esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Description of the Non-Contact Measuring Device  
For non-contact measurement of roughness, we used a laser profilometer (Figure 3), which consisted of basic and additional parts. The basic parts included a massive aluminum support structure made of components with vertical positioning of the measuring tube (z axis), a work plate that was fixed to stepper motors with movement in the x and y axes, a laser beam source, a backlight, and a detector, which was a CMOS camera (STMicroelectronics, Geneve, Switzerland) (complementary metal oxide semiconductor). The camera was chosen as it had sufficient camera features and due to its immediate availability, as it is part of the laboratory equipment. Additional parts included a computer with LPM View control and evaluation software, and an image splitter.  
Placing the experimental sample in an anti-vibration material and eliminating vibrations from the surrounding environment is of great importance when measuring with a non-contact system. These measures can ensure the stability of the sample and minimize the possible effects of vibrations on the measured data. In this way, it is possible to achieve higher accuracy and reliability in the measurements of surface roughness.  
In our experiment, the light source was an exchangeable laser light (red with a wavelength of 635 nm, green with a wavelength of 520 nm, and blue with a wavelength of 445 nm) (Figure 4).  
Laser profilometry enables the measurement and evaluation of the surface roughness parameters of samples in line with ISO 4287 standards [31] (including Rq, Rv, Rz, Ra, and Rp). The obtained roughness data can be exported in CSV format, facilitating further processing of the experiments as raw data. In our analysis, we focused on evaluating the surface roughness parameters Ra and Rz using laser profilometry.  
When measured by non-contact laser profilometry, the surface of the sample was scanned in 45 lines (count of scan lines) with a step size of 110 \(\upmu\)m on the length of the scan line in the x-axis, which measured 4000 \(\upmu\)m.  
Figure 4: Laser lights used: (**a**) wavelength of light \(\lambda=635\) nm; (**b**) wavelength of light \(\lambda=520\) nm; (**c**) wavelength of light \(\lambda=445\) nm.  
Figure 3: The laser profilometer assembly. (1) Tube with the laser and camera; (2) LPM frame (z-axis); (3) PC with operating and evaluation software; (4) worktop with step motors that are movable in x and y axes.
## 3 Results and Discussion
### Freezing and Melting Characteristics
#### 3.2.2 Melting Process
The solidification rate reduces gradually during the process, especially near the end of process where the solid layer covers the whole of the capsule except a small region [33]. In both the freezing and melting experimentations, it was observed that the addition of nanoparticles enhanced the freezing and melting rates, and the maximum enhancement was observed with the 0.5% volume percentage of GnP.
# Development of a Metabolic Syndrome Prediction Model Using HOMA-IR and Multivariate Factors
## 2 Materials and Methods
### Variables  
#### 2.2.1 Analysis of MetS-Related Factors  
In this study, Diabetes Mellitus was defined as a fasting blood glucose level of >126 mg/dL. The diagnosis of MetS was based on the Adult Treatment Panel III (ATP III) criteria. According to these criteria, MetS is diagnosed when three or more of the following conditions are met:  
* Waist circumference: \(\geq\)102 cm in men and \(\geq\)88 cm in women;
* Hypertriglyceridemia: a blood triglyceride level of \(\geq\)150 mg/dL;
* Decreased high-density lipoprotein (HDL) cholesterol levels: HDL cholesterol levels of <40 mg/dL in men and <50 mg/dL in women;
* Hypertension: Blood pressure of \(\geq\)130/85 mmHg or treatment for hypertension;
* Hyperglycemia: a fasting blood glucose level of \(\geq\)100 mg/dL or treatment for Diabetes Mellitus.  
We assessed the presence of MetS using these criteria for subsequent data analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Excluded (_n_ = 1976)** & **Included (_n_ = 6134)** & \(p\) \\ \hline Men & 990 (50.1) & 3120 (50.8) & \\ Women & 986 (49.9) & 3014 (49.2) & 0.45 \\ Age (years) & 52.31 \(\pm\) 16.72 & 51.7 \(\pm\) 16.93 & 0.45 \\ Waist circumference (cm) & 83.52 \(\pm\) 13.34 & 84.08 \(\pm\) 10.44 & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of selected variables between excluded and included participants.  
Figure 1: Study population.
## 4 Model Analysis for UAV Multispectral Measurements
### Parameters Information Content Analysis  
Furthermore, the information of each parameter contained in the multispectral observation is quantified using the degrees of freedom of the signal (DFS) [56]. The DFS is defined as the partial derivative of the posterior estimate values with respect to state parameters and can be calculated by the following equation:  
\[\frac{\partial\hat{\mathbf{x}}}{\partial\mathbf{x}}=\left(J^{\mathrm{T}}S^{-1}_{\mathrm{ Obs}}J+S^{-1}_{\mathrm{pri}}\right)^{-1}J^{\mathrm{T}}S^{-1}_{\mathrm{Obs}}J \tag{9}\]  
Ideally, the state vector can be completely determined by measurements, that is, all the parameters can be obtained for observation data. In this case, the amount of information for each parameter is equal to 1 and the sum of the information of all parameters is equal to the number of parameters. However, subject to observation and systematic errors, the DFS is always less than 1 for each parameter. Therefore, the signal degrees of freedom can characterize the ability of observations and models to invert parameters. The closer the parameter's DFS is to 1, the more adequate information the observation contains about that parameter.  
