esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Additionally, with the progress of science and technology and the rapid development of social economy, the demand for power is increasing. Accurate power load forecasting is very important for the stability of power system, the guarantee of power service and the rational utilization of power. Scholars have put forward a variety of prediction methods, including time series prediction methods, multiple linear regression prediction methods [22; 23] and so on. However, with the development of intelligence, the data of power load is becoming more and more complex. Power load forecasting is a nonlinear time series problem, and more accurate forecasting needs to rely on machine learning algorithms. In recent years, the deep learning methods are playing a vital role in this field [24]. For these issues above, we have done the following study: based on recurrent neural network (RNN) and Long-Short Term Memory (LSTM) models, the method we propose in this paper is used instead of the manual method to determine the hyper-parameters, and the power load forecasting is carried out on the real time series data set. The experimental results show that the method is effective in the hyper-parameter tuning of machine learning model and can be effectively applied to power load forecasting. The remainder of this paper is organized as follows: BO, PSO, RNN and LSTM are introduced in Section 2. The BO-PSO is introduced in Section 3. Furthermore, the experimental results are demonstrated in Section 4. The paper is concluded in Section 5.
## 6 Robustness Considerations
### Integral Action  
The assumption that \(\Delta(v_{r},t)\) is known is particularly restrictive. The aerodynamics of aircraft is highly uncertain. Moreover, the explicit computation of \(\Delta(v_{r},t)\) requires the knowledge of the surrounding flow field. Although the airspeed can be measured using a small pitot-static tube, equipment that measures the flow angles \(\alpha\) and \(\beta\) is usually not readily available for small UAVs. There exists some available technologies [80], but such equipment can be expensive, too large or too heavy, or just impractical to install on small UAVs that often perform belly landings [81]. Several approaches for flow angle estimation have been proposed in the literature [81; 82; 83; 84], but it remains a challenging problem. Therefore, we focus our attention to instead estimating the aerodynamic moments directly. The control input during trim, \(u_{\text{trim}}\) can often be quite easily identified during manual flight, so we turn our attention to estimating \(\Delta(v_{r},t)\) instead of \(h_{r}(v_{r})\). This also removes the need for an explicit estimate of \(M_{p}\).  
Consider the tracking error dynamics (18), and let \(\hat{\Delta}\) be an estimate of \(\Delta(v_{r},t)\). Define the estimation error \(\hat{\Delta}\triangleq\hat{\Delta}-\Delta(v_{r},t)\), let \(K_{2},K_{3}\) be symmetric, positive definite matrices and define the control input as  
\[u=u_{\text{trim}}+\frac{1}{V_{d}^{2}}B^{-1}\big{[}u_{\text{pd}}+u_{\text{ff}} -\hat{\Delta}\big{]} \tag{40}\]  
where  
\[u_{\text{pd}} =-k_{1}e_{\eta}-K_{2}z \tag{41}\] \[u_{\text{ff}} =J\hat{\omega}_{d}-S(J\hat{\omega}_{d})\hat{\omega}_{d}-V_{a}D \hat{\omega}_{d}, \tag{42}\]  
where the update law for \(\hat{\Delta}\) is given by  
\[\hat{\Delta}=K_{3}z, \tag{43}\]  
and the matrix \(K_{2}\) is chosen such that  
\[\lambda_{\text{min}}^{K_{2}}-\lambda_{\text{max}}^{I}(\alpha_{\omega_{d}}+ \kappa)\geq\gamma, \tag{44}\]  
for some \(\gamma>0\). Then the following holds:  
* There are two closed-loop equilibria, given by \((\eta,z,\tilde{\Delta})=(\pm\eta_{d},0,0)\).
* The equilibrium \((\eta,z,\tilde{\Delta})=(-\eta_{d},0,0)\) is unstable.
* The desired equilibrium \((\eta,z,\tilde{\Delta})=(\eta_{d},0,0)\) is almost globally asymptotically stable and locally exponentially stable.  
See Appendix D.  
While the controller in the previous section is of PD type, this is a PID controller with feedforward terms. Integral action removes any steady-state error between the desired and actual angular velocity.
