esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup  
This section describes the practical scenario where the proposed D&F relay node co-operative 5G network has been executed and verified. Figure 7 shows the scenario that has been created with the goal of validating the proposed D&F protocol co-operative hardware interface in the context of a specific use case (outdoor-to-indoor).  
A series of real field measurements have been performed in _E.T.S.I. de Telecomunicacion_, _Universidad Politecnica de Madrid_, in particular in an outdoor-to-indoor environment, on the hall of the fourth floor of Building C and 406 office, as can be seen in Figure 7. The experimentation scenario is composed of one gnodeB, which will serve the one UE through the D&F relay node. In addition, the base station is physically available at the hall window of the front courtyard of Building C, as shown in subfigure of Figure 7. The backhaul link is represented in Figure 7 with a red arrow, which transmits the 5G downlink signal over antennas that are outside of the building. In the scenario, we consider that gnodeB and D&F protocol have line-of-sight (LOS) and the distance between them is approximately 55 m. Therefore, the data information is received by the relay node employing other antennas placed outside of the opposite building, specifically in the window of the 406 office. Therefore, the received 5G signal is processed in the D&F protocol and retransmitted to the UE inside the 406 office through the access link, which is represented by the light blue arrow in Figure 7. It should be mentioned that D&F relay node and UE have a LOS channel and a separation distance of 3 m. Finally, a third link has been established between gnodeB and UE nodes, named as direct link, which has non-line-of-sight (NLOS), and the distance between them is 58 m. It should be highlighted that the idea behind the third link is to compare the performance over a 5G downlink signal between the relay co-operative system (gnodeB-to-D&F-to-UE) and a direct communication (gnodeB-to-UE). Consequently, the UE calculates the KPIs metric for the two kinds of link-levels individually and performs the comparison between them.
# A Failure Risk-Aware Multi-Hop Routing Protocol in LPWANs Using Deep Q-Network
## 5 Detailed Description of the Proposed Protocol
### DQN-Based Routing Decision Mechanism  
At time step \(t\), all ENs that respond to a REQ packet form the candidate relay set \(\mathbb{N}_{t}^{cf}\), from which DEN selects the optimal next-hop router. However, an EN selected as a relay will consume more energy, which may lead to unbalanced energy distribution across the network and potentially cause coverage holes. Furthermore, selecting candidates with high RFRV increases transmission disruption probability. Given these, we propose a low-latency, long-lifetime, and high-success-rate routing decision mechanism. The optimal relay selection is formulated as follows:  
* Actions: By executing action \(a_{t}\) at time step \(t\), the agent selects the corresponding EN as the next-hop router, i.e., \(a_{t}=i(i\in\{1,2,\ldots,N\})\) indicates that \(EN_{i}\) is chosen as the relay.
* Reward Function: To determine the optimal relay in Equation (14), the reward function in FRDR is designed to guide the agent towards solutions that maximize residual energy, minimize RFRV, reduce distance to the GW, and minimize hop count. It is defined as follows: \[r_{t}=\left\{\begin{aligned} R_{\max},& C_{1}\\ -R_{\max},& C_{2}\\ \hat{r}_{t},& C_{3}\\ \end{aligned}\right.\] (17) In Equation (17), \(C_{1}\) represents successful packet delivery to the GW, for which a positive reward \(R_{\max}\) is granted. Conversely, \(C_{2}\) denotes transmission disruption, which is penalized with \(-R_{\max}\). All other cases fall under \(C_{3}\), where the composite reward \(\hat{r}_{t}\) implements the optimization objectives from Equation (14) through specific reward components: \[r_{t}^{1}=dst_{i}/L,\] (18) \[r_{t}^{2}=e_{res}(i,t)/e_{init},\] (19) \[r_{t}^{3}=RFRV_{i,t},\] (20) \[r_{t}^{4}=h_{t}/H_{\max},\] (21) Note that \(r_{t}^{1}\), \(r_{t}^{2}\), and \(r_{t}^{3}\) pertain to the attributes of ENs, while \(r_{t}^{4}\) is a path attribute. By  
Figure 4: Framework of DQN-based routing decision mechanism.  
