esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Hertzian Elastohydrodynamic Contact Stress Calculation of High-Speed Ball Screws
## 3 Experiments and Verification
### Experimental Results  
The friction coefficient [32] of BSs can be written as  
\[\mu=\frac{\mu_{a}Q_{a}+Q_{f}}{Q} \tag{21}\]  
Figure 5: Experimental setup of the DNBSs. 1. Motor. 2. Coupling. 3. Base. 4. Guideway. 5. Table. 6. Screw. 7. Nut. 8. Clamping device. 9. Dowel bar. 10. Tension–compression sensor.  
\[\iint_{A_{h}}\tau_{f(i,j)}dA_{h(i,j)}=Q_{f} \tag{22}\] \[\tau_{f}=\tau_{0}\text{arcsin}h\Big{(}\frac{\eta\Delta u}{\tau_{0}h} \Big{)} \tag{23}\]  
where \(Q\) is the total normal contact force, including the film bearing force \(Q_{f}\), and the asperity bearing force \(Q_{a}\). The ratio of the asperity bearing stress is \(R_{S}\), \(R_{S}=Q_{a}/Q\). \(\mu_{a}\) is the dry friction coefficient and \(\mu_{a}=0.1\). \(\tau_{f}\) is the film shear stress proposed by the Eyring model [33]. \(A_{h}\) is the film hydrodynamic contact area, \(\eta\) is the film viscosity, \(\Delta u\) is the relative sliding velocity, and \(\Delta u=u_{1}-u_{2}\). \(u_{1}\) and \(u_{2}\) are the same as that of Equation (14). \(h_{0}\) is the film central thickness.  
The results of the friction torque test are shown in Figure 6. The friction coefficient curve of the DNBSs is indicated by the blue lines in Figure 6b, which is in good agreement with the schematic Stribeck curve [34] shown in the green box (Figure 6a). The friction coefficient is composed of the asperity friction coefficient and the hydrodynamic lubrication friction coefficient. When the screw speed is lower than 502.65 mm/s, the BSs is in the mixed lubrication state. When the screw speed is higher than or equal to 502.65 mm/s, the BSs is in the hydrodynamic lubrication state. The comparison of the simulated and experimental friction torque is shown in Figure 6c. The comparisons showed that the experimental friction torque was consistent with the simulated friction torque, which verified the correctness of the non-Hertzian elastohydrodynamic contact stress calculation method.  
Figure 6: Results of the friction torque test. (**a**) A schematic Stribeck curve [34]. (**b**) Friction coefficient curve. (**c**) Comparison of the simulated and experimental friction torque.
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 5 Conclusions  
A novel GPR system architecture was designed using a fast ray tracing algorithm. The proposed architecture was validated using FDFD, even taking into account the surface roughness. This new system exploits the advantages of FLGPR (with the transmitter looking ahead of a vehicle) and DLGPR (with the receiver on a drone), mainly in terms of less clutter and good penetration and resolution. Compared to a multimonostic FLGPR (on a vehicle at a stand-off distance), it provides higher signal strength and resolution, since the receiver is closer to the investigation domain. Compared to a multimonostatic DLGPR (on board a UAV), it helps to mitigate the strong clutter from the air-soil interface. For the analyzed aperture size and TX-RX distance, it was also shown that the results with the transmitter stationary (multistatic) or moving synchronously with the receiver (multibistatic) are almost the same.  
**Author Contributions:** [PERSON]-[PERSON] developed the ray-tracing and inversion codes, ran the simulations and wrote the manuscript; [PERSON] developed the FDFD code; [PERSON]-L. and [PERSON] provided some suggestions and improvements, and reviewed the manuscript; and [PERSON] guided the research and reviewed the manuscript.  
**Funding:** This work was supported by Government of Spain (project TEC2014-55290-JIN and grants FPU15/06341 and EST17/0777) and Government of Principado de Asturias (project GRUPIN-18-000191).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 15: SAR image from FDFD simulations with a flat-top dielectric target buried under a rough surface (dashed line indicates the soil interface) for: multimonostatic DLGPR (**a**); multimonostatic FLGPR (**b**); multistatic FLGPR–DLGPR (**c**); and multibistatic FLGPR–DLGPR (**d**) configurations.
