esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Data Analysis for Identified Parameter
#### 3.1.2 Flood Vulnerability Parameters
Information was also verified in the records and data of Odiongan Water District. From Barangay Rizal down to the Poblacion (Dawapan, Liwanag, Liwayway, Ligay, Tabin-Dagat) area have access to pipe water. According to the data validated from the Odiongan Water District, 11 out of 25 barangays were supplied by piped water, where the main tank and reservoir are from Barangay Rizal. However, as Romblon is given such a water source, water supply from electric pumps and wells was installed for some barangays. The emergency prepared data were based on a survey questionnaire's knowledge and input from the head of barangays. The emergency preparedness map is shown in Figure 7g and was classified into three (3) categories, e.g., prepared, well prepared, and very well prepared. It was noticed that only 5 out of 25 barangays, namely, Progresso Weste, Progresso Este, Tulay, Poctoy, and Panique, have been categorized as \"prepared\" barangays during calamities. 3. Type of Built-up Structures  
The combination of information was taken from the barangay profile as of 2018, and a questionnaire survey was conducted. Figure 7h shows the types of built-up structures and are classified into three (3) categories. These are the (a) permanent, (b) semi-permanent, and (c) temporary shelters. Permanent buildings are structures with concrete foundations and walling, GI sheets as roofing, and other solid materials. Semi-permanent structures are a combination of lumber and concrete elements. Temporary shelters use sawali, bamboo, nipa, and cogon as construction materials. The map shows that most of the barangay have permanent and semi-permanent structures. This indicated that most of the homes in Odiongan are more resilient in terms of flood events. However, eight (8) barangays in the elevated area have residential structures categorized as temporary shelter. Consistent findings were proven in the previous research regarding households' housing types as a significant factor in flood vulnerability [43]. Coordinates were noted and listed during site investigations and measured the floor area to estimate the capacity of every room area during calamity (see Figure 7). Figure 7i shows the ideal coverage of every evacuation area in the municipality on which 500 to 2000 m circles around each evacuation center were drawn and categorized into five (5) levels. The map shows the number of people who can reach the facility within an acceptable walking distance. In this map, the ideal number of people have been identified for each evacuation center during a disaster. The Sphere standards imply that in the instant aftershock of a disaster, particularly in dangerous climatic conditions where quarter materials are not readily available, an area of no less than 3.5 square meters per person is suitable to save lives and provide adequate short-term shelter. As an evacuation center is utilized preferably only for a short duration, a center's maximum 'event sheltering' capacity should permit no less than 1.5 square meters per person [55].
## 2 Background  
### Fuzzy C-Means Clustering Algorithm  
For a sample set \(X=(x_{1},x_{2},\ldots,x_{j})\), it can be classified into \(c\) groups. Each sample in \(X\) can be sorted into a category according to membership \(\mu_{ij}\), where \(\mu_{ij}\) donates the membership value of the \(j\)-th sample belonging to the \(i\)-th category [12]. For the \(j\)-th sample, \(\mu_{ij}\) satisfies the condition as follows:  
\[\sum\limits_{i=1}^{c}\mu_{ij}=1,\ \mu_{ij}\in[0,1]. \tag{1}\]  
The objective function in FCM can be presented as follows:  
\[J_{m}(U,P)=\sum\limits_{j=1}^{J}\sum\limits_{i=1}^{c}{(\mu_{ij})^{m}(d_{ij})^{ 2}}, \tag{2}\]  
where \(m\in(1,+\infty)\) is weighted index and \(d_{ij}\) is distance of \(x_{j}\) and \(i\)-th clustering center \(v_{i}\). The target of the clustering process is to minimize the objective function. Considering some constraint conditions of \(\mu_{ij}\), the Lagrange multiplier method is applied to solve the extremum problem. The membership value \(\mu_{ij}\) and the clustering center \(v_{i}\) can be calculated as follows:  
\[\mu_{ij}=\left\{\begin{array}{ll}\frac{1}{\sum\limits_{i=1}^{c}{(\frac{d_{ ij}}{d_{ij}})^{\frac{2}{n-1}}}}&d_{kj}\
eq 0\\ 1&d_{kj}=0,k=i\\ 0&d_{kj}=0,k\
eq i\end{array}\right\}, \tag{3}\]  
\[v_{i}=\frac{\sum\limits_{j=1}^{J}{(\mu_{ij})^{m}x_{j}}}{\sum\limits_{j=1}^{J}{ (\mu_{ij})^{m}}}. \tag{4}\]  
The iterative process of FCM algorithm can be generalized as follows:  
Step 1.1: Initialization. Initialize weighted index \(m\), category number \(c\), original clustering center \(v^{(0)}\), stopping value of the iterative process \(\xi\) and the maximal iteration number \(N\).  
