esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Tunnel Blasting Vibration Velocity Considering the Influence of Free Surface
## 2 Effect of Free Surface on Rock Blasting Mechanism  
The tunnel of the blasthole is according to the role of the different divided into cutting blasthole and non-cutting blasthole. Cutting blastholes are pre-blasted to form a free surface to provide favorable blasting conditions for subsequent blastholes [19; 20]; The non-cutting blasthole (breaking blasthole, bottom blasthole, and contour blasthole) rely on the free surface opened up by the cutting hole blasting and are thrown out of the rock by blasting.  
The blasting crack of a single hole can be illustrated in Figure 1. According to the damage degree of the rock near the blasthole, it can be divided into a crushing zone, a crack zone, and an elastic vibration zone in turn [21; 22]. At the moment of the explosion, an overpressure shock wave is formed, causing the rock around the blast hole wall to break up and form a crushing zone. Then, the explosion gas expands outward, causing the rock to crack in all directions, forming a crack zone. When the stress wave passes through the crack zone and decays into an elastic vibration wave, the energy is low, and the rock cannot be broken, causing mainly rock vibration. However, the stress wave is reflected by the free surface to form a tensile wave, which is superimposed with the original stress wave, resulting in tensile cracks in the surrounding rock and forming a laminated crack zone on the free surface.  
When the free surface is less, the surrounding rock is more constrained, and the proportion of blast energy converted into vibrational energy in the elastic zone is higher [23]. The increased free surface intensifies the superposition of vibration waves, resulting in more rock cracks and optimizing the crushing effect. Therefore, the rock-breaking mechanism of blasting in the cutting blasthole and non-cutting blasthole is different. By creating a new free surface via blasting the cutting hole, the energy transmission during subsequent blasting is altered. Therefore, it is reasonable to consider the effect of the free surface when analyzing the effect of blasting vibration in different types of blastholes.
# Mouth Sounds: A Review of Acoustic Applications and Methodologies
## 2 Mouth Sounds and Their Acoustic Features
### Power/Energy-Based Features (PEBF)
#### 2.2.2 Frequency Domain Features  
Frequency domain analysis is a valuable tool in audio signal processing. The frequency domain is mostly studied to observe the periodicity and spectral composition of signals. To analyze a signal in the frequency domain, it is necessary to convert the time domain signal using a mathematical transformation, such as Fourier transformation [34; 35]. The most recurrent features in this review can be classified into three categories: (1) Tonality-Based Features (TBF), including jitter, Harmonic-to-Noise Ratio (HNR), Noise-to-Harmonic Ratio (NHR), fundamental frequency (\(\mathrm{f}_{0}\)) or pitch, and formants; (2) Spectral-Based Features (SBF), including spectral centroid or spectral brightness, spectral roll-off, spectral spread, spectrum envelope, spectral flux, spectral kurtosis, and entropy; and (3) Cepstral Features (CF), including the Mel-Frequency Cepstral Coefficient (MFCC), Bark Frequency Cepstral Coefficient (BFCC), Linear Prediction Cepstral Coefficients, Cepstral Peak Prominence (CPP), Cepstral Peak Prominence-Smoothed (CPPS), Linear Frequency Cepstral Coefficients (LFCC), Rasta-PLP, Teager energy cepstral coefficient, and Teager energy based Mel-frequency cepstral coefficient. Features in these categories are described in this section, and the rest can be found in Appendix B.  
C. Cepstral Features (CF)  
_Mel-Frequency Cepstral Coefficient (MFCC)._ MFCC is a technique for speech feature extraction [35], which represents the power spectrum based on discrete cosine transform [34]. This feature is presented in Figure 3.  
Figure 2: Illustrations of the time features on a fragment of a speech signal. (**a**) Time signal. (**b**) ZCR. (**c**) STE.  
_Bark Frequency Cepstral Coefficient (BFCC)._ This is a process that combines perceptual linear prediction processing of the spectra and cosine transformation to compute the cepstral coefficients [36]. This feature is presented in Figure 3.
## 2 Geometric Description of Groin Vaults  
The groin vaults are considered to have a square footprint. Their three dimensional geometry is obtained by the extrusion in two orthogonal directions of the parametric cross-section defined in Figure 1. This cross-section is defined by four parameters: its base length \(l_{0}\), the radius \(r\), the springing angle \(\beta\), and the thickness \(t\) (Figure 1).  
