esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Plastic Waste on the Workability and Mechanical Behaviour of Asphal Concrete
## 4 Discussion
### Resistance to Permanent Deformation  
Regarding the resistance to permanent deformation of asphalt concrete, the findings show that the types of plastic waste used in this project have significant potential. Nevertheless, the achieved resistance to permanent deformation depends on plastic kind and content. Therefore, a fundamental mix design including performance testing to permanent deformation resistance should support the decision on the composition of the asphalt mixture with plastic waste. The findings of this project are aligned with the literature, which presents  
Figure 16: Black diagram derived from four-point bending tests at 20 \({}^{\circ}\)C.  
some conflicting results for different types of plastic waste. For example, [PERSON] et al. [63] observed increased permanent deformation by adding polyethylene (PE) and polypropylene (PP) to the asphalt mixture, whereas polystyrene (PS) reduced the resistance. [PERSON] et al. [64] and others [7, 22] also found improvements in rutting resistance with plastic film (LDPE) incorporation using a wheel-tracking test at 60 \({}^{\circ}\)C.
## 5 Conclusions  
This paper proposes a key distribution method based on the characteristics of the fiber channel, which uses the BER measured at both ends of the transmitter and receiver to generate a consistent key after quantization and coding. This scheme describes the process of the key agreement scheme with single end measurement in detail, and builds an experimental platform to verify the key performance. Finally, when the segment length is 1000 bits, the maximum achievable rate of the system is 10 kps, and the key consistency is higher than 98% of the security key. By setting different state base thresholds and analyzing different BER segment lengths, it can be seen that the higher the BER threshold, the higher the key generation rate, but the worse the consistency. When the segment length is longer, the key generation rate is smaller, and the consistency is higher. Therefore, we must appropriately adjust the threshold value and segment length value to make the consistency of generated keys and the coding rate meet the requirements of the transmission system.  
Conceptualization, X.W. and X.Y.; methodology, X.W.; software, X.Y., D.W. and L.Z.; validation, X.W., L.Z. and H.Z.; formal analysis, B.W.; investigation, B.W.; resources, B.L.; data curation, Z.Y.; writing--original draft preparation, X.W. and X.Y.; writing--review and editing, D.W.; visualization, D.W.; supervision, L.Z.; project administration, X.W.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the Special Fund of Advantageous and Characteristic disciplines (Group) of Hubei Province. This research work was supported in part by BIGC Project (Ec20201); The Initial Funding for the Doctoral Program of BIGC (27170122006). This research work was supported in part by Scientific Research Project of Fuyang Normal University (2022 KYQD0004) and Key scientific research project of Universities in 2021 (KJ2021A0897), Anhui Education Department. This research work was supported in part by Guiding project of Scientific Research Plan of Education Department of Hubei Province (B2022338), and by University Natural Science Research Project of Anhui Province (Grant No.: 2022 AH051338). This research work was supported in part by University-level Natural Youth Funding Project of West Anhui University (Project No.: WXZR201907).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** This study does not report any data.  
**Acknowledgments:** We acknowledge the support given by [PERSON] and [PERSON] during the project.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 9: (**a**) Key distribution rate, (**b**) key consistency rate, and (**c**) key bit 0/1 proportion as a function of BER sampling interval.
# Advancements in Glucose Monitoring: From Traditional Methods to Wearable Sensors
## 2 Invasive Methods
### Hexokinase Method  
The hexokinase method, often referred to as the photometric method, involves a sequence of enzymatic reactions. Initially, glucose reacts with hexokinase in the presence of adenosine triphosphate (ATP) and magnesium ions, resulting in the formation of glucose-6-phosphate (G6P) and adenosine diphosphate (ADP). In the subsequent step, G6P undergoes oxidation in the presence of glucose-6-phosphate dehydrogenase and nicotinamide adenine dinucleotide (NAD), producing 6-phosphogluconate and reduced nicotinamide adenine dinucleotide (NADH). The quantity of NADH generated is directly proportional to the glucose concentration in the sample (Figure 3a). NADH absorbs light at a wavelength of 340 nm, enabling glucose levels to be quantified using spectrophotometric methods based on light absorption [20; 41]. In a recent application, hexokinase (HKJI and II have been used to test metabolic rates associated with different subcellular distributions [42]. Effects of HKI and HKII on glucose utilization in CHO cells has been studied here, and their data suggested that HKI activity, which is primarily associated with mitochondria (Figure 3b(i-iv)), increases glucose metabolism, while increased HKII activity, which is found at least in part in the cytosol, slows glucose metabolism (Figure 3c(i-iv)) [42]. This may be an important mechanism by which HK's allow cells to adapt to changing metabolic conditions to maintain energy balance and avoid injury, thus allowing hexokinase to track their metabolic activity.  
