esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In this work, solar concentrating heliostat fields are modeled using accurate solar-tracking algorithms and a wide range of rotation models to investigate the parameters controlling the mechanical efficiency of these solar facilities. Iterative procedures are first described to determine the rotation angles needed to properly orient a single heliostat for the most commonly used mechanical models, including the azimuth-elevation, tilt-roll, and target-aligned models. These mathematical techniques were integrated into our Light Analysis Modelling (LAM) software and used to study a realistic heliostat field with six different mechanical rotation models for the full year of 2024 and a daily working range of 08:00-16:00 Local Time (LCT). Two locations were chosen, representing the highest and lowest latitudes from the SFERA-III EU list of solar concentrating facilities with heliostat fields: Julich (Germany) and Protaras (Cyprus). The results obtained show that tilt-roll models require less angular rotation (\(-15.2\%\) in Julich, \(-20.2\%\) in Protaras) and a narrower angular range (\(-14.5\%\) in Julich, \(-20.2\%\) in Protaras) than azimuth-elevation models. Seldom-used target-aligned models are more efficient with tilt-roll rotations (compared with the tilt-roll model: \(-35.1\%\) rotations in Julich, \(-29.2\%\) in Protaras; \(-12.3\%\) angular range in Julich, \(-14.3\%\) in Protaras) and less efficient with azimuth-elevation rotations (compared with the azimuth-elevation model: \(+53.2\%\) rotations in Julich, \(+39.2\%\) in Protaras; \(+96.2\%\) angular range in Julich, \(+87.5\%\) in Protaras).  
heliostat mechanical models; azimuth-elevation; tilt-roll; target-aligned; heliostat modelling; heliostat rotation; ray-tracing simulations 2020
## 3 Results and Discussion
### Chemical Characteristic of Mashes and Fermentation Results
The dextrin content in the mashes prepared from wheat bread did not exhibit a statistically significant difference (\(p\leq 0.05\)) between the SHF and SFF methods. Conversely, the SHF method resulted in a higher dextrin content in the mashes made from wheat-rye bread than the SSF method. It is likely that the high concentrations of liberated fermentable sugars may have caused product inhibition of the activity of the enzymes responsible for catalysing starch hydrolysis [37]. The native starch hydrolysis method without starch 'activation' resulted in the highest level of dextrin being found in the wheat bread-based mash. An application of initial 'activation' of starch during the preparation of wheat-rye bread-based mashes significantly improved the initial degree of starch saccharification (\(p<0.05\)), which in turn resulted in a decrease in dextrin concentration in comparison to the samples without starch 'activation' (\(p\leq 0.05\)) (Table 2). It is due to the disruption of the bonds between the glucose molecules in the starch chain that occurs during the activation process [38]. The highest concentration of dextrin (68.50 \(\pm\) 0.09 g/L) was found in the wheat-rye-based mash prepared using the NSH-N/A method. It may be supposed that one of the probable reasons of lower results of starch hydrolysis is, apart from the lack of its 'activation', the ongoing starch retrogradation, which initially involves the rapid recrystallisation of amylose molecules and is followed by a slow recrystallisation of amylopectin molecules [39]. Moreover, wheat starch exhibits a lower degree of retrogradation in comparison to rye due to the presence of higher levels of phosphorus (phospholipids) [40], which provides an explanation for the observed differences in the degree of starch hydrolysis in the wheat and wheat-rye bread-based mashes. The chemical analysis of fermented mashes entailed the determination of pH, apparent extract, as well as the concentration of ethanol, reducing sugars (glucose, maltose, and maltotriose) and dextrins. Additionally, the concentration of other sugars (xylose, arabinose) as well as organic acids and glycerol was determined (see Table 3). During the process of fermentation, yeast secretes H\({}^{+}\) ions, which causes a decline in pH levels within the medium. The pH value of the waste wheat bread-based mashes after process completion decreased from 5.1 up to 4.3, whereas for wheat-rye bread-based mashes, the pH value decreased from 4.5 up to 4.1. These findings are consistent with the data presented in the literature and confirm the correct duration of fermentation [41].
