esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Experimental Setup
As noted in Section 2.1, compression is known to reduce the performance of rPPG solutions. However, compression is also very useful for ensuring the manageability of data collected in terms of both storage capacity and playback compatibility. The parameters chosen for shooting were: high resolution (1080p), lowest video compression ratio available (28 Mbps H.264), zoom to frame face (50 mm), highest aperture possible at the zoom position (\(t/3.4\)), and slowest feasible shutter speed to create the longest exposure possible whilst shooting at 50 FPS (1/60 s). The decision was made to use a progressive encoding format, rather than an interlaced format (e.g., 1080i), due to the fact _PyVHR_ and _OpenFace 2_ both use frame-wise processing. The shutter speed was deemed appropriate as no noticeable motion blur was found when recording facial actions in a pre-study test, as assessed using frame-by-frame inspection. The only difference in camera settings between the three room illuminating was the ISO setting, which relates to the sensitivity of the sensor to incoming light. The bright, medium, and dark sensitivity settings were set to ISO400, ISO1600, and ISO6400, respectively. Maintaining the ISO400 sensitivity would render the participant's face invisible in all but the bright illumination. The data compression applied by the camera provides a roughly 100:1 reduction in data, as uncompressed video at the same specification would have a bit rate of 2.98 Gbit/s. This value is obtained by multiplying the number of channels (3) by the bit depth of each channel (8) and then multiplying this by the resolution (1080 \(\times\) 1920) and the frame rate (50). [PERSON] et al. found that applying a similar compression ratio artificially led to an increase in average error from 1.78 BPM to 2.68 BPM. However, it should be noted that their system did not apply skin patch processing and the correlation between rPPG and contact sensor readings is not reported in this publication [42]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Illumination** & **Direction** & **Maximum (lux)** & **Minimum (lux)** \\ \hline Bright & Upward & 370 & 349 \\  & Forward & 270 & 192 \\ \hline Medium & Upward & 60 & 46 \\  & Forward & 93 & 23 \\ \hline Dark & Upward & 5 & 0 \\  & Forward & 20 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Description of the illumination conditions, made up of the illumination incident on the participant due to the screen and the room lighting. The minimum illumination measurement was made with the television displaying a black screen and the maximum with a white screen. Figure 3: Photograph taken in the room where the experiment took place, showing the participant’s chair, lighting, and position of the camera on a tripod under the television (highlighted by the white circle).
# Copy Number Variation: Methods and Clinical Applications
## 1 Introduction  
Among the least understood types of genetic variation are copy number variants (CNVs), a class of unbalanced structural variants characterized by deletions, insertions, duplications or even multiplications of DNA segments ranging in size from a few dozen of bp up to several Mb. Currently, the lower limit for CNV length is 50 bps, but this value has been gradually decreasing due to continuous methodological progress. The shift is mainly due to an increased resolution of used methods, allowing for detection of a wider variety of variant lengths and for an increase of CNV detection capacity (Figure 1). Considering this remarkable shift and the fact that generally used distinguishing criteria are somewhat vaguely defined, it has also been suggested that CNVs include a wider spectrum of variants. However, for practical reasons, we will focus on the conventional concept of CNVs in this review.  
Pushing the limits of CNV detection revealed that they are widespread in human populations with a 5-10% difference of genomic sequences between normal individuals [1, 2, 3]. As a significant aspect of our heterogeneity, CNVs may disrupt gene function or alter gene dosage by direct gain or loss of coding sequences [4], but several indirect mechanismsincluding alteration of non-coding RNAs [5; 6] and topologically associated domains [7] have been described. Since these may affect the phenotype, CNVs may threaten the ability to survive or, on the contrary, enhance chances of survival in disadvantage environments [8].  
CNVs are an important cause of genomic disorders with Mendelian inheritance, and may also contribute to complex diseases with multifactorial etiology [9]. Since the introduction of high throughput technologies for CNV detection, namely array-based comparative genomic hybridization (aCGH) and massively parallel sequencing (MPS), the number of novel variants is constantly increasing. However, a lot of detected CNVs are still categorized as variants of uncertain significance (VUS) with unknown clinical impact [4], suggesting a need for their reliable classification. Therefore, all available information has been translated to the standards of interpretation and reporting of constitutional CNVs as recently published by the American College of Medical Genetics and Genomics and the Clinical Genome Resource [10]. However, the age of high throughput technologies comes with an ever increasing amount of generated data. Researchers must continuously improve bioinformatic softwares and decision support tools to help clinicians handle the problem.
# Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization
## 2 State of the Art
### Volume and Weight Sensors  
The main sensors currently applied in smart waste systems can provide information about the waste volume of a container. These transducers may be capacitive (2010), ultrasonic (2011), infrared, Time of Flight (ToF) (2012), or Guided wave radar (GWR). Each one of these transducers has different features that must be considered, such as range, measuring accuracy and amplitude of the working angle (described in Table 1).  
