esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 HFAJAYA for the Multi-Constraint OPF Problems
### Discussion on Results  
In this section, the statistical results of the proposed method including max, mean, min, Std., and computational time, compared to the FA algorithm for all cases are given. The results for all single-objective cases are given in Table 12. The proposed HFAJAYA in all cases had a good advantage over the FA algorithm and on the other hand its optimization process was simple so that no additional time was required to perform the optimization process. HFAJAYA was also the most robust algorithm between the two algorithms. The important point is that as the dimension increased, the issue of HFAJAYA superiority over the FA algorithm became more prominent. Moreover, the ability of this algorithm to solve Optimal Power Flow Problem Considering Wind and Solar Power Generations can be seen.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Optimizer & Min & Mean & Max & Std. & Time (s) \\ \cline{2-5}  & & & Case 1 & \\ \hline
**HFAJAYA** & **800.4800** & **800.5095** & **800.5378** & **0.0095** & **20.6** \\ \hline FA & 800.7502 & 8001.0924 & 801.6420 & 1.82 & 20.6 \\ \hline Optimizer & Min & Mean & Max & Std. & Time (s) \\ \cline{2-5}  & & & Case 2 & \\ \hline
**HFAJAYA** & **646.5020** & **646.6267** & **646.7001** & **0.017** & **20.7** \\ \hline FA & 647.2612 & 647.5939 & 647.2614 & 0.823 & 20.7 \\ \hline Optimizer & Min & Mean & Max & Std. & Time (s) \\ \cline{2-5}  & & & Case 3 & \\ \hline
**HFAJAYA** & **832.1798** & **832.3673** & **832.6049** & **0.0097** & **20.7** \\ \hline FA & 832.5596 & 832.8512 & 833.1725 & 0.746 & 20.7 \\ \hline Optimizer & Min & Mean & Max & Std. & Time (s) \\ \cline{2-5}  & & & Case 7 & \\ \hline
**HFAJAYA** & **782.2795** & **782.5472** & **782.7363** & **0.0648** & **21.4** \\ \hline FA & 783.8409 & 784.6110 & 785.4923 & 1.84 & 21.5 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Statistical results to show the performance of the proposed HFAJAYA and FA algorithms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Optimal Values** \\ \hline \(Q_{G3}\) (MVAR) & 35.0868 \\ \(Q_{\text{avg2}}\) (MVAR) & 30.0 \\ \hline \(Q_{\text{ss}}\) (MVAR) & 18.2947 \\ \hline Fuelvlvcost (USD/h) & 422.6759 \\ \hline Wind gen cost (USD/h) & 255.1889 \\ \hline Solar gen cost (USD/h) & 115.4701 \\ \hline Total Cost (USD/h) & 810.5517 \\ \hline Emission (t/h) & 0.86084 \\ \hline Power losses (MW) & 5.2775 \\ \hline V.D. (p.u.) & 0.47175 \\ \hline Carbon tax (USD/h) & 17.2168 \\ \hline \hline \end{tabular}
\end{table}
Table 11: _Cont._In our proposed HFAJAYA, the population initialization, and the firefly position update by the combinatorial movement were included. Let \(O(F)\) be the computational time complexity of the objective function \(F(X)\). In the HFAJAYA, the computational time complexity of population initialization was \(O(F^{*}N)\), and the computational time complexity of position update was \(O(F^{*}N^{2})\). Hence, by analyzing other references ([PERSON], 2008), we know the total computational complexity of the HFAJAYA was the same as the basic FA that was \(O(F^{*}N^{2})\).
# Communication System Based on Magnetic Coils for Underwater Vehicles
## 3 Description of the Communication System
### Receiver System
#### 3.2.2 Demodulation  
The signal obtained in the receiver coil must be demodulated to obtain logic levels that can be correctly interpreted in a microcontroller. This stage was carried out using an envelope detector, avoiding frequency and phase problems that appear in synchronous detection. In this case, the signal that arrives at the receiver coil is passed through a nonlinear device (diode 1N4001) and a low pass filter, the diode rejects the negative part of the amplitude shift keying signal, and the low-pass filter cannot follow the variations of the signal, so it only keeps the envelope of the signal that the diode allows to pass.  
