esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Decision Algorithm for Health Impact Assessment in a 5G Environment
## 6 Results and Discussion  
The presented multiparameter fuzzy decision-making algorithm takes into account almost all key parameters according to ICNIRP guidelines. All of the data and frequency ranges, as well as the corresponding membership functions and fuzzy intervals of the observed parameters in the fuzzy model, were also made based on recommendations from ICNIRP guidelines. The presented algorithm was practically realized by software tools [35]. The input parameters are the following: frequency, intensities of magnetic and electrical magnetic fields, SAR, penetration depth, social group (gender, age, and occupation), exposure time, etc.  
Fuzzy rules are simple statements that specify a relationship between a system's inputs and output. They consist of an antecedent (the if portion) and a consequent (the then portion) connected by the implication operator. The _if-then_ rules were created by using the ICNIRP guidelines (Figure 1) and research papers [6; 10; 11; 12]. Attention should be paid to the max and min values of 3D surfaces shown on the graphs that show which adverse health effects are dominant at the given frequencies.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 3 Composite Islanding Detection Solution  
### Basic Principle  
From the analysis in Section 2, the VVS relay monitors the sudden voltage phase angle change at the instant of islanding and ROCOF relay monitors the change of frequency after islanding. One example for an islanding condition of the synchronous generator is presented in Figure 3 with voltage cycle length before and after islanding. The voltage cycle length is measured by the zero-crossing detection method. The islanding condition happens at time 0 s. The voltage cycle length suddenly increases from 20 ms to 20.13 ms which means the voltage vector shifts about 2.6\({}^{\circ}\) due to the sudden current change. After islanding, the cycle length increases and the frequency decreases. The rate of change of frequency remains greater than 0.5 Hz/s until time 0.6 s. Hence the sudden islanding condition in this example results in a voltage jump and then the frequency changes.  
Compared with the voltage phase angle at the instant of islanding, there is an accumulated phase angle drift due to the frequency change. The accumulated phase angle drift is expressed as:  
\[\sum{Ang(t)}=2\pi\cdot\int{[f(t)-f(t_{0})]dt} \tag{5}\]  
In Equation (5), where \(f(t_{0})\) is the frequency at the instant of islanding.  
The state machine of the composite phase angle drift (PAD) solution is shown in Figure 4.  
Figure 4: State machine of composite phase angle drift (PAD) method.  
Figure 3: Voltage cycle length before and after islanding.  
The basic principle includes four main criteria:  
1. If any voltage phase angle changes per cycle \(\Delta Ang_{A},\Delta Ang_{B},\Delta Ang_{C}\) are larger than the threshold \(\Delta Ang\_set\), the stable frequency and phase angles just before the phase angle jump is recorded.
2. If three phase angle changes are in the same direction, the calculation of the phase angle drift \(\sum Ang\) is initiated. The calculation of the phase angle drift is based on the recorded last stable frequency value, as Equation (5).
3. If the maximum rate of change of frequency in five power cycles is less than the threshold \((df/dt)\_set\), which means the frequency is almost stable, the calculation of the accumulated phase angle drift is terminated and the value of \(\sum Ang\) is reset to zero.
4. If the accumulated phase angle drift \(\sum Ang\) is larger than threshold \(\Delta Ang\_set\), the islanding condition is claimed.
## 3 Materials and Methods
### Classification Methods
#### 3.2.1 Support Vector Machine  
Support vector machine (SVM) is a classification algorithm used for finding an optimal hyperplane that maximizes the margin between classes. That hyperplane is orientated in such a way that it is as far as possible from the closest data points from each of the classes. These closest points are called support vectors [27]. The key element of the SVM algorithm is the kernel function. It transforms a non-linear feature space into a linear one before the hyperplane search [28].
