esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Creation of the Prototype and Establishment of the Dynamic and Static Testing Systems
### Manufacturing Process of the Pressure Sensor Encapsulation  
To achieve accurate measurement of fluid pressure within a biological cavity, the pressure sensor should have the following characteristics: (1) Long-term impermeability. The sensor (BMP280; Bosch) needs to operate in a conductive liquid environment for extended periods. If liquid penetrates the sensor interior, it will not only cause the entire monitoring system to fail but will also contaminate the fluid to be measured. (2) Biocompatibility. Since the system monitors biological entities, and the sensor is placed in a catheter connected to the fluid to be measured, the encapsulating materials used by the sensor can cause protein adhesion, leading to blockages, making biocompatibility a necessary condition to ensure the health of the biological entity.  
To meet these requirements, a layer of Parylene C protective coating is attached to the external surface of the existing pressure sensor [31]. Parylene is prepared by vacuum chemical vapor deposition, can coat surfaces of various shapes, and has excellent electrical insulation and protective qualities. It effectively prevents moisture, mold, corrosion, and salt spray [32].  
The coating process uses a low-pressure chemical vapor deposition (LPCVD) method, with the equipment being a PDS2010 Parylene Coating System (Specialty Coating Systems, Inc., Indianapolis, IN, USA). The materials used for encapsulation are Parylene C (Specialty Coating Systems, Inc.) and a silane coupling agent. The specific procedure for this single-layer coating experiment is as follows: Place the pressure sensor to be encapsulated in the vacuum deposition chamber of the device. Before placing it in the deposition chamber, clean the surface of the pressure sensor, then place 5 mL of silane coupling agent in the evaporation chamber of the Parylene coating system. Next, place the solid dimer form of para-xylylene into the PDS2010 evaporation chamber. During deposition, every 0.6 g of type-C material corresponds to a 1 \(\upmu\)m film. The silane coupling agent vaporizes when a vacuum is applied, filling the chamber with silane gas, and forming a nanoscale layer of coupling molecules on the device surface to enhance the adhesion between Parylene C and the silicon piezoresistive sensor. The gaseous monomer of para-xylylene enters the PDS2010 vacuum deposition chamber at room temperature and a pressure of 13.3 Pa, where it polymerizes and deposits on the device surface. The vacuum level in the vacuum deposition chamber is maintained at 1.3 Pa, resulting in a high-fidelity and dense Parylene C polymer film.
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 3 System Diagram
### The DC Gain of the Channel Model  
When an optical input and optical output are considered, the Direct Current (DC) gain of the channel model is given by \[H_{\text{ref, opt}}=H_{\text{LoS}}+H_{\text{NLoS},(1)}+H_{\text{scatter}}\, \tag{5}\]  
where \(H_{\text{LoS}}\) is the LoS component, \(H_{\text{NLoS},(1)}\) is the single-hop NLoS component, and \(H_{\text{scatter}}\) is the NLoS contribution of light scattering off dust particles. Due to the insignificance of the following bounces in terms of the received power and time dispersion, only one hop is evaluated [25; 37]. In Appendix A, we provide descriptions of reference model equations, which describe the most applicable reference models utilised for the UM-VLC Single input single output channel (UM-VLC SISO) [24; 25]. In [38], it is assumed that particles are spread through a two-dimensional disc with irregular walls, nondeterministic diffuse reflections, shadows, and a scattering component.
