esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics
## 2 Materials and Methods
### Strategies for \(\epsilon\) Determination  
#### 2.3.1 Fixed Size Strategy  
The most straightforward way is to handle \(\epsilon\) as a constant value. It seems easy to set this value as an input parameter of the HC method. This method is quite simple but raises several issues [16].  
First of all, it is hard to determine the ideal value in advance. This depends on the problem, the attributes of the search space, the availability of resources, and the requested quality of final solutions. It is usually based on several trial-and-error experiments, but it usually cannot be satisfactory because of the first issue.  
As a second issue, it is not guaranteed that the same \(\epsilon\) value will be optimal at the beginning and end of the search. It is common at the beginning of any metaheuristic search to strengthen the exploration behavior, having a relatively large step size in this case. After several iterations, the algorithm should continuously and gradually switch to the exploitation phase, which requires smaller step sizes.  
Besides the number of iterations, it is also important to look at the number of fitness calculations. For many optimization problems, the fitness function is quite complex, and its evaluation takes up most of the optimization time. For this reason, it is more important to keep the number of fitness calculations lower than the number of iterations.  
The number of fitness calculations is computed as follows:  
\[c_{f}=\iota*\delta \tag{1}\]  
where \(\iota\) is the number of iterations, and \(\delta\) is the number of directions the method checks around the current position.
## Materials and Methods
### Data Analysis  
The data collected were analyzed with the SPSS version 24 statistical program. A descriptive analysis was performed through which the main dispersion indexes, as well as frequencies and percentages, were analyzed. An inferential analysis was also performed to evaluate the relationship between variables, assuming a significance of at least 0.5. For the design of the artificial neural network (ANN), a multilayer perceptron network with a backpropagation algorithm was used. The dependent variable was \"Burnout (presence versus absence)\", of a nominal nature (presence of burnout = 0 and absence of burnout = 1). The order in which the variables were introduced into the system was as shown in the following lines. Any change in the order modifies the predictive capacity of the network. Three nominal factors were selected: gender, marital status, and children. Of a quantitative nature, the seven covariates used were: age, experience, burnout, cynicism, efficiency, resilience, and COVID-19 stress. The scale change of the covariates was normalized. Regarding the partitioning of the total cases, the network was instructed to use 60% for the training phase, 20% to perform the test phase, and 20% for the reserve phase, although the network itself adjusted the percentage during its setup through a random assignment procedure. Thus, finally 64.4% (270) of the cases were assigned to the training phase, 17.4% (73) were assigned to the testing phase, and 18.1% (76) to the holdout phase.
# System Dynamics Modeling for Estimating the Locations of Road Icing Using GIS
## 2 Materials and Methods
### Modeling Road Icing
#### 2.4.3 Road Icing  
When road temperature and road moisture are derived, road icing per hour can be calculated [47]. The road icing model is basically expressed as an integral value for the amount of change in freezing and thawing [50; 51]. For freezing, the road temperature and road moisture calculated above are involved. In the presence of road moisture, the amount of change in freezing occurs if the road temperature satisfies certain conditions. For thawing, Hillshade and air temperature are involved. A higher value of Hillshade means larger exposure to sun energy [29; 52]. In addition, when road moisture freezes, it is affected by the air temperature because it rises to the top due to a reduction in density, and more thawing occurs as its value increases. This is expressed in Equation (16). Ft is the amount of change in freezing over time and Tht is the amount of change in thawing over time.  
\[RI=\int{(F_{t}}^{\prime}-Th_{t}{}^{\prime})dt \tag{16}\]  
Contents on the amount of change in freezing, Ft, are shown in Equation (17). Ft is a function of the road temperature (Tr), road moisture (Mr), and precipitation (P). The value of Ft is determined according to the conditions of the precipitation and road temperature. In the presence of precipitation, road moisture freezes when road temperature is below 1 \({}^{\circ}\)C. In the absence of precipitation, road moisture freezes when road temperature is below 0 \({}^{\circ}\)C.  
