esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Shake Table Model Tests
### Results and Analysis
#### 3.4.4 Discussion  
Based on the aforementioned test results, it was apparent that strain, earth pressure, and acceleration were slightly larger along the fault section and its vicinity under the excitation of three waveforms. In most cases, the index amplification effect was most pronounced at the interface of the fault zone surrounding rock. This demonstrated that the presence of a fault zone had a significant amplification effect on the acceleration, principal stress, and lining stress of the tunnel lining structure. The amplification effect extended a certain distance from the boundary surface of the surrounding rock of the fault zone to the complete surrounding rock section, with the maximum extension being 10 m. It was advisable to extend the minimum length of seismic fortification at both ends of the fault section by 10 to 20 m beyond the specifications outlined for tunnels with the recommended section in the Chinese industry standard (JTG D70-2004). Furthermore, after the installation of seismic joints and shock absorption layers, there were alterations in the positions of the maximum seismic response peaks at the fault interface sections. To comprehensively analyze the shock absorption effect of surrounding rock grouting, seismic joints, and shock absorption layer, the maximum reduction ratio of strain, earth pressure, and acceleration peak values for each seismic measure was summarized in Table 4. In general, the trends in anti-vibration effects across various waveform excitations remained largely consistent, affirming the reliability of the model test outcomes. A comprehensive analysis revealed that the reinforcement of surrounding rock through grouting, the installation of seismic joints, and the addition of a shock absorption layer had noticeable seismic effects. Following the grouting reinforcement, the reduction ratios of hoop strain, longitudinal strain, earth pressure, peak acceleration value in the X direction, and peak acceleration value in the Y direction were \(-56.3\%\), \(-48.5\%\), \(-38.5\%\), \(-27.9\%\), and \(-42.6\%\), respectively. Subsequently, after the installation of seismic joints, the reduction ratios of hoop strain, longitudinal strain, earth pressure, X acceleration peak, and Y acceleration peak were \(-40.2\%\), \(-32.4\%\), \(-16.3\%\), \(-18.8\%\), and \(-11.0\%\), respectively. Finally, with the addition of the shock absorption layer, the reduction ratios of hoop strain, longitudinal strain, earth pressure, X acceleration peak, and Y acceleration peak were \(-34.3\%\), \(-29.7\%\)
## 2 Materials and Methods
### Risk Exposure  
The risk exposure study of genotoxic and carcinogenic compounds such as aflatoxin M\({}_{1}\) is approached based on the Margin of Exposure (MOEs), corresponding to the ratio between benchmark dose lower limit (BMDL) and the estimated daily exposure. A specific BMDL is not available for AFM\({}_{1}\), thus the BMDL established for AFB\({}_{1}\) (0.4 \(\upmu\)g/kg or 400 ng/kg body weight per day) was used in this study applying a potency factor of 0.1. MOE values lower than the threshold set at 10,000 are considered of concern [39; 40; 41]. Regarding milk consumption, the reference data were taken from the INRAN-SCAI 2005-06 Italian National surveys on food consumption provided to EFSA and elaborated taking into account 65 kg as reference body weight, and the upper boundary that is 95 th percentile (P95) [39; 42; 43]. The following population subgroups were considered: children (3-9 years), adolescents (10-17 years), adults (18-64 years), and elderly (65-97 years) (females + males, whole population). Regarding scenario, the high exposure was evaluated using the mean concentration of non-complaint samples as the reference value; moreover, a very interesting and useful parameter, named \"most-likely exposure\", already proposed elsewhere [44] was  
\begin{table}
\begin{tabular}{c c c c} \hline
**Time (min)** & **Water\%** & **Acetonitrile\%** & **Methanol\%** \\ \hline
0.0 & 55 & 40 & 5 \\
6.0 & 55 & 40 & 5 \\
7.0 & 15 & 80 & 5 \\
10.0 & 15 & 80 & 5 \\
11.0 & 55 & 40 & 5 \\
17.0 & 55 & 40 & 5 \\ \hline \end{tabular}
\end{table}
Table 1: Optimized elution gradient of HPLC/FLD method.  
