esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bus Signal Priority Control Method Based on Deep Reinforcement Learning
## 3 Bus Signal Priority Control Reinforcement Learning Agent Training  
Considering that the traffic state space is difficult to be exhaustively characterized by the traditional Q-value table, this paper uses a deep Q network (DQN) to solve the problem of Q-value table storage. At the same time, by employing the mechanism of experience playback [48], the correlation of data in agent training is eliminated, ensuring the fast iterative convergence of the training. The deep Q network adopts a three-layer network structure. The agent training framework is shown in Figure 3.  
The architecture of this model includes the following modules: state extraction module, Deep Q network module, action selection module, action execution module, reward calculation module, experience pool, and model training module.  
Among them, the state extraction module observes the bus state, intersection operation state, and signal timing state at the current moment, and concatenates them as the intersection state vector \(S\), which is taken as the inputs of the Deep Q network module, and the output of the Deep Q network module is the probability Q value for each action (phase). Then, through the action selection module, we select the next phase based on the greedy strategy. At the same time, the signal controller changes the green light duration time through the action execution module. After the execution is completed, the state extraction module observes and extracts the intersection state vector again and runs the cycle according to the above process. After each decision action is executed, the reward value \(R\) is calculated through the reward calculation module, and the previous state, action, next state, and reward are saved to the experience pool. In every step, the model training module randomly selects a certain number of samples in the experience pool to train and update the deep Q network.  
The detailed training process is shown in the Algorithm 1:  
```
1:Build SUMO simulation environment
2:Initialize network \(net\), the experience pool \(M\), observation interval \(N_{u}\), update interval \(N_{u}\), iterations \(N\), single simulation duration \(N_{t}\), decision time point \(s_{n}\), action \(a_{n}\)
3:For\(i=0\):\(N\)
4:For step = 0:\(N_{t}\)
5:If step = \(s_{n}\) Then
6: Observe the traffic state \(S_{l}\)
7: Compute the next action \(a_{n}\) based on \(S_{l}\)
8: Execute action \(a_{n}\)
9: Update \(s_{n}\)
10: Observe the traffic state \(S_{n}\)
11: Compute the reward \(R\)
12: Save (\(S_{l}\), \(a_{n}\), \(S_{n}\), \(R\)) to the memory pool \(M\)
13: Count = Count + 1
14: Step = Step + 1
15:End if
16:If Count > \(N_{0}\) and \(N_{0}\) % \(N_{u}\) == 0 Then
17: Randomly select training samples in \(M\)
18: Using \(net\) computes predictive \(Q\) value for all actions
19:\(Q_{l}\) = \(R\) + Gamma * Max[\(Q(S_{n}\), all actions)]
20: Train and update \(net\)
21:End if
22:End for
23:End for \(net\) \(M\).
```  
**Algorithm 1**Detailed training process  
Figure 3: Bus signal priority control under the deep reinforcement learning framework.
# Intelligent Reflective Surfaces for Wireless Networks:
## 5 Practical Challenges, Open Issues and Future Research Lines
### Open Issues and Future Research Directions
#### 5.2.3 User Balancing and Scheduling  
Appropriate user scheduling results are decisive to perform practical implementations which balance the number of served users according to the power transmission constraints and/or the QoS. The schemes of user balancing in IRS-assisted systems have not been approached in-depth treatment. Limited works can be found regarding user scheduling in IRS-assisted systems, e.g., the authors in [66] analyze the optimization of the IRS phase-shift matrix by considering the detection order of the received users by a common BS in a NOMA MIMO system. These ideas can be extended to mobile networks by balancing the amount of served users and the overall system performance as it has already been considered in conventional non-IRS systems, e.g., ([96; 97], Algorithms 1-3). The use of the IRS technology together with an efficient scheduling strategy constitutes a novel and challenging open research line.
## 3 Theory of Solar Radiation Intensity and Sunshine Temperature Field
### Sunshine Temperature Field Theory and Test Comparison
#### 3.2.2 Comparison of Actual and Simulated Shadow Areas  
The structure of U-shaped girder bridges is unique, resulting in shading and shadow effects under solar radiation, as illustrated in Figure 7. Since the surfaces of these shadowed areas do not directly receive solar radiation, it is crucial to accurately calculate their impact on the temperature field of U-shaped girder bridges. This study employs a ray tracing algorithm [38; 39] to calculate the distribution of sunlight shading in the finite element model of a U-shaped girder bridge.  
Figure 8 shows a numerical simulation of the changes in sunlight shadows on the U-shaped girder bridge throughout the day. The black areas represent parts of the U-shaped girder bridge surface that are directly exposed to sunlight, while the white areas indicate parts of the structure that are in shadow. It can be seen that at 8:00, the shadow on the outside of the web is highly consistent with the shadow areas predicted by the numerical simulation. Likewise, the shadows on the inner side of the web at 16:00 are accurately represented through numerical simulation. In general, the shadow areas obtained from  
Figure 6: Two-dimensional triangular thermal element.  
