esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Present PV Technologies  
The criteria of low cost, high efficiency, long service life, and availability of raw materials are currently best met by crystalline silicon modules. There are In, Te, and Se source limits for CdS/CdTe and CIGS thin film modules. For both CdTe and CIGS modules, the maximum production limit was set at 20 GW\({}_{\text{p}}\) per year [20], which is well below the expected future demand of several hundred GW\({}_{\text{p}}\) per year. Amorphous silicon modules have low efficiency and other technologies have not yet reached the required service life level [19].  
During technological development since 1970, there have been several important changes in the design and technology of crystalline silicon cells and modules (four generations of crystalline silicon cell technology were considered in [21], depending on the complexity of the technological process).  
During their development in the 1980s, back surface field (BSF) technology was introduced into mass production, enabling the production of cells from a P-type starting material without costly photolithography and vacuum deposition. The standard cell structure (BSF) is shown in Figure 8. BSF technology solved the problem of front surface reflexivity by combining the application of surface texture and an anti-reflective layer with the back contact low resistance of the PP\({}^{+}\)-metal structure realized by the Si-Al alloy deposition process. No technological step requires a process temperature higher than 900 \({}^{\circ}\)C, which has made it possible to produce cells with acceptable efficiency even from multicrystalline wafers. The boron-doped P-type starting material was used in accordance with the easier production of crystalline rods with a uniform resistivity distribution due to the high boron segregation coefficient.  
However, during crystalline silicon fabrication, the molten silicon is kept in a quartz crucible, from which a certain amount of oxygen is incorporated in the crystalline silicon.  
Figure 8: BSF cell structure.  
This is a disadvantage, as oxygen forms B-O complexes, which can act as recombination centres and reduce the carrier's lifetime. It was found that the defect concentration is directly proportional to the boron concentration and squared to the interstitial oxygen concentration [22].  
During this development, there were many improvements in the structure and technology of cells (optimization of impurity concentration, contact configuration, passivation of grain boundaries, optimization of the anti-reflective coating, reduction of wafer thickness, etc.), leading to increased efficiency and production costs. In the period 2008-2016, the share of starting multicrystalline material increased to 66%, but thereafter it quickly decreased below 20% in 2020 [23].
# A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods
## 4 Applications Overview of Deep Learning in the Field of Self-Driving Cars
### Other Applications
#### 4.4.4 Traffic Signs and Lights Recognition  
Traffic signs and lights recognition is an important task in the self-driving system. The traffic signs recognition can provide some helpful information for the navigation and safe driving (see Figure 21a). The traffic lights perception at intersections and crosswalks is a necessary function for self-driving cars to observe the traffic regulations and prevent fatal accidents (see Figure 21b). Deep learning methods have demonstrated prominent representation capacity, and achieved outstanding performance in the traffic sign and light recognition, which are reviewed simply as follows.  
In the traffic signs recognition, [PERSON] et al. [82] proposed a traffic signs recognition approach based on a CNN algorithm. First, the structural information of the traffic sign image is extracted based on the hierarchical significance detection method. Then, a neural network model is used to extract the features of the region of interest. Finally, the traffic sign is classified by the Softmax classifier to complete the detection of the traffic sign. [PERSON] et al. [83] designed a deep-learning-based architecture and applied it in the real-time traffic sign classification. The proposed architecture in [83] consists of two convolutional layers, two max-pooling layers, one dropout layer and three dense layers.  
In the traffic lights recognition, [PERSON] and [PERSON] [84] proposed a DNN-based method to detect traffic lights in images. The detector in this paper has a DNN architecture of encoder-decoder. The encoder is used to generate feature maps from the images by the ResNet-101. Then, the decoder is used to generate a refined feature map from the results of the encoder, to output the final classification results for the traffic lights. [PERSON] et al. [85] proposed a traffic light recognition method based on deep learning, which consists of a semantic segmentation network and a fully convolutional network. The semantic segmentation network is employed to detect traffic lights and the fully convolutional network is used for traffic light classification.