## 5 Experimental Analysis
### Evaluation Metric and Methodology  
We implemented decision trees, random forests, logistic regression, KNN, and LSTM for validating the proposed exploratory cyber-physical safety framework for commercial UAM. Furthermore, we evaluated the algorithms by utilizing confusion matrices that could  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline K-range (KNN) & \((1,100)\) \\ Cross-validation (KNN) & 10 \\ N-neighbors (KNN) & 79 \\ C (logistic regression) & 0.8 \\ Random state (random forest) & 42 \\ Cross-validation (random forest) & 5 \\ N-estimators (random forest) & 50 \\ Cross-validation (decision tree) & 10 \\ LSTM units in cell & 50 \\ No. of epochs (LSTM) & 100 \\ Batch size (LSTM) & 155 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of experiment set-up.  
Figure 5: Principal component analysis (PCA) of the considered dataset for detecting attacks (spoofing or jamming) in UAM attitude control.  
allow us the measurement of false positive and false negative results [5]. Furthermore, we evaluated the proposed schemes using well-established evaluation metrics such as the precision, recall, F1-score, and accuracy of the AI mechanism. In other words, we fed the data to the KNN, logistic regression model, random forest classifier, decision tree classifier, and LSTM model for predicting the accuracy of attacks from the attacker. We have not provided a detailed working procedure of each AI algorithm, since we used and deployed the Scikit APIs [26] for each ML model. We refer to the following base paper reference for further reading on the decision tree [9], random forest [10], logistic regression [11], KNN [12], and LSTM approaches [13].  
We illustrate a block diagram of the overall methodology with the inputs and outputs in Figure 6. We considered UAM attitude control parameters such as the input timestamp, reference point, roll and longitudinal, transverse and pitch, vertical and yaw, reference change, roll change, pitch change, and yaw change inputs as shown in Figure 6. The inputs were prepossessed by missing value interpretation and adding a label of jamming or spoofing and benign. We implemented decision trees [9], random forests [10], logistic regression [11], KNN [12], and LSTM [13] on the Python platform [26,27]. Then, each trained model was tested by the test dataset and used to predict the jamming or spoofing and benign behavior of UAVs. Based on this detection, the proposed framework found the root cause and effect of the servo rail voltage and RSSI pin voltage for the switching state (PX4 IO Status) parameters. Furthermore, the proposed framework could also analyze the impact of radio status parameters such as the RSSI, remote RSSI, transmit buffer, noise, and remote noise for finding the impact of jamming or spoofing and benign cases (as shown in Figure 6). Therefore, our cyber-physical safety analyzer could detect all kinds of attacks such as jamming and spoofing from attitude control system and explain the root cause on a radio frequency-based communication signal.
# Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
## 4 Simulation and Discussion
### Fuzzy Controller Drive
#### 4.3.1. Starting Characteristics  
The initial torque of the motor is 12 nm, and it is 41.6% more than the PI-controller-driven five-phase BLDC motor. While starting the motor with no load, the overshoot percentage is \(-0.0722\) (99.67% less than the PI controller), the peak time value is 0.7664 s,  
Figure 7: Transient performance of the drive with a fuzzy controller.  
the rise time is observed as 0.0697 s, and the observed settling time is 0.0881 s. The simulation result from t = 0 s to t = 1 s is the same across both types of load variations. It is seen that the speed settles at 1398 rpm (2 rpm less), the steady state speed for all load conditions.
# Student Behavior Recognition in Classroom Based on Deep Learning
## 5 Experiment and Analysis
### Metrics  
In this paper, to comprehensively evaluate the performance of the proposed classroom behavior detection model, the following metrics are selected:  
1. Precision: Precision measures the proportion of correctly identified positive samples among all the positive predictions made by the model. This metric is chosen because it effectively assesses the model's ability to minimize false positives. The calculation is represented in Equation (9):  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Behavior** & **Criteria** \\ \hline Raising\_hand & Student is raising one hand or both hands \\ Standing\_up & Student is in a standing position \\ Writing & Student is holding a pen and paper \\ Slippage & Student is in a head-turning posture \\ Listening & Student’s gaze is directed at the blackboard or teacher \\ \hline \hline \end{tabular}
\end{table}
Table 2: Criteria for judging students’ classroom behaviors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Raising\_hand** & **Standing\_up** & **Writing** & **Slippage** & **Listening** \\ \hline Encoding & 0 & 1 & 2 & 3 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Category code.  
\[Precision=\frac{TP}{TP+FP}\] (9) Among them, \(TP\) stands for True Positives and \(FP\) stands for False Positives.
