esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Volatile Compounds
#### 2.2.2 Volatiles Derived from Aminoacids  
Aminoacid metabolism generates aromatic, aliphatic, and branched chain alcohols, acids, carbonyls, and esters that are important to the flavour of fruit. 1-Hexanol, 3-methylbutan-1-ol and 3-methyl-2-buten-1-ol are products of the deamination of the relevant \(\alpha\)-aminoacids through oxidation reactions that occur during aerobic storage of cherry fruit [29]. [PERSON] _et al._[30; 31; 32] demonstrated that valine, leucine, alanine and aspartic acid can be converted to short chain carbonyls by tomato extracts. Radioactive labeling studies have shown that valine and leucine are transformed into branched chain flavour compounds that are essential to banana flavour (2-methyl propyl esters and 3-methyl butyl esters, respectively). The initial step in such transformations is deamination of the aminoacid followed by decarboxylation. Various reductions and esterifications then lead to a number of volatiles that are significant to fruit flavour (acids, alcohols, and esters). Further work has shown that aminoacids play a role in apple flavour as well. For example, isoleucine is the precursor of 2-methylbutyl and 2-methylbutenyl esters in apples [33; 34]. The benzoic acids may be further transformed by esterification to yield benzyl esters and by reduction to yield various benzaldehydes and benzyl alcohols. Decarboxylation would yield phenols.
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 4 GeoSOT-AS-Based Gridding Modeling Method for Airspace Information
### Height Information Encoding Conversion Algorithm  
The height dimension information coding conversion algorithm was based on the aforementioned altitude dimension dissection method presented in Section 3.2, which can obtain the coding of the highest level based on the underlying structure of the 150 m segmented flight altitude layer and obtain the coding representation of the lower level by intercepting the prefix based on the clustering of the coding prefix. The specific algorithm flow is shown by Algorithm 2:  
```
Input: Height information for ADS-B data point source (Height), level L Output: ADS-B data height dimensional grid coding set
1. function EncodeHeight(Height,L)
2. \(h_{0}\)=1 ifHeight>0 else h=0
3. \(\{\Delta H\}_{i=1}^{L}\)=150m*211/i
4. for\(i\) in range(L) :
5. ifHeight<\(\Delta H_{i}\);
6. \(h_{i}\)<0
7. else:
8. \(h_{i}\)<-1,Height=height-\(\Delta H_{i}\)
9. \(H_{code}\)=\(h_{0}\)\(h_{1}\)\(h_{2}\)...\(h_{L}\)
10. End if
11. End for
12. return\(H_{code}\)
13. End function
```  
**Algorithm 2** Grid set generation algorithm for height
###### Abstract  
As a fundamental and important task in remote sensing, remote sensing image scene understanding (RSISU) has attracted tremendous research interest in recent years. RSISU includes the following sub-tasks: remote sensing image scene classification, remote sensing image scene retrieval, and scene-driven remote sensing image object detection. Although these sub-tasks have different goals, they share some communal hints. Hence, this paper tries to discuss them as a whole. Similar to other domains (e.g., speech recognition and natural image recognition), deep learning has also become the state-of-the-art technique in RSISU. To facilitate the sustainable progress of RSISU, this paper presents a comprehensive review of deep-learning-based RSISU methods, and points out some future research directions and potential applications of RSISU.  
deep learning; remote sensing image scene understanding (RSISU); remote sensing image scene classification; remote sensing image scene retrieval; remote sensing image object detection
## 2 Related Work
### Object Detection Method Following the Top-Down Approach  
Object detection is a basic task in computer vision, with the purpose of identifying categories and predicting the position of objects in image sequences. It is widely used in fields such as pedestrian recognition [46], autonomous driving [47], and crop planting [48].  
Object detection algorithms can be separated into top-down and bottom-up methods, with the main distinction being the period of holistic object generation and evaluation. The top-down approach is still the most commonly used method nowadays, including two-stage and one-stage methods:  
1. Two-stage method. This method uses a proposal mechanism to decrease negative candidates generated by anchors and outputs the object detection results consisting of prediction bounding boxes and corresponding probabilities of object category through the detection network, such as CNNs. R-CNN [21], proposed by [PERSON] et al., combines region proposals with CNNs and can predict and partition objects by applying high-capacity convolutional neural networks. [PERSON] [22] designed the FastR-CNN object detection model, which uses deep convolutional networks to classify object proposals efficiently; R-FCN [49], developed by [PERSON] et al., contains position-sensitive score maps to resolve the contradiction between image classification and object detection. However, this method often results in a long training time and slow testing speed due to a large amount of repeated computation of convolutional features.