The volume level estimations of the waste in a container are based on a measure of distance between the upper side of the container and the surface of the waste. Given the extent of the measurement calculated by sensors (Table 1), one of the best options is the ultrasonic sensor because it provides data from a wider area than that provided by others.  
However, when carrying waste, the truck grinds and crushes the content so the waste volume of the container is highly reduced. Once waste is in the truck, the problem lies in the total weight of it, as road safety regulations do not allow trucks with excess weight to travel.  
Regarding the weight of measuring containers, load cells have been traditionally used for this purpose (2013; 2014). The operation of load cells is based on strain gauges which are sensors using the piezoresistive effect (the electrical resistance variation produced by an effort or stress on a material) of their built-in materials. Load cells provide a data accuracy of 0.03% to 0.25%. They are suitable for virtually every industry application.  
Sensors intended to measure volume and weight in this context must additionally provide great efficiency and autonomy. Complex nodes meet this requirement through the different sensors and components they include, such as the communication network employed by the device which is essential for the final consumption results of the device.
## 3 Test Results and Discussion
### Results of Repeated Impact Tests
#### 3.2.2 Dynamic Mechanical Properties  
Figure 9 shows that the peak strength decreases while the failure strain increases with increasing impact times, which is consistent with the waveform analysis results. In addition, it can be seen that the increase in the number of impacts gradually decreases the elastic section of the sandstone, i.e., the dynamic elastic modulus gradually decreases. The main factor for sandstone failure is the accumulation of damage, which in turn causes an increase in strain. The internal porosity of the sandstone increases, and cracks propagate under repeated impacts, resulting in a decrease in longitudinal wave velocity and an increase in damage.  
Figure 8: Waveforms of repeated SHPB impact tests.
# Experimental and Numerical Study on Dynamics of Two Footbridges with Different Shapes of Girders
## 2 Materials and Methods
### Theory of Mode Shapes and Frequencies Validation Criteria  
The combination of modal pairs, i.e. frequencies and modal shapes, creates a representation of the basic dynamic properties of the structure. Mode shapes can be the basis for many further analyses, such as validation or updating of numerical models. Thus, they enable the assessment of the condition of the structure or are the basis for diagnostic analyses.  
Conducting advanced analyses based on comparing dynamic characteristics determined in experimental research of real structure and characteristics obtained as a result of numerical analyses requires the use of appropriate comparative techniques. One of the most significant problems is related to the 'pairing' mode shapes, both in terms of frequencies and mode shapes. This concerns particularly more complex structures, for which modal shapes may seem to be similar. Another important issue is  
Figure 14: FEM eigenvectors and eigenvalues of the footbridge above the University Route in Bydgoszcz.  
the difference in scale of the amplitude of the modal shapes obtained from the numerical model and experimental studies.  
Modal assurance criterion (MAC) is one of the most popular criteria which is often used for comparing the mode shapes of free vibrations [81]. It can be defined as  
\[\text{MAC}_{ij}=\frac{\left|\Phi_{mi}^{\text{T}}\Phi_{aj}\right|^{2}}{\left( \Phi_{aj}^{\text{T}}\Phi_{aj}\right)\!\left(\Phi_{mi}^{\text{T}}\[PERSON]_{mi}\right)} \tag{1}\]  
where \(\Phi_{mj}\)\(\[PERSON]_{aj}\) are the experimental and numerical mode shapes, respectively, and symbol T denotes the transposition.  
The criterion compares only the mode shapes, which means that it is necessary to simultaneously use the criterion of comparing the natural frequencies. The result is a MAC matrix in which the elements take values between zero (no match) and one (complete match). This presentation allows us, together with the frequency comparison, to select the appropriate modal pairs from experimental study and numerical analysis. When the MAC value is greater than 0.90, it is assumed that the compatibility of mode shapes is high [50; 66].  
The MAC criterion was introduced together with the modal scale factor (MSF), which is the scaling factor for the mode shapes obtained from experimental tests in relation to the parameter obtained from numerical analyses. The coefficient is expressed by the formula  
\[\text{MSF}_{i}=\frac{\Phi_{ai}^{\text{T}}\Phi_{mi}}{\Phi_{mi}^{\text{T}}\Phi_{ mi}} \tag{2}\]  
The difference between experimental and numerical frequencies can be defined as  
\[\Delta f=\frac{f_{a}-f_{m}}{f_{m}}\cdot 100[\%] \tag{3}\]  
where \(f_{m}\)\(f_{a}\) are the experimental and numerical frequencies, respectively.