integrating Equations (18)-(21), the composite reward \(\hat{r}_{t}\) is derived as Equation (23). Here, \(\hat{r}_{t}^{1}\) and \(\hat{r}_{t}^{3}\) are the normalized versions of \(r_{t}^{1}\) and \(r_{t}^{3}\) via Equation (7), while \(r_{t}^{2}\) is normalized using Equation (22).  
\[\vec{x}=\left(x_{\max}-x\right)/\left(x_{\max}-x_{\min}\right), \tag{22}\]  
\[\hat{r}_{t}=\sum_{i=1}^{3}\mu_{i}\cdot\hat{r}_{t}^{i}-\eta\cdot r_{t}^{A}, \tag{23}\]  
The weights \(\mu_{i}\) in Equation (23) are calculated via AHP using Equations (8)-(10). The pairwise comparison matrix for decision criteria in Equation (23) is presented in Table 4, with a CR of 0.0158. This CR value is well below the threshold of 0.1, confirming the logical coherence of the pairwise comparisons and the reliability of the weight results.
# A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars
## 4 Scenario Description and Basic Definition
### The Concept of the t-RSU and Its Mechanism
Furthermore, we can calculate the cluster's score changing with the time quantitively, as shown in Equation (1):  
\[S(cluster_{k},t)=\sum_{j=1}^{m}{Score_{j,t}} \tag{1}\]  
where \(j=1\dots m\); \(j\) is the sequence number of the parked cars in the given cluster, and m is the parked car's total number for the current second. The subscript \(t\) indicates that the score represents the cluster's state at the current second, given that we set the updating frequency as 1 Hz. The subscript \(k\) means the sequence number for the cluster, thanks to the fact that there are many clusters in an area. The cluster's score can represent the current mesh network's working status. To step further, with the appropriate threshold for a single parked car, the cluster can provide a networking service in a much bigger range and better quality with many t-RSUs working at the same time. The relatively fixed cluster can, therefore, be treated as a huge unconventional t-RSU. So far, the first part of our proposed hybrid hierarchical roadside unit deployment scheme has been accomplished. The second part is naturally transferred into an optimization problem within an area with already-existing fixed unconventional \"huge t-RSUs\" distributed in different coordinates of the given area.
## 4 Synthetic Examples Tests
### Example I: A Single Prism Model
In the calculation, only the grid cell where the prism is located was considered, rather than all of the grid cells. In this way, the recovery degree of the prism can be reflected more intuitively. Figure 3: Imaging results from separate inversion and joint inversion of synthetic resistivity method and TDIP data for example I. The separate inversion results are shown in (**a**,**c**), and the joint inversion results are shown in (**b**,**d**). The top panels (**a**,**b**) are the vertical slice diagrams of the resistivity model and polarizability model with X = 0 m, and the bottom panels (**c**,**d**) are plan sections at Z = 8 m in depth. The cross-gradient maps shown in the third column are plotted to examine the crossgradient constraint and structural similarity between the resistivity model and the polarizability model. The values for separate inversion were \(\zeta_{p}=7.59\) and \(\zeta_{\eta}=0.64\), while those for joint inversion were \(\zeta_{p}=4.02\) and \(\zeta_{\eta}=0.47\), which indicates that the results of the joint inversion are more similar to the true model. As shown in Figure 3a,c, there are structural differences between the resistivity and polarizability models of separate inversion, which are reduced in the joint inversion (Figure 3b,d). To verify the degree of structural similarity between the models with different physical properties, we also compared the cross-gradient values of the separate inversion model and the joint inversion model (Column 3 in Figure 3). The cross-gradient values obtained by single inversion were higher, while the values were significantly decreased by joint inversion. This observation was also confirmed by a comparison of the average cross-gradient values of the final model:  
\[\overline{t}=\frac{1}{M}\sqrt{\sum_{i=1}^{M}\Bigl{[}(t_{x,i})^{2}+(t_{y,i})^{ 2}+(t_{z,i})^{2}\Bigr{]}}, \tag{19}\]  
where \(M\) represents the number of cells in the grid. The value of \(\overline{t}\) obtained in separate inversion was \(5.74\times 10^{-4}\), while it was \(2.34\times 10^{-4}\) in joint inversion, indicating that the resistivity and polarizability models obtained by the joint inversion are more similar in structure.
