esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology Statement
### Source-Load Uncertainty Modeling and Scenario Generation
#### 2.2.1 The Uncertainty Modeling of the Wind Power and Load  
Regarding the uncertainty of wind power, the Weibull distribution is usually used to describe the probability distribution of wind speed; however, its parameters are fuzzy and difficult to obtain accurately. Therefore, the stochastic fuzzy variables can be adopted to model the uncertainty of wind speed [34] as (8)-(11). The membership function of the parameters of the Weibull distribution \(k\) and \(c\) are shown as (8) and (9), respectively.  
\[\mu_{k}(k)=\left\{\begin{array}{ll}\frac{k-\xi_{k}^{1}}{\xi_{k}^{2}-\xi_{k}^{ 1}}&\xi_{k}^{1}\leq k<\xi_{k}^{2}\\ \frac{\xi_{k}^{2}-\xi_{k}^{1}}{\xi_{k}^{2}-\xi_{k}^{2}}&\xi_{k}^{2}\leq k<\xi_ {k}^{3}\\ 0&k<\xi_{k}^{1}&or&k>\xi_{k}^{3}\end{array}\right. \tag{8}\]  
\[\mu_{c}(c)=\left\{\begin{array}{ll}\frac{c-\xi_{c}^{1}}{\xi_{c}^{2}-\xi_{c}^{ 1}}&\xi_{c}^{1}\leq c<\xi_{c}^{2}\\ 1&\xi_{c}^{2}\leq c<\xi_{c}^{3}\\ \frac{\xi_{c}^{4}-c}{\xi_{c}^{4}-\xi_{c}^{3}}&\xi_{c}^{3}\leq c<\xi_{c}^{4}\\ 0&c<\xi_{c}^{1}&or&c>\xi_{c}^{4}\end{array}\right. \tag{9}\]  
where \(k\) is the shape parameter of the Weibull distribution, described as the triangle fuzzy variable \(\xi_{k}=\left(\xi_{k}^{1},\ \xi_{k}^{2},\xi_{k}^{3}\right)\); \(c\) is the size parameter, described as the trapezoidal fuzzy variable \(\xi_{c}=\left(\xi_{c}^{1},\ \xi_{c}^{2},\xi_{c}^{3},\xi_{c}^{4}\right)\). The wind speed chance measure distribution function of the Weibull distribution can be expressed as (10), and the typical wind power output characteristics are shown as (11).  
\[F(\xi_{v})=Ch(v<\xi_{v})=1-e^{-(\frac{v_{c}}{\xi_{c}})\xi_{k}^{k}} \tag{10}\]  
\[P_{max}^{WT}=\left\{\begin{array}{ll}0&v\leq v_{ci}&or&v\geq v_{co}\\ \frac{v^{3}-v_{ci}^{3}}{\vartheta-v_{ci}^{2}}P_{wt,r}&v_{ci}\leq v\leq v_{r}\\ P_{wt,r}&v\geq v_{cr}\end{array}\right. \tag{11}\]  
where \(\xi_{v}\) is the stochastic fuzzy model variable of wind speed; \(P_{max}^{WT}\) and \(P_{wt,r}\) represent the actual maximum output and rated output power of the wind turbine, respectively; \(v\), \(v_{ci}\), \(v_{r}\) and \(v_{co}\) represent the actual wind speed, cut-in wind speed, rated wind speed, and cut-out wind speed, respectively.  
For the uncertainty of power load, the typical daily load curve with a random deviation which follows a normal distribution was adopted to model the randomness of power load as (12) and (13).  
\[P^{L}=P^{L,Typical}+\Delta P^{L} \tag{12}\]  
\[\Delta P^{L}\sim N\left(0,\sigma^{2}\right) \tag{13}\]  
where \(P^{L}\) and \(P^{L,Typical}\) represent the power load and typical daily load curve, respectively; \(\Delta P^{L}\) denotes the random load deviation.
# Viscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network
## 5 Conclusions  
To accurately predict the viscosity ratio between ethylene glycol/water nanofluids and a base fluid, a RBF neural network based model was developed and evaluated in the present study. Based on the comparative analysis, the following conclusions were obtained.  
1. Considering the complex effects of different factors including temperature, nanoparticle properties (such as volume fraction, density, diameter), and viscosity of the base fluid on the viscosity ratio and the effect of Spread on modeling performance of the RBF neural network, the final network structure was determined to be 5-56-1 neurons.
