esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optimal Allocation of Water Resources
#### 2.2.3 Interval Linear Programming  
Due to climate change and human activities, numerous uncertainties exist in the supply and demand system of agricultural water resources. Traditional deterministic programming models, which neglect the impact of such uncertainties, diminish the referential value of research outcomes. To address these uncertainties, parameters or variables can be represented by interval numbers in a linear programming model [31]. This approach not only yields relatively stable solutions without requiring specific distribution information of the parameters and variables but also manages the uncertainties within the objective function and the constraints of the model [32; 33]. The general form of an interval linear programming model is as follows [28]:  
Figure 3: Flowchart of optimal allocation.  
(1) Objective function:  
\[\text{Maxf}^{\pm}=\text{c}^{\pm}\text{-}\text{x}^{\pm}, \tag{1}\]  
(2) Constraints:  
\[\text{a}^{\pm}\text{-}\text{x}^{\pm}\leq\text{b}^{\pm}, \tag{2}\]  
\[\text{x}^{\pm}\geq 0, \tag{3}\]  
where \(\text{a}^{\pm},\text{b}^{\pm},\text{c}^{\pm},\text{x}^{\pm}\) all represent the interval number; \"\(-\)\" represents the lower limit of the interval; and \"\(+\)\" represents the upper limit of the interval.  
In order to solve the above model, it can be transformed into two linear programming submodels [34]:  
(1) Upper linear programming model  
a. Objective function:  
\[\text{Maxf}^{+}=\sum_{\text{j}=1}^{\text{k}}\text{c}_{\text{j}}^{+}\text{-} \text{x}_{\text{j}}^{+}+\sum_{\text{j}=\text{k}+1}^{\text{n}}\text{c}_{\text{j} }^{+}\text{-}\text{x}_{\text{j}}^{-}, \tag{4}\]  
b. Constraints:  
\[\sum_{\text{j}=1}^{\text{k}}\big{|}\text{a}_{\text{j}}\big{|}^{-}\text{Sign} \Big{(}\text{a}_{\text{j}}^{-}\Big{)}\text{x}_{\text{j}}^{+}+\sum_{\text{j}= \text{k}+1}^{\text{n}}\big{|}\text{a}_{\text{j}}\big{|}^{+}\text{Sign}\Big{(} \text{a}_{\text{j}}^{+}\Big{)}\text{X}_{\text{j}}^{-}\leq\text{b}_{\text{j}} ^{+}, \tag{5}\]  
\[\text{x}_{\text{j}}^{+}\geq 0,\text{x}_{\text{j}}^{-}\geq 0,\forall\text{j}, \tag{6}\]  
(2) Lower-level linear programming model  
a. Objective function:  
\[\text{Maxf}^{-}=\sum_{\text{j}=1}^{\text{k}}\text{c}_{\text{j}}^{-}\text{-} \text{x}_{\text{j}
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 6 Open Issues
### Security  
Despite ongoing efforts to provide secure communication in HSTNs, the intrinsic nature of wireless communications makes it subject to assaults such as denial of service (DoS), spoofing, jamming, and other forms of interference. Physical-layer security (PLS) in HSTNs is an emerging framework in addition to standard cryptographic approaches. However, because of the sophisticated and complicated nature of today's eavesdroppers, using the PLS architecture for secure transmission in HSTNs remains critical.
## 3 Results and Discussion
### High Temperature Tests
#### 3.1.3 Thermal Cycling between Maximal Temperature and 300 \({}^{\circ}\)C  
Thermal cycling tests are performed on specimens between the maximal temperature (400, 500, and 600 \({}^{\circ}\)C) and 300 \({}^{\circ}\)C. Two cases with different thermal cycles--i.e., 5-cycles and 10-cycles--are carried out. Figures 11 and 12 show the comparison of concrete specimens before and after exposure to 5-cycles and 10-cycles, respectively. It is found that color change of concrete occurs after thermal cycles. Besides, micro-cracks are observed on the surface of the specimens. More micro-cracks appear in the concrete in the higher temperature tests. Compared with 5-cycles, 10-cycles may create more intensive micro-cracks in the specimens.  
