esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Principle of Operation for the STPP  
The STPP type of power generation operates by transmitting solar irradiation on to a central receiver mounted on a tower, which attains a high temperature heat transfer medium via several heliostats [53]. The sunlight is tracked and reflected onto the receiver by the heliostats, which are computer controlled. The solar radiation that is collected is converted into heat in the receiver by HTF. The HTF energy is transferred to water/steam via exchangers, and it is then used for the generation of electricity in a turbine generator [54; 55].  
The receiver comprises of wall pipes that play the role of heat exchangers. The heat transfer fluid that is generally used is molten nitrate salt, it is also used as a storage fluid. The molten salts are mostly used by solar tower systems as a result of its energy storage and superior heat transfer capabilities. The molten salts are stored using storage tanks so that it can be used when sunlight is not available [15; 54].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Financial Parameters** & **Value** & **Ref.** \\ \hline Real discount rate (per year) & 10\% & [15; 16; 43; 45; 47; 48; 49] \\ \hline Nominal discount rate (per year) & 12.42\% & Calculated value \\ \hline \multicolumn{3}{c}{_Project tax and insurance rates_} \\ \hline Annual Insurance rate & 0.5\% & [48; 49] \\ \multicolumn{3}{c}{of the install debt} \\ \hline Sales tax (of total direct cost) & 18\% & [50] \\ \hline Federal income tax rate (per year) & 25\% & [51] \\ \hline \multicolumn{3}{c}{_Project term debt_} \\ \hline Debt Percent & 70\% & [48; 49] \\ \hline Tenor (years) & 18 & [11] \\ \hline Annual interest rate & 6.5\% & [52] \\ \hline Moratorium (years) & 5 & [33; 34] \\ \hline Up-front fee & 2.75\% & [35] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Financial parameters used in the SAM simulation.  
Figure 2: The configuration of the studied STPP technology (obtained from SAM [42]).
## 3 Linear Optical Methods for Pulse Characterization
### Spectrally Resolved Interferometry
We can neglect the dispersion of the medium surrounded by the interferometer (usually air), since the dispersion difference in the arms is greater by several order of magnitudes. The spectral phase difference can be written as a modified form of Equation (10), namely  
\[\varphi_{SPP}(\omega,t^{\ast})=\varphi_{S}^{(0)}(\omega)-\varphi_{R}^{(0)}( \omega)+\omega\cdot t^{\ast}. \tag{41}\]  
The first derivative by \(\omega\) of Equation (41) gives  
\[0=GD_{S}^{(0)}(\omega^{\ast})-GD_{R}^{(0)}(\omega^{\ast})+t^{\ast}, \tag{42}\]  
if \(\omega\)* is the frequency where the stationary phase point is observed. Eventually, scanning through the spectrum of the pulse, the difference of the group delay between the arms can be measured with respect to the \(\omega\)*, since \(t^{\ast}\) can be calculated easily from length position of the precision translator. The first and second order coefficients of polynomial fitted on the \(t^{\ast}\)(\(\omega\)*) relationship will provide the _GDD_ and the _TOD_ difference between the arms, respectively. It can be seen that the larger the _GDD_ to be measured, the more accurate the _SPP_ method; therefore, it is especially appropriate for the measurement of stretched pulses. However with decreasing the relative _GDD_, the width of the interference fringes (_i.e._, their extension along the \(\omega\)-axis) increases, and the determination of the position of the stationary phase point becomes unreliable. In such a case, the position can only be obtained by fitting a phase modulated cosine function. Although the _SPP_ method can improve the accuracy of dispersion measurements, no additional information is provided compared to the  
Figure 10: A typical example of spectrally and spatially resolved interferograms with the stationary phase point. dispersion calculated from the parameters of the phase-modulated cosine function fitted on the data at one single delay [80]. It is worth mentioning that the elliptical nature of the two-dimensional fringes (as illustrated in Figure 10) was observed as early as in 1927 for glass plates used as samples [81]. A spectrograph equipped with two-dimensional detector for this measurement in order to ensure collinearity has been suggested. However, the position of the stationary phase point can be determined even if only a one-dimensional detector (e.g., a diode array) is available, positioned parallel to the wavelength axis. Stationary phase point method does not exploit the information about the dispersion of the sample encoded in the two-dimensional structure of the fringes. The accuracy of measurement by _SRI_ method was thoroughly examined by [PERSON] _et al._[66; 67; 68]. Among other error sources, spectral calibration issues, detector response inaccuracies, resolution and sampling problems were investigated. Experimental demonstrations showed about 1% relative error in the determination of _GDD,_ which is a little bit worse comparing to _SSRI_[57].
