esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
From a practical perspective, the use of healthy recreational adults from across the general population, iPhone X with a 240 Hz high-speed camera, the relatively large number of participants, and field-testing conditions rather than a precise laboratory space all signify strengths of the current research. However, the main limitation was that we did not use force plate, which is considered as the \"gold standard\" in measuring vertical jump in various populations. Nevertheless, comparing My Jump app with Optojump is more appropriate because both use the flight time to measure jump height. Additionally, different methods for determining the height of the vertical jump exist, which can also impact the validity of instruments [32]. Most of the research has compared methods that calculate jump height to methods that calculate flight distance. [PERSON] and [PERSON] [33] mention a method based on a force-displacement curve. The method used to calculate jump height should be determined by the equipment available and the definition of jump height used by the practitioner [34]. Furthermore, a possible limitation of our study was that some participants might not have been familiar with the SJ jump style and the usage of hands in CMJAS test, especially among female participants. Relatively high variability obtained in SJ may be due to a lack of proper technique among recreational athletes, while previous research was conducted on elite athletes [28] with greater experience performing these jumps. Additionally, in comparison to male participants, females have a little difference in achieved jump height between CMJ and CMJAS. We can speculate that females did not swing with their arms correctly and use them to enhance their jump performance. During this jump, the arms reduce the pressure on the ground by moving downward toward the ground, which creates a negative effect, and later the arm swing creates a positive effect by moving upward and increasing the pressure on the ground [35]. Optimal jump is performed when the arms move in the jumping movement direction [36]. Additionally, female athletes show the trend for the increased differences in jump height between the two devices with increasing jumping height, which was confirmed by [PERSON] et al. [37]. Furthermore, another limitation was that we did not check for the inter-rater reliability because some factors could contribute to differences in scores (i.e., the experience of the tester, the different variability of scores, testers' seat position, and assessment view angle) [38]. Therefore, future study should include a larger number of observers to compare results and to account for probable human error. Nevertheless, our results support the usage of smartphone apps in measuring vertical jump in recreationally active males and females. Due to its advanced technology, popularity, low cost, and portability, smartphone apps will soon be commonplace for measuring variables associated with physical fitness and health with great precision [39].
## 3 Improved DBSCAN Algorithm Based on Sparrow Search Algorithm
### Fitness Function Selection  
In the case that the real classification label is unknown, the quality of clustering can be described by internal metrics, and calculating the contour coefficient is a commonly used internal cluster measurement method which evaluates the similarity of a sample point with its own cluster rather than other clusters. The specific formula is defined as follows:  
\[s(i)=\frac{b(i)-a(i)}{\max\{\,a(i),b(i)\}} \tag{14}\]  
where \(a(i)\) denotes the mean proximity of the \(i\)-th entity to its fellow members within its cluster. Conversely, \(b(i)\) signifies the mean proximity of the \(i\)-th entity to entities residing outside its own cluster, encompassing all other clusters. Values nearing 1 in the silhouette coefficient denote superior quality in cluster formation.  
The contour coefficient is used in Sparrow Search Algorithm as a fitness function and serves as an important indicator to measure the clustering effect because it does not need to calculate the density and dispersion between clusters without specifying the clustering center, and has robustness. Compared with other indicators such as DBI and CH, the silhouette coefficient index is the most widely used indicator. It is possible to evaluate datasets containing various shapes, and the comprehensive effect is optimal. By using this feature of the profile coefficient, the optimal values of Eps and MinPts parameters in DBSCAN can be calculated.
## 3 Results
### Complexity Analysis
#### 3.1.1 Data Preprocessing with MediaPipe  
* Description: The preprocessing stage uses the MediaPipe API to detect keypoints in the images.
* Complexity: The computational complexity is approximately \(O(n)\), where \(n\) is the number of frames or images processed. Each frame is processed independently to extract 21 keypoints, making the overall complexity linear for the number of frames.