2. By comparing the RBF predictive values and the experimental data published in various studies, it was demonstrated that the RBF neural network not only exhibited good modeling accuracy (\(RMSE=0.04615\), \(MAPE=2.12738\%\), \(SSE=0.46007\), \(R^{2}=0.99925\)), but also could effectively predict the influences of temperature, nanoparticle volume fraction, and diameter on the viscosity ratio of different ethylene glycol/water based nanofluids.
3. Compared to the typical viscosity models, namely the Batchelor model and Chen model, the RBF neural network has a good ability to predict the viscosity ratio of different ethylene glycol/water based nanofluids. However, the prediction performance can be affected by the size of the data set  
Figure 9: (**a**) Predicted comparisons and (**b**) relative errors of the RBF predicted \(\mu_{qf}/\mu_{f}\) and the experimental data [14] for CuO-ethylene glycol/water (60:40 by weight ratio) nanofluids at \(d_{p}=30\) nm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Criteria** & **216 Data** & **200 Data** & **160 Data** & **120 Data** \\ \hline \(RMSE\) & 0.04615 & 0.07017 & 0.09547 & 0.37982 \\ \(MAPE\) (\%) & 2.12738 & 2.88920 & 3.64470 & 8.75159 \\ \(SSE\) & 0.46007 & 0.98468 & 1.45844 & 17.31116 \\ \(R^{2}\) & 0.99925 & 0.99832 & 0.99505 & 0.90323 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance evaluation of the RBF neural network with different viscosity data.  
The present investigation may play an active role for developing the modeling of nanofluid viscosity. However, how to extend the application of ANN to predict other thermo-physical properties of nanofluids is still worthy of study in the future.  
The authors acknowledge the financial support by the Fundamental Research Funds for the Central Universities (No. HEUCF160307). [PERSON] was responsible for the main parts of this manuscript, which includes the collection of viscosity data and the results analysis. [PERSON] provided the program codes of RBF neural network and the conventional viscosity models, and wrote the basic theory of the RBF neural network.  
We declare that we have no conflict of interest.
## 2 The Algorithm  
As shown in Figure 1, the proposed algorithm contains four key steps, namely seed point extraction, expansion, conditional initial value modification and outlier filtering. Firstly, sparse initial seed points are extracted from input images through a process of image selection, followed by a correspondence-search along epipolar lines; Secondly, new points are expanded on the basis of the initial positions and normals set from the seed points, and optimized for a potential increase in accuracy; Thirdly, the expanded points are modified and re-optimized on the condition that their neighbor points are sufficiently dense and centered on them. Note that once a point has been successfully modified and re-optimized, it will not be modified again; Fourthly, the outliers are deleted via consistency filtering with regard to smoothness, depth and normal. The successfully-expanded points are then used as new seeds and the last three key steps loop again and again until the final 3D point cloud becomes dense enough. More detailed descriptions are as follows.
# Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks
## 6 Conclusions  
In this paper, the hybrid beamforming problem in millimeter-wave MIMO systems was studied. In order to obtain system performance similar to that of pure digital beamforming, an iteratively updated minimum mean square error algorithm was proposed. The algorithm uses the orthogonality of the initial digital matrix on the basis of the digital matrix. Compared with the classical OMP algorithm and the approximate optimal MD-HP algorithm in [24], the proposed algorithm system performance was significantly better than that of the OMP algorithm [20], especially when the number of RF links was equal to the number of data streams and the number was small. The performance of the proposed algorithm always approximates the performance of pure digital beamforming. Compared with the approximate optimal MD-HP scheme, the proposed performance was slightly better than that of the MD-HP algorithm, and both were close to the performance of pure digital beamforming. In terms of algorithm complexity, the proposed Alt-MMSE hybrid beamforming had lower complexity than the OMP and MD-HP algorithms. The next study will extend this work, and comprehensively analyze the impact of cost and power consumption on system performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Complexity** \\ \hline PP [18] & \(\mathcal{O}\big{(}40N_{t}N_{t}^{RF}N_{s}\big{)}\) \\ PC-SIC [19] & \(\mathcal{O}\big{(}4N_{t}^{2}N_{t}^{RF}+16N_{t}N_{t}^{RF}+4N_{t}^{RF}\big{)}\) \\ OMP [20] & \(\mathcal{O}(N_{t}^{2}\big{(}N_{t}^{RF}\big{)}N_{s}\big{)}\) \\ MO-AltMin [24] & \(\mathcal{O}\big{(}10N_{t}^{2}N_{t}^{RF}N_{s}\big{)}\) \\ Proposed Alt-MMSE & \(\mathcal{O}\big{(}N_{t}N_{t}^{RF}N_{s}\big{)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complexity comparison.  
