esa-sceva/satcom-chunk-collection huggingface dataset:
# MEMS and IoT in HAR: Effective Monitoring for the Health of Older People
## 4 Materials and Methods  
In innovative healthcare, monitoring behaviors and analyzing the movements of older adults provides valuable insights for diagnostic and therapeutic planning. Sensors allow for the recognition of a range of human activities, from simple to complex. Currently, various sensors employing different technologies are available [36]. This article presents a technology-based system (MEMS) that detects patient movements and transmits this information to a monitoring system. The motor state is assessed by analyzing the recorded movements. To achieve this, a miniature sensor node, known as a MEMS Inertial Measurement Unit (IMU), is integrated into a Microcontroller Unit (MCU). The motion detection results are also displayed on smartphones, using BLE module (Bluetooth Low Energy). Data preprocessing and feature extraction occur onsite, which minimizes data transmission and reduces the load on the host machine. The MEMS data related to the activities performed are processed using sci-kit-learn to identify movements such as grasping, circular arm movement, walking, and leg flexion. The system is powered by the STM32 WB55 microcontroller (STMicroelectronics NV-Agrate, Agrate Brianza, Italy), facilitating the development of IoT applications that utilize Edge Machine Learning technology [37] (Figure 2).  
The following is a description of the device used, aimed at enhancing the technical understanding and reproducibility of the system. It highlights the performance optimizations and safety measures that have been implemented. Information regarding the firmware components offers insight into the interaction between hardware and software, ensuring integration and compatibility with other technologies. Additionally, the firmware description emphasizes advancements such as Bluetooth Low Energy and dual-core microcontrollers.  
Furthermore, by providing firmware details, this document supports research by serving as a benchmark for other researchers working on similar projects, ultimately facilitating knowledge-sharing and collaboration.
# Exploring the Cutting Process of Coaxial Phase Change Fibers under Optical Characterization Tests
###### Abstract  
Urban heat islands (UHI) are a growing issue due to urbanization, causing citizens to suffer from the inadequate thermal properties of building materials. Therefore, the need for climate-resistant infrastructure is crucial for quality of life. Phase change materials (PCMs) offer a solution by being incorporated into construction materials for thermoregulation. PCMs store and release heat as latent heat, adjusting temperatures through phase changes. Polymeric phase change fibers (PCFs) are an innovative technology for encapsulating PCMs and preventing leaks. This study produced PCFs via wet-spinning, using commercial cellulose acetate (CA, Mn 50,000) as the sheath and polyethylene glycol (PEG 2000) as the core. The PCFs were cut using a hot-cutting method at three different temperatures and washed with distilled water. Morphological analysis was conducted with a bright-field microscope, and chemical analysis was performed using Fourier transform infrared spectroscopy (FTIR) before and after controlled washing. Additionally, the washing baths were analyzed by UV-visible spectroscopy to detect PEG. The PCFs displayed a well-defined core-shell structure. Although some PEG 2000 leakage occurred in unsuccessful cuts, cuts at 50 \({}^{\circ}\)C showed sealed ends and less material in the baths, making it viable for civil engineering materials.  
hot cutting process; controlled washing; phase change fibers; commercial cellulose acetate; polyethylene glycol 2000; wet-spinning Article
## 2 Experimental Section
### Identification of Volatiles Produced by CL-1 Using SPME-GC-MS  
Twenty mL glass vials were used for capturing the volatiles produced by CL-1. After inoculation with a 5 mm disc of fresh culture, each vial was closed with a sterile cotton plug and incubated for 5 days. After purging the headspace with sterile synthetic air, the vial was sealed gas tight. The vial was further incubated for 6 h before sampling of volatiles. The volatiles were collected using solid phase microextraction (SPME) fiber assemblies (Supelco, Bellefonte, PA, USA) for 30 min at 25 \(\,\)C. The fiber was coated with 65 \(\,\)um polydimethylsiloxane (PDMS)/divinylbenzene (DVB). Following the sampling, the SPME fiber was immediately inserted into the GC injector, and the volatiles bound to the fiber were thermally desorbed for 3 min at 250 \(\,\)C. A Varian VF-5 MS (5% diphenyl-95% polymethylsiloxane) capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\,\)um) (Varian, Palo Alto, CA, USA) was used for volatile separation. Helium was used as the carrier gas at a flow rate of 1 mL min\({}^{-1}\). The oven temperature program consisted of 40 \(\,\)C (hold 2 min), 10 \(\,\)C/min to 200 \(\,\)C, and 25 \(\,\)C/min to 260 \(\,\)C (hold 5 min). The quadrupole mass detector was operated at 150 \(\,\)C in the electron impact ionisation (EI) at 70 eV. The ion source temperature was set at 230 \(\,\)C, and the transfer line was set at 280 \(\,\)C. The mass acquisition range was 40-400 \(m/z\).  