## 5 Discussion
### Suitable Interpolation Method for Soil Salinity Prediction  
The standard measurement of accuracy assessment (ME, RMSE, R\({}^{2}\), and PBIAS), as suggested by [56; 57] with the rank-sum technique [58], was applied to identify a suitable interpolation method for soil salinity prediction. As a result, OCK, as a multivariate geostatistical method [39], was suitable for soil salinity prediction at the topsoil level (HH mode). In a similar study, [PERSON] et al. [28] conducted predictive mapping of soil electrical conductivity as a proxy of soil salinity in southeast Algeria by measuring 42 ECe samples collected from topsoil (0-15 cm). Three models were compared: OK, OCK, and IK (Indicator Kriging). They found that the OCK was the best, with the lowest ME and RMSE values, where the ME was 9 mS/m, and the RMSE was 153 mS/m.  
Meanwhile, a suitable method for soil salinity prediction at subsoil level (VV mode) was IDW as a non-geostatistical method [39]. This finding agrees with [PERSON] and [PERSON] [77], who mapped soil salinity in the Eastern Nile Delta, Egypt, by analyzing estimated 92-point ECe values using cross-validation of IDW with power 3. The ME value was 12 mS/m, and the RMSE value was 39 mS/m.  
Nevertheless, the derived ME and RMSE values of OCK and IDW in this study were relatively high because the ECe data for soil salinity interpolation was not directly measured in the field. Still, they were estimated based on the derived equation of each measuring mode using LRA.
## 2 Materials and Methods
### Study Selection and Data Extraction  
The titles and abstracts of the retrieved papers were blindly screened by two reviewers ([PERSON] and [PERSON]) for the eligibility criteria. Any disagreement was resolved by discussion with another author ([PERSON]). Finally, full texts of the remaining articles were reviewed independently and in duplicate by two reviewers ([PERSON] and [PERSON]) before final inclusion. Data were extracted independently from the included studies according to the PICOS question and arranged in the following fields: general information (author name, publication year, nationality), participants (number of patients, number of teeth being treated, tooth number), intervention/comparison (type of graft material), and outcomes (root coverage, probing depth, width, and keratinized tissue after 1, 3, 6, and 12 months from the treatment).
## 4 Conclusions  
In order to manufacture a high-densification magnetic sheet, it is necessary to apply a nanopowder process that improves the coarse powder-fine powder filling method currently used. In this research, a highly densified magnetic sheet for a power inductor was successfully fabricated by a magnetic-sheet casting process, using Fe-6.5 wt.% Si coarse powder, fine iron powder, and nano iron powder. Using a surface treatment with a silane coupling agent, we effectively implemented Fe nanopowder with a high specific surface area with the binding system used in the current mass production process. The magnetic properties of the sheet fabricated by using trimodal powder were highly improved, compared to the magnetic sheet fabricated by using bimodal powder. The optimum conditions for the manufacture of high-filled magnetic sheets were determined through the experiments presented here and culminated in the study outcomes as follows.  
1. Nanopowder is receptive to surface treatment by epoxy-based silane coupling agents.
2. The surface treatment condition of the nanopowder involves mixing the nanopowder with 1% silane coupling agent, followed by aging for 24 h at 3 \({}^{\circ}\)C.
3. An effective dispersion process for effective dispersion of nanopowders was developed.
4. A press-sheet casting process to fill powder effectively when forming a sheet was developed.  
Conceptualization, M.C.; methodology, Y.S., Y.-S.L. and T.L.; validation, S.C., T.L., and H.J.; formal analysis, Y.S., Y.-S.L. and H.J.; investigation, Y.P., M.C., Y.K., and S.C.; writing--original draft preparation, M.C. and H.J.; writing--review and editing, M.C., H.J., and Y.K.; visualization, M.C. and H.J.; supervision, Y.S. and D.-H.K.; project administration, M.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Technology Innovation (20010938, Development of 630 V high capacity MLCC array module for power train), funded by the Ministry of Trade, Industry & Energy (MOTIE, Sejong City, Korea).  
The authors declare no conflict of interest.  
