esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This research investigates the instability mechanism of the double-angle trench section's continuous wall through a combination of theoretical analysis, numerical simulation, and on-site measurement. A calculation model for trench wall instability is proposed by summarizing the types and calculations of instability. The circular ground connecting wall of the shield receiving well foundation pit in Yunnan Province, China is taken as a case study, and the results of various calculation methods are compared and examined. The study ultimately determines the minimum slurry weight required to ensure the stability of the diaphragm wall. The findings of this study are summarized as follows.  
1. In this study, we have developed a novel calculation model to assess the overall stability of the double-angled trench. Drawing upon the Mohr-Coulomb strength theory and limit equilibrium conditions, our model accounts for the influence of the positive angle of the sliding body of the double-angled trench section. Specifically, a minimum slurry weight calculation formula to ensure the total stability of the diaphragm wall was derived.
2. A theoretical calculation model for the maintenance of the local stability of the diaphragm wall is established by utilizing the strip method and the limit equilibrium condition to analyze the force of the micro-soil unit. This model allows for the consideration of the impact of various factors on local instability. By employing this model, a formula for the calculation of the minimum slurry weight required to ensure the local stability of the diaphragm wall was derived.
3. The stability of the double-angled trench section's wall was analyzed using the finite element method that employed the strength reduction approach. The results showed that the center of the inner side of the trench wall was the most susceptible to damage, followed by the inner corner of the trench wall, and then the middle and outer corner of the outer side of the trench wall.
4. The model proposed in this paper is verified to be valid, but more extensive validation is still needed, such as considering different geologic conditions and trenching methods. In addition, the description of the connections between the groove segments and the methods of strengthening the reliability of the connections will be the focus of further research.  
Q.X.: conceptualization, formal analysis, investigation, writing--review and editing, funding acquisition, project administration. J.X.: data curation, formal analysis, methodology, investigation, writing--review and editing. Z.S.: methodology, investigation, formal analysis, validation, writing--original draft, writing--review and editing. L.L.: investigation, data curation, formal analysis. H.Y.: investigation, validation, writing--original draft, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Key R&D Program of Shandong Province (No. 2021 CXGC011203).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.
# An Overview of Position Sensorless Techniques for Switched Reluctance Machine Systems
## 3 An Overview of Recent Development in Position Estimation Methods of SRM
### Magnetic-Model-Based Position Sensorless Methods  
Equation (2) shows that the rotor position estimation has a direct mapping relationship to flux-current and torque-current. A large number of methods have been proposed based on magnetic models and magnetic equations [44].
# Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment
## 3 HGSA Algorithm
### Mutation
In Figure 7a, UAV1 and UAV2 each visit 10 targets, while in Figure 7b, UAV2 visits 10 targets and UAV1 only visit nine targets. The results indicate that increasing the turning radius will lead to a longer path; therefore, the optimal routing scheme for the classic TOP violates the UAV endurance constraint. For example, in Figure 7b, if the 61# target is visited by UAV1, the path length of UAV1 will reach 17.6784, which exceeds \(T_{MAX}=17.5\) of UAV1. The results of Numerical Experiment 1 show that if the impact of the turning radius on the path is not considered in the route planning, the path planned is likely to be impossible for the UAV to execute, which leads to task incompletion or even failure. #### 4.1.2 Numerical Experiment 2

To present the influence of the heading angle dispersion on the routing scheme, we select six benchmark instances with routing number \(K=2\) and maximum endurance \(T_{\max}=20\) in the TOP benchmark instances for numerical experiments. In each instance, the UAV turning radius is set to \(\rho=0.1\), and the set of heading angle dispersions is \(N_{\theta}\in\{3,6,8,15,24,36,60\}\); thus, we obtain 42 benchmark instances. Table 4 records the best results for the 42 benchmark instances, which was run 10 times under the same experimental conditions. The optimal results of the 10 runs are listed in the \(R_{MAX}\) column and the average computational time of the 10 runs in seconds are listed in the \(CPU_{AVG}\) column. For comparison, we also list the known traditional TOP optimal solutions in the \(R_{BEST}\) column. Figure 7: Optimal routing scheme diagrams of the p6.2.e instance for Set 6 of the TOP benchmark instances under different turning radii. (**a**) Optimal routing scheme for the p6.2.e instance under turning radius \(\rho=0\); (**b**) Optimal routing scheme for the p6.2.e instance under turning radius \(\rho=0.7\). For each instance, except p2.2.j, \(R_{MAX}\) tends to increase until \(N_{\theta}\) = 8; with increasing \(N_{\theta}\), the computational time increases continuously. The main reason is that greater discretization will lead to more accurate heading angle for UAV, which will effectively shorten the path length of the UAV, in turn it will help the UAV to access more targets to obtain greater profit. However, a larger heading angle dispersion increases the computational complexity of the problem and increases the computational time. The results of Numerical Experiment 2 show that under the premise of ensuring a high-quality solution, setting appropriate heading angle discretization can effectively improve the time efficiency of the algorithm.
