esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The approximate probability distribution of frequency-locked detector output has been theoretically derived by statistical approach. The mean-time-to-lose-lock (MTLL) of the frequency tracking loop has been analyzed based on the statistical characteristic of the FLD output to evaluate the performance of the tracking loop, as well as to compare with the PLD output. MTLL is a kind of evaluation criteria to measure FLD or PLD performance. The relationship among mean-time-to-lose-lock, detection threshold, and \(C/N_{0}\) can be revealed with this method, which shows the difference between threshold settings in the phase-locked detector and the frequency-locked detector. The FLD threshold is, as demonstrated by the analysis results, much lower in comparison to the PLD threshold in the PLL with the same MTLL performance. The FLD output is an accumulation of adjacent periods, which means that FLD can be used to indicate a weaker signal to ensure the stability of the tracking loop. Therefore, a theoretical basis for the threshold setting in the frequency-locked detector of the GPS receiver is provided.  
Meanwhile, the result of the experiment using the actual data indicates several suggested thresholds under different \(C/N_{0}\) values, which can be used as a reference for the threshold setting criteria of FLD in receiver.  
**Acknowledgments:** Sponsored by National Natural Science Foundation of China(61471017, 41374137) and Guangxi Key Laboratory of Precision Navigation Technology and Application, Guilin University of Electronic Technology (No DH201514).  
**Author Contributions:** [PERSON], [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] developed the algorithms; [PERSON], [PERSON] and [PERSON] coded and debugged the algorithms; [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON] and [PERSON] analysed the data; [PERSON] provided field data; [PERSON], [PERSON] contributed field surveying tools; all authors wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 14: FLD output of actual data with different \(C/N_{0}\) (\(M=20\)).
# Integrated Hydrological Analysis of Little Akaki Watershed Using SWAT-MODFLOW, Ethiopia
## 2 Materials and Methods
### Groundwater Modelling Method and Procedures
#### 2.3.6 MODFLOW Grid  
With the coverage's setup, the MODFLOW grid was created. The number of cells in the x and y dimensions were generated automatically because the number of rows and columns and the location of the cell boundaries were controlled by the refine point data at the wells. The grid dimensions of the model automatically assigned due to the refinement assigned for the vicinity of the pumping wells. The model divided into 228 rows and 232 columns with the size of each cell being varied from 40 m near the wells to 140 m. The grid cells outside and inside the model domain (Little Akaki catchment) were designated as \"inactive\" and as \"active\", respectively. The total number of active cells of the model were 29,840.
## 2 Materials and Methods
### Machine Learning-Based Analysis  
Medical image analysis is one of the leading artificial intelligence (AI) applications in the medical field. Machine learning and deep learning are now being considered for adaptation for lesion detection, differential diagnosis, and region extraction. The dramatic improvement in AI performance has led to the proposal of the use of AI in clinical sites related with CT and MRI, especially by radiologists [45; 46; 47; 48; 49; 50]. As an example of machine learning applied in tumor diagnosis related in this study, [PERSON] et al. achieved 94% accuracy by adapting Random Forests, which is an ensemble learning method used for classifications, to a bladder cancer diagnostic tool using AFM [51]. Thus, recent advances in machine learning technology have enabled the effective analysis of tumors.  
However, [PERSON]'s modulus is not a categorical but continuous value. Therefore, to obtain stiffness mapping and stiffness spectrum using numerical values in CADe by L-RFA as in cell evaluation using AFM, [PERSON]'s modulus should be estimated using a regression method rather than using a simple classification problem such as stiffness or softness.  
To realize CADe by analyzing the data obtained from the L-RFA, stiffness prediction was conducted based on machine learning using L-RFA data for agarose samples with different [PERSON]'s moduli. The analysis was based on support vector regression (SVR), which was used in a previous study [52]. Three explanatory variables obtained from the vibrational frequency spectrum in the L-RFA were analyzed by SVR to predict the [PERSON]'s modulus at the laser-irradiated area. Three types of explanatory variables, namely, the frequency center of gravity, intensity center of gravity, and peak intensity in the measured frequency region, were introduced into SVR as explanatory variables.  
Support vector regression is an adaptation of the support vector machine (SVM) [53; 54], which is a pattern recognition method, to regression, and can be adapted to nonlinear problems by incorporating a kernel function. In this study, we used a Gaussian kernel (RBF kernel), which generally exhibits a higher speed and accuracy. The basic principles of SVR and SVM are the same, and the regression performance by SVR leads to the classification performance in SVM. Therefore, SVR is the most optimal machine learning method that can generalize the performance of L-RFA hardness measurement, which will be considered as a benchmark.  
