esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Photocatalytic Tests  
The photocatalytic efficiency of the TP-Hy/N-TiA and the TP-Hy/P25 coatings was tested by the colorimetric method using the Techkon Spectrodens colorimeter. The photodegradation of methyl orange (MeO, 0.1 mM) and methylene blue (MB, 0.1 mM), usually used as pollutant simulants in photocatalytic experiments, was monitored under artificial solar irradiation of a 300 W Ultra Vitalux lamp (Osram, GmbH, Munich, Germany). Stained samples were placed 14 cm from the lamp: the irradiance on the samples was \(\sim\)56 mW/cm\({}^{2}\) in the 300-800 nm range and \(\sim\)5.5 mW/cm\({}^{2}\) in the UVA + B range, as reported in [86]. To estimate the photocatalytic discoloration of MeO and MB stains over time, the normalized chroma change \(\Delta\)C\({}^{\star}\) at different exposure times t, was used:  
\[\Delta\)C\({}^{\star}(\%)=\sqrt{\frac{\left(a^{\star}(t)-a^{\star}(0)\right)^{2}+ \left(b^{\star}(t)-b^{\star}(0)\right)^{2}}{\left(a^{\star}_{c}-a^{\star}(0) \right)^{2}+\left(b^{\star}_{c}-b^{\star}(0)\right)^{2}}}\times 100 \tag{2}\]  
where \(a^{\star}(t)\) and \(b^{\star}(t)\) and \(a^{\star}(0)\) and \(b^{\star}(0)\) are the CIELAB space colorimetric coordinates at time \(t\) and \(t\) = 0, respectively, whereas \(ac^{\star}\) and \(b_{c}\)\({}^{\star}\) are the coordinates measured on clean stones [87]. At least nine regions of a few mm\({}^{2}\) areas were examined, and the results were averaged for three samples for each case.  
Two different photocatalytic experiments were performed. In one experiment, the TP-Hy/N-TiA and the TP-Hy/P25 treatments were applied directly to the _Lecce stone_ with and without the paint layer, and the organic pollutants, MeO and MB, were spread by brush on the treated stone. In this way, the coating's ability to prevent degradation due to dirt deposits was tested. In the other experiment, carried out only on painted stone, the organic pollutants were spread directly on the stone, and the treatments were applied on the stains only subsequently. In this way, the cleaning capacity of the coating was assessed.
## 1 Introduction
This innovative method, termed Beam Sweep-enabled Search and Rescue (BWSAR), offers a promising solution to enhance the precision and efficiency of search and rescue operations. BWSAR performs a three-stage SAR mission based on downlink Synchronization Signal Blocks (SSBs) and Positioning Reference Signals (PRSs). Initially, the drone flies towards the direction estimated by coarse-grained beam sweeping with SSBs. Afterwards, the drone launches finer-grained beam sweeping process with PRSs within the best direction of the initial process, in which case the three-dimensional coordinate of UEs can be estimated. Moreover, the paper proposes a trajectory updating algorithm to guide the drone to reach emergency areas as quickly as possible. Several simulation experiments are conducted and the results demonstrate that BWSAR performs good in a SAR mission. Our main contributions are highlighted as follows:  
* To expedite the SAR operation time, this paper introduces an innovative three-stage SAR methodology that harnesses the potential of 5G NR beams. By deploying an extensive array of antennas within 5G NR base stations, we enable the attainment of precise 3D positioning estimates for UEs, leveraging fine-grained beam sweeping angles. * An innovative 3D localization approach utilizing downlink PRSs from 5G NR base stations is introduced. To combat the issue of signal attenuation that frequently plagues disaster-affected regions, our method allows for the adaptive adjustment of downlink signal power based on the specific environmental conditions encountered. * This paper presents a UAV trajectory updating algorithm that meticulously guides UAVs towards disaster-stricken areas. By designing the drone's flight path, the proposed algorithm ensures that communication signals between 5G NR base stations and UEs propagate predominantly within the Line of Sight (LoS), thereby mitigating the detrimental effects of signal obstruction caused by obstacles prevalent in disaster-affected zones. The remaining sections of this paper are structured as follows. Section 2 summarizes the existing positioning methods based on Radio Access Technology (RAT) and the SAR methods using drones with 5G NR base stations. Section 3 presents the system model and problem formulation of this paper. Section 4 describes the proposed UAV trajectory progress updating algorithm. Several BWSAR simulation experiments are conducted and results are analyzed in Section 5. Finally, in Section 6, we provide concluding remarks and possible future research tracks.
