esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 6 Test and Analysis
### Compensation Algorithm Verification  
According to the compensation methods and steps presented in this article, the following compensation results are calculated:  
\[K_{i}=\begin{bmatrix}-0.0251&0.0113&-0.0087\\ -0.0384&0.3031&0.0034\\ 0.093&-0.0074&-0.0059\end{bmatrix}B_{p}=\begin{bmatrix}34.2870&746.2563&67. 3204\end{bmatrix}^{T}\]  
The measurement result of a single axis changes with change in the attitude angle of the carrier. Therefore, the measurement data of a single axis lacks standard comparison data. To solve this problem, we have used the total magnetic intensity to perform an algorithmic effect analysis. The relationship between the three axial magnetic field strengths and total magnetic field strength is:  
\[H_{b}=\sqrt{h_{bx}^{2}+h_{by}^{2}+h_{bz}^{2}}\]  
The local magnetic field strength, original measurement data, and compensated data are shown in Figures 8 and 9, respectively.  
Figure 7: Spatial distribution of data after calibration.  
From the analysis of the data in Figure 8, after using the compensation algorithm, the deviation of the data is considerably reduced compared to the local magnetic field, and the fluctuation of the data is smaller. As shown in Figure 9, the measurement data error is significantly reduced after compensation. After further analysis, it can be observed that the average absolute error of the original measurement data compared to the local magnetic field strength is 17.80%, and the average absolute error of the measurement data compensated by the compensation algorithm is 6.86% compared to the local magnetic field strength. From the data, error distribution, and absolute error analysis results, after using the compensation algorithm, the absolute measurement error of the magnetometer is reduced by 61.49%, which effectively compensates for external or system interference.
# Yeast Starter Culture Identification to Produce of Red Wines with Enhanced Antioxidant Content
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Strain** & **Quercetin** & **Gallic Acid** & **Catechin** & **Oenin** & **Trans-Resveratrol** & **Trans-Coutaric** & **Caftaric Acid** \\ \hline \multicolumn{7}{c}{mg/L} \\ \hline
6993 & 2.857 \(\pm\) 0.091 b & 32.872 \(\pm\) 0.486 b & 58.207 \(\pm\) 2.593 b & 115.251 \(\pm\) 1.096 b & 1.412 \(\pm\) 0.118 a & 14.097 \(\pm\) 0.113 b & 52.723 \(\pm\) 0.900 b \\
6920 & 2.433 \(\pm\) 0.152 b & 30.381 \(\pm\) 0.549 & 49.160 \(\pm\) 4.002 a & 120.966 \(\pm\) 0.643 a & 2.766 \(\pm\) 0.097 b & 16.039 \(\pm\) 0.214 b & 56.158 \(\pm\) 0.751 b \\
8766 & 2.369 \(\pm\) 0.391 b & 25.187 \(\pm\) 0.590 b & 44.896 \(\pm\) 0.375 a & 134.179 \(\pm\) 1.229 b & 1.710 \(\pm\) 0.069 a & 9.447 \(\pm\) 0.157 b & 43.842 \(\pm\) 0.614 b \\
14093 & 2.456 \(\pm\) 0.295 b & 30.530 \(\pm\) 344 & 50.545 \(\pm\) 3.924 b & 12.750 \(\pm\) 0.924 b & 1.721 \(\pm\) 0.016 a & 8.806 \(\pm\) 0.059 b & 42.426 \(\pm\) 0.198 a \\
9500 & 3.375 \(\pm\) 0.441 b & 27.015 \(\pm\) 0.427 b & 48.366 \(\pm\) 0.335 a & 131.042 \(\pm\) 2.551 b & 3.085 \(\pm\) 0.067 b & 8.341 \(\pm\) 0.052 b & 42.143 \(\pm\) 0.332 a \\
9501 & 2.132 \(\pm\) 0.91 b & 29.516 \(\pm\) 0.673 a & 42.295 \(\pm\) 3.506 a & 121.213 \(\pm\) 3.725 a & 3.528 \(\pm\) 0.088 b & 0.554 \(\pm\) 0.000 b & 3.130 \(\pm\) 0.113 b \\
9507 & 2.184 \(\pm\) 0.115 b & 24.349 \(\pm\) 0.179 b & 44.407 \(\pm\) 0.086 a & 127.983 \(\pm\) 1.007 b & 2.139 \(\pm\) 0.446 b & 14.093 \(\pm\) 0.046 b & 62.812 \(\pm\) 1.713 b \\
9508 & 2.902 \(\pm\) 0.189 b & 26.848 \(\pm\) 0.875 b & 47.368 \(\pm\) 0.815 b & 125.542 \(\pm\) 3.056 a & 1.442 \(\pm\) 0.028 a & 8.703 \(\pm\) 0.18 b & 54.666 \(\pm\) 1.254 b \\