Figure 3: (**a**) Hexokinase method for glucose measurement. (**b**) Subcellular distribution of HKI and HKII linked to YFP in CHO cells. (**i**) HKI-YFP demonstrates that HKI is bound to mitochondria with little or no fluorescence found in cytosol. (**ii**) Distribution of HKII-YFP showed both mitochondrial labeling and diffuse fluorescence in cytoplasm. (**iii**) HKI-YFP together with HKII (ratio 1:3) shows  
that HKI-YFP is attached to mitochondria and is not affected by excess HKII. (**iv**) HKII-YFP together with HKI (ratio 1:3) shows that HKII-YFP was no longer associated with mitochondria in presence of excess HKI. (**c**) Effects of HKI and HKII on glucose utilization in CHO cells. These data illustrate how overexpression of HKI (Panel B) and HKII (Panel C) affects glucose clearance measured in presence of CytoB. (**i**) Control signal. (**ii**) Compared to control, HKI increased rate of glucose clearance. (**iii**) In comparison with control, HKII is observed to have opposite effect and decreases this rate. (**iv**) Quantification of clearance time for HKI and HKII. Reprinted with permission from [42].
## 3 Hardware Design
### Introduction to the Main Controller Module  
This system adopts an STM32F407 VET6 MCU leverages ST's NVM technology and ART Accelerator(tm) as the main control chip (STMicroelectronics, Geneva, Switzerland). The core uses a 32-bit high-performance Arm(r) Cortex(r)-M4 processor (Arm, San Jose, CA, USA) with a clock frequency of up to 168 MHz; supports FPU (floating-point arithmetic)  
Figure 2: Block diagram of edge monitoring device hardware.  
and DSP instructions; has three low-power modes of sleep, stop, and standby; and has abundant on-chip peripheral resources. The STM32 minimum system part includes an ISP one-button download circuit, a crystal oscillator circuit, and an SD card storage circuit.
# Validation of Acoustic Emission Waveform Entropy as a Damage Identification Feature
## 3 Materials and Methods
### Experimental Evaluation Procedure  
#### 3.2.1 Performance Against Threshold  
The performance of AE entropy against threshold was evaluated with a realistic waveform that resembles damage in metal. The realistic waveform was generated through the pencil lead break (PLB) test. In this test, a pencil lead of 0.5 mm diameter and 3 mm length was broken by pressing it on a metal plate (12 m in length, 12 m in width and 5 mm in thickness). The configuration of the pencil lead was chosen in accordance with ASTM standard E976 [36]. Fracture of the lead resulted in the generation of an elastic wave in the metal plate. The frequency response of this elastic wave was unknown prior to the test. Therefore, in order to successfully capture the true nature of this elastic wave, an AE sensor (VS-900 [37]) sensitive to a wide range of frequencies was chosen. The AE sensor and its frequency sensitivity spectrum are shown in Figure 4. A single AE sensor was placed approximately 400 mm away from the point where the pencil lead was broken. Figure 5 shows the experimental setup. The  
Figure 3: Probability distribution of a typical AE waveform.
