esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm
## 3 Materials and Methods
### Deployment  
The purpose of the deployment was to enhance the implementation of inference during the safety belt detection experiments carried out within the vehicle. To achieve this objective, it was imperative to transform the weights of the trained YOLOv7 model into a more lightweight format that could be effectively processed on the Jetson Nano development board. The process consisted of three distinct steps, as illustrated in Figure 8.  
The YOLOv7 model was trained using the PyTorch platform, as stated in Stage 2 of the flow diagram. The weights for each training process were generated in the PyTorch format (.pt). The weights with the \".pt\" extension are typically unsuitable for deployment on devices with limited memory capacity, such as the Jetson Nano. To facilitate compatibility  
Figure 8: The steps used to deploy the YOLOv7 model to a Jetson Nano development board.  
Figure 7: The original YOLOv7 architecture as presented by [10].  
with the Jetson Nano platform, we performed a conversion process wherein the weights in the \".pt\" format were transformed into TensorRT weights in the \".trt\" format. This conversion was carried out with the objective of achieving lighter weights that could be effectively managed by the Jetson Nano platform. In order to convert weights to the Tensor RT format, it is necessary to first convert the \".pt\" format to the \".wts\" format. Subsequently, the \".wts\" format needs to be converted to the \".trt\" format.
## 3 Underwater Channel Characterization
### Electro-Magnetic Properties of Water
#### 3.2.2 Dielectric Permittivity of the Medium  
The relative permittivity (\(\mu\)), also known as a dielectric constant, is an ability of a medium to transmit an electric field [11]. Usually, the value of relative permittivity is considered as 81 [11; 20], but in actual this parameter is complex valued and it further depends on the salinity of water, temperature and carrier frequency [21]. The dielectric permittivity in Arabian and Indian ocean costal lines has effectively been calculated and is estimated to be approximately \(\mu\) = 81 [22].
## 4 Results
### Presence Questionnaire
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PQ** & **Training** & **Mean (SD)** & **Median (95\% CI)** & **Z-Score** & \(p\)**-Value** \\ \hline \multirow{2}{*}{Realism} & SET & 40.09 (6.29) & 41.00 (37.30–42.88) & \multirow{2}{*}{\(-2.667\)} & \multirow{2}{*}{0.008} \\  & ET & 37.00 (7.25) & 39.00 (33.79–40.21) & & \\ \hline \multirow{2}{*}{Possibility to Act} & SET & 22.18 (3.88) & 22.00 (20.46–23.90) & \multirow{2}{*}{\(-3.213\)} & \multirow{2}{*}{0.001} \\  & ET & 18.77 (4.81) & 20.00 (16.64–20.91) & & \\ \hline \multirow{2}{*}{Quality of Interface} & SET & 14.59 (3.42) & 15.00 (13.08–16.11) & \multirow{2}{*}{\(-1.049\)} & \multirow{2}{*}{0.294} \\  & ET & 13.86 (3.36) & 13.00 (12.38–15.35) & & \\ \hline \multirow{2}{*}{Possibility to Examine} & SET & 16.55 (3.08) & 17.00 (15.18–17.91) & \multirow{2}{*}{\(-1.858\)} & \multirow{2}{*}{0.063} \\  & ET & 15.41 (3.40) & 16.00 (13.90–16.92) & & \\ \hline \multirow{2}{*}{Self-Evaluation of Performance} & SET & 11.77 (1.57) & 12.00 (11.08–12.47) & & \\ \cline{2-2}  & ET & 10.82 (2.46) & 11.00 (9.73–11.91) & \multirow{2}{*}{\(-1.998\)} & \multirow{2}{*}{0.046} \\ \hline \multirow{2}{*}{Sounds} & SET & 17.05 (3.51) & 18.00 (15.49–18.60) & & \\ \cline{2-2}  & ET & 16.91 (3.80) & 18.00 (15.22–18.60) & & \\ \hline \multirow{2}{*}{Haptic} & SET & 11.73 (2.27) & 12.00 (10.72–12.73) & & \\ \cline{2-2}  & ET & 10.05 (2.59) & 11.00 (8.90–11.19) & & \\ \hline \multirow{2}{*}{Total 1  & SET & 112.59 (12.82) & 117 (106.91–118.27) & & \\ \cline{2-2}  & ET & 104.00 (15.83) & 107 (96.98–11.02) & \(-3.114\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the presence questionnaire (PQ) for the strength endurance training (SET) and endurance training (ET).