For convenience of the parametric analysis the following geometrical parameters and their ranges are defined:  
* The radius over length (\(r/l_{0}\)) defines the \"pointiness\" of the vault. This parameter varies from 0.5 to 1.5, in steps of 0.05. When \(r/l_{0}=0.5\), a rounded cross vault is achieved, and the section becomes a semicircular arch.
* The springing angle (\(\beta\)) denotes the inclination at the impost line. This parameter varies from \(0^{\circ}\) to \(40^{\circ}\), and is considered in steps of \(10^{\circ}\); when \(\beta=0^{\circ}\), the effective span (\(s\)) of the structure is equal to the base length \(s/l_{0}=1\), and if \(\beta>0^{\circ}\) the effective span is smaller (i.e., \(s/l_{0}<1\)) according to Equation (1).
* The adimensional thickness over span (\(t/s\)) is used to properly compare and describe the vaults' thicknesses with different springing angles. The thickness is applied through a homotetical translation in the normal direction of the middle surface defined by the aforementioned parameters, as in Figure 1.  
All the results are provided with different \"pointiness\" values described by \(r/l_{0}\), and the minimum thickness are reported in terms of the thickness over span \(t/s\). The relationship among \(s\) and \(l_{0}\) can be described by the following equation:  
\[\frac{s}{l_{0}}=2\bigg{(}\frac{r}{l_{0}}(\cos{(\beta)}-1)+\frac{1}{2}\bigg{)}, \tag{1}\]  
Figure 1: A groin vault’s section defined by its base length \(l_{0}\), radius \(r\), springing angle \(\beta\), and thickness \(t\). For a proper parametric description, the adimensional radius over length (\(r/l_{0}\)) and thickness over span (\(t/s\)) are used.  
The parameters adopted in the proposed analysis, particularly referring to the springing angle \(\beta\), allow straightforward modelling of the typical fill near the supports of groin vaults. Indeed, once the fill has gone through compacting, it is sufficient for the vault's stability that the thrust lies within the reduced geometry defined by a given springing angle \(\beta\), as in [1; 27; 37].  
Figure 2 depicts the three-dimensional geometries obtained for the ranges \(r/l_{0}=[0.5-1.0]\) and \(\beta=[0^{\circ}-40^{\circ}]\), considering increments of 0.05 and \(10^{\circ}\), respectively.
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 3 System Modeling and Problem Description
### Delay Model  
The latency mainly refers to the transmission delay when the user offloads data to the edge or cloud servers.  
We assume that \(\delta_{k}\) is the volume of data to be offloaded from each IoT end device \(D_{k}\) and take \(\alpha_{k}\) as the offloading decision variable, where \(\alpha_{k}\in\{0,1\}\). When \(\alpha_{k}=0\), \(D_{k}\) does not select the edge server for task offloading; when \(\alpha_{k}=1\), \(D_{k}\) offloads all the task data to the edge server for processing. Therefore, the total volume of task data \(\lambda_{k}\) that the user offloads to the edge server for processing is  
Figure 1: Scenario diagram of server placement.  
\[\lambda_{k}=\alpha_{k}\delta_{k}. \tag{2}\]  
When the user offloads task data to the edge server, the transmission rate \(r_{k}^{e}\) of the \(k\)th user offloading the task to the edge server is calculated using [PERSON]'s formula. The calculation formula is  
\[r_{k}^{e}=\mathrm{w}\times\log_{2}\left(1+\frac{P\times l_{k}^{-\alpha}\times h _{1}}{N_{0}}\right), \tag{3}\]  
where \(w\) denotes the channel bandwidth, \(P\) denotes the Internet of Things (IoT) terminal transmission power, \(l_{k}\) denotes the distance between the \(k\)th IoT terminal and edge server, \(\alpha\) denotes the path loss factor, \(h_{1}\) denotes the channel gain between the user and edge server, and \(N_{0}\) denotes the noise power.  