As the aim of the current work was to prove the growth promotion potential of volatiles of CL-1 in plants and also to identify them, SPME-GC-MS analysis focused on the qualitative analysis of the volatiles. Individual peaks were identified on the basis of their fragmentation patterns and identities were confirmed by comparing the mass spectra with the data system libraries (Wiley and NIST-2007).
## 2 Materials and Methods
### Characterizations of TiO\({}_{2}\)  
The morphology, particle size, and phase of TiO\({}_{2}\) powder were studied using transmission electron microscope (TEM) (JEM-2100, JEOL Ltd., Tokyo, Japan, software of 2013) operated at 200 kV. X-ray diffraction (XRD) (D8 Advance, Bruker, Germany) patterns were obtained with a Bruker D8 ADVANCE equipped with Cu source (Cu K\(\alpha\) = 0.154 nm, 40 kV, 40 mA). The scattered radiation was detected in the angular range of 10-80\({}^{\circ}\) (20) with a scan rate of 0.02\({}^{\circ}\). A commercial Total Pattern Analysis Solution software (TOPAS version 4.2, Bruker, Germany, 2011) was employed to determine the crystallite size and phase content of TiO\({}_{2}\) powder. The absorption edge and energy band gap of TiO\({}_{2}\) were investigated by diffuse reflectance spectroscopy (DRS) (Cary Series, Agilent Technologies, Santa Clara, CA, USA) with the wavelength range of 200-800 nm (Agilent carry 5000). The energy band gap of synthesized TiO\({}_{2}\) with different calcination temperatures was calculated by using Tauc plot from the equation as follows:  
\[(\alpha\mathrm{hv})^{\frac{1}{n}}=\mathrm{A(hv-E_{g})} \tag{1}\]  
where \(\alpha\) is the extinction coefficient, h is the Planck's constant (J-S), v is the light frequency (s\({}^{-1}\)), A is the absorption constant, E\({}_{g}\) is the energy band gap (eV) and n is the value of the specific transition, n = 2 for indirect band gap, and n = \(\frac{1}{2}\) for direct band gap [31].
###### Abstract  
A sustainable use of active heating, ventilation, and air conditioning (HVAC) systems is crucial for minimum energy consumption. Currently, research studies are increasingly applying adaptive setpoint temperatures, thus reducing considerably the energy consumption without influencing comfort levels excessively. Most of them, however, are focused on the limit values of adaptive comfort standards without considering the tolerance in users' adaptation capacity. This research study analyzed various tolerance ranges in the recent adaptive thermal comfort model from EN 16798-1:2019 used in setpoint temperatures. The study focused on the south of Europe, considering 47 cities in Spain, 18 cities in Portugal, 13 cities in Greece, and 20 cities in Italy. In addition, such cities were analyzed in three climate scenarios: present time, 2050, and 2100. The results showed that values prefixed by EN 16798-1:2019 for new buildings (tolerance of 0.00 \({}^{\circ}\)C) produced significant savings with respect to the static model and that each progressive improvement in users' thermal expectations in 0.25 \({}^{\circ}\)C increased the energy consumption between 6.57 and 9.31% in all scenarios analyzed. Even applying a thermal tolerance of 1.50 \({}^{\circ}\)C, energy savings are currently produced with respect to the static model. This tendency increases in future scenarios until a thermal tolerance of 1.75 \({}^{\circ}\)C. The results of this paper provide greater knowledge about the possible energy increase that the improvement in users' expectations would produce.  
