esa-sceva/satcom-chunk-collection huggingface dataset:
# IPCB: Intelligent Pseudolite Constellation Based on High-Altitude Balloons
## 7 Simulations and Discussions
### Simulation Settings  
The parameters used in the simulations are listed in Table 1.  
The initial geometry configuration of the IPCB is listed in Table 2, as Figure 5a,b illustrates. Users are distributed uniformly in the service area, as Figure 5c illustrates. The average GDOP of the IPCB with initial geometry configuration is 7.47, whose distribution is illustrated in Figure 5d.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Physical Meaning** & **Value** \\ \hline \(n_{p}\) & number of IPBs in a constellation initially & 6 \\ \(n_{t}\) & expected service duration of an IPCB & 24 h \\  & & \(-\)0.1338, 1.3189, \\  & & \(-\)1.9669, \(-\)2.3772, \\ \(c_{m}\) & meridional wind coefficients & 4.0187, 1.4032, \\  & & \(-\)1.4290, \(-\)0.5504 \\  & & 2.1927, \(-\)7.6660, \\ \(c_{z}\) & zonal wind coefficients & \(-\)3.0280,28.8161, \\  & & \(-\)2.6879, \(-\)41.5979, \\  & & 2.0248, 21.8084 \\ \(\lambda_{min}\) & minimal longitude of the approved airspace & 107\({}^{\circ}\) E \\ \(\lambda_{max}\) & maximal longitude of the approved airspace & 109\({}^{\circ}\) E \\ \(\phi_{min}\) & minimal latitude of the approved airspace & 39\({}^{\circ}\) N \\ \(\phi_{max}\) & maximal latitude of the approved airspace & 41\({}^{\circ}\) N \\ \(h_{min}\) & feasible minimal flight altitude of an IPB & 20 km \\ \(h_{max}\) & feasible maximal flight altitude of an IPB & 24 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used in simulations.
# Performance Analysis of Time Synchronization Protocols in Wireless Sensor Networks
## 1 Introduction
Section 5 discusses the evaluation results for FTSP and GTSP in terms of message delay jitter, length of synchronization period, topology, and packet loss. In this section, an extended version of FTSP (E-FTSP) is described and evaluated to prove its advantages over FTSP. Finally, we conclude the paper and suggest future works in Section 6.
## 2 CMOS and MTJ Technologies Operating at Cryogenic Temperatures
### Related Work
A detailed explanation of the adopted modeling can be found in [43]. Our cryogenic-aware MTJ modeling has been validated in previous works [30]. Table 1 summarizes the main physical and electrical characteristics of the MTJ devices, where the temperature-dependent parameters are explicitly provided for both room and cryogenic temperatures. Lower temperatures increase _Ic_ and HRS, thereby improving reliability in terms of read operation but sacrificing speed and energy during write memory access [30]. All the temperature trends are in agreement with the experimental studies previously reported in the literature [37; 40; 41; 49]. For more detailed information regarding the temperature-dependent equations and trends in the electrical characteristics of the MTJ models, the reader is referred to [29; 43]. Note that the magnetic parameters reported in [29; 43] and used in our analysis mainly refer to a CoFeB FL.
## 3 Results
### Analysis of Current Modular Design and Prosumer Methods
#### 3.1.3 Analysis of Methods and Tools for the Prosumer  
Figure 3 includes a total of 29 methods and tools used in the creation of products for the prosumer collected in a previous investigation [21]. As in Figure 2, a series of colors has been used to highlight some of the identified methods. In this case, essential methods for the prosumer are highlighted in red, interesting methods in yellow and methods that have a potential use with modular design are highlighted in green. Unlike Figure 2, in Figure 3, there are several methods highlighted in more than one color because they fulfill more than one condition at the same time.  
Figure 3: Analysis of current methods and tools for the prosumer.  
As previously observed in Figure 1, there is a variety of methods throughout all phases of the process. A greater presence is detected in the feasibility study, detailed design and production plan, with these last two phases belonging to the product development stage. The phase with the greatest absence of methods is that of the distribution plan, where only a method based on sharing resources through online platforms and open access is identified--something deeply rooted in the maker philosophy. On the other hand, most of the academic methods are located in the feasibility study, since their objective is to analyze the feasibility of the development of a product through the study of the end users.  
