esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Face Profilometry Based on Galvanometer Scanner with Infrared Fringe Projection in High Speed
## 4 Discussion  
Compared with the DLP fringe projection, the 3D data measured using this proposed MEMS galvanometer has fringe-like errors (Figure 6). The error may be caused by the inaccuracy of the fringe boundary corresponding to the high-frequency phase. The closer the scanning boundary is, the greater the error will be, as shown in Figure 6d.  
When the phase-shifted fringes are projected with DLP projector The DMD chip contains numerous micro-mirrors, each of which is projected as one pixel. Projection fringe pattern is generated by a computer according to the equal period of pixels. As shown in Figure 10a, when the focal length f of the projector and the distance z between the projector and the object are fixed, the ratio between \(d_{1}\) and \(d_{2}\) is equal to that between \(x_{1}\) and \(x_{2}\). Because the pixel spacing is equal, as long as the high-frequency fringes are integer times of the low-frequency fringes on the DMD, the fringe boundary of the measured object surface is also aligned, as shown in Figure 10b. In this case, the corrected unwrapping phase can be obtained.  
The period of projection fringes is determined by the number of pixels in the DMD, but the period can only be controlled by the scanning angle of the MEMS, as shown in Figure 5c.  
If the range of scanning angle is divided equally to generate the fringe with different periods pattern, the width of one fringe which is projected to the object surface must be different for different scanning angles \(\theta\). Figure 11a illustrates that the tangent values are not linear when from \(-30\) degrees to \(0\) degrees and from \(0\) degrees to \(30\) degrees, and the derivatives in different scanning angles is different (Figure 11b). Therefore, the equal interval scanning angle as a period to produce fringes pattern will cause the misalignment in boundaries of the fringes between the low-frequency pattern and the high-frequency pattern. That will inevitably lead to an inaccurate phase unwrapping of the fringe boundary. In the next work, how to achieve non-equiangular calibration will be focused on.  
Figure 10: The DLP projector. (**a**) The projection geometry; (**b**) The upper and lower images on the left are projection patterns with the fringe frequency of two and four. The right images are a line of phase values corresponding to the blue line of the left image, respectively. The fringe boundary on the projection plane coincides with the fringe boundary on the DMD completely.
# An Intelligent Control Method for Servo Motor Based on Reinforcement Learning
## 3 Problem Description  
### Control Mode of Servo Motor  
The general servo motor has three control modes: position control, torque control, and speed control.  
The position control mode is usually assigned to the displacement directly through the external input. Because the position mode can control the speed and position strictly, it is generally applied to the positioning device.  
The speed control mode is used to control the motor rotation speed through the external input, which has been quite widely applied. In addition, there is a need for a rapid response of the continuous speed control system, the upper closed-loop positioning system, and the need for a rapid change in the speed system.  
Torque control mode is used to change the size of the motor's external output torque through the external input. It is mainly used in winding and unwinding devices with strict requirements regarding the force of the material, such as a winding device or fiber-pulling equipment.  
In this work, we only consider the torque control mode and speed control mode, since position control can be achieved via speed control in definite time. The speed and torque of the motor are determined by the duty cycle, which denotes the ratio of the up-level time to the whole-cycle time within a pulse period. The range of the duty cycle is [0, 1], and its value is proportional to the velocity and torque.
## 3 Results and Discussion
### Wind Tunnel Test Results  
_Structural Feasibility of ETMs and Flexible Skin_  
Beyond validating the numerical results, the wind tunnel tests also aimed to assess the structural integrity and feasibility of the ETMs and flexible skin for SMTE flap morphing. Throughout all 11 tests, the deformation of the ETMs and the performance of the flexible skin were carefully observed. The results of the structural analysis in [37] showed that the ETM could achieve the minimum required deflection of 66 mm. This was confirmed by experimental tests, where the vertical flap deflection reached 30.815 mm (Table 6), which corresponds to 66.13 mm when scaled by a factor of 0.466. Figure 22 presents the SMTE flap after its morphing.  
Figure 21: Comparison of CFD and wind tunnel results of (**a**) lift and (**b**) drag coefficient variations with angle of attack.  
Figure 22: UAS-S45 wing section with SMTE flap after morphing in the wind tunnel: (**a**) lower surface view, (**b**) upper surface view.  
