esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation Models for Distributed PV Project
### Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) Evaluation Method  
In this paper, all evaluation indexes are quantitative. In order to keep the data information optimal, a technique for order preference by similarity to an ideal solution (TOPSIS) is chosen to evaluate the core abilities of a distributed PV generation project due to its methodological advantages [36]. The steps of the TOPSIS evaluation model are introduced as follows.  
1. Matrix construction of multi-objective decision (\(R\)). Assume that \(C=\{C_{1},\,C_{2},\,\ldots,\,C_{m}\}\) is the solution set of objective problems and \(X=\{x_{1},\,x_{2},\,\ldots,\,x_{n}\}\) is the index set, the evaluation value of \(X\) to \(C\) is \(r_{ij}\) (\(i=1,\,2,\ldots,\,m;j=1,\,2,\ldots,\,n\)). Then, the matrix \(R\) can be represented as Equation (19). \[R=\left(\begin{array}{cccc}r_{11}&r_{12}&\ldots&r_{1m}\\ r_{21}&r_{21}&\ldots&r_{2m}\\ \ldots&\ldots&\ldots&\ldots\\ r_{n1}&r_{n2}&\ldots&r_{nm}\end{array}\right)\] (19)Positive and negative ideal point setting. Combining this with the weighting result \(W\), the decision matrix \(Z=(z_{ij})_{m\times n}\) can be calculated with \(R\). From the decision matrix, the positive and negative ideal points can be set as \(Z^{+}=({z_{1}}^{+},{z_{2}}^{+},\ldots,{z_{m}}^{+})\) and \(Z^{-}=({z_{1}}^{-},{z_{2}}^{-},\ldots,{z_{m}}^{-})\) which can be measured via Equation (20). \[\left\{\begin{array}{l}{{z_{i}}^{+}}=\max({z_{i1}}^{+},{z_{i2}}^{+},\ldots,{ z_{im}}^{+})\\ {{z_{i}}^{-}}=\min({z_{i1}}^{-},{z_{i2}}^{-},\ldots,{z_{im}}^{-})\end{array}\right.\] (20)
3. Distance calculation of solution to ideal point. Set \({D_{j}}^{+}\) and \({D_{j}}^{-}\) as positive ideal point and negative ideal point respectively, the calculation method is shown in Equation (21). \[\left\{\begin{array}{l}{{D_{j}}^{+}}=\sqrt{\sum\limits_{i=1}^{m}{{(z_{ij}-{z _{i}}^{+})}^{2}}}\\ {{D_{j}}^{-}}=\sqrt{\sum\limits_{i=1}^{m}{{(z_{ij}-{z_{i}}^{-})}^{2}}}\end{array} \right.,i=1,2,\ldots,m;j=1,2,\ldots,n\] (21)
4. Evaluation result ranking. According to the evaluation results, the solutions can be ranked by their distances to the negative ideal point. The solution values can be calculated via Equation (22). \[C_{j}=\frac{{{D_{j}}^{-}}}{{{D_{j}}^{-}}+{{D_{j}}^{+}}},j=1,2,\ldots,n\] (22)
## 5 Simulations
### Channel Performance in Presence of Surface and Bottom Reflections
This is because in the latter losses as a result of bottom-up reflection would be higher, resulting in a decrease in the SNR in the receiver and a mandatory decrease in the amount of data transmitted per unit of time. Considering lifetime, the simulations show (Table 6) that by increasing the number of reflections the network lifetime decreases, as the acoustic communication capacity decreases. As a modem spends more energy during the transmission, this would increase the energy consumed for transmission and decrease the network lifetime. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Reflections** & **Capacity (kbps)** & **Delay (ms)** & **Lifetime (cycles)** \\ \hline
2 & 831.51 & 160.16 & 4573 \\
4 & 738.02 & 291.55 & 4065 \\
6 & 656.81 & 424.40 & 3618 \\
8 & 580.46 & 557.71 & 3197 \\
10 & 506.77 & 691.29 & 2789 \\
12 & 434.77 & 825.11 & 2391 \\
14 & 363.95 & 959.22 & 1998 \\
16 & 294.02 & 1093.78 & 1611 \\
18 & 224.91 & 1229.14 & 1227 \\
20 & 157.06 & 1366.30 & 852 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of simulation for basalt material. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Reflections** & **Basalt** & **Limestone** & **Gravel** & **Sand** \\ \hline
2 & 831.51 & 808.27 & 765.24 & 751.17 \\
4 & 738.02 & 699.61 & 617.82 & 590.63 \\
6 & 656.81 & 600.98 & 479.43 & 438.94 \\
8 & 580.46 & 506.76 & 345.23 & 291.45 \\
10 & 506.77  
