esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Computational Framework
### Work Calculation of the Failure Mechanism
The work rates associated with soil weight \(W_{\gamma}\) is shown as:  
\[W_{\gamma}=\alpha\sum_{i}\sum_{j}\gamma(\sin\beta_{i,j}R_{i,j}V_{i,j}+\sin \beta^{\prime}_{i,j}R^{\prime}_{i,j}V^{\prime}_{i,j}) \tag{30}\]  
where the \(\beta_{i,j}\) (or \(\beta^{\prime}_{i,j}\)) can be obtained from the coordinates of the centroid of the little cube \(V_{i,j}\) (or \(V^{\prime}_{i,j}\)) and the center of rotation. The \(R_{i,j}\) (or \(R^{\prime}_{i,j}\)) represents the distance between these two points. Only the expression of \(\gamma\) varies in these two contexts:  
\[\gamma=\left\{ \begin{array}{l}\gamma_{1},\ X_{N}x_{i,j}+Y_{N}(y_{i,j}-dt)+Z _{N}z_{i,j}\leq 0\\ \gamma_{2},\ X_{N}x_{i,j}+Y_{N}(y_{i,j}-dt)+Z_{N}z_{i,j}>0\end{array} \right. \tag{31}\]  
where \((x_{i,j},y_{i,j},z_{i,j})\) is also the Cartesian coordinate of the centroid of the facet \(S_{i,j}\) (or \(S^{\prime}_{i,j}\)) and the \(V_{i,j}\) (or \(V^{\prime}_{i,j}\)) is seen as a rectangular prism since the facet is small enough and its volume is equal to the product of the area of the plane and the distance from the plane's centroid to the axis of rotation of the tunnel's rotational symmetry. The work rate related to the support force is as follow:  
\[W_{\sigma_{T}}=-\omega\sigma_{T}\sum_{j}\Sigma_{j}R_{j}\cos\beta_{j} \tag{32}\]  
where \(\Sigma_{j}\) is subdividing the entire palm surface into small horizontal strip-like planes that approximate rectangles. The \(R_{j}\) is the distance between the rotational center and the centroid of the former plane and the \(\beta_{j}\) can also be derived from the angle between the line connecting these two points and the horizontal direction. As the kinematic approach enables an approximation of the maximum ultimate loads or a determination of the minimum support needed, the minimum critical face pressure \(\sigma_{T}\) can be derived as the lower limit:  
\[\sigma_{T}=\frac{W_{\gamma}-W_{c^{\prime}}-W_{c_{app}}}{\sum_{j}\Sigma_{j}R_{ j}\cos\beta_{j}} \tag{33}\]  
The lower limit is determined by evaluating the different potential failure mechanisms in space. By varying the location of the rotational center and adjusting the parameter \(t\) using pseudo-dynamic methods, the optimized critical face pressure can be obtained. The location of the rotational center can be decided by the parameters \(r_{E}\) and \(\beta_{E}\) shown in Figure 2.
## 2 Multi-Infeed LCC-HVDC System
### Multi-Infeed Schemes  
The trend of installing HVDC for bulk power transmission is increasing with every passing year. Situations are occurring, such as in the southeast coast of China, where several HVDC links are installed to meet the industrial load. The existence of more than one HVDC link in a close area initiates a new configuration termed \"multi-infeed HVDC\". MI-HVDC raises new challenges because the working of each converter is disturbed by other converters in proximity. Therefore, MI-HVDC has received much attention in recent research. There are three main configurations of MI-HVDC systems [11].  
(i). Ring type;  
(ii). Chain type;  
(iii). Mesh type  
The ring-type multi-infeed DC system is shown in Figure 1a. It is similar to a scheme where several HVDC links encircle a city and feed at a specific point. If two HVDC links feed a city (same AC system), then the schematic diagram will become as shown in Figure 1b. The schematic diagram in Figure 1b is often called dual-infeed HVDC system.  