2. One-stage method. This method is created to realize object detection directly from anchors after extracting the input image features without using any proposal elimination mechanisms. [PERSON] et al. [24] presented the SSD, which removes the generating proposal and image feature extraction modules and implements all the work of the model into a single network. RetinaNet [50] is designed to address imbalance problems of object detection by optimizing the standard cross entropy loss. [PERSON] et al. [25] converted the object detection task into a regression problem of object bounding boxes and corresponding category probabilities while proposing a single object detection network, YOLO, which has attained optimal accuracy and speed performance. This type of method can optimize the detection performance from end to end and has very high computation efficiency.  
Regardless of whether a two- or a one-stage method is used, inherent problems arise in the object detection model, such as frequent ID switching of the detected object may result from occlusions. Therefore, extra models such as DeepSORT [20] can be used to improve object detection results. DeepSORT introduces deep learning into the SORT [51] algorithm and reduces identity switching by adding appearance descriptors.
# Load Frequency Optimal Active Disturbance Rejection Control of Hybrid Power System
## 2 Dynamic Model of Load Frequency Control
### Linearized Models for LFC Components  
The linearized models of thermal turbine generators, hydroelectric generators, WTGs, and PVG are introduced in this subsection.  
The transfer function of a reheat thermal generator is shown in Equation (4).  
\[G_{T}(s)=\overbrace{\left(\frac{1}{1+T_{G}s}\right)}^{\text{Governor}}\underbrace{ \left(\frac{1+F_{HP}T_{RH}s}{(1+T_{CH}s)(1+T_{RH}s)}\right)}_{\text{Turbine}} \tag{4}\]  
where \(T_{G}\), \(T_{CH}\), and \(T_{RH}\) are the time constants of the governor, turbine, and reheating system separately. \(F_{HP}\) is the fraction of the output power.  
The linearized representation of the wind turbine generator can be expressed as Equations (5)-(7).  
\[G_{WTG}(s)=K_{PC}G_{\omega p}(s)G_{\omega l}(s) \tag{5}\]  
\[G_{\omega p}(s)=(\frac{K_{P1}(T_{P1}s+1}{s+1})(\frac{1}{T_{P2}s+1}) \tag{6}\]  
\[G_{\omega l}(s)=\frac{K_{P2}}{s+1} \tag{7}\]  
where \(G_{\omega p}\) (s) describes the changes in generated powers. \(K_{P1}\) is a gain of the actuator, and \(T_{P2}\) and \(K_{P2}\) are time constants of the process.  
The linearized models of the photovoltaic generator are represented by Equations (8) and (9).  
\[G_{PV}(s)=\frac{\Delta p_{pv}}{\Delta p_{ref}}\approx 2.18(\frac{s+10}{s+21.76}) \tag{8}\]  
\[G_{PV,\text{load}}(s)=\frac{\Delta p_{pv}}{\Delta G}=\frac{K_{pv}}{T_{pv}s+1} \tag{9}\]  
A hydraulic generator includes two cascaded transfer functions: a governor and a turbine. The linearized model of the hydraulic generator is represented by Equation (10).  
\[G_{h}(s)=\overbrace{(\frac{1}{1+T_{G}s})(\frac{1+T_{RS}}{1+(\frac{R_{T}}{R_{P} })T_{RS}})}^{\text{Governor}}(\underbrace{\frac{1-T_{\omega}s}{1+0.5T_{\omega }s}}_{\text{Turbine}}) \tag{10}\]
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 4 Results
### Evaluation of Filtering Based on Staring Measurements
The standard deviation filter was used in a two-sigma configuration and the CNR-threshold filter, as well used in the combined filter approaches, in a parametrisation of \(\alpha_{le}=-24\) dB and \(\alpha_{ue}=-8\) dB. To the best of our knowledge, we were also porting the filter approach by [PERSON] et al. (2017) for the first time to staring mode and horizontally scanned LiDAR data. So far, this filter approach has been applied only for VAD measurements. Further, we tested the proposed quality control from [PERSON] et al. with Leosphere Windcube 200s data for distances beyond those in the original publication [PERSON] et al. (2016).
