esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feature Selection Algorithm Performance Metric for Comparative Analysis
## 2 Background and Related Work
### Algorithm Selection  
The algorithm selection problem is concerned with finding the best algorithm to use, assuming the availability of a number of alternative algorithms that may be applied to a specific instance of a problem [14]. An instance of a problem, in the context of the feature selection problem, is a classification dataset that feature selection algorithms are applied to. The per-instance algorithm selection problem [2] was first formulated as an abstract model consisting of a problem space, algorithm space, feature space and performance space [6]. The feature space of the algorithm selection model should not be confused with the feature space of the feature selection problem. The features describing the feature selection problem are the features that expose the various complexities of the problem. In order to avoid confusion, the features describing the feature selection problem are referred to as problem characteristics.  
The per-instance algorithm selection problem assumes a set of instances \(I\) for a problem \(P\), the set \(F_{d}\) of measurable problem characteristics describing the problem instance \(i\in I\), the set \(A\) of algorithms and a performance metric \(m:A\times I\rightarrow\mathbb{R}\)[2; 14]. Given these essential components of the algorithm selection model, the objective of the per-instance algorithm selection model is to find the selection mapping, \(S(f_{d}(i))\), to algorithm space \(A\), where \(i\in I\) and \(f_{d}\in F_{d}\) such that the performance, \(m(\alpha(i))\), of the selected algorithm \(\alpha\in A\) is maximised [14].  
Given a set of features \(F\) in the feature selection domain, the feature selection problem is concerned with finding the best set of features \(F^{\prime}\subseteq F\) such that the performance of the underlying classifier is maximised while minimising the number of features selected, i.e., \(|F^{\prime}|\). As an example, the algorithm selection model for the feature selection problem consists of \(P\) as the general feature selection problem, where \(I\) is a set of specific feature selection problems as applied to a specific classification dataset. Candidate problem characteristics may include characteristics such as the level of feature interdependence, the number of features and fitness landscape characteristics. \(A\) is the set of candidate feature selection algorithms. The interaction between the respective components of the algorithm selection model is illustrated in Figure 1.  
Performance complementarity is the phenomenon where there is no single algorithm that dominates the performance of all other algorithms on different instances of the same problem [2]. The per-instance algorithm selection problem is of particular importance when there is a high level of performance complementarity between the set of algorithms \(A\) on a set of problem instances \(I\). The use of per-instance algorithm selectors shows promising opportunity to achieve better algorithm performance on problem instances for classification problems [15].
## 4 Numerical Simulation
### Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Monitoring Position**} & \multirow{2}{*}{**Axial Force F\({}_{x}\)/kN**} & \multicolumn{2}{c}{**Bending Moment**} & \multirow{2}{*}{**Failure Type**} & \multirow{2}{*}{**K**} & **Meets Safety Factor** \\  & & & & & **Requirement?** \\ \hline Vault & \(-\)38.29 & 3.91 & Tensile & 3.7 & Yes \\ Left 45\({}^{\circ}\) & \(-\)953.28 & \(-\)0.72 & Compressive & 6.5 & Yes \\ Left 60\({}^{\circ}\) & \(-\)1435.70 & \(-\)6.87 & Compressive & 4.2 & Yes \\ Left 90\({}^{\circ}\) & \(-\)549.84 & 10.61 & Compressive & 10.3 & Yes \\ Left football & \(-\)314.32 & \(-\)51.61 & Tensile & 2.7 & Yes \\ Right 45\({}^{\circ}\) & \(-\)959.65 & 0.08 & Compressive & 6.5 & Yes \\ Right 60\({}^{\circ}\) & \(-\)1444.89 & \(-\)6.98 & Compressive & 4.2 & Yes \\ Right 90\({}^{\circ}\) & \(-\)540.27 & 11.08 & Compressive & 10.4 & Yes \\ Right football & \(-\)316.01 & \(-\)52.13 & Tensile & 2.6 & Yes \\ Invert center line & 552.08 & 0.11 & Compressive & 11.3 & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Safety factor of each position after collapse treatment. in extreme weather (such as continuous rainfall) and implement advanced support in a timely manner to prevent the tunnel from collapsing.
# Performance of Payement Temperature Prediction Models
## 4 Discussion
### Materials Performance  
The performance of the models varied based on the material tested. The results showed that all three models performed better at predicting maximum pavement temperature than minimum pavement temperature.  