Step 1.2: Iteration. Update \(\mu_{ij}\) and \(v_{i}\) according Equations (3) and (4).  
Step 1.3: Comparison. Terminate the iteration process if \(\mid v^{(b+1)}-v^{b}\mid\mid\leq\xi\) or the present iteration number \(n=N\); otherwise, repeat steps 1.2 and 1.3.
## 3 Security Attacks in Smart Healthcare Systems
### Phishing Attacks  
A phishing attack is an attempt to acquire the victim's valuable information, such as login credentials (e.g., username and password), bank details, credit card numbers, company data, and other personal information that potentially has value. In a smart healthcare system, phishing is performed by creating a fake web link with malicious code running in the backend. So, when a user clicks on the link, the malicious code silently installs the malware on the healthcare system, which will spread rapidly to various medical devices. The attackers use social engineering techniques (e.g., baiting, pretexting, scareware, etc.) to create fake web links, which encourage the hospital staff or patients to deliberately click on them. In April 2022, three medical institutions, i.e., Charleston Area Medical Center, Central Minnesota Mental Health Center, and Christie Clinic were targeted by phishing attacks and email security incidents. The attackers have collected many employees' login credentials, and social security numbers, which impacted 54,000 individuals [63]. Another incident oocrred in August 2022, when Allegheny Health Network was compromised by a phishing attack that affected \(\approx\)8000 patients, causing them to lose their login accounts. To respond to this attack, the organization immediately shut down (temporarily disabled) the compromised accounts, reset login credentials, and applied monitoring controls [64]. In addition, the Health Sector Cybersecurity Coordination Center reported that attackers have developed a new phishing scheme that lures the victims to redirect to a phishing web page (containing malicious Trojans) that steals your valuable information. Mostly, the phishing attack is launched via emails, where catch phrases are framed to manoeuvre the human brain to click the malicious web page [65]. Therefore, to protect the smart healthcare system from fraud and phishing detection, [PERSON] et al. [66] applied ML and DL models such as Random Forest, K-nearest Neighbor (KNN), Decision Tree, Naive Bayes to identify the financial fraud detection in modern healthcare systems. Finally, the KNN model yielded the best accuracy compared with the other conventional models used for mitigate the phishing detection in healthcare.
# Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder
## 3 Methodology
### Implementing Feature Selection Methods  
Feature selection is required to identify significant attributes that improve the performance of machine learning models [26]. Many state-of-the-art works were found where various significant features were identified to detect autism more efficiently [27]. On the toddler, child, adolescent, and adult datasets, various feature selection techniques such as the Boruta algorithm, Correlation-based Feature Selection with Harmony Search (CFS-Harmony Search), Repeated Incremental Pruning to Produce Error Reduction (RIP-PER), and Recursive Feature Elimination (RFE) were used to explore various feature subsets. A brief description about them is given as follows:  
* **Boruta algorithm** is a wrapper algorithm based on the random forest [28] where it finds the importance of a feature by creating shadow features [29]. It is an extended system where each feature of the given data set is replicated. Then, the values of replicated variables are randomly combined, which are called shadow features. It performs its feature selection process using RF on the extended data set and evaluates the importance of each feature. Additionally, it computes the z-score of real and shadow features. It compares higher z-score values of real features than the maximum z-score value of its shadow features at every iteration. In this process, it constantly eliminates features that are deemed highly unimportant. Finally, the algorithm ends either when all features are approved or rejected or it obtains a particular limit of RF runs [30]. * **Correlation-based Feature Selection (CFS-Harmony Search)** is a heuristic function that evaluates the ranks of features based on their correlation [31]. For harmony search, it illustrates the point of intersection with the following parameters: the number of harmonies in memory \(N\), the number of indicator \(M\), the number of possible values of indicator \(D\), the number of optimal indicator \(i\) in the harmony memory \(=E_{i}\), and the rate of harmony memory \(E_{t}\). The probability is calculated by the following equation,
## 2 An Isolated, High-Power, Photovoltaic Pumping System (Monitored System)
### Hydraulic Network  
The irrigation (hydraulic) network consists of 55 m of a vertical well steel pipe (diameter of 30 cm), a hydro cyclone filter, 3 mesh filters, 1100 m of horizontal distribution polypropylene pipe (diameter of 30 cm), several 45 and 90 degree elbows, 6 tees and 6000 m of drip irrigation system working at a pressure range between 1.5 and 2.5 bars. The irrigation network is divided into 7 sectors. In Table 1 are presented the irrigation system (hydraulic network) design parameters (pumping head and discharge) of each sector.  