Figure 1: (**a**) Ring-type multi-infeed Dc system; (**b**) dual infeed ring-type multi-infeed Dc system.  
Chain-type multi-infeed HVDC systems can be imagined as shown in Figure 2. Here, the HVDC links lie in close vicinity and have very short electrical distance. In a chain-type MI-HVDC system, the HVDC converters do not have direct connection, but they are connected via nearby links. Another possible structure of the MI-HVDC system can be formed as in Figure 3. The configuration is a meshed network where several HVDC links feed at various buses in the network. This structure is more complex than previous and commonly found structures in the Southern Power Grid of China.  
Figure 3: Mesh-type multi-infeed HVDC system.  
Figure 2: Chain-type multi-infeed HVDC system.
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 5 Conclusions
Furthermore, as RO measurements contain important parameters of the atmosphere and ionosphere, we could explore the possible relationships between space weather or stratospheric dynamics and weather forecast in the troposphere to improve NWP and TC forecast [91]. We believe that the development of RO will bring more promising results for NWP and TC forecasts. Writing--original draft/editing, W.B., N.D.; Data curation G.T., Z.L., X.L., N.D.; Resources, W.B., N.D., Y.S., C.L. ; Funding acquisition, Y.S., Q.D., J.X., X.W., X.M., D.Z., C.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by the National Natural Science Foundation of China, grant numbers 42074042, 41505030, 41606206, and 41775034; Strategic Priority Research Program of Chinese Academy of Sciences, grant number XDA15007501. **Conflicts of Interest:** The authors declare no conflict of interest.
# Non-Invasive and Accurate Blood Glucose Detection Based on an Equivalent Bioimpedance Spectrum  
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2 School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; [EMAIL_ADDRESS] ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))). ([EMAIL_ADDRESS].  
through lifestyle and medication coordination [9; 10]. Recently, tech advances and rising health awareness, especially among the youth, have increased acceptance of \"three highs\" and demand for convenient health monitoring devices [11; 12]. Blood glucose detection methods are invasive or minimally invasive, painful, and can cause infection. Non-invasive methods are painless, infection-free, convenient, comfortable, and cost-effective, offering accurate, real-time monitoring and early detection of metabolic abnormalities with substantial value. Therefore, non-invasive blood glucose detection technology has become a research hotspot in recent years [13; 14].  
The concept of non-invasive blood glucose detection was proposed in the 1980s. Currently, non-invasive blood glucose detection methods can be broadly categorized into two types based on their detection principles: optical methods and non-optical methods. Non-optical methods primarily include the energy metabolism conservation method [15], the microwave detection method (MS) [16], the electromagnetic detection method (ES) [17], and bioelectrical impedance spectroscopy (BIS) [18]. BIS refers to the conductive and dielectric properties exhibited by organisms when weak alternating currents below the excitation threshold are applied. This technique can estimate fluctuations in blood glucose levels, thereby facilitating the prediction of blood glucose concentrations [19].  
In this paper, a type 2 diabetes mellitus recognition method based on an equivalent bioelectric impedance spectrum is introduced. It uses automatic balance bridge technology to design an impedance spectrum acquisition circuit and then extract impedance spectrum characteristics using the K-nearest neighbor algorithm [20]. Then, blood glucose samples are processed via multiple regression to establish a blood glucose-electrical impedance spectrum model and implement non-invasive blood glucose detection. According to the experimental results, the proposed method can accurately detect the fluctuation of personal blood glucose and reliably identify high blood glucose samples in multi-person tests.
## 3 Model Development and Analysis
### Cooling Load Temperature Difference (CLTD) Method for Cooling Load  
To estimate the cooling load for the test room, the CLTD method was used. The CLTD initially calculates the sensible cooling load required based on the transfer function method (TFM). The manual procedure was adopted to estimate the cooling load for two occupants inside the room.  
* The CLTD is used to calculate the sensible cooling load for the exterior walls and roofs.