9530 & 0.671 \(\pm\) 0.010 a & 26.277 \(\pm\) 0.834 b & 46.072 \(\pm\) 0.704 b & 92.511 \(\pm\) 0.632 b & 0.515 \(\pm\) 0.070 b & 7.157 \(\pm\) 0.154 a & 37.062 \(\pm\) 0.796 b \\ CM & 0.677 \(\pm\) 0.059 & 29.559 \(\pm\) 0.009 a & 47.319 \(\pm\) 3.573 a & 124.157 \(\pm\) 0.709 b & 1.565 \(\pm\) 0.079 a & 6.832 \(\pm\) 0.017 a & 40.754 \(\pm\) 0.010 a \\ \hline \multicolumn{7}{c}{CM, commercial yeast strain. Data are mean \(\pm\) S.D. and are representative of three different assays performed. Data were submitted to one-way analysis of variance (ANOVA), [PERSON]’s post hoc method was applied to establish differences between each yeast strain versus control. Different letters indicate statistically significant differences (\(p<0.05\)).} \\ \end{tabular}
\end{table} 
Table 4: Polypphenols content (mg/L) of wines obtained by the nine selected yeasts and the commercial strain (CM).
# A Comparative Analysis of Feature Detectors and Descriptors for Image Stitching
## 2 Overview of Feature Detectors and Descriptors
### MSD Detector  
[PERSON] [71] proposed a new detector, Maximal Self-Dissimilarities (MSD), which is able to detect features across multi-modal image pairs. It is based on the intuition that image patches which are very distinct across a reasonably large area of their surroundings are repeatable and distinctive. This idea of contextual self-dissimilarity inverts the essential paradigm of previous successful techniques based on the occurrence of similar rather than different patches. Furthermore, it extended the local self-dissimilarity concept embedded in existing detectors of corner-like interest points to contextual information, resulting in improved repeatability, distinctiveness and localization accuracy.
## 3 Hybrid-DBR GaN-based VCSELs  
These approaches do not necessarily work well when fabricating GaN-based VCSELs. The major researching interest in this case is the formation of nitride semiconductor DBRs for n-side mirrors. [PERSON] et al. reported the first demonstration of continuous wave lasing from a GaN-VCSEL, in which the n-side DBR comprised 29 AlN/GaN bilayers grown directly on a GaN substrate by MOCVD [4; 5]. In this device, a half-wavelength AlN stack was inserted between every four pairs of AlN/GaN quarter-wavelength stacks to produce the DBR. These AlN stacks suppressed the formation of cracks potentially resulting from the lattice mismatch between GaN and AlN.  
In contrast, other groups have reported a DBR made of AlInN and GaN. Doping AlN with In reduces the lattice mismatch with GaN to zero [6; 7], so this approach is expected to produce crack-free layers. The incorporation of this DBR structure into GaN-based VCSELs has been intensively investigated, primarily by a group at Meijo University [3; 7; 21] and a significant volume of data regarding threshold current, efficiency and optical output has been reported. This type of GaN-VCSEL has recently demonstrated a remarkably high output power above 10 mW together with a peak wall plug efficiency of 8.9% by Stanley Electronics [22].  
However, the small refractive index difference between the two layers is problematic because a large number of layers is required to obtain high reflectivity. It has also been reported that the AlInN growth rate is so low that 12 h are required to grow a DBR with 40 AlInN/GaN DBRs bilayers [7]. The narrow stopband width of epitaxially grown n-side DBRs is another concern with regard to eventual mass production. The lasing wavelength of a VCSEL should ideally be located on the plateaus at which both DBRs exhibit high reflectance, meaning that the lasing mode makes use of the small mirror loss observed upon lasing. However, variations in the fabrication process could disrupt this set of conditions. As an example, a change in the thickness of the DBR layers would shift the position of the stopband. The stopband width for AlGaN/GaN bi-layers is reported to be 19 nm above 90% [7]. Thus, a change in the thickness of a DBR layer having a reflective center at 450 nm by only a small percentage, which is likely to occur in mass production, will shift the reflectance peak by approximately 10 nm. This shift would be sufficient to displace the stopband from the precisely-designed lasing wavelength. Therefore, there will likely be challenges associated with any future mass production of hybrid-DBR GaN-based VCSELs.
