esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Hybrid PSOGWO Principle
#### 2.2.1 Pso  
Particle swarm optimization (PSO) [35; 36] was first proposed in 1995. It is a population intelligent optimization algorithm inspired by the foraging behavior of birds. In PSO, the solution of each optimization problem represents a bird of the search space, called 'particle'. All particles update their speed and location by tracking two 'extremum'. About these two extrema, one is the optimal solution found by the particle itself, called the individual extreme value. The other is found in the current iteration of the whole population, called the global extremum. The updating equation of particle velocity and location during iteration is as follows:  
\[V_{ij}^{t+1}=wV_{ij}^{t}+c_{1}r_{1}(\textit{Pbest}_{ij}^{t}-L_{ij}^{t})+c_{2}r _{2}(\textit{Gbest}_{j}^{t}-L_{ij}^{t}) \tag{1}\]  
\[L_{ij}^{t+1}=L_{ij}^{t}+V_{ij}^{t+1} \tag{2}\]  
where \(w\) is the inertial factor. By adjusting the size of \(w\), the performance of global optimization and local optimization can be promoted. \(c_{1}\) and \(c_{2}\) are the acceleration constants. \(c_{1}\) and \(c_{2}\) represent the individual learning factor and social learning factor of each particle, respectively. \(r_{1}\) and \(r_{2}\) are random numbers in the interval [0,1], which can increase the randomness of the search. \(\textit{Pbest}_{ij}^{t}\) and \(\textit{Gbest}_{j}^{t}\) represent the historical optimal location of particle \(i\) population in the \(j\)th dimension and \(t\)th iteration, respectively. \(V_{ij}^{t}\) and \(L_{ij}^{t}\) represent the velocity vector and location of particle \(i\) in the \(j\)th dimension and \(i\)th iteration. The implementation process of PSO is shown in Algorithm 1, where the input is the velocity \(V_{i}\) and the location \(L_{i}\) of each particle and the maximum number of iterations \(T\), and the output is the optimal solution \(L_{t}\).  
```
Input: the location \(L_{i}(i=1,2,\cdots,n)\) and velocity \(V_{i}(i=1,2,\cdots,n)\) of each particle Output: the optimal solution \(L_{t}\)
1 initialize \(w\), \(c_{1}\) and \(c_{2}\)
2 set the number of iteration \(T\)
3 set global best fitness as \(g\_best\) and local best fitness as \(p\_best\)
4 while(\(t<T\))do
5 forachparticle\(i\)do
6 calculate the fitness value \(f_{i}\) of each particle
7 forachparticle\(j\)do
8 if\(f_{ij}<p\_best\)then
9\(p\_best=f_{ij},L\_best=L_{i}\)
10
11 if\(p\_best<g\_best\)then
12\(g\_best=p\_best,L\_best=L\_{pbest}\)
13 update the location and velocity by Equations (1)-(2)
14\(t=t+1\)
```  
**Algorithm 1**The implementation process of PSO.
# Organic Nanostructured Materials for Sustainable Application in Next Generation Solar Cells
### The Operatzon Principle of OPVs
#### 1.2.3 Free Charge Carriers Transport  
After the exciton dissociation into free charge carriers' step, the charges are transported towards the respective electrodes as shown in Figure 4c above. The transportation of charge carriers in organic semiconductors mostly takes place by hopping from one localized state to the next [38]. Separated holes and electrons are dispersed inside the donor and acceptor phases, respectively, after charge transfer occurs at the D/A contact. The transport of holes and electrons to their respective electrodes is then aided by an internal electric field derived from the Fermi level difference between the electrodes, with efficiency varying depending on their mobilities during the hopping process [39]. Concomitantly, nongeminate recombination (NGR) is typically in competition with the collection of photogenerated charge carriers to each electrode not only at the short-circuit but also at the open-circuit conditions, and therefore impacts on both external quantum efficiency (EQE) and fill factor (FF). Therefore, optimised active layers should, in some cases, be kept as thin as possible (typically \(\sim\)100 nm) to avoid NGR loss. This, in return, may decrease its effectiveness of solar light absorption. As such, a key to efficient EQE and FF is suppressing NGR beyond limitations arising from the Langevin recombination.
# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 4 Discussion
### Implications for Understanding Extreme Heat in Mid-Atlantic Cities
One promising direction is the use of public health records that can identify acute vulnerabilities to urban heat within the community, which offer means for identifying equitable policies and strategies that seek to alleviate the burden of extreme heat on vulnerable communities [42]. This may include focusing research attention on high-resolution, city-scale climate modeling where future redevelopment and land use change is incorporated alongside projected increases to air temperature, extreme precipitation, and other climate change impacts such as sea-level change and the intrusion of invasive species into urban areas. Future work will explore and show how boundary organizations and volunteers included in these heat assessment campaigns have since leveraged this study's findings to advocate for, design, and implement resilience strategies in their respective cities.
# Multi-Issue Negotiation Protocol with Pre-Domain Narrowing
###### Abstract  
Consensus building among agents is crucial in multi-agent systems because each agent acts independently according to its utility function, and conflict among agents can occur. Therefore, automated negotiation is an essential technology for efficiently resolving conflicts and forming consensuses while also maintaining agents' privacy. As the domain to be negotiated is large, the computational cost of reaching a consensus increases and the agreement rate decreases. Some negotiation protocols have been proposed wherein a mediator collects the utility information of each agent and creates multiple alternatives of agreements to handle large-scale multi-issue negotiations. However, in such protocols, a limitation is placed on agents' privacy because all agents have to disclose their private information by following the mediator and pre-decided negotiation rules. In this study, we propose a negotiation protocol with a predomain-narrowing phase to enable efficient negotiations in large-scale domains which can maintain the privacy of information that agents should not disclose to their opponents or the mediator. The proposed protocol divides the negotiation process into a predomain-narrowing phase and the main negotiation phase. In the proposed protocol, the parts subject to negotiation are first narrowed in upon, and then the main negotiation is performed. We also propose two narrowing methods: issue- and option-narrowing. Further, we propose naive agent strategies considering the predomain-narrowing phase. We perform comparative simulation experiments between the baseline negotiation protocol without a domain-narrowing phase and the proposed negotiation protocol with the predomain-narrowing phase. The experimental results show that the proposed protocol achieves higher agreement rates in less negotiation time than the baseline.  
automated negotiation; pre-negotiation; domain narrowing; multi-agent system +
Footnote †: journal: Applied
# Multi-Antenna Jammer-Assisted Secure Short Packet Communications in IoT Networks
## Appendix A  
For the delivery of \(B\) information bits through \(N\) channel uses, the decoding error probability of the ZFB scheme can be characterized by:  