###### Abstract  
Optical communication systems face challenges like nonlinear noises, particularly Kerr-induced phase noise, which worsens with higher-order m-QAM formats due to their dense data-symbol sets. Advanced signal processing, including machine learning, is increasingly used to enhance signal integrity during demodulation. This paper explores the application of a spectral clustering algorithm adapted to deal with data streaming to mitigate nonlinear noise in long-haul optical channels dominated by nonlinear phase noise, offering a promising solution to a pressing issue. The spectral clustering algorithm was adapted to handle data streams, enabling potential real-time applications. Additionally, it was combined with a demapping process for m-QAM to resolve labeling inconsistencies when processing windowed data. We demonstrate that the spectral clustering algorithm outperforms the k-means algorithm in the face of nonlinear phase noise in \(-90\), \(-100\), and \(-110\) dBc/Hz scenarios at 1 MHz in a simulated 10 GHz symbol rate channel.  
spectral clustering; data streaming; optical communications; nonlinear phase noise Article
## 3 Results
### Low-Current Electrical Fatigue: Electrical Properties and Wire Microstructure  
Under low-current electrical-fatigue conditions (0.35 A and 0.37 A), electrical properties and microstructure were monitored up to 300 cycles. Beyond this point, severe embrittlement (0.35 A) and thermal fusing (0.37 A) prevented further testing; therefore, 300 cycles mark the beginning of irreversible degradation.  
Resistance measurements (Figure 8) began at 100 cycles, once contact instability subsided and microstructural evolution became detectable. At this point, the two currents gave comparable resistance, providing a common baseline. Beyond 100 cycles, resistance decreased at 0.35 A but increased at 0.37 A, indicating competing recovery and oxidation-driven mechanisms. XRD, optical microscopy, and EPMA analyses of the 0.35-C200 and 0.37-C200 specimens (Figures 9-11) were performed to investigate this divergence. (Figures 9-11).  
Figure 8: _Cont._  
Figure 7: Electrical fatigue properties: (**a**) Electrical fatigue life; (**b**) physical appearance; (**c**) surface morphology after fusing under different currents.  
The as-received wire consisted predominantly of strain-induced martensite with high dislocation density. XRD, optical microscopy, and EPMA (Figures 9-11) of 0.35-C200 revealed a uniform \(\sim\)1.4 \(\upmu\)m Cr\({}_{2}\)O\({}_{3}\)/SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) oxide layer and an approximately 5% narrowing of the (110) peak FWHM, indicating partial lattice-strain relaxation and homogeneous grain growth [25]. In contrast, the 0.37-C200 specimen developed a thicker \(\sim\)3.8 \(\upmu\)m scale dominated by Fe\({}_{2}\)O\({}_{3}\), Cr\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), and Al\({}_{2}\)O\({}_{3}\)--reducing the conductive cross-section by approximately 15% and sharply increasing resistance. Subsurface EPMA mapping identified fine carbides and Al-Si-rich phases.  
Figure 8: Electrical properties: (**a**) I–V characteristics; (**b**) resistance behavior after high-cycle electrical fatigue at 0.35 A and 0.37 A.  
Figure 9: X-Ray diffraction pattern of As, 0.35-C200 and 0.37-C200.  
I\({}_{\rm{fuse}}\) data (Table 3) support these trends: oxide growth at 0.37 A significantly reduces I\({}_{\rm{fuse}}\), leading to accelerated thermal failure, whereas 0.35 A maintains a stable I\({}_{\rm{fuse}}\) with recovery-enhanced conductivity. Overall, 0.35 A favors uniform recovery and stability, while 0.37 A causes oxidation-dominated degradation.
# Individual Differences in Signal Perception for Takeover Request in Autonomous Driving
## 4 Results  
### Correlation Analysis  
To examine the correlation of TOR reaction time with individual background and modality, Pearson correlation analysis was performed. The results are presented in Table 3. Age was positively correlated with auditory (\(r=0.44\)) and tactile (\(r=0.42\)) cues. As age increases, reaction times to auditory and tactile cues increase. However, for visual cues, there was no correlation with age. The visual cue was correlated with driving frequency (\(r=0.21\)). This indicates that the higher the frequency of driving per week, the higher the reaction time to visual cues. Next, a Pearson correlation analysis was conducted with individual background by level of each modality's sub-attribute (tactile: amplitude, frequency; auditory: pitch, loudness; visual: size, brightness). The results are presented in Appendix B.  