## 4 Results and Discussion
### Performance Comparison with the Dynamic Retraining Module
group, as shown in Figure 4. Although our method shows a stable overall performance, the conventional methods show large deviations between different days. This means that if the conventional methods are used alone, changes in OTT trends cannot be reflected, resulting in a decreased classification accuracy. In contrast, our framework facilitates a proper response to trend changes over time.
# GAT-ABiGRU Based Prediction Model for AUV Trajectory
## 4 Experiments and Results
### Analysis of Forecast Results
#### 4.4.3 Overall Experiments  
Figure 11 shows the comparison results of all models with the actual trajectory in terms of latitude and longitude, with each of the six methods displaying distinct predictive outcomes.  
It is clear from Figure 11 that the best performer of these algorithms in terms of prediction accuracy is our proposed GAT-ABiGRU model, whose predicted trajectory is closest to the actual trajectory. This is followed by the neural network models GAT-BiGRU, ABiGRU, CNN-LSTM, GRU, and RNN.  
Figure 12 and Table 3 present the specific performance of all models in terms of the evaluation metrics, such as the mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). Our proposed GAT-ABiGRU model achieves the best performance in all evaluation metrics, with the lowest MSE, RMSE, and MAE values. This result fully demonstrates the high accuracy characteristics of the GAT-ABiGRU model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Metrics** & **Latitude (*N)** & **Longitude (*E)** \\ \hline \multirow{3}{*}{ABiGRU} & MSE & 0.000984 & 0.000825 \\  & RMSE & 0.031368 & 0.028722 \\  & MAE & 0.044362 & 0.040620 \\ \hline \multirow{3}{*}{GAT-BiGRU} & MSE & 0.000318 & 0.000205 \\  & RMSE & 0.017832 & 0.014317 \\ \cline{1-1}  & MAE & 0.025219 & 0.020248 \\ \hline \multirow{3}{*}{GAT-ABiGRU} & MSE & **0.000163** & **0.000142** \\  & RMSE & **0.012767** & **0.011916** \\ \cline{1-1}  & MAE & **0.018055** & **0.016852** \\ \hline \hline \end{tabular}
\end{table}
Table 2: MSE, RMSE, and MAE value comparison among GAT-ABiGRU, GAT-BiGRU, and ABiGRU models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Metrics** & **Latitude (*N)** & **Longitude (*E)** \\ \hline \multirow{3}{*}{RNN} & MSE & 0.009232 & 0.007548 \\  & RMSE & 0.096083 & 0.086879 \\  & MAE & 0.135882 & 0.122865 \\ \hline \multirow{3}{*}{GRU} & MSE & 0.003807 & 0.002014 \\  & RMSE & 0.061700 & 0.044877 \\  & MAE & 0.087258 & 0.063466 \\ \hline \multirow{3}{*}{CNN-LSTM} & MSE & 0.002324 & 0.001847 \\  & RMSE & 0.048207 & 0.042976 \\  & MAE & 0.068176 & 0.060778 \\ \hline \multirow{3}{*}{ABiGRU} & MSE & 0.000984 & 0.000825 \\  & RMSE & 0.031368 & 0.028722 \\  & MAE & 0.044362 & 0.040620 \\ \hline \multirow{3}{*}{GAT-BiGRU} & MSE & 0.000318 & 0.000205 \\  & RMSE & 0.017832 & 0.014317 \\ \cline{1-1}  & MAE & 0.025219 & 0.020248 \\ \hline \multirow{3}{*}{GAT-ABiGRU} & MSE & **0.000163** & **0.000142** \\ \cline{1-1}  & RMSE & **0.012767** & **0.011916** \\ \cline{1-1}  & MAE & **0.018055** & **0.016852** \\ \hline \hline \end{tabular}
\end{table}
Table 3: MSE, RMSE, and MAE value comparison among all models.