## 4 Results
### Crop Parameters Retrieval Validation
#### 4.2.3 Chlorophyll Retrieval Validation  
Figure 10 illustrates the accuracy of chlorophyll retrieval, represented by the ratio T850 nm/T720 nm and based on the GCVI, utilizing various angular correction methods. When examining the overall chlorophyll retrieval accuracy across the five correction methods, it is observed that, apart from the VJB method, the distinctions among the remaining methods were not pronounced. Specifically, the variance in R\({}^{2}\) and RMSE among these four methods was minimal, with differences of only 0.003 and 0.01, respectively. The VJB method yielded the poorest accuracy for chlorophyll retrieval, which is even lower than that achieved using the original reflectance data. In contrast, the Fixed-Jilin-1 method outperformed the others, showing an increase in R\({}^{2}\) of 0.12 and a decrease in RMSE of 0.013 when compared to the chlorophyll retrieval results from the original reflectance.  
In general, the angular effect exerted a negligible influence on the accuracy of chlorophyll extraction. However, when the chlorophyll ratio (T850 nm/T720 nm) exceeded 1.2, the chlorophyll estimates derived from the original reflectance tended to be higher than  
Figure 7: NIR band reflectance validation based on field measured data. The _X_-axis is the measured reflectance, the _Y_-axis is the corrected reflectance, the black solid line is the 1:1 line, and the different color scatter points represent the different observation geometry. (**a**) Original, (**b**) MODIS-based, (**c**) AFX, (**d**) VJB, (**e**) Fixed-Landsat, And (**f**) Fixed-Jilin-1.  
the measured values. This discrepancy was significantly mitigated following angular correction, particularly with the Fixed-Jilin-1 method.  
Figure 8: LAI validation based on field measured data. The \(X\)-axis is the measured reflectance, the \(Y\)-axis is the corrected reflectance, and the black solid line is the 1:1 line. (**a**) Original, (**b**) MODIS-based, (**c**) AFX, (**d**) VJB, (**e**) Fixed-Landsat, and (**f**) Fixed-Jilin-1.  
Figure 9: FVC validation based on field-measured data. The \(X\)-axis is the measured reflectance, the \(Y\)-axis is the corrected reflectance, and the black solid line is the 1:1 line. (**a**) Original, (**b**) MODIS-based, (**c**) AFX, (**d**) VJB, (**e**) Fixed-Landsat, and (**f**) Fixed-Jilin-1.  
In summary, aside from the VJB method, the remaining four methods for angular correction were shown to enhance the retrieval accuracy of the LAI, FV, and chlorophyll content. The MODIS-based, AFX, and Fixed-Landsat methods exhibited comparable performance in improving the retrieval accuracy of these crop parameters. Notably, the Fixed-Jilin-1 method demonstrated superior accuracy in retrieving the LAI, FVC, and CHL across the board.  
Furthermore, the retrieval of the LAI, FVC, and CHL exhibited varying sensitivities to the angle effect. This effect also influenced the retrieval accuracy of crop parameters differently, depending on the specific growth stage of the crops.
## 8 Introduction to Proprietary and Standards-based Solutions
### Standards-Based Technologies
#### 8.2.4 LTE-M  
LTE-M is also a 3 GPP standard-based technology and operates in the licensed LTE spectrum. It is compatible with LTE networks and provides the connection for machine to machine (M2M) traffic. Also, the migration path from legacy 2G and 3G networks is available. It provides extended coverage as compared with LTE networks, coverage for M2M applications similar to 5G networks, and offers a seamless path towards 5G M2M solution [8]. LTE-M is focused on providing variable data rates and support for both real-time and non-real time applications. It supports low latency applications, as well as deferred traffic applications that can operate with latencies in the range of a few seconds. It has low power requirements and supports operations ranging from low bandwidth to bandwidth as high as 1 Mbps. It also supports devices with a very wide range of message sizes. As it is derived from LTE as a base, mobility is supported as part of standard LTE functionality, but not in extended coverage scenarios [33]. It is software upgradable from LTE. It has capacity for up to 100,000+ devices per base station for applications where devices have very low data-throughput requirements.
## 3 Mathematical Model for Optimal Trajectory Planning
### Constraint Function Design
#### 3.3.4 Operational Area Constraint (OAC)  
For feasible path planning, the UAV must stay inside the operational area to avoid ambiguity. Thus, a negative penalty P is added in the constraint function if any unwanted event occurs. The operational area constraint of a mission can be formulated as follows:  
\[OAC=0,\;OAC=\sum_{i=1}^{N_{w}}OAC_{i} \tag{22}\]where,  
\[OAC_{i}=\left\{\begin{array}{c}0,\,if\,\,\text{waypoint}\,\,in\,\,map\big{(}x_ {j},y_{j}\big{)}\\ P,\,\text{otherwise}\end{array}\right.\]  
where,  
\[range(x_{j},y_{j})\,\,=\,\,\{l_{x}\leq x_{j}\leq u_{x}\}\,\cap\,\{l_{y}\leq y_{ j}\leq u_{y}\}\]  
where \(N_{w}\) is the number of waypoints, \(l_{x},l_{y}\) are the lower bounds of \(x\) and \(y\), \(u_{x}\), \(u_{y}\) is the upper bounds of \(x\), and \(y\), respectively.