The personal background that correlated most strongly with the sub-attribute was age. For all the tactile and auditory sub-attributes, results indicated an increase in reaction time as age increased. Gender correlated with some of the sub-attributes for both tactile and auditory, particularly for auditory, where women had faster reaction times than men for stimuli at 70 and 80 dB. Women also had faster reaction times to stimuli at 4000 Hz than men. Driving frequency, number of accidents, and driving speed were also correlated with sub-attributes of visual stimuli. In particular, the number of accidents was correlated with  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Response Time** & **Auditory** & **Tactile** & **Visual** \\ \hline Age & 0.44 ** & 0.42 ** & \(-\)0.05 \\ Gender & \(-\)0.17 & \(-\)0.16 & \(-\)0.17 \\ Experience & 0.10 & 0.12 & 0.10 \\ Frequency & 0.15 & 0.14 & 0.21 * \\ Accident & 0.02 & \(-\)0.02 & 0.07 \\ Driving style & 0.14 & 0.11 & \(-\)0.01 \\ Driving speed & 0.08 & 0.03 & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The result of correlation analysis.  
the smallest and second smallest visual stimuli. In other words, the more accidents a driver had, the slower they responded to the smaller visual stimuli.
## 2 Experimental Methods
### Targets  
The cylindrical granite targets made of a combination of granite target core, steel casing, and concrete filler were used in the test. Figure 2 displays the dimensions and the photos of a typical granite target. The target core with the diameter of 100 cm was assembled from five layers of granite discs. Three of the five granite discs were 10 cm in thickness and two were 25 cm in thickness, and the five granite discs were mirror-polished and successively conglutinated together by epoxy. The adhesive strength of the epoxy used was about 18 MPa and much higher than the tensile strength 4.2 MPa of the granite measured by the Brazilian disc experiment. That provided enough strength to prevent spallation from the conglutinations between plates. The total length of the target cores is 80 cm. A set of thin-film pressure sensors was installed at the center of the interfaces between the granite discs to record the initial stress waves caused by the impact. The granite target core was encased by a steel casing, which was 5 mm in thickness, 120 cm in diameter, and 80 cm in length. To fix and confine the granite target core, concrete with cubic compressive strength of 60 MPa was cast in the gap between the granite target core and the steel casing. Based on experimental results obtained by [PERSON] et al. [34], the diameter of the granite targets used in this study was 50 times more than the maximum diameter of the projectiles, such that the impact crater would not exceed the target in diameter. The steel casing provided a circumferential restraint for the granite target to prevent the target disintegration from the radial crack of the impact crater extending to the circumference of the granite target.  
Figure 1: Dimensions and photographs of projectiles: (**a**) schematic of dimensions of projectile; (**b**) photograph of TC4 projectile; (**c**) photograph of 30 CrMnSiA projectile; (**d**) photograph of 93 W projectile.  
Figure 2: Dimensions and photographs of a target sample.  
The target granite was light grayish white with dark spots, which was quarried from the Tongbai Mountains in Central China. X-ray diffraction (XRD) analysis was performed, and the result shows that the granite mainly consisted of 54.4% quartz, 14.3% anorthoclase, 13.4% albite, 12.7% biotite, and 5% orthoclase. The density (\(\rho_{\mathrm{t}}\) = 2.59 g/cm\({}^{3}\)) was obtained by dividing the weight of the sample by the volume. The porosity was 0.77%. Uniaxial compressive strength \(Y\) was 131.24 MPa, which was measured based on unconfined cylindrical granite samples (50 \(\times\) 100 mm) in a uniaxial test machine at a displacement rate of 0.02 mm/s.
