esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Material and Methods
### Adaptive Gaussian Filter  
The AGF operates in a slightly different manner from Gaussian filtering. The standard deviation dynamically changes, pixel by pixel. The weight of the AGF is defined as follows:  
\[f(\mathbf{p},\mathbf{q}) := \exp(\frac{\|\mathbf{p}-\mathbf{q}\|_{2}^{2}}{-2\sigma_{\mathrm{s}}(\mathbf{ p})^{2}}), \tag{5}\]  
where \(\sigma_{\mathrm{s}}(\mathbf{p})\) is a pixel-dependent parameter found in a parameter map.  
Here, we use this filter for refocusing. In this application, we change the parameter of the Gaussian distribution using a depth map [45; 46] as the parameter map. The detail of the AGF based on the depth map is defined as follows:  
\[f(\mathbf{p},\mathbf{q}) := \exp(\frac{\|\mathbf{p}-\mathbf{q}\|_{2}^{2}}{-2(\sigma_{\mathrm{s}}+ \alpha|d-\mathbf{D}(\mathbf{p})|)^{2}}), \tag{6}\]  
where \(\mathbf{D}\) is the depth map, \(d\) is the focusing depth value, and \(\alpha\) is a parameter of the range of the depth of field. The function of the kernel shape \(\mathcal{N}(\mathbf{p})\) in Equations (1) and (2) is different for each pixel of interest \(\mathbf{p}\).  
Blurring is minimal at the focused pixel, and most of the kernel weights may become zero. In this case, the region whose kernel weights are not zero can be regarded as an arbitrary kernel depending on \(r^{\prime}\), which is less than the actual \(r\), due to the property of the Gaussian distribution. This means that, if the processing pixel of interest is in focus, we can only process a small kernel depending on \(r^{\prime}\). Pixel loop vectorization, pixel loop unrolling, and kernel loop unrolling are restricted in terms of the kernel shape function. Therefore, the largest kernels in a vector of the pixel of interest should be used to maintain the restriction. Further, within kernel loop vectorization, arbitrary kernel loop unrolling, and color loop unrolling, the amount of processing can be reduced using small kernels.  
In the AGF, multiple LUTs are prepared to compute kernel weights whose size is \(\mathcal{D}\times(2r+1)\times(2r+1)\). \(\mathcal{D}\) is the number of elements in the depth range. The utilized LUT is switched by the depth value. In kernel loop vectorization, kernel loop, and color loop unrolling, the LUT is sequentially accessed within a single LUT. In pixel loop vectorization and pixel loop unrolling, the LUT is instead discontinuously accessed across multiple LUTs.
## 3 Materials and Methods
For example, as shown in Figure 6, for the natural decay of a 50 kg satellite with 400 kg/m\({}^{3}\) density from an initial elliptical orbit with 275 km perigee and 700 km apogee, the predicted deorbit time with the integration of the equations of motion is 347 days, while with the approximated model (50 km sampling distance) is 349 days (0.6%). Such an accurate prediction comes with a dramatically reduction in computational time. Figure 7 shows that the variation of the semi-major axis during one orbit is negligible and that the eccentricity is very low, justifying the corresponding hypotheses. It is also worth noting from Figure 8 that the flight-path angle is very small in LEO, as any elliptical orbit has a very low eccentricity anyway. \[r\approx\frac{r_{0}}{\left(1-\frac{F}{m}\frac{t}{t_{0}}\right)^{2}} \tag{13}\]8 Error of the exponential density model compared with the USSA1976 for: (**a**) 10 km sampling spacing; (**b**) 100 km sampling spacing. Parametric with initial altitude (250 to 1500 km at 25 km steps). \[r\approx\frac{r_{0}}{\left(1-\frac{F}{m}\frac{t}{t_{0}}\right)^{2}} \tag{13}\]9 Comparison between the USSA1976 density model and approximate exponential model: (**a**) density vs. altitude; (**b**) error with altitude of the exponential model for a 50 km sampling spacing. \[a=\frac{2a^{2}}{\mu}dE\rightarrow\Delta a_{rev}=\frac{2a^{2}}{\mu}\Delta E_{rev} \tag{14}\]0 Natural orbital decay from an initial elliptical orbit (275 \(\times\) 700 km): (**a**) approximate model; (**b**) numerical integration of the equations of motion. \[a=\frac{2a^{2}}{\mu}dE\rightarrow\Delta a_{rev}=\frac{2a^{2}}{\mu}\Delta E_{rev} \tag{14}\]1 Orbital decay calculated with the approximate model: (**a**) relative variation of the semi-major axis for each orbit (**b**) eccentricity evolution with time. Consequently, the direction of the spacecraft velocity is almost perfectly orthogonal to the instantaneous orbit radius even far from the apsides and, therefore, gravitational losses are negligible even for a continuous thrusting system.
