esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Organic-Inorganic Hybrids in Chemical Analysis, Bio- and Environmental Monitoring
## 2 Classification and Synthesis of Organic-Inorganic Hybrids (OIH)
### Synthesis  
#### 2.2.1 Sol-Gel and Solvothermal Methods  
There are numerous methods and approaches of hybrid materials synthesis. The first chemical method to allow the mixing of inorganic and organic components at the nanometric scale was the sol-gel process [44]. Sol-gel processes are a method of forming dispersed inorganic component in organic solvents, through the growth of metal-oxo polymer. The sol-gel approach is a simple and low-temperature method to produce OIH which allows to control the chemical composition of products. Compounds produced by the sol-gel process have many applications in superhydrophobic surfaces [45], electrolytes [46], biosensors [47], corrosion protection [48], etc.  
Another method for preparing OIH, similar to the sol-gel process, is solvothermal synthesis [49]. The technique involves separation of organic and metal reagents using immiscible solvents in high temperatures, which can exceed the boiling point of the solvents.
## 5 Application Scenario
### Writing Paper
#### 5.2.6 Test Case Generation  
With the total number of test paths calculated using a flow control diagram of the _CPN4M_ state space, it is necessary to generate a description for each of these paths that will be our test cases.  
For example, let us take the fifth path, using Table 4 to identify the place and the token that belong to a given node. It is possible to identify our test case's goals and roles. In Table 3, the description of each goal is extracted. Therefore, it is possible to generate Table 5 with our first test case.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**State** & **Place** & **Token** \\ \hline
7 & _wsec_ & writer \\ \hline
8 & _wref_,_wcom_ & writer, editor \\ \hline
9 & _finish_ & true \\ \hline
10 & _sv_ & true \\ \hline
11 & _wp_ & editor \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 16: State-space diagram of _Writing Paper_.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Node** & **Goal** & **Role** & **Description** \\ \hline
1 & start & _bool_ & Test Start. \\ \hline
2 & wtitle & _editor_ & An _editor_ writes the title of the article. \\ \hline
3 & wabs & _editor_ & An _editor_ write the abstract. \\ \hline
5 & wsectile & _editor_ & An _editor_ write the title of the sections. \\ \hline
6 & fds & _bool_ & First draft version was written. \\ \hline
7 & wsec & _writer_ & A _writer_ write a section. \\ \hline
8 & wcon, wref & _editor_, _writer_ & An _editor_ writes the conclusion while a _writer_ write references. \\ \hline
9 & finish & _bool_ & conclusion and references were written. \\ \hline
10 & sv & _bool_ & second version was written. \\ \hline
11 & wp & editor & The paper was finished by _editor_. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test case for _Writing Paper_ with _state transition path_ criterion.  
Figure 17 shows an execution of the system for test case number 5. With two agents where [PERSON] represents the _editor_ and [PERSON] the _writer_, the program execution was successful, with the agents accomplishing the respective goals correctly. The following subsection will analyze some test cases where failures occur.
## 5 Conclusions  
This study analyzed the application of LAI inversed from ZY-3 MUX, GF-1 WFV, and HJ-1 CCD data. The method of LAI extraction was based on the SVI-LAI relationship for ZY-3 MUX, GF-1 WFV, and HJ-1 CCD, which was simulated from the PROSAIL model. The LAI inversions were validated using LAI field measurements of maize in Huailai, Hebei Province, China. Regarding the sensor band settings of three Chinese satellite sensors (ZY-3 MUX, GF-1 WFV, and HJ-1 CCD), the performances of these satellite sensors were comparable to that of Landsat-8 OLI. However, the reflectance of the ZY-3 MUX, GF-1 WFV, and HJ-1 CCD images, which was influenced by SZA, VZA, and data processing methods, differed to varying degrees from the reflectance of the Landsat-8 OLI image. The ZY-3 MUX with similar observation geometry conditions as those of Landsat-8 OLI, showed better performance than did GF-1 WFV and HJ-1 CCD in the study area. When compared with the LAI field measurements, the results showed that the performances of the LAI that was inversed from the NIRv-LAI exponential relationships for ZY-3 MUX (R\({}^{2}\) = 0.62, RMSE = 0.54 and BIAS = \(-\)0.02) were better than the others. However, the performances of the LAI that was inversed from the NIRv-LAI relationships for GF-1 WFV and HJ-1 CCD did not perform as well because of the larger variations in the NIR reflectance. In contrast, the LAI inversions from the NDVI-LAI relationships for ZY-3 MUX, GF-1 WFV, and HJ-1 CCD were much more stable because the NDVI is a normalized index that can eliminate the fluctuations in the reflectance in the red and NIR bands. Overall, LAI inversions tended to be systematically underestimated, especially for the higher LAI values. The scaling effects of the different spatial resolutions could not be ignored, which demonstrated that the LAI inversion differences increased with larger variations in the spatial resolution between the two sensors, especially between ZY-3 MUX and HJ-1 CCD, for mixed pixels. However, more vegetation types and multi-temporal data at different spatial resolutions in LAI inversions need further study.  
