esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Support Vector Machines and SVR Models  
Support vector machine (SVM) algorithms have also been successfully applied to flyrock prediction, especially in cases with complex, nonlinear parameter interactions. SVM-based models generally outperform linear regression and even perform favorably against ANNs in certain cases. [PERSON] et al. (2016) tested SVM for predicting flyrock at the Sungun copper mine and found it \"faster and more precise\" than an ANN model on the same data. [PERSON] and [PERSON] (2017) developed an SVM regression model for flyrock prediction and compared it to MVRA. The SVM achieved a coefficient of determination R\({}^{2}\)\(\approx\) 0.95, dramatically higher than the 0.44 achieved by multiple regression, and reduced prediction error (MAE) to about 3.1 m versus 7.7 m for MVRA ([PERSON] and [PERSON], 2017). These results demonstrate SVM's ability to capture nonlinear patterns that simple regressions miss, yielding much improved accuracy ([PERSON] and [PERSON], 2017). The success of SVM is further reflected in its variants like support vector regression (SVR). [PERSON] et al. (2018) proposed an ensemble of SVR models (with different kernels) combined via a regularized linear meta-learner. This stacked SVR model achieved excellent performance (test R\({}^{2}\) = 0.993 and RMSE \(\sim\) 3.74 m), significantly better than any single SVR (whose R\({}^{2}\) ranged from 0.92 to 0.97). Likewise, [PERSON] et al. (2019) utilized a hybrid SVR optimized by the Harris Hawks algorithm, which attained test R\({}^{2}\)\(\approx\) 0.97 and RMSE \(\sim\)9.7 m, outperforming not only a standard SVR but also other machine learners like ANN and extreme learning machine (ELM) on the same dataset. These studies confirm that SVM-based approaches are highly capable for flyrock modeling, often matching or exceeding ANN accuracy. A practical consideration with SVM/SVR models is the need to tune kernel and regularization parameters. Advanced optimization techniques (genetic algorithms, particle swarm, etc.) have been applied to automate this tuning with great success in flyrock problems (e.g., yielding the R\({}^{2}\)\(\sim\) 0.97 noted above for hybrid SVR ([PERSON] and [PERSON], 2017)). As with ANNs, SVM models benefit from adequate training data--in fact, [PERSON] and [PERSON]'s SVM was trained on the same small dataset (from one mine) as their regression yet generalized far better ([PERSON] and [PERSON], 2017). This robustness suggests SVMs can be a powerful tool even when data are limited or noisy, provided the model parameters are properly optimized.
## 4 Results and Discussions
### Comparison between the RVE Model and the Full-Size Model
#### 4.1.2 Heat Flux Field  
To analyze the influence of ATC on the heat transfer in the 2.5-D braided CMC, Figure 8 shows the heat flux distributions of the RVE model and the full-size model. From Figure 8a,b, it can be seen that the heat flux fields were heterogeneous and that the heat flux of the diagonal braided yarns was larger than the heat flux of the axial yarns on the \(Y\)-axis and the horizontal braided yarns on the \(x\)-axis. The reason for this was that the axial and radial thermal conductivities of the fibers bundle were 9.66 W/(m\(\cdot\)K) and 1.48 W/(m\(\cdot\)K), and the thermal conductivity of the matrix was 6.5 W/(m\(\cdot\)K). In the diagonal braided yarns, there was a component of the bigger thermal conductivity (9.66 W/(m\(\cdot\)K)) on the \(z\)-axis, leading to an enhancement of the heat conduction on the \(z\)-axis.  
Figure 8c,d shows the distributions of the heat flux fluctuation \(\delta_{\mathit{lf}}\) for the middle sections of the \(Z\) axes of the two models. The results show that the heat flux fluctuations were significant and that the distributions for those two models were similar. For the RVE model, the maximum \(\delta_{\mathit{lf}}\) was 255.58%. For the full-size model, the maximum \(\delta_{\mathit{lf}}\) reached 280.40%.