As mentioned in Section 2.5, the buckling behavior of the flexible skin was mitigated by pre-tensioning the skin before attachment to the SMTE flap's lower surface using a screw-driven pre-tensioning device. Figure 22a illustrates the lower surface of the SMTE flap at its maximum deflection. As shown in this figure, no buckling signs are present, and the skin maintains a smooth surface across the entire flap, demonstrating the effectiveness of the flexible skin. Additionally, through continuous variation in the SMTE flap deflection angle, it was confirmed that all four ETMs, along with the connecting rubbers in the transition sections, worked properly without any malfunctions (Figure 22b).
# Study on Deformation Characteristics of Low-Highway Subgrade under Traffic Load
## 4 Numerical Calculation and Analysis of Subgrade Deformation
### Numerical-Calculation Model  
In this research, a 3D numerical-calculation model is established according to the design size of a two-way six-lane highway, as shown in Figure 20. The subgrade height is 3 m, the pavement width is 42 m, and the foundation thickness is 14 m. The finite-element mesh attributes are hexahedral and reduced linear elements (C3D8R), and the number of elements is 29,526. After mesh-convergence check, the model mesh convergence is good. The parameters of the numerical model are shown in Table 5. The load in the table is determined by axial-load spectrum, and the parameters of subgrade soil are measured by dynamic triaxial test in Section 2. The subgrade model's bottom- and four-side-boundary conditions are set as displacement fixed-boundary conditions.  
Figure 19: Comparison chart of numerical simulation results and test results. (**a**) Consolidated undrained triaxial-shear-test comparison chart; (**b**) Dynamic-cycle-test comparison chart.  
Figure 20: Numerical-calculation model. (**a**) Model mesh; (**b**) Boundary condition.  
Moreover, the nonlinear relationship between the elastic modulus and strain level is reflected through modulus reduction. In the process of ABAQUS numerical simulation, material parameters can be set to change with the change in temperature or field variables, and the change in field variables can be controlled by modifying the model input file. According to the modulus-reduction curve drawn from the dynamic triaxial test results, the elastic modulus and shear modulus are defined as parameters that vary with the field variable, and the condition is added that the modulus decreases with the number of vibrations.
# Detection of Logos of Moving Vehicles under Complex Lighting Conditions
## 3 Methodology  
There are three kinds of images in vehicle logo image datasets: low brightness images, high brightness images, and medium (suitable) brightness images. Image enhancement is purposed to reasonably balance the pixel distribution, adjust the brightness and contrast, and improve the distinction between logos and their backgrounds in images under different lighting conditions. Image enhancement can make vehicle logos on outdoor roads more recognizable.  
Considering the complexity of vehicle logo sizes and style diversities for logo detection of vehicles in motion, this study improves a target detection algorithm and designs a network structure for the multi-scale problem of vehicle logos in different images.
###### Abstract  
(1) Background: Change of direction (COD) is crucial for agility in team sports. Complex contrast training (CNT), alternating between heavy and light exercises, is a newer method currently gaining attention, but its effectiveness compared to others (strength training, ST; glyometric training, PT; complex descending training, DT; complex ascending training, AT) is unclear. (2) Methods: A systematic review and meta-analysis following PRISMA guidelines included studies with CNT interventions. The effect size (ES) was measured using Hedges' g, with subgroup analyses for moderating factors. (3) Results: CNT improved COD performance more than PT (ES = 0.65), ST (ES = 0.88), and controls (ES = 1.24), with no significant difference from DT (ES = \(-\)0.08) or AT (ES = 0.19). CNT was particularly effective for athletes under 18 (ES = 1.13), females (ES = 1.59), amateurs (ES = 1.02), and COD measures with more than three turns (ES = 1.08). (4) Conclusions: CNT enhances COD performance, proving superior to standalone strength or glyometric training. However, its effectiveness is comparable to other combined-training models, suggesting that the integration of high-load strength and high-velocity power exercises is the primary driver of adaptation. The benefits are most pronounced in younger, female, and amateur athletes. Future large-scale studies are needed to confirm these findings and refine protocols for diverse populations.  
complex contrast training; strength training; glyometric training; agility; COD +
Footnote †: journal: Applied sciences
## 1 Introduction
### Robustness of Demagnetization Fault Identified Signal
#### 2.4.2 Effect of Speed on Demagnetization Fault Identified Signal  
The speed of machines is variable in actual applications. To diagnose the demagnetization faults under various speeds, the fault identified signal needs to be robust to the speed. This part is focused on the study and analysis of the demagnetization fault identified signal under various speeds (the load of the test machine is rated load, and the demagnetization severity is 10%). Two speed conditions are studied, including rated speed and 50% rated speed. Take Mode 3 as an example to analyze the waveforms of \(SI_{\rm m}\) under different speed conditions, which are depicted in Figure 9.  