# Electron Pumping and Spectral Density Dynamics in Energy-Gapped Topological Chains
## 2 Theoretical Description
are necessary to obtain the occupancies, satisfy the following set of integro-differential equations (\(t_{0}=0\) is assumed):  
\[i\frac{\partial U_{i,\vec{k}_{j}}(t)}{\partial t} = \sum_{i^{\prime}}V_{ii^{\prime}}(t)e^{i(\varepsilon_{f}-\varepsilon _{i})t}U_{i^{\prime},\vec{k}_{j}}(t)-V_{i\vec{k}_{j}}(t)e^{i(\varepsilon_{i}- \varepsilon_{\vec{k}_{j}})t}\] \[- |V_{i\vec{k}_{j}}(t)|^{2}\int_{0}^{t}dt^{\prime}\int d\varepsilon D _{j}(\varepsilon)e^{i\varepsilon(t-t^{\prime})}e^{i\varepsilon_{i}(t-t^{ \prime})}U_{i,\vec{k}_{j}}(t^{\prime})\,,\]  
where \(D_{j}(\varepsilon)\) is the \(j\)-th lead's spectral density function. Assuming the wide-band approximation which is justified for flat leads DOS or in the case when the lead's DOS varies slowly in the vicinity of the central system electron energies, the effective chain-electrode coupling can be expressed by \(\Gamma_{i}(\varepsilon)=2\pi\sum_{\vec{k}}|V_{i\vec{k}}|^{2}\delta(\varepsilon -\varepsilon_{\vec{k}})=\Gamma_{i}\), which is energy independent. Within this approach Equation (4) for the left electrode takes the form:  
\[\frac{dU_{i,kL}(t)}{dt} = -i\sum_{i^{\prime}}V_{ii^{\prime}}(t)e^{i(\varepsilon_{f}- \varepsilon_{i})t}U_{i^{\prime},\vec{k}_{L}}(t)-iV_{1,\vec{k}_{L}}(t)e^{i( \varepsilon_{1}-\varepsilon_{\vec{k}_{L}})\,t}-\frac{\Gamma_{L}}{2}U_{i,\vec{ k}_{L}}(t)\,, \tag{5}\]  
and similar for the right lead. The current flowing from the left electrode is obtained from the time derivative of the total number of electrons in this reservoir:  
\[j_{L}(t)=-e\frac{d}{dt}\sum_{\vec{k}_{L}}n_{\vec{k}_{L}}(t)\,, \tag{6}\]  
where the occupancies \(n_{\vec{k}_{L}}(t)\) can be expressed similarly to Equation (3) by the corresponding evolution operator matrix elements. In this case, the spectral density function at each chain site for the zero temperature satisfies the relation:  
\[H = \sum_{i=1}^{N}\varepsilon_{i}a_{i}^{\dagger}a_{i}+\sum_{i=1}^{N} \sum_{\vec{k}_{i}}(\varepsilon_{\vec{k}_{i}}a_{\vec{k}_{i}}^{\dagger}a_{\vec{k }_{i}}+V_{\vec{k}_{i}}a_{\vec{k}_{i}}^{\dagger}a_{i})+\sum_{i,j}^{N}V_{i,j}(t )a_{i}^{\dagger}a_{j}+H.c. \tag{1}\]0  
where \(U_{i,\alpha}\) elements are calculated numerically from Equation (4) or Equation (5). In this manuscript we use the unit of energy \(\Gamma_{L}=\Gamma\equiv 1\), the time unit is \(\hbar/\Gamma\), and the current is expressed in the units of \(e\Gamma/\hbar\). Thus, for \(\Gamma=1\) meV the units of time and current are \(0.66\) ps and \(0.25\,\upmu\)A, respectively. Please note that the reference energy point is the left electrode Fermi energy, \(E_{F}=0\).
