esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Performance Forecasting of Residential Buildings Using Fuzzy Approaches
## 3 Fuzzy Approaches for Energy Performance of Buildings Forecasting
### Performance Criteria  
In order to test the performance of FIR and ANFIS fuzzy models we use 10-fold cross validation (10-fold CV). Model parameters are obtained using the training subset and model validation is performed using the testing subset. For statistical confidence, the training and testing processes are repeated 100 times to the whole dataset, randomly permuted in each run prior to splitting in training and testing subsets.  
Three evaluation criteria are used to evaluate the performance of each of the models. Two of them are error measures, i.e., root mean square error (RMSE) and the mean absolute error (MAE), described in Equations (2) and (3), respectively. These errors have been chosen because they are the ones most frequently used in previous studies [17; 19; 20; 21], and it is intended to compare the fuzzy proposed techniques with the methodologies presented in those works.  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left|y_{i}-\hat{y_{i}}\right|^{2}} \tag{2}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\left|y_{i}-\hat{y_{i}}\right| \tag{3}\]  
In Equations (2) and (3), \(\hat{y}(t)\) corresponds to the predicted output, \(y(t)\) to the system output, and \(N\) is the number of samples. Both RMSE and MAE are commonly used accuracy measures that are useful when comparing different methods on the same data, as is the case in this research. The MAE is a linear score, which means all individual differences are weighted equally in the average. The RMSE gives a relatively high weight to large errors, since the errors are squared before they are averaged. This means that the RMSE is most useful when large errors are particularly undesirable. The MAE and the RMSE can be used together to diagnose the variation in the errors in a set of forecasts.  
The third evaluation criteria has the goal to obtain a comprehensive performance measure. To this end, RMSE and MAE measures are combined into a synthesis index, SI, as defined in Equation (4).  
\[I=\ \frac{1}{2}\sum_{i=1}^{2}\frac{Meas_{i}-Meas_{min,i}}{Meas_{max,i}-Meas_{ min,i}} \tag{4}\]  
where \(Meas_{i}\) is the performance measure, i.e., RMSE, MAE, and \(Meas_{min,i}\) and \(Meas_{max,i}\) are the minimum and maximum values of the corresponding measure for the set of prediction models, respectively. The SI range is [0, 1] and values close to 0 indicates a highly accurate prediction model.
## 4 Example for Damper Design
### Targeting for a Single Mode  
The parameters of the HDR damper and the TM-HDR-D are designed for mitigating the cable's first mode of vibration, assuming that the installation position of the HDR damper is \(x_{1}/L=0.02\), and the TM-HDR-D targets the cable's first vibration mode, i.e., \(\rho=1\). The loss factor of high-damping rubber is set to \(\varphi=0.5\) for both the HDR damper  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Length \(L\) (m)** & **Tension \(T\) (kN)** & **Mass \(m\) (kg/m)** & **Fundamental Frequency (Hz)** \\ \hline
154.08 & 3831 & 70.1 & 0.759 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the cable for the present study.  
Figure 8: First modal damping of a cable with two dampers versus the TM-HDR-D location \(x_{2}/L\) (\(\varphi=0.5\), \(\rho=1\)), with (a) \(x_{1}/L=0.02\) and (b) \(x_{1}/L=0.05\).  
Figure 9: Second modal damping of a cable with two dampers versus the TM-HDR-D location \(x_{2}/L\) (\(\varphi=0.5\), \(\rho=0.5\)), with (a) \(x_{1}/L=0.02\) and (b) \(x_{1}/L=0.05\).  
and the TM-HDR-D. Figure 10 shows the dampers' design process by considering the cable's first vibration mode.  
For the HDR damper, there is an optimal stiffness parameter when the installation location and loss factor are determined. As shown in Figure 10a, the optimal damper stiffness is 1140 kN/m, and the maximum modal damping ratio is 0.0024. Note that the required cable damping ratio for suppressing rain- and wind-induced vibrations is about 0.005 [33]. Therefore, in this case, a single HDR damper cannot meet the vibration reduction requirements. In addition, an additional TM-HDR-D is used to make up for the deficiency of the damping contribution of a single HDR damper. Three different masses of TM-HDR-D, i.e., 20 kg, 30 kg, and 40 kg, are used to improve the first modal damping of the cable system, as depicted in Figure 10b. It can be seen that the effect of the TM-HDR-D on the system's modal damping is very significant, and a small installation mass (such as 20 kg) can still meet the target damping requirements. At the same time, in order to reduce the TM-HDR-D's installation height, its mass should be appropriately increased.
