esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Cement Sheath-Rock Damage Mechanism--A Case Study on Water Injection Wells
## 4 Numerical Simulation Analysis of Water Channeling outside Casing in Water Injection Wells
### Effect of Confining Pressure on Bonded Interface Rock Damage  
Damage occurs on the bonded rock surfaces of cemented formations with increasing confining pressures of 1 MPa, 3 MPa, 5 MPa, 8 MPa, and 10 MPa. Higher confining pressure correlates with an escalation in the injection pressure at which damage is observed. Based on Figures 7 and 8, observation shows that at 326, the levels of confining pressure (5, 8, and 10 MPa) significantly affect the occurrence of damage on the bonded rock surfaces. The damage propagates along the bonded surfaces of the rock-cement interface while also extending to one side of the cement sheath at the model boundary. Injected water diffuses along both sides of the crack, and due to the damaged bonded surfaces, there is preferential flow along the rock-cement interface. Higher confining pressures result in higher water pressures within the preferential flow channels.  
Figure 7: Evolution of damage in cemented interface rock at various confining pressures.  
Numerical simulations were performed using COMSOL software to model the damage evolution in the bonded rock under fluid-solid coupling. The resulting numerical solutions were compared with analytical solutions derived from the damage model calculations. The evolution of shear stress on both sides of the crack demonstrates a similar pattern, as shown in Figure 9. With the increase in confining pressure, there is a corresponding rise in the peak stress during the damage process of the bonded rock surfaces. This suggests that inducing damage becomes more challenging as the confining pressure increases. Figure 10 illustrates the variation in peak stress during the damage process of the bonded rock surfaces under varying levels of confining pressure. The analytical solutions for the peak stress values at confining pressures of 1, 3, 5, 8, and 10 MPa are approximately 4.087, 5.021, 5.55, 6.66, and 7.22 MPa, respectively. The corresponding numerical solutions for peak stress are approximately 4.068, 4.952, 5.517, 6.592, and 7.096 MPa, respectively. The average error between the numerical and analytical solutions is 1.03%. Thus, the numerical simulation results accurately and scientifically describe the damage characteristics of the bonded rock surfaces under varying confining pressures.  
Figure 8: Fluid pressure variation with confining pressure.  
Figure 10: Variation of peak stress during damage of cemented interface rock at various confining pressures.  
Figure 9: Shear stress solutions at various confining pressures: numerical and analytical approaches.
## 3 Method
### Delays  
The third step of the method is the calculation of the vehicles and pedestrian delays. The average uniform delay per vehicle (du) is calculated in seconds per vehicle (s/veh) using Equation (11), where u is the effective green ratio and Q is the capacity.  
This equation helps estimate the delay experienced by each vehicle at an intersection based on the effective green time (u) and the intersection's capacity (Q).  
\[du=\frac{C{\cdot}{\left(1-u\right)}^{2}}{2(1-y_{i})} \tag{11}\]  
\[u=\frac{v_{ei}}{C} \tag{12}\]  
\[Q=u\cdot S \tag{13}\]  
In the case of pedestrian delay (dp), it is calculated in seconds per pedestrian (s/peat) using adapted equations from [37]. This adapted equation considers various factors, such as pedestrian flow, the time required for pedestrians to cross, and the interaction between vehicle and pedestrian movements, to estimate the average delay experienced by pedestrians at an intersection. The aim is to accurately model the time pedestrians spend waiting to cross the street, as well as the time spent crossing, depending on factors such as traffic conditions and pedestrian traffic volume. It is assumed that pedestrians arrive at the intersection using a uniform distribution; the cycle time C is fixed and the green time for pedestrians (G) is calculated as an isolated signalized intersection.  
\[dp=\left(\frac{\left(C-G\right)^{2}}{2\cdot C}\right) \tag{14}\]  
Figure 3: Movements at the 3-phase signalized intersection: (a) phase 1; (b) phase 2; (c) phase 3.
