esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application Results and Discussion
### Estimation of RCCP Mechanical Characteristics Using Classification-Based Regression Methods
#### 3.2.3 Flexural Strength  
The applicability of tree-based models, namely RF, M5 rule, M5p, and CHAID was investigated for estimation of the flexural strength of RCCP. The statistical evaluation of the developed models in the simulation of FS is presented considered in Table 5. In the 75%-25% data split of this study, the RF model outperformed the other ML methods in both training (R = 0.988 and RSD = 0.049 MPa) and testing stages (R = 0.970 and RSD = 0.108 MPa), respectively. RF has the lowest RMSE (0.197 MPa) and highest NSE (0.939); it enhanced the precision of testing phase in terms of NSE of the M5 rule, M5p and CHAID by 28%, 27%, and 30%, respectively. Figures 8 and 9 show the plots for comparison of the actual results with those of the four models inspired of tree-based regression methods. It can be shown in the aforementioned figures of the proposed models that the RF model has the highest accuracy in the simulation of FS during the training and testing steps. It is also evident from this plot that RF had a slightly higher precision in estimation of the local maximum and minimum FS values compared to the other ML methods.
## 4 Conclusions  
In this paper, an ICS-based ELM is applied to BOD soft sensing modeling to predict the effluent water quality. It overcomes low prediction accuracy and poor stability of basic ELM algorithm with an improved cuckoo search algorithm. The input weights and the hidden layer biases of ELM are optimized with an offline training process. Results show that ICS-based ELM BOD soft sensing model can improve the accuracy of the prediction with better anti-interference and generalization abilities than basic ELM algorithm. Because of the accurately prediction of the BOD in the process, it would be helpful to the energy saving in the aeration operation in the future research. In summary:  
(1) The fuzzy rough monotone dependence (FRMD) algorithm was used to do the data attribute reduction process. This allowed finding out the input parameters closely related to BOD based on the membership function degrees of each conditional attribute. This would help to reduce the input layer nodes, which would save the computational cost when training the model and make the process more efficient.  
(2) A periodic change of \(p_{\text{a}}\) and an adaptive dynamic adjustment strategy are designed to improve the cuckoo search algorithm. The input weights and the hidden layer biases of the proposed ICS-based ELM are optimized during the offline training process. Through the verification of the results, the proposed method can effectively improve the accuracy of the prediction with better anti-interference and generalization abilities.  
(3) Comparing the simulation results of ICS-based ELM, CS-based ELM, basic ELM, LS-SVM, and BP models showed that no matter how the weather condition changes, a well-trained ICS-based ELM still can predict the effluent BOD with smaller errors compared to other five models considered in this paper.  
Conceptualization, P.Y., J.C., V.J. and X.D.; methodology, X.D.; software, P.Y.; validation, P.Y., J.C., V.J. and X.D.; formal analysis, P.Y. and X.D.; investigation, X.D.; resources, P.Y.; data curation, J.C.; writing-original draft preparation, P.Y. and X.D.; writing-review and editing, V.J.; visualization, P.Y.; supervision, J.C. and V.J.; project administration, X.D.; funding acquisition, J.C. and X.D.  
This research was funded by the National Natural Science Foundation of China, grant number (No. 61563032), the Natural Science Foundation of Gansu Province (No. 1506 RJZA104, No. 2017 GS10945), University Scientific Research Project of Gansu Province (No. 2015B-030), and the Excellent Young Teacher Project of Lanzhou University of Technology (No. Q201408).  
Figure 8: Prediction of effluent BOD under stormy weather condition with a storm event, (**a**) Effluent BOD concentration, (**b**) error in predicting the measured effluent BOD.
# Variational Multiscale Nonparametric Regression: Algorithms and Implementation
## 2 Theoretical Properties of Variational Multiscale Estimation Methods  
In this section, we briefly review some theoretical reconstruction properties of multiscale variational estimators within model (1). Recall that, in the nonparametric regression model (1), we have access to noisy samples of a function \(f\) at locations \(x_{i}\in\Gamma_{n}\). We have some flexibility in choosing the underlying grid \(\Gamma_{n}\): it could be for instance an equidistant \(d\)-dimensional grid (e.g., as pixels in an image), but other choices are possible (e.g., a polar grid).
