esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Findings
### Feedback on App Usability and Workflow Modifications
#### 3.3.2 Scanning Form  
Scanning a Scan-compatible form requires the user to take a picture of the paper form using smartphone's built-in camera. This lets the application process the form in the background and detects machine-readable, hand-marked data fields. Once the processing of image is finished, a notification appears in the top toolbar. Field workers understood the importance of taking a good quality picture, and one described it as follows:  
\"A good picture is one which is not blurred, and properly focuses the form and covers all the edges of forms. No corner should be cut.\"  
Field workers considered this step important and easy to learn. Knowing this step to be important for having accurate and better classification results, they preferred to take image at a relatively calmer place. One field worker said, \"Because this is delicate task and to make its editing error-free, I conduct this exercise at home.\"
## 3 Proposed Architecture
### Particle Filter (PF) with Time Difference of Arrival (TDOA) Measurement
The operation of the PF for estimating the position of the moving target using the relation between the system and the measurement is shown in Algorithm 1. The overall algorithm of the PF using the TDOA measurement is as follows. First, the state of the system for each particle is updated. Then, each particle predicts the observed value using the relational expression between the system state and the measurement values from each updated state. Additionally, the weight of each particle is calculated according to the probability distribution of the actual observed TDOA measurement value and the predicted measurement value. After that, the effective sample size (ESS) is calculated, then it is compared with threshold \(N_{th}\)[23; 24]. The \(N_{th}\) is defined as a predefined threshold for resampling. In our implementation, the threshold \(N_{th}\) is defined as 0.8 times of number of particles. Therefore, if the ESS is below a predetermined number of samples, which is denoted as threshold \(N_{th}\), the weight of each particle is resampled. The resampling process removes low weighted particles and selects  
\[s_{k}=As_{k-1}+p_{k} \tag{1}\]0 Scalable target position estimation using GPU. particles with high weight to reduce errors due to probability distributions. The PF estimates the position of the moving target more accurately using the resampling process. ```
1:Set initial state \(s_{0}\)
2:Generate particles \(s_{0(i)}\ (i=1,2,...,N)\)
3:Set initial weights \(W_{0(i)}=1/N\ (i=1,2,...,N)\)
4:for\(k=1,2,3,...\)
5:\(s_{k(i)}=As_{k-1(i)}+p_{k}(i=1,2,...N)\)
6:\(z=TDOA\) measurement values
7: Reweight \(w_{k(i)}=W_{k-1(i)}p(z\mid s_{k(i)})\)
8: where \(p(z\mid s_{k(i)})\sim\mathcal{N}(H(s_{k(i)}),Q_{v})\)
9: Normalize \(W_{k(i)}=w_{k(i)}/\sum_{i=1}^{N}w_{k(i)}\)
10:\(ESS=1/\sum_{i=1}^{N}(w_{k(i)})^{2}\)
11:if\(ESS\leq N_{th}\)
12: Resampling
13:endif
14:\(s_{k}=\sum_{i=1}^{N}W_{k(i)}s_{k(i)}\)
15:endfor
```  
**Algorithm 1** PF
## 3 Experiments and Result Analysis
### Results and Analysis  
We now report the comparison of our method with other data compression schemes and classifiers. First, 9-dimensional compressed features were derived from the original 252-dimensional multi-temporal features using various methods, namely LLE, PCA, stacked sparse auto-encoder (S-SAE) and the proposed NC-SAE. Then, the compressed 9-dimensional features were fed into the SVM, CNN and the proposed MSCDN classifiers. The ratio of the training samples for each classifier was 1%.
## 3 Linear Mathematical Model of the Bearing Stationary State
### Discussion of the Results of Static Characteristics Calculating  
The following parameters are among the input quantities that affect the static compliance of the bearing: \(L\), \(\lambda_{1}\), \(\lambda_{3}\), \(R_{1}\), \(P_{s}\), \(\chi\), \(\varsigma\) and \(K_{e}\), where \(\lambda_{1}=L_{1}/L\) and \(\lambda_{3}=L_{3}/L\). Figures 2 and 3 show the dependences of the bearing compliance \(K\) on the adjustment coefficients of the resistance of the SECT at various values of the elasticity (compliance) \(K_{e}\) of the elastic suspension for the design point corresponding to the values of the parameters \(L=1.5\), \(\lambda_{1}=0.8\), \(\lambda_{3}=0.95\), \(\chi=0.5\), \(R_{1}=1.1\) and \(P_{s}=4\). For the parameter \(\varsigma\), as the study of a number of aerostatic bearings shows, both from the point of view of statics and, especially, dynamics, the best values are \(0.1<\varsigma<0.25\)[12; 26; 27]. When calculating the static characteristics, \(\varsigma=0.15\) was taken.
# Induction Coil Design Considerations for High-Frequency Domestic Cooktops
## 3 General Structure of Induction Coils
### Skin Effect  
The skin effect is the non-uniform distribution of the current density of a conductor through which alternating current flows. The skin effect occurs because the magnetic field created by the current flowing through a conductor induces eddy currents in the conductor itself and pushes the current outwards from the center due to the resulting back EMF. As a result, the current density is maximum at the surface of the conductor and minimum at its center. The resistance resulting from the decrease in the surface on which the current flows increases. For this reason, the resistance under DC (R\({}_{\text{DC}}\)) and the resistance under AC (R\({}_{\text{AC}}\)) are different. Figure 8 shows how the skin effect occurs in a conductor.  
Figure 6: The basic structure of an induction coil.  
Figure 7: The effects of induction coil design parameters.  
The basic precaution to be taken for the induction coil is to produce the coil with litz wire. Thanks to the litz wire structure, the surface area over which the current flows is increased, and the winding resistance is reduced.  
The mathematical modeling of skin depth is shown in (9). As a result of skin depth, current density changes exponentially, as shown in (10).  
\[\delta=\sqrt{\frac{\rho}{\pi\mathrm{f}\mu_{0}\mu_{\mathrm{r}}}} \tag{8}\]  
\[\mathrm{J}=\mathrm{J}_{\mathrm{surface}}\mathrm{e}^{-\frac{4}{8}} \tag{9}\]  
The skin effect at different wire diameters is shown in Figure 9. As can be seen in the figures, when the wire diameter is decreased, the skin effect is lower. However, multiple windings must be used to obtain enough current carrying capacity.  
Figure 8: Skin effect in a conductor.  
Figure 9: Skin effect at 100 kHz for different wire diameters: (**a**) 2.5 mm; (**b**) 1.0 mm; (**c**) 0.5 mm; (**d**) 0.1 mm.
###### Abstract  
Glasswort (_Salicornia herbacea_ L) is a halophyte plant known to contain high contents of minerals and phytochemicals. The purpose of this study was to investigate the effect of fermented glasswort on gut health in D-galactose (D-gal)-induced aging rats. Forty rats were randomly divided into five groups: control (CON), D-gal alone (CD), D-gal plus fructooligosaccharide as a positive control (FOS, 400 mg/kg), D-gal plus low dose fermented glasswort (LGW, 10 mg/kg), and D-gal plus high-dose fermented glasswort (HGW, 20 mg/kg). Each treatment was orally administered to rats of each group for eight weeks. All groups except for the CON group (treated with saline) were intraperitoneally injected with D-gal (150 mg/kg). Our results showed that butyric acid among short-chain fatty acids (SCFAs), goblet cells of colon, and thickness of mucus layer in colon were increased in fermented glasswort groups. In addition, fermented glasswort reduced levels of thiobarbituric acid-reactive substances (TBARS, a related oxidative stress marker) and expression levels of inflammation-related proteins such as IL-1\(\beta\). These results suggest that fermented glasswort can improve age-related gut health.  
Keywords:aging; gut health; glasswort; fermentation; goblet cells +
Footnote †: journal: Applied
## 4 Results
### Modalities
#### 4.4.2 Output  
We also identified two different output modalities that were used in the reviewed studies. Table 7 presents the output modalities with the number of output sources of the modality used in the reviewed studies. The table also lists the total number of studies that used each output modality and the ratio of each output source.  
\"Vision\" is the first identified output modality that was used in every study that we analyzed. We identified two unique output sources for the vision modality: (1) the presentation of 2D or 3D graphics and (2) presentation of controls to manipulate IoT devices. The first output source (presentation of 2D or 3D graphics) could be found in all studies, as all of the analyzed MI systems that were used an interaction device that displayed graphical content including AR. However, the second output source could be found in 11 out of 23 studies that used vision as an output modality. When an MI system allows for an agent to manipulate IoT devices through a touchscreen [10; 11; 34; 40; 42; 48; 49; 50; 51; 52] or by hand gestures [12], the result of IoT device manipulation is visually presented in the real-world to the agent. Thus, we categorized 11 studies that allowed the agent to control IoT devices into the vision modality category.  
