esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sensitivity Analysis
### PCE-Based Sobol' Indices  
Sudret [20] showed how Sobol' indices of a finite-variance computational model can be obtained by post-processing the coefficients of its PCE metamodel. The principle idea is that the [PERSON]' decomposition (Equation (1)) of a truncated PCE can be obtained by rearranging the polynomials of a truncated PCE into summands of increasing dimensions:  
\[\begin{split} f^{PCE}(\mathbf{x})=y_{0}+\sum_{i=1}^{n}\sum_{ \alpha\in A_{i}}y_{\alpha}\cdot\Phi_{\alpha}(x_{i})+\sum_{1<i_{1}<i_{2}\leq n }\sum_{\alpha\in A_{i_{1},i_{2}}}y_{\alpha}\cdot\Phi(x_{i_{1}},x_{i_{2}})+... \\ +\sum_{\alpha\in A_{1,2,...,n}}y_{\alpha}\cdot\Phi(x_{1},x_{2},...,x_{n})\end{split} \tag{22}\]  
where \(A_{i_{1},...,i_{s}}\) is a set of tuples where only \((i_{1},...,i_{s})\) are non-zero. Comparing the Sobol' decomposition from Equation (1) with Equation (22), the summands of the Sobol' decomposition can be written as:  
\[f_{i_{1},...,i_{s}}(x_{i_{1}},...,x_{i_{s}})=\sum_{\alpha\in A_{i_{1},...,i_{ s}}}y_{\alpha}\cdot\Phi_{\alpha}(x_{i_{1},...,i_{s}}) \tag{23}\]  
where the polynomial function on the right side depends only on input variables \((i_{1},...,i_{s})\). Due to the orthonormality of the polynomial basis, the squared integral of the polynomial sum (right side of Equation (22)), as defined in Equation (6), reduces to squaring the coefficients \(y_{\alpha}\). Thus, Sobol' indices of model inputs can be obtained by summing and normalizing squared coefficients of the PCE:  
\[S_{i_{1},...,i_{s}}=\sum_{\alpha\in A_{i_{1},...,i_{s}}}\frac{y_{\alpha}^{2}} {D} \tag{24}\]  
Therefore, once the PCE coefficients are obtained, Sobol' indices are calculated analytically with, practically, no computational effort. As obtaining a PCE of a computational model often requires fewer samples than calculating MC-based Sobol' indices, calculating Sobol' indices using the coefficients of a PCE is a viable alternative to the MC-based calculation, assuming that a sufficiently accurate PCE metamodel of the considered computational model can be obtained.
# Random Forests and Cubist Algorithms for Predicting Shear Strengths of Rockfill Materials
## 4 Conclusions  
This paper investigates the potential of random forest and Cubist regression techniques for predicting the shear strength of RFM and identifies useful parameters that affect the prediction of rockfill shear strength. This study concluded that Cubist and random forest regression algorithms are more suitable for predicting the shear strength of RFM compared with conventional multiple linear regression and ANN in terms of explanatory value and error. In this study, the Cubist model (RMSE =0.0959, R\({}^{2}\) = 0.9697 and MAE = 0.0671) appeared to be superior to random forests (RMSE = 0.1133, R\({}^{2}\) = 0.9548 and MAE = 0.0665) with this type of dataset but random forests can achieve reasonable prediction accuracies in some cases. Another important conclusion of this study is that Cubist and random forest regression algorithms can effectively identify useful input parameters that affect the shear strength of RFM. Supervised machine learning based approaches are very sensitive to the quality and type of data, and their performance may change depending on the dataset, the quality and number of training datasets and the scale at which the experiments are conducted. Future studies can explore whether the accuracy can be improved by increasing or reducing the training dataset to validate the proposed model, by adjusting/tuning the hyperparameters of the algorithms or implementing a new predictive model. Additional experiments using a larger number of datasets are suggested to estimate the performance of the random forest and Cubist algorithms in forecasting RFM values that exceed the range of the pre-existing training dataset.  
