esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
As aforementioned, 66 LEO satellites, which can transmit GNSS-like signals, have been recruited to shorten the PPP convergence time. For the satellite visibility, eight to ten LEO satellites can be observed in polar areas while only one to three can be tracked in low and medium latitude regions because of the small footprint of LEO satellites and their polar orbits. Compared to those in hours flying overhead of GNSS satellites, LEO satellites fly overhead just in five to ten minutes, which offers the fast-geometric change with respect to the ground stations, resulting in the short convergence time. The experimental results for four stations show that the convergence time can reach to five minutes in polar areas where enough LEO satellites can be observed. Even though a few LEO satellites can be tracked in low and medium latitude regions, the improvement of the convergence time is greater than that of multi-GNSS. The global kinematic PPP results show that in most part of the world, one can achieve cm-level precise positioning just in five minutes.  
There are still some issues to be investigated, such as the bounds of the convergence time of the LeGNSS as a function of the number of LEOs. Moreover, since the world population is between latitudes 35\({}^{\circ}\) N and 35\({}^{\circ}\) S, the short convergence time in this area is much more expected than that in polar areas. As a result, not only the number of LEO satellites, but also the constellation configuration of LEO satellites, should be taken into consideration in our future studies. If it comes true, the precise pointing technique will see a great change since there is no need for dense reference stations anymore. One can achieve the cm-level positioning results immediately without any additional augmentations.
## 2 Analytical Methods for Phytocannabinoid Profiling
### Instrumental Analysis
#### 2.2.2 LC-Based Methods
MS information from HRAM-MS acquisitions, that is, the accurate mass and mass fragmentation patterns, is used for untargeted phytocannabinoid profiling [19; 63; 65; 66; 83]. Compound identification is performed using one or more MSLs, such as mzCloud (High-Chem LLC, Bratislava, Slovakia) [196], in-house MSL, such as the recent MSL of LC-MS/MS spectra of 94 phytocannabinoids accompanied with metadata (names, Rs, accurate masses, fragmentation patterns and fragments structures) [19], compound DBs, such as and Chem-Spider [197] and Human Metabolome DB (HMDB) [198] or cheminformatics software, such as Compound Discoverer (Thermo Fischer Scientific, Waltham, MA, USA). HPLC-UV high-resolution MS (HRMS) is employed for simultaneous quantification of the two main impurities in \"pure\" commercial CBG samples (cannabigerovarin (CBGV) and cannabinoid (CBGB)) with subsequent confirmation by comparison with synthesized compounds [193]. * Matrix Effect  
The sample, i.e., matrix type greatly affects selection of extraction technique, extraction solvent(s), HPLC column, mobile phase and detection method, which further enhances method sensitivity, selectivity and specificity. All three validation parameters are affected by the presence of matrix constituents that co-extracts with phytocannabinoids, causing signal alteration (suppression or enhancement). In that spirit, matrix effect is frequently reported during LC-MS-based phytocannabinoid profiling. Cannabis plant material is complex matrix with high fat, pigment and polar compounds content, being flavonoids and terpen most prominent. Cannabis-based products are much more versatile in terms of fat-, sugar- and polar-interferences content, thus being more prominent to expressing significant matrix suppression during instrumental analysis. Significant polar matrix interferences are reported to occur when EtOH is used for extraction of phytocannabinoids from honey, which also co-extracts several interfering matrix components, such as flavonoids [194]. Matrix effect is also examined during phytocannabinoid profiling of commercial products including oils, creams, and plant material. No significant matrix effect is observed in oil for \(\Delta^{9}\)-THC, CBD, CBDA and \(\Delta^{9}\)-THCA (110.4 \(\pm\) 116.0%, 105.4 \(\pm\) 112.2%, 96.3 \(\pm\) 117.8% and 92.7 \(\pm\) 107.8%, respectively); acceptable matrix effects for \(\Delta^{9}\)-THC and CBD in plant material (102.0 \(\pm\) 112.8% and 91.2 \(\pm\) 129.4%, respectively) and in creams (79.4 \(\pm\) 93.1% and 83.8 \(\pm\) 100.2%, respectively). Matrix effect of CBDA was especially pronounced in plant material, with signal enhancement particularly at low concentrations [173].