For maximum temperature prediction, the Viljoen model performed best for asphalt and gravel, while the Diefenderfer model performed best for concrete. The SHRP Superpave model performed best at predicting maximum temperature in gravel; while it is not the best for predicting maximum temperature for gravel, it could be used as an alternative. High variability was observed in model performance for predicting maximum temperature for block paving.  
Higher variability was observed for the minimum predicted temperatures. This is in agreement with what was observed in Australia by [PERSON] [19]. Nonetheless, the Diefenderfer model performed best for all the materials examined. The minimum temperature prediction models, however, would require revising to improve their accuracy for reliable use in pavement design.
## 3 Mathematical Formulation of Our Proposed Models
\right] \tag{6}\]  
The least squares estimator vector, \(\beta\), is obtained by minimizing Equation (7), now expressed in matrix form:  
\[S=\sum_{i=1}^{n}\epsilon_{i}^{2}=\epsilon^{\prime}\epsilon=(y-X\beta)^{\prime }(y-X\beta) \tag{7}\]  
\(S\) can be expressed as follows in Equation (8):  
\[S=y^{\prime}y-\beta^{\prime}X^{\prime}y-y^{\prime}X\beta+\beta^{\prime}X^{ \prime}X\beta=y^{\prime}y-2\beta^{\prime}X^{\prime}y+\beta^{\prime}X^{\prime}X\beta \tag{8}\]  
From Equation (8), the least-squares estimators must satisfy  
\[\frac{\partial S}{\partial\beta}\bigg{|}_{\beta}=-2X^{\prime}y+2X^{\prime}X \hat{\beta}=0 \tag{9}\]  
This implies that  
\[X^{\prime}X\hat{\beta}=X^{\prime}y \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \(Y\) & \(X_{1}\) & \(X_{2}\) & & \(X_{k}\) \\ \hline \(Y_{1}\) & \(X_  
To solve Equation (10) in terms of \(\hat{\beta}\), both its sides are multiplied by the inverse \(X^{\prime}X\). Thus, the least squares estimator of \(\beta\) is  
\[\hat{\beta}=\left(X^{\prime}X\right)^{-1}X^{\prime}y \tag{11}\]  
The adjusted model is, therefore, expressed as  
\[\hat{y}=X^{\prime}\hat{\beta} \tag{12}\]  
In scalar form, the adjusted model is expressed as  
\[\hat{y_{i}}=\hat{\beta_{0}}+\sum_{j=1}^{k}\hat{\beta_{j}}\;x_{ij}\;i=1,\;2,\; \ldots,\;n \tag{13}\]  
Starting from the \(\hat{\beta}\) values obtained in Equation (13), we can express the model in Equation (1) as  
\[Y_{\left(\text{QoE}\right)}=\beta_{0}+\beta_{1}\;X_{1}+\beta_{2}\;X_{2}+\beta_{ 3}\;X_{3}+\beta_{4}X_{1}^{2}+\beta_{5}X_{2}^{2}+\beta_{6}X_{3}^{2}+\beta_{7}X_ {1}X_{2}+\beta_{8}X_{1}X_{3}+\beta_{9}X_{2}X_{3} \tag{14}\]  
Hence, in the model proposed in Equation (14), let \(X_{1}\) = Delay, \(X_{2}\) = Jitter and \(X_{3}\) = Packet Loss. For more convenient readability and interpretation, let \(D\) = Delay = \(X_{1}\), \(J\) = Jitter = \(X_{2}\) and \(P_{L}\) = Packet Loss = \(X_{3}\). This yields Equation (15) and, after the \(\beta\) value replacement, Equation (16):  
\[\text{QoE}=\beta_{0}+\beta_{1}D+\beta_{2}J+\beta_{3}P_{L}+\beta_{4}D^{2}+\beta _{5}J^{2}+\beta_{6}\;P_{L}^{2}+\beta_{7}DJ+\beta_{8}DP_{L}+\beta_{9}JP_{L} \tag{15}\]  
\[\text{QoE}=0.65+0.35\times 10^{-3}D+254J-5.8P_{L}-1\times 10^{-6}D^{2}-0.069 \text{RV}_{\text{R}}-8890\text{V}_{\text{R}}{}^{2}+0.0275 DP_{L}-698 JP_{L}+30. 1P_{L}{}^{2} \tag{16}\]  
The model in Equation (16) is a second order model, featuring interactions of the three QoS parameters, which can be used to obtain the QoE associated with a certain video by identifying the value of its three QoS parameters [32]. Considering the type of models that will be obtained, the next section explains the experimental design to obtain them. Models are proposed depending on the video's degree of motion (low, medium or high).