## 6 Functional Properties and Applications of Nanocomposites
### Biomedical sciences  
New materials with potential biomedical applications have been developed with respect to direct, supplement or substitute the functions of living tissues [248]. Biocompatible materials must fulfill a chemical, biological, and physical as well as a structural, _i.e.,_ mechanical behavior, compatibility with the surrounding host tissues. In case of mechanical properties especially the elastic modulus (Young's modulus), strength, stiffness and optimal load transmission are of particular interest. In addition to polymers, ceramics and metals polymer-composites allow for a tailoring of the aspired physical properties. In the following a few examples for the application of polymer-filler-composites are summarized [248]:  
* bone fracture repair: Epoxide-carbon fibers-composite for external fixators
* bone plates and screws: Epoxide, PMMA, polypropylene, polyethylene, PS, Nylon, polybutylerephthalate, PEEK, reinforced with carbon fibers
* joints replacement: Ultrahigh molecular weight polyethylene or PEEK-carbon fibers composites for total hip replacement
* bone cement: PMMA-glass powder
* dental applications: Acrylates, filled with surface modified nanosized SiO\({}_{2}\) or ZrO\({}_{2}\)
* catheters: Urethanes or silicone rubber, reinforced with nanosized SiO\({}_{2}\)
* prosthetic limbs: Thermosets, reinforced with glass, carbon, or Kevlar fibers.  
A comprehensive overview was published by [PERSON] and coauthors in 2001 [248]. In a recent publication the impact of the particle size, distribution, and geometrical shape on the resulting mechanical properties of different composites suitable as dental filling materials for substituting amalgams was summarized [249]. The authors found, that especially the homogenous nanoparticle distribution in the matrix ensured a long dental filler lifetime. In contrast to composites with passive fillers improving mainly the thermomechanical properties, the ferroelectric PVDF, filled with nanosized (20-60 nm) hydroxyapatite for improved biocompatibility and nanosized (100 nm) BaTiO\({}_{3}\) with its high dielectric constant, could be used as bioelectroactive bone regeneration composite [250]. PMMA/SiO\({}_{2}\) nanocomposites containing calcium salts can be used as bioactive bone substitute or as filler for PMMA bone cement [251]. Fluorescent, stable aqueous ZnOat Polymer core/shell nanoparticles exhibit a high potential for application as fluorescent probes _in vitro_, as they are almost nontoxic for human cells [139]. Finally, as already shown in section 6.2., there is a huge application potential for superparamagnetic nanocomposites in the field of biology, medicine, diagnostics and therapy [53,54,159-171].
# Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure
## 7 Conclusions and Future Work  
In this paper, a novel data extraction method based on local adjacent modules for images of industrial Data Matrix barcodes captured by smartphone cameras is presented. Different CNN models are established to learn the colors of the two adjacent modules and label them depending on whether they have the same color or not. Based on the label matrix of a Data Matrix image, an edge image is produced based on the KM algorithm. A Data Matrix symbol is, finally, reconstructed and decoded. Based on the experiments with our barcode image database, we achieve a 99.5% recognition rate by using CNN network VGG19. The execution environment of our experiment is the CPU. If GPU is used, the time-consuming issue can be kept within an acceptable range. The proposed method has good generalizability to images captured by different smartphone cameras and performs well under different lighting conditions. Experimental results on Data Matrix images demonstrate that our data extraction method can also be extended to barcode patterns with similar features. One interesting future work is to focus our method on extremely poor quality modules in barcode images, where other high-contrast modules are identified by a binarization algorithm to save decoding time.  
Conceptualization, L.L. and J.L.; project administration, C.L.; resources, C.L.; supervision, L.L. and J.L.; writing--review and editing, J.L. and C.L.; writing--original draft, L.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Software** & **The Size of the Set** & \
\begin{tabular}{c} **The Number of** \\ **Recognized Samples** \\ \end{tabular} &
\begin{tabular}{c} **Recognition Rate** \\ **(\%)** \\ \end{tabular} \\ \hline Google ZXing & 200 & 3 & 1.5 \\ Onbarcode.NET & 200 & 10 & 5 \\ Dynamsoft barcode & 200 & 103 & 51.5 \\ LEADTOOLS & 200 & 63 & 31.5 \\ Libdmtx & 200 & 17 & 8.5 \\ Inlite barcode & 200 & 3 & 1.5 \\ [PERSON] [15]+Dynamsoft & 200 & 175 & 78.5 \\ Our solution (four & 200 & \textgreater{}192 & \textgreater{}96.5 \\ networks) & 200 & 199 & 99.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The recognition results in the CNN test set.  
Figure 19: (**a**) Original barcode image; (**b**) binarization result of Yang [15]; (**c**) our result.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** This work was supported by the key R&D plan of Shandong Province (2019 GGX104098).  
**Conflicts of Interest:** The authors declare that there is no conflict of interest regarding the publication of this paper.