## 3 Experimental Results
### Coupled Two-Rod Waveguides, \(N=2\)  
In the case of two coupled rod waveguides, the coupling matrix \((M_{2\times 2})\) of Equation (1) and (3) takes the form: \(M_{2\times 2}=(1,-1;-1,1)\). Therefore, the two spatial mode eigen vectors corresponding to the eigen values \(\lambda_{1}=0\), and \(\lambda_{2}=2\), are: \(e_{1}=\frac{1}{\sqrt{2}}\!\!\left(\begin{array}{c}1\\ 1\end{array}\right)\), \(e_{2}=\frac{1}{\sqrt{2}}\!\!\left(\begin{array}{c}1\\ -1\end{array}\right)\), and the associated dispersion relations are \(\omega_{k}^{2}=(\beta k)^{2}\) and \(\omega_{k}^{2}=(\beta k)^{2}+2(\alpha)^{2}\). Here, we drive the coupled waveguides with amplitudes corresponding to the symmetric eigen vector \(e_{1}\) and anti-symmetric eigen vector \(e_{2}\). The transmission spectra of the two spatial eigen modes are shown in Figure 4a. It is clear from the figure that the transmission spectra depends on the stimulation types. The \(e_{2}\) anti-symmetric mode shows negligible transmission below around 20 kHz. Above 20 kHz, the transmission spectrum shows well defined resonances with non-uniform frequency spacing. The resonances appear to be spaced more closely at low frequency. Therefore, the spectra show a passing band with well-defined resonances. In addition, in this band the experimental conditions enable us to resolve resonances with frequencies very close to the cutoff frequency, which helps us to calculate the dispersion relation. Figure 4b shows the resultant dispersion relation for both bands. For the symmetric band, we use the technique discussed above and use \(k=1/\lambda=n/2L_{e}\). We find the speed of sound \(\beta=4502\) m/s. To calculate the dispersion relation of the \(e_{2}\) band, i.e., \(\omega_{k}^{2}=(\beta k)^{2}+2(\alpha)^{2}\), we need \((k,\omega_{k})\) i.e., the wave number and the associated resonant frequency. Similarly, to the case of the symmetric band, the wave numbers for the anti-symmetric band are multiples of \(2L_{e}\) (since finite length waveguides only support standing waves). Therefore, we label each resonance with the lowest resolvable frequency as being \(m=1\) and rewrite the dispersion relation as \(\omega_{k,m}^{2}=\beta^{2}\Delta k^{2}(m_{0}+m)^{2}+2(\alpha)^{2}\). The _Isqcurvefit_ function of MATLAB is used to determine \(\alpha\) and \(m_{0}\) from the frequency resonances of Figure 4a, and we obtain \(\alpha=14.31\) kHz and \(m_{0}=1\). The cutoff frequency of \(\left(\sqrt{2}\alpha=\right)20.24\) kHz is consistent with the measured transmission spectra of Figure 4a.  
Figure 3: (**a**) Transmission spectrum of the single rod waveguide \((N=1)\). The inset in (**a**) identifies the first two resonances at frequency \(<\)10 kHz. The transmission amplitude is in arbitrary units. (**b**) Band structure determined and calculated from (**a**). The asterisks in (**b**) are obtained from the resonances of (**a**) and the solid line associated with the asterisks is a fit to resonances with frequency less than 60 kHz.
# A Dataset of Pulsed Thermography for Automated Defect Depth Estimation
## 4 Data Labeling  
### Principal Component Thermography Image-Based Method (Manual Method)  
Figure 3 provides a visual comparison between an original infrared image and the result images generated by the three methods. In the original infrared frame, only four defects are discernible. However, after applying these methods, nine defects become visible, demonstrating a notable enhancement in defect contrast and detectability. Among these methods, the PCT images exhibited sufficient contrast for defects, making PCT the preferred choice for labeling tasks. Following the PCT process, one of the EOF images that contained the most-relevant information about the defects was selected for further analysis. The labeling of the defects' position can, therefore, be facilitated by examining this selected EOF image. Once the defect locations had been identified, the subsequent step involved integrating the depth information from the defect plan, as illustrated in Figure 4.  
Specifically, one or more than one PCT images with the highest contrast indicating potential defects were selected for further processing. Subsequently, red masks were then manually drawn over these images to highlight abnormal regions. If the process involved multiple images, they were combined to create the final composite mask.  
While this manual method enables precise defect localization through the careful adjustment of the masks to match abnormal regions, it is not without its drawbacks. Firstly, manually placing masks for each defect is a time-intensive task, especially when a large number of defects are present on a specimen. Moreover, accurately defining defect boundaries can be challenging, as the boundaries of abnormalities on the PCT image may not align perfectly with the actual defect boundaries. Secondly, some defects may not be visible in a single EOF image, often due to depth variations. In such cases, labeling must be performed on multiple EOF images, each of which is then merged to form the final composite mask. This additional step introduces complexity and potential inaccuracies in the labeling process.  
Figure 4: PCT images and labeled images, where different grayscale values represent different depths.  
Figure 3: Comparison of original infrared frame and resulting images generated by TSR, PPT, and PCT, respectively.
# A Hybrid Linear Quadratic Regulator Controller for Unmanned Free-Swimming Submersible
## 4 Stability Criterion of UFSS  
Before introducing controllers into the UFSS, the stability is evaluated using the Routh Hurwitz method, initially provided in [19]. To check the stability, the values of \(K_{1}\) (in Equations (3) and (10)) determines the stability of UFSS [20]. By using the denominator of Equation (3) we design the Routh Table 2 for pitch.  