Therefore, the transmission latency \(T_{e}\) when users offload task data to the edge server can be expressed as follows  
\[T_{e}=\sum_{k=1}^{Z}\frac{\lambda_{k}}{r_{k}^{e}}. \tag{4}\]  
When users offload task data to the cloud server for processing, the transmission rate \(r_{k}^{e}\) can be calculated using [PERSON]'s formula. The calculation formula is as follows  
\[r_{k}^{e}=\mathrm{w}\times\log_{2}\left(1+\frac{P\times l^{-\alpha}\times h _{2}}{N_{0}}\right), \tag{5}\]  
here, \(l\) and \(h_{2}\) denote the distance and channel gain between the user and cloud server, respectively. Therefore, the transmission delay for all users to offload their task data to the cloud server \(T_{c}\) is expressed as  
\[T_{c}=\sum_{k=1}^{Z}\frac{\lambda_{k}}{r_{k}^{e}}. \tag{6}\]  
Thus, the average latency of offloading task data to the edge or cloud server for all users \(T\) is expressed as  
\[T=\frac{T_{e}+T_{c}}{z}. \tag{7}\]
## 2 Electrical Resistivity/Impedance Measurement: Is It Really that Simple?
### Curing and Hydration
In particular, after 3 weeks in air (T = 20 \({}^{\circ}\)C, RH = 80%) \(\rho\) increases by a factor 3-5, stronger in outer layers (due to pore water evaporation); at approximately 30 weeks, \(\rho\) increases by a factor 6-20 in the outer layers (10 mm depth) and by a factor 10-30 in the inner parts (50 mm depth) compared to 1-week values. [PERSON] et al. [96] highlighted that, since pozzolanic reactions occur later, after the first 28 days in concrete with SCMs the electrical resistivity increases more than in plain concrete [164]. In fact, for concrete mixes with the addition of SCMs a curing period of 56 days is recommended [165]. [PERSON] et al. [102] evaluated the effects of different curing regimens (immersion in tap water or 3.5% NaCl solution, exposure to fog room, high humidity, and laboratory humidity) on the electrical resistivity of concrete. In general, the exposure to fog room and high humidity levels cause lower resistivity values with respect to curing at room conditions. [PERSON] et al. [166] evaluated the effect of thermal cycling (between \(-\)24 \({}^{\circ}\)C and \(+\)24 \({}^{\circ}\)C, at a rate of 1 \({}^{\circ}\)C/h) on the electrical resistivity of concrete at early ages. Resistivity increases when temperature decreases and when the pore solution freezes (phase transition temperature, typically between \(-\)5 \({}^{\circ}\)C and 0 \({}^{\circ}\)C); in fact, ice is an insulator and limits ion mobility [167]. It is worth noting that phase transition temperature varies with concrete composition (e.g., GGBFS decreases it, because of changes in the ionic combination of the pore solution) and decreases with the ageing of concrete (because of different water content and, consequently, different ionic concentrations influencing the ice formation). Moreover, the phase transition temperature is higher during thawing cycles than freezing ones, meaning that in the former, there is more ice than in the latter, at the same temperature. Also, the setting time of concrete has been evaluated by means of electrical resistivity measurements. [PERSON] et al. [14] explored the relationship between setting time and electrical resistivity in the early hydration of cement-based materials; in particular, they identified linear relationship between the minimum of electrical resistivity curve and initial setting time (with R\({}^{2}\) > 0.956) and between the first peak of electrical resistivity curve and final setting time (with R\({}^{2}\) > 0.986). Also, [PERSON] et al. [125] evaluated the setting time of mortar specimens by means of electrical resistivity, noticing that electrical resistivity increases with hydration depending on mix and chemical admixtures. In particular, they considered the rising time of electrical resistivity (indicating the increase onset) and the increasing ratio over time, which reflects the evolution of the cement-based materials microstructure.
## 2 Data and Methods
### Methods  
#### 2.3.1 Chang Trends of NPP  
A linear regression analysis was conducted to explore the long-term trends of NPP in YRB from 2000 to 2019, the formula is as follows (Equation (1)). The trend slope of regression represents inter-annual NPP change, and the slope indicates the direction and magnitude of the interannual variation in NPP. A positive slope shows that climate change is conducive to forest growth, while a negative slope represents that climate change hinders forest growth. In addition, a slope of zero means that climate change does not affect forest  
Figure 2: The distributions of (**a**) 2000 forest, (**b**) 2017 forest, (**c**) old forest (Fold), and (**d**) new forest (Fnew) in Yangtze River Basin, China.  
net primary productivity. Moreover, the significance of variation is investigated using \(t\)-tests to represent the confidence level of variation (\(p<0.05\)).  
\[Slope=\frac{n\times\sum_{j=1}^{n}j\times\mathrm{NPP}_{j}-\sum_{j=1}^{n}\mathrm{ NPP}_{j}\sum_{j=1}^{n}j}{n\times\sum_{j=1}^{n}j^{2}-\left(\sum_{j=1}^{n}j \right)^{2}} \tag{1}\]  
where _Slope_ is the inter-annual variation rate of NPP; \(n\) is the study period, from 2000 to 2019; and \(\mathrm{NPP}_{j}\) is the forest NPP in the \(j\)th year. The correlation between NPP sequences and time sequences(year) is used to determine the significance of interannual variation in NPP.