## 4 Application Classification of Intelligent Algorithms for Hydrodynamic Noise
### Noise Prediction  
The combination of intelligent algorithms and computational fluid dynamics technology holds great potential for the accurate prediction of fluid noise. While computational fluid dynamics (CFD) technology has the capacity to simulate the process of fluid flow and sound wave propagation, addressing complex flow fields remains a significant challenge. The computational demands of such simulations are substantial. The integration of intelligent algorithms, particularly in-depth learning technology, facilitates the extraction of noise features from a substantial volume of experimental and simulated data. This approach enables the rapid and precise prediction of noise phenomena. [PERSON] [99] developed a novel systematic approach for noise prediction and evaluation, termed the Noisy Spectrum Deep Learning Model (NSDL). The algorithm utilizes an encoder and decoder architecture to extract noise features and reconstruct noise data. The model's training is effectively guided by a loss function that considers both broadband noise and tonal noise recognition. This refinement enhances the training efficiency and the prediction accuracy.  
The primary benefit of a deep learning algorithm is its capacity to discern the salient characteristics of fluid flow, thereby facilitating the rapid prediction of the distribution of both the flow field and the sound field. This approach effectively addresses the challenges posed by high dimensions and non-linearities in noise prediction, enhancing the precision of predictions. When employed in conjunction with CFD technology, researchers can simulate and optimize noise control schemes under diverse operating conditions, providing substantial theoretical underpinnings for practical applications. [PERSON] [100] developed a closed-loop model by directly applying a feedback control algorithm to a multi-coupled structural-acoustic method. This model was specifically designed for active noise and vibration control. It simulates and designs a fluid-loaded lightweight structure with surface-mounted piezoelectric actuators and sensors, effectively reducing sound radiation and vibration. This approach enhances the reliability of noise prediction and provides a scientific foundation for subsequent noise control strategies.
## 2 Information as a Variable in Media Ecology Analysis
Moreover, the communication that is represented by the model is without context or situation, which is to say that it is without environment. Given the inadequacies of the Shannon-Weaver Model for representing something other than the electronic signaling, it is still possible to consider the transmission of information as a medium-specific phenomenon. For example, the way in which information is transmitted via telegraph differs from the way in which messages are transferred via telephone, and from communication by way of wireless technology. Information shared via speech can be distinguished from information shared through writing. The kind of information transmitted through images is not the same as the type of information disseminated by words. Receiving information when alone and in the comfort of one's home is not the same as receiving the same information as part of a crowd in a public place. One of the main tenets of media ecology is that different media have different kinds of biases (one of the meanings of _the medium is the message_). To analyze the biases of different media, it is useful to break down the concept of _transmission_ into more specific characteristics, such as communication over space or distance, speed of transmission, dissemination or relationship between sources and receivers, conditions of attendance for the audience, accessibility of information to the audience, access to and control of the transmission process, information storage, volume or amount of information, and the form that information takes or the code that is used [6, 23, 24, 25, 26]. Each of these characteristics can be examined in their own right, but it is essential to remember that they are all interrelated and cannot truly be isolated from one another.
## 3 Diversity and Reliability
### Outage Probability Analysis
#### 3.1.2 Transmit Antenna Selection with Silencing  
In this scheme, the CRAN chooses the best channel among the \(\text{RRH}_{0}\) and the \(k\) cooperating RRHs in order to serve the UE. After selecting the best channel, the RRHs that are cooperating remain silent while the non-cooperating RRHs cause interference. This scheme reduces the interference and improves overall average system performance by enabling diversity transmission. We can evaluate the SIR in case of TAS with silencing from (1) as  
\[\text{SIR}_{\text{TAS}}=\frac{\max(h_{0}d_{0}^{-\alpha},(h_{1},\cdots,h_{k})d^ {-\alpha})}{\sum_{j=k+1}^{N}h_{j}d^{-\alpha}}, \tag{5}\]  
while expressing the outage probability in Theorem 2,  
Theorem 2: _The CDF of the \(\text{SIR}\) in the case when UE is served through TAS with silencing is_  
\[F_{\text{SIR}_{\text{TAS}}}(\theta)=\sum_{t=0}^{k}\binom{k}{t}(-1)^{t}\Bigg{(} (1+\theta t)^{k-N}-\Big{(}1+\theta t+\theta\delta^{\alpha}\Big{)}^{k-N}\Bigg{)}. \tag{6}\]  
Proof: Please refer to Appendix B.  