Conceptualization, J.Z.; Data curation, E.L.; Formal analysis, C.L.; Supervision, H.W.; Writing--review and editing, J.Z., Q.Q. and D.J.A. This research was funded by the National Science Foundation of China (41807259, 51504082), the Natural Science Foundation of Hunan Province (2018 JJ)3693), the China Postdoctoral Science Foundation funded project (2017M622610) and the Shenghua Lieying Program of Central South University (Principle Investigator: [PERSON]). The authors confirm that this article content has no conflict of interest.
# Intelligent Traffic Management in Next-Generation Networks
## 3 DL/ML Applications in Software-Defined Networking
### Traffic Prediction
#### 3.2.1 Existing Solutions
Their experiments show that all three models, especially the GRU model, perform well but still produce significant predicting errors when the traffic is suddenly shot. However, BiLSTM consumes the most resources in training and predicting. Additionally, to ensure dynamic optimization of the allocation of network resources in a proactive way, [PERSON] et al. [106] proposed machine-learning-based traffic prediction. Specifically, the GRU model was used in order to predict the mobile network traffic matrix in the next hour. Moreover, with the scale and complexity expected for the networks, it is essential to predict the required resources in a proactive way. To do so, network traffic forecasting is the need of the hour. In this context, [PERSON] et al. [116] used both linear and non-linear forecasting methods, including machine learning, deep learning, and neural networks, to improve management in 5G networks. Through these forecasting models, a multi-slice resource management approach has been proposed, and the experimental results show that it is possible to forecast the slices' needs and congestion probability efficiently and, accordingly, maintain the QoS of the slice and the entire network. Last but not least, since the ML/DL-based models need continuous training because the network situation changes quickly, sending all raw data to a central entity can slow the convergence of the models as well as introduce network congestion. To solve these issues, [PERSON] et al. [75] used the LSTM model in a collaborative way in order to predict the future load to optimize routing decisions (i.e., select the best path). More specifically, the authors used federated architecture with a multi-agent control plane, where each controller trainsthe LSTM model locally then sends only its model parameters to the Cloud for global aggregation (Figure 7).
## 5 Discussion
### Comments and Further Improvements  
In the previous literature using WHU-CD for algorithm evaluation, different splits and data pre-processing steps, for example, different patch sizes and cropping methods, are used [49; 50; 51; 52]. Thus, it is difficult to fairly compare the different approaches in related studies. In Section 4.1, we compare our approach to some typical models to demonstrate their performance in a single-task learning setup, after which we show the benefits of MTL to further improve the CD accuracies in Section 4.2.  
We observed that satellite images from Challenge were not orthophotos, and high buildings were downwards to the side, as shown in Figure 5. This observed phenomenon also happens in other BE and CD datasets, leading to some label noises, which makes it difficult to acquire sharp boundaries for building roofs or footprints. This phenomenon makes the BE and CD approaches infeasible for some subsequent specific applications such as stereo matching and 3D reconstruction of buildings.  
One limitation of our proposed MTL idea is that it requires BE labels in addition to CD labels, as both the BE and CD branches are optimized in a supervised manner, where annotations are needed during the training process. In reality, CD datasets do not necessarily have BE labels. Therefore, further improvements are also needed for the proposed method. In the future, we can either simulate pseudo CD labels using simulated paired BE samples, as carried out in [40], or resort to self-supervised learning techniques such as those proposed in [53; 54] for representation learning to decrease the amount of required labels.
## 3 Results
### Receiver Operating Characteristic Curve  
In order to graphically show the diagnostic value of each proposed risk-stratification system and remove the influence of the cut-off values, a Receiver Operating Characteristic Curve (ROC curve) was drawn for each proposal. In the following figure (Figure 1), all the ROC curves are plotted alongside the ROC curve for a \"chance\" diagnostic.  