# A Packet Content-Oriented Remote Code Execution Attack Payload Detection Model
## 5 Experimental Results and Analysis  
### Experimental Setup  
The construction steps of the experimental dataset (the dataset used in this paper can be obtained from [[https://github.com/HJX-zhanS/RCE-Detection-Dataset](https://github.com/HJX-zhanS/RCE-Detection-Dataset)]([https://github.com/HJX-zhanS/RCE-Detection-Dataset](https://github.com/HJX-zhanS/RCE-Detection-Dataset)) (accessed on 7 March 2024)) are as follows:  
1. Building the vulnerability environment based on Github's open source project Vulhub;
2. Collecting XXE, ELi, and IDSER payloads (benign and malicious) from the Internet;
3. Testing collected payloads in the built environment and collecting network packets.  
We obtain 18 malicious XXE packets and 100 benign packets, 72 benign OGNL expressions and 58 malicious OGNL expressions, 96 benign SpEL expressions and 48 malicious SpEL expressions, and 97 benign class files and 55 malicious class files. Based on these, we construct 340 pieces of data to evaluate the effect of the ELi and IDSER detector on ELi detection, and 122 pieces of data to evaluate its effect on IDSER detection. We use seven metrics as shown in Table 2 to evaluate the effect of the proposed model. It should be noted that positive means malicious and negative means benign.
## 2 Materials and Methods
### Amplification, Sequencing and Analysis of 16S and ITS rRNA  
We extracted DNA of samples using the MO-BIO PowerSoil(r) kit following the manufacturer's protocols under sterile conditions in a laminar flow hood. Sequencing was conducted at the Environmental Sample Preparation and Sequencing Facility at Argonne National Laboratory (Lemont, IL, USA) following the protocol previously described in 2 \(\times\) 150 and 2 \(\times\) 250 Miseqs runs (16S and ITS, respectively) [2].  
We used DADA2 (version 1.30) to infer the amplicon sequence variants (ASVs) present in each sample [25]. ASVs are more accurate and reproducible than OTUs defined at a constant level (97% or other) of sequence similarity [26]. DADA2 pipeline was run following the procedure described previously [2], with minor modifications. Thus, 16S forward and reverse reads were truncated at 140 nt, whereas ITS forward and reverse reads were not truncated. Bacterial ASVs were inferred from forward and reverse reads, whereas fungal ASVs were inferred from forward reads only because more ASVs were assigned than when using forward and reverse reads (502 and 363 ASVs in forward and merged forward and reverse reads, respectively). Taxonomic assignment was conducted using Silva (version 138) and UNITE (version 9.0) databases for 16S and ITS genes, with the RDP naive Bayesian classifier [27; 28]. We removed ASVs unclassified at phylum level (0.85 and 15% of sequences for 16S and ITS, respectively). At the end of the DADA2 pipeline, we retained 708 bacterial ASVs that comprised 103,220 sequences (mean: 6881, SD: 1307) and 376 fungal ASVs that comprised 308,497 sequences (mean: 20,532, SD: 9778). Rarefaction curves showed that sampling depth was fine (Supplementary Figure S1). To assess the impact of earthworms on human bacterial pathogens (HBPs), we used the assignSpecies command from DADA2 to obtain the taxonomy at the species level. We set the allowMultiple option to FALSE to ensure unambiguous identifications, which is appropriate for 16S amplicon data [29]. After obtaining the taxonomic classification at the species level, we compared it with the comprehensive list of HBPs from [PERSON] et al. [30] to identify which species were present in the sewage sludge and to evaluate the effect of earthworms on them. Sequence data were uploaded to the GenBank SRA database under accession numbers PRJNA1090289 (16S) and PRJNA1090297 (ITS).
# A Systematic Review of the Usability of Telemedicine Interface Design for Older Adults
## 4 Discussion
Qualitative studies generally had smaller sample sizes and were often used to analyze or address specific diseases, such as fall prevention programs [52; 65] and studies on blind and visually impaired individuals [18; 82], while the target populations of quantitative studies were relatively broader. Study settings also vary from urban environments in the USA to culturally specific designs in Arab nations. Population characteristics, such as age range, gender distribution, and health conditions (e.g., Alzheimer's disease [86], diabetes [20], or cognitive impairment [83]), further highlighted the heterogeneity. Thesedifferences may lead to biased conclusions, especially when generalizing the research findings to different populations. We observed that larger and more diverse samples tend to produce more universal insights, while smaller-scale or culturally specific studies provide in-depth but narrow perspectives. To address potential biases, future studies should strive to adopt standardized methods and enhance representativeness in both the environment and population.