# Development of Active Microvibration Isolation System for Precision Space Payload
## 2 System Composition
### Principle of Vibration Isolation Module  
A diagram of a single active vibration isolation module of the isolation platform is shown in Figure 2. The equivalent stiffness and damping of the system are represented as \(k\) and \(c\), respectively, and the total mass of the loaded platform is \(m\), which constitutes the passive structure. The active control force \(u\) provided by the voice coil motor can reduce the influence of the noise \(a_{1}\) transmitted from the base plane noise \(a_{0}\), \(z_{0}\) and \(z_{1}\) correspond to the displacements of the base platform and the load platform, respectively. The dynamic equation can be expressed as  
\[m\ddot{z}_{1}+c\dot{z}_{1}+kz_{1}=u+c\dot{z}_{0}+kz_{0}, \tag{1}\]  
The diagram of the SISO control loop for a single vibration isolation module in the system is shown in Figure 3, which includes feedforward controller and feedback controller. The control block \(C_{0}\) provides the feedforward control force \(u_{ff}\) using the acceleration \(a_{0}\) measured base platform, and the control block \(C_{1}\) provides the feedback control force \(u_{fb}\) using the acceleration \(a_{1}\) of the load platform. The resultant force \(u\) acts on the AVIS to suppress the disturbance on the load platform by the voice coil motor.  
Figure 1: Structure of active vibration isolation system.  
The objective of the control is to design appropriate controllers \(C_{0}\) and \(C_{1}\) so as to suppress the noise disturbance on the load platform as much as possible in the required frequency band. According to the above control force design, the dynamic equation can be expressed as follows:  
\[m\ddot{z}_{1}+c\dot{z}_{1}+kz_{1}=c\dot{z}_{0}+kz_{0}+C_{0}\ddot{z}_{0}+C_{1} \ddot{z}_{1}, \tag{2}\]  
After Laplace transform, the function is given as:  
\[\Big{(}ms^{2}+cs+k-C_{1}s^{2}\Big{)}z_{1}=\Big{(}cs+k+C_{0}s^{2}\Big{)}z_{0}, \tag{3}\]  
Owing to the relationship between displacement and acceleration \(z_{0}=\frac{a_{0}}{s^{2}}\), \(z_{1}=\frac{a_{1}}{s^{2}}\), the transfer function can be expressed as  
\[G(s)=\frac{a_{1}}{a_{0}}=\frac{cs+k+C_{0}s^{2}}{ms^{2}+cs+k-C_{1}s^{2}}, \tag{4}\]  
It can be seen from Equation (4) that vibration suppression can be achieved by designing controllers \(C_{1}\) and \(C_{0}\).  
Figure 3: Control block diagram of the AVIS.  
Figure 2: Schematic diagram of the vibration isolation module.
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 5 Numerical Simulations
### Performance Comparison
#### 5.1.1 Accuracy Comparison  
Figures 3 and 4 show the simulation results of the four available methods. As is shown, ADMM_P had higher accuracy than other three methods. In Figure 3, the CDF curve of ADMM_P is in the leftmost side of the three other methods. This means that the estimation results of ADMM_P were more concentrated around lower errors. Especially at point Error = 2.5 m, the CDF of our method improved by up to 50% compared with the ADMM_SF, which did not utilize any NLOS mitigation techniques. In Figure 4, we further analyzed the performance of the four methods in varying environments with different \(\sigma_{n}\) and \(\alpha_{\text{NLOS}}\), respectively. The simulation results show that ADMM_P has higher accuracy, and performance is more stable in different environments.
