esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation of IDAQ System
### Compressive Signal of the IDAQ System  
Next, for the data compression technology required to operate and manage the wireless (RF) communication and measurement database of this study, the peak-picking algorithm was designed and reflected in the artificial filter bank, and the peak signal in the time and frequency domains were picked based on the reconstruction signal and compared as shown in Figure 14. Figure 14 shows a diagraming of the reconstruction and compression signals that were created in the signal processing on the first and second stage of the CAFB, in comparison with primitive ones. They were found to faithfully reflect the modal information of primitive signals.  
Additionally, in Figure 14a, only the peak values of the reconstruction signal were selected by the peak-picking algorithm completely and in Figure 14b, the mode information was clearly contained in the peak signal. Therefore, it was observed that the peak-picking algorithm was effective in selecting only the peak value containing the mode information from the entire reconstruction signal.  
Figure 14: _Cont._  
Figure 13: Comparison of time and frequency domain of reconstruction signal using the IDAQ system: (**a**) time domain (original vs. reconstruction signal); and (**b**) frequency domain (original vs. reconstruction signal).  
To evaluate the response performance of the CAFB shown in Figure 14 quantitatively, the error ratios for the frequency for the reconstruction signal and the peak signal compared to the wireless raw data are shown in Table 4, and the reconstruction efficiency of the reconstruction signal compared to the raw data was calculated and shown in Table 5. In Table 4, the natural frequency in the reconstruction signal exactly matched that of the raw data, and the error ratio of the natural frequency in the peak signal was less than 2.5%, completely reflecting the mode information of the raw data. In Table 5, the reconstruction error and the compression ratio were 0.4461 and 0.095, respectively, meaning the developed CAFB can expect to effectively reproduce 55.39% of the signal with 90.5% of the data compression compared to the entire response signals.
## 2 Methods
### Open-Path Multi-Depot Asymmetric Traveling Salesman Problem (OP-MATSP)  
We can model the multi-robot task assignment in an intelligent warehouse as an open-path multi-depot asymmetric traveling salesman problem (OP-MATSP). Each task is considered to be a city node, each robot is considered to be a traveling salesman, and the initial position of a robot is considered to be a starting depot.  
OP-MATSP is another variant of TSP, consisting of \(m\) traveling salesmen, \(m\) depots, and \(n\) city nodes. Only one traveling salesman can visit each city node. The \(m\)th traveling salesman starts from the \(m\)th depot to visit respective city nodes until \(m\) traveling salesmen visit all city nodes. Each traveling salesman stays at the last city node without returning to the starting depot. OP-MATSP can be described as a directed weighted graph \(G=(V,A)\) with set \(V=V_{n}\cup V_{m}\) and set \(A\). \(V=\{1,\cdots,n,n+1,\cdots,n+m\}\) is the city node and depot set. \(A=\{(i,j)|i,j\in V,i\
eq j\}\) is the arc set. Subset \(V_{n}=\{1,\cdots,n\}\) represents \(n\) city nodes. Subset \(V_{m}=\{n+1,\cdots,n+m\}\) represents \(m\) depots. \(C=\left(c_{ij}\right)\) is the cost matrix associated with each arc \((i,j)\in A\), and \(c_{ij}\
eq c_{ji}\) (asymmetric).