2. Recall (R): The recall rate measures the proportion of true positive samples that the model correctly identifies, reflecting its sensitivity. This metric is crucial for evaluating the model's ability to detect all relevant targets, ensuring comprehensive performance assessment. As shown in Equation (10): \[Recall=\frac{TP}{TP+FN}\] (10) \(FN\) stands for False Negatives.
3. F1-Score: The F1-Score is the harmonic mean of precision and recall, balancing accuracy and sensitivity. It is particularly valuable for evaluating model performance on imbalanced data, as shown in Equation (11). \[F1=2\times\frac{Precision\times Recall}{Precision+Recall}\] (11) (4) mAP: mAP measures the average detection accuracy of the model on multiple categories. This metric is chosen because it can comprehensively evaluate the overall performance of the model in multi-category tasks. As shown in Equation (12): \[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i}\] (12)  
Among them, \(AP_{i}\) stands for the average precision of class \(i\), and \(N\) stands for the total number of classes.
# URSIM: Unique Regions for Sketch Map Interpretation and Matching
## 4 Segmenting the Maps and Building the Anchor Sets
### Creating the Sets of Anchors from the Unique Regions of the Sketch and Metric Maps  
Given a sketch and metric map, the number of unique regions found in each map might be very different, e.g., if one map has multiple similar looking regions compared to the other map, or if a map represents a bigger part of the environment than the other. As presented in Section 5.1, the URSIM method uses the position of all unique regions to build region descriptors representing the topology of the environment and the position of the anchors. Hence, having a different number of unique regions between the sketch and metric maps may create dissimilar region descriptors between otherwise equivalent regions. In this section, we present how, before creating the region descriptors, URSIM reduces both sets of unique regions to the same length by finding the most similar unique regions between the sketch and metric maps.  
We define the disparity score \(d\) between two regions R and R' as the mean of the differences of their attributes standardized:  
\[d=\frac{1}{N}\sum_{n=0}^{N}a_{n}^{s}-a_{n}^{s^{\prime}} \tag{2}\]  
and use the Hungarian matching algorithm [29], with weights given by the disparity score, to find the set of unique regions that are the most similar.  
In practice, the Hungarian algorithm finds correspondences between the two sets of unique regions, effectively making both sets of unique regions equal in length if we disregard the regions that were not matched. However, since the topology of the environment is not yet taken into account,  
Figure 3: Example of a map segmentation on the left and unique regions found using the map attributes on the right. Non-unique regions are left white. (**a**) MAORIS map segmentation of a sketch map. (**b**) Coloured are the unique regions found in the map shown in (a) using the size and shape attributes.  
the correspondences are often not correct, as can be seen in Figure 4. Thus, this step is only used to reduce the sets of unique regions to two sets of equal length by removing the most dissimilar unique regions between the maps, i.e., the regions unmatched by the Hungarian matching. However, the Hungarian matching correspondences themselves are not used in the rest of the method.  
We refer to the regions in the reduced sets as the _anchors_ of the maps.
## 4 Model and Design of SPIDEP Platforms
### Analysis of Software Attributes  
This preliminary step is fundamental for the design and development of applications, since it is necessary to define an adequate set of software attributes according to the needs identified [7].  
