esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Hertzian Elastohydrodynamic Contact Stress Calculation of High-Speed Ball Screws
## 2 Materials and Methods
### Non-Hertzian Normal Contact Stress Calculation
The contact equation between the ball and raceway can be expressed by a matrix equation:  
\[\mathbf{c}=\overset{-}{\mathbf{c}}+\mathbf{K}\otimes\mathbf{P} \tag{7}\]  
\[c_{i,j}\geq 0,p_{i,j}\geq 0,c_{i,j}p_{i,j}=0 \tag{8}\]  
\[\widetilde{c}_{i,j}=d_{0}+c_{gi,j}+c_{ri,j} \tag{9}\]  
where \(\otimes\) indicates cross product. According to the variational principle, the contact problem described by Equations (7)-(9) can be transformed to the conditional extremum problem of quadratic function, which is derived by:  
\[W(\mathbf{P})=\mathbf{c}^{T}\mathbf{P}+\frac{1}{2}\mathbf{P}^{T}\mathbf{K}\mathbf{P} \tag{10}\]  
The boundary condition of Equation (10) is given by:  
Figure 1:0The fast convergence of the contact Equation (11) in the cyclic iteration is achieved by using the conjugate gradient method. The flowchart is shown in Figure 1:1 The detailed derived process and iteration solution process of the non-Hertzian contact stress is listed as follows [22]:  
1. The initial normal contact stress \(\mathbf{P}\) is given, \(p_{ij}\geq 0\). The normal contact stress distribution \(p_{i,j}\) is also satisfied in the mesh region. 2. The discretized normal contact deformation \(\mathbf{V}\) is determined by Equation (4). 3. The contact gap \(\mathbf{c}\) between the two contact surfaces is calculated by Equations (3)-(6), and the contact gap is satisfied with the boundary condition. 4. According to the variational principle, the contact in Equation (7) can be transformed by the conditional extremum of the quadratic function, the normal contact stress \(p_{ij}\) is modified by Equation (10) satisfying the boundary condition in Equation (11). 5. The current relative error is determined by: Figure 1:2 (12)
6. If \(\varepsilon\geq\varepsilon_{0}\), the iteration solution process of the non-Hertzian contact stress continues from steps (2)-(5), otherwise the iteration solution process ends. The classic solution between the ball and smooth plane can be determined by Hertzian contact theory [23]; it is expressed as  
Figure 1:3  
The comparison between the Hertzian solution and non-Hertzian solution of the contact stress under the helix angle is zero is shown in Figure 1:4 As shown in Figure 3, the non-Hertzian solution is in good agreement with the Hertzian solution in the contact stress calculation results, which is verified the correctness of the non-Hertzian contact stress calculation method. Figure 1:5 Flowchart for the computation of the non-Hertzian contact stress.
## 2 Materials and Methods
### Equipment  
The study was conducted on a slide vibration board (SVB) (Patent, P201630075, Vislide; Viequipment, Movilani System SCP, Sant Joan Despi, Barcelona, Spain (Frequencies: 20, 25 and 30 Hz; Amplitude: 2 mm; Total size: 2.27 \(\times\) 0.74 \(\times\) 0.24 m; sliding surface size: 2.00 \(\times\) 0.59 m). Players wore a pair of nylon socks over their shoes to be able to slide on the polyethylene surface of the SVB. The different intensities of the incremental test were controlled through different rhythms using a metronome (Korg KDM-3 Digital Metronome; Tokyo, Japan) to help the player keep the rhythm by providing auditory feedback and synchronizing the beep with the end of the slide action [16]. For the WBV condition, the frequency selected was 30 Hz [32,33].  
Throughout each test, the trunk acceleration of the ice hockey players was measured using a wireless inertial measurement unit (IMU) (WIMU; Realtrack Systems, Almeria, Spain). Signals from the 3-axial accelerometer (range: \(\pm\)100 G; sampling frequency: 1000 Hz) were used.  
The IMU was securely attached near the players' sacrum using an elastic waist belt. This specific placement was chosen as it provides the most accurate representation of whole-body movement, given its proximity to the players' centre of mass [34]. SampEn was quantified in arbitrary units (a.u.) from the module of the acceleration signal [19] using specific routines written in MATLAB(r) (version R2020a, The MathWorks, Natick, MA, USA). The physiological parameters monitored included expired volume (VE), respiratory exchange ratio (RER), carbon dioxide production (VCO\({}_{2}\)), and oxygen uptake (VO\({}_{2}\)). These  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **n = 10** \\ \hline Age (years) & 20.4 \(\pm\) 2.07 \\ Height (m) & 1.79 \(\pm\) 0.04 \\ Leg height (m) & 0.92 \(\pm\) 0.03 \\ Weight (kg) & 75.97 \(\pm\) 5.43 \\ BMI & 23.64 \(\pm\) 1.64 \\ \hline \hline \end{tabular} BMI: body mass index.  
\end{table}
Table 1: Participants’ characteristics.  
were measured on a breath-by-breath basis using a K4b2 (Cosmed(r); Rome, Italy) portable gas exchange analyser, which was calibrated as per the manufacturer's guidelines before each testing session. Heart rate (HR) data were gathered via radiotelemetry using a SP0180 Polar Transmitter (Polar Electro INC., Kempele, Finland). The subjective perception of exertion was assessed by means of the rating of perceived exertion (RPE) questionnaire with the Borg scale [35]. When participants finished each series the evaluators asked, \"How hard did you find the set you performed?\". The scale consisted of 15 items in a range of scores from 6 to 20 (6 = very, very light; 20 = very, very difficult).
## 3 Results and Discussion
### Structural Properties of RD-DF
#### 3.1.2 Crystalline and Molecular Structure  
The XRD patterns for D-RB, RB-DF, EH-DF, and HPH-DF (Figure 2) showed a main diffraction peak at 22.5\({}^{\circ}\), which is attributed by cellulose and hemicellulose (20 is 15-25\({}^{\circ}\)) [26]. However, diffraction peak intensity values do change significantly during sample processing (\(p<\)0.05). The crystallinity of EH-DF decreased from 22.22% \(\pm\) 1.23% to 11.76% \(\pm\) 0.70% (\(p<\)0.05) compared to that of raw D-RB. The main reason for this is the hydrolysis of starch and protein in rice bran, and the random and amorphous hemicellulose attached to cellulose microfibers surface is preferentially hydrolyzed during cellulase hydrolysis, resulting in a significant decrease in fiber crystallinity [27]. Subsequently, the crystallinity of HPH-DF increased from 11.76% \(\pm\) 0.7%, to 15.78% \(\pm\) 0.3%. Cellulose is a linear polymer of D-glucopyranoside linked by \(\beta\)-1,4 glycosidic bonds, having a crystalline region and an amorphous region. The shear force generated by HPH likely promoted the cleavage of hydrogen bonds between cellulose molecular chains and destroyed the amorphous regions of cellulose. With the dissociation of the amorphous region, the arrangement regularity of cellulose molecule increased, resulting in an increase in crystallinity. Thus, HPH treatment leads to cellulose destruction of the connection between crystals, but does not affect the structural framework of the cellulose polymer backbone.  
Figure 1: Scanning electron microscopy images of rice bran dietary fiber at different treatment stages (**A**) defatted rice bran, (**B**) rice bran dietary fiber, (**C**) cellulase enzymatic treatment rice bran dietary fiber, (**D**) high pressure treated rice bran dietary fiber.