Numerous essential methods for creating products for the prosumer have been identified by the high frequency of their use. Although they cannot be considered to be completely essential, they are of great relevance to achieve products aimed at this user category. It is necessary to take this into account if we aim to create a methodological model, since these tools, such as DIY and tutorials, and methods such as redesign are very present in the prosumer methodology. Six methods of interest have also been identified for the future model or design method to be developed, among which 3D printing and methods focused on end consumers stand out. Finally, there are 10 methods and tools that can potentially be used in conjunction with modular design, where five of them are at the same time essential methods. For this reason, we can conclude that the implementation of methods and tools that are both essential and potential are necessary to unite prosumer and modular design in a future method or model. These five methods and tools are new idea/innovation, DIY, adaptation, upgrade and reuse. Specifically, these last three methods are also identified later in _Modular Design-Prosumer Relationship_ as common design goals between prosumers and modular design.
###### Abstract
The features of a malignant nodule on US include microcalcifications, marked hypoecheogenicity, being microlobulated or irregularly circumscribed, and suspicious cervical lymph nodes with long axis perpendicular to the skin [7]. However, due to the complex nature of the nodules, there may be some some overlap between the features of benign and malignant nodules on complex US images. [12]. Therefore, they are not always satisfactorily distinguished by operator-dependent US. In recent years, some thyroid scoring systems have been developed (K-TIRADS, EU-TIRADS, ACR-TIRADS, ATA, BTA, etc.) to diagnose benign and malignant thyroid nodules. These scoring systems are used to score cancer rate. According to the cancer score ratio, clinicians determine whether to perform surgical intervention [13; 14; 15]. Now, machine learning and artificial intelligence are dominating various industries. Among them, medicine and healthcare may be ripe for these state-of-the-art applications. Machine learning has been widely adopted in different medical practices (blood count, sperm count, cancer screening, diagnoses, etc. ), yielding high-performance results. The relevant literature also contains many studies oriented to machine learning applications for thyroid nodules [16; 17; 18; 19; 20; 21; 22]. In this study, we retrospectively investigated the thyroid treatment process, obtained through US imaging techniques, adopting different machine learning algorithms (Random Forest, J48 Decision Tree (J48 DT), Adaboost, and Support Vector Machine (SVM)) with current thyroid nodule scoring systems (K-TIRADS, EU-TIRADS, ACR-TIRADS, ATA, and BTA) with the aim to prevent unnecessary interventions and costs. Then, we compared the diagnostic performances of the hybrid algorithm with the evaluations of two experienced radiologists. In the end, we created a decision-support mechanism with high accuracy for radiologists. In other words, intelligent models were integrated into current thyroid nodule scoring systems. These hybrid models are used to distinguish benign from malignant nodules. The main contributions of this paper can be summarized as follows:  
* An original dataset is collected to carry out this study. * Some intelligent models are employed for assisting thyroid scoring systems with the aim to determine the correct surgical intervention for thyroid nodules and to reduce operation costs by preventing unnecessary interventions and surgical procedures. We aimed to increase the efficiency and robustness of the scoring systems. Therefore, while integrating the intelligent models to scoring systems, we attempted to prevent unnecessary operation costs. We aimed to build a decision-support system to doctors. The rest of this paper is arranged as follows: Section 2 briefly explains materials and methods. Section 3 gives the experimental results. Section 4 introduces discussions, and Section 5 give conclusions.
# Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
## 1 Introduction
* Maghrebi: A dialect of Arabic spoken in North Africa, except Egypt. Figure 1 illustrates the geographical layout of the above Arabic dialects [12]. We implemented an Arabic dialect identification system to localize COVID-19 data so as to provide region-based insights and analysis. Dialect identification is a form of language identification in which the purpose is to differentiate between dialects that are closely related. Our experiments provided 3.4% absolute enhancement over the state-of-the-art result [13] in dialect identification accuracy on the region level. Additionally, we achieved more than 3% accuracy improvement between the three major Arabic dialects after excluding the MSA set. Moreover, the developed models have been successfully applied on a recently collected dataset of COVID-19-related Arabic tweets to identify the source country of each tweet with a weighted average accuracy of 97.36%. We believe that the achieved accuracy is notable and can be effectively used to support decision-makers  
with a valuable source of information regarding people's reactions towards the current pandemic. The rest of the paper is organized as follows: Section 2 reviews the previous effort for Arabic Dialect identification, Section 3 describes the implemented models, and Section 4 includes the description of the used data sets and the results. Finally, Section 5 concludes the paper with future directions.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 2 Model Description
### RAS Telescope Model  
The interference characteristics of an RAS telescope depend not only on the characteristics of the interfering system but also on its own characteristics, such as the location of the RAS station and the receiving antenna model of the RAS telescope. The number of visible interfering satellites also varies depending on the location of the telescope, with the number of visible satellites increasing with latitude for the polar-orbiting constellation; the location of the RAS station also leads to a different density of user terminals around it, with more terminals requiring more beam resources in the area. The above characteristics lead to changes in the interference to which the RAS telescope is subjected. In this study, an in-depth analysis of the interference characteristics was carried out depending on the location of the RAS telescope and the user requirements around it. Based on ITU R RA.1631, the following mathematical model of the receiving antenna was used in the compatibility analysis between non-GSO systems and the RAS telescope at frequencies above 150 MHz.  
\[[G_{r}(\phi)]=\left\{\begin{array}{ll}G_{max}-2.5\times 10^{-3}(\frac{D}{ \lambda}\phi)^{\alpha}&0<\phi\leq\phi_{m}\\ G_{1}&\phi_{m}<\phi\leq\phi_{r}\\ 29-25\log_{10}\phi&\phi_{r}<\phi\leq 10\\ 34-30\log_{10}\phi&10<\phi\leq 34.1\\ -12&34.1<\phi\leq 80\\ -7&80<\phi\leq 120\\ -12&120<\phi\leq 180\\ \end{array}\right., \tag{17}\]  
where \(D\) and \(\lambda\) are the diameter of RAS telescope and the wavelength of the observation frequency band, respectively. The expressions of the parameters in Equation (17) are formulated below,  
\[G_{max}=20\log_{10}(\frac{D}{\lambda})+20\log_{10}\pi \tag{18}\]  
\[G_{1}=-1+15\log_{10}\frac{D}{\lambda} \tag{19}\]  
\[\phi_{m}=\frac{20\lambda}{D}\sqrt{G_{max}-G_{1}} \tag{20}\]  
\[\phi_{r}=15.85(\frac{D}{\lambda})^{-0.6} \tag{21}\]  
According to ITU Rec. RA.1631, the maximum antenna gain of a typical RAS telescope in the 10.6\(\sim\)10.7 GHz band is 81 dB. The power flux density (PFD) threshold in the band at the RAS receiver is \(-160\) dBW/m\({}^{2}\)/100 MHz. RAS telescopes are used to observe very faint celestial activity in the Universe and therefore the requirements for RAS interference protection are very stringent. The permissible percentage of individual NGSO satellite systems exceeding the PFD threshold is 2 percent, while the permissible percentage for all NGSO systems is 5 percent.
## 3 Experimental Section
### Algal Cultivation and Harvest  
_Neochloris oleoabundans_ (UTEX LB 1185) was obtained from the University of Texas at Austin Algal Culture Collection and KAS 603, a saltwater species of _Chlorella_, was provided by Kuehnle Agro Systems. _Neochloris_ was cultivated in freshwater Bold 3N (B3N) medium [30] using an airlift bioreactor illuminated with cool white fluorescent lights on a 12:12 light:dark cycle and aerated with ambient air using an oil-free diaphragm pump. KAS 603 was cultivated in the same way using f/2 saltwater medium [31]. For nitrogen starvation experiments, _Neochloris_ was grown for 14 days in B3 Nmedia, harvested by centrifugation, resuspended in sodium nitrate-free B3N, and cultured for an additional 21 days. For resin binding and FAME synthesis, algae were harvested by centrifugation and resuspended in distilled water to concentration of 0.4 g/L.
