esa-sceva/satcom-chunk-collection huggingface dataset:
# Reconfigurable Intelligent Surface Assisted Target Three-Dimensional Localization with 2-D Radar
## 4 Simulation Analysis
### Target Localization with RISs Site Errors  
The target localization depends on the accurate location of the RISs. When the site errors of the RISs exist, the target localization accuracy will be reduced to a certain extent. The GDOP with the RISs site errors is simulated in the same single target scenario in Section 4.2. All the simulation parameters of the targets and the RIS-assisted 2-D radar are the same.  
A normally distributed site error is added to the position of the RISs, and it is assumed that the errors of the x-axis, y-axis and z-axis of each RIS meet the independent co-distribution. With the site error as 1 m and 5 m, the simulated GDOP in the detection area of \([-100,100]\times[-100,100]\) km is given in Figure 8. To reflect the effect of the site error, the difference of the GDOP with the site error as 5 m and 1 m is shown in Figure 9.  
Compared with Figure 3(a), the target localization accuracy reduces with the site errors of the RISs. And it is shown in Figure 9 that the difference between the GDOP is always greater than zero, which indicates that the GDOP becomes higher with the increase in the site error. Therefore, the decline of the localization accuracy becomes more serious with the increase in the site error. This is because the target localization model in the proposed method does not consider the randomness of the site error, leading to the mismatch of the algorithm model.  
Figure 8: \(Cont.\)
# Physiological and Biochemical Variations in Celery by Imidacloprid and Fenpyroximate
## 3 Results
### Peroxidase Activity  
Figure 3 manifests four histograms of the POD activity in celery (including stem and leaf) with pesticide treatments during the experimental period at 2 h, 1, 3, 5, 8, 10, 14, 21 and 28 d. POD activities in the stem were all significantly altered (increased or decreased) by pesticide treatments with IMI and FEN at a concentration of 10 F (Figure 3A,B). As shown in Figure 3A, the POD activity in one day was promoted, obviously, with a gradual enhancement of IMI concentration. The transition phase was 3 to 5 d, and then POD activity ascended steadily until it returned to the initial state. Apparently, variation (increased or decreased) was observed in 2 h (Figure 3B). Totally speaking, the celery stem was greatly affected by FEN at concentration 1 F from 3 to 15 d, which was contrary to the result of IMI. The lowest and highest POD activities were 16.39 U/mg prot and 124.55 U/mg prot of concentration 1 F at 5 and 28 days, respectively. The value of POD was perpetually steady between 50 U/mg prot and 85 U/mg prot at a concentration of 10 F, while the range of lower concentration was 16.39 U/mg prot to 73.82 U/mg prot, which indicated that FEN in 1 F concentration had a great influence on POD compared to FEN in 10 F concentration.  
POD activity in celery leaf was further analyzed at various pesticide exposure concentrations of IMI and FEN (Figure 3C,D). Evident stress response can be seen in Figure 3C and appeared in 3 to 21 d (concentration dropped), and the value of POD was steady between 10 U/mg prot and 18 U/mg prot after IMI treatment until the initial state was restored at 28-d. The highest POD activity was 25.81 U/mg prot and 29.43 U/mg prot with an IMI of concentration of 1 F and 10 F at 1 and 28 d, respectively. As presented in Figure 3D, marked elevation has taken place from the beginning, which suggests that celery leaf could efficiently remove peroxides and free radicals from the body in the initial period under the function of FEN. However, it then declined 43.69% and 26.18% compared to non-pestic treatment when FEN at 10 F and 1 F concentration in 1 to 3 d. Overall, the value of POD in celery was a dynamic process, with the content in stem being much higher than that in leaf regardless of pesticide presence or absence.  
Figure 3: The activities of peroxidase (POD) in stem (**A**,**B**) and foil (**C**,**D**) after spraying IMI and FEN at the 1 F and 10 F, respectively (***:**\(p<0.05\)).
## 2 Materials and Methods
### Methods
#### 2.2.1 Support Vector Machine (SVM) + Clustering  
The proposed SVM + Clustering method consisted of several steps, as presented in Figure 1. To build the dataset for training, image masks that discriminate leaf and non-leaf pixels were needed. Since building this is a really time-consuming task if done manually, a clustering pre-process was implemented as an alternative to facilitate the dataset generation.  