## 3 Proposed Approach
### Decision Maker  
This process is individual for each agent, and it is the intelligent method to obtain an optimal decision with minimal information. High intelligent performance at this level reduces the communication cost. Three cognitive styles characterize a decision-making algorithm: satisfiers, who try to find a \"good enough\" solution; maximizers, who try to make an optimal decision; and intelligent, who may also learn or use knowledge to achieve their goals. The core point of this process is how to coordinate agents to cover the environment efficiently. The primary purpose of the exploration is to cover the whole environment in a minimum of time. As a result, the agents must be aware of which areas of the environment have been explored previously. The task allocation implicates a new grid cell assignment where each agent should go and the path to get there.  
The proposed intelligent approach uses a utility function to calculate the reaching suitability of each point. For this reason, a utility function \(U_{i}\) based on the trade-off between the costs of reaching the grid cell and its gain in coverage is proposed and shown in Equation (2). The proportional cost \(P(C_{Ri})\), as in Equation (3), defines agent suitability as a function of the time required to move to a particular grid cell \(t_{Rj}\) compared to other agents in terms of maximum time to get there. This guarantees the best solution for the team and allows it to be implemented into a multi-agent system. The unexplored area size gives the proportional gain \(P(G_{Ci})\) of grid cells that an agent can cover with its sensors when reaching a grid cell.  
\[U_{i}=P(G_{Ci})-P(C_{Ri})\ for\ i=1,2,\ldots,\ n \tag{2}\]  
where \(n\) is the number of grid cells in the environment.  
\[P(C_{Ri})=\frac{t_{Rj}}{\max(t_{R1},t_{R2},\ldots,\ t_{Rm})}\ for\ j=1,\ 2,\ \ldots,\ m \tag{3}\]  
where \(m\) is the number of agents in the environment.
## 3 Results  
### Analysis of THR  
#### 3.1.1 Tablets Containing Clindamycin  
For this preparation, at each time point, all the radiation was reflected in the 480-600 nm spectral range, and thus THR was the highest in this band. In turn, the lowest THR on day 0 was found for 335-380 nm and 1700-2500 nm bands, amounting to about 35% and 46%, respectively. It can be seen that the mean THR increases in the visible range (ranges of 400-540 nm, 480-600 nm, and 590-720 nm), and from the infrared range of 700-1100 nm, it starts to decrease compared to the ranges of 480-600 nm and 590-720 nm.  
Comparing the THR values between subsequent days of analyses, significant differences were found for all spectral bands except for 1700-2500 nm. In these ranges, mean THR values were the highest on day 0 of the experiment. In turn, for the 335-380 nm spectral band, THR significantly differed for the day 0 vs. day 3B and day 0 vs. day 7B comparisons (\(p\) = 0.001 and \(p\) = 0.010, respectively). Statistically significant differences in mean THR values are shown in Figure 2.  