## 1 Introduction
### Objectives  
This paper builds on previous work to present a new, fully automated approach to perform change detection and identify individual rockfalls on complex rock slopes. Specifically, the paper introduces:  
1. A streamlined approach to analyze time series point clouds to isolate individual failure clusters (_i.e._, rockfall events) on the rock slope, to quantify volumes of each cluster, and to generate magnitude-frequency relationships from those clusters.
2. An evaluation of the influence of surface modeling parameters such as the degree of hole filling and modeling resolution on the resulting magnitude-frequency relationships, rockfall detection, and overall erosion volume computations. In this study, resolution is defined in terms of the model resolution, rather than actual sampling resolution.  
While an interesting, semi-automated approach for rockfall clustering [33, 34] has recently been developed, the proposed approach is an alternative method of performing this analysis that is fully automated. Additionally, [33, 34] do not consider the influence of hole filling and modeling resolution on the resulting magnitude-frequency relationships, but rather work directly on the point cloud.  
The proposed approach was applied to several study sites with active, unstable rock slopes in the State of Alaska, U.S.A. Results from three representative study sites are presented to demonstrate the effectiveness of this approach. However, it is worth noting that the level of automation developed by this study has enabled this procedure to efficiently evaluate rockfalls along much larger sections of highway than what can be presented in this paper.
## 2 Results and Discussion  
### Sensory Analysis  
Sensory data show that the scores for aroma properties were spread over the full range (scale 0-10), although the highest values were poorly represented, as expected, because of the intrinsic characteristics of the samples, such as species, origin, primary processing, and the life-span of the study. The standard deviations (SD) of the attributes (Table 1) are very low considering the high numbers of both of the samples investigated (184) and judges (6). A high Coefficient of Variation (CV) was observed for some attributes, fruity, flowery and spicy, meaning that these sensory properties were rated as very high or very low by the panel. CV relates the SD to the mean values of the aroma properties and provides a more representative evaluation of the importance of SD. In addition, it is a useful measure for comparing the dispersions of two or more attributes measured on different scales.  
ANOVA analyses and a post-hoc Tukey's test provided information on the judges' ability to evaluate the sensory attributes. Figure 1 shows that judge 3 does not perform similarly to the others for the aromatic intensity coffee property, and, together with judge 2, for acid notes, while judge 2 has a different evaluation for bitter attribute compared to the others. All judges are, however, aligned in rating the scores of the other attributes. These two judges were therefore not taken into consideration for the attributes in which their variance was not comparable to the others. The score averages were used as the \"main scores\" for the nine attributes in the following elaborations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Attributes** & **Mean** & **S.D.** & **Minimum** & **Maximum** & **CV** \\ \hline
**Acid** & 1.79 & 1.68 & 0.10 & 7.80 & 0.93 \\
**Bitter** & 1.50 & 1.70 & 0.20 & 9.00 & 1.14 \\
**Aromatic Intensity** & 6.71 & 1.29 & 1.00 & 10.00 & 0.19 \\
**Flowery** & 1.08 & 1.76 & 0.00 & 9.00 & 1.62 \\
**Fruity** & 0.69 & 1.49 & 0.00 & 10.00 & 2.16 \\
**Nutty** & 1.41 & 2.06 & 0.00 & 9.00 & 1.46 \\
**Woody** & 1.36 & 2.02 & 0.00 & 8.00 & 1.49 \\
**Spicy** & 0.76 & 1.57 & 0.00 & 8.00 & 2.07 \\
**Overall Quality** & 6.63 & 1.46 & 0.60 & 10.00 & 0.22 \\ \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics for the sensory attributes of coffee samples.  
Figure 1: ANOVA and post-hoc Tukey’s test results on the ability of the judges to rate the different attributes. The same letter means that the judges involved rate the attributes in the same way at a confidence level of 95%.