Figure 9 shows the comparison between the emitted signal prior to the modulation stage (yellow color) and the received signal after the demodulation stage (pink color). In this figure, it can be seen that communication between the two systems is established.  
Communication was established adequately through the air, and the transmitted information was verified using laboratory instruments. To evaluate the application of the proposed system for underwater vehicles communication, a submersible module was designed and attached to an underwater robot as shown in following section.
## 3 Optimal Attack Strategy
### Optimal Attack Strategy Solving Based on PSO
After that, adaptive mutation is performed according to the threshold \(c_{m}\) to provide the possibility for the particle to jump out of the local optimum. Since the optimization problem in this paper introduces energy constraints, the current position of the particle needs to be corrected, and the optimal position \(\{G_{i,\text{best}}\}_{i=1}^{S}\) and \(Z_{\text{best}}\) are updated. After the requirements of iteration accuracy or maximum number of iterations are met, the loop is terminated and the optimal attack strategy, \(\Omega^{*}\), is given. Under the premise that the attacker knows the threshold, \(C_{l}\), the input vector, \(\phi_{l}\), and the system parameter, \(\theta\), in the system, Algorithm 1 can satisfy the energy constraints and obtain \(\Omega^{*}\). ```
Data: Number of particles \(S\), maximum number of iterations \(T\), initial position of particles \(\{\Omega_{i}^{0}\}_{i=1}^{S}\), and the initial velocity of particles \(\{v_{i}^{0}\}_{i=1}^{S}\), the learning factor \(c_{1}\) and \(c_{2}\), the inertia weight \(w\), the threshold \(c_{m}\) of adaptive mutation, the particle historical optimal position \(\{G_{i,\text{best}}^{0}\}_{i=1}^{S}\) and the population historical optimal position \(Z_{\text{best}}^{0}\), the upper boundary vector \(\Omega_{\text{max}}\) and lower boundary vector \(\Omega_{\text{min}}\) of the particle, the termination condition \(Z_{\text{min}}\). Result: Optimal attack strategy \(\Omega^{*}\) which is the global optimal position of the population \(Z_{\text{best}}^{*}\). 1 for\(t=1:T\)do
2\(\{v_{i}^{t}\}_{i=1}^{S}=w\{v_{i}^{t-1}\}_{i=1}^{S}+c_{1}*rand*(\{G_{i,\text{ best}}^{t-1}\}_{i=1}^{S}-\{\Omega_{i}^{t-1}\}_{i=1}^{S})+c_{2}*rand*(Z_{ \text{best}}^{t-1}-\{\Omega_{i}^{t-1}\}_{i=1}^{S})\);
3\(\{\Omega_{i}^{t}\}_{i=1}^{S}=\{\Omega_{i}^{t-1}\}_{i=1}^{S}+\{v_{i}^{t}\}_{i=1} ^{S}\);
4 if\(rand>c_{m}\)then
5 A dimension \(j\) of the particle is randomly selected;
6\(\{\Omega_{i}^{t-1}\}_{i=1}^{S}(j)=\Omega_{\text{min}}(j)+rand*(\Omega_{j,\text {max}}(j)-\Omega_{\text{min}}(j))\);
7 end for
8 Energy constraint correction according to (9)-(11);
9 Calculate the fitness value of \(\{\Omega_{i}^{t}\}_{i=1}^{S}\) based on (5);
10 Update \(\{G_{i,\text{best}}^{t}\}_{i=1}^{S}\) and \(Z_{\text{best}}^{t}\);
11 if\(Z_{\text{best}}^{t}<Z_{\text{min}}\)then
12 Break;
13
14 end if
15
16 end for
```  
**Algorithm 1**The PSO algorithm to solve \(\Omega^{*}\)
## 3 Results and Discussion
After depositing the DBR structure, based on the modeled EL spectrum for Device A, one cavity mode lies at 500 nm (FWHM 13 nm) and another lies at 588 nm (FWHM 16 nm) (Figure 7a). Figure 4: (a) CE versus luminance and (b) PE versus luminance for device C.  