In response to these considerations, it is of utmost importance to mention our needs; they are as follows: (i) neutral technology--those responsible for the development, implementation and monitoring of the assigned services may decide which is the best technology to use according to their objectives; (ii) automation and continuous deployment--each component should be autonomous, independent and focused on a specific task according to the established decomposition (e.g., by verb or use case). In addition, it must have the individual ability to replicate to balance the load, as necessary; (iii) scalable to the demand--the services require a horizontal scalability that allows a faster and more precise reaction to the demand peaks; (iv) interoperable--each service must run a lightweight communication mechanism (e.g., RESTful) to communicate between the different applications or services, either their own or those of third parties. In addition, this mechanism should not have access to visual elements, i.e., it must be separate from the front-end; and (v) high performance--the application must support a large volume of queries of the REST methods (e.g., GET, POST, PATCH and DELETE) from the integration, transfer and storage of the biometric sensors from the different nursing homes (Edge) to the various services offered by the SPIDEP hybrid (Cloud).  
As explained in Section 2.3 (SOA vs. MSA), it was decided to adopt the concept of versatile high-performance, since it allows us to meet the needs of the project [21].
# Blockchain-Based Decentralised Authentication in Closed Environments
## 7 Conclusions  
In this study, we proposed a decentralised authentication protocol based on blockchain technology specifically designed to operate in closed or private environments. Such environments include academic or financial institutions and healthcare facilities, where security and privacy are crucial.  
The proposed protocol leverages a network of validator nodes that are representative of different departments or centres within these organisations. Each department or centre acts as a validator, contributing to the consensus process and ensuring that authentication requests are processed securely and efficiently. This structure not only enhances the integrity of the authentication process but also creates trust among users, as each validator is a recognised entity within the organisation.  
By implementing this decentralised approach, our protocol addresses several key challenges encountered by traditional centralised authentication systems, including a single point of failure. The distributed nature of the blockchain ensures that sensitive user data remain protected while allowing transparent and auditable authentication processes.  
While this study provides a framework for decentralised authentication using blockchain technology, there are certain limitations. The proposed protocol primarily targets closed environments, which may restrict its applicability in more open settings. In other words, while our current work assumes that pre-registered validator nodes are suitable for closed environment, we acknowledge that different scenarios may require more flexible registration mechanisms. Future work will, therefore, focus on enhancing the protocol's adaptability to diverse environments.  
Conceptualisation, W.K. and S.B.; methodology, S.B.; validation, S.B.; formal analysis, S.B.; investigation, W.K. and S.B.; resources, W.K. and S.B.; writing--original draft preparation, S.B.; writing--review and editing, S.B.; visualization, S.B.; supervision, S.B.; project administration, S.B.; funding acquisition, S.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Suranaree University of Technology (SUT), Thailand Science Research and Innovation (TSRI), and the National Science Research and Innovation Fund (NSRF) (NRIS no.FF11-1199-67-12-114).  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 5 Conclusions  
The S\({}_{21}\) link between the wrist and footwear toe-cap is reported for a subject standing and walking. A monopole antenna is optimized for different on-body locations at 2.45 GHz, 3.95 GHz and 7.25 GHz. The results are summarized in terms of Cumulative Distribution Functions and the Maximum Likelihood Estimation criteria are used for K-factor parameter fit to a Rician distribution.  
In the subject standing pose, the S\({}_{21}\) remains persistent for each of the three frequencies. When the subject is walking, the S\({}_{21}\) varies rapidly with lower S\({}_{21}\) values for lower frequencies. This is attributed to channel fading and shadowing by the hand during the arm swing motion associated with walking. At the lower frequency, the fading is more noticeable because the selected channel is narrowband.  