Figure 1: The isolated, high-power, photovoltaic pump system, located in the Aragon region of Spain.
# Improved Exponential Phase Mask for Generating Defocus Invariance of Wavefront Coding Systems
## 3 Simulated Results
The simulation results of the improved exponential phase mask of image \"Cameraman\" are shown in Figure 3:2 In Figure 10, the six lines represent the results of the improved exponential phase mask, exponential phase mask, cubic phase mask, sinusoidal phase mask, improved logarithmic phase mask, and tangent phase mask, respectively, and four rows represent the defocus value of 0, 20, 40 and 60, respectively. To quantified compared the performance of six phase masks, the differences between restored images and reality images are calculated by the following formula [21]:  
Figure 3:3  
Figure 3:4 Peak position difference variation with defocus of MTF of six kinds of phase masks. where RI is the reconstructed image and O the object, which have been normalized, \(i\) and \(j\) the coordinates of the pixels, and \(\left|\overline{\left|\text{RI}_{ij}-\text{O}_{ij}\right|^{2}}\right|^{2}\) denote the average values over all the pix els. The better the imaging quality, the higher the RIQ. The results are presented in Figure 3:5 It can be seen that the improved exponential phase mask offers the best image quality for defocus values ranging from 20 to 60, and is less expensive than the sinusoidal phase mask and the exponential phase mask at defocus values of 0. Figure 3:6 Simulation results of six different phase masks at defocus value of 0, 20, 40 and 60. (**a**) Improved exponential phase mask. (**b**) Exponential phase mask. (**c**) Cubic phase mask. (**d**) Sinusoidal phase mask. (**e**) Improved logarithmic phase mask. (**f**) Tangent phase mask.
## 3 Flow Control Devices for Gust Load Alleviation
### Passive Control Devices  
Comparing with the active control system, a passive control device is usually simpler and does not rely on extra energy. [PERSON] et al. investigated the effects of a passive twist wingtip as a gust-load alleviation device on a flying-wing configuration [69] and a 200-seater airliner [23], respectively. This concept is to use a separate wing-tip section connected to the main wing by a spring. As the shaft is located ahead of the aerodynamic centre, this device will have a nose-down twist under the gust-induced aerodynamic force resulting in gust load alleviation. The results showed significant reduction in gust-induced wing-tip displacement and root bending moment. Compared to the current active control methods, this passive control concept is attractive as it requires no energy input.  
Similar to the ideas by [PERSON] et al., [PERSON] et al. [70; 71; 72] investigated the effects for the alleviation on wing root bending moments at gust conditions by a flexible wing-fold device. The key idea was to introduce a hinge line which was not parallel to the incoming flow direction but was rotated outboard with a hinge orientation \(\Lambda\) to allow the wing tip to rotate. Therefore, folding the wingtip with the angle of \(\theta\) will reduce the local angle of attack, which can be calculated as \(\Delta\alpha=-tan^{-1}(tan\theta sin\Lambda)\). The results indicated that suitable designs of the control device are capable for gust load alleviation. It was also observed that the load alleviation capabilities are highly sensitive to the stiffness of the hinge spring and the wing-tip mass. It will be a problem in application as it is very challenging practically to change the mass or the hinge spring stiffness according to different incoming flows.