## 4 UbiPriSEQ: The Proposed Framework
### Device Module (DevM)
#### 4.3.3 DevM: QoS and Energy-Efficiency  
DevM provides higher QoS by selecting the fog or cloud nodes that have SINR higher than a threshold. Moreover, it improves QoS further by utilizing latency constraints of tasks in setting task priorities, and offloading lower-priority tasks to fog or cloud only if needed. Energy-efficiency is improved by DevM by attempting to execute tasks locally if possible, otherwise on fog, and then on cloud.
## 2 Literature Review  
### Online Entertainment Platforms  
An online entertainment platform is a digital service that offers a diverse array of multimedia content, including streaming videos, music, games, and social media, all of which are accessible via the internet. These platforms provide on-demand entertainment options, enabling users to access a wide range of content, from movies and TV shows to music, live streaming, and interactive experiences, often through subscription-based or ad-supported models. They have revolutionized how people consume and engage with entertainment, making it easily accessible across a variety of devices and contributing to the digital transformation of the entertainment industry [1].  
The future of online entertainment platforms is characterized by a seamless fusion of immersive technologies, artificial-intelligence-driven personalization, and diverse content offerings. These platforms will not only transcend the traditional boundaries between streaming, gaming, and social interaction but also create entirely new dimensions of interactive and shared experiences [12]. Users may expect hyper-personalized recommendations, high-quality live streaming, and a wealth of original content while platforms explore novel monetization models, global expansion, and deeper integration with emerging technologies, such as augmented reality, virtual reality, and the metaverse [3]. However, they will also grapple with increasing regulatory scrutiny and environmental sustainability concerns as they strive to provide a continuously evolving, interconnected, and ethically sound entertainment landscape.  
Online entertainment platforms exhibit a distinctive set of characteristics. First, they emphasize a user-friendly interface tailored for touch input, ensuring effortless navigation and an aesthetically pleasing user experience. Second, user accounts are often essential as they enable personalization, content storage, and smooth platform interactions. Third, push notifications are commonly employed to keep users informed about new content and personalized recommendations even when the app is inactive. Fourth, integration with social media platforms fosters content sharing and community engagement, thereby expanding the platform's reach. Fifth, in-app purchases, including virtual goods and premium features, generate revenue. Sixth, global positioning systems and location services are harnessed to provide location-based features, offering users local content, events, and deals. Seventh, personalization is central, with platforms tailoring content recommendations to user preferences to enhance engagement. Eighth, robust security measures, including encryption, secure payments, and authentication mechanisms, are implemented to protect user data [13; 14].
## 3 Methods
### Creation of Land-Cover Maps  
To parameterize the model to estimate GPP, the study area was classified into seven land-cover types, including EBF, deciduous broadleaf forest (DBF), grassland, cropland, built-up, bare land, and water bodies. In Wuhan, the needle tree is not the dominant tree type, and is usually mixed with broadleaf trees. Shrubs are mainly used to decorate roads. Thus, we excluded needle leaf forest and shrubs from our study, because it was difficult to accurately differentiate needle trees and shrubs from other vegetation types at 30-m resolution in the study area. Built-up areas, bare land, and water bodies were treated as non-vegetated areas.  
Maximum likelihood classifier, a widely-used supervised classification approach, was employed in this study for the land-cover classification. Classification was conducted for each year on the selected image shown in Figure 2. Samples were collected for each image through the visual interpretation of Landsat images, with the help of a 2013 summer image of WorldView-2, higher resolution images in Google Earth, and field observations. A random stratification procedure was performed to generate training (80%) and testing (20%) datasets for each year. The NDVI values derived from Landsat images of different seasons were used to build a decision tree to classify forests into EBF and DBF.
## 3 Results  
The systematic literature search of the three bibliographic repositories produced 1458 documents. In detail, 443 documents resulted from the search on PubMed, 559 resulted from the search on Scopus, and 456 resulted from the search on WoS. Of these, 762 documents were automatically recognized as duplicates, so the remaining 696 were left for the next selection stage. After manually screening the documents according to document type, 668 remained. Of these, 4 documents did not have full text available. From the manual evaluation of title, abstract and full text, 621 documents were excluded according to the chosen exclusion criteria. Specifically, in 418 documents, the normalization was defined as the restoration of the physiological condition; in 93 documents, the normalization was included in the standard definition of a parameter/not applied after parameter extraction/applied to parameters not extracted from the ECG; in 33 documents, normalization was intended as a default preprocessing step for a machine learning application; and in 77 documents, there was no explanation of why and/or how normalization was applied. So, 43 documents met all the established inclusion criteria. Among them, 4 documents were animal studies and so were further excluded. Lastly, 39 documents were included in this review [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57]. Using a flowchart, Figure 1 shows the entire process of the literature search, from the application of the query on each bibliographic repository to the manual screening leading to the selected documents. The majority of the selected documents were original research articles [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 51; 53; 54; 55; 56], while only one document was a conference proceeding [52]. Overall, the included documents were published between 1986 and 2024, with more than half published in the last 10 years. The distribution of the document publication years is detailed in Figure 2.  