## 4 Multiple Objective Motion Optimization Problem
### Motion Optimization Metrics
#### 4.1.1 Safety  
Safety is the most critical requirement. The MASS must avoid collisions with surrounding objects. Based on the VO model, this objective can be formulated as \(X_{t+1}\) must be out of the collision regions of all surrounding objects. To achieve this goal, it requires that:  
\[(V_{t}+A_{t}\Delta_{t})\cap VO_{t}=\emptyset \tag{7}\]  
where \(VO_{t}\) is the union of the velocity obstacle region output by Algorithm 1 for the MASS at time \(t\).
## 2 Materials and Methods
### Estimation of Antioxidant Activity  
2.5.1. DPPH* (2,2-Diphenyl-1-picrylhydrazyl) Radical Scavenging Assay  
A DPPH* hydromethanolic solution (70% \(v/v\)) (Sigma-Aldrich, Darmstadt, Germany), at the concentration of 60 \(\times\) 10\({}^{-6}\) M was prepared and kept in the dark during the experiment.  
The range of the final concentrations tested as far as the extract is concerned was 3.6 to 19.80 ug/mL. The experiment took place as previously described by [13]. Measurements were performed in triplicate at 517 nm. The following equation was used to calculate the inhibition percentage for DPPH scavenging:  
\[\%\]  
radical scavenging activity = [(Acontrol \[-\]  
Asample)/Acontrol] \[\times\] 100  
where Acontrol is the absorbance of the control solution and Asample is the absorbance of the tested extract. The experiment took place in triplicates.  
A scatter plot of \(\%\) inhibition versus concentration (ug/mL) was constructed and results were expressed as IC\({}_{50}\) value which was calculated using the equation derived from the plot. The antioxidant activity of Trolox (Acros organics, Waltham, MA, USA) and caffe acid (Sigma-Aldrich, Darmstadt, Germany) was also calculated under the same conditions.  
2.5.2. ABTS** (2,2'-Azinobis[3-ethylbenzthiaazoline-6-acid]) Radical Scavenging Assay  
For the ABTS assay, 7 mM of ABTS+ reagent (Sigma-Aldrich, Germany) and 2.45 mM of potassium persulfate (Sigma-Aldrich, Germany) were mixed. The solution was left at room temperature, in the dark for 18 h. The ABTS+ stock solution was then diluted with 70\(\%\) methanol, so the absorbance of the final working solution was 0.700 \(\pm\) 0.01 at 734 nm. The range of the final concentrations used, as far as the extract is concerned, was 1.98 to 12.87 ug/mL The experiment took place as described by [14], in triplicates. Control solution and calculation of the results were carried out as previously described for the DPPH assay.
# A Study on the Optimization of the Coil Defect Detection Model Based on Deep Learning
## 3 Methodology
### Preprocessing of Images
#### 3.4.2 Structural Similarity Index Measure  
For the image evaluation technique, the SSIM (Structural Similarity Index Measure) [24], which compares the similarity of images, is used because we aimed to enhance contrasts while maintaining characteristics similar to those of the original.  
SSIM evaluates the similarity of two images in terms of luminance, contrast, and structure. These elements can be expressed using the following equations:  
\[l(x,y)=\frac{2\mu_{x}\mu_{y}+C_{1}}{\mu_{x}^{2}+\mu_{y}^{2}+C_{1}},c(x,y)= \frac{2\sigma_{x}\sigma_{y}+C_{2}}{\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2}},\;s(x, y)=\frac{\sigma_{xy}+C_{3}}{\sigma_{x}\sigma_{y}+C_{3}} \tag{1}\]  
The SSIM values of these elements are calculated using the final equation:  
\[\text{SSIM}(\text{x},\text{y})=[l(x,y)]^{a}\cdot[c(x,y)]^{\beta}\cdot[s(x,y)]^ {\gamma} \tag{2}\]  
These equations verify that SSIM evaluates image quality using the main elements in an image instead of a simple comparison between pixels.