## 4 Analysis and Results
### Data Analysis of Teachers' STEM Education Intention
#### 4.2.2 Assessment of Hypothesized Relations  
According to the model fit test, the fit of this model was good. Through the path coefficients and hypothesis testing, we obtained the model of teachers' intentions to implement STEM education, as shown in Table 7. When the value of the critical ratio (C.R.) was 1.96 or higher, the value of the coefficient (P) was considered significant at the level of 0.05 [53]. In this research, we used the path coefficients (\(\beta\)) of the fitted model and the \(p\)-value in the analysis and verified that 12 hypotheses, including H1, H3, H1a, H1b, H1c, H2a, H2b, H2d, H3a, H3b, H3c, and H3d were valid, while two hypotheses, namely H2 and H2c, were rejected. Therefore, we constructed the model of teachers' intentions to implement STEM education and its factor path coefficients (as shown in Figure 2). Every path constructed from one latent variable to another showed their relationships, and the path coefficient points of the valid hypotheses were between 0.093 and 0.890, which indicates a positive correlation among the exogenous variables and endogenous variables of the twelve valid hypotheses. In other words, the paths had statistical significance and supported the model.  
The value of the SMC (squared multiple correlations) represents the strength of the structural relationships [61]. The value of the SMC ranges between 0 and 1, where stronger relationships are indicated by values close to 1 [62]. The explained maximum variance of the three exogenous variables (perceived behavioral control, subjective norms, and attitudes) to behavioral intention was 0.90. The SMC value of the main endogenous latent construct, i.e., behavioral intention, was 0.53. That is, 53% of the variance among behavioral intention, attitudes, subjective norms, and perceived behavioral control could be explained by behavioral intention.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Obtained** & \
\begin{tabular}{c} **Values for** \\ **Excellent Fit** \\ \end{tabular} & **Values for Good Fit** &
\begin{tabular}{c} **Evaluation of Fit for** \\ **Final Model** \\ \end{tabular} \\ \hline x\({}^{2}\)/df & 1.954 & \(\leq\)2 & \(\leq\)3 & Excellent fit \\ \hline SRMR & 0.0485 & \textless{}0.05 & \textless{}0.08 or 0.09 & Excellent fit \\ \hline IFI & 0.953 & \(\geq\)0.95 & \(\geq\)0.90 & Excellent fit \\ \hline TLI & 0.948 & \(\geq\)0.95 & \(\geq\)0.90 & Good fit \\ \hline CFI & 0.953 & \(\geq\)0.95 & \(\geq\)0.90 & Excellent fit \\ \hline RSMEA & 0.045 & \textless{}0.05 & \textless{}0.08 & Excellent fit \\ \hline \end{tabular} Note. The fit indices are based on the work of [PERSON] et al. [59], [PERSON] [55], and [PERSON] [60]; x\({}^{2}\)/df: chi-square-to-degree of freedom ratio; SRMR: standardized root mean square residual; IFI: incremental fit index; TLI: Tucker–Lewis index; CFI: cumulative fit index; RMSEA: root mean square error of approximation.  
\end{table}
Table 6: Statistics for the final models.
