esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Photovoltaic Properties
#### 3.3.5 Excited-State Lifetime (\(\tau\))  
The excited-state lifespan in a DSSC is the duration during which a dye molecule remains in an excited state before returning to its ground state. The transition to ground state can be through various mechanisms such as fluorescence, non-radiative decay, or electron injection into the semiconductor. The excited-state lifespan regulates the likelihood of electron injection into the semiconductor and also the competition between electron injection and alternative decay modes [45]. The excited-state lifetime can be calculated using the following Equation (6).  
\[\tau=(h\times c)/(\Delta E\times\varphi) \tag{6}\]  
where:  
\(\tau=\) Excited-state lifetime  
\(h=\) [PERSON]'s constant (6.626 \(\times\) 10\({}^{-34}\) J-s)  
\(c=\) Velocity of light in vacuum  
\(\Delta E=\) Excitation energy (in joules)  
\(\varphi=\) Oscillator strength.  
Table 3 shows the excited-state lifetimes of the natural dyes in both the gas and ethanol solvent phases. The order of excited-state lifetimes in the gas phase is cyanidin (70.4 ns) > delphinidin (43.86 ns) > pelargonidin (39.04 ns) > petunidin (17.17 ns) > peonidin (7.078 ns), while in the ethanol solvent phase, the order shifts to cyanidin (86.72 ns) > pelargonidin (29.82 ns) > delphinidin (19.14 ns) > peonidin (17.59 ns) > petunidin (17.19 ns).  
The length of time it takes for an excited dye molecule to return to its ground state is known as the excited-state lifetime. The claim emphasizes that petunidin, both in the gas phase and the ethanol solvent phase, has the lowest excited-state lifetime among the dyesunder study. This is explained by the oscillator strength's maximum value, which denotes a more powerful interaction between the dye's electronic transitions and the incident light.  
Additionally, petunidin is anticipated to have a better energy injection procedure and a greater light harvesting efficiency without affecting the DSSC's short-circuit current (\(J_{\text{SC}}\)). This suggests that petunidin has a greater potential for effectively absorbing light and injecting electrons into the semiconductor material's conduction band. Moreover, solvents with higher polarities have longer exciting lifetimes. This implies that the dyes' excited-state lifetime is extended in more polar solvents like ethanol. Since polar liquids have a longer lifespan than nonpolar ones, more energy can be transferred, and electrons may be injected for longer periods of time, potentially improving device performance.
# Automated Evaluation of C-ITS Message Content for Enhanced Compliance and Reliability
## 2 Materials and Methods
### Types of C-ITS Messages
#### 2.1.1 Cooperative Awareness Messages (CAMs)  
CAMs are a key element within C-ITSs, facilitating communication between vehicles and infrastructure by providing crucial information about the current state and location of vehicles. CAMs are standardized according to ETSI EN 302 637-2--V1.3.1 [23], which defines the structure and content of these messages.  
A CAM is divided into several main containers, as shown in Figure 1, which include specific parameters [24]:  
* Basic Container:
* stationType: This parameter specifies the type of the device sending the message (e.g., passenger car, truck, etc.). It helps recipients of the message understand the context in which the message was sent.
* referencePosition: Contains information about the geographical location of the device at the time the message was sent. This includes latitude, longitude, altitude, and measurement accuracy. This parameter is crucial for determining the vehicle's position and for applications such as navigation and proximity detection.
* High-Frequency Container:
* This container includes data that changes frequently and is transmitted at a higher frequency. It typically includes information about speed, direction, and the current state of the vehicle. The high-frequency container is mandatory, and its content is important for applications requiring current and dynamic information about the vehicle.
* Low-Frequency Container:
* This container contains less frequently changed information, transmitted at a lower frequency. It may include static data such as vehicle properties (type, size). According to the standard, the low-frequency container must be included in all CAMs from the first message generated after the activation of the cooperative awareness basic service. Additionally, it must be included if the time elapsed since the last CAM generation with the low-frequency container is equal to or greater than 500 ms.