Figure 1: Process of literature search and study selection. The general characteristics of the populations studied by the included documents are reported in Table 1. Specifically, the table details population size, sex (expressed as number of males and number of females), age (expressed as fetus, newborn, infant, child, adolescent, young adult, or adult), and clinical information, which includes health status (expressed as healthy or diseased) and details about the pathology, when present. Information about population sex was not available for 5 documents, while details about the pathology were not applicable when the studied populations were healthy.
## 2 Mission and Satellite Design
### Imaging Modes
#### 2.3.1 High-Resolution Mode  
Stripmap imaging is the most fundamental SAR technique, and the stripmap mode provides the finest cross-resolution that can be achieved by continuously acquiring SAR images without interruption. Unlike alternative SAR imaging techniques, angular steering is not performed with respect to the transmitted and received beams during image acquisition. Moreover, the antenna beam is fixed in the broadside direction. A schematic of the SAR operation in the stripmap mode is illustrated on the left-hand side of Figure 2. In S-STEP SAR, the stripmap mode is operated at a high resolution of 1 m, and it is designed to combine the advantages of the stripmap and spotlight modes. Thus, the acquisition of continuous high-resolution SAR images is possible in a long strip by selecting the antenna size with a high resolution of 1 m in the stripmap mode. This delivers an effective mission performance such as faster target detection and identification for surveillance applications. In general, the high-resolution mode can also be operated in the traditional stripmap imaging technique. The main advantage of an S-STEP SAR payload is that it can achieve a resolution of 1 m in the stripmap mode with high-beam agility owing to its full elevation steering capability. This is possible because of the extremely short antenna length in the along-track direction and the presence of one phase center per element in the elevation for the stripmap mode operation. This is extremely attractive, especially for military, surveillance, and security applications. The representative examples of these SAR satellite systems include Excubitor and Nimbus [30].
## 2 Materials and Methods
The intensity distribution recorded for the object \(O\) can be expressed as \(I_{\rm O}=O\bigotimes I_{\rm PSF}\), where \(I_{\rm PSF}\) is the intensity distribution obtained by temporally summing the intensity distributions during pixel-by-pixel scanning. There are two main mechanisms to extract \(O\) from \(I_{O}\) using \(I_{\rm PSF}\): direct cross-correlation [4] and an iterative maximum likelihood approach such as the LRA and LRRA [14; 15; 16]. In our recent study [14], it was found that the LRRA performed better than the LRA, whose schematic is shown in Figure 2. The (\(n\)+1)th reconstructed image is given as \(I_{\rm R}^{n+1}=I_{\rm R}^{n}\left\{\left(\frac{I_{\rm O}}{I_{\rm R}\otimes I _{\rm PSF}}\right)\otimes\right._{\rm P}^{\rm a}I_{\rm PSF}\right\}\), where '\(\otimes\)' refers to NLR which is defined for the two functions A and B as \(\left|\mathcal{F}^{-1}\left\{\left|\widetilde{A}\right|^{\alpha}exp\left[jarg \left(\widetilde{A}\right)\right]\right|\left|\widetilde{B}\right|^{\beta}exp \left[-jarg\left(\widetilde{B}\right)\right]\right\}\right|\), where \(\widetilde{X}\) is the Fourier transform of X. The \(\alpha\) and \(\beta\) are tuned between \(-1\) and \(1\). The number of iterations was increased from \(1\) until a minimum entropy was obtained. The algorithm begins with an initial guess, which is the recorded object intensity \(I_{O}\) and convolves it with the \(I_{\rm PSF}\). The ratio between \(I_{O}\) and the resulting distribution \(I_{O}\)' is correlated with the using NLR and the residue is then inverse Fourier transformed and multiplied to the first guessed solution I\({}_{R}\)\({}^{n}\) (=\(I_{O}\) when \(n\) = 1) to generate the next solution I\({}_{R}\)\({}^{(n+1)}\). This process is iterated with the new solution until the maximum likelihood solution is obtained. However, unlike the LRA, the LRRA has a rapid convergence and better estimation of the solution.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 6 Security Analysis
### Authentication Proof Using BAN Logic  
We present the security analysis utilizing the BAN logic [25] to prove the secure user authentication of our protocol. In Table 4, we present the notations used for BAN logic. We present the security rules, the security goals, the idealized forms and the assumptions that are essential to BAN logic. We assessed that our scheme ensured mutual authentication among \(MU\), \(FA\), and \(HA\).