\[F_{t}=f(T_{r},M_{r},P)_{t}=\left\{\begin{array}{c}M_{t}*\frac{-T_{t}+1}{5}, \ (if\ P\ >\ 0\ \text{and}\ T_{r}<\ 1)\\ M_{r}*\frac{-T_{t}+1}{5},\ (if\ P=0\ \text{and}\ T_{r}<\ 0)\end{array}\right. \tag{17}\]  
Contents on the amount of change in thawing (Tht) are shown in Equation (18). Tht is a function of Hillshade (H) and air temperature (Ta). Tht is determined according to the conditions of the air temperature. If air temperature is higher than 0 \({}^{\circ}\)C, thawing occurs in proportion to the amount of Hillshade (0 to 254). If it is lower than 0 \({}^{\circ}\)C, no thawing occurs.  
\[Th_{t}=f(RI,H,\ T_{a})_{t}=\left\{\begin{array}{c}RI_{t-1}*\frac{H}{254}, \ (T_{a}>\ 0)\\ 0,\ (T_{a}\ \leq\ 0)\end{array}\right. \tag{18}\]
# Improvements of Computational Ghost Imaging by Using Sequenced Speckle
## 1 Introduction
We experimentally investigate the influence of different rules for sequenced speckle pattern projection on the contrast-to-noise rate (CNR) of the retrieved ghost images in a DMD-based computational sequenced GI (SGI) system. Furthermore, we perform simulations under ideal conditions to verify the feasibility of this sequenced method. Our results demonstrate that SGI effectively improves image quality and reduces sampling requirements compared to traditional GI (TGI). The SGI approach holds great promise for achieving high-quality GI with flexible wavelength capabilities. Moreover, it can be readily applied to PGI and DMD or SLM-based CGI, providing a versatile solution for various imaging scenarios.
## 3 Results and Discussion
### Dosimetric Characterization
#### 3.2.5 Reproducibility  
The high reproducibility of radiation dosimeters is important in clinical radiation dosimetry. The reproducibility is highly influenced by dosimeter homogeneity and irradiation history [38]. The reproducibility of the MgB4O7:Zn glass was assessed for seven consecutive cycles. The reproducibility test evaluates the thermal treatment applied to the dosimeters to desenstize the dosimeters, as well as to assess the homogeneity of the samples.  
It can be evidenced from Figure 11, identifying radiotherapy dose measurement uncertainty limits, that both groups produce a similar trend over seven cycles, as the experimental procedures were carried out concurrently. The measurements were acquired with 6.8% uncertainty for annealed samples and 5.2% uncertainty for unannealed samples. The measurement uncertainties demonstrate that the dosimeter could be deployed for clinical dosimetry with proper dosimeter calibration. The maximum variation is slightly higher than the dose measurement uncertainty requirement, \(\pm\)3.5%, for dose measurements in radiotherapy [39]. The fluctuation in the results could be attributed to the irregular shapes of the samples that affect the contact area between the sample surface and the heating planchet, resulting in a temperature gradient. Moreover, it was observed that the TL material could be desensitized adequately by read out at 350 C, which is well above the optimum annealing temperature and is sufficient enough to de-trap the majority of the trapped electrons from their traps.  