Figure 9: Complexity analysis of algorithms with increasing number of BS antennas.  
**Author Contributions:** Conceptualization, S.L.M. and I.K.; methodology, M.H.A.; software, M.A.; validation, M.H.A.; formal analysis, S.L.M.; investigation, M.A.; resources, M.H.A.; data curation, I.K.; writing--original-draft preparation, S.L.M.; writing--review and editing, I.K.; visualization, M.A.; supervision, M.H.A; project administration, S.L.M.; I.K.; funding acquisition, M.H.A.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Project Example Analysis  
### Overview of Jiaoping Expressway Yellow River Bridge Project  
The starting point of the Jiaoping Expressway that spans across the Yellow River is located in Wen County, Jiaozuo City, and the end point is located in Xingyang City. The main bridge of the Jiaoping Expressway has a total length of 3650 m, with a left and right framing design, a bridge deck width of 2 \(\times\) 16.55 m, and a span layout of 6 \(\times\) (6 \(\times\) 100 m) + 1 \(\times\) 50 m, totaling seven sections. The main girder of the upper structure adopts steel-concrete composite box girder construction, the pier of the lower structure adopts vase pier construction, the abutment is a gravity abutment, a steel sheet pile cofferdam is used for construction, the foundation adopts bored pile foundation construction, and the main girder adopts jack-up construction. The maximum cantilever length is 100 m. The design speed adopts the technical standard of a 120 km/h, two-way six-lane expressway, and the standard roadbed width is 34.5 m.  
Figure 6: The construction safety risk evaluation index system of the studied Yellow River bridge.  
The bridge site is located in a temperate monsoon climate with abundant sunshine, where it is cold in winter, hot in summer, warm in spring, and cool in autumn, with four distinct seasons. The annual average temperature is 14.3 \({}^{\circ}\)C and the annual average annual precipitation is 608.2 mm. The temporal and spatial distributions of precipitation are uneven, and it is rainy in summer. Summer months account for about 65% of the precipitation, and there is little rain and snow in winter. The lithology of the bridge site area is mainly composed of silty clay, silt, silty fine sand, fine sand, round gravel, pebbles, and cemented layers. Yellow River ice floods, earthquake liquefaction, bank slope collapse, and slope instability are prone to occur in the site area.
# A Novel Approach for Acute Mental Stress Mitigation Through Adapted Binaural Beats: A Pilot Study
## 2 Materials and Methods
### Instruments  
The ECG was collected through the PolarH10 chest strap. The signal was sampled at 130 Hz and streamed in real time to a workstation (HP Laptop 14s-dq0 xxx, Intel i5-8265U @ 1.60 GHz CPU and 8 GB RAM-HP, Palo Alto, CA, USA). The tones were presented to the left and right ears (see Figure 1A) and were created through a Python (version 3.11) script, with a carrier (\(f_{0}\)) at 400.0 Hz. The beat frequencies (\(f_{BB}\)) ranged within the theta range (4.0-8.0 Hz), and the beat modulation was obtained by modifying the frequency of the right tone (\(f_{R}\), see Figure 1A). Sennheiser HD 201 headphones were used to deliver the sounds. To avoid discomfort during the auditive stimulation, a relaxing song [33] in mono format (i.e., identical for both channels) was summed to the tones, thus not altering the creation of the BBs given the exact contribution to both the left and right channels. It is worth noting that other studies also consider the possibility of embedding the BBs into sounds like pink noise [34] and natural sounds (e.g., the sound of the sea [35]). The signal-to-music ratio chosen was \(-\)6.0 dB, and the sounds were reproduced at 40 dB(A) (measured with the Decibel X app--version 9.9.0 and iPhone 12 mini, Apple Inc., Cupertino, CA, USA).  