Figure 3: Chromatogram of a contaminated milk sample (AFM\({}_{1}\) = 0.158 \(\upmu\)g/kg).  
elaborated, considering the overall mean contamination. In this case, a concentration equal to the LOD of ELISA method (0.005 ug/kg) was assigned to samples with \"not detectable\" amount of AFM\({}_{1}\). This is a protective measure for both human health and the environment proposed by the Italian Institute of Health and known as \"upper-bound\" approach [45]. The elaborations were made from both raw and processed milk samples regarding \"most-likely exposure\" scenario, and for all samples relating to \"high exposure\" scenario.
## 2 Data
### Datasets for Evaluating Smoke Detection  
#### 2.6.1 VIIRS Aerosol Products  
The NOAA S-NPP Data Exploration (NDE) version of VIIRS level 2 aerosol products, including aerosol optical depth (AOD) and aerosol detection products (ADP) ([[https://www.avl.class.noaa.gov/saa/products/](https://www.avl.class.noaa.gov/saa/products/)]([https://www.avl.class.noaa.gov/saa/products/](https://www.avl.class.noaa.gov/saa/products/))), were used to evaluate the SSDA smoke detection results. The VIIRS AOD product provides aerosol optical depth at 0.55 \(\upmu\)m, defined as the vertically-integrated column total extinction at the wavelength of 0.55 \(\upmu\)m [45]. This product is derived based on the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model [45].  
The VIIRS ADP product identifies VIIRS pixels as clear, smoke, or dust categories during daytime [28]. Two algorithms, including deep-blue based algorithm and IR-visible based algorithm, have been developed for operational smoke detection in the ADP product [28]. Specifically, the deep blue algorithm detects smoke by using the absorbing AI and a dust smoke discrimination index (DSDI) derived from three TOA reflectance bands: 0.41, 0.44, and 2.2 \(\upmu\)m. AI generally increases as absorbing aerosol loadings (including smoke and dust) increase. The DSDI value of dust is typically greater than that of smoke since the 2.2 \(\upmu\)m band is sensitive to the existence of dust but not smoke. Therefore, the combined use of absorption AI and DSDI can help distinguish smoke from dust and also  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region ID** & **Region of Interest** & **Acquisition Date** & \begin{tabular}{c} **Acquisition** \\ **Time (UTC)** \\ \end{tabular} & **Sample Types** \\ \hline a & West North America & 20180820 & 21:27 & \begin{tabular}{c} Smoke, cloud, \\ bright surface \\ \end{tabular} \\ b & East North America & 20190319 & 18:48 & \begin{tabular}{c} Smoke, cloud, \\ bright surface \\ \end{tabular} \\ c & South America & 20160912 & 17:08 & \begin{tabular}{c} Smoke, cloud, \\ bright surface \\ \end{tabular} \\ d & Africa & 20160917 & 12:10 & \begin{tabular}{c} Smoke, cloud, \\ bright surface \\ \end{tabular} \\ e & Indonesia & 20160828 & 06:43 & \begin{tabular}{c} Smoke, cloud \\ \end{tabular} \\ f & Siberia & 20170807 & 04:26 & \
\begin{tabular}{c} Smoke, cloud \\ \end{tabular} \\ g & Australia & 20180917 & 03:54 &
\begin{tabular}{c} Smoke, bright \\ surface \\ \end{tabular} \\  & Sahara & 20180810 & 12:43 & Dust \\  & Sahara & 20180818 & 13:36 & Dust \\ \hline \hline \end{tabular} Note: the region ID is corresponding to that in Figure 1. The Sahara region is not shown in Figure 1 because it is not a fire-prone region and we selected only dust samples from this region. The bright surface is mainly bare soil.  
\end{table}
Table 2: The acquisition locations and times of VIIRS imagery for selecting training samples.  
clear land. Since the deep blue algorithm does not work well in some surface reflectance combinations, smoke detections from the IR-visible based algorithm are also provided in the ADP product.
## 2 Method
### Simulation Method  
To validate the accuracy of the analytical solutions presented in the previous sections, a comparison and verification with numerical simulations will be conducted. For the single-cell case, the energy conservation equations (Equations (1)-(5)), thermal decomposition reaction equations, and the heating/cooling equations are directly solved numerically using an explicit method. For the multiple-cell case, the one-dimensional energy conservation equation (Equation (24)), along with the thermal decomposition reactions andheating/cooling equations for each cell, are evaluated. In both cases, the stage-based division introduced in the analytical solutions is not applied. The parameters used in the calculations are based on typical cathode materials (NCM) and anode materials (graphite), with values referenced from previous studies (summarized in Tables 1-3). The numerical simulations have been performed using in-house MATLAB code (MATLAB 2022a).
