esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Problem Formulation  
### Communication Scenario  
As depicted in Figure 1, we consider a rectangular disaster area with line-of-sight (LoS) characteristics and damaged communication infrastructure. In this area, a set of drones \(\mathcal{U}\)--\(u=1,2,\ldots,U\)--serves as mobile base stations to provide services for ground UEs. We use \((x_{u},y_{u},H)\) to represent the position of the drone, \(u\), where \(H\) represents the height of the drone. This article assumes that all drones fly on a horizontal plane at a constant height from the ground. Each drone is equipped with a fixed-capacity lithium battery and can only provide service for a limited time. When a drone's energy falls below a certain threshold, it will seamlessly switch with a backup drone. We also assume that each drone has a high-capacity fronthaul link, such as a millimeter-wave link, to a ground base station equipped with an agent central unit. This central processing agent receives the drones' observations of the dynamic environment and their own status information. It then stably learns the optimal trajectory strategy to minimize energy consumption and manages the cooperation among the deployed drones. As illustrated in Figure 2, we consider a time period divided into \(2T\) time slots. At the beginning of each time slot, the drone first moves to a new position, with the duration of this time slot being uncertain. In the next time slot, the drone hovers and provides communication services for a duration of \(\Delta t\).  
In this study, we consider a fixed number of rescuers, \(\mathcal{N}\)--\(n=1,2,\ldots,N\)--beginning from different rescue centers to conduct detailed searches of specified regions. The mission  
Figure 1: Drone-assisted communication region.  
Figure 2: Drone-assisted communication region.  
of drones is to cooperate within a specified time period to provide the maximum possible communication coverage to these rescuers. We assume that the drone can communicate simultaneously with multiple UEs within its coverage area and perform interference-free communication by allocating appropriate orthogonal resources. In this post-disaster scenario, our subsequent analysis relies on the following basic assumptions:  
* Each drone is randomly distributed in the certain region at the initial time. When a drone is almost exhausted, it retains sufficient energy to return to the charging station and then seamlessly switches with a backup drone. For simplicity, we keep the drone numbers the same before and after the switch. If a drone fails, we ignore the arrival time of the backup drone and also assume a seamless switch by default.
* Whether each drone can provide services to a particular UE depends on the number of UEs within its coverage. Additionally, each UE covered in the scenario is guaranteed a specific quality of service (QoS).
* The energy consumption of drones is mainly determined by flight and hovering. In this scenario, the energy consumption for communication is small and, thus, ignored [18].
## 5 Field Monitoring
### Crown Settlement Monitoring  
In order to explore the state of the surrounding rock at the top of the tunnel in different construction stages, three sections d3k279 + 875-896 were selected for analysis, as shown in Figure 14a. The final crown settlement of 12 sections in d3k279 + 896-955 in the open excavation period and concealed excavation period was counted, as shown in Figure 14b. By analyzing the data in the diagram, we can determine that:  
1. The crown settlement increases gradually with the progress of construction, and the growth trend of each curve is basically consistent. During the open excavation period, the crown settlement gradually increases with the construction process of the step method, and it can be clearly seen in Figure 14a that it has an obvious growth gradient.  
Figure 13: Time-curve of cumulative layered settlement of section d3k279 + 915: (**a**) 20–30 m; (**b**) 32–40 m.  
In the period of concealed excavation, due to the influence of grouting and other reinforcement measures, the crown settlement is fluctuating and continues to rise as part of the whole trend.
2. The crown settlement of the d3k279 + 885 section is the largest of the three sections in both open excavation and concealed excavation, and the maximum settlement in the two periods reaches 28.5 mm and 37.3 mm, respectively. In addition, the crown settlement can reach a stable state through reinforcement measures, such as continuous grouting behind the lining.
3. Crown settlement is considered to be the main displacement in the tunnel. The final settlement of each section at the end of the open excavation period and concealed excavation period is shown in Figure 13(b). The settlement range of the whole monitoring section is 0.3-13.6 mm and 4.4-15.2 mm, respectively. The crown settlement difference between sections is relatively large. Therefore, it is necessary to pay attention to the change in load caused by the change in surrounding rock stability and to strengthen the monitoring of the surrounding rock in different areas and carry out timely supplement grouting for reinforcement.
# In-Situ Seawater Gamma Spectrometry with LaBr\({}_{3}\) Detector at a Nuclear Power Plant Outlet
## 2 Materials and Methods  
### LaBr\({}_{3}\) Scintillator  
One 2\({}^{\prime\prime}\)\(\times\) 2\({}^{\prime\prime}\) LaBr\({}_{3}\) scintillator (Huakailong(r), Beijing, China) and a photomultiplier tube (PMT) connected to it were encapsulated together in a 0.5 mm thick aluminum alloy case. Then, the other electronic modules were encapsulated together with this aluminum alloy case in a waterproof PVC container. At the experiment site, the PVC container is suspended below the sea surface by a buoy. It collects data in-situ and transmits them to the land base via Global System for Mobile Communication (GSM). Figure 1 shows the external appearances of the aluminum alloy case and the PVC container of the detector.
# Automatic Addition of Fault-Tolerance in Presence of Unchangeable Environment Actions +
## 5 Addition of Fault-Tolerance
### Algorithm to Add Failsafe Fault-Tolerance
Then, we focus on creating new invariant, \(S^{\prime}\), for the revised program. \(S^{\prime}\) cannot include any transition in \(ms_{2}\), as starting from any state in \(ms_{2}\), there is a computation which violates the safety. In addition, the set of program transitions of the revised program, \(\delta^{\prime}_{p}\), cannot include any transition in \(mt\), as by any transition in \(mt\) a state in \(ms_{2}\) is reached. Thus, we initialized \(\delta^{\prime}_{p}\) with \(\delta_{p}|S-mt\). Note that \(S^{\prime}\) must be closed in \(p^{\prime}\cup\delta_{e}\) and it cannot include any deadlock state. (Note, as we said in Section 2, for original deadlock states we have added self-loops. Thus, any deadlock state at this point is created because of removing transitions. Having these deadlock state in the invariant create new behavior inside invariant which is not acceptable.) Thus, whenever we remove a state from \(S^{\prime}\) we ensure that the \(S^{\prime}\) is closed in \(p^{\prime}\cup\delta_{e}\) and does not include any deadlock state by Line 12. To satisfy condition **C1**, in the loop on Lines 13-21, we remove certain states that cause new computations for \(p^{\prime}\) that are not computations of \(p\). Suppose starting from \(s_{0}\) there exists environment transition \((s_{0},s_{1})\). In addition, there exists a program transition \((s_{0},s_{2})\) in the set of program transitions of the original program, \(\delta_{p}\). Set \(ms_{3}\) includes any state like \(s_{0}\). If \(s_{0}\) is reached by environment transition \((s_{3},s_{0})\), in the original program, according to the fairness assumption, \((s_{0},s_{1})\) cannot occur. Thus, sequence \(\langle s_{3},s_{0},s_{1}\rangle\) cannot be in any \(p[\lfloor 2\delta_{e}\) computation. However, if we remove program transition \((s_{0},s_{2})\) in the revised program, \(\langle s_{3},s_{0},s_{1}\rangle\) can be in a \(p^{\prime}[\lfloor 2\delta_{e}\) computation. Therefore, we have to remove any state like \(s_{3}\) (i.e., all states in \(ms_{4}\) from the invariant (Line 20)). After creating invariant \(S^{\prime}\), we add program transitions outside it to \(\delta^{\prime}_{p}\). Note that outside \(S^{\prime}\), any program transition which is not in \(mt\) is allowed to be in the final program. In Line 15, the algorithm declares that no solution to the addition problem exists, if \(S^{\prime}\) is empty. Otherwise, at the end of the algorithm, it returns \((\delta^{\prime}_{p},S^{\prime})\) as the solution to the addition problem.
# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 4 Results and Discussion
### Single-View Consistency Evaluation
#### 4.1.2 Quantitative Evaluation  
The mean, standard deviation, and average gradient were used as the objective evaluation indicators for the color consistency of the single images. The mean value could represent the average light and the average chroma value of the image. The standard deviation indicated the overall color difference. The average gradient represented the detail contrast and the character of the texture change. The ideal images should retain rich original texture information and reasonable contrast and show a slight increase in average gradient, but not excessive. The mean value is calculated as follows:  
\[mean=\frac{\sum f(x,y)}{num}, \tag{25}\]  
where \(f(x,y)\) is the value of pixel \((x,y)\) and \(num\) is the number of pixels participating in the calculation. The sample standard deviation is calculated as follows:  
\[std=\sqrt{\frac{1}{N}\sum(f(x,y)-mean)^{2}}, \tag{26}\]  
where \(N\) is the degrees of freedom, where we let \(N=num-1\). The average gradient is calculated as follows:  
\[\
abla\tilde{G}=\frac{1}{MN}\sqrt{\sum_{i=1}^{m}\sum_{j=1}^{n}\frac{\triangle_ {x}f(i,j)^{2}+\triangle_{y}f(i,j)^{2}}{2}}, \tag{27}\]  
where \(\triangle_{x}f(i,j)^{2}\) and \(\triangle_{y}f(i,j)^{2}\) are the first-order differences of pixel \((i,j)\) in the \(x\) and \(y\) directions, respectively; \(m\) and \(n\) are the height and width of the image, respectively; \(M\) and \(N\) are the degrees of freedom; \(M=m-1\) and \(N=n-1\).  
The images in Figures 9a,c and 10d were evaluated quantitatively. For each image, we randomly extracted four areas of the same size as sub-regions and calculated the mean, variance, and average gradient of \(R\), \(G\), \(B\), and \(I\) (brightness). The selected sub-regions are shown in Figure 10. The results before and after processing are shown in Figure 11.  
Figure 11a-c show that the results after processing tended to appear as straight lines. This demonstrated that the mean values of the different parts in the same image were similar, which indicated that there were similar brightness and chroma values in each part. In Figure 11d-f,  
Figure 10: The sub-region locations and ranges. The three images shown are named F1, F2, and F3. The rectangles are the sub-regions of each image, labeled A, B, C, and D, from left to right.  
the results after processing showed a lower standard deviation for each part, indicating that the color and brightness distributions of the image were more concentrated. In addition, the results after processing were very close to the original images and showed little increase in the average gradient in Figure 11g-i, suggesting that the average gradient of each image showed an appropriate increase. The results shown in Figure 11 indicated that the proposed method improved the richness of the details and allowed clearer visual perception.
# Image-Based Arabian Camel Breed Classification Using Transfer Learning on CNNs
## 2 Literature Review
### ML-Based Approaches for Animal Breeds Classification
The study indicated that the VGG architecture would be the best choice for training if given a limited training time and limited hardware facilities, while ResNet50 generates the highest accuracy of 95.90%; however, it takes a long time to converge during the training phase. [PERSON] et al. [29] conducted a similar study by carring out an exhaustive analysis of five pre-trained CNN models, including VGG16, VGG19, Resnet50, InceptionV3, and Xception to detect horse breeds using two horse images datasets. Furthermore, [PERSON] et al. [30] proposed a novel method using transfer learning of pre-trained deep CNN architectures including MobilenetV2, Mobilenet, Xception, VGG16, and VGG19 to solve the same task based on a horse images dataset. Their results showed that transfer learning on the MobilenetV2 architecture achieved the best accuracy of 89.34%, which confers the best approach to horse breed classification compared to other deep CNNs given a small dataset. [PERSON] et al. [31] presented a computer-vision-based recognition system to categorize buffalo breeds. Their suggested framework combines self-activated convolutional neural networks with self-transfer learning. Additionally, rich feature vectors are obtained by further transferring the feature maps that were generated from the CNN. To classify the feature vectors, they used various machine learning (ML) classifiers. Their suggested framework is tested on various buffalo breeds. They reached a maximum accuracy of 93% using SVM and 85% using other recent learning models including Fine-KNN, Medium-KNN, Coarse-KNN, LP-Boost, Total-Boost, and Bag-Ensemble. For classifying sheep into different breeds based on their images, [PERSON] et al. [32] tried to find the appropriate design for CNNs by experimenting with two methodologies. The first applies Bayesian optimization to set the parameters for a convolutional neural network automatically, whereas the second uses AlexNet, a pre-trained CNN model. Their hybrid approach of using a CNN with Bayesian optimization achieved an accuracy of 98%, while their second approach of using pre-trained AlexNet achieved an accuracy of 97.5%. [PERSON] et al. [33] proposed an ensemble model using the ResNet50 and VGG16 CNN architectures to predict sheep breeds. Five cutting-edge transfer learning models, namely ResNet50, VGG16, VGG19, InceptionV3, and Xception, were compared to their proposedensemble model. Their ensemble model performed the best and achieves 97.32% accuracy according to their experiments. In addition, [PERSON] et al. [34] used the Inception-v3 CNN architecture to recognize and classify goat images into six different breeds. As training such a large network end-to-end is very time-consuming, the authors instead used transfer learning to overcome this problem. The result showed that their modified Inception-v3 correctly classified the animals in 93.33% of the cases with more than 95% confidence.