As it is a supervised method, input-output pairs of data are required for SVM training. The output was defined as a binary value that classifies every pixel as leaf (\"1\") or non-leaf (\"0\"). In the case of inputs, the original pictures were taken in the Red-Green-Blue (RGB) colour space. Nonetheless, other colour spaces were used, as more relevant information for segmentation can be obtained [37; 38]. Thus, a hybrid colour space formed by some channels of several colour spaces was defined. The colour spaces considered were: RGB, I\({}_{1}\)I\({}_{2}\)I\({}_{3}\), HSV and CIE (International Commission on Illumination) L*a*b*. The procedure for choosing the channels consisted of representing each of them in a grayscale picture together with its histogram for different test images. This allowed visual determination of their sensitivity to discern between the leaves and the background. Finally, the hybrid colour space consisted of the selected channels: I\({}_{3}\) from I\({}_{1}\)I\({}_{2}\)I\({}_{3}\), a* and b* from CIE L*a*b*, and H from HSV.
# Lem-Detector: An Efficient Detector for Photovoltaic Panel Defect Detection
## 2 Related Work  
### Data Augmentation  
Data augmentation is a widely used technique in computer vision [11], employed to generate synthetic training samples from existing data. By applying various transformations and processing, data augmentation increases the diversity and quantity of datasets, ultimately enhancing the model's generalization ability and robustness [12; 13].  
Data augmentation methods encompass a broad spectrum, ranging from traditional image processing techniques to model-based approaches. Traditional methods include transformations such as flipping, rotation, scaling, translation, cropping, color manipulation, noise addition, blurring, and affine transformations. Model-based approaches, on the other hand, leverage techniques like Generative Adversarial Networks [14], Variational Autoencoders (VAEs) [15], and adversarial sample enhancement [16].  
Prior research has explored the application of data augmentation in photovoltaic panel defect detection. [PERSON] et al. [17] proposed an automatic classification method for monocystalline silicon solar cell defects that utilizes geometric morphological features for data augmentation, requiring only a limited number of training samples. [PERSON] et al. [18] employed Generative Adversarial Networks to generate synthetic images of surface defects, training the generator to synthesize new defect images based on random noise. [PERSON] et al. [19] utilized a linear weighted fusion method for fabric defect image reconstruction, producing a residual map that highlights defects and facilitates pixel-level detection of various defect types. [PERSON] et al. [20] addressed the challenges of limited training data and data imbalance in photovoltaic panel detection models through data augmentation and weighted classification methods, demonstrating significant performance improvements.  
These studies collectively highlight the significant impact of data augmentation methods in enhancing the detection performance of photovoltaic panels, emphasizing the importance of diversifying and expanding training data for robust and accurate defect detection.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Authors** & **Data** & **Type** & **Type of Data Processing** & **Form Maps (Models)** & **Interpolation** & **Determination of Anisotropy** & **Additional Isoline Corrections** \\ \hline
[25] & Precise leveling & Point & Adjustment & Analog & Linear & No & Geological \\ \hline
[26] & Precise leveling & Point & Adjustment & Analog & Linear & No & Geological \\ \hline
[27] & Precise leveling & Point & Adjustment & Analog, numerical (grid: \(20^{\prime}\times 20^{\prime}\)) &
\begin{tabular}{c} Collocation \\ using the \\ \end{tabular} & No & No \\ \hline
[28] & Data from GNSS stations & Point & Development of time series & Analog &
\begin{tabular}{c} Kriging method \\ with the linear \\ semivariogram \\ \end{tabular} & No data & No \\ \hline
[29] & Data from GNSS stations & Point & \
\begin{tabular}{c} Development of \\ time series, \\ adjustment \\ \end{tabular} & Analog &
\begin{tabular}{c} Kriging method \\ with the linear \\ semivariogram \\ \end{tabular} & No & No \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of maps (cartographic models) of the vertical movements of the Earth’s crust on the territory of Poland [25; 26; 27; 28; 29]. The obtained results show how the method of combining data sets (interpolation, network adjustment) affects the final cartographic model.