## 1 Introduction  
Rapid technological development and accelerated natural resource consumption have largely increased the demand for efficient, environmentally-friendly, cost-effective, and safe energy storage devices [1; 2; 3; 4]. In the last decade, supercapacitors--the new devices between conventional physical capacitors and lithium-ion batteries--have been extensively recognized as one of the most promising candidates for energy storage devices due to their high power density, long cycling lifespan, and fast charge/discharge process [5; 6; 7; 8; 9; 10; 11]. In general, supercapacitors can be divided into two categories according to their energy storage mechanism: One is the electric double-layer capacitor (EDLCs), which is mainly made of carbonaceous materials [12; 13; 14; 15; 16]; the other is the faradic redox reaction pseudocapacitor (PsCs), which usually utilizes transition metal oxides/hydroxides as electrode materials [17; 18; 19; 20; 21]. In particular, pseudocapacitors deliver much higher specific capacitance in comparison with electric double-layer capacitors, and receive considerable interest today [22; 23].  
Among various transition metal compounds, Co\({}_{3}\)O\({}_{4}\) occupies a crucial position due to its superior theoretical specific capacitance (3560 F/g), nontoxicity, and low cost [24; 25; 26]. Meanwhile, the hollow structure stands out because of its novel interior geometry and surface functionality; this can possibly provide a large surface area and extra active sites, thus dramatically boosting the electrochemical properties [27; 28]. To date, a great deal of efforts have been devoted to synthesizing Co\({}_{3}\)O\({}_{4}\) with hollow morphologies such as hollow spheres [28], hollow nanocubes [29], and hollow cages [30]. Despite the great progress that has been made, the specific capacitance is still significantly below the theoretical value, especially at high current densities. Therefore, it is still a challenge to fully take advantage of this powerful hollow nanoscaled Co\({}_{3}\)O\({}_{4}\) to achieve a large specific capacitance and a good rate capability.  
In this work, we successfully prepared porous thin-wall hollow Co\({}_{3}\)O\({}_{4}\) spheres from a zeolitic imidazolate framework-67 (ZIF-67) precursor through a simple and fast reaction. This nanostructure offers a large accessible surface area with numerous pathways. As a consequence, it exhibits a large specific capacitance of 988 F/g at 1 A/g with satisfactory cycling stability (96.6% retention after 6000 cycles). In particular, the specific capacitance of 925 F/g at even 20 A/g (slight decay of less than 7%) is extremely competitive among Co\({}_{3}\)O\({}_{4}\)-based electrode materials reported in literature. The pseudocapacitive performance manifests the great potential of the porous thin-wall hollow Co\({}_{3}\)O\({}_{4}\) spheres as electrode materials for applications in supercapacitors.
## 2 Network Architecture
### Twin Delayed Deep Deterministic Policy Gradient  
The implementation of reinforcement learning algorithms such as DDPG, SAC and TD3 all follow the Markov Decision Process (MDP) as sequences (\(S\), \(A\), \(S\), \(R\)), where \(S\), \(A\), \(S\)' and \(R\) are state, agent, next state and reward, respectively. The trained agent gets two elements (a state containing information from the environment and a reward calculated based on the previous state) to find suitable actions, and the agent will then perform these actions in the next state. The loop will continue as the environment sends the next state and rewards to the agent to find the next action. Each set of parameters (\(S\), \(A\), \(S\), \(R\)) received will be stored in a replay buffer \(B\) to serve the training process of the model. The main purpose of reinforcement learning is to find an optimal policy \(\pi_{opt}\) (with \(\pi:S\to A\)) for the highest cumulative reward [40; 41], the cumulative reward is represented as (8):  
\[R_{t}=\sum_{i=t}^{T}\gamma^{i-t}r(s_{i},\;\;a_{i}) \tag{8}\]where \(\gamma\) is a discount factor (\(0<\gamma<1\)), \(R_{t}\) is the total reward the agent receives from timestep t to the end of an episode and the reward at each timestep is \(r(s_{i},\,a_{i})\). For problems that need to compute continuous actions through each state, the actor-critic method is appropriate. The actor network receives an input state that the agent obtained from the environment and calculates the actions, and the actor network is the policy that the agent needs to find an optimal one. The critic network returns a \(Q\)-value that is used to evaluate the calculated actions from the actor network, and during the training process, the learning parameters are updated to maximize this \(Q\)-value. TD3 is one of these actor-critic methods, the structure and the steps of TD3 algorithms [20] are presented as follows:  
* The algorithm uses an actor network \(\pi_{\phi}(s)\), two critic networks \(Q_{\theta_{1}}(s,a),\ Q_{\theta_{2}}(s,a)\), corresponding to these three networks are their target networks: one target actor network \(\pi_{\phi^{\prime}}(s^{\prime})\) and two target critic networks \(Q_{\phi^{\prime}_{i}}(s^{\prime},\,a^{\prime})\) (\(i=1,\,2\)). Where \(\phi\) and \(\theta_{i}(i=1,\,2)\) are the learning parameters of the actor and critic network, respectively, and similarly \(\phi^{\prime}\) and \(\theta_{i^{\prime}}\) are the parameters of the target actor network and the target critic networks;
* In order for the agent to explore a variety of states in the environment, actions computed from the actor network have a noise added \(\epsilon\)[42]. This noise helps the data in replay buffer \(B\) to be augmented, and the noise in TD3 algorithms follows a Gaussian distribution [20]. Both the action \"\(a\)\" and the target action \"\(a^{\prime}\)\" are added a noise \(a\sim\pi_{\phi}(s)+\epsilon\), \(a^{\prime}\sim\pi_{\phi^{\prime}}(s^{\prime})+\epsilon\) (with \(\epsilon\sim\mathcal{N}(0,\,\sigma)\)). The Bellman equation [43] is used to calculate a target value \(y(r,s^{\prime})\) as (9).