# Monthly Precipitation over Northern Middle Atlas, Eastern Morocco: Homogenization and Trends
###### Abstract
Unfortunately, it is usually difficult to obtain complete and reliable metadata, especially in developing countries such as Morocco, where even the recorded climate data available are very limited. To identify irregularities in observed climate data sets, and to make their subsequent adjustments, several statistical homogeneity testing techniques are available [22]. These techniques are generally classified into two groups, called absolute and relative methods [23]. The choice of which method to use generally depends on the task and the state of available resources and data [20]. For our study, we opted for the Climatol relative method (version 3.1.1) developed by [PERSON] [24]. This is available as an open-source R package and uses an adaptation of the Paulhus and Kohler [25] method. The choice of this method is justified by the simplicity of its software implementation; the short time length it requires; and its robustness in the homogenization of the series and the filling of the climatic gaps [26], even if the different station records do not cover the same observation period [27; 28]. We used Climatol here to process the data of 22 stations, without needing to carry out manual homogenization. The assessment of spatial and temporal trends in precipitation plays an important role for actors and decision-makers in a given region. In this context, several studies have been conducted in many countries and regions to detect spatial and temporal changes in climate parameters [29; 30; 31; 32; 33]. Several statistical methods and tests have been defined and used by researchers. A brief literature review of the latter showed the parametric and non-parametric methods widely used to detect significant trends within hydrometeorological series. Among these, the Mann-Kendall test is one of the most frequent non-parametric methods [34; 35; 36]. This test allows us to show the trend significance of a statistical series, assuming that any trend is monotonic but not necessarily linear. Therefore, this work aims to homogenize the rainfall series that cover the northern Middle Atlas. Further to this, we evaluated the precipitation trends in the homogenized series at a monthly scale, using the Mann-Kendall test.
## 4 Results
### Application in Streamflow Prediction
In addition, it shows a generally decreasing trend for NSE values and a generally increasing trend for RE values with the increasing lead days. In general, taking into account both the NSE and RE, the results show that the ECMWF is the most skillful model. It can add an additional lead time up to the seventh lead day after post-processing with the GPP method. [PERSON], [PERSON] [22] identified a ten lead day period in streamflow predictions using post-processed precipitation forecasts. The diminished performance of this study may be ascribed to the inherent challenges of estimating precipitation in the complex terrain of the mountain river basin. The results above show that the ECMWF is more skillful in streamflow prediction among the four EPFs. Figure 11 further presents the hydrograph of ensemble streamflow driving using raw and post-processed ECMWF data at one lead day during the summer of 2017. The observed streamflow is also presented. The figure shows that the trend of observations is well captured by predictions both for the peak and low flows. The peak occurrence time predicted by raw ECMWF data closely matches the observations, while peak streamflow is considerably underestimated during July. The ECMWF ensemble underestimates the first peak event (9 July) and the second peak event (15 July) by about \(-16.51\)% and \(-6.57\)%, respectively. By using the GPP method, the prediction of GPP-ECMWF is improved in terms of generating the hydrograph. Specifically, GPP-ECMWF ensemble underestimates the first peak event (9 July) and the second peak event (15 July) by about \(-11.87\)% and \(-3.83\)%, respectively. Although the underestimation of peak streamflow was reduced, a certain degree of underestimation still could be observed. This is probably resulted from the underestimation of heavy rain rates (Figure 2). In addition, it can be seen that the ensemble prediction driven by GPP-ECMWF can better cover observations than that driven by raw ECMWF data. By using the BMA method, there is little improvement in predicting the ensemble mean streamflow compared with raw ECMWF data. The ensemble range cannot cover observed streamflow, with the maximum BMA-ECMWF ensemble member underestimating observations. Figure 10: RE for the ensemble mean of streamflow simulated by the raw and post-processed EPFs (CMA, ECMWF, JMA, NCEP) at 1—7 lead days during the summer of 2017. The mean value of the absolute RE for all seven lead days is also presented. Figure 9: NSE for the ensemble mean of streamflow simulated by the raw and post-processed EPFs (CMA, ECMWF, JMA, and NCEP) at 1—7 lead days. The mean value of NSE for all seven lead days is also presented.
## 3 Visualization and Clustering
### Self-Organizing Maps  
There are many different clustering methods which can be used in text analysis [18; 19; 20]: artificial neural network (ANN), k-means, agglomerative hierarchical clustering, etc. The SOM is one of the most popular artificial neural network models, proposed by Professor [PERSON] [21]. New extensions and modifications are developed constantly. SOMs can be used to cluster, classify, and visualize the data. The main advantage of this method is to show results in visual form [22]. There are many different tasks where SOM can be used and solve it. SOM can be useful in text mining, too [23; 24]. The main aim of SOM is to preserve the topology of multidimensional data when they are transformed into a lower dimensional space (usually two-dimensional). The SOM is a set of nodes, connected to one another via a rectangular or hexagonal topology. The rectangular topology of SOM is presented in Figure 3.  