# Improving Text Recognition Accuracy for Serbian Legal Documents Using BERT
## 2 Related Work
### Optical Character Recognition  
The goal of optical character recognition research is to create an AI-based model that can automatically extract and process text from text-containing documents. Text-containing documents of interest vary from handwritten text to printed or scanned text images, and their processing is performed with the goal of transforming them into an editable digital format for deeper and further processing. The extraction process is not a trivial one, since it encounters some major obstacles such as the font characteristics of the characters within documents and the quality of images. Unfortunately, OCR technology is still not as sophisticated as human ability.  
The quality of input documents directly affects OCR's efficiency and accuracy. Consequently, the information extraction process tends to be noisy and often suffers from blur effects, faded text, scanning quality, and wrinkles. When scanned documents are used, the low accuracy is caused by these noises. Preprocessing operations including binarization and contrast enhancement may occasionally be necessary to improve the scanned documents' visual quality. For OCR to perform at its best, a high degree of accuracy and minimal processing latency are necessary [22].  
To improve OCR accuracy, various approaches have been considered and investigated. OCR engine performance analysis was reported in [23], while the significance of OCR accuracy was emphasized in [24]. In the legal domain, OCR has in many cases been used for named entity recognition, coupled with machine learning techniques. This coupling of technologies includes a rule-based approach for data extraction from court decisions [25], CRFs (Conditional Random Fields), and BiLSTM (Bidirectional Long Short-Term Memory) [26]. In many cases, the Tesseract OCR engine was used for different scanned documents ranging from small scanned bill documents to sample images [27; 28; 29]. The accuracy of Tesseract measured in these cases was based on string matching and varied from 90% to 97%. In addition to string matching, a study reported by [PERSON] et al. [30] uses conversion time, NER (named entity recognition) time, string match accuracy for precision, and a recall measure for the number of acquired entities to measure OCR engine effectiveness--not only for Tesseract OCR but also for the Foxit and PDF2 GO OCR engines. Although the differences between the engines studied in [30] are not very large, Tesseract OCR has still shown the highest accuracy level and F1-Score.  
The previously described state of OCR engines has led us to decide to rely on Tesseract OCR for the research we present in this paper. Although there are OCR adjustments for the Serbian language [3; 31], these adjustments usually refer to restoring texts written using Serbian Cyrillic or Latin with diacritics, which is not the case in our research. Cyrillic is not unique to the Serbian language; thus, we have decided to rely on the capabilities of the engine that has proven its usability--Tesseract OCR.
## 3 Concept of Spatial Mode Diversity
\[A_{i}=\upmu|\rho_{i}\alpha_{ii}+\sum_{k\in N,K\
eq i}\rho_{k}\alpha_{ki}|^{2} \tag{16}\]  
During our analysis, we assume that the channel state information for the signal amplitude fading \(\mid\alpha_{ii}\mid\) at the receiver end is estimated by collecting the received power and exciting the \(i_{th}\) spatial multiplexed mode. In this case \(\rho_{i}\alpha_{ii}\) is obtained directly from the spatial mode multiplexing calculations. The photon rate obtained from Equation (16) is a half normal distribution, whose intensity fluctuation is represented by a probability density function (pdf) under atmospheric turbulence conditions. The results obtained from Equations (15) and (16) are used in calculations of probability density function as well as the channel capacity given by the Equations (27) and (28) in Section 4, respectively. The instantaneous asymptotic optical signal-to-noise ratio [20] (OSNR) can be calculated by using Equation (17). Each channel in the proposed system carries high-speed, 16-QAM modulated information, resulting in \(N\) multiplexed OAM channels. The probability of error of the 16-QAM modulation [22] under atmospheric turbulence is given by  
\[\gamma_{i}=\frac{|\alpha_{ii}|^{2}}{\sum\limits_{k\in N,K\
eq i}\frac{E[|\alpha _{ki}|^{2}]}{E[|\alpha_{ki}|^{2}]}} \tag{17}\]  
\[P_{e}=3Q\left(\sqrt{\frac{4E_{avg}}{5N_{0}}}\right)\left[1-0.75Q\sqrt{0.8\frac {E_{avg}}{5N_{0}}}\right] \tag{18}\]  
where, \(N_{0}\) and \(Q(. )\) are the FSO link's noise power spectral density and complementary error function, respectively. The received field strength for the \(i_{th}\) spatially multiplexed signal [20] at the received plane for the Lagrange Gaussian (LG) polynomial \(L_{0}^{i}\) is expressed by  
\[u_{i*}(\omega_{z},\phi,0)=\sqrt{\frac{2}{\pi|i|}}\frac{1}{\omega_{0}}\left( \frac{\sqrt{2}\omega_{z}}{\omega_{0}}\right)^{|i|}L_{0}^{i}\left(\frac{2\omega _{z}^{2}}{\omega_{0}^{2}}\right)e^{-(\frac{\omega_{z}^{2}}{\omega_{0}^{2}}+ji \phi)} \tag{19}\]
## 3 Methodology
### Speed Computation and Data Augmentation Based on Movement Feature  
BFRS also causes acceleration vertical to the road surface and a decrease in driving speed. Hence, the acceleration along all 3-axis should be taken into consideration to detect BFRS. The speed is computed using consecutive GPS samples as denoted in (6).  