Figure 7: Partial shading of the U-shaped girder bridge at representative times: (**a**) the outer shadow of the web at 08:00; (**b**) the inner shadow of the web at 16:00.  
numerical simulation are highly consistent with the actual distribution of shadows under sunlight conditions, which not only verifies the effectiveness of the numerical simulation approach but also demonstrates its ability to accurately predict the sunlight conditions of U-shaped girder bridges, including the duration of sunlight and the size of shadow areas. In addition, the numerical simulation not only offers images of shadow distribution at particular moments but also illustrates the dynamic process of shadows throughout the sunlight cycle, supplying data support for analyzing changes in the temperature fields of U-shaped girder bridges.
## 3 Results and Discussions  
In the following subsections, we present the results and discussions of the synthesis and analysis of data extracted from primary studies to answer the research questions. Most of the results presented  
\begin{table}
\begin{tabular}{c c c c} \hline
**Objective** & **Data Item** & **Objective** & **Data Item** \\ \hline \multirow{4}{*}{General data} & Title & RQ5 & Context \\  & Author(s) & Q1 & Study Objectives \\  & Year of publication & Q2 & Context Description \\  & Venue & Q3 & Research Project description \\  & Paper Summary & Q4 & Data analysis \\ \hline RQ1 & Approach Used & Q5 & Presentation of conclusions \\ \hline RQ2 & Application domain & Q6 & Critical analysis \\ \hline RQ3 & Support Mechanisms & Q7 & Credibility and limitations \\ \hline RQ4 & Evidence level & & \\ \hline \end{tabular}
\end{table}
Table 4: Items extracted from each study, related research questions and quality criteria.  
\begin{table}
\begin{tabular}{c c c} \hline
**Level** & **Classification** & **Description** \\ \hline
0 & Without evidence & No evidence of validation or evaluation. \\ \hline
1 & Demonstration or usage example & The authors describe an application and provide an example to aid in its description. \\ \hline
2 & Expert Notes & Some textual, qualitative assessments or opinions are provided. For example, it compares and contrasts the advantages and disadvantages. \\ \hline
3 & Laboratory experiment & The result is obtained from simulations with artificial data used in real experiments. Evidence is collected informally or formally. \\ \hline
4 & Empirical Investigation & Investigate the behavior of the proposed approach within a real context. \\ \hline
5 & Strict analysis & Use of a more formal methodology to evaluate and validate the study. For example, defining questions and variables to be analyzed while applying the approach. \\ \hline \end{tabular}
\end{table}
Table 5: Levels.  
in this section are based on the systematization of data collected directly from the reviewed studies. Interpretations of the results by the authors of this SLR have been limited to a minimum in order to primarily focus on what has been reported in the revised primary studies. However, some of the revised primary studies may not have provided sufficient information to solidly answer the RQs, so interpretations and inferences to some extent were unavoidable. In these situations, attempts have been made to examine other available resources on the revised studies (e.g., the authors' home page and other online available information on approaches) to make interpretations and inferences as reliable as possible in this type of effort.
###### Abstract  
This study demonstrates a rapid non-contact ultrasonic inspection technique by visualization of Lamb wave propagation for detecting impact damage in carbon fiber reinforced polymer (CFRP) laminates. We have developed an optimized laser ultrasonic imaging system, which consists of a rapid pulsed laser scanning unit for ultrasonic generation and a laser Doppler vibrometer (LDV) unit for ultrasonic reception. CFRP laminates were subjected to low-velocity impact to introduce barely visible impact damage. In order to improve the signal-to-noise ratio of the detected ultrasonic signal, retroreflective tape and a signal averaging process were used. We thus successfully visualized the propagation of the pulsed Lamb A\({}_{0}\) mode in the CFRP laminates without contact. Interactions between the Lamb waves and impact damage were clearly observed and the damage was easily detected through the change in wave propagation. Furthermore, we demonstrated that the damage could be rapidly detected without signal averaging. This method has significant advantages in detecting damage compared to the conventional method using a contact resonant ultrasonic transducer due to the absence of the ringing phenomenon when using the LDV.  
non-destructive inspection; laser ultrasonic imaging; Lamb wave; delamination; composite laminate +
Footnote †: journal: Applied Physics Letters
## 5 Conclusions  
The main conclusions are summarized as follows:  
1. The change in the cooling water flow rate from 60 to 120 kg/h had no significant impact on the distribution of the Nu\({}_{\text{t}}\) number along the tube for the analyzed inlet flue gas temperatures for Re\({}_{\text{in}}\)\(\approx\) 9500; however, for Re\({}_{\text{in}}\)\(\approx\) 21,000, the influence of the cooling water flow rate is more pronounced.
2. For Re\({}_{\text{in}}\)\(\approx\) 21,000 and the lower cooling water flow rate (60 kg/h), Nu\({}_{\text{t}}\) was increasing along the tube and reached its maximum value almost at the end of the tube. However, at the higher cooling water flow rate (120 kg/h), the Nu\({}_{\text{t}}\) remