esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study provided valuable insights into the dynamic behavior of lithium-ion batteries during both the charging and discharging processes. We observed that varying terminal voltages, sensitivity coefficients, and energy transfer coefficients significantly impact the SOC and discharging rates. Higher terminal charging voltages, as demonstrated by our results, can lead to faster SOC increases, making them advantageous for applications requiring rapid charging. However, it is crucial to strike a balance, as excessive values of these parameters can result in battery degradation, overheating, and energy wastage. On the other hand, in the discharging process, a higher terminal voltage leads to a slower discharging rate, which is beneficial for extending battery life. Adjusting sensitivity coefficients can provide a more stable voltage during discharging, essential for applications demanding a consistent power supply. Yet, a high discharging rate can significantly reduce battery life and lead to over-discharging. These findings underscore the necessity of carefully selecting the operating conditions to achieve a balance among fast charging, time efficiency, and battery longevity in practical applications. However, certain factors, such as internal resistance, temperature, charging/discharging behavior, and aging, which can have an impact on the SOC, were not taken into account in this study. Therefore, for greater accuracy of the model, the inclusion of these factors should be considered in the future.  
Conceptualization and methodology, P.K.N., I.R.M., A.G. and P.L.; software and validation, P.K.N., I.R.M., A.G. and P.L.; writing--original draft preparation, P.K.N. and I.R.M.; writing--review and editing, A.G. and P.L. All authors have read and agreed to the published version of the manuscript.  
This paper is based on DAAD project 57611107, which received funding from the Federal Ministry of Education and Research (BMBF).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The first two authors wish to thank DAAD and the Institute for Automation and Systems Engineering at Technische Universitat Ilmenau, Germany, for a visit during which part of this work was completed.  
Figure 3: (Color online.) The discharging profile of a lithium-ion battery as a function of time for different values of discharging rate \(\alpha_{2}\) for \(V_{n}=12.8\) V, \(U_{dis}=12\) V, \(\beta_{dis}=95\%\), and \(\mu_{100}=100\%\).
## 6 Conclusions  
The effect of fiber contents and curing time on \(q_{\text{u}}\) and \(q_{\text{t}}\) of BFUSM and BFCSM was analyzed on the basis of UCS tests and STS tests. Meanwhile, the developed MNLR models were established to predict the \(q_{\text{u}}\) and \(q_{\text{t}}\) of BFUSM and BFCSM. Furthermore, the fiber-reinforced mechanism was revealed by a SEM test. The most important conclusions were as follows:  
Figure 12: The microscopic mechanism of fiber reinforced: (**a**) SM0B, (**b**) SM2P0B, (**c**) SM0.2B and (**d**) SM2P0.2B.  
The UCS and STS of BFUSM and BFCSM both increased as the curing time increased, which reached the maximum value after curing for 28 days. The UCS and STS of BFCSM were obviously higher than that of BFUSM or cemented silty sand;
2. The UCS and STS of BFUSM and BFCSM both increased first and then decreased with the fiber contents, and both obtained the maximum value at 0.2% dosage, which was the optimal fiber content. BF had a more significant impact on STS than that on UCS;
3. The UCS and STS of BFUSM and BFCSM both had a good linear correlation and the correlation coefficients were 0.093 and 0.149, respectively. The interaction between fiber contents and curing time on the UCS and STS was significant. The developed MNLR models had high correlation coefficients and could be used to effectively predict the \(q_{\text{u}}\) and \(q_{\text{t}}\) of BFUSM and BFCSM;
4. The microscopic mechanism of fiber reinforced cemented silty sand was attributed to the physical action of fiber reinforcement and the chemical action of cement hydration reaction to form a fiber-cement-soil skeleton structure with greater bonding capacity between particles to enhance the strength.  
Conceptualization, S.S. and H.L.; methodology, S.S.; validation, H.L. and C.S.; formal analysis, S.S.; investigation, L.X. and Y.S.; resources, S.S. and C.S.; data curation, L.X. and Y.S.; writing--original draft preparation, S.S.; writing--review and editing, S.S. and C.S.; visualization, Y.S.; supervision, H.L. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Jilin Provincial Department of Education Scientific Research Project (JJKH20230348 KJ) and the Graduate Innovation Fund of Jilin University (101832020 CX155).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank the Jilin Provincial Department of Education Scientific Research Project (JJKH20230348 KJ) and the Graduate Innovation Fund of Jilin University (101832020 CX155). We appreciate Jilin University for providing the test instruments and equipment as well as the test site. The authors are grateful to the editor and the anonymous reviewers for their comments.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Bacterial Characterization by Raman Imaging  
Bacterial samples of _E. coli_ ATCC 8739 prepared as described in paragraph 2.3 were analyzed with a DXRTMxi confocal Raman microscope (Thermo Scientific). Measurements were performed with a laser excitation wavelength of 532 nm. Each spectrum was acquired at a laser power of 8 mW with an exposure time of 0.033 s for 100 scans using a confocal pinhole aperture of 50 \(\upmu\)m diameter. Maps of single bacterial cells were collected in the range 3300-650 cm\({}^{-1}\) with a spectral resolution of 5 cm\({}^{-1}\) using a 100\(\times\) objective with a numerical aperture of 0.9 (Figure 1). Before every measurement, calibration of the instrument was achieved with a neon lamp and white LED lamp [18] and verified with monocrystalline silicon and a polystyrene standard. The frequency uncertainty was determined by the grating resolution of 5 cm\({}^{-1}\); the intensity uncertainty was demonstrated to be lower than 5% using the polystyrene standard. A spectrum of monocrystalline silicon used as standard was taken and compared with a reference spectrum acquired in the same conditions to verify the success of the process (the 520 cm\({}^{-1}\) characteristic Raman band of silicon is taken into consideration for this procedure). Proper calibrations are beneficial in the scope of this work in order to identify chemical compounds using reference and literature data, and to compare measurements acquired in different conditions (which could arise, for example, from the changes in environmental and instrumental parameters), in particular for the comparisons between freeze-dried and air-dried cells. Of course, the total uncertainty includes systematic biases that can manifest between measurements as well as random contributions affecting each spectrum, hence the intra-image variability is lower than the total one: multivariate analysis on single Raman maps is not affected by the former, and is inherently robust against the latter. For the bacteria band assignment, a polynomial fit at 6 th order was applied to the average spectra of each sample in order to delete the fluorescence effects. Each spectral region of interest was analyzed, peaks were deconvolved in order to obtain the wavenumber of each maximum, and chemical meaning was assigned. Raman chemical images were collected with an image lateral resolution of 0.1 \(\upmu\)m, provided by the step size of the sample stage. Then, the resolution is greater than the laser spot-size of approximately 0.45 \(\upmu\)m.
# Improving Localization of Deep Inclusions in Time-Resolved Diffuse Optical Tomography
## 2 Methods
### Noise Correlations
In Figure 8, an example of the typical correlation matrices of standard, Mellin-Laplace, Gaussian and Tukey windows are shown. It is evident that, the greater the order of the standard moment, the more correlated it is with the rest of moments, see Figure 8a to get an intuitive idea. In the case of Mellin-Laplace window for a fixed \(p\) value, as the order \(n\) increases, its get more and more correlated with neighbour windows, see Figure 8b, although not as much as standard moments. However, for Gaussian and Tukey windows, the correlation can be minimized by separating them far enough; in the example given the Gaussian windows are not correlated with any other windows, because the windows did not overlap. The same thing could have been done with Tukey windows. In time-domain reconstruction, the heteroscedasticity [41] is present in the noise, that is, the noise at each time bin has different variance but they are not correlated with each other (this is due to photon noise physics). After applying overlapping windows, such as Mellin-Laplace or Fourier transform, non-zero covariance values will appear. Therefore, in these cases, Least Squares (LS) will not be a Best Linear Unbiased Estimator (BLUE) anymore. Nevertheless, Generalized Least Squares (GLS) (also known as Aitken's estimator) can be used instead, which is also a BLUE [41]. In theory, LS and GLS will reach the same solution for different noise realizations, however since for GLS the inverse of covariance matrix must be computed, some problems could arise if it is ill-posed. So, noise correlations should be avoided whenever possible.