As shown in Figure 9, even when the mode of demagnetization severity is 10%, the amplitudes of \(SI_{\rm m}\) are almost the same for various speeds, with small differences. Consequently, \(SI_{\rm m}\) is slightly affected by the variation in the machine speed. The behaviors of \(SI_{a1}\) and \(SI_{a2}\) are similar to \(SI_{\rm m}\). As a result, \(SI_{\rm m}\), \(SI_{a1}\), and \(SI_{a2}\) are considered robust and discriminant characteristic signals to accurately locate demagnetization faults under various speeds.
# Enhancing Cement Paste Properties with Biochar: Mechanical and Rheological Insights
## 3 Results
### Mechanical Properties  
The mechanical test results related to the 7 days of curing are shown in Figure 8A, showing that adding microparticles to the cement mix significantly improves the mechanical properties of plain cement. Regarding flexural strength, the most effective additions were B 1% and 5%, while compressive strengths were 1% and 2%, respectively. Besides the fracture energy, the most effective percentage of B was 3%. However, adding 1% of B achieved an almost 130% improvement with respect to the reference mixture. Results regarding the mechanical properties related to 28 days of curing are shown in Figure 8B, and they are not as promising as those of 7 days. In this case, the best performance for flexural and compressive strength was seen in B with the addition of 2%, yielding improvements of 30% and 13% with respect to the reference sample, respectively. A similar trend was observed in the work of [PERSON] et al. [32]. Besides this, evaluating the fracture energy, we found that a biochar content of 5% presented the best performance. Furthermore, the B contents for additions of 1 to 3 wt. % of cement showed the same positive performance with respect to those samples without biochar.  
Figures 9-12 show how the fracture surface changes when biochar is added to the cement matrix. The use of biochar alters the crack path within the cementitious matrix, thereby creating a winding crack path, consequently increasing the fracture energy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Storage Modulus (Pa)** & **Loss Modulus (Pa)** \\ \hline Reference OPC & 30,340 & 5034 \\ \hline B 1\% & 45,790 & 10,760 \\ \hline B 2\% & 54,050 & 12,250 \\ \hline B 3\% & 63,380 & 14,440 \\ \hline B 5\% & 181,300 & 29,360 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Initial storage and loss modulus value.  
Figure 7: Dynamic oscillatory shear measurement using strain sweep: (**a**) cement paste, (**b**) cement paste + 1% B, (**c**) cement paste + 2% B, (**d**) cement paste + 3% B, (**e**) cement paste + 5% B.  
Figure 8: Mechanical properties of cement composite containing biochar (**A**) after 7 days of curing and (**B**) after 28 days of curing.  
Figure 10: Pseudo-linear crack path and rough fracture surface. B 3% at 7 days.  
Figure 9: Linear crack path and smooth fracture surface. Plain cement (OPC) at 7 days.
# On Cross-Layer Interactions for Congestion Control in the Internet
## 3 TCP Variants
### TCP Compound  
TCP Compound (C-TCP) [23] is a Microsoft algorithm. It was implemented in Windows XP (as a patch), Vista, and Windows 7. It is also available in Windows 10 and Windows Server 2016, as well as in the Linux kernel. According to the work in [2], it was the most deployed version of TCP in 2010, owing to the fact that it has replaced the conventional congestion control in Windows operating systems since Windows Vista and Windows 2008 Server. However, in Windows 10, the default congestion control was set to New Reno and Windows Server 2019 uses Cubic by default, therefore its commonness dropped.  
