esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
Linear trends were calculated by the non-parametric Mann-Kendall trend test [41; 42]. This test consists of a comparison of each value of the time series with the remainingsequential values, calculating the number of times the remaining values are higher than the current tested value. The non-parametric Mann-Kendall trend test has been vastly used in trend analysis studies of meteorological variables in the NEB [43; 44; 45]. The following different statistical significance levels were used: 0.001, 0.01, 0.05, and 0.1 \(p\)-values. Trends tests were used for the entire air temperature, relative air humidity, and wind speed data series (January 2008 to December 2018). The results were analyzed as hourly trends and therefore are unprecedented to the region.  
The statistical analyses were carried out in the R software [46], and figures were generated using the ggplot2 package [47]. Boxplots were used because they simultaneously represent different important statistical aspects of data, such as central tendency, variability, symmetry, and the presence of outliers. The boxplot shows three quartiles, and the minimum and maximum data in a vertically aligned rectangular box. The box comprises the interquartile interval, with its lower limit representing the first quartile (Q1) and its upper limit representing the third quartile (Q3). A dashed line within the box represents the second quartile (median). In both extremes of the box, there is a vertical line extending towards the maximum and minimum observed values. These are the so-called whiskers that extend to a distance of 1.5 from the limits of the box (Q3-Q1) ([PERSON] et al. (1983)). All data beyond these limits are highlighted as outliers (values lying too far from the majority), represented by blank circles. In addition, the mean value was also added to the boxplot represented by a filled circle.
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 4 Experimental Design
### Dataset 5: Sealing Group  
The fourth dataset is collected from a thermoforming machine that can produce up to 320 products per minute. In particular, a component-level analysis is conducted on the sealing group, which is responsible for sealing the upper and bottom parts of the product. The group is made of two tables in which the film flows continuously, allowing the sealing at a low temperature. In each cycle, the top table goes up and closes; the cover is sealed to the base, and the tables open before returning to the initial position. The health condition of the sealing group is monitored through a displacement sensor that records the positions of the two tables. Figure 5 depicts the trend of the measured signal during a sealing cycle, which lasts 800 milliseconds, and the sampling frequency of the sensor is 1000 Hz. One nominal condition and three unacceptable working conditions, nominal, fault 1, fault 2, and fault 3, are available. Fault conditions do not cause the system breakdown. However, the quality of the final product is compromised, and the improper working condition has to be detected as soon as possible to avoid production losses.  
Figure 4: Temperature signals collected during the two nominal conditions by M1 (on the **top**) and M2 (on the **bottom**).
# Energy-Efficient Region Shift Scheme to Support Mobile Sink Group in Wireless Sensor Networks
## 3 Efficient Mobility Support for Sink Group Utilizing Region Shift
### Group Region Discovery by Region Shift Mechanism
```
1:\(MMI_{p}\): member sink movement information set of pivot node \(p\)
2:\(c_{prev}\): previous center point coordinates of a group region
3:\(c_{new}\): new center point coordinates of a group region
4:\(r\): radius of a group region
5:\(Dist(a,b)\): euclidean distance between geographic position \(a\) and \(b\)
6:\(MMI_{p}\leftarrow\) collect movement information during a certain time since the first movement report
7:for all\(i\in MMI_{p}\)do
8:if\((i\cdot x_{2}-c_{prev}\cdot x)^{2}+(i\cdot y_{2}-c_{prev}\cdot y)^{2}>r^{2}\)then\(\triangleright\) if \(i\)'s movement deviates the previous region
9:\(c_{new}\cdot x\gets c_{prev}\cdot x+Dist(i\cdot t_{x},i\cdot x_{2})\)
10:\(c_{new}\cdot y\gets c_{prev}\cdot y+Dist(i\cdot t_{y},i\cdot y_{2})\)
11:\(c_{prev}\gets c_{new}\)
12:endif
13:endfor
14:broadcast the new region information tuple \(<\)\(c_{new}\), \(r\)\(>\) into the region such that \((x-c_{new}\cdot x)^{2}+(y-c_{new}\cdot y)^{2}\leq r^{2}\)
```  
**Algorithm 1** Basic algorithm of region shift at a pivot node. A pivot node is elected as the closest node to a center point. Replacement rule of the pivot node is not strictly defined and it could be determined according to sink group parameters e.g., region size or moving speed. We defined the rule in this paper as the time until a pivot belongs to the current group region for easy understanding and clear presentation. When the pivot calculates a new group region and the pivot is out of the region, it also delivers a _new_election_ message when the new region is announced. Then, pivot \(P_{2}\) is selected and announces the new region message with the new pivot information.
