esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 7 Conclusions and Future Work  
The dual-decomposition method and LoRaWAN D2D communication modules are used in this paper to design a P2P optimization-based voltage control system. A multi-agent, multi-layer microgrid testbed has been constructed at the EnergyVille premises to validate the performance of the proposed P2P system. Experimental results show the ability of the proposed system to solve the voltage rise problem within 3 min.  
Future work includes expanding the P2P setup, by connecting the microgrid to a virtual network. The virtual network can be modelled using the Real-Time Digital Simulator (RTDS) [37]. Power Hardware-in-the-Loop (PHIL) can be used to connect the real inverters to virtual ones, and Control Hardware-in-the-Loop (CHIL) can be used to connect the real agents to virtual agents. The setup will be used to test distributed control algorithms that have faster convergence.  
Figure 14: Communication delays: The observer agents update the control signals every 1.5 s, with very little deviation. The actuator agents issue their updates more slowly, with a significant difference between both actuator agents.  
As demonstrated in [16], the Alternating Direction Method of Multipliers (ADMM) has a much faster convergence than the dual decomposition.  
[PERSON] conceived and designed the dual-decomposition-based peer-to-peer voltage control system. [PERSON] and [PERSON] designed the laboratory testbed, performed the experiments, and analyzed the data. [PERSON], [PERSON] and [PERSON] designed and implemented the device-to-device communication modules. [PERSON] wrote the paper. [PERSON] oversaw the work and proofread the paper.  
This work is partially supported by: (1) P2P-SmarTest project (European Commission-Grant number 646469); and (2) an SB PhD fellowship from FWO-Vlaanderen.  
The authors declare no conflict of interest.
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
## Appendix A Proof of Theorem 2  
Let \(\{t_{i,r}\}_{r=0}^{M}\) be the solution for phase \(i\) RAP and \(\{t^{\prime}_{i,r}\}_{r=0}^{M}\) be that after we increase the resource for phase \(i\). According to Algorithm 1, we have \(t^{\prime}_{i,r}\geq t_{i,r}\) for all \(r\).  
Let \(X_{r}\sim\text{Bin}(t_{i,r},p)\) and \(Y_{r}\sim\text{Bin}(t^{\prime}_{i,r},p)\). We have that \(Y_{r}\) first-order stochastically dominates \(X_{r}\) ([128], Example 4.24), denoted by \(Y_{r}\succeq X_{r}\). That is, \(\Pr(Y_{r}\geq s)\geq\Pr(X_{r}\geq s)\) for all \(s\). For \(X_{r}^{+}\sim\text{Bin}(t_{i,r},p,r)\) and \(Y_{r}^{+}\sim\text{Bin}(t^{\prime}_{i,r},p,r)\), their cumulative distributions are the same as that of \(X_{r}\) and \(Y_{r}\), respectively, from 0 to \(r-1\). At \(r\), we have \(\Pr(Y_{r}^{+}\geq r)=\Pr(X_{r}^{+}\geq r)=1\). Therefore, \(Y_{r}^{+}\succeq X_{r}^{+}\).  
Let \(X\) and \(Y\) be the random variables of the phase \((i+1)\) innovative rank when the solutions for the phase \(i\) RAP are \(\{t_{i,r}\}_{r=0}^{M}\) and \(\{t^{\prime}_{i,r}\}_{r=0}^{M}\), respectively. We have \(X~{}=~{}\sum_{r_{i}}\Pr(r_{i})\)\((r_{i}-X_{r_{i}}^{+})\succeq\sum_{r_{i}}\Pr(r_{i})\)\((r_{i}-Y_{r_{i}}^{+})=Y\).  
Suppose the resource allocated to phase \((i+1)\) is fixed. Let \(\{t_{i+1,r}\}_{r=0}^{M}\) and \(\{t^{\prime}_{i+1,r}\}_{r=0}^{M}\) be the solution of phase \(i+1\) RAP when the phase \((i+1)\) innovative ranks are \(X\) and respectively. According to ([73], Theorem 8), we have \(t_{i+1,a}>t_{i+1,b}\) and \(t^{\prime}_{i+1,a}>t^{\prime}_{i+1,b}\) for all \(a>b\). Hence, by ([73], Theorem 2), we have \(E(a,t_{i+1,a})\geq E(b,t_{i+1,a})\geq E(b,t_{i+1,b})\). Similarly, we have \(E(a,t^{\prime}_{i+1,a})\geq E(b,t^{\prime}_{i+1,b})\).  