The set of weights forms a vector \(M_{ij},i=1,\ldots,k_{x},\ j=1,\ldots,k_{y}\) that is usually called a neuron or codebook vector, where \(k_{x}\) is the number of rows, and \(k_{y}\) is the number of columns of the SOM. All texts of the analyzed dataset converted to SOM are given as a matrix (1). The learning process of the SOM algorithm starts from initialization of the components of the vectors (neurons) \(M_{ij}\). They can be initialized at random (usually these values are random numbers from the interval (0, 1)) or by the principal components. At each learning step, an input vector \(X_{p}\) is passed to the SOM. The vector \(X_{p}\) is compared to all neurons \(M_{ij}\). Usually, the Euclidean distance between this input vector \(X_{p}\) and each neuron \(M_{ij}\) are calculated. The vector (neuron) \(M_{w}\) with the minimal Euclidean distance to \(X_{p}\) is designated as a neuron winner (the best match unit). All the neuron's components are adapted according to the learning rule:  
\[M_{ij}(t+1)=M_{ij}(t)+h_{ij}^{w}\big{(}X_{p}-M_{ij}(t)\big{)} \tag{2}\]  
Figure 3: Two-dimensional self-organizing map (SOM) (rectangular topology).  
Here \(t\) is the number of learning step, \(h_{ij}^{w}\) is a neighboring function, \(w\) is a pair of indices of the neuron winner of vector \(X_{p}\). The learning is repeated until the maximum number of learning step \(T\) is reached.
# Effects of Distracted Pedestrian Behavior on Transportation Safety: Causes and Contributing Factors
## 1 Introduction
### Strategies Implemented to Address Pedestrian Distraction
#### 1.4.2 Infrastructure-Based Interventions  
Infrastructure-based interventions focus on engineering solutions to modify the physical environment, and enhance safety of pedestrians. Examples include:  
* Inbuilt Technology: Technologies such as road markings and flashing lights can warn distracted pedestrians and facilitate safer walking behaviors. For example, traffic lights installed at ground level and LED visual warning devices can improve visibility and alertness. In Germany, 16 red LEDs are embedded in the pavement next to tram crossings to increase safety.
* Road Signs: In Stockholm, road signs are used to warn drivers about distracted pedestrians on the roadway.
* Designated Pedestrian Areas: Innovative approaches include the \"phone lane\" in a theme park in China, specifically designed for distracted pedestrians. Additionally, Brick Lane in London is the UK's first \"Safe Text\" street with a designated pedestrian lane [31].
## 1 Introduction  
Millimeter wave (mmWave) communication systems provide a high peak data rate and system capacity, but they result in restricted spectral efficiency (SE) because of severe signal attenuation [1, 2]. Therefore, the beyond Fifth-Generation (B5G) and Sixth-Generation (6G) mobile communication systems require a more efficient use of the wireless resources and a more powerful means to manage the interference to provide services with a higher throughput and quality of service (QoS) [3]. The reconfigurable intelligent surface (RIS) and rate-splitting multiple access (RSMA) have attracted attention as promising technologies for B5G and 6G because of the improved SE [4, 5, 6, 7, 8].  
The RIS, also called the intelligent reflecting surface (IRS), is composed of a large number of passive elements that can reflect the wireless signal independently in the desired direction. When the channel link of the base station (BS) and user equipment (UE) is blocked, the SE improvement is significant in RIS-based wireless communication. The RIS has various advantages, which are the ease of fabrication and deployment, low energy consumption, and compatibility with the techniques of conventional wireless networks. Withthese advantages, the applications of the RIS in diverse wireless communication networks have dramatically increased [9; 10]. In recent years, a new RIS structure has been proposed, whereby each RIS element can be controlled through an interconnected impedance network, unlike the existing structure controlled by the impedance connected to the ground [11; 12; 13; 14; 15]. In [11; 15], a reconfigurable impedance network matrix that maximizes the received power was found for single-user single-input single-output (SU-SISO) systems. A matrix with a continuous value was directly optimized by using the quasi-Newton method in [11], but a matrix with a discrete value was optimized by designing the discrete reactance codebook in [15]. In [12; 13; 14], the matrix that maximizes the sum-rate was optimized for multi-user multiple-input multiple-output (MU-MIMO) systems. An RIS architecture that supports reflecting and transmitting mode was considered in [12; 14], and a multi-sector RIS structure with multiple RIS faces was proposed in [13].  
RSMA can partially decode the interference and treat it as noise by splitting the user message into common and private messages. The split messages are independently encoded into common and private streams. The common streams are shared by multiple users, and the private streams are for each user. Due to the flexibility of the interference management, RSMA outperforms other multiple access techniques [3]. This technique has robustness to imperfect channel state information (CSI) [16; 17]. Therefore, RSMA can mitigate the disadvantages of the RIS, which has inaccurate CSI for the RIS-aided wireless channel because of the passive elements of the RIS.