esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Computational Aspects  
In this paper we concentrate on mathematical details of the OPTCON algorithm. However, it is important to mention the computational characteristics of this algorithm. In order to show computational time and accuracy we used two different models: the MacRae model and the ATOPT model. As it would go beyond the scope of the present paper, we do not discuss these models and their economic interpretation (see detailed descriptions in [18]) but provide computational details of applying different versions of the OPTCON algorithm. We compared four different optimization strategies: deterministic solution (det), stochastic open-loop solution (OL) as described in Section 3.1, stochastic passive learning solution (OLF) as described in Section 3.2, and stochastic active learning solution (AL) as described in Section 3.3. In the case of the active learning strategy, a grid search with 100 grid points for each control variable was applied (see parameter \(\Pi\) in Step-I-[2] in Section 3.3). All calculations were performed on a Windows 10 computer with 16 GB RAM.  
The OPTCON algorithm is programmed in MATLAB (MATLAB R2020a); the MATLAB computer code is available on request.  
The MacRae model is a simple linear model with one state and one control variable. One parameter is stochastic and the optimal control was calculated for two periods only. Table 1 summarizes the computational performance of the OPTCON algorithm for the MacRae model. In the case of a linear-quadratic deterministic optimal control framework convergence can be shown analytically. This is true for the MacRae model, at least for the deterministic and the OL case. Moreover, as a check for the special case of a linear system, we compared the results of OPTCON with those of the same model calculated by available software and found them to be the same apart from round-off errors.  
The ATOPT model is a small nonlinear model consisting of three equations, one control variable, and two stochastic parameters. The optimal control is calculated for five periods. Table 1 summarizes the computational performance of the OPTCON algorithm for the MacRae model. Table 2 summarizes the computational performance of the OPTCON algorithm for the ATOPT model.
## 2 Materials and Methods
### Biomass Analysis
#### 2.3.2 Pigments Extraction and Quantification  
The algae biomass obtained from biomass concentration measurement was freeze-dried and ground into powder, of which 25 mg was suspended in 1 mL of methanol 90% (V\({}_{\text{methanol}}\)) and kept at 60 \({}^{\circ}\)C in a water bath until the biomass was colorless. After extraction, the mixture was allowed to cool until it reached room temperature, and the tube was centrifuged at 1500 \(g\) for 5 min. The supernatant was used to carry out optical density measurements to determine pigment content.  
The chlorophyll-a and total carotenoid concentrations were calculated by reading the absorption of the extract in a spectrophotometer (Jenway, 6305, UK) at different wavelengths (650, 665, and 480 nm) and using the following equation adapted from [21]:  
\[\text{Chlorophyll a }\left(\text{mg L}^{-1}\right)=\frac{\text{V}_{\text{methanol }}}{\text{V}_{\text{samp}}}\left(16.5\text{ OD}_{665\text{nm}}-8.3\text{ OD}_{650\text{nm}}\right)\]  
\[\text{Total carotenoids }\left(\text{mg L}^{-1}\right)=\frac{\text{V}_{\text{methanol }}}{\text{V}_{\text{samp}}}\left(4\text{ OD}_{480\text{nm}}\right)\]  
As presented in [PERSON] et al. [16], a \"stress factor\" can be obtained from the ratio of absorbances at 480 and 665 nm (named OD\({}_{480}\)/OD\({}_{665}\) hereafter), which are proportional to the carotenoid and chlorophyll content, respectively.