# Mobile Charging Scheduling Approach for Wireless
###### Abstract  
Wireless energy transfer technology (WET)-enabled mobile charging provides an innovative strategy for energy replenishment in wireless rechargeable sensor networks (WBSNs), where the mobile charger (MC) can charge the sensors sequentially by WET according to the mobile charging scheduling scheme. Although there have been fruitful studies, they usually assume that all sensors will be charged fully once scheduled or charged to a fixed percentage determined by a charging upper threshold, resulting in low charging performance as they cannot adjust the charging operation on each sensor adaptively according to the real-time charging demands. To tackle this challenge, we first formulate the mobile charging scheduling as a joint mobile charging sequence scheduling and charging upper threshold control problem (JSSTC), where the charging upper threshold of each sensor can adjust adaptively. Then, we propose a novel multi-discrete action space deep Q-network approach for JSSTC (MDDRL-JSSTC), where [PERSON] is regarded as an agent exploring the environment. The state information observed by MC at each time step is encoded to construct a high-dimensional vector. Furthermore, a two-dimensional action is mapped to the charging destination of MC and the corresponding charging upper threshold at the next time step, using bidirectional gated recurrent units (Bi-GRU). Finally, we conduct a series of experiments to verify the superior performance of the proposed approach in prolonging the lifetime compared with the state-of-the-art approaches.  
mobile charging; multi-discrete action space; deep Q-network; Bi-GRU; wireless rechargeable sensor networks +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### The Molecular Electrostatic Potential (MEP)  
The MEP can be calculated using the following formula [66]:  
\[V(r)=\sum_{A}\biggl{(}\frac{Z_{A}}{|R_{A}-r|}-\int\frac{\rho(r)dr^{\prime}}{r^{ \prime}-r}\biggr{)}\]  
where \(Z_{A}\) denotes the charge of the nucleus A located in \(R_{A}\) and \(\rho(r)\) is the density of electric charge. It displays probable regions for the electrophilic interaction of charged reagents with a given molecule [68; 69]. The total electron density as well as the charge density of DIT-SAL are shown in Figure 15.  
For the DIT-SAL molecule, the negative charges are mainly localized on the oxygen atoms, which have a higher electronegativity value and consequently have a higher electron density around them. The molecular electrostatic potential surface shows that the negative potential site is around the electronegative oxygen atoms and the positive potential sites are around the hydrogen and carbon atoms, as can also be seen from the distribution of charge. A pronounced area with negative values of potential and charge density is around the ester  
Figure 15: Distribution of total charge density and mean electrostatic potential (MEP) around DIT-SAL molecule. Positive and negative values are depicted in red and blue, respectively.  
bond, which may allow for the attack of polarized water (OH\({}^{-}\)...H\({}^{+}\)) on carbonyl oxygen, leading to esterase-catalyzed DIT-SAL hydrolysis. Carboxyl esterases, acetylcholinesterase,cholinesteraseerases, butyrylcholinesteraseerases, paraoxonases, arylesterases, and biphenyl hydrolase-like protein (BPHL) are examples of enzymes that are responsible for the hydrolytic bioactivation of ester prodrugs.
## 3 RUL Prediction Methodology
### Bayesian RUL Prediction Based on Extended Kalman Filter  
#### 3.4.1 Definition of Bearing RUL  
Based on the illustration in Figure 3, the RUL of bearings is a time-dependent variable defined as the length from the current time \(t\) to the end-of-life time \(t_{EOL}\) jointly determined by the selected health indicator, ESI, and the EOL threshold \(\overline{\gamma}\),  
\[\mu l_{t}=t_{EOL}-t\ (with\ t_{EOL}\geq t). \tag{7}\]  
In practical prediction of RUL, the estimated EOL time \(\overset{\sim}{t}_{EOL}\) is extrapolated as the time when the health indicator reaches the EOL threshold. Hence, let \(x_{t}\) be the state of bearing degradation at time \(t\); the estimated RUL at time \(t\) is derived as the interval from \(t\) to the estimated EOL time \(\overset{\sim}{t}_{EOL}\), or the inferior limit of \(\Delta T\) that drives the degradation state to exceed the threshold \(\overline{\gamma}\), i.e.,  
\[\overset{\sim}{\mu l_{t}}=\overset{\sim}{t}_{EOL}-t=inf\{\Delta T:x_{t+\Delta T }\geq\overline{\gamma}\}. \tag{8}\]  
Figure 3: An example of bearing degradation process. Bearing health indicator vs. operation time. The bearing lifetime is typically divided into the healthy stage and the degradation stage.