\"Audition\" is another output modality that was used in four of the reviewed studies. This output modality includes three different output sources. Two studies used sound cues to inform the agent regarding a specific event. For example, the MI system proposed by [PERSON] et al. [38] notifies the agent by a sound alarm about an abnormal environment state based on values, such as temperature and CO\({}_{2}\), collected from IoT devices. The MI system that was proposed by [PERSON] et al. [49] plays a sound cue to notify the agent that he or she is approaching IoT devices. Another output source is the sound effects of AR content. This was exemplified by [PERSON] et al. [37], who developed a mobile application that displays an animated 3D model of the human heart. This application also plays the sound of a heartbeat, which is synchronized with the real heart rate of the agent. Moreover, the beating animation of the 3D heart model is also synchronized with the real heart rate. The final output source that we identified in the audition modality category was sound from an external device. The aforementioned three studies that utilized audition as an output modality used a built-in audio device to deliver auditory stimulation to the agent. However, one study used an external device to provide sound signals to the agent. [PERSON] et al. [12] developed an MR interface that an agent can use to control a smart speaker to select and play music. The agent can also increase and decrease the volume of the smart speaker.
## 6 Experimental Results and Analysis
### Analysis of Experimental Results
#### 6.3.5 Comparative Analysis with Previous Studies  
In order to further verify the effectiveness of the breast cancer classification model developed in this paper, the classification results of this paper are compared with those of other studies. Firstly, the breast cancer classification model proposed in this paper is compared with the results of existing studies without considering privacy protection, and the results are shown in Table 8.  
As can be seen from Table 8, through comparative analysis, the prediction method for breast cancer classification proposed in this paper outperforms previous research results with an accuracy of 0.9912. Therefore, the hybrid feature selection method and the BGD-LR model used in this paper provide the best classification results.
## 5 Instrument Used in Milan and the Specific Mapping Approach Solution Applied to the Case Study
### Quality and Use of Pictures  
In surveys with geospatial goals, the photographic component of the survey plays a critically important role, as it is from this information that operators can recognize assets and populate the asset management database. Indeed, MM technicians must be able to navigate through virtual tours along the routes and recognize the details required to populate the comprehensive database. An important factor in the choice of the Heron(r) system, therefore, was the quality of the image and its resolution, appropriately tailored to the needs of the project. Considering that the surveys mostly take place inside the buildings and very close to the building's body, it was considered sufficient for the resolution of the RGB images to be greater than or in the order of 5K, with a strong emphasis on the overall image quality, which should not be blurred, dark, or over/underexposed. Indeed, the quality of an image is often confused with its mere resolution. Therefore, the characteristics of the images had to be such that the technicians could recognize the items and assets present in the mapped areas from the photos, and in particular the attributes associated with them. In the case of the Milan project, the company that won the contract was required to have iMMS systems equipped with lighting to ensure image quality even for measurements taken in the evening hours or under low-light conditions. For more specific details, such as names on intercoms or technical identification plates of machinery in building thermal power plants, the option to associate an externally acquired photograph (from a smartphone, PDA, or tablet) as an integrative document, attached as an attribute to the surveyed asset, was provided. Obviously, a mobile system with higher photographic resolutions would have made it possible to avoid such procedures. A system capable of acquiring very high resolution images, however, would require the mobile system to have simultaneously large computing, data management, and storage capabilities capable of handling the high resolution acquired in the field. However, this implies (as already stated in Section 3) that the instrument would have high power consumption and therefore require a sufficiently large battery pack on board. All of this leads to a larger system footprint and greater weight for the instrumentation, details that make the instrument less flexible and harder to use in articulated and complex spaces with numerous scales, as required by the Milan project.
# Electrospun Nanofibrous Membranes for Tissue Engineering and Cell Growth
## 3 Materials for Tissue Engineering and Cell Growth Experiments  
Generally, the most important prerequisite of a material usable for tissue engineering, or more generally as a scaffold for cell growth, is its biocompatibility. Interestingly, this does not necessarily mean that all substances used during electrospinningning and a possible post-treatment step, e.g., crosslinking, have to be nontoxic themselves, as directly visible by the often highly toxic crosslinking chemicals which nevertheless can be used to prepare nontoxic nanofiber scaffolds [40]. Besides biocompatible in the sense of \"bioinert\" materials, there have also been attempts to achieve a positive interaction between substrate and cells [51]. On the other hand, it is necessary to distinguish between biodegradable and permanent materials, which can be used for different purposes.  
