esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Recently, they have been successfully validated for the degradation of a fungicide by generating singlet oxygen under visible light irradiation, as demonstrated with AHMPD in real wastewater samples [31]. The objective of this research is to investigate the application of these photoactive materials for wastewater treatment through advanced oxidation processes involving singlet oxygen while evaluating the effect of pH and direct solar irradiation. The novelty of this study lies in the first-time investigation of Acetamiprid degradation via visible light photocatalysis using O\({}_{2}\)(\({}^{1}\Delta_{\text{g}}\)) generated from supported RB photocatalysts and, additionally, to optimize physicochemical parameters, such as pH, to identify the optimal application range of the photoactive materials for Acetamiprid degradation. Their efficiency is evaluated under both laboratory conditions using simulated sunlight and natural sunlight in Murcia, one of the European regions with the highest solar irradiation. Previous results with AHMPD demonstrated the effectiveness of supported RB polymers [31], achieving high degradation rates in basic media despite slow kinetics; this study also aims to evaluate the kinetic O\({}_{2}\)(\({}^{1}\Delta_{\text{g}}\)) performance of the photoactive materials for the degradation of Acetamiprid, an insecticide with a chemical structure distinct from AHMPD (a pyrimidine-based fungicide). The effectiveness of the photo-oxidation process will be compared to Fenton and photo-Fenton methods for Acetamiprid degradation, particularly in basic or slightly basic media. The application also will be studied both in laboratory samples and in real-scenario settings using effluent samples from an (IWWTP) in the agri-food sector.
## 2 Methodology Setup
### Measurement Method  
In our measurement method, the center vertex of the DOE's substrate was scanned and measured along the diameter direction. In this way, the measuring path could go through all step structures, and the rotationally symmetrical workpiece could be measured multiple times by rotating the path to realize the characterization and evaluation of the whole DOE surface. The measurement system platform needed to provide at least four degrees of freedom for the relative motion between the probe and the workpiece, the linear motion in the three-axis directions of the X-axis, the Y-axis, and the Z-axis, and the rotational motion degree of freedom in the Z-axis direction. All motion freedoms needed to ensure high motion accuracy.  
The measurement system ensured that the optical axis of the confocal probe was perpendicular to the horizontal plane that the measured workpiece was fixed on it, and that the probe kept a fixed height relative to the measured workpiece in its measurable range. In this method, the probe was independently responsible for collecting the measured contour data points at a fixed frequency, and the motion system was independently responsible for driving the probe along the DOE substrate contour at a constant speed. In this paper, a special method for data processing was proposed. The complete 3D surface reconstruction of a DOE can be realized by matching and coordinate transformation, and then the geometric characteristic of the measured DOE surface can be extracted by analysis. This method effectively avoided the disadvantage that the scanning point must be accurately positioned in the system coordinate one by one in real time, without acquiring the system coordinates in real time, reducing the dependence on the positioning coordinate points in the process. This ensured high-precision measurements, while improving measurement efficiency. There were some issues that should be taken into account in this method. The tilt of the measured workpiece resulted in the overall tilt of the measured data, and the measurement path may not cross the central vertex of the surface base, which resulted in a deviation in the DOE feature information. Using the data processing measurement method proposed in this paper could solve the main problems in the measurement method, and achieve high-precision and high-efficiency measurements, getting precise measurement results of the main characteristic of the curve-based DOE, like tooth pitch and tooth height.
## 3 Experimental Set-Up for Investigation of Soil Water Retention Behavior
### Soil Sample Specification  
Two types of sandy soil were involved in this experimental set-up. One is the beach sand collected from Bribe Island, and the other one is the Budget Brickies loamy sand collected from an unknown construction site, Queensland, Australia. The particle sizeof the two types of sand ranges from 0.075 to 2 mm. The \(D_{50}\) of beach sand is 0.35 mm, which is slightly larger than the mean size of loam, 0.27 mm. The sieving analysis was implemented for both types of sand in accordance with ASTM standards of particle analysis for coarser soil [48].  
