esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of PV and Battery Energy Storage Size in Grid-Connected Microgrid
## 3 Proposed Algorithm
### Energy Management Strategy  
The management of the power flow is an important process for optimizing the system components and the efficient operation of the system. The proposed energy management strategy can be divided into two parts as island mode and grid-connected mode operation. Figure 4 shows the flowchart of the proposed energy management strategy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Efficiency (\(n_{BESS}\)) & 95 & \% \\ Capital, Operation and Maintenance and Replacement Cost & 493 & USD/Unit \\ \hline \hline \end{tabular}
\end{table}
Table 2: BESS cost parameters.  
In the island mode, the MG operates without grid support. The load demand can be satisfied by PV generation and/or BESS available capacity. There is always a balance between the available PV power, BESS capacity and load. Net energy (\(E_{net}(t)=E_{PV}(t)-E_{Load}(t)\)) is followed, and it is decided that the battery is charged if \(E_{net}(t)>0\) and \(B_{SOC}(t)<E_{bat,max}(t)\). Batteries are charged with \(E_{Rab}(t)\) until their \(E_{bat,max}(t)\) level. If BESS reaches its charge limit and there is still available power in the PV system, this remaining power cannot be used or sold to the grid due to the island mode operation. If \(E_{net}(t)=0\), then there is no excess energy, and thus, load demand is equal to PV generation. If there is not enough PV generation to satisfy load demand (\(E_{net}(t)<0\)), EMS controls \(Bosc(t)\) level at that time. If BESS has available energy, batteries can be discharged until their \(E_{bat,min}(t)\) level. Finally, if both \(E_{net}(t)<0\) and \(B_{SOC}(t)<E_{Load}(t)\), but there is still some available PV power generation (that is, \(0<E_{PV}(t)<E_{Load}(t)\)), then batteries are charged by PV power.  
Figure 4: Proposed energy management flowchart.  
For the grid-connected mode operation, the MG operates with grid support. In this study, the aim is to find optimum PV and BESS size for mostly self-sufficient MG in a yearly period. The grid energy is only used for supplying load demand if there is not enough energy in the PV and batteries. In this study, the grid is not used for charging batteries. It is assumed that it is costly to obtain energy from the grid. Thus, there is a grid cost limitation. The MG can be partially or fully supplied from the grid only for a limited time when there is either no or not enough energy in the BESS and PV. The EMS tracks the current energy level of the system components, the status of the PV system and BESS, and if there is not enough energy to be able to supply the load demand (\(E_{PV}(t)+B_{SOC}(t)<E_{Load}(t)\)), then the MG can obtain energy from the grid with penalty. There is a flag that holds the record of obtained energy from the grid. If energy from the grid exceeds a previously defined limit value, the flag increases. Hence, the PSO algorithm, which is explained below, decides to increase the PV or BESS module capacity to minimize the dependency of grid connectivity by considering the total installation cost.
## 2 Materials and Methods
### Identification of Dust Sources and Characterization of the Events  
Records of African dust intrusions over the Canary Islands were obtained [23; 24] to determine the events registered at the sampling site. Dust surface concentration maps were acquired using the MONARCH model ([[https://dust.aemet.es/products/daily-dust-products](https://dust.aemet.es/products/daily-dust-products)]([https://dust.aemet.es/products/daily-dust-products](https://dust.aemet.es/products/daily-dust-products)) (accessed on 7 May 2022)) [25; 26] to ascertain the source and analyse the distribution of each event.  
The Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT) ([[https://www.ready.noaa.gov/HYSPLIT.php](https://www.ready.noaa.gov/HYSPLIT.php)]([https://www.ready.noaa.gov/HYSPLIT.php](https://www.ready.noaa.gov/HYSPLIT.php)) (accessed on 7 May 2022)) [27] was used to obtain 72 h backward trajectories, with the aim of determining the source region of the air masses bound to each dust intrusion. To that end, we used the vertical velocity model, and the altitude was set to 750, 1500 and 2500 m.a.g.l., respectively. The length of each backward trajectory was measured in Google Earth.  
