esa-sceva/satcom-chunk-collection huggingface dataset:
# Visible Light Communication: A System Perspective--Overview and Challenges
## 4 Hybrid VLC
### Ir-Vlc  
Most of the current research relies on IR or RF for uplink transmission [73]. Infrared (IR) indicates the light with a wavelength less than red light. It has a frequency range between 300 GHz and 430 THz. IR band is unlicensed and has the potential to provide high-speed connectivity. Since the frequency is above 300 GHz, it cannot penetrate objects, hence used mostly in the indoor LOS environment. According to [PERSON] et al. [65], IR can achieve up to 1 Gbits/s of data rate over a distance of several meters. IR communication can take place in defused mode, also called scatter mode. In this type of communication transmitter and receiver do not need to be in the line of sight (LOS). However, for such communications devices need to be close to each other which can be an issue when used for developing indoor wireless networks. Researchers are now taking particular interests in using IR to complement VLC network in the uplink transmission.  
[PERSON] et al. [74] have proposed fast adaptive beam steering IR (FABS-IR) system to enhance the received optical power signal, speed up the adaptation process and reduce the channel delay during high data rate operation. The proposed scheme employs IR for uplink transmission and VLC for the downlink transmission. FABS-IR was coupled with imaging receivers to improve the system performance. Results showed that employing the IR-VLC network increases the data rate up to 2.5 Gb/s for a mobile scenario in an indoor environment. [PERSON] et al. [75] have proposed a VLC-IR system for internet access to passengers on flights. The VLC-IR system can also be used to provide personalised entertainment using wireless media without interfering with airline radio systems. The proposed system claims to have minimal interference as every lamp serves as VAP dedicated to each seat. Experiments have shown significant minimisation in transmission error for a short distance (2 m), good downlink signal strength for receivers, and functional capability to work in the presence of other illumination sources. Further, the proposed system is tested for other onboard entertainment such as collaborative games and live video streaming.  
Few researchers have investigated IR for uplink transmission in the VLC as compared to WiFi. WiFi is already available, and it has hardware and software support available, whereas integrating IR in experiments are difficult due to less support of hardware and software. IR has less data rate and a lower range of communication, which made it less attractive for the researcher to investigate. Furthermore, IR transceiver has to be integrated into the VAP and in the user device, which further increases the complexity of the VLC system. Nevertheless, researcher community has shown a high interest in improving the IR-VLC in an indoor communication system for specific applications such as in-flight communication system, point-to-point communication. IR communication could be one of the areas, which would complement VLC either in the uplink or during mobility.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.14. Magnetic Compass  
The magnetic compass is installed on ships to determine and display heading information without any power supply [71]. Magnetic compasses generate several errors; for these to remain within the limits determined by the SOLAS, the compass should be mounted in a proper binnacle with correcting devices [72].
## 4 Simulation
### Coordinated Controller  
To test the effect of the proposed coordinated controller of active suspension and DYC, the stability performance under no control, independent control, and coordinated control are compared and analyzed under a ramp and sinusoidal steering maneuver. First, no control refers to the use of passive suspension in the vertical direction and no DYC control in the horizontal direction. Second, an independent control means that the active suspension and the DYC controller works independently where the anti-roll torque is evenly distributed between the front and rear axes. Finally, a coordinated control refers to the integrated control of the active suspension and DYC with the coordinated controller.
# A Decision Support System for Melanoma Diagnosis from Dermoscopic Images
## 4 Implemented CAD System
### Feature Extraction
#### 4.2.2 Border Detection and Processing  
The border detection block extracts the segmented lesion area. The segmented area is used to extract borders that are then chain-coded. Each boundary pixel of the segmented image is represented by a real and an imaginary part so that a sequence of complex numbers is defined to describe the lesion border in relation to the lesion centroid. The real and imaginary part of the detected border is shown in Figure 4, as well as the extracted contour.  