To study the contribution of the phenotypic parameters to observation, Figure 6a shows the variation of all parameters information obtained by different wavelengths' observation; in other words, the number of parameters can be retrieved from single wavelength observation. In order to describe the observation capabilities of the P4 multispectral UAV, we carried the forward simulation of multispectral bands. Finally, the DFS of each parameter is calculated according to the information content analysis method, as shown in Figure 6b.  
From Figure 6a, the total DFS of different wavelengths between 400 and 1200 nm is less than 1, which indicated that the single-band observation has insufficient retrieval ability for multiple parameters. It is because the single band cannot obtain sufficient information. From Figure 6b, (1) the DFS of LAI, Cab, and N were 0.83, 0.71, and 0.58, respectively, significantly higher than Cm (DFS = 0.2). It indicated that the observation contains more effective information on these parameters and can further be better extracted. (2) The DFS of ALA, Car, and Psoil were lower than 0.2, indicating that these parameters are difficult to be retrieved by multispectral observation and can be used as a priori value in the inversion framework.
# Earthquake Prediction for the Duzce Province in the Marmara Region Using Artificial Intelligence
## 6 Conclusions and Future Work  
In this study, earthquake prediction and prediction rates were investigated using the RNN method for the Duzce Province, which is geographically located in the Marmara region of Turkey with GPS coordinates of 40\({}^{\circ}\)49\({}^{\prime}\)59\({}^{\prime\prime}\) N and 31\({}^{\circ}\)10\({}^{\prime}\)0\({}^{\prime\prime}\) E between the years 1990 and 2022. The dataset used included earthquake magnitude, depth, distance of the Moon from the Earth, b value, and d value. Although different methods are used in the studies, it can be seen that it is challenging to estimate 100% of the seismic data due to the irregular data structure.  
This study found that the magnitude values estimated via the dataset created according to the research results and the applied method are close to the actual earthquakes' magnitudes.  
It is expected that this study will be helpful for future earthquake prediction studies.  
Methodology, data curation, software, writing--original draft, validation, T.P.; Formal analysis, writing--review and editing, P.G., A.G. and A.A.H.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
## 3 Field Experiments
### Experimental Results  
In wireless communications, distance attenuation is unavoidable. In addition, mmWave communications are more strongly affected by distance attenuation than microwaves. As for the system design of access UAVs, the placement and other factors will vary greatly depending on how much coverage can be secured. In addition, if the number of UEs on the ground is large, multiple access UAVs are assumed to work near each other. However, since both will be communicating toward their associated ground terminals, earth-reflected wave interference will be an issue.  
Using the above equipment, three types of experiments were conducted in this paper to determine the distance and coverage characteristics and the extent to which earth-reflected wave interference occurs:  
* Direct Experiment This experiment examined changes in throughput by varying the height of the UAV against the fixed ground user.
* Coverage Measurement This experiment varied the ground user's location against a UAV BS hovering at a certain height to measure its coverage.
* Ground Reflection Experiment When there are two UAV BSs, this experiment investigated how much ground reflection affects the communications.  
Details about each experiment are explained as follows.
# A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap
## 2 Materials and Methods
### Description of the Evaluation Criteria for Stitching Algorithms  
Five criteria are used to evaluate each stitching algorithm in the four stitching test cases: visual inspection (qualitative criterion), stitching algorithm run time, stitching algorithm memory usage, mean repositioning error and stitching quality estimator (quantitative criteria).  
Only a few methods exist for evaluating stitching quality: mean height difference (or root mean square--_RMS_) between a known topography and a stitched version of this topography [17; 58; 59], or a size comparison along \(x\) and \(y\) axes between a stitched topography size and the true size of a known object [8; 60]. However, these methods have two major drawbacks: they required a reference surface of known dimensions (that sometimes is not available in day-to-day usage of stitching) and they do not take into account measurement apparatus uncertainties, especially along \(x\) and \(y\) axes. Our method solves these two problems. It only uses the stitched dataset and registration information obtained during the stitching procedure, and takes into account measurement apparatus and its uncertainties. However, this method cannot determine the source of uncertainties, nor give a quantitative value for each uncertainty type.