Figure 5: Tapping-mode atomic force microscopy (AFM) height images of the DBR film. The scan area of the image is 2 \(\times\) 2 \(\upmu\)m.  
Figure 6: The reflectance spectrum of the distributed Bragg reflector (DBR) and the electroluminescence (EL) spectrum of a reference device without a DBR structure with the same configuration as Device A. The modeled EL emission shows the coordinates of (0.37, 0.48), which is within that of a warm white color. To optimize the luminance from the perspective of design, the antinodes of the confined cavity field, at 500 and 588 nm, respectively, are placed near the interface between the NPB layer and Alq\({}_{3}\) layer, and excitons come into being around the interface [25; 26]. Additionally, Device B also exhibits a very narrow EL spectrum with three emission peaks (Figure 7b). More importantly, with the application of driving voltage, we can see the white light along the normal direction from the side of the glass. Figure 7a displays the EL spectrum at the range of 5-13 V in a normal direction. The emission color remains stable, although the voltages change due to the simple structure and single emitting layer. Therefore, the recombination zone hardly changes with increasing voltage [27]. With the increase in the operation voltage, the resonator mode in the microcavity does not change. Thus, the ratio of emission intensity with respect to the inherent emission changes little, resulting in a good chromatic stability [28]. The CIE coordinates exhibited by device A shows a negligible slight variation of (\(\pm\)0.01, \(\pm\)0.01). Furthermore, Device C also yields a high color purity white emission when the luminance changes between 100 cd m\({}^{-2}\) and 10,000 cd m\({}^{-2}\), and there are four resonant peaks of 420 nm, 488 nm, 552 nm, and 620 nm (Figure 8).
## 3 Heuristic Solution Approach
### Addressing Coverage Constraints  
Having available the solution from the unconstrained problem, we can solve the problem subject only to the coverage constraints. The optimization problem as described in [7] can be described from the following equations:  
\[\max(\frac{1}{L|U|}\sum\
olimits_{i\in I}\sum\
olimits_{u\in U}{\dot{r}iu*xiu} )\, \tag{8}\]  
subject to:  
\[Cov(c,x)=\frac{1}{|Ic|}\sum\
olimits_{i\in Ic}\sum\
olimits_{u\in U}{\dot{x}u \geq Kc},\ xiu\in\{0,1\}\, \tag{9}\]  
\[\sum\
olimits_{i\in I}{\dot{x}iu=L}\,\ \text{for all}\ u\in\mathrm{U} \tag{10}\]  
where all the symbols have been described in the previous section. Specifically, the goal is to maximize the total predicted rating of the new recommendation list, while average coverage for the items of each provider should be at least \(Kc\) (items of provider, \(c\), should be recommended on average to at least \(Kc\) users. The last constraint denotes that each user receives exactly \(L\) recommendations.  
As described also in [7], the optimization problem with coverage constraints is linear and hence solvable and can be solved in polynomial time. The results of the problem subject to coverage constraints are a list of recommended items for each user; these have the highest predicted rating by each user, that allow coverage constraints to be fulfilled. The aforementioned recommendation lists will be used as a starting point, in order to find a solution that also fulfills the constraints for diversity.
# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## 5 Experimental Design and Results
### Evaluation Metrics  
The Machine Learning community commonly uses four evaluation metrics to assess the performance of models in classification tasks like ours:* Accuracy measures the proportion of correctly classified samples out of the total number of samples in the dataset. It is calculated by dividing the number of correctly classified samples by the total number of samples.
* Precision measures the proportion of true-positive predictions (correctly classified positive samples) out of all positive predictions (samples classified as positive). It is calculated as true positives divided by the sum of true positives and false positives.