# Improving Congestion Control of TCP for Constrained IoT Networks
## 7 Conclusions  
ulP TCP may suffer lots of retransmissions and low throughput when RTT fluctuates wildly due to the hidden terminal problem, which may worsen with RDC enabled. One of the main reasons for such poor performance is that uIP TCP sets the retransmission timer using the fixed RTO for retransmitted segments. To address this problem, we propose a new scheme for retransmission timer management. Our scheme adjusts the retransmission timer according to the estimated RTT for retransmitted segments, as well as first-transmitted segments. In addition, our scheme adopts the notion of weak RTT estimation of CoCoA, exponential backoffs with variable limits, and dithering. Simulation results show that our proposed scheme enhances throughput and significantly reduces retransmissions, particularly when an RDC mechanism is enabled.  
In this work, we only used a periodic reporting scenario without bursty background traffic. For future work, we plan to extend our scheme considering various scenarios. Furthermore, we will examine the effect of extending the TCP window size to more than one segment.  
**Funding:** This research was supported by the 2019 Hongik University Research Fund.  
**Conflicts of Interest:** The author declares no conflict of interest.  
Figure 8: Performance with respect to random loss rates in a \(4\times 4\) grid topology network when ContikiMAC is used.  
Figure 7: Performance with respect to random loss rates in a \(4\times 4\) grid topology network when an RDC mechanism is not enabled.
## 2 Materials and Methods
### Data Analysis  
Two examiners with certified experience in CBCT analysis independently screened 580 three-dimensional CBCT files in DICOM (.dcm) format for matching pairs (RCF tooth with contralateral n-RCF tooth) of teeth that met the inclusion criteria from March to June 2019. To prevent fatigue of the observers, the examinations were limited to a maximum of eight per day.  
Both examiners measured all teeth separately according to the stated protocol. Mean values of both measurements served for statistical analysis (SPSS, IBM SPSS Statistics for Windows 26.0, IBM Corp., Armonk, NY, USA). Kolmogorov-Smirnov and Shapiro-Wilk tests revealed that the data were not normally distributed. Therefore, the Kruskal-Wallis test served for statistical analysis. The level of significance was set at 0.05. Regression analysis was performed to determine the strength of the relationship between the identified variables.
# Multi-Fidelity Gradient-Based Optimization for High-Dimensional Aeroelastic Configurations
## 3 Results
### Summary and Comparison  
For reference, Table 7 lists the initial and final fuel burns and constraint values for the rigid cases (best observed design from either BFGS-based optimizer), while Table 8 lists them for the aeroelastic cases. Note that the initial designs are not trimmed and could therefore predict significantly different fuel burns when evaluated at level cruise. In particular, the trimmed rigid baseline designs yield fuel burns of 134,318 and 95,035 kg for the 7- and 17-DoF rigid cases, with the corresponding aeroelastic values being 159,717  
Figure 13: Optimization results for structural sizing cases, variable airfoils. (**a**) Penalty-constrained objective; (**b**) Feasibility; (**c**) Percent reduction, all; (**d**) Percent reduction, difference. and 84,744 kg. Among the rigid cases, the higher-dimensional airfoil parameterization yielded slightly lower initial and final fuel burn values when compared to the lower dimensional one. This is to be expected, as the baseline 17-DoF geometry yielded slightly better aerodynamic performance; among optimized geometries, it was able to more finely tune the geometry to improve performance. The same trend was observed among aeroelastic shape-only cases. For comparison, the SLSQP P1a solution shown in Section 3.2 (via direct constraint handling) yielded a final fuel burn of 64,015.9 kg with the constraints being \([3.6437\times 10^{-4},\ -1.9921\times 10^{-2},\\ -7.9465\times 10^{-5}]^{\intercal}\), which equates to roughly 2% less than the BFGS optimizers. However, \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Problem** & **FB\({}_{0}\) (kg)** & **FB\({}^{*}\) (kg)** & **Initial Constraints** & **Final Constraints** \\ \hline \multirow{3}{*}{P1a} & \multirow{3}{*}{84,166.8} & \multirow{3}{*}{51,899.3 (\(-\)38.34\%)} & \(5.0709\times 10^{-1}\) & \(8.4127\times 10^{-3}\) \\  & & & \(-3.6960\times 10^{-2}\) & \(-3.2446\times 10^{-2}\) \\  & & & \(1.6960\times 10^{-2}\) & \(1.2446\times 10^{-2}\) \\ \hline \multirow{3}{*}{P1b} & \multirow{3}{*}{79,272.0} & \multirow{3}{*}{48,874.9 (\(-\)38.35\%)} & \(2.5849\times 10^{-1}\) & \(2.5544\times 10^{-3}\) \\  & & & \(-5.4214\times 10^{-2}\) & \(-3.6096\times 10^{-2}\) \\ \cline{1-1}  & & & \(3.4214\times 10^{-2}\) & \(1.6096\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of rigid optimization cases (subscript “0” and superscript “*” denote initial and final designs.)
###### Abstract  
Atopic dermatitis (AD) is a chronic and allergic skin disease; however, there is no cure for the disease. Red ginseng is well known to have anti-AD potential, while red ginseng marc (RGM) remaining after ginseng extraction is regarded as useless and discarded. However, it has recently been reported that RGM, particularly fermented RGM (fRGM), still contains bioactive properties. Thus, the anti-allergic effects of fRGM were examined in a 2,4-dinitrochlorobenzene-induced AD-like mice model. The model was topically treated with distilled water (control), dexamethasone, or fRGM for six weeks. Treatments of fRGM alleviated skin lesions and reduced serum IgE levels, compared with the control. The fRGM also reduced skin levels of lipid peroxidation and superoxide anion; however, it increased glutathione contents, with downregulated gene expression for inflammatory mediators. Histopathological analyses demonstrated that fRGM suppressed epidermal thickening, collagen deposition, and inflammatory cell and mast cell infiltration, which involved anti-inflammatory, antioxidant, and anti-apoptotic effects. Further, fRGM suppressed hypertrophic changes and inflammatory responses in the spleen and lymph nodes. The beneficial effects were observed in the dexamethasone and fRGM groups; however, the antioxidant effects were evident only in the fRGM treatments. These results provide useful information for developing fRGM as a therapeutic source for AD.  
Keywords:AD; allergy; skin; DNCB; _Panax ginseng_; red ginseng marc; fermentation +
Footnote †: journal: Applied Sciences
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 3 Proposed Algorithm
### Global Optimal Layer Selection via BayesOpt
#### 3.4.1 Gaussian Process (GP) Regression  
GP regression is a Bayesian statistical approach for modeling an objective function [29]. Just as Gaussian distribution is a distribution over a random variable, completely specified by its mean and covariance, GP regression is a distribution over objective functions, identified by its mean function and covariance function. Any finite set of \(k\) points in \(n\), \(n_{1:k}\) produces a \(k\)-dimensional Gaussian distribution on \(\mathbb{R}^{k}\), taken to be a distribution on \(\{L(n_{i})\}_{i=1}^{k}\). GP regression considers this \(k\)-dimensional Gaussian distribution as the prior distribution with a mean vector \(\mu(n_{1:k})\) and covariance matrix \(\Sigma_{0}(n_{1:k})\). The resulting prior distribution on \(\{L(n_{i})\}_{i=1}^{k}\) is defined as follows:  
\[L(n_{1:k})\sim Normal(\mu_{0}(n_{1:k}),\Sigma_{0}(n_{1:k},n_{1:k})), \tag{5}\]  
where \(\mu_{0}(n_{1:k})\) means a vector which is constructed by evaluating a mean function \(\mu_{0}\) at each \(n_{i}\) in \(\{L(n_{i})\}_{i=1}^{k}\), \(\Sigma_{0}(n_{1:k},n_{1:k})\) denotes the covariance matrix which is composed by evaluating a covariance function \(\Sigma_{0}\) at each pair of points \(n_{i}\), \(n_{j}\) in \(\{L(n_{i})\}_{i=1}^{k}\). In GP regression, the covariance function should produce large positive correlation when \(n_{i}\) and \(n_{j}\) are closer in the input space.  