## 1 Introduction  
Cyber-Physical Systems (CPSs) are regarded as most useful due to the fact that they combine the advantages of distributed systems, hardware developments and networked information. One of the main issues that these developments present is the transmission of data between the different entities taking part in the communications. Even though there is a collection of technologies proven to be fully functional in regular environments, having a suitable protocol to interchange information among nodes becomes way more challenging when constrained environments, such as underwater and subsea environments, are providing the transmission medium.
## 1 Introduction
This provided context on weather routing principles, global best practices, technological advancements (e.g., algorithms [19; 20; 21], AI applications [22], low-carbon technologies [23; 24]), international market leaders [25], and general challenges faced by the industry worldwide (e.g., observation gaps [26; 27; 28], modeling difficulties [29; 30; 31; 32], communication constraints [33]). Official Policy Documents and National Reports: Key Chinese government policy documents were analyzed to understand the strategic priorities and official objectives related to maritime meteorology. Crucially, the \"Meteorological High-Quality Development Outline (2022-2035)\" and analyses of the \"National Marine Meteorological Support Project\" provide authoritative context for national goals, framing the assessment of challenges (Section 3) and the rationale behind future prospects (Section 4). Operational Expertise and Internal Documentation: As frontline operational and research personnel at the National Meteorological Centre (NMC), the authors possess direct, long-term experience with the development, implementation, and daily operation of China's primary national weather routing service. This \"insider perspective\" provides unique insights into specific operational workflows, historical context (e.g., CMRC transition, NMC service relaunch), internally developed technologies (e.g., speed loss algorithms, typhoon avoidance methods [34]), specific challenges encountered in the Chinese con text (e.g., integration of domestic NWP models), and internal strategic considerations for future development. The analysis presented in Sections 2-4 represents a synthesis of these three information streams. We explicitly acknowledge that insights derived from operational experience are not as verifiable as peer-reviewed publications. However, excluding this perspective would result in an incomplete picture. By clearly identifying the authors' affiliation and integrating this expert perspective with verifiable public sources, this paper aims to provide a uniquely valuable and nuanced understanding of China's weather routing services. We explicitly acknowledge that insights derived from operational experience and internal documentation lack the direct verifiability of peer-reviewed publications or publicly released datasets. This is an inherent limitation when reviewing the specific status and strategy of a national operational entity. However, we argue that excluding this perspective would result in an incomplete and potentially misleading picture, reliant solely on the generic global literature or high-level policy statements. By clearly identifying the authors' affiliation and integrating this expert perspective with verifiable public sources, this paper aims to provide a uniquely valuable and nuanced understanding of China's weather routing services, contributing practical insights relevant to researchers, industry stakeholders, and policymakers interested in this specific and globally significant maritime context.
# Energy-Efficient and QoS-Aware Computation Offloading in GEO/LEO Hybrid Satellite Networks
## 2 Materials and Methods
### Computation Offloading Method Based on Minimum Cost Maximum Flow
#### 2.2.1 Overview of MCMF  
The MCMF problem is the optimization problem of traffic delivery among nodes of a directed graph, which aims at finding the minimum cost way to deliver the maximum flow, \(f_{max}\), from the source node, \(S\), to the sink node, \(T\).  