## 2 Theoretical Background
### Theory of Transmission Loss
In the stiffness-controlled region, the sound insulation of the plate is proportional to its stiffness, and decreases as the frequency increases. With the frequency increases, the resonant effect excites large amplitudes and produces an obvious transmission effect. The TL drops to a minimum at the first-order resonance frequency. Then, the effect of resonance gradually disappears with the frequency continuing to increase, and the vibration of the plate begins to be affected by the inertial mass, that is, into the mass-controlled region. When the frequency rises to a certain value, the projection of the wavelength of the incident sound wave on the plate is equal to the flexural wave wavelength of the plate. At this time, due to the bending stiffness effect, the sound wave excites the natural vibration of the plate, so that the sound insulation capability of the plate is greatly reduced. Finally, at very high frequencies, the transmission loss rises again and gradually approaches the extension of the mass law portion of the original curve, which is called the damping-controlled region. When the frequency is in the range from 1.5 times the first-order resonance frequency of the plate to \(f_{c}/2\), at which in the mass-controlled region, the expression of field incident transmission loss is:  
\[\tau=\frac{E_{transmitted}}{E_{incident}} \tag{4}\]1  
At greater than or equal to the critical frequency, the field incident transmission loss of the isotropic panel is:  
\[\tau=\frac{E_{transmitted}}{E_{incident}} \tag{4}\]2  
While in the frequency domain of \(f_{c}/2\) to the critical frequency, the TL can be approximated by linearly interpolating the corresponding two TL values of \(f_{c}/2\) and critical frequency.
## 3 Orbit Determination Method for GEO Satellite Based on RDSS Data  
### Principles of Orbit Determination  
According to the RDSS measurement principle, the orbit accuracy of GEO satellites based on RDSS data considerably depends on the time delay accuracy of RDSS equipment. The time delay for the MCC and satellite is calibrated by a special instrument before being adopted in the BDS. The user equipment time delay is usually calibrated in the special equipment calibration field and the accuracy is better than 10 ns. Owing to equipment aging and hardware replacement, the time delay of RDSS equipment involves the problem of long-term drift [21; 22; 23]. The common method is recalibration and retesting; during this process, the equipment of the MCC needs to be off the line and the user equipment needs to be returned to the equipment calibration field for recalibration. Therefore, the time delay recalibration of RDSS equipment is tedious and time-consuming, potentially affecting the RDSS service of BDS, especially since the recalibration of the satellite equipment delay cannot become true [24; 25; 26; 27]. For navigation satellites, with centimetre- or millimetre-scale precision, SLR is a high-accuracy measurement method that is independent of radio measurementsand is also an important means of evaluating orbit accuracy [10; 28; 29]. All the BeiDou satellites in orbit are equipped with laser reflectors, laying an important foundation for the accuracy evaluation and time delay calibration of the RDSS data in the BDS.  
For the new method of POD for GEO satellites by only using the RDSS data, first the time delay of the RDSS equipment is calculated based on SLR data, and then in the POD process, the corrected RDSS time delay value is fixed. The satellite orbit and dynamic parameters are solved only by using the RDSS data.
# Maximum Transmit Power for UE in an LTE Small Cell Uplink
## 3 Network and Propagation Model
### Homogenous Macro/Small Cell Deployment
#### 3.1.4 Spectral Efficiency  
To see the impact of variation in the \(P_{\rm max}\) limit on the system performance, the cell (or average) spectral efficiency \(\overline{SE}\) (in bps/Hz/cell) for the proposed system can be obtained using the classical Shannon formula and Equation (11) as:  
\[\overline{SE}=\frac{2}{R^{2}-r_{\rm o}^{2}}\int\limits_{r_{\rm o}}^{R}\log_{2 }(1+SIR_{Rx}(r))rdr \tag{12}\]  
where, \(r_{\rm o}\
eq 0\) represents the minimum distance between a UE and its serving eNodeB. Equation (12) can be employed for both a small and macro cell network, where \(r_{\rm o}\) = 5 m and 50 m are employed for small and macro cell cases, respectively. To evaluate the spectral efficiency for a UE at a certain distance from the serving eNodeB, Equation (12) can be rewritten as:  
\[SE(r)=\log_{2}(1+SIR_{Rx}(r)) \tag{13}\]