The mean PM10 concentration--registered during the sample collection period--bound to each dust event was estimated (Table 1) using data from the \"Polideportivo Afonso-Arucas\" station, located in the North of Gran Canaria (28\({}^{\circ}\)06\({}^{\prime}\)41\({}^{\prime\prime}\) W, 15\({}^{\circ}\)31\({}^{\prime}\)15\({}^{\prime\prime}\) W). The station cited previously belonged to the \"Red de Vigilancia y Control de la Calidad del Aire de Canarias\" [28].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Sampling Date** & **Period of the Event** & **PM10 (\(\mu\)g/m\({}^{3}\))** \\ \hline M1 & 2 October 21 & 1–3 October 2021 & 47.0 \\ \hline M2 & 6 February 22 & 1–14 February 2022 & 65.4 \\ \hline M3 & 17 April 22 & 16–19 April 2022 & 54.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of dust samples from African sources.  
Figure 1: Description of the sampling area. Images retrieved from Google Earth.
# Crowd Evacuation Guidance Based on Combined Action Reinforcement Learning
## 1 Introduction
### Deep Reinforcement Learning
In the process of minimizing the loss function, the Q function gradually converges to the optimal value, and finally an optimized strategy \(\pi\) is obtained. DQN has made breakthroughs in tasks such as mastering Atari video games that use images as input. Based on DQN, researchers have proposed improvement methods such as the double DQN (DDQN) method, which uses the current Q network to select the target action [22], and priority experience replay method [23], which uses the TD error to set the priority of the sample in the experience pool. However, the action space output by DQN is discrete, and an output layer node is used to evaluate each possible action combination. Therefore, when the action dimension increases, the network complexity will increase exponentially. In the crowd evacuation guidance problem, the agent uses the display state of dynamic guidance signs as the output action, and the discrete action of each sign forms an independent action dimension. When there are numerous dynamic guidance signs in complex architectural scenes, the output layer of the DQN will become excessively large for the algorithm to actualize.
###### Abstract
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Smart Education** & **Traditional Education** \\ \hline Flexibility is a key benefit of smart schooling. It enables students to study in their own environment. Teachers can be accessed as needed [14]. & Traditional education is less adaptable. Students must attend classes in person [15]. \\ Location and time are not factors in smart education. Students can access lessons anytime, anywhere [16]. & Traditional education relies on specific locations and is bound by time constraints. \\ Smart education reduces operational costs through paperless work, reducing travel, testing, and administrative expenses. Smart education enables educators to teach on a global scale, offering a diverse array of courses [17]. & Traditional education relies on physical presence, limiting the variety of courses. \\ Smart education offers various collaboration and communication tools (e.g., Zoom [18], Google Meet [19], Skype [20]). Smart education’s deficiency in social interaction impacts the social and communication skills of students [21]. & Traditional education promotes social learning through face-to-face interaction, improving social skills [15]. \\ Smart education lacks extracurricular activities. & Traditional education provides extracurricular activities, enhancing students’ readiness for learning [22]. \\ While operational costs are minimized, smart education requires a significant capital investment for system installation. & Traditional education remains expensive overall. \\ \hline \hline \end{tabular}  The leading challenges to the traditional education system are as follows:  
1. Educational technology (EdTech) has transformed teaching and learning methods. Teachers and learners follow outdated patterns that are not comfortable with cutting-edge innovations. 2. Traditional methods for monitoring employee and learner progress are time-consuming and do not ensure accurate information for decision-makers, which results in wrong decisions.
## 3 Results and Discussion
### The Effect of Back Contact Materials  
The work function is defined as the minimum energy of the photon required for an electron to come out of a metal surface [48]. In order to find out the effect of the work function of the back contact, as summarized in Table 2, various electrodes such as Cu, Ag, Fe, and Au were used [33].  
