esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 4 Result Analysis
### Dataset Description  
Various intelligent equipment, such as drones and dashcams, have made the automatic collection of road damage data much more accessible in recent years. Especially the dashboard-mounted camera is quicker to install and more effective than manual scanning techniques in detecting road damage. Moreover, classifying road damage data manually is a time-consuming process. An existing benchmark dataset was employed to train the road damage detection model. Most top-view datasets do not have the class name; hence, classification is impossible. The road damage detection dataset (RDD 2022) is the largest front-view road damage dataset ever compiled about road pavement damage, encompassing four types of damage in six countries. This dataset follows the preceding RDD dataset and is an expanded version of the earlier RDD 2020 [45] and RDD 2018 [26] datasets. While the RDD 2018 dataset has 9053 photos and the RDD 2020 dataset contains 26,336 images from three countries, the RDD 2022 dataset [46] comprises 47,420 images compiled from numerous sources in six nations. It includes Japan, the United States, Norway, the Czech Republic, India, China Motorbike, and China Drone. Figure 5 depicts the distributions of damage types (of the four main damage types) among the six countries. It also illustrates the diversity of road pavement damage across countries by showing that India has a more significant number of D40, whereas D00 is adequately represented in the dataset for all countries. Moreover, Japan, India, and Norway have a higher rate of pavement damage than the other three nations. From the present standpoint, this cutting-edge dataset is the most practical and effective for road pavement damage detection and classification.
# A Novel Radial Artery P-S Curve Model Based on Radial Vibration of Vascular Wall
## 2 Models and Methods
### Model of Blood Movement  
The walls of human vessels are composed of three layers. The inner layer is composed of endothelial cells and the stromal membrane, the middle layer is composed of collagen and smooth muscle fibers, which is elastic, and the outer layer is a more relaxed connective tissue. We took a small segment of vascular microns in the radial artery, assumed that the blood vessels were cylindrical tubes of various isotropy, and constructed the vascular model shown in Figure 1.  
In this model, the \(x\)-axis direction is the axial direction of the blood vessel, \(r\) is the radial direction, \(\theta\) is the circular direction, \(\mu\) and the \(v\) are the blood axial and radial flow rate, \(O\) is the coordinate origin, and \(R\) is the blood vessel radius. In this column coordinate system, the basic equation for blood movement is as follows [21,22].  
Continuity equation:  
\[\frac{\partial\mu}{\partial x}+\frac{1}{r}\frac{\partial}{\partial r}(rv)=0 \tag{1}\]  
Axial and radial equations of motion:  
\[\frac{\partial\mu}{\partial t}+\mu\frac{\partial\mu}{\partial x}+v\frac{ \partial\mu}{\partial r}=\frac{1}{\rho}\frac{\partial p}{\partial x}+\eta \left(\frac{\partial^{2}\mu}{\partial r^{2}}+\frac{1}{r}\frac{\partial\mu}{ \partial r}+\frac{\partial^{2}\mu}{\partial x^{2}}\right) \tag{2}\]  
\[\frac{\partial v}{\partial t}+\mu\frac{\partial v}{\partial x}+v\frac{ \partial v}{\partial r}=\frac{1}{\rho}\frac{\partial p}{\partial r}+\eta \left(\frac{\partial^{2}v}{\partial r^{2}}+\frac{1}{r}\frac{\partial v}{ \partial r}+\frac{\partial^{2}v}{\partial x^{2}}-\frac{v}{r^{2}}\right) \tag{3}\]  
where \(\rho\) is the blood density and \(\eta\) indicates the motility viscosity of the blood. Due to the periodic ejection of the heart, the blood travels within the blood vessel in the form of pressure waves and assuming that the pulse is propagated by a sine wave with a wave velocity of \(a\) and a circular frequency of \(\omega\), the flow rate is expressed in the form of a plural, as shown in Formula (4).  
\[\left\{\begin{aligned} &\mu=\mu_{0}(r)e^{i\omega(t-\frac{x}{a})}\\ & v=v_{0}(r)e^{i\omega(t-\frac{x}{a})}\end{aligned}\right. \tag{4}\]  
Substituting Formula (4) into Formula (1) yields a radial initial flow rate of blood \(v_{0}\) and the order of magnitude relationship (expressed in \(O\)) of the axial initial flow rate \(\mu_{0}\), as shown in Formula (5).  