## 4 Discussion
### Performance Metrics Analysis
The results in Table 4 and in Table 6 indicate that VGG19 had the best metrics, suggesting that it is the most robust model for this task. MobileNetV2, although efficient, performed slightly less well, which may be an expected trade-off given its lightweight design. When performing a fairness analysis of the model, based on Table 6, it can be observed that despite the high overall classification performance, analysis across skin tone categories reveals notable disparities, particularly affecting lighter skin tones. While the model performs consistently well on darker skin tones, achieving F1-scores of 98-99% for \"Tan\", \"Brown\" and \"Dark\" categories, it shows reduced performance on \"Light\" and \"Intermediate\" tones. Most notably, the \"Light\" group exhibits the lowest F1-score (84%), with comparatively lower precision (81%) and accuracy (84%). This discrepancy is not solely attributable to small sample size, as other minority classes (e.g., \"Very Light\") perform better (F1-score 97%) despite similarly low support. Instead, the reduced precision for \"Light\" skin tones, where 11 false positives were observed, suggests systematic misclassification or confusion with neighboring classes, which may stem from insufficient representation or ambiguous ITA thresholds. On the other hand, the \"Very Light\" skin tones also showed to have good results. This may be due to a more obvious difference between skin tone and -ulcer-related colours. It is also interesting to observe that \"Intermediate\" class showed lower performance than \"Very Light\" class despite its higher representation in the dataset.
## 2 Background on FA, WDO, DSO, and Fuzzy Logic
### Drone Squadron Optimization (DSO)  
In the DSO algorithm, the drones can navigate autonomously, and this method was originally developed in [28] and [29], and [PERSON] et al. [30] describes some applications of the method. The sensors in DSO can communicate over long distances and use solar energy, and one of the most important features is that they can be updated not only regarding hardware, but also when changing their software.  
The DSO algorithm consists of the simulation of a drone squadron with different equipment and a command center. The command center uses information collected by the drones to perform two operations:  
1. Maintain partial control of the search.
2. Develop new control software for drones. In general, the DSO consists of:  
* Coordinates: representing a numerical solution.
* Scan the landscape: calculate the objective function and obtain the fitness value.
* Firmware: distinct rules/configurations to evolve the population.
* Team: group of agents that share a firmware, but operate on possibly different data.
* Squadron: group of teams with different firmware. The following Figure 1 shows the DSO search cycle.  
DSO has two main equations that establish the rules of drone movement as shown below in Equations (6) and (7):  
\[P=Departure+Offset() \tag{6}\]  
\[TC=calculate(P) \tag{7}\]  
where \(P\) is the complete perturbation formula that should be calculated to return the trial coordinates, the variable _Departure_ is a particular coordinate, \(Offset\) is a function that returns the actual perturbation movement, and \(TC\) generates new trial coordinates through perturbation.  
\[violation_{team}=\sum\
olimits_{drone=1}^{N}\sum\
olimits_{j=1}^{D}\left\{ \begin{array}{l}\left|TmC-UB_{j}\right|\\ +\\ \left|LB_{j}-TmC\right|\end{array}\right. \tag{8}\]  
Equation (8) is applied when a drone commits a violation when leaving the limits of the search space, where \(violation_{team}\) is equal to the infraction, \(TmC\) represents the equipment coordinates, \(UB_{j}\) and \(LB_{j}\) correspond to the upper and lower limits, respectively, \(D\) is equal to the number of dimensions of the problem, and finally \(N\) represents the number of drones per team.  
To better explain the difference between the used methods, a comparison is made among them, as illustrated in Table 1, with respect to one of the most used metaheuristics: particle swarm optimization  
Figure 1: Drone Squadron Optimization (DSO) search cycle.  
(PSO) as developed by [PERSON] and [PERSON] in [31; 32]; applications of the method are also described in [33]. PSO is a bio-inspired method in nature, to be more specific in the behavior of the birds, where each one represents a particle. Considering that the WDO algorithm is inspired by PSO, the following comparison table is made, and it should be noted that PSO is not currently used in this methodology, but rather only used in this table for comparison purposes.
# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 3 Electric Vehicle Characteristics Model
### Spatial and Temporal Characteristics of Electric Vehicles Travel  
The timing of EV travel is influenced by user behavior and exhibits a distinct bimodal pattern. Morning and evening are the two major time periods when EVs are most commonly used, especially for trips between residential areas and workplaces. This observation is particularly applicable to EVs following the travel chain of residential area-workplace-residential area. This information is based on data from the U.S. National Household Travel Survey, which suggests that EVs have a similar travel timing distribution as conventional fuel-powered vehicles [29]. According to a study referenced as [30], the probability density functions for morning and evening EV travel can be approximated by Equation (16) and Equation (17), respectively, with adjustments made in this article. For other types of travel chains, the timing of vehicle travel is randomly distributed throughout the morning and afternoon time periods.  
\[f_{\rm{B1}}(t)=\omega_{1}\exp\left[-\left(\frac{t-\alpha_{1}}{\beta_{1}} \right)^{2}\right]+\omega_{2}\exp\left[-\left(\frac{t-\alpha_{2}}{\beta_{2}} \right)^{2}\right] \tag{16}\]  
In the equation, \(\omega_{1}\) = 0.389, \(\alpha_{1}\) = 7.046, \(\beta_{1}\) = 1.086, \(\omega_{2}\) = 0.066, \(\alpha_{2}\) = 15.610, \(\beta_{2}\) = 9.667.  
\[f_{\rm{B2}}(t)=\left\{\begin{array}{ll}\frac{1}{\sigma\sqrt{2\Omega}}\exp \left[-\frac{(t+24-\mu)^{2}}{2\sigma^{2}}\right],&0<t<(\mu-12)\\ \frac{1}{\sigma\sqrt{2\Omega}}\exp\left[-\frac{(t-\mu)^{2}}{2\sigma^{2}} \right],&(\mu-12)<t\leq 24\end{array}\right. \tag{17}\]  
In the equation, \(\mu\) = 17.6, \(\sigma\) = 3.4.  
The spatial distribution characteristics of EVs primarily involve the description of their travel behavior in space, specifically the analysis of origin-destination (O-D) pairs, which includes considerations of travel between different zones of the road network. Common methods, such as O-D analysis, involve inferring the O-D probability matrix based on road traffic flow to allocate starting and ending points for EVs.  
This study relies on survey data on user travel behavior and divides the road network into different regions. Following this, a method is employed to allocate O-D points within the corresponding regions for private cars on different travel chains based on the proportion of travel destinations in the data. Different types of EVs have varying travel characteristics, which also determine differences in their location distribution. For example, private cars have travel demands related to commuting and entertainment, with travel chains primarily consisting of residential areas-work areas-residential areas, or residential areas-commercial areas. As a result, their initial positions are mainly concentrated near residential areas.
# Enhanced Performance of nZVI/MXene@CNTs for Rapid As(III) Removal from Aqueous Solutions
## 2 Materials and Methods  
### Materials  
The titanium aluminum carbide (Ti\({}_{3}\)AlC\({}_{2}\)) was purchased from 11 Technology Co., Ltd. in Jilin, China. The multi-walled carbon nanotubes (MWCNTs) were purchased from Suzhou CarbonFocus Graphene Technology Co., The nitric acid (HNO\({}_{3}\)), sulfuric acid (H\({}_{2}\)SO\({}_{4}\)), hydrochloric acid (HCl) and sodium hydroxide (NaOH) were obtained from Tianjin Hengxing Chemical Reagent Manufacturing Co., Ltd. (Tianjin, China). The lithium fluoride (LiF), anhydrous ethanol (C\({}_{2}\)H\({}_{5}\)OH), thiourea (CH\({}_{4}\)N\({}_{2}\)S), ascorbic acid (C\({}_{6}\)H\({}_{8}\)O\({}_{6}\)), ferrous sulphate (FeSO\({}_{4}\)\(\bullet\)7H\({}_{2}\)O), calcium nitrate (Ca(NO\({}_{3}\))\({}_{2}\)), magnesium nitrate (Mg(NO\({}_{3}\))\({}_{2}\)), sodium chloride (NaCl) and sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)) were purchased from Sinopharm Group Chemical Reagent Co., Ltd. (Hong Kong, China). The sodium borohydride (NaBH\({}_{4}\)), potassium borohydride (KBH\({}_{4}\)) and sodium arsenite (NaAsO\({}_{2}\)) were purchased from Xilong Science Co., Ltd. (Guangdong, China). All of the chemicals used were analytically pure and the water used in the experiments was ultrapure.