Figure 11: Concrete specimens subjected to 5 thermal cycles before (**left**) and after (**right**). (**a**) Test 400-300-5; (**b**) Test 500-300-5; (**c**) Test 600-300-5.  
Figure 12: Concrete specimens subjected to 10 thermal cycles before (**left**) and after (**right**). (**a**) Test 400-300-10; (**b**) Test 500-300-10; (**c**) Test 600-300-10.  
Figure 13 shows mass loss of the concrete specimens after thermal cycles. The mass loss is attributed to both leakage of water vapor and thermal decomposition accompanied by release of carbon dioxide. The water content in specimens may have minor differences, while the mass loss is close from different experiments. The average value of mass loss is about 6.0%, and is similar to the results in the non-cycling tests and the thermal cycling tests with a lower temperature of 25 \({}^{\circ}\)C.
# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 3 Methodology
### Near Real-Time Burned Area Detection  
In this study, the burned area data were identified based on each Himawari-8 image using Equation (1). From this equation, the burned area data at timestamp \(i\) equates to the sum of the active fire data in the past two hours. The two-hour interval was found to be suitable for the area chosen in this study. However, the suitability of this time interval should be explored further, if applied to other wildfires. The active fire detection algorithm developed by [PERSON] et al. [46] was used to detect the Esperance wildfire. The algorithm was modified from the active fire detection algorithm used in the MOD64 collection 6 product [52]. This modification is based on a certain threshold required to detect a possible active fire and then eliminating any false positives (e.g., water and cloud masking). The pixels that match the Equation (3) are classified as active fire. The empirical thresholds used in Equation (2) were specifically adjusted for the case study.  
\[BA_{i}=AF_{i}\cup AF_{i-1}\cup\cdots\cup AF_{i-12} \tag{1}\]  
\[(abs(A_{0.64})<0.01)\cap(abs(A_{0.86})<0.01):night-time \tag{2}\] \[\left(\frac{T_{39}-mean(T_{39})}{std(T_{39})}>0.8\right)\cap\left( \frac{T_{39}-mean(T_{39})}{std(T_{39})}-\frac{T_{112}-mean(T_{112})}{std(T_{11 2})}>1.5\right):possible-AF\] \[A_{2.3}>0.05:not-water\] \[(A_{0.64}+A_{0.86}<1.2)\cap(T_{12.4}<265K)\cap((A_{0.64}+A_{0.86}< 0.7)\cup(T_{12.4}>285K)):not-cloud\] \[\begin{split}&\text{\emph{possible}}-AF\cap not-water \cap not-cloud:AF\\ &\text{\emph{possible}}-AF\cap night-time:AF\end{split} \tag{3}\]  
where BA and AF represent burned area and active fire, respectively. \(A_{0.64}\) denotes the albedo value at 0.64 \(\upmu\)m, \(T_{3.9}\) denotes the brightness temperature value at 3.9 \(\upmu\)m, mean(\(T_{3.9}\)) and std(\(T_{3.9}\)) denote the average and standard deviation of \(T_{3.9}\) within the study area.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 5 Performance Evaluation
### Visual Analysis
#### 5.2.2 Impact of Hyper-Parameters  
To further investigate the impact of different hyperparameters on model performance, we designed systematic comparative experiments, focusing on the functional mechanisms of three key parameters: experience replay buffer size, learning rate, and target network update interval.  
The sensitivity analysis of the learning rate reveals a clear convergence boundary effect, as illustrated in Figure 7a. When the learning rate is too low (lr = 0.0001), insufficient weight updates slow down convergence; conversely, an excessively high learning rate (lr = 0.01) causes parameter oscillations, leading to significant fluctuations in the average reward during the convergence process. This phenomenon confirms the strong constraint of the learning rate on the gradient descent path in reinforcement learning. Therefore, during the training process, the training effect is the best when the learning rate is set to 0.005 or 0.001.  
As shown in Figure 7b, by comparing the effects of different experience replay capacities on the agent's average global reward, we find that when the storage space is set to 10,000, the larger sampling range effectively breaks data correlation, resulting in significantly better training stability compared to other capacity configurations. However, it should be noted that an excessively large experience replay buffer can lead to a sharp increase in video memory usage. Experimental results show that when the storage space exceeds the system's memory threshold, a dynamic data replacement strategy must be adopted to maintain training efficiency.  