## 3 Results
### Fourier Transform Infrared Spectroscopy (FTIR-ATR)  
Figure 8 shows the FTIR spectrum of the PVA-Chi sample, which represents the film containing only the polymer matrix PVA-Chi. The spectrum showed the following signals: in the region of 3500 cm\({}^{-1}\), a stretching vibration of the NH bond; at 3100-3300 cm\({}^{-1}\), a stretching vibration of the hydroxyl group \(-\)OH from PVA and Chitosan molecules; at 2900 cm\({}^{-1}\), a stretching of the C-H bond; at 1790 cm\({}^{-1}\), a stretching band of C=O; at 1600 cm\({}^{-1}\), a bending vibration of the \(-\)OH bond due to the formation of hydrogen bridges as a result of the adsorption of water; at 1050 cm\({}^{-1}\), a stretching vibration of the C-O bond in the C-O-C and C-OH groups (these functional groups are present in both PVA and Chi). In samples B2 PVA-Chi/PPy0.1, B3 PVA-Chi/PPy0.2, and B4 PVA-Chi/PPy0.3, in which PPy (0.3, 0.2 and 0.1 g respectively) was added to the PVA-Chi matrix, the FTIR spectra showed a decrease in the signal between 3100 and 3300 cm\({}^{-1}\), which corresponded to the stretching of the O-H groups. The stretching vibration of the N-H bond in Chi and PPy is more clearly observed at 3500 cm\({}^{-1}\). A sharp signal can also be observed at 1600 cm\({}^{-1}\), corresponding to the stretching of the C=C bond in PPy. The signal at 1700 cm\({}^{-1}\) corresponds to the stretching of the C=O bond in Chi. There is no significant change in the chemical structure of the components, forming hydrogen bridges.  
Figure 4: Result of average Contact angle.  
Figure 5: SEM micrographs (300\(\times\)) of samples (**A**) PVA, (**B**) B1 PVA\(-\)Chi.  
Figure 9 shows the FTIR spectrum of samples B6 PVA-Chi/PEDOT: PSS0.2 to B10 PVA-Chi/CB0.2. These images show the samples with PVA-Chi matrix containing a single filler of either PEDOT: PSS, MWNTC or CB. As can be seen, the spectra are very similar, with no great differences in vibration signals between the mentioned fillers. Figure 10 shows the FTIR spectra of samples B1 PVA-Cs, which contained only the polymer matrix, and of the samples B11 PVA-Chi/PPy0.1/PEDOT: PSS0.1/MWCNT0.1/CB0.1,  
Figure 6: Micrographs (300\(\times\)). (**A**) B3 PVA\(-\)Chi/PPy 0.2 g; (**B**) B6 PVA\(-\)Chi/PEDOT: PSS0.2; (**C**) B8 PVA\(-\)Chi/MWCNT0.2; (**D**) B10 PVA\(-\)Chi/CB0.2.  
B12 PVA-Chi/PPy0.1/PEDOT: PSS0.2/MWCNT0.2/CB0.2, B13 PVA-Chi/PPy0.2/PEDOT: PSS0.1/MWCNT0.1/CB0.1, B14 PVA-Chi/PPy0.2/PEDOT: PSS0.2/MWCNT0.2/CB0.2, B15 PVA-Chi/PPy0.3/PEDOT: PSS0.1/ MWCNT0.1/CB0.1 and B16 PVA-Chi/PPy0.3/PEDOT: PSS0.2/ MWCNT0.2/CB0.2, which also contained the four fillers under study (PPy, PEDOT: PSS, MWCNT, and CB).
## 3 Model Assumption and Toss-and-Catch Algorithm
### Handover Stage  
In the final handover stage, the focus is on the timing of handovers. We defined \(\text{RSS}_{\text{th},i}\) as the RSS threshold of SBS \(i\) and used it as a basis for determining the handover timing in a specific range. It can be expressed as  
\[\text{RSS}_{\text{th},i}=-20\log P_{\text{TX}}KK_{\text{th}}d_{i}, \tag{31}\]  
where \(K_{\text{th}}\) is the threshold constant, which has a value between 0 and 1. We defined \(\text{RSS}_{\text{th},1}\) and \(\text{RSS}_{\text{th},2}\), respectively, as the RSS threshold of the SBS currently connected to the OIS and the RSS threshold of the SBS selected through the scoring mechanism. These two thresholds are calculated using (31). If RSS is less than \(\text{RSS}_{\text{th},1}\), the next step is to determine if RSS is greater than \(\text{RSS}_{\text{th},2}\) with a handover being executed if this is true; otherwise, the operation would continue until the conditions are met at which point a handover would be executed.  