## 2 Materials and Methods
### The HJA-N Model  
The HJA-N model is cast as a set of mass balance equations, and uses various strategies to represent rate terms. The formulation is designed to explicitly track fluxes and masses that pass through a set of roughly defined environmental storages, differentiated in both vertical and lateral terms. It is a dynamic model that incorporates transient input data at monthly time steps, and as such is useful for evaluating potential effects of seasonality on N cycling and long term trends in N cycling in response to environmental disturbances including climate change, increasing CO\({}_{2}\) concentration, and large scale vegetation manipulation. It is, however, not currently designed for the short time scales necessary to consider the time scale of storm events. Table 1 outlines the naming convention used in tables outlining the model. The full list of mass balance equations is included in Table 2, with the rate terms outlined in Tables 3-6. Model parameters are listed in Tables 7 and 8. The model was implemented using the Stella\({}^{\copyright\) systems modeling framework [31] to more fully communicate the developing model structure with the disciplinary experts providing the perceptual model of catchment process. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**System Component**} & \multicolumn{2}{c}{**Mass**} & \multicolumn{2}{c}{**Additional Information**} \\ \hline
**Name** & **Short Name** & **Name** & **Short Name** & **Short Name** & **Name** \\ \hline Biomass & B & Carbon & C & Root Zone & R \\ \hline Detritus & D & Particulate Nitrogen & N & Below Root Zone & B \\ \hline Soil Organic & & Available Nitrogen & & & \\  & S & (DIN) & & & \\ Matter (SOM) & & Unavailable Nitrogen & & & \\  & & (DON) & & & \\ \hline Aquatic & & & & & \\ Biomass & A & Water & Wa & & \\  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definition of naming convention used in the mathematical description of the model. Terms used in rate equations follow the form: \(\mathit{ComponentMass,Location}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Equation**} & \multicolumn{2}{c}{**Defines**} \\ \hline \(\frac{dB_{{}_{C}}}{dt}=P_{{}_{O}}-T_{{}_{C}}\to\) & _where_ & _i = foliage_, wood_, _fine \(\cdot\) _roots_ & Carbon in biomass \\ \(\frac{dB_{{}_{S}}}{dt}=U_{{}_{g}}+FS_{{}_{N}}-T_{{}_{N}}\to\) & _where_ & _i = foliage_, wood_, _fine \(\cdot\) _roots_ & Nitrogen in biomass \\ \(\frac{dD_{{}_{C}}}{dt}=T_{{}_{C}}-D_{{}_{C}}-R_{{}_{C,D}}\to\) & _where_ & _i = foliage_, wood_, _fine \(\cdot\) _roots_ & Carbon in detritus \\ \(\frac{dD_{{}_{F}}}{dt}=FA_{{}_{N}}+T_{{}_{N}}-D_{{}_{N}}\to\) & _where_ & _i = foliage_, wood_, _fine \(\cdot\) _roots_ & Nitrogen in detritus \\ \(\frac{dS_{{}_{C,R}}}{dt}=D_{{}_{C}}+D_{{}_{C}}+D_{{}_{G}}-R_{{}_{C,R}}-T_{{}_{C}}\) & Carbon in the root zone SOM  
\end{table} 
Table 2: The mass balance equations (differential equations) used to define the HJA-N model.
