esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### TOC and TOA Retrieval Models
* Another way how to further optimize the training LUT for operational mapping is by using sample distributions that reflect reality more, e.g., normal or log-normal distributions for key variables. A more refined LUT may be necessary to mitigate the drawback of the LAI saturation. It is expected that by refining the LUT, e.g., excluding unrealistic situations the LAI model will be greatly improved, e.g., that saturation only occurs at higher LAI (>6). This is also the strategy in the official S2 vegetation algorithms as found within the SNAP toolbox [95]. * There are some aspects of the obtained maps from L1C and L2A data that require clarification. For instance, the fact that L2A-retrieved Cab is more pronounced than the one from L1C might indicate that the atmosphere has still some impact on the Cab. Indeed, aerosol properties have some influence in the AOT (although according to the GSA results this influence is residual <5%). The same holds for LAI, since LAI is also sensitive to the visible part (not only in the SWIR). Regarding Cw, their similarities in the obtained L1C vs L2A maps can be explained from the GSA, since Cw is mostly impacting in the SWIR range, where outside the water absorption bands the atmosphere has little influence. In this respect, it can be understood that Cw achieves the same performance from L1C or from L2A data. * As a final remark, the TOA reflectance to TOA radiance conversion as well the Sen2 Cor TOA (L1C) to BOA (L2A) conversion is done with routines based on the libRadtran RTM. These differences may lead to discrepancies as compared to the here used MODTRAN routines. For instance, the S2 processing uses the Thuillier [96] solar irradiance, while MODTRAN uses the Kurucz [97]. The role of using different atmospheric RTMs in atmospheric correction and in TOA radiance biophysical variables retrieval is yet to be investigated. Given these topics for improvements, it would be premature to apply the obtained models into an operational context, but that is also not the aim of this study, as here the tools have been created to facilitate the developments of hybrid (i.e., RTM-based) TOA retrieval algorithms. It is foreseen that in follow-up studies the processing chain will be applied for dedicated TOA-based mapping applications.
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 6 Simulations and Measurements Results
### Efficiency  
Antenna efficiency is the term used to describe the relationship between the amount of radiated power and the power delivered to the antenna. Antenna efficiency often helps identify any problems with the antenna design itself, and also helps identify other factors that may interfere with the antenna's ability to receive signals efficiently. For the proposed antenna, only the simulation process was performed to determine the antenna efficiency as a function of frequency. The efficiency value of the proposed antenna as a function of frequency is shown in Figure 11. The proposed antenna has a high energy efficiency in the range of 80-96.68%.
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 6 Performance Evaluation Results
### Results of Pollution Use Case Evaluation
#### 6.2.2 VSL in Previous Sections  
For the pollution use case, tests applying the strategies to previous sections have also been conducted. Unlike in the first tests, speed limits were only changed to 60 km/h, 70 km/h, and 80 km/h. The reason for that is that these speeds are the ones providing the most convenient results for this use case, so there is no need to test all the other options. For each of these speed strategies, the speed was modified in the selected section, and in one, two, or three previous sections.  
In Figure 12, we observe the total results for section N_5 in the northbound direction, without any scaling factor. As the number of previous sections increases, the average speed decreases, although the variation is not significant, and the same happens with the speed deviation. The intensity values hardly experience significant changes. Total travel  
Figure 11: Pollution simulation results for variable speed limit strategy.  
time values grow when the speed limit is modified in a higher number of sections, and it increases even more as the speed decreases. Then, in the pollution KPIs graphs, the opposite behavior is observed for the CO, NO\({}_{\times}\) and PM\({}_{2.5}\) parameters. These indicators decrease with every increase in the number of sections modified. Furthermore, they keep reducing with every 10 km/h interval decrease.  
Analyzing the general behavior, we could say that the KPIs benefit from different strategies. While total travel time and average speed improve with higher speed limits and lower number of sections, the pollutants enhance their performance with lower speed limits and higher number of sections. Subsequently, the decision would be a trade-off, prioritizing a strategy with a high number of previous sections and a low or intermediate speed limit. For the pollutants, two possible options would be a 60 km/h limit in the section and in three previous ones (60_3), or a 70 km/h limit in the section and in three previous ones (70_3). To avoid significantly deteriorating the traffic-related KPI values, the 70_3 strategy would be the preferred choice.