## 4 Experiment Setup and Evaluation Metrics
### Evaluation Metrics: AWS Cloud Metrics
```
1:Import board
2:Import adafruit_dht
3:Import paho.mqtt.client as mqtt
4:From Crypto.Cipher import ABS
5:Import base6
6:Define the DHT sensor type (DHT11)
7:Define the Raspberry Pi's input/output pins to which the DHT11 is connected
8:Define a Python library (adafruit_dht:DHT11) to read the DHT series of humidity and temperature sensors on a Raspberry Pi with one argument, DHT pin connected
9:Define the key length which must be either 16, 24, or 32 bytes long
10:Define humidity topic variable and temperature topic variable for each IoT device (DHT11 attached to Raspberry Pi) in each experiment
11:Define MQTT broker variable
12:Define the variable of MQTT port
13:While True do
14:Define a connection function
15:Connect to Internet
16:If (the connection is established) then
17:Print \"connected\"
18:Else (the connection is not established) then
19:Try reconnecting to Internet
20:Define a message function
21:Read humidity degree from Raspberry Pi serial port using (dhtDevice.humidity)
22:Read temperature degree from Raspberry Pi serial port using (dhtDevice.temperature)
23:Print humidity degree
24:Print temperature degree
25:Generate keys containing a set of attributes for each sensor data type in each IoT device
26:Generate a first key containing a set of attributes for temperature sensor in each IoT device
27:Generate a second key containing a set of attributes for humidity sensor in each IoT device
28:Create the cipher config for first key (temperature sensor)
29:Create the cipher config for second key (humidity sensor)
30:Use the cipher of the first key to encrypt the humidity degree message using cipher.encrypt
31:Use the cipher of second key to encrypt the temperature degree message using
32:Cipher.encrypt
33:Encode the cipher and humidity degree message using the base64 module
34:Encode the cipher and temperature degree message using the base64 module
35:Print the encrypted message
36:Publish humidity topic with its encrypted message to fog device
37:Publish temperature topic with its encrypted message to fog device
38:End while
39:Create a client to connect to fog device
40:Make the client run connect, and message function
41:Enable the transport layer security using fog device certificates and MQTT protocol version
42:Connect the client to the MQTT broker using fog device's IP address and MQTT port 1883
43:Call a loop_start() method for the client connection
```  
**Algorithm 1**Collect data from the IoT device and send it to the fog device-IoT-Fog-Cloud architecture. This algorithm provides authentication and authorization operations from IoT devices (DHT11 sensor + Raspberry Pi) to fog devices.
## 3 Results
### Zeta Potential  
Pyrolysis temperature significantly influences the zeta potential attributed to the colloidal biochar surface charge. The increased negative charges may indicate that biochar particles form stable suspensions to be more mobile in subsurface environments. As seen in Figure 5, the zeta potential of SF had the lowest value (\(-\)14.3) compared to the three produced biochar types. However, there was no significant change between the different biochar types, which ranged from \(-\)23.1 (SB-1) to \(-\)25.4 (SB-3), indicating that all biochar particle samples carried negative charges on their surfaces.  
Figure 4: H/C, O/C, and (O+N)/C molar ratios of sesame feedstock (FS) and corresponding biochar types: parabolic dish collector (SB-1 and SB-2) and conventional method pyrolyzed at 300 \({}^{\circ}\)C.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 9 Micromegas for Precise Timing
This is an improvement of 32 ps compared to the single photoelectron time resolution of the first prototype of \(76.0\pm 0.4\) ps with 200 \(\upmu\)m drift distance, a drift field of 21 kV/cm and an amplification field of 35 kV/cm [118]. Resistive PICOSEC-Micromegas prototypes have been also tested in muon and high rate pion beams. Resistive strip Micromegas with different resistivities and discrete resistive [14] floating strip [120] Micromegas were tested. The floating strip resistive detector reaches a similar time resolution in the muon beam as the non-resistive PICOSEC-Micromegas, and the diffusion of the charge on the resistive strip Micromegas causes a degradation of the time resolution. All prototypes were operated in stable conditions for twelve hours in a pion beam with an average flux per spill of \(\sim\)2.2 \(\times\) 10\({}^{3}\) pions per cm\({}^{2}\) per  
Figure 26: The PICOSEC-Micromegas detection concept, described in detail in the text. second, demonstrating the possibility of adapting the PICOSEC-Micromegas concept for robust detectors in high-rate environments [121]. The higher particle flux also produces a higher ion back-flow in the detector, which is even more severe due to the electrons generated in the pre-amplification avalanche. The CsI photocathodes, providing up to \(10.40\pm 0.40\) photoelectrons per muon, degenerate from the ion bombardment and a part of the PICOSEC-Micromegas project, focuses on the development of more robust photocathodes with sufficient quantum efficiency [121]. Different alternative photocathodes have been tested in the muon beam and in a monochromator. The most promising material is DLC [122; 123], reaching 3.7 photoelectrons per muon [124], and B\({}_{4}\)C [125] with even better results than the DLC in the monochromator. Pure metallic and nano-seeded diamond photocathodes are robust alternatives with a lower quantum efficiency [121]. The PICOSEC-Micromegas detection concept is additionally adapted to prototypes with larger active areas and segmented anodes. The timing properties of the smaller prototypes are preserved with the multipad prototype with hexagonal segmented readout. Further demonstrators of (resistive) multipads are in preparation in order to investigate the full potential of the PICOSEC-Micromegas for future applications.