Due to optimality, we know that  
\[\sum_{r}\Pr(X=r)E(r,t_{i+1,r})\geq\sum_{r}\Pr(X=r)E(r,t^{\prime}_{i+1,r}).\]  
On the other hand, we have  
\[\sum_{r}\Pr(X=r)E(r,t^{\prime}_{i+1,r})\geq\sum_{r}\Pr(Y=r)E(r,t^{\prime}_{i+1,r})\]  
because \(X\succeq Y\) if and only if \(\mathbb{E}[F(X)]\geq\mathbb{E}[F(Y)]\) for any increasing function \(F\)[129]. Therefore, we conclude that under the same available resources for phase \((i+1)\), the expected rank achieved at the next node by phase \((i+1)\) RAP when the phase \((i+1)\) innovative rank \(X\) is no smaller than that when the phase \((i+1)\) innovative rank is \(Y\). Finally, if we reduce the resources for phase \((i+1)\), according to Algorithm 1, the expected rank achieved at the next node decreases, which completes the proof of this theorem.
# AI-Enabled Compressive Spectrum Classification for Wideband Radios
## 2 Materials and Methods  
We assumed a WB signal of \(W\) Hz divided into M sub-bands is at the disposal of the CR node. The received WB signal is given as  
\[z(t)=\sum_{k=0}^{N-1}h_{k}(t)*S_{k}(t)+n(t), \tag{1}\]  
\(S_{k}(t)\) shows the \(k\)-\(th\) NB signal, \(h_{k}(t)\) is the \(k\)-\(th\) Rayleigh fading channel coefficient, * represents the convolution operation, and n(t) denotes the AWGN. NB signals \(S_{k}(t)\) were produced using various modulation techniques, for example, binary modulation schemes such as binary phase shift keying (BPSK), binary amplitude shift keying (BASK), binary frequency shift keying (BFSK) and multi-level modulation schemes such as quadrature amplitude modulation (QAM), quadrature phase shift keying (QPSK) as depicted in Figure 1. Moreover, the PUs in the vicinity may adopt any other modulation scheme over time.  
Figure 1: Wideband signal made up of multiple transmissions, whereas a stealthy jammer with pulsed jamming capabilities tries to disrupt multiple ongoing transmissions.  
For our system model, it is considered that the stealthy jammer embedded an FD, making it capable of identifying the modulations of the targeted signal and employing the most effective jamming techniques against them, as presented in Figure 1. The pulsed (modulated) jamming is especially successful in energy-constrained conditions, and the jamming strategies against different target signals are provided in Table 1. The transmitted signals are generated by the signal model:  
\[S_{k}(t)=\sum_{i=-\infty}^{\infty}A_{i}g(t-iT_{s})e^{(j2\pi(m_{i}-1)/M+j2\pi f _{c})} \tag{2}\]  
here \(A\) denotes the amplitude of the transmitted signal, \(g(t)\) represents the root-raised-cosine filter (RRC) pulse shaping filter with a roll-off factor \(\alpha=0.5\), \(T_{s}=1/Rs\) denotes the symbol period, \(Rs\) denotes the symbol rate, and, \(M\) denotes the number of phases, \(f_{c}\) denotes the carrier frequency, and \(m_{i}\) denotes the \(i\)-th transmitted symbol.