The L-RFA data from 11 different agarose samples with different [PERSON]'s moduli, as shown in Figure 2, were used to train the SVR used in this study. The hyperparameters required for learning SVR were determined with the least squares error in the grid search as follows: the insensitivity coefficient \(\varepsilon\) ranged from \(2^{-20}\) to \(2^{9}\), the regularization coefficient \(C\) ranged from \(2^{-10}\) to \(2^{10}\), and the RBF kernel function \(\gamma\) ranged from \(2^{-15}\) to \(2^{9}\). The combination (\(\varepsilon=2^{-4}\), \(C=2^{3}\), and \(\gamma=2^{-2}\)) was used in this analysis.
## 2 Materials and Methods
### Emulgel Preparation  
Firstly, a nanoemulsion was prepared based on the report of the experimental method of [PERSON] et al. [43] with minor adjustments. For this purpose, a coarse emulsion was obtained by adding to the aqueous phase (Tween 80 and Span 20 (HLB 12) [43] and deionized water) the appropriate amount of lemon essential oil using an Ultraturrax T-50 (Ika Werke GmbH and Co. KG, Breisgau, Germany) rotor-estator device for 40 s at 2000 rpm and then for an additional 90 s at 4000 rpm. The particle size was then reduced by passing the sample through a laboratory-scale microfluidicizer (M-110P, Microfluidics International Corporation, Westwood, MA, USA) having a Y-type (F12Y, with 75 \(\upmu\)m minimum internal dimension) and \(Z\)-type (H30Z, with 200 \(\upmu\)m minimum internal dimension) interaction chamber arranged in series, at 20,000 psi and 2 passes.  
Secondly, a pectin gel was obtained. Pectin powder (0.3 wt%) was dispersed in a citrate buffer solution (pH = 4.2) and deionized water employing a rotor-estator homogenizer (Ultaturrax T-50) at 2000 rpm for 1 h. Subsequently, gels were obtained by adding CaCl\({}_{2}\) in solution (7% \(w/v\) stock solution) following the method developed by [PERSON] et al. (2015) [38] and [PERSON] et al. (2013) [44], that is, stirring at 8000 rpm for 30 s using the same device. The sample was kept at an ambient temperature for 24 h to ensure that the pectin was properly hydrated.  
Finally, emulgels were obtained by mixing different proportions of freshly prepared nanoemulsions and pectin gel using an IkaVisc MR-D1 device (Ika Werke GmbH and Co. KG, Breisgau, Germany) and a sawtooth-type impeller. Then, they were kept at 25 \({}^{\circ}\)C and allowed to stand until the analyses.  
A schematic diagram of the emulgel preparation process can be found in Figure 1.  
Table 1 shows the composition of the emulgels. These compositions have been selected as intermediate values of the two extreme conditions that correspond to the nanoemulsion and the pectin gel. The AP/BE nomenclature will be used to denote the emulgels, with AP being the pectin gel mass proportion and BE being the emulsion mass proportion. It should be noted that the initial emulsion contained 15 wt% lemon essential oil and 15 wt% emulsifiers, while the initial pectin gel contained 0.3 wt% pectin and a CaCl\({}_{2}\)/pectin mass ratio of 1.
## 4 Simulation and Results  
We trained and tested the intelligent algorithm on an actual section of the track modeled in SUMO. Signal propagation simulation parameters:  
* Terrain resolution is 4000 \(\times\) 4000 dots.
* Serving area radius is 600 m.
* Infrastructure object coordinates are 34.765406 N, 113.650334 E.
* The height of the antenna suspension of the infrastructure object is 5 m. The basic traffic simulation parameters in SUMO:
* The simulation steps number for all vehicles was 100.
* The average vehicles speed on the highway was 15.11 m/s.
* The cars and trucks ratio was 50/50%.