From the above prior distribution, the conditional distribution of \(L(n)\) can be computed by Bayesian rule.  
\[L(n)|L(n_{1:k})\sim Normal(\mu_{k}(n),\sigma_{k}^{2}(n)), \tag{6}\]  
where \(\mu_{k}(n)\) is  
\[\Sigma_{0}(n,n_{1:k})\Sigma_{0}(n,n_{1:k})^{-1}(L(n_{1:k})-\mu_{0}(n_{1:k}))+ \mu_{0}(n)\]  
and \(\sigma_{k}^{2}(n)\) is  
\[\Sigma_{0}(n,n)-\Sigma_{0}(n,n_{1:k})\Sigma_{0}(n_{1:k},n_{1:k})^{-1}\Sigma_{ 0}(n_{1:k},n).\]  
This conditional distribution is called a posterior probability distribution and quantifies the uncertainty of loss function value \(L(n)\) on the unidentified sharing layer points \(n\).  
The performance of GP regression to represent a valuable distribution on the objective function depends on the covariance function. Therefore, in the proposed algorithm, we utilize automatic relevance determination (ARD) Matern \(5/2\) function as a covariance function:  
\[\Sigma_{0}(n,n^{\prime})=\theta_{0}\bigg{(}1+\sqrt{5r^{2}(n,n^{\prime})}+ \frac{5}{3}r^{2}(n,n^{\prime})\bigg{)}\exp\bigg{(}-\sqrt{5r^{2}(n,n^{\prime}) }\bigg{)}, \tag{7}\]  
where \(r^{2}(n,n^{\prime})\) is \((n-n^{\prime})^{2}/\theta_{1}\), \(\theta_{0}\) and \(\theta_{1}\) are hyper-parameters. This covariance function results in twice differentiable sample functions, a hypothesis that corresponds to those made by, quasi-Newton methods, but without requiring the smoothness of the squared exponential.
# Dynamic and Stability Analysis of Multibolt Plane Joints under Normal Forces
## 2 Surface Fractal Contact Model and Verification
A reasonable approximation of the total elastic force \(P_{e}\) can be written as [21]  
\[P_{e}\Big{(}a_{l}^{\prime}\Big{)}=\int_{a_{c}^{\prime}}^{a_{l}^{\prime}}p_{e} (a^{\prime})n(a^{\prime})da^{\prime}. \tag{6}\]  
Then, substituting Equations (3) and (5) into Equation (6) yields  
\[P_{e}\Big{(}a_{l}^{\prime}\Big{)}=\left\{\begin{array}{ll}\frac{2^{\frac{9- 2D}{2}}D^{D-1}E\left(\ln\gamma\right)^{\frac{1}{2}}\psi^{\frac{3-D}{2}}}{3\pi ^{\frac{3-D}{2}}\left(3-2D\right)}&a_{l}^{\prime}\geq a_{c}^{\prime},D\
eq \frac{3}{2}\\ 2\pi^{-\frac{3}{2}}G^{\frac{1}{2}}E\left(\ln\gamma\right)^{\frac{1}{2}}\psi^ {\frac{1}{2}}a_{l}^{\prime\frac{3}{4}}\ln\frac{a_{l}^{\prime}}{a_{c}^{\prime}},&a_{l}^{\prime}\geq a_{c}^{\prime},D=\frac{3}{2}\\ 0,&a_{l}^{\prime}<a_{c}^The truncated contact area is related to \(n(a^{\prime})\) as follows [21]:  
\[A^{\prime}_{r}\Big{(}a^{\prime}_{l}\Big{)}=\int_{0}^{a^{\prime}_{l}}a^{\prime}n(a ^{\prime})da^{\prime} \tag{8}\]  
Introducing Equation (5) into Equation (8) gives  
\[a^{\prime}_{l}(A^{\prime}_{r})=\frac{2-D}{D}\psi^{\frac{D-2}{2}}A^{\prime}_{r}. \tag{9}\]  
