esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Stress Characteristics
#### 3.2.1 Analysis of Blade Surface Stress  
The simulation result illustrates that the equivalent stress distribution on a blade surface during a change from maximum yaw to zero yaw (\(30^{\circ}\)\(\rightarrow\)\(0^{\circ}\), \(-30^{\circ}\)\(\rightarrow\)\(0^{\circ}\)) was opposite to that during a change from zero yaw to maximum yaw (\(0^{\circ}\)\(\rightarrow\)\(30^{\circ}\), \(0^{\circ}\)\(\rightarrow\)\(-30^{\circ}\)). Therefore, stress cloud diagrams under dynamic yaw conditions (\(30^{\circ}\)\(\rightarrow\)\(0^{\circ}\), \(-30^{\circ}\)\(\rightarrow\)\(0^{\circ}\)) are not shown.  
Shown in Figure 11 are the variations in the stress distributed on the windward side of the three blades under the dynamic yaw condition (\(0^{\circ}\)\(\rightarrow\)\(30^{\circ}\), \(0^{\circ}\)\(\rightarrow\)\(-30^{\circ}\)). When the wind turbine blades yawed to the same position, the stress at the same position of the blades increased with the increase in tip speed ratio, and the stress variation was more obvious. Stress concentrations occurred concurrently at the positions of the radial direction, the maximum and intermediate chord lengths, and both. The stress-concentration area decreased obviously with increases in yaw. The concentrated stress region was at its largest at a yaw angle of \(\theta\) = \(0^{\circ}\), and smallest at \(\theta\) = \(\pm 30^{\circ}\). In the process of positive yaw, \(\theta\) \(\in\) (\(0^{\circ}\)\(\rightarrow\)\(30^{\circ}\)), the stress-concentration of blade 1 decreased the most slowly with increases in the yaw angle, and the stress-concentration area in the middle part of blade 3 decreased the most quickly. In the process of negative yaw, \(\theta\) \(\in\) (\(-30^{\circ}\)\(\rightarrow\)\(0^{\circ}\)), with increases in yaw angle, the area of concentrated stress in blade 3 decreased the most slowly, while the rate of blade 2's reduction in downwind yaw was moderate, and that of blade 1 in headwind yaw decreased the most quickly. The results show that the maximum stress always occurred at the position of maximum chord length on the windward side of blade 1 during yaw. To compare the changes in concentrated stress at the maximum chord length more clearly, only the maximum stress position of the blade is shown in the cloud chart at \(\lambda\) = 6 and yaw angle = \(0^{\circ}\). As shown in Figure 12, under the dynamic yaw condition (\(0^{\circ}\)\(\rightarrow\)\(30^{\circ}\), \(0^{\circ}\)\(\rightarrow\)\(-30^{\circ}\)), the stress variation trend on the leeward side of the three blades was consistent with that on the windward side. The speed of the stress concentration was lowered to ordinals of blades 3, 2, and 1 in the process of increasing the positive yaw, with increases in the yaw angle. The speed of the stress concentration was lowered to ordinals of blades 1, 2, and 3 during the negative yaw process.  
Figure 10: Output power response of wind turbine under dynamic yaw.
# Evaluating the Effect of Financing Costs on PV Grid Parity by Applying a Probabilistic Methodology
## 2 Materials and Methods
### Economic Parameters Used in the Study
#### 2.3.3 Internal Rate of Return  
The \(IRR\) is a metric used in the capital budget to estimate the profitability of the potential investments [34]. The \(IRR\) is a discount rate that makes the net present value (\(NPV\)) [35] of all cash flows of a project zero [15].  
The \(IRR\) with a loan is much higher than the \(IRR\) without a loan because a loan at a relatively low interest rate favours the \(IRR\). The \(IRR\) with a loan assumes that the project has a financing scheme typical of the PV industry [4].  
The \(IRR\) calculation procedure is based on determining which \(IRR\) value results in the \(NPV\) being zero, as shown in Equation (5):  
\[NPV=\sum_{t=1}^{n}\frac{F_{t}}{\left(1+IRR\right)^{t}}-I=0 \tag{5}\]  
where \(F_{t}\) is the cash flow during the year \(t\), \(n\) is the number of years of the investment and \(I\) is the initial investment (\(\mathbf{\hat{c}}\)).  
The \(IRR\) was calculated using the interest rate (\(r\)), operation and maintenance (OM) cost, annual energy production (E), tax rate (20%) and loan length (12 years), therefore it comprises all the variables and PDFs defined previously.  