# Advanced Metal Matrix Nanocomposites
## 2 Fabrication Methods
### Semi-Solid Casting (SSC)  
Semi-solid casting (SSC) is a high-integrity die casting technique, wherein metal is injected into a mold cavity in a semi-liquid/solid state with a \"slushy\" consistency with attributes of lower energy demands and less porosity, is more easily handled and requires less post-molding machining than metal cast using high-pressure die casting. However, it is difficult to generate the globular microstructure (globular solid particles suspended in the remaining liquid phase) needed for SCC. Zinc alloy AC43A nanocomposite with 0.5 wt% SiC \(\beta\) nanoparticle additions were cast at a 30% solid fraction [39]. AM60/SiC nanocomposite was obtained in a uniform microstructure form via semi-solid casting and a 19.3% and 7.9% increase in the elongation and the UTS, respectively, was discerned through the addition of 0.1 vol% of the nanoparticles [40].  
The combination of strength enhancement of the MMNC material with the beneficial processing of SSC was reported by [PERSON] et al. [39; 41] with reduced shrinkage, progressive mold filling and increased ductility for the semi-solid cast zinc alloy AC43A with 0.5 wt% SiC \(\beta\) (20-30 nm) nanoparticle addition compared to liquid cast monolithic AC43A zinc alloy and liquid cast AC43A/SiC nanocomposite, as shown in Figure 5.  
Figure 4: Schematic diagram of disintegrated melt deposition setup [7].  
The observed strength increase in the liquid cast AC43A/SiC systems is relatively small compared to other nanocomposite systems. There are no available mechanical testing results of semi-solid cast aluminum-based nanocomposites in the literature [39].
# Improving Sorbents for Glycerol Capture in Biodiesel Refinement
## 3 Materials and Methods
### Characterization  
Nitrogen adsorption-desorption analysis was performed on a Micromeritics ASAP 2010 or a TriStar II Plus porosimeter at 77 K (Micromeritics Instrument Corporation, Norcross, GA, USA). Samples were degassed to 1 \(\upmu\)m Hg at 100 \({}^{\circ}\)C prior to sorption analysis. Surface area was calculated by the Brunauer-Emmett-Teller (BET) method, pore size by the Barrett-Joyner-Halenda (BJH) method, and total pore volume by the single point method at relative pressure (_P_/_P\({}_{0}\)) 0.97.  
Batch type experiments were used to characterize the binding capacity and affinity of the sorbent materials. Experiments were conducted in 20 mL scintillation vials (EPA Level 3; clear borosilicate glass; PTFE/silicone-lined cap) using a fixed mass of sorbent (indicated in text and figure captions).  
Target samples (10 mL) were prepared in 18.2 M\(\Omega\) Milli-Q deionized water or in biodiesel synthesized in-house. Target solutions were added to the sorbents in the vials with a portion of the sample retained for use as a control during chromatographic analysis. Serial dilution of the retained sample was used for generation of a standard curve. Vials were incubated overnight on rotisserie mixers. Samples were filtered using 25 mm Acrodisc 0.2 \(\upmu\)m syringe filters with PTFE membranes prior to processing and analysis. Difference method analysis was applied to determine the target removed from solution.  
Analysis of glycerol was based on the method described by ASTM D6584-13 [27]. For initial characterization, all samples and control solutions were aqueous; water was removed (5 mL) using a Centrifan PE-T (Model 78-0070, KD Scientific, Holliston, MA, USA). A stock solution of N-methyl-N-(trimethylsilyl)trifluoroacetamide (MSTFA; 160 \(\upmu\)L, Gelest, Inc., Morrisville, PA, USA) was added to the dried samples followed by incubation for 20 min. Heptane (1.6 mL) was then added to the samples, and they were transferred to autosampler vials for GC analysis. GC-MS analysis was performed using a Shimadzu GCMS-QP2010 (Shimadzu Scientific, Columbia, MD, USA) with AOC-20 autoinjector equipped with a Restex Rtx-5 (30 m \(\times\) 0.25 mm ID \(\times\) 0.25 \(\upmu\)m df) cross bond 5% diphenyl 95% dimethyl polysiloxane column. A GC injection temperature of 200 \({}^{\circ}\)C was used with a 1:1 split ratio at a flow rate of 3.6 mL/min at 69.4 kPa. The oven gradient ramped from 50 \({}^{\circ}\)C (1 min hold time) to 180 \({}^{\circ}\)C at 15 \({}^{\circ}\)C/min and then to 300 \({}^{\circ}\)C at 20 \({}^{\circ}\)C/min where it was held for 5 min. For samples prepared in biodiesel, no water removal was necessary. A sample of the solution (100 \(\upmu\)L) was added to MSTFA (100 \(\upmu\)L). All other aspects of the protocol were identical to that described for samples in aqueous solution.