## 5 Derivation of Semi-Empirical Models  
### CLS Derivation  
The CLS trends obtained with respect to the various considered parameters were examined to derive appropriate individual variable functions for each parameter, while taking any significant inter-relationships between the constitutive factors into consideration. Taking a cue for successful development of semi-empirical models for prediction of mechanical performance in pipelines from recent numerical studies [58; 59; 61], a multiplicative approach was employed for development of six nonlinear mathematical expressions, each formed as a product of the individual variable functions for the respective constitutive parameters. The basic form of the nonlinear expressions for the CLS of YPT and RHT pipes under the three loading conditions investigated is:  
\[\varepsilon_{cr}=f_{cr}[\pi_{1},\pi_{2},\pi_{3},\pi_{4},\pi_{5},\pi_{6}]=f_{1 }.f_{2}.f_{3}.f_{4}.f_{5}.f_{6} \tag{4}\]  
where \(f_{1},\ f_{2},\ f_{3},\ f_{4},\ f_{5}\), and \(f_{6}\) represent the \(D/t\) ratio function \((f_{dt})\), the pressure factor function \((f_{fp})\), the strain hardening function \((f_{sh})\), the heel factor function \((f_{hf})\), the knee-to-heel ratio function \((f_{kh})\), and the compression factor function \((f_{fc})\), respectively. Here, \(\pi_{1}\) represents the \(D/t\) ratio \(\left(\frac{D}{t}\right)\), \(\pi_{2}\) represents the pressure factor \(\left(\frac{p}{p_{y}}\right)\), \(\pi_{3}\) represents the yield plateau length \((YPL)\) for YPT pipes or the PLUS ratio \(\left(\frac{\sigma_{pl}}{\sigma_{us}}\right)\) for RHT pipes, \(\pi_{4}\) represents the heel factor \((h_{f})\), \(\pi_{5}\) represents the \"_knee-to-heel_\" ratio \(\left(\frac{k_{f}}{h_{f}}\right)\), and \(\pi_{6}\) represents the compression factor \((f_{c})\).  
For simplicity of presentation, the three loading conditions investigated are hereafter assigned the following alphanumeric designations: LC1 for uniform axial compression, LC2 for uniform bending, and LC3 for combined axial compression + bending.  
An iterative process was thoroughly implemented for deriving the final form of the individual variable functions by targeting the highest possible value of the coefficient of multiple determination  
Figure 17: Plots of CLS vs. \(f_{c}\) for RHT DT1 pipes with PL/US = 0.577.  
(\(R^{2}\)). An \(R^{2}\) value greater than 0.95 was considered to be a satisfactory goodness-of-fit between the FEA-derived CLS values and the predictions of the derived nonlinear expressions.  
The individual variable functions for YPT pipes are outlined thus:  
\[\begin{array}{ll}f_{1}=f_{dt}&=a_{1}.(\pi_{1})^{b_{1}}\\ f_{2}=f_{fp}&=\left[\begin{array}{c}a_{2}+(b_{2}+c_{2}\pi_{1}).Exp(\pi_{2})^{  
The applicable ranges for the constitutive factors of the developed semi-empirical models are determined by the range of the parameters used in the FE analyses, given in Table 3 as follows.
## 3 Results and Discussion
### Membrane Morphologies in NIPS vs. VIPS
At this point the pathway shifts from B to F, following a path similar to D but at a different initial point in the two-phase region. Ultimately, the membrane forms with different surface morphology, pore properties and porosity [18]. Pathway C is similar to B but with an even higher RH and a different exposure time. Here, the membrane is immersed in the coagulation bath after it crosses the spinodal line [21] moving from path C to E.  
Unlike the NIPS process, in which introducing the cast film into the coagulation bath promotes instantaneous demixing (Figure 1), slow uptake of water from humid air allows the composition change to cross or approach the crystallization region in the phase diagram (Figure 2) and form crystalline morphology, allowing the crystal nuclei to grow thanks to the semi-crystalline property of PVDF, as observed in the case of VIPS-37%, VIPS-60% and VIPS-80% in Figure 1[23]. This way, crystal nuclei were formed and they had sufficient time to grow and produce a spherulitic structure in the resulting membrane as illustrated in Figure 1. The large PVDF agglomerates are highly crystalline spherulites (micron size), while the nodule-like morphologies over the surface of the spherulites are the ends of the PVDF polymer chains. This pathway also alters the membrane pore size to be much larger, as shown in Figure 3. On one hand, the rapid demixing process disallows further growth of the polymer-lean phase, resulting in small pore size of NIPS membranes [24]. On the other hand, slow uptake of water from humid air gives sufficient time for the polymer-lean has for growth and coalescence after the composition enters the meta-stable region and before phase inversion is completed, creating the large pores in the VIPS-made membranes. Figure 2: A three-component phase diagram for the polymer, solvent and non-solvent system generally depicting the VIPS and NIPS processes. Path A and D represent delayed (VIPS) and spontaneous (NIPS) de-mixing, respectively. Paths B and C represent an initial VIPS process followed by a NIPS. A is initial polymer solution composition and \(\bigcirc\) is composition of final membrane. DMAC: dimethylacetamide.
# Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks
## 3 Proposed CATR Routing Protocol for OppIoT Networks
### Overall Trust Computation  
The overall trust of a node \(n\) as computed by node \(m\) is obtained as follows:  
\[OT_{mn}=\rho*DT_{mn}+\phi*\Pi_{mn} \tag{16}\]where \(\rho,\phi\) are constants such that \(\rho+\phi=1\). This value is then compared against a prescribed threshold, and nodes having a value less than that threshold are considered malicious. The procedure to calculate \(OT_{mn}\) is given in Algorithm 7. The pseudo-code of our proposed CATR model is given in Algorithm 8.
```
0: Direct Trust \(DT_{in}\), Indirect Trust \(IT_{in}\)
0: Overall Trust Computation \(OT_{in}\)
1: Calculate Direct Trust \(DT_{in}\)
2: Calculate Indirect Trust \(IT_{in}\)
3: Calculate Overall Trust using \(OT_{in}=\rho*DT_{in}+\phi*IT_{in}\)
```
**Algorithm 7** Calculation of Overall Trust Computation (\(OT_{mn}\)).
```
1:\(calcBT()\): Method that computes and returns the Beta Trust of a node \(i\) on node \(j\) (as per Algorithm 1).
2:\(calcEF()\): Method that computes and returns the Encounter Frequency Degree between two nodes (as per Algorithm 2).
3:\(calcPF()\): Method that computes and returns the Packet Forwarding Degree of a node (as per Algorithm 3).
4:\(calcRC()\): Method that computes and returns the Recent Contact Degree between nodes (as per Algorithm 4).
5:\(calcCD()\): Method that computes and returns the Contact Durability Degree between two nodes (as per Algorithm 5).
6:for each encountered neighbor node \(n\) of node \(i\)do
7:\(/\) Node \(i\) computes the Beta Trust of node \(n\)
8:\(BT_{in}=calcBT()\)
9:\(/\) Node \(i\) computes the Encounter Frequency Degree of node \(n\)
10:\(EF_{in}=calcEF()\)
11:\(/\) Node \(i\) computes the Packet Forwarding Degree of node \(n\)
12:\(PF_{in}=calcPF()\)
13:\(/\) Node \(i\) computes the Recent Contact Degree of node \(n\)
14:\(RC_{in}=calcRC()\)
15:\(/\) Node \(i\) computes the Contact Durability Degree of node \(n\)
16:\(CD_{in}=calcCD()\)
17:\(/\) Computes the Direct Trust of node \(i\) on node \(n\).
18:\(DT_{in}=\omega_{1}*BT_{in}+\omega_{2}*EF_{in}+\omega_{3}*PF_{in}+\omega_{4}* RC_{in}+\omega_{5}*CD_{in}\)
19:\(/\) Computes the Reputation Index for node \(n\) based on reputations from neighbors of node \(n\).
20:for each neighbour \(k\) of node \(n\)do
21:\(RI_{nk}=(s_{n}^{k}+1)/(s_{n}^{k}+u_{n}^{k}+2)\)
22:endfor
23:\(/\) Compute the Indirect Trust x, \(K\) being the total neighbors (as per Algorithm 6)
24:\(IT_{in}=\frac{\sum\limits_{i=1}^{N}RI_{nk}}{K}\)
25:\(/\) Compute the Overall Trust (OT) (as per Algorithm 7)
26:\(OT_{in}=\rho*DT_{in}+\phi*IT_{in}\)
27:\(/\) Compare OT with a threshold trust value \(\tau\)
28:if\(OT_{in}\geq\tau\)then
29: Forward the message from node \(i\) to node \(n\)
30:endif
31:endfor
```  
**Algorithm 8** CATR routing protocol for OppIoT networks.
# Impacts of Arc Length and ECOM Solar Radiation Pressure Models on BDS-3 Orbit Prediction
## 3 Results and Analysis
### Analysis of Different ECOM SRP Models for Orbit Prediction
In addition, the ECOM7 and ECOM9 SRP models show similar orbit prediction accuracy, and both are better than the ECOM5 SRP model. The reason may be that the added parameters absorb more orbital errors. In addition, we study the orbit prediction accuracy for different ECOM SRP models with different arc lengths. Figure 8 shows the orbit prediction accuracy for different ECOM SRP models out of the eclipse seasons with the prediction interval of 24 h.  