## 2 Related Work
\\ \hline Complementing IoT Services Through Software Defined Networking and Edge Computing: A Comprehensive Survey & [PERSON] et al. [37] & 2020 & SDN and edge computing for limited resources computed intensive tasks. \\ \hline A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security & [PERSON] et al. [27] & 2020 & IoT security threats and solutions using machine learning and deep learning. \\ \hline Security, Privacy and Trust for Smart Mobile-Internet of Things (M-IoT): A Survey & [PERSON] et al. [28] & 2020 & Threats and countermeasures comparison of existing works for Smart Mobile-Internet of Things. \\ \hline From Pre-Quantum to Post-Quantum IoT Security: A Survey on Quantum-Resistant Cryptosystems for the Internet of Things & [PERSON] et al. [31] & 2020 & Comparison of traditional and quantum security vulnerability and impacts. \\ \hline A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues & [PERSON] et al. [32] & 2020 & Discussed present challenges and solution of IoT forensics. \\ \hline A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems & [PERSON] et al. [33] & 2020 & 5G layers, effect of 5G on IoT and review on low-power wide-area network for 5G. \\ \hline Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies & [PERSON] et al. [38] & 2021 & Emerging technology like SDN, NFV, Blockchain in the smart agriculture related application. \\ \hline A Survey on the Integration of Blockchain with IoT to Enhance Performance and Eliminate Challenges & [PERSON] et al. [41] & 2021 & Challenges and attacks resistance using blockchain technology. \\ \hline Applications of the Internet of Things (IoT) in Smart Logistics: A Comprehensive Survey & [PERSON] et al. [36] & 2021 & How IoT can be applied in smart logistics. \\ \hline A Survey on Security and Privacy Issues in Edge-Computing-Assisted Internet of Things & [PERSON] [39] & 2021 & Improvement of data processing and resistance of attacks using edge-computing. \\ \hline Lightweight Cryptographic Protocols for IoT-Constrained Devices: A Survey & [PERSON] et al. [34] & 2021 & Discussed IoT architecture and lightweight cryptographic protocols. \\ \hline Public Blockchains for Resource-Constrained IoT Devices—
# Quantitative Modeling of Near-Field Interactions in Terahertz Near-Field Microscopy
## 3 Results and Discussion
### Edge Effect
The influence of the edge effect increasingly diminishes because the maximum electric field strength of the blue line is significantly reduced. In addition, the influence of the edge effect becomes very small when it comes to S\({}_{3}\). Therefore, for the THz s-SNOM system, the higher-order signal can effectively eliminate the influence of the edge effect and improve the resolution of image. For this system, with the increase of the order of the extracted signal, the edge effect will continue to decrease, but the increase of the order of the signal will also increase the difficulty of extracting the signal, and the decline of the signal-to-noise ratio will lead to the deterioration of imaging quality. Therefore, in practical applications, it is necessary to extract the appropriate signal order to obtain better imaging quality. A more intuitive understanding of the edge effect in the harmonic signal is represented in our proposed model.