Figure 2 illustrates a case of a minimum cost maximum flow network where each edge, \(e(u,v)\), has two weights, i.e., capacity, \(c(u,v)\), which denotes the maximum amount of flow shipped on \(e(u,v)\), and unit cost, \(w(u,v)\), of shipping flow from node \(u\) to node \(v\) on \(e(u,v)\). We use \(f(u,v)\) to denote the flow amount shipped on \(e(u,v)\). Then, the cost of sending a flow of \(f(u,v)\) units on \(e(u,v)\) is equal to \(f(u,v)\times w(u,v)\). As shown in Figure 2, the minimum cost maximum flow network has 6 nodes and 9 edges. The flow is sent from the source node, \(S\), to the sink node, \(T\). Take \(e(v_{1},v_{2})\) for example, the maximum capacity of this edge is 6 and the unit cost of this edge is 9. We assume the current amount of flow through this edge is 2. Then, the cost of this edge is equal to \(2\times 9=18\). Given such a flow network of \(G=(V,\mathcal{E})\), finding a maximum flow that minimizes the total cost of the flow can be expressed as  
min \[\left(\sum_{e^{\prime}(u,v)\in E}f(u,v)w(u,v)\right)\] (19) s.t. \[0\leq f(u,v)\leq c(u,v),\quad\forall e(u,v)\in\mathcal{E}\] (20) \[\sum_{e(u,v)\in\mathcal{E}}f(u,v)-\sum_{e^{\prime}(v,u)\in \mathcal{E}}f(v,u)\] \[=\left\{\begin{array}{ll}f_{max},&\quad u=S\\ -f_{max},&\quad u=T\\ 0,&\quad u\
eq S,T\end{array}\right.\] (21)  
where \(f_{max}\) represents the maximum flow obtained by solving the problem and Equation (20) denotes that the amount of flow shipped on the edge cannot exceed its capacity. From Equation (21), it can be seen that the outflow from the source node is equal to the inflow at the sink node, and, for all intermediate nodes except the source and sink node, the outflow is equal to the inflow.  
The objective of our work is to find an offloading decision (mapping between the on-board missions and the satellites) that minimizes the total energy consumption, since MCMF problems have been extensively researched over the years and several algorithms have been proposed to solve the problems efficiently in polynomial time [28]. By transforming the computation offloading problem of on-board missions into the MCMF problem using a flow network, it is also possible to solve the computation offloading problem in polynomial time. This is because a solution of MCMF can be extracted as a mapping between the nodes in the flow network. By setting the corresponding capacity and unit cost on the edges in the flow network, MCMF algorithms can find the offloading decision (mapping) that minimizes the total energy consumption for GEO/LEO hybrid satellite networks. Therefore, we focus on how to transform the computation offloading problem into an MCMF problem and how to solve it.
## 3 Data
### Data Pre-Processing  
A reasonable division of the database is crucial for improving model performance. The rock mass classification database was randomly divided into a training set and a test set, with proportions of 70% and 30%, respectively. Although random division cannot completely ensure consistent data distribution between the training and test sets, it maintains the objectivity of model evaluation. The training set contains 91 sample points, and the issue of class imbalance remains.  
This study employed adaptive synthetic (ADASYN) oversampling technology [24] to increase the number of minority class samples without disrupting the structure of the majority class rock mass samples, thereby achieving class balance in the rock mass classification data. The core idea of ADASYN is to adjust the generation process of synthetic samples based on the density of each minority class sample in the feature space. For each minority class sample, ADASYN calculates its ratio to its k-nearest majority class samples, representing the density ratio of the minority class sample. A certain number of synthetic samples are generated according to the density ratio; fewer synthetic samples are generated for higher density ratios and more for lower density ratios. To ensure that the synthetic samples are closer to the decision boundary, ADASYN introduces a weighting factor that weights the synthetic samples according to the density ratio of each minority class sample. By adaptively generating synthetic samples based on the dataset characteristics, ADASYN increases the coverage of the minority class samples and enhances the diversity and authenticity of the synthetic samples.  
Data analysis revealed that different rock mass indicators in the database have varying magnitudes and scales, which can affect model performance and interpretation. Therefore, data preprocessing was necessary before model training. This study used the Z-score method for normalization, converting the data to a standard normal distribution with a mean of 0 and a standard deviation of 1, as shown in Equation (7). Z-score normalization eliminates the influence of different feature scales while preserving the relative order and information content of the data, improving the stability and reliability of the model.  
\[x^{*}=\frac{x-\mu}{\sigma} \tag{7}\]  
where \(x\) is the value of the original data sample, \(\mu\) is the mean of the original data sample, and \(\sigma\) is the standard deviation of the original data sample.