# The Effect of Countermeasure Readability on Security Intentions
## 6 Discussion
Concerning the last supported hypothesis, the data point out that subjective norm is a significant determinant for security intentions (H15). This signifies that one is more inclined to act safely regarding cyber threats and measures when people (that individual relates to) think they should adopt protective security behavior. Finally, the proposed model explains 58% of the variance in the variable SI. According to [PERSON] et al. [61], this is a moderate effect size. \(F^{2}\) indicates an effect for 11 of the 15 relationships (Table 2). \(F^{2}\) measures the change in \(R^{2}\) (on a dependent variable) when an independent variable is left out of the model. [PERSON] [79] proposes \(F^{2}\) values of 0.02, 0.15, and 0.35 account for small, medium, and large effect sizes, respectively. Contrary to other research (e.g., [12, 44, 48] relationships concerning threat appraisal constructs are not supported (H3, H4, H10, H11). However, this is in line with work by [PERSON] and [PERSON] [10] and [PERSON] et al. [49], suggesting PMT is strongly context-dependent as all related work displayed in Figure 1 differ slightly in their contextual application. Section 6.1 delves into other possible reasons for this lack of support. Besides the model, Section 5.1 demonstrated there is a significant difference in perceived readability between 'easy' and 'hard' countermeasure texts, as classified by five readability metrics (see Appendix A). The distributions of the indicators for countermeasure readability are highly negatively skewed. This explains why the means of 'hard' and 'easy' countermeasures do not differ much. As a result, it can be concluded that readability metric scores of textual readability indeed affect the perceived readability of cybersecurity countermeasure texts. Consequently, if a countermeasure text scores low on readability according to a readability formula it will be perceived as poorly readable, and vice versa.
## 4 Assessment
### Setup  
The technology that has been used for implementation and the crowded events measurement campaign is from the open experimentation platform MONROE (Measuring Mobile Broadband Networks in Europe) [39]. The MONROE platform is a software environment created from an H2020 project that manages more than 300 virtualized probes distributed across Europe. It offers an isolated environment for running applications by wrapping the software in a complete file system containing everything needed for execution (i.e., virtual containers). In this way, containers share the underlying host resources but only include what they need to run applications. It is, therefore, possible to schedule multiple experiments and measurements to run on a probe to compare the performances of different mobile networks.  
The measurement campaigns have been executed by programming two probes equipped with LTE Cat6 radio interfaces and integrated with a container-based virtualization system developed by MONROE consortium [40]. These probes or nodes have been placed in Lerkendal Stadium of the Rosenberg BK team, located in Trondheim (Norway). In this area, two different  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **MODEM Log** \\ \hline \multirow{6}{*}{Identifiers} & Integrated Circuit Card Identifier (ICCID) \\  & Physical Cell Identifier (PCI) \\  & Location Area Code (LAC) \\  & Cell ID (CID) \\  & eNodeB ID \\ \hline \multirow{6}{*}{Radio Parameters} & Frequency \\  & Band \\  & Received Signal Code Power (RSCP) \\ \cline{1-1}  & Reference Signal Received Power (RSRP) \\ \cline{1-1}  & Reference Signal Received Quality (RSRQ) \\ \cline{1-1}  & Received Signal Strength Indicator (RSSI) \\ \cline{1-1}  & ratio of Signal to Interference (ECIO) \\ \hline \hline \end{tabular}
\end{table}
Table 2: MODEM log parameters.  
operators A and B, have their base stations placed around the stadium, which enables the study of the influence of crowded events on the user-perceived service quality. In Figure 2, the approximate distribution of the base stations is shown according to a crowdsourced database online [41].  
Beyond assessing the capabilities of the proposed framework, the research aimed to answer in this specific scenario whether the development of an event affects the quality of the service, and if it does, to determine the most influential factors and to which extent they have an impact. Additionally, it was of interest to find behavioral differences between operators.
# On Evaluating IoT Data Trust via Machine Learning
## 5 Evaluation
### Considered ML-Based Approaches  
In the literature, both supervised and unsupervised ML algorithms have been used to classify the trustworthiness of IoT data or sensors/devices. Supervised ML algorithms are trained for classification by minimizing the error between the predictions and the ground truth. Semi-supervised ML algorithms can also be utilized for learning classification models from a mixture of labeled and unlabeled data. Unsupervised ML (clustering) algorithms are generally not designed for classification, but instead are useful for understanding existing patterns and relationships within the data. However, they can be used to classify unseen data based on their cluster assignment. After training a clustering algorithm using data with no class label, the data instances assigned to each learned cluster can be presumed to have the same class label. Unseen data can then be classified according to their proximity/distance to different clusters. In this way, we can compare the classification accuracy of supervised and unsupervised ML algorithms when used to evaluate data trustworthiness.  
The lack of publicly available benchmark datasets makes the comparison of the existing approaches for IoT data trust evaluation difficult. Therefore, we base our comparisons on augmented versions of the Intel Lab dataset that contain untrustworthy data synthesized via the methods described in Section 3.  