* Recall, also known as sensitivity, measures the proportion of true-positive predictions out of all actual positive samples in the dataset. It is calculated as true positives divided by the sum of true positives and false negatives.
* F1 score is the harmonic mean of precision and recall. It provides a balanced measure of a model's performance by taking into account both false positives and false negatives.  
In this work, we evaluate the performance of our classification models using only accuracy and F1 because those are the metrics used in the literature, which will allow us to conduct a fair comparison of results. The only exception is the work of [PERSON] et al. [2], which only evaluated the accuracy of their models. As shown in Figure 10, we had class imbalance. To ensure a fair evaluation, we employed weighted averages for calculating the F1 score. A weighted F1 score takes into account class imbalance by assigning weights proportional to the number of samples in each class when calculating the average.  
Table 7 provides a summary and comparison of our experimentation parameters.
# Influence of MOSFET Parasitic Capacitance on the Operation of Interleaved ZVS Boost Converters
## 7 Possible Output Voltage Regulation Method
The model was simulated for two main scenarios, possible in a real application:  
* **Scenario 1:** start-up of the converter and step response for reference output voltage set to 200 V and 50 \(\Omega\) load resistance;
* **Scenario 2:** sudden change of the load resistance from \(R\) = 50 \(\Omega\) to 200 \(\Omega\) and subsequently back to 50 \(\Omega\) for \(V_{\mathrm{ref}}\) = 400 V.  
Figure 12: Simplified diagram of the proposed converter control method. The results of both simulations show that the proposed control method is feasible, thus making the analyzed converter topology applicable in renewable energy generation systems, other DC power supplies or as a boost stage in any device. It is possible to control the output voltage with little overshoot, which could be further reduced by fine-tuning the PI controller. The output voltage can also be controlled for different load resistance values. However implementing the proposed control method in a prototype converter requires further analysis of extreme work conditions, such as very or very low loads. For example, a very low load (high load resistance) could result in the voltage ratio rising above the voltage rating of the converter components, which would lead to damaging the converter. Therefore over-voltage detection and protection are most likely required. Despite that the controller must be able to control the output voltage even for very low loads, thus a feasible solution to the described problem must be found. A possible solution could be the implementation of a \"cutoff\" mode, similarly to LLC resonant converters. Further work is needed to confirm the feasibility of such a solution in a prototype setup.
# Smart Contract Design Pattern for Processing Logically Coherent Transaction Types
## 3 The Pattern Design and Implementation
### Concrete Layer  
Smart contracts are widely used in energy applications. Their usages were recently reviewed by Vionis and Kotsilieris [41]. The _Concrete_ layer of the pattern uses the example of energy transfer between different stakeholders in the distributed renewable energy system. In such systems, energy can be exchanged between prosumers within the same community and between prosumers in different communities. Additionally, electricity can be sent to the power grid. The schema Figure 3 shows a UML Use case diagram for the _SendEnergy_ use case. The Use case diagram employs stereotype \(<<\)IntegratedSystem\(>>\) from the _UML Profile for Messaging Patterns_ defined by Gorski [42]. The stereotype denotes actors representing applications external to the prosumer one. The action performed in the use case is the same, but the conditions checked differ depending on the transaction type. The example assumes the following set of verification rules used by the three transaction types considered:  
* The source of the transaction must be different from the target of the transaction;
* Energy quantity to transfer must be greater than zero;
* Energy surplus in the source node must be greater or equal energy quantity to transfer;
* Source community must differ from target community;
* Target need must be greater or equal to energy quantity to transfer;