## 3 Retention Modeling
### Evaluation of the Models  
As explained above, we have discussed two categories of retention models, DIMs and MRMs. Each category contains several models, derived with different isotherm types or extended versions. Furthermore, even with the same model, fits can be performed using different input data arrangements and lead to significantly different results. To keep the text clearer, we introduce the following notation. The name of each method contains three parts: (1) either a DIM or MRM, (2) either linear (Lin) or Langmuir isotherm (Lan), and (3) the symbols of input data. Taken together, all these elements form what we call a method. Table 2 gives an overview of the employed methods. All regressions (linear and nonlinear) were performed in Origin 8.5.1 (25), through chi-square minimization with a tolerance of \(10^{-9}\), and a statistical weighting method was applied. A summary of fitting parameters applied for each method and the ranges of R\({}^{2}\) values of regressions are given in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Equation Used** & **Input Data** & **Fitted Parameters** \\ \hline DIM-Lin-kc & \(\text{lg}k=\text{lg}k_{0}-Ac_{mod}\) & \(k,c_{mod}\) & \(A,\,k_{0}\) \\ DIM-Lan-kc & \(\text{lg}k=\text{lg}k_{0}+B\frac{K_{eq,L}\epsilon_{mod}}{1+K_{eq,L}\epsilon_{ mod}}-ac_{mod}\) & \(k,\,c_{mod}\) & \(B,\,a,\,k_{0}\) \\ DIM-Lan-kc & \(\text{lg}k=\text{lg}k_{0}+B\frac{K_{eq,L}\epsilon_{mod}}{1+K_{eq,L}\epsilon_{ mod}}-ac_{mod}\) & \(k,\,c_{mod}\) & \(B,\,a,\,k_{0}\), \(K_{eq,L}\) \\ DIM-Lan-kcK\({}^{*}\) & \(\text{lg}k=\text{lg}k_{0}+B\frac{K_{eq,L}\epsilon_{mod}}{1+K_{eq,L}\epsilon_{ mod}}-ac_{mod}\) & \(k,\,c_{mod}^{corr}\) & \(B,\,a,\,k_{0}\) \\ MRM-Lan-kcK & \(k=\frac{k_{0}+K_{eq,L}\epsilon_{0}(1+M)c_{mod}}{\big{(}1+K_{eq,L}\epsilon_{ mod}\big{)}(1+Mc_{mod})}\) & \(k,\,c_{mod},\,K_{eq,L}\) & \(k_{0},\,k_{c}\), \(M\) \\ MRM-Lan-kcKM & \(k=\frac{k_{0}+K_{eq,L}\epsilon_{0}(1+M)c_{mod}}{\big{(}1+K_{eq,L}\epsilon_{ mod}\big{)}(1+Mc_{mod})}\) & \(k,\,c_{mod},\,K_{eq,L},M\) & \(k_{0},\,k_{c}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of employed methods: each method is a combination of a model (DIM or MRM), adsorption isotherm, and a set of fitted parameters.
## 4 Results and Interpretation
### Bulk Geochemical Parameters  
The abundance and quality of organic matter in the source rock determine the hydrocarbon generation capacity. For the quantitative characterization of the organic matter abundance, many parameters have been proposed, such as TOC, extractable organic matter (EOM) and the HI, while TOC refers to the mass percentage of organic carbon in a unit mass of rock [12; 13]. According to the test, the TOC content of the first member is 0.49-1.25 wt% (average 1.06 wt%), the TOC content of the second member is 0.63-3.49 wt% (average 1.38 wt%), and the TOC content of the third member is 0.14-1.47 wt% (average 0.79 wt%). The TOC value measured in this study is the residual organic matter abundance of the source rock after massive hydrocarbon expulsion. [PERSON] [12] stated that the organic matter abundance index of a source rock cannot be applied to a source rock with a high maturity because the initial TOC value may be much higher than the TOC measured at present [12].  
TN is an important index in the study of the characteristics of organic matter in sedimentary rocks [14]. The TN content of the first member is 0.26-0.59 wt% (average 0.43 wt%), that of the second member is 0.08-0.6 wt% (average 0.27 wt%), and that of the third member is 0.05-0.26 wt% (average 0.12 wt%). The average TS values of the first, second and third members of the Hengtongshan Formation are 0.12, 0.12 and 0.06 wt%, respectively (Table 2).  