# A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM
## 6 Conclusions  
Two novel methods for diagnosing rolling bearing faults have been proposed, MFO-optimized VMD and DE-OSELM. The objective of these methods are to tackle the challenge of signal degradation, caused by environmental noise, which presents difficulties in identifying subtle fault features. The method presented in this paper presents a new idea for fault diagnosis of rolling bearings.  
1. The original MFO algorithm is subject to low accuracy and slow convergent speed, probably due to falling into the local optimal solution. This issue is solved through the proposed dynamic adaptive weight factor and crossover operator, which significantly improves the algorithm's global optimization ability.
2. The traditional VMD decomposition method faces a challenge of modal aliasing due to suboptimal selection of critical parameters, including the number of decomposition layers and quadratic penalty factor. To address this problem, this study introduces an improved MFO algorithm, utilizing the Mean Absolute Envelope Error (MAEE) as the fitness function, to adaptive select these two parameters and effectively improve the quality of VMD decomposition.
3. False IMF components generated by VMD decomposition are removed using the EWCS index. The real components containing fault feature information are subsequently extracted.
4. This study proposes a fault diagnosis method that utilizes MFO-optimized VMD and DE-OSELM. Numerical analysis and field test data analysis were conducted to verify the effectiveness and feasibility of the proposed method. Our results demonstrate that the proposed approach displays superior anti-noise performance and accuracy in fault identification when compared to other commonly used diagnostic techniques.  
Conceptualization, Y.J. and J.S.; methodology, Z.S. and C.X.; software, C.T.; validation, M.H. and L.Z.; resources, Y.J. and J.W.; writing--original draft preparation, C.X.; writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant 51405449, Grant 51575497) and the Zhejiang Provincial Natural Science Foundation of China under Grant No. LZ22E050001.  
Not applicable.  
This research used the published rolling bearing datasets of Western Reserve University. The access location of the datasets is [[https://engineering.case.edu/bearingdatacenter/](https://engineering.case.edu/bearingdatacenter/)]([https://engineering.case.edu/bearingdatacenter/](https://engineering.case.edu/bearingdatacenter/)) welcome (accessed on 12 November 2022).  
Many thanks to editors and reviewers for their comments and help.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fault Diagnosis Method** & **Accuracy Rate \%** \\ \hline EMD and OSELM & 92.70 \\ VMD and KNN & 97.13 \\ improved VMD and DE-OSELM & 98.80 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Fault classification accuracy of three methods.
# Space Debris In-Orbit Detection with Commercial LiDAR Sensors
## 2 Materials and Methods
### Maximum Distance from LiDAR to Object at Orbital Velocities
\[\text{Scanning Time per Orbit}=\frac{2\pi}{\Delta\sigma} \tag{4}\]  
In the example, with object 2 being just 50 m higher, the sensor for object 1 would catch up to object 2 in approximately 18.6 years. In Figure 3, there is a graphical representation of the time needed to scan the entire orbit as a function of the sensor's range or distance to the object. In this figure, you can clearly see something that is paradoxical from an Earth-based perspective: the total scan time decreases as the object is farther away. In other words, it takes longer to see the objects that are closer than the objects that are farther away from the sensor. At this point, we observe how the time required to scan a single orbit can become excessively long for the closest distances. Thus, the greater the sensor's range, the less time is needed to scan a complete orbit located at the farthest distance from the sensor. However, if the sensor has a wider range, this means that the host satellite must be larger to meet the necessary requirements for power, size, and weight to ensure the operability of the sensor. Therefore, another approach to reduce the scanning time of a complete orbit with a short range sensor is to deploy multiple sensors along the same orbit. By doing so, we effectively decrease the scanning time proportionally to the number of sensors scanning the orbit and distributed at an angular distance equivalent to the full orbit divided by the number of sensors. Given the orbit example, where we are examining 840 km altitude with a sensor range of 50 m easily installable on a small sat or CubeSat, the time required to scan a complete orbit would be 1.85 years if we use 10 sensors in a constellation configuration. In Figure 4, we show the scanning time for different low-Earth orbits (LEO) accessible to Cubesats, considering a constellation of 10 satellites for different detection ranges. In Figure 4, it can be observed that for a typical LEO altitude of 1000 km, the orbital scanning duration is less than one year only for detection ranges above 100 m, which is is suitable for ID sensor's 1 to 4, described in Figure 1.  