## 2 Principles of MDP and Reinforcement Learning  
### Markov Decision Process  
A Markov decision process (MDP) represents a discrete time-sequence control process that provides a general framework for the decision-making procedure of a stochastic, time-varying, and dynamic control problem. The formulation of the MDP requires the control problem to have a \"Markov property\", indicating the next state of the system is only dependent upon the current state. For the problem of coordinated voltage control, a 4-tuple can formulate the MDP (\(\mathbf{S}\), \(\mathbf{A}\), \(\mathbf{P_{a}}\), \(\mathbf{R_{a}}\)). \(\mathbf{S}\) stands for a vector of system states, including voltage magnitudes, phase angles, and line flow across the system or areas of interest; \(\mathbf{A}\) is a list of actions to be taken, e.g., generator terminal bus voltage setpoints, generator active power outputs, and the status of shunts and tap ratios of regulating transformers; \(\mathbf{P_{a}}(s,s^{\prime})=\mathbf{Pr}(s_{t+1}=s^{\prime}|s_{t}=s,a_{t}=a)\) stands for the transition probability from the current state \(s_{t}\) at time \(t\) to a new state at the next time stamp \(t+1\), \(s_{t+1}\), after an action is taken at time \(=t\), \(a_{t}\); \(\mathbf{R_{a}}(s,s^{\prime})\) is the reward value calculated after reaching state \(s^{\prime}\) to quantify the overall control performance. The MDP is solved to determine an optimal policy, \(\pi\)(s), which can specify actions based on states so that the expected accumulated rewards, typically modeled as a Q-value function, \(Q^{\pi}(s,\ a)\), can be maximized in the long run, as follows:  
\[Q^{\pi}(s,\ a)=\mathbb{E}\Big{(}r_{t+1}+\gamma r_{t+2}+\gamma^{2}r_{t+3}+ \ldots\Big{|}s,\ a\Big{)} \tag{1}\]  
Then, the maximum achievable value is given as  
\[Q^{*}(s,\ a)=\max_{\pi}Q^{\pi}(s,\ a)=Q^{\pi^{*}}(s,\ a) \tag{2}\]  
Once \(Q^{*}\) is known, the agent can act optimally as  
\[\pi^{*}(s)=arg\max_{a}Q^{*}(s,\ a) \tag{3}\]  
The MDP problem can be solved using effective RL algorithms.
## 4 Random Linear Network Coding for Mobile Video Delivery  
In this section, we bring together the material presented in previous two sections and discuss integration of RLNC sublayer across different layers of 4G LTE mobile video delivery environment.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 6 Conclusions  
In this study, we propose a laboratory testing platform, which consists of logical plane, control plane, data plane, and measurement plane, for the fundamental problems with SN and its applications. Based on the SDN and traffic control mechanism, the networking of the platform can change automatically like that of a real space communication environment. In addition, integrating with container technology, various emulation scenarios are reconstructed flexibly with the same deployment as real target networks. Furthermore, the platform has an extensible structure which supports the horizontal extension of hardware resources and the access of external physical emulation devices. Lastly, we reproduce the Earth-Mars communication scene. The experimental results and the comparison results are nearly the same, which verifies the reliability of the proposed platform. Furthermore, more emulation experiments are carried out for different network protocol architectures, network scenarios, and improved protocol technologies to prove the flexible emulation ability of the platform. Compared with existing SN emulation methods, the proposed platform effectively balances the authenticity, flexibility and extendibility of the emulation. Hopefully, the proposed platform can provide a reliable basis for accurately grasping the future development direction of the key technologies in SN.  
Conceptualization, D.H.; data curation, D.H.; funding acquisition, K.Z.; methodology, D.H.; supervision, S.D.; visualization, D.H.; writing--original draft, D.H.; writing--review and editing, K.Z. and W.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 62131012 and the Fundamental Research Funds for the Central Universities under Grant 021014380187.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 20: Packet loss ratio at different bandwidths.
## 2 Materials and Methods
### Experimental Methods
#### 2.3.2 Preparation of Contaminated Fruits and Treatment  
A 10 g sample of the fruit was accurately weighed (to 0.01 g) and placed in a 50 mL centrifuge tube, followed by the addition of 10 mL of acetonitrile. The mixture was vortexed for 15 min, followed by the addition of 2 g of sodium chloride, and vortexed again. The mixture was centrifuged at 4000 rpm for 5 min, and the supernatant was quantitatively transferred to a centrifuge tube containing a water removal agent and a purification material (25 mg of PSA per milliliter of extract). The mixture was vortexed for 1 min, and the supernatant was filtered through a 0.22 \(\upmu\)m filter membrane and diluted, if necessary, for further analysis.  