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 1 Introduction  
The advent of digital technology has fueled significant advancements in various transformative technologies such as big data and artificial intelligence. Machine learning (ML)-powered mobile applications are revolutionizing various aspects of modern life [1]. Nevertheless, ML training tasks typically demand a significant volume of data from diverse terminals with varying computational capabilities. Uploading data to remote cloud servers for processing, which is the conventional approach, presents challenges like privacy infringement, network congestion, and significant transmission delays. These challenges hinder the complete utilization of the abundant data [2]. In 2016, the Google Research Institute introduced the concept of federated learning (FL) with the aim of mitigating network bandwidth constraints and addressing vulnerabilities in data privacy [3]. FL is a collaborative training and sharing approach that eliminates the need for accessing raw data, aligning with decentralized collection and data minimization principles, where updates are shared with a central server or a coordinator. This decentralized approach adheres to principles of data minimization and eliminates the requirement for centralized data collection. Raw data remain securely stored on individual devices without direct access. FL facilitates local model training on each device while uploading only aggregated updates or model parameters to the central server.  
Hierarchical FL (HFL) [4] is an FL framework where edge servers serve as intermediaries between mobile devices and the cloud. These servers facilitate the aggregation oflocal models received from neighboring devices at the edge. By enabling the cloud server to effectively process data from a larger number of terminal devices, HFL successfully addresses the challenges encountered during data uploading to the cloud. However, the common HFL framework employs synchronous aggregation of global models, wherein the server does not conduct cloud aggregation until it receives the model parameters of all clients. This synchronous approach leads to a phenomenon known as the straggler effect, wherein the delay in cloud aggregation is determined by the slowest client. The straggler effect adversely affects model training in terms of delayed convergence and greater resource consumption. Notably, a delay in uploading parameters by one or more clients result in an overall delay in the training process, prolonging the training time. Moreover, a delayed aggregation of client parameters impedes the convergence of the global model, potentially affecting the overall accuracy performance of the trained model. These negative effects underscore the significance of addressing the straggler effect in FL to ensure efficient and timely model updates.
# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 3 Proposed Methods
### Method 1: Drowsiness Detection and Prediction Based on Facial Landmarks
#### 3.1.4 Step 4: Drowsiness Detection and Prediction  
The input data are a video tracked from a camera on a vehicle. This video is split into clips with lengths of 300 s. Through the experiments, we tested many different values before reaching the recommended optimal value for \(Counter_{\mathit{EyeLimit}}=15\) and \(Counter_{\mathit{YawnLimit}}=25\). The algorithm by pseudocode for the proposed method is presented in Algorithm 1.  
Figure 8: An example of detecting faces and facial landmarks.  
Figure 7: Illustration of extracting image frames from input videos.  
Figure 9: Determination of the coordinates of points on the eyes and mouth.  