Figure 16 illustrates the two scenarios that could occur based on the intersecting relationship between the coverage of the SBS currently connected to the OIS and the SBS selected through the scoring mechanism (areas outlined with the black solid lines), as well as the intersecting relationship between their threshold coverage (areas outlined with red dashed lines). Scenario 1: the coverage of the two thresholds overlap; in which case, the handover is executed in the overlapping area. Scenario 2: the coverage of the two thresholds do not overlap; in which case, the OIS disconnects from the currently connected SBS when it leaves this SBS's coverage and executes a handover when it enters the threshold coverage of the selected SBS.  
After the handover is completed, the five stages are repeated until the OIS is shut down.
## 1 Introduction
However, some of the limitations include the initial parameters required for the optimization, the amount of data required from the PV grid for a correct search, and the validation through controlled simulations without considering real variations of external factors. In other works, the GA and the PSO are used to define the best topology of electrical devices for optimizing commercial building microgrids [24]; yet, these solutions need to be adapted for the particular microgrid and the specific user requirements. Finally, metaheuristic techniques have been used in the identification of model parameters of PV generation systems for simulation and design purposes, such as the crow search algorithm (CSA) [25] and the whale optimization algorithm (WOA) [26]. Nevertheless, in both cases, the validation of the estimation of the mathematical model is performed through simulations. Therefore, the use of these modern optimization techniques might be helpful if applied to explore the PQ analysis of PV generation systems in the predictive modeling field. Thus, there exists a need for developing methodologies that help in the optimization of models for the prediction of the generation process, including its quality. This work proposes the use of GA for parameterizing mathematical models that can estimate the power delivered by a PV system and the level of distortion in the voltage signals associated with harmonic contamination. The use of four different parameters to fit the mathematical models is proposed: sun irradiance, cell temperature, DC voltage, and DC current. The proposed methodology was applied to signals from a real PV generation plant. Experimentation was performed during a year, and a significant sample of 8 days was taken for its analysis. These days were selected to be representative cases of the four seasons of the year with different weather conditions. Results prove that sun irradiance, cell temperature, DC voltage, and DC current can describe the behavior of the power delivered by the PV inverter, and also the behavior of the total harmonic distortion (THD) in PV generation. Moreover, the resulting models were compared with the results from an ANN, which is one of the most common techniques in forecasting tasks. It is demonstrated that the estimations performed by the GA are better than those delivered by the ANN.
## 2 Methods
### Data Collection Tools and Techniques  
Data collection was conducted using an online questionnaire via Google Forms, comprising 22 questions with closed-ended formats--Yes/No, multiple choice, multiple select, Likert scale, and semantic differential scale questions [10; 11]. The questions are: (Q1) gender; (Q2) physician specialty; (Q3) experience; (Q4) employment organization; (Q5) work location; (Q6) patients seen per day (slots); (Q7) official hours work per day; (Q8) actual hours work per day; (Q9) workload rating; (Q10) ways of obtaining patients' medical history; (Q11) history-taking steps followed by physicians; (Q12) most challenging tasks in history taking; (Q13) resources to manage patients' medical histories; (Q14) time spent history taking; (Q15) history-taking challenges; (Q16) causes of history-taking challenges; (Q17) workplace efficiency; (Q18) consequences of defective history taking; (Q19) difficulties in making a clinical decision; (Q20) consequences of excess workload; (Q21) history visualization system acceptability; (Q22) necessary items to include in the visualization system. Specific queries on experience, working hours, patient count, etc., offered 5-6 selectable slots. Certified physicians in Bangladesh were invited to participate, ensuring anonymity and privacy by collecting emials to prevent duplication and securing prior consent. Duplication and error checks were performed, identifying no errors. All fields were made compulsory, ensuring no missing values or incomplete forms. A total of 104 eligible forms were analyzed. _Python_ programming, utilizing libraries like _Pandas_ and _Numpy_, was employed for data pre-processing, visualization, and model training. Feature engineering techniques, such as one-hot encoding and label encoding, were applied. Visualization tools like _Matplotlib_, _Plotly_, and _Seaborn_ were utilized, and the ordinal regression model was constructed using _Statsmodels_ and _Sklearn_.