Standard curve preparation process:  
The standard stock solution (100 ug/mL) was diluted with acetonitrile to prepare a series of standard solutions with concentrations of 1.0, 2.0, 5.0, 10, 20, 50, 100, 200, and 500 ng/mL, which were used to construct the standard curve.  
A mixed standard working solution of avermectin, bifenazate, spiroclofen, etoxazole and lufenuron was injected and detected under the above instrument conditions. The concentrations of the mixed solution were 1.0, 2.0, 5.0, 10, 20, 50, 100, 200, and 500 ng/mL. The obtained MS response values (peak area, Y) were used to construct a linear regression equation with the injection concentration (X). The results showed that there was a good linear relationship between 1.0 and 500 ng/mL. The obtained standard curves for bifenazate and spiroclofen were Y = 992.8659X + 532.6314, Y = 3931.2446X + 30,000.7846, Y = 27,198.9796X + 99,277.9849, Y = 138,192.2898X - 133,027.2403 and Y = 17,879.8776X - 3481.7118, respectively. The determination coefficients (R\({}^{2}\)) were 0.9985, 0.9951, 0.9974, 0.9992 and 0.9990, respectively. Under these detection conditions, the limit of quantitation (LOO) was calculated to be 0.0005 mg/kg using a signal-to-noise ratio (S/N) of 10, indicating the high sensitivity of the method.  
Chromotographic conditions:  
Column: Waters ACQUITY UPLC BEH C18 1.7 \(\upmu\)m 2.1 \(\times\) 100 mm; flow rate: 0.3 mL/min; column temperature: 40 \({}^{\circ}\)C; injection volume: 5.0 \(\upmu\)L; mobile phase: A: 0.1% formic acid water, B: acetonitrile. The chromatographic separation gradient elution program is shown in Table 1.  
(4) Pesticide degradation rate:  
The pesticide degradation rate is calculated using Formula (1):  
\[\text{Pesticide degradation rate (\%) = (C0 - Ct)/C0 \times 100} \tag{1}\]  
where C0 is the pesticide residue before sample treatment and Ct is the pesticide residue after sample treatment.
# Bindings and RESTlets: A Novel Set of CoAP-Based Application Enablers to Build IoT Applications
## 2 Constrained Application Protocol (CoAP)
In such applications, clients need to have an up-to-date representation of data from servers. Polling (sending periodic requests to servers) is not optimal in constrained environments since the values may not change as often as the pol  
Figure 1: (**a**) CoAP GET Operation; (**b**) CoAP PUT Operation. Figure 2: CoAP Header. resulting in unnecessary packet transmissions. Introducing a mechanism that triggers transmissions only if changes occur improves the communication significantly. Resource Observation [5] is an interesting extension of the CoAP protocol that introduces such a mechanism. With the observe extension, clients can inform servers about their interest in getting an up-to-date representation of a resource by adding the observe option along their GET request. As a result, the server registers the client as an observer and sends the current representation. After that, the server only sends values, called notifications, when there is change in the resource representation. This method of communication is proposed based on the well-known observer design model. Figure 3 shows this communication model by taking the light sensor example discussed above. Instead of repeatedly requesting for the current representation, the client sends a GET with an observe option. This is called registration at the server. The server responds by including the current representation and registering the client as an observer. Subsequent changes in the resource representation will trigger notifications to be sent back to the client. In this communication model, the original request and subsequent notifications are matched using the Token. In frequently changing environments, the representations may arrive out of order at the client. The observe option value is incremented each time to identify the latest value. For a resource to be observed by clients, it must be defined as observable, also indicated by the obs attribute in the resource definition. A significant performance gain is obtained by using resource observation instead of polling. Nevertheless, not every event change might be significant enough for the client to store it or to trigger any action. Therefore, all those insignificant messages will be dropped after being transmitted over the constrained network. Those unnecessary transmissions can be suppressed by combining resource observation with server side filtering. Conditional Observation [6] provides a mechanism for clients to specify notification criteria during registration. As a result, servers will filter notifications and send only those that meet the notification criteria. Detailed implementation and evaluation of Conditional observation is given in [6]. The core-interfaces draft [8] also allows server side filtering by allowing clients to specify notification criteria in URI-Queries. Figure 3: CoAP Observe Operation.
