esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis
## 6 Results and Discussion
### Discussion  
As illustrated in Figure 8 and Tables 3-5, significant performance improvement in terms of recognition accuracy has been demonstrated. This is achieved by improving a previously reported algorithm by the authors through the utilization of new vertical-edge histogram analysis and candidate size calculation to minimize the number of candidate regions and remove the image background if it exists. A de-rotation stage is also added to the segmentation to correct the plate's tilt angle before the recognition.  
The vertical-edge histogram analysis has contributed to improving the overall success rate of detection from 82% to 94.4%. This improvement is found to be more effective in the European-standard plates as compared to the American-standard plates. This is mainly because the aspect-ratio classification showed more false results with European-standard plates. Many candidate regions demonstrated an aspect ratio similar to the real one, so the vertical-edges analysis is found to be more effective in the detection stage.  
The suggested de-rotation stage also improved the overall success rate from 74.4% to 91.9%. The big difference here is caused by (i) the accumulated nature of the license plate recognition algorithm since an improvement in one stage affects the next stage and (ii) the segmentation improvement caused by the de-rotation. Unlike the vertical-edge histogram analysis impact, this improvement is found to be more effective in the American-standard plates as compared to the European-standard plates. The American-standard plates consist of two lines, so any segmentation error would have a larger impact on the results as compared to the European-standard plates. This causes the de-rotation to be more effective in the American-standard plates.  
Minimizing the number of candidate regions in the detection and improving the program's structure has contributed to maintaining the execution time of the improved  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{\(T_{exec}\)**(s) of the Old ALPR Algorithm**} & \(T_{exec}\)**(s) of the Improved ALPR Algorithm** \\ \hline Detection & 1.0025 & 1.0043 \\ Segmentation & 0.4454 & 0.4621 \\ Recognition & 1.0147 & 1.0141 \\ Total Time & 2.4626 & 2.4805 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between the execution time of the old and improved ALPR algorithms.  
algorithm approximately equal to that of the old one, despite the addition of the suggested vertical-edge histogram analysis and the de-rotation, as illustrated in Table 5.
## 2 Methodology
### Fault Tree Analysis (FTA)  
Fault tree analysis (FTA) is a top-down Boolean logic tool commonly used to identify the potential hazards that may lead to fail of an undesired top event; the fault tree takes the system failure as the top event. Then, the common events leading to the top event are systematically categorized with a top-down approach until the basic events are identified. Boolean logic gates are used to graphically describe the logical relationship between these events, in which the lower-level events are used as the input of the gate, and the higher-level events are used as the output of the gate [33]. In this way, the undesired events and the logical relationship of failure events can be clearly described. Therefore, the fault tree model can be used to qualitatively analyze the failure mode of the system. Before modelling the system, the following assumptions are usually made to simplify the analysis.  
1. When the system starts running, all components are normal.
2. Component failure rates obey the exponential distribution.
3. The states of all components are statistically independent.
4. Failure rates are constant.  
A fault tree model is established consisting of different components such as the top event (T), intermediate events (M) and basic events (X). It helps to understand which possible combination of basic events will cause the top event in the fault tree [34].
## 3 Results and Discussion  
### The Corrosion Potential Result  
The corrosion potential results are presented in Figure 3. The results showed a relatively smaller potential value than previous studies [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). The reason for this is that the corrosion potential uses the principle of the potential difference between embedded rebar in concrete and electrode [PERSON] et al. (2018). The embedded rebar is protected by the passive film from the alkalinity of concrete. Additionally, the alkalinity gives a large difference of the measured potential, and the usual eigenvalue of the potential of rebar is the range of \(-400\) to \(-700\) mV [PERSON] et al. (2018). The specimens of this experiment did not embed in concrete but were only exposed in the air condition. Therefore, the potential of this study came out as a relatively smaller value of potential than other studies because of the absence of alkalinity [PERSON] et al. (2018); [PERSON] et al. (2018); [PERSON] et al. (2018). In addition, the case of using only the metal specimens showed small potential [PERSON] et al. (2018); [PERSON] et al. (2018). Therefore, the results could come out with small values of potentials.  
The potentials fluctuated because the specimens were located in an uncontrolled air temperature environment, which affected the specimens in terms of temperature and specimen diameter. The usual results of corrosion potential in a controlled environment show clear linear behavior with few fluctuations [PERSON] et al. (2018); [PERSON] et al. (2018). Therefore, fluctuations here can be read as a temperature effect.  