## 4 ADRC-Based ABS Slip Ratio Tracking Control
### Design of ADRC
#### 4.2.2 Tracking Differentiator (TD) Design  
The optimal slip ratio obtained from the upper-level road surface recognition method serves as the target slip ratio for the transitional process. The tracking differentiator (TD) is capable of smoothing input signals, filtering out high-frequency noise, while rapidly tracking the trend of signal changes and estimating the differential value of the signal. This results in obtaining the tracking signal and differential signal of the target slip ratio,  
Figure 4: Structure of ABS control strategy in ADRC.  
simultaneously serving to alleviate overshoot and oscillations. The tracking equation for the target slip ratio B is as follows:  
\[\left\{\begin{array}{l}f_{h}=f_{ham}[\lambda_{\text{TD}}(k)-\lambda_{\text{ref }}(k),\dot{\lambda}_{\text{TD}}(k),r_{0},h_{0}]\\ \lambda_{\text{TD}}(k+1)=\lambda_{\text{TD}}(k)+h\dot{\lambda}_{\text{TD}}(k) \\ \dot{\lambda}_{\text{TD}}(k+1)=\dot{\lambda}_{\text{TD}}(k)+hf_{h}\end{array}\right. \tag{22}\]  
where \(\lambda_{\text{ref}}\) is the target slip rate output by the upper pavement recognition module; \(\lambda_{\text{TD}}\) and \(\dot{\lambda}_{\text{TD}}\) are the tracking signal and its differential signal of \(\lambda_{\text{ref}}\), respectively; \(r_{0}\) is the velocity factor; and \(h_{0}\) is the sampling step. According to [31], the \(f_{h}\) function can be expressed as follows:  
\[f_{h}=f_{ham}(c_{1},c_{2},r_{0},h_{0}) \tag{23}\]  
\[\left\{\begin{array}{l}d=r_{0}h_{0}{}^{2}\\ a_{0}=h_{0}c_{2}\\ y=c_{1}+a_{0}\\ a_{1}=\sqrt{d(d+8|y|)}\\ a_{2}=a_{0}+sign(y)(a_{1}-d)/2\\ s_{y}=[sign(y+d)-sign(y-d)]/2\\ a=(a_{0}+y-a_{2})s_{y}+a_{2}\\ s_{a}=[sign(a+d)-sign(a-d)]/2\\ f_{ham}=-r_{0}[a/d-sign(a)]s_{a}-r_{0}sign(a)\end{array}\right. \tag{24}\]
# Reliability Assessment of Space Station Based on Multi-Layer and Multi-Type Risks
## 7 Reliability Assessment of Space Station Configuration Mission  
According to above mentioned model and assessment data, the space station configuration mission reliability is assessed and the result is shown in Table 7. The LOM state has the highest state probability, which needs to be analyzed carefully.  
According to the fault model of KTV phase, the LOM state is influenced by the three layers and three types of risks. Thus, the risk plan should include platform equipment reliability growth and management process improvement.
## 3 Identification and Division of Urban Functional Zones
### Urban Functional Zone Division Based on Cluster Analysis
[86] used the MapReduce framework from the Hadoop platform to realize the KNN parallel algorithm by writing java language and classified urban land use types in the travel zone based on the time sequence characteristics of the residents' call volume reflected in the CDR data. Logistic regression is a common algorithm model for binary classification. [PERSON], et al. [87] used a logistic regression model to determine urban functional zones based on the category proportions of the POIs after autocorrelation processing. This approach avoided the limitation of identifying functional zones only based on the POI ranking and yielded high identification accuracy. [PERSON] and [PERSON] [88] considered the properties of adjacent parcels, such as the size and compactness and the point density of POI data and determined the model parameters through logistic regression. A cellular automata (CA) model was used to identify the parcels automatically. Classification tree is robust and extensible to abnormal data. [PERSON], et al. [27] used a classification tree method and verified that the characteristics of taxi trajectory data are closely associated with various land-use types and intensities. When the depth of the decision tree is too large, over-fitting phenomenon may occur. In order to solve it, random forest (RF) can be used. [PERSON], et al. [24] used the random forest algorithm to identify clusters of locations with similar zones and mobile phone activity patterns to understand the land-use pattern in Boston. [PERSON] and [PERSON] [89] proposed a word vector model that integrated POI and check-in data and used the co-location quotient method and KDE to obtain weighted feature vectors to identify urban functional zones. Some studies used SQL Server to process travel data and matched the functional zones with the actual land use utilizing the expectation maximization (EM) algorithm [2; 90; 91]. The efficiency and quality of most clustering algorithms are well performed when they are implemented based on C/C++, java, MATLAB, and Python, which can provide good identification accuracy for urban functional zones [92]. However, for some complex and heterogeneous urban areas, these methods using shallow features of data may have poor classification accuracy.
# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 3 Network Architecture and System Model
### Task Forwarding Model
As shown in Figures 6 and 7, at a specific moment, node 1 has two tasks at the same time, whose F and C are 300 packet/s and 200 packet, respectively. Task 1 has 100 cached packets on the node, and task 2 is a newly input task with 200 packets. According to the node port routing table and task destinations, it is assumed that the next-hop routes for the two tasks are node 2 and node 3. At this point, node 1 initiates the sequential arrangement of the two tasks. Tasks are ordered based on a prioritization scheme where high-priority tasks take precedence, followed by medium-priority tasks, then low-priority tasks, while tasks of the same priority are randomized. Then, node 1 assigns forwarding and caching capacities to tasks. \(F\) and \(C\) are allocated based on the sorting result; the more advanced task in the queue is preferred to be allocated with the same number of \(F\) and \(C\) as its packets until the allocation is completed. Therefore, the forwarding and caching sizes of task 1 are 100 and 100, respectively, and the sizes of task 2 are 200 and 100, respectively. In cases where multiple tasks are present in the egress link simultaneously, each task is assigned the transmission rate \(R\) according to the same prioritization principle employed for \(F\). Upon completion of the assignment,  
Figure 5: Single-task forwarding model. the output flow of each task can be compared and calculated according to the single-task forwarding model, so as to realize the traffic forwarding of multiple tasks. Based on the above analysis, when multiple nodes in the network handle multiple tasks, the methodology outlined can be employed to compute the input flow, output flow, cache flow, etc., for each node. This involves directing the traffic from each specific task to its designated forwarding port, allowing for seamless forwarding in conjunction with routing. It ultimately achieves the traffic simulation of the overall network, so as to explore the distribution of the network traffic in different conditions. This approach establishes a groundwork for further investigations into network performance metrics, such as packet loss rates.
# Improving Localization of Deep Inclusions in Time-Resolved Diffuse Optical Tomography
## 2 Methods
### Temporal Windows
#### 2.3.4 Tukey Window  
The centralized Tukey window [37] is defined as  
\[w(t)=\begin{cases}1,&0\leq|t|\leq\alpha t^{*}\\ 0.5\left[1+\cos\left(\pi\frac{t-\text{sign}(t)\,\alpha t^{*}}{\text{sign}(t) \,(t^{*}-\alpha t^{*})}\right)\right],&\alpha\,t^{*}\leq|t|\leq t^{*},\\ 0,&\text{elsewhere},\end{cases} \tag{18}\]  
where \(t^{*}\) is the limit of the window and \(0\leq\alpha\leq 1\) parameter controls the smoothness of the window.  
Figure 5: (**Left**) Several Gaussian functions with different \(\sigma\) values. (**Right**) Spectrum magnitude of previous Gaussian functions. The more spread the Gaussian function, the narrower its Fourier transform magnitude.  
Figure 6: (**Left**) Several exponential windows with different \(p\) values. (**Right**) Spectrum magnitude of previous functions.  
If \(\alpha=1\) the Tukey window is rectangular and its Fourier transform is defined as  
\[w(t)=\text{rect}(t/\tau)\longleftrightarrow W(f)=\tau\text{sinc}(\pi f\tau). \tag{19}\]  
These windows have a high selectivity of photon arrival time. The spectrum magnitude of rectangular function (\(\alpha=1\)) is a sinc function with infinite oscillations, which can make difficult to integrate them numerically. Nevertheless, as the \(\alpha\) value decreases, the oscillations at high frequencies disappear without losing much temporal selectivity. Note that before presenting this novel computation method, rectangular windows were only used for functional near-infrared spectroscopy analysis [38] but not for tomography since they could not be calculated without computing the full time-resolved curve.