## 5 Conclusions  
This paper presents a novel signal decomposition method which is applied to AE signal processing. To eliminate the impact of noise interference or frequency dispersion, the LWM segmentation approach is used to search the dominant local maxima and the segmentation boundaries. Through the presented algorithm, useful components for damage analysis are decomposed. The decomposition results from the simulation and practical experiment show that the meaningful empirical modes, which are more reflective of damage characteristics, are obtained. Additionally, compared with the original EWT, components decomposed by the proposed LWM-EWT are much more reasonable for the AE signals. Since the frequency of AE signals can be influenced by different structures, further research work should concentrate on acquiring more signals from other structures, to validate the proposed method.  
Conceptualization, S.D. and M.Y.; Methodology, Q.W.; Software, S.D. and Q.W.; Validation, S.D., M.Y. and Z.L.; Formal Analysis, S.D.; Investigation, S.D.; Resources, S.D.; Data Curation, S.D.; Writing-Original Draft Preparation, S.D.; Writing-Review & Editing, S.D., Q.W. and Z.L.; Visualization, S.D.; Supervision, M.Y.; Project Administration, M.Y.; Funding Acquisition, M.Y.  
The authors acknowledged the financial support of the National Natural Science Foundation of China (NSFC) under the Grant No. 51375030, and the special funds for improving basic condition for central universities of China  
The authors declare no conflict of interest.  
Figure 19: The spectrum of separated signals of sensor 2.
###### Abstract  
Background: To employ optical coherence tomography angiography (OCTA) to evaluate the effects of hydroxychloroquine (HCQ) on the superficial capillary plexus (SCP) and deep retinal capillary plexus (DCP) in patients affected by rheumatoid arthritis (RA). Methods: Patients with recent diagnosis of \"definite RA\", based on 2010 Rheumatid Arthritis Classification Criteria, were included in a prospective, observational imaging study carried out by the G.B. Bietti Foundation between March 2019 and January 2020. Vessel density (VD) of SCP and DCP, central foveal thickness (CFT) and foveal avascular zone (FAZ) values were collected by OCTA. The primary outcome measure was the VD alteration of SCP and DCP in RA-patients after one year of HCQ treatment. Results: OCTA data analysis showed no statistically significant reduction in the mean VD of SCP and DCP, including the mean global area, central subfield, inner ring and temporal, superior, nasal, and inferior sectors, as well as in the mean CFT and FAZ areas. Conclusions: OCTA demonstrated no early change in the VD in the SCP and DCP, in RA-patients after one year of HCQ treatment. A longer monitoring period would more precisely establish the treatment's effect on the VD and its correlation with HCQ toxicity.  
deep retinal capillary plexus; optical coherence tomography angiography; rheumatoid arthritis; superficial retinal capillary plexus; hydroxychloroquine
# Production and Maintenance Planning for a Deteriorating System with Operation-Dependent Defectives
## 6 Sensitivity and Results Analysis
### Influence of the Backlog Cost  
The effect of the variation of the backlog cost on the production policy is presented in Figure 7, where we illustrate the production traces for two cost values, \(c^{-}=50\) and \(c^{-}=250\). From the obtained results, we realize that at reducing the backlog cost to \(c^{-}=50\) the production thresholds are less extended on the computational domain. Then at increasing the backlog cost to \(c^{-}=250\), the production thresholds increase as protections against backlog, since shortages are more penalized. The influence of the deterioration process on the production policy is evident, because the production thresholds increase as the machine ages. Additionally, we note that the machine operates at a reduced pace (i.e., zone \(u_{1}\) and \(u_{2}\)) when there is some inventory, and the maximum production rate is devoted just in case of backlog or to maintain some protection stock when the machine reaches higher levels of deterioration.  
Regarding the results of Figure 8, it is apparent that the backlog cost also influences the repair/major maintenance switching policy. For this cost parameter, we compared three cases, \(c^{-}=50\), \(100\) and \(200\). In analyzing such scenarios, we notice that when the backlog cost decreases to \(c^{-}=50\), major maintenance is less recommended, and so more minimal repairs are performed. If the backlog cost increases to \(c^{-}=100\), the major maintenance zone grows. Moreover, when the backlog cost is set to \(c^{-}=200\), even more major maintenance is recommended. This increment is explained because at increasing the backlog cost, the effects of deterioration on shortages and defectives are more penalized. Hence, maintenance options that serve to mitigate effectively the effects of such deterioration, (as major maintenance), are more recommended. With respect to the inventory cost, we notice that is has the opposite effects that the backlog cost.  
Figure 7: Sensitivity of the backlog cost on the production policy. (**a**) \(c^{-}=50\); (**b**) \(c^{-}=250\).