This work was supported by the GF6 Project under Grant 30-Y20A03-9003017/18, the National Natural Science Foundation of China (No. 41401393 and No. 41671374). [PERSON], [PERSON] and [PERSON] conceived, designed and produced the method of LAI estimation from ZY-3 MUX, GF-1 WFV and HJ-1 CCD based on the PROSAIL model; [PERSON], [PERSON] and [PERSON] accomplished the multi-sensor data preprocessing; [PERSON] provided the LAI spatial representativeness evaluation.  
The authors declare no conflict of interest.
## 4 Discussion
### ORL Database Analysis and Discussion  
The ORL dataset contains 40 classes of target faces. As shown in Table 12, when BPLRC identifies random occlusion of 10%, 20%, and 30% of faces, 10% vertical occlusion of faces, and 20% diagonal occlusion of faces, its number of precision, recall, and F1 score categories greater than 0.7 is greater than other related methods. The number of precision, recall, and F1 score categories equal to 0 is less than or equal to different related algorithms. Therefore, the BPLRC method solves the face occlusion problem more effectively in the ORL datasets than the other related algorithms, showing stronger robustness than LRC, SRC, CRC, ESRC, and Module LRC. Combined with the identification accuracy obtained from the previous experiments, we fully confirm that BPLRC improves upon both LRC and Module LRC. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Occlusion Rate and Method** & \multicolumn{3}{c}{**Other Measurement Model**} & \multicolumn{6}{c}{**Method**} \\ \cline{3-8}  & **Predinance Indicators** & **LRC** & **SRC** & **CRC** & **ESRC** & **Module LRC** & **BPLRC** \\ \hline \multirow{4}{*}{10\% (Vertical Occlusion)} & Precision \textgreater{} 0.7 & 39 & 24 & 33 & 29 & 37 & **40** \\  & Recall \textgreater{} 0.7 & 38 & 27 & 32 & 27 & 37 & **40** \\  & F1 Score \textgreater{} 0.7 & 39 & 18 & 30 & 25 & 37 & **40** \\  & Precision, recall, and F1 Score = 0 & 0 & 1 & 0 & 1 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 38 & 14 & 27 & 13 & 36 & **40** \\
20\% (Random Occlusion) & Recall \textgreater{} 0.7 & 38 & 13 & 26 & 22 & 37 & **39** \\  & F1 Score \textgreater{} 0.7 & 37 & 9 & 23 & 10 & 36 & **40** \\  & Precision, recall, and F1 Score = 0 & 0 & 4 & 1 & 2 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 24 & 11 & 15 & 12 & 36 & **38** \\
30\% (Random Occlusion) & Recall \textgreater{} 0.7 & 26 & 10 & 17 & 15 & 34 & **38** \\  & F1 Score \textgreater{} 0.7 & 16 & 5 & 8 & 7 & 34 & **38** \\  & Precision, recall, and F1 Score = 0 & 1 & 7 & 1 & 6 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 14 & 5 & 18 & 5 & 39 & **39** \\
40\% (Random Occlusion) & Recall \textgreater{} 0.7 & 18 & 6 & 18 & 11 & 37 & **37** \\  & F1 Score \textgreater{} 0.7 & 9 & 1 & 10 & 2 & 37 & **37** \\  & Precision, recall, and F1 Score = 0 & 7 & 13 & 6 & 12 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 36 & 12 & 19 & 15 & 38 & **40** \\
10\% (Vertical Occlusion) & Recall \textgreater{} 0.7 & 36 & 15 & 24 & 25 & 36 & **39** \\  & F1 Score \textgreater{} 0.7 & 36 & 8 & 15 & 11 & 36 & **39** \\  & Precision, recall, and F1 Score = 0 & 0 & 3 & 0 & 1 & 0 & **0** \\  & Precision \textgreater{} 0.7 & 9 & 5 & 8 & 2 & 35 & **35** \\
20\% (Diagonal Occlusion) & Recall \textgreater{} 0.7 & 9 & 6 & 14 & 8 & 34 & **34** \\  & F1 Score \textgreater{} 0.7 & 4 & 1 & 4 & 1 & 33 & **33** \\  & Precision, recall, and F1 Score = 0 & 25 & 27 & 21 & 27 & 0 & **0** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: _Cont._