Figure 10: Dose response of MgB4O7:Zn glass S35654 for \({}^{137}\)Cs source \(\gamma\)-rays.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algal Species**} & \multicolumn{2}{c}{**Cultivation**} & \multirow{2}{*}{**CBZ**} & \multirow{2}{*}{**Removal**} & \multirow{2}{*}{**Reference**} \\  & **Conditions** & & & \\  & **(Culture Media,** & & & \\  & **Temperature and** & & & \\  & **Cultivation Time)** & & & \\ \hline \multirow{2}{*}{_Spirulina platensis_} & Zarrouk medium, 27 \(\pm\) & 1–5 mg/L & 30–16 (\%) & \multirow{2}{*}{[24]} \\  & FACHB-431 & 1 \({}^{\circ}\)C, & 1–5 mg/L +0.5 g/L & & \\  & 10 days & glucose & & 50–20 (\%) & \\ \hline \multirow{2}{*}{_Chlorella sp. and_} & Synthetic wastewater, & \multirow{2}{*}{5 \(\mu\)g/L} & \multirow{2}{*}{20\%} & \multirow{2}{*}{[25]} \\  & 23 \(\pm\) 5 \({}^{\circ}\)C, 10 days & & & \\ \hline \multirow{2}{*}{_Ceratozamia_} & Bold’s basal medium & \multirow{2}{*}{1–25 mg/L} & \multirow{2}{*}{37–21\%} & \multirow{2}{*}{[26]} \\  & 27 \({}^{\circ}\)C, & & & \\  & 10 days & & & \\ \hline \multirow{2}{*}{_Scenedesmus_} & Bold’s basal medium & \multirow{2}{*}{1–25 mg/L} & \multirow{2}{*}{30–13\%} & \multirow{2}{*}{[26]} \\  & 27 \({}^{\circ}\)C, & & & \\  & 10 days & & & \\ \hline Microalgae & Domestic wastewater, & \multirow{2}{*}{\(<\)33 ng/L} & \multirow{2}{*}{no removal} & \multirow{2}{*}{[27]} \\  & 21 \({}^{\circ}\)C, HRT = 4.5 days & & & \\ \hline \multirow{2}{*}{_Chlorella sorkiniana_} & Anaerobic black water & \multirow{2}{*}{100–35 \(\mu\)g/L} & \multirow{2}{*}{10\%} & \multirow{2}{*}{[28]} \\  & 35 \({}^{\circ}\)C, 31 days & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Removal of carbamazepine (CBZ) from aqueous solutions by microalgae. noted that about 50% of the initial CBZ had been removed after 60 days. At the moment, it is still unknown whether these algae are able to degrade or transform this refractory pharmaceutical molecule and even less about the degradation mechanism. The aim was to confirm this result at various CBZ initial concentrations using two culture media, the inorganic Bold's Basal (BB) medium and a proteose peptone (PP) medium, to enhance autotrophic and heterotrophic growth, respectively, both for different inoculum concentrations as a function of cultivation time. First, the influence of CBZ on the growth of the algae will be studied. Then, the elimination of CBZ was investigated quantitatively over time. Finally, the mechanism of CBZ removal was analyzed.
## 2 Study Area and Data
### Different SAR Datasets  
Sentinel-1A is the first satellite developed by the European Commission and the European Space Agency for the Copernicus Global Earth Observation Project, which was launched in April 2014 with C-band imaging. Sentinel-1A contains four operating modes: SM, IW, EW, and WV, including an interferometric wide-field mode with a resolution of  
Figure 1: The main urban area of Zhengzhou. The black dotted line represents the boundary of the main urban area of Zhengzhou. The red diamond shows the location of Zhengzhou on the map of China.  
5 m \(\times\) 20 m and an amplitude of 250 km and a revisit period of 12 days. Sentinel-1A SAR data are freely available, providing rich data support for global scholars to monitor the global land and coastal zone.  
The GF-3 satellite, the first Chinese high-resolution SAR remote sensing satellite with 1 m spatial resolution, was launched on 10 August 2016, using C-band imaging and containing 12 imaging modes. Depending on the imaging modes, GF-3 can provide multi-polarized SAR images with 1 m to 500 m resolution and 10 km to 650 km width to achieve global monitoring of ocean and land resources [23]. The imaging mode used in this paper is fine striping with HV polarization and 10 m azimuthal resolution of the image.  
Due to the satellite revisit cycle at the time of the \"7-20\" rainstorm disaster, this paper combines two kinds of heterogeneous SAR satellites, Sentinel-1A and GF-3, as well as the observation data of the \"7-20\" rainstorm in Zhengzhou. The Sentinel-1A VH SAR image was imaged on 15 July 2021. The two views of GF-3 SAR images were imaged on 20 and 22 July 2021, respectively, and HV polarization was used. The specific parameters of the heterogeneous SAR images with different phases are detailed in Table 1. Each SAR image is shown in Figure 2.