## 6 HVDC Converters
### System Restorations with VSC-HVDC  
Even though VSC-HVDC technologies serve the main purpose of transmitting bulk power over great distances, several characteristics of their behavior make them excellent tools for participating in activities other than AC/DC power conversion and transmission [17]. VSC-based systems are able to feed networks with no local generation at the moment [55] and to control, in an easy and fast way, active and reactive power independently [56].  
Justifying auxiliary purposes for increasing the development of VSC-HVDC technologies may boost their maturity, as any additional benefit that a HVDC link can deliver to the grid will increase its attractiveness and, therefore, will speed up the spread of this type of link on actual grids. The next section of this paper explores the ancillary services and support activities that a VSC-HVDC link, be it single or multi-terminal, can provide to the grid during restoration events.
## 3 Nonlinear Model Predictive Control Formulation
### Laguerre Functions  
The Laguerre functions are the set of the orthonormal basis functions, and they will be used to approximate the control signals to decrease the computational time of the model predictive control [28; 29]. The set of Laguerre functions can be arranged in the matrix form as  
\[\mathbf{L}(t)=e^{\mathbf{A}_{l}t}\mathbf{L}(0) \tag{18}\]  
where:  
\[\mathbf{A}_{l}=\left[\begin{array}{cccc}-p&0&\cdots&0\\ -2p&-p&\cdots&0\\ \vdots&\vdots&\ddots&\vdots\\ -2p&\cdots&-2p&-p\end{array}\right]_{N\times N},\mathbf{L}(0)=\sqrt{2p}\left[ \begin{array}{c}1\\ 1\\ \vdots\\ 1\end{array}\right]_{N\times 1},\ p>0 \tag{19}\]  
in which \(p\) is a scaling factor which implies the exponential decay rate; \(N\) is the number of coefficients. As \(N\) increases, the estimated input signal is closer to the real value. Figure 1 shows an input signal approximated by using the Laguerre functions. It is worth to note that the Laguerre functions have the property of orthonormality.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Transformation** & **JL** & **FL** &
\begin{tabular}{c} **SDC** \\ **Parameterization** \\ \end{tabular} & **TSF Model** \\ \hline Redefine states and & & & & \\ inputs & & & & \\ Linear or linear-like & & & & \\ Approximated or & & & & \\ same representations & & & & \\ Condition satisfied & & & & \\ Design flexibility & & & & \\ Formulation & & & & \\ complexity & & & & \\ Computation cost & & Low & Low & High & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of the four transformations.
# Comparative Study Between Active AMD and ABS Devices by Using \(\mu\)-Synthesis Robust Control
## 3 Description of the Model
### Simulations and Analysis  
Using MATLAB software, we developed two controllers based on synthesis theory to control the described uncertain structure model. The two active systems considered in this study were AMD and ABS.  
Concerning uncertainties, to show that the controllers we designed were strong, as we already talked about, we looked at the specific case of parametric and dynamic uncertainties affecting the design of the controllers. When large calculation errors occur in certain parameters, like stiffness, it becomes crucial to protect the structures, as this parameter significantly affects the structure's rigidity. We assumed 30% of the errors in the stiffness of each floor, modeling these uncertainties as additive errors.  
When considering dynamic uncertainties, we specifically took into account the errors in modeling the dynamics of the actuators. The actuators served as active systems, translating the calculated control into a force that directly acted on the structure to counteract the seismic effect. Because they created a mechanical force that caused a non-negligible dynamic, mistakes in the calculation of this dynamic were an important part of the study to make sure that the controller would work well in real-world situations. \(W_{unc}\) models these errors as multiplicative input errors.  
The computation of the two controllers was based on the measurements of the feedback state vector, which included the displacements and velocities of the three floors. The first controller, referred to as \(\mu_{\text{C-MD}}\) for simplicity, synthesized and treated the case of an AMD system placed on the last floor, which was the suited place for this type of system. The second controller, known as \(\mu_{\text{C-BS}}\), addressed the ABS on the first floor, which experienced the highest deformation.  