## 3 Methodology
### Design of the Data Service Container
#### 3.1.3 Access and Invocation of Data Service
In addition, the monitoring process will monitor the execution status of the data service in real time, such as the input data status, running status, output status, error status, etc., to feed back the execution information of the data service to the container process in real time. When the monitoring process detects that the data service is abnormal, it immediately terminates the data service-execution process and reports the exception information to the container process. In addition, the temporary files generated during the execution of the data service and the applied system resources will be cleared and released to ensure the efficiency of the container operation.
# A New Design Method of Shield Tunnel Based on the Concept of Minimum Bending Moment
## 3 The Design and Calculation of the Zero Bending Moment Shield Tunnel Cross-Section  
### The Calculation of the Rational Axis for Shield Tunnel Cross-Section  
The expression of the rational axis is required to be calculated when designing the rational axis for the shield tunnel cross-section. In Figure 5, the left cross-section of the shield tunnel is cross-section \(S\), and its coordinate is \((x,y)\). As in Figure 6, cross-section \(S\) is composed of four parts, as follows:  
\[M_{s}=N_{1}x-\frac{1}{2}P_{2}x^{2}-\frac{1}{6a}P_{3}x^{3}-\frac{1}{2}P_{1}y^{2} \tag{6}\]  
where \(M_{s}\) is the bending moment of cross-section \(S\); \(x\) is the horizontal coordinate of cross-section \(S\); \(y\) is the vertical coordinate of cross-section \(S\).  
Figure 4: Structural mechanical model of semi-shield tunnel cross-section (the semi-structure of Figure 3, after rotating clockwise 90 degrees).  
Figure 5: Structural mechanical model of rational axis for zero bending moment shield tunnel cross-section.  
Equation (6) shows that the first three bending moments \(M_{s}\) are all caused by the loading on the \(Y\)-axis and the values are only associated the value of \(x\); the fourth \(M_{s}\) is caused by the loading along the \(X\)-axis, the value of which is only related with \(y\). The first three bending moments and the corresponding simply supported beam (as shown in Figure 7) share the shame bending moment expression when subjected to the same loading along the \(Y\)-axis. Assuming the bending moment expression of the simply supported beam at horizontal coordinate \(x\) is as follows:  
\[M_{x}=N_{1}x-\frac{1}{2}P_{2}x^{2}-\frac{1}{6a}P_{3}x^{3} \tag{7}\]  
where \(M_{x}\) is the bending moment at the position of horizontal coordinate \(x\).  
In order to realize the zero bending moment of the cross-section \(S\), the vertical coordinate for cross-section \(S\) can be calculated as Equation (8).  
\[y=\sqrt{2M_{x}/P_{1}}=\sqrt{\left(2N_{1}x-P_{2}x^{2}-\frac{1}{3a}P_{3}x^{3} \right)/P_{1}} \tag{8}\]  
However, the bending moment of the right cross-section \(S\) is composed of five parts, as shown in Figure 8. The bending moment of the cross-section \(S\) on the right of Figure 8 caused by the \(P_{1}\) loading in the left direction exerted on curve \(CS\) shares the same value as the bending moment of the cross-section \(S\) caused by the \(P_{1}\) loading in the right direction exerted on curve \(DC\) on the left of Figure 8. The two bending moments are in opposite directions so they can offset each other. Therefore, Equation (8) can be applied to any section in Figure 5.  
Figure 6: Internal force analysis for a section of zero bending moment shield tunnel cross-section 1.  
Figure 7: Corresponding simply supported beam model for zero bending moment shield tunnel cross-section.