* Special Vehicle Container:
* This container holds specific information about vehicles that may have special requirements or characteristics. Similar to the low-frequency container, the special vehicle container must be included in the first CAM after the activation of the CA basic service. It must also be included if the time elapsed since the last CAM generation with the special vehicle container is equal to or greater than 500 ms.  
Figure 1: CAM structure [23].  
This text provides a basic overview of CAMs and their container structures. For the purposes of this article, this information is sufficient. The following sections will provide a more detailed examination of specific CAM parameters that were tested and the methods used to verify them.
## 3 Results and Discussions
### Data Preparation and Analysis of Vulnerability Factors
#### Net Recharge  
Recharge plays a key role in the transfer of water from the ground surface to the aquifer. To estimate this parameter in the Tebessa-Morsott alluvial aquifer, the results of the mathematical model established in the region was used [38]. The map of the \"Net recharge\" parameter (Figure 9b) shows three classes: (i) The first class covers the major central part of the plain, where, due to a lithological composition mainly of clay-loamy alluvium, the recharge does  
Figure 9: The thematic maps for the DRASTIC model, including depth to water (**a**), net recharge (**b**), aquifer media (**c**) and soil media (**d**), topography (**e**), impact of the vadose zone (**f**) and hydraulic conductivity (**g**).  
not exceed 50 mm/year; (ii) a second class with a recharge of 50 to 100 mm/year occupies the banks of the wadis (El kebir, El Ksob and Chabro wadis); (iii) a third class occupies the piedmonts of the Maestrichtian and Turonian limestone borders with a recharge of 100 to 180 mm/year.
# Study on the Seismic Effect of the Pebble Soil Site in the Zhongwei Basin
## 2 Seismology and Geology Conditions of the Zhongwei Basin  
The Zhongwei Basin is located in the arc-shaped structural belt on the northeastern margin of the Tibetan Plateau, which is the product of strong tectonic movements and fluvial geology. As shown in Figure 1, The largest earthquake ever recorded was the 1709 Zhongwei 71/2 \(M\)s earthquake. According to historical records, this earthquake caused heavy disaster losses. The seismic structure of the 1709 Zhongwei 71/2 \(M\)s earthquake was the Xiangshan-Tianjingshan fault zone, which was less than 15 km away from the Zhongwei Basin. The Xiangshan-Tianjingshan fault is an active Holocene active fault that has a risk of earthquakes of 8.0 \(M\)s in the future.  
Relying on the \"The Active Fault Detection Project in Zhongwei City\" and the \"First National Natural Disaster Risk Census Project\", the working group conducted many drilling and geophysical explorations in the Zhongwei Basin (Figure 2). At the same time, some borehole and high-density electrical data were also collected. Among them, 25 boreholes and four high-density electrical sections were drilled in the active-fault detection project [21], one borehole was drilled in the risk-survey project, and 20 holes and 8 high-density electrical sections were drilled in the seismic-safety evaluation and seismic-microzoning project. In this work, the basin strata deposition was determined. Multiple boreholes and high-density electrical profiles revealed that the thickness of the pebble layer in the basin was between 25 and 100 m, covered by a silty clay layer ranging between 0 and 5 m, and a sand layer of varying thickness below it. In response to the work requirements, field tests of the borehole shear wave velocity and dynamic parameter tests of some samples were conducted. By collecting the completed seismic safety evaluation reports in the basin and referring to the previous research results, the basic soil model parameters required for the calculation were comprehensively determined.  
Figure 1: Seismic and tectonic background map of Zhongwei Basin.
# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
## 3 Proposed Approach
### Feature Extraction
#### 3.1.2 Deep Feature Extraction  
To generate the CAM [50] of the 14 pathological regions and extract deep features, we use the pretrained DenseNet-121 [44], which was trained for pneumonia detection from our targeted ChestX-ray14 dataset. The model architecture of the DenseNet-121 is shown in Figure 2. Compared to the other pretrained CNNs, DenseNet-121 can improve the intake of information and gradients through the network, in which a layer obtains a collective knowledge from all previous layers; passes on its own feature maps to subsequent layers; and then concatenates them into the depth dimension. Thus, the network can be thinner and compact due to having fewer layers. The training error is able to be easily propagated to the previous layers more directly and more computational efficiency tends to be drawn. The network can also learn more diversified and richer feature patterns as the classifier in DenseNet uses features from all complexity levels giving more smooth decision boundaries when training data is insufficient.  