The THR values for both non-blister and blister tablets with clindamycin significantly decreased on day 3 of UV exposure for ranges of 400-540 nm, 480-600 nm, 590-720 nm, and 700-1100 nm. Figure 3 shows the course of the spectra for the above-mentioned bands throughout the experiment (time points 0, 3, and 7). For non-blister tablets, on day 7 of the experiment, THR increased slightly compared to day 3, while for blister tablets, THR continued to decrease. In the remaining spectral ranges, i.e., for the UV (335-380 nm) and infrared range (two bands from 1000 nm to 2500 nm), the THR values were below 0.8, which would mean that the waveforms in these four ranges of visible light and near-infrared would not be visible. Furthermore, no such obvious differences in the course of the spectra over time were found; therefore, they are not visible in Figure 3.  
Figure 2: Mean THR values of clindamycin tablets between subsequent days of experiments. THR—total hemispherical reflectance; B—blister tablets. Only significant differences are shown.
## 8 System Implementation Experimental Studies
### Decoder Implementation  
The developed Raptor-like decoding algorithm, presented in Section 6, can be implemented on any computing platform utilized in the system gateway (Figure 1), including CPU, GPU, ASIC or FPGA. For the the experimental investigation in this paper, Matlab software implementation on a personal computer was used. However, additionally, we developed the C language implementation for the STM32 microcontroller, the same as that used for the encoder. In Figure 8, we present the decoding time measurements of this implementation.  
The microcontroller with the developed decoding algorithm is able to decode information portions efficiently, with lower computing time requirements if the received signal is of high quality (with small number of errors) and higher computing time consumption if the received signal is of low quality. In case of an erroneous received signal, the algorithm adapts and continues decoding until the correct result is achieved. During decoding, subsequent parity checks are used from \(\mathbf{H}_{\text{IR}}\). With every additional chunk, the total (cumulative) decoding time increases, as shown in Figure 8. The figure provides averaged measured cumulative decoding time versus the number of chunks required for successful decoding. This means that the first (leftmost) point in every curve presents the time of decoding just the core HR part (with 0 additional chunks), and then the subsequent points present the total decoding times if the number of required chunks increases.  
Figure 7: Observed encoding time intervals for initial HR block (chunk No. 0) and IR chunks \(1,\ldots,L\).
## 3 Materials and Methods
### Functional Requirements  
In terms of functional requirements, the following operations are mainly considered:  
* Creation of a LoRaWAN gateway network in remote and isolated regions with redundant coverage, in which at least one gateway will be connected to the Internet (3G/4G);
* Secure connection service with user registration, authentication and validation;
* Data collection function of sensors attached to the device (GPS, accelerometer, temperature, pulse, body humidity);
* Housing sensor data collection function (temperature, gases, smoke, flood);
* User data sending function via LoRa communication;
* Portable device parameterization and configuration function;* Real-time analysis of the data collected, to detect deviations beyond the permitted tolerances;
* Alerts when there is an abnormal occurrence in the user's device (sent to family, friends or entities and security forces), namely when the equipment signal is lost, or a fall occurs.
# Prediction of Arrival Time of Vessels Considering Future Weather Conditions
## 2 Previous Studies
### Overview  
To prevent collisions among vessels and save lives, the installation of an automatic identification system (AIS) for vessels has been mandatory since 2002 for passenger ships, vessels of 500 gross tonnage (GRT) or more, and vessels of 300 GRT or more that are engaged in international logistics [7]. The AIS relays dynamic information for a vessel, such as position information and the bow direction, as well as static information, such as the ship identification number, total length, and total tonnage, in international very high frequency (VHF) to terrestrial facilities. The AIS also receives information about other vessels from terrestrial facilities.  
The introduction of AIS has facilitated the acquisition of operation data of vessels and led to the emergence of data providers, such as Marine Traffic, who accumulate and publish operational data of vessels worldwide [8]. Consequently, the use of operational data is being actively researched for routing and scheduling operations [9].  
