several data preprocessing techniques to improve the quality and meaning of the data.  
The first step we took was data cleaning. This involves identifying and fixing or removing incorrect, corrupt, incorrectly formatted, duplicate, or incomplete data within the dataset [26]. In our dataset, we identified and removed any invalid or missing values, which resulted in a clean dataset with 4700 observations of class 0 and 209 observations of class 1.  
The dataset contained five columns with non-numeric variables--gender, work type, ever married, residence type, and smoking status. In order to use these variables in the machine learning models, we used label encoding to convert the current dataset's string variables to integer values. This transformation helps the machine learning algorithms to process the data efficiently and improves the accuracy of the models.  
In addition to data cleaning and label encoding, we also used other data preprocessing techniques such as feature scaling and feature selection. Feature scaling is used to standardize the range of features in the dataset, which helps avoid bias toward certain features. Feature selection, on the other hand, involves selecting a subset of the most important features in the dataset, which helps reduce the complexity of the model and improve its performance. Overall, data preprocessing is a critical step in building accurate and effective machine learning models. By applying techniques such as data cleaning, label encoding, feature scaling, and feature selection, we were able to improve the quality and meaning of the data and build models that can accurately predict the risk of stroke. However, it is important to note that data preprocessing is not a one-time task, and it may need to be repeated as new data become available, or as the requirements of the problem change.
# Multimodal Biometric Template Protection Based on a
## 4 Experiments
### Ablation Studies  
This section presents an ablation study on the proposed CSMoFN. We first explore the accuracy performance of the sole feature extraction module with cosine distance, which is equivalent to an unprotected biometric system, and serves as a baseline. We then examine the feature extraction module + PSMoT, and lastly the entire CSMoFN. The latter two are CB systems.  
From Table 6, we can observe that the baselines for the face and periocular region alone perform better than their CB counterparts (i.e., PSMoT and CSMoFN). This is as expected, and can be attributed to the performance-security tradeoff made in CB systems,  
Figure 9: Average EER (%) for face, periocular, and fused biometric traits, for various \(m\) and \(q=32\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Equal Error Rate (EER) (\%)**} \\ \hline \hline  & \multicolumn{5}{c}{**Face**} \\ \hline \multirow{2}{*}{\(q\)} & \multicolumn{5}{c}{\(m(k)\)} \\ \cline{2-7}  & **256(32)** & **512(64)** & **1024(128)** & **2048(256)** & **4096(512)** \\ \hline
8 & 15.68 & 14.82 & 13.83 & 12.87 & 12.56 \\
16 & 18.06 & 15.27 & 14.30 & 13.66 & 13.01 \\
32 & 17.65 & 15.85 & 14.14 & 13.62 & 13.16 \\
64 & 17.83 & 15.87 & 14.90 & 13.70 & 13.31 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(q\)} & \multicolumn{5}{c}{**Periocular**} \\ \cline{2-7}  & **256(32)** & **512(64)** & **1024(128)** & **2048(256)** & **4096(512)** \\ \hline
8 & 16.35 & 15.43 & 15.61 & 15.00 & 15.20 \\
16 & 18.35 & 17.96 & 16.04 & 15.57 & 15.02 \\
32 & 19.44 & 17.75 & 16.74 & 15.94 & 15.33 \\
64 & 19.70 & 18.34 & 17.27 & 15.91 & 15.29 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{\(q\)} & \multicolumn{5}{c}{**Fused**} \\ \cline{2-7}  & **256(32)** & **512(64)** & **1024(128)** & **2048(256)** & **4096(512)** \\ \hline
8 & 13.63 & 10.73 & 8.53 & 7.35 & 6.99 \\
16 & 14.33 & 10.69 & 8.53 & 7.43 & 6.73 \\
32 & 13.88 & 10.47 & 8.58 & 7.48 & 6.67 \\
64 & 13.64 & 10.53 & 8.44 & 7.49 & 6.71 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average performance of unimodal and multimodal CB systems on six datasets for varying values of \(m\) and \(q\).  
where the performance may be degraded after the CB transformation. However, the use of face-periocular fusion largely restores the verification performance for CSMoFN.