## 3 Results and Discussion
### Assessment of the Behavior of the GRC Composites Subjected to Chemical Attacks
#### 3.3.2 Exposing the GRC Composites to a H\({}_{2}\)SO\({}_{4}\) Solution  
Figure 13 shows the mass loss of the composites immersed in a 0.5 M H\({}_{2}\)SO\({}_{4}\) solution. When exposed to the aggressive solution, the control specimens underwent significant mass loss (close to 60%) after 40 exposure days. On the other hand, very stable behavior was noted for the specimens with pozzolanic materials on the first exposure days. [PERSON] et al. [35] found similar behavior for low water/cement Portland cement concrete. Subsequently, a significant increase in mass was recorded after 28 exposure days, especially for the composites with Acc (close to 15% mass gain). This can be attributed to sulfate salts forming in the cementing matrix by the reaction of the calcium-aluminate-based hydrates and SO\({}_{4}\)[2] to form gypsum (CaSO\({}_{4}\).2H\({}_{2}\)O) or ettringite [35].  
Figure 14 shows the load vs. displacement curves from the bending tests for the attacked samples. After immersion in the acid solution for 28 days, the control composite showed less mechanical behavior than the pozzolanic composites. A brittle fracture occurred after maximum loading. On the other hand, for the pozzolan GRC-containing samples, significantly higher loads and toughness values were obtained. The composites prepared with an accelerated pozzolanic reaction (the Acc series) showed better mechanical behavior after exposure to the aggressive agent than the GRC mortars cured under normal conditions.  
For the calculated toughness (Figure 15), the samples with pozzolans yielded higher values because they had hydrated products (generated by the portlandite reaction with  
Figure 14: Load–displacement curves (load rate 0.03 \(\pm\) 0.003 mm/s) for the GRC composites immersed for 28 days in a 0.5 M H\({}_{2}\)SO\({}_{4}\) solution.  
Figure 13: Mass variation for the GRC composites immersed in a 0.5 molar (0.5 M) H\({}_{2}\)SO\({}_{4}\) solution.  
pozzolans), which were much more resistant to the acid attack. After exposure to the acid solution, the toughness of the control reduced to values close to zero (brittle fracture).
## 3 Results and Discussions
### Flow Regime Mapping for Horizontal Flow
#### 3.1.2 Air/non-Newtonian flow map  
Researchers have also developed different flow pattern maps for horizontal, vertical, and inclined gas/non-Newtonian flow. For horizontal gas/non-Newtonian fluid, [PERSON] [56] developed a flow pattern map by slightly modifying the [PERSON] et al. [9] horizontal flow pattern map (Figure 8). This map was developed for evaluating the literature and verified using 3700 data points of gas/non-Newtonian shear-thinning liquid mixture flow with 70% certainty. However, there was not enough data to confirm the [PERSON] [56] flow map for annular and slug flows.  
In Figure 8, the experimental flow regime almost matches with the [PERSON] [10] flow map, but the slug to dispersed bubble flow transition started slightly earlier for this experiment. [PERSON] & [PERSON] [10] used the particulate suspension of china clay, aqueous polymer solutions, limestone, and coal, which is a significantly more viscous shear-thinning non-Newtonian fluid compared to the 0.1% solution of xanthan gum that was used in this experiment. This is why the dispersed bubble flow regime started earlier. It is well-known that flow patterns of gas/non-Newtonian fluid are not significantly different from gas/Newtonian fluid for horizontal flow. However, due to high viscosity, the bubbles and slug could not break easily and collided together to form more prominent and well-defined bubbles.  
Figure 6: Comparison of the [PERSON] & Dukler [7] (adapted) flow map with experimental data for horizontal gas/Newtonian flow. Copyright AIChE Journal, 2020.  
Figure 7: Comparison of the [PERSON] et al. [9] (adapted) flow regime map with experimental data obtained for horizontal gas/Newtonian flow. Copyright Elsevier, 1974.  