The sale price of energy was considered equal to the regulated tariff for low-power consumers, in Spain, who do not want to negotiate with the manufacturers. In this work we assumed a regulated tariff of 17.4 c/kWh, including taxes [1].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Inputs** & **PDF** & **Range** & **Unit** \\ \hline Power & Constant value & 6–600 & kWp \\ Lifetime of the system & Constant value & 25 & Years \\ Annual Yield & Normal & 1100–1500 & kWh/kWp \\ Capital expenditure & Log-normal & 0.2–1 & \(\mathbf{\hat{c}}\)/Wp \\ Interest rate & Triangular & 4–12 & \% \\ Retail cost & Constant & 0.174 & \(\mathbf{\hat{c}}\)/kWh \\ \hline \hline \end{tabular}
\end{table}
Table 2: PDFs used in the Monte Carlo calculation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**PV Module Cost (\(\mathbf{\hat{c}}\)/Wp)**} \\ \cline{2-5}  & **1** & **0.8*** & **0.6** & **0.4** & **0.2** \\ \hline PV module (6 kWp) & 6000 & 4800 & 3600 & 2400 & 1200 \\ Fixed installation costs & 6780 & 6780 & 6780 & 6780 & 6780 \\ TOTAL PV installed cost (\(\mathbf{\hat{c}}\)) & 12,780 & 11,580 & 10,380 & 9180 & 7980 \\ VAT (21\%) & 2684 & 2432 & 2180 & 1928 & 1676 \\ \hline TOTAL PV installed cost, & & & & & \\ including taxes (\(\mathbf{\hat{c}}\)) & 15,464 & 14,012 & 12,560 & 11,108 & 9656 \\ Total specific cost (\(\mathbf{\hat{c}}\)/Wp) & 2.58 & 2.34 & 2.09 & 1.85 & 1.61 \\ Total specific cost (\(\mathbf{\hat{c}}\)/Wp) & 3.09 & 2.80 & 2.51 & 2.22 & 1.93 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cost of a PV system at 6000 Wp (5000 W inverter).
# Robotics Applications in the Hospital Domain:
## 4 Robotic Systems in Hospitals Settings
### Rehabilitation Robots  
Rehabilitation robots are specially designed machines that help people with physical disabilities during their recovery process. These robots are particularly beneficial for individuals dealing with conditions like stroke, multiple sclerosis (MS), traumatic brain injury (TBI), spinal cord injuries (SCIs), and Spina bifida (SB) [33; 34; 35; 36].  
In the case of spinal cord injuries, where individuals may face challenges with upper and lower body movements, rehabilitation robots address specific needs. Lower limb rehabilitation robots target leg movements, helping with walking and balance, while upper limb robots focus on arm and hand rehabilitation, helping with tasks such as reaching and grasping objects [36]. Overall, rehabilitation robots play a crucial role in individualizing recovery programs designed by physiotherapists. By targeting specific areas of impairment, these robots can play an important role in aiding patients in regaining their natural motor skills and promoting overall well-being [37].
## 4 LTC Architecture
### PoT Gateway
#### 4.2.2 PoT Gateway as a Wi-Fi Access Point  
The PoT gateway is configured as an access point using the built-in dual-band WLAN interface on Raspberry Pi, as depicted in Figure 3. The access point is used to connect to the distributed WiFi-enabled door entry nodes of the LTC system. It allows the PoT gateway to act as an autonomous router to handle the door entry nodes' traffic. Additionally, it provides seamless integration to the existing network infrastructure within the premise, without further configuration to handle the system traffic. The PoT gateway programmatically, dynamically, and securely handles the system's network configuration, even when the system scales up, using software-defined networking (SDN) [25] technology under the hood.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Configuration** \\ \hline Server Location & Toronto, Canada \\ Server Type & Linux, Ubuntu 18.04 \(\times\) 64 \\ No. of CPUs & 1 \\ RAM & 1 GB \\ Storage & 25 GB SSD \\ Bandwidth & 500 GB \\ Cost & \$5 USD/month \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic configuration of the VPS instance used in LTC.  
Figure 1: Location Transparency Call (LTC) system overview.  
Figure 3: Overview of the PoT gateway as a Wi-Fi access point to the door-entry nodes.  
Figure 2: Overview of LTC OpenVPN implementation to establish secure tunnels for traffic exchange.