In our experiments, we consider the relevant approaches that are based on supervised, unsupervised, and semi-supervised ML. They include the fully supervised approach based on learning a binary classification model from a dataset containing ground-truth data trust labels and the unsupervised approach of learning a classification model from a dataset whose data trust labels are obtained via clustering as proposed in [11; 12]. We use two classification models, namely, linear SVM and multi-layer perceptron (MLP) with a single hidden layer. For cluster analysis, we use the \(k\)-means and Gaussian mixture model (GMM) clustering algorithms. In addition, we consider a semi-supervised approach based on the label propagation algorithm and evaluate its performance over partially labeled datasets.  
We compare the cross-validated performance of the considered ML-based approaches, in terms of the accuracy of data trustworthiness classification, over multiple datasets augmented by different methods and represented by different feature sets. We also evaluate the classification accuracy of each approach on datasets that differ from the one used for training. The purpose of this evaluation is to assess how well each approach generalizes to unseen data.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 5 Results and Discussion  
The performance of the CNN-based model was evaluated against the dataset of heart patients. The deep learning algorithms MLP, CNN, LSTM, and RNN were used to predict heart failure patients and compare with the machine learning algorithms. A training-to-testing ratio of 70% to 30% was set for all models to measure the performance of the models. The Python libraries Keras and TensorFlow were used to implement deep learning algorithms. The hardware and software specifications are shown in Table 6. The system took approximately one hour time to train the data and give the final results.  
Table 7 shows the performance analysis of the deep learning models regarding the accuracy, precision, recall, and F1 scores. The CNN model leads, with an accuracy score of 0.9398 and precision, recall, and F1 score values of 0.95 each.  
The performance of the CNN model is better than the other deep learning models. Its performance was compared to two other models from study [59]. Reference [59] performed experiments using different scenarios, including one involving the use of oversampling by the synthetic minority oversampling technique (SMOTE). The SMOTE is utilized with CNN and Extra Tree Classifier (ETC) models. Results comparison given in Table 7 indicates that the performance of the proposed CNN model is better than both the CNN and ETC, which is employed with SMOTE in [59].  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Sr. No.** & **Hardware** & **Software** \\ \hline
1 & RAM 8 GB & Windows 10 \\ \hline
2 & DDR4 & Anaconda 1.0.0 \\ \hline
3 & Core i-5 & Pycharm 2023.1.1 \\ \hline
4 & 6 th Generation & Python 3.7.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: System specifications.  
Table 8 shows the accuracy with other models employed in [59], including Decision Tree (DT), Logistic Regression (LR), Stochastic Gradient classifier (SGD), etc. All these models are utilized with the SMOTE oversampling approach. Performance comparison indicates that the proposed CNN deep learning model shows better results than all the models used in [59]. Despite the use of SMOTE in [59], the CNN model performs better than these approaches.
###### Abstract
For previous researches, they concentrated more on emergency management for large passenger flow scenarios, strategy optimization for adding backup trains, and optimization of cross-line train organization plans. However, to the best of our knowledge, the problem of train rescheduling in the context of cross-line operation has not been fully discussed, and previous methods cannot be used to solve the problem. In practice, the cross-line operation is becoming increasingly common, and corresponding management problems are worth paying attention to. However, cross-line trains have a connection relationship between different lines. Interactions among planned trains and cross-line backup trains on different lines and other unique constraints need to be considered (see Section 3.1), therefore, the problem is more complex. To bridge this gap, this paper takes the scenario of large transfer passenger flow as the background and combines the strategy of adding the cross-line backup train to study the train rescheduling methods, including the corresponding models and solution approaches. The main contributions of this study include: (1) a mixed integer programming (MIP) formulation with quadratic terms is proposed for the train rescheduling problem, considering the service of transfer passengers and the addition of a cross-line backup train. The model focuses on the large transfer passenger flow scenario and optimizes the train rescheduling scheme, considering the approach of adding the cross-line backup train. (2) We propose a solution approach for solving the formulation in conjunction with the Gurobi solver, which can obtain one or more sets of feasible solutions for decision-makers to choose from. (3) We implemented a case study to analyze the impact of demand changes on the results of train rescheduling. The remainder is organized as follows. Section 2 further elaborates on this paper's problems. In Section 3, a detailed description is given of the optimization model considering dynamic passenger flow, which is a nonlinear model that includes objective functions and constraints. In Section 4, some linearization lemmas and model-solving approaches are proposed, and the corresponding lemmas are used to reconstruct the nonlinear model into a MIP model. Section 5 presents the case study, and the results of the study are discussed in Section 6. Finally, we summarize this paper in Section 6.
# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 4 The Proposed System
### Evaluation Metrics  
During the classification process, the evaluation metric is crucial for discriminating and obtaining an optimal classifier. Hence, five essential metrics are considered in this paper. Recall (\(r\)) is one of the metrics to find the percentage of the correctly classified positive pattern that the classifier should collect. Precision (\(p\)) determines the fraction of positive patterns in a positive class that are correctly predicted out of the overall predicted patterns. Both terms are often used to assess the goodness of a model in predicting observed instances [29; 30]. Recall and precision are defined in Equations (8) and (9), respectively:  
\[r=\frac{t_{p}}{t_{p}+t_{n}} \tag{8}\]  
\[p=\frac{t_{p}}{t_{p}+f_{p}} \tag{9}\]  
where \(t_{p}\) and \(t_{n}\) are the positive and negative instances that are correctly classified, respectively, and \(f_{p}\) is the number of misclassified positive patterns.  
Recall and precision are known to detect the sensitivity and positive predictive value of a classifier, respectively. However, they cannot be individually employed to evaluate the handover prediction in the proposed simulation. In that, the F-score metric (\(F\)) is the harmonic mean between recall and precision values. Further, the F-score combines both metrics to obtain a scalar value as in Equation (10).  
\[F1-score=\frac{2 pr}{p+r} \tag{10}\]  
Another related metric is the accuracy (\(A\)), which is defined as the proportion of correct predictions to all the predictions conducted (i.e., the total number of instances calculated). While the accuracy is a significant metric, it cannot be used alone to determine the robustness of the classifier. The accuracy (\(A\)) metric is calculated as shown in Equation (11).  
\[A=\frac{t_{p}+t_{n}}{t_{p}+f_{p}+t_{n}+f_{n}} \tag{11}\]  
where \(f_{n}\) is the number of misclassified negative instances.  
Finally, a receiver operating characteristic (ROC) curve is a graphical representation of the success of a classifier across all classification thresholds. The true positive rate and false positive rate are the main parameters that build this graph. An ROC curve can be generated by repeatedly running a logistic regression model. However, an aggregate measure of performance is determined for the overall possible classification thresholds,which is known as the area under the curve (AUC). It is utilized to calculate the 2D area under the ROC curve from \((0,0)\) to \((1,1)\).
## 2 Proposed UWB Antenna with Quadruple Band Rejections
### Performance Analysis and Current Distribution
When the antenna works at 5.2 GHz and 5.8 GHz, most of the current is concentrated at the interdigital resonant structure of the interdigital resonant slots. The structure resonates around 5.2 GHz and 5.8 GHz, and the energy cannot be radiated effectively, so a band stop is formed. Figure 12a,d show the surface current distribution of the antenna working at 3.5 GHz and 7.6 GHz. For 3.5 GHz, the current directions are opposite along the inside and outside of slot (a), and the currents are canceled by each other, so the antenna does not radiate, the surface current is concentrated in the curled C-shaped slot, and a frequency notch is achieved around the 3.5 GHz [23]. When the antenna works at 7.6 GHz, the current direction generated by the curled split-ring resonant on the backside of the antenna is opposite to the current direction generated by the corresponding position on the front side of the antenna. Thus, the currents in opposite directions cancel each other out, causing energy to be stored in the curled split-ring resonator rather than radiated into the air. Figure 12: Simulated magnitude of current distribution at (a) 3.5, (b) 5.2, (c) 5.8, and (d) 7.6 GHz.