## 3 Analysis of I2O WBAN Systems
### Configuration and Human Safety Analysis  
Implantable biomedical antenna design in WBAN systems is affected by posture, body mass index, aging and so on. Detailed information about biocompatible in-body antenna design can be found in the review by [PERSON] et al. [4]. Here, we focus our attention mainly on I2O body communication systems analysis. An efficient and multi-layered heterogeneous human body model (Figure 3) proposed in [23] for WBANs, at the frequency of 2.45 GHz, is investigated in this paper. The configuration consists of a layer of air followed by layers equivalent to the frontal thorax of an adult, making it possible to divide the human body into numerous areas of a multi-layered model. We employ dipole antennas because they are well-studied antennas in free space and have a simple structure [5; 15; 17]. In agreement with [23], we place the Tx antenna of length 3.9 cm in the liver region and the Rx antenna is a free space, half wavelength dipole with a length of 6.12 cm, located 2 cm from the human body surface. Both the Rx and Tx antennas are made of perfect electric conductors (PEC) and are directionally aligned, with a thickness of 2 mm. The simulations use a current source and the simulation methods are the same for all the cases. The equivalent 3D human body model contains multiple layers that are built on dry skin, subcutaneous adipose tissue (SAT), breast tissue (BT), muscle tissue and liver; whose thicknesses are 2 mm, 5 mm, 1 mm, 10 mm and 10 mm with dielectric properties as shown in Table 1. Since it is difficult for both manufacturers and researchers to  
Figure 2: (**a**) The computational 3D human body model in CST; (**b**) the cross section of the human frontal thorax; (**c**) equivalent human frontal thorax model; (**d**) typical healthcare applications.  
investigate their systems on an actual human body, the proposed human body model offers a viable alternative to investigate the performance of I2O body WBAN systems.  
As reported in [5,7], signal propagation in the human body leads to high attenuation, which will result in heating of tissues and organs and to an increase in the temperature of the human body. Biological effects and health risks may occur by exposure to RF electromagnetic fields. The IEEE standard and the ICNIRP safety guidance specify that the averaged SAR over 10 g of tissue should be no more than 1.6 W per kg and 2 W per kg, respectively [8,9]. With an input power of 1 W provided to the implanted sensor or device, the finite-difference time domain (FDTD) approach provided by CST was used in association with the 3D human model investigated to calculate the SAR [25]. Results demonstrate that the SAR of the human body model is far lower than both the regulations. The maximum 1 g and 10 g SAR values calculated tissue are given in Table 2 for the skin minimal distance (skin region) and maximum distance (liver region).
## 3 Results and Discussions
### Energy Analysis of Corn Drying with the IRCC Corn Dryer
#### 3.2.2 Radiant Energy Recovery  
In preheating section, due to the fact that the radiator was inserted in the far infrared section and surrounded by the loose corn, the total radiation angle coefficient of the device to the corn is equivalent to 1, this means, the radiation energy caused on the outer surface of the far-infrared generation device will be totally absorbed by the corn. According to the previous research results, grains tend to acquire relatively high absorptivity at near 9-\(\upmu\)m far-infrared wavelength [52]. The far-infrared wavelength was calculated according to the Wien displacement law [36]. In the present work, the wavelength of the radiator varies from 7.68 \(\upmu\)m to 8.3 \(\upmu\)m, as shown in Figure 10. Moreover, during the whole drying process, the recovered radiant energy rate is between 15.578 kW and 17.2 kW, and the overall recovered radiant energy is 674,339.3 kJ, which can be utilized to dehydate about 270.14 kg at the  
Figure 8: Variations of specific energy consumption (SEC) of the whole dryer and the DC during drying process.  
Figure 9: Variations of energy efficiency of the whole dryer and the DC during drying process.  
present ambient temperature. The drying method proposed in this paper can not only recover the radiation energy to strengthen the drying process, but also improve the drying quality of corn.
# Energy Accumulation Law of Different Forms of Coal-Rock Combinations
## 4 Energy Accumulation Law of Coal-Rock Combination Body
### The Influence of Coal Thickness on the Energy Accumulation Characteristics of the Combination  
When the coal thickness varies, we extract the relevant data from Table 1 and depict the resulting energy change profile for different combinations of coal thickness, as shown in Figure 7.  
Figure 7a demonstrates that the CS and CF pre-peak energy of the binary combinations are arranged in descending order as CS1:3 < CS1:2 < CS1:1 < CS2:1 < CS3:1 and CF1:3 < CF1:2 < CF1:1 < CF2:1 < CF3:1, respectively. In addition, the FCS pre-peak energy of the ternary combination is arranged in descending order as FCS1:1 < FCS1:2:1 < FCS1:3.1. This observation suggests a gradual increase in the pre-peak energy of the combination as the percentage of C thickness is augmented.  