\[e^{ZFB}\Big{(}\gamma_{b}^{ZFB},\gamma_{e}^{ZFB}\Big{)}\] \[=Q\Bigg{(}\sqrt{\frac{N}{V_{b}^{ZFB}}}\Bigg{(}\ln\frac{1+\gamma_{ b}^{ZFB}}{1+\gamma_{e}^{ZFB}}-\sqrt{\frac{V_{e}^{ZFB}}{N}}Q^{-1}(\delta)-\frac{B}{N} \ln 2\Bigg{)}\Bigg{)}, \tag{11}\]  
conditioned on \(\gamma_{b}^{ZFB}>\gamma_{e}^{ZFB}\). Note that, \(\epsilon^{ZFB}=1\) if \(\gamma_{b}^{ZFB}\leq\gamma_{e}^{ZFB}\). In order to obtain a closed-form expression of the secrecy throughput, we use a linear approximation of \(Q\)-function [35] and approximate the decoding error probability \(\epsilon^{ZFB}\) as:  
\[\epsilon^{ZFB}\Big{(}\gamma_{b}^{ZFB},\gamma_{e}^{ZFB}\Big{)}\approx\Bigg{\{} \begin{array}{cc}1,&\gamma_{b}^{ZFB}<\varsigma^{ZFB},\\ \frac{1}{2}+v^{ZFB}(\gamma_{b}^{ZFB}-\theta^{ZFB}),&\varsigma^{ZFB}\leq\gamma _{e}^{ZFB}\leq\varsigma^{ZFB},\\ 0,&\gamma_{b}^{ZFB}>\varsigma^{ZFB},\end{array}  \tag{12}\]  
where \(\theta^{ZFB}=e^{\sqrt{\frac{V_{e}^{ZFB}}{N}}Q^{-1}(\delta)+\frac{B}{N}\ln 2 }\big{(}1+\gamma_{e}^{ZFB}\big{)}-1,\;v^{ZFB}=-\sqrt{\frac{N}{2\pi\theta^{ZFB} \big{(}\theta^{ZFB}+2\big{)}}}\), \(\zeta^{ZFB}=\theta^{ZFB}+\frac{1}{2v^{ZFB}}\), and \(\zeta^{ZFB}=\theta^{ZFB}-\frac{1}{2v^{ZFB}}\). Let \(X=\gamma_{b}^{ZFB}\) and \(Y=\gamma_{e}^{ZFB}\); the secrecy throughput of the ZFB scheme can be approximated as:  
\[T^{ZFB} =\frac{B}{N}\int_{0}^{\infty}\int_{y}^{\infty}\Big{(}1-\epsilon^ {ZFB}(x,y)\Big{)}f_{\gamma_{b}^{ZFB}}(x)f_{\gamma_{e}^{ZFB}}(y)dxdy\] \[\stackrel{{(a)}}{{\approx}}\frac{B}{N}\int_{0}^{ \infty}\int_{0}^{\infty}\Big{(}1-\epsilon^{ZFB}(x,y)\Big{)}f_{\gamma_{b}^{ZFB }}(x)f_{\gamma_{e}^{ZFB}}(y)dxdy\] \[\stackrel{{(b)}}{{\approx}}\frac{B}{N}\int_{0}^{ \infty}\Bigg{(}1+v^{ZFB}\int_{\varsigma^{ZFB}}^{\varsigma^{ZFB}}F_{\gamma_{b}^ {ZFB}}(x)dx\bigg{)}f_{\gamma_{e}^{ZFB}}(y)dy\] \[\stackrel{{(c)}}{{\approx}}\frac{B}{N}\int_{0}^{ \infty}\Big{(}1-F_{\gamma_{b}^{ZFB}}\Big{(}\theta^{ZFB}(y)\Big{)}\Big{)}f_{ \gamma_{e}^{ZFB}}(y)dy, \tag{13}\]where \((a)\) is based on the fact that \(\epsilon^{ZFB}\to 1\) when \(N\rightarrow\infty\) and \(x<y\), while \(\epsilon^{ZFB}\to 1\) as \(N\to 0\), \((b)\) is obtained by using (A2), \((c)\) is obtained by leveraging the Riemann integral approximation [36], \(f_{\gamma_{b}^{ZFB}}(x)\) is the PDF of \(\gamma_{b}^{ZFB},f_{\gamma_{e}^{ZFB}}(y)\) is the PDF of \(\gamma_{e}^{ZFB}\), and \(F_{\gamma_{b}^{ZFB}}(x)\) is the cumulative distribution function (CDF) of \(\gamma_{b}^{ZFB}\).
## 2 Related Work
### Long Short-Term Memory (LSTM)  
LSTM is an extension of RNN. The vanishing of gradients in the training of vanilla RNN was proposed. It has a special memory mechanism that gives it an advantage over the vanilla RNN. The memory mechanism enables the network to capture long-term dependencies, in particular the LSTM appraoch in [23], which succeeded in minimizing dimensionality and outstanding performing concerning the precise classification of opinion. Reducing the input functions is an important task for the classification of sentiment based on machine learning techniques. Therefore, the suggested method could be a promising solution for better classification with scalability. The proposed approach would be particularly suitable for applications that have a large dataset such as the detection of sentiment for product and service reviews.  
