esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sprint** & **Variable** & \begin{tabular}{c} **GPEXIt** \\ **Mean** \\ **(SD)** \\ \end{tabular} & \begin{tabular}{c} **TGs** \\ **Mean** \\ **(SD)** \\ \end{tabular} & \begin{tabular}{c} **ICC** \\ **(95 Cl)** \\ \end{tabular} & \begin{tabular}{c} **\% Bias** \\ **(SD)** \\ \end{tabular} & \
\begin{tabular}{c} **_p_** \\ **(SD)** \\ \end{tabular} &
\begin{tabular}{c} **ES** \\ **(SD)** \\ \end{tabular} \\ \hline CSL & t\({}_{0-10}\) (s) & 2.26 (0.13) & 1.99 (0.14) & 0.66 (0.32–0.84) & 13.10 (5.54) & \(<\)0.001 & 2.40 & very large \\  & t\({}_{0-20}\) (s) & 3.84 (0.24) & 3.51 (0.25) & 0.85 (0.67–0.93) & 8.79 (3.71) & \(<\)0.001 & 2.47 & very large \\  & t\({}_{0-30}\) (s) & 5.40 (0.33) & 5.01 (0.37) & 0.98 (0.78–0.96) & 7.65 (3.11) & \(<\)0.001 & 2.56 & very large \\  & MSS (m s\({}^{-1}\)) & 6.58 (0.43) & 6.73 (0.60) & 0.87 (0.70–0.94) & \(-\)2.19 (3.85) & 0.015 & \(-\)0.58 & small \\ \hline CSR & t\({}_{0-10}\) (s) & 2.28 (0.15) & 1.97 (0.14) & 0.71 (0.41–0.87) & 14.60 (5.33) & \(<\)0.001 & 2.78 & very large \\  & t\({}_{0-20}\) (s) & 3.86 (0.27) & 3.51 (0.26) & 0.89 (0.75–0.95) & 9.73 (3.50) & \(<\)0.001 & 2.90 & very large \\  & t\({}_{0-30}\) (s) & 5.45 (0.38) & 5.02 (0.39) & 0.93 (0.83–0.97) & 8.22 (2.89) & \(<\)0.001 & 3.00 & very large \\  & MSS (m s\({}^{-1}\)) & 6.50 (0.47) & 6.68 (0.54) & 0.96 (0.91–0.99) & \(-\)2.57 (2.09) & \(<\)0.001 & \(-\)1.29 & large \\ \hline LS & t\({}_{0-10}\) (s) & 2.21 (0.14) & 1.98 (0.12) & 0.70 (0.40–0.87) & 11.10 (4.66) & \(<\)0.001 & 2.32 & very large \\  & t\({}_{0-20}\) (s) & 3.61 (0.24) & 3.36 (0.21) & 0.89 (0.75–0.95) & 7.31 (2.96) & \(<\)0.001 & 2.42 & very large \\  & \({}_{0-30}\) (s) & 4.93 (0.37) & 4.66 (0.32) & 0.94 (0.86–0.98) & 5.52 (2.34) & \(<\)0.001 & 2.25 & very large \\  & MSS (m s\({}^{-1}\)) & 7.86 (0.74) & 7.94 (0.79) & 0.98 (0.94–0.99) & \(-\)0.95 (1.62) & 0.015 & \(-\)0.58 & small \\ \hline  & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Concurrent validity of the GPEXE It with respect to timing gates (TGs) for the measurement of linear and curvilinear short, intermediate, and long distance sprint times, and maximum sprinting speed (MSS). Figure 2: Bland–Altman plot showing differences between the GPEXE It and timing gates in split times of short distance (t\({}_{0-10}\)), intermediate distance (t\({}_{0-20}\)), long distance (t\({}_{0-30}\)), and maximum sprinting speed (MSS) for linear sprint. Each plot depicts absolute systematic bias, 95% limits of agreement (\(\pm\)1.96 standard deviation; dashed), and the regression line (solid). The strength of relationship (R\({}^{2}\)) is also presented in each plot.
###### Abstract  
In this work, we demonstrate In\({}_{0.52}\)Al\({}_{0.48}\)As top/backside-illuminated avalanche photodiodes (APD) with dual multiplication layers for high-speed and wide dynamic range performances. Our fabricated top-illuminated APDs, with a partially depleted p-type In\({}_{0.53}\)Ga\({}_{0.47}\)As absorber layer and thin In\({}_{0.52}\)Al\({}_{0.48}\)As dual multiplication (M-) layer (60 and 88 nm), exhibit a wide optical-to-electrical bandwidth (16 GHz) with high responsivity (2.5 A/W) under strong light illumination (around 1 mW). The measured bias dependent 3-dB O-E bandwidth was pinned at 16 GHz without any serious degradation near the saturation current output. To further increase the speed, we downscaled the active diameter and adopted a back-side illuminated structure with flip-chip bonding for batter optical alignment tolerance. A significant improvement in maximum bandwidth was demonstrated (25 versus 18 GHz). On the other hand, we adopted a thick dual M-layer (200 and 300 nm) and 2 um absorber layer in the APD design to circumvent the problem of serious bandwidth degradation under high gain (>100) and high-power operation which significantly enhanced the dynamic range. Due to dual M-layer, the carriers could be energized in the first M-layer then propagate to the second M-layer to trigger the avalanche process. In both cases, despite variation in thickness of the absorber and M-layer, the cascade avalanche process leads to values close to the ultra-high gain bandwidth product (GBP) of around 460 GHz with a responsivity of 0.4 and 1 A/W at unit gain for the thin and thick M-layer devices, respectively. We successfully achieved a good sensitivity of around \(-20.6\) dBm optical modulation amplitude (OMA) at a data rate of 25.78 Gb/s, by packaging the fabricated APDs (thin dual M-layer (60 and 88 nm) version) with a 25 Gb/s trans-impedance amplifier in a 100 Gb/s ROSA package. The results show that, the incorporation of a dual multiplication (M) layer structure in the APD opens a new window to obtaining the higher GBP in order to meet the requirements for high-speed transmission without the need of further downscaling the multiplication layer.  
avalanche photodiode photodiode photodetector
###### Abstract  
This work describes a new, low-cost and simple-to-use method for the determination of free biothiols in biological fluids. The developed method utilizes the interaction of biothiols with gold ions, previously anchored on micellar assemblies through electrostatic interactions with the hydrophilic headgroup of cationic surfactant micelles. Specifically, the reaction of \(\mathrm{AuCl_{4}}^{-}\) with the cationic surfactant cetyltrimethyl ammonium bromide (CTAB) produces an intense orange coloration, due to the ligand substitution reaction of the \(\mathrm{Br}^{-}\) for \(\mathrm{Cl}^{-}\) anions, followed by the coordination of the \(\mathrm{AuBr_{4}}^{-}\) anions on the micelle surface through electrostatic interactions. When biothiols are added to the solution, they complex with the gold ions and disrupt the \(\mathrm{AuBr_{4}}^{-}\)-CTAB complex, quenching the initial coloration and inducing a decrease in the light absorbance of the solution. Biothiols are assessed by monitoring their color quenching in an RGB color model, using a flatbed scanner operating in transmittance mode as an inexpensive microtiter plate photometer. The method was applied to determine the biothiol content in urine and blood plasma samples, with satisfactory recoveries (i.e., \(>\)67.3-123% using external calibration and 103.8-115% using standard addition calibration) and good reproducibility (RSD \(<\) 8.4%, \(n=3\)).  
biothiols; blood plasma; gold nanoparticles; colorimetric probes +
Footnote †: journal: Medical Physics Letters
## 5 Modeling of Existing Processes and Situations. Results and Discussion  
### Simulation of Changes in Air Buoyancy Coefficient C  
Buoyancy is a force acting on a body that is in a gaseous or liquid medium, directed opposite to the force of gravity, the latter arising due to Earth's gravity. In mass measurements, the effect of buoyancy is equal to the mass of the displaced medium corresponding to the volume occupied by the weighed body. The buoyancy coefficient, provided in formulas (3) and (4), reflects this change in body mass, taking into account the buoyancy of the steel reference weights used during the calibration of scales in the air. The influence of individual components on the buoyancy coefficient C was analyzed through modeling: when weighing oil or oil products in an unsealed container and applying formula (4) for buoyancy evaluation, the effects of air density \(\rho_{a}\), weight density \(\rho_{r}\), and the density of the oil product \(\rho\) were modeled.