# Driver Behavior Analysis via Two-Stream Deep Convolutional Neural Network
## 3 Method
### Temporal Stream ConvNet  
Although distracted behavior analysis was studied in recent works [19; 24; 25; 26], only spatial information was considered while temporal information was discarded. In previous studies of action recognition, many network configurations were designed to integrate the spatial and temporal information, for example, 2D ConvNets + LSTM [41; 42], 3D ConvNet [43; 44], and two-stream network [27]. Instead of using the famous 3D ConvNet [43; 44] that would suffer from large parameters and overfitting when there is lack of a large training dataset [27], the two-stream network was used to design the temporal stream ConvNet. In order to extract the motion information, TVL\({}^{1}\) optical flow [28] with default parameters in OpenCV is firstly applied to obtain vertical and horizontal flow frames between two consecutive frames.  
Then, the optical frames of two directions are concatenated, and a stack of 20 flow images with the size of \(224\times 224\times 20\) pixels are input to the temporal stream ConvNet. Figure 3 shows the configuration of the temporal stream ConvNet. Following the input layer, the network is composed of 13 convolution layers with a kernel size of \(3\times 3\) pixels, while five max pooling and the ReLU activation function are set in each layer. Note that, in our work, unlike the original VGG-16 configuration, the  
Figure 2: Configuration of spatial stream ConvNet.  
fully connected layers were not used in the proposed temporal stream ConvNet. Here, a feature map with the size of \(7\times 7\times 256\) pixels from the last convolution layer is extracted as the temporal features and fused with the spatial features in the following fusion network.  
In order to train the temporal stream ConvNet, the pre-trained dataset, consisting of videos of \(N\) (\(N=10\)) actions involving hand motions from the UCF-101 dataset, as used in the training process of the spatial stream ConvNet, was applied. Data augmentation involving random cropping and horizontal flipping was also performed to enlarge the dataset. The vertical and horizontal motion information was estimated between two frames via TVL\({}^{1}\) optical flow. Additionally, in order to reduce the impact caused by the camera movement, the mean flow frame of each direction is calculated, and the flow frames were subtracted from the corresponding mean frame. For the pre-training process, a softmax layer with the size of \(1\times 1\times N\) was added after the last convolutional layer, which was removed in the fine-tuned process, and the hyper-parameters were the same as used in the spatial stream ConvNet. The pre-trained model was then obtained, which was used to initialize the temporal stream ConvNet in the training process of the whole network.
## 3 Position-Information Feature Added Two-Stream CNN with Pedestrian Detector and Tracker
### Position-Information Feature-Image Added Two-Stream CNN for Cross-Attribute Recognition  
Whether pedestrians cross at road is independent of their behavior and depends on their position [35]. For example, if a walking pedestrian is located in the center of the image, the pedestrian is considered to be crossing the road because the pedestrian is walking on the crossroad. If the pedestrian is located on the horizon edge of the image, the pedestrian is not considered to be crossing the road because the pedestrian is walking on the sidewalk. Therefore, information on where the pedestrian is located in the image is required to recognize the cross attribute. This paper defines this information on pedestrian crossing as a position-information feature image. The position-information feature image is generated using a two-dimensional Gaussian filter applied to the area where the pedestrian is located in the RGB image, as illustrated in Figure 4.  