The first objective of TCP Compound is to fully utilize the available bandwidth in high-speed and long-distance networks (high BDP) and provide RTT- and inter-TCP fairness at the same time. The algorithm uses a delay-based estimator of the network state (based on TCP Vegas [24]) to combine the Reno-type congestion control with a scalable component of congestion control derived from High-Speed TCP (HS-TCP) [20].  
The congestion window increases exponentially in the Slow Start phase, identical to TCP Reno Slow Start. In the Congestion Avoidance phase, the window size (\(win\)) is the sum of the standard congestion window (\(cwnd\)), calculated as in Reno and the additional parameter (\(dwnd\)), which is calculated as follows:  
\[dwnd(t+1)=\begin{cases}dwnd(t)+(\alpha*win(t)^{k}-1),&\text{if }\Delta<\gamma,\\ dwnd(t)-\eta*\Delta,&\text{if }\Delta\geq\gamma,\\ win(t)*(1-\beta)-cwnd/2,&\text{if loss is detected},\end{cases} \tag{2}\]  
where \(\eta\) and \(\gamma\) are predefined constants, \(\alpha\) is an increase coefficient in the Congestion Avoidance phase, and \(\beta\) is a decrease factor after a loss detection. \(\Delta\) is the Vegas estimator of network buffering. If it is below \(\gamma\), the congestion window increases according to the HS-TCP rule. If it exceeds the threshold, the additional component is gently reduced to provide a smooth transition between the aggressive behavior of HS-TCP and the much slower Reno mode.  
The main flaw of C-TCP is inherited from TCP Vegas and affects the accuracy of the estimator. If a competing flow observes the minimal RTT value that already consists of a buffering delay caused by an already existing flow, it behaves much more aggressively and unfairly to other flows.
## 4 Materials and Methods
### HMEC-1 Damage Assay  
The effect of MRSA strains on EC damage was determined using a well-established 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) assay as described previously [13; 37; 38]. Briefly, logarithmic phase MRSA cells (1 \(\times\) 10\({}^{5}\) CFU/well) were added to HMEC-1 cells in 384-well plates with a density of -5 \(\times\) 10\({}^{3}\) EC/well in MCDB131 medium to reach a multiplicity of infection (MOI) of 20, which JE2 parental caused \(\sim\)50% HMEC-1 damage as established in our pilot experiments. After 3 hr invasion, extracellular MRSA cells were killed by adding lysostaphin (10 ug/mL) in full medium MCDB131 (Sigma-Aldrich, St. Louis, MO, USA) supplemented with 20% bovine calf serum, 2 mM glutamine, 100 IU/mL penicillin, and 100 mg/mL streptomycin [13; 37]. At 18 hr incubation at 37 \({}^{\circ}\)C, MTT (5 mg/mL; Sigma-Aldrich, St. Louis, MO, USA) in Hank's Balanced Salt Solution (HBSS, Thermo Fisher Scientific, Waltham, MA, USA) was added and incubated for 2 h, then the medium was replaced with 0.04 M HCl in absolute isopropanol (Thermo Fisher Scientific, Waltham, MA, USA) to stop the reaction and lyse the cells. Absorbance was measured at 560 nm (A\({}_{560\text{nm}}\)) using a microplate reader Synergy 2 (BioTek, Winooski, VT, USA). Uninfected HMEC-1 served as a negative control, and wells containing medium alone were used for background correction in each round. In addition, EC infected with \(\Delta\)_arIR_ in JE2 was selected as an additional control group as it was reported that _arISR_ inactivation leads to \(>\)70% reduction in human EC damage vs. JE2 parental strain [13]. EC damage was calculated using the following formula: 1 \(-\) (A\({}_{560\text{nm}}\) of test well/A\({}_{560\text{nm}}\) of 0% \(-\) damage control well) as previously described [37]. Each experiment was performed three times in triplicate.
# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
## 7 Challenges and Opportunities
As image preprocessing techniques can potentially improve the results, even of a robust model, analysis of the integration of these two components can be relevant. Another interesting factor is the performance of end-to-end systems. Researchers are focused on text spotting development that benefits from a joint optimisation of detector and recogniser. Nevertheless, some limitations need to be addressed, such as the effective way to link and communicate text detection and recognition and to enhance joint optimization. This is a recent area that requires more investigation. Another area that has drawn attention is the introduction of a language model. The verse of NLP methods in STR to acquire linguistic information has yielded promising results [101; 143], with the disadvantage of increased computational costs. The effective way to fuse visual and linguistic information while maintaining high efficiency still raises doubts. The most significant difficulties in identifying grocery products are noted along with potential research directions:  
* **Poor image quality**, as blurring and perspective distortion can be enhanced with image preprocessing techniques. * **Lack of datasets** can be overcome by creating synthetic datasets, applying data augmentation or selecting one-shot or unsupervised learning. * **Shift environment** between training data and test data requires strategies such as GAN network, data augmentation and transfer learning. * **New classes** are regularly added and the use of incremental learning or one-shot learning prevents the model from being completely retrained. Ultimately, we conclude that grocery product recognition is still a challenging task because of intra-class variance and inter-class similarity; new data arriving frequently; lack of huge quality data; cross-domain between training samples and test samples; complex background; noise and perturbation of query images; irregular texts; multi-lingual texts, and the need for a light model to perform in real-time. More research is clearly required in this field in order to make progress, hence some guidelines for each problem were suggested. Conceptualization, P.C., P.V., V.G. and J.N. ; methodology, P.C. and P.V. ; validation, V.G., J.N., P.V. and P.C. ; investigation, V.G. and J.N. ; writing--original draft preparation, V.G. and J.N. ; writing--review and editing, V.G., J.N., P.V. and P.C. ; visualization, V.G., J.N., P.V. and P.C. ; supervision, P.C. ; funding acquisition, P.C. and P.V. All authors have read and agreed to the published version of the manuscript. This work is financed by National Funds through the Portuguese funding agency, FCT - Fundacao para a Ciencia e a Tecnologia, within project LA/P/0063/2020. Not applicable. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results and Discussion
### Characterization of Aroma Compounds in RZT Identified by SBSE-GC-MS
Figure 3 presents our hypothesis regarding the impact of low-temperature stress on fatty acid derivatives in RZT, based on the volatiles detected in this study. Research has shown that unsaturated fatty acids in tea can be oxidized by lipoxygenase (LOX) and subsequently decomposed into six-carbon aliphatic aromatic compounds. The metabolic pathways diverge based on the fate of hydrogen peroxide, an LOX oxidation product, into two branches: the LOX-AOS (lipoxygenase-allene oxygenase) and LOX-HPL (lipoxygenase-hydro peroxide lyases) pathways [27; 28; 29]. Unsaturated fatty acids can be converted into green leaf volatiles such as (Z)-3-hexen-1-ol and (Z)-3-hexen-1-ol acetate through oxidation, reduction, and esterification in the LOX-HPL pathway, and converted to jasmonic acid (JA) through oxidation and reduction in the LOX-AOS pathway [30]. Both pathways are significantly affected by environmental stresses. Low-temperature stress can enhance the accumulation of jasmonic acid in fresh tea leaves, and the concentration of its derivative, methyl jasmonate (MeJA), is also substantially elevated [31]. MeJA, known for its floral, fresh magnolia, and oily waxy scent, acts as a signaling factor that can induce changes in the levels of aroma precursors [32]. According to pertinent studies, the entire JA pathway in tea leaves can be rapidly activated by external MeJA, significantly affecting the biosynthesis of terpenoid backbones and leading to an increase in volatiles like linalool and geraniol [33]. External MeJA can also efficiently promote ROS (reactive oxygen species) scavenging and maintain cell membrane stability under cold stress [34]. Jasmine lactone, a cyclic volatile resulting from fat degradation [4], is characteristic of Oolong tea, and its accumulation in tea can be triggered by external stress such as low temperatures [35]. Therefore, it is hypothesized that the low-temperature stress caused by its high-latitude geographical location could be a major factor affecting the elevated levels of some unsaturated fatty acid derivative volatiles in RZT. Although 1-butyl-2-isobutyl phthalate was abundant in RZT samples, there are no reports indicating its direct contribution to tea aroma. The same applies to hexadecanoic acid and alkanes; they are presumed to have negligible effects on the flavor of RZT samples due to their undetectable odor thresholds, and they cannot they be detected by GC-O analysis; however, it is hypothesized that these odorless volatiles may interact with other components such as amino acids and tea polyphenols, thereby influencing the flavor of tea.