## 3 Results
### The Influence of Relative Azimuth Angle on the Prediction Results
#### 3.2.2 The Influence of Relative Azimuth Angle on the Prediction Model of SSC  
Table 4 presents the results of the SSC content prediction model using multispectral data at different relative azimuth angles. The best modeling effect was observed at an azimuth angle of 180\({}^{\circ}\) in both the PLSR and SVM models. For the PLSR model, the R\({}_{\rm c}\) of the training set was 0.4562, the RMSEC of the training set was 2.8416, the Rp of the prediction set was 0.4184, and the RMSEP of the prediction set was 2.5181. Similarly, for the SVM model, the R\({}_{\rm c}\) of the training set was 0.4141, the RMSEC of the training set was 3.1531, the R\({}_{\rm p}\) of the prediction set was 0.5176, and the RMSEP of the prediction set was 2.6538.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Relative Azimuth/\({}^{\circ}\)**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Prediction Set**} \\ \cline{3-6}  & & **R\({}_{\rm c}\)** & **RMSEC** & **R\({}_{\rm p}\)** & **RMSEP** \\ \hline \multirow{8}{*}{PLSR} & 0 & 0.2474 & 3.2755 & 0.2788 & 3.2263 \\  & 45 & 0.3342 & 3.2092 & 0.2595 & 3.2407 \\  & 90 & 0.5798 & 2.7122 & 0.3733 & 3.1731 \\  & 135 & 0.2947 & 3.0731 & 0.3502 & 3.0611 \\  & 180 & 0.3381 & 3.2090 & 0.1887 & 3.3870 \\  & 225 & 0.3632 & 3.0566 & 0.1098 & 3.2212 \\  & 270 & 0.3123 & 3.2170 & 0.1294 & 2.9910 \\  & 315 & 0.1832 & 3.2663 & 0.0938 & 3.3545 \\  & 0 & 0.2224 & 3.3730 & 0.2760 & 3.2873 \\  & 45 & 0.3362 & 3.3725 & 0.2744 & 3.3204 \\  & 90 & 0.5409 & 3.2294 & 0.5036 & 3.2907 \\  & 135 & 0.3000 & 3.1772 & 0.3388 & 3.1867 \\  & 180 & 0.3211 & 3.3983 & 0.1359 & 3.3822 \\  & 225 & 0.3058 & 3.2836 & 0.2694 & 3.1748 \\  & 270 & 0.2463 & 3.3401 & 0.4720 & 3.1074 \\  & 315 & 0.1887 & 3.3147 & 0.0523 & 3.3521 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Prediction results of MC under different relative azimuth angle conditions.  
Figure 6: Prediction results of the MC of jujube fruit when the relative azimuth angle was 90\({}^{\circ}\). (a) PLSR (b) SVM.  
Figure 7 presents the prediction results of the SSC of jujube fruit at a relative azimuth angle of 180\({}^{\circ}\). Despite being the optimal angle, the model constructed using PLSR and SVM exhibited low correlation and accuracy. Hence, relying solely on the multispectral data at this specific azimuth angle is insufficient for the quantitative detection of the SSC in jujube fruit.
# Numerical Simulation of Rockfill Materials Based on Fractal Theory
## 4 Analysis of the Results of the Numerical Test
### Micromechanical Response  
The numerical simulation allows the real-time monitoring of the evolution of rockfill materials in the mesostructure [14; 15]. The coordination number \(M\) refers to the average number of contacts in the specimen that reflects the contact characteristics of rockfill materials from a meso point of view A great coordination number corresponds to more compact filling of coarse particles and thus higher structure stability. The evolutionary curve of the coordination number is shown in Figure 6. Figure 6 shows that in the initial stage of loading, the finer particles are gradually filled between the coarse particles, the contact points between the particles are increasing, the corresponding coordination number is also increasing, and the sample is in a compressed state; After the coordination number reaches the maximum value, with the continuous increase of load, relative sliding occurs between some coarse particles, the number of contact points between particles decreases, the corresponding coordination number decreases, and the sample is in the state of shear expansion; The coordination number changes corresponding to the four fractal dimensions \(D\) are similar. The larger the fractal dimension \(D\) is, the larger the coordination number is, but it does not coincide, indicating that the influence of fractal dimension \(D\) on numerical simulation calculation is objective.  
Figure 5: Relationships between index of strength and deformation characteristics with fractal dimension \(D\). (**a**) strength characteristics, (**b**) deformation characteristics.  
Figure 6: Evolutionary curves of the coordination number.