## 5 Conclusions  
We performed a series of uniaxial ratcheting experiments on super duplex SAF2507 stainless steel and evaluated the effects of stress amplitude, mean stress, and pre-strain on ratcheting strain. In addition, a three-dimensional ratcheting strain boundary diagram containing stress amplitude, mean stress, and ratcheting strain rate was created. Numerical analysis was performed in Abaqus by incorporating an isotropically hardened AF-OW II model. The following conclusions were drawn:  
1. The ratcheting strain increased with increasing mean stress and stress amplitude for the same stress conditions. In addition, the ratcheting strain decreased as the pre-strain level increased.
2. With the help of experimental results for certain groups of mean stress and stress amplitude conditions, three-dimensional ratcheting boundary surface maps were drawn. With the experimental results of a finite group, multiple stress combinations and their corresponding ratcheting strain rates were obtained. Thus, experimental cost and time were saved.
3. Through a comprehensive comparison of the simulated and experimental values of the ratcheting strain of SAF2507 stainless steel under diverse loading scenarios, the proposed model demonstrated its ability to reasonably predict the ratcheting strain of SAF2507 stainless steel at room temperature.
4. Pre-strain showed an inhibitory effect on the ratcheting effect of SAF2507 stainless steel, which could be further investigated.  
Conceptualization, H.L. and X.C. ([PERSON]); investigation, H.L.; methodology, X.Z.; project administration, X.C. ([PERSON]); validation, H.L.; writing--original draft, H.L. and X.Z.; writing--review and editing, H.L. and X.C. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
## 2 Methods and Materials
### Sample Preparation and Testing Conditions  
The material used for the illustration of the capacity of the combination of all chosen in situ techniques with a developed setup was the commercial Inconel-625 alloy with the nominal composition NiCr21 Mo8.3 Nb3.5 (in wt.%) manufactured by Bohler Edelstahl GmbH & Co KG (Vienna, Austria). The material was annealed at 930 \({}^{\circ}\)C for 75 min and quenched in water, resulting in a reasonably uniform microstructure with \(\approx\)10 \(\upmu\)m grain size. The tensile specimen had a dog-bone geometry with a gauge length of 12 mm, 3 mm width, and 2 mm thickness. The compact tension specimens were made according to the ASTM E647-15e1 standard [90], with 50 mm width \(W\) and 4 mm thickness. All specimens were shaped by electric discharge machining.  
To facilitate infrared measurements, and improve the signal-to-noise ratio through the increase in the emissivity coefficient of the test object, the dog-bone tensile specimens were coated with a black matt paint (with the emissivity of 0.95) that does not peel off at large strains. The surface facing the DIC camera was additionally covered with white dots sprayed to create a speckle pattern.  
The compact tension (CT) specimen for FCG tests were ground down to FEPA #2000 grade. The surface observed by the IRT method was covered with the same black paint, and the RVI side was further treated depending on the purpose of the test. For deformation microstructure observations, the specimens were polished to 1 \(\upmu\)m, and optionally etched. For the DIC analysis, the surface was grounded with FEPA #4000 paper in one direction, and random scratches were made with grade #2000 and #4000 papers. If the spatial alignment between the cameras was required, reference lines were made on the surface of the specimen.  