## 4 Metro Track Geometry Defect Identification Model and Its Optimization Method
### MTGDI-CNN Based on DARTS
#### 4.2.5 Coping with Dataset Class-Imbalance Problem  
Considering that the metro TGD represents an aberration and the limited number of physical instances of TGD in normal-operation metro tracks, the generated CVD-TGD example dataset, as detailed in Section 4.1, is a class-imbalance dataset. In general, the number of no-defect examples is considerably greater than that of level-II-defect examples [14]. The direct application of such a dataset for MTGDI-CNN training will lead to a detrimental effect [54], wherein the model will experience difficulties in learning features of a minority class of severity level, thereby reducing the effectiveness of the identification performance of this severity level. This problem is usually solved by weighting the model classification threshold or resampling the example dataset in the ML-based method. However, the threshold-weighting method must estimate the proportion of each class in advance; therefore, this approach is not applicable to CVD-based metro-TGD identification problem. The resampling method is classified into oversampling (OS) and undersampling (DS) methods. In particular, OS methods can be further classified as random OS (ROS) and synthetic minority oversampling techniques (SMOTE) [55]. SMOTE is a cluster-based synthesis method for minority class examples. Using the DS method results in several unlearned no-defect examples, and employing SMOTE yields several learned non-existing examples. Considering the high imbalance ratio and nonlinearity in CVD-based metro-TGD identification problem, the DS and SMOTE methods reduce the identification performance. Therefore, this study proposes the ROS method to address the class-imbalance problem in the MTGDI-CNN development, that is, to copy examples of a minority class of severity levels randomly.  
Notably, the MTGDI-CNN was constructed in two stages: CAS and final model validation (FMV). To ensure that the model had sufficient generalization capability, a strategy using ROS only in the FMV training process was proposed. Considering that in CAS, the model architecture changes according to the features of the input example, the ROS method is not used in this stage to prevent excessive adaptation of the model architecture to the features of a minority class. In the FMV, the ROS method is employed to process the training dataset such that the model achieves improved training results.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 2 Background
### Specific Characteristics of Remote Monitoring in Urban VANETs
#### 2.2.1 Urban Environment Constraints  
In general, the vehicular environment has unique characteristics that pose considerable challenge for efficient communication [5; 6; 7]. This environment consists of both stationary roadside units (RSUs) and mobile vehicles, and the mobility in this environment can vary. The mobility tends to be high in the case of fast moving vehicles and slow when the vehicles are moving at low speed. Both extreme cases of mobility have their own challenges. For high mobility scenarios, the wireless communication among vehicles has to circumvent issues such as frequent link failures, Doppler effect, large ETE delay, etc. For the low mobility scenarios, issues such as high packet collision, channel fading, interference, etc., become considerable. Apart from this, there exist significant density variations in vehicular environment, with high density being observed usually during peak hours or in case of traffic jams and low density when few vehicles are moving around. Density variation may pose various constraints for efficient communication among vehicles. High density scenarios face the issue of duplicate packet reception due to redundant messages sent by multiple vehicles. In contrast, low density scenarios face the issue of packet drop due to the unavailability of vehicles for further forwarding the message. Another characteristics of the vehicular environment is the constrained movement of vehicles due to the predefined paths (roads). Thus, location information of the paths can be used to further improve the efficiency of the wireless communication in this context. In addition to the general constraints of the vehicular environment, the urban environment has its own unique characteristics. The work in [8] demonstrated various parameters that have a significant impact on network performance in an urban environment. Vehicles in this environment face drastic speed decay due to the presence of road intersections and traffic signals. Therefore, the average velocity of a vehicle has minimum impact on the network performance as it does not reflect the actual velocity in urban traffic [8; 9].