###### Abstract  
The frequency-domain analysis using the fast Fourier transform (FFT) for diagnosis of eccentricity fault has been widely used in squirrel-cage induction motor (IM). However, with the restriction of sampling frequency and time acquisition, FFT analysis could not provide ideal results under low levels of dynamic eccentricity (DE). In this paper, a combined use of the wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) method is presented to diagnose the IM fault under low degrees of purely DE. The proposed method is based on the decomposition of apparent power signal and extracts the characteristic component. The fault severity factor (FSF) has been defined to evaluate the eccentricity severity. Simulation results using the finite element method (FEM) are tested to verify the effectiveness of the presented method under different load conditions.  
induction motor (IM); dynamic eccentricity (DE); wavelet packet decomposition (WPD); empirical mode decomposition (EMD); finite element method (FEM) +
Footnote †: journal: Applied Physics Letters
## 2 Methodology
### Evaluation Metrics
#### 2.6.1 Mean Absolute Error (MAE)  
The Mean Absolute Error (MAE) is the average of the absolute errors between the actual observations and the forecasted values. The formula for its calculation is as follows:  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}}|y_{i}-\hat{y}_{i}| \tag{34}\]  
where \(y_{i}\) is the actual observation value of the i instance, \(\hat{y}_{i}\) is the corresponding forecasted value, and \(n\) is the number of forecasting periods. The smaller the value of the MAE, the higher the accuracy of the forecast model.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
## 2 WSNs Architecture Overview
The delivery period is considerable, which may create significant delays and make it unsuitable for time-sensitive applications [17]. The nodes are split into numerous branches, leaf nodes, and parent nodes in tree-based routing. Data are sent to the leaf node toward its parent node, next to another parent node, and so on, until they reach the sink node. The disadvantage of tree constructions is that when a tree's parent node fails, all data transfer within its branches would be lost. Furthermore, if indeed the branch has a large number of sensor nodes, data transfers are delayed, and energy demand may rise [18]. The whole network space is split into several grids, each with its own cluster head. All sensor nodes  
Figure 1: The typical network architecture of sensor nodes in a WSN. in a grid will transmit data to its cluster head, which will transmit it to the cluster head of another grid until it arrives at the base station [19]. Unfortunately, when a grid has a large number of sensor nodes, perhaps there is a lot of data traffic, and the cluster head node's energy is depleted quickly. The tree, chain, and grid-based routing have significant issues in that long-distance communication is not possible due to the lack of scalability for various sensors. Network clustering is attempted in various routing techniques to address these disadvantages. The cluster-based method divides networks into several clusters. Each cluster is made up of several sensor nodes, one of which is chosen as the cluster head. Due to the decreased number of data packets to be delivered, the bandwidth is kept to a bare minimum. In the cluster-based technique, the data-aggregating method decreases the quantity of immediately transmitted data to the base station as well as the amount of energy that is required due to the shorter transmitted power [20].
## 3 Survey Results
### In-Network Processing  
In-network preprocessing, aggregation and distributed algorithm usage is shown in Table 11 and visualized in Figure 8. Application level aggregation is considered here--data averaging and other compression techniques with the goal to reduce the size of data to be sent.  
\begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Nr** & **Codename** & **Raw data preprocess** & **Advanced distributed algorithms** & **In-network aggregation** \\ \hline
1 & Habitats & n & n & n \\
2 & Minefield & y & y &? \\
3 & Battlefield & y & n & y \\
4 & Line in the sand & y & y &? \\
5 & Counter-sniper & y & n & y \\
6 & Electro-shepherd & n & n & n \\
7 & Virtual fences & n & n & n \\
8 & Oil tanker & n & n & n \\
9 & Enemy vehicles & y & y & y \\ \hline \hline \end{tabular}
\end{table}
Table 11: Deployments: in-network processing.  
Figure 7: **Deployment network topologies—**Almost half (47%) use a multi-hop mesh network. One-hop networks are used in 25% of cases; 15% use multiple one-hop networks.  