* The solar cooling load (SCL) factor is added to represent the solar heat gain at that hour and also reflects the thermal storage effect due to various types of material used for building construction and for floor covering.
* The cooling load factor (CLF) is used to calculate the internal sensible cooling load.
* The following is the detailed procedure for calculating the cooling load method using CLTD [40].  
Space cooling load due to heat gain through exterior walls, roofs, and transparent glass is calculated using Equation (11) [49].  
\[\mathrm{Q_{Roof,Wall}}=\mathrm{U}\times\mathrm{A(CLTD)_{c}} \tag{10}\]  
where \(\mathrm{(CLTD)_{c}}=\mathrm{(CLTD)_{Roof,Wall}}+\mathrm{(78-TR)}+\mathrm{(TM-85)}\)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Equation of** & **Decination, \({}^{\circ}\)** & **A, W/m\({}^{2}\)** & **B** & **C** \\ \hline JAN & \(-11.2\) & \(-20.2\) & 1202 & 0.141 & 0.103 \\ FEB & \(-13.9\) & \(-10.8\) & 1187 & 0.142 & 0.104 \\ MAR & \(-7.5\) & 0 & 1164 & 0.149 & 0.109 \\ APR & \(1.1\) & \(11.6\) & 1130 & 0.164 & 0.12 \\ MAY & \(3.3\) & \(20\) & 1106 & 0.177 & 0.13 \\ JUN & \(-1.4\) & \(23.45\) & 1092 & 0.185 & 0.137 \\ JUL & \(-6.2\) & \(20.6\) & 1093 & 0.186 & 0.138 \\ AUG & \(-2.4\) & \(12.3\) & 1107 & 0.182 & 0.134 \\ SEP & \(7.5\) & 0 & 1136 & 0.165 & 0.121 \\ OCT & \(15.4\) & \(-10.5\) & 1166 & 0.152 & 0.111 \\ NOV & \(13.8\) & \(-19.8\) & 1190 & 0.142 & 0.106 \\ DEC & \(1.6\) & \(-23.45\) & 1204 & 0.141 & 0.103 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of solar constants for clear sky [48].  
Space cooling load due to heat gain through electric lights is calculated using Equation (12) [49].  
\[\mathrm{Q_{e}}=3.413\times\mathrm{W_{light}}\times\mathrm{F_{use}}\times\mathrm{F_{ al}} \tag{11}\]  
Space cooling load due to heat gain through equipment and appliances is calculated using Equation (13) [40].  
\[\mathrm{Q_{ea}}=2546\times\mathrm{P_{hp}}\times\mathrm{F_{load}}\times\mathrm{F_ {use}} \tag{12}\]  
Space cooling load due to heat gain from occupants and people is calculated using Equation (14) [49].  
\[\mathrm{Q_{P}}=\mathrm{N_{p,t}}\times\left(\mathrm{SHG_{p}}\right)+\mathrm{N_{ p,t}}\times\left(\mathrm{LHG_{p}}\right) \tag{13}\]
# Artificial-Intelligence-Based Charger Deployment in Wireless Reshargeable Sensor Networks
## 5 Simulation Results
### Comparison of Time Complexity
The number of iterations required for GABC to converge to a near-optimal solution depends on the convergence criterion and the quality of the initial population. The convergence criterion can be based on the number of generations, the fitness improvement or the solution quality. Overall, the time complexity of GABC can be approximated as O(\(knm\)), where \(k\) is the number of generations required to converge to a near-optimal solution. The time complexity of ACOBC depends on several factors, such as the size of the input dataset, the number of ants, the pheromone-update rule and the convergence criterion. The main steps of the ACOBC algorithm include generating an initial pheromone matrix, constructing solutions using probabilistic transition rules based on the pheromone matrix, updating the pheromone matrix based on the quality of the constructed solutions and repeating the process until convergence. The time complexity of each iteration of ACOBC is O(\(n^{2}m\)), where \(n\) is size of the input dataset, \(m\) is the number of ants and the exponent 2 is due to the need to calculate the transition probabilities for each ant. The pheromone-update rule is used to update the pheromone matrix based on the quality of the constructed solutions. The time complexity of the pheromone-update rule can vary depending on the implementation and the complexity of the problem. The number of iterations required for ACOBC to converge to a near-optimal solution depends on the convergence criterion and the quality of the initial pheromone matrix. The convergence criterion can be based on the number of iterations, the pheromone level or the solution quality. Overall, the time complexity of ACOBC can be approximated as O(\(kn^{2}m\)), where \(k\) is the number of iterations required to converge to a near-optimal solution. The layoff algorithm can efficiently remove the solution that cannot lead to an optimal solution. It needs to be used with SABC, TSBC, GABC and ACOBC. The time complexity of layoff algorithm can be approximated as O(\(k\)), where \(k\) is the number of generations required to converge to a near-optimal solution and \(n\) is size of the input data. Compared with the algorithm before adding the layoff approach, time complexities are similar to the original algorithm. However, the layoff approach is more efficient in practice, due to its ability to remove unnecessary solutions.