Figure 12 shows the current density-voltage (_J-V_) curves for various back contact materials. It can be seen that the output parameter value increased as the work function value increased. The V\({}_{\rm oc}\) increased from 0.87 V to 1.04 V, and the PCE increased from 17.36% to 23.07%. This is consistent with the results of the previous literature, which reported that solar cell efficiency increased as the work function value increased [49]. As the work function value increased, the barrier height value of the majority carriers decreased, which made the electrode more of an ohmic type. Therefore, the V\({}_{\rm oc}\) and PCE values increased. Accordingly, the best results were obtained when Au, which had the largest work function of 5.1 eV, was used as the back electrode in this experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Cu** & **Ag** & **Fe** & **Au** \\ \hline Work function (eV) & 4.6 & 4.7 & 4.8 & 5.1 \\ \hline PCE (\%) & 17.36 & 19.65 & 21.62 & 23.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Work function of various back contact materials for PSC.  
Figure 12: Comparison of the current density-voltage (_J–V_) behavior for different work functions of back contact.
## 1 Introduction
Several channel models have been reported in the literature, with two main approaches described: stochastic and statistical. The former relies on the definition of the probability density function (PDF) of the channel parameters depending on the geometry of the environment, while the latter exploits real measures on which to base the definition of the channel. Each of these solutions has possible drawbacks, but a hybrid approach can be chosen, firstly, stochastically, characterizing the channel parameters through their PDF, and subsequently leveraging real data to confirm and eventually adapt them. In this investigation, we rely on the latest 3D stochastic channel model for 5G mmWave massive MIMO communications released by 3 GPP in TR 38.901 [22; 23]. It covers the entire range of 0.5-100 GHz, with particular relevance to the new challenges represented by mmWave communications, where some phenomena, such as path attenuation, noise, interference, and atmospheric attenuation, can seriously impact the signal transmission. One of its main characteristics is the modelling of the channel through a certain number of clusters distributed over the area, which groups the scatterers and so produces different replicas of the propagated signal (i.e., different rays). It is categorised as a clustered delay line (CDL) model. Moreover, while, in 4G, the propagation waves were considered only in the horizontal plane, thus producing a 2D channel model, mmWave communications (and thus 5G) require a 3D channel model, characterising the signal propagation depending on the elevation coordinate. The channel model generation is performed inside a link-level simulator designed by TIM, which also provides the resource blocks generation, transmission and reception for a 5G 3 GPP-compliant communication, with evaluation of the primary downlink shared channel (PDSCH) throughput and the block error rate (BER).
## 4 Results
#### 4.2.2 Correlation Between Frequency Bands and Performance Metrics
To further understand how rectifier design affects system-level performance, correlation analyses are conducted between rectifier types and four critical variables: antenna area, radiation efficiency, operating frequency, and gain. This multi-dimensional approach enables deeper understanding of how rectifier selection impacts RF-to-DC conversion effectiveness in practical energy harvesting applications. Figure 17: Boxplot showing gain distribution of band frequency for energy harvesting.