## 4 Results and Discussion
### Analysis of Random Field Results  
In this section, there are in total 150 random field analysis results obtained from five different \(l_{x}\) and five different COVs under six different WLs. The detailed computational setup can be found in the Appendix A, and corresponding FOSs are displayed in Figure 9.  
Figure 8: The results of deterministic analysis.  
Figure 7: The choice of element number.  
Furthermore, to compare with the deterministic results in Figure 8, during the slow water drawdown, one group of random field results with the parameter set, COV = 0.1, \(l_{x}=50\) m, corresponding to six water levels are plotted as shown in Figure 10. It shows the variation of the statistical FOSs during the slow drawdown of the WL, during which the FOSs present a gradual upward trend with the decrease in the WL. Such a result comes from two reasons. The first one is that the effective stress of soil increases with the decrease in WL because the pore water pressure disappears. The other one is that the increase in mechanical bite force leads to the soil slope being more stable.  
With the same WL, all FOSs obtained from the random field analysis are smaller than the results obtained from the deterministic analysis. In other words, the deterministic analysis method may sometimes overestimate the stability of the embankment slope, having potential risk. It implies that it is more reasonable to adopt the random field analysis, especially when the objective is the natural geomaterials. It is interesting to note that all the other results from the different parameter sets have a similar trend to Figure 10.
# Screening of Scaffolds for the Design of G-Quadruplex Ligands
## 4 Conclusions  
Different scaffolds of phenyl, quinoline, naphthalene, phenanthroline and arcidine types have been investigated for their G4 binding potential. Four of these scaffolds (Napht-3, Napht-5, DPPZ and PhenCOOCH\({}_{3}\)) were synthetized and screened for the first time against a G4 structure. Concerning their synthesis, the derivatives were obtained with moderate to good yields. The FRET-melting experiments showed that the arcidine compounds are the most promising scaffolds and, most importantly, C\({}_{8}\)NH\({}_{2}\) can discriminate among different G4 structures. Overall, the different molecules did not present a high pronounced toxic effect in normal cell lines, in contrast to cancers cells. Additionally, these scaffolds hit the drug-like criteria. This study could lead to the identification of G4-interactive molecules, deserving further optimization of their structures towards the development of drug candidates.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12042170/s1](https://www.mdpi.com/article/10.3390/app12042170/s1)]([https://www.mdpi.com/article/10.3390/app12042170/s1](https://www.mdpi.com/article/10.3390/app12042170/s1)), Figure S1: \({}^{1}\)H NMR and \({}^{13}\)C NMR spectra of synthetized compounds; Figure S2: Normalized FRET melting curves of the oligonucleotide sequences in the presence and absence of ligands.  
[PERSON] performed the methodology investigation and wrote the original draft. [PERSON], [PERSON], [PERSON] and [PERSON] performed chemical synthesis of arcidine derivatives and reviewed the manuscript. [PERSON] performed a formal analysis and reviewed the manuscript. [PERSON] supervised the work, analyzed the data, wrote and reviewed and edited the manuscript. [PERSON]-[PERSON] and [PERSON] acquired funding and administrated projects. All authors have read and agreed to the published version of the manuscript.  
This work was supported by PESSOA program ref. 5079 and project \"Projeto de Investigacao Exploratoria\" ref. IF/00959/2015 entitled \"NCL targeting by G-quadruplex aptamers for cervical cancer therapy\" financed by Fundo Social Europeu e Programa Operacional Potential Humano. Thanks are due to FCT/MCT, for the financial support, to CICS-UBI UIDB/00709/2020 research unit, project UID/Multi/04349/2019, POCI-01-0145-FEDER-02122 research unit PPBI-Portuguese Platform of Bioimaging, and to the Portuguese NMR Networks (ROTEIRO/0031/2013-PINFRA/22161/2016), through national funds, and, where applicable, co-financed by the FEDER through COMPETE 2020, POCI, PORL and PIDDAC. [PERSON] acknowledges the doctoral fellowship grant from FCT ref. SFRH/BD/145106/2019. [PERSON] acknowledges the grant from FCT ref. UIDP/00709/2020. [PERSON] acknowledges support from INCa PL-Bio (G4 Access) and ANR-20-CE12-0023 grants and SYMBIT project [CZ.02.1.01/0.0/0.0/15 003/0000477] financed by the ERDF.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 4 Simulation and Experimentation
### Separation of Dependent Source Signals
The separation results of the BAS-BCA algorithm are significantly better than those of the BCA algorithm, and the average SIR of the BAS-BCA method increased by 2.87. Traditional blind source separation algorithms such as FastICA and SCA are only applicable to mutually independent source signals, so the separation results are poor. Such algorithms have the best separation accuracy when the sources are independent, i.e., q is close to zero. When the source signals are not mutually independent, the separation accuracy of the FastICA and SCA algorithms decreases rapidly with the increase in the relevant parameter q. The average SIR of the BAS-BCA algorithm is 9.35 and 8.46 higher than that of the FastICA and SCA algorithms, respectively. Therefore, the superiority of the BAS-BCA algorithm in the separation of dependent source signals is verified.
# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 5 Performance Evaluation
### Evaluation of Algorithm Overhead  
In our simulation, we measured the overhead of the algorithm under different network scales, including memory usage, the number of iterations, and the training time per iteration. In the experiments, we used a 13 th Gen Intel(r) Core(tm) i9-13900K CPU without utilizing a GPU. For network scales involving 10 and 40 users, the CPU usage for a single training did not exceed 10%. As shown in Table 3, the differences in the number of training iterations and the time required for each iteration across different network scales were minimal. However, when the number of users increased to 40, memory usage rose by approximately 30 MB compared to when there were 10 users. This indicates that as the problem scale increases, the overhead of the algorithm increases only slightly. We also observed that the duration of each iteration increased from 0.24 s to 0.938 s, due to the expansion of the neural network size caused by the increase in users and edge servers, which in turn increased the computational load. To address this growth, GPU acceleration could be considered in practical applications to effectively reduce the training time.  
Figure 12: The impact of data size on average failure rate.  
Figure 13: The impact of network scale expansion in an environment with 40 users and 20 ESs. (**a**) Average response delay. (**b**) Average failure rate.
## 4 The Development of 3 SMVI Software and Integration
### Data Acquisition  
This non-contact roundness measurement method automatically generates data acquisition by rotating the workpiece at a specific point using a machine vision system and capturing images with a high-end webcam camera placed directly in front of the object with 90 degrees of freedom. For one cycle of the measurement part, 330 images of the target region were captured, with each measurement performed after rotating the workpiece by 1.8 degrees using an Arduino-controlled two-phase stepper motor, which provides higher resolution due to its smaller step angle and usable torque. The application connects to MQTT clients over the internet by specifying the client's IP address and port number through visual network computing. Once connected, the VNC module displays the image file and serves as the primary interface for the machine vision platform. A sample image obtained from the vision system is shown in Figure 6.  
Table 2 presents the surface feature of a hole pocket with four circle hole diameter values, namely 22 mm, 22 mm, 22 mm, and 22 mm, generated at different angles ranging from 0 degrees to 360 degrees in the 3 SMVI vision system. The average circular error in micrometers for each hole was also calculated.
## 3 Development of Wireless Laser Displacement Monitoring Equipment  
### The Principle of Laser Ranging Measurement  
At present, there are two main types of laser ranging methods: time-of-flight (TOF) ranging and non-time-of-flight (NTOF) ranging. Two methods are mainly used for TOF, including phase laser ranging and pulse laser ranging, while triangular laser ranging is mainly used for NTOF. This paper mainly uses the principle of TOF ranging to develop wireless laser displacement monitoring equipment [20; 22; 23; 24; 25].
# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 3 Crowd Monitoring (CM) Approaches
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Ref** & **Process** & **Frameworks/Methods** & **Performance** & **Conclusion/Result** \\ \hline \multirow{3}{*}{[46]} & Crowd behavior analysis(Behavior) & Spatio-temporal model & Accuracy 98\% and 88\% & Visual descriptors have extracted and considered for both individual and interactive behaviors \\ \cline{2-6}  & Crowd evacuation & & Correlation scores were positive & Reduced evacuation time \\ \cline{2-6}  & (Excitation behavior) & & & & \\ \cline{2-6}  & Detection of anomaly & Optical flow and Hom & Computation of distance between centroids & A novel approach of abnormal event detection has proposed \\ \cline{2-6}  & (Normal and abnormal) & & The STT method demonstrates comparable results of Spatio-temporal & Crowd anomaly detection framework was introduced \\ \cline{2-6}  & Crowd behavior detection (Identify behavior) & Spatio-Temporal Texture model & Spatio-temporal Compepositions (STC) and Inference by Composition (IBC) & Crowd anomaly detection framework was introduced \\ \hline \multirow{3}{*}{[50]} & Crowd behavior detection(Behavior) & Approximate median filter and foreground segmentation algorithm & Lower false rate & A robust unsupervised abnormal crowd behavior detection has achieved \\ \cline{2-6}  & \multirow{3}{*}{Volent behavior detection} & Hybrid random matrix (HEM) and deep neural network & Accuracy 90.17\% and 91.61\% & Combining compressive sensing and deep learning to identify violent crowd behavior \\ \cline{2-6}  & \multirow{3}{*}{Convd behavior detection} & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & Crowd behavior detection & Holistic approach & The performance of this methods yields better results & Holistic approach for abnormal crowd behavior detection has proposed \\ \hline \multirow{3}{*}{[1]} & Crowd behavior detection (Real time) & Scale-invariant feature transform (SIFT) & Accuracy 95\% & The combination of SIFT and genetic algorithm has achieved better simulation results \\ \cline{2-6}  & \multirow{3}{*}{[53]} & Crowd behavior monitoring & Fixed-width clustering algorithm and YOLO & Accuracy is between 80\%-95.7\% & The approach has a superior performance on six videos \\ \cline{2-6}  & \multirow{3}{*}{[54]} & Abnormal behavior detection(Abnormality) & Optical flow method and SVM & 87.4\% accuracy & Higher detection rate for anomaly \\ \cline{2-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 4 Results
### Advantages and Limitations of Using the DEA-ANN Method  
Since DEA offers some benefits to other approaches such \"as \"(1) DEA is able to handle multiple inputs and outputs (2) DEA does not require a functional form that relates inputs and outputs (3) DEA optimizes on each individual observation and compares them against the \"best practice\" observations. (3) DEA can handle inputs and outputs without knowing a price or knowing the weights and (4) DEA produces a single measure for every DMU that can be easily compared with other DMUs and also have some limitation as (1) DEA only calculates relative efficiency measures and (2) As a nonparametric technique statistical hypothesis test are quite difficult\" [71; 72]. \"Neural networks offer a number of advantages, including requiring less formal statistical training, ability to implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect all possible interactions between predictor variables, and the availability of multiple training algorithms. Disadvantages include its \"black box\" nature, greater computational burden, proneness to overfitting, and the empirical nature of model development\" [73]. However, overfitting can be controlled by the penalty method. Previously, DEA was popular with its multi-stage properties, but it has shortcomings with respect to its prediction capabilities, which reduces/limits its application. So, a powerful technique, ANNs, has been joined with DEA to fill that gap. Finally, the predictive potential of ANNs and the optimization capacity of DEA perform complementary features, thus envisioning a prominent modelling option [3; 6].
# DIVA Meets EEG: Model Validation Using Formant-Shift Reflex
## 2 Materials and Methods
### Experimental Phase
#### 2.2.5 EEG Acquisition and Analysis  
EEG was recorded using the ActiveTwo BioSemi system (BioSemi, Amsterdam, Netherlands) with ActiView acquisition software (BioSemi) with 64 scalp electrodes (10-20 electrode placement). External electrodes were placed in periocular locations to record blinks and eye movements. Analog filters were set at 0.03 and 100 Hz. During the analog/digital conversion, signals were sampled at 4096 Hz, with 24 bits of resolution. The EEG signal was pre-processed offline using standard procedures implemented in Brain Vision Analyzer 2.0\({}^{\text{\textregistered}}\) (Brain Products GmbH, Munich, Germany). Recordings were re-referenced to the average of all channels and band-pass filtered between 0.1 and 40 Hz using a zero-phase shift Butterworth filter of order 8. Data were downsampled to 512 Hz. Independent Component Analysis (ICA) was used for correcting EEG artifacts induced by blinking and eye movements (following [54]). Data were segmented from \(-\)200 to 500 ms around the onset of vocalization. Semiautomatic criteria implemented in Brain Vision Analyzer were used for rejecting noisy epochs. ERPs were obtained by averaging baseline-corrected epochs. N1 and P2 peaks were identified using semiautomatic procedures. Electrodes in occipital, parietal locations and in the midline were pooled (Iz, O1, O2, Oz, P10, P7, P8, P9, PO7, PO8), and N1 and P2 amplitudes were computed as the average voltage in a two-point window around the corresponding peak amplitude. The amplitude of the N1-P2 complex was obtained and compared between conditions (unperturbed feedback, up-shifted pitch, and down-shifted pitch) using a repeated measure ANOVA (\(p\leq 0.05\)).