As shown in Figure 7c, experimental results regarding the target network update interval indicate that adopting a longer update cycle (update rate = 100) slows down the model's convergence speed, while an excessively short update cycle (update rate = 5) also hinders convergence. This finding is consistent with the theoretical analysis of policy synchronization in reinforcement learning. Specifically, within the deep reinforcement learning framework, the update frequency of the target network directly affects the synchronization level between the policy network and the target network, thereby influencing the stability and efficiency of the overall learning process. When the update interval is too long, the target network parameters lag behind those of the policy network, leading to  
Figure 6: Deployment result of the emergency communication network. (**a**) Greedy strategy. (**b**) Reinforcement learning strategy (ours).  
significant estimation bias in the target values and weakening the guiding effect during learning, which in turn reduces the convergence speed. Conversely, if the update interval is too short, the frequent changes in the target network result in unstable learning signals, making the training process prone to oscillation and negatively affecting the model's stable convergence. As a result, setting the update rate to 10 yields optimal convergence behavior.
## 3 Results and Discussion
### Mechanical Properties  
#### 3.2.1 Tensile Properties  
The effects of CPH addition on the tensile strength and modulus of the composites are shown in Figure 3a. UPT resin showed a tensile strength of 29 MPa. It can be seen that as the CPH content was introduced at 10%, the tensile strength of the unsaturated polyester composites decreased. Further reduction was noticed with an increment in filler content until it reached 9.465 MPa at 14 weight percent CPH. Similar decrease in UPT strength with addition of filler has been reported by [PERSON] et al. [57]. The particle size of the CPH could play a critical role in the reduction of the tensile strength by increasing the area of stress concentration zones [58]. Furthermore, the lower tensile strength at higher CPH content may be due to formation of agglomeration of CPH particulates leading to poor crosslinking of polyester chains. Although the tensile strength decreased with increasing filler content, the produced composites showed favourable results (9.465-15.735 MPa) that may be similar or higher in some cases when compared to published reports on some construction materials. For example, [PERSON] et al. [59] reported 4.23 MPa for 60% weight bauxite residue while foam glass, mineral fibres and polystyrene reported 0.689, 0.069, and 0.310 MPa, respectively. More so, the obtained tensile strength values are higher than the values reported by [PERSON] et al. [60] of 4.35 and 1.25 MPa for fine and coarse crumb rubber polyester composites, respectively. However, for the tensile modulus, 10% initial inclusion of CPH resulted  
Figure 2: Scanning electron micrographs of (**a**) unsaturated polyester resin (UPT) (**b**) a representative mono reinforced composite (**c**) representative of hybrid composite.  
in a slight drop in the modulus value of the composite. The maximum tensile modulus obtained is 1.605 GPa, i.e., 12% CPH content and 88% polyester. Beyond that, the tensile modulus ranges from between 1.509 to 1.231 GPa. This drop in tensile modulus is probably due to an increase in filler content, which results in poor load transmission between the fibres. In addition, high fibre concentrations imply resin-depleted areas, resulting in insufficient fibre-matrix adhesion for stress transfer [61]. The addition of clay further reduced the tensile strength by over 47.57% while increasing the tensile modulus by 136.44% as shown in Figure 3b. The reduction in tensile strength could be due to agglomeration in the hybrid resulting in non-uniform stress transfer while the increase in the modulus could be due to the absence of voids or pores and good interfacial bonding [62]. This is similar to the observation of [PERSON] et al. [63] who reported reduction in tensile strength and improvement in tensile modulus with addition of clay in clay-polypropylene nanocomposite.
# Automating Visual Blockage Classification of Culverts with Deep Learning
## 6 Detection-Classification Pipeline for Visual Blockage Detection  
In light of the reported insights from the presented experiments in Section 5, a detection-classification pipeline is a potential future work in the development process to address the background clutter issue. The idea is to detect the culvert openings from the image using the object detection model (i.e., Faster R-CNN [35]) at the first stage and classify the detected culvert openings as \"blocked\" or \"clear\" using a deep learning classification model (i.e., ResNet50). Figure 13 shows the conceptual block diagram of the detection-classification pipeline. A preliminary system with such a pipeline has already been deployed on a cutting edge hardware for testing purposes [18]. For culvert opening detection, m@AP50 of 0.95 has been achieved using the Faster R-CNN model while an improved 94% blockage classification accuracy has been achieved using the ResNet50 model.  