\[v_{i}=\sqrt{\left(x_{i+1}-x_{i-1}\right)^{2}+\left(y_{i+1}-y_{i-1}\right)^{2}} \ast H_{GPS}/2 \tag{6}\]  
In (6), the average speed \(v_{i}\) is taken as the instant speed of \(P_{i}(x_{i},y_{i})\), and it is computed based on the distance between \(P_{i-1}(x_{i-1},y_{i-1})\) and \(P_{i+1}(x_{i+1},y_{i+1})\), along with the sampling frequency \(H_{GPS}\). In addition, since the accelerometer and GPS sampling frequencies are usually different and BFRS are usually detected based on the time stamp of the accelerometer, the computed speed needs to be interpolated to align with the accelerometer time stamp. Here, the linear interpolation method is applied, as denoted in (7).  
\[v_{k}=a(v_{k}-v_{i})+b,\ v_{k}\in D_{spead} \tag{7}\]  
In (7), \(v_{i}\) is the computed average speed at point \(Pi\) in (6); \(v_{k}\) is the speed interpolated for time stamp \(k\); and \(a\) and \(b\) are coefficients of the linear interpolation function. When aligning the speed with the accelerometer time stamp, the speed \(v_{k}\) is computed using the precomputed speed \(v_{i}\) and time stamp \(k\) in sequence.  
The movement feature refers to the speed and direction of the vehicle, which is recorded in the multi-sensor stream data by different driving behaviors. Based on the modeling result and movement feature, recordings containing BFRS are manually selected. However, there are only limited bump features in the training dataset. The intuitive solution is to simulate the driving behavior and augment the dataset based on the movement feature. In our work, two kinds of data augmentation methods, as shown in Figure 3, are used.  
Figure 3: _Cont._
## 4 Experimental Setup  
The acoustic models for the initial (bootstrap) phone recognizer have been trained on generic speech databases in Basque and Spanish: CommonVoice (cv-corpus-5.1-2020-06-22) [29], OpenSLR (SLR76) [36], Aditu [30] and Albayzin [31] (see Table 2). The development and test sets of Aditu and Albayzin were used to validate and evaluate phone recognition performance. The training, development and test sets have durations of 332.21, 3.96 and 4.03 h, respectively. Note, however, that Spanish and Basque are highly imbalanced in the training set (with a 3:1 ratio). PRR on the test sets of Aditu (Basque) and Albayzin (Spanish) were of 4.6% and 6.9%, respectively.  
To build the phone recognizer, an off-the-shelf, close to state-of-the-art end-to-end neural network-based ASR system is used: Facebook AI Research wav2 letter++ (consolidated into Flashlight), applying the Gated ConvNet recipe presented in [37]. Note that the phone recognizer requires neither lexical models nor a language model. For the semisupervised data collection step, all the BP plenary sessions from 2014 to 2021 (amounting to more than 1200 h) are used. Despite having access to BP sessions from 2010 to today, the audios prior to 2014 were recorded and stored using different formats and protocols, which prevented us from using those audios in this work.  