The expected value of \(\text{SIR}_{\text{TAS}}\) is given as  
\[\begin{array}{ll}\mathbb{E}[\text{SIR}_{\text{TAS}}]&=\int_{0}^{\infty}(1-F _{\text{SIR}_{\text{TAS}}}(\theta))d\theta,\\ &\stackrel{{(a)}}{{=}}\int_{0}^{\infty}1-\sum_{t=0}^{k}\binom{k}{t }(-1)^{t}\Bigg{(}(1+\theta t)^{k-N}-\Big{(}1+\theta t+\theta\delta^{\alpha} \Big{)}^{k-N}\Bigg{)}d\theta,\\ &=\frac{\delta^{-\alpha}}{N-(k+1)}+\sum_{t=1}^{k}\binom{k}{t}(-1)^{t}\Big{(} \frac{\beta^{\alpha}}{t(N-k-1)(\theta^{\alpha}+1)}\Big{)}.\end{array} \tag{7}\]where (_a_) comes after substituting the respective CDF. Solving the integral in (_a_) and with simplification we reached (_7_). The first term in (_7_) is the same as (_4_) whereas the second term gives some of the diversity gain from TAS with slight increments in_ \(\mathbb{E}[\text{SIR}_{\text{TAS}}]\) _as compared to silencing._
# Comparison of PCR Primers for Analyzing Dentirifying Microorganisms in the Hyporheic Zone
## 1 Introduction  
The nitrogen cycle is one of the most important nutrient cycles in terrestrial ecosystems and is carried out via nitrogen fixation, mineralization, nitrification, and denitrification [1]. The nitrogen cycle involves biological and non-biological processes, where the biological processes usually involve microorganisms [2]. Denitrification is the last step of the nitrogen cycle, in which the nitrogen in fixed forms, such as nitrates, returns to the atmosphere in the form of nitrogen gas. This process can prevent nitrate contamination of groundwater and surface water that arises from the overuse of nitrogen fertilizers [3].  
Microorganisms involved in the nitrogen cycle are labeled as either nitrogen-fixing, nitrifying, or denitrifying microorganisms according to their characteristics [2]. Of these, denitrifying microorganisms are typically found in the soil, mud, sewage, sea, and wetland, and are phylogenetically diverse, belonging to _Firmicutes_[4], _Actinobacteria_[5], _Proteobacteria_[6], _Archaebacteria_[7], and fungi [8, 9]. In genetic and biochemical studies, six genes, including _nitK_, _nitS_, and _nosZ_, have been reported to be involved in the denitrification process (Figure 1, [9, 10]).  
It has been reported that non-culture-based methods are generally used to analyze denitrifying microorganisms in soil and water environments [11, 12, 13, 14]. Methods used to analyze the diversity of microorganisms include denaturing gradient gel electrophoresis (DGGE), cloning, and pyrosequencing [15, 16, 17, 18]. The analysis of denitrifying communities typically uses a molecular approach [19, 20, 21, 22].  
The hyperbie zone is an environment where the mixing of groundwater and surface water occurs and it is critical for denitrification. Hyporheic exchanges are of great interest because they make the hyperbie zone highly productive and complex environments. In particular, the exchange patterns play an important role in removing contaminants or attenuating contamination under certain conditions with indigenous microbes when contaminants or polluted water pass-through the hyporheic zone. It has been consistently shown that when surface water with a high nitrate concentration is passed through the hyporheic zone, the nitrate-nitrogen concentration of the surface water is decreased by indigenous microorganisms living in hyporheic zones. Therefore, this study aimed to perform comparative analyses and sample testing of PCR primer sets for each denitrification-associated gene for the analysis of denitfriying microorganisms in environmental samples extracted from hyporheic zones.