The experimental data were used to reconstruct the flow maps and validated with the existing literature for the identification of the two-phase flow regimes for this experimental setup. Due to the targeted velocity condition pre-requisite for hole cleaning operations and the limitation of the existing flow loop system (that is, not designed for the extreme conditions of higher flow rates and high-pressure environments), the experiment was performed under moderate pressure and velocity conditions. Therefore, the results are effectively limited by the bubble flow and plug flow range, which can provide stratified, wavy, or annular flow. The [PERSON] & Dukler [7] and [PERSON] et al. [9] flow maps for air/water two-phase horizontal flow and the Chhabra & Richardson [56] flow map for air/xanthan gum solution horizontal two-phase flow represented the flow regimes of the experimental setup relatively accurately. Nonetheless, the boundary of the flow regime varied due to the unpredictable characteristics of the transition of the flow pattern.
# A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8
## 4 Experimentation and Analysis
### Evaluation Indicators  
In target detection tasks, P (precision), R (recall), and mAP (mean average precision) are usually chosen to evaluate the performance of target detection algorithms. The specific formulas for P and the R are presented as follows:  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{11}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{12}\]  
where TP is the number of positive samples correctly detected by the algorithm, FP is the number of negative samples incorrectly detected as positive samples, and FN is the number of positive samples incorrectly detected as negative samples by the algorithm. A P-R curve can be plotted according to the values of P and R, and integration of the curve can obtain the value of AP, that is, the detection accuracy of a single category in the dataset. The specific formula is shown below:  
\[\text{AP}=\int_{0}^{1}\text{PRdR} \tag{13}\]  
For target detection with multiple categories, mAP is obtained by averaging the sum of the AP of each category:  
\[\text{mAP}=\frac{\sum_{\text{k}=1}^{n}\text{AP}_{\text{k}}}{\text{n}} \tag{14}\]
## 4 Simulation Results Analysis
### ECM Simulation Results
#### 4.2.2 The Performance Analysis of Different Evolution Strategies  
Figure 8 shows the radar detection power map of a single radar interfered with by multiple jammers. When the main lobe of the radar antenna points to the direction of jammer distribution, multiple jammers effectively suppress the radar exposure area.  
In order to verify the influence of the number of jammers on the performance of the radar, the relationship between the number of UAVs and the signal-to-noise ratio of the system is shown in Figure 9. As the evolution process progresses, the number of UAVs in jamming state gradually increases, and the signal-to-noise ratio of the system decreases accordingly.  
Figure 8: Radar detection power map.  
Figure 7: Time domain and frequency domain of the jamming signal.  
The curve of the radar target detection probability with different number of jammers is shown in Figure 10. The detection probabilities under various jammer transmit power are compared. In an actual scenario, the number of jammers and the jamming power should be considered comprehensively. The experimental results show that to optimally allocate jamming resources, the number of jammers and the interference power can be adjusted according to actual application requirements.  
Figure 11 shows the influence of different evolution strategies on the radar detection probability. It can be seen that as the evolution progresses, the number of UAVs in the jamming state gradually increases, and the radar detection probability gradually decreases. From the previous analysis results, the AS strategy and NS strategy 1 converge to multiple local centers with only certain individuals evolving to the interference state. Due to the lack of sufficient interference power, these two strategies cannot effectively interfere with the radar. On the contrary, both NS strategy 2 and NS-AS strategy can eventually achieve effective interference with the radar. Meanwhile, as the convergence speed of the NS-AS strategy is faster than the NS strategy 2, it achieves effective interference faster.  
Figure 10: The curve of the radar target detection probability.  
Figure 9: The influence of the number of jammers on the system SNR.  
When radar performs the searching and tracking task, it usually uses pulse compression technology to improve the target detection capabilities. The effect of a few jammers on the radar pulse compression output is described in Figure 12. It can be seen that as the evolution process progresses, the number of UAVs in the jamming state gradually increases, and the radar detection probability gradually decreases. The target echo is detected normally when the SNR is large in Figure 11(a). However, with the constant transition of the agent state, more agents are transformed into the jamming state. As can be seen in Figure 11(b), the target echo submerges into background noise with the jamming power increasing. It verifies the effectiveness of the interference signal by comparing the results of pulse pressure output under different signal-to-noise ratios.