# Numerical Simulation Analysis of Wellbore Stability in Weak Interlayer of Igneous Rock
## 3 Numerical Simulation Model for Wellbore Stability
### Linear Elasticity Constitutive Equation  
Using the general Hooke's law to express the stress-strain relationship:  
\[\left\{\begin{array}{c}\varepsilon_{11}\\ \varepsilon_{22}\\ \varepsilon_{33}\\ \gamma_{12}\\ \gamma_{12}\\ \gamma_{23}\\ \end{array}\right\}=\left[\begin{array}{cccccc}C_{1111}&C_{1122}&C_{1133} &C_{1112}&C_{1123}&C_{1131}\\ &C_{2222}&C_{2233}&C_{2212}&C_{2223}&C_{2231}\\ &&C_{3333}&C_{3312}&C_{3323}&C_{3331}\\ &&&C_{1212}&C_{1223}&C_{1231}\\ &&&C_{2332}&C_{2331}\\ &&&C_{3131}\\ \end{array}\right]\left\{\begin{array}{c}\sigma_{11}\\ \sigma_{22}\\ \sigma_{33}\\ \sigma_{12}\\ \sigma_{23}\\ \sigma_{31}\\ \end{array}\right\} \tag{1}\]  
where \(C_{ijkl}\) is the flexibility tensor, \(\varepsilon_{ij}\) is the strain tensor, and \(\sigma_{kl}\) is the stress tensor.  
Considering the material to be transversely isotropic, the properties are considered constant on the isotropic plane. Furthermore, upon analyzing the material as isotropic, the matrix form of the stress-strain expression is as follows:  
\[\left\{\begin{array}{c}\varepsilon_{11}\\ \varepsilon_{22}\\ \varepsilon_{33}\\ \gamma_{12}\\ \gamma_{13}\\ \gamma_{23}\\ \end{array}\right\}=\left[\begin{array}{cccccc}1/E&-v/E&-v/E&0&0&0\\ -v/E&1/E&-v/E&0&0&0\\ -v/E&-v/E&1/E&0&0&0\\ 0&0&0&1/G&0&0\\ 0&0&0&0&1/G\\ \end{array}\right]\left\{\begin{array}{c}\sigma_{11}\\ \sigma_{22}\\ \sigma_{33}\\ \sigma_{12}\\ \sigma_{13}\\ \sigma_{23}\\ \end{array}\right\} \tag{2}\]  
Figure 4: Improved two-dimensional model of igneous rock formation.  
The relationship between the 3 elastic constants is as follows:  
\[G=\frac{E}{2(1+\
u)} \tag{3}\]  
where \(E\) is the modulus of elasticity, GPa; \(G\) is the modulus of rigidity, GPa; \(\
u\) is the Poisson's ratio.  
The only parameters involved here are the modulus of elasticity and Poisson's ratio, which can vary with temperature and other variables [23].
# A Case Study on the Design and Implementation of a Platform for Hand Rehabilitation
## 1 Introduction
The developed platform RehabHand demonstrates the use of different rehabilitation aids (even custom-made). A fusion of the mentioned technologies, bearing in mind the equipment's total cost, and providing a customized rehabilitation aid for the individual patients, is our project's vision. The goal was to develop an aid for therapists who work within rehabilitation centers and engage patients in rehabilitation with modern solutions. The main contributions outlined in this paper are:  
* the design of an affordable rehabilitation aid with cutting-edge technology that addresses some of the emerging challenges in health care and rehabilitation,
* a unique platform which enables therapists to define exercises independently of hardware and software (see Figure 1),
* a platform that can be upgraded with a new device (i.e., rehabilitation tool, robotic-assisted device, assistive technology) without changing the interface for the therapist,
* an example of local production for a rehabilitation aid where patients can work on their treatment autonomously to improve the functional capabilities with hand movement disabilities inside or outside rehabilitation centers,
* to support the rehabilitation community with a platform to share rehabilitation aid designs, rehabilitation exercises, and treatments. The remainder of this paper is organized as follows--a background on hand rehabilitation challenges is given in Section 2. Section 3 discusses the motivation for our work. The architecture and other generalized ideas for the RehabHand application are described in Section 4. Results implementing and using RehabHand are described in Section 5. A discussion about our experience in implementing the rehabilitation platform follows in Section 6. The related work on rehabilitation is discussed in Section 7. Finally, the concluding remarks are summarized in Section 8.
## 4 Experiments
### Evaluation Protocols and Metrics  
In this study, we adopted two evaluation protocols (items 1 and 2 below) and two evaluation metrics to assess model performance (items 3 and 4 below).  
1. Holdout method: In this method, datasets are split into three sets, i.e, training set, validation/holdout set, and testing set. Here, the training set is the subset of data used to learn the temporal pattern in the time-series data. The error associated with this is called training error. Validation or hold-out set is the subset of data used to guide the selection of hyperparameters. The error associated with this is called validation error. The test set is the subset of data used to measure the model's performance on a new unseen sample. The error associated with this is called testi error. The split ratio affects the model's performance.