The mineralogy of the two samples comprised of quartz at the specific gravity of 2.65. The coefficients of uniformity (\(C_{u}\)) for beach sand and loamy sand are 1.71 and 2.39 separately, and the coefficients of gradation (\(C_{c}\)) are 0.99 and 1.11. Based on the Unified Soil Classification System (USCS), both of them are poorly graded uniform sand (SP). The beach sand is medium sand without any fine content, while the loamy sand is fine sand with a fine content of 5%. The loamy sand is adopted for a demonstration of the results collected by the experimental platform in this study.
# Cross-Correlation Wavelet-Domain-Based Particle Swarm Optimization for Lightning Mapping
## 2 Materials and Methods
### Optimization Constraints and PSO Parameters  
The performance of the PSO-based algorithm is affected by how to arrange the searching process. In the optimization process, the determination of the parameters that are used in the objective function should satisfy and meet the problem constraints. The two main constraints include the upper and lower window size values. These constraints are limited to \(W_{Slower}=100\) and \(W_{upper}=512\), respectively. The inequality constraint is the constraint considered in the PSO optimization problem, which represents the best solution and best fitness of the extracted lightning mapping. Furthermore, PSO-based algorithm usually influenced by a number of control parameters--namely, the dimension of the problem (D), number of particles, acceleration coefficients \((c_{1},\ c_{2})\), inertia weight \((w_{min},\ w_{max})\), number of iterations \((MaxIteration)\), population size, and random values. The population size is set to (\(Npop=10\), the demission is set to D = 1. The lower and upper of the dynamic inertia weight are set as \(w_{min}=0.4\) and \(w_{max}=0.9\). The study iteratively executed the PSO algorithm several times and computed the optimal values of the objective functions and window size at each iteration. The performance of the proposed PSO-based algorithm was evaluated using the experimental simulated results of four aspects iterations and the iterative curve.
## 2 Comparative Analysis of Old Pavement Evaluation Models  
The oldest worldwide model for assessing asphalt pavement performance is the present serviceability index (PSI) established by the American Association of State Highway Officials (AASHO) in the 1960s [24]. The introduction of the PSI marked a significant milestone in the road management sector. The AASHO established threshold indicators for road surface condition, with a PSI of 2.5 for primary roads and 2.0 for secondary roads. If the PSI fell below the specified threshold, remedial technical measures should be considered for maintenance. Subsequently, many countries adopted PSI variations tailored to their contexts, including the ride comfort index (RCI) in Canada, the maintenance condition index (MCI) in Japan, and the pavement quality index (PQI) model in China [25].  
While pioneering, these early models primarily relied on limited regression analysis and contemporary expert knowledge, which presents issues when evaluating aged pavements in reconstruction projects. Firstly, evaluation data itself demonstrates randomness, complexity, and fuzziness characteristics difficult to fully capture through single-factor regression. This can induce discrepancies between modeled and actual relationships between factors and outputs. Secondly, the models were mainly developed for maintenance and repair rather than reconstruction, requiring investigation into their suitability given reconstruction's unique demands.  
Notably, the widely used PQI model in China incorporates indices like overall cracking, rutting, depression, and others [24]. However, related research shows cracks and ruts, respectively, account for over 60% and 30% of pavement distress on Chinese highways [23]. The PQI model does not sufficiently prioritize these predominant failure modes. Furthermore, the PQI calculation equally weights all indices, which may not optimally guide decisions if a particular problem drastically worsens. Fixed weights cannot flexibly reflect actual priorities and importance levels for reconstruction projects.  
In summary, while pioneering works, conventional models exhibit several shortcomings limiting applicability to aged pavement performance appraisal for major reconstruction projects. A new approach considering data characteristics and project requirements is warranted to scientifically guide engineering decision-making. The proposed normal cloud framework addresses the abovementioned issues by enabling holistic representation of indicators, weights, and condition fuzziness.
# Review of Traffic Assignment and Future Challenges
## 3 Traffic Assignment: Theoretical Background  
Research on traffic assignment can be classified in various ways, such as being based on problem formulation, expected objectives, methodologies, models used, and assumptions about demand, etc. In the following, we consider the classification commonly used in the literature, which distinguishes approaches based on the consideration of traffic dynamics over time. Thus, we first present a static traffic assignment, introducing concepts, definitions, and problem formulations. We then move on to dynamic assignment, where the concepts and algorithms used will be discussed.
