esa-sceva/satcom-chunk-collection huggingface dataset:
# Hardware Design for Cascade-Structure, Dual-Stage, Current-Limiting, Solid-State DC Circuit Breaker
## 2 Topology and Principles  
### Hardware Topology Structure  
The proposed bidirectional solid-state DC circuit breaker topology and the unidirectional hardware topology are shown in Figure 1a,b. The hardware topology consists of a steady-state branch, a dual-stage current-limiting branch (including primary and auxiliary current-limiting branches), a transfer branch, a surge arrester branch, and an energy dissipation branch. The main branch integrates a modular current detection circuit, while the steady-state branch, transfer branch, and short-circuit branch achieve control over the conduction and turn-off of core power electronic devices through a microcontroller STM32(Zheng dian yuan zi, Guangzhou, China).  
The power supply employs modular capacitors to simulate the system voltage of the circuit breaker and is equipped with a capacitor power switch connected in series to start or shut down the capacitor power supply. To enable the detection of the current carried by the main branches, modular current detection circuits are introduced in each main branch, which can be implemented using sampling resistors or Hall current sensors for current measurement. In the steady-state branch, the current detection branch converts the current signal into a voltage signal, which is then sent to the AD analog-to-digital conversion module and subsequently transmitted to the STM32 microcontroller for signal processing. The STM32 sends control signals to the optocoupler and drive circuit to achieve the conduction and turn-off of power electronic devices. The steady-state branch is composed of thyristors T\({}_{0}\) and MOSFETs VT\({}_{1}\) connected in series, while the transfer branch is formed by the series connection of modular cascaded SM sub-modules. Each SM contains a power electronic branch made up of a thyristor and a MOSFET connected in series, along with a buffer branch connected in parallel, consisting of resistors, capacitors, and diodes in series and parallel configurations. The control method for the power electronic branch is the same as that for the steady-state branch. A voltage measurement circuit is introduced on the high side of the metal oxide varistor (MOV) to achieve precise measurement of the voltage across the MOV. A simulated short-circuit circuit is also installed on the load side to perform short-circuit operations on the load.
## 5 Benchmark Testing and Performance Analysis of MRSO  
In this section, we evaluated the efficiency and reliability of the proposed Modified Rat Swarm Optimizer (MRSO) by testing it against 23 classical benchmark functions and 10 benchmark functions from the CEC 2019 competition. These benchmark functions are commonly used by researchers to validate the performance of new algorithms, providing a standard basis for comparison [24; 25].
## 2 The ELD Problem Formulation
### Considered Constraints in the ELD Problem
#### 2.3.2 POZ, SR, and RRL Constraints  
Technically, TGUs are not allowed to work in restricted areas because negative impacts on these units will cause damage to steam turbines, even the bearings can be destroyed. Therefore, POZ is a constraint that must be strictly observed in the operation process of TGUs. The fuel-power curve of TGUs will be interrupted due to the presence of POZs, so the determination of the power position of the units in neighboring POZs is quite complicated. In addition, if a TGU in the system is stopped due to a breakdown, the power balance constraint must still be met. Therefore, each unit must reservepower to ensure stability for the system. The solution to this problem is called a spinning reserve. Another important constraint is the ramp rate limit (RRL), which does not allow the TGU to generate power with values beyond a predetermined space. This constraint considers increase and decrease limits for power output of the TGU. The details of the constraints are as follows:  
Prohibited operating zones: According to the POZ constraint, the TGU can operate at power ranges as shown in the following model.  
\[p_{i}=\left\{\left\{p_{i,(h-1)}^{u},p_{i,h}^{l}\right\}\right\},h=1,\;2,\;3,\; \ldots,\;(nz+1);\forall i\in\Omega \tag{9}\]  
where \(p_{h}^{l}\) and \(p_{h}^{u}\) are lower and upper power output of the \(i\)th at the \(h\)th zones; \(p_{i,0}^{u}=p_{i,\text{min}}\) considering \(h=1\) and \(p_{i,nz+1}^{l}=p_{i,\text{max}}\); \(nz\) is the number of POZs.  
Determining the power generated by the TGU beyond the POZs must be done first and then other constraints such as spinning reserve and online power balance can be considered.  