After plotting the ROC curves, the area under each curve was calculated using the trapezoidal rule. The resulting areas with values between 0.5 for chance and 1 for a theoretically perfect differentiating method are shown in the following table (Table 8). This allows for a quick, one-parameter comparison between the diagnostic powers of the different proposed risk-stratification systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Benign** & **Malignant** & **Total** \\ \hline
**FLR \textless{} 2.8** & 889 & 10 & 899 \\
**FLR 2.8–4.5** & 360 & 11 & 371 \\
**FLR \textgreater{} 4.5** & 66 & 69 & 135 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Semiquantitative real-time elastography scores and malignancies (1405 nodules).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Se [\%]** & **Sp [\%]** & **PPV [\%]** & **NPV [\%]** & **ACC [\%]** \\ \hline
**BIRADS US** & 74.23 & 63.95 & 13.53 & 97.79 & 65 \\
**Semiquantitative RTE (FLR)** & 79.57 & 95.20 & 51.39 & 98.35 & 94 \\
**Qualitative RTE (UENO)** & 55.15 & 95.96 & 50.93 & 97.07 & 94 \\
**EFSUMB BIRADS** & 71.23 & 81.55 & 22.68 & 97.99 & 81 \\
**BIRADS TM** & 81.23 & 75.84 & 20.35 & 98.81 & 77 \\
**Worst-case scenario BIRADS** & 84.23 & 58.23 & 13.29 & 98.84 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Diagnostic value of proposed methods (1405 nodules).  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **AU ROC** \\ \hline
**Ueno score** & 0.883327 \\
**FLR** & 0.901603 \\
**BIRADS US 2B** & 0.773243 \\
**BIRADS EFSUMB** & 0.800632 \\
**BIRADS TM** & 0.862293 \\
**Worst case Scenario BIRADS** & 0.836948 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Area under the Receiver Operating Characteristic (ROC) curve for each diagnostic method.
## 1 Introduction
Current studies on the improvement of the SPME technique often propose new coating materials, which may lead to the improvement of the extraction process taking advantage of their high selectivity, thermal and mechanical resistance as well as multiple uses. As a result, commercially available SPME sorbents including polydimethylsiloxane (PDMS), polyacrylate (PA), divinylbenzene (DVB) in some applications may be replaced by metal-organic frameworks (MOFs), molecularly imprinted polymers (MIPs), magnetic nanoparticles (MNs), ionic liquids (ILs) [26; 27]. In this paper, a new coating sorbent based on a deep eutectic solvent (DES) is proposed. DESs can be synthesized by mixing hydrogen bond acceptors (HBAs) with hydrogen bond donors (HBDs). Their properties resemble ionic liquids, but they are cheaper, often less toxic, non-flammable, biodegradable and easier to synthesize without any purification [32; 33]. DESs may be used as extractants in liquid-phase microextractions [34]. Nevertheless, their usage in SPME is limited as there are only a few publications presenting modifications of popular SPME sorbents with these compounds. This may be caused by weak mechanical stability resulting in low reproducibility. Our research team recently prepared DES based on trihexyl(tetradecyl)-phosphonium chloride and n-docosanol to preconcentration of parabens in surface waters [35]. In this study, a DES sorbent was applied to the mesh support and used for preconcentration of formaldehyde (after derivatization) from the environmental water samples.
# Integrated Digital-Twin-Based Decision Support System for
## 4 Developing a DT Platform
### Digital Layer
In the second stage, data management and visualization are facilitated by a geo-database that consolidates information from various sources into a unified system. Advanced spatial-temporal visualization tools allow stakeholders to interact with 3D models and analyze data trends over time. The third stage involves deploying the integrated platform into an online environment, making it accessible to project participants through an intuitive web-based interface. This platform includes dashboards, visualization tools, and decision support features that enhance collaboration and streamline project workflows. The digital layer's capabilities extend beyond visualization to include analytics and simulation. By synchronizing virtual models with real-time IoT data, the platform enables facility managers to monitor ongoing projects and simulate future scenarios. For instance, the platform can assess the logistical feasibility of reusing modules by analyzing factors such as transportation distances, module conditions, and project requirements. These insights support effective decision making, ensuring that resources are allocated optimally and waste is minimized. Figure 4: DT platform architecture for reused module allocation plan.