## 3 Results  
The expansion of fractures exhibits symmetry, with both sides of the fracture (referred to as \"edge fracture\" hereafter) behaving similarly. Therefore, the analysis focuses on the expansion behavior of one edge fracture and the central fracture (referred to as \"mid fracture\" hereafter). Figure 4 illustrates the expansion of the mid fracture and edge fracture at 30 s, 200 s, and 400 s. It can be observed that in the initial stage of fracture propagation, there is little difference in the morphology of the two fractures. As time progresses, the mid fracture becomes longer and narrower, while the edge fracture becomes wider and shorter. Additionally, the mid fracture exhibits less fracture compared to the edge fracture. This is because during expansion, the edge fractures on both sides exert a compressive effect on the mid fracture, limiting its expansion towards the sides while promoting its forward expansion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Elastic modulus/GPa & 12 \\  & Poisson’s ratio & 0.18 \\ Reservoir & Permeability coefficient/m\(\cdot\)s\({}^{-1}\) & \(1\times 10^{-7}\) \\  & Porosity ratio & 0.11 \\  & Filtration coefficient/m\(\cdot\)(Pa\(\cdot\)s)\({}^{-1}\) & \(1\times 10^{-13}\) \\ \hline  & Elastic modulus/GPa & 18 \\  & Poisson’s ratio & 0.13 \\ Interlayer & Permeability coefficient/m\(\cdot\)s\({}^{-1}\) & \(1\times 10^{-8}\) \\  & Porosity ratio & 0.03 \\  & Filtration coefficient/m\(\cdot\)(Pa\(\cdot\)s)\({}^{-1}\) & \(1\times 10^{-14}\) \\ \hline  & Tensile strength/MPa & 6 \\  & Pore pressure/MPa & 28 \\  & Displacement/m\({}^{2}\)\(\cdot\)s\({}^{-1}\) & 0.01 \\  & Viscosity/Pa\(\cdot\)s & 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters for model calculation.  
Figure 3: Comparison of simulated results for a single crack with PKN calculations. (**a**) The variation in fracture width with time. (**b**) The variation in fracture length with time.  
Figure 5 reflects the variation in pore pressure at the injection points during fracture expansion. The edge fracture injection point ruptures and fracture expansion begins when the pore pressure reaches 47.85 MPa, while the mid fracture pore pressure needs to reach 49.31 MPa for fracture expansion to start. Therefore, the edge fracture expands before the mid fracture, exerting a certain suppression effect on the width of the mid fracture and promoting its length. Additionally, the forward expansion of the mid fracture also has a certain inhibitory effect on the expansion of the edge fractures on both sides. As the fractures continue to expand, the pore pressure at the mid fracture  
Figure 4: The expansion of fractures at different time points (left—edge fracture, right—mid fracture).  
injection point and the edge fracture injection point eventually stabilize at around 23.37 MPa and 24.34 MPa, respectively.
## 2 Material and Methods
### Parameter Estimation  
Some parameters from Tables 2 and 3 were empirically estimated to ensure responses to the epidemiological variables. The percentages of infected agents \(\beta_{i}\) and immune agents \(c_{i}\) were defined to represent the complete disease dynamics, according to the SEPAI3R3O model.  
