esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Compressive Strain Rate on Auxetic Foam
## 3 Results
### Young's Moduli  
Increasing the applied strain rate from 0.005 to 200 s\({}^{-1}\) approximately doubled Young's modulus in most cases (Figure 6), but almost tripled the unconverted open cell foam's Young's modulus (Figure 6c), with values reported in supplementary Table S1. Both closed cell foams exhibited quasi-static Young's moduli of \(\sim\)1 MPa (Figure 6d), as expected [62]. Increases in Young's modulus with applied strain rate were similar across the stress vs. strain (Figure 6a,b) and Young's modulus vs. strain rate relationships (Supplementary Figure S2). Standard deviations indicate differences between samples at the lowest strain rate were also relatively small (maximum standard error = 10% of mean Young's modulus, Figure 6c,d).  
Figure 5: (**a,b**) Mean Poisson’s ratios of (**a**) open cell foams; (**b**) closed cell foams at applied strain rates of 0.005 s\({}^{-1}\) and 200 s\({}^{-1}\), measured to 2% compression. Error bars show 1 standard deviation.  
Figure 6: (**a,b**) Stress vs. axial engineering strain at minimum and maximum strain rates, for samples with median stress at 10% compression of (**a**) open cell and (**b**) closed cell foam. (**c,d**) Mean Young’s moduli of (**c**) open cell foams and (**d**) closed cell foams at minimum and maximum applied strain rates, measured to 2% compression, error bars show 1 standard deviation. Tested axis labelled for each foam.
###### Abstract
Measurement campaigns were carried out with the UAV flying at different heights and speeds in three regions defined by the environments: (i) lake, (ii) Caatinga biome, and (iii) both lake and Caatinga. From the measured data, we characterize the large-scale attenuation (path loss and shadowing) and small-scale fading, comparing fading statistical distributions and using second-order statistics such as the level crossing rate (LCR) to characterize the Doppler scattering. The rest of the paper is organized as follows. Section 2 describes the materials used for the wireless channel measurements. We present the mathematical formulation and the measurement data processing in Section 3. The scenario where the measurements were obtained is depicted in Section 4. Section 5 discuss the results of our analysis. Finally, Section 6 proceeds with the conclusion of the paper.
# Synchrotron X-ray Microprobes: An Application on Ancient Ceramics
## 3 Results
### SR Based X-ray Methods Using Microprobes  
#### 3.2.1 \(\upmu\)XRF Maps  
\(\upmu\)XRF maps collected on representative samples of black gloss ceramics allowed to distinguish different productions. It also showed the usefulness of the cross-sectional set-up in data collection to detect enrichment/depletion in gloss vs. clay paste (Figure 1). The different productions examined were thus non-destructively discriminated based on microchemical tracers identified on the black gloss vs. the clay paste.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
The thin (-20 \(\upmu\)m) shiny, compact, deep black gloss proper of Attic production is characterized by a prominent Fe-based black-gloss, and a clay paste enriched in Ni and Cr. Laconian vessels are characterized by a very thin (about 10 \(\upmu\)m) matte black gloss characterized by Fe-rich composition and a clay paste rich in Rb and Ca. The thinner (-15 \(\upmu\)m), matte and brownish (even reddish) gloss typical of vessels from the Sicilian Greek colony of Gela is due to Fe-based black-gloss enriched in Rb and Mn, and a K-rich clay paste.  
South Italian colonial vessels (Chalcidian and probable Lacrian products) showed peculiar features. The matte and compact black/black-bluish gloss is in fact characterized by Fe- and Zn enrichment and a clay paste marked by Ni, Cr signature (less than in Attic products), while Rb, K, Mn in Chalcidian black gloss ceramics. Accordingly, previous mineralogical and geochemical investigations on reference groups assessed characteristics geochemical fingerprints [35; 37] for this production, providing criteria based on destructive methods useful to locate the products in the south-Italian Greek colonies (Strait of Messina area and Ionian cost) and discriminate them from Sicilian and Attic ones. However, bulk analysis could not trace a quite interesting chemical marker, which is also useful for ceramic technological studies. The SR based non-invasive investigation revealed this to be a Fe-Zn-rich black gloss.
## 2 Materials and Methods
### Data Processing and Statistical Analysis  
Using ITK-SNAP, the plaques were manually segmented in the intensity and the retardation data [39]. The resulting volumetric segmentation data were saved as binary files. Using the 3D Objects Counter plugin [40] in Fiji and the segmentation data of the plaques, their diameter, area and plaque density (in plaques per mm\({}^{3}\)) were quantified over the whole volume [37]. The mean plaque diameter in \(\upmu\)m and plaque density were also extracted by the 3D Objects Counter tool. For plaque diameters, the average of the horizontal and vertical diameter was calculated.  