Figure 6 represents the seismic signal acceleration of the El Centro earthquake (1979), to which the structure must submit, in the time domain. The examination of this figure informs the maximal acceleration (0.3 g) and the duration (45 s) of the shaking of the earthquake considered in this work.  
We computed both the uncontrolled and controlled responses of the excited structure based on the previously described assumptions. The figures presented below illustrate the simulation of the effect of the selected ground acceleration on displacements and accelerations of the studied structure before and after control in the time domain. We also compute and compare the control forces provided for each type of control system.
# Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System
## 3 Proposed Sensors
### Triangulation
The error in Figure 6 is illustrated up to a distance of 4 \(\times\) 10\({}^{8}\) km from the Sun, where each celestial source is measured by the sensor up to an accuracy \(\sigma_{\theta}\) of 0.01\({}^{\circ}\). This is similar to the performances delivered by the star tracker described in Section 3.1, where the technique of measuring each celestial body source is very similar. These results make an assumption that the planets are constrained to the solar system horizontal plane. The DOP of the plane varies from 10\({}^{8}\) to 10\({}^{10}\), and thus the illustrated best case position error would be 10 km in certain regions, moving towards 1,000 km in others. This performance would be unsuitable for localised orbit determination, but perhaps is applicable for navigation in interplanetary travel.
## 4 The Principle of the Balanced Cross-Layer Fuzzy Logic Routing Algorithm
### The Design of the Fuzzy Logic Inference System  
Fuzzy logic has the advantages of easy implementation, robustness, and ability to approximate to any nonlinear mapping [41,42]. It is relatively simple to convert knowledge of domain experts to control algorithms. The design of a fuzzy-logic controller starts with constructing the membership functions for linguistic input/output and if-then rules. In BCFL, the input of the fuzzy logic inference system is the parameter dispersion and the output is the cross-layer parameter weight. The new fuzzy logic inference system is a single input-single output system, which is shown in Figure 3. In the rest of this section, we will introduce the membership functions and the if-then rules of BCFL in detail. The input and output membership functions are shown in Figure 4, and the if-then rules are shown in Table 4. The linguistic variables of input and output are: _very small (vsmall), medium small (msmall), small, medium, large, medium large (mlarge), very large (vlarge)_, which are shown in Table 4.  
As shown in Table 4, the large dispersion has small weight and the small dispersion has large weight. The reason is that the parameter whose dispersion is large will have a greater effect on the algorithm performance; however, in the BCFL, we intend to get the most balanced solution, so this situation should be avoided as much as possible. Therefore, in the new routing algorithm, the larger the dispersion is, the smaller value of \(\alpha_{n}\).  
Figure 2: (**a**) The result of the traditional fuzzy logic-based algorithm (used in [19,20]) with the raw data; (**b**) The result of the new routing algorithm with the data in Table 2.  
From Figure 4 and Table 4, we can conclude that the if-then rules and membership functions will keep constant even when the network condition and topology change; moreover, the if-then rules are very simple in this algorithm. The BCFL is easy to design and is robust. On the contrary, in the traditional fuzzy logic-based routing algorithm (the algorithm used in [18,19]), the if-then rules will become unacceptable with the increasing influence of the parameters. In addition, when the network condition or the topology changes, the if-then rules need to be redesigned, which is not flexible.  
Once inputting the parameters' dispersion into the fuzzy logic system, the fuzzy logic inference will set the weights for each parameter based on the dispersion, which will be used to calculate the utility of each node.
## 3 System Overview  
The work presented in this study is based on a modified Google's Cartographer technique. Cartographer is a SLAM method which focuses on real-time performance and portability. It uses grid map-based representation with flexible resolution and sensor choices. The Cartographer technique is divided into two processes. The first process is called local SLAM which uses a Ceres scan matcher on a small map, known as a submap, to estimate the pose and orientation of the vehicle. The second process is known as global SLAM which optimizes the pose by utilizing a larger scan matcher on global maps (a global map generated from assembling all submaps). Some modifications are made to the Cartographer's local SLAM integrated with the AMDS to control the sensor distances, and the scan matcher's search window during map generation and pose generation.