# Least-Squares Reverse Time Migration in Imaging Domain Based on Global Space-Varying Deconvolution
## 6 Conclusions  
Utilization of the point spread function and the global space-varying high-dimension deconvolution algorithm effectively solves the demand of excessive computation in the iterative data-fitting process of classical least-squares migration and alleviates the high dependency on the accuracy of seismic wavelet and velocity model. Both the theoretical analysis and the model experiment demonstrate that the global space-varying point spread function can effectively approximate the Hessian and the high-dimensional spatial deconvolution algorithm can replace the deburring effects from the Hessian operator. The application on a field 3D data suggests that the method is effective in improving the imaging resolution, especially for caves and fractures, and provide higher-quality seismic images for ultra-deep fractured-cavity reservoirs.  
Figure 11: Comparison in vertical-wave-number spectrum (RTM: red line; LSM: blue line). The unit of y-axis is decibel, and x-axis indicates the wavenumber component.  
Figure 12: Comparison between results of amplitude attributes. (**a**) zoomed results from RTM; (**b**) zoomed results from LSM. The target caves are corresponding to the ones marked by red arrows in Figure 10.  
**Author Contributions:** Conceptualization, B.L.; methodology, M.S., Y.B.; software, M.S., C.X. and B.L.; validation, M.S., C.X.; formal analysis, B.L., Y.B.; investigation, M.S., B.L.; resources, M.S., C.X. and B.L.; writing--original draft preparation, M.S.; writing--review and editing, M.S., C.X. and B.L.; supervision, B.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (No. 2017 YFB0202900), and China Postdoctoral Science Foundation (Grant No. 2020M671539).  
Not applicable.  
The authors declare no conflict of interest.
# Concrete Cracks Detection Based on FCN with Dilated Convolution
## 4 Experiments
### Results and Analysis
#### 4.4.1 Performance on Validation Set  
Compared with the performance on the training set, the performance on the verification set can better reflect the generalization ability of the model. According to the indexes of Figure 12, the proposed model performed better on the verification set. As shown in Figure 12a, the loss of the proposed model on the verification set decreased fastest. After training of 400 epochs, the loss of the proposed model was the lowest, followed by FCN-VGG. The final losses of FCN-Resnet-18 and FCN-Resnet-50 were the same. In the training process, the losses of FCN-Resnet-18 and FCN-Resnet-50 had great fluctuations. Although Figure 11a reports that all models had similar losses, it can be seen from Figure 12a that the generalization ability of FCN-Resnet-18 and FCN-Resnet-50 is deficient. As shown in Figure 12b, the PA of FCN-Resnet-18 and FCN-Resnet-50 had obvious fluctuations. After training of 400 epochs, the PA of FCN-Resnet-18, FCN-Resnet-50 and FCN-VGG were similar, but there was still a gap with the proposed model. From the situation shown in Figure 12c, the performance difference of each model was more obvious. The best was the proposed model, followed by FCN-VGG, then FCN-Resnet-18, and finally  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Training Time (s)** & **Number of Layers** & **Parameters (Millions)** \\ \hline The Proposed Model & 2756.91 & 29 & 14.38 \\ FCN-VGG & 3139.70 & 19 & 18.64 \\ FCN-Resnet-18 & 2049.80 & 29 & 15.10 \\ FCN-Resnet-50 & 2866.19 & 62 & 34.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of parameters, layers and training time of different models.  
FCN-Resnet-50. On the verification set, both the proposed model and FCN-VGG were relatively stable, while the proposed model could achieve better MIoU. The same situation also occurred in FWIoU, as shown in Figure 12d. By comparing Figure 12 with Figure 11, it is obvious that the generalization ability of the proposed model is the best, which proves the effectiveness of dilated convolutions with different dilation rates and the multi-branch fusion strategy.  
Figure 11: The results on training set. (**a**) Shows the change of the loss of each model on the training set with training epoch. (**b**) Shows the change of the Pixel Accuracy (PA) of each model on the training set with training epoch. (**c**) Shows the change of the Mean Intersection over Union (MIoU) of each model on the training set with training epoch. (**d**) Shows the change of the Frequency Weighted Intersection over Union (FWIoU) of each model on the training set with training epoch.  
Figure 12: The results on validation set. (**a**) Shows the change of the loss of each model on the validation set with training epoch. (**b**) Shows the change of the PA of each model on the validation set with training epoch. (**c**) Shows the change of the MIoU of each model on the validation set with training epoch. (**d**) Shows the change of the FWIoU of each model on the validation set with training epoch.