# The Effects of Concussions on Static Postural Stability
## 4 Discussion
### Effects of Multiple Concussions on Postural Stability  
In the analyses of postural sway variables between the participants who had a single concussion (ONCE) and multiple concussions (MULTIPLE), the results revealed that the ONCE group had higher postural sway compared to the MULTIPLE group, indicating greater postural decrements. More specifically, the ONCE group demonstrated greater COP-Y max and 95 EA in foam BL EO condition of CTSIB, as well as average displacement along Y in ND EO condition of unilateral stability test. This finding that the participantswith a history of one concussion (ONCE) had a greater postural sway in both anterior-posterior and medial-lateral directions compared to the group who had a history of multiple concussions (MULTIPLE) contradicts the researchers' original hypothesis that multiple concussions would have a greater negative effect on postural stability compared to a single concussion.  
These findings could have occurred due to multiple reasons. One reason could be testing the young healthy individuals who are shown to recover from concussions faster and better [26]. Additionally, in the present study, the severity of the concussion(s) and post-concussion management/therapy that the study participants underwent were not taken into consideration, which could have affected the results. For example, an individual who sustained a grade 3 concussion once could potentially have higher balance decrements compared to an individual who had two grade 1 concussions. Recent evidence indicates that early initiation of clinical interventions, including vestibular therapy following concussion, positively impacted the recovery time [28; 29] and functional outcomes [30]. Moreover, in the current study, the timing of concussion(s) was not taken into consideration. Therefore, a participant who had a single concussion recently will have more effects on postural stability compared to someone who had multiple concussions a few years ago. The authors assume the results would differ if the severity and timing of concussions were taken into consideration when categorizing participants into ONCE and MULTIPLE groups. In addition to these, the individual participants' activity level, training history, and type of footwear worn could have contributed to the findings.
# Gibbs Sampling Detection for Large MIMO and MTC Uplinks with Adaptive Modulation
## 6 Adaptively Modulated MIMO Detection  
In a MTC-dominated context, when a number of different (single-antenna) devices transmits data to a BS, they should not be all transmitting using the same modulation order. This can happen due to ad hoc deployment of terminals (such as sensors) or in a planned manner. In an AM-MIMO system, the various transmitters adjust their parameters (e.g., their transmit power, or modulation order [19]) according to the channel state information (CSI) in order to attain a target objective (e.g., data throughput, spectral efficiency). Since each antenna is affected by different channel conditions, this adjustment feature often leads to non-negligible gains when compared to the most often studied fixed MIMO.  
In this paper, we focus on solving the problem of detection when the different users have dissimilar constellation sizes using the proposed GS method. In fact, since the sampling procedure in (6) is done sequentially and because each instance corresponds to a given user, the adaptation of Algorithm 2 is quite straightforward, and can be summarized as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variant** & **Mixing Ratio** & \(q_{\infty}\) (\(\mathbf{a^{2}}=\infty\)) & \(q_{1}\) (\(\mathbf{a^{2}}=1\)) & \(q_{\text{SNR}}(\mathbf{a^{2}}=\mathbf{a^{2}_{opt.}})\) \\ \hline MGS & \(1/2N_{T}\) & 1 & 0 \\ \hline OGS & 0 & 0 & 1 \\ \hline T-MGS & \(1/20N_{T}\) & 1/2 & 1/2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: “Temperature” parameters and corresponding mixing ratios used to compare the various GS methods, including the proposed one, for \(N=16\) and when a 16-QAM constellation size is used.  
Figure 8: BER performance as a function of the SNR for the optimized, mixed and triple mixed Gibbs sampling algorithms with multiple restarts for \(N_{R}=N_{T}=16\) and a 16-QAM constellation, using \(R=15\) restarts.  
Resorting to the CSI, determine the constellation size of each user according to a given criteria (for instance, using the proposed scheme in [19]). Each user has then an assigned alphabet from which is allowed to transmit, denoted by \(\mathcal{A}_{k}\).
2. Apply Algorithm 2 to the received vector (1), taking into account that the construction of the sampling probabilities in (6) is user dependent, i.e., each candidate entry to be sampled can only be chosen from \(\mathcal{A}_{k}\).  
To best of the authors' knowledge, this is the first time that a detector based on a randomized algorithm is utilized to perform detection in AM-MIMO (alternatives in the literature include either linear detection [17] or SD-based methods [20], whose performance-complexity trade-offs render them useless in large dimensions). Further, given the random nature of the proposed detector, it can be easily extended to generate soft-output values [23], which are of paramount importance when coded transmissions are used.
# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
###### Abstract
The findings from the experiments indicate that TransECA-Net surpasses almost all other models, achieving superior performance across the datasets utilized (including a classification accuracy enhancement of 3.2% and a convergence acceleration of 37.4%). * A novel hybrid architecture integrating local feature enhancement with global interaction is proposed. * A tailored solution for classifying encrypted traffic is developed, effectively overcoming the data dependency and processing limitations characteristic of traditional methods. * The model markedly enhances efficiency and generalization capabilities for the high-precision, real-time classification of non-VPN encrypted traffic.
# From Symptom Tracking to Contact Tracing:
## 3 Results  
### Accessibility  
The framework's questions around app accessibility prompt users to consider what apps and features are available and readily accessible to them. Our analysis revealed that for COVID-19 apps, many were not applicable or accessible to the general public. Of the first 100 apps generated for COVID-19 in both May and June, only 58 and 69, respectively, were able to be downloaded and used, whereas the remaining 42 and 31 apps, respectively, required national identification codes, local country phone numbers, or research study codes to access. Moreover, when specifically looking at the top 10 results as of June 8 2020, we found that half of the apps were created for or by countries other than the US, with apps from Vietnam, Pakistan, Czech Republic, Russia, and the UAE being generated in some of the top ranked spots. Thus, we were restricted in our ability to apply the framework across many apps due to the lack of app accessibility in the COVID-19 app space.
## 2 Materials and Methods
### Production of Docosahexaenoicoic Acid ( DHA) and Total Carotenoids (TC)  
Aliquots taken every 24 h from the bioprocesses mentioned above were used to determine the amount of DHA and TC. Determination of DHA was carried out in three stages: (1) saponification extraction, (2) solvent changes and (3) chromatographic analysis,according to [20]. The saponification reaction was performed using 100 mg of wet biomass adding 1 mL of NaOH at 0.5 M in 96% ethanol. The cellular rupture was performed using an Ultra Turnrax for 1 min. After this, it was centrifuged at 7000 rpm for 5 min, which dismissed any solid residue. One milliliter of HC1 was added to the obtained supernatant at 0.6 N and 3 mL of ethyl acetate placed in a vortex mixer for 1 min. The samples were incubated for 30 min at room temperature; to obtain phase separation, the upper phase was dried with N\({}_{2}\) (g) at room temperature. After this, the phases were freeze-dried to eliminate water residue. The freeze-dried samples were diluted in 400 uL of filtered methanol with polyethersulfone membranes RF-Jet Syringe Filter of 13 mm, with 0.45-um pores and, after this, they were stored at \(-20\)\({}^{\circ}\)C for analysis. For chromatographic analysis, a HPLC VWR\({}^{\text{TM}}\) HITACHI was used, a column LC-18 Supelco(r) of 15 cm \(\times\) 4.6 mm. The mobile phase was performed at a gradient of 100% of A (75% of ACN LiChrosolv(r)) with a flow of 1 mL min\({}^{-1}\) at 50% of B (pure ACN LiChrosolv(r)) with a flow of 2 mL min\({}^{-1}\) over 15 min; consecutively, the flow changed to 1 mL min\({}^{-1}\) until the 30-min cycle finished. Both A and B solutions were acidified with 0.12% of acetic acid (Sigma-Aldrich(r), St. Louis, MO, USA). The detection was performed at 195 nm with an injection volume of 10 uL per sample. The DHA concentration was determined by a calibration curve performed using standard docosahexaenoicenoic acid (Sigma-Aldrich(r)).  
The determination of total carotenoids (TC) was performed by a methodology outlined by [PERSON] [20]. For this, 50 mg of wet biomass were weighed, obtained from aliquots during the bioprocesses. Once the samples were incubated and were centrifuged at 11,000 rpm for 10 min, the reached supernatant was transferred to a new tube for a further spectrophotometric analysis. Prior to the analysis, a potassium dichromate calibration curve was built of a stock solution of 3.6 mg mL\({}^{-1}\). Both the dilutions used to build the calibration curve and the samples were measured at 560 nm in glass cells. The TC concentration was determined, considering that 0.036% of potassium dichromate was equivalent to 2.6 mg mL\({}^{-1}\) of TC.