adaptive comfort; setpoint temperature; energy consumption; climate change; energy efficiency +
Footnote †: journal: Applied Sciences
# Gait Recognition via Deep Learning of the Center-of-Pressure Trajectory
## Materials and Methods
### Verification  
In most biometric applications, the goal is not necessarily to identify an individual but rather to verify whether an individual is an authorized user or not. To test this type of verification scenario, 100 different training and test sets were built to challenge the best CNN in verification tasks. First, a full training set including 48,500 strides (24,250 segments) of the 36 participants was gathered. An independent test set including 5500 strides (2750 segments) was also built for testing the performance of the classifier. The segment labels identifying each participant (ID 1 to 36) were modified as follows. Each participant could be considered an authorized user (label = 1) or an unauthorized user (label = 0); the repartition between authorized users and unauthorized users was randomly chosen. Four different levels of repartition were chosen--10 authorized users vs. 26 unauthorized users, 15 vs. 21, 20 vs. 16, and 25 vs. 11. Twenty-five repetitions for each repartition level were performed, each repetition including a random assignment of individuals between groups. Like for transfer learning (see above), the best pre-trained CNN was modified for the new classification task. The CNN weights were frozen, except those of the two last convolutional layers. Given the binary nature of the classification task, the output layer was replaced with a logistic classifier (sigmoid activation). The mini-batch size was 128. The learning rate was 0.0002. Ten epochs were performed to fine tune the pretrained CNN. Two different performance indexes were used, the area under the receiving operator characteristic curve (AUC) and the equal error rate (EER). AUC is a recommended index in case of unbalanced classes [50]. EER--also known as crossover error rate--is a common metric in biometrics recognition. It is defined as the threshold where the false acceptance rate and the false rejection rate are equal [51].
# Experimental Evaluation of Hybrid Fibre-Wireless System for 5G Networks
## 3 5G New Radio Hybrid MIMO-Based Fibre-Wireless System
### CNN Based Digital Pre-Distortion  
For VCSEL-based RoF connections [60] and DFB-based RoF links [53], DPD has been widely investigated. DPD is a useful tool for reducing nonlinearities in OFH connections. DPD has been proposed using a Volterra-based memory system and a generalised memory polynomial [1, 2, 60]. Machine learning approaches have recently been presented as a method for DPD. When compared to Volterra-based conventional designs, neural network-based DPD appears to yield a considerably greater reduction in signal impairments. As a result, this research employs deep neural network-based DPD. Both carrier frequencies are analysed using DPD In the following subsection, a detailed explanation of the working principle of the proposed CNN-based DPD is discussed.
## 3 Results
### Flat-roof Building
#### 3.1.2 Surface Pressure  
The flow pattern above the roof can also be analysed based on surface pressure. In addition, surface pressure can provide more reliable validation as it usually has lower uncertainty levels compared to velocity measurements. Figure 6 shows the contours of the mean surface pressure coefficient of the flat-roof building, at the four wind angles. The pressure coefficient (\(C_{p}\)) is defined as  
\[C_{p}=\left(p-p_{\infty}\right)/\left(0.5\rho U_{ref}^{2}\right) \tag{2}\]  
where \(p_{\infty}\), \(\rho\) and \(U_{ref}\) are the free-stream pressure, the air density and the reference velocity, respectively.  
Figure 6a shows a significant reduction in the surface pressure at 0\({}^{\circ}\) close to the upstream edge due to the flow separation, followed by a subsequent increase downstream. A similar distribution of the surface pressure is found at 15\({}^{\circ}\) with a slightly higher reduction in the pressure close to the upstream corner. At higher wind angles of 30\({}^{\circ}\) and 45\({}^{\circ}\), the contour maps are similar to each other, with a high reduction in surface pressure located at a limited area along both upstream edges. This pattern of pressure contours indicates the existence of two intense conical structures, as observed from the velocity measurements presented in Figure 5. As expected, for the 45\({}^{\circ}\) wind angle, the surface pressure distribution is symmetrical around the diagonal line of the roof. The non-uniform arrangement of the measuring taps might be responsible for the slightly asymmetric pattern in Figure 6d.  
To provide an improved understanding of the effect of the wind direction on the surface pressure, Figure 7 shows the mean pressure coefficient and its standard deviation along two alignments on the roof, at \(\chi/D=0.35\) and \(\chi/D=0.8\). For all wind angles in Figure 7a, a characteristic upstream hump shape is observed, which is typical for a flow with a separated region followed by a reattachment [57]. The hump shape is related to the high negative pressure values in the separation region. The largest suction lies directly below the average moving vortex core [23]. The length of the mean recirculation region is related to the peak location of the standard deviation value, considering that the peak occurs just upstream of the mean reattachment position [57]. For wind angles of \(0^{\circ}\) and \(15^{\circ}\), the hump shape is wider than in the case of \(30^{\circ}\) and \(45^{\circ}\). This is the consequence of the large separation bubble generating at the upstream edge, which entails a long recirculation region. Comparing Figure 7a,b, the growth in the size of the conical vortices along the side of the building can be noticed for the \(30^{\circ}\) and \(45^{\circ}\) wind angles.  