Figure 1: (**A**) Graphical representation of the proposed adaptive stimulation approach considering the features of the electrocardiogram (ECG) and ECG-derived respiratory (EDR) signals. (**B**) Experimental protocol. It consists of a sequence of relaxing videos and mental arithmetic tasks, acting as stressors. Steps 1 and 2 were considered for training the random forest regressor, whereas steps 4 and 6 were used to test the effect of constant and adapted stimulations (STIM ON). Notably, during the viewing of the relaxing videos (steps 3 and 5), the auditive stimulation was turned off (STIM OFF). At the end of each task, a visual analog scale (VAS) questionnaire was presented to the participants with values ranging from 0 (no stress) to 5 (high stress) to assess the perceived stress. The temporal duration of the tasks was 2 and 4 min, respectively, for the relaxing videos and mental arithmetic tasks.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 4 Dataset Validation  
We validated the algorithm using the TUM dataset [22], which is a dataset of indoor and outdoor images collected by the Computer Vision Group of Ludwig Maximilian University. The dataset contains calibration files, five types of data, and the real trajectory of the camera captured by the motion-capture system. The true values of the motion capture system were used instead of the GNSS data to validate the visual dynamic localization method with GNSS assistance proposed in this paper. To verify the performance of the algorithms for the GNSS continuous aid and GNSS initial aid, three frames of the GNSS coordinates were randomly added for the constraints after the 78 th frame. Table 2 shows the TUM dataset camera parameters.
# Analytical Solution and Experimental Validation of the Electromagnetic Field in an IPT System
## 7 Conclusions  
To reveal the energy transfer mechanism in the IPT system, it is important to study the distribution of the time-harmonic electric and magnetic fields in the IPT system. In this paper, the analytical solution of the time-harmonic electric and magnetic fields in an IPT system was deduced in detail, and the numerical integration method was used to find the distribution of the \(E\) and \(H\) fields on the basis of the analytical solution.  
Firstly, based on the Maxwell's equations, the Laplacian of the electric field and the magnetic field strength around the current-carrying coil were derived theoretically. The analytical expression of the electric field was calculated in an integral form, and the result can be obtained by the simple trapezoidal integration method. The final solutions were obtained by combined analytical derivation and numerical calculations.  
Secondly, the correctness of the analytical results for the electromagnetic field in the IPT system was verified utilizing the finite element software COMSOL.  
Finally, an IPT system was set up, and the electric-field strength between the primary and secondary coils was measured. The experimental results further verify the correctness of the analytical solution. The analytical solution developed in this paper can be applied to analyze electric and magnetic fields in any uniform medium.  
[PERSON] proposed the idea, obtained the results and wrote the paper. [PERSON] and [PERSON] built the experiment setup. All authors together contributed by drafting and critical revisions.  
This work was supported by the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University.  
The authors declare no conflict of interest.
## 5 Performance Evaluation  
We evaluate and compare the performance gain and coexistence performance of MLO under various channel access schemes through simulation (We used a Matlab-based in-house simulator). In addition to the two basic channel access schemes of EHT MLO, i.e., Async and Sync, we also evaluate Sync-PL and Sync-FT as well as the coexistence solutions for Sync-FT described in the previous section. In this evaluation, we consider two deployment scenarios:  
* _Single-spot deployment_: All APs and devices are deployed at a single spot. APs/devices of the same type have the same modulation and coding scheme (MCS). This deployment scenario enables us to clearly observe and analyze the coexistence performance.
* _Indoor random deployment_: Following the 3 rd Generation Partnership Project (3 GPP)'s indoor evaluation scenario for coexistence in unlicensed spectrum [22], the APs are evenly placed, and devices with link adaptation are randomly distributed to allow the coexistence performance to be investigated under more realistic conditions. The deployment area considered in this scenario and the placement of the APs are shown in Figure 16.  
The coexistence performance of MLO is investigated by comparing the throughput and latency performance of legacy devices between the cases without and with MLDs. To this end, two phases of simulation are performed, as illustrated in Figure 17 and described below:  
* (Phase 1) Legacy devices only: Only legacy devices exist in the simulated environment.
* (Phase 2) MLD vs. legacy devices: Half of the legacy devices from the first phase are replaced with MLDs.  
In the transition from Phase 1 to 2, one legacy device is replaced with one MLD. The purpose is to evaluate the coexistence performance of MLO when the number of coexisting devices remains unchanged. Phase 1 reflects the baseline performance of the conventional single-link channel access scheme. Phase 2 reveals, compared with the performance in Phase 1, how much of a performance gain MLO achieves and how much of a performance degradation the legacy devices suffer. As the performance of the legacy devices in Phase 2 decreases relative to that in Phase 1, the MLDs coexist more poorly with the legacy devices.  