## 6 Evaluation and Analysis
### Simulation Results
#### 6.3.4 Parameter Effects on Context-Aware Protocol Set
As D\({}_{\text{freq}}\) increases from 0.5 to 10 packets per second, the delay can increase by up to 5.6 times for N\({}_{\text{ami}}\) = 25, D\({}_{\text{size}}\) = 512, 6.1 times for N\({}_{\text{ami}}\) = 25, D\({}_{\text{size}}\) = 1024, 5.1 times for N\({}_{\text{ami}}\) = 50, D\({}_{\text{size}}\) = 512, and 3.7 times for N\({}_{\text{ami}}\) = 50, D\({}_{\text{size}}\) = 1024. Expectedly, the delay performances of the four settings are ordered according to the amount of sensor data bits transmitted in each setting, with the lowest delay, and highest delay, incurred by the setting N\({}_{\text{ami}}\) = 25, D\({}_{\text{size}}\) = 512, and N\({}_{\text{ami}}\) = 50, D\({}_{\text{size}}\) = 1024, respectively. Figure 19d shows the energy cost per packet delivered increases as D\({}_{\text{freq}}\) increases. This is because more energy is expended to transmit an increasing amount of sensor data, while less of these data can be delivered due to increasing network congestion. The result also shows that doubling the packet size from 512 to 1024 bits has a greater detrimental impact on the energy efficiency, _i.e._, higher energy cost per packet, than doubling the number of AmI users from 25 to 50 nodes. This may be due to more packet reception errors and subsequently more retransmissions when long packets are used. (B) Effects of Node Density and Mobility Parameters  
In this section, the node density and node mobility are varied as shown in Table 7. Only one parameter is varied at a time (node density or node mobility, but not both). Results are obtained for N\({}_{\text{ami}}\) = 25, D\({}_{\text{freq}}\) = [0.5, 2, 10] packets per second, and D\({}_{\text{size}}\) = [512, 1024] bits, as shown in Figure 20.  
With high node mobility, the throughput and PDR expectedly decrease as more routing paths between sensor nodes/Aml users and the VB nodes become unstable. The resulting path re-discoveries inevitably increase the end-to-end delay and energy consumption. With high node density, the throughput and PDR improve due to more routing paths available. The reduction in end-to-end delay is also noticeable as routing paths are generally shorter with less number of hops, which in turn reduces the energy cost by as much as 50% compared to the case of normal node density.
# Low-Power Rectennas in Microwave Wireless Power Transmission
## 1 Introduction  
Wireless power transfer (WPT) has emerged as a revolutionary technology, enabling the untethered operation of electronic devices across a spectrum of applications, ranging from industrial automation to biomedical implants [1, 2]. This technology was initially proposed by [PERSON] at the end of the 19 th century [3]. Early WPT efforts primarily targeted high-power, short-range transmission using inductive or resonant coupling [4, 5]. Recent advancements in microwave engineering have paved the way for efficient far-field energy harvesting [6, 7, 8]. In particular, rectennas have garnered increasing attention for their ability to capture and convert low-power-density radio frequency (RF) energy into dc power.  
While high-power WPT systems' development is hampered by their bulky size, high implementation, costs and safety concerns arising from elevated radiation levels, low-power-density rectenna systems have found widespread application opportunities as they are uniquely suited to meet the demands of the rapidly expanding Internet of Things (IoT) ecosystem, where vast numbers of distributed sensors and devices require maintenance-free, long-term energy solutions. The proliferation of ambient RF sources, such as cellular base stations, Wi-Fi routers, and broadcasting towers, has created an environment rich in harvestable electromagnetic energy, albeit at relatively low power densities (<100 mW/m\({}^{2}\)) [6, 9]. This scenario has fueled a surge of innovation focused on maximizing the efficiency and practicality of rectenna designs under real-world operating conditions.  
The core challenge in low-power rectenna research lies in optimizing the overall energy conversion process, from the initial RF energy harvesting to efficient rectification into usable dc power. This process must be achieved within the strict constraints of limited available RF power and the need for device miniaturization. Achieving high efficiency in rectenna systems is particularly challenging due to several technical bottlenecks, such as nonlinear losses in rectifying diodes, signal attenuation along transmission lines, and impedancemismatches between antenna and rectifier circuits. Moreover, antenna characteristics, including radiation efficiency, frequency band, polarization orientation, and beam coverage, directly influence the performance of low-power wireless power transmission systems in practical applications.  