The rock pyrolysis data for the Hengtongshan Formation are shown in Table 2. The analysis shows that the HI values of the three members of the Hengtongshan Formation are quite different; among them, the HI values of the second member are very high as a whole, with values of 238.07-1276.47 mg HC/g TOC and an average of 560.93 mg HC/g TOC. In contrast, the average HI values of the first and third members are 247.01 mg HC. The T\({}_{\text{max}}\) values of all samples are between 445 and 460 \({}^{\circ}\)C, indicating a high maturity, but the samples with sampling depths between 233 and 238 m and between 287 and 294 m are all approximately 480 \({}^{\circ}\)C. Combined with the geological conditions of the Tonghua Basin and the core observation results, this may be due to the short-term volcanism that enhanced the thermal maturity of the source rock [9; 10], resulting in over maturity of the source rocks in the two sections.
## 2 The Proposed DCLNN Method for Failure Time Prediction
### Convolutional LSTM Neural Network
#### 2.4.1 Convolutional Neural Network  
Convolutional neural networks are a type of deep neural network with the ability to act as feature extractors. Such models are able to learn multiple layers of feature hierarchies automatically (also called'representation learning'). A typical CNN network structure is shown in Figure 4. This typical CNN includes an input layer, a convolution layer (Conv), a maximum pooling layer (MP), a fully connected layer (FC), and an output layer.  
The structure of the CNN network mainly has two characteristics: Sparse connection and shared weights. As shown in Figure 5, unlikely a fully connected neural network, CNNs use the locally connected mode. The neurons in the m layer are connected to the adjacent neurons in the m-1 layer. The weights of every neuron in the m-1 layer are shared to the neurons in the m layer, which means the weights with the same color in Figure 4 are the same. Due to these advantages, the CNN has the effect of regularization, which can improve the stability and generalization ability of neural networks and avoid over-fitting. At the same time, these two advantages reduce the total amount of weight parameters, which is beneficial to the rapid learning of neural networks. CNNs have good representation learning ability, which can learn excellent features through network training. It is very suitable for a learning process lacking prior knowledge or clear features.  
Figure 4: Convolutional neural network (CNN) network structure.  
In this paper, the failure time in the failure recall data is a typical time series, so a one-dimensional (1D) convolution operation is used to extract the features of failure time sequences.
# Photodegradation Assessment of Calicotriol in the Presence of UV Absorbers by UHPLC/MSE
## 2 Experimental
### Method Validation
#### 2.5.2 Linearity  
The linearity of the method was assessed by analyzing six methanolic calcipotriol solutions in the concentration range of 15-150 \(\upmu\)mL\({}^{-1}\). Each of the solutions was injected into a column three times in the amounts of 10 \(\upmu\)L. Linear regression analysis was conducted using Statistica 13.3 (StatSoft) based on the peak areas and known concentrations of calcipotriol solutions. The slope of the regression line, y-intercept, standard deviation of the slope and intercept, correlation coefficient, R\({}^{2}\) value, and standard error of residuals for the calibration curve were calculated. The Shapiro-Wilk test was used to determine whether the residuals of the dependent variable have a normal distribution. Furthermore, the homoscedasticity of the random component was evaluated using the Bartlett test, and the presence of autocorrelation in the residuals was examined using the Durbin-Watson test.