# Hybrid Active-Passive Reconfigurable Intelligent Surface for Cooperative Transmission Systems
## 3 Performance Analysis
### OP of the Hybrid Active-Passive RIS System  
As previously mentioned, the HAPR system enables flexible switching of operating modes based on the channel conditions. That is, when the instantaneous SNR of all active REs exceeds that of all passive elements, RIS chooses to reflect signals using only active REs; conversely, it employs only passive REs for reflection. The e2e SNR of the hybrid HAPR system can be expressed as follows,  
\[\gamma=\text{max}\big{(}\gamma_{a},\gamma_{p}\big{)} \tag{30}\]  
Then, the outage probability of the system can be written as  
\[P_{out}=\text{Pr}\big{(}\text{max}\big{(}\gamma_{a},\gamma_{p}\big{)}\leq\gamma _{th}\big{)}=F_{\gamma}(\gamma_{th}) \tag{31}\]  
**Theorem 1**.: _By a series of mathematical means, \(F_{\gamma}(\gamma_{th})\) is provided by_  
\[F_{\gamma}(\gamma_{th})=1+\frac{\Gamma(m_{D},\text{Y}_{D}y_{1})}{\Gamma(m_{D} )}-S_{1}(\gamma_{th})-S_{2}(\gamma_{th})-S_{3}(\gamma_{th}) \tag{32}\]  
\[S_{1}(\gamma_{th})=\sum_{k=0}^{m_{S}-1}\frac{\text{Y}_{D}^{m_{D}}(\text{Y}_{S }c)^{k}}{\Gamma(m_{D})k!}\bigg{[}2\bigg{(}\frac{\text{Y}_{S}c}{\text{Y}_{D}} \bigg{)}^{\frac{m_{D}-k}{2}}K_{m_{D}-k}\Big{(}2\sqrt{\text{Y}_{S}\text{Y}_{D} c}\Big{)}-S_{n_{1}}\big{]} \tag{33}\]  
\[S_{2}(\gamma_{th})=\sum_{q=0}^{m_{D}-1}\sum_{r=0}^{m_{S}+q-1}\binom{m_{S}+q-1 }{r}\frac{\text{Y}_{S}^{m_{S}}\text{Y}_{D}^{q}\text{b}^{m_{S}+q-1}}{\Gamma(m_ {S})q!a^{m_{S}+q}}e^{-\frac{\text{Y}_{S}\text{S}+\text{Y}_{D}}{q}}S_{n_{2}} \tag{34}\]  
\[S_{3}(\gamma_{th})=\sum_{q=0}^{m_{D}-1}\frac{\text{Y}_{S}^{m_{S}}(\text{Y}_{D }c)^{q}}{\Gamma(m_{S})q!}\Bigg{[}2\bigg{(}\frac{\text{Y}_{D}c}{\text{Y}_{S}} \bigg{)}^{\frac{m_{S}-q}{2}}K_{m_{S}-q}\Big{(}2\sqrt{\text{Y}_{S}\text{Y}_{D} c}\Big{)}-S_{n_{3}}\Bigg{]} \tag{35}\]  
_where_  
\[S_{n_{1}}=\frac{y_{1}}{6n_{1}}\Bigg{\{}-f(y_{1})+\sum_{l=1}^{n_{1}}\Big{[}4f \Big{(}y_{l+\frac{1}{2}}\Big{)}+2f(y_{l})\Big{]}\Bigg{\}}\]  
\[S_{n_{2}}=\frac{ax_{2}}{6n_{2}}\Bigg{\{}f(-b)-f(ax_{2}-b)+\sum_{l=1}^{n_{2}} \Big{[}4f\Big{(}t_{l+\frac{1}{2}}\Big{)}+2f(t_{l})\Big{]}\Bigg{\}}\]  
\[f_{y}(y)=y^{m_{D}-k-1}e^{-\text{Y}_{D}y-\frac{\text{Y}_{S}c}{y}}\]  
\[f_{t}(t)=t^{r-q}e^{-\frac{\text{Y}_{S}}{q}-\frac{\text{Y}_{D}c}{q}t^{-1}}\]  
\[f_{x}(x)=x^{m_{S}-q-1}e^{-\text{Y}_{S}x-\frac{\text{Y}_{D}c}{q}}\]  
\(a=\frac{\text{N}^{2}P_{A}}{7n_{D}^{2}}\), \(b=\frac{NP_{A}\sigma_{D}^{2}}{P_{S}\sigma_{D}^{2}}\), \(c=\frac{7n_{D}\sigma_{D}^{2}}{N^{2}P_{S}}\), \(y_{1}=\frac{2c}{ac-\sqrt{a^{2}c^{2}-4 bc}}\), \(x_{2}=\frac{ac+\sqrt{a^{2}c^{2}-4 bc}}{2}\) and \(n_{1}\), \(n_{2}\), \(n_{3}\) are the equipartition points of the original integration interval of \(S_{1}\), \(S_{2}\) and \(S_{3}\) respectively.  