###### Abstract  
Various state-of-the-art gridded satellite precipitation products (GPPs) have been derived from remote sensing and reanalysis data and are widely used in hydrological studies. An assessment of these GPPs against in-situ observations is necessary to determine their respective strengths and uncertainties. GPPs developed from satellite observations as a primary source were compared to in-situ observations, namely the Climate Hazard group Infrared Precipitation with Stations (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA). These products were compared to in-situ data from 51 stations, spanning 1998-2016, across Pakistan on daily, monthly, annual and interannual time scales. Spatiotemporal climatology was well captured by all products, with more precipitation in the north eastern parts during the monsoon months and vice-versa. Daily precipitation with amount larger than 10 mm showed significant (95%, Kolmogorov-Smirnov test) agreement with the in-situ data, especially TMPA, followed by CHIRPS and MSWEP. At monthly scales, there were significant correlations (R) between the GPPs and in-situ records, suggesting similar dynamics; however, statistical metrics suggested that the performance of these products varies from north towards south. Temporal agreement on an interannual scale was higher in the central and southern parts which followed precipitation seasonality. TMPA performed the best, followed in order by CHIRPS, MSWEP and PERSIANN-CDR.  
precipitation; gridded precipitation products; in-situ observations; comparison; Pakistan 2019 11 628 10.3390/rs11060628
# Monitoring of Sugarcane Harvest in Brazil Based on Optical and SAR Data
## 2 Materials
### Sugarcane Harvesting Technologies in Brazil  
In Brazil, sugarcane is planted twice a year: in winter (August-September) and summer (February-April) periods. The sugarcane harvest dates are influenced by the local climate. Precipitation affects negatively the sugar content in the sugarcane stalks and creates difficulties with manual harvesting. Therefore, harvest is usually organized during dry periods, when moisture content of the harvested material is low and saccharose concentration is highest. The lowest amount of precipitation is observed in winter. Correspondingly, most sugarcane areas are harvested in one and a half year after the summer planting, and in a year after the winter one (Figure 2) [37]. According to [37], the ratooning harvest begins 10-12 months after the previous harvest.  
Figure 1: Location of the study area in the state of São Paulo (Brazil).  
To monitor the harvest, time series of Sentinel-2 satellite optical data and Sentinel-1 SAR data were used in the research. The optical data advantages are in the shorter time interval (5 days) and high sensitivity of the reflection coefficients in spectral bands to vegetation changes within the fields. The disadvantage is in possible long-term periods of data nonavailability due to the cloud cover. Cloud-independent Sentinel-1 data allow the ability to construct regular time series (except for some gaps due to the changed survey plan). However, for the Brazilian territory, the Sentinel-1 revisiting period is much longer, being 12 days. Taking into consideration the indicated advantages and disadvantages, sets of optical and SAR data were used separately to determine the sugarcane harvest dates.