# Acceleration of OCT Signal Processing with Lookup Table Method for Logarithmic Transformation
## Materials and Methods
### Lookup Table Method  
In our experiment, the structural signal after Fourier transformation varies in a range of 80 dB. Logarithmic transformation of the signal in such a large range based on the conventional calculation is time-consuming. In current system, it takes 13.3 ms to perform logarithmic transformation in a frame with 1024 \(\times\) 1000 pixels, which occupies more than 60 percent of total processing time. Since an excessive precision of logarithmic calculation is not necessary in grayscale imaging, a lookup table consisting of pre-calculated logarithmic values can be used to perform logarithmic transform.  
The recorded raw data \(Y\) can be expressed in scientific notation as \(Y=X\times 10^{n}\), in which the significand \(X\) is a decimal less than 10 and the integer \(n\) is the order of magnitude. Based on the properties of logarithmic calculation, the logarithmic value of \(Y=X\times 10^{n}\) can be expressed as:  
\[\log_{10}(X\times 10^{n})=\log_{10}(X)+n\]  
If the logarithmic value of \(X\) and the order of magnitude \(n\) are known, the logarithmic value of \(Y\) can be calculated by simply adding them together. The calculation accuracy is determined by the number of digits of \(X\). Considering the precision requirement of logarithmic calculation in OCT data processing is relatively low, the number of digits of \(X\) can be reduced correspondingly. Figure 2 shows the calculation process of the lookup method based on a table of 1000 components. To calculate the logarithmic value of \(Y=X\times 10^{n}\), the order of magnitude \(n\) and significand \(X\) were determined first  
Figure 1: Schematic of the swept source optical coherence tomography (OCT) system, ADC: analog to digital converter, PC: polarization controller.  
and then \(X\) was rounded to two decimal places. A table consisting of logarithmic values of integers from 1 to 1000 was built beforehand. Rounding of \(X\times 100\) was used as the address to look up in the pre-calculated table. The corresponding logarithmic value of \(Y\) can be obtained by adding \(n\)-2 to the lookup value.
## 3 Results and Discussion  
### Preliminary Washings with Sugarcane Wine and Pineapple Wine  
The results of this preliminary washing experiment are shown in Figure 2. The initial total TEQs of the soil is 10024.15 ng-TEQ/kg, which was contributed mostly by OCDF (5112.51 ng-TEQ/kg) and 1,2,3,4,6,7,8-HpCDF (3865.65 ng-TEQ/kg), while the TEQ concentration of OCDD was only 1046.00 ng-TEQ/kg. After the third wash, sugarcane wine washing resulted in 60.7% total TEQ removal, decreasing from 10024.15 ng-TEQ/kg to only 3938.74 ng-TEQ/kg, while the experiment with pineapple wine achieved only 50.2%, finishing at 4991.33 ng-TEQ/kg. After the last wash, the soil in the washing experiment with sugarcane wine contained approximate TEQ concentrations of OCDF (1586.62 ng-TEQ/kg) and 1,2,3,4,6,7,8-HpCDF (1824.73 ng-TEQ/kg). Similarly, the last washing in the experiment with pineapple wine also led to the soil being mostly occupied by OCDF (1985.16 ng-TEQ/kg) and 1,2,3,4,6,7,8-HpCDF (2329.11 ng-TEQ/kg). The removal efficiencies for each congener in the sugarcane wine washing were better than those of the pineapple wine washing, e.g., 1,2,3,4,6,7,8-HpCDF (52.8% versus 39.8%) and OCDD (49.6% versus 35.3%) (H value \(>\chi^{2}_{\text{U}}\)). The difference between the highest (69%) and lowest (49.6%) removal efficiencies in the sugarcane wine washing experiment was 19.4%, which was lower than that in the pineapple wine washing experiment (25.9%), indicating that sugarcane wine washing might be more non-selective than pineapple wine washing and that sugarcane wine washing might at the same time reduce different PCDD/F congeners. As sugarcane wine proved to be the better solvent of the two, it was chosen for a later experiment determining its capacity to reach a higher removal efficiency after six washing cycles.  
Figure 2: Changes in dioxin toxic equivalent quantities (ng-TEQ/kg dry wt.) and final removal percentages in a preliminary three-cycle soil washing experiment using both sugarcane wine and pineapple wine solvents (HpCDF: Hepta-chlorinated dibenzo-p-furans; OCDF: Octa-chlorinated dibenzo-p-furans; OCDD: Octa-chlorinated dibenzo-p- dioxins).