Figure 12: Conceptual block diagram of framework for partial automation of visual blockage classification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Model Processing Time (s)** \\ \end{tabular} & \begin{tabular}{c} **Total Execution Time (s)** \\ **Image 1** \\ \end{tabular} & \
\begin{tabular}{c} **Image 2** \\ \end{tabular} &
\begin{tabular}{c} **Image 3** \\ \end{tabular} \\ \hline DarkNet53 & 0.05 & 0.12 & 0.2 & 0.35 \\ DenseNet121 & 0.09 & 0.17 & 0.24 & 0.39 \\ InceptionResNetV2 & 0.14 & 0.21 & 0.29 & 0.44 \\ InceptionV3 & 0.09 & 0.17 & 0.24 & 0.39 \\ MobileNet & 0.06 & 0.13 & 0.21 & 0.36 \\ ResNet152 & 0.13 & 0.20 & 0.28 & 0.43 \\ ResNet50 & 0.08 & 0.15 & 0.23 & 0.38 \\ VGG16 & 0.08 & 0.15 & 0.23 & 0.38 \\ EfficientNetB3 & 0.09 & 0.16 & 0.24 & 0.39 \\ NASNet & 0.15 & 0.22 & 0.30 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CNN model processing times for three different size images.
# Simulating Intraday Electricity Consumption with ForGAN
## 1 Introduction
The goal of ForGAN is to simulate the next value in a time series \(x_{t+1}\), given the historical data \(c=(x_{1},\ldots,x_{t})\). Unlike traditional forecasting models, ForGAN learns the underlying conditional probability distribution \(p(x_{t+1}|c)\), enabling the generation of multiple plausible outcomes. Again, this distinction between simulation and forecasting is crucial: forecasting aims to provide one single \"best guess\" for the future. Simulation explores the range of possible outcomes via simulating multiple realizations of \(x_{t+1}\). Hence, ForGAN enables the generation of synthetic data for computing statistics or risk measures of \(x_{t+1}\). Additionally,the set of generated values for \(x_{t+1}\) can be used to compute a prediction for the actual value, such as the mode or the mean of the generated conditional distribution. Given this task, the main innovations in this contribution are as follows:  
* The accessible introduction of ForGAN as a well-suited data generation tool for time series data in applications related to electricity consumption. * The presentation of our realization of ForGAN in detail and a display of its ability to learn the conditional distribution of a time series' next value. * To provide a framework that does not need data transformations nor parametric, simplistic model assumptions. * The description of the full training process of ForGAN and its problems/dangers that have to be overcome. * A collection of quality checks that a GAN has to pass before it is ready to generate reliable time series data. The next section describes the basic structure and algorithms of a GAN in general. The focus will then shift to aspects of ForGAN. Accessible descriptions of its special structure and ingredients will be provided. In Section 3, ForGAN is presented in action by dealing with the problem of simulating the intraday electricity consumption data of Germany. These data are publicly available and are not as unpredictable as stock prices. They display a behavior in time that is a mixture of a (non-linear) deterministic tendency with moderate random fluctuations. A purely data-based approach is followed. In particular, it is not claimed that a new model for the behavior of energy consumption evolution has been found. The preprocessing of data before handing them to ForGAN is also explained. Further, hyperparameter tuning and algorithmic decisions before its application are considered. Section 4 demonstrates the effectiveness of ForGAN when applied to the time series of intraday electricity consumption data. By leveraging its ability to model true data distributions, ForGAN produces highly realistic simulations. This will be checked by testing it on the final 40% of the time series. The following sections aim to provide a comprehensive and reproducible example of how conditional GANs can be effectively adapted for time series simulations in real-world settings, such as electricity consumption forecasting. Finally, the results and properties of the method are discussed, and a conclusion is provided.