* The target is the subnet energy grid. Using the _AdapT_ pattern, the _Send energy_ use case can be implemented with one smart contract. Figure 4 depicts a UML Class diagram with classes that both constitute the abstract layer of the _AdapT_ pattern and implement classes of the concrete _ExchangeEnergy_ smart contract. Figure 4: The _AdapT_ pattern with abstract and concrete classes. Figure 3: The _SendEnergy_ use case with various external users. The concrete smart contract _ExchangeEnergyContract_ handles three transaction types: in-community (_TransactionIn_ class), cross-community (_TransactionCross_ class), and to-grid (_TransactionGrid_ class). The drawing Figure 5 presents all classes declared for transaction types in both layers of the pattern for this concrete smart contract. One additional class the _Transaction_ has been defined to gather common attributes of transactions. The class counteracts the redundancy of attributes. The _Transaction_ class is also abstract. One can only instantiate objects of the three specific transaction classes. The class that inherits from the _AbstractSmartContract_ abstract class is responsible for handling various transaction types. In the presented example the _ExchangeEnergyContract_ class inherits from that abstract class. In the constructor of the smart contract class, both the verification rule list and configurations for transaction types are initiated. The constructor source code of the _ExchangeEnergyContract_ class presents Listing 3.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 5 Performance Analysis
### Angle Error  
Consider a unit norm target vector \(t\in\mathbb{R}^{N}\) that shall be approximated by a scaled version of one out of \(K\) codewords \(b_{k}\in\mathbb{R}^{N}\) that are random and jointly independent. Denoting the angle between the target vector \(t\) and the codeword \(b_{k}\) as \(\alpha_{k}\), we can write (the norm of) the angle error as  
\[a_{k}=|\sin\alpha_{k}|. \tag{22}\]  
The correlation coefficient between target vector \(t\) and codeword \(b_{k}\) is given as  
\[\rho_{k}=\frac{\langle t;b_{k}\rangle}{\|t\|_{2}\,\|b_{k}\|_{2}}=\cos\alpha_{k}. \tag{23}\]  
The angle error and the correlation coefficient are related by  
\[a_{k}^{2}=1-\rho_{k}^{2}. \tag{24}\]  
We will study the statistical behavior of the correlation coefficient in order to learn about the minimum angle error.  
Let \(\mathrm{P}_{\rho^{2}|t}(r,t)\) denote the cumulative distribution function (CDF) of the squared correlation coefficient given target vector \(t\). The target vector \(t\) follows a unitarily invariant distribution. Thus, the conditional CDF does not depend on it. In the sequel, we choose \(t\) to be the first unit vector of the coordinate system, without loss of generality.  
The squared correlation coefficient \(\rho_{k}^{2}\) is known to be distributed according to the beta distribution with shape parameters \(\frac{1}{2}\) and \(\frac{N-1}{2}\) Section III.A in [48], and given by  
\[\mathrm{P}_{\rho^{2}}(r)=\mathrm{B}\bigg{(}\frac{1}{2},\frac{N-1}{2},r\bigg{)}. \tag{25}\]  
Here, \(\mathrm{B}(\cdot,\cdot,x)\) denotes the regularized incomplete Beta function [49]. It is defined as  
\[\mathrm{B}(a,b,x)=\frac{\int\limits_{0}^{x}\xi^{a-1}(1-\xi)^{b-1}\mathrm{d}\xi }{\int\limits_{0}^{1}\xi^{a-1}(1-\xi)^{b-1}\mathrm{d}\xi} \tag{26}\]  
for \(x\in[0;1]\) and zero otherwise. With (24) and (26), the distribution of the squared angle error is, thus, given by  
\[\mathrm{P}_{a^{2}}(r)=\mathrm{B}\bigg{(}\frac{N-1}{2},\frac{1}{2},r\bigg{)}. \tag{27}\]
# A Hybrid Route Selection Scheme for 5G Network Scenarios: An Experimental Approach
## 4 CenTri: Reinforcement Learning Model and Algorithm
### Reinforcement Learning Models
#### 4.1.1 Centralized Route Selection  
In the centralized mechanism, a proactive route selection mechanism is deployed in the CC to establish backbone routes among MC BS and femtocell nodes \(fc\). Backbone routes, which do not have PU activities, help to maximize the packet delivery ratio in networks. The CC evaluates the available routes based on network-wide information gathered from the BSs and femtocell nodes \(fc\). The routes are prioritized as \(k_{1}=fc_{s}-mc-fc_{d}\), \(k_{2}=fc_{s}-fc_{3}-fc_{6}-fc_{d}\), \(k_{3}=fc_{s}-fc_{1}-fc_{4}-fc_{7}-fc_{d}\), and \(k_{4}=fc_{s}-fc_{2}-fc_{5}-fc_{8}-fc_{d}\). The evaluated routes from the CC are prioritized based on the number of hops without taking PU activities into consideration. The CC tends to give priority to the shortest route (i.e., the backbone route), which goes through MC BS. The routes are given to source nodes proactively to save processing times incurred in determining routes. Figure 3 shows that the proactive routes are provided by the CC to distributed source nodes in the data plane.  