Also, from Table 7, results that as the number of reflections increases, the total delay will increase as in addition to the fact that the signal is transmitted to the receiver through longer paths. By increasing the number of reflections, the acoustic communication capacity decreases, so it will take even longer to send the packet. Two other bottom materials, sand and gravel, are taken into consideration in simulation presented in Tables 5-7. Sand is the most porous material among the four materials mentioned. So, any reflected signal at a sand composite bottom environment would suffer the highest loss compared to the reflections in the other three environments, thus enabling lower acoustic capacity. The acoustic communication capacity performed in an area where the bottom environment is composed of gravel, would be higher than the capacity for a sand composite bottom material. This is due to the loss that this material has on the reflected signal is much lower than the loss caused by the sand. Furthermore, lifetime decreases for basalt-limestone-gravel-sand crossings while the total delay to data acquisition would increase (decreased data transmission capacity). These results are also relevant for cases where acoustic modems will be designed for specific applications by the manufacturer.
## 4 Statistical Prediction Model
### SWIPE Model Flowchart  
Figure 3 shows the general flowchart of the SWIPE model training and predicting based on the RF algorithm. From this figure, a unified strategy from tracking to identifying is used to classify convective storm system into three categories. It roughly contains three key steps: First, it tracks potential convective cloud clusters using three continuous imageries from H08, and then collocates the H08/AHI and GFS NWP data with GPM IMERG rain rate data (benchmark) in a same spatiotemporal scale. The second step is to divide the convective storm system dataset into three different types (weak, medium, and severe). A classical sample-balance technique is used here to further improve the performance of models. Finally, the RF algorithm is used to train and develop a convection intensity classification statistical model - SWIPE.
## 2 Theory of Infiltration and Seepage in Unsaturated-Saturated Soils  
[PERSON]'s law governs water flow in unsaturated and saturated soils [19]. According to [PERSON]'s law, the velocity of discharge is proportional to the gradient of hydraulic head:  
\[v=-k\,\frac{\delta h}{\delta y} \tag{1}\]  
where \(k\) is the coefficient of permeability, \(h\) is the hydraulic head, and \(\delta h/\delta y\) is the hydraulic gradient in the \(y\) direction.  
The hydraulic head \(h\) is composed of an elevation head \(y\) and a pore water pressure head as follows:  
\[h=y+\frac{u_{w}}{\rho_{w}g} \tag{2}\]  
where \(u_{w}\) is the pore water pressure, \(\rho_{w}\) is the density of water, and \(g\) is the acceleration due to gravity. For a saturated soil, the coefficient of permeability can be considered as a constant value. However, for unsaturated soils, the coefficient of permeability is normally regarded as a function of negative pore water pressure (matric suction) and tends to decrease with increasing suction.  
The relationship between matric suction and the coefficient of permeability for an unsaturated soil is referred to as the permeability function. [PERSON]'s law and the equation of continuity, the governing equation for two-dimensional transient flow through an unsaturated soil, can be expressed as:  
\[\frac{\delta}{\delta x}\bigg{(}k_{x}\frac{\delta h}{\delta x}\bigg{)}+\frac{ \delta}{\delta y}\bigg{(}k_{y}\frac{\delta h}{\delta x}\bigg{)}=\frac{\delta \theta_{w}}{\delta t} \tag{3}\]where \(k_{x}\) is the coefficient of permeability in the \(x\) direction, \(k_{y}\) is the coefficient of permeability in the \(y\) direction, and \(\theta_{w}\) is the volumetric or gravimetric water content.  