## 2 Materials and Methods
### Model Building
#### 2.4.1 Construction of a Water Quality Prediction Model Based on the ARIMA Model  
The normalized time series exhibits consistent patterns regardless of the specific point in time at which it is observed. Three parameters, namely p, d, and q, are used to describe the three main components of an ARIMA model. There exist multiple approaches for transforming a non-smooth time series into a smooth time series, and one of these methods involves generating D through differencing. The ARIMA model can be extended to the ARIMA (p, d, q) model. The process of time-series analysis involves three main steps: model identification, model estimation, and model testing. It is crucial to select a model that is suitable for the specific time-series data at hand.  
In the autoregressive (AR) model, the variable of interest is predicted using past values of the variables. This model is suitable for data that exhibit smoothness. On the other hand, the moving average (MA) model represents a weighted average of past prediction errors, similar to a regression model. Equation (7) represents the AR model, while Equation (8) represents the MA model:  
\[y_{t}=c+\phi_{1}y_{t-1}+\phi_{2}y_{t-2}+\ldots+\phi_{p}y_{t-p}+\varepsilon_{t} \tag{6}\]  
\[y_{t}=c+\varepsilon_{t}+\phi_{1}\varepsilon_{t-1}+\phi_{2}\varepsilon_{t-2}+ \ldots+\phi_{q}y_{t-q} \tag{7}\]  
The identification of the ARIMA model is determined by analyzing the autocorrelation function (ACF) and partial autocorrelation function (PACF) for the difference order (d), autoregressive order (p), and moving average order (q). After identifying the appropriate ARIMA (p, d, q) model, it is imperative to estimate the chosen model and assess its suitability through testing. In this study, the quality of the statistical model is assessed using the Akaike Information Criterion (AIC) as a testing method (Equation (9)). The AIC is a valuable tool for comparing different estimated models, with lower values indicating better model quality. The time series model that has been developed has the capability to accurately forecast and identify anomalies by incorporating temporal features in the provided data. In this paper, p, d, and q are 0, 1, and 0, respectively.  
\[AIC=-2\log(L)+2(p+q+k+1) \tag{8}\]
# Evaluating the Effectiveness of Large Language Models in Converting Clinical Data to FHIR Format
## 5 Methodology
### Hypotheses
#### 5.1.3 Influence of Temperature  
Temperature settings control the randomness of the model's output. Higher temperatures allow the model more flexibility, potentially generating more diverse and numerous resources. Lower temperatures, on the other hand, restrict the model's variability, leading to more consistent and precise outputs that better adhere to FHIR standards.  
**H3.1:**: _Higher temperature will increase the number of resources that the model is generating compared to low temperature._  
**H3.2:**: _Lower temperature settings will yield more consistent and precise FHIR bundles._
# Design of an Energy Harvester Based on a Rubber Bearing Floating Slab Track
## 6 Conclusions  
For the rubber bearing floating slab track, a spherical energy harvester was designed, and the mechanical and electrical properties of the disk, cymbal, and spherical energy harvesters were compared by numerical simulation. Furthermore, the geometric parameters of the spherical energy harvester were optimized. The conclusions are as follows:  
1. Compared with the disk and cymbal energy harvester, the spherical energy harvester had better electrical and mechanical properties and showed less stress concentration. The output voltage of the spherical energy harvester was 11 times that of the disk one, and the total electricity output was 20% more than that of the cymbal energy harvester.
2. By calculating the output power of the energy harvesters under load match, we found that the output power of the spherical energy harvester was two orders of magnitude higher than that of the disk one and 53% higher than that of the cymbal one, being able to reach the microwatt level under impedance match.
3. The optimal dimensions of the spherical energy harvester were piezoelectric layer thickness h = 2 mm, piezoelectric layer radius r = 16 mm, spherical shell axis ratio I = 0.4, and spherical shell thickness t = 4 mm.
4. The spherical energy harvesters had better mechanical and electrical properties than the traditional energy harvesting structure, so it has broad prospects in the field of energy harvesting of rail transit and other energy harvesting fields.
5. This paper creatively proposed a new spherical energy harvesting structure and applied it to energy harvesting in the field of rail transit. In the future, it can also be applied to the field of energy harvesting under long-term heavy load such as road surface. The summary of abbreviations and symbols is shown in Appendix A.  
Conceptualization, S.Y.; methodology, H.J.; software, Z.L.; validation, J.Y.; formal analysis, D.Y.; investigation, S.Y.; resources, Z.L.; data curation, D.Y.; writing--original draft preparation, Z.L.; writing--review and editing, S.Y.; visualization, H.J.; supervision, J.Y.; project administration, H.J.; funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [the Natural Science Foundation of Jiangsu Province] grant number [BK20211173], [China Postdoctoral Science Foundation] grant number [2022M722958], and [Key R&D Program of Zhejiang] grant number [2023C03182].  