To make the border description insensitive to image rotation, the complex data sequence is transformed in the frequency domain via Fast Fourier Transform (FFT). The signal energy computed within a tunable number of bands in the frequency domain is performed to identify the frequency feature to be used in the classification phase.  
With \(N\) as the number of the FFT bins to be used and \(M\) intervals in the frequency domain, the feature vector is computed as:  
\[E(m)=\sum_{i=0}^{\frac{N}{M}-1}\left\{\left|X\left[m\frac{N}{M}+i\right]\right| ^{2}\right\},\ m=1,\ 2,\ \ldots,\ M \tag{3}\]  
Figure 4: (**a**) Border of the detected lesion area; (**b**) real (blue line) and imaginary parts (red line) of the complex coding of the border.  
Figure 3: Result of the Boolean EXOR operation for the same image of the PH\({}^{2}\) database, previously segmented. The yellow zones are the non-overlapping pixels between the two areas.  
in which \(E(m)\) represents the energy in each sub-band of contiguous frequency bins in the frequency domain, \(m\) is the index of the \(m^{\text{th}}\) band chosen to describe the feature, and \(X(mN/M+i)\) is the FFT-computed value.  
To compare different contour lengths, the FFT is computed on a fixed length by zero-padding and multiplied by a scaling factor, named \(SF\), defined as the ratio between the chosen FFT length, \(N_{FFT}\), and the original contour length, \(N_{cont}\).  
\[SF=\frac{N_{FFT}}{N_{cont}} \tag{4}\]
# Modelling of ML-Enablers in 5G Radio Access Network-Conceptual Proposal of Computational Framework
## 4 Discussion  
The general idea of using AI/ML as a component of network optimization, undertaken by 3 GPP, is adopting an intelligence to automated data collection through the intended RAN optimization use case. The outlined architecture for AI/ML adoption in RAN possesses generalization abilities in the context of the three essential types of 5G communication (_eMBB_, _URLLC_, and _mMTC_). Applying ML practices without considering the usage sc  
Figure 10: An example of a random forest-based decision for 5G traffic evaluation in association with multiple RAN optimization use cases.  
narios for 5G traffic can run into a problem with suboptimal optimization tasks or ML algorithm outcomes. For instance, maximally optimizing cell coverage does not guarantee _URLLC_ service, characterized by stringent requirements such as reliability and latency. The ability to adapt the ML-based RAN framework for the converged evaluation criteria (i.e., 5G traffic requirements and RAN optimization use cases requirements) is envisioned to improve the robustness of ML algorithms. The applied data classification with the ANN technique can help to reduce losses and avoid redundancy in data collection campaigns, thus saving computing cost and optimization processing time, while raising accuracy. The proposed evaluation technique can help to distinguish data assessment for a single user and dedicated 5G traffic type in a unit area, where the significance of different needs, varies. The distinction of the approaches can be applicable and may serve different purposes, such as the collective traffic evaluation in a certain area or the user's specific evaluation. Both are based on the key performance metrics generated and collected from 5G users. While the availability of the real network data from 5G traced traffic appears challenging, the method can be applied to data generated in 5G testbeds and prototypes [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34].
## 2 Theory Background
### Improved Singular Spectrum Decomposition  
As reported in [24], a limitation of SSD is that the decomposed components are always affected by end effects. This requires the extension of both ends of the original signal to isolate the distortion as far as possible from the outside of the signal to be analyzed. However, this extension cannot be blind extension, but to make the extended waveform conform to the natural trend of the original signal as much as possible, to maximize the maintenance of the change trend of the original signal and achieve a smooth transition between the extended waveform and the original signal. Thus, a novel waveform matching degree evaluation index was proposed by combing the correlation coefficient and root mean square error to adaptively perform the waveform matching extension. An improved singular spectrum decomposition (ISSD) approach was developed by using this waveform matching extension method to improve the decomposition accuracy.  