As the results show, raw data preprocessing is used in 52% of deployments, _i.e._, one out of two deployments reports raw data without processing it locally. The situation is even worse with distributed algorithms (voting, distributed motor control, _etc._) and data aggregation: it is only used in 20% and 10% of cases, respectively. Therefore, sensor network theoretical assumptions, \"smart devices taking in-network distributed decisions\" and \"to save communication bandwidth, aggregation is used\", prove not to be true in reality. Raw data preprocessing and distributed decision-making is performed at the application layer; no responsibility for the operating system is imposed. Aggregation could be performed at the operating system service level. However, it seems that such additional service is not required for most of the applications. Data packet aggregation is optional and should not be included at the OS level.  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**Nr** & **Codename** & **Raw data preprocess** & **Advanced distributed algorithms** & **In-network aggregation** \\ \hline
10 & Trove game & n & y & n \\
11 & Elder RFID & n & n & n \\
12 & Murphy potatoes & n & n & n \\
13 & Firewxnet & n & n & n \\
14 & AlarmNet & y & n & n \\
15 & Ecuador Volcano & y & y & n \\
16 & Pet game & n & n & n \\
17 & Plug & y & n & n \\
18 & B-Live & y & n & n \\
19 & Biomotion & n & n & n \\
20 & AID-N & y & n & n \\
21 & Firefighting & n & n & n \\
22 & Rehabil & n & n & n \\
23 & CargoNet & n & n & n \\
24 & Fence monitor & y & y & n \\
25 & BikeNet & n & n & n \\
26 & BriMon & n & n & n \\
27 & IP net & y & n & n \\
28 & Smart home & y & n &? \\
29 & SVATS & y & y & n \\
30 & Hitchhiker & n & n & n \\
31 & Daily morning & n & n & n \\
32 & Heritage & y & n & n \\
33 & AC meter & y & n & n \\
34 & Coal mine & y & y & y \\
35 & ITS & y & n & n \\
36 & Underwater & n & y & n \\
37 & PipeProbe & y & n & n \\
38 & Badgers & y & n & n \\
39 & Helens volcano & y & n & n \\
40 & Tunnels & n & n & n \\ \hline \hline \end{tabular}
\end{table}
Table 11: _Cont._
# A QoS-Guaranteed and Congestion-Controlled SDN Routing Strategy for Smart Grid
## 4 Experiments and Performance Analysis  
### Implementation Details  
In this section, the proposed algorithm is implemented by using the floodlight controller [54] in the experimental environment of Linux Ubuntu14.04. In the floodlight controller, we mainly add two modules. One is used to detect link congestion periodically, and another one is to find the optimal route according to the optimization function.  
The controller adds the code of the QCORS algorithm and recompiles the simulation software. The experiment topology was built by using Mininet 2.0.0, and the southbound interface protocol used Openflow 1.3. SDN network topologies of 2 to 36 nodes are used to simulate the network of smart power grid neighbors, and the link bandwidth is 100 M, and the maximum length of each queue is 1000 packets. The simulation time is 600 s, and the average value of each experiment is repeated 10 times. The value of \(V\) (defined in Equation (12)) is 1000. An example of SDN network simulation with 10 nodes in our experiments is shown in Figure 4. Moreover, some NAN applications are configured and differentiated as shown in Table 2. It is noted that those applications have all been activated in the simulation simultaneously. In other words, though the congestion level varies over time, the network is always congested.
## 4 Results
### Mapping of Groundwater Potential
#### 4.5.2 Final Synthesized Map of Groundwater Potential in the ACC  
The resulting synthesized map of groundwater potential is shown in Figure 10. NGPI values were classified into four levels by JNB as low (0.20-0.50 NGPI), medium (0.25-0.56 NGPI), high (0.56-0.63 NGPI), and very high (0.63-0.97 NGPI) groundwater potential. The percentages of the low, medium, high, and very high potentials were 26%, 32%, 27%, and 15%, respectively. NGPI values, which range from 0 to 1, were compared with the well yield data, as shown in Table 4. There was a positive correlation between the NGPI and well yields.
## 2 Related Works

Table 1: Summary of the related works. [PERSON] et al. [31] developed an efficient and less complex model to increase coronary heart disease risk prediction on the Cleveland dataset using a random search algorithm (RSA) and Random Forest (RF) model. The dataset was split into 70% for training and 30% for testing to evaluate the model. The model achieved an accuracy of 93.33% using a subset of 7 features that accounted for a 3.3% improvement compared to standard RF. The AUC of 0.947 is obtained with the improved version of the RSA-RF model. [PERSON] et al. [32] proposed a ranking-based feature selection method for medical data classification. The RF classifier with 10-fold cross-validation was implemented on the selected ten distinct disease datasets, including Statlog heart data. The feature ranking methods such as Information Gain, Gain Ratio, Correlation, OneR, and Relief attribute evaluators were utilized from the Weka tool. It can be concluded that the RF with 12 attributes selected from the Relief method (excluding feature 'chol') performed better than other techniques. It achieved 