The mean intensity and retardation signals (\(\upmu\)) of OCT image data were calculated for the plaques (\(\upmu\)p) and the surrounding brain parenchyma (\(\upmu\)BP) using the retardation averaging method 1 [41]. To estimate the signal levels in the brain parenchyma, the same amount of pixels used for the evaluation of the signal in the plaques was used. Values adjacent to the plaques at the same depth (located 10 \(\upmu\)m to the left in OCT tomograms) were chosen. Using a custom Matlab (Version R2015b, The MathWorks, Inc., Natick, MA, United States) code, the Weber contrast (WC) [42] was calculated in both contrast modalities for the plaques compared to the surrounding cortical gray matter:  
\[\text{WC}=\frac{\upmu_{\text{P}}-\upmu_{\text{BP}}}{\upmu_{\text{BP}}} \tag{1}\]  
WC was evaluated for the intensity data (Int) and for the retardation data (Ret) in linear scale based on the plaque segmentations performed in both the intensity data (Int\({}_{\text{Seg}}\)) and the retardation data (Ret\({}_{\text{Seg}}\)), resulting in four different WC analyses. The same evaluation was performed for the signal to noise ratio (SNR), where \(\upsigma_{\text{P}}\) and \(\upsigma_{\text{BP}}\) are the standard deviation of the signals in plaques and the brain parenchyma, respectively [42]:  
\[\text{SNR}=\frac{\upmu_{\text{P}}-\upmu_{\text{BP}}}{\sqrt{\upsigma_{\text{ P}}^{2}+\upsigma_{\text{BP}}^{2}}} \tag{2}\]  
Furthermore, the plaque overlap (i.e., the percentage of plaques segmented in both intensity and retardation images) was evaluated using Fiji. Finally, histograms and box plots were generated for visual representation. Student's t-tests were used to analyze statistical differences for equal mean values and Bonferroni correction was applied when evaluations on multiple groups were performed. The data are presented with their associated standard deviations, unless otherwise stated.  
For a direct comparison to histology, the number of amyloid-beta plaques in fields of view (FOV) of 500 \(\upmu\)m \(\times\) 500 \(\upmu\)m \(\times\) 2.5 \(\upmu\)m (corresponding to the thickness of a histological section) were quantified for OCT intensity images, PS-OCT retardation images and the corresponding histological section of one specimen. The dense neuritic and diffuse plaques in one histological slice were manually counted in five randomly chosen regions and the mean plaque diameter and the areal plaque density (in plaques per mm\({}^{2}\)) were evaluated.
## 4 Simulation Experiment and Analysis
### Performance Evaluation of Multipath Routing Algorithms
#### 4.1.3 Network Throughput  
The network throughput is measured by the service reception rate of the destination node. From the simulation data Figure 10, it can be seen that the throughput increases with the increase of the service packet transmission rate in both cases. However, the throughput performance of this algorithm proposed in this paper is significantly better than that of other algorithms.  
It can be seen from the above that this algorithm can obtain all the shortest paths from source nodes to other nodes, avoiding the limitation of only one shortest path, and is able to choose the best path according to the load of neighboring nodes and their links. It can  
Figure 8: Average business wait delay comparison chart.  
Figure 9: Packets loss comparison chart.  
improve link utilization, avoid data packet loss problems caused by link congestion or failure, and have a smaller wait delay and better network throughput performance.
## 3 Results
### Spectrophotometer Measurements and Bleaching Data Analysis for Each Treatment  
In this study, we utilized a spectrophotometer to evaluate the brightness of teeth before and after various treatments. The brightness measurements were expressed in terms of optical density (OD) and statistically analysed using a Student's \(t\)-test. The results are presented graphically in Figure 3.  
The data showed that BlancOne ULTRA\({}^{+}\) significantly improved tooth brightness in all three experiments, which consisted of six mandibles each, with a statistically extremely significant difference (\(p<0.0001\)) observed before and after the treatment. The mean \(\pm\) standard deviation values before and after the treatment were 21.75 \(\pm\) 0.91 and 14.08 \(\pm\) 0.46, 21.61 \(\pm\) 0.60 and 13.99 \(\pm\) 0.56, and 21.91 \(\pm\) 0.65 and 14.79 \(\pm\) 0.40, respectively.  