From Routh Table 2 we get;  
\[\frac{K_{1}^{2}-21.216K_{1}-121.65}{11.22-0.25K_{1}}=0 \tag{13}\]  
Similarly, By this using its denominator Equation (10) we get Routh Table 3 for head.  
From Routh Table 3 we get;  
\[\frac{0.0625K_{1}^{2}-1.388K_{1}-6.95}{11.461-0.25K_{1}}=0 \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \(s^{4}\) & \(1\) & \(3.457\) & \(0.0416+0.109K_{1}\) \\ \(s^{3}\) & \(3.456\) & \(0.719+0.25K_{1}\) & \(0\) \\ \(s^{2}\) & \(\frac{11.22-0.25K_{1}}{3.456}\) & \(\frac{0.143+0.376K_{1}}{3.456}\) & \(0\) \\ \(s^{1}\) & \(\frac{K_{1}^{2}-21.216K_{1}-121.65}{11.22-0.25K_{1}}\) & \(0\) & \(0\) \\ \(s^{0}\) & \(\frac{0.143+0.376K_{1}}{3.456}\) & \(0\) & \(0\) \\ \hline \end{tabular}
\end{table}
Table 2: Routh table of pitch.  
Figure 4: Open loop block diagram of the head control system.  
Figure 5: Closed-loop representation of the head control system.  
\begin{table}
\begin{tabular}{c c c c} \hline \(s^{4}\) & \(1\) & \(3.465\) & \(0.109K_{1}\) \\ \(s^{3}\) & \(3.483\) & \(0.607+0.25K_{1}\) & \(0\) \\ \(s^{2}\) & \(\frac{11.46-0.25K_{1}}{3.483}\) & \(0.109K_{1}\) & \(0\) \\ \(s^{1}\) & \(0.0625K_{1}^{2}-1.388K_{1}-6.95\) & \(0\) & \(0\) \\ \(s^{0}\) & \(0.1146-0.25K_{1}\) & \(0\) & \(0\) \\ \(s^{0}\) & \(0.109K_{1}\) & \(0\) & \(0\) \\ \hline \end{tabular}
\end{table}
Table 3: Routh table of head.
# Solar Radiation Pressure Models for BeiDou-3 I2-S Satellite: Comparison and Augmentation
## 2 Characteristics and SRP Models for BeiDou-3 I2-S Satellite
### Box-Wing Model
Figure 1: One-way satellite laser ranging (SLR) residuals of (**a**) Extended CODE Orbit Model (ECOM1), (**b**) extended ECOM model (ECOM2), and (**c**) adjustable box-wing model (ABW) orbit solutions of BeiDou-3 I2-S satellite with respect to the \(\varepsilon\) angle. Figure 2: One-way SLR residuals of (**a**) ECOM1 and (**b**) ECOM2 orbit solutions of BeiDou-3 I2-S satellite with respect to the \(\mu\) angle. Figure 3 shows the daily OBD values in along-track, cross-track, and radial direction for these three solutions, and Table 2 lists the mean values. It is worth noticing that some days were excluded as orbit maneuvers were detected, e.g., 25 August and 20 September 2017. In general, the three solutions show almost same performance, although ABW has slightly better performance in along-track and radial direction. The large OBDs in cross-track direction might be related to some onboard tests in March 2017. The radial OBDs show \(\beta\) angle dependent variations, and became larger with an increase of \(\beta\) angle. In addition, the slightly worse OBDs have been identified in eclipse periods, in particular for ECOM2 solution in radial direction and ABW solution in along-track direction. For ABW solution, it is possibly caused by deviations between the nominal yaw attitude and the real one, as we did not model the yaw maneuvers. The above results demonstrated that better orbit consistency and accuracy could be achieved by use of ABW model. In addition, the \(\varepsilon\)-angle-dependent error can also be reduced by using ABW model, hence, it is possible to establish a better SRP model for the BeiDou-3 I2-S satellite based on ABW model, as we did for QZSS Michibiki mission [11].
## 4 Experimental Results
### Data Augmentation  
The psoriasis images were captured using smartphones or digital cameras in outpatient rooms. For performance evaluation, there were 52 unconstrained psoriasis images with spatial resolutions of 3000 \(\times\) 4000 pixels. These high-resolution images were partitioned to obtain 400 psoriasis images with 640 \(\times\) 480 pixels. Some unconstrained psoriasis images are shown in Figure 5. As shown in Figure 5, we can observe some factors. These psoriasis images have different backgrounds, the sizes of psoriasis regions are different, and the psoriasis regions have different severity levels and arbitrary boundaries. For example, the left image in the top row of Figure 5 has a background including clothes and a chair. Furthermore, these psoriasis regions have different sizes and severity levels, as shown in Figure 5. After image partition, these 400 images were divided into training, validation, and testing sets for model training and performance evaluation.  