The behavior of the corrosion potentials was more stable in the D16 and D19 specimens than in D10 and D13, especially after the inflection point. This means that the rebar diameter was also a factor that affected the corrosion potential. The inflection point can be confirmed comparing with the non-corroded and corroded trend (black and blue lines). Usually, the corroded potential followed the trend of non-corroded results. However, the behavior showed an opposite or different trend at a certain point. For example, from Figure 3a, when the non-corroded potential showed an increasing trend but the corroded potential showed a decreasing trend at the inflection point.  
[PERSON] et al. [PERSON] et al. (2018) showed experimentally that rebars with different diameters have different thermal expansion coefficients. In that study the thermal expansion coefficient was decreased as the diameter increased; hence, the larger specimens were less affected by the temperature because of the thermal coefficient. Therefore, in all of the specimens the temperature effect was evident in the corrosion potential behavior.
## 4 Concerns about and Encounters with Underwater Wireless Sensor Network
### Underwater Sensor Network Organization
#### 4.2.2 Use of Static Clustering to Form Equal-Sized Clusters  
The BS divides the entire sensing region into a set number of equal-sized rectangular clusters. We will use the example of the nine clusters that the BS generated to explain how the protocol functions. UWSN offers consistent connectivity and coverage. Equal-sized clusters consuming the same amount of energy assure energy-efficient cluster formation.
# Significance of Single-Interval Discrete Attributes: Case Study on Two-Level Discretisation
## 2 Discretisation
### Unsupervised Discretisation Transformations  
In the unsupervised methods, the range of continuous values of a considered attribute is divided into subranges by the user-specified width (range of values) or frequency (number of values in each interval). In this group of algorithms, equal width binning and equal frequency binning are the most popular. Both are relatively simple, but they are sensitive to a user-provided input parameter \(k\), which determines the number of bins to be constructed for a given attribute.  
The equal width method divides the range of attribute values into \(k\) intervals of equal width. The equal frequency method divides the sorted values of a discretised attribute into \(k\) intervals so that each interval contains approximately the same number of values. The weakness of these methods is that some information can be lost after the discretisation process. It can occur in a situation of uneven distribution of the considered attribute values. If outliers of values are present in the data, they can affect the cut points between intervals of the considered attribute. In the case of the equal frequency method, it is important to keep the same values of a discretised attribute in the same interval. Consequently, it is not always possible to generate \(k\) equal frequency bins.
## 3 Fatigue Life Prediction Modeling
### Modeling Process
#### 3.3.1 Determine the Data Augmentation Factor  
Experiments were conducted based on Dataset 1, where the hyperparameters of the ANN model are shown in Table 2. Three evaluation indexes including RMSE, MAE and R\({}^{2}\) were used to evaluate the prediction effect.  
Figure 3: Schematic diagram of the modeling process of the prediction model based on GRW-DBA and ANN.  
Figure 4 shows the performance and training time of the model in different augmented data amounts. From the results of the evaluation metrics of the comparative experiments, it is found that when the model is trained and tested using real experimental data or a smaller number of multiples of the augmented data, the prediction accuracy of the model is not high due to the small amount. This result proves that the model tends to be overfitted when the amount of data is small [23]. With the increase of the augmentation multiplier, the model could fully exploit the hidden features of the input data while avoiding the overfitting phenomenon. The prediction accuracy shows an overall increasing trend.  
However, the increased amount of data also brings the problem of time consumption for model training. As shown in Figure 4c, the time used for model training is significantly longer when using 1000 times the augmented dataset compared to 500 times the data. Furthermore, the improvement in prediction accuracy is low when combined with Figure 4a,b. Therefore, the fatigue life prediction model is trained using 500 times (13,500 sets) of augmented data.
## 3 Results  
The process of study identification and selection is described in Figure 1, following the PRISMA methodology. The systematic search in electronic databases yielded a total of 665 records. After removing 310 duplicates, 355 titles and abstracts were screened, of which 276 were excluded for not meeting the eligibility criteria. Subsequently, 79 full-text articles were assessed. Of these, 18 were excluded for various reasons, including populations outside the defined age range or the absence of a structured physical intervention. Finally, sixty-one studies were included in the qualitative synthesis (systematic review), and of these, eight met the methodological and statistical criteria to be included in the meta-analysis.
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## 3 SatSINS
### Module 2: Analysis of Medium-Scale Sea Ice Characteristic  
This analysis was conducted when [PERSON] _Xuelong_ was approaching the ice edge. The data sources and the logical decisions are included in Figure 3. The 6-h MSLP data was integrated with the data from MODIS and the SIC maps to assess the severity of the ice conditions and the need for more details of ice conditions from SAR images to support navigational analysis of ice conditions in Prydz Bay.  