Figure 6: Mean surface pressure coefficient (\(C_{p,mean}\)) distributions on the flat roof for different approaching flow angles: (**a**) \(0^{\circ}\), (**b**) \(15^{\circ}\), (**c**) \(30^{\circ}\) and (**d**) \(45^{\circ}\). Black dots on the surface plot represent the measurement taps.
## 2 Background and Related Concepts
### Security Challenges  
The term \"security\", in the context of an organization, pertains to the safeguarding of valuable assets, including information, resources, processes, and records [13]. Companies that prioritise security allocate significant resources to establish an effective information security system to protect their data. However, despite these efforts, they remain a target for various threats [25].  
Managing security poses challenges for large corporations with multiple departments or small branches sharing the same network. In the latter scenario, this structure makes it easier for cybercriminals to target small branches before moving on to headquarters. Security plays a crucial role in supporting organizational operational processes and methods. Therefore, organizations must protect confidential information from potential threats or harm resulting in damage, loss, modification, or unauthorized disclosure.  
According to [PERSON] and [PERSON] [26], an organisation's security should be a complex system that includes computer systems supporting the organisational environment, software applications and databases securing data, as well as policies, procedures, training, and other human-reliant components. The primary objective of securing an organization's critical information resources and assets is to implement security policies across three layers: onboarding access control, authentication access control, and risk access control policies utilizing related technologies [13].  
[PERSON] and [PERSON] further suggest that the objectives of security policies should encompass the protection of confidentiality, integrity, and availability of information-reliant entities and information-delivering systems, ultimately fulfilling privacy requirements [26].  
Hence, confidentiality, integrity, and availability objectives are essential for accomplishing security policy objectives.
## 2 Swarm Intelligence Algorithms
### Bat Algorithm (BA)  
The Bat Algorithm (BA) is an SI algorithm inspired by the echolocation behavior of bats. The BA demonstrates unique characteristics that set it apart from other metaheuristic algorithms [8].  
The algorithm begins by initializing a population of bats, where each bat represents a potential solution to the optimization problem. Bats fly through the search space, continuously adjusting their positions and velocities based on their knowledge. BA implementation is illustrated in Algorithm 3[1].  
```
1:\(n\): number of bats
2:\(d\): dimension of the search space
3:\(t_{max}\): maximum number of iterations
4:\(A\): loudness
5:\(r\): pulse rate
6:\(\alpha\): cooling factor
7:\(\gamma\): wavelength
8:\(x_{i}\): position of bat \(i\)
9:\(f(x_{i})\): fitness of bat \(i\)
10:\(x_{i}^{*}\): best position of bat \(i\)
11:\(f(x_{i}^{*})\): best fitness of bat \(i\)
12:
13:\(x^{*}\): optimal solution
14:Function BA()
15:for\(i=1\) to \(n\)do
16: Initialize \(x_{i}\) randomly
17:endfor
18:while\(t<t_{max}\)do
19:for\(i=1\) to \(n\)do
20: Generate a new position \(x_{i}^{\prime}\)
21: Calculate the fitness of \(x_{i}^{\prime}\)
22:if\(f(x_{i}^{\prime})<f(x_{i})\)then
23:\(x_{i}\gets x_{i}^{\prime}\)
24:endif
25: Update loudness: \(a_{i}\leftarrow\alpha_{i}-1\)
26: Update pulse rate: \(\beta_{i}\leftarrow\beta_{i}+1\)
27:endfor
28:for\(i=1\) to \(n\)do
29: Probability of loudness: \(p_{i}=\frac{1}{\alpha_{i}}\)
30: Probability of pulse rate: \(q_{i}=\frac{1}{\beta_{i}}\)
31:if\(p_{i}>q_{i}\)then
32:\(x_{i}\gets x_{i}+\gamma(x_{i}^{*}-x_{i})\)
33:else
34:\(x_{i}\gets x_{i}-\gamma(x_{i}-x_{best})\)
35:endif
36:endfor
37:\(t\gets t+1\)
38:endwhile
39:\(x^{*}\gets x_{best}\)
40:\(f^{*}\gets f(x_{best})\)
41:endFunction
```  
**Algorithm 3** BA Pseudocode
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 4 Challenges and Opportunities
#### 2.2.5 Multi-information fusion  
The electric network method, surface electromagnetic field method, time difference positioning method, and electromagnetic imaging method consider the physical characteristics of the grounding grid corrosion. However, these methods do not reveal the nature of corrosion and can only solve the problem of corrosion degree detection. The electrochemical method can detect the corrosion rate but not the degree of corrosion. Multi-information fusion [52], combined with multiple methods, is expected to simultaneously achieve corrosion degree and corrosion rate detection. Therefore, tapping the potential of existing methods, implementing two or more corrosion detection methods in one device, solving the problem of simultaneous detection of corrosion degree and corrosion rate, and improving the practicality of the detection method are future development directions.