# Algorithmic Modified Denavit-Hartenberg Modeling for Robotic Manipulators Using Line Geometry
## 4 Algorithmic MDH Parameter Extraction
### Joint Offset Angle \(\theta_{i0}\)  
It is defined as the angle from \(\hat{x}_{i-1}\) to \(\hat{x}_{i}\) about the \(\hat{z}_{i}\)-axis as shown in Figure 1. Since the \(\hat{x}_{i}\)-axis is determined by Equation (37), the sign \(s_{iz}\) should be reflected as follows:  
\[\theta_{i0}=s_{iz}\text{atan2}(\|\hat{x}_{i-1}\times\hat{x}_{i}\|,\hat{x}_{i-1} \cdot\hat{x}_{i}). \tag{43}\]  
The outputs of Algorithm 1 are consecutively arranged into:  
\[1\text{st joint} : \{\hat{x}_{1},\alpha_{1},a_{1},d_{10},\theta_{10}\}\text{from}L_{0},L_{1},L_{2},\hat{x}_{0},\] \[2\text{nd joint} : \{\hat{x}_{2},\alpha_{2},a_{2},d_{20},\theta_{20}\}\text{from}L_{1 },L_{2},L_{3},\] \[\vdots\] \[N\text{th joint} : \{\hat{x}_{N},\alpha_{N},a_{N},d_{N0}\theta_{N0}\}\text{from}L_{N -1},L_{N},L_{N+1},\]  
where it is noted that the \(\hat{x}_{0}\)-axis is required in advance and it is commonly chosen as \([1,0,0]^{T}\). To verify the completeness of Algorithm 1, several examples are suggested in the following section.  
The algorithm does not include any iterative procedures and is composed of fixed geometric operations such as dot products and cross products. The line geometry block contains no loops, and Algorithm 1 uses a single for-loop over \(N\) joints. The overall computational complexity is \(\mathcal{O}(N)\).
# A Review of Worldwide Strategies for Promoting High-Temperature Heat Pumps
## 2 The Decarbonization of the Industrial Sector
### Electrification of Industrial Heat Demand
Such a shift entails a departure from the conventional system reliant on dispatchable thermal power plants and the concept of \"base load\" in favor of a system accommodating variable renewables [49]. Energy-intensive processing industries have the potential to become flexible \"swing consumers\", using electricity to produce materials (power-to-products) rather than reducing excess solar and wind energy. For example, producing hydrogen for various industrial processes on demand could become a major flexible load in the future power system. This shift in economic thinking would also influence the geographic locations of these industries. Traditionally, they have been located near raw material sources or energy supplies. In a future dominated by RES, these industries might instead move closer to sources of renewable electricity [36]. Furthermore, the process of electrification and the broader transformation of the electricity system entail technological changes and cost considerations that must be carefully evaluated. To achieve viability, any zero-carbon heating technology must effectively meet the end user's heat demand at a competitive cost. Heat demand is influenced by two primary factors: temperature and load. For instance, industrial processes like steelmaking and cement production require temperatures that can exceed 1400 \({}^{\circ}\)C. Industrial heat, typically fueled by cheap coal and NG in certain regions, often represents a relatively inexpensive energy source compared to residential retail electricity prices. Despite the decreasing costs of renewable electricity, many industrial electricity tariffs remain high, resulting in electrified heating--especially without the use of HPs--often costing two to three times more than fossil-based heat generation [36]. To bridge this cost gap, electrified heating technologies must significantly improve in efficiency, and HP technologies are precious allies for this purpose. These systems can achieve a thermal energy output per unit of electricity input (MWhth/MWhe) greater than 1, thereby offsetting the higher operating costs associated with electricity. However, achieving this efficiency gain at a low capital cost is essential for competitiveness. Moreover, traditional fuel sources possess the capability to generate flame temperatures well beyond what is required for industrial processes. The affordability and high performance of modern carbon-based heat generation, which stem from decades of scientific and engineering advancements, pose a substantial challenge for the development of economically competitive decarbonized alternatives [34].
## 5 Conclusions and Future Trends  
### Summary  
This study presents a detailed examination of intelligent arterial traffic management systems spanning multiple intersections, focusing on the synergistic potential VLC and DRL techniques to enhance safety and efficiency. By leveraging vehicle-to-vehicle VLC messages and intelligent state representations, we developed dynamic traffic control models capable of managing traffic efficiently across interconnected intersections.  