The ASR system is also based on wav2 letter++. In this case, besides the acoustic models, lexical and language models are also estimated, based on the minutes and translations of all BP plenary sessions from 2010 to 2021, which comprise more than 33 million words and around 279 thousand different entries. For each word in the vocabulary, a single pronunciation baseform is considered, as provided by our in-house G2P converter. A trigram language model is computed using KenLM [38] (without pruning), including close to 16 million trigrams.  
\begin{table}
\begin{tabular}{c c c} \hline
**Name** & **Basque** & **Spanish** \\ \hline CommonVoice & 24.75 & 250.30 \\ Aditu (train) & 47.40 & - \\ OpenSLR (SLR76) & 5.66 & - \\ Albayzin (train) & - & 4.10 \\ \hline Total & 77.81 & 254.40 \\ \hline \end{tabular}
\end{table}
Table 2: Databases used to train the baseline (bootstrap) acoustic models. Durations are expressed in hours.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 1 Introduction
### AC/DC Power Flow for MT-HVDC Systems Considering Droop Parameters  
Controlling the droop parameters in MT-HVDC systems has a significant impact on the PF of AC and DC grids after an outage [4]. In [16], the concept of distributed DC voltage control with the droop parameters on the PF problem in MT-HVDC systems is considered. In [22], a methodology to determine the mean voltage instead of a single slack converter station in MT-HVDC systems to solve the DC PF is proposed. By interconnecting MT-HVDC systems to the large-scale AC grids, solving the AC/DC PF goes through a complicated process, and a combined solution of AC/DC PF considering all the system's variables and constraints is required.  
To address all the above-mentioned challenges, a mixed AC/DC PF algorithm for hybrid AC/DC grids with MT-HVDC systems is proposed in this paper. The proposed strategy is a fast and accurate method, which is capable of optimizing the AC/DC PF calculations. Except for the high accuracy and optimized performance, considering all operational constraints and control objectives of the integration of MT-HVDC systems into the large-scale AC grids is the other contribution of this paper. The calculated results by the mixed AC/DC PF problem can be used for the planning, scheduling, state estimation, small-signal stability analyses. The mixed AC/DC PF algorithm is applied to a five-bus AC grid with a three-bus MT-HVDC system and the modified IEEE 39-bus test system with two four-bus MT-HVDC systems (in two different areas) which are simulated in MATLAB software. To check the performance of the mixed AC/DC PF algorithm, different cases are considered. The obtained results show the accuracy, robustness, and effectiveness of the mixed AC/DC PF algorithm.
## 3 Data  
Three sources of datasets were aggregated to create a final dataset. The data sources include traffic, crash, and barrier geometric characteristics. The Wyoming Department of Transportation (WYDOT) provided the crash and traffic datasets between 2007 and 2017. The traffic dataset includes predictors such as average annual daily traffic (AADT) and average annual truck traffic (ADTT).  
The second dataset was the barrier geometric dataset including different roadway, and barrier geometric characteristics such as types, length, offset, and height of barriers. This dataset also includes various roadside geometric characteristics such as shoulder width. In addition, the data incorporated the starting and ending mileposts of traffic barriers on the roadways, and the roadway ID along with its direction. In addition, only crashes involving hitting a barrier as their first harmful event were included in the dataset. The crash dataset was aggregated across various barriers based on the milepost, direction of travel, and highway system.  
Table 1 presents summary statistics of those predictors that were found to be important in the statistical analysis. A few points are worth mentioning from Table 1. Some barriers experienced more than a single crash. For those scenarios, the average of various drivers' characteristics, and weather or road conditions were used as explanatory variables across those barriers. For instance, if two drivers with two residencies, Wyoming residence as 0 and non-Wyoming residence as 1, hit a barrier ID of 1234, the average of those crashes as 0.5 would be set for residency variable for barrier ID 1234.  
The concrete barriers accounted for a total of 2% of all barriers and the majority of those barriers in the dataset were higher than 42 inches. As a higher concrete barrier could not result in underride crashes, those barriers were excluded from the optimization process. A binary category was assigned to shoulder widths. A shoulder width less than or equal to 1.5 feet was categorized as 0 while other values were considered as 1. It should be noted that clusters I to IV are based on the FMM model.