# Adaptive Neural-Network-Based Lossless Image Coder with Preprocessed Input Data
## 6 Experiments  
The performance of the new algorithms is compared to that of some other methods. Table 2 shows the advances in AdNN performance caused by the new concepts; additionally, data for two popular fast codecs are presented, JPEG-LS and CALIC [48]. The list of images form a widely used benchmark for the comparison of lossless image coders [49]; see Figure 5. The new AdNN versions are better, while less computationally complex than the previous ones (see Section 5) and outperform JPEG-LS by approximately 10%. The AdNN codec is clearly better for images with a high variance of pixel values (e.g., noisesquare), while for artificially generated ones the difference is smaller (shapes). Data for another simple and popular codec, WebP, can be found in Table 3; its performance and complexity are of the same rank as JPEG-LS. Table 3 presents a comparison of the new AdNN algorithms with up-to-date ANN-based codecs; the table is an extension of the appropriate table from [17]. Note that the LCIC, L3C, CWPLIC, and LCIC duplex are deep-learning ANN algorithms. As can be seen, there is only one image (Comic) for which a pair of codecs considered in [17] outperform the new algorithms (CWPLIC, LCIC duplex). As a result, the mean performance of the new codecs is the highest. When realized on CPU, the Concept 2 codec is faster; however, as over 99% of Concept 1's complexity is linked with AdNN on-line training, advances in the implementation of ANNs may revert this relation.  
Figure 5: Some images used in the experiments: coastguard, noisesquare, monarch, ppt3, zebra, flowers, pepper, mandrill, shapes.
# Enhancing Heterogeneous Communication for Foggy Highways Using Vehicular Platoons and SDN
###### Abstract  
Establishing a safe and stable routing path for a source-destination pair is necessary regardless of the weather conditions. The reason for this is that vehicles can improve safety on the road by exchanging messages and updating each other on the current conditions of both roads and vehicles. This paper intends to solve the problem of when foggy roads make it difficult for drivers to travel, especially when people encounter emergency situations and have no other option but to drive in foggy weather. Although the literature offers few solutions to the problem, no one has considered integrating software-defined networking into vehicular networks for foggy roads to create an optimal routing path. Moreover, it is of significance to mention that vehicles in adverse weather conditions travel following each other and maintaining a constant safety distance, which leads to the formation of a platoon. Considering this, we propose a heterogeneous communication protocol in a software-defined vehicular network to establish an optimal routing path using platoons on foggy highways. Different cases were tested to show how platoons behave in high connectivity and sparsity, achieving a maximum delivery ratio of 99%, a delay of 2 ms, an overhead of 55%, and an acceptable number of hops compared to reference schemes.  
Keywords:local controllers (LCs); main controller (MC); platoons; software-defined vehicular network (SDVN); vehicle-to-infrastructure (V2I); vehicle-to-vehicle (V2V) +
Footnote †: journal:
## 3 Analysis of Factors Affecting Shear Behavior of Reinforced Concrete Beams
### Effect of the Shear Reinforcement Ratio  
To investigate the effect of the shear reinforcement ratio on the shear behavior of strengthened beams, taking the strengthened beam B-1 as the reference, keeping the original parameters unchanged, the stirrup spacing was adjusted to 250 and 300 mm, with the corresponding reinforcement ratios being 0.15% and 0.13%. The parameters of the relevant specimens are shown in Table 3. Figure 13 is a comparison diagram of the load-midspan deflection curves of the strengthened beams under different shear reinforcement ratios. The detailed data and comparison are listed in Table 9. As the ratio increases, both the ultimate load of the strengthened beam and the effective strain of the FRP grid increase slightly, which intuitively reflects that the shear reinforcement ratio is positively correlated with both the shear behavior capacity and the grid strain. When the stirrup spacing decreases and the reinforcement ratio increases, the range of restraining diagonal cracks becomes larger, which can better delay the appearance and expansion of diagonal cracks. Compared with the reference beam B-1 with a shear reinforcement ratio of 0.19%, for beams B-S1 and B-S2 with ratios of 0.15% and 0.13%, the ultimate loads decrease by 2.37% and 4.51%, respectively. Thus, it can be seen that the influence of the shear reinforcement ratio on the shear behavior capacity of the strengthened beam is indeed smaller compared with that of the shear span ratio and the concrete strength. However, the grid strain change rates of beams B-S1 and B-S2 are \(-\)7.85% and \(-\)13.03%, respectively, indicating that the reinforcement ratio has a certain influence on the effective strain of the FRP grid, which cannot be ignored. Therefore, to ensure the correctness and practicality of the effective strain formula derived later, its effect should be taken into account.  