Spinning reserve: To ensure energy security issues, the system always reserves a power under the following inequality:  
\[\sum_{i=1}^{n}pr_{i}\geq pr_{all} \tag{10}\]  
where \(pr_{i}\) is the real power reserve of the \(i\)th TGU; and \(pr_{all}\) is the required spinning reserve of the whole system. The spinning reserve is managed by the following equation:  
\[\left\{\begin{array}{l}pr_{i}=\left\{\begin{array}{l l}p_{i,\text{max}}-p_{ i}&\text{if}\quad pr_{i,\text{max}}\text{>}\;(p_{i,\text{max}}-p_{i})\\ pr_{i,\text{max}}&\text{otherwise}\end{array}\right.;\forall i\
otin\Omega\\ pr_{i}=0&\text{;}\quad\forall i\in\Omega\end{array}\right. \tag{11}\]  
where \(\Omega\) is the set of TGUs that can supply reserve power to loads.  
Ramp rate limit constraint: Increasing or lowering of the power generated by the TGU in the process of operation must be managed to keep the output within a permissible limit. If \(RUL\) is the ramp up limit and \(RDL\) is the ramp down limit of the \(i\)th TGU, the ramp rate constraint is defined as follows:  
\[p_{i,0}-RUL_{i}\leq p_{i}\leq p_{i,0}+RDL_{i} \tag{12}\]  
where \(p_{i,0}\) is the initial power output from the previous operating hour of the \(i\)th TGU.
## 4 Experiments and Results
### Comparison with the Existing Correction Model  
In this section, the HFM in the current study is compared with the existing correction model in Yao and Zhao [14], which is named the Yao model in this work for convenience. In the Yao model, a similar exponential relationship between the scale factor and the height is established, as follows [14]:  
\[\alpha=a+b\epsilon^{\frac{1}{4}}, \tag{25}\]  
where \(\alpha\) is the scale factor, and \(a\) and \(b\) represent the coefficients of the scale factor, respectively. \(h\) denotes the height of the intersection point between the effective side signals and the side face of the tomography area. To compare the performance of the HFM and Yao model in retrieving the three-dimensional water vapor field, three further schemes were designed for tomography modeling based on all GNSS station observations from the Hong Kong SatRef, in addition to the HKSC station. The tomographic region for the three schemes covers from \(113.82^{\circ}\)E to \(114.36^{\circ}\)E in longitude and \(22.16^{\circ}\)N to \(22.56^{\circ}\)N in latitude. Other tomographic model configurations, such as a horizontal and vertical resolution, are consistent with the four special experimental schemes. The three schemes in this section are as follows. * **Scheme I:** The traditional tomography method that only considers the GNSS signals crossing the top boundary is adopted to construct the observation equations;
* **Scheme II:** The Yao model is used to estimate the SWV of side rays and build the equations with both rays passing from the top and side boundary;
* **Scheme III:** The HFM is employed to calculate the \(SWV_{side}\) and structure the tomography system with top and side observations. Based on GNSS data at two tomography epochs of 00:00-00:30 UTC and 12:00-12:30 UTC during the period of DOY 213-243, 2017, the scale factor in the Yao model was calculated and is illustrated in \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scheme** & **Data** & **RMSE** & **STD** \\ \hline Scheme East & \begin{tabular}{c} _Integral_ only \\ _Integral_+_inside_ \\ _Integral_ only \\ _Integral_+_inside_ \\ _Integral_ only \\ _Integral_+_inside_ \\ _Integral_ only \\ _Integral_+_inside_ \\ _Integral_ only \\ _Integral_+_inside_ \\ _Integral_+_inside_ \\ \end{tabular} & 1.60 & 1.59 \\ Scheme West & \begin{tabular}{c} _Integral_+_inside_ \\ _Integral_ only \\ _Integral_+_inside_ \\ _Integral_ only \\ _Integral_+_inside_ \\ _Integral_+_inside_ \\ \end{tabular} & 1.62 & 1.61 \\ Scheme South & \begin{tabular}{c} _Integral_+_inside_ \\ _Integral_ only \\ _Integral_+_inside_ \\ _Integral_+_inside_ \\ \end{tabular} & 1.12 & 1.08 \\ Scheme North & \
\begin{tabular}{c} _Integral_ only \\ _Integral_ only \\ _Integral_+_inside_ \\ \end{tabular} & 1.66 & 1.64 \\ Scheme North &
\begin{tabular}{c} _Integral_ only \\ _Integral_+_inside_ \\ \end{tabular} & 1.63 & 1.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The mean RMSE and STD for the conventional method and proposed method during the 31-day period from DOY 213 to DOY 243, 2017 (unit: g/m\({}^{3}\)).