Here, there are many research works about routing scheduling operations for other modes of transportation such as automobiles and aircrafts. For example, one study pre  
Figure 1: Growth in number of containers over last 10 years from 2008 to 2018 [3].  
dicted the short-term airport arrival time flow [10], and another study predicted an aircraft flight course and arrival time [11]. However, unlike other modes of transportation, vessels have some freedom in selecting routes, which has resulted in numerous studies on vessel routing. In recent years, the Dijkstra algorithm [12], which is often used to find the shortest path in problems, and its derivative, the A* algorithm [13; 14; 15], have been used in studies to calculate routes considering weather conditions. The machine learning models of support vector regression (SVR) and differential evolution (DE) have also been combined for use in route prediction [16]. Moreover, there are several studies which predict route and arrival delay with neural networks [17; 18]. The effect of weather conditions on a vessel's future voyage speed has also been predicted [19] by using equations of motion to model vessel motion. The prediction model includes parameters related to waves/winds, such as the wave height, wave period, and wind power, as well as parameters pertaining to dynamic vehicle information, such as the number of engine and propeller rotations, as well as the bow direction. The voyage speed is calculated using known parameters, such as the en route weather conditions and the engine output. Motion equations also are used to predict ship performance [20].  
A representative study by [PERSON] et al. [6] predicted the arrival time at a destination port. This method consists of two steps: (A) predicting the route from the current position to the destination port and (B) predicting the en route voyage speed to calculate the time of arrival at the destination port.
# Physarum-Inspired Bicycle Lane Network Design in a Congested Megacity
Finally, constraints of BLN design are different from a typical network design, and hence appropriate mechanisms are sought for BLN design. A novel network optimization technique has recently been identified from natural Physarum polycephalum, a brainless multi-headed single-celled slime mold organism [15]. Despite lacking brain and neurons, it shows complex decision-making ability in food foraging behavior and develops a reticulated network shape [16; 17]. Its ability to optimize its shape attracted the research community to solve complex optimization tasks, such as optimal network design [16]. The computational model of Physarum's network design technique is expected to solve complex design problems more effectively than the existing bioinspired and traditional methods [11; 12; 13]. The aim of this research is to develop a Physarum-inspired novel BLN design method to improve the mobility of a congested megacity. The basic mathematical model of Physarum is modified to handle various BLN constraints of megacity contexts in designing an optimal BLN. Notably, we introduced hinder issues (e.g., footpath conditions, traffic conditions, aside marketplace conditions, etc.) in conductivity assignments to expose individual links' feasibility, and modified flux measurements adapted to a BLN to make the optimization technique effective, as well as efficient. The proposed Physarum-inspired BLN design method is applied to the megacity Dhaka to design two BLNs. The first BLN covers the congested central city zone, and the second one covers a broader area that includes the central zone and other major locations of the city. The designed BLNs are analyzed to evaluate the expected improvements in green mobility (in terms of travel time) in congested megacities and other benefits, in terms of transportation cost, health, and environmental impacts. Section-wise overviews of the rest of the paper are as follows. Section 2 describes natural Physarum and mentions important tasks inspired by it. Section 3 presents the proposed Physarum-inspired BLN design method. Section 4 demonstrates BLN design in the megacity Dhaka, employing the proposed method. Section 5 gives an overall discussion on model significance, outcomes and related issues. Finally, Section 6 is given to the conclusions of this study, together with an outline of future directions for research opened up by this work.
## 2 Theoretical Background
### Concept of Fractal Dimension  
The essential idea of fractional has a long history in mathematics that can be traced back to the 1600s. It was originally used to describe objects that have self-similarity features. The terms fractal and fractal dimension were coined by the mathematician [PERSON] in 1975 [28,29]. It was used to extend the concept of theoretical fractional dimensions to geometric patterns in nature. For example, the fractal dimension of a coastline quantifies how the number of scaled measuring sticks required to measure the coastline changes with the scale applied to the stick.  