## 3 Time-Frequency Characteristics of Video Signals
To illustrate the above statement, the amplitude spectra of two models of the revealing emission signals obtained in an analytical manner, i.e., from reading the pixel values corresponding to the RED channel information of the images stored in the form of the BMP file, were determined. Two images, presented in Figure 2, were analyzed: vertical black and white stripes 8 pixels wide and the screen of the MS Word editor. The above can be compared with the case of displaying data on the displays of wireless communication devices. The lack of knowledge of the display operation mode requires carrying out the analyses necessary to obtain data about the parameters of the displayed image in order to recreate it from the recorded revealing emission. The images sizes correspond to a screen resolution of 1920 \(\times\) 1080 pixels. Based on the information contained in [7], models of video signals corresponding to the vertical refresh rate of 60 Hz were created. Under these conditions, the horizontal frequency is 67.5 kHz and the pixel frequency is 148.5 MHz. In order to map the characteristics of the real propagation channel (radiated type) of the reveal signals, the obtained signal was subjected to the differentiation operation. The amplitude spectra of the above-mentioned signals determined with the use of the FFT transform were shown in Figures 3-5. It was assumed that the signal was sampled at 297 MS/s, and 256 MS samples were included in the calculations. Figure 1: Timing relationships between video signals and (**a**) horizontal and (**b**) vertical sync signals for VGA monitor (video graphics array) operating in 640 \(\times\) 480/60 Hz mode. Figure 2: Test images: (**a**) 8 \(\times\) 8 vertical stripes and (**b**) a window of MS Word editor. Figure 4: _Cont_. Figure 3: (**a**) Amplitude spectrum of the test signal (strips 8 \(\times\) 8) and magnification of its fragments corresponding to the (**b**) horizontal and (**c**) vertical sync frequencies. When analyzing the above-presented fragments of the spectrum corresponding to the horizontal and vertical spectral lines, it can be noticed that even in the case of a signal built strictly according to the recommendations [7], the finite accuracy of the calculations causes the values of these frequencies determined with the use of the FFT transform to differ from the assumed ones. This indicates the need to look for solutions that can approximate the values of the above parameters with greater accuracy [34]. At the same time, it should allow to use of coherent summation with a greater multiplicity. Then, it will be possible to obtain an improvement in the value of the SNR.
## 4 Technical Analysis  
### IFC Information Extraction and Storage  
In order to better support the cooperation of all participants in the whole life cycle of buildings, buildingSMART [58] developed the IFC standard to create a BIM model, which is becoming the most widely used open standard for BIM data. In the IFC format, data objects are described by predefined entities and data types using the Express language.  
In general, the IFC format not only defines instances but also describes models through geometry, spatial relations, and other types of information. Spatial relations describe the association between instances and other types of information. Geometric information  
Figure 1: Technology roadmap.  
includes instance materials, spatial positions, and geometric expressions, which can be described by solid geometry, extrusions, patches, boundaries, etc.  
In IFC files, components are mapped from local coordinates to global ones by multiple transformations. The component coordinate is generally represented by the _IfcLocalPlacement_ class, which contains the coordinate origin, X-axis, and Z-axis directions.  
The IFC model is a semantic data model, which defines elements contained in the AEC domain as well as their relationship. For instance, _IfcRelDecomposes_ describes the composition relationship among IFC entity objects, and _IfcRelDefines_ is used to link instances with types or property sets. Figure 2 shows the spatial hierarchy of building elements in the IFC file. According to the IFC format, a project can contain one or more sites, and a site can contain one or more buildings, while a building may have one or more stories. All the building elements are attached to stories. This hierarchy is also referred to as the IFC Tree. Generally, the leaf nodes of the IFC file include columns, beams, plates, and so on. They contain some basic attributes and can be linked with property sets, which involve extended attributes.  
Generally, an IFC file has a large amount of data and complex references. Moreover, varieties of geometric descriptions increase the complexity of visualization. Therefore, in order to achieve efficient information storage, retrieval, and rendering, the developed system needs to be optimized according to the characteristics of the original IFC file.