\[v_{0}=O\left(\frac{\omega R}{a}\mu_{0}\right) \tag{5}\]  
[PERSON], [PERSON], [PERSON], [PERSON] et al. [3,23] analyzed the changes in arterial blood flow under different stenosis degrees based on the blood continuity equation and momentum equation. On this basis, this study introduced the transformation equation of blood flow radial velocity and axial velocity. In addition, we analyzed the magnitude relationship between radial velocity and axial velocity, and provided an important basis for the elastic vibration of the arterial wall.
# The Protection Role of Cysteine for Cu-5 Zn-5 Al-1 Sn Alloy Corrosion in 3.5 wt.% NaCl Solution
## 2 Materials and Methods  
A commercial Cu5 Zn5 Al1 Sn alloy in ther form of a 1 mm thick sheet (89 wt.% Cu, 5 wt.% Zn, 5 wt.% Al, and 1 wt.% Sn, equivalent to 84 at.% Cu, 11 at.% Al, 4.5 at.% Zn, and 0.5 at.% Sn) was obtained from Aurubis (Hamburg, Germany). The specimens were wrapped in epoxy resin, exposing a 1 cm\({}^{2}\) surface area. Before any test, the alloy was ground by silicon carbide paper from #800 to #2000 and then successively polished with diamond paste to 2.5, 1.5, and 0.5 \(\upmu\)m, respectively. The diamond polished specimens were rinsed with analytical grade ethanol and dried in air.  
3.5 wt.% sodium chloride (NaCl) was used as the corrosive solution. It was made from an analytical grade reagent of NaCl and deionized water. Cysteine (KC90277-100 gm, Suzhou itanke Co. Ltd., Suzhou, China) was used as the inhibitor and stock solution of \(1\times 10^{-2}\) M was prepared by mixing a suitable amount of cysteine in distilled water. Solutions with cysteine concentrations of \(1\times 10^{-3}\) to \(1\times 10^{-5}\) M were set from the stock solution using a dilution method.  
The electrochemical cell used was a three-electrode glass cell with a capacity of 400 mL. A silver/silver chloride electrode (Ag/AgCl) was used as the reference electrode. All potential values in the manuscript were referred to using Ag/AgCl. A flat platinum sheet (4 cm\({}^{2}\)) was used as the counter electrode. An electrochemical workstation Gamry (Reference 600) with a high input resistance of \(10^{14}\)\(\Omega\), a current detection limit of 60 pA, and a current resolution of 20 aA was used to conduct electrochemical measurements. Prior to the tests of electrochemical impedance spectroscopy (EIS), the specimen was deepened in the test solution for 60 min. EIS was carried out under potentiostatic conditions in the frequency range of 100 kHz to 0.01 Hz at open circuit potential (OCP) with an amplitude of 5 mV.  
The EIS data was analyzed by Zsimpwin software to determine a simulated circuit and respective parameters. Tafel curves were obtained from potentiodynamic polarization study which was performed by varying the electrode potential automatically from \(-800\) mV to \(+400\) mV with respect to OCP at a rate of 1 mV/s.  
An FEI Quanta 250 scanning electron microscope was used to observe the surface morphology of the Cu5 Zn5 Al1 Sn alloy specimen with and without the cysteine inhibitor in the 3.5 wt.% NaCl solution. X-ray photoelectron spectroscopy (XPS) and Auger electron spectroscopy (AES) measurements were performed on an ESCALAB 250 Xi spectrometer with Al K\(\alpha\) X-ray (1486.6 eV) irradiation as the photo source. Binding energy (BE) was calibrated against the C1s line of the aliphatic carbon contamination set at 284.8 eV. Xsppeak41 software was used for data fitting, which was carried out graphically within the constraints of Gaussian peak shapes.