## 3 Intrinsic and Extrinsic Factors Associated with Hair Graying
### Genetics and Epigenetics  
Although most people experience hair graying as they age, some people show symptoms of premature hair graying at a much earlier age [62; 63]. The age criteria for classifying premature hair graying vary according to race [18; 22; 62]. Premature hair graying occurs mainly due to genetic factors, although it is also influenced by various other factors [22; 64; 65]. A familial history of premature hair graying is an important risk factor for the expression of this phenotype [66; 67; 68].  
MITF plays a critical role at multiple stages of the melanocyte life cycle across species, with its expressional depletion resulting in a fully depignmented hair or coat [69]. The heterozygosity for MITF exacerbates hair graying in transgenic (_DctSox10_) mice that conditionally overexpress SOX10 by upregulating a type I interferon (IFN)-regulated gene expression signature associated with innate immunity [70].  
Genome-wide association studies revealed that interferon regulatory factor 4 (IRF4) downregulation is associated with scalp hair graying [71]. IRF4 interacts with MITF and transcription factor AP-2\(\alpha\) (TFAP2A), inducing gene expression of melanogenic enzymes, such as TYR [72]. Accordingly, hair-graying prediction models have been proposed by combining age, sex, and 13 unique single nucleotide polymorphisms (SNPs), including those in IRF4, maestro heat-like repeat family member 2A (MROH2A), kinesin family member 1A (KIF1A), and non-structural maintenance of chromosomes element 1 homolog (NSMCE1) [73].  
A recent study has reported that the S519N variant of SAM and SH3 domain-containing protein 1 (SASH1) causes dysfunction in MSCs maintenance, contributing to the development of certain aging-related pigmentation disorders, namely senile lentigo of the skin and scalp hair graying in a Hispanic family [74]. SASH1 is a scaffold protein binding to tankyrase 2 (TNKS2) and regulating the division of MSCs, but its S519N variant does not function normally due to a low binding affinity to TNKS2 [74; 75].  
MicroRNAs, produced by the dicer (endoribonuclease type 3), are involved in lowering target gene expression by inducing mRNA degradation or suppressing translation [76]. Mimicking stress conditions, the inactivation of dicer causes the misplacement of melanocytes within the hair follicle, resulting in reduced melanin transfer to keratinocytes in the growing hair [77]. These effects were partly mediated by miR-92b, which targets integrin alpha chain 5 (ITGA5) involved in the regulation of melanocyte migration, implicating that such epigenetic mechanisms might link stress to premature hair graying.  
From a mechanistic perspective, it is considered that certain genetic or epigenetic variations may cause acquired hair graying by negatively affecting MSC maintenance and melanocyte migration, whereas some genetic or epigenetic variations resulting in differences in the melanogenic function of melanocytes may affect natural hair color.
# Study on Asymmetric Support of Anchor Cable with C-Shaped Tube in Inclined Coal Seam Roadway
## 2 Research on the Mechanical Characteristics of Anchor Cable with C-Shaped Tube
### Preparation for Test  
To compare the shearing ability of the prestressed \(ACC\) and the prestressed anchor cable under shear load, the self-developed tension shear test equipment was employed in a double shear test. In the test, a 21.8 mm \(ACC\) (composed of a 21.8 mm anchor cable and a C-shaped tube having an internal diameter of 24 mm and an exterior diameter of 28 mm) and a 21.8 mm anchor cable commonly used in roadway support were selected. The length of the anchor cable and the embedded anchor cable in the \(ACC\) was 1200 mm, while the length of the C-shaped tube was 850 mm. The steel grade of the anchor cables was 82B carbon steel, while the steel grade of the C-shaped tubes was Q345B. Table 1 shows the test scheme.  