In the next sections, we will give an overview of some often used and highly interesting materials used for the electrospinning of scaffolds for cell growth, such as poly(lactic acid) (PLA), polyurethane, poly(acrylonitrile) (PAN), poly(\(\varepsilon\)-caprolactone) (PCL), collagen and gelatin, chitosan and chitin, and others. It should be mentioned that due to the large bandwidth of possible polymers and polymer blends applicable as electrospun scaffolds, our choice of a few of them is necessarily subjective. However, we hope to show some interesting blends of well-known materials and give some inspiration for researchers starting with this topic.
# Physics of Within-Tissue Wave Propagation Generated by Pulse Propagation in the Carotid Artery
## 4 Results  
### Mesh Sensitivity Analysis: Shear and Rayleigh Wave Simulations  
For the shear wave simulations, shear wave speeds were calculated for both the forward (positive z-direction) and backward (negative z-direction) directions with respect to the loading location as visualized in Figure 2a. Their averaged value is shown in Table 2 for all considered mesh grids together with the computational time. All meshes reproduced the shear wave propagation with a good degree of approximation with respect to the theoretical shear wave velocity value of 4.82 m/s (deviations varied between \(-\)0.6% for the finest mesh to \(-\)10.0% for the coarsest mesh). The mesh with 0.20 mm element size was chosen as the optimal mesh for our study purpose due to the combination of result accuracy, reasonable computational time and minimized residual computational noise after load release.  
The accuracy of the selected mesh size of 0.20 mm for wave propagation analysis was further verified by simulating Rayleigh waves. Figure 3a compares the spatial characteristics for simulation and theory, whereas the temporal characteristics of the simulated Rayleigh wave at z = 5 mm are shown in Figure 3a for three depth values (y-direction) together with the theoretical solution. This figure demonstrates that both the spatial and temporal characteristics of the Rayleigh waves were well reproduced in the simulation. Furthermore, the estimated surface wave velocity is 4.63 m/s in the simulation, deviating only 0.9% from the theoretical value (4.59 m/s).
## 6 Performance Evaluation
### Communication Cost Analysis and Comparison  
In ECC, the length of cyclic group \(G(p)\) is 40 bytes and the size of \(p\) is 20 bytes. In bilinear pairing, the length of cyclic group \(G_{1}(p^{-})\) is 128 bytes and the \(p\) size is 64 bytes. In addition, the timestamp and the size of integer item \(Z_{q}^{*}\) are 4 bytes and 20 bytes, respectively.  
In the proposed scheme, registered participating vehicles broadcast \(\{AlO_{i},\ R_{i},\ M_{i},\ T_{i},\ \sigma_{i}\}\) to others, where \((R_{i}\in G)\), \((AlO_{i},\sigma_{i}\in Z_{q}^{*})\) and \(T_{i}\) is the timestamp. Consequently, the total size of the message signature is \((40+20^{*}2+4)=84\) bytes. The same method is also used in the analysis of other related schemes.  
Table 5 shows that the communication cost of the proposed scheme is lower than the related schemes of [PERSON] et al. (2015), [PERSON] et al. (2016), [PERSON] et al. (2019), [PERSON] et al. (2019), [PERSON] et al. (2019), [PERSON] et al. (2019), and [PERSON] et al. (2019).  
Figure 4: Computation cost for signing and verifying messages.  
Figure 5: Computation cost in the batch verification of multiple messages.
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
## 2 Ship Hydroelasticity Theory in Regular and Irregular Waves  
### Basic Assumption and Description  
The general description of ship hydroelastic responses in regular or irregular waves is demonstrated in Figure 1. In the figure, the fluid field \(\Omega_{F}\) is assumed to be infinite in the horizontal direction, with parameters \(R\), \(H\), \(S_{\infty}\), and \(S_{F}\) respectively being denoted as the infinite radius of the finite or infinite depth of \(\Omega_{F}\), the infinite boundary of \(\Omega_{F}\) in the horizontal direction, and the free surface of \(\Omega_{F}\). \(S_{B}\) denotes the wet hull surface.  