The X-ray input image is encoded by a densely connected CNN, similar to DenseNet's in [43]. At the first stage, we resize the X-ray image into a 224 \(\times\) 224 grid to be fed into the pretrained DenseNet-121. The weights of DenseNet-121 are initialized with a pretrained model on ImageNet [7] using Adam standard parameters [51]. Next, we freeze all the weights from lower convolutional layers, replace the final fully connected layer with the fully connected layers of a 14-dimensional output, and treat the DenseNet-121 as a fixed feature extractor. In the second stage, we fine-tune the weights from all layers by continuing the back-propagation. Each training iteration aims to optimize the cross-entropy losses through the following equation,  
\[L\Big{(}Y,Y^{(P)}\Big{)}\ =\ \sum_{c\ =\ 1}^{14}Y_{c}\log\Big{(}Y_{c}^{(P)} \Big{)}+(1-Y_{c})\log\Big{(}1-Y_{c}^{(P)}\Big{)}, \tag{1}\]  
where \(Y\) is the ground truth vector and \(Y^{(P)}\) is the predicted label vector in which each element is binary; 1 and 0 represent the existence and nonexistence of the corresponding diseases, respectively. In this study, the batch size is 16; the momentum is 0.9 (as an optimizer); the initial learning rate is 0.001, which decays by a factor of 10 after each iteration during the validation loss process; and the maximum number of iterations is 50,000.
## 4 Conclusions
A lower % of transmittance at 3380 cm\({}^{-1}\) in binary AAECs with reagent 2 and 0.6% MWCNTs (B2C6), compared to their counterpart with 5% MgO (B2M5), confirmed a weaker bond and lower compressive strength even though MWCNTs can act as nucleation sites and accelerate polymerization;
* The UPV values ranged from 3067 m/s to 4068 m/s, showing an increase with the addition MWCNTs/rGO/MgO, with the highest values (19% higher) being observed for MWCNT-incorporated AAECs. This is an indication of a higher capacity to transmit ultrasonic waves, better conductivity, and a better self-sensing ability. A linear correlation revealed an increase in compressive strength with an increase in the UPV content of AAEC mixes at 28 days;
* This research demonstrated the viability of producing ambient-cured powder-based AAECs, as more green and sustainable alternatives to conventional ECCs, that incorporate carbon-nano materials and MgO additives, as they have satisfactory workability, mechanical, and microstructural characteristics. The developed multifunctional AAECs have the potential to be used for structural applications and to produce high-performance resilient durable bridge-building infrastructures with self-healing/sensing and 3-D printing potentials. Conceptualization, K.M.A.H. and M.A.H. ; Methodology, K.M.A.H. and M.A.H. ; Formal analysis, K.M.A.H. and M.A.H. ; Investigation, K.M.A.H. and M.A.H. ; Resources, K.M.A.H. ; Writing--original draft, K.M.A.H. and M.A.H. ; Writing--review & editing, K.M.A.H. ; Supervision, K.M.A.H. ; Project administration, K.M.A.H. ; Funding acquisition, K.M.A.H. All authors have read and agreed to the published version of the manuscript. This research was funded by Natural Sciences and Engineering Research Council (NSERC) Canada, grant number RGPIN-5613-2019. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Acknowledgments:** The authors gratefully acknowledge the financial support provided by NSERC, Canada. Supports provided by the technical staff of Concrete and Advanced Concrete Material laboratories of Toronto Metropolitan University are also acknowledged. **Conflicts of Interest:** The authors declare no conflicts of interest.
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP  
CoAP is being used in WSNs and IoT environments in various domains. The implementation of CoAP in different applications has highlighted some shortcomings in the basic architecture of CoAP. Therefore, various enhancements have been made in the CoAP architecture. The current section discusses in detail the enhancements made in CoAP congestion control mechanism, CoAP security, CoAP interoperability and so on.