According to [PERSON] _[PERSON]_ navigation information (Section 2.2), the vessel can travel quickly and safely in sea ice with SIC \(<\) 70% and she will travel slowly and even be trapped in SIC \(>\) 70% when the SIT exceeds 1.0 m. So if the regional SIC distribution in Prydz Bay is below 70%, there would be no need for further detailed information on the ice or the atmospheric conditions. In this case, SIC maps from SSMIS/AMSR-2 along with available MODIS images would be sufficient for the route planning. Note that sea ice modelling results and observation data indicate that the SIT does not exceed 1.5 m in the Bay, though it can be deformed and broken pieces accumulate to bring the thickness up to 3.0 m, as observed in the West Ice Shelf [11; 12]. Ice thickness up to 1.5 m does not constitute an obstacle for [PERSON] _[PERSON]_ to travel and reach Zhongshan Station, though it would certainly slow her travel. However, if the SIC in Prydz Bay is over 70%, the need for high-resolution imagery is identified pending further information from the MSLP data. The information will identify a possible presence of cyclones or anti-cyclones over the region. If anti-cyclones are proven to exist, the atmospheric conditions in the bay will be largely quiescent and cloud-free. In this case, MODIS data will be sufficient (as a fine-enough resolution data source) to provide the ice information for the vessel's route planning. Alternatively, if the MSLP data reveal several consecutive cyclones passing by the bay, it means that clouds prevail and the turbulent atmospheric conditions will cause higher mobility of sea ice and therefore more surface deformation. This condition, combined with an estimate of SIC \(>\) 70%, triggers the need for the higher resolution SAR images. Consequently, in our examples, either Envisat or Radarsat-2 images were acquired in Wide-Swath or ScanSAR modes (Table 1). The imagery would be available within 2 h of the satellite overpass.
# Design and Depth Control of a Buoyancy-Driven Profiling Float
## 2 System Design  
The overall design goal was to create a system that controlled buoyancy through a linear actuator and could accurately maintain depth and control speed through a low-cost depth sensor. Each part of the float system was modular in design, allowing different observation instruments and communication methods to be selected according to different observation scenarios.  
The hardware system used was mainly composed of VBS, communication system, positioning system, battery system, electric control system, and sensor unit. The details and distribution of the specific parts can be seen in Figure 1a,b, showing the SolidWorks perspective view on the back of the float (a), and the physical picture of the float on the front (after removing the transparent cylindrical acrylic tube (b)).
## 4 Results and Discussions
### Joint LUT and GS-Based CD Pre-Compensation Algorithm at the Transmitter  
GS shows clear advantages in compensating for CD-induced power fading under 40 km SSMF transmission. As shown in Figure 6, the value of BER was reduced with the help of the GS-based CD pre-compensation algorithm. However, the BER arrived at an error floor when increasing the number of FFE taps. The reason is that FIR-based FFE algorithm is a linear equalizer. It is important to compensate for residual ISI for improving the system performance when there is no nonlinearity or weak nonlinearity. Based on the premise that dispersion and PD work together to produce nonlinear distortion, when  
Figure 4: Simulation setup and the DSP flow of (**a**) joint LUT and GS-based pre-compensation, (**b**) joint LUT and GS-based post-compensation, and (**c**) the transmission magnitude of EAM versus control voltage.  
Figure 5: BER versus ROP without and with 7-symbol LUT for 28 GBaud PAM-4 under BTB case.  
the modulator nonlinearity is large, the GS algorithm alone cannot compensate for power fading. After using the LUT algorithm, the system nonlinearity was mostly compensated. The joint LUT and GS-based CD pre-compensation algorithm can reach the threshold of \(3.8\times 10^{-3}\). As shown in Figure 6, the BER decreased gradually when increasing the number of taps. After 125 taps, the decreasing trend of BER was no longer obvious. Thus, we fixed the tap number at 125 in the subsequent simulations in this subsection.  
We then investigated the BER versus ROP, as shown in Figure 7. The BER was \(3\times 10^{-2}\) when the GS-based CD pre-compensation algorithm was absent. After using the GS-based CD pre-compensation algorithm, the BER dropped to \(1.5\times 10^{-2}\). However, the existence of nonlinearity seriously affected the performance of the system. The joint LUT and GS-based CD pre-compensation algorithm showed improved performance. The LUT considering more symbols performed better. The receiver sensitivity arrived at \(-4\) dBm with the help of the 7-symbol LUT and GS-based CD pre-compensation algorithm.  
Figure 6: BER versus the number of FFE taps for different algorithms for 28 GBaud PAM-4 under 40 km SSMF transmission at an ROP of 0 dBm.  
Figure 7: BER versus ROP for different algorithms for 28 GBaud PAM-4 under 40 km SSMF transmission.