## 3 Results and Discussion
### Global LAI and fAPAR Derived from GlobAlbedo  
Figures 1 and 2 show examples of the global retrievals of GlobAlbedo-derived fAPAR and LAI respectively for day of year (DOY) 185 in 2002 at 25 km resolution in sinusoidal projection. Also shown are the retrieval uncertainties in each case. These uncertainties are a core part of the parameter retrieval process outlined in Section 3 below _i.e._, posterior parameter uncertainty covariance \(\mathbf{C}_{\mathbf{X}_{post}}\) from Equation (3).  
The fAPAR and (effective) LAI values shown above display similar spatial patterns (unsurprisingly) _i.e._, high fAPAR values co-occur in general with high LAI values, albeit more evenly distributed globally in terms of mean and maximum values. LAI reaches a maximum of around 3 in parts of the US Midwest, Amazonia and Siberia, with the greening of the boreal regions being quite obvious. The uncertainty in fAPAR, \(\sigma_{\text{fAPAR}}\), lies mainly between 0.1 and 0.4; uncertainty in LAI, \(\sigma_{\text{LAI}}\), is generally highest in areas of high LAI, with some exceptions in Western Europe, China and SE Asia. In these regions, persistent cloud likely causes the higher retrieval uncertainty. This ability of the two-stream inversion package, JRC-TIP, to provide parameter retrieval uncertainty as a consistent part of the retrieval process is a key reason for using such an approach. Data assimilation applications for example, require explicit consideration of uncertainty in driving observations [41; 42; 43]. The GlobAlbedo product includes uncertainty estimates on a per pixel basis. This is a key difference from other global EO-derived estimates of fAPAR and LAI, for which parameter retrieval uncertainty is either absent, or quantised and hard to interpret as a \"real\" uncertainty [44].
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
## 5 Experiment Environment
### Experimental Setup for Performance Evaluation  
In this work, we consider a 100k-node (\(k=10\), 7, 4) randomly disseminated in WSN with an area of \((500\times 500)\)m \(\times\) m with \(k_{c}=100\) clusters. In each cluster, a fixed number of nodes \(\eta_{c}\) are distributed randomly. All of the sensors have a range, \(R_{i}=50\) m \(\pm\)\(\epsilon\), where \(\epsilon=10\%\) perturbation is introduced, as, due to obstacles, all sensors' actual ranges may vary. Range is used for neighbors selection, choosing candidates, and forwarding nodes. The variation in initial energy levels of sensor nodes is also accommodated by introducing \(\epsilon=5\%\) noise. Furthermore, different network sizes and FTRs are used to test the robustness of our approach on diverse setups and environments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Sensors nodes & 800 \\ Sensor field size & \((500\times 500)\) m \(\times\) m \\ BS location & \((250,\ 0)\) m \\ \(R_{i}\) & 50 m \\ Cluster h/w & 50 m \\ \(E_{alc}\) for Tx and Rx & 50 n/bit \\ \(E_{amp}\) & 100 pJ/bit/m\({}^{2}\) \\ Node energy & 1 Joules \\ MAC verification & 20 mJ \\ Data packet & 32 bytes \\ Round & 800 bits \\ \(FTR\) & 60\% \\ Path loss constant (\(\lambda\)) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters setup for optimization.  
The experimental evaluation setup parameters with network size 1000, 700, and 400 sensor nodes are shown in Table 2. The \(BS\) is located at (250 m, 0 m) and knows node IDs, locations, and \(k_{n}\) keys of each node. At start-up, the boot-up process with localization is initialized. In the simulation, we execute the model of the proposed system as described in Section 4. Table 3 highlights the equations along with the respective context used in the simulation modeling.  
The simulation experiments have been carried out building a C++ simulator using Microsoft Visual Studio 2010 ([PERSON], Washington, USA).