# Propulsion Performance of the Full-Active Flapping Foil in Time-Varying Freestream
## 3 Results and Discussions
### The Influence of the Time Varying Freestream
#### 3.2.3 The Effect of the Time-Varying Flow in Multiple Periods  
In the previous section, the influence of the time-varying flow on the propulsion performance of the full-active flapping foil in different periods is investigated. We note that the simulation time can affect the hydrodynamic performance of the flapping foil under the action of the time-varying freestream. The oscillating amplitude and frequency determine the average velocity of the time-varying flow in different periods, which affects the propulsion performance of the foil. Therefore, we infer that the average velocity of the time-varying flow under different oscillating amplitude and oscillating  
Figure 8: The evolution of the mean propulsion power coefficient for different reduced frequency and pitching amplitude, (**a**) \(\tau=0.2\), (**b**) \(\tau=0.8\).  
frequency coefficient in multiple periods will tend to the constant velocity. Thus, with the extension of simulation time, the effect of the time-varying flow on the flapping foil will gradually weaken. [PERSON] [29] and [PERSON] [30] also found a similar phenomenon when studying the effect of time-varying flow on the energy harvesting performance of the full-active flapping foil. The average value of the propulsion power coefficient in 10 cycles with the variation of the oscillating frequency coefficient is shown in Figure 9. Referring to Figure 8, we know that a larger pitching amplitude and reduced frequency can increase the maximum change rate of the mean propulsion power coefficient of the flapping foil affected by the time-varying flow. Referring to Figure 9, when \(f^{*}=0.68\) and \(\theta_{0}=60^{\lx@math@degree}\), the maximum change rate of the flapping foil is only 4.8%. Therefore, we conclude that the time-varying freestream has little effect on the propulsion performance of the full-active flapping foil at different pitching amplitude and reduced frequency. When we calculate the hydrodynamic performance of the flapping foil by using numerical simulation, we can simplify the time-varying freestream to the steady flow.
## 3 Results and Discussion  
### Strain Rate-Time Graph  
Figure 3 shows the strain rate-time curves generated from the Taylor impact test numerical simulations conducted at the prescribed velocities of 130 m/s, 200 m/s, and 250 m/s. The range of impact velocity from 130 m/s to 250 m/s was selected to ensure sufficient noticeable plastic deformation of the numerical model.  
It is clear from this figure that there was a sharp increase in strain rate in all three cases. Furthermore, the rate of increase in strain rate was higher for higher simulation velocities and vice vasa. At a prescribed velocity of 250 m/s, the strain rate initially peaked at a time of about 2.6 us before going through a series of rises and drops and subsequently attaining a maximum value of strain rate at 6.01 us. The range of strain rates attained at these times were 61,320 s\({}^{-1}\) and 78,946 s\({}^{-1}\), respectively. The time for the initial peak strain rate at a simulation velocity of 200 m/s was at about 4.11 um before dropping and rising again and reaching a maximum at 7.61 um. The strain rate within this range of times was between 40,061 s\({}^{-1}\) and 52,982 s\({}^{-1}\), as seen in Figure 3. At the lower simulation velocity of 130 m/s, the initial peak strain rate was approximately 38,875 s\({}^{-1}\), followed by a series  
\begin{table}
\begin{tabular}{c c c c} \hline
**Variable** & \multicolumn{3}{c}{**Measured Value**} \\ \hline  & Samples C & Samples D & Samples E \\ Average grain size & 2.5 μm & 6.0 μm & 9.0 μm \\ Initial dislocation density (m\({}^{-2}\)) & \(5.73\times 10^{14}\) & 5.09 \(\times\) 10\({}^{14}\) & 7.00 \(\times\) 10\({}^{14}\) \\ Material constant (\(\zeta\)) & 207 & 210 & 210 \\ Effective damping coefficient (\(\chi\)) & 0.00032 & 0.00030 & 0.00020 \\ \hline \end{tabular}
\end{table}
Table 2: Measured and calibrated model parameters for different forms of LPBF Ti6 Al4V (ELI) alloy.  