## 2 Material and Methods
### QA and QC  
#### 2.5.1 PAHs  
The quantification of PAHs was carried out through a linear calibration curve, which was built on matrix-matched standards (dust on filters). For the determination of the curve a standard polyaromatic solution containing secondary PAHs derivatives was used as internal standards. The solutions were of known concentration (0.05-10 ng/\(\mu\)L). The expanded uncertainty (Uexp k = 2) ranged from 6.3% to 23%, while the LOD of the method ranged from 0.6 pg/\(\mu\)L to 12.8 pg/\(\mu\)L.
# Human Activity Recognition Based on Deep Learning Regardless of Sensor Orientation
## 4 Experimental Setup, Results, and Analysis
### Deep Learning Baseline Analysis  
In light of the potential randomness associated with single training, we opted for a five-fold cross-validation approach, utilizing the KFold method for experimentation.  
Figure 11: The accuracy (**left**) and loss (**right**) curve of the optimized ResNet-34 with data fusion.  
Figure 12: The confusion matrix of the optimized ResNet-34 with data fusion.  
Figure 10: The accuracy (**left**) and loss (**right**) curve of the optimized ResNet-34.  
Three deep-learning baseline models were used for comparison. The results are detailed in Table 3.  
The device angle change problem has a significant impact on the accuracy of human motion recognition. Upon comparing the first two columns of Table 3, namely, ACC_0 and ACC_All, noticeable trends emerge. When MLP was utilized, the accuracy decreased from 86.93% to 82.42%; similarly, employing CNN-2D resulted in a decrease from 91.25% to 89.62%. In the case of ResNet-34, despite observable fluctuations (the five-fold cross-validation accuracy and loss and the average confusion matrix are shown in Figure 13), the average accuracy declined from 92.08% to 91.14%.  
Figure 13: Loss/accuracy per fold (**top**) and average confusion matrix (**bottom**) of ResNet-34 on ACC_0.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **ACC\_0** & **ACC\_All** & **6D** & **3D** & **9D** \\ \hline MLP & 86.93 & 82.42 & **87.49** & 80.12 & 86.57 \\ CNN-2D & 91.25 & 89.62 & 86.33 & 90.60 & **92.25** \\ ResNet-34 & 92.08 & 91.14 & 92.31 & 96.52 & **96.58** \\ Optimized & 95.83 & 96.24 & 96.25 & 96.16 & **97.13** \\ ResNet-34 & & & & & \\ \hline \hline \end{tabular} Note: The titles are explained in Section 3.1.1.  
\end{table}
Table 3: User-dependent performance comparison based on five-fold cross-validation (Accuracy %).  
The utilization of the optimized ResNet-34 resulted in an elevation in accuracy from 95.83% to 96.24%, showcasing minimal impact. This effect can be attributed to the greater volume of training data, enabling deeper networks to more comprehensively capture data features in the past.  
As shown in the last row of Table 3, the optimized ResNet-34 outperformed other baselines; as model complexity increased, the influence of the angles gradually diminished and the accuracy remained steady at around 96%. After fusion, ResNet-34 and optimized ResNet-34 exhibited comparable results.  
The performance of the MLP (Multi-Layer Perceptron) with the fused data was not as satisfactory as expected. The accuracy decreased from 87.49% to 80.12%. This decline in performance can be attributed to the fact that the fused data lose some of the original information that is advantageous for classification. Therefore, we concatenated the original data with the fused data, which proved effective (e.g., CNN-2D was marked by its highest result of 92.25%). The optimal outcome was demonstrated by the optimized ResNet-34, which achieved an accuracy of 97.13%. Details on its accuracy and loss per fold are shown in Figure 14.
## 3 Results
Figure 7: Original UAV NDVI image (**a**); predicted NDVI images with missing parts of 10% (**b**), 30% (**d**), and 50% (**f**); and the calculated NDVI images (based on the imputed R and NIR spectral bands) with missing parts of 10% (**c**), 30% (**e**), and 50% (**g**). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Predictor** & **Missing Data** & **Agreement** & \multicolumn{2}{c}{**NDVI**} \\ \cline{3-5}
**Imagery** & **Percentage** & **Parameter** & **Predicted** & **Calculated** \\ \hline \multirow{4}{*}{PS + S2} & \multirow{2}{*}{10\%} & r & 0.62 & 0.62 \\  & & d & 0.74 & 0.74 \\ \cline{2-5}  & & r & 0.66 & 0.65 \\ \cline{2-5}  & & d & 0.77 & 0.77 \\ \cline{2-5}  & & r & 0.63 & 0.63 \\ \cline{2-5}  & & d & 0.75 & 0.75 \\ \hline \hline \end{tabular} Where: r = [PERSON]’s correlation coefficient, d = [PERSON]’s agreement index.  
\end{table} 
Table 3: Spatial agreement between the original, predicted, and calculated UAV NDVI images.