The simulation parameters are listed in Table 2. Each MLD has two links working in individual channels. The cases of MLDs both with and without the STR capability are considered. A transmission frame is an aggregation of up to 64 MAC protocol data units (MPDUs), where each MPDU is 1500 bytes long. We consider full-buffer traffic conditions. For the coexistence solution of CW set switching, CW set A has a CWmin of 16 and a CWmax of 1024, while CW set B has a CWmin of 32 and a CWmax of 2048. The timeout time for switching (\(t_{switch}\)) is set to 100 ms. All simulation results are obtained by averaging over 5 runs; in each run, 50 s of time are simulated.  
Figure 16: Indoor random deployment scenario.
## 4 Results and Discussion
### Performance Evaluation on Binary Dataset  
We validated the model using another standard, publically available dataset [47] that comprises 250 BC pictures, 100 benign and 150 malignant, in order to further examine the performance and generalizability of the proposed customized framework and other pretrain models. We used 70% of around 175 of the images for training and the remaining 30% for testing. Table 6 presents the performance of all pretrain models and proposed model for BC detection and classification and shows that all the models achieved good results except AlexNet. The results of DL models were analyzed and assessed using four assessment metrics, i.e., accuracy, precision, recall and F1-Score. It can be witnessed that the ResNet-18 achieves the highest accuracy of 99.50% and the Alexnet achieves the lowest accuracy of 97.40%. While the suggested model achieves the second-highest Accuracy of 99.63.%.The F1-Score provides an overall assessment of the classifier's robustness. The greater the score of F1-score, the better the classifier's performance. The maximum F1-score value is 100% for the ResNet-50 model, while the lowest F1-score value is 97.30%percent for the AlexNet model. Table 8 represent the experimental results on binary BC dataset. Additionally in future study, By training and evaluating the suggested technique on tasks such as identifying COVID-19 from chest radiograph images, brain Tumours detection, mask removal, pest detection, and forecasting heart diseases, we want to find and replicate its performance.
## Glossary/Nomenclature/Abbreviations  
ANN Artificial Neural Networks AI Artificial Intelligence ML Machine Learning IoT Internet of Things MSE Mean square error PV Photovoltaic RCI Reliability Centred Maintenance RE Renewable Energy SVM Support Vector Machines Objective: A IMEP (Ideal models for energy production forecasting) B MFM Models incorporating meteorological forecasting C ARAM (Asset reliability assessment models) Methodology: D Neural Network E Comparison Models F Others Parameters: T Temperature D Date H Humidity WH Work Hour SR Solar Radiation WS Wind Speed RF Rainfall HD Hydrometric Data WP Water Pressure O Others
# Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute
## 2 Background  
### Imaging Model  
The most commonly used imaging model for CCC is the one under the assumption of Lambertian reflection [9]. According to this model, the camera's raw intensity response \(f_{c}(\mathbf{x})\) is given by [9; 16]:  
\[f_{c}(\mathbf{x})=\int_{\Omega}I(\lambda,\mathbf{x})R(\lambda,\mathbf{x})\rho_ {c}(\lambda)d\lambda, \tag{1}\]  
where \(c\) is a RGB color channel, \(c\in\{R,G,B\}\), \(\mathbf{x}\) is a pixel location of the given image, \(\lambda\) is the wavelength of the light, \(\Omega\) is the spectrum range of the visible light, \(\lambda\in\Omega\), \(I(\lambda,\mathbf{x})\) denotes the spectral distribution of light source, \(R(\lambda,\mathbf{x})\) denotes the surface reflectance of imaging objects, and \(\rho_{c}(\lambda)\) denotes the camera sensitivity function of color channel \(c\).  
To make the problem straightforward, it is commonly assumed the image is uniformly illuminated by a single light source. Considering the case that the image scene is an ideal white surface, it yields \(R(\lambda,\mathbf{x})=1\), then the observed illuminant color \(\hat{\ell}=[\hat{\ell}_{R},\hat{\ell}_{G},\hat{\ell}_{B}]^{T}\) will be obtained as:  
\[\hat{\ell}=\int_{\Omega}I(\lambda)\rho_{c}(\lambda)d\lambda. \tag{2}\]Once we know \(\hat{\ell}\), the image can be successfully chromatically adapted under the help of von Kries diagonal transformation [3]. However, with only image pixel responses \(f_{c}\) given, but both \(I(\lambda)\) and \(\rho_{c}(\lambda)\) unknown, the estimation of \(\hat{\ell}\) is an under-determined issue, which needs additional assumptions to be taken.