Figure 3: Binary descriptor dense SIFT flow-based two-stream CNN.  
The improved network model of the conventional two-stream CNN is presented in Figure 5. A short stream called a position stream is added to apply the position-information feature image to the conventional two-stream CNN that does not degrade the performance of action-attribute recognition and increases the performance of cross-attribute recognition. As the input image of the network model, one RGB image enters the spatial stream, and \(N\) frames in the \(x\)- and \(y\)-direction of binary descriptor dense SIFT flow images enter the temporal stream similar to the conventional two-stream CNN. Then, the \(N\) frames of the position-information feature images also enter the position stream. The first convolutional layer is the same as the fifth convolutional layer of the other stream, and three fully connected layers are the same as the other fully connected layers to avoid increasing the number of parameters as much as possible.
## 5 International Roadmap to Mandate Connectivity on Board
### When to Mandate Connected Vehicles in Europe  
In 30 November 2016, in Brussels, the European Commission adopted a European Strategy on C-ITS, a milestone initiative towards cooperative, connected and automated mobility [18]. The Strategy will make it possible to deploy vehicles that can talk to each other and to the transport infrastructure on EU roads as of 2019. The strategy aims to avoid a fragmented European market in the field of C-ITS and to create synergies between different initiatives. It addresses the most critical issues, including cyber-security, data protection and interoperability and recommends action at different levels to meet the 2019 target date. In 17 May 2018, in Brussels, the European Commission proposes a comprehensive EU approach towards connected and automated mobility, setting out a clear, forward-looking and ambitious European agenda to identify supporting actions for developing and deploying key technologies, services and infrastructure. The communication also ensures that EU legal and policy frameworks are ready to support the deployment of safe connected and automated mobility [50]. The Commission recognizes that the new market of automated and connected vehicles is expected to grow exponentially with large economic benefits, with, for instance, revenues exceeding 620 billion Euro by 2025 for the EU automotive industry. In addition, the Commission will offer support in 2018 for testing the use of 5G connectivity to enable highly automated driving functions and new mobility services with a budget totaling around 50 million Euro [50]. For what concerns V2V communications, the Commission follows a technology-neutral approach in line with the EU Strategy and has not proposed mandatory deployment of specific technologies. However, it is stated in [50] that _to enable safety-related services that require very low latency, several manufacturers have committed to also fit short-range communication devices (Wi-Fi based) on vehicles from 2019 and road operators have also started to pre-deploy roadside communication infrastructure, allowing direct interaction between vehicles or between vehicles and the road infrastructure. As of 2020, 5G connectivity infrastructure is equally expected to be an important enabler of connected and automated mobility as well as empower innovative digital ecosystems around cars._ The document remarks the expected coexistence of different radio technologies in the 5.9 GHz band.
###### Abstract  
This study introduces a novel Unmanned Aerial Vehicle-mounted (UAV-mounted) Laser Doppler Vibrometer (LDV) system for detecting debonding damage in Hidden Frame-Supported Glass Curtain Walls (HFSGCW). The established system enables UAVs to transport the LDV to high altitudes for operation. The vibration signals acquired by the UAV-LDV system are decomposed into different energy bands by wavelet packet analysis, and then the occurrence and location of the damage are identified by the Sum of Squared Differences (SSD) of the wavelet packet bands' energy. This paper investigates the potential factors affecting the performance of the Unmanned Aerial Vehicle-Laser Doppler Vibrometer (UAV-LDV) system, including the arrangement of measuring points, measuring distance, noise level, and wind speed through the first-order natural frequency, the normalized frequency response functions, and the SSD indicator. Experimental and simulation results confirm the effectiveness of the UAV-LDV system, highlighting its advantages over traditional methods by offering remote, non-contact, and efficient debonding detection. This method not only indicates the presence of the damage, as traditional indicators do, but also pinpoints the exact location of it, ensuring safety and cost-effectiveness in high-rise inspections. The proposed method and indicator offer advantages in terms of convenience, visualization, and efficiency. The study discusses the impact of measurement point arrangement, measuring distance, noise levels, and wind speed on the system's performance. The findings demonstrate that while the UAV-LDV system introduces new capabilities in rapid and reliable structural damage assessment, operational challenges such as wind and noise levels significantly influence its accuracy.  