Figure 10: Evaluation of the volume fraction and permeability of the magnetic sheet manufactured under each condition.
# Analysis of Cyclone Spinning Effect with Different Guide Vane Heights
## 3 Numerical Calculations and Validation of Results
### Governing Equations and Boundary Conditions  
The basic governing equations for the RNG k-\(\varepsilon\) model are as follows: \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{3}{c}{**Average Pressure Difference Between Inlet and Outlet Sections (pa)**} \\ \cline{2-5}
**Guide Vane Height H** & \multicolumn{3}{c}{**Maximum Mesh Size**} \\  & **5 mm** & **4 mm** & **3 mm** & **2 mm** \\ \hline \(H=20\) mm & 255.9523 & 256.4531 & 256.2118 & 255.6531 \\ \(H=25\) mm & 310.0521 & 308.3142 & 308.0582 & 307.5165 \\ \(H=30\) mm & 358.9304 & 359.8461 & 360.6469 & 361.3437 \\ \(H=35\) mm & 444.8207 & 443.5515 & 444.1006 & 444.8023 \\ \(H=40\) mm & 462.1532 & 464.1413 & 463.5691 & 464.4322 \\ \hline \end{tabular}
\end{table} 
Table 2: Grid independence verification. \begin{table}
\begin{tabular}{c c c} \hline
**Computational Models** & **Advantages** & **Disadvantages** \\ \hline \multirow{8}{*}{Standard k-\(\varepsilon\)} & It has a wide range of applications, high & \multirow{4}{*}{Poor performance when simulating complex flows with high curvature and sharp changes in pressure gradients;} \\  & cost-effectiveness and high accuracy; & & \\ \cline{1-1}  & Able to meet the convergence and & & \\ \cline{1-1}  & accuracy requirements of most & & \\ \cline{1-1}  & engineering calculations; & & \\ \cline{1-1}  & Ideal for design decisions and & & \\ \cline{1-1}  & parametric analysis. & & \\ \hline \multirow{8}{*}{RNG k-\(\varepsilon\)} & It is suitable for simulating flows of & \multirow{4}{*}{Due to the limitations of the eddy viscous homogeneity assumption, it is not possible to accurately predict the phenomenon of strong vortices.} \\ \cline{1-1}  & certain complexity, such as separated & & \\ \cline{1-1}  & flows, secondary flows, and vortex flows; & & \\ \cline{1-1}  & Capable of handling complex shear flows & & \\ \cline{1-1}  & with fast strain and moderately & & \\ \cline{1-1}  & strong vortices. & & \\ \hline \multirow{8}{*}{Realizable k-\(\varepsilon\)} & Similar to the RNG model but with more & \multirow{4}{*}{The vorticity viscosity coefficient of turbulence is related to the strain rate, resulting in relatively low computational efficiency.} \\ \cline{1-1}  & accurate calculations, especially for & & \\ \cline{1-1}  & simulating circular jets. & & \\ \cline{1-1} \cline{2-5}  & & & \\ \cline{1-1} \cline{2-5}  & & & \\ \end{tabular}
\end{table} 
Table 3: Comparison of the advantages and disadvantages of k-\(\varepsilon\) models.