[PERSON] [24] used a long short-term model for the sentiment of the restaurant reviewers for RNNs. Several approaches are used to compare the gathering results using the following techniques: simple recurrent neural networks, logistic regression, bidirectional RNNs, and bidirectional long-term memory. Deep bidirectional LSTM with numerous hidden layers yielded the best performance across all RNN models.  
A vector representation of single words can be considered as a system training parameter to simplify the analysis of the general model text data. Thus, we can initiate values for single words with random values of vector representations to replicate certain variables.  
[PERSON] et al. [25] performed the LSTM to solve identifications of the romantic phrases extracted from film reviews and estimation of somatitizing sentence pair's tasks.  
[PERSON] et al. [26] presented the Treebank sentiment and recursive neural tensor networks to solve their feeling detection project. In the case of single-sentence positive/negative cataloging, by using recursive neural tensor networks, performance increased from 80% to 85%.  
The prediction of fine-grained feeling labels for all sentences was 80.7% higher than the 9.7% increase with a bag of features for approaches, such as SVM and Naive Bayes using the recursive neural tensor network process. Neural networks are effective for managing text data analysis tasks as variants in the LSTM model. The use of these models is an innovative approach to describe the social networking emotions of users.  
The idea with LSTM is to have a self-regulating flow of information through the cells that can be forgotten or modified based on the information input to the cell. Therefore, some extra parameters have been added to each recurrent cell to enable the RNN to propagate information and overcome optimization issues. The added parameters, as shown in Figure 3, act as filters to allow the cell to select which information is worth remembering and which is worth forgetting.
## 5 Experimental Results
### SPR-TPOF Sensors without Buffer Layer and with Different Taper Ratios
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & **B2** & **B1** & **Intercept** & **Adj. R-Square** \\ \hline A & 11,301.90 & \(-\)29,023.40 & 18,607.20 & 0.9997 \\ B & 11,819.05 & \(-\)30,274.00 & 19,355.60 & 0.9995 \\ C & 12,665.17 & \(-\)32,483,31 & 20,797.75 & 0.9977 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the quadratic fitting of data, Equation (2), for sensor configurations A, B, and C.  
Figure 8: Sensitivity as a function of refractive index for all SPR-TPOF sensor configurations. Finally, a comparative analysis has been carried out considering the signal-to-noise (SNR) parameter, which can be defined as [28; 29]  
\[\text{SNR (n) }=\left[\frac{\delta\lambda}{\delta\lambda_{SW}}\right]_{n} \tag{3}\]  
where \(\delta\lambda_{SW}\) is the spectral width of the SPR curve corresponding to the same reference level of the transmitted power and can be calculated as the full width at half maximum (FWHM) of the SPR curve. Figure 9 reports both (1) FWHM and (2) SNR defined in Equation (3) as functions of the refractive index for all three configurations. As can be seen, configuration \"C\", which has shown to have the better sensitivity, presents the highest values of FHWM and the worst SNR. This duality between sensitivity and SNR is a well-known result, and it depends on the influence of the higher-order modes over both parameters [28]. To resume discussing the obtained experimental results, Table 2 shows an overview of the calculated performance parameters at a fixed refractive index of 1.35 for all three tested SPR-TPOF sensor configurations. Moreover, in Table 2, we have also reported the parameters obtained by different  
Figure 9: (**a**) Full width at half maximum (FWHM) and (**b**) signal-to-noise ratio (SNR) as a function of refractive index for all SPR-TPOF sensor configurations. configurations of SPR D-shaped POF sensors based on non-tapered POFs [17; 25]. In this table, the different performances of the configurations based on the same buffer layer deposited on a D-shaped POF when the POF's diameter is 1000 \(\upmu\)m as reported in [17; 25] are due to the different depths of the \"D\" area [27].