As we know, the more data a machine learning-based scheme can access, the more powerful and robust the scheme can be. Here, data augmentation [31] was adopted to increase the variability of the input images for model training, enabling the proposed scheme to process unseen images well. The generic ways for augmenting images are to perform geometric transformation [7] and photometric change [7]. Here, some geometric transformations such as flipping and translation, two photometric changes, and noise insertion were adopted. A combination of these operations was also used for data augmentation. Thus, we obtained 38,400 augmented psoriasis images for model training. Figure 6 shows some examples of augmented psoriasis images. In Figure 6, the first column (Figure 6(a1,a2)) shows the original images and the second column (Figure 6(b1,b2)) shows the results of brightness adjustment. Figure 6(c1,c2) are the resulting images after combining three operations: Flipping, brightness increasing, and translation. Figure 6(d1,d2)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Input size & \(550\times 550\) \\ IOU\_THRESHOLD & 0.5 \\ EPOCH & 36 \\ Learning rate & 0.0001 \\ Momentum & 0.9 \\ Batch size & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of YOLACT++ for model training.  
Figure 5: Examples of psoriasis images for performance evaluation.  
are the resulting images after combining three operations: Flipping, brightness decreasing, and translation.
## 3 Numerical Simulations
Figure 10: CDF of MSE of the retrieved excitations using proposed method in the case \(Q=20\) and the standard \(\ell_{1}\) norm minimization; solid line: \(\ell_{1}\) norm minimization; dashed line: proposed method. Figure 9: CDF of MSE of the retrieved excitations using proposed method in the case \(Q=5\) and the standard \(\ell_{1}\) norm minimization; solid line: \(\ell_{1}\) norm minimization; dashed line: proposed method. is enough to have satisfactory result. Correspond to this is the fact that according to the computational burden displayed in Table 2, if we use moderate non-convex norm \(\ell_{0.5}\), the average computational time of proposed method is around 1.4 s. Although this time will be around 9.1 s if we use \(\ell_{0.1}\) norm as the associated non-convex feature strengthens, it still appears to be acceptable from the angle of significant reduction of acquisition time of far-field sampling data compared to the matrix method. The goal of the last experiment is to validate the effectiveness of the proposed method as a function of \(M\) ranging from 9 to 29. In Figures 11 and 12, the curves of probability of exact reconstruction of the recovered excitations are plotted considering strong non-convex norm (\(p=0.1\)) and moderate non-convex norm (\(p=0.5\)) with the number of random starting points \(Q=20\) and \(Q=5\), respectively. In the same figure the probability curve of the \(\ell_{p}\) (\(0<p<1\)) norm minimization using MCCA and the standard \(\ell_{1}\) norm minimization using CVX are also plotted in order to make comparisons. The estimation of the excitations is considered to be exact if MSE is lower than \(-30\) dB and the rate of success (i.e., the number of trials terminated with an MSE lower than \(-30\) dB over the total number of trials) is evaluated for each \(M\) considering 300 trials. The results demonstrate that the proposed method is effective to overcome the non-convex feature of \(\ell_{0.1}\) and \(\ell_{0.5}\) norm minimization and perform better than the standard \(\ell_{1}\) norm minimization using an acceptable number of random starting points. For example, supposing that a probability of success rate of 0.95 or higher, a number of measurements \(M=25\) is required for both \(\ell_{0.1}\) norm minimization and \(\ell_{0.5}\) norm minimization taking advantage of the proposed random perturbation technique, while \(\ell_{1}\) norm minimization requires at least \(M=27\) measurements, not to mention the worse performance of \(\ell_{p}\) (\(0<p<1\)) norm minimization using MCCA, which uses more than 29 measurements. These results have confirmed again the better performance of the proposed method. It is also worth noting that increasing the number of random starting points will not bring any significant improvements apart from a higher computational burden. Figure 11: Probability of exact reconstruction of the recovered excitations as a function of \(M\) using \(\ell_{0.1}\), \(\ell_{0.5}\) norm minimization and the standard \(\ell_{1}\) norm minimization; red line: \(\ell_{1}\) norm minimization; green and black line: \(\ell_{0.1}\) and \(\ell_{0.5}\) norm minimization.