The water phase constitutive relationship proposed by [PERSON] and [PERSON] [20] for an unsaturated soil can be expressed as follows:  
\[d\theta_{w}=m_{1}^{w}d(\sigma-u_{a})+m_{2}^{w}d(u_{a}-u_{w}) \tag{4}\]  
where \(\sigma\) is the total stress; \(u_{a}\) is the pore air pressure; \(m_{1}^{w}\) is the slope of the water volume vs. \(\sigma-u_{a}\) relationship when \(d\) (\(u_{a}-u_{w}\)) is equal to zero; \(m_{2}^{w}\) is the water storage coefficient representing the slope of the water volume vs. (\(u_{a}-u_{w}\)) relationship when (\(u_{a}-u_{w}\)) is equal to zero.  
For an analysis involving transient seepage, it can be assumed that the pore air pressure remains at atmospheric conditions while the total stress is constant. Then, a change in porewater pressure can be related to volumetric water content as follows:  
\[d\theta_{w}=m_{2}^{w}d(u_{a}-u_{w}) \tag{5}\]  
Substituting Equations (2) and (5) into Equation (3) yields the following governing equation for water flow through an unsaturated soil:  
\[\frac{\delta}{\delta x}\bigg{(}k_{x}\frac{\delta h}{\delta x}\bigg{)}+\frac{ \delta}{\delta y}\bigg{(}k_{y}\frac{\delta h}{\delta x}\bigg{)}=m_{2}^{w}\rho _{w}g\frac{\delta h}{\delta t} \tag{6}\]
# Study of Discharge Characteristics on Ignition Performance via High-Speed Imaging in a CVCC
###### Abstract  
Advanced combustion technologies, like highly boosted and lean or dilute combustion, have been employed to meet the demands of high efficiency and low emissions in SI engines, which have increased the challenges of ignition control. It is essential to find a suitable ignition strategy due to the need to develop a next-generation spark ignition system. In this study, simultaneous visualization by a high-speed infrared camera (FLIR X6900 sc) and a conventional high-speed camera (FASTCAM SA-X) is carried out to obtain deeper insights into the ignition process in a constant volume combustion chamber (CVCC). Infrared images have provided a more accurate way of measuring the initial flame and are able to analyze quantitatively. Ignition performance is studied with various mixture dilutions, flow conditions, and discharge characteristics. Two types of ignition coils that have the same discharge energy were analyzed in particular. The results show that extending the discharge duration is more helpful in improving the ignition performance under the increasing dilution ratio, compared to the enhanced discharge current at the same discharge energy. However, the discharge current plays a more vital role in perfecting the ignition performance under the increasing local flow velocity than the discharge duration.  
discharge characteristics; ignition performance; simultaneous visualization Article  
Study of Discharge Characteristics on Ignition Performance via High-Speed Imaging in a CVCC  
1
## 4 Discussion
### Purpose and Significance of the Study  
The current study underscores the significance of assessing the total carbon footprint and resilience as crucial steps toward achieving carbon neutrality in parks. This aids in developing strategies to enhance parks that fall short of carbon neutrality. The study's four research objectives encompass calculating the carbon footprint of six parks' landscape engineering, evaluating park resilience, summarizing ideal LEP area ratios, and conducting correlation analysis of LEP, NDVI, and CN. The research successfully fulfilled all these objectives.  
Past research on park carbon footprints revealed certain limitations. [PERSON] et al. [53] addressed the carbon footprint of entire life cycles but omitted factors like irrigation practices and carbon emissions from certain park facilities. [PERSON] et al. [23]focused solely on tree carbon sequestration, neglecting grasslands and other vegetation. [PERSON] et al. [42] employed generalized construction material carbon calculations, unlike this study, which localized data for accurate results. Furthermore, while previous studies explored NDVI, RI, and CN in urban parks, none investigated their interconnectedness through field research and data analysis. Thus, this study bridges a crucial research gap.  
The study's significance lies in several aspects: (1) Comprehensive life cycle carbon footprint calculations, encompassing labor, shrubs, and facilities, providing a more holistic assessment. (2) Inclusion of NDVI, RI, and CN, yielding insights into the impact and resilience of carbon-neutral urban parks. (3) Development of ideal LEP ratios tailored to different park types, offering valuable guidance for park construction, enhancement, and carbon neutrality achievement. (4) Provide landscape designers and managers with a tool to assess the carbon neutrality and resilience of parks swiftly and effortlessly, enabling rapid and effective decision-making prior to the design and improvement stages of park projects.