## 2 Instrument Design
### Additional Sensors
#### 2.2.2 Thermal Infrared Camera  
One of the optical cameras integrated in MERITXELL is a TIR camera designed to provide thermographic imagery and repeatable temperature measurements. The data obtained with the TIR camera may be used to increase the accuracy of the radiometric measurements since it provides a real-time estimation of the physical temperature.  
The TIR camera model is a FLIR A320 camera (FLIR Systems, Wilsonville, OR, USA) [30] with a spectral range from 7.5 \(\upmu\)m to 13 \(\upmu\)m, a Field-Of-View (FOW) of 25\({}^{\circ}\)\(\times\) 18.8\({}^{\circ}\), a detector resolution of 320 \(\times\) 240 pixels, and a maximum image frequency of 30 frames per second. In addition, the physical temperature can be measured with a thermal sensitivity (i.e., radiometric resolution) of 50 mK at 300 K.
## 2 Test and Result Analysis
### Test Results Analysis
#### 2.3.3 Vertical Displacement  
Figure 11 shows the dynamic vertical displacement curves at the center of each compartment's baseplate for specimens G, PCG, and PCGR. Under explosive loading, all specimens exhibited a rapid increase in displacement, followed by a rebound and oscillation process, eventually stabilizing with residual deformation. For specimen G, the maximum vertical displacements at the centers of chambers 1, 2, and 3 were 8.68 mm, 19.26 mm, and 4.87 mm, respectively, with corresponding residual displacements of 2.58 mm, 4.68 mm, and 1.60 mm. This indicates that the explosive impact varied significantly across different locations, with chamber 2, located closest to the blast center, experiencing the most pronounced deformation. In the case of specimen PCG, subjected to the same explosive load, its displacement response was significantly reduced. The maximum vertical displacements in chambers 1, 2, and 3 were 2.93 mm, 4.86 mm, and 2.13 mm, with residual displacements of 0.84 mm, 1.23 mm, and 0.64 mm, respectively. Notably, the peak and residual displacements of compartment 2, directly beneath the blast center, were reduced by 74.8% and 73.7%, respectively, compared to specimen G. This clearly demonstrates the effectiveness of the polyurea coating in mitigating structural deformation induced by the explosion. For specimen PCGR, subjected to repeated explosive loads, the displacement response increased markedly. The maximum vertical displacements in compartments 1, 2, and 3 were 15.61 mm, 34.39 mm, and 8.09 mm, with residual displacements of 2.89 mm, 8.85 mm, and 2.90 mm, respectively. This highlights the significant cumulative damage effect on the box girder under repeated explosive loads, further amplifying the displacement response. In conclusion, the comparative analysis of the displacement patterns of specimens G, PCG, and PCGR reveals that while the polyurea coating effectively reduces displacement under a single explosive load, the cumulative damage effect under repeated explosive loads is substantial, leading to increased displacement response.  
Figure 10: Dimensions of the hole of mid-chamber (PPCG). (**a**) Dimensions of the hole of the top surface (longitudinal direction). (**b**) Dimensions of the hole of the top surface (transverse direction). (**c**) Dimensions of the hole of the bottom surface (transverse direction).  
Figure 9: Dimensions of the hole of mid-chamber (PCG). (**a**) Dimensions of the hole of the top surface (longitudinal direction). (**b**) Dimensions of the hole of the top surface (transverse direction). (**c**) No damage.