The extension of the signal includes the left and right ends, and the following is an example of  
whole data. Finally, the left data of the optimal matching waveform will be extended to the left of the data. The specific steps are given as follow:  
(1) Find out the starting point of all matched waveform \(S_{i}\) (\(i\) = 1, 2, 3... ), whose corresponding time can be calculated as:  
\[t_{S_{i}}=\frac{t_{M_{1}}t_{N_{i}}-t_{N_{i}}t_{M_{i}}}{t_{M_{1}}-t_{N_{1}}}. \tag{4}\]  
The waveform \(S_{i}\)-\(M_{i}\)-\(N_{i}\) is defined as the matched waveform \(\mathbf{W}_{i}\).  
(2) Calculate the proposed matching degree of the characteristic waveform and the matching waveform by using the following equation:  
\[D_{i}=\frac{corr(\mathbf{W}_{1},\mathbf{W}_{i})}{RMSE(\mathbf{W}_{1},\mathbf{W}_{i})}, \tag{5}\]  
where, \(corr(\mathbf{W}_{1},\mathbf{W}_{i})\) and \(RMSE(\mathbf{W}_{1},\mathbf{W}_{i})\) represent the cross-correlation coefficient and root mean square error between \(\mathbf{W}_{1}\) and \(\mathbf{W}_{i}\), respectively. If the correlation between the two waveforms is stronger and the root mean square error is smaller, the matching degree is higher.  
(3) Treat the matching waveform which has the largest value of \(D_{i}\) as the optimal matching waveform. When there are multiple optimal matching waveforms, the one which is farthest from the starting point is regarded as the optimal matching waveform.  
(4) Extend the data on the left side of the optimal matching waveform to the left side of \(S_{1}\).  
Similarly, the same steps can be used to complete the right extension of the data. The extended data will be decomposed by SSD to generate a series of SSCs with physical meanings. Finally, the SSCs with the same length as the original signal data can be obtained by intercepting based on the pre-extension position. As the ISSD will be combined with HT to conduct the time-frequency analysis for the rotor fault signal in this paper and HT also has end effects, the interception process will take place after the HT process.
## 2 Data and Methods  
### Experimental Datasets  
CNNs extract a large number of abstract features of target elements via multi-layer convolution calculations to achieve high accuracy in image classification tasks. Therefore, a large number of parameters need to be solved, which require the input of a large number of samples in the training process to improve the reliability and robustness of the classification model. We constructed outdoor test areas (including grass, soil and crops, rocks, and other objects), put in third-instar, fifth-instar, and adult AM locusts, adult rice locusts, and adult cotton locusts (close to 2000 for each type) to obtain a sufficient number of training samples. Thus, we collected images of locusts with different poses (because specimen appearance varies with the orientation they are being shown) and background using a visible light camera under the condition of \"normal lighting and shooting.\" Then, an open-source software \"labelImg\" was applied to manually label the locust location border information and species' information on the original images to generate XML files, as shown in Figure 1. In this regard, each of the original images was cut into several sample blocks according to the content of a corresponding XML file (each sample block corresponds to one locust); and all sample blocks were extracted and stored in a corresponding sample dataset. Particularly, we manually obtained 6000 sample blocks for each type of locust, i.e., a total of 30,000 sample blocks were extracted, which took one of our co-authors around 300 h. Afterward, we divided the dataset (in a random manner) into training, validation, and test datasets. To be specific, the training set contained 4000 samples, the validation set contained 1000 while the test set contained 1000 samples as shown in Table 1.  
Figure 1: Schematic diagram of sample annotation based on labelImg.