# New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach
###### Abstract  
In the Korea Flux Monitoring Network, Haenam Farmland has the longest record of carbon/water/energy flux measurements produced using the eddy covariance (EC) technique. Unfortunately, there are long gaps (i.e., gaps longer than 30 days), particularly in 2007 and 2014, which hinder attempts to analyze these decade-long time-series data. The open source and standardized gap-filling methods are impractical for such long gaps. The data-driven approach using machine learning and remote-sensing or reanalysis data (i.e., interpolating/extrapolating EC measurements via available networks temporally/spatially) for estimating terrestrial CO2/H2O fluxes at the regional/global scale is applicable after appropriate modifications. In this study, we evaluated the applicability of the data-driven approach for filling long gaps in flux data (i.e., gross primary production, ecosystem respiration, net ecosystem exchange, and evapotranspiration). We found that using a longer training dataset in the machine learning generally produced better model performance, although there was a greater possibility of missing interannual variations caused by ecosystem state changes (e.g., changes in crop variety). Based on the results, we proposed gap-filling strategies for long-period flux data gaps and used them to quantify the annual sums with uncertainties in 2007 and 2014. The results from this study have broad implications for long-period gap-filling at other sites, and for the estimation of regional/global CO2/H2O fluxes using a data-driven approach.  
eddy covariance; long-term database; gap-filling; long gap; data-driven approach; uncertainty +
Footnote †: journal: _arxiv.org/abs/1206.00002_
# Swin Transformer Assisted Prior Attention Network for Medical Image Segmentation
## 4 Experimental Results
### Experiment Setup  
#### 4.1.1 Datasets  
For overall comparison with other state-of-the-art methods in direction of Transformer complements CNNs, gland segmentation dataset [38] and MoNuSeg segmentation dataset [39] are selected to evaluate our proposed method. Glands are important histological structures as the main mechanism for secreting proteins which its nuclear images have been used to assess the degree of malignancy. Similarly, MoNuSeg dataset is obtained by carefully annotating tissue images of patients' tumors of different organs. Gland segmentation dataset (GlaS) contains 85 images for training and 80 images for testing. MoNuSeg segmentation dataset (MoNuSeg) contains numbers of 30 images for training and numbers of 14 images for testing. Experiments on GlaS and MoNuSeg datasets followed the same experimental protocols with the recent research [5], in which the datasets are divided according to the training and test datasets provided by the competition, and the five-fold cross-validation strategy is conducted on training and testing datasets for fair comparison.  
Furthermore, for investigating the performance of the proposed modules and further demonstrating the effectiveness of Swin-PANet, the skin lesion segmentation task in International Symposium on Biomedical Imaging (ISBI) from the year 2016 is implemented on the proposed Swin-PANet. The skin lesion segmentation datasets are acquired from different medical centers, and archived by the International Skin Imaging Collaboration(ISIC). The ISIC conducts a challenging competition called skin lesion analysis toward melanoma detection for enhancing the quantity of melanoma diagnosis. ISIC 2016 dataset contains a number of 900 images in total for training and a number of 379 images in total for testing.
## 4 Risk Analysis for Current Protocols
### OneNet
#### 4.5.1 Denial of Service  
Operating through Ethernet network, OneNet devices can obtain transfer speeds up to 10 Gbps [40]. As such, we suggest that, because of the high throughput, OneNet devices have a better resistance against DoS attacks, depending on hardware and software configuration.
# Experimental Investigation on Effect of Temperature on FDM 3D Printing Polymers: ABS, PETG, and PLA
###### Abstract  
Four-dimensional printing is a process in which a 3D-printed object is intentionally transformed in response to an external stimulus such as temperature, which is useful when the final geometry of a 3D-printed part is not easily manufacturable. One method to demonstrate this is to print a part made of thin strips of material on a sheet of paper, heat the part, and allow it to cool. This causes the part to curl due to the difference in the thermal expansion coefficients of the paper and plastic. In an attempt to quantify the effect of different temperatures on various materials, samples of three common 3D printing filaments, acrylonitrile butadiene styrene (ABS), polyethylene terephthalate glycol (PETG), and polylactic acid (PLA), were heated at different temperatures (85 \({}^{\circ}\)C, 105 \({}^{\circ}\)C, and 125 \({}^{\circ}\)C) for intervals of 15 min and then allowed to cool until curling stopped. This heating and cooling cycle was repeated three times for each sample to determine if repeated heating and cooling influenced the curling. Each sample was filmed as it was cooling, which allowed the radius of curvature to be measured by tracking the uppermost point of the part, knowing the arc length, and calibrating the video based on a known linear length. After three cycles, all three materials showed a decrease in the radius of curvature (tighter curl) as heating temperature increased, with PLA showing the trend much more predominantly than ABS and PETG. Furthermore, for PETG and PLA, the radius of curvature decreased with each cycle at all temperatures, with the decrease being more significant from cycle 1 to 2 than cycle 2 to 3. Conversely, ABS only shared this trend at 125 \({}^{\circ}\)C. The findings of this work can provide guidelines to users on the temperature dosage for the mass manufacturing of complex geometries such as packaging, self-assembly robots, and drug delivery applications.  