Figure 3: The strain rate–time graph for the Taylor impact numerical simulations conducted at various imposed velocities.  
of rises and drops. The time for the initial peak strain rate at this simulation velocity was approximately 7.21 \(\upmu\)m. In all cases, the strain rate eventually decreased to zero, as seen in Figure 3. This difference in times for the initial peak strain rates in each case was ascribed to the difference in times taken for the impact wave, generated by impact of the cylinder onto the rigid surface, to travel through the cylinder. It is clear from these times that the impact wave was faster at high impact velocities and vice versa.
# Possibility of Replacing Sugar with Apple Puree in Muffins
## 1 Introduction
Apple puree can replace sugar in many recipes, but it is important to note that changes must be adjusted to maintain the correct balance of ingredients. For example, due to the high water content of apple puree, it may be necessary to adjust the amount of liquids in the recipe to avoid a too wet or gummy texture [12]. Apple puree adds a subtle fruit flavor to baked goods that can complement or enrich the overall taste. Depending on the recipe, this flavor can be an advantage, contributing to the complexity of the final taste. It should be taken into consideration that choosing the type of apples used for puree can further influence the flavor profile, as different apple varieties have varying levels of sweetness and acidity [53; 54; 55; 30]. Apple puree can be used in a variety of baked goods, from cakes and muffins to cookies and cakes. It can be used as a full or partial sugar substitute, depending on the recipe and personal preference. It is also a great option for people following a vegan diet or looking to cut down on processed sugars [12; 25; 26; 50]. This paper presents a study of the effects of replacing sugar in muffins with different percentages of apple puree on the modulus of elasticity and viscosity of the dough, depending on the frequency and temperature, texture, physical characteristics, and color of the muffins.
## 2 Mathematical Modeling  
### Process Description  
In Figure 1a, a layout diagram of two cycles named gas cycle (GC) and steam cycle (SC) and integration (combined cycle (CC)) were demonstrated. In Figure 1b, the integration of this CC with the heliostat solar receiver (HSR) cycle is presented. The natural gas-based combined GC and heat recovery steam generator (HRSG) system is illustrated in Figure 1a. Additionally, Figure 1b depicts the combined gas turbine (GT)-HRSG-HSR system that employs solar energy by a heliostat solar receiver alongside natural gas to provide the thermal energy requirement of the plant. Both configurations are designed for power production using a gas turbine and a steam turbine. In the GT-HRSG combined system, Figure 1a, natural gas as fuel is sent to a combustion chamber (stream No. 11) by a booster compressor (BC), and air enters the combustion chamber (stream No. 2) from a compressor (C). The combustion chamber output goes to a gas turbine (stream No. 3) for power production. The blue gas of the gas turbine enters an HRSG unit (stream No. 4) to provide heat for a Rankine cycle (RC). The superheated steam leaves the HRSG unit to the turbine (T; stream No. 7) for more power production. The output fluid of the steam turbine is sent to a condenser (stream No. 8), and then is pumped back to the HRSG unit (stream No. 6).  
In the integrated GT-HRSG-HSR configuration, Figure 1b, part of the heat energy demand is provided by the HSR. In HSR, Therminol VP-1 absorbs the heat reflected by the heliostat field as the working fluid and enters a storage tank (stream No. 3). The working fluid after heat exchange with exhaust pressurized air from the air compressor (stream No. 5) enters a pump (stream No. 1) to be pumped back to the receiver (stream No. 2). The hot air goes to a combustion chamber (stream No. 6), which includes natural gas as the fuel enters (stream No. 15). The combustion chamber output fluid enters a gas turbine (stream No. 7) for power production. The outlet stream goes to an HRSG unit to provide heat for the RC (stream No. 8). The steam of RC after gaining heat leaves the HRSG unit as superheated steam to be exploited in a steam turbine (stream No. 11). After power production, the exhausted fluid is sent to a condenser (stream No. 12) and is pumped back to the HRSG unit (stream No. 10) to complete the RC.  