The parameter related to the population size on Joana Bezerra Island, identified as \(\gamma_{3}\), assumes significant importance as it directly impacts the execution time of simulations. Meanwhile, the population density of the neighbourhood, calculated using formula \(\gamma_{3}/(\gamma_{1}\gamma_{2})\), revealed that the 12,629 inhabitants occupy an area of 0.87 km\({}^{2}\). This calculation produces a population density of approximately 14,516 inhabitants per km\({}^{2}\), highlighting the notable population density in the area in question.  
Few studies have used agent-based models to simulate the economic impacts of COVID-19. For example, authors of [64] quantified that a one-month lockdown in Tokyo would lead to a total production loss of 5.3% in Japan's annual gross domestic product (GDP). Authors of [65] proposed a tool to analyse the pandemic's health, social, and economic impacts when the government implements a series of interventions, such as school closures, requiring employees to work from home, and providing subsidies to the population. Based on this premise, we surveyed socio-demographic and economic data, such as the total GDP of Joana Bezerra Island, represented by parameter \(\delta_{2}\), along with the percentage rates attributed to different economic agents (\(\delta_{3}\) for public GDP, \(\delta_{4}\) for business GDP), which are abstract representations of economic dynamics within a closed local system. The net minimum wage, indicated by \(\delta_{5}\), reflects the nominal income after tax deductions, establishing a floor for labour remuneration in the region. On the other hand, the minimum expense, denoted by \(\delta_{6}\), corresponds to the estimated cost of a basic set of goods and services necessary for subsistence in the area, reflecting the minimum cost of living for the inhabitants.  
These parameters are crucial for understanding the local economic structure and planning policies. The total GDP provides an overview of the region's economic output. In contrast, the specific rates by type of agent provide insights into the relative contributions of different sectors to the economy. The net minimum wage is an essential indicator of workers' economic well-being, and the minimum expenses measures the affordability of essential goods and services. Together, these parameters form a basis for detailed economic analyses, thus informing policy and business decisions for Joana Bezerra Island.
# Pattern Recognition in Older Adults' Activities of Daily Living
## 3 Materials and Methods  
### System Architecture  
The primary aim of this study is to develop a highly autonomous and scalable system designed to identify the daily life patterns of older adults. Additionally, the system is intended to detect deviations in their behavior throughout the day, thereby enabling the transmission of alerts to their family members or caregivers. The construction of a system capable of fulfilling these objectives is essential. The architectural framework of the project is depicted in Figure 1 and is divided into two stages.  
The first stage corresponds to the configuration of devices to gather data on older adults' daily activities. This involves beacons emitting periodic Bluetooth Low-Energy (BLE) signals, which are then assigned to several rooms within the household. Concurrently, two applications for data collection and configuration are developed. Firstly, an Android mobile application facilitates the setup of the physical system and maps beacon representations within the application interface. Subsequently, user association with the beacons is achieved. A smartwatch widget application compatible with Garmin OS is developed to enable this. Both systems, the mobile application and the smartwatch widget, encompass distinct functionalities that allow the integration of components for data storage. These two developed applications are out of this paper's scope. The data collection process encompasses acquiring beacon locations through the mobile application and identifying users through the smartwatch widget application, facilitating the capture of vital biometric data to monitor the user's physiological signs. This phase culminates in the generation of data stored within a MongoDB database.  
Subsequently, these accumulated data undergo processing to derive insights into usage patterns. The mobile application configures the environment by associating all household beacons with the individual, ensuring scalability by accommodating unlimited environments. Conversely, the smartwatch widget application captures periodic beacon signals, stores them, and records the user's heart rate. The stored data are then transmitted to an API, bridging the applications with the database. The database assumes responsibility for storing all pertinent data gathered by the smartwatch throughout the user's daily activities, which subsequently serves as the basis for pattern recognition and outlier detection.  
The second stage is the focus of this paper. This phase encompasses data processing, analysis, and decision-making. The project is designed to operate autonomously, devoid of any requirement for human intervention in decision-making processes. So, as more dataare obtained, the assembled algorithm becomes more accurate and prepared to make decisions and not create false positives concerning the detection of outliers.