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 3 Numerical Tests and Results
### Results
#### 3.3.1 Multi-Layer Random Stratigraphic Models
Figure 10.0 Velocity profile comparisons for multi-event test dataset of multi-layer random stratigraphic models (**a**) at a horizontal distance of 0.96 km; (**b**) at a horizontal distance of 0.6 km; (**c**) at a horizontal distance of 1.1 km. To address the issue of inaccuracies in edge characterization, several approaches can be considered. One option is to train the model with a larger dataset that includes a significant proportion of geological structures with four layers or more. This can enhance the model's ability to represent complex geological structures effectively. Another avenue of improvement is refining our neural network architecture. Additionally, a complementary approach involves combining DL results with traditional methods. For instance, using the outcomes from DL as an initial model for FWI can lead to precise subsurface velocity models.
## 5 Case Study
### Operational Benefits
The indicators for evaluating operating data and eco-driving were calculated as follows:  
1. Operating data (power consumption efficiency = 0.67): * Monthly power consumption, 3955/0.67 = 5903 (kWh); * Carbon emissions, 5903 \(\times\) 0.554 = 3270 (kg); * Driving cost, 2.6253/0.67 = 3.9 (NTD/km). 2. Eco-driving (power consumption efficiency = 0.73): * Monthly power consumption, 3955/0.73 = 5418 (kWh); * Carbon emissions, 5418/0.554 = 3002 (kg); * Driving cost, 2.6253/0.73 = 3.6 (NTD/km). According to the above evaluation indicators, eco-driving can improve the monthly operational improvements of each vehicle as follows:  
**Energy saving** Energy consumption was reduced from 5903 kWh to 5418 kWh (by 485 kWh), increasing the benefit by about 8.2%;  
**Carbon reduction** Carbon emissions were reduced from 3270 kg to 3002 kg (by 268 kg), increasing the benefit by about 8.2%;  
**Operation** The driving cost was reduced from 3.9 NTD/km to 3.6 NTD/km, saving 0.3 NTD/km. Before we demonstrate the statistics on monthly mileage and power consumption in operating data, we formally define the terms \"energy consumption\" and \"power consumption\". In this study, energy savings, carbon reduction, and driving energy consumption were the indicators used to evaluate the operational benefits. When the power consumption efficiency increased, the benefits of energy saving and carbon reduction would be improved. The greater the mileage, the less the driving cost. This section concludes that the eco-driving of the research vehicles could improve energy savings and carbon reduction by 8.2% and reduce the monthly driving cost by NTD 1187.
## 2 Materials and Methods
### Protein Production and Purification  
All proteins--Hep-A200, MW = 87.9 kDa; Syn-A200, MW = 86.8 kDa; Hep-SELP, MW = 60.0 kDa; Syn-SELP, MW = 58.6 kDa; CM4-SELP, MW = 60.8 kDa; BMAP18-SELP, MW = 59.2 kDa)--were expressed in _E. coli_ BL21(DE3) by means of auto-induction [6]. Cell cultures were allowed to grow for 22 h at 37 \({}^{\circ}\)C and 200 rpm, with a volumetric ratio of 1:4 in Terrific Broth (12 g tryptone, 24 g yeast extract, 5.04 g glycerol, 12.54 g K\({}_{2}\)HPO\({}_{4}\), 2.31 g KH\({}_{2}\)PO\({}_{4}\), per liter), supplemented with 2 g/L lactose for auto-induction (TBlac), and 100 mg/L ampicillin (AMP-SELP) or 50 mg/L kanamycin (AMP-A200).  
After protein production, bacterial cells were collected by centrifugation, resuspended in TE buffer solution (50 mM Tris-HCl + 1 mM EDTA at pH 8.0) and disrupted by sonication using a Vibra cell(tm) 75043 ultrasonic liquid processor (Bioblock Scientific) with a 25 mm diameter probe (3 s pulse on, 9 s pulse off, total sonication time: 10 min). The pH of the crude cell lysate was adjusted to 3.5 with 1.6 M HCl to precipitate endogenous _E. coli_ contaminants [6; 45] and removed by centrifugation at 11,500\(\times g\) for 20 min at 4 \({}^{\circ}\)C. Samples were kept at ice-cold temperature during the entire process. Purification of AMP-A200 proteins (Hep-A200 and Syn-A200) was achieved by means of inverse transition cycling (ITC) based on previously described methodologies [45; 46]. Briefly, the AMP-A200 proteins were subjected to three cycles of hot (37 \({}^{\circ}\)C) and cold (4 \({}^{\circ}\)C) incubation (2 h at each temperature) and centrifugation steps (11,500\(\times g\) for 20 min) in ultrapure water (mQ; Milli-Q\({}^{\otimes}\), Millipore). AMP-SELP proteins were purified by ammonium sulfate precipitation with 20% saturation followed by dialysis against mQ water (refer to [6] for detailed description), using natural cellulose with a 12,000-14,000 Da molecular weight cut-off (Medicell Membranes Ltd.). Protein production and purification was assessed by sodium dodecyl sulfate-polyacrylamideamide gel electrophoresis (SDS-PAGE) on a 10% acrylamide gel with 0.3 M copper chloride (CuCl\({}_{2}\)) staining. Purified polymer fractions were freeze-dried (Christ Alpha 2-4 LD Plus from Bioblock Scientific) and stored at room temperature prior to use.