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 1 Introduction
The motivation for predicting therapist behaviour and client talk type is that accurate models for these tasks can automatically label utterances and therefore facilitate therapy quality monitoring and provide feedback for the therapist (Section 2.1), thus ultimately improving counselling quality. We also note that points 3 and 4 are distinctly different from previous work on automated MISC/MITI coding, since (a) the dataset is annotated following a MISC/MITI-inspired scheme rather than the original MISC/MITI, and (b) we are focused on establishing baselines for the new tasks and probing the impact of dialogue topics on the performance, instead of pursuing the state-of-the-art. After listing the background and related work in Section 2, we describe our MI video acquisition, transcription, and annotator recruitment in Section 3. The annotation scheme is detailed in Section 4, while we show the results of inter-annotator agreement in Section 5. We present thorough analyses of AnnoMI in Section 6 and then introduce the utterance-level prediction tasks and their baselines in Section 7. Topic-related analyses are shown in Section 8 and discussions over the creation and application of AnnoMI are given in Section 9, before this work is concluded in Section 10. The data collection process is described in its entirety--as opposed to only the elements related to the newly introduced utterance attributes--to facilitate a better understanding of this work. The same applies to the analyses in Section 6.1, which have appeared in [PERSON] _et al._ (2018), as they provide a basis and context for the subsequent analyses and experiments in this study.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 3 Results
### Spatiotemporal Variations in GPP, ET, and WUE  
The variation in trends of each pixel could be effectively obtained through the Sen trend analysis and the Mann-Kendall test, and the spatial variations in GPP, ET, and WUE of the Three-North region of China are shown in Figure 4. In Figure 4, only pixels passing the Mann-Kendall test (the trend is significant) are selected. As shown in Figure 4a, the southern and eastern parts of the research regions demonstrate significant increasing GPP trends, which are similar to those of WUE, as shown in Figure 4c. For ET, as shown in Figure 4b, regions with significant variation are much different from those of WUE and GPP. For GPP, 78.53% (21.47%) of the Three-North region of China had an increasing (decreasing) trend, and 36.86% (2.94%) showed a significant increasing trend. For ET, 69.97% (30.03%) of the total research area showed an increasing (decreasing) trend, and 5.28% (0.01%) showed  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sen (WUE \(\times\) 10\({}^{-2}\))** & **\textless\textless\textgreater{}2** & **\textless\textgreater{}1** & **\textless\textgreater{}1** & **\textless\textgreater{}0.2** & **\textless\textgreater{}0.2** & **\textless\textgreater{}1** & **\textless\textgreater{}2** & **\textless\textgreater{}2** \\ \hline \multirow{2}{*}{Level} & Significant & Moderate & \multirow{2}{*}{inhibition} & \multirow{2}{*}{Slight inhibition} & Almost no effect & Slight promotion & Moderate promotion & Moderate promotion \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gradings of impacts of climate factors and human activities on WUE.  
a significant increasing (decreasing) trend. For WUE, 67.60% (32.4%) of the total research region showed an increasing (decreasing) trend, and 32.44% (13.04%) of the research region showed a significant increasing (decreasing) trend. For most parts of Three-North region, the variation trends of WUE are not significant. In western parts, mainly negative variation trends were discovered. It is especially important that, in southeastern parts of Three-North region, large areas of significant increasing trends were discovered. This area is the core areas of ecological restorations and abundant precipitation, suggesting that those large scales of ecological restorations were implemented across the entire Three-North region, and the most effective areas are still in the southwestern boundary regions. For the whole Three-North region, GPP, ET, and WUE all showed small increasing trends, as shown in Figure 5.  
Figure 3: Spatial distributions of annual average GPP (**a**), ET (**b**), and WUE (**c**) in the Three-North region of China from 2001 to 2017.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 3 Proposed Method
### Multi-View Scaling Strategy  
Training new convolutional neural networks often requires a large amount of data. However, the amount of data in the existing high-resolution remote sensing datasets is still limited, which cannot meet the requirements of designing an ideal CNN model. Therefore, this paper proposes a dataset amplification method--multi-view scaling strategy. Since high-resolution remote sensing images are a snapshot of the earth, objects on the surface of the earth are usually randomly distributed in the scene. As a result, the distribution mode of surface objects with different perspectives and scales is also very important for the scene classification of high-resolution remote sensing images.  
In fact, in order to solve the problem of limited labeled data in the field of high-resolution remote sensing image scene classification, many dataset amplification methods have been proposed. Different from the method of cutting the four corners and the center of the images to increase the number of labeled data proposed in [43], the basic idea of the multi-view scaling strategy proposed in this paper is to randomly cut the training data of high-resolution remote sensing image into different sizes and then stretch them. The specific steps are shown in Figure 4.  