This review comprehensively overviews recent technological progress in low-power-density rectenna systems. Section 2 discusses key innovations in rectifier circuit design, nonlinear device modeling, and RF-dc efficiency enhancement. Section 3 examines the design requirements of antennas for various application scenarios, highlighting how antenna configurations are optimized to meet diverse operational demands. Section 4 explores the practical applications of low-power rectenna systems in industrial IoT, implantable medical devices, and space solar power stations (SSPs). Finally, concluding remarks are provided in Section 5.
## 4 Optimal Control Problem
### Objective Functions  
Different objective functions have been defined for the problem of walking, which can be combined by means of weighting factors:  
Figure 4: Collision avoidance with rounded caps. The distance between the two is calculated as \(d\).  
Figure 5: Approximation of the support area of the foot. The darker grey zones delimit the support areas for single support cases. The lighter grey zone changes according to the positions of the feet.  
* Minimization of joint torques squared, which is always included to ensure smooth torques (with small weighting factor): \[\Phi_{\tau}(\cdot)=\mathbf{W}\cdot\mathbf{u}^{\mathcal{T}}\mathbf{u}.\] (14)
* Minimization of absolute mechanical work: \[\Phi_{work}(\cdot)=\mathbf{W}\cdot\sum_{i=0}^{n_{adv}-6}\mid\dot{q}_{i-6}\cdot u _{i}\mid.\] (15)
* Minimization of joint accelerations squared in order to obtain smooth velocity trajectories (with small weighting factor): \[\Phi_{\bar{q}}(\cdot)=\mathbf{W}\cdot\bar{\mathbf{q}}^{T}\bar{\mathbf{q}}.\] (16)
* Torso orientation minimization (ort. min., which is sometimes also referred to as torso stabilization) in terms of torso movements with respect to the world reference: \[\Phi_{torso}(\cdot)=\theta_{torso}-\theta_{world}.\] (17)  
The matrix \(\mathbf{W}\) is a weighting matrix that allows one to weight each joint differently, as not all joints have an equal contribution and an equal order of magnitude. For the time being, the same \(\mathbf{W}\) is used for all objective functions.  
The reason for which the torso orientation minimization has been included as one of the objectives is that it has been observed that the torso performs large movements in order to compensate for the angular momentum, given that the robot does not have arms. Since the robot also has cameras on the torso, which faces toward the front, it is desired that the movements of the torso with respect to the world reference not be too large; this is similar to the necessity of head stabilization in many humanoid robots.  
The above-listed objective functions can then be combined as one weighted objective:  
\[\Phi_{L}(\cdot)=c_{\tau}\Phi_{\tau}+c_{work}\Phi_{work}+c_{\bar{q}}\Phi_{\bar{q }}+c_{torso}\Phi_{torso}. \tag{18}\]  
The weighting factors serve to scale the contribution of each objective, but also to take into account the difference in order of magnitudes. Different combinations of the weighting factors are explored in the next section.
# Review on Electrical Impedance Tomography: Artificial Intelligence Methods and its Applications
## 3 How EIT Works
### Forward Problem and Data Collection
#### 3.1.2 Finite Element Method (FEM)  
The forward problem can be solved analytically for simple shapes. For the complex medium, it is necessary to discretize the whole domain into small elements called meshes. For the discretized medium, there are many numerical techniques present to solve this forward problem. Some of these methods include finite volume and finite difference approaches that generally need regular grids. However, the drawback of these methods is that they need a regularity requirement while representing curved structures. The finite element method (FEM) is usually used for EIT applications. The advantage of the FEM is that it does not need any regularity of the discretization. The increase in the number of elements in the meshes will result in the FEM solution moving towards the real solution of the partial differential equation. To formulate the FEM problems, three types of approaches are usually used.  
* Direct Method
* Variational Method
* Method of Weighted Residuals (MWR)