Figure 7: Accuracy of BDS-3 predicted orbits with different ECOM SRP models when satellites move in and out of eclipse seasons (6 h prediction interval). As shown in Figure 8, the optimal arc lengths using the ECOM5, ECOM7, and ECOM9 SRP models are 42-48 h. The difference in orbit prediction accuracy between ECOM7 and ECOM9 SRP models is within 2 mm, and the performances of both ECOM7 and ECOM9 SRP models surpass the ECOM5 SRP model. Moreover, when the fitted arc length is less than 48 h, the orbital accuracy of the ECOM7 and ECOM9 SRP models is better than that of the ECOM5 SRP model. However, when the fitted arc length is longer than 48 h, the ECOM5 SRP model exhibits improved orbit prediction accuracy. The reason is that the ECOM5 SRP model shows improved orbit prediction accuracy in the along-track direction. Figure 9 shows the orbit prediction accuracy of different ECOM SRP models during the eclipse seasons with the prediction interval of 24 h. The optimal arc lengths using the ECOM5, ECOM7, and ECOM9 SRP models are 42-48 h. The ECOM7 and ECOM9 models have good performances with respect to orbit prediction during the eclipse seasons, for which the orbital UREs are both 0.065 m. However, the orbit prediction accuracy of the ECOM5 SRP model during the eclipse seasons is unsatisfactory, particularly in the cross-track direction. When the arc length is longer than 36 h, the orbit prediction accuracy in the cross-track direction becomes worse, and the cause needs to be further studied. Therefore, using the ECOM9 or ECOM7 SRP model is recommended when performing precise orbit predictions for the BDS-3 MEO satellites during eclipse seasons. Figure 8: Average RMS and URE of BDS-3 predicted orbits with different ECOM SRP models out of eclipse seasons (24 h prediction interval).
# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 3 Variants of the HSA
### Basic Versions of the HSA  
[PERSON] et al. in [52] presented a new method to improve the basic HSA by analyzing the influence of its adjusting parameters, such as HMCR and PAR. The proposed method offers changes in the basic HSA by taking precise rates of these adjusting parameters and enabling them to grow exponentially through the development processes. The proposed version of the HSA has been assessed on standard data clustering datasets. The clustering production of the method is compared with K-means, HA, and an enhanced variant of the HSA. The proposed algorithm gives better results than other similar methods in regard to precision, recall, inter-cluster, G-measure, and intra-cluster distance.  
[PERSON] et al. in [54], proposed a clustering method based on the basic HSA. In the proposed method, the power of the HSA is employed to develop the best variety of clusters automatically and to find the areas of cluster centers. By straightforwardly incorporating the concept factorization for each harmony solution, the proposed method converts the variables of cluster centers at every iteration. The cluster validity criterion is employed as an objective function to verify the clustering result obtained from every solution. The proposed method is tested on well-known datasets, and the results show that the proposed method obtains the correct variety of clusters and areas of centers.  
[PERSON] and [PERSON] proposed in [64] a novel method based on using the HSA to solve the clustering problem in energy load balancing. The proposed method is examined in an extensive sample network. The results show that the proposed method reached the optimal solution more quickly and produces secure and green balanced clustering matched to the basic HSA and other various comparable approaches in the literature. Furthermore, the robustness and influencing parameters of the proposed HSA are also examined for numerous instances of Wireless Sensor Networks (WSNs).