## 2 Materials and Methods
### Mathematical Modeling
#### 2.2.1 Governing Equations  
By applying the first law of thermodynamics to the control volume in Figure 2b, we have  
\[\dot{Q}-\dot{W}=\sum_{o}\dot{m}_{k}\left(h+gz+\frac{w^{2}}{2}\right)_{k}-\sum _{i}\dot{m}_{k}\left(h+gz+\frac{w^{2}}{2}\right)_{k}, \tag{1}\]  
where \(\dot{Q}\) and \(\dot{W}\) are the heat flux and mechanical power developed by the thermodynamic system, respectively. \(\dot{m}_{o}\) and \(\dot{m}_{i}\) are the mass flow rates at the system's outlet and inlet. h, z, and v are the enthalpy, height, and fluid velocity, respectively, and g is gravity.  
Figure 1: Physical model of the system: (**a**) FSC system; (**b**) sectional view of FSC.  
The energy flow in the thermodynamic system, according to the first law of thermodynamics, can be represented as  
\[I_{c}A_{c}T_{\rm s}a_{\rm s}=q_{u}+q_{loss}+\frac{de_{c}}{dt}, \tag{2}\]  
where \(\tau\) and \(\alpha\) are the transmittance and absorptance of the transparent cover. \(q_{u}\) and \(q_{loss}\) are the useful heat and heat losses of the solar collector. The third term of Equation (2) represents the change in the system's energy over time.  
The useful heat absorbed by the working fluid, \(Q_{u}\), according to [24], is given by  
\[Q_{u}=\dot{m}Cp(T_{o}-T_{i}), \tag{3}\]  
where Cp is the specific heat of the fluid, and \(T_{\rm o}\) and \(T_{\rm i}\) are the outlet and inlet temperatures of the system, respectively.  
The useful heat can also be determined as the difference between the useful heat and the heat lost from the solar collector, according to [24]:  
\[Q_{u}=F_{\rm R}A_{c}[G_{\rm T}\tau\alpha-U_{L}(T_{i}-T_{a})], \tag{4}\]  
where \(F_{\rm R}\) and \(U_{\rm L}\) are the heat removal factor and the overall loss coefficient, \(G_{\rm T}\) is the irradiance, \(\tau\alpha\) is transmittance absorptance and \(T_{a}\) is the ambient temperature.  
The instantaneous collector efficiency, \(\eta_{\rm c}\), is determined by [25]  
\[\eta=\frac{Q_{u}}{A_{c}I}=\frac{\dot{m}Cp(T_{o}-T_{i})}{A_{c}I}, \tag{5}\]  
\[\eta=F_{\rm R}\tau\alpha-F_{\rm R}U_{L}\frac{(T_{i}-T_{a})}{G_{\rm T}}, \tag{6}\]
# Low-Complexity GSM Detection Based on Maximum Ratio Combining
## 5 Simulation Results and Computational Complexity Analysis
### Simulation Result and Computational Complexity Analysis for M-PSK Constellation
#### 5.2.2 Computational Complexity Analysis  
Table 4 lists the computational complexity of the above-mentioned three algorithms for the M-PSK constellation.  