Mechanical Engineering Department, Widener University, One University Place, Chester, PA 19013, USA; [EMAIL_ADDRESS]  
* Correspondence: [PERSON]@wideneredu  
**Citation:** [PERSON]; [PERSON] Experimental Investigation on Effect of Temperature on FDM 3D Printing Polymers: ABS, PETG, and PLA  
**Appl. Sci. 2023, 13, 11503. [[https://doi.org/10.3390/app132011503](https://doi.org/10.3390/app132011503)]([https://doi.org/10.3390/app132011503](https://doi.org/10.3390/app132011503))**  
**Keywords:** 4D printing; polymer; PLA; ABS; additive manufacturing; FDM 3D printing
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 4 The Proposed Scheme
### Data Management Processes  
In this subsection, we provide the processes for data creation and storage, data access by the user, and data access by the third party.  
First, consider the case in which a new user, such as a health professional, general user, or third party participates in the system. When a new member participates in the proposed system, a private key should be created and a public key calculated for the PRE scheme, as mentioned in Section 4.3.2. In addition, to open the stored file, a re-encryption key that transforms the encrypted symmetric key to another encrypted key that can be decrypted by the user is generated, as mentioned in Section 4.3.5.  
Next, we consider the case in which the data are created by the medical institute. For this case, the process described in Section 4.3.3 is executed. When a health professional, such as a doctor, creates an EHR, a symmetric key is created first, and then the data are encrypted with the key using a symmetric encryption scheme such as the AES. Subsequently, the symmetric key is encrypted using the second level of the PRE with the manager's public key. Next, a pair of data and encrypted keys are transferred to the data server, and the access authority for the data is recorded in the blockchain system.  
When a manager wishes to view the stored file, he/she must first request the file server to send the corresponding file. Subsequently, the server verifies the requester's accessibility for the file using the blockchain and returns the encrypted file and encrypted key to the manager. Next, the manager retrieves the symmetric key using the second-level decryption, as mentioned in Section 4.3.4, and decrypts the file using the symmetric key. Finally, the manager obtains the data.  
In addition to the manager, a general user, such as a patient or a third party, may require the stored data. Prior to this process, the third party should obtain the right to access the data and create a re-encryption key using the procedure detailed in Section 4.3.5. When the file server receives a data request, it verifies the accessibility of the blockchain server. If the verification is passed, then the file server sends the requested data to the proxy server, and the proxy re-encrypts the symmetric key. Finally, the encrypted data and the re-encrypted key are transmitted to the end user, and the user decrypts the key and data.
# Utilization of an OLED-Based VLC System in Office, Corridor, and Semi-Open Corridor Environments
## 2 Simulation  
### Simulation Features  
To determine the detected optical power and path lengths from the Tx to the Rx, non-sequential ray-tracing approach was used in the 3D environment. It evolves the specification and location of the Tx and the Rx, features of the CAD models of objects, wavelength-dependent reflectance of surfaces (wall, ceiling, floor, and objects), and transmission/reflection coefficient of glass windows. Next, the captured output data of the OpticStudio(r) is processed in MATLAB to obtain the CIR expressed as given by [27; 32]:  
\[h(t)=\sum_{i=1}^{N}P_{i}\delta(t-\tau_{i}), \tag{1}\]  
where \(P_{i}\) and \(\tau_{i}\) are the power and the propagation time of the \(i\)th ray, respectively. \(\delta\) is Dirac delta function and \(N\) is the number of rays received at the Rx. Note, a number of reflections from the floor, ceiling, walls, and other objects are considered until the normalized intensity of rays after intercepting an object drops to \(10^{-3}\).  