# Research on Computational Method of Supersonic Inlet/Isolator Internal Flow
###### Abstract  
In this research, a CFD solver is developed for solving the 2D/3D compressible flow problem: the finite volume method based on multi-block structural grids is used to solve the compressible Reynolds averaged Navier-Stokes equations (RANS). Included in the methodology are multiple high-order reconstruction schemes, such as the 3 rd-order MUSCL (Monotone Upstream-centered Schemes for Conservation Laws), 5 th-order WENO (Weight Essentially Non-Oscillatory), and 5 th-order MP (Monotonicity-Preserving) schemes. Of the variety of turbulence models that are embedded, this solver is mainly based on the shear stress transport model (SST), which is compatible with OpenMP/MPI parallel algorithms. This research uses the CFD solver to conduct steady-state flow simulation for a two-dimensional supersonic inlet/isolator, incorporating these high-precision reconstruction schemes to accurately capture the shock wave/expansion wave interaction and shock wave/turbulent boundary layer interaction (SWTBLI), among other effects. By comparing the 2D/3D computation results of the same inlet configuration, it is found that the 3D effects of the side wall cannot be ignored due to the existing strong lateral flow near the corner. To obtain a more refined turbulence simulation, the commercial software ANSYS Fluent 18.0 is used to carry out the detached eddy simulation (DES) and the large eddy simulation (LES) of the same supersonic inlet, so as to reveal the flow details near the separation area and boundary layers.  
supersonic inlet; shock wave; high-order reconstruction scheme; RANS/DES/LES +
Footnote †: journal: Applied Sciences
## 4 Proposed Control Method
### Control Objective  
The body rate vector \(\omega=[p\ q\ r]^{T}\) in Equation (3) of an aircraft is treated as the control variable. Therefore, the control objective is to create a control input \(u\equiv u_{\phi}=[\delta_{a}\ \delta_{e}\ \delta_{r}]^{T}\) in Equation (5) such that \(\omega\rightarrow\omega_{cmd}\) where \(r_{cmd}\equiv\omega_{cmd}\) is our reference command. Figure 2 shows the block diagram of the proposed controller law. The baseline controller is an incremental nonlinear dynamic inversion controller with the first-order low-pass filter as our state derivative estimator and NDO for our uncertainty estimation.  
Equation (3) can be expressed in incremental form as in (6),  
\[\dot{\omega}=\dot{\omega}_{0}+\hat{B}_{\delta}u_{\delta}+\epsilon_{\omega}, \tag{22}\]  
where \(\hat{B}_{\delta}\) is the a priori control allocation matrix and \(\epsilon_{\omega}\) is the uncertainty term. The detailed derivation can also be found in [24]. Consequently, the control input (15) becomes  
\[u_{\delta}=u_{\delta_{0}}+\hat{B}_{\delta}^{-1}\Big{(}\
u_{\omega}-\dot{ \omega}_{0}-\dot{\epsilon}_{\omega}\Big{)} \tag{23}\]  
where \(\
u_{\omega}=\big{[}\
u_{p}\ \
u_{q}\ \
u_{r}\big{]}^{T}\) is the pseudo control input. Applying the above control input to Equation (22), the state derivative Equation (16) is expressed as follows:  
\[\dot{\omega}=\
u_{\omega}+\Big{(}\dot{\omega}_{0}-\dot{\omega}_{0}\Big{)}+( \epsilon_{\omega}-\dot{\epsilon}_{\omega}) \tag{24}\]  
where \(\dot{\epsilon}_{\omega}\) is the true and estimated uncertainty.
# Three-Dimensional Resource Allocation in D2D-Based V2V Communication
## 3 Matching and Resource Block Allocation Scheme  
The first step in maximizing capacity is to calculate the optimum power. The optimum power for the case of V2I-V2V users has been evaluated in [10]; we have modified that method for the case of three types of users since the case of three types of users cannot be solved directly. We tried to offer the minimum required SINR for the CUEs as an assumption; another assumption we had made while  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol.** & **Definition** \\ \hline \(h_{j}\) & Desired channel gain for CUE \\ \(h_{k}\) & Desired channel gain for V2I \\ \(h_{l}\) & Desired channel gain for V2V \\ \(h_{k\,j}\) & Interference channel gain from V2I to CUE \\ \(h_{l\,j}\) & Interference channel gain from V2V to CUE \\ \(h_{j\,k}\) & Interference channel gain from CUE to V2I \\ \(h_{l\,k}\) & Interference channel gain from V2V to V2I \\ \(h_{j\,l}\) & Interference channel gain from CUE to V2V \\ \(h_{k\,l}\) & Interference channel gain from V2I to V2V \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different channel gains.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **V2V Link** & **V2I Link** & **CUE Link** \\ \hline Path loss model & \(127+30\) log\(10(\ell)\) & \(128.1+37.6\)log\({}_{10}(\ell)\) & \(128.1+37.6\)log\({}_{10}(\ell)\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Channel model.  
deriving is that the interference is much greater than Additive White Gaussian Noise (AWGN), and hence we have dropped AWGN from the equations for SINR.