# Climate Change Impact on Corrosion of Reinforced Concrete Bridges and Their Seismic Performance
## 4 Case Study
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Corrosion Level**} & \multicolumn{6}{c}{**Ductile Failure Mechanism**} \\ \cline{2-9}  & \multicolumn{2}{c}{**50 Years**} & \multicolumn{2}{c}{**75 Years**} & \multicolumn{2}{c}{**100 Years**} \\  & \multicolumn{2}{c}{**X**} & \multicolumn{2}{c}{**Y**} & \multicolumn{2}{c}{**X**} & \multicolumn{2}{c}{**Y**} \\ \hline \multirow{3}{*}{\(RI_{PCA}\)} & \multirow{3}{*}{Slight} & 5.342 & 3.936 & 5.342 & 3.936 & 5.342 & 3.936 \\  & & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) \\  & & 5.100 & 3.799 & 5.083 & 3.801 & 4.537 & 3.004 \\  & & (\(-4.20\%\)) & (\(-4.14\%\)) & (\(-4.84\%\)) & (\(-3.43\%\)) & (\(-15.01\%\)) & (\(-23.68\%\)) \\  & & 4.968 & 3.782 & 3.194 & 2.348 & 1.114 & 0.876 \\  & & (\(-7.00\%\)) & (\(-4.56\%\)) & (\(-40.21\%\)) & (\(-40.35\%\)) & (\(-79.15\%\)) & (\(-77.74\%\)) \\ \hline \multirow{3}{*}{\(RI_{RP}\)} & \multirow{3}{*}{Slight} & 9.804 & 6.464 & 9.804 & 6.464 & 9.804 & 6.464 \\  & & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) \\  & & 9.480 & 6.200 & 9.322 & 6.208 & 8.374 & 5.233 \\  & & (\(-3.30\%\)) & (\(-4.08\%\)) & (\(-4.92\%\)) & (\(-3.96\%\)) & (\(-14.58\%\)) & (\(-19.04\%\)) \\  & & 8.879 & 6.122 & 4.862 & 3.198 & 2.256 & 1.635 \\  & & (\(-9.34\%\)) & (\(-5.29\%\)) & (\(-50.41\%\)) & (\(-50.53\%\)) & (\(-76.99\%\)) & (\(-74.70\)) \\ \hline \multicolumn{9}{c}{**Britic failure mechanism**} \\ \hline \multirow{3}{*}{\(RI_{PCA}\)} & \multirow{3}{*}{Slight} & 1.379 & 0.959 & 1.379 & 0.959 & 1.379 & 0.959 \\  & & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) \\  & & 1.144 & 0.632 & 1.003 & 0.413 & 0.829 & 0.222 \\  & & (\(-17.04\%\)) & (\(-34.09\%\)) & (\(-27.26\%\)) & (\(-56.93\%\)) & (\(-39.88\%\)) & (\(-76.85\%\)) \\  & & 0.937 & 0.299 & 0.442 & 0.241 & 0.123 & 0.054 \\  & & (\(-32.05\%\)) & (\(-68.82\%\)) & (\(-67.95\%\)) & (\(-74.87\%\)) & (\(-91.08\%\)) & (\(-94.37\%\)) \\ \hline \multirow{3}{*}{\(RI_{RP}\)} & \multirow{3}{*}{Slight} & 1.547 & 0.949 & 1.547 & 0.949 & 1.547 & 0.949 \\  & & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) & (0.00\%) \\ \cline{1-1}  & & 1.200 & 0.627 & 1.102 & 0.389 & 0.966 & 0.237 \\ \cline{1-1}  & & (\(-22.43\%\)) & (\(-33.93\%\)) & (\(-28.76\%\)) & (\(-59.01\)) & (\(-37.56\)) & (\(-75.03\%\)) \\ \cline{1-1}  & & 0.984 & 0.267 & 0.503 & 0.230 & 0.159 & 0.062 \\ \cline{1-1}  & & (\(-36.39\%\)) & (\(-71.87\%\)) & (\(-67.49\%\)) & (\(-75.76\%\)) & (\(-89.72\%\)) & (\(-93.46\%\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Risk indices obtained considering the \(i_{corr}\) values suggested by [54]. Figure 10: Variation in seismic risk index RI\({}_{\text{FGA}}\) for the brittle mechanism in the \(Y\) direction, for different exposure and climate scenarios at the bridge location.