# Study on Seismic Isolation of Long Span Double Deck Steel Truss Continuous Grider Bridge
## 3 Seismic Response of Structures
### Combined Seismic Dissipation and Isolation (CSDI) Structure  
Combined seismic dissipation and isolation (CSDI) is adopted, that is, the 0#, 2# and 3# piers are equipped with viscous dampers, and the 1# pier has an installed FPB with the abovementioned optimized parameters. When the E2 earthquake occurs, the anti-seismic bolt of the fixed pier is cut off, so the FPB and viscous dampers can play a role in CSDI. Now assuming that the damping exponent is 0.5, and the damping coefficient ranges from 1000 to 10,000, the influence of damping coefficient C on the performance of the damper can be studied. The seismic response of the structure is shown in Figure 12.  
Figure 11: Seismic response of the longitudinal bridge direction (friction coefficient \(\mu\)). (**a**) Bending moment at the bottom of pier. (**b**) Relative displacement between pairs and beams.  
It can be seen from Figure 12 that, in the change process of C from 1000 to 10,000, compared with the seismic structure, the relative displacement between the piers and beams can be effectively reduced; however, with an increase in C, the output of the damping force will be larger, which will limit the displacement of the upper structure, but is not conducive to dissipating the seismic inertia force; the upper inertia force is transmitted to the lower structure, which increases the bending moment at the pier bottom.  
It is proposed to set damping coefficient C as 6000, and the damping exponent as 0.3, 0.4, 0.6, 0.8, and 1.0. When the damping exponent is 1.0, the damper is a linear damper. The specific results are shown in Figure 13.  
With the increase in the damping exponent, the bending moment at the bottom of the pier first decreases and then increases; however the bending moment of the pier bottom of the 2# pier decreases gradually; for the relative displacement between the piers and beams, with an increase in the damping exponent, the displacement response gradually increases.  
Figure 12: Seismic response of the longitudinal bridge direction (damping coefficient C). (**a**) Bending moment at the bottom of pier. (**b**) Relative displacement between piers and beams.  
Figure 13: Seismic response of the longitudinal bridge direction (damping exponent \(\alpha\)). (**a**) Bending moment at the bottom of pier. (**b**) Relative displacement between piers and beams.  
Based on the above research rules, the following parameters can be selected: C = 4000-6000, \(\alpha\) = 0.3.
## 7 Security Considerations for Proposed Mobility Schemes  
This section discusses the security model of the proposed mobility schemes in this paper. The following assumptions and notations are used in order to describe the security processes and cryptographic operations in this paper:  
1. In order to establish an individual key shared between the GW and a node, and a pairwise key shared between a node and each of its intermediate one hop neighbors, we can use LEAP [22]. In the proposed scheme, the MSPs share an individual key with the GW and pairwise key with each of its intermediate MSP. The SNs also share pairwise key with another SN and an individual key with the GW. It should be noted that both individual key and pairwise key are symmetric keys.
2. G: The gateway (GW) which is assumed to be trusted [23].
3. MSP\({}_{\rm i}\), MSP\({}_{\rm j}\): Two intermediate neighboring MSPs located in a path between a source MN and a destination MN.
4. K\({}_{\rm ij}\): A symmetric pair-wise shared key between MSP\({}_{\rm i}\) and MSP\({}_{\rm j}\)
5. K\({}_{\rm s}\): Session key shared between source and destination MN.
6. K\({}_{\rm ig}\): Individual shared key between MSP\(i\) and the GW.
7. K\({}_{\rm m}\): Mobile Key between MN and its future SNs
8. K\({}_{\rm c}\): Communication key which is shared between a MN and the GW.
9. M1\(|\)M2: The concatenation of messages M1 and M2
10. \(\{\rm M\}_{\rm\cdot<kij\,C\rm\rm\rm\rm\rm\rm\rm\rm\rm\rm\rm\rm\rm\rm\rm\rm\rm\rm\rm\rm\rm
## 3 Results and Discussion
### Prediction Results of the Validation Set  
The validation set contains in-cylinder pressure data from six different operating conditions, of which there are 8646 samples. Using the optimized model to predict the validation set, the regression analysis is shown in Figure 9. As can be seen in Figure 9, most of the samples lie around the diagonal line, and only a few validation samples with larger values deviate, which means that the model obtained very accurate prediction results on the validation set.  