Figure 6: Energy variation of different coal–rock combinations.  
As indicated in Figure 7b, as the C thickness percentage increases, the post-peak energy of the combination shows the trend of CS and FCS increasing and then decreasing; on the contrary, CF shows the direction of reducing and then expanding, and the fluctuation range of post-peak energy of the combination CS, CF, and FCS are 2.07\(\sim\)2.55 (\(10^{2}\) MJ\(\cdot\)m\({}^{-3}\)), 2.08\(\sim\)3.43 (\(10^{2}\) MJ\(\cdot\)m\({}^{-3}\)), and 2.87\(\sim\)3.43 (\(10^{2}\) MJ\(\cdot\)m\({}^{-3}\)), respectively. There is no apparent regularity in the post-peak energy of the combination with the more significant coal thickness percentage; the range of post-peak energy fluctuation of the CF combination is more extensive than that of the CS combination, which shows that the larger the rock strength and elastic modulus are, the larger the range of post-peak energy fluctuation of the combination.  
According to Figure 7c, binary combination CS and CF exhibit a fluctuation range of impact energy index between 3.29 and 4.15 and 4.61 and 7.82, respectively. Additionally, ternary combination FCS shows a fluctuation range of impact energy index between 3.94 and 5. 21. The impact energy index of combination is affected by the lithology, with greater peak strength and modulus of elasticity in rocks resulting in the broader fluctuation range. The lithology affects the combination's impact energy index, with greater peak strength and modulus of elasticity in rocks, resulting in a broader fluctuation range. Generally, an increasing trend in the impact energy index of the combination is observed with a more significant proportion of coal thickness.  
The results suggest that a higher percentage of coal thickness in the combination leads to an increase in the pre-peak energy accumulation. The post-peak energy displays no clear patterns, while the impact energy index indicates a rising trend.
## 3 Results and Discussion
### Experimental Results
#### 3.1.4 Influence of the Number of Connections  
The effect of placing another connection symmetrically to the centre of the beam is discussed here. Figure 13 shows the evolution of the load per connection as a function of \(l_{\mathrm{b}}\) for beams with single (1D) and double symmetrical (2D) connections, for the two groups of beam depths tested (100 and 200 mm). It is observed that the load of beams with a single connection is higher than that of beams with two connections in the case of \(l_{b}\) of 360 mm, i.e., a connection spacing \(l_{c}\) of 230 mm. Considering the linear relationship plotted in Figure 13, the value of \(l_{c}\) required for a multicomponent beam to exceed the load corresponding to its single-connection counterpart would be approximately 420 mm and 280 mm for \(h=100\) mm and \(h=200\) mm, respectively (\(l_{c}\approx 4.2h\) and \(1.4h\), respectively). This could be due to an influence of the \(\alpha\) parameter, suggesting that a higher \(\alpha\) requires higher \(l_{c}\) connection spacing.  
In the case of beams with \(h=100\) mm, it is observed that the load per connection in beams with multiple connections corresponding to \(l_{b}\) distances of 120 mm and 240 mm (\(l_{c}\geq 470\) mm) is approximately 4% higher than the equivalent case with a single connection. However, in the case of beams with \(h=200\) mm, this increase does not remain constant and amounts to 4% and 31% for \(l_{b}\) distances of 120 mm and 240 mm, respectively. In this case, there is a significant gain for \(l_{b}=240\) mm. It should be noted that this distance \(l_{b}\) corresponds to the geometry for which the most critical case of its equivalent with one connection occurs. In both groups of beam depths, for a certain distance between connections (\(l_{c}\)), there is an inflexion point at which beams with multiple connections reach more load than their counterparts with a single connection. Therefore, a beam with multiple connections arranged symmetrically to the centre of the beam reaches more than twice the splitting load than its counterpart with a single connection.  
According to the findings in Section 3.1.3, the total load of a beam with two dowels located at off-centre of the beam does not double the load of the reference connection, i.e., a connection with two dowels located at mid-span. However, when comparing similar geometries, there is a certain connection spacing (\(l_{c}\)) where the stresses introduced into the beam by each connection do not interfere with each other and, in such cases, the total load  
Figure 13: Splitting failure load per connection for single and double symmetrical connections as a function of the distance to the support \(l_{b}\), for the two different beam depths.  
of a beam with multiple connections can be more than twice the load of its counterpart with a single connection.