Similarly, Opalescence Extaboost (21.46 \(\pm\) 0.68 before and 17.06 \(\pm\) 0.99 after treatment) and Zoom WhiteSpeed (21.69 \(\pm\) 0.66 before and 19.07 \(\pm\) 0.49 after treatment) were found to have a positive effect on tooth brightness, with a statistically extremely significant difference (\(p<0.0001\)) observed in both cases.  
However, the difference before (21.95 \(\pm\) 0.69) and after (20.91 \(\pm\) 0.54) Pola Office treatment was found to be the least evident, despite there being significant statistical evidence with a \(p\)-value of less than 0.05.  
Figure 3: Effect of professional bleaching treatments. Histograms show the means \(\pm\) standard deviations of the spectrophotometric brightness measure (O.D.) carried out before and 48 h after the treatment. \({}^{*}\) and \({}^{**}\) symbols indicate a significant and an extremely statistically significant difference (Student’s \(t\)-test), respectively, comparing the teeth brightness before and 48 h after bleaching treatment.
###### Abstract  
The primary objective of this research is to evaluate the feasibility of two of the most prevalent agricultural residues in Ecuador, banana peels and pineapple peels, as a carbon source in the culture medium of _Komagtaeibacter hansenii_ for the production of bacterial cellulose (BC) and BC-based paper. This analysis includes an assessment of the productivity parameters of the obtained BC and the quality parameters of the BC-based paper, employing multivariate statistical methodologies. The experimental design consisted of fifteen treatments: T1 served as the control using the standard HS medium, while treatments T2-T8 used banana peel extracts (BPE), and treatments T9-T15 used pineapple peel extracts (PPE) at concentrations from 10% to 40% (\(v/v\)). Extracts were prepared with tailored pretreatments for each type of peel to optimize bioactive compound recovery. Standardized fermentation and purification conditions were applied, and once the cellulose was obtained, additives and coating agents were incorporated to produce paper samples from each treatment. The results indicated that higher BPE concentrations (T5, T6, T7, and T8) correlated significantly with increased Weight and Yield of BC, as well as improved grammage and water content in the BC-based paper. This highlights that efficient paper production is influenced by the quality of the bacterial cellulose used, with BPE-based media yielding optimal results due to their nutrient composition, which promotes bacterial growth and metabolic activity. This approach suggests a pathway for advancing sustainable and economical paper production.  
agricultural residues; bacterial cellulose; carbon source; culture medium; multivariate statistics; paper; productivity; quality +
Footnote †: journal: Applied sciences  
Article  
Data Mining for the Characterization of a Paper Prototype Obtained with Bacterial Cellulose Derived from Banana and Pineapple By-Products
## 2 Result and Discussion
### Illustrating the Biosynthetic Pathway of DHA  
Thraustochytrids were proposed to use two pathways fatty acid synthase (FAS) and PKS pathways) to synthesize fatty acids [8; 31; 32]. Genomic and transcriptomic mining revealed that _Aurantiochytrium_ sp. SK4 consisted of almost all the genes for the two pathways. The candidate genes encoding for the enzymes involved in _FAS_ pathway included a type I fatty acid synthase (_FAS_), 6 fatty acid desaturases (_FAD_: _\(\Delta\)-9_, _\(\Delta\)-12_, \(\Delta\)-6, _\(\Delta\)-5_, \(\omega\)-3, and _\(\Delta\)-4 desaturases_), and 4 fatty acid elongases (Figure 1A). Transcriptomic data showed that these genes remarkably expressed except for the \(\Delta\)-9, _\(\Delta\)-6 and \(\omega\)-3 desaturases_ (Table S2). Interestingly, no _\(\Delta\)-15 desaturase_ genes were found in both the genome and transcriptome sequences. The genes (_pfaA_, _pfaB_ and _pfaC_) encoding for the subunits of the polyketide synthase (PKS) were identified, which expressed at moderate levels according to the transcriptomic data (Figure 1A, Figure S4). To further correlate the accumulation of fatty acids with the expression of the key genes involved in the FAS and PKS pathways, the transcription of one FAS and three PKS genes were detected at eight different culturing times by quantitative real-time PCR (qRT-PCR). The transcription trends of the four genes increased rapidly at the logarithmic phase but decreased at stationary phase and decline phase (Figure 1B). Gas chromatography-mass spectrometry (GC-MS) detection revealed four main fatty acids, i.e. myristic acid (C14:0), palmitic acid (C16:0), docosapentaenoic acid (DPA) and DHA. DHA and palmitic acid were the predominant fatty acids (Figure 1C,D). The contents of total lipids and DHA increased and reached a maximum at 48 hours and thereafter decreased during the decline phase (Figure 1C,D). Thus, the expression of the genes correlated well with the accumulation of fatty acids. The presence of DPA and the absence of _\(\Delta\)-15 desaturase_ genes suggest _Aurantiochytrium_ sp. SK4 might synthesize DHA via the PKS pathway rather than the FAS pathway. The growth curve of _Aurantiochytrium_ sp. SK4 of Figures 1 and 2 were shown in Figure S5.