```
Input: Input video monitored from a camera Output: Drowsiness_Detection = True/False (state of drowsiness or non-drowsiness) Begin \(\triangleright\) video is split into clips with lengths of 300 s for each clip in video do \(Counter_{\textit{eye}}=0\); \(\triangleright\) initialize variables to count blinks and yawns respectively. \(Counter_{\textit{yawn}}=0\); \(Drowsiness\_Detection\) = False; for each\(i=1\ldots n\)do \(\triangleright\)\(n\) is the number of image frames extracted from clip  Calculate \(\mathit{EAR}_{i}\) by Equation (1);  Calculate \(\mathit{LIP}_{\textit{distance}}\) by Equation (2); endfor \(\triangleright\) compute an adaptive eye-opening threshold for each driver  Calculate \(\mathit{EAR}_{\textit{thresh}}=\mathit{Average}(\mathit{EAR}_{1},\mathit{EAR}_{2}, \ldots,\mathit{EAR}_{n})\); for each\(i=1\ldots n\)do \(\triangleright\) Count blinks and yawns if\(\mathit{EAR}_{i}>=\mathit{EAR}_{\textit{thresh}}\)then \(Counter_{\textit{eye}}\) += 1 ; endif if\(\mathit{LIP}_{\textit{distance}}>\mathit{LIP}_{\textit{thresh}}\)then \(Counter_{\textit{yawn}}\) += 1; endif endfor \(\triangleright\)\(Counter_{\textit{eyeLimit}}\) and \(Counter_{\textit{yawnLimit}}\) are thresholds of blinks and yawns if\(Counter_{\textit{eye}}>Counter_{\textit{eyeLimit}}\)then \(Drowsiness\_Detection\) = True; \(\triangleright\) Turn on a driver drowsiness alarm: Wake up, please! endif if\(Counter_{\textit{yawn}}>Counter_{\textit{yawnLimit}}\)then \(Drowsiness\_Detection\) = True; \(\triangleright\) Turn on a driver drowsiness alarm: Wake up, please! endif endfor
```  
**Algorithm 1** Drowsiness Detection and Prediction
# Earthquake Risk Probability Evaluation for Najin Lhasa in Southern Tibet
## 3 Data Processing Results
### Determination of Earthquake Parameters  
#### 3.2.1 Earthquake Parameters of the Statistical Area  
The activity parameters of the earthquake statistical area mainly include the upper limit of the magnitude, \(m_{uz}\); the starting magnitude; the background earthquake; the \(b\) value; the annual average occurrence rate; and the average annual occurrence rate of the background earthquake, \(\mathrm{R_{M\;\geq\;4}}\)[34]. Table 2 lists the earthquake parameters of the Central Tibet and Himalayan Earthquake Statistical Area, in which the starting magnitude M\({}_{0}\) is prescribed to be M = 4.0. The upper limit of the magnitude of the background earthquake is smaller by 0.5 than that of the delineated potential source area. The \(b\) value in the magnitude-cumulative frequency relationship represents the proportion between the frequencies of earthquakes of different sizes in a region. Hence, \(b\) specifies the density distribution function of earthquakes of various magnitudes within the earthquake statistical area [35]. The annual average occurrence rate \(\mathrm{R_{M\;\geq\;4}}\) of M \(\geq\) 4 earthquakes has a great influence on the calculation results of earthquake risk analysis, and the main factor affecting \(\mathrm{R_{M\;\geq\;4}}\) is \(b\). The \(b\) value is selected from the statistical period of the data, and the statistical period is required to represent the seismicity level within a century (Table 2).