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 3 UWB Positioning System
### UWB IPS Topologies
#### 3.2.3 Phase Difference of Arrival (PDoA)  
This method is based on measuring the difference in phase between the ultra-wideband waves received from the transmitter (tag or anchor). Using this algorithm, the location can be found with the smallest possible number of anchors and tags by integrating two or more antennas (antenna array) to receive the signal in the receiver. This algorithm is used to estimate the angle of arrival or departure (AoA or AoD), as well as the distance between the sender and the receiver (ToA or ToD). In the previous methods, UWB units containing one antenna were used to calculate the time of flight and convert it into distance. In this method, a location can be found between a tag and an anchor, one of which contains two or more antennas with a distance d between them, without the need for additional system infrastructure. The recipient measures the phase difference between the signal received on each of the two antennas, and from this information, the algorithm can find the angle of the received UWB waves and determine the values of t1 and t2 as well as the distance between the sender and each antenna separately by calculating the time-of-flight values t1 and t2, and this enables the location to be determined without the need for additional anchors. This algorithm is widely used for angle-of-arrival or angle-of-departure (AoA or AoD) estimation in IPSs. Figure 8 explains the structure of the PDoA algorithm [19; 20; 21; 22].  
Figure 7: Topology of the TDoA positioning algorithm.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 2 Overview of Smart Grid
### Smart Grid Infrastructure
#### 2.1.2 Architecture  
What separates Smart Grids from traditional electrical grids are the interaction and communication between the different domains. The SG infrastructure can be structured by dividing it in four layers: the application layer, the communication layer, the power control layer, and the power system layer. On the customer side, the application layer enables various applications such as home automation and real-time pricing. On the grid side: automation of grid and power distribution applications. The communication layer is important in distinguishing Smart Grids from traditional power grids, and in enabling SG applications. It is divided into three categories classified by geographic area (Wide Area Network, Neighborhood Area Network/Field Area Network, and the Premise Area Network). Depending on the type of network, different communication technologies are used. The power control layer enables management, control and monitoring of the power grid, and utilizing equipment such as switches, sensors, and metering devices. The power system layer handles power generation, transmission/distribution, and the customer premises.
## 5 Simulation of Coded Laser Pilots-Based HILPB WPT System
### Simulation Results
#### 5.2.1 Pairing Algorithm Based on Maximum Receivable Power  
Table 7 and Figure 14 are simulation results of the first pairing algorithm in which the PTX that can provide the most significant power is selected by calculating the product of MTP and PTE of PTX. When the power provided by the first selected PTX is insufficient, PTXs that provide the most significant power to compensate for the insufficient amount of power are sequentially selected. In the first round, the PRX R1 selected PTX T1, transmitting the most significant power among all the PTXs. The PRX R1 received 289.8 kW power through the WPC (162, 189) of PTX T1, which is greater than the MDP 200 kW. The pair between PRX R1 and PTX T1 is established, and then desired power is supplied. In the second round, the PRX R2 selects PTX T2 and T3, transmitting the highest power among the remaining PTXs. PRX R2 received power from PTX T2 and T3 together, so it received 409.7 kW of power then the MDP 300 kW. The PTX T2 used a WPC (173, 333), and the PTX T3 used a WPC (154, 283). In the third round, the PRX R3 received power from the unallocated PTX T4 and provides 144.9 kW, which is lower than the required power of 150 kW. When the algorithm for selecting the PTX with the most significant MTP is used, the PTX with the most significant maximum transmission power is paired in turn, regardless of the MDP of the PRX. Finally, the PRX R1 provided 145% of the MDP, the PRX R2 received 145%, but the PRX R3 received 87% less power.