building curtain wall; structural damage detection; laser doppler vibrometer; remote vibration measurement; unmanned aerial vehicle +
Footnote †: journal: Applied Sciences
## 2 Model Specification of Data Processing
### Denoising Methodologies  
Before unwrapping the interferogram, the image obtained with pre-processing technique had to be denoised. A simple and fast method coupled is sequentially applied: \"fast denoising method based on the sine-cosine transform (SCT) and stationary wavelet transform (SWT) [15].  
This simple and fast method uses a two-dimensional median filter. It is applied using the'medfilt2' function in MatLab [16]. This function has three inputs: the image to be denoised, the window size, and the padding. The output pixel is the result of median filtering a given number of neighbourhood pixels by the window size. This window size is a square to obtain the same resolution in both dimensions. The padding is a way of extending the image boundaries, three options are offered: zero padding where the image is extended with zeros, symmetrical padding where the image boundaries are extended symmetrically, and indexed padding where the image extension is one.  
The second method used to denoise the enveloped phase [15] shown in Figure 2 is a combination of the sine-cosine average (SCA) method [17; 18] and the stationary wavelet transform method. In this method, firstly, the SCA (green in the figure) is the decomposition of the noisy interferogram into a sine and a cosine image. Then, these two types of images are recombined with the 'atan2' function after the SWT method. For each image (yellow parts in the figure), three steps are applied at this stage. An SWT transformation by the'swt2' function is applied with a five-level decomposition and different analysis wavelets were considered. Before the application of this method, the image has to be expanded to meet the condition that the images have to be divided by \(2^{N}\), where \(N\) is the chosen level (in our case the condition is \(2^{5}\)). This is performed by the'vextend' function with the periodic extension of the image with the 'per' mode. A Birge-Massart threshold is defined with the function 'wbmpen' which takes as input an alpha, the tuning parameter for the penalty term, defined as [15] equal to 6.25 and a sigma, the standard deviation, obtained with 'wnoisest'. After the application of the threshold, the inverse SWT is applied by 'iswt2' and finally, the two images are recombined. To further denoise the wrapped phase, two different methods are used. The first denoising algorithm is a simple 2D median filter with a window size of \(3\times 3\) and symmetric filling. This method is followed by the SCA algorithm with SWT with a Birge-Massart threshold, an alpha equal to 6.25, and a default sigma.  
After this step, the image denoising is sufficient allowing us to continue the general data processing.
###### Abstract
[20] proposed the D2 HC-RMVSNet architecture, where existing 3D reconstruction methods expand the receptive field in the feature extraction part by downsampling, which causes feature loss and thus affects the reconstruction completeness, and cited a new feature extraction structure, DRE-Net, which enlarges the receptive field by inflated convolution and no longer performs downsampling [21; 22]. Overall, MVS inherits the stereo geometry theoretical basis of stereo matching, and with the help of more image viewpoints, it effectively improves the influence of the occlusion problem and achieves a big improvement in both accuracy and generalization. However, the existing methods still have room for improvement in terms of feature extraction effect and matching customers in specific practical agricultural production scenarios. Aiming at the above practical problems of production, this paper is dedicated to the use of remote sensing and artificial intelligence technology to realize the rapid and high-quality acquisition of fruit tree information and realize accurate reconstruction, and makes the following contributions:  
1. An automatic key frame extraction algorithm based on the DSST target tracking algorithm was designed to realize efficient and high-quality remote sensing image data acquisition of a multi-angle view of a jujube tree in a large-scale jujube tree garden. 2. The composite U-Net network structure is proposed as a feature extraction network model to make the extracted features more accurate. 3. The parallel self-attention mechanism is designed to improve the feature-enhanced improvement network of DRE-Net, which utilizes the self-attention mechanism and residual connectivity to make the model more complete. 4. For the cost volume regularization link, a multi-scale cost volume information fusion network based on a hierarchical feature body decoder is designed so that the information within the cost volume can be passed layer by layer to improve the estimation quality of the depth map.