## 4 Discussion  
### Influence of Noise Levels on the New Algorithm  
In practical applications, the prior noise level of the observed data is unknown, while the random signal is partially dependent on the prior noise level. Thus, we set three noise levels, \(\sigma_{0}^{2}=0.1\), \(\sigma_{0}^{2}=1\), and \(\sigma_{0}^{2}=10\), to analyze the influence of different noise levels on the fitting and estimation results of the TLSC and ALSC algorithms (Table 2).  
Table 2 shows that the TLSC are affected significantly by the observation noise levels. When higher observation accuracy is gained artificially (small noise level, such as \(\sigma_{0}^{2}=0.1\)), most of the residuals will be absorbed as signals. Therefore, the observation corrections will be smaller, resulting in the illusion that the fitting and estimation results have higher accuracy. However, for the ALSC, the fitting  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multirow{2}{*}{\begin{tabular}{c} **Noise Levels** \\ **(\(\sigma_{0}^{2}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **RMS (mm/a)** \\ **(Fitting Points)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **RMS (mm/a)** \\ **(Check Points)** \\ \end{tabular} } \\ \cline{3-5}  & & & **(E)** & **(N)** & **(E)** & **(N)** \\ \hline \multirow{3}{*}{TLSC} & 0.1 & 1.82 & 0.64 & 2.42 & 1.04 \\  & 1 & 2.08 & 0.91 & 2.73 & 1.18 \\  & 10 & 2.43 & 1.32 & 3.21 & 1.86 \\ \hline \multirow{3}{*}{ALSC} & 0.1 & 2.15 & 0.96 & 2.82 & 1.24 \\  & 1 & 2.15 & 0.96 & 2.82 & 1.24 \\ \cline{1-1}  & 10 & 2.15 & 0.96 & 2.82 & 1.24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Influence of different noise levels on fitting and estimation results of the traditional LSC algorithm (TLSC) and ALSC algorithms in the actual case.  
Figure 5: Correlation analysis between the fitting and observed velocity in the east (**a1**, **b1**, **c1**, and **d1**) and north directions (**a2**, **b2**, **c2**, and **d2**).  
and estimation results are not affected by the noise levels, and the results are more stable. Because the ADLSC algorithm incorporates the ALSC algorithm, it will also not be affected by the noise levels.
# Analysis of BDS-3 Onboard Clocks Based on GFZ Precise Clock Products
## 3 Analysis of Long-Term Clock Bias, Frequency, Drift Rate, and Noise
### Analysis of Drift Rate Series  
The series of clock drift rates for BDS-2 and BDS-3 are shown in Figures 5 and 6. The clock drift rate of BDS-2 onboard clocks is maintained on the order of \(10^{-18}\), varying from \(-4\times 10^{-18}\) to \(4\times 10^{-18}\) s/s\({}^{2}\). In addition, it can also be found that the drift rate series of the BDS-2 clock has some fluctuations. The drift rate series of C08, C09, and C10 represent obvious periods, which may be caused by the aging of satellite hardware facilities  
Figure 6 plots BDS-3 satellite onboard clock drift rate series. The drift rates of BDS-3 satellite clocks (except two GEO satellites, C59 and C60) vary within the range of \(-2\times 10^{-18}\)-\(2\times 10^{-18}\) s/s\({}^{2}\), which is smaller than that of BDS-2 clocks. Note that the drift rates of C41 and C44 are worse than other PHMs of the MEO satellites, which may have resulted from the lower accuracy of their clock products, since there are only a few MGEX stations that could be employed to track these satellites. Compared to BDS-2 clocks, the drift rate series of BDS-3 onboard clocks show unobvious periodic variations.