If the rough surface obeys the Gaussian distribution, the truncated contact area also can be expressed as a function of the rms height, the separation \(x_{s}\) between the mean planes of the joint interface and the nominal contact area \(A_{a}\), which is formed as [17]  
\[A^{\prime}_{r}(x_{s})=\frac{A_{a}}{\sqrt{2\pi}}\int_{\frac{x_{s}}{\sigma}}^{ \infty}e^{-\frac{t^{2}}{2}}dt=\frac{A_{a}}{2}\text{erfc}\Bigg{(}\frac{x_{s}}{ \sqrt{2}\sigma}\Bigg{)} \tag{10}\]  
where \(\text{erfc}()\) is the complimentary error function and \(x_{s}=x_{s0}-\Delta x_{s}\), in which \(x_{s0}\) is the initial separation and \(\Delta x_{s}\) is the decrease of the distance between the two surfaces. Hence, the relationship of the separation \(x_{s}\) and the force \(P_{e}\) is obtained mathematically. The normal contact stiffness can be acquired by taking the derivative of \(P_{e}\) with respect to \(x_{s}\). In addition, [PERSON] et al. have proposed a stiffness model based on the fractal theory and carried out an experimental study utilized cast iron, which is the same as the material of the contact parts in this paper [23]. Figure 1 performs the comparison of numerical simulations of the present model, [PERSON]'s model, and experiment results of [PERSON]. It shows the theory curve has good coherence to the experimental data. Here, the pressure of the interface \(Q\) and the stiffness per unit area \(K_{unit}\) are obtained as  
\[Q\Big{(}a^{\prime}_{l}\Big{)}=\frac{P_{e}\Big{(}a^{\prime}_{l}\Big{)}}{A_{a}} \tag{11}\]  
\[\sigma=\frac{G^{D-1}L^{2-D}}{2\sqrt{\left(2-D\right)\ln\gamma}}. \tag{2}\]0  
where the stiffness of contact surfaces \(K_{s}(\Delta x_{s})=dP_{e}(\Delta x_{s})/d\Delta x_{s}\). \[\sigma=\frac{G^{D-1}L^{2-D}}{2\sqrt{\left(2-D\right)\ln\gamma}}. \tag{2}\]1 Dependence of the stiffness per unit area on the pressure: (**a**) milling/milling joint, (**b**) scraping/scraping joint, (**c**) grinding/grinding joint.
# Assessing the Applicability of Machine Learning Models for Robotic Emotion Monitoring: A Survey
## 2 Background
### Human Signal Sources
#### 2.1.7 Speech Signals  
Speech is the most commonly used and one of the most important mediums of communication for humans. The signals generated from human voice or audio clips are considered speech signals. Speech signals can contain information regarding the message, speaker, language, and emotion [48]. Therefore, speech signals have been of great interest to researchers for emotion recognition. Similar to imaging signals, the general approach for speech emotion recognition has three stages; signal preprocessing, feature extraction, and feature classification [49]. Besides audio extracts, speech signals are usually collected from smartphones and wearable devices where local interaction of on-body, environmental, and location sensor modalities are merged [50].