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameter**} & **T1** & **T2** & **T3** & **T4** \\ \hline \multirow{6}{*}{R1} & WPC & (162, 189) & (173, 333) & (154, 283) & (150, 236) \\  & Highest irradiance cell location & PV (0, \(-\)1) & PV (0, \(-\)1) & PV (\(-\)1, \(-\)1) & PV (\(-\)1, 0) \\  & RCPI of the pilot signal & 44.426 \(\mu\)J & 46.678 \(\mu\)J & 42.642 \(\mu\)J & 39.836 \(\mu\)J \\  & MDP & 289.8 kW & 248.25 kW & 185.4 kW & 129.9 kW \\  & MTE & 96.6 \% & 99.3 \% & 92.7 \% & 86.6 \% \\ \hline \multirow{6}{*}{R2} & WPC & (153, 103) & (149, 56) & (161, 283) & (173, 153) \\  & Highest irradiance cell location & PV (\(-\)1, \(-\)1) & PV (\(-\)1, \(-\)1) & PV (\(-\)1, \(-\)1) & PV (0, \(-\)1) \\  & RCPI of the pilot signal & 42.366 \(\mu\)J & 39.836 \(\mu\)J & 44.436 \(\mu\)J & 45.678 \(\mu\)J \\  & MDP & 276.3 kW & 216.5 kW & 193.2 kW & 148.95 kW \\  & MTE & 92.1 \% & 86.6 \% & 96.6 \% & 99.3 \% \\ \hline \multirow{6}{*}{R3} & WPC & (173, 63) & (154, 13) & (150, 326) & (162, 279) \\  & Highest irradiance cell location & PV (0, 0) & PV (0, 0) & PV (\(-\)1, 0) & PV (0, 0) \\ \cline{1-1}  & RCPI of the pilot signal & 45.678 \(\mu\)J & 42.642 \(\mu\)J & 39.836 \(\mu\)J & 44.436 \(\mu\)J \\ \cline{1-1}  & MDP & 297.9 kW & 231.75 kW & 173.2 kW & 144.9 kW \\ \cline{1-1}  & MTE & 99.3 \% & 92.7 \% & 86.6 \% & 96.6 \% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Possible PTX-PRX combinations according to the maximum receivable power.
# Study on Dynamic Mechanical Properties and Failure Pattern of Thin-Layered Schist
## 2 SHPB Test
### Test Equipment  
The dynamic compression test employs the split Hopkinson pressure bar system (SHPB), comprising a bullet, incident bar, transmission bar, absorption bar, and data acquisition and recording system. All rods (incident rod, transmission rod, and absorption rod) have a diameter of 50 mm. The incident rod measures 2 m in length while the transmission rod is 1.5 m. Specimens are vertically positioned between the incident and transmission rods with strain gauges attached to each. These strain gauges are linked to a super-dynamic strain gauge and oscilloscope system. During impact, the wave signal from the strain gauge located between the incident and transmission rods is transmitted to the oscilloscope for analysis. Subsequently, computer analysis software processes this waveform diagram to calculate the corresponding loading pulse curve. The schematic diagram of SHPB devices is depicted as Figure 3.  
Figure 1: Thin-layered schist with different schistosity angles.  
Figure 2: Thin-layered schist specimens in SHPB test.  
The dynamic compression test was carried out at a constant 0.4 MPa impact pressure. In order to ensure the accuracy of the obtained test results and reduce the data dispersion, three independent specimens were prepared for each schisting angle; that is, three repeated tests were carried out under each working condition to improve the reliability and stability of the results.
### Potential Challenges in Underground Mines  
#### 2.2.1 Extreme Path Loss and Multipath Fading  
Path loss is one of the major concerns for propagated signals in underground mines. Communication channels share several characteristics in the critical environment of mines, which leads to difficulty in reliable operation. As the signal propagates away from the transmitter, extreme path loss is observed in the gallery of a mine due to irregular geometric effects [6]. The link distance has a direct relationship with the attenuating factors and inverse relationship with propagated signal power [14]. The dielectric medium of underground mines changes its properties with a change in temperature and moisture, which leads to signal absorption [15]. Reflecting surfaces of underground mines and mining equipment used for mineral extraction are the prime sources of disruption for propagated signals. These obstructions produce a multipath propagation effect due to the reflection of signals, which leads to fading and fluctuations of the propagated signal [16]. When the signal travels through multiple routes and ends up at a particular receiver with a specific time delay, this effect reflects the dispersive properties of the channel, which leads to induction of the delay spread [17].