## 3 Results and Discussion
### The Application of the IANN Model  
To further verify the practicability of the established IANN model, the 117 experimental data sets acquired in 12 November (\(-\)3.01 \({}^{\circ}\)C \(\leq T_{0}\leq\) 5.94 \({}^{\circ}\)C, 40.35\({}^{\circ}\)\(\leq RH_{0}\leq\) 63.23%) were trained by the model. The comparison between experimental values and predicting results of the _MC_, _Er_, and \(\eta_{ex,h}\) are depicted in the following Figure 9. Obviously, it can be seen from the figure that the regression coefficient of determination (\(R^{2}\)) for _MC_, _Er_, and \(\eta_{ex,h}\), respectively, are 0.998, 0.992, and 0.980, mean squared error (_MSE_) of 0.067, 0.00296, and 0.0057, mean absolute error (_MAE_) of 0.16, 0.04, and 0.0063. The values of the above statistic indexes indicate that the 9-2-12-3 IANN model has an excellent prediction performance and can be used in engineering practice. Moreover, the prediction results of the first cycle (the measured _MC_ between 28.9% d.b. and 32% d.b.) are slightly deviated from the experimental results, which might be caused by the fact that there is a measurement error for the on-line moisture meter in the high moisture region of cereal grains [40].  
Based on the established 9-2-12-3 IANN model, a control model of the drying process is proposed, as shown in the Figure 10. During the drying process, the experimental data collected by the corresponding sensor are used to train, and get, the optimal IANN model. As can be seen from Figures 2 and 10, the drying process is controlled by the DD and the flue gas valve on the flue gas pipeline.  
Figure 9: The comparison of experimental values and predicting results for the _MC_ (**a**), _Er_ (**b**), and \(\eta_{ex,h}\) (**c**).  
When one of the inputted factors changes, the established IANN model can automatically calculate and analyze the predicted corresponding output indexes at the same time. Accordingly, the intelligent optimized controller optimizes and calculates the grain discharging electric motor (or flue gas valve), and the optimal rotational speed of the electric motor (or optimal openness of the valve) is given to the frequency converter, so as to control the drying process, and realize the optimal control of the \(MC\), \(Er\), and \(\eta_{ex,h}\). The proposed control model based on the IANN model may strengthen the practical implications.
## 3 Analysis of Results
### Velocity Boundary Conditions  
With a boundary condition of \(v=0.2\) m/s, the grout diffusion and pressure dissipation are shown in Figures 12 and 13. In this case, the flow rate of the grout is \(0.04\) m\({}^{2}\)/s, which is approximately one-quarter of the flow rate achieved under the pressure boundary condition when it stabilizes.  
Figure 11: Flow rate variation at grouting port.  
As shown in Figure 12b, at \(t=2\) s, the grout volume fraction at the \(110^{\circ}\) position is 0.935, indicating that the grout has completely filled the pores at this location. In contrast, at the \(70^{\circ}\) position, the grout volume fraction is only 0.119, indicating that only a small amount of grout exists in the pores at this location. The grout is diffusing downward faster than upward. This phenomenon is also observed under pressure boundary conditions, and it is likely due to the slower injection speed in the pressure boundary case, which results in a more pronounced difference in diffusion speed under the velocity boundary condition.  
The pressure at the grouting port increases gradually over time, and the lower the position of the grouting port, the higher the pressure. When the grouting port is at \(30^{\circ}\), the maximum pressure is 16.9 kPa, while at \(150^{\circ}\), the maximum pressure reaches 31.5 kPa, approximately twice the value at \(30^{\circ}\). Additionally, when grouting takes place at \(150^{\circ}\), the pressure increases at a relatively steady rate, while in other cases, the pressure growth rate accelerates over time.  
Figure 14 illustrates the variation in grouting pressure under the velocity boundary condition. From 0 to 0.5 s, the grouting pressure remains nearly constant, as a large number of pores in the backfilling layer have not yet been filled, resulting in low resistance to grout flow. As time progresses, the grouting pressure gradually increases. The lower the position of the grouting port, the faster the pressure increases, indicating that greater resistance is encountered during grout diffusion at lower positions. From 1.7 to 2 s, the grouting pressure at \(30^{\circ}\) and \(90^{\circ}\) suddenly increases, possibly because the grout has diffused into narrower pore regions, where the obstruction of grout movement leads to a rise in pressure.  
Figure 14: Variation in grouting pressure.  
Figure 13: Grout pressure distribution in the backfilling layer under velocity boundary conditions. (a) \(\alpha=30^{\circ}\); (b) \(\alpha=90^{\circ}\); (c) \(\alpha=150^{\circ}\).