# Learning-Assisted Rain Attenuation Prediction Models
## 4 Insights of LARA Models
### LARA Model with Two Factors  
[PERSON] et al. [12] established an ANN model that uses horizontal or vertical polarized frequency components of the input and extinction cross-section as output, varying from 1 to 100 GHz and raindrop diameter (Figure 6). They used the Bayesian regularization adaptive training algorithm to train the neural network. They used prespecified mean square error (MSE) as the threshold level to determinethe optimized extinction cross-section in addition to the correlation coefficient between the actual and predicted values (\(R^{2}\)) for India and Singapore experimental dataset datasets.  
**Advantages:** The technique has a wide range of frequency supporting facilities (1-100 GHz). Maybe this technique can be applied easily with the frequency scaling in the satellite downlink channel as the technique is already using the drop size distribution (DSD)-based raindrop distribution.  
**Disadvantages:** It is not mentioned whether the training data is from the terrestrial or earth-space link.  
**Future improvement:** As the technique already supports the vertical and horizontal polarization, it needs to add circular polarization.  
[PERSON] et al. [10] proposed short-term rain attenuation for ground wireless communications using k-nearest neighbor (kNN) with an artificial neural network (ANN). The time-series of relation attenuation measured from a collection of high-precision radar precipitation maps was used as training data.  
**Advantages:** As the specific rain attenuation rate is calculated from the radar-originated rain map, the rain information is more a granular type, e.g., comparatively, almost real rain distribution is attainable. This result leads to the training of the ANN network being built with comparatively accurate data.  
**Disadvantages:** The technique used here requires a 5 min rain rate. In the case of a sparse rain distribution in a map, then the kNN and ANN-based techniques may give an erroneous result.  
**Future improvement:** Although the radar map-based rain attenuation extracting technique may be helpful without deploying the rain gauge data collected, another procedure may have some truncation error. The real wireless device that can be deployed to collect the attenuation data might give a high-precision reduction of rain attenuation.  
**Application area:** The model is useful for short-term rainfall model application, e.g., FMT operation
## 3 Proposed Dataset
### Data Statistics  
The initial 800 images in the dataset were manually annotated by a three-person team, and the annotation and review processes took 8 and 4 worker-hours, respectively, with an average time of 0.9 min per image. In the semi-automatic labeling stage, 800 unlabeled images were fed into the model trained in the previous stage in each cycle. After 12 cycles, 9600 semi-automatically labeled images were obtained in total. In each cycle, we trained a YOLOv5s model from scratch; the maximum training epoch was set to 50, and the model with the highest mean average precision (mAP) was selected for detecting unlabeled data. All detected instances produced in each cycle were reloaded into CVAT, checked, and modified by three people in the team. In the end, 9600 images consumed 26.6 worker-hours, an average of 0.16 min per image, which is one-fifth of the manual annotation process.  
We obtained a total of 13 crack detection models in the manual labeling and semi-automatic labeling stages, and the mAP of each model is shown in Figure 5. At the beginning, the mAP value increased rapidly; after that, the performance of the model barely improved.  
In the process of reviewing the semi-automatic labeled images, the most annotation errors we found occurred on the pavement's white lines. White line damage was mistakenly identified by the model as cracks or sealed cracks. Another annotation error was that the model sometimes misidentified pavement stains as cracks or sealed cracks, but this error gradually decreased with the number of training cycles, and a richer dataset would also help to reduce these issues.  
Overall, the dataset we developed contains 10,400 pavement images. Among these images, 202,840 bounding boxes were created, of which 132,012 are bounding boxes for cracks and the rest are for sealed cracks. There was a difference between the manually annotated data and the semi-automated annotated data, e.g., in our statistics, the average number of instances per sample was 13 in the first 800 images, but in the other semi-automated annotated image data, the number was 20. The reason for this is our proposed dense and redundant annotation method. Common sense considers a crack to be a complete crack, while we divided each whole crack into sub-cracks, which is the way we labeled them. Each part of the crack has a chance to be detected by the model, thus generating more detected instances than manual annotation. Although the semi-automatic annotation method is more dense and more redundant, after checking, we found that the result was still good because it satisfied the two criteria of crack annotation: consistency and coverage. Figure 6 shows a comparison of manual annotation with semi-automatic annotation.  
Figure 5: The mAP value for each model over 13 cycles.