## 4 Analysis of Simulation Results  
### Model Match  
In this paper, based on BP neural network algorithm and Smith prediction compensation principle, the liquid fertilizer pH adjustment system based on the Smith prediction compensator, PID-Smith prediction compensator, and BP-PID-Smith prediction compensator were designed, respectively, and the final obtained simulation results were compared and analyzed. According to the actual situation, the initial pH value of liquid fertilizer was 7.5, the pH value of adjusted liquid fertilizer was set to 6.8, and the simulation time was set to 500 s. The focus of this paper is on the dynamic process of the system, and this process is described by the dynamic performance metrics, which are composed of rise time, peak time, regulation time, and overshoot of the controller. The rise time is the time required for the response to rise from zero to the final value for the first time; the peak time is the time required for the response to exceeding its final value to reach the first peak; the regulation time is the minimum time required for the response to reach and remain within \(\pm\)5% (or \(\pm\)2%) of the final value; and the overshoot is the percentage of the ratio of the maximum deviation of the response \(c(t_{p})\) to the final value \(c(\infty)\), i.e., \(\sigma\%_{o}=\frac{C\left(t_{p}\right)-C(\infty)}{C(\infty)}\times 100\%\). The rise time, peak time and regulation time are important indicators to evaluate the response speed of the system, and the overshoot reflects the stability of the system control process. The response curves of the three controllers when the models are matched are shown in Figure 11, the training error curves of the BP-PID-Smith controller are shown in Figure 12, and the dynamic performance comparisons are shown in Table 1.  
According to the data in Table 1, the dynamic performance index of the BP-PID-Smith controller is significantly better than the other two controllers, so the BP-PID-Smith controller has a faster response and better steady-state performance compared with the PID-Smith controller and Smith controller. The Smith controller has a slow regulation time and rise time, reflecting its slow response time. The rise time and regulation time of the modified PID-Smith controller become shorter compared with the Smith controller, but at the same time, a certain amount of overshoot is generated, and the overall control effect is not as good as that of the BP-PID-Smith controller.
## 4 Results and Discussion
### Summary of Key Insights  
1. Surface Finish and Precision: SLA achieves \(\sim\)2 um roughness and \(\pm\)0.05 mm tolerances, outperforming FDM.
2. Mechanical Properties: SLA exhibits higher tensile strength (68.9 MPa) but lower ductility (4.4% elongation at break). FDM ABS, while weaker in tensile strength (47.5 MPa), offers superior toughness (8.7% elongation at break), making it suitable for impact-resistant applications.
3. Cost and Throughput: FDM is significantly more economical (\(\sim\)2.30\(\sim\)2.50 EUR/part) and faster (45-55 min per part) compared to SLA (\(\sim\)5.90 EUR/part and 90-100 min per part). This makes FDM ideal for rapid prototyping and cost-sensitive projects.
4. Environmental and Safety Considerations: FDM waste (PLA/ABS) is biodegradable or recyclable, whereas SLA resin waste is hazardous if uncured. However, SLA generates slightly less waste by mass (10% vs. 15% for FDM).
5. Reliability and ease of Use: FDM has an 80% success rate, with failures primarily due to nozzle clogs or warping. SLA achieves a 90% success rate but requires stricter handling of resin and post-processing, increasing operational complexity.  
Overall, the choice between FDM and SLA depends on specific application requirements. SLA is indispensable for high-detail, high-strength, or precision-demanding parts, while FDM is better suited for rapid, cost-effective prototyping or flexible components. The subsequent section (Section 5) provides further recommendations for aligning these results to specific use cases.
## 2 Description of the Classification Method
### The Embedding Layer with the Center Loss Function and Softmax  
To address the face verification problem, center loss [22] is first exploited to compensate for softmax loss in 2016. For the UATR task, we improve generalization by leveraging such a simple but useful cost function, since it learns a center for the features of each class and meanwhile tries to pull the deep features of the same class close to the corresponding center [5]. Basically, for classification task \(\{(x_{i},y_{i})\}_{i=1}^{N}\) consisting of \(N\) samples \(x_{i}\) and their  
Figure 4: The overall framework of an 18-layer Residual Network (ResNet18).  
corresponding labels \(y_{i}\in\{1,2,\ldots,Y\}\). Here, \(x_{i}\) is embedding into a new vector \(f(x_{i})\) with a DNN [17]. The center loss function used here is defined as:  
\[L_{c}(class=j)=-ln\left(\frac{e^{W_{j}^{T}x+b}}{\sum\limits_{i=1}^{N}e^{W_{i}^{ T}x+b_{i}}}\right)+\lambda\sum\limits_{i=1}^{N}D(f(x_{i}),c_{i}), \tag{2}\]  
where the first term denotes the softmax loss, the second term denotes the penetration term for clustering, and \(\lambda\) is a balance parameter within \([0,1]\). Here,\(c_{i}\) is the center of class \(i\) and the function \(D(\cdot)\) stands for the distance function. During training, the center loss will encourage instances between samples of the same classes to be closer to their learnable class center. Here, \(j\) is the class number, \(x\) is the input, \(W_{j}\) is weight, \(b_{j}\) is the bias, and \(N\) is the total number of the classes.