A concrete test block was used as the supporting material in the test, which was 300 mm by 300 mm by 300 mm in dimension, with 32 mm holes set aside in the center. Water was the raw material, and the proportion of cement, fine sand, and gravel was 1:2:4:4. When making each set of concrete test blocks at the same time, small test blocks were reserved with a size of 100 mm by 100 mm by 100 mm for uniaxial compression testing. Once the cured concrete test specimen reached its design strength, it was placed inside a shear box. The \(ACC\) was then inserted into prefabricated holes, and an axial force sensor was placed on the exposed portion of the \(ACC\). The \(ACC\) was then tensioned using a prestressing jack to achieve the design prestress. Then, the shear box was placed within a vertical loading system, with the side shear boxes fixed using transverse beams while the middle shear box remained suspended. At the beginning of the experiment, the vertical loading system applied a loading rate of 2 mm/min. The internal system was equipped with stress sensors and displacement sensors to record variations in shear load and shear displacement throughout the test. The test concluded when the \(ACC\) fully ruptured. The processes of test material assembly and test loading are shown in Figure 3. The findings of the uniaxial compression test revealed that the average strength of the test blocks for the double shear test was 40 MPa.
# REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs
## 1 Introduction
* **Performance of collection devices:** WSN requires efficient security algorithms to work efficiently. EMR systems use WSN to collect patient data. However, a WSN is source-constrained in terms of energy, computing and memory. Therefore, when using signature mechanisms, security and performance should be efficient. The efficiency of these algorithms is a major challenge in HC applications. Namely, the sensor nodes should be very efficient to collect patient data accurately and for a long time while protecting the data collected from the penetration [10; 11]. Many attacks undermine the security of the WSN of collecting patients' data and threaten the privacy of the EMR repository. These attacks have been classified into passive, active, internal, and external attacks [12; 13; 14]. For instance, potential attacks on data transferred or stored in an EMR repository by WSN are a serious risk to HC systems. As shown in Figure 1, Intruder 1 can listen to data as they are transferred from the patients' sensors or \(CH\)s to the server (\(LS\)/\(BS\)). When the attacker intercepts the message, he/she can obtain information about the physical location of the patient, ID, timestamps, source address, target address, and the medical report sent by the sensors or directed by medical staff devices. The patients' data transmitted through the sensor networks requires complete security, especially when movement through the network does not require the consent of the patient, such as moving the data of an emergency case [15]. Additionally, Intruder 2 can perform an attack on the \(LS\)/remote Central Server (\(CS\)) to penetrate the datasets to obtain patient information. An attacker can also get information from the datasets, such as the patient's name, age, address, type of disease, and the seriousness of the disease. This information allows the attacker to harm the patient in different ways, such as changing or destroying data [16]. However, designing a patients' data collection scheme with strong and heavy signing mechanisms without regard to network performance in data collection is useless and infeasible for HWSN systems. Therefore, performance and security issues are essential to provide care services in HC applications.
## 4 Measurement Results and Discussion  
### Measurement Setup  
The measurement setup is illustrated to obtain the responsivity of the detector in Figure 6. A 16.67 GHz signal with a power of 5 dBm from the signal generator is transmitted to the input of the mixer. The signal applied to the mixer input is multiplied by the multiplier corresponding to 12 of the mixer to output a 200 GHz signal. The amplitude of the THz signal is electrically modulated as opposed to the mechanical chipper by applying the pulse waveform with an amplitude of 5 V from the function generator to the Transistor-Transistor Logic (TTL) mode of the mixer. The frequency of the modulation signal is set as 200 Hz to easily obtain the detected signal in a measurement environment  
Figure 4: Architecture of the two THz detectors: (**a**) Differential detector directly connected to the external low-noise amplifier; (**b**) Detector including the subthreshold preamplifier.  