# Punching Shear Strength of Reinforced Concrete Flat Plates with GFRP Vertical Grids
## 2 Shear Strength Equations
### Eurocode 2  
In Eurocode 2, the critical section is estimated as 2d away from the column face in the concrete flat plates without shear reinforcement. The critical section is on the perimeter 1.5d away from the end of the shear reinforcement in the concrete flat plates with shear reinforcement. Unlike in ACI 318 and BS 8110, in Eurocode 2 the corners of the critical section form four quadrants. Figure 2c shows the Eurocode 2 critical section. [PERSON] and [PERSON] [17] reported that the shear strength was overestimated when the ACI 318 punching shear strength equation included a low flexural reinforcement ratio and when the slab was very thick. In addition, in the case of low flexural reinforcement, the shear strength was reduced. Considering the depth of the slab and the amount of the flexural reinforcement, Eurocode 2 considers the size effect of the effective depth of the slab as \(1+\sqrt{200/d}\) and proposes a shear strength equation that considers the effects of the tension reinforcement ratio and the concrete compression strength.  
The shear strength of Eurocode 2 is determined by the sum of the shear strength of the concrete and shear reinforcement (Equation (1)). The shear strength of the concrete is described in Equation (6). The shear strength of the shear reinforcement can be computed by multiplying by 1.5 as shown in Equation (7).  
\[V_{c,EC2}=0.75*C_{Rd,c}\cdot k\cdot(100\cdot\rho_{l}\cdot f_{ck})^{\frac{1}{3}}b _{0}d. \tag{6}\]  
\[V_{f,EC2}=1.5 nA_{f}f_{fu} \tag{7}\]  
where \(V_{c,EC2}\) is the nominal shear strength provided by the concrete (according to Eurocode 2) (kN) and \(V_{f,EC2}\) is the nominal shear strength provided by the GFRP plate (according to Eurocode 2) (kN).
## 2 Related Work  
### Object Detection Method Based on a Deep Convolutional Neural Network  
Since the United States launched the first SAR satellite in 1978, scholars from various countries have proposed various ship detection methods for SAR images. Scholars from various countries have proposed traditional ship detection methods based on CFAR [16], visual saliency [17] and polarization decomposition [18] for SAR images.  
Deep learning improves traditional object detection methods [19]. The current deep learning-based target detection methods are divided into two-stage target detection and one-stage target detection. The fast region-based convolutional neural network (Fast R-CNN [20]) is a typical two-stage object detection method. Compared with traditional object detection methods, the speed and accuracy of two-stage object detection have been greatly improved, but they still cannot meet the real-time requirements.  
The algorithm based on convolutional neural network (CNN) has the ability to automatically extract deep features of images, showing high robustness and efficiency. Researchers try to use the excellent characteristics of CNN to promote CFAR. [PERSON] et al. creatively used the region proposal generated by Faster R-CNN [21] as the protection window of CFAR, and combined Faster R-CNN with CFAR to detect small-sized ships [22]. [PERSON] et al. used the CNN (SLS-CNN) detector based on land-ocean segmentation for accurate ship detection [23]. These experiments verify the feasibility of CNN in SAR ship detection. The structure of You Only Look Once (YOLO) [24; 25; 26] is that of a typical one-stage object detection method. The one-stage target price detection algorithm ignores the candidate frame generation stage, regards location information as potential targets, and tries to divide each area into detection targets and backgrounds. YOLOv5 [27] with a lightweight model size is basically on par with YOLOv4 [28] in terms of detection accuracy.  
In recent years, artificial intelligence technology has emerged in the field of image target detection [29; 30]. With the introduction of machine learning methods in the field of radar remote sensing, many new solutions and ideas have been introduced into the field of SAR image ship target detection.  
[PERSON] et al. [31] proposed an enhanced graphics processing unit (GPU)-based deep learning method for ship detection in SAR images. [PERSON] and [PERSON] [32] proposed an improved YOLOv4-Tiny detection algorithm. The improved algorithm introduced attention mechanism units to strengthen feature extraction, which was more prominent.