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 1 Introduction
Specifically, mmWave APs, small-cell base stations (SBSs), and WiFi APs are clustered into groups according to their geographic locations. Each cluster is separately controlled by a subordinate controller, which is also under the control of a superior controller. The superior controller can collect global network information and make comprehensive decisions in a centralized way, whereas each subordinate controller performs the corresponding resource management distributively. * To further improve the network performance, we then propose a novel self-optimizing traffic steering strategy which can steer traffic to specific cells according to the dynamic network and traffic environments. The procedures of the proposed self-optimizing traffic steering mainly include three steps: information collection, mode selection, and decision making. The input parameters for mode selection in each subordinate controller include the feedback of the previous network reconfiguration response and the key performance indicators (KPIs) provided by the real-time environment and traffic information. * Based on the proposed framework, self-optimizing traffic steering can be preformed flexibly to provide further benefits for both networks and users. To analyze the potential advantages, several use cases of the proposed self-optimizing traffic steering are discussed. The remainder of this article is organized as follows. In Section 2, we introduce the characteristics of 5G networks and devices before presenting a novel triple-band network structure. In Section 3, we propose a novel self-optimizing traffic steering strategy and investigate the corresponding use cases. Performance evaluation results are presented in Section 4 and the article is finally concluded in Section 5.
# Application of Sorbent-Based Extraction Techniques in Food Analysis
## 3 SPE in Food Analysis--Main Fields of Applications
### Limitations of SPE for Food Analysis  
There are various formats and sorbents used for SPE; however, this method also did not avoid certain limitations. Commercially available SPE cartridges, discs, and multi-well plates are relatively expensive and generate a substantial amount of plastic. Moreover, they have a limited capacity and plugging can occur [85]. And although there are many sorbents developed to date, these sorbents are also a limiting factor and SPE cannot be used for the analysis of every compound of interest.
###### Abstract  
This research includes modeling and studying the performance improvement of a hybrid renewable energy power plant using the modeling software Greenius in Idlib, Syria. The system consists of solar parabolic trough collectors and an anaerobic digester for generating biogas. This study included a practical experiment for generating biogas using five identical digesters operating at five different temperatures. The raw material was a mixture of 81% food waste and 19% human waste, and average temperatures were as follows: 49.6, 45.9, 43.5, 37.5, and 33.2 \({}^{\circ}\)C. Modeling operations were conducted for each case, as well as for the case corresponding to the highest growth rate of methanogenic bacteria theoretically. The modeling processes were conducted at 11 different values for the storage capacity from Full Load Hours (FLHs) 0 to 10 and by varying the solar multiple factor (SM) from 1 to 8. This study showed that when operating as a net solar plant, the lowest value for the cost of produced electricity (LCOE) was 0.1785 EUR/kWh at FLHs = 5 h and SM = 2, while the annual electricity production was 25.21 GWh. The maximum annual electricity production was 48.66 GWh, achieved at FLHs = 10 h, SM = 8, and the LCOE = 0.2896 EUR/kWh. It is possible to obtain annual electrical energy of 39.7 GWh, which was about 82% of the maximum possible annual production, at a cost of LCOE = 0.1864 EUR/kWh, which is less than 5% higher than the lowest possible cost. When operating as a hybrid plant with an annual capacity factor of 1 (full load), it is discovered that the lowest value of energy produced is in the third scenario at \(\mathrm{t_{AD}}\) = 43.52 \({}^{\circ}\)C and \(\mathrm{t_{c}}\) = 63.5 \({}^{\circ}\)C, with FLHs = 0 h and SM = 1, with the LCOE = 0.1283 EUR/kWh.  
a +
Footnote †: journal: applied sciences
# A GPU-Accelerated Method for 3D Nonlinear Kelvin Ship Wake Patterns Simulation
## 4 GPU Parallel Computing Framework
### GPU Solver Implementation  
#### 4.2.1 Creating Nonlinear System  
The programming for creating a nonlinear system on a GPU by using CUDA language is briefly shown in Algorithm 1. On the whole, the dimension of the GPU grid is the equivalent of the size of the mesh, which means that one block can complete the relevant equations of one node in the mesh. One block has 1024 threads; these threads can calculate Equation (1), Equation (2) and Equations (4)-(7) simultaneously.  