## 1 Introduction
Current mobility management standards of the IETF have not addressed these constraints on the design of mobility management architecture and protocols. In this article, we propose the CoAP-based Mobility Management Protocol (CoMP), which can provide mobility management for mobile CoAP sensor nodes. Because CoMP uses a separate location management function, which is based on CoAP, low signaling overhead can be obtained due to simplicity of the mobility management architecture. The tunneling scheme is not used for architectural simplicity. CoMP enables the IP addresses of mobile CoAP sensor nodes to be kept track of, allowing monitored sensing data to be reliably delivered to Web clients using both HTTP and CoAP. To the best of our knowledge, there have been no previous research attempts at providing direct IP mobility functionality to mobile CoAP nodes. Compared with other related works, the originality of our approach may be summarized as follows:  
* Instead of designing new signaling protocols for mobility management, CoMP employs the IETF standard CoAP protocol for mobility management in an application layer, without changing the lower layer. This achieves the simple seamless connectivity of wireless constrained sensor node without the modification of the existing network infrastructure. * CoAP messages and methods are extended to implement the mobility management functions of a mobile CoAP node, which imparts not only simplicity in the mobility management architecture, but also has significantly low signaling overhead, compared to other protocols, such as MIPv4/v6 and its variants. * In the existing IETF MIPv6 mobility management protocol, a bi-directional tunnel scheme has been used for transparent handover operation. Instead of a bi directional tunnel, CoMP uses two modes of operation, holding and binding, for fast and reliable data transmission. The contributions of our research are as follows:  
* The detailed architecture and functions of CoMP have been designed for mobility management. A separate location management function to support CoAP service mobility has been designed. * The sleep mode operation of sensor node in CoMP is considered to provide reliable service. * Detailed signaling procedure and an address management method were designed for supporting seamless connectivity and reliable transmission. * To enhance interoperability, we extended CoAP; more specifically, CoAP messages and methods were extended to exchange messages for managing IP addresses between CoAP nodes. The remainder of this paper is organized as follows. In Section 2, we describe the overview and limitations of the CoAP standard. We also describe the comparison of CoMP with the existing standard mobility management protocols such as Mobile IP and SIP-MM. In Section 3, we describe the architecture and message formats of the proposed CoMP. In Section 4, we present a mathematical analysis of the proposed CoMP handover mechanism for a performance evaluation. In Section 5, we describe the performance results of the proposed scheme. Finally, in Section 6, we provide some concluding remarks regarding this research.
# Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
## 2 Materials and Methods  
### Literature Search and Article Selection  
This systematic review adhered to the guidelines delineated in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Statement (PRISMA) [32]. To ensure thoroughness, articles published up to 2024 (until April) were considered, and the search commenced on 1 May. PubMed Medline, Web of Science (Web of Science SM; Current Contents Connect), and Scopus were utilized. The search strategy aimed to identify articles that used artificial intelligence, mainly machine learning, in tennis.  
Utilizing a systematic approach, the search strategy entailed combining the term \"tennis\" with various AI-related terminologies using the Boolean operator \"AND/OR\" [33]. These terminologies encompassed \"artificial intelligence\", \"machine learning\", \"deep learning\", \"neural network\", \"support vector machine\", \"nearest neighbor\", \"random forest\", \"Bayesian logistic regression\", and \"predictive modeling\". This search strategy aimed to capture all relevant studies investigating the application of AI and its subfields, including machine learning, within the domain of tennis performance.  
Hence, two evaluators independently analyzed the titles and abstracts of identified articles. In instances of ambiguity regarding eligibility, the full text was procured for further assessment. Subsequently, two independent reviewers assessed the articles for eligibility based on predetermined criteria. Each article underwent a two-tier evaluation process:initially predicated on title and abstract, followed by a thorough examination of the full text. Any discordance concerning eligibility was resolved through collegial discourse and, if requisite, settled with the involvement of a third author.
# An Efficient Algorithm for De-Interleaving Staggered PRI Signals
###### Abstract  
Resolution and mapping bandwidth are the two most important image performance indicators that reflect satellite synthetic aperture radar (SAR) imaging reconnaissance capability. The PRI-staggered signal can simultaneously achieve high resolution in azimuth and wide swath during SAR imaging, and is an important signal form of SAR. It is important for anti-SAR reconnaissance to de-interleave the staggered PRI signal from the mixed signals. To address the problem that the existing staggered signal de-interleaving algorithms cannot accommodate PRI jitter and are computationally inefficient, this paper proposes an efficient algorithm for de-interleaving staggered PRI signals. A clustering-based square sine wave interpolation method and a threshold criterion are proposed, improving computational efficiency while suppressing interference between sub-PRIs and the frame period of the staggered PRI signal. In addition, a sequence retrieval algorithm incorporating matched filter theory is proposed to improve the separation accuracy of radar pulse sequences. The simulation shows that the novel algorithm can adapt to PRI jitter and de-interleave staggered PRI signals from mixed signals with high efficiency. Compared with the existing staggered signal de-interleaving algorithm, the computational efficiency is improved by an order of magnitude.  