# Research on Optimized Energy Scheduling of Rural Microgrid
## 4 Short-Term Rural Electricity Load Forecast  
In terms of agricultural load forecasting, we first set up a database of crop growth environment indicators. Combined with the crop growth characteristics, it is necessary to explore the relationship between the crop growth environment and the electrical energy consumption of greenhouses. Then we select the data samples from the database and build a short-term agricultural load-forecasting model based on the XGBoost algorithm.  
In terms of residential load forecasting, the effective features are directly selected from the database as data samples to train the prediction model. Finally, the overall load forecasting results of the rural area are obtained, which will help us to grasp the trend of electricity consumption and work out the appropriate energy-dispatching scheme.
## 5 Case Study: An Actual Deployment at the City Campus of the University of Auckland  
To validate the reliability and practical applicability of our proposed HWASN and the proposed end-to-end CCBA model, we conducted a small-scale experimental deployment at the City Campus of the University of Auckland. This case study aims to assess the system's performance in a real-world environment, addressing potential challenges in network communication and sound classification accuracy.
# CasOmniMVS: Cascade Omnidirectional Depth Estimation with Dynamic Spherical Sweeping
## 4 Experiments
### Ablation Studies  
**Evaluation of Dynamic Spherical Sweeping.** We compared the performance of the proposed method with and without dynamic spherical sweeping (dss). VAR represents using the variance used in [10,18] to estimate the uncertainty \(\sigma_{t}\). All networks had data augmentation disabled during training. Table 5 shows the ablation studies of the proposed dynamic spherical sweeping. For the non-dynamic spherical sweeping method, \(h_{2}\) and \(h_{3}\) were set to 2.0 and 1.0, respectively. The variance was defined as  
\[var_{t}=\sum_{j=S_{t,min}}^{S_{t,max}}(s_{t}^{pred}-j)^{2}\times p_{t,j}, \tag{15}\]  
where \(var_{t}\) is the variance in stage \(t\). The results demonstrated the superiority of our method over the fixed-interval and variance-based uncertainty prediction approaches.  
**Evaluation of Cascade Strategy.** We used the fine-tuned model to test the results of different stages and conducted a quantitative comparison, as shown in Table 6. The results demonstrated that our cascade strategy could progressively improve the prediction accuracy. Figure 8 illustrates that the network predictions were continuously refined from coarse to fine as the stages advanced.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Network Setting** & **MAE (\(\downarrow\))** & **RMSE (\(\downarrow\))** & **>1 (\(\downarrow\))** & **>3 (\(\downarrow\))** & **>5 (\(\downarrow\))** \\ \hline w/o dss & 1.1803 & 3.3381 & 20.7600 & 7.8810 & 4.6502 \\ w/dss(VAR) & 1.2627 & 3.1713 & 25.6427 & 9.3490 & 5.1254 \\
**w/dss** & **1.0676** & **2.9761** & **20.2148** & **6.8897** & **3.8971** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative results (index) on the omnithings dataset with dynamic spherical sweeping. The best results are in bold. ‘\(\downarrow\)’ indicates that the smaller the value, the better.  
Figure 7: Qualitative results on the real data. Visualizing the inverse of the depth, with a minimum depth of 1.25 m.
# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## Materials and Methods
### Methods  
#### 3.2.1 Pre-processing  
The Z coordinates of points on trees also contains the elevation of terrain, which can affect the segmentation when the parameters are relevant to the tree height. A common procedure is to normalize the elevation with respect to the ground. Ground classification from airborne light detection and ranging (lidar) data is a well-studied topic [47], and there are both proprietary and free and open-source software available for ground filtering. Lastools ([[https://rapidlasso.com/lastools/](https://rapidlasso.com/lastools/)]([https://rapidlasso.com/lastools/](https://rapidlasso.com/lastools/))) is adopted here to identify ground points, which are used to normalize other points, so that tree bases are at zero height, and the Z coordinate corresponds to the tree height. After ground filtering, extra points can be observed at ground level, resulting from understory vegetation, e.g., grass or small bushes. As these are not of interest and can affect the segmentation, a 1 m buffer is applied to filter out these points, as suggested by [PERSON] et al. [22]. This height buffer can vary depending on the vertical structure of studied forests. A bigger buffer might be more appropriate if the understory vegetation is higher [28]. The remaining points are considered to be on trees of interest, and will be segmented.