Tensile tests were performed under velocity-controlled, nominally constant strain rate of \(1\times 10^{-2}\) s\({}^{-1}\). FCG tests were performed using the pre-cracked CT specimen cyclically loaded under the load-control mode with the maximum and minimum loads of 7000 N and 1400 N, respectively (the fatigue ratio \(R\) = 0.2).
# SMARTEN--A Sample-Based Approach towards Privacy-Friendly Data Refinement
## 5 Evaluation
### Performance Measurement
To address this issue, we have also implemented the processing engine using _Modin 0.13.3_ (see [[https://modin.readthedocs.io/](https://modin.readthedocs.io/)]([https://modin.readthedocs.io/](https://modin.readthedocs.io/)), accessed on 31 July 2022) and _Dask 2022.3.0_ (see [[https://docs.dask.org/](https://docs.dask.org/)]([https://docs.dask.org/](https://docs.dask.org/)), accessed on 31 July 2022) as its compute engine. Dask is a Python library that supports data partitioning and parallel computations. For this purpose, it provides a data structure called _Dask DataFrame_, which is internally composed of several Pandas DataFrames. The data contained in the Dask DataFrame are partitioned row-wise, and each partition is stored in a Pandas DataFrame. The Dask DataFrame has an identical API as the Pandas DataFrame, yet all commands are executed in parallel. The abstraction framework Modin takes care of the data distribution and assigns the tasks to the available processor cores. Due to these properties, switching the SMARTEN processing engine from Pandas to Modin is pretty straightforward. The results of the data throughput measurements are surprising at first glance. Apparently, the Modin-based implementation has the worst performance for all three operators. However, it has to be considered that Modin causes an organizational overhead due to the initial partitioning and distribution as well as the merging of the results. This overhead is amortized for large amounts of data, as can be seen for the 12 GB of base data. Here, the Modin-based implementation approximates the baseline even further. That is, with a combination of a Pandas-based implementation for small datasets and a Modin-based implementation for large datasets, SMARTEN has a throughput behavior which is asymptotically similar to the one of the baseline. With regard to the memory footprint, there are no noticeable differences for the selection and projection (see Figure 7b,d). However, for aggregation (see Figure 7f), the memory consumption is significantly higher in the Pandas-based implementation. This is due to the fact that Pandas loads all data into memory, while the other two implementations process the data group-based. Figure 7: Evaluation results regarding the overhead caused by SMARTEN. (**a**) Data throughput of the filter operator; (**b**) memory usage of the filter operator; (**c**) data throughput of the map operator; (**d**) memory usage of the map operator; (**e**) data throughput of the reduce operator; (**f**) memory usage of the reduce operator.
## 5 Conclusions  
The theoretical analysis and test results reveal that the vehicle with ESP can realize control over handling stability and that CDC can greatly improve the comfort of passengers under the action of sky-hook control. MFCDC-ESP combines these two functions organically and optimizes ride comfort. The control effect of the ESP can be improved, and extremely dangerous conditions can be effectively prevented when rollover occurs with MFCDC-ESP through the redistribution of the vertical force.  
MFCDC-ESP is superior to P-ESP in terms of ride comfort from the quantitative point of view; it also has an obviously different damping force and roll angle. In terms of stability control, MFCDC-ESP can effectively use the rear axle for load redistribution; the rear axle has a certain auxiliary lifting effect on the ESP. MFCDC-ESP has a significant advantage in preventing rollover (because P-ESP has no control strategy to inhibit rolling). Moreover, MFCDC-ESP has strong real-time sensitivity to the control state of the damping force of each wheel. MFCDC-ESP can maintain the optimized distribution of damping forces through its own compensation in the case of possible instability. it can even predict the critical stable state to some extent.  
In general, MFCDC-ESP vehicles can ensure the comfort of passengers under good driving conditions while having strong adaptability and control effect under various extreme working conditions. MFCDC-ESP perfectly meets the requirements of comfort, handling stability, and rollover prevention. In practical engineering simulation and application, fuzzy control can use human expert control experience to realize process automatic control for nonlinear and complex objects. Fuzzy control has a certain ability to judge and deal with uncertainty and it can realize the design of vehicle control systems which have a certain degree of intelligence.  