## 3 Results and Discussion
### FTIR Spectra of Pellets  
Figure 3 shows the changes in function groups of different wood/RDF pellet samples. In 100/0 pellet samples with 100% wood composition, the broad absorption near the peaks around 3300-3400 cm\({}^{-1}\) and 2800-3000 cm\({}^{-1}\) corresponds to the hydrogen-bonded O-H stretch and C-H stretch, respectively. These structures are commonly found in lignocellulosic materials, which are the most abundant in the literature. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Sample**} & \multicolumn{8}{c}{**Elemental Analysis**} \\ \cline{2-13}
**Type** & **K** & **Na** & **Cl** & **Zn** & **Cu** & **Pb** & **Cd** & **Cr** & **As** & **Hg** \\
**(Sawdust/RDF)** & **Mean** & **Mean** & **Mean** & **Mean** & **Mean** & **Mean** & **Mean** & **Mean** & **Mean** & **Mean** \\  & **(\%)** & **(\%)** & **(\%)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** & **(mg/kg)** \\ \hline \multirow{3}{*}{\(100/0\)} & 0.228 \({}^{\text{a}}\) & 0.016 \({}^{\text{a}}\) & 0.017 \({}^{\text{a}}\) & 10.190 \({}^{\text{a}}\) & 5.475 \({}^{\text{a}}\) & 27.163 \({}^{\text{a}}\) & 0.001 \({}^{\text{a}}\) & 19.014 \({}^{\text{a}}\) & 0.180 \({}^{\text{a}}\) & 0.0001 \({}^{\text{a}}\) \\  & \(\pm\)(0.01) & \(\pm\)(0.001) & \(\pm\)(0.001) & \(\pm\)(1.78) & \(\pm\)(0.94) & \(\pm\)(3.05) & \(\pm\)(0.00) & \(\pm\)(1.79) & \(\pm\)(0.01) & \(\pm\)(0.00) \\ \hline \multirow{3}{*}{\(70/30\)} & 0.237 \({}^{\text{a}}\) & 0.101 \({}^{\text{b}}\) & 0.092 \({}^{\text{b}}\) & 328.492 \({}^{\text{b}}\) & 61.763 \({}^{\text{b}}\) & 57.449 \({}^{\text{b}}\) & 1.517 \({}^{\text{b}}\) & 44.014 \({}^{\text{b}}\) & 2.306 \({}^{\text{b}}\) & 0.0001 \({}^{\text{a}}\) \\  & \(\pm\)(0.09) & \(\pm\)(0.003) & \(\pm\)(0.004) & \(\pm\)(16.90) & \(\pm\)(5.67) & \(\pm\)(7.82) & \(\pm\)(0.20) & \(\pm\)(2.22) & \(\pm\)(0.16) & \(\pm\)(0.00) \\ \hline \multirow{3}{*}{\(60/40\)} & 0.234 \({}^{\text{a}}\) & 0.122 \({}^{\text{c}}\) & 0.116 \({}^{\text{c}}\) & 380.599 \({}^{\text{c}}\) & 70.076 \({}^{\text{b}}\) & 48.275 \({}^{\text{b}}\) & 2.287 \({}^{\text{c}}\) & 64.274 \({}^{\text{c}}\) & 2.927 \({}^{\text{c}}\) & 0.0001 \({}^{\text{a}}\) \\  & \(\pm\)(0.01) & \(\pm\)(0.002) & \(\pm\)(0.003) & \(\pm\)(7.74) & \(\pm\)(11.09) & \(\pm\)(7.51) & \(\pm\)(0.14) & \(\pm\)(3.45) & \(\pm\)(0.19) & \(\pm\)(0.00) \\ \hline \multirow{3}{*}{\(50/50\)} & 0.237 \({}^{\text{a}}\) & 0.144 \({}^{\text{d}}\) & 0.125 \({}^{\text{d}}\) & 400.015 \({}^{\text{d}}\) & 70.496 \({}^{\text{b}}\) & 61.996 \({}^{\text{c}}\) & 3.360 \({}^{\text{d}}\) & 64.735 \({}^{\text{c}}\) & 2.725 \({}^{\text{c}}\) & 0.0001 \({}^{\text{a}}\) \\  & \(\pm\)(0.01) & \(\pm\)(0.004) & \(\pm\)(0.002) & \(\pm\)(12.27) & \(\pm\)(3.43) & \(\pm\)(5.34) & \(\pm\)(0.34) & \(\pm\)(5.34) & \(\pm\)(0.34) & \(\pm\)(0.00) \\ \hline \end{tabular}
\end{table} 
Table 4: Elemental analysis of samples.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 5 Discussion
### Future Work  
Having such diverse options of PHYs available, it is possible to build a mesh network with heterogeneous radio settings. We see the need to design an agile MAC layer with the following characteristics:  
* capable of choosing the most convenient PHY for each pair of nodes.