85.5% of accuracy and an AUC of 0.915. [PERSON] et al. [33] developed a meta-heuristic technique called Parasitism-Predation Algorithms (PPA) for selecting feature subsets to improve the classification accuracy by combining cat swarm optimization (CSO), cuckoo search (CS), and crow search algorithm (CSA). The PPA achieved an accuracy of 86.17% in 49.13 s when the KNN classifier was applied to the selected four features extracted from the Statlog heart dataset using 10-fold cross-validation. [PERSON] et al. [34] proposed a novel feature reduction (NFR) model for effective heart disease risk prediction on Cleveland, Hungarian, Statlog, and Switzerland datasets. The missing values have been replaced with a mean value of the column, and 70:30 hold-out validation is applied. The performance of Logistic Regression (LR), RF, boosted regression tree (BRT), stochastic gradient boosting (SGB), and SVM algorithms were evaluated by reducing the features based on various subset combinations with maximum accuracies, AUC values, and the slightest difference between them. The results of the proposed model reported an accuracy of 92.53% and an AUC of 0.9268 using LR (9 features) on the Cleveland dataset. [PERSON] et al. [35] projected a feature subset selection method to improve the cardiovascular risk prediction outcomes using mean fisher score-based feature selection algorithms (MFSFSA), forward feature selection algorithm (FFSA), and reverse feature
# A Direction-Sensitive Microwave Sensor for Metal Crack Detection
## 5 Conclusions  
A microwave direction-sensitive sensor for metal crack detection is proposed in this paper. The proposed sensor adopts two feeding ports to obtain perpendicular polarization currents, which realize the direction detection. In addition, the orientation of metal cracks can be determined by combining the resonant frequencies of two feeding ports. Additionally, compared with the direct feeding method, a coupled feeding method is proposed in the design to improve the current distribution on the patch resonator surface, and this results in a better detection performance. Due to the advantages of a simple structure, low profile, large coverage area and high sensitivity, the proposed sensor has great application potential in nondestructive detection fields. Future work will concentrate on the conformal microstrip sensor with flexible material for the cracks occurring on irregular metal plates. Furthermore, the detection performance of irregular cracks will be studied in future work.  
Conceptualization, L.W.; investigation, L.M.; project administration, Z.X.; supervision, T.H.; validation, B.Q.; writing--original draft, B.Q.; writing--review and editing, L.W. and J.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology on Near-Surface Detection Laboratory (Grant No TCGZ2017A005).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Performance Evaluation  
We evaluate the performance of the eDSA implemented in the C++ simulator. Each sensor has limited resources and is equipped with an Omni-directional antenna.  
* Single-event model (see Figure 9): the initial ENs that adequately covers a randomly selected circle area \(\{(x-10)^{2}+(y-10)^{2}=\mathrm{R_{circle}}^{2}\}\) to irritate the event. At every time slot, EN propagates by picking up a random number of neighbors to join the event (non-ENs\(\rightarrow\)ENs). In this way, the event is guaranteed to be fully connected.
* Multi-event model: randomly picks up three points that should be geo-separated over at least 50 m distance in the coordinate and then initiate events in a similar way as in the single-event model.  
\begin{table}
\begin{tabular}{|c|c|} \hline _Parameter_ & **Value** \\ \hline Network Area & 100 m\({}^{2}\) \\ The location of SS & (50,175), \\ Transmission range (\(r\)) & 20 m \\ Time slots (T) & 100 (seconds) \\ Initial Energy/sensor & 2J/battery \\ All the coefficients & 1 \\ (\(\mu_{\mathrm{SS}}^{E}\), \(\mu_{\mathrm{SS}}^{V}\),\(\eta_{\mathrm{SS}}^{V}\), \(\mu_{\mathrm{MS}}^{E}\) and \(\eta_{\mathrm{MS}}\)_etc._) & \\ Message size (L) & 100 Bytes \\ MS Velocity (\(v\)) with \(m=1\) & 5\(\sim\)10 m/sec \\ E\({}_{\mathrm{elec}}\) & 50 nJ/bit \\ E\({}_{\mathrm{fs}}\) & 10 pJ/bit/m2 \\ \(\mathcal{E}_{\mathrm{amp}}\) & 0.0013 pJ/bit/m4 \\ E\({}_{\mathrm{DA}}\) & 5 nJ/bit/signal \\ \(\mathbf{\mathcal{d}_{0}}\) & 80 m \\ \hline \end{tabular}
\end{table}
Table 7: Simulation parameters.  
Figure 9: Single-event model with the initial ENs that adequately covers a circle area \(\{(x-10)^{2}+(y-10)^{2}=\mathrm{R_{circle}}^{2}\}\) to generate the event.
# Numerical Simulation-Based Investigation of the Limits of Different Quasistatic Models
## 4 Numerical Validation
### Industrial Application
#### 4.1.2 Electric Potential and Electric Field  
The electric scalar potential along the magnetic core is presented in Figure 6a, which shows a decreasing linear variation in its magnitude. In addition, the electric field distribution in a sectional view is also given in the magnetic core, as shown in Figure 6b. It can be observed that the electric field is located in the periphery of the core due to the skin effect that occurs, and reaches a maximum value of 12 V/m.  
Figure 4: Detail of winding.  
Figure 5: Considered mesh.