Figure 3: Time required to scan the complete orbit vs. the sensor’s detection range at 840 km altitude.
# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## 3 Dataset, Model and Parameters Configuration
### Parameters Configuration
#### 3.4.1 Adam Optimizer and Learning Rate  
The optimizer selected implements the Adam algorithm [30], acronym for Adaptive Moment Estimation, for the optimization of the descent of the stochastic gradient, applying the principles of the RMSProp and AdaGrad optimizers [31].  
_Gradient descent_ is a technique used for determining the global maximum and minimum points of a function of several variables. The stochastic approximation is applied when the cost function is too expensive at each iteration and breaks down the addends at each iteration into a sum. In the context of artificial neural networks, the descent of the gradient evaluates the model by using an input corresponding to a known output and corrects each parameter of the model in a proportional quantity (but of opposite sign) with respect to its contribution to the result error.  
The Adam algorithm is computationally efficient, requires little memory and is suitable for handling large data volumes or numbers of parameters. The fundamental parameter that [PERSON] receives is the learning rate, that is, a floating point value that indicates the size of the passage at each iteration and that influences the criterion for evaluating whether the newly acquired information is more important than the past information item. Therefore, the learning rate must be neither too high--otherwise, learning will jump above the minima of the loss function; nor too low--otherwise the convergence will happen too slowly, with the possibility of getting stuck in an undesired local minimum. During the training phase, it is advisable to adjust and adapt the learning rate in the right way as it does not change, but remains unchanged throughout the execution of the model. For the proposed model, the value of the learning rate was set at the default value recommended for the Adam algorithm, equal to \(10^{-3}\). Then, the optimal value was determined experimentally, by varying it in the range \([10^{-4}\div 10^{-2}]\), using a logarithmic spacing for the search process.  
From the relevant literature, the good robustness and speed of Adam, compared to other optimizers, emerges [32]. However, performance comparisons of various optimizers are strongly dependent on the specific workload and hyperparameter tuning. Hence, Adam has been selected after a preliminary experimental comparison with alternative optimizers such as SGD and Adagrad. In our analysis, Adam provided the best performance in a limited training time. Moreover, coherently with our results, the Adam optimizer is widely used in neural networks for Image Segmentation [17; 33; 34; 35], since it provides better results compared with some alternatives even with varying network architectures [33].
# Performance Limits of GNSS Code-based Precise Positioning: GPS, Galileo & Meta-Signals
## 7 Results: Time-Delay Estimation Accuracy Limit for Some Representative GNSS Signals
### GPS L1 C/A vs. GPS L1C and Galileo E1 Open Service  
First, we assessed the CRB for the GPS and Galileo civil signals broadcasted in the L1 band. Figure 4 summarizes the results for both time-delay CRB and MLE, the left panel for the GPS L1 C/A vs. GPS L1C comparison and the right panel for the GPS L1 C/A vs. Galileo E1B/E1C one. Notice that we used a sampling rate \(F_{s}=10\) MHz for the GPS L1 C/A, \(F_{s}=12\) MHz for both GPS L1C pilot and data components, and \(F_{s}=12\) MHz for both E1B and E1C components.  
A first look at the CRB results in Figure 4 clearly show the improvement on the time-delay estimation performance between the GPS L1C or Galileo E1 OS with respect to the GPS C/A. This is mainly due to the subcarrier modulation, that is, GPS L1C and Galileo E1 OS (data and pilot components) are modulated by BOC-type subcarriers, which results in narrower ACFs. In addition,these new L1 GNSS signals have longer PRN codes; consequently, for the same chip rate, the coherent integration time is longer, which translates to a higher SNR\({}_{\rm out}\) operation point. We also can see a slight improvement of the GPS L1C pilot component with respect to the GPS L1C data component. Again, this is mainly due to the ACF shape, which is narrower for the TMBOC modulation than for the BOC(1,1). Moreover, at the signal generation point, 75% of the power is dedicated to generate the pilot component of the GPS L1C signal, while only 25% is used to generate the GPS L1C data component. In the Galileo E1 OS case, we obtain the same results for both E1B and E1C signals.  