# Accuracy of Assessing Weight Status in Adults by Structured Observation
## 2 Materials and Methods
### Recruitment and Training of the Observers  
In order to recruit observers, an email was sent to the directors of all undergraduate courses at the School of Health Sciences of the Polytechnic of Leiria, asking for the dissemination of the study through their students, who were invited to collaborate as observers. A total of six students were interested in collaborating and were, thus, recruited. All observers were finishing their undergraduate degrees in Dietetics and Nutrition, except one, who was in the course of Physiotherapy. Three students in Dietetics and Nutrition were also healthcare workers with a previous degree and professional experience in another health field.  
Training aimed at providing the observers with theoretical knowledge and practical skills for estimating weight status using a body image scale, as well as standardizing procedures. After explaining the study, its objectives, and methodology, the Stunkard Figure Rating Scale [17] was presented, including (1) its rationale and what it consists of; (2) the silhouettes that compose it; and (3) the BMI corresponding to each figure. Then, practical exercises were performed, in which the figures were randomly projected on a white board (three times each) and classified by the observers regarding the correspondent BMI, at the end of which the observers were given feedback regarding their evaluations.
# An Unsupervised Machine Learning Approach for UAV-Aided Offloading of 5G Cellular Networks
## 5 Conclusion and Future Directions  
### Conclusions  
The on-demand deployment of aerial base stations can improve the communication quality via offloading the terrestrial cellular network in highly loaded scenarios, where connectivity and service provisioning is threatened. UDP, a UAV-aided offloading scheme for 5 th generation terrestrial cellular networks, utilizing the k-medoid, was presented in this context. Through UDP, both the offloading percentage of the terrestrial network and the average received signal strength of the UEs are improved, compared to traditional offloading strategies utilizing pico base stations, PDPP/ PDPR, and to KDP, which is also a UAV-aided offloading scheme utilizing k-means ML technique. Moreover, UDP outperforms the compared methods in terms of system sum rate, user average throughput, and spectral efficiency. Finally, UDP outperforms the compared methods in terms of the  
Figure 9: Impact of the increased localization inaccuracy on the achieved average RSS value for the UDP and KDP scheme, given the deployment of 5 UAV-BSs.
# An Exploration into Damage Repair and Manufacturing Technology of Photomask Glass Substrates
## 2 Equipment
### CMP Subsurface Damage Model  
Subsubsection  
The residual subsurface damage of the surface layer of chemically polished quartz glass mainly occurred at the surface hydrolysis layer and the subsurface defect layer. The surface hydrolysis layer included the polishing impurities from the shallow surface flow layer, plastic scratches, and the polishing process, whose concentration decreased with the increase in the depth. The subsurface defect layer may include subsurface cracks, brittle scratches, and residual stresses left in the polishing process, as well as plastic scratches introduced by the polishing process [16]. The residual subsurface cracks, brittle scratches, and residual stresses in the polishing process can be eliminated gradually by optimizing the polishing technology and prolonging the polishing time. Figure 4 presents a quartz glass polishing subsurface damage model.  