## 1 Introduction  
While deep reinforcement learning (RL) has emerged as a viable solution for complicated and high-dimensional decision-making problems [1], including games such as Go [2], chess [3], checkers [4], and poker [5; 6], robotic locomotion [7; 8], autonomous driving [9; 10], and recommender system [11; 12], the determination of an effective reward function remains a challenge, especially in multidomain task-oriented dialogue systems. Many recent studies have struggled in sparse-reward environments and employed a handcrafted reward function as a breakthrough [13; 14; 15; 16]. However, such approaches typically are not capable of guiding the dialogue policy through user goals. For instance, as shown in Figure 1, the user cannot attain the goal because the system (S1) that exploits the handcrafted rewards completes the dialogue session too early. Moreover, as the dialog progresses, the user goal will frequently vary.  
Due to these problems, systems that exploit the handcrafted rewards fail to assimilate user goals and guide users through user goals, achieving low performance, while humans self-judge from dialog context using well-defined reward function in their minds and generate appropriate responses despite multidomain circumstances.  
MaxEnt-IRL [17] and Inverse reinforcement learning (IRL) [18; 19] tackle the problem of recovering the reward function automatically and using this reward function to generate optimal behavior. Although generative adversarial imitation learning (GAIL) [20], which applies the GANs framework [21], has proven that the discriminator can be defined as a reward function, GAIL fails to generalize and recover the reward function. Adversarial inverse reinforcement learning (AIRL) [22] enables GAIL to take advantage of disentangled rewards. Guided dialogue policy learning (GDPL) [23] uses the AIRL framework to construct the reward estimator for multidomain task-oriented dialogues. However, these approaches often encounter difficulties in balancing the performance of the reward estimator and policy generator and produce excessively uninformative gradients.  
In this paper, we propose the variational reward estimator bottleneck (VRB), a novel and effective regularization algorithm. The VRB uses information bottleneck [24; 25; 26] to constrain unproductive information flows between dialogue internal representations and state-action pairs of the reward estimator, thereby ensuring highly informative gradients and robustness. The experiments show that the VRB achieves state-of-the-art (SOTA) performances on a multidomain task-oriented dataset.  
The remainder of this paper is organized as follows: Section 2 presents the brief background to set the stage for our model. Section 3 describes the proposed method in detail along with mathematical calculations. Section 4 outlines the experimental setup, whereas Section 5 presents the experiments and the results thereof. Section 6 provide discussions and the conclusions of this study.
# Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning
## 3 Results
### Lightning Analysis
#### 3.2.1 Overall Statistics
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Mean** & **99\% Conf. Int. (\(\pm\))** & **Samples** &
\begin{tabular}{c} **RCG \textgreater{} 10** \\ **(Mean)** \\ \end{tabular} & **Rain \textless{} 6 mm h\({}^{-1}\)** \\ \hline CYGNSS FDS with GLM & & & & \\ (m s\({}^{-1}\)) & & & & \\ (m s\({}^{-1}\)) & & & & \\ \hline CYGNSS FDS without & & & & \\ GLM (m s\({}^{-1}\)) & & & & \\ \hline CYGNSS YSLF with GLM & & & & \\ (m s\({}^{-1}\)) & & & & \\ \hline CYGNSS YSLF without & & & & \\ GLM (m s\({}^{-1}\)) & & & & \\ \hline ASCAT with & & & & \\ GLM (m s\({}^{-1}\)) & & & & \\ \hline ASCAT without GLM & & & & \\ (m s\({}^{-1}\)) & & & & \\ \hline IMERG with GLM & & & & \\ (mm h\({}^{-1}\)) & & & & \\ \hline IMERG without GLM & & & & \\ (mm h\({}^{-1}\)) & & & & \\ \hline CYGNSS Std. Dev. with & & & & \\ GLM (m s\({}^{-1}\)) & & & & \\ \hline CYGNSS Std. Dev. & & & & \\ without GLM (m s\({}^{-1}\)) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary statistics for winds and precipitation both associated and not associated with GLM lightning. The overall statistical results, common to all lightning instruments, were the following. CYGNSS FDS winds were stronger when lightning was not present (by \(\sim\)0.2-0.4 m s\({}^{-1}\)), and this was largely robust against controls for RCG or precipitation rate. ASCAT winds tended to be stronger when lightning was present (by \(\sim\)0.4-0.6 m s\({}^{-1}\)), but this difference was reduced significantly when controlling for heavier precipitation. Meanwhile, the percentage change in precipitation when lightning was present was much larger than the change in winds (\(\sim\)200-270% increase in rain rate when lightning occurred).