Comparing these results at a particular operation point, that is, SNR\({}_{\rm out}=25\) dB, we obtained the following time-delay standard deviation: (i) 2.24 m for the legacy GPS L1 C/A; (ii) 92 cm for the GPS L1C data component; (iii) 75 cm for the GPS L1C pilot component; (iv) 78 cm for the Galileo E1B data component; and (v) 77 cm for the Galileo E1C pilot component. Then, using these BOC-type subcarrier modulations, we gain around a factor 2.5 on the standard deviation, but still we are far from calling it a precise positioning solution.
## 4 Results and Discussion  
In this section, we evaluate the performance of the proposed adaptive approach to maximize the system throughput of the WLAN downlink MU-MIMO channel with the least cost in terms of a delay increase by considering the effects of channel conditions, heterogeneous traffic patterns, and number of stations. The simulation was conducted based on the IEEE 802.11 ac standard for the MAC and PHY layer specifications. Table 1 shows the detailed simulation parameter settings considered in the experiment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Symbol** & **Value** \\ \hline Number of antennas at AP & N\({}_{\text{Ant}}\) & 4 \\ \hline Number of stations & Num\({}_{\text{STA}}\) & 2–4 \\ \hline Average data frame length & LData & 100 bytes for VoIP, \\  & & 1000 bytes for video \\ \hline Traffic rate & & 10 Kbps for VoIP, \\  & & 100 Mbps for video \\ \hline Data rate & & 260 Mbps per STAs \\ \hline Minimum window size & CW-min & 15 \\ \hline Transmission time slots & Tslot & 16 microseconds \\ \hline Average A-MSDU length & & 11,454 Byte \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
## 4 Conclusions  
A simple mouse eye optical model at a wavelength 1.3 \(\mathrm{\SIUnitSymbolMicro m}\) is introduced for the first time. This model informed SAR-OCT system design. Extensive design analysis and performance characteristics are presented for murine retinal imaging system, including laser and fiber optic components, the 2D MEMS mirror for beam scanning, the value of corneal contact for mouse eye imaging, dispersion compensation, PME computational modeling, and system construction. Image processing techniques are described in relation to unique SAR-OCT demands, including intensity processing, angiography, and retinal segmentation. Finally, the results of the analysis are presented in murine eye. The reported SAR-OCT system is shown to be sensitive to angular scattering up to eight degrees off normal. Example B-scans of mouse retinas, angiography images, and retinal segmentation results are presented to demonstrate the capacity of SAR-OCT to maintain retinal  
Figure 16: The left image is segmented, and the boundaries on the right. The boundaries displayed are the vitreous-ILM, IPL-INL, INL-OPL, OPL-ONL, ONL-RPE boundaries. Scale bars represent 300 \(\mathrm{\SIUnitSymbolMicro m}\).  
Figure 17: \(\mathrm{L/H_{2}}\) is the ratio of Pathlengths 1 and 2. The segmented retinal layers can be superimposed on \(\mathrm{L/H_{2}}\) to sort the \(\mathrm{L/H_{2}}\) values of the retina into their corresponding layers. Scale bars represent 300 \(\mathrm{\SIUnitSymbolMicro m}\).  
imaging fidelity, similar to that of other reported devices, while also adding the contrast mechanism due to scattering angle. Using a SAR-OCT imaging device such as the one described here enables new methods of scattering angle analysis for animal disease models that affect the morphology of biological structures in the retina, such as Alzheimer's disease, glaucoma, and traumatic encephalopathy.  
Conceptualization, N.K., M.R.G., H.G.R.III, and T.E.M.; methodology, N.K., M.R.G., H.G.R.III, and T.E.M.; software, M.R.G. and A.S.R.; validation, M.R.G.; formal analysis, M.R.G.; investigation, M.R.G.; resources, H.G.R.III and T.E.M.; data curation, M.G. and H.G.R.III; writing--original draft preparation, M.R.G.; writing--review and editing, M.R.G., H.G.R.III, and T.E.M.; visualization, M.R.G.; supervision, H.R.G. and T.E.M.; project administration, H.G.R.III and T.E.M.; and funding acquisition, H.G.R.III and T.E.M.  
This research was supported by a grant from The University of Texas System Neuroscience and Neurotechnology Research Institute. The Cancer Prevention and Research Institute of Texas (DP150102). [PERSON] was supported by the NIH T32 training grant EB007507 and is currently supported as a Fulbright Scholar by the US Department of State, hosted at the University of Bahrain.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.