Figure 10 shows the comparison of the computational complexity of the proposed two algorithms with the ML algorithm when \(N_{t}=16\), \(N_{r}=16\), \(M=16\) and \(N_{p}=2\). It can be seen that the computational complexity of all three algorithms is almost independent of SNR. Especially when the SNR is 20 dB, the computational complexity of the MRC-RS algorithm is about 11980, which accounts for 12% of the ML algorithm. The computational complexity of the MRC-MP algorithm is about 15975, which is about 16% of the ML algorithm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Detectors** & **Computational Complexity** \\ \hline ML & \(6N_{r}MN_{C}\) \\ MRC-RS & \((6N_{r}+3)N_{C}+(6N_{r}+3)L+(12N_{r}+8)*round(L/2)\) \\ MRC-MP & \(\sum\limits_{\beta=1}^{N_{r}}(8N_{r}N_{t}+3N_{t}+12N_{r}\phi_{\beta}+\phi_{ \beta}+1)+(12N_{r}+8)\Phi\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of above-mentioned detectors for the M-PSK constellation.  
Figure 9: Average number of TACs finally detected by MRC-RS and MRC-MP detectors when \(N_{t}=16\), \(N_{r}=16\), \(M=16\) and \(N_{p}=2\).
## 3 Earth Surface Modelling Advances and Links with EO Datasets  
In this section, we describe some of the progress that has been made over the last few years in modelling that can be directly linked to EO data availability. For instance, advances in different land-surfaces components introduced in the ECMWF operational forecasts can be linked to availability of key observational datasets. It is acknowledged that surface related developments need to be discussed in the wider context of Earth system modelling. Common areas of development in surface parameterisation going towards environmental applications are presented by clustering the following groups: land-surface reservoirs, land-atmosphere fluxes, land-surface properties, inland and open waters.
## 4 Proposed Method
### Pre-Processing of Data
#### 4.1.1 Segmentation Using FCN  
In computer vision, segmentation is a process in which each coherent region of the image is assigned by a class. This can be done in particular by classifying each pixel of the eye image. The size of the images varies and is not necessarily square. So the size of 256 \(\times\) 256 is retained for the size of the input images. A padding is then performed on all the images of the base that they are this size.  
In this work, we use FCN which reside of convolutional layers, pooling layers and unsampling, minimize computation time and number of parameters. FCN does not hold fully connected layers like traditional CNN. Furthermore, the network can work without each number fixed of original image size, FCN in input entire iris eye image without any repeated computation overlapped regions. Thus it make 2 bottlenecks of CNNs and segmentation iris very fast and more accurate. We can calculate the final output O by the equation bellow:  
- Softmax layer  
\[F_{(p,d)}^{(\theta)}=-\sum_{f=1}^{M}1\{y_{(p,d)}=f\}log\frac{Exp(\theta_{f}^{T} x_{(p,d)})}{\sum_{l=1}^{M}Exp(\theta_{l}^{T}x_{(p,d)})} \tag{2}\]  
\(F_{(p,d)}^{(\theta)}\) represents the output of softmax layer. \(\theta\) is parameter of the model, \(M\) represents the number of classes and f the ith number of column of class \(M\). \(x_{(p,d)}\) represents the response at coordinate \((p,q)\). If \(O\) is the output of the heat map containing width, height and the number of label classes of \(m\)\(\times\)\(n\)\(\times\)\(c\) of the input image. So the loss of any pixel \((p,q)\) between final output and the ground truth map can be calculated as follows  
\[loss=\sum_{(p,d)\in O}F_{(p,d)}^{(\theta)} \tag{3}\]  
Hence, whole loss at each pixel can be summarized to measure the accuracy. Figure 1 shows illustration of FCN introduced in this work, which contains 23 convolutional layers and 5 pooling layers. In our model, Fusion layer manipulates the sum of the output of these layers after upsampled to the input size. Figure 2 shows segmentation results obtained by FCNs.  
Figure 1: The framework for iris recognition using FCN segmentation then Normalization, feature are next extracted by using MCNN.