# Blind Spot Detection Radar System Design for Safe Driving of Smart Vehicles
## 5 BSD Radar System Performance Test
### Performance Evaluation  
This paper described the design of a BSD radar system and validated its performance by proposing five performance test scenarios that consider the driving environment of the vehicle. The operation of the BSD radar was verified in the proposed driving environments, and the scenarios were written in accordance with the ISO 17387 standard for the BSD radar specifications.  
Case 1. BSD longitudinal detection performance evaluation, target vehicle overtaking test vehicle:  
* The test vehicle drives at a constant speed of 40 kph, while the target vehicle approaches from behind at a constant speed of 45 kph and overtakes the test vehicle.  
The longitudinal distance of the target vehicle is measured based on the timing of when the warning was turned ON or OFF.
2. Warning ON: The moment when the front of the target vehicle enters the BSD area.
3. Warning OFF: The moment when the rear of the target vehicle exits the BSD area.
4. Case 2. BSD longitudinal detection performance evaluation, test vehicle overtaking target vehicle: 1. The target vehicle drives at a constant speed of 35 kph, while the test vehicle approaches from behind at a constant speed of 40 kph and overtakes the target vehicle. The longitudinal distance of the target vehicle is measured based on the timing of when the warning is turned ON or OFF.
5. Warning ON: The moment when the rear of the target vehicle enters the BSD area.
6. Warning OFF: The moment when the front of the target vehicle exits the BSD area.
7. Case 3. BSD lateral detection performance evaluation, target vehicle changing lanes laterally: 1. The test vehicle drives at a constant speed of 40 kph, while the target vehicle changes lanes from the adjacent lane to the next lane at the same speed. The distance of the target vehicle is measured based on the timing of when the warning is turned ON or OFF.
8. Warning ON: The moment when the inner side of the target vehicle relative to the test vehicle enters the BSD area.
9. Warning OFF: The moment when the inner side of the target vehicle relative to the test vehicle exits the BSD area.
10. Case 4. Cyclist detection performance evaluation, cyclist approaching test vehicle: 1. The test vehicle drives at a constant speed of 10 kph and the distance is measured when a cyclist approaches the BSD area of the test vehicle.
11. Warning ON: The moment when the cyclist enters the BSD area.
12. Warning OFF: The moment when the cyclist exits the BSD area.
13. Case 5. Pedestrian detection performance evaluation, pedestrian approaching test vehicle: 1. The test vehicle drives at a constant speed of 10 kph and the distance is measured when a pedestrian approaches the BSD area of the test vehicle.
14. Warning ON: The moment when the pedestrian enters the BSD area.
15. Warning OFF: The moment when the pedestrian exits the BSD area.
## 3 Structure of MRAS  
The MRAS is mainly composed of a reference model, an adjustable model, and an adaptation mechanism. The system block diagram of the MRAS is shown in Figure 1, where \(u\) is the input; \(i\) is the output of reference model; \(\hat{i}\) is the output of adjustable model; \(e\) is the error of \(i\) and \(\hat{i}\); \(\psi_{f}\) and \(\hat{L}\) are estimated parameters, respectively.  
The reference model is a state space equation containing the information of the parameters to be identified. According to the reference model, a model with the identified parameters as adjustable variables is established. The error, defined as the difference between the output of the reference model and that of the adjustable model, is sent to the adaptation mechanism to estimate the unknown parameters, which are fed back to the adjustable model. When the error between those two models converges to zero, the identified parameters are extraordinarily close to the true value. Here, Equation (2) is the reference model.
## 3 Results
### Study Characteristics
#### 3.3.3 Study Settings and Research Focus  
The research settings demonstrated significant methodological diversity. Twenty-four-hour monitoring was conducted in six studies [21,23; 24,26; 27; 28], while five studies focused on free-living conditions [7,8,18; 19,22]. Laboratory settings were used in two studies [17,19], and one study implemented supervised free-time observation [20].  