# Calculation Method for Electricity Price and Rebate Level in Demand Response Programs
## 5 Numerical Simulation Results
### Numerical Simulation Results for Price-Based DRs  
By using Equations (18)-(20), the optimal electricity prices and the acceptable conditions in the price-based DR were calculated as:  
\[p_{t}^{\prime  
Tables 4 and 5 summarize the calculation results in the case that the power suppliers set the DR target as a 1% decrease (Scenario 1) or increase (Scenario 2) of the standard electricity consumption. Figures 7 and 8 illustrate changes in the utility functions of the power suppliers and the consumers and those in the social welfare functions.  
In Table 4, the calculation results in scenario 1 satisfied with their acceptable conditions, and as a result, the optimal electricity prices were slightly increased (less than 1%) as compared to the standard electricity price (23.90 JPY/kWh). By contrast, as shown in Table 5, the power suppliers could not request a 1% increase in the electricity consumption in scenario 2, because the calculation results did not satisfy their acceptable conditions. In particular, the electricity consumption became higher than its annual average (103.94 kW) from 18:00 to 22:00, and therefore, \(\underline{p_{t}^{-}}\) exceeded the standard price. In the other periods, the power suppliers can request the DR cooperation, until the condition shown in Equation (38) is violated. With reference to Figure 2 we can understand that the calculated electricity prices changed inversely to the profile of electricity consumption in Table 4, while synchronously in Table 5. These results indicated that the consumers can reduce the power demand during the peak periods, and it becomes difficult in the off-peak periods. That is, the calculated prices reflected the controllability in the electricity consumption on each time slot.  
In Figures 7 and 8, there were no significant differences in the social welfare by the DR; however, we can confirm that the price-based DRs took burden on the consumers in Figure 7 or the suppliers in Figure 8. As displayed in Figure 9, the optimal electricity price became 25.01 JPY/kWh, and the increment of the price exceeded 1 JPY/kWh when the DR target was set to 7% of the actual electricity consumption. As for the reference, the optimal electricity price in scenario 2 is shown in Figure 10.  
Figure 8: Changes in each function by the 1% DR request in the day of the lowest electricity consumption. In this scenario, the power suppliers could not request the DR cooperation in the target period.  
Figure 9: Results on the typical time slots in scenario 1 (at 22:00).  
In the numerical simulation model, the authors made the operational cost function of the power suppliers relying on the fuel costs of thermal power units. However, in the actual operations, the other factors such as the surplus power of renewable energy sources have influences on the operational cost. If we reflect them appropriately in the model, the results in scenario 2 can be activated.  
From these results, the authors concluded that the authors' proposal functioned properly in the price-based DR.
# Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network
## 3 Methodology
Since each set of input data contains the variables of the previous 1-hour, 2-hour, and 3-hour passenger flows, we set s=1 as the parameter of time step. This shows that the previous passenger flows at time step (\(t\)-1), (\(t\)-2), and (\(t\)-3) are used as the input variables to forecast the passenger flow at time step t. It is worth noting that the dimension of the input vector \([X_{t}]\) is changed by multiplying the weight matrixes based on Equations (1)-(4). As a result, the vectors of \(i_{t},\ f_{t},O_{t},\ \widetilde{C}_{t},C_{t},\) and \(h_{t}\) have the same dimensions of \(a\) as the size of the hidden nodes and are not equal to the dimension of \(n\) in input vector \([X_{t}]\). At the output  
Figure 4: A cell in long short-term memory (LSTM). layer, all the hidden outputs are fully connected to obtain \(\widetilde{Y}_{t}\), whose dimension is returned to \(n\). \(\widetilde{Y}_{t}\) is the forecasting result for the passenger flow and \(Y_{t}\) is the actual value of the passenger flow given in the training dataset. This dataflow structure shows that the hourly passenger flow forecasting model can be transformed into a supervised learning problem using a deep LSTM_NN. In other forecast models, \(Y_{t}\) was defined as one of the input variables contained in input vector \([X_{t}]\)[35] to fit the model, or as \([Y_{t-k},Y_{t-(k-1)},\ldots Y_{t-1}]\), which were a series of previous k-hour passenger flows, to be used as the only input variables to fit the model [36]. In summary, different forecasting tasks should be constructed with the most suitable input variables to obtain the optimized results. Finally, we calculated the loss \(l(t)\) between \(Y_{t}\) and \(\widetilde{Y}_{t}\), where\(f\) refers to a loss function in Equation (7). Equation (8) was used to minimize the error between \(Y\) and \(\widetilde{Y}\) over an entire sequence of length \(T\) using one of the state-of-the-art optimization algorithms [37]. To deeply and rapidly extract the relationship between the input variables and passenger flow, the deep structural LSTM_NN was applied in the experiment. \[l(t)=f\Big{(}Y_{t}-\widetilde{Y}_{t}\Big{)} \tag{7}\]  
\[\text{Input gate}:\ i_{t}=\int_{1}(\omega_{t}\cdot[x_{t},h_{t-1}]+b_{i}) \tag{1}\]0