Proof of Theorem 1.: See Appendix A for details.
## 3 Methodology  
This research mainly focuses on the accurate extraction of recommendation sentences from CPGs, irrespective of the CPG target disease and format. The process flow of the proposed sentence extraction mechanism is depicted in Figure 1. Our proposed methodology consists of four major steps: document preprocessing, salient terms extraction, the pattern extraction process, and sentence classification. In the Document preprocessing step, we prepare the contents of the CPG according to the required format (sentences in our case). Salient extraction then identifies and extracts sentence decision terms using machine-learning interpretable models. This is followed by Pattern extraction, which provides the steps required for extracting the Heuristic Patterns and their generalizations (POS patterns and UMLS patterns). Finally, Sentence classification applies the extracted patterns and analyzes the CPG sentence characteristics to distinguish between the recommendation and non-recommendation sentences.
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 5 Hyperparameter Optimization and Model Training
### Training Result  
According to the balanced loss function presented in Section 5, the effectiveness of the model can be observed from the trend of the loss on the training set. For the VPP, STFT spectrogram, scatter constellation, and NDC in Section 3, the model is trained by changing the number of input channels of the network. The training process is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Description** & **Values** \\ \hline \(K_{bs}\) & Batch size & 5 \\ \(lr\) & Initial learning rate & \(6.1884\times 10^{-6}\) \\ \(ep\) & Number of epochs & 20 \\ \(c_{1}\) & Size of convolutional layer 1 & 9 \\ \(c_{2}\) & Size of convolutional layer 2 & 1 \\ \(c_{3}\) & Size of convolutional layer 3 & 4 \\ \(n_{1}\) & Number of convolutional layer 1 & 4 \\ \hline \(n_{2}\) & Number of convolutional layer 2 & 19 \\ \(n_{3}\) & Number of convolutional layer 3 & 18 \\ \(fc\) & Size of fully connected layer & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training parameters setting.  
Figure 7: Training loss of the model with different inputs from 0 to 50 epochs.  
It can be obtained from Figure 7 that the network with all structures converges well. With the increase in iterations, the loss in the initial stage decreases rapidly. In the case of applying the Adam optimizer, the model without the VPP will converge the slowest in the double-input model. This shows that the performance parameters of the data link are closely related to the learning process of the network for mapping.  
In general, the closer the loss of the model is to 0, the higher the accuracy of the model on the training set is. In the three-input model, the network with the constellation diagram has the fastest convergence rate, with a convergency value of approximately 0.27. The convergence rate of the network with NDC is not the fastest, but the value of the convergency is the lowest at approximately 0.2, which indicates a higher accuracy in the training process.
## 2 Results
### Validating Allele-Specific Knockout of Mutant G56R NR2E3 at the Protein Level
Loading controls, an anti-tubulin antibody detected a 55-kDa protein (in green) **(A)**; an anti-actin antibody detected a 42-kDa protein (in red) **(B)**. **(C)** Representative images following IF analysis of COS7 cells transfected with the pCDNA3 constructs expressing wild type (left), G56R (middle) or G56R-CRISPR2 (right) NR2E3 in red; nuclei labelled in blue. **(D)** Quantification of the percentage of COS7 cells transfected with the pCDNA3 constructs expressing wild type or G56R NR2E3 showing the mixed perinuclear and nuclear versus mainly nuclear localization profiles. Results are expressed as mean \(\pm\) SEM of three independent experiments (\(n=3\); \(*\)\(p<0.05\); Mann and Whitney test); each experimental value represented the mean of five separate fields. **(E)** Western blot analysis following differential centrifugation of cytosolic (left) and nuclear (right) fractions of HEK293 cells mock-transfected (lanes 1) or transfected with the