## 3 Results and Discussion
### Air Broadening Coefficient  
For the measurement of HCl-Air broadening coefficients, the same procedure as for the HCl-Ar and HCl-O\({}_{2}\) broadening coefficient determinations was employed by using the HCl desorption effect from cell inner surface after evacuation. Synthetic Air (N\({}_{2}\):O\({}_{2}\) = 80:20, 1% uncertainty of the ratio) was filled into the vacuum cell for HCl-Air broadening measurements.  
Figure 6a shows the measured HCl absorption spectrum at a pressure of 100 mbar. The measured spectrum was averaged for 10 scans and fitted by the Voigt profile. For this 15.1 umol/mol HCl spectrum, the SNR is 102 as the residuals were around (1\(\sigma\)) 4 \(\times\) 10\({}^{-4}\) (optical density). The respective Lorentzian widths show very good linearity with pressures with an intercept of (2.30 \(\pm\) 6.41) \(\times\) 10\({}^{-4}\) cm\({}^{-1}\). The experiments yielded a HCl-Air broadening coefficient \(\gamma_{\rm N2}\) = 55.26(99) \(\times\) 10\({}^{-3}\) cm\({}^{-1}\)/atm at 294 K.
## 4 Experiment
### Pose Estimation Experiments in Indoor and Outdoor Environments
#### 4.4.3 Experimental Results on Scannet and MegaDepth  
To validate the effectiveness of the proposed integrated matching model, we improved an existing open-source algorithm and conducted comparative tests between the enhanced matching method and other feature matching algorithms on the MegaDepth and ScanNet datasets.  
For the MegaDepth dataset, we selected 1500 image pairs for evaluation and established three thresholds for pose estimation error: 5\({}^{\circ}\), 10\({}^{\circ}\), and 20\({}^{\circ}\). To solve the camera pose estimation problem, we employed the RANSAC algorithm to compute the fundamental matrix from the predicted matches. The evaluation results of the improved matching algorithm, compared to other open-source algorithms, are presented in Table 2. The evaluation metric used is the Area Under the ROC Curve (AUC) at various thresholds.  
For the ScanNet dataset, we selected 1500 image pairs for testing, using three pose estimation error thresholds: 5\({}^{\circ}\), 10\({}^{\circ}\), and 20\({}^{\circ}\). To recover the camera pose, we employed the RANSAC algorithm to compute the fundamental matrix from the predicted matches. The evaluation results of the improved algorithm, compared with other matching algorithms, are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Pose Estimation AUC\%**} \\ \cline{3-5}  & & **AUC@5\({}^{\circ}\)** & **AUC@10\({}^{\circ}\)** & **AUC@20\({}^{\circ}\)** \\ \hline \multirow{4}{*}{Sparse} & SP [32] + NN & 31.7 & 46.8 & 60.1 \\  & SP [32] + SG [35] & 49.7 & 67.1 & 80.6 \\  & SP [32] + LG [38] & 49.9 & 67.0 & 80.1 \\ \hline \multirow{4}{*}{Semi-Dense} & DRC-Net [45] & 27.0 & 42.9 & 58.3 \\  & LoFTR [36] & 52.8 & 69.2 & 81.2 \\ \cline{1-1}  & QuadTree [46] & 54.6 & 70.5 & 82.2 \\ \cline{1-1}  & MatchFormer [44] & 53.3 & 69.7 & 81.8 \\ \cline{1-1}  & TopicFM [47] & 54.1 & 70.1 & 81.6 \\ \cline{1-1}  & OURS & **55.6** & **70.6** & **82.1** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pose Estimation Results on the MegaDepth Dataset. Bold values represent the best results among all methods.  
The results presented in Tables 1 and 2 show that, after integrating the matching framework with the open-source algorithm model, the improved algorithm consistently achieves significantly higher AUC values across all thresholds compared to other methods. This integrated framework not only improves accuracy on the MegaDepth dataset but also outperforms other algorithms on the ScanNet dataset. The evaluation metrics generally exceed the performance of individual models, demonstrating that the proposed matching method effectively enhances matching accuracy.  
These findings indicate that, without requiring additional training on the dataset, the proposed algorithm framework successfully improves matching accuracy while reducing computational resource consumption. Validation on public datasets further confirms the effectiveness and feasibility of the proposed framework.