# Ex-Situ Evaluation of Commercial Polymer Membranes for Vanadium Redox Flow Batteries (VRFBs)
###### Abstract
They found that swelling behavior has a significant effect on the degree of membrane degradation due to the oxidation of polymers by V\({}^{5+}\) ions. [PERSON] et al. also evaluated three commercial AEMs--Selemion APS, AHA and AFN--as separators in VRFBs [28]. More recently, the possibility of commercial Fumasep AEMs for VRFB application has also been examined by several research groups [29; 30; 31]. [PERSON] et al. [29] evaluated the permeation rate of the different vanadium ions across Fumasep(r) FAP-450 with a comparison to N115, and [PERSON]'s group [30] examined FAP-450 with respect to vanadium ion uptake under both static equilibria and after cell operating conditions. Besides ion exchange membranes (IEMs), non-ionic porous membranes have also been tried for VRFB such as polyvinylidene fluoride (PVDF), polytetrafluoroethylene (PTFE), polybenzimidazole (PBI) and polyvinylpyrrolidone (PVP)-based porous membranes due to their excellent mechanical stability, high chemical resistance and low cost [32; 33; 34; 35; 36]. Many detailed studies have been conducted using ex-situ methods of characterization of various commercial membranes in VRFBs over the last few decades. These research works have different evaluation approaches and are under a diverse set of test conditions that are not easily comparable after a certain point. There is a high demand from VRFB developers and researchers to harmonize these evaluation methods, and establish the effective and appropriate evaluation/diagnostic tools to determine the most essential properties of membranes linked to VRFB performance. In this work, five (5) key properties, namely, chemical stability, mechanical strength, proton conductivity/resistance, vanadium ion permeability/ion selectivity, and swelling property, were identified as a set of ex-situ membrane assessment criteria to screen the membranes for VRFB application. Among them, membrane chemical stability was first-time evaluated by membrane tensile strength changes before and after soaking V\({}^{5+}\) solution for ten days. Eight (8) current state-of-the-art, commercially available membranes made by PFSA and hydrocarbon polymers were selected and evaluated by the methods of ex-situ characterizations mentioned. The results were used to access the qualification of these membranes in the VRFB applications. This work could provide the VRFB researchers and industry a reference for selecting the membrane materials before the VRFB in-situ testing.
# Tunable non-Hermiticity in Coupled Photonic Crystal Cavities with Asymmetric Optical Gain
## 4 Conclusions  
In summary, we have investigated the cavity modes in a non-Hermitian doubly coupled PhC cavity system with the systematic manipulation of the intercavity coupling constant. In the doubly coupled PhC cavity, the coupling constant can be tuned by changing the cavity structure, such as by varying the sizes of the shared airholes or via the discrete vertical/lateral airhole shifting of one of the component cavities. We found that discrete airhole shifting, which yields a tunable coupling constant with a constant fundamental eigenfrequency, is more desirable for non-Hermicity control. In addition, we analyzed the evolution of eigenmodes and eigenfrequencies in the non-Hermitian doubly coupled PhC cavity system by varying the coupling constant via the vertical shifting of one of the component cavities and by systematically controlling the spatially asymmetric optical gain. It was revealed that the interplay between the gain contrast and the coupling constant is crucial for determining the EP. This unique non-Hermitian behavior with EP can provide a new method to control laser emission by the additional tuning knob and asymmetric optical gain, which helps in increasing the design flexibility of the cavity structures. We believe that the proposed method of manipulation of non-Hermicity in the coupled PhC cavity system can allow us to develop a novel actively tunable PhC cavity laser, using a fixed cavity structure, that exclusively excites a steady and robust single amplifying laser mode.  
Conceptualization, K.-H.K. and Y.-S.N.; methodology, M.S. and E.S.H.K.; validation, K.-H.K., E.S.H.K., and Y.-S.N.; writing--original draft preparation, K.-H.K.; writing--review and editing, Y.-S.N.; funding acquisition, K.-H.K., E.S.H.K., and Y.-S.N. All authors have read and agreed to the published version of the manuscript.  
K.-H.K. acknowledges support from the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2019R1C1C1006681). E.S.H.K. acknowledges support from the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (2020R1A2C1102558). Y.-S.N. acknowledges support from the National Research Foundation of Korea (NRF) grant funded by the Korean government (2018R1C1B3001130).  
The authors declare no conflict of interest.
# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
## 3 Results and Discussion
### Model Robustness  
To test the model robustness, Gaussian white noise with different intensities is utilized and added into testing data sets of actual data. This approach has been utilized by relevant literature, such as in [38; 39]. The data is split into training dataset and multiple testing sets with each set corresponding to a defined noisy intensity. Gaussian white noise is generated with four different intensities of 20%, 40%, 60%, and 80% to assess the robustness of the Prophet and Holt-Winter models. The generated Gaussian white noise samples are distributed randomly at each testing data set, with the corresponding noise intensity, and model robustness is evaluated by the effect of the noise intensity over the accuracy of the model, measured by the coefficient of determination R\({}^{2}\). Low variance in R\({}^{2}\) indicates high robustness where high variance indicates low robustness. Table 2 presents the results of the R\({}^{2}\) of both models under various noise intensities.  
A reduction in the coefficient of determination is associated with the increase in the noise level, as depicted in Figure 10. The Prophet model showed high R\({}^{2}\) values across the different noise intensities with a minimum value of 0.9604 at 80% noise intensity. On the other hand, the Holt-Winters model showed high values of R\({}^{2}\) but relatively lower than the Prophet model. The minimum value of R\({}^{2}\) for Holt-Winters was 0.8883 at 80% noise intensity. The results indicate that the Prophet model is more robust than the Holt-Winters model under various Gaussian white noise intensities.
# Experimental Evidence of Specimen-Size Effects on EN-AW6082 Aluminum Alloy in VHCF Regime
## 4 Conclusions  
In the present investigation, fully reversed ultrasonic fatigue tests at a loading frequency of 20 kHz were performed on hourglass specimens made of EN-AW6082-T6 aluminum alloy up to \(10^{9}\) cycles. Furthermore, three different dimensions of specimens were considered in order to investigate the specimen-size effects in the VHCF regime. The geometry of the specimens was designed by using a commercial finite element code. Thus, strain gauge calibration was carried out before the tests to check the stress distribution of the designed hourglass specimens. From the obtained experimental results, it emerges clearly that a decrease in the fatigue resistance with the specimen size is expected. Furthermore, the fractography analysis shows that the fatigue crack nucleation always originated from the subsurface. Subsequently, P-S-N curves were obtained for the three different specimen sizes, which show that the specimen size effect prevails on the scatter of experimental data. Scale-invariant P-S'-N probabilistic fractal Wohler curves are obtained by exploiting the concept of lacunar fractality, and the decrease in the fatigue life with the specimen size is theoretically justified and assessed. Although the extrapolation of the ultrasonic test result to structural components is not yet unanimously agreed, and the influence of loading frequency is still controversial, the P-S'-N probabilistic fractal Wohler curves can be used to provide objective values of the fatigue strength of full-size components subjected to ultrasonic VHCF regimes.  
conceptualization, S.I., F.M. and A.C.; methodology, S.I., F.M. and A.C.; investigation, F.M. and S.I.; data curation, F.M.; writing-original draft preparation, S.I. and F.M.; writing-review and editing, S.I. and A.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors wish to thank the company Italsigma(r) for the collaboration and its kind availability. SEM fractography was carried out at the J-Tech center of the Politecnico di Torino, whose collaboration is deeply acknowledged.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l l l l} VHCF & Very High Cycle Fatigue \\ SLM & Selective Laser Melting \\ AISI & American Iron and Steel Institute \\ P-S-N & Probabilistic Stress-Life \\ UFTM & Ultrasonic Fatigue Testing Machine \\ SEM & Scanning Electron Microscope \\ CDF & Cumulative Distribution Function \\ MLM & Maximum Likelihood Method \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{\Delta\sigma^{*}_{0,80\%}}\) **(N mm\({}^{-1.85}\))** & \(\mathbf{d_{\sigma}}\) & \(\mathbf{n}\) & \(\mathbf{R^{2}}\) & \(\mathbf{\pm^{*}}\) & \(\mathbf{\bar{\beta^{*}}}\) \\ \hline
1221 & 0.15 & 18.5 & 0.966 & 3.8522 & 1.1428 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Best-fitting of free-parameters entering Equations (8) and (9).