The spatial intensity distribution of light emitted from the light source is determined by the optical radiation pattern profile. The luminous intensity defined in terms of the angle of irradiance \(\phi\) is given as [1]:  
\[I(\phi)=\frac{m_{L}+1}{2\pi}I(0)\cos^{m_{L}}(\phi),\phi=[-\frac{\pi}{2},\frac {\pi}{2}] \tag{2}\]  
where \(I(0)\) is the center luminous intensity of the OLED and \(m_{L}\) is Lambertian order, which is defined in terms of the Tx semi-angle \(\phi_{1/2}\) as [1]:  
\[m_{L}=-\frac{\ln(2)}{\ln[\cos(\phi_{1/2})]}. \tag{3}\]  
As inputs of the simulator, the measured characteristics of a flexible OLED from UNISAGA with a size of \(200\times 50\) mm\({}^{2}\), see Figure 1a, were used. The measured beam pattern of the flexible OLED for flat and a half-circular configuration is depicted in Figure 1b, showing symmetry but not fitting with Lambertian radiation pattern (the solid line for \(m_{L}=1\)). A close match between the simulated and the measured beam patterns can be seen in Figure 1b. The measured spectrum profile of the flexible OLED is presented in Figure 1c, showing the red, green, and two blue components at 620, 553 and 454 and 480 nm, respectively.  
Figure 1: The flexible organic light emitting diodes (OLED) panel and its characteristics adopted in the simulation: (**a**) photograph, (**b**) the emission pattern of light source modeled for a flat and curved OLED, which is closely matched with the measured data, and (**c**) the normalized optical spectrum where the peak wavelengths are marked.
## 3 Results and Discussion
### Pore Structure Evolution  
The changes in volume fraction of mineral components and hydration products were evaluated by the HYMOSTRUC3D model, the results were shown in Figure 8. As shown in Figure 9, the 3D microstructures of the cement stone with the _w/c_ ratio of 0.44 at 0, 1, and 28 days were reconstructed by the Para View visualization software.  
Figure 7: Measured and simulated capillary porosity of cement stone.  
At the initial hydration stage, the volume content of pores accounts for a relatively higher proportion than those at the later hydration stage. The pores are mostly connected pores and occupied by water (Figures 8 and 9a). As the hydration reaction proceeded and the degree of hydration increased, unhydrated cement particles and water decreased sharply. Lots of hydration products such as C-S-H gel and CH were generated gradually, resulting in a volumetric expansion effect, and they occupied the pore spaces (Figures 8 and 9). The lower capillary porosities indicated that the pores were filled with continuously formed hydration products [60]. As a result, the microstructures become more compact with the increase in hydration time at the same _w/c_ ratio (Figure 9).  
With the further hydration of cement, hydration products were continuously generated and wrapped onto the surface of unhydrated cement (Figure 9b,c). Meanwhile, the hydration products continuously formed and hindered the migration of water and ions, resulting in the hydration rate of GCC gradually slowing down, the isolation of pores, and the gradual optimization of pore structure. At the later hydration stage, capillary water was almost consumed absolutely, finally forcing the hydration reaction to stop. Meanwhile, the skeleton of cement slurry was basically constructed, and almost all pores were no longer connected [61].  
Capillary pores are commonly considered the remnants of the initially water-filled space [62, 63]. With the _w/c_ ratio increasing from 0.40 to 0.44 and 0.44 to 0.50, the initial water volume ratio increased by 2.34% and 3.09% respectively. Correspondingly, the pore spaces and the capillary porosity increased with the increase in the _w/c_ ratio at the same hydration time (Figure 10). On the contrary, the mechanical strength decreased as the _w/c_ ratio increased.  
Figure 8: Time-varying curves of volume fraction of mineral components and hydration products under different _w/c_ ratios using HYMOSTRUC3D. **(a)** 0.40; **(b)** 0.44; **(c)** 0.50.  
Figure 9: Microstructure of cement stone under different hydration time using HYMOSTRUC3D **(a)** 0 days; **(b)** 1 day; **(c)** 28 days.