## 3 Results
### Parameter TV--Dependence on CNC Parameter Settings
The corner jerk threshold values are displayed as vertical dashed lines. The (normalized) test values of \(TV\) are displayed as full dots. The results for the limit frequency of 10 Hz require a different modelling approach; see Figure 10.  
Figure 10: Regression models describing the joint dependence of a (normalized) \(TV\) coefficient on the corner jerk limit and the axis jerk limit for the low-tolerance low-limit frequency test combinations: (**a**) contour tolerance 10 μm; limit frequency 10 Hz (corner jerk threshold 2.5 m/s3); (**b**) contour tolerance 10 μm; limit frequency 15 Hz (corner jerk threshold 5 m/s3); (**c**) contour tolerance 20 μm; limit frequency 10 Hz (corner jerk threshold 5 m/s3). The colors of the response surfaces correspond to the respective values of the normalized \(TV\) coefficient. In practice, low-limit frequency settings are typical for large machine tools or budget machine tools with lower-quality mechanical components. As can be seen from the results in Figure 10, if a low-contour-error oscillation is required in corner neighborhoods in the case of low-contour-tolerance and low-limit-frequency settings, the corner jerk limit should be kept to minimal values (\(\sim\)1 m/s\({}^{3}\)) and axial jerk limits should ideally not exceed 30 m/s\({}^{3}\). An illustrative demonstration of the effect of corner jerk limit settings on the distribution of toolpath deviations in corner neighborhoods and its effect on the \(TV\) parameter is presented in Figure 11.  
Figure 11: A visualization of the effect of the corner jerk on the deviation error distribution in corner neighborhoods (Valley toolpath). Both figures show a comparison of three corner jerk settings (corner jerk values 7.5, 45, and 100 m/s\({}^{3}\)) at the limit frequency of 25 Hz, the axis jerk of 45 m/s\({}^{3}\), and the tolerance limit of 100 \(\mu\)m leading to increasingly larger values of the \(TV\) parameter: (**a**) Deviation from ideal surface as a function of toolpath arclength; a comparison of the three cases. (**b**) Deviations of the workpiece surface from the ideal surface visualized using material removal simulation. The visualizations are ordered from the highest \(TV\) value (top) to the lowest \(TV\) value (bottom).
## 2 Materials and Methods
### DEM-Based Shadow Coarse Detection Method
#### 2.3.2. Shade Analysis Radius
Then, a shadow mask is generated by judging the shadow attribute for each point in the DEM raster. The pseudo-code for DEM-based shadow coarse detection algorithm is shown in Algorithm 1. Figure 7: Shade analysis radius calculation (The red solid line indicates the length of the global shade radius \(r_{g}\) and the green line for the local shade radius \(r_{l}\)). ```
Input: DEM, the time of image acquisition Output: Rough shadow mask
1 calculate the solar azimuth angle \(\theta_{a}\) and solar elevation angle \(\theta_{e}\) at the time of image acquisition;
2 foreach grid point \(p\) in DEM do
3 calculate the global shade radius \(r_{g}\) and the local shade radius \(r_{l}\);
4 locate the grid points on the ray toward the Sun within \(r_{l}\);
5 calculate the maximum terrain height angle \(\theta_{l}\);
6 if\(\theta_{l}<\theta_{e}\)then
7\(p\) is illuminated;
8
9 else
10\(p\) is non-illuminated;
11 return rough shadow mask;
```  
**Algorithm 1**DEM-based shadow coarse detection  
Because the resolution of the DEM used is generally lower than that of the image, the shadow mask obtained from DEM is generally smaller than the original image. So, we extend the DEM shadow mask to the same size as the image by interpolation. The shadow masks derived from this method are rough due to factors such as the accuracy of the DEM, the error in the calculation of the solar angles, and the ortho-correction of the image. If the shadow mask is derived using the solar elevation angle and azimuth angle at the moment the image was taken, the DEM-based method can predict the approximate location of shadows in the image, which can guide the subsequent shadow fine detection.
# An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevine
## 3 Results
### Transfer Learning Results  
As was mentioned, the transfer learning approach that was followed in this work was the fine tuning of the whole convolutional base of both MobileNet V2 and EfficientNet B0. Table 4 illustrates the validation accuracy and the F1 score of the two DL models, which were developed and trained with fine tuning using the EdenLibrary grapevines dataset. The values refer to the average of the classification process (average from all six classes). It is apparent that both of the models had an excellent detection performance for the possible health status of the plants. MobileNet V2 has a slightly better performance when comparedto the EfficientNet B0. Additionally, the F1 scores are almost identical to the validation accuracy results, with less than 1% difference between the two metrics. This validates the balance between the training data from the six classes that were discussed.  