## 5 Probability of Data Error in IEEE 802.15.4  
The received path of the commercial 802.15.4 compatible transceivers, such as TICC2420 [29] and Freescale MC13224 [30], is composed of two main blocks: an O-QPSK demodulator and a de-spreader to obtain the data symbols from the received chips. In the previous section, we have obtained the chip error rate, \(P_{c}\), by assuming both coherent and non-coherent detection; this error probability is used here to derive bounds for the symbol error probability, \(P_{d}\), and for the corresponding packet reception rate, \(P_{r}\).
## 3 Results and Discussion
### Removing the Temperature Effect  
Figure 6 presents the results of removing the temperature effect from the corrosion potential. The red lines in Figure 6 are the results when the TPI was applied. The trend of the corrosion potential was not changed and clearly confirms the behavior of the corrosion potential. After the temperature effect was removed, the diameter effect was clearly confirmed (that the thermal coefficients vary by the diameter of the rebars), as shown in Figure 6. The maximum gap of the red and blue lines shows how much the temperature affected the specimens. The maximum gap was calculated by subtracting each data after the inflection point from the results and found the maximum gap. The maximum gap of red to blue lines increases as the diameter reduces, indicating that the larger specimens were less affected by the temperature because of the thermal effect [37].  
Figure 5: The fitting trend flow of GPR: **(a)** air temperature; **(b)** D10; **(c)** D13; **(d)** D16; **(e)** D19.  
Thus, these corrosion potential results reflect the temperature effect. Furthermore, in the case of D19 shown in Figure 6, the corrosion was progressing but at a slower rate than the other specimens. Previous studies indicated that temperature is an important factor in rebar corrosion [14,19], and this study confirmed this with experimental applications of the GPR method. This suggests that GPR could be an appropriate method for analyzing the temperature effect on corrosion potential.
## 2 Spent Nuclear Fuel in Disposal Conditions  
Alpha, beta, and gamma radiation are classified as ionizing radiation, which leads to the cleavage of chemical bonds in the traversed medium in a process called \"radiolysis\". Neutrons are also emitted within the spent fuel matrix (by fissile radionuclides), but their flux is small and they will interact with water mostly by formation of proton recoils. Radiation (ionizing and not) deposits energy within the spent fuel and any surrounding media such as water. The question as to whether spent fuel self-irradiation affects the  
Figure 1: Schematic illustration of management and direct disposal of spent nuclear fuel according to the KBS-3 method (Copyright: Posiva Oy).  
dissolution rates has been pondered for decades. It has been found that the activity of the fuel does have an effect on dissolution rates that depends on the fuel age (inversely proportional to its activity) and on the environmental conditions. These dependencies are more fully discussed below. After about 1000 years, the radioactivity of spent fuel will be dominated by alpha-emitting radionuclides. Given that spent fuel is not expected to be in contact with groundwater for at least several thousands of years [3], even in the presence of an initially defective canister [4], the relevant type of ionizing radiation to be considered for its effect on fuel dissolution is alpha radiation. The effects of alpha radiolysis are also relevant to fuel dissolution processes because of the relatively short range (up to 30 micrometters) from the fuel surface in which the energy is deposited, as discussed below.  