Figure 1: \(Cont.\)
###### Abstract
The results demonstrated that MCSS achieved 70.63% and 70.93% shorter end-to-end delay, 17.89% and 18.80% higher aggregate throughput, and 111.96% and 116.28% higher average harvested energy compared with the legacy TSCH MAC and TSCH multiple slotframe scheduling (TMSS), respectively. The remainder of this paper is organized as follows. Section 2 presents a system model for MCSS. We describe the design of MCSS in detail in Section 3. We provide the simulation configuration and results in Section 4. Finally, Section 5 concludes this paper.
# A Mapping Model of Propeller RANS and LES Flow Fields Based on Deep Learning Methods
## 4 Experiments
### Experimental Results of Feature Extraction Mapping for Flow Fields  
Based on the experiments mentioned above, it can be concluded that the two-stream network for three velocity components is more advantageous for feature extraction and exhibited better performance. Therefore, we selected the two-stream network for three velocity components as the backbone feature extraction network for training the mapping model.  
Table 4 summarizes the results of experiments for the mapping module on the test dataset, which consists of approximately 200 samples. In the experiments, we first pre-trained the mapping module using the two types of features obtained and then integrated them into a complete model for training. From the table, it can be observed that all the mapping error rates for the three velocity components are below 10%, indicating excellent model performance. Furthermore, we interpolated the mapping results with the ground truth values and obtained the interpolation plots as shown in Figure 12. It is obvious from the plots that the errors in the predicted data are mainly concentrated around the location of the propeller blades.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{_V\_axial_ Error Rate**} & _V\_tangential_ Error** & _V\_radial_ Error** & **Average Prediction** \\  & & **Rate** & **Rate** & **Time (Seconds)** \\ \hline Resnet-18 single speed & 3.58\% & 7.25\% & 9.04\% & 5.06 \(\times\) 3 \\ Resnet-18 Triple Speed & 3.78\% & 7.88\% & 9.59\% & 5.67 \\ Two-stream network single speed & 2.46\% & 6.89\% & 8.92\% & 6.34 \(\times\) 3 \\ Two-stream network triple speed & 2.72\% & 7.65\% & 8.89\% & 6.58 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Regression error rate for feature extraction of RANS velocity in the data set (the average prediction time represents the running time of every hundred samples in the neural network).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{_V\_axial_ Error Rate**} & _V\_tangential_ Error** & _V\_radial_ Error** & **Average Prediction** \\  & & **Rate** & **Rate** & **Time (Seconds)** \\ \hline Resnet-18 single speed & 3.58\% & 7.25\% & 9.04\% & 5.06 \(\times\) 3 \\ Resnet-18 Triple Speed & 3.78\% & 7.88\% & 9.59\% & 5.67 \\ Two-stream network single speed & 2.46\% & 6.89\% & 8.45\% & 6.34 \(\times\) 3 \\ Two-stream network triple speed & 2.72\% & 7.65\% & 8.89\% & 6.58 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Regression error rate of feature extraction for LES velocity in the dataset.  
We have extracted some data from the propeller center area and presented it in Table 5. Table 5 includes the \((x,y,z)\) coordinates of each point, along with the predicted and actual results for the three speed parameters at those points.
# Monocular Vision Guidance for Unmanned Surface Vehicle Recovery
###### Abstract  
The positioning error of the GPS method at close distances is relatively large, rendering it incapable of accurately guiding unmanned surface vehicles (USVs) back to the mother ship. Therefore, this study proposes a near-distance recovery method for USVs based on monocular vision. By deploying a monocular camera on the USV to identify artificial markers on the mother ship and subsequently leveraging the geometric relationships among these markers, precise distance and angle information can be extracted. This enables effective guidance for the USVs to return to the mother ship. The experimental results validate the effectiveness of this approach, with positioning distance errors of less than 40 mm within a 10 m range and positioning angle errors of less than 5 degrees within a range of \(\pm\)60 degrees.  
unmanned surface vehicle; perspective-n-point; artificial markers; visual guidance +
Footnote †: journal: Applied  
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