The source node re-prioritizes (or reranks) the given routes based on D2D communication with the minimum number of intermediate nodes (or the number of hops) and traffic intensity. Hence, the source node gives the lowest priority to the backbone route since it does not use D2D communication; this helps in distributing traffic from the MC BS. The presence of PUs has a direct impact on the throughput and delays the performance of packet transmission. Routes with lesser channel switches have lower signaling overheads, leading to higher stability and bandwidth availability [56].
# Logistics Transportation Vehicle Supply Forecasting Based on Improved Informer Modeling
###### Abstract  
This study focuses on the problem of the supply prediction of logistics transportation vehicles in road transportation. Aiming at the problem that the supply data of logistics transportation has the characteristics of long sequential data, numerous influencing factors, and a significant spatiotemporal evolution law, which leads to the lack of accuracy of supply predictions, this paper proposes a supply prediction method for logistics transportation based on an improved Informer model. Firstly, multidimensional feature engineering is applied to historical supply data to enhance the interpretability of labeled data. Secondly, a spatiotemporal convolutional network is designed to extract the spatiotemporal features of the supply volume. Lastly, a long short-term memory (LSTM) model is introduced to capture the supply volume's long- and short-term dependencies, and the predicted value is derived through a multilayer perceptron. The experimental results show that mean square error (MSE) is reduced by 73.8%, 79.3%, 82.24%, 78.58%, 77.02%, 53.96%, and 40.38%, and mean absolute error (MAE) is reduced by 52%, 59.5%, 60.36%, 57.52%, 53.9%, 31.21%, and 36.58%, respectively, when compared to the auto-regressive integrated moving average (ARIMA), support vector regression (SVR), LSTM, gated recurrent units (GRUs), a back propagation neural network (BPNN), and Informer and InformerStack single models; compared with the ARIMA + BPNN, ARIMA + GRU and ARIMA + LSTM integrated models, the MSE is reduced by 74.88%, 71.56%, and 74.07%, respectively, and the MAE is reduced by 51.31%, 50%, and 52.02%; it effectively reduces the supply prediction error and improves the prediction accuracy.  
Keywords:logistics transportation; supply forecast; informer model; data feature engineering; artificial neural networks +
Footnote †: journal: Applied Sciences
## 2 Modeling of UAV Observations
### Atmospheric Radiative Transfer  
To calculate the downward radiation term \(T_{\lambda}^{\downarrow}\), the UNL-VRTM model is adopted, which is an open-source atmospheric radiative transfer model [38; 39]. The inputs of UNL-VRTM include the atmospheric parameters (pressure, altitude, temperature, etc.), aerosol parameters (the aerosol optical depth, particle size distribution, and complex refractive indices), and observation geometries (solar zenith angle, viewing zenith angle, and relative azimuth angle). The outputs of UNL-VRTM include not only the Stokes vector but also their sensitivities (Jacobians) with respect to aerosol and surface model parameters. However, the UNL-VRTM's built-in surface model is the kernel-driven BRDF (Bi-directional Reflectance Distribution Function) model, and none of the BRDF kernels contains the phenotypic parameters of the crop. Therefore, the UNL-VRTM model cannot be used directly as a forward model for crop parameter inversion. The main input parameters of the UNL-VRTM model are given in Table 1.