Figure 31: Optimal size model of the spherical energy harvester.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Unit** & **Description** \\ \hline H & 2 & mm & Piezoelectric layer thickness \\ R & 16 & mm & Piezoelectric layer radius \\ r\({}_{1}\) & 5 & mm & Vertical axis radius of the inner cavity of the spherical shell \\ r\({}_{2}\) & 12 & mm & Horizontal axis radius of the inner cavity of the spherical shell \\ I & 0.4 & 1 & Axis ratio \\ T & 4 & mm & Spherical shell thickness \\ \hline \hline \end{tabular}
\end{table}
Table 5: Optimal size parameters.
# Preliminary Global NO2 Retrieval from EMI-II Onboard GF5B/DQ1 and Comparison to Tropopmi
Meanwhile, the combination of multiple instruments with different overpass times can improve the temporal resolution compared to a single polar orbiting satellite. This combination can also improve the daily coverage data and diminish the problem of missing data caused by cloud interference. In addition, the observed spectra at different overpass times contain information about the diurnal variation in the detection target. Combined OMI and GOME-2 observations have been used to investigate the diurnal variation in global formaldehyde [20], to more accurately measure the near-surface NO\({}_{2}\)[21], and to reconstruct spatially and temporally coherent tropospheric NO\({}_{2}\)[22]. The EMIs onboard GF5B and DQ1 with different overpass times have the advantages of networking observation and can avoid errors caused by differences in instrument design specifications and calibrations. This ability must be evaluated. In summary, in this work, we focus on assessing the detection performance of the EMI-II for global NO\({}_{2}\) monitoring. In addition, combined GF5B and DQ1 observations were investigated and discussed. Section 2.1 briefly describes the EMI-II data and TROPOMI NO\({}_{2}\) data, which are used as a validation source. Section 2.2 discusses the DOAS-based retrieval method. Section 3 presents the derived NO\({}_{2}\) column results, an intercomparison between GF5B and DQ1, and the validation results with TROPOMI. Section 4 discusses the potential advantages of a dual-satellite network (e.g., increasing the frequency of observations and facilitating the study of the diurnal variation in NO\({}_{2}\)). Finally, Section 5 presents the conclusions.
# Effects of Glyphosate and Its Metabolite AMPA on Aquatic Organisms
## 2 Glyphosate (N-(phosphonomethyl)glycine)
### Environmental Fate  
Although a strong bond to the soil amount of GLY leaching up or runoff into surface or ground water is low [40], the aerial applications of glyphosate spray drifts from the ground and may enter into aquatic ecosystems (Figure 1) [41]. Height application rates, rainfall, and a flow route that does not include transportation of GLY through the soil from watersheds pose the highest risk to offsite transport of GLY [9]. For example, the United States Environmental Protection Agency [15] reports predicted GLY concentration from direct applications into a standard pond in 103.8-221.5 ug/L for daily peak,101.8-217.5 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L for 21-day average, and 98.4-210 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L for 60-day average. In water bodies, the glyphosate-based herbicide is usually detectable as glyphosate acid equivalent at the range level from 0.01 mg/L to 0.7 mg/L and has the worst impact on surface waters with the value of 1.7 mg/L [42; 43; 44]. [PERSON] et al. [9] reported concentration of GLY for Mississippi, Iowa, and France ranged from 0.03 to 73 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L, 0.02 to1.6 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L, and 1.9 to 4.7 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/L, approximately.  
This herbicide is unique for its ability to transform itself to the major metabolite AMPA due to microbial degradation [16; 40], and its physiochemical properties: water solubility 11.6 g/L at 25 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/C, low lipophilicity LogP \(<\)\(-\)3.2 at 20 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/C, dissociation constant of 2.3 at 25 \(\mathrm{\SIUnitSymbolMicro\text{g}}\)/C [40]. Under aerobic conditions, the halflife of GLY ranges from 1.8 to 109 days in soil and 14-518 days in water-sediment systems; however, in anaerobic water-sediment systems it ranges from 199 to 208 days [15]. Nevertheless, according to the published data the halflife of GLY ranges from 7 to 14 days [40].  