pCDNA3 constructs expressing WT (lanes 2), G56R (lanes 3) or G56R-CRISPR2 (lanes 4) NR2E3. Bands corresponding to 45-kDa NR2E3 were detected in both fractions for WT and G56R-transfected cells, whereas a 16-kDa protein was only detected in the cytosolic fraction of G56R-CRISPR2-transfected cells. To control for fraction purity, both of the membranes were hybridized with anti-tubulin (55 kDa; in green) and anti-histone H3 (15 kDa; in green). A non-specific 40-kDa band could be detected after incubation with the H3 antibody on both membranes. Furthermore, and in direct contrast to WT and G56R, the expression of the truncated G56R-CRISPR2 NR2E3 protein was much weaker and localized throughout the cytosol (Figure 5C, right). The cytosolic and nuclear fractions of HEK293 (Figure 5E) and COS7 cell lines that were transfected with the WT, G56R, and G56R-CRISPR2 plasmids were separated by differential centrifugation and analyzed by western blot analysis in order to confirm this localization profile. Consistent with the (peri)nuclear labelling detected by IF, WT and G56R proteins could be detected in both fractions, whereas the 16-kDa G56R-CRISPR2 protein was exclusively localized to the cytosolic fraction; no band was detected in the nuclear fraction. To verify the purity of the fractions, the membranes were hybridized with an anti-tubulin antibody, as a cytosolic marker, and an anti-histone H3 antibody, as a nuclear marker. A 55-kDa tubulin was only detected in the cytosolic fraction and an \(\sim\)15-kDa H3 in the nuclear fraction; a non-specific \(\sim\)40-kDa band that was associated with the anti-H3 antibody was seen in both fractions. Taken together, the overexpression of allele-specific _NR2E3_ knockout results in a truncated and mis-localized protein in cell lines.
# Finite Element Modeling of the Soil-Nailing Process in Nailed-Soil Slopes
## 6 Performance Comparison of FE and Laboratory Models of Nailed-Soil Slopes
### Horizontal Movement of the Slope Face  
Figures 23-25 show the horizontal slope movement (D/H, %) at different soil densities for both laboratory and FE models. It can be seen from the figures that, for loose sand, at the construction stage, the highest horizontal movement occurs at the lower point of the slope in the FE model, whereas it is at the middle point in the laboratory model. In the overburden pressure stages, the maximum movement is at the mid-point in both models. The highest horizontal slope movement occurs at an overburden pressure of 30 kN/m\({}^{2}\) in both cases. The percentage of difference in highest movement varies between 16.15% at overburden pressure of 30 kN/m\({}^{2}\) to 27.0% at overburden pressure of 10 kN/m\({}^{2}\).  
Figure 23: Horizontal movement of the slope from the laboratory and FE model (loose sand). (**a**) Construction stage 5. (**b**) Overburden pressure stage 6. (**c**) Overburden pressure stage 9.  
Figure 24: Horizontal movement of the slope from the laboratory and FE model (medium sand). (**a**) Construction stage 5. (**b**) Overburden pressure stage 6. (**c**) Overburden pressure stage 9.  
Figure 25: Horizontal movement of the slope from the laboratory and FE model (dense sand). (**a**) Construction stage 5. (**b**) Overburden pressure stage 6. (**c**) Overburden pressure stage 9.  
For medium sand and dense sand, at the construction stage, the highest horizontal displacement occurs at the lower point of the slope in the FE model, whereas it is at the middle point in the laboratory model. The highest movement is at mid-point under overburden pressure stages, and the highest horizontal slope movement occurs at an overburden pressure of 30 kN/m\({}^{2}\) in both FE and laboratory models. The percentage of variation in highest movement ranges between 14.68% at overburden pressure of 30 kN/m\({}^{2}\) to 29.01% at overburden pressure of 10 kN/m\({}^{2}\) for the medium dense sand slope. For the dense sand slope, the difference in highest movement ranges from 17.58% at surcharge 30.0 kN/m\({}^{2}\) to 15.38% at overburden pressure of 10 kN/m\({}^{2}\). It can be concluded that the FE model and the laboratory model exhibit similar trends in the behavior of the nailed-soil slope.