## 3 Results and Discussion
### Physicochemical Parameters of Cucumber Flesh during Storage
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Day 1** & **Day 7** & **Day 13** & **Day 16** & **Day 22** & **Day 28** \\ \hline aw & 0.9934 \(\pm\) 0.0028 a & 0.9910 \(\pm\) 0.0021 ab & 0.9890 \(\pm\) 0.0019 ab & 0.9885 \(\pm\) 0.0023 ab & 0.9897 \(\pm\) 0.0012 ab & 0.9882 \(\pm\) 0.0018 b \\ \hline Moisture & 96.10 \(\pm\) 0.22 a & 95.48 \(\pm\) 0.32 b & 95.34 \(\pm\) 0.52 b & 95.21 \(\pm\) 0.38 b & 95.32 \(\pm\) 0.29 b & 94.44 \(\pm\) 0.52 c \\ \hline Firmness (N) & 14.87 \(\pm\) 1.22 ac & 18.07 \(\pm\) 1.15 b & 17.18 \(\pm\) 1.58 ab & 18.00 \(\pm\) 1.75 b & 13.60 \(\pm\) 1.15 c & 9.03 \(\pm\) 0.88 d \\ \hline L* inner & 63.34 \(\pm\) 1.47 a & 68.42 \(\pm\) 3.09 b & 71.37 \(\pm\) 2.47 bd & 68.90 \(\pm\) 2.01 bc & 66.51 \(\pm\) 2.45 c & 73.87 \(\pm\) 2.64 d \\ \hline A* inner & \(-\)11.11 \(\pm\) 0.49 a & \(-\)10.48 \(\pm\) 0.75 a & \(-\)9.01 \(\pm\) 0.69 b & \(-\)8.54 \(\pm\) 0.50 b & \(-\)7.28 \(\pm\) 0.53 c & \(-\)7.47 \(\pm\) 0.73 c \\ \hline B* inner & 21.33 \(\pm\) 1.06 a & 21.55 \(\pm\) 1.54 a & 20.02 \(\pm\) 1.29 ab & 18.25 \(\pm\) 1.13 bd & 16.17 \(\pm\) 0.72 c & 17.31 \(\pm\) 1.42 cd \\ \hline h inner & 117.65 \(\pm\) 0.39 a & 115.70 \(\pm\) 0.80 b & 114.87 \(\pm\) 0.73 bc & 115.06 \(\pm\) 0.48 bc & 114.27 \(\pm\) 1.06 bc & 112.40 \(\pm\) 1.49 c \\ \hline \end{tabular} Different letters in the same row present statistically different values (\(p<0.05\)).  
\end{table} 
Table 4: Water activity, moisture content, firmness, and color parameters (L*, a*, b*, h) of the cucumber flesh over the storage period of 1, 7, 13, 16, 22, and 28 days. activity of tissue-degrading enzymes, as well as moisture content decrease during storage, results in flesh softening [12; 42; 43; 44].
## 4 Simulation Results and Discussion
### Superior Optimization Method
#### 4.2.2 Network Loss and Deviations  
Figure 10 shows the power loss changes in the electricity, heating, and gas markets. Due to the constant gas power in the hub and gas station, gas power losses are constant. According to Figure 10, the heating power loss from 1:00 to 15:00, which is low, has an increasing behavior over time, from 16:00 to 24:00. It can also be seen that the electric power loss is high between 1:00 and 7:00, which is due to the high demand of the EH during these hours.  
In Figure 11, the daily changes in voltage deviation, pressure, and temperature in EGHNs are presented. For the gas network, the rate of change of 0.1 p.u for 24 h is reported because of the high gas demand in the EH. The temperature difference is also low for the hours from 1:00 to 15:00 and for the hours from 16:00 to 24:00, which is 0.1 p.u. In addition, the maximum voltage deviation occurred due to the demand of the EH and the power system in the time period 1:00 to 15:00 and 23:00 to 24:00, and the lowest voltage deviation occurred in the hours of 16:00 to 22:00, because of the delivery of active and reactive power above the hub into the power grid has been achieved.