GLY contamination has emerged as a pressing issue their high-water solubility and extensive usage in the environment (especially in shallow water systems). Therefore, the exposure of nontarget aquatic organisms to these herbicides is a concern of ecotoxicologists [16; 37]. Many objects of ecotoxicologists studies are the toxicity of GLY to different species of fauna and flora and as the final food chain link human. For example, the recent review by [PERSON] [45] summarizes only the impact on marine invertebrates but compares glyphosate and its commercial formulations. On the other hand, we report published data about adverse effects of GLY on more aquatic species as a summarisation of harmful impact on aquatic biota.
## 2 Fundamentals
### LU Decomposition Methods
The factorisation consists of two main steps: first, a symbolic factorisation to determine the resulting column structure; second, a numeric factorisation with partial pivoting using the Gilbert-Peierls algorithm. 3. Refactorisation: Numeric factorisation of the whole matrix without partial pivoting reusing the scaling, permutation and pivoting choices can be conducted for different numerical values, but only for the same symbolic pattern. 4. Solution: The RHS is solved using forward/backward substitution on the entire matrix.
## Methods
### Fiber-Optic Interferometry and Signal Processing
The intensity of the interferometer output is converted to an electric current by means of an optical receiver (a photodetector):  
\[i=\varepsilon I_{0}\alpha\cos\left(\phi_{d}+\phi_{s}\sin\omega t\right), \tag{3}\]  
where \(\varepsilon\) is the sensitivity of the photodetector, \(\alpha\) represents the losses on the interferometer, \(I_{0}\) is the medium signal value, \(\phi_{d}\) is the changing phase shift, \(\phi_{s}\) is the duration of the amplitude, and \(\omega\) symbolizes the frequencies applied to the sensing arm of the interferometer [38; 39; 40; 41; 42]. The monitoring application (PC) for vehicle detection and frequency analysis measurement was developed in LabVIEW (2015, National Instruments, Austin, TX, USA). The sampling frequency was set to 1 kHz (this value is sufficient with regards to the measured range of 1-80 Hz stated in ISO 4866:1990 for rail transport). Measurement of the vibration spectrum (generated by a tram) is triggered by the tram detection algorithm. The algorithm (for the interferometric sensor) evaluates the overall RMS value of vibrations generated by a passing tram. The algorithm searches for the first amplitude spike (caused by the first axle of the passing tram) over the set level (minimum 3 dB), which is determined with respect to the measured background noise of the interferometric sensor. To improve the sensor's tolerance (immunity) of external influences (noise) occurring in the vicinity of the sensor, each segment of the measured data was converted from an absolute value to a relative level \(L_{noise}\), which relates to background noise as follows:  
\[L_{noise}=10\log\left(\frac{P_{noise}}{P_{0}}\right), \tag{4}\]  
Figure 2: A simplified block diagram of the evaluation unit (detection part). HP, high-pass. where \(P_{noise}\) is the current RMS value calculated from the signal segment on the filter's output and \(P_{0}\) is the average RMS noise value. The value of \(P_{0}\) was calculated as a moving average (MA) over period \(T\) according to:  
\[P_{0}=\frac{\sum_{t=0}^{T}\sqrt{\frac{1}{N}\sum_{i=0}^{N-1}x\left[n\right]^{2}} }{T}, \tag{5}\]  
where \(x\left[n\right]\) are individual samples in the data window with a size of \(N\). The duration of the data segment in which the calculations were performed depended on the number of samples acquired for the calculation of the \(P_{noise}\) RMS value. The default duration was 100 ms, with the algorithm showing more immunity to noise for larger data segments, however with a less accurate detection of the passage of time and an overall reduction in sensitivity. Once a passing tram is detected, the application begins recording the signal samples \(x_{n}\) from the interferometer. The length of recording \(N_{REC}\) is a fixed pre-set value, usually several seconds. The recorded signal is then used to calculate the frequency spectrum \(X_{k}\), where:  
\[X_{k}=\sum_{n=0}^{N_{REC}-1}x_{n}\left(\cos\left(-2\pi k\frac{n}{N_{REC}} \right)+j\sin\left(-2\pi k\frac{n}{N_{REC}}\right)\right),k\in Z. \tag{6}\]