When discussing the interaction between ionizing radiation and spent fuel, the heterogeneous nature of the system cannot be neglected (see Figure 2). The system is composed of the spent fuel matrix, the solid-water interface, and (bulk) water. The cladding surrounding the UO\({}_{2}\) pellets is also in the immediate surroundings but its role in the spent fuel-water interaction will not be discussed in this paper.
## 6 Precise Orbit Determination Methods
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Developers** & **Characteristics** & **Inputs** & **Outputs** & **Main Equations** \\ \hline \multirow{3}{*}{[PERSON] [80]} & Established the first analytical & Initial states of a & Prediction of & \multirow{2}{*}{\(R_{1}=GMA_{2}(I/3-Isin^{2}i/2)(I-e^{2})^{-3/2}/a^{3}\)} \\  & precise orbit determination & close earth satellite & & \\  & method & & & \\ \hline \multirow{3}{*}{[PERSON] et al. [88]} & Proposed a dual-satellite optical & Initial state of the & \multirow{2}{*}{\
\begin{tabular}{c} Optimal \\ estimation of the \\ orbits of space \\ targets \\ \end{tabular} } & \multirow{2}{*}{\((\mathbf{H}^{T}\mathbf{R}^{-1}\mathbf{H}+\mathbf{P}_{0}^{-1})\hat{\mathbf{x}}_{0}=\)} \\  & Orbit determination method for & & \\  & GEO satellites & & \\  & & & \\  & & & \\ \hline \multirow{3}{*}{[PERSON] [94]} & Proposed an analytical method & & \multirow{3}{*}{
\begin{tabular}{c} Prediction of \\ satellite orbit \\ \end{tabular} } & \multirow{3}{*}{\(\hat{\mathbf{r}}=\mathbf{f}(\hat{\mathbf{r}},t)_{3\times 3}\)} \\  & for solving the orbit propagation & Initial states of a & \\  & to determine orbit fast and & close earth satellite & \\  & accurately & & \\ \hline \multirow{3}{*}{[PERSON] et al. [95]} & Presented a method for orbit & Initial states of & Predicted orbits & \\  & prediction in large-scale space & nominal space & \\  & debris clusters & debris & cluster & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the mentioned precise orbit determination methods in this section. state of space situational awareness systems and the anticipated future demands for orbit prediction, essential research in this field can be focused on the following key areas.
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 4 Results and Discussion
### Sensitivity Analysis  
The sensitivity analysis in cost modeling needs to account for uncertainties and various extreme scenarios. The inputs for the cost model should be varied from case to case. Firstly, the CAGR and number of factory customers are varied, and the cost savings for each of the three scenarios are compared. The calculations are performed with the growth scenarios ranging from 6% to 40%, and the number of different factories ranging from 2 to 500. Secondly, the cost of isolation is estimated for extreme cases of full isolation and no isolation between the computing resources.
## 4 Experimental Results and Discussion
### Experimental Setup and Evaluation Metrics  
#### 4.1.1 Experimental Setup  
In this paper, tongue image capturing devices developed by Signal and Information Processing Lab at Beijing University of Technology are used. As shown in Figure 4, the lighting condition in the device simulates the standard D65 illumination, which is intended to approximate the daylight with a color temperature of 6500 K. The color rendering index (CRI) of the Light-Emitting Diode (LED) lights is above 92. The brightness of the device can be adjusted. The default shooting camera is Canon EOS 1200D (Canon Inc., TaiChung, Taiwan), which has been corrected using a gray card. The simulated lighting condition can be adjusted slightly to model deviation between different individual devices. In our experiments, we considered five cases in Section 4.2.  
In our paper, the model is trained using the MatConvNet software package. The weight of the proposed network is initialized by the method in [25]. We use an Adam optimization algorithm and a mini-batch size of 128 in our OCC network. The configuration of the computer is Intel Core i7-6700 CPU @ 3.40 GHZ and TITAN X (Pascal) GPU.
## 4 Simulation Study  
Numerical simulations are conducted on a Z-axis gyroscope model using Matlab/Simulink environment to evaluate the effect of the proposed APPSMC method. \(\mathbf{D}\), \(\mathbf{K}\), and \(\mathbf{\Omega}\) are system parameters matrices and actual gyroscope parameters (elements of \(\mathbf{D}\), \(\mathbf{K}\), and \(\mathbf{\Omega}\)) are listed in Table 1.  
And in the simulation study, we compared control performances of adaptive sliding mode control, sliding mode control, and adaptive prescribed performance control. Table of used acronyms and corresponding full names are provided in Table 2.  