Figure 5: Photographs of the fabricated complementary metal-oxide-semiconductor (CMOS) plasmon detector integrated circuits: (**a**) Differential detector including the integrated antenna and the bias circuits; (**b**) Detector including the integrated antenna, the bias circuits, and the subthreshold preamplifiers.  
with DC offset and noise floor. The 200-GHz signal output from the mixer is radiated into free space through a horn antenna with 23 dBi gain. The input power received at the detector is calculated as \(-22.10\) dBm given the distance from the antenna port to CMOS detector corresponding to 6.7 cm by the Friis transmission equation [28]. The output signal of the detector is connected to the 50-\(\Omega\) input impedance of the low noise voltage amplifier. The signal amplified by the voltage gain of 49 dB in the low noise voltage amplifier is measured using a signal analyzer. The voltage gain of the low noise amplifier is set to the maximum gain that is obtained from the magnitude of the detector output signal.
## 6 Comparison  
The Mkurtogram [46] method proposed by [PERSON] et al. is used for comparison firstly. In Mkurtgram, envelope autocorrelation analysis of the original signal is carried out for the estimation of FCF firstly. Secondly, the targeted hunting frequency zone is set automatically according to the estimated FCF. Subsequently, the analyzed signal is decomposed by 1/3 binary filter banks and the frequency domain multipoint kurtosis (FDMK) of each decomposed signal is calculated. At last, the frequency band with the maximum FDMK is selected. As verified in [46], Mkurtogram has more advantages than the classical Kurtogram method and another new FBS method, that is the frequency domain correlated kurtosis [47] method. The analyzing results of the original experimental signal using fast Mkurtogram are presented in Figure 11. Though the calculation efficiency of Mkurtogram is higher than the proposed method and the FCF of the experimental bearing's inner race is extracted as presented in Figure 11c, the harmonics of FCF are not extracted and the reason is due to the effect of the low sampling frequency when resampling being required using the Mkurtogram method. Similarly, Figure 12 provides the analyzing results of the original engineering signal using Mkurtogram, based on which not only an evident impulsive characteristic could not be observed, but also the FCF of the engineering bearing is not extracted.  
The second compared method is the multi-objective informative based FBS (MOIFBS), in which the grey wolf optimizer is used for capturing the impulsiveness and cyclostationarity characteristics to adaptively determine the parameters of FBS, and its excellent performance is verified through two cases of slight bearing faults. Figure 13 presents the MOIFBS results of the original experimental signal and the impulsiveness characteristic is enhanced evidently by comparing Figure 13c with the original experimental signal. Unfortunately, the FCF of the experimental bearing's inner ring could not be identified evidently in Figure 13d. Similarly, though the FCF and its harmonics of the engineering bearing could be identified based on the analysis results as presented in Figure 13 by applying MOIFBS on the original engineering signal. By comparing Figure 13d with Figure 9, the extraction effect of the latter is better: the spectral line amplitudes located on the FCF and its harmonics are evident based on Figure 13d, but they are relatively weaker compared with the spectral line amplitude located on the rotating frequency of the experimental  
Figure 10: Engineering verification of the proposed method. (**a**) The original vibration signal of engineering bearing. (**b**) Envelope spectral of the signal shown in (**a**). (**c**) The filtered signal of the signal shown in (**a**) using the proposed AMF. (**d**) Envelope spectral of the signal shown in (**c**). (**e**) Analysis result of the signal shown in (**c**) using the proposed FBS method. (**f**) Envelope of the filtered signal of the signal shown in (**c**) using the proposed FBS method. (**g**) FFT of the signal shown in (**f**).
## 4 The (Near) Future: 2015-...
### Viscoelastic Tensor  
The complex C-tensor identification scheme discussed in Section 2 is a procedure solely based on the analysis of the amplitude P-UPS landscape. That system identification procedure has its merits, but still suffers from drawbacks. First, the amplitude-based inversion procedure is ill-defined for samples with a high degree of viscoelasticity. Further, for certain materials, small discrepancies between the characteristic patterns and the critical bulk wave angles were observed [19], thus requiring special care during inversion. Finally, the current amplitude-based inversion is unable to provide the complete set of orthotropic viscoelasticity parameters.  