mixed signals; pulse de-interleaving; sequence retrieval; square sine wave interpolation; staggered PRI +
Footnote †: journal:  
applied  
sciences
# Quantum Computing for Healthcare: A Review
## 3 Applications of Quantum Computing for Healthcare
### Diagnosis Assistance  
Early diagnosis of the diseases could render better prognosis and treatment, as well as lower the healthcare cost. For instance, it has been shown in the literature that the treatment cost lowers by a factor of 4, whereas the survival rate could be decreased \"by a factor of 9 when the colon cancer is diagnosed at an early stage\" [59]. In the meantime, the current diagnostics and treatment for most of the diseases are costly and slow, having deviations in the diagnosis of around 15-20% [60]. The use of X-rays, CT scans, and MRIs has become critical over the past few years with computer-aided diagnostics developing at a faster pace. In this situation, diagnoses and treatment suffer from noise, data quality, and replicability issues. In this regard, quantum-assisted diagnosis has the potential to analyze medical  
Figure 5: An illustration of quantum computing harnessing massive multimodal data to facilitate precision medicine.  
images and oversee the processing steps, such as edge detection in medical images, which improves the image-aided diagnosis.  
The current techniques use single-cell methods for diagnosis, while analytical methods are needed in single-cell sequencing data and flow cytometry. These techniques further require advanced data analytic methods, particularly combining datasets from different techniques. In this context, cell classification on the basis of biochemical and physical attributes is regarded as one of the main challenges. While this classification is vital for critical diagnoses, such as cancerous cell integration from healthy cells, it requires an extended feature space where the predictor variable becomes considerably larger. Quantum ML techniques, such as quantum vector machines (QVM), enable such classifications and enable single-cell diagnostic methods. The discovery and characterization of biomarkers pave the way for the study of intricate omics datasets, such as metabolomics, transcriptomics, proteomics, and genomics. These processes could lead to increased feature space, provoking complex patterns and correlations that are nearly impossible to be analyzed using classical computational methodologies.  
During the diagnosis process, quantum computing may help to support the diagnostics insights, eliminating the need for repetitive diagnosis and treatment. This paradigm helps in providing continuous monitoring and analysis of individuals' health. It also helps in performing meta-analysis for cell-level diagnosis to determine the best possible procedure at a specific time. This could help to reduce the cost and allow extended data-driven diagnosis, bringing value for both medical practitioners and individuals.
# Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset
## 2 Material and Methods
### Datasets  
#### 2.1.1 Lung CT Segmentation Challenge 2017  
The Lung CT Segmentation Challenge (LCTSC) 2017 [19] dataset was part of a competition in which the goal was the development of algorithms for the segmentation of several organs at risk in CT images for radiation treatment planning. The data was collected from 3 different institutions, making a total of 60 CT scans. The dataset is divided into 2 subsets: 1 contains 36 scans that are intended to be used for training (36-LCTSC), and the other subset contains 24 scans intended to be used for the assessment of the developed models (24-LCTSC). The number of slices along the \(z\)-axis per scan varies between 103 and 279 and their axial resolution is 512 \(\times\) 512. The slice spacing is of 1.02 \(\pm\) 0.11 mm and the slice thickness is of 2.65 \(\pm\) 0.38 mm. The ground truth of the original images contains the delineation of five anatomical structures: esophagus, heart, left and right lungs, and spinal cord. Given that the lungs are the only organs of interest for this work, a binary ground truth containing solely the pulmonary regions was generated for each slice, using the information regarding these organs extracted from the DICOM RSTRUCT file.