Figure 9 depicts the accuracy performance of each of the two DL models in the validation phase, per epoch, of the fine-tuning process. From this figure it can be concluded that the higher performance of the MobileNet V2 network, in comparison with the EfficientNet B0 is observed even from the first epoch of the training and is maintained until the end of the training phase in the 20 th epoch.  
Additionally, MobileNet V2 has succeeded in converging to the maximum accuracy faster than EfficientNet B0. Furthermore, it is noteworthy that both models reached a high accuracy even before the completion of the fifth epoch. This indicates that the DL models were successful in quickly learning the patterns and features presented in the Eden Library dataset, demonstrating their ability to generalise well and make accurate predictions, even in early stages of the training process. Finally, the performance of each class in the training phase for both of the models is described in Figure 10.  
The results shown in Figure 10 validate the good performance of both DL models in accurately distinguishing between the different health status classes. It is noteworthy that all classes have equal performance, and there is no class with extreme misclassified instances. Additionally, the spread of the misclassified instances is not biased as the misclassified examples are detected in the rest of the classes and not in a specific class.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **EfficientNet B0** & **MobileNet V2** \\ \hline Validation accuracy & 0.965 & 0.985 \\ F1 score & 0.958 & 0.983 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Validation accuracy results of the EfficientNet B0 and MobileNet V2, trained with the EdenLibrary dataset images, on the 20 th training epoch.  
Figure 9: Validation accuracy in the different training epochs for the two different pretrained models.
# Towards the Internet of Smart Trains:
## 2 Communication Systems in Railway Scenarios  
Railway lines can be categorized mainly into one of four classes: urban, urban/inter-city, inter-city and/or high-speed. It is necessary to analyze lines or networks separately, given that their differences may have impact on their requirements (Table 1). Furthermore, railway communication systems can be divided into three main application groups: safety and control, operator, and customer oriented networks. In this Section, the communications in the most representative railway scenarios (Figure 2) are described: train-to-infrastructure communications, inter-car communications, intra-car communications, communications inside the station, infrastructure-to-infrastructure communications,  
Figure 1: Overview of the topics related to the Internet of Smart Trains that are covered in this article.  
and wireless sensor networks. In the following subsections, future directions of wireless systems in railways are addressed.
## 6 Numerical Simulation Results and Discussions
### Simulation Assumptions  
The simulation was conducted based on the following assumptions; (i) the quadrotor attitude is determined by an inertial navigation system (INS), (ii) the quadrotor altitude is measured either by a barometric sensor or a laser sensor, and (iii) the quadrotor horizontal position is provided by a commercial global positioning system (GPS) receiver module. The controllers are implemented in Matlab/Simulink with a sampling time of 0.0025 s, corresponding to an operating frequency of 400 Hz. The quadrotor parameters and the control saturation limits used in the simulation (Table 1) are chosen by making use of the parameters of a real F450 quadrotor platform [55], which is equipped with four 2312E motors and four 9450 propellers [56]. The controller gains, initial conditions, desired trajectory, and heading angle for the numerical simulations are listed in Table 2. By choosing the trajectory reference as a three-dimensional (3D) spline (Figures 2 and 3), we demonstrate that our new controller is capable of tracking complex desired trajectories. Candidate functions of the lumped disturbances, \(\mathbf{d}_{1}(t)\) and \(\mathbf{d}_{2}(t)\), are introduced into the simulation as [51]  
\[\mathbf{d}_{1}(t) =0.1[sin(0.1t),sin(0.1t+0.3),sin(0.1t-0.1)]^{T} \tag{60}\] \[\mathbf{d}_{2}(t) =0.15[sin(0.3t+0.2),sin(0.3t-0.25),sin(0.3t)]^{T}\]
## 4 Machine Learning Techniques for Forecasting
### ELM Neural Networks  
ELM generalizes single hidden layer feedforward networks [53; 54; 55]. For an input sample \(\mathbf{x}\in R^{\mathrm{D}_{1}\times 1}\), ELM constructs a hidden layer with \(L\) nodes and the output of the \(i\)-th node is denoted by \(h_{i}(\mathbf{x})\), where \(h_{i}(\mathbf{x})\) is a nonlinear feature mapping. We can choose the output of all hidden layer nodes as follows:  
\[\mathbf{h}(\mathbf{x};\ \mathbf{W},\mathbf{b})=\left(h_{1}(\mathbf{x}),\ldots,h_{L}(\mathbf{x})\right)^{ \mathrm{T}}=g(\mathbf{W}\mathbf{x}+\mathbf{b}) \tag{15}\]  
where \(\mathbf{W}\in\Re^{L\times D_{1}}\) and \(\mathbf{b}\in\Re^{L\times 1}\) are the weight matrix and the bias vector in the hidden layer, respectively, and \(g(\cdot)\) is the mapping function. Subsequently, the linear combination of \(\{h_{i}(\mathbf{x})\}_{i=1}^{L}\) is used as the resulting output of the prediction  
\[\mathbf{y}\approx f_{\mathrm{ELM}}(\mathbf{x})=\left(\mathbf{h}(\mathbf{x};\ \mathbf{W},\mathbf{b})^{ \mathrm{T}}\mathbf{\beta}\right)^{\mathrm{T}} \tag{16}\]  
where \(\mathbf{\beta}\in\Re^{L\times D_{2}}\) is the output weight matrix. Figure 7 illustrates the diagram of an ELM neural network with one single hidden layer.  