[PERSON] conceived the control method, completed the modeling, analyzed the results, and finished the manuscript. [PERSON] provided overall guidance for the study and managed the project. [PERSON] and [PERSON] collected the data, reviewed, and revised the paper. [PERSON] and [PERSON] give crucial suggestions about fuzzy continuous damping force control strategy and perform simulation experiments. [PERSON] and [PERSON] put forward the idea and debugged the model in MATLAB/Simulink. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Science and Technology Development Plan Program of Jilin Province (Grant No. 20200401112 GX) and Industry Independent Innovation Ability Special Fund Project of Jilin Province (Grant No. 2020C021-3).  
The authors declare no conflict of interest.
# An Analysis of Spatial Variation in Human Impact on Forest Ecological Functions
## 2 Materials and Methods
### Econometric Methods
#### 2.3.3 Geographically Weighted Regression Model  
The GWR model is unique in that it considers spatial non-stationarity, which leads to variable relationships that differ by location [41]. The formula is as follows:  
\[y_{i}=\beta_{0}(u_{i},v_{i})+\sum_{i=1}^{k}\beta_{j}(u_{i},v_{i})x_{ji}+e_{i} \tag{5}\]  
where \(y_{i}\) denotes the FEFI of county \(i\); \(x_{ji}\) represents the human activity indicator; \(k\) is the sample size; \(e_{i}\) is the random error term; \((u_{i},v_{i})\) is the spatial location of county \(i\); \(\beta_{0}(u_{i},v_{i})\) is the intercept at location \(i\); and \(\beta_{j}(u_{i},v_{i})\) is the local estimated coefficient of variable \(x_{ij}\).  
We employ the Gaussian function to determine the weight and the Akaike information criterion (AIC) method to determine the optimal bandwidth [42]. Moreover, the adjusted coefficient of determination \(\mathrm{R}^{2}_{\mathrm{adj}}\) and AIC are used to compare and confirm the accuracy of MLR and GWR models in this study [42]. Generally speaking, the model with a higher \(\mathrm{R}^{2}_{\mathrm{adj}}\) and a lower AIC has a better fit for the data, and if the difference between AIC values is greater than 3, there is a significant difference between the model and the actual data.
## 5 Results
### E\({}_{0}\) and Compensation Factors  
The published _E\({}_{0}\)_ values for RapidEye are 2003, 1824, 1571 and 1117 for the blue, green, red and NIR bands, respectively [88]. In Table 4, the exo-atmospheric irradiance coefficients for FASat-C (W\(\cdot\) m\({}^{-2}\cdot\) sr\({}^{-1}\cdot\) um\({}^{-1}\)) and the compensation factors are provided. For consistency, _E\({}_{0}\)_ values of RapidEye were obtained and compared against the given coefficients, and differences within the range 0.4-4.8 W\(\cdot\) m\({}^{-2}\cdot\) um\({}^{-1}\) were obtained, where the maximum discrepancy corresponds to the NIR band.  