Figure 12: Effect of concrete strength.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Beam ID**} & **Concrete** & **Shear** & **Ultimate** & **Change** & **FR Grid** & \multirow{2}{*}{**Change**} & **Failure** \\  & **Strength** & **Cracking** & **Load (kN)** & **Load (kN)** & **Rate (\%)** & **Strain** & **Rate (\%)** & **Mode** \\ \hline B-1 & 37 & \(111\) & 328.27 & & 0.0054 & & SF \\ B-H1 & 41 & \(115\) & 331.23 & 0.90 & 0.0057 & 5.56 & SF \\ B-H1 & 33 & \(107\) & 319.66 & \(-\)2.62 & 0.0050 & \(-\)7.09 & SF \\ B-H2 & 29 & \(101\) & 311.92 & \(-\)4.98 & 0.0048 & \(-\)11.48 & SF \\ \hline \end{tabular}
\end{table}
Table 8: Effect of concrete strength on reinforced beams.
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 4 Experiment and Discussion  
In order to verify the effectiveness of the proposed method, a dynamic geomagnetic measurement experiment was carried out in an outdoor field with a stable geomagnetic environment. In the experiment, the magnetometer and inertial navigation system are combined to form a strap-down measuring system, which is mounted on the truss for dynamic measurement. Through the standard deviation of the geomagnetic vector dynamic measurement value and its comparison with the static measurement calibration value, the measurement accuracy and the effectiveness of the compensation method are verified. The procedure of the experiment is: (1) geomagnetic measurement system establishment; (2) geomagnetic field calibration; (3) dynamic measurement and compensation; and (4) compensated geomagnetic measurement value assessment.
## 4 Discussion  
Figure 14 shows the SCB time series extracted from the Undifferenced-SCB model. For the convenience of observation, the time series of each satellite is shifted to different degrees. As shown in the figure, although those SCBs are not stable constants, their time-varying characteristics are not dramatic. Therefore, the constant part of the SCB is usually absorbed by the satellite clock in estimation, and the time-varying part is driven into the code residual to generate stable clock products. On the one hand, the time-varying part of the SCB is not dramatic, and it can be driven into the code residual after a long time of convergence. On the other hand, it is also to eliminate the rank deficiency and obtain the full rank model. However, when the filtering system needs to converge again, the time-varying SCB will seriously damage the convergence speed and stability of the clocks.  
Figure 13: The mean convergence time for various PPP solutions from DOY 031 to 037.  
\begin{table}
\begin{tabular}{c c c c} \hline
**PPP Solutions** & **East** & **North** & **Up** \\ \hline IGS solution & 2.43 & 2.03 & 5.36 \\ Undifferenced solution & 6.61 & 4.49 & 12.14 \\ Undifferenced-SCB solution & 3.77 & 3.44 & 7.78 \\ \hline \end{tabular}
\end{table}
Table 5: The mean RMS (cm) for different PPP solutions when compared to IGS weekly solutions at 57 stations from DOY 031 to 037.  
The key to accelerating the convergence of the satellite clock to a stable state is to get rid of the disturbance of the time-varying part of the SCB. Additionally, there are two methods to solve this problem. The first is that a robust filtering model needs to be constructed to accelerate the time-varying part of the SCB being driven to code residual. The advantage of this method is that the estimated satellite clocks are consistent with that of the IGS, which avoids trouble at the user end. However, how to build a robust filtering model needs more research. The second method is to extract the SCB directly. The advantage of this method is that it can directly avoid the adverse effects of SCBs and other unmodeled satellite related errors on clocks, but the influence of SCB should be considered in PPP at the user end. The latter method is adopted in this study.  
At the user end, the full rank SCB extraction model cannot be constructed, as in the network processing. In addition, due to the contamination of the code observation, the SCB extracted from network processing cannot be used as a correction in PPP directly. In this study, the weight of the code equation is reduced to eliminate the negative effect of SCB, and experiments show that this is a simple and feasible strategy.