Other researchers have already tried to tackle the problem of (visco)elastic tensor determination by considering TOF (and amplitude) analysis of obliquely incident waves [37; 38; 39; 40]. Unfortunately, their approaches also have inherent limitations. As they consider only a limited set of orientation angles \(\varphi\), prior knowledge about the material symmetry axes is required [37; 40; 41]. Secondly, those inversion schemes often rely on bulk wave approximations, which has the consequence that the plate thickness \(d\) should match the ultrasound frequency \(f\). Thirdly, phase effects at the liquid-solid and solid-liquid interface are often ignored, though some authors incorporated the phase effect by considering an additional iterative step [38].  
One of our current research lines is trying to reconcile the above two inversion procedures, _i.e._, combining the analysis of amplitude P-UPS landscape with TOF analysis, in order to obtain a robust, stable and widely applicable procedure for extracting viscoelastic material parameters. In order to reach this goal, we consider a two-step inversion approach [21; 22]. The schematic roadmap of this approach is presented in Figure 15. First, the elastic properties are extracted on the basis of the TOF landscape of a P-UPS recording. Second, damping parameters are obtained through analysis of the amplitude landscape of a P-UPS recording.  
As the forward P-UPS simulation model (both amplitude and TOF) accounts for the true physical parameters, its validity is not restricted by the bulk wave approximation. Further, the optimization procedure includes a wide range of incidence angles \(\Psi(\varphi\),\(\theta)\), which makes prior knowledge about material symmetry ubiquitous. Finally, the large amount of redundant data will without doubt lead to robust and stable inversion results, even for noisy data. As such, we believe that our current effort may lead to a comprehensive approach in obtaining viscoelastic parameters for a wide range of (orthotropic) materials. As a matter of fact, we already successfully tested this system identification procedure for synthetic data [21; 22], and we are now in the stage of applying this procedure to real experimental data.
## 3 Results
### Importance of Processing Parameters  
The S/N ratio is an important measure of robustness used to determine control variables that decrease variability in a manufacturing process by reducing the influences of uncontrollable (or noise) variables [14; 15]. The importance of each processing parameter on the product quality can, therefore, be decided by the alteration of S/N. In line with the data presented in Figure 10A, the significance ranking of each parameter for the strength of FDM printed parts was fill density (\(\Delta\)S/N = 8.91 dB), print speed (\(\Delta\)S/N = 5.94 dB), oven temperature (\(\Delta\)S/N = 5.72 dB), print orientation (\(\Delta\)S/N = 3.98 dB), and nozzle temperature (\(\Delta\)S/N = 3.44 dB). For the parameters chosen in this analysis, fill density, print speed, and oven temperature were identified as the most important factors affecting the tensile properties of FDM printed parts.  
Based on Figure 10B, the importance of each parameter for the tensile strength of solution extrusion-printed parts was, from high to low, oven temperature (\(\Delta\)S/N = 5.13 dB), fill density (\(\Delta\)S/N = 4.86 dB), PLA/DCM ratio (\(\Delta\)S/N = 3.95 dB), print speed (\(\Delta\)S/N = 2.98 dB), and print orientation (\(\Delta\)S/N = 1.10 dB). For the variables chosen in this analysis, oven temperature, fill density, and PLA/DCM ratio were identified as the most important parameters affecting the strength of solution extrusion-printed parts.
## 4 Experimental Results
### Concentration  
We conducted single factor variable analysis to examine the learning concentration for the students of the experimental group; the results are given in Table 5. The Levene standard deviation was used to describe the variable difference, where the result was \(F=12.644\), \(p<0.01\). The analysis result indicated that the experimental group students (who were taught by AR teaching videos) were more easily immersed in the situational context learning than the control group students. Regarding the test group student feedback, the AR technique-assisted language-learning approach provided them with enjoyable and interesting immersion experiences; further, the AR videos could be repeatedly played, providing an effective method for learning. The research hypothesis 1, regarding the difference of concentration between the experimental and control group, has thus been validated.