According to SBAF values, for all FASat-C bands, but B3, values lower than unity show a higher amount of in-band reflectance in comparison to RapidEye, either for Cal or Eva compensation factors, the cause being the shape of the FASat-C spectral responses and the features of the Hyperion TOA reflectance spectra employed to derive the SBAF values. [PERSON] et al. [21] mentioned that SBAF is more sensitive to variations in slope or spikes present in hyperspectral TOA reflectance spectra rather than to magnitude disparities. This point may explain the low standard deviation values, especially for SBAF\({}_{\text{Cal}}\). The NIR band presents higher variability due to the presence of water vapor  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Band**} & \multirow{2}{*}{\(\mathbf{E_{0}}\)} & \multirow{2}{*}{**Illumination**} & \multirow{2}{*}{**SBAF\({}_{\text{Cal}}\)**} & \multicolumn{2}{c}{**St Dv**} & \multicolumn{2}{c}{**St Dv**} & \multirow{2}{*}{\(\mathbf{A_{\text{Eva}}}\)} \\  & & & & **SBAF\({}_{\text{Cal}}\)** & & & **SBAF\({}_{\text{Eva}}\)** & \\ \hline B1 & 1975.85 & 1.03172 & 0.96608 & 0.0009 & 0.99672 & 0.97882 & 0.0115 & 1.00987 \\ B2 & 1825.06 & 1.01715 & 0.99860 & 0.0001 & 1.01573 & 0.99971 & 0.0011 & 1.01686 \\ B3 & 1536.95 & 1.02042 & 1.00583 & 0.0003 & 1.02637 & 1.00516 & 0.0010 & 1.02569 \\ B4 & 1027.58 & 1.10630 & 0.97358 & 0.0016 & 1.07707 & 0.97219 & 0.0023 & 1.07553 \\ \hline \end{tabular}
\end{table}
Table 4: Parameters and compensation factors estimated for the cross-calibration of FASat-C.  
Figure 6: Highly homogeneous and reflective areas in Frenchman Flat are shown in blue. The samples for the cross-calibration were extracted from the highlighted areas (red boxes).  
absorption features [8; 21; 54; 61]. Nonetheless, for SBAF\({}_{\text{Cal}}\) and SBAF\({}_{\text{Eva}}\), this variability may also reveal a temporal variation in TOA reflectance due to the differences in observation, illumination or atmospheric conditions. Besides, in the case of SBAF\({}_{\text{Eva}}\), the differences could possibly be related to a seasonal component (e.g., phenology of vegetation and soil moisture variation).
## 2 Materials and Method
### Vibration Signal Capturing and Processing for TWCM System
#### 2.1.1 Experimental Set-Up  
To determine the condition of the tool and workpiece during the operation based on the vibration signals, four (4) cutting tool categories were used for the machining experiment. An indexable tungsten cutting tool with CCMT09T3034 carbide inserts was used for the turning operations. The first class of tools was brand-new tools, the second class was used good tools, and the third and fourth classes were rough and worn-out tools, respectively. A new tool has 100% of its useful life remaining, while a worn tool has zero (0%) life remaining. A good tool has a useful life of about 70% or more. A worn tool has less than 50% useful life, however, it can be used depending on the surface finish requirement of the job or product. A standard recommended value in defining a tool life criterion based on ISO 3685:1993 requires that cutting tool inserts with flank wear of 0.3 mm be discarded. [PERSON], [PERSON] [21] states that the ranges of flank wear values were divided into three classifications, normal wear (VB = 0-0.15 mm), medium wear (VB = 0.15-0.25 mm), and critical wear (VB = 0.25-0.35 mm), adhering to the ISO 3685:1993 standard. Similarly, this study classified tools using flank wear, where a new tool was presumed to have 0 flank wear, a good tool was taken to have the same range as that of normal wear (VB = 0-0.15 mm), a rough tool had medium wear (VB = 0.16-0.29 mm), and a worn tool had flank wear similar to the critical wear range (VB > 3.0 mm). As tools gradually move from one phase of useful life to another during the turning operation, it becomes very important to monitor the tool conditions, so as to optimize production by reducing the chances of nonconformities in the product quality. Bright carbon cylindrical steel workpiece materials, BS 970 080m40 and BS 970 070m55, were used for running the test, and the corresponding vibration during the  
Figure 1: Flow Chart of the Proposed Methodology.  
machining operation was recorded. The set-up of the back-end for remote collection of the captured data and installation of sensors for data capturing is illustrated in Figure 2.  
The experiment was repeated by capturing the vibration signals from the respective cutting tool class used for the machining process. The data were recorded as the captured vibration signals, the time intervals and labels that indicate the tool category used for the machining process. The vibration data may not be applied to analyses for decision making because they are time-series data, and because of the transient conditions that may have contaminated the precision of the signal. Hence, the data were processed using advanced signal analysis techniques, and a feed-forward neural network algorithm was employed to classify the signals into each condition class.