# Occupancy Reward-Driven Exploration with Deep Reinforcement Learning for Mobile Robot System
## 4 The Proposed Occupancy-Reward-Driven Exploration
### The Robotic Exploration Problem Formalization  
Robotic mapping is a process where a real environment is converted into a digital model by a robot or a group of robots. If we decompose this process into entities, we assume that we obtain two main objects: a robot and an occupancy map. The robot object has a sensor, a position, and velocity parameters. The occupancy map object is massive, with a certain number of cells and their probabilistic values being modified at each time step (Figure 4). The robot begins to run from the initial position. Operating the simulation, this position can be any x-, y-coordinates of the free space on the map. In the real-world experiment, the initial position has zero values on the map, no matter where the robot is currently in a room [21].  
As the robot moves in the environment, the occupancy map is updated from every robot position, expanding the terrain acquisition. Step by step, the laser sensor touches new areas or seen areas, which return different probabilities values from the occupancy map. The values from the occupancy map at time \(t\) are known as explored segments in this study.  
With the aim of continuous and safe driving, some others aspects should be analyzed and planned for the correct sequential decisions. One of these is obstacle avoidance. The decision of turning left or right can be made based on the available visibility for the robot as detected by the laser sensor. The maximum sensing range is a known parameter, depending  
Figure 4: The occupancy map visualization with occupancy probability values of uncertainties (0.5) and explored values (varying from 0.0010 to 0.5). The map size is defined as 20 by 10. It is only for representing the figure window of the simulation and is not used in the algorithm computation.  
on the sensor model. Based on the maximum range value, the minimum threshold for the reaction to an obstacle can be defined through test runs. The other parameters for continuous driving are the linear and angular velocities. Their values govern and characterize the robot's behavior. The linear velocity is responsible for forward and backward motions. When the robot turns, the angular velocity deals with the turning motions.  
These metrics, such as the explored segments at each time step, the minimum distance threshold for the obstacle avoidance, and the linear velocity and angular velocity, are synchronized and adjusted in the reward function below.
# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 3 The Proposed Algorithm  
The objective of the proposed method is the enhancement of a low-light image by enhancing the brightness and contrast of the illuminance map. Firstly, a neighborhood-based approach is proposed to estimate the illuminance component, and then obtain the reflection component through the retinex theory. Secondly, differential enhancement is applied to each region in the illuminance component, that is, the luminance is substantially increased in darker regions and hardly enhanced in brighter regions. Contrast enhancement uses the form of piecewise function to update the image pixel by pixel to reduce noise. Then, the two enhanced images are used to obtain the adjusted illumination map using the Gaussian-Laplacian pyramid fusion algorithm. Finally, the improved illumination map is multiplied by the reflection component to obtain the final enhancement result. Figure 3 shows the flowchart of the algorithm in this paper. As follows, we introduce our method in this section with all necessary details.  
Figure 2: \(Min\_g\), \(Max\_v\), and \(Min\_v\) of an image. (**a**) Divided images. (**b**) Corresponding area label maps after image division. (**c**) Contents of some areas. (**d**) Figure legend.
## 5 Conclusions  
Based on validation by using CFD and experimental data, the UAM is suitable for the unsteady aerodynamic force calculation of a flapping wing with large pitching amplitude at low flight velocity. For the experimental case where the pitching angle is idealized as \(-18^{\circ}\)-\(44^{\circ}\) and flight velocity is zero, the average lift forces between the UAM and CFD method have a small difference of 1.3%, but a large difference of 18% occurs when compared to experimental result. For the study case of flight velocity at 4m/s and pitching angle \(0^{\circ}\)-\(60^{\circ}\), the average lift force difference of 2.3% and drag force difference 2.1% between the UAM and CFD remain in the same level as the zero velocity case.  