# Effect of Physical Exercise on the Release of Micorparticles with Angiogenic Potential
## 8 MPs and Physical Exercise  
Physical exercise, particularly endurance exercise, is known to stimulate shear stress due to its hemodynamic effects [96]. Intraluminal shear stress has a beneficial effect on vasculature because it is able to ameliorate vascular dilation through mechanisms mediated by nitric oxide [97]. In addition, exercise-induced shear stress can modulate the production of both E-MPs and Plt-MPs. For example, it was seen that high shear stress can reduce the concentration of E-MP while low shear stress is prone to increase such concentration [98]. Moreover, both high and low shear stress can elicit the production of MPs from platelets [99] (Figure 2). Acute exercise leads to an augmentation of the sympathetic activity, increasing both epinephrine and norepinephrine concentrations [100]. In this regard, studies report that norepinephrine is able to stimulate Plt-MPs release [101]. Along with hormonal response, acute exercise stimulates the secretion of many cytokines from skeletal muscle, named myokines [102]. Among these myokines, IL-6 is released in the bloodstream and elicits multiorgan effects [103]. It has been reported that IL-6 is also able to stimulate Plt-MPs formation [104], thus exercise can be a potent stimulus to enhance such mechanism. The following sections provide an overview of various studies (summarized in Table 2) investigating the effects of physical exercise and training on MPs concentration, with an emphasis on endothelial and platelet-derived MPs, due to their specific involvement in angiogenesis [17; 105; 106; 107]. Figure 2: This figure shows the possible mechanisms involved in the production of platelet-derived MPs and endothelial-derived MPs and their role in stimulating angiogenesis. Both platelets and endothelial cells are activated in response to mechanical and chemical stimuli provided by exercise, that in turn elicit the MPs production by ectocytosis. The branching point refers to the site in which MPs stimulate neo-vascularization. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Participants** & **Exercise Protocol** & **MPs Antigen Expression** & **MPs Collection Time Point** & **Effects of Exercise on MPs** \\ \hline [PERSON] et al. & Twenty-five professional & & No experimental protocols, blood was analyzed at rest & CD2*/CD3* & At rest, after 12 h of fasting & MPs concentration did not differ \\ (2017) [52] & half-martathon runners & and compared with that of healthy controls & (PI-MPs/CD3* [E-MPs]) & At rest, after 12 h of fasting & MPs concentration did not differ \\  & & & healthy controls & (PI-MPs/CD3* [E-MPs]) & between runners and controls \\ \hline [PERSON]. [PERSON]. et al. (2013) [53] & Eleven healthy men & 5 days of reduced physical & CD31*/CD2* (“opeptide & CD31*/CD2* [E-MPs]) & At baseline, days 1, 3, 5 of \(\uparrow\) CD31*/CD2* [E-MPs] \\  & (2S \(\pm\) 2 year) & activity & E-MPs/CD2* (“activated & reduced physical activity & CD32*/ [E-MPs] \\  & &
# Magnitude Modeling of Personalized HRTF Based on Ear Images and Anthropometric Measurements
## 4 Performance and Evaluation
### Objective Evaluation  
The spectral distortion (\(SD\)) error was used to evaluate the accuracy of personalized HRTF. The indicator is defined as follows:  
\[SD^{(d)}(H,\hat{H})=\sqrt{\frac{1}{K}\sum_{k=f_{min}}^{f_{max}}(20\log_{10}\| \frac{H(k)^{d}}{\hat{H}(k)^{d}}\|)^{2}} \tag{7}\]  
where \(H(k)^{d}\) and \(\hat{H}(k)^{d}\) denote the magnitude of the true HRTF and the predicted HRTF in direction \(d\), \(k\) presents the frequency, \(f_{min}\) = 3 kHz and \(f_{max}\) = 15 kHz, with \(K\) being the total frequency points. The pinna usually affects the personalized HRTF in the range of 3\(\sim\)15 kHz, and the magnitude of the low-frequency HRTF is generally flat, the \(SD\) value of the low-frequency should be discarded. Hence, this paper only calculates the average \(SD\) value in the frequency range 3\(\sim\)15 kHz, that is, the frequency index is 9-44.  
Then we cover the entire discrete space and use global \(SD\) to evaluate global performance.  
\[SD(H,\hat{H})=\sqrt{\frac{1}{D}\sum_{d=1}^{D}{LSD^{(d)}(H,\hat{H})}} \tag{8}\]  
where \(D\) is the number of directions.
# Energy Consumption Analysis of ISO/IEC 29192-2 Standard Lightweight Ciphers
## 5 Discussion
These comparative results indicate that LEA maintains a consistent and scalable energy advantage over CLEFIA, making it a more suitable choice for power-constrained environments such as embedded systems and IoT devices. A comparative evaluation between LEA andPRESENT further highlights the efficiency of LEA in low-power encryption scenarios. When using LEA with a 128-bit key and PRESENT with an 80-bit key under identical test conditions, LEA demonstrated a substantial energy saving of approximately 96.5%. Furthermore, LEA completed the encryption process in 96.1% less time compared to PRESENT. These results clearly indicate that LEA not only consumes significantly less power, but also operates with much greater speed, making it a more suitable option for resource-constrained environments, such as IoT and embedded systems, where energy efficiency and real-time performance are critical.