The microscopic removal mechanism of polishing materials can be explained according to the established polishing subsurface damage model. Firstly, the hydrolysis of quartz glass broke its inherent network structure, and the silicic acid gel was formed on the surface, which eventually induced a decrease in the surface density, especially the decrease in the surface hardness. Subsequently, the large-scale polishing particles embedded in the polishing film would remove the material on the soft surface hydrolysis layer by the two-body wear manner, and produce plastic scratches. The polishing particles bearing a large normal load can penetrate the hydrolysis layer to wear the matrix material. However, small-scale polishing particles rolled and moved between the polishing film and the quartz glass surface, and they can remove the material or be embedded in the hydrolysis layer by adhesion. With the progress of polishing, the temperature rise caused by the friction between the polished film and the quartz glass surface, the instantaneous high temperature of microprotrusion on the friction surface, the residual structural defects during polishing, and the stress exerted during polishing would promote the hydrolysis of quartz glass and the softening of the hydrolyzed layer. Finally, the friction force between the polishing film and the quartz glass surface promoted the plastic flow of the shallow surface flow layer. This plastic flow can quickly cover the grinding damage exposed in the polishing process and the plastic scratch introduced by the polishing, thus ensuring the smoothness of the surface.  
Figure 4: Subsurface damage model for quartz glass after chemical mechanical polishing.
## 4 Security and Privacy of IoNT
For example, a hacker could potentially gain access to a nanorobot used in medical procedures and alter its functionality by compromising the security of microscale network devices or end-point devices through infected malware, leading to potentially fatal consequences [51; 52; 53; 54]. Another threat is the potential for nanodevices to be used for surveillance and tracking. Because these devices are small and can be easily hidden, they could be used to monitor individuals without their knowledge or consent, which would violate one's privacy. This could have serious implications for privacy and civil liberties, particularly if the devices are used by governments or other powerful entities. Owing to the fact that medical information should be protected from unauthorized access and that such access can have a significant impact on people's lives due to the data's sensitivity and confidentiality, the healthcare industry, which is one of the major domains that highly utilizes the IoNT, raises significant concerns about the privacy of medical data [31; 32; 33; 34; 35; 36]. According to a previous publication [9], in a typical healthcare system, the attacker is able to attack private data, such as biological data gathered by either in-body or wearable sensors, disrupt medical applications in the form of denial-of-service attacks (this would lead to severe consequences as the legitimate instructions may not be able to reach to the destination/vital body organs on time), and modify the communication links at the nano-communication level or the gateway to the Body Area Network (BAN), posing a serious threat to the lives of patients as we have already mentioned [37; 38; 39; 40; 41; 42; 43; 55; 56; 57; 58; 59; 60]. To address these threats, it is important for organizations and individuals to implement strong security measures, such as encryption and authentication protocols, to protect against unauthorized access to nanodevices. It is also important for governments and industries to develop and enforce regulations and standards to ensure the secure and responsible development and deployment of these technologies [34; 35; 36]. The COVID-19 global pandemic, which started in December 2019, has influenced many domains across the world, owing to the deadly nature of the virus; it has killed nearly seven million people as of the date of publication. Owing to its contagious nature, the pandemic has changed the nature and working conditions of companies across the world. As employees continue to work remotely, companies need to automate their operations even further. IoNT developers are currently working on an implementation of available IoNT techniques, which will boost the growth of the IoNT market in the future. Day by day, the IoNT is becoming an essential part of our daily life and technology itself is reaching its maturity. Thus, it is essential to focus higher attention on the security and privacy of the IoNT, towards the growth of the technology. In this regard, the following subsection briefly discusses the security challenges the IoNT faces, and highlights the privacy issues of the IoNT.