To verify the robustness of the APPSMC, parameter deviations and random signal disturbance are set in the control system where there exist 10% parameter deviation between actual gyroscope parameters and the origin values (nominal values) in APPSMC such that \(\mathbf{D}(0)=0.9\mathbf{D},\mathbf{K}(0)=0.9\mathbf{K}\), and \(\mathbf{\Omega}(0)=0.9\mathbf{\Omega}\). Parameters for the PPB and parameters for the proposed controller including robust gain and gains for adaptive laws are listed in Table 3.  
Figures 4 and 5 depict trajectory tracking performances using SMC1, SMC2, ASMC1, and ASMC2. Parameters of the controllers above mentioned are listed in Table 3. In Figures 4 and 5, red and green dotted line depict actual MEMS trajectory using SMC2 and SMC1, while yellow and purple dotted lines depict system trajectory using ASMC1 and ASMC2. Reference trajectory are depicted by a blue solid line. It is obvious in Figures 4 and 5 that system trajectory using SMC1 (green) and ASMC1 (yellow) can hardly track the reference trajectory while system trajectory using SMC2 and ASMC2 can \begin{table}
\begin{tabular}{c c c} \hline \hline
**System Parameters** & **Symbol** & **Value** \\ \hline X axis spring coefficient & \(K_{\mathrm{x}}{}^{2}\) & 355.3 \\ Y axis spring coefficient & \(K_{\mathrm{xy}}{}\) & 532.5 \\ Coupling spring coefficient & \(K_{\mathrm{y}}{}^{2}\) & 70.99 \\ X axis damping coefficient & \(D_{\mathrm{xx}}\) & 0.01 \\ Y axis damping coefficient & \(D_{yy}\) & 0.002 \\ Coupling damping coefficient & \(D_{yy}\) & 0.01 \\ Angular velocity & \(\Theta\) & 0.1 \\ External disturbance & \(d\) & Rand(1) \\ X axis command trajectory & \(q_{dx}\) & \(\sin(4.17t)\) \\ Y axis command trajectory & \(q_{dy}\) & \(1.2\sin(5.67t)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Nominal Parameters of the \(Z\)-axis gyroscope. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Acronym** & **Full Name** \\ \hline PPB & Prescribed Performance Bound \\ APPSMC & Adaptive Prescribed Performance Sliding Mode Control \\ ASMC1 & Adaptive Sliding Mode Control 1 \\ ASMC2 & Adaptive Sliding Mode Control 2 \\ SMC1 & Sliding Mode Control 1 \\ SMC2 & Sliding Mode Control 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table of acronyms.
# Joint Embedding of Semantic and Statistical Features for Effective Code Search
## 3 Approach
### Joint Embedding of Semantic and Statistical Features
#### 3.2.2 Natural Language Embedding Network  
Like code embedding networks, query embedding networks embed natural language descriptions into a vector space. Suppose a natural language description \(D=w_{1},w_{2},\ldots,w_{N_{D}}\) is a sequence of \(N_{D}\) words. The specific embedded network is as follows:  
* Embedding layer: Like the Code Embedding Network, the role of this layer is to convert each word \(w_{i}\) into a corresponding vector representation \(e_{i}\). For each the word vector matrix \(W\in\mathbb{R}^{d\times|V|}\), each word can be converted into a representation of its word vector: \(e_{i}=W\eta^{i}\). The natural language description named \(D\) is transformed into a matrix \(emb_{D}=e_{1},e_{2},\ldots,e_{N_{D}}\) as the input of the next layer of the model.
* Attention layer: Like the Code Embedding Network, this study uses the attention mechanism to assign normalized weights to each word according to its importance to generate sentence vectors. For the output generated by the previous layer \(emb_{D}=e_{1},e_{2},\ldots,e_{N_{D}}\), we use the following formula to calculate the normalized weight \(\alpha_{i}\) of each word vector \(e_{i}\): \[\alpha_{i}=\frac{exp(a_{d}\cdot e_{i}{}^{T})}{\sum_{i=1}^{N_{D}}exp(a_{d}\cdot e _{i}{}^{T})},\] (5) where \(a_{d}\) is a trainable d-dimensional vector. Then, the weighted sum of these individual word vectors is used to obtain the final natural language description embedding vector \(E_{D}\): \[E_{D}=\sum_{i=1}^{N_{D}}\alpha_{i}\cdot e_{i}.\] (6)