## 5 Conclusions
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Works** & **Year** & **Technique/Algorithms** & **Performance Metrics** \\ \hline \multicolumn{4}{c}{**Machine and Deep Learning Algorithms**} \\ \hline E. [PERSON], et al. [25] & 2020 & Semantic Role Embeddings & F1-Score: 99\% \\ \hline A. [PERSON], et al. [28] & 2020 & GRU Model for News Article Detection & Acc: 94\% \\ \hline G. [PERSON], et al. [29] & 2021 & MLP, LSTM, Bi-LSTM, Bi-GRU, BERT & F1-Score: 78.03\% \\ \hline C. [PERSON], et al. [30] & 2021 & Bi-GRU, GloVe & Accuracy: 96.98\% \\ \hline [PERSON], et al. [31] & 2021 & BERT, MTL & Accuracy: 97.90\% \\ \hline [PERSON], et al. [32] & 2021 & NB, MNB, Bernoulli NB, LR, SGD, Linear SVC, RNN & Accuracy: 84\% \\ \hline [PERSON], et al. [33] & 2022 & Supervised and Unsupervised BERT-NER & F1-Score: 76\% \\ \hline [PERSON], et al. [26] & 2022 & NLP Pipeline with LSTM, BERT, RoBERTa & F1-Score: 97\% \\ \hline [PERSON], et al. [34] & 2023 & BERT, DGCN, Caps-DGCN & Accuracy: 86.78\% \\ \hline [PERSON], et al. [35] & 2023 & KNN, DT, RF, SVM, ANN, AdaBoost + RF & Accuracy: 63.9\% \\ \hline [PERSON], et al. [36] & 2023 & RF, LR, TF-IDF & ROC-AUC: 98.1\% \\ \hline [PERSON], et al. [37] & 2023 & SVM, RF, MLP, DT & Accuracy: 93\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparisons with similar studies in terms of technical and performance. An important contribution of this study is to demonstrate the applicability of NLP techniques in the early detection and prevention of expressions of violence to improve the safety of healthcare workers. Integrating these models into healthcare systems as part of early detection mechanisms will significantly contribute to the prevention of violent incidents. In the future, the use of larger and diversified datasets will increase the generalizability of the models in cultural and linguistic contexts. Furthermore, real-time data analytics and the development of mobile-based applications have been identified as imminent targets in the detection and prevention of expressions of violence. M.V.A. : Conceptualization, review and editing, software. M.A.Y. : Validation, writing--original draft preparation, methodology. R.G. : Conceptualization, writing--original draft preparation, methodology. A.A.: XAI Algorithms, methodology. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Isparta University of Applicable Sciences Ethics Committee has official ethics committee permission dated 4 September 2024 and numbered E-96714346-050.04-129339. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data that support the findings of this study will be made available from the corresponding author upon reasonable request. The authors declare no conflicts of interest.
## 4 Experiment Results
### Results and Analysis  
We compared the experimental results of the four methods mentioned above in the environments described as Tables 2 and 3. The results are shown in the Figures 4-17.The Figure 4 represents the team reward of every episode, which is a macro-performance evaluation index. It means the total cost of the supply chain in the environment. The Figures 6-17 represent the average step inventory costs of the main manufacturer and the 22 suppliers, which are micro-performance evaluation indexes in the environment. For the convenience of the method policy updates and observations, we uniformly processed the positive and negative of all evaluation indexes: a higher numerical value indicates better performance of the method in the corresponding evaluation index. The comparison of the result curve in the same result figure concerning the variation of episodes is also a contrasting reference for the impact of the number of iterations on the experiment results.  
\(-380\) and \(-420\), respectively. The DualPolicy-IT(ours) method achieves approximately a \(45\%\) performance improvement compared to MAPPO.  
In our proposed method, to minimize the impact of the learning rate on method performance, we used a trick called learning rate decay. In order to explore the influence of the learning rate on the method, we conducted a set of control experiments, and the experiment results are shown in Figure 5. The control group used \(7\times 10^{-4}\) as the initial learning rate, and the initial learning rates of the experimental group were \(7\times 10^{-2}\), \(7\times 10^{-3}\), and \(7\times 10^{-5}\). The experiment results show that the large or small learning rate has a certain degree of influence on the performance of the method. When the learning rate is too large, such as \(7\times 10^{-2}\), the learning rate will cause the span of each update of the method to be too large, failing to converge to the optimum, which will degrade the performance of the method. However, we set learning rate decay. It can be clearly seen that as the training progresses, the impact of excessive learning rate has been alleviated to a certain extent, and finally converged to a final level slightly lower than the results of the control group at about episode \(950\). When the learning rate is too small, such as \(7\times 10^{-5}\), the learning rate will result in a small update range, making the performance of the method trapped in a local optimum. The \(7\times 10^{-5}\) only finally converged to about \(-1500\) at episode \(600\).
# Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network
## 2 System Description
### EV Power Demand Calculation  
EV power demand is essential for ANN data training. According to physical laws, power demand can be calculated by analyzing all forces that act in the vehicle and road surfaces. From the force diagram in Figure 2, all forces that act in the vehicle can be evaluated where \(F_{a}\), \(F_{air}\), \(F_{c}\), and \(F_{r}\) denote, respectively, the force for acceleration, the aerodynamic force, a load related to steepness, and rolling resistance. It is possible to calculate the total power requirement by calculating the sum of all force multiplied by the vehicle's speed, as described in Equation (2).  
\[P_{t}(t)=\left[m\cdot\frac{dv(t)}{dt}+m\cdot g\cdot\sin\theta+m\cdot g\cdot f _{r}\cdot cos\ \theta+\frac{1}{2}\cdot\rho\cdot A_{f}\cdot C_{d}\cdot(v(t)+v_{w})^{2}\right] \cdot v(t) \tag{2}\]  
where:  
* \(P_{t}\) = power traction (W);
* \(m\) = vehicle mass (kg);
* \(v\) = speed (m/s);
* \(g\) = gravity force (m/s\({}^{2}\));
* \(\theta\) = slope angle (rad);
* \(f_{r}\) = rolling resistance coefficient;
* \(\rho\) = air density (kg/m\({}^{3}\));
* \(A_{f}\) = vehicle's frontal area (m\({}^{2}\));
* \(C_{d}\) = aerodynamics resistance coefficient;
* \(v_{w}\) = wind speed (m/s).
# Deep Feature Fusion Based Dual Branch Network for X-ray Security Inspection Image Classification
## 3 Method  
### Dual Branch Network Architecture  
The deep feature fusion model is in charge of improving the model's perception ability to small-sized objects, and works at the feature level. The focal loss function balances the importance between positive and negative samples; moreover, it distinguishes difficult samples from easy ones, which works at the loss level. To effectively combine their advantages, instead of intuitively adopting the focal loss function on loss calculation of the deep feature fusion model, this paper proposes a dual branch network architecture. Each branch of the network corresponds to one of the above two methods and implements the integration of two branches on the final loss calculation. The specific architecture is shown in Figure 1.  
The network output on the left branch is based on a CNN model, and the loss calculation adopts the focal loss function. The network output on the right branch is based on the deep feature fusion model, and the loss calculation adopts a cross entropy loss function. The dual branch network enables the two branches to supervise the training process on each other due to the integration brought about by late fusion, effectively combining the advantages of different branches and further improving the perfoBased on the design of dual branch architecture, the entire network will yield two various outputs and losses. However, in principle, a deep neural network is an end-to-end system, so that the output and loss of the network should be unique. As for the output, we evaluate the performance of the outputs of the two branches on the test dataset and select the output with better performance. Assume that on each branch, as samples of the test dataset delivered into the network, \(o_{\text{I}}\) and \(o_{\text{II}}\) denote the yielding output, respectively. \(M(o,t)\) denotes the performance measurement function. \(t\) denotes ground-truth labels of test dataset. Then, the output of network will be determined as follows:  
\[o_{\text{net}}=\text{argmax}_{\{o_{\text{I}},o_{\text{II}}\}}M(o,t) \tag{4}\]  
while the loss calculation of the network adopts the form of weighted sum. Assume that \(L_{\text{I}}\) and \(L_{\text{II}}\) denote loss on each branch, respectively. Then, the loss of network is calculated as follows:  
\[L_{\text{net}}=\lambda_{1}\cdot L_{\text{I}}+\lambda_{2}\cdot L_{\text{II}} \tag{5}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are hyperparameters preset before training. For the loss of each branch, it controls the contribution to the network, respectively.