* with a Time-Slotted Channel Hopping (TSCH) approach, to reduce power consumption by eliminating idle listening and reduce collision probabilities. This adds a level of determinism, therefore nodes switch on their radios only when their schedule indicate that they are expected to transmit/receive frames. The rest of the time nodes can sleep, saving electric charge.
* a CCA procedure, to be able to increase the duty cycle from 0.1% to the maximum 2.8%.  
Conceptualization, Software and Data curation, J.M. and T.W.; Investigation and Visualization, J.M. and T.C.; Methodology, J.M. and X.V.; Writing--original draft, J.M.; Writing--review & editing, J.M., T.C. and X.V.  
This research received no external funding.  
This work is partially supported by the European Commission through the H2020 F-Interop project.  
The authors declare no conflict of interest.
## 3 Platform Subsystem Design
### Communication Subsystem  
The communication subsystem's role is to transmit and receive telemetry, mainly payload and housekeeping data between the ground station and the satellite. For the uplink telecommands and downlink house-keeping data, the subsystem utilizes ultra high frequency (UHF) for communication. The UHF Digital Radio has a sensitivity of \(-118\) dBm and uses a frequency between 430-440 MHz. It operates at 2400 bps with GFSK2 modulation technique. The UHF transceiver is coupled with a UHF dipole antenna that has a gain of 2 dBi. Both UHF transceiver and antenna have an operating temperature between \(-40\) and \(+85\)\({}^{\circ}\)C. Moreover, for downlink of payload data, the subsystem utilizes S-band for communication. The S-band transceiver has a sensitivity of \(-110\) dBm and uses a frequency of 2035.5 MHz for uplink and 2208.2 MHz for downlink. It has a transmission rate of 1.28 Mb/s for uplink and 5.12 Mb/s for downlink using GMSK modulation technique. Additionally, the S-band is coupled with a directional antenna, and a S-band patch antenna which has a gain of 6 dBi. The communication subsystem also acts as a messenger between the OBC and the ground station. In the case of uplink telemetry, the subsystem receives the data from the ground station and passes it to the OBC, which translates it to commands. While in the case of the downlink, the subsystem receives the information, housekeeping data, or payload data from the OBC and sends it to the ground station.
## 3 Results and Discussion  
### Total Construction Waste Generation  
The total waste generation for this case study was estimated by the method and parameters described in the previous section. Table 3 shows the results of the total waste generation of the solid slab, flat plate slabs, and the void slab system. As shown in Table 3, a total of 101,361.385 kg or 43.642 m\({}^{3}\) of construction was estimated to be generated from the solid slab system, which is the highest among the four slab systems. The waste generation rate for the solid slab system was approximately 105.220 kg/m\({}^{2}\) of the construction floor area. On the other hand, the void slab system produced the lowest amount of construction waste, which was 87,603.958 kg or 33.648 m\({}^{3}\) in total. For the waste generation rate of the void slab system, it showed about 90.939 kg/m\({}^{2}\) of the construction floor area. In addition, the total construction waste from the flat plate slab with the thickness of 180 and 210 mm produced about 98,657.409 kg and 99,335.161 kg, respectively. The construction waste generated by building materials from the different slab systems is depicted in Figure 3, which shows the percentage of each type of construction waste. As indicated in Figure 3, ready-mixed concrete produced the largest proportion of construction waste, at about 65% by weight in all cases. Although the material loss rate of ready-mixed concrete is relatively lower than other building materials, it was found that the amount of waste generated from this material was higher than for other building materials. In addition, the material loss rate of rebars was relatively higher than other materials because the reinforcement work at the construction site would be dependent upon the skill level of the construction workers. Moreover, the forms for concrete moulds of this case study building used plywood forms, which would make it possible to easily create various forms of concrete structures. However, the plywood forms would be difficult to recycle or reuse, since they are cut into many different sizes for creating building structures. Figure 2: Schematic description of the slab systems: (**a**) Solid slab, (**b**) The flat plate slab, (**c**) The void slab system. In this study, the void slab system required additional building materials to build the slab system of the building. While void formers, steel decking, and anchoring metals were applied to the void slab system, the total amount of the construction waste showed the  
Figure 3: Construction waste generation by materials: (**a**) Solid slab system, (**b**) Flat plate slab (180 mm), (**c**) Flat plate slab (210 mm), (**d**) The void slab system.