## 5 Degradation Analysis  
With the increasing product reliability, prediction of lifetime through accelerated life test can be too long, and degradation analysis is usually employed. However, in most degradation tests, the measuring processes may cause variation in the observed measures.  
As the measuring process is inherent to the degradation testing, it is important to establish schemes that define a certain level of permissible measurement error such that a robust reliability estimation can be obtained. [PERSON] et al. proposed an approach to deal with this measurement error based on a deconvolution operation. An illustrative example based on a fatigue-crack growth dataset is presented to illustrate the applicability of the proposed scheme.
## 5 Results and Analysis
### Suburban (Scenario II)  
We optimized the best locations of RRUs using metaheuristic approaches for suburban areas. We designed a radio access network to serve 2500 users in 4 km \(\times\) 4 km i.e., 16 km\({}^{2}\) area. The minimum number of macro-RRUs we need, as illustrated in Table 4, is min(max(12, 22), max(30, 60)), i.e., 22. Using 60 micro-RRUs is not feasible in suburban areas because 22 macro-RRUs are sufficient for full coverage. For capacity fulfillment  
Figure 21: Mixed cell with existing 4G sites.  
Figure 22: Mixed cell in the field.  
requirements in suburban areas, we need max(max(18, 35), max(9, 18)), i.e., 35, because 18 micro-RRUs are sufficient for capacity constraints but not for coverage constraints. To meet both requirements, the maximum (coverage, capacity), i.e., 35, is selected for simulation for both 5G greenfield and 5G with existing 4G infrastructure for suburban areas.
## 3 Multi-Scale Numerical Modeling of Rock Failure Process under Compression
### Mesoscale Simulation
#### 3.2.1 Mesoscale Numerical Model  
Figure 8 shows the CT images of the shale samples. The resolution of a single CT image is 1000 pixels \(\times\) 1000 pixels, and the pixel size is about 4 \(\upmu\)m \(\times\) 4 \(\upmu\)m (Figure 8b). The scan spacing is about 8 \(\upmu\)m, and a total of 1250 layers were scanned. Therefore, the diameter of the original 3D micro-CT cylinder of the shale sample obtained was 4 mm and the height was 10 mm (Figure 8a). In our study, the results of the micro-CT image analysis show that the white mineral grains are calcite-dominated carbonates, the gray minerals are clay minerals, and the dark gray minerals are organic matter. The objective of this section is to select the region of interest from Figure 8a, with a diameter of 4 mm and a thickness of 2 mm. As a result of the need to increase computational speed without compromising the computational accuracy of the model, the pixels were reduced 4-fold when constructing the numerical model, and images were taken once at each interval (16 \(\times\) 16 \(\times\) 16 \(\upmu\)m), for a total of 125 images. Due to the principle of CT imaging, circular artifacts (images that are bright in the middle and dark around) are inevitable in CT images. Therefore, in order to ensure the authenticity of the model and improve threshold segmentation accuracy, this subsection refers to the improved threshold segmentation method proposed by [50] regarding CT images, and the processing results are shown in Figure 8c. Next, the three-dimensional numerical model of Brazilian shale was reconstructed on the mesoscale  
Figure 7: Internal fracture evolution in micro shale sample sliced along different Z planes. (**a–f**) The slices at different loading stages, corresponding to points A–F in Figure 6g, respectively. ( organic matter, carbonate minerals,  pyrites,  damage elements, fractures).  
based on the method introduced in Section 2.3, as shown in Figure 9 ((a) front view, (b) back view), which is represented by the elastic modulus diagram. By comparing Figure 9 with Figure 8a, we can see that the reconstructed numerical model reproduces the mesostructure of the shale, and that the addition of the inhomogeneity coefficient makes the reconstructed numerical model more natural. There are approximately 7.81 million finite elements in the Brazilian disc model. It consists of organic matter, calcite-based carbonate particles, and clay matrix, at 2.98%, 92.88%, and 4.14%, respectively. The Brazilian disc model exhibits a highly inhomogeneous structure, with X-type organic matter and large calcite-dominated carbonate particles distributed on the front side. However, only a few small grains of calcite-dominated carbonates are distributed on the backside of the model. Afterward, the mechanical input parameters from Table 1 are used to establish the meso shale Brazilian disk numerical sample. In the vertical direction, load is applied at a constant rate of \(4.8\times 10^{-5}\) mm/step.