In fractal geometry, the fractal dimension is a ratio providing a statistical index of complexity comparing how detail in a fractal pattern changes with the scale at which it is measured. Moreover, it has been characterized as a measure of the space-filling capacity of a pattern that tells how a fractal scales differently from the space it is embedded in.  
According to the definition, the fractal dimension could describe the density and complexity of the cracks in tunnel lining. In this case, it is a promising method to estimate the stability of tunnel lining according to the fractal dimension of cracks. The fractal dimension of cracks in a plane could be explained intuitively thinking of them as an object too detailed to be one-dimensional, but too simple to be two-dimensional. Therefore its dimension might best be described not by its usual topological dimension but by its fractal dimension, which in this case is a number between one and two.
# In Operando Health Monitoring for Lithium-Ion Batteries in Electric Propulsion Using Deep Learning
## 3 Experimental Setup
### On-Ground Testing  
On-ground tests were conducted to analyze the performance of newly developed BMS before deploying it in electric propulsion vehicles. The on-ground tests involved applying a load to the battery, allowing it to charge and discharge by connecting the BMS between the battery pack and the BAn, as shown in Figure 3b. The NEWARE Powerwall CT-4004-20V20A system BAn, capable of charging and discharging up to a combined 20 V and 20 A, was used as a load during on-ground testing. The voltage, current of the battery pack, and surface temperature of each cell were monitored, and the BMS logged the data. To place the RTD on the battery, the protective film of the battery was removed at the center of the 18650 cell, and the orientation of the battery pack was noted to maintain consistency in recording surface temperature, as shown in Figure 3d. Additionally, the voltage and  
Figure 2: A drone with the BMS on top stores the voltage, temperature, and current. Current was also used to calculate the SOC of the battery, which helps to predict the discharge capacity by using CD-Net.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **Value** \\ \hline Cell chemistry & NCA \\ Cell form factor & 18650 \\ Nominal capacity & 3120 mAh \\ Nominal voltage & 3.6 V \\ Standard charge & CCCV, 1 C, 4.2 V \\ Standard discharge & Constant charge, 1 C, 2.5 V \\ Weight & 46.4 \(\pm\) 1.5 g \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of the individual cells in the LIB battery pack used in this study.  
applied current on the battery pack were collected by the BAn. Following the standard cycling on the battery pack, 42 cycles were performed to check the performance of the newly developed BMS. The cycling pattern was similar to certification experiment bed by NASA for small satellites [40].
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 3 Methodology
### Explanatory Variables
#### 3.2.4 Arrival/Departure Pressure  
Arrival/departure pressure can reflect congestion in the airport. Air traffic controllers (ATCO) control take-off and landing based on certain rules, including wake turbulence. Therefore, if the airport is very crowded, ATCO will delay a flight's arrival or departure.  
In this study, arrival/departure pressure is defined as the number of scheduled flights and the number of actual flights in a unit of time. The time interval was set to half an hour before and after the scheduled flight time of the flight \(i\):  
\[NS_{i}=n_{t_{i}^{s}+\frac{M}{2}}^{s}-n_{t_{i}^{s}-\frac{M}{2}}^{s} \tag{3}\]  
\[NA_{i}=n_{t_{i}^{s}-\tau}^{a}-n_{t_{i}^{s}-\tau-\Delta t}^{a} \tag{4}\]  
where \(n^{s}\), \(n^{a}\) denote the number of scheduled flights and the number of actual flights, respectively; the subscript \(t_{i}^{s}\) is the scheduled time of the flight \(i\). Since flight delays are predicted \(\tau\) hours in advance, the actual number of flights is calculated using data from \(\tau\) hours earlier.
# Current Context and Research Trends in Linear DC-DC Converters
## 6 LDO's Recent Research Trends  
With the growing market for LDOs, researchers are focused on further expanding the horizons of currently available LDOs. With the high level of LDO applications in portable devices, major LDO manufacturers and researchers are keen to further enhance the performance of LDOs [39]. The efficiency of LDOs, size of LDOs, and the noise and EMI levels of LDOs are the major key areas of high concern for enhanced performance of portable devices.