# A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
## 2 Materials and Methods
### Loop Closure Detection Acceleration Based on Grayscale Histogram
If the number of interior points is greater than a certain threshold, a more stringent search radius and Hamming distance are used to match again. Otherwise this process is repeated to obtain a relative pose with the highest accuracy. After the \(T_{am}\) is calculated, in order to prevent the loop closure failure, it is necessary to check the keyframes like a collection card. This process follows the geometric consistency check in [17] and the loopback continuity check in [5]. Firstly, the geometric consistency check is performed. This process does not depend on time continuity, and \(K_{M}^{\prime}\) is verified by the degree of common-view between its keyframes and \(KF_{i}\). If the common-view keyframes of \(K_{M}^{\prime}\) have enough matching points with \(KF_{i}\), it is recorded as a successful verification. Traverse these common-view keyframes, and if at least three of them pass the check, exit the check process. Otherwise, the loopback continuity check is performed. The loop closure detection results are verified by projecting \(X_{m}\) to the newly generated keyframes for matching, and the pose of Sim3 is optimized. If the number of matching points meets the requirements, it is recorded as a validation success. If the total number of verification successes (geometric consistency check and loopback continuity check) reaches three times, the detection process will be directly exited, and the final roll angle, pitch angle and yaw angle will be judged. In ORB-SLAM3, the roll angle and pitch angle are required to be less than 0.46\({}^{\circ}\), and the yaw angle is less than 20\({}^{\circ}\), otherwise the loopback result is considered bad. If the above conditions are met, loopback correction will be performed.
## 1 Introduction
Based on routing issues in these networks, more bandwidth will be utilized, frequent routing updates will occur, packet losses will be observed, low convergences will occur in the network, and frequent link failures will be noticed. Energy is another significant factor in UAVs' networks which has a direct relation with overhead and data delay. There is a need to design a routing solution for UAVs' networks based on more feasible and efficient routing metrics for routing decisions with less energy consumption parameters. In addition, to complete the pre-planned task and improve time and distance, and optimize the cost, the solution will be based on balanced parameters as per network requirements. The main objective of this paper is to design a routing solution for UAV-based IoT networks. The other objectives of this paper are as follows:  
* To design a decision-based routing protocol to address the data delay and network overhead issues. * To design a protocol to handle nodes' mobility and the dynamic topologies of UAVs' networks by considering feasible routing parameters. * To design an intelligent routing protocol for balanced data communication and the best path findings for data transmission. The rest of this paper is organized as follows: Section 2 presents the literature review in the field of IoT and UAV networks and discusses the possible solutions gap. Section 3 illustrates the design and development phases of the proposed solutions. Section 5 presents the results and the last section concludes the paper with future directions.
# LTE: Lightweight Transformer Encoder for Orbit Prediction
## 2 Materials and Methods
### Data Preprocessing  
There are various mathematical representations of the same orbit, but specific parameter sets of six orbital elements are commonly used in fields like astronomy and orbital mechanics. These traditional orbital parameters are known as the six Keplerian elements, named after [PERSON] and his laws of planetary motion [25]: the Semi-major axis, Eccentricity, Inclination, RAAN, the Argument of perigee, and True anomaly. Although True anomaly is the parameter used for precisely indicating a satellite's position along its orbit, it is challenging to calculate due to the nonlinear motion of objects in elliptical orbits. Instead, Mean anomaly is often used, as it increases linearly over time, providing a simpler and more predictable measure of position. The Semi-major axis is not included in the TLE set directly. However, by using [PERSON]'s third law [26], which states that the square of an orbital period is proportional to the cube of the Semi-major axis, we can substitute Mean motion with the Semi-major axis [27]. Thus, we can use the Mean anomaly of the TLE set instead of the True anomaly and calculate the Semi-major axis from the Mean motion of the TLE set. As a result, we extracted six elements from the TLE set for satellite orbit prediction, and their descriptions are provided in Table 1.  
Given a time \(t\), if \(S_{t},E_{t},I_{t},R_{t},A_{t},M_{t}\) denote the six orbital elements extracted from the TLE set--namely, the Semi-major axis, Eccentricity, Inclination, RAAN, Argument of perigee, and Mean anomaly--then the final vector \(X_{t}\) can be expressed using the following formula:  
\[X_{t}=(S_{t},E_{t},I_{t},R_{t},A_{t},M_{t}),\quad t=0...n, \tag{1}\]Figure 2: Plot of the elements from the KOMPSAT-3 satellite dataset, displaying the data points from 300,000 to 302,000. In the case of RAAN, a different range of data points (300,000 to 1,700,000) was provided since it has a long-period repetitive pattern compared to the other elements. Each element exhibits a unique period and wave shape, but a repetitive pattern is observed within each element individually. However, the data show subtle deviations in fluctuation even within these repetitive pattern, and the errors caused by these subtle deviations in fluctuation can lead to major satellite orbit changes.