2. Cross-validation method: In this method, datasets are split into k nonoverlapping subsets. In each trial, one of them is chosen as a test set and the rest is used as the training set. Test error is estimated by taking the average test error across k trials.  
Figure 9: Semi non-overlapping windowing data generation technique. The red box indicates an input sequence with ten features. With a window stride of fixed, the green box indicates the next ten input features.  
Accuracy: This metric denotes the total number of correct predictions of classes against their actual labels.
4. F1 score: This metric is the weighted average of precision and recall. Precision is the ratio of correctly classified positive observations to the total classified positive observations. Recall is the ratio of correctly classified positive observations to all observations in actual class.
## 4 Results and Discussion
### Path Model Equations
Comparison with a Simpler Regression Model  
By using multiple linear regression [86] (lm function) and the same sample dataset to derive the path model, we obtained the following regression equation for the air change rate \(\overline{\text{acc}}^{*,m}\) (\(F=380;\)\(p<0.001;\)\(R^{2}=0.566\)):  
\[\overline{\text{acc}}^{*,m}=-1.3405+0.1050\overline{T}_{\text{out}}+0.0181 \Sigma E_{\text{sol}}-0.1163\text{ER}^{*}+0.0252\overline{S}_{c}^{*}, \tag{4}\]  
with statistically significant (\(p<0.01\)) intercept and regression coefficients. Observing Equations (3a) and (4), the similarities between coefficients are obvious; moreover, the \(R^{2}\) values for the regression and path models are also identical (\(R^{2}=1-V(\zeta_{1})=0.575\) for the path model). In [87], the authors also reported identical results obtained with the path model and multiple linear regression, which is understandable given that path modeling uses a collection of statistical techniques that include multiple regression [42]. In spite of the similarity between the regression and path model in terms of results, it is important to recall that the goal of the path model analysis presented in this paper is to assess the internal structure proposed in Figures 4 and 5, not to improve the fit between model estimates and sample data. Indeed, the analysis presented thus far has used continuous data on occupancy and environmental physical variables to extend the understanding of natural ventilation with window airing by validating and quantifying direct, mediation, and feedback effects taking place in the classrooms of a Portuguese school. Additional insights made available by the path model are described in the next section.
# Software Defined Networks in Wireless Sensor Architectures
## 3 Wireless Sensor Networks
### Wireless Sensor Networks Architecture  
Wireless Sensor Networks (WSN) [11; 45] are typically a set of distributed autonomous sensors that are used to monitor physical or environmental conditions such as temperature, sound, and humidity, among others. WSNs are composed of a few or several devices called nodes, which contain a radio transceiver with an internal antenna, a microcontroller and an energy source. A WSN is also composed of a base station that stores the collected data and an application server as Figure 3 depicts.  
Figure 3: Wireless sensor network architecture.  
A WSN includes several characteristics such as:  
* Fault tolerant: nodes have the ability to cope with node failures.
* Heterogeneity of nodes: all nodes do not have to contain the same brand or the same characteristics.
* Energy harvesting: power consumption constraints for nodes using batteries.
* Durability: ability for resilience hard environmental conditions.
* Scalability: big deployments of nodes do not interfere in the performance of the network.  
In order to test these types of networks, there are some available simulators to check its behaviour. For example, simulators as OPNET [46], NetSim [47] or NS2 [48] are the most known nowadays.
# Performance Optimization of MANET Networks through Routing Protocol Analysis
## 3 Results
### PDR  
The PDR results of the OLSR and AODV protocols are shown in Figure 3. The PDR of the default AODV protocol has an average value of 84.6%, while that of the OLSR protocol is 77.45%. This result shows that the AODV protocol is better than the OLSR protocol in the moving node scenario. While reducing the RREQ_RETRIES value to 2 (AODV 2) increases the PDR performance by 1.8%, increasing the RREQ_RETRIES value to 4 (AODV 3) decreases the PDR performance by 1.9%. These results also apply to the reduction in the value MAX_RREQ_TIMEOUT, which increases PDR performance. By decreasing the MAX_RREQ_TIMEOUT value of 5 s (AODV 4), this increases the PDR performance by 5.3%, while increasing the MAX_RREQ_TIMEOUT value of 12 s (AODV 10) decreases the PDR performance by 2.1%.