# Gaussian Mixture Modeling for Detecting Integrity Attacks in Smart Grids
## 3 Probabilistic Modeling
### k-Nearest Neighbor Clustering Using Kullback-Leibler Divergence
Compute the parameter vectors of the \(j-th\) dynamic model \(\theta_{j}\) with respect to \(W_{j}\);
7. Estimate the respective GMM \(G_{novel}=\{\mu_{novel},\sigma_{novel},w_{novel}\}\);
8. Compute the KL distances with respect to \(G_{novel}\), i.e., \(D_{novel}=KL(novel||N)=\int p(x_{novel}|\mu_{novel},\sigma_{novel},w_{novel}) \log\frac{p(x_{N}|\mu_{N},\sigma_{N},w_{N})}{p(x_{novel}|\mu_{novel},\sigma_{ novel})}dF_{n}\);
9. if\(D_{novel}>T_{h}\)then  Raise alarm: integrity attack detection; else  Nominal data detected;  end if 10. \(j=j+1\); untilDetector turned OFF;
```  
**Algorithm 1**The algorithm for detecting integrity attacks by receiving data from the physical layer of the interdependent CIs and modeling them by means of GMMs operating on the parameter space of LTI models.
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 2 Error Control Scheme (ECS)
### Forward Error Correction (FEC)
#### 2.2.1 Block Codes  
Block codes are a large family of ECCs where encoding is performed in blocks. There are varieties of block codes with various applications. In block codes, the information bits to be transmitted will be broken down into smaller blocks of pre-defined length. Then, the channel encoder converts these blocks into codewords. Block codes may be defined as binary and non-binary fields. In this paper, we limit the review of block codes in binary Galois Field \(GF(2)\), but the concept can be generalised to non-binary fields. Mathematically, a block code is represented by the triple \((n,k,t)\) where \(n\) is codeword length i.e, the number of bits in codeword, \(k\) is information length, i.e., the number of information bits in the codeword and \(t\) is the error correction capability i.e., the number of bits errors that can be corrected. Figure 2 illustrates information bits and parity bits of a systematic block codes.  
As we discussed earlier, there are various types of binary block codes with a wide range of applications. A binary block code is termed as a linear code if, and only if, the modulo-2 sum of the two codewords produces a new codeword. Hamming codes [16] and extended Hamming codes are examples of linear binary block codes. Hamming distance of Hamming code determines the error correction and detection capability. Single Error Correction and Double Error Detection (SEDED) and Double Error Correction and Triple Error Detection (DECTED) are two examples of extended Hamming code with extra error detection capability. This can be achieved by adding extra parity bits on regular Hamming code [17].  
Cyclic code is another special class of linear block code where a circular shift of a codeword transforms into a new codeword. Bose-Chaudhuri-Hocquenghem (BCH) [18; 19] and Reed-Solomon (RS) [20] codes are classified into binary and non-binary cyclic codes, respectively.  
Low-Density Parity Check (LDPC) [21], and Repeat Accumulate (RA) [22] codes are two linear block codes with high error correction capability. These codes are considered as the most powerful codes in the current literature. LDPC codes are binary linear block codes with sparse parity-check matrices where the number of ones per column and per row is very small. LDPC code is represented by factor graph [23] in graphical form. An example of a parity check matrix of a regular LDPC code with degree of variable node \((d_{p})=2\) and degree of check node \((d_{c})=3\) is represented as parity check  
Figure 2: A systematic block code of codeword length \(n\) with \(k\) information bits and \((n-k)\) parity bits.  
matrix in Equation (4). The factor graph of the parity check matrix given in Equation (4) is shown in Figure 3. Details of encoding and decoding operations of LDPC and RA codes can be found at [24]:  
\[\mathbf{H}=\begin{pmatrix}1&1&0&1&0&0\\ 0&1&1&0&1&0\\ 1&0&0&0&1&1\\ 0&0&1&1&0&1\end{pmatrix} \tag{4}\]