Research objectives clustered into two primary domains: (a) physical activity analysis (\(n\) = 10; [7,8,17; 18; 19; 20; 21,22; 23,25]) encompassing activity classification, intensity prediction, motor competence evaluation, and energy expenditure estimation; and (b) sleep analysis (\(n\) = 4; [24,26; 27; 28]) concentrated on sleep analysis, investigating duration prediction, pattern characterization, and relationships with behaviour and weight status.
# Road Passenger Load Probability Prediction and Path Optimization Based on Taxi Trajectory Big Data
## 3 Data Preparation and Data Preprocessing
### Data Description  
This study selected one week's worth of taxi trajectory data from Chengdu as the research subject. The daily data file is approximately 1 GB in size, and the data file format is CSV. During the exploration of road passenger volume prediction, the trajectory data for the entire month of August were used. The data fields are shown in Table 2.
## 2 Materials and Methods
### Photocatalytic Activity Test  
The photocatalytic activity of prepared cement plates towards the degradation of air pollution was also proved. The NO gas (1.989 ppm \(\pm\) 0.040 ppm, Air Liquide, Krakow, Poland) was used as model pollution. NOx removal was evaluated using the experimental installation, which scheme is presented in Figure 1.  
Eight studied cement plates (one at dimensions of 2 cm \(\times\) 2 cm \(\times\) 0.5 cm) were placed in the central part of the cylindrical reactor (pyrex glass; \(\partial\)\(\times\) H = 9 cm \(\times\) 32 cm). Used NO was diluted with humidified synthetic air (50% humidity) to obtain 1 ppm and 0.2 ppm  
Figure 1: The scheme of the system to photocatalytic removal of NO (S—a source of pollution; M—mass flower; H—humidifier; R—photocatalytic reactor with irradiation source; A—NOx analyzer [9].  
initial concentration, the flow rate through reactor was 500 cm\({}^{3}\)/min, gas temperature was 25 \({}^{\circ}\)C. The equilibrium of dark conditions reach after 35 min, then UV lamps were turned on for 30 min. The irradiation cumulative intensity of 100 W/m\({}^{2}\) UV and 4 W/m\({}^{2}\) Vis was used for measurements. T200 NOx analyzer (Teledyne, San Diego, CA, USA) was used for NO and NO2 measurements.  
In Figure 2 the scheme of installation for photocatalytic removal of BTEX is presented.Two studied cement plates (one at dimensions of 8 cm \(\times\) 4 cm \(\times\) 1 cm) were placed inside the reactor (Heraeus type reactor UV-RS-2; \(\mathcal{O}\)\(\times\) H = 8 cm \(\times\) 21 cm) with a medium pressure mercury vapor lamp (TQ-150, \(\lambda_{\text{max}}\) = 365 nm, I = 146 W/m\({}^{2}\)) The irradiation time during photocatalysis process was 10 min. The BTEX mixture (benzene 5 ppm, ethyl benzene 5 ppm, toluene 5 ppm, \(\circ\)-, p-, m-xylene 5 ppm) (AG Gases & Equipment, Stoke-on-Trent, UK) was diluted with humidified synthetic air (50% humidity) in a ratio 1:1. The oxygen and water molecules were necessary for forming oxidative species, which are essential in photocatalytic reactions. The polluted air flowed through the reactor for 10 min at a rate of 500 cm\({}^{3}\)/min. After 10 min outlet and flow were stop. Smart SPME Arrow (phase type: Divinylbenzene (DVB)/Polydimethylsiloxane (PDMS), phase thickness 120 \(\upmu\)m) were used for gases analysis. Smart SPME Arrow was introduced into the reactor for 2 min and after that to injector into the GC-MS apparatus (Nexis GC-2030, Shimadzu, Japan), which consisted of a single quadrupole mass spectrometer model QP2020 NX ISQ, (Shimadzu, Japan). A capillary column Zebron-PAH (30 m length, 0.25 mm I.D. The starting oven temperature was held at 45 \({}^{\circ}\)C for 2 min and ramped to 105 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min.
# Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows
## 6 Conclusions  
This research work presents an R-DEAR strategy for SWs in an IaaS cloud environment. The motive for presenting the R-DEAR strategy is to address the issues of energy consumption and task failure for real SWs. Real-world SWs, including Montage and CyberShake, are examples of data- and computing-intensiveness. For their implementation, SW applications need high-performance computational resources and a huge amount of storage. Scientists in most scientific fields, such as astronomy, high-energy physics, and bioinformatics, conduct research that requires and produces terabytes of data obtained from physical devices. The workflow tasks are linked based on computational and data interdependence. Considering the high volume and variety of SWs, the resources of the IaaS cloud model require efficient energy management without failure or loss.  
The proposed strategy, R-DEAR, is a resource-provisioning strategy that works with replication-based fault-tolerant mechanisms. The proposed R-DEAR strategy also manages the power consumption of IaaS cloud resources dynamically through a load-sharing process. Simulation was performed on a WorkflowSim simulator. The results reveal that the proposed R-DEAR strategy minimizes energy consumption, execution cost, and execution time by 9%, 15%, and 18%, respectively, against the existing state-of-the-art strategy.  
The proposed study is limited to scientific workflows. Therefore, for business workflows, we need to develop another strategy based on the nature of business workflows. The proposed study implements a cloud platform for evaluation; however, a fog environment can be used in some aspects. In the future, this work will be strengthened to implement workflow scheduling and fault tolerance in fog environments. Similarly, business workflow-based implementation of a similar study will also be achieved.  
Figure 7: Execution time for CyberShake scientific workflow.  
**Author Contributions:** [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper; [PERSON], [PERSON] and [PERSON] performed the simulations and analyzed the results; [PERSON] and [PERSON] technically reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R138), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
**Data Availability Statement:** No data were used to support this study. However, any query about the research conducted in this paper is highly appreciated and can be answered by the corresponding authors upon request.  
**Acknowledgments:** The authors appreciate the support from Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R138), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Lateral Constraint Inversion Theory
### Optimization of Lateral and Vertical Weighting Factors
A comparison with the inversion results of the low-resistivity layer model presented in Figure 6 indicates that following the application of the lateral weighting factor, the inversion outcomes of both high- and low-resistivity models exhibit superior spatial continuity in the lateral direction, with the inclined plane of the low-resistivity model being more distinct than that of the high-resistivity model. The lateral weighting factor was set to 0, while the vertical weighting factors were set to 0.1, 0.5, and 5. The corresponding inversion outcomes are depicted in Figure 4.2 The Figure illustrates a progressive enhancement in the smoothness and vertical continuity of the inversion model as the vertical weighting factor increases from 0.1 to 5. Moreover, gradual alterations in the vertical transition characteristics of the lower interface of the intermediate  
Figure 4.3 Inversion results of high-resistivity fault valley model with different lateral weighting factors. (**a**) Lateral weighting factor 0.1; (**b**) lateral weighting factor of 0.5; (**c**) lateral weighting factor of 5.  
high-resistivity layer are observed. Upon reaching a vertical weighting factor of 5, the lower interface of the model tends towards homogenization, significantly diminishing the detailed features of the lower interface of the high-resistivity layer model. Through a comparative analysis of the three inversion results concerning vertical continuity and structural intricacies, the optimal vertical weighting factor for the model is determined to be 0.1. Based on the outcomes of the aforementioned control univariate analysis, the lateral weighting factor of 0.5 and the vertical weighting factor of 0.1 are deemed optimal for inverting the model of the high-resistivity fault valley. As illustrated in Figure 13, the inverted structure exhibits favorable continuity both laterally and vertically, effectively capturing the morphological features of the high-resistivity stratum. A comparison with Figure 8 reveals superior depiction of the depth and orientation of the low-resistivity layer in contrast to the high-resistivity layer. Notably, the inversion outcomes pertaining to the interface and orientation under the high-resistivity layer deviate somewhat from the actual model. Figure 14 depicts the corresponding decrease in fitting error relative to Figure 4.4 Analogous to the resistive fault valley model, the fitting error diminishes rapidly for the high-resistivity fault valley model. Following three iterations, the fitting error reaches a relatively low level, aligning the inversion results closely with the intended model. Figure 4.5 Inversion results of high-resistivity fault valley model with different vertical weighting factors. (**a**) Vertical weighting factor 0.1; (**b**) vertical weighting factor of 0.5; (**c**) vertical weighting factor of 5.