In order to represent the prediction results of the validation set more intuitively, the predicted values of each validation operating condition were fit to the actual values; the results are shown in Figure 10. The horizontal axis is the crank angle and the vertical axis is the in-cylinder pressure value. The black curve represents the actual value acquired in the tests, and the rest of the curves in different colors are the predicted values of the prediction model. As can be seen in Figure 10, the predicted values from operating condition 1, 2, and 4 are in good agreement with the actual values, and the predicted in-cylinder pressure values at the rest of the operating conditions only deviate from the actual values in the peak region.
## 4 System Model and Description of the Proposed Protocols
### Proposed Protocol 1 (EP-VIR-Three)  
In this subsection, the network architecture of EP-VIR-Three is discussed in detail (see Figure 9). Then, detailed theoretical analysis (including propagation and data transmission) is presented. In the end, the steps for EP-VIR-Three working are given in detail (see Algorithm 1).
## 3 Materials and Methods  
### Materials  
#### 3.1.1 Dataset  
The dataset provided by AI Hub [25] consists of 77,899 RGB images labeled with five beef quality grades: 1++, 1+, 1, 2, and 3. The dataset includes corresponding segmentation masks and was used without modification in this study. The original images have a resolution of 1080 \(\times\) 1920 pixels and were resized to 512 \(\times\) 512 pixels. The dataset was randomly split into training, validation, and test sets with a ratio of 7:1:1, resulting in 60,571 training images, 8664 validation images, and 8664 test images. Table 1 shows the number of images per grade in each subset.  
In addition to the images, segmentation masks are provided to isolate the lean meat region by removing non-meat areas. These pre-labeled masks served as ground truth annotations for training the segmentation networks used in the two-stage pipeline. The segmentation masks were provided together with the images by AI Hub. According to the dataset description, the masks were constructed using a combination of automatic and manual labeling techniques based on OpenCV, focusing on removing the outer fat surrounding the beef cross-sectional area. These pre-generated masks were used directly for training the segmentation models in the two-stage pipeline. To better illustrate the dataset composition, Figure 1 shows representative examples of RGB images and corresponding segmentation masks for each of the five quality grades (1++, 1+, 1, 2, and 3). These masks isolate the lean meat area used for training 2-stage models.  
The two-stage classification models were trained using the images with segmentation masks applied, whereas the one-stage models were trained directly on the original unmasked images.  
To evaluate performance under different proportions of lean meat, we used the segmentation masks to calculate the cross-sectional area of meat in each image. Based on this ratio, the dataset was divided into two groups: images with over 20% loin area (405 samples) and those with under 10% loin area (2346 samples).  
Figure 2 shows representative examples from the dataset, including beef cross-sectional images, labeled with different quality grades. The images exhibit varying degrees of intra-muscular fat distribution, which is the key visual cue for determining beef quality.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Grade** & **Training** & **Validation** & **Test** \\ \hline
1++ & 17,280 & 2473 & 2473 \\
1+ & 15,855 & 2265 & 2265 \\
1 & 14,930 & 2137 & 2137 \\
2 & 8748 & 1250 & 1250 \\
3 & 3758 & 539 & 539 \\ \hline Total & 60,571 & 8664 & 8664 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of images across beef quality grades in each subset.  
Figure 1: Examples of beef carcass images and their corresponding segmentation masks for each quality grade. For each row, the left image shows the original RGB input, and the right image displays the associated mask highlighting the loin region.
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Development in Smart Clothes Sensor Materials
#### 3.5.1 Carbon-Based Sensor Materials
Graphene oxide (GO) is a single-atomic layered material, developed by the oxidation of graphite. GO films are deposited onto substrate to convert it into a conductor. [PERSON] et al. [124] presented a simple, scalable, and cost-effective method that can produce e-textiles. Graphene oxide (GO) was employed to ensure stable reduced graphene oxide (rGO) dispersion, which was applied to the textile using simple pad-dry techniques. The laser scribed (LS) technique is useful for putting graphene layers onto various flexible substrates. For example, functional patterns are produced with the use of graphene oxide (GO) films. In addition, the electronic properties of the layer can be adjusted broadly by varying the laser power and irradiation time [125].