1. Labeling with boxes: In order to extract different information from different perspectives, high-resolution remote sensing images are labeled with two boxes.
2. Random cropping: Different parts of the labeled boxes are cropped randomly and get different pictures each time.  
Figure 4: Schematic diagram of the multi-view scaling strategy.  
Scale stretching: Four different size adjustment algorithms, including bilinear interpolation, nearest neighbor interpolation, bicubic interpolation, and area interpolation method, are used to stretch the intercepted image, so that the stretched image is the size of the input layer of the constructed convolutional neural network.
* Random flipping: Flip the image randomly with a certain probability.
* Image standardization: Normalize the image, so that the mean brightness value becomes 0 and the variance becomes 1, as shown in Equation (11). \[image\_standardization=\frac{X-\mu}{\max(\sigma,\frac{1.0}{\sqrt{N}})},\] (11) where \(X\) represents the image matrix, \(\mu\) represents the mean value of the image, \(\sigma\) represents the standard variance and \(N\) represents the number of pixels in the image.
## 4 Results and Analysis  
### Performance of the Composites for Reducing Cloud Contamination  
#### 4.1.1 Statistics of the Cloud Cover Percentage  
The 8-day, 16-day and monthly composites were generated from all the HJ-1A/B imagery in 2010. Figure 4 shows the statistics of the mean cloud cover percentage before and after the composition. The grey blocks with the grey bar were the average cloud cover percentage and its maximum and minimum value in the 8-day compositing period. The blue rounds, red diamonds and green triangle points were the residual cloud cover percentage of the 8-day, 16-day and monthly composites, respectively.  
In general, the residual cloud cover percentages of 8-day, 16-day and monthly composites were significantly reduced. Before the temporal composition, the mean cloud cover percentage in 8 days was 46.25%, and the standard deviation (SD) was 33.41%. The 8-day composites yielded substantially lower residual cloud cover percentage than that of original images, with the mean value of 16.37%, and SD of 21.63%. The mean cloud cover percentage for 16-day composites was further decreased to 4.79%, and the SD was decreased to 8.24%, which meant that the 16-day composites were taken almost under clear-sky conditions. The monthly composites were totally cloud free. The lower cloud cover of composites when compared with origin images was expected, because temporal composition can integrate multi-temporal information by compositing a time-series of cloudy images into a representative \(n\)-day cloudless image. This is consistent with many previous studies [11, 15, 19].
## 4 Experiments and Analysis
### Ablation Experiment
#### 4.5.6 Effect of Dilation Rate  
In order to further analyze the impact of the dilation rate on model performance, we selected a smaller dilation rate (r = [4, 8, 12]) for ASPP-Small and a larger dilation rate (r = [12, 24, 36]) for ASPP-Large and obtained corresponding evaluation results. Table 7 shows the comparison results, from which it can be seen that ASPP-Small performs better than ASPP-Large in terms of IoU, Precision, OA, and F1-Score in both regions. This may be due to the inability of ASPP-Large to effectively enhance the model's focus on small buildings, thereby affecting its performance. In addition, our method outperforms ASPP-Small in terms of IoU, F1-Score, and Recall, indicating that our chosen dilation rate is more appropriate.
# Investigation of Microservice-Based Workflow Management Solutions for Industrial Automation
## 4 Strategies for Workflow Management in a Microservice Architecture
### FITMAN-CBPM  
FITMAN-CBPM is a web-based platform for the semantically enhanced design and execution of business processes in companies [43]. Targeted at the manufacturing domain and specially focused on collaboration among the roles of BPM and manufacturing [76], the platform is built using cloud-based architecture to allow one to access the user interface without needing to install anything on their machine. The database stores BPMN recipes, semantic/ontological data, and user data in a central server.  