# Research on Offshore Vessel Trajectory Prediction Based on PSO-CNN-RGRU-Attention
## 1 Introduction
While composite neural networks have stronger learning capabilities than single neural networks, they come with complex network structures, large computational demands, and the challenge of determining the hyperparameters of the network model. Existing hyperparameter optimization methods, such as manual search, grid search, and random search, generally suffer from issues like high blindness and the tendency to fall into local optima. As a result, heuristic algorithms like Particle Swarm Optimization (PSO) have gradually become the preferred approach for optimizing network model hyperparameters for researchers both domestically and internationally. The PSO [15] algorithm has been used to optimize the LSTM neural network. By analyzing input data statistics and the performance of hidden units, the model predicted the closing price of the Indian Energy Exchange with minimal error, providing an efficient solution for electricity price forecasting. The Sparrow Search Algorithm (SSA) [16] has been applied to optimize the GRU model, utilizing data decomposition to obtain feature components. Combining actual company data, they compared various models to provide a better solution for short-term load forecasting in the power grid. The Artificial Bee Colony (ABC) [17] algorithm has been used to optimize the LSTM and construct a hybrid model. Based on a big data platform to compute sentiment polarity indices, the model was used to analyze and predict stock data, significantly improving the accuracy of stock market predictions through multi-model comparisons. The Whale Optimization Algorithm (WOA) [18] has been employed to optimize the LSTM model. The linear components of air pollution data were first extracted and the nonlinear components were output. WOA was then used to search for LSTM hyperparameters. Ultimately, the model predicted air pollutant data and was compared with several related models, providing more accurate reference data for pollutant concentration prediction in air pollution control. In this context, this paper focuses on the important and complex task of vessel trajectory prediction, addressing the challenges associated with determining hyperparameters in existing models and the limitations encountered when processing time series data, particularly the insufficient capture of long-term dependencies and limited generalization capabilities. We propose an optimization scheme for a CNN-RGRU-Attention [19] fusion model based on the Particle Swarm Optimization algorithm to enhance the accuracy of vessel trajectory prediction. The model integrates CNN for extracting local features, residual connections to alleviate gradient vanishing, max pooling for downsampling, and fully connected layers for dimensionality reduction. Additionally, it introduces an attention mechanism to enhance sensitivity to key information while the dual-layer GRU captures long-term dependencies, improving prediction accuracy and the model's generalization ability.
## 5 Simulation Results
### Trajectory Tracking via Attitude Stabilization  
In this subsection, the results of the last performed simulations are presented.  
Figure 9 shows the results of the position tracking, and as can be seen, the desired position is achieved. In both cases, the PD and PID controllers drive the quadcopter to the desired fixed point in a settling time of approximately 1 s with the PD controllers, and less than one second with the PID controllers. On the contrary, an overshoot of 30% is present in the \(x\) and \(y\) response, corrected by the PID controllers. These results were obtained after many trials to find out both the optimal and good-performing PD/PID controllers' gains.  
The attitude (\(\phi\) and \(\theta\)) behaviors corresponding to the position step response and the sinusoidal design are presented in Figures 10 and 11, respectively. Moreover, figure 12 shows the tracking in each axis, so that each axis can be examined independently. As in the previous subsection, a simulation was performed for a circular trajectory to test the maneuverability and the validity of this proposed controller.  
The 3D plot in Figure 13 illustrates a comparison between the reference trajectory and the actual controlled quadrotor trajectory. As in the previous trajectory tracking simulation, the quadcopter is located initially at the origin \((0,0,0)\). The quadrotor follows the desired path closely in the fourth cases (TTRM-PD, TTRM-PID, TTGA-PD, and TTGA-PID designs). In contrast, as mentioned above, the PID for the position (\(x\) and \(y\)) responses presented a higher overshoot. Therefore, it is preferred to adopt PD controllers for the trajectory tracking via attitude stabilization.  
Figure 9: Quadcopter position step response achieved via attitude stabilization, on the basis of the TTRM and TTGA approaches.  
Figure 11: Quadcopter attitude response (corresponding to sinusoidal consign for the position) achieved via attitude stabilization, on the basis of the TTRM and TTGA approaches.  
Figure 12: Quadcopter position trajectory tracking using the TTRM and TTGA approaches.  
Figure 10: Quadcopter attitude response (corresponding to step consign for the position) achieved via attitude stabilization, on the basis of the TTRM and TTGA approaches.  
On the basis of these results, the new proposed strategy of trajectory tracking for quadcopter gives a good performance in term of settling time (less than 1 s) and state error when compared to the hierarchical strategy (Ts about 3 s).  
In conclusion, the control methodology based on trajectory tracking via attitude stabilization was approved and its performance was tested.