```
0: Initial values and model parameters
0: Nonlinear System for Device Part do for_device_double Integral(double a,double b,double c,double d,double e) do  double val= b/sqrt(c)*log(2*c*a + d*b+ 2*sqrt(c*(c*a*a + d*b*b + e*b*b))); endfor for_device_double Integral(double a,double b,double c,double d,double e) do  double val = a/sqrt(e)*log(2*d*b + d*s+ 2*sqrt(e*(c*a*a + d*a*b + e*b*b))); endfor  threadPos = threadIdx.x; k = blockIdx.x; l = blockIdx.y ; forthreadPos = 0 to \(M\times N\)do  Calculate necessary values  endfor for\(threadIdx.x=blockDim.x-1\) to \(blockDim.x-16\)do  Calculation of the 16 parts to the closed integral endfor for\(i=locDim/2\) to 0 do  Sum up all thread contributions endfor if\((k==0\&\&\&\ |==0\ \ldots\ k==0\&\&\ |==5)\)then  Split the free surface condition and radiation conditions between 5 blocks endif endfor for Host Part do  InitialData(double* cpuData, double* gpuData);  cudaMemcpy(cpuData, gpuData, Datasize, cudaMemcpyHostToDevice);  dim3 block(1024, 1); dim3 grid(M, N - 1);  Nonlinear\(\ll\) grid, block\(\gg\)(gpuData);  cudaMemcpy(gpuData, cpuData, Datasize, cudaMemcpyDeviceToHost); endfor return Nonlinear System
```  
**Algorithm 1** The programming of creating nonlinear system on the GPU  
In the device part, there are two device functions which are utilized by the kernel function multiple times. These two device functions are formed to solve the singularity in the second integral of the boundary integral equation. The 16 special threads are set separately for fast computation. Other threads with the same CUDA code are used to complete the calculation of the remaining parts of the boundary integral equation. After the threads have finished computing, all thread contributions are summed up, and \((N-1)M\) nonlinear equations are built. Then, we arbitrarily choose five blocks to calculate the free surface condition and the radiation condition, after which \((N+3)M\) nonlinear equations can be obtained. Therefore, these equations are formed by using the \(1024\times(N-1)M\) threads on the GPU.  
In the host part, the environment variables are configured firstly. Then, the data are transferred from the CPU to GPU, and the parallel instruction is sent to the GPU. Finally, the CPU obtains the computation results from the GPU.
# Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway
## 4 Proposed Energy Efficient Sensing Network  
### Topology and Implementation  
In our aerial-ground approach, the UAV was the wireless network gateway, which was responsible for communicating with each individual sensor node that was deployed in the field. Although there are many wireless protocols that can be configured for these sensors, we chose the XBee (a modified Zigbee wireless protocol) wireless module for constructing the network, since its power consumption is relatively low. The XBee protocol allows three types of network topologies, which include: star, mesh, and cluster-tree. Although mesh and cluster-tree networks have a very flexible network structure, they both require some sensor nodes in the network for relaying data to the gateway. This means that these relay nodes have to either always be active or be duty-cycle active, further consuming a significant amount of power. Alternatively, the star topology does not require any node to relay information, and they can be kept in sleep mode for most of the time. When using the star topology, the UAV will be the XBee coordinator (gateway) for collecting data from multiple ground nodes in the communication range. More importantly, the high-mobility UAV will wake up multiple ground sensor nodes from their sleeping state based on the demand of the UAV, which can fly overhead to perform sensor activation and data collection on an individual or group of sensor nodes. Therefore, the star topology was considered the most appropriate topology for our proposed aerial-ground network.  
One concern is that using UAV as a flying gateway may consume more energy per UAV flight than what could potentially be saved in our wake-up mechanism. In most cases, the batteries in sensor nodes are hard to replace due to a variety of reasons, e.g., the position of the node may be difficult to reach, or the battery is sealed in a box and buried in soil or within structures to avoid harsh weather conditions. The key concept of our approach is to reduce the consumption of the sensor-node batteries in the field as much as possible to sustain service times as long as possible. For the power consumption, we state that in reality multiple identical UAVs with multiple high-capacity battery backups can be used to perform their functions in remote sensing and serving as the network gateway.  
Last, for implementing the sensing nodes, we use Libelium's Waspmote(r) (Zaragoza, Spain) as our ground sensing nodes [77]. The Waspmote sensing node contains a low-power MCU, embedded sensors, and optional wireless module slots. It consumes quite a small amount of energy when its sleeping mode is selected.