The workflow recipes are written in the BPMN format and are designed using COMPEL, a lightweight semantic workflow composition tool, which is based on Activiti. COMPEL uses of ontologies to allow users to semantically annotate service tasks through the user interface [77]. Users select existing web services from a catalog and associate them with BPMN service tasks to automate their execution at runtime. Human tasks are supported intrinsically by the Activiti engine, allowing one to assign responsibilities to specific users or roles. The advantage of this approach is the support of standardized business processes using formal notation languages such as BPMN. Thus, the workflow recipes can be imported and/or exported from/to different platforms.  
Collaboration tools are provided as a part of their platform within the Liferay portal, which is a web-based software that includes tools for document management, forums, and wikis. A REST API is also provided to manage the execution and monitoring of processes.  
Among the limitations, there is the restricted service discovery feature that allows the selection of very specific service repositories due to technical reasons and the lack of support for REST services for binding them to service tasks, as the platform supports only the Web Services Description Language (WSDL).
# Optimal Scheduling of Hydro-PV-Wind Hybrid System Considering CHP and BESS Coordination
## 5 Discussion
### Comparison of Different Models  
Considering the high cost of investment about BESS and charging piles, we compared the optimal results without the BESS or charging piles. The results of four different models are shown in Table 9. The tie-line power with power grid is represented in Figure 5.  
BESS has the ability to store energy, so it can discharge at high prices and charge at a low price to reduce the economic cost and stabilize the tie-line power. Comparing scheduling models where BESS participates and where it does not, we found that the total cost and risk all dropped, and the economic objective was lower in Table 9, which is consistent with the fact. Regarding the hybrid energy system,  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Time**} & **CHP/** & **BESS1/** & **BESS2/** & **EV1/** & **EV2/** & **EV3/** & **EV4/** & **Hydro/** & **Tie-Line Power/** \\  & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** & **kW** \\ \hline
88 & 150 & 0 & 0 & 0 & 0 & 0 & 0 & 2764 & 0 \\
89 & 150 & 0 & 0 & 0 & 0 & 0 & 0 & 125 & 2287.7 \\
90 & 150 & 0 & 0 & 0 & 0 & 0 & 0 & 125 & 2284 \\
91 & 150 & 0 & 0 & 0 & 0 & 0 & 0 & 125 & 2279.6 \\
92 & 150 & 0 & 0 & 0 & 0 & 0 & 0 & 125 & 2290.1 \\
93 & 150 & 36 & 0 & 0 & 0 & 0 & 0 & 2117 & 0 \\
94 & 150 & 18 & 0 & 0 & 0 & 0 & 0 & 2117 & 0 \\
95 & 150 & 18 & 0 & 0 & 0 & 0 & 0 & 2117 & 0 \\
96 & 150 & 0 & 0 & 0 & 0 & 0 & 0 & 2117 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Cost/RMB** & **CVaR/RMB** & **Objective/RMB** \\ \hline Results & 111,275 & 120,306 & 147,366 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimal results of the proposed model.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Include All** & **No BESS** & **No EV** & **No BESS and EV** \\ \hline Cost/RMB & 111,275 & 111,291 & 110,664 & 110,680 \\ CVaR/RMB & 120,306 & 120,324 & 119,693 & 119,710 \\ Objective/RMB & 147,366 & 147,388 & 146,572 & 146,593 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Optimal results of the compared models.  
the charging piles of EV are opposite to the BESS. The charging piles can be seen as a controllable load, so the total cost and risk will drop without them.  
Figure 5 shows the tie-line power curve of the compared model. Throughout the time of operation, the curve of the model including all is much smoother than the compared model without BESS and EV. At periods 28 to 42, 46 to 59, and 70 to 76, the power of the tie-line in the proposed model is lower than those of the compared models without BESS and EV, respectively. Therefore, with the diversification of load demands, expanding the types of energy is important to peak shaving and valley filling.  
In order to show the advantages of the proposed method, the computational time and the comparison results of the hybrid energy system with different situations are given in the following part.