# Power Consumption Modeling of Discontinuous Reception for Cellular Machine Type Communications
## 5 Performance Metrics  
In this paper, we evaluate the performance of the proposed semi-Markov model based on metrics such as power-saving factor \((P_{S})\) and wake-up latency \((d)\). \(P_{S}\) is defined as the percentage of time a device spends in sleep mode to reduce its power consumption. Hence, states \(S_{6},S_{10},\ldots,S_{2N+4}\) are the corresponding short sleep states, whereas the \(S_{L+1}\) represents long sleep state, as given in Figure 3. \(P_{S}\) can be calculated using Equation (20), whereas the expressions for \(\pi_{i}\), \(\pi_{L}\), \(H_{i}\), and \(H_{L}\) can be determined using Equations (15), (17) and (18).  
\[P_{S}=\frac{\sum_{i=1}^{N}\pi_{2i+4}H_{2i+4}+\pi_{L}H_{L}}{\sum_{i=1}^{L+1}\pi _{i}H_{i}} \tag{20}\]  
The wake-up latency \(d\), which is defined as the time interval between the packet arrival and departure, is usually experienced in the case of the RRC connection state and sleep modes. Furthermore, the packet arrival over the short DRX cycle and long DRX cycle follows, on average, a uniform distribution in the case of the Poisson arrival process. In the corresponding short and long DRX sleep states, the latency of short and long sleep modes can be interpreted as \(d_{S}=(T_{SC}-T_{ON})/2+T_{RC}\) and \(d_{L}=(T_{LC}-T_{ON})/2+T_{RC}\), respectively. Similarly, the latency values in the on duration state and RRC connection state are \(T_{RC}\) and \(T_{RC}\)/2, respectively. Therefore, the wake-up Latency \(d\) can be calculated according to Equation (21):  
\[d=T_{RC}(\sum_{i=1}^{N}P_{2i+5}+P_{L+1})+d_{S}\sum_{i=1}^{N}P_{2i+4}+d_{L}P_{L}+ \frac{P_{1}T_{RC}}{2}, \tag{21}\]  
with \(P_{j}=\frac{\pi_{j}H_{j}}{\sum_{j=1}^{L+1}\pi_{j}H_{j}}\).
# Sigmoidal Mathematical Models in the Planning and Control of Rigid Pavement Works
## 3 Materials and Methods
### Sigmoidal Models
#### 3.3.2 Logistics Model  
It is a sigmoidal function used to model growth processes in resource-constrained systems, where initial growth is exponential, but it gradually decelerates as system constraints are reached [22; 23], such as production capacity or material availability. This approach is particularly relevant in the field of rigid pavement construction, where factors such as availability of machinery, skilled labor, and budget constraints can influence demand responsiveness.  
The logistics model was employed because it represents the growth of demand, allowing for the anticipation of not only the initial increase in demand as new projects are implemented or existing infrastructures are expanded, but also the eventual stagnation as production times become saturated. This integral perspective is imperative to circumvent issues such as overproduction, the squandering of resources, and delays in the execution of works. Equation (13) delineates this model:  
\[f(x)=\frac{c}{1+e^{-(x-b)/a}} \tag{13}\]  
It describes a symmetric growth, where the inflection point x\({}_{0}\), occurs at 50% of c, marking the productivity peak. Its limits \(\lim\limits_{x\rightarrow-\infty}\!\!f(x)=0\;\mbox{y}\lim\limits_{x \rightarrow\infty}\!\!f(x)=c\) ideal for projects with balanced mobilization and demobilization. However, its symmetry may underestimate the initial slowness in some activities.  
Demonstrations of the limits of extremes of intervals 0 and 1.  
Verification of \(f(0)=0\)  
\[f(0)=c\cdot\frac{L(0)}{1-L(0)}\;=0 \tag{14}\]  
Limit when \(x\rightarrow\infty\):  
\[L(x)\to 1\Rightarrow f(x)\to c\]  
Derived from \(L(x)\):  
\[L^{\prime}(x)=\frac{e^{-(x-b)/a}}{a(1+e^{-(x-b)/a})^{2}}\Rightarrow f^{\prime }(x)=\frac{c}{1-L(0)}\cdot L^{\prime}(x) \tag{15}\]  
This model is symmetric concerning \(x\;=\;b\), which makes it suitable for processes where the growth and deceleration phases are similar.  
Figure 4: Proposed model.  
\(f(x)\) is the valued cost of the project ($), \(a\) is the parameter that reflects the rate at which costs increase over time, \(b\) represents the moment in time when costs increase at the highest rate, \(c\) indicates the expected total cost of the project in the long term, and \(x\) is the project execution time (Month) (See Figure 5).