From the parametric study of the flapping wing, it is revealed that an optimal pitching amplitude and change rate of pitching angle near the end of up-stroke play a critical role for the lift force. The kinematics of motion of a flapping wing can be improved by a quick reduction of pitching angle in up-stroke during \(\hat{t}\) = 0.25-0.5 to achieve maximum lift force.  
For this aircraft model, the maximum lift occurs in the range of pitching angle amplitude between \(60^{\circ}\)-\(70^{\circ}\) when the flight velocity is reduced from 5 m/s-0 m/s during landing. In general, the drag increases with the flight velocity. As the forward flight velocity decreases during landing, the aircraft should continuously increase the wing pitching angle up to \(75^{\circ}\) to gain maximum lift. In the same time, the increased pitching angle results in increased drag to decelerate the aircraft motion.  
Conceptualization: B.J., S.G., and Z.Z.; methodology: S.G.; software: Z.Z.; validation: [PERSON], [PERSON] ([PERSON]), and Q.Z.; formal analysis: F.Y.; investigation: F.Y. and Q.Z.; resources: S.C.; data curation: S.C. and Z.Z.; writing--original draft preparation: [PERSON]; writing--review and editing: [PERSON], [PERSON], and S.G.; visualization: [PERSON]; supervision: [PERSON], [PERSON], and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Key R&D Program of China, grant number 2018 YFB1305400; the National Natural Science Foundation of China, grant number 61673246 and 81301294, and the Research and Development Program of Jinan, grant number 201907064.  
The authors declare no conflict of interest.
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
###### Abstract  
Vehicular networks are the combination of transport systems and the internet systems formed with the main motive to increase the safety of passengers, although non-safety applications are also provided by vehicular networks. Internet of Things (IoT) has a subsection called Mobile Ad hoc Network (MANET)m which in turn has a subsection called Vehicular Ad hoc Network (VANET). Internet of Energy (IoE) is a new domain that is formed using electric vehicles connected with VANETs. As a large number of transport systems are coming into operation and various pervasive applications are designed to handle such networks, the increasing number of attacks in this domain is also creating threats. As IoE is connected to VANETs extension with electric cars, the future of VANETs can be a question if security measures are not significant. The present survey is an attempt to cover various attack types on vehicular networks with existing security solutions available to handle these attacks. This study will help researchers in getting in-depth information about the taxonomy of vehicular network security issues which can be explored further to design innovative solutions. This knowledge will also be helpful for new research directions, which in turn will help in the formulation of new strategies to handle attacks in a much better way.  
VANET; security; networks; survey; attacks; solutions +
Footnote †: journal: Applied Sciences
## 4 Experimental Results  
### Database  
Two databases were used to analyze the performance of the deep model for time-frequency representations in the ECG biometrics. The first database was PTB-ECG, which was built by the National Metrology Institute of Physikalisch-Technische Bundesanstalt. PTB-ECG has 27,000 recordings, which were acquired from 290 people sitting in a comfortable state in a chair. The ECG data were acquired from subjects that included patients with heart disease and healthy people. The ECG data were acquired using twelve standard leads and three rank leads at 1000 samples/s. Two to three recordings of varying length from 23 s to two minutes were acquired for each person, with an average time difference of 500 days between any two measurements [39; 40]. The second database was CU-ECG, which was directly constructed for biometrics at Chosun University (CU). The database was acquired from 100 people, which included males and females, aged 23 to 34. The ECG data were acquired from subjects seated in a comfortable state in a chair. Sixty ECG signals were recorded for each person using a short length. The data were acquired with a sampling rate of 500 kHz for 10 s each time, with only the lead I signal recorded. The ECG acquisition equipment was developed using Keysight, MSO9104, Atmega8, and non-invasive electrodes for constructing the CU-ECG database. Figure 9 shows the environment for acquiring the CU-ECG signals [18].