When all parameters \(\{\mathbf{W},\mathbf{b},\mathbf{\beta}\}\) are unknown, the above prediction function can be regarded as the combination of RBF networks and BP neural networks with only one hidden layer. To simplify the network model, extreme learning machines generate randomly the hidden node parameters \(\{\mathbf{W},\mathbf{b}\}\) according to some probability distributions. In other words, \(\mathbf{W}\) and \(\mathbf{b}\) do not need to be trained explicitly, resulting in a remarkable efficiency.  
Let \(\mathbf{H}=\left(\mathbf{h}(\mathbf{x}_{1};\ \mathbf{W},\mathbf{b}),\ldots,\mathbf{h}(\mathbf{x}_{N};\ \mathbf{W},\mathbf{b})\right)^{\mathrm{T}}\), \(\mathbf{Y}=\left(\mathbf{y}_{1},\ldots,\mathbf{y}_{N}\right)^{\mathrm{T}}\). The weights matrix \(\mathbf{\beta}\) connecting the hidden layer and the output layer can be solved by minimizing the squared error loss:  
\[E(\mathbf{\beta})=\frac{1}{2}\sum_{i=1}^{N}\left\|f_{\mathrm{ELM}}(\mathbf{x}_{i})-\bm {y}_{i}\right\|^{2}=\frac{1}{2}\|\mathbf{H}\mathbf{\beta}-\mathbf{Y}\|_{\mathrm{F}}^{2} \tag{17}\]  
where \(\left\|\cdot\right\|_{\mathrm{F}}\) is the Frobenius norm of one matrix.  
Figure 7: Diagram of an ELM neural network with a single hidden layer.
# Seismic Performance of Reinforced Concrete Short Columns Subjected to Freeze-Thaw Cycles
## 1 Introduction
For the purpose of observing the decay of both shear capacity and flexure capacity, three RC columns with a shear span-to-depth ratio of 2.5, designed to fail in flexure-shear mode, were subjected to different numbers of FTCs and cyclic lateral loading in sequence. Various seismic performance indicators were analyzed, which included comprising failure pattern, hysteretic behaviors, skeleton curves, shear deformation, and energy dissipation.
## 2 Methods
### Model Selection
#### 2.3.2 SynthSeg  
For comparison to the nnU-Net, we employed SynthSeg, a publicly available state-of-the-art segmentation model, as an example of an off-the-shelf solution. SynthSeg is also a 3D U-Net-based DL model originally trained on a large synthetic dataset (N = 1020) [18]. The segmentation process was executed using the pretrained SynthSeg model integrated into FreeSurfer (v7.3.2). As the SynthSeg model was trained exclusively on synthetic MRI scans, its application during inference was limited to T1 CE MRI data in both the internal and external test sets. SynthSeg was used for the segmentation of the left and right lateral ventricles, left and right inferior lateral ventricles, and the third and fourth ventricles. These segmentations were combined to create a single total ventricle structure for each patient. Because of the model's capacity to generalize across heterogeneous data, no additional fine-tuning or retraining was performed. Using an Intel Core i7\(-\)9700K CPU with 32 GB of DDR4 RAM (Rocky Linux 9), the median inference time per patient was 107 s.