The earth fixed Cartesian frame \(Ox_{0}y_{0}z_{0}\), the translational Cartesian frame \(oxyz\), and the ship hull fixed Cartesian frame \(Gx_{b}y_{0}z_{b}\) are adopted to study the fluid structure interaction problem. Initially, reference frames \(Ox_{0}y_{0}z_{0}\) and \(oxyz\) are spatially identical. Coordinate planes \(Ox_{0}y_{0}\) and \(oxy\) are at the calm waterplane with their origins being positioned at the After Perpendicular (A.P.). The translational Cartesian frame \(oxyz\) translates at the constant ship velocity \(U\) and keeps parallel to \(Ox_{0}y_{0}z_{0}\). The hull fixed reference frame \(Gxy_{b}y_{2}z_{0}\) moves identically with the ship, and \(G\) is the Center of Gravity (CG) of the ship. The Galilean transformation relation between these three reference frames at any time \(t\) is shown below:  
\[\left\{\begin{array}{c}x\\ y\\ z\end{array}\right\}=\left[\begin{array}{ccc}\cos\beta&\sin\beta&0\\ -\sin\beta&\cos\beta&0\\ 0&0&1\end{array}\right]\left\{\begin{array}{c}x_{0}\\ y_{0}\\ z_{0}\end{array}\right\}+\left\{\begin{array}{c}-Ut\\ 0\\ 0\end{array}\right\}\approx\left\{\begin{array}{c}x_{b}\\ y_{b}\\ z_{b}+z_{G}\end{array}\right\} \tag{1}\]  
where \(\beta\) denotes the azimuth of the ship.
## 5 Result Analysis
We have varied the unit transmission cost on the wireless link, \(\mu\), from 1 to 10 and compared the total cost for the proposed architecture with IP multicasting and the typical PMIPv6 scheme. Because only the PMIPv6 is adjusted to permit inter-domain handover and the intra-domain handover has no impact on the latency, the basic PMIPv6 and the proposed technique share the same intra-domain handover latency. Originally, inter-domain handover was not supported by PMIPv6. For inter-domain handover, the IETF takes into consideration the usage of the previously suggested technique,  
Figure 6: Effect of varying setup cost on intra-domain handoff. Figure 7: Effect of varying unit transmission cost (wired link) on intra-domain handoff. Mobile IPv6 (MIPv6). The problems in MIPv6 include DAD that uses 1000 s latency, which causes a higher delay during inter-domain handover. Our proposed architecture suggests a new approach to facilitating inter-domain handover by using the method of multicasting the authentication information with PMIPv6, in which no problem of duplicate addresses is present. In the proposed architecture, when the TTP authenticates a vehicle, it sends the authentication response to the MAG, and the TTP also multicasts this profile information to the neighboring TTPs so that, later, during the other inter-domain handover, this information can be used. Figure 9 shows the effect of the varying setup cost on inter-domain handoff when setup cost, Tsetup, is varied from 100 to 500. Figure 10 depicts the effect of the varying unit transmission cost on the wired link, \(\tau\), from 1 to 100, and shows that the total cost increases proportionately with an increase in transmission cost, whereas the effect of the varying unit transmission cost on the wireless link, \(\mu\), is represented by Figure 11. If we compare the cost of the intra-domain handoff and inter-domain handoff of a vehicle in PMIPv6 with IP multicasting, we observe a slight variation in inter-domain handoff, which may be reduced later. Figure 8: Effect of varying unit transmission cost (wireless link) on intra-domain handoff. Figure 9: Effect of varying setup cost on inter-domain handoff.
## 3 Results and Discussion
### Qualitative Observations
When inspecting the uXCT image stacks, it was possible to detect that some reconstructed stacks contained blurring noise. After the image segmentation process, it became obvious that the segmented images in these stacks did not correspond to the expected cellular structure. For example, portions of the cell wall material were classified as air or random lumina classified as cell wall, as shown in Figure 8. Also, for a given specimen tested at different RH conditions, the blurring effect was random and only present in scans under certain RH conditions. Therefore, the effect was not specimen dependent. It is likely that this random blurring effect was from lateral vibrations caused by the sample becoming loose in the mounting clay and vibrating during rotations. It is also possible that airflow through the RH chamber caused specimen vibrations. These blurred image stacks will need to be removed in future analyses for calculating moisture swelling because the cell walls cannot be reliably segmented. Future experiments should use mechanical clamping instead of mounting clay to minimize this blurring effect.