# First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields
## 5 Discussion
### Impact of Our Assumption of Spatial Homogeneity of Soil Moisture and Roughness on VOD Calculation
#### 5.1.2 Impact of Soil Roughness Spatial Homogeneity
Similarly, the RMSE increases from 0.02 for NDVI = 0.4 to 0.05 for NDVI = 0.8. Accordingly, the assumption about the equivalence between \(\sigma_{\text{soil}}^{0}\) of vegetated plots and that of bare plots in the surrounding 5 km \(\times\) 5 km considered in order to solve Equation (3) would not considerably impact VOD estimation.
## 2 Related Work
The numerical analysis of existing approaches is presented in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Author** & **Method** & **Results (\%)** \\ \hline \multirow{8}{*}{2015} & \multicolumn{3}{c}{Segmentation:Morphological operators} & 96.67 \\ \cline{3-3}  & & Classification: & KNN: 95.23 \\  & [32] & SVM & SVM: 90.47 \\  & & ANN & ANN: 95.23 \\  & & K-means & KM 85.71 \\ \hline \multirow{8}{*}{2015} & \multirow{8}{*}{[5]} & RGB to Grey Scale & \\  & & Median filtering & \\ \cline{3-3}  & & SegmentationK-Mean Clustering & F-Measure 93.44 \\ \cline{3-3}  & & Classification SVM & \\ \cline{3-3}  & & Segmentation Zack Algorithm & \\ \cline{3-3}  & & Classification SVM & 93.57 \\ \hline \multirow{3}{*}{2015} & \multirow{3}{*}{[29]} & Segmentation K-Means Clustering & SNN classification 97.7 \\ \cline{3-3}  & & Classification SNN & 93.5 \\ \hline \multirow{3}{*}{2015} & \multirow{3}{*}{[27]} & Classification SVM & 89.8 \\ \cline{3-3}  & & Segmentation: K-Means Clustering & \\ \cline{3-3}  & & Classification SVM & 94.56 \\ \hline \multirow{3}{*}{2016} & \multirow{3}{*}{[35]} & Classification SVM & 94.56 \\ \cline{3-3}  & & Segmentation bounding box technique & \\ \cline{3-3} \cline{3-3}  & & Classification: & NB 81.66 (avg) \\ \cline{3-3}  & & NB & KNN 83.46 (avg) \\ \cline{3-3}  & & KNN & BPNN 58.7 (avg) \\ \cline{3-3}  & & BPNN & SVM 89.76 (avg) \\ \cline{3-3}  & & SVM & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Systematic comparison of existing approaches in terms of different morphological operators.
## 4 Analysis of Test Variation Parameters
### Type of Mixing Water  
Figure 8 shows the effect of type of mixing water (fresh water and sea water) on the initial rigidity, cracking and peak load, peak deflection, energy dissipation index and ductility index of reinforced sea sand concrete beam specimens.  
As can be seen in Figure 8A, the initial rigidity of the specimen changed from 52.35 kN\(\cdot\)mm\({}^{-1}\) to 43.33 kN\(\cdot\)mm\({}^{-1}\) when the type of mixing water was changed from fresh water to sea water, which decreased by 15.6%.  
Figure 8: The performance index varies with the type of mixing water.  
As seen in Figure 8B, the cracking load and peak load of the specimens (the cracking load and peak load are 30.4 kN and 161.3 kN, respectively) with mixing sea water increased by 33.9% and 10.9%, respectively, compared to those with freshwater mixing and raising, which indicates that the increase in concrete strength by seawater mixing and raising increased the bearing capacity of the specimens. This is because the _cl_- ions in sea water have a certain promotion effect on the hydration of cement, and its hydration products fill the concrete pores, so the microstructure of concrete becomes denser and the ultimate bearing capacity of the specimens is increased. As seen in Figure 8C-E, the peak deflection of the specimens with mixing sea water decreased by 24.9%, the energy consumption index decreased by 26.7% and the ductility coefficient decreased by 24.9% compared with the specimens with freshwater mixing and raising. This indicates that although the mixing sea water has improved the bearing capacity of the specimens, it has reduced the ductility of the specimens, so that the peak load is reached in advance, and therefore the peak deflection and energy consumption indexes are reduced.  
In conclusion, mixing sea water can improve the peak load and cracking load of sea sand concrete beams under a tidal environment, but it has a certain degree of reduction on its initial rigidity, peak deflection, energy dissipation index and ductility index.