# Review on Wearable Technology in Sports: Concepts, Challenges and Opportunities
## 7 Applications of Wearables in Sports
### Optimizing Athletic Performance  
In sports, success is a balance between high athletic performance and the negative consequences of excessive training and overexertion, such as severe fatigue, overuse injuries, muscle soreness, and decreased performance. Training at high volumes and intensities can be both the key to success and failure. Training volumes below what is considered optimal do not lead to adaptation, while training volumes above optimal can lead to \"overtraining syndrome\" [165].  
Technological advancements enable athletes, coaches, and doctors to maximize performance by using wearable sensors to track functional movements, workload, biomechanics, and key physiological indicators (biovital markers) such as heart rate, blood pressure, oxygen saturation, and other relevant physiological [100; 166; 167]. Coaches can collect measurable data using these tools to determine which training method yields the best results [151]. The generation of large volumes of data by wearable sensors allows for the identification of patterns in athletic performance. Continuous real-time data provided before, during, and after training, as well as during activities, can be analyzed to optimize sports performance.  
[PERSON] et al. [140] indicated that WT, when used to monitor preseason and regular-season training programs, can assist team athletic training and health personnel in developing programs to optimize player performance. Studies have also indicated that wearable technologies provide independent information related to performance and can be effective tools for monitoring athlete performance. For example, PlayerLoad (Catapult) is used to identify and measure external training load with IMU. Data from additional sensors can be integrated to calculate advanced motion models and measure the load in indoor sports [168].  
Due to the accessibility of wearable IMU, recent research primarily focuses on external load studies during training and/or competition. IMUs are applied in elite sports populations, particularly in indoor sports where GNSS signals are not available, to better understand movement demands [169]. IMUs allow for the examination of kinematic, technical [9], and biomechanical [3] characteristics in competitions, providing benefits for optimizing performance-related qualities such as timing, power, and speed.
## 2 Materials and Methods
### Data Acquisition  
To build robust classification models, the RGB images of the chickpea sampled were captured using two different cameras (smartphone camera and industrial camera) to accommodate potential variations due to imaging systems. The smartphone (Samsung Galaxy A6 Plus) used comes with a dual camera, where the primary camera has 16 MP, followed by 5 MP for the secondary camera with an f/1.7 aperture opening. First, the chickpeas were placed in a tray in a manner such that it was fully filled with chickpeas. In order to collect images through a smartphone, the smartphone camera was set on top of the chickpea tray in such a way that it could capture the maximum area of the tray without including the borders of the tray. The images were 3456 \(\times\) 4608 in resolution and a total of 150 images were collected for each variety. As for the industrial camera, a GigE colour industrial camera (Model: DFK 33 GX273; Specifications: 1.6 MP, 60 FPS; The Imaging Source, USA) was used. The chickpea seeds were placed over a conveyor belt and the speed of the belt was fixed at 1 m/min to avoid image distortion. The industrial camera was set up on top of a conveyor belt and the video of the chickpea variety was captured. Thereafter, the video was processed and each frame of the video was extracted as an image. The extracted images were used to build our chickpea dataset with different cameras. Thereafter, a total of 400 images were collected for each variety from captured video with 1440 \(\times\) 1080 resolution. Thereafter, all the images were combined (smartphone and industrial camera) and the final dataset consisted of 550 images per variety. The reason behind using two different sources of dataset (i.e., industrial camera and smartphone) is to increase the variability in the dataset which helps in better generalisation by the model. In other words, it will increase the robustness of our trained model since each source of data is exposed to different environmental conditions and, by combining the data, the model becomes more tolerant to changes in lighting, noise, and other factors, producing more reliable and accurate results. Further, this variability will make the model better generalise on datasets acquired with different camera resolution and conditions. Figure 2 displays the collected images for the four chickpea varieties.  
Figure 1: Flow diagram of the experimental process.