Using Multi-Agent Reinforcement Learning integrated with the SUMO simulator, we demonstrated the effectiveness of our approach in optimizing traffic flow and reducing congestion. The simulations accounted for various critical factors, including vehicle and pedestrian volumes, velocities, pedestrian clearance times, and waiting zone occupancy. These parameters are pivotal for ensuring robust and efficient traffic management systems. Our approach proved highly adaptable to dynamic traffic scenarios, showcasing the importance of continuous learning in fluctuating urban environments.  
The results highlight the efficacy of rerouting in enhancing arterial road efficiency while demonstrating tangible benefits, such as shorter queue lengths and fewer stopped vehicles at intersections. When analyzing the three scenarios, standard, symmetric rerouting, and asymmetric rerouting, it is evident that rerouting significantly enhances traffic flow by redistributing vehicles more effectively. In the symmetric rerouting scenario, green times were more evenly allocated across all intersections, resulting in smoother traffic flow and reduced bottlenecks. The prioritization of phases, such as pedestrian crossing and left turns, facilitated better synchronization and balance in traffic management, accommodating both vehicles and pedestrians. The asymmetric rerouting scenario further demonstrated its advantage by tailoring traffic adjustments to prioritize high-traffic directions, such as W > E, while reducing activations of less critical phases, such as N > S and S > N. This targeted approach addressed localized congestion issues effectively, ensuring a more balanced distribution of traffic without overwhelming specific intersections. Both rerouting scenarios also demonstrated substantial benefits for pedestrian safety by reducing vehicle queues and enabling more frequent pedestrian phases. The overall reduction in halted vehicles and improved traffic fluidity contributed to a safer and more efficient traffic environment for all road users.
## 3 Experiment  
### Sample Preparation  
DR1-doped PMMA fibers are made with fixed DR1 concentration, but of different chain lengths, by adding different concentrations of 1-butanethiol chain transfer agent (CTA) to the MMA monomer during the polymerization process, which terminates chain growth through the transfer of radicals [66; 67]. Figure 4 shows the chemical structures of MMA, PMMA, and DR1 trans and cis isomers.  
The initiator acts as a polymerization catalyst by generating free radicals in the MMA monomer. This results in a cascade where the product radical added to the C=C bond forms a new radical upon polymerization, which can freely react with another MMA molecule to generate a polymer chain. Radical formation and chain growth propagation terminates when it encounters the chain transfer agent molecule [46; 68], and thus lowers the molecular weight in proportion to the CTA concentration [42]. The lower molecular weight should lower Young's modulus [69] and \(T_{g}\)[70] because of reduced chain entanglement [70]. Furthermore, the material's elasticity should decrease with shorter chain length.  
Figure 4: Chemical structures of (**a**) Methyl Methacrylate (MMA), (**b**) Poly (Methyl Methacrylate) (PMMA), (**c**) Disperse Red 1 (DR1) trans isomer, and (**d**) Disperse Red 1 (DR1) cis isomer.  
The MMA has been purchased from Sigma Aldrich and contains \(\leq\)30 ppm MEHQ (4-methoxyphenyl), which acts as an inhibitor. The inhibitor has been removed from the methyl (metacrylate) (MMA) monomer solution by passing it through an alumina gravity column. DR1 has been also purchased from Sigma Aldrich with a stated purity of 95%.  
Preforms are made in a test tube by adding 1-butanethiol CTA to MMA in concentrations of 20, 30, and 40 \(\upmu\)L per 15 mL of MMA, yielding volume fractions of 1.33 \(\times\) 10\({}^{-3}\), 2 \(\times\) 10\({}^{-3}\) and 2.66 \(\times\) 10\({}^{-3}\), respectively. Additionally added is 40 \(\upmu\)L Tert-butyl peroxide initiator per 15 mL of MMA and 0.5 wt % DR1 in MMA. After polymerization, the resulting solid cylinder is machined into a rectangular shape and fibers are drawn as described in the literature [17; 71].  
A typical preform makes fibers of a length measured in tens of meters. Widths are 0.26 mm and thicknesses range from 0.12 mm to 0.15 mm. These are cut to the desired lengths and mounted in the sample holder, as described below. The lengths are cut to be longer than the clamp spacing to provide extra length on the ends for the grippers. The effective length of the sample is the clamp spacing, which is set to \(L_{0}=8.28\) mm for both the photomechanical stress response measurement and the temperature-dependent stress measurement. The room temperature [PERSON]'s modulus measurement uses a clamp separation of \(L_{0}=14.05\) mm while the temperature-dependent [PERSON]'s modulus measurement uses \(L_{0}=14.84\) mm.