The UWB channels are an easier fit to a Rician distribution, with small changes of the K-factor. In the 2.45 GHz case, the Rician K-factor is \(\sim\)10 dB lower than that for the UWB channels. This is due to  
Figure 11: **(a)** Measured S\({}_{21}\) for a walking subject; **(b)** CDF of the path loss for the five cases.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Minimum path loss [dB]** & **Maximum path loss [dB]** & **K-factor [dB]** \\ \hline Meas 1 & 52 & 54 & 20.9 \\ Meas 2 & 52 & 54 & 23.7 \\ Meas 3 & 52 & 54 & 26.0 \\ Meas 4 & 52 & 54 & 24.9 \\ Meas 5 & 52 & 54 & 23.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: K-factor estimation for the different measurements for the 7.25 GHz channel.  
the larger beamwidth at 2.45 GHz which increases the signal scattering from surrounding environment. The minimum path loss for each of the UWB channels is the same due to higher antenna efficiency. The 7.25 GHz channel is characterized by the largest K-factor due to its smaller beamwidth. The communication link complies with the standard for each of the measured cases for a subject standing and walking.  
Further work will consider the receiving node in different body locations; hence it will establish the optimum frequency band for different body locations. The antenna will be tested in an outdoor environment and with different transceivers to mitigate cable effects.
# Virtual Environments and Augmented Reality Applied to Heritage Education. An Evaluative Study
## 1 Introduction
### This Study  
In order to identify what work is being carried out on these new relationships between heritage sites, digital applications (particularly AR and VR and virtual environments) and education processes, we evaluated a selection of cultural heritage education programs which specify the use of virtual environments or virtual or augmented reality in their educational design. The main aim of this study was to analyse the educational components of heritage education programs which make use of these emerging technologies.  
The relationship between heritage education and technology is gaining presence in research in Spain [30]. Led by the Observatorio de Educacion Pattrominal en Espana (the Spanish Heritage Education Observatory--SHEO), there has been a large number of educational research projects carried out in recent years in heritage contexts with ICT resources (Information and Communication Technology). Particularly worthy of note are the analyses of the use of digital technologies in the areas of archaeology and heritage education [31], the use of ICT and contemporary heritageization processes [32], and the most recent projects regarding the study of apps [33; 34; 35] which follow in the footsteps of the initial projects carried out in Great Britain [23]. Over the past few years, studies on virtual heritage [36], Big Data [37] and heritage, ICT, and inclusion [38] have been carried out in other parts of the world.  
Although there are previous studies on the evaluation of heritage education programs, there are none which evaluate whether the fact of introducing these types of devices into the educational design supposes an alteration of their planning, or whether these types of devices are already totally integrated or if the process of implementation is still in progress. The initial hypothesis of this research suggests that we are facing an emerging process in which there is an increase in heritage educational programs that integrate virtual environments, AR and VR, which do not have a quality didactic or evaluative structure.  
Therefore, the specific objectives of this study are:  
SO1: To analyse the typology of heritage education programs which use VR, AR, or virtual environments. SO2: To evaluate the quality of the educational design of heritage education programs which specify the use of virtual environments, AR and VR in their planning.  
SO3: To analyse the correlations between the quality standards of these heritage education programs. SO4: To differentiate the quality of the programs according to whether they are publicly or privately owned, the specificity of the program and the category of heritage in which these digital resources are implemented.
## 1 Introduction  
### Motivation  
Distribution system operators (DSOs) aim to improve the distribution system operation state constantly [1]. The operation indices include items such as low energy loss, smooth voltage profile, and a high-power factor of the distribution substation. In the distribution network, there are various ways to access the desired state of operation [1]. For example, if the network has local resources (resources at the demand site) such as distributed generation (DG), their energy management may considerably enhance the operation indices [2]. Moreover, generally, the distribution network has tie lines. Hence, by implementing the reconfiguration program, it is possible to take steps to improve various technical indices [3] so that it can obtain a low voltage drop by supplying the consumers through the distribution substation via a short path. Following this, a more favorable voltage profile is obtained. In addition, the reduction of voltage drop is proportional to the reduction of power and energy losses [3]. Therefore, system reconfiguration is a desirable program in the distribution network to improve the operating situation. According to the mentioned cases, appropriate energy management of local resources and optimal planning of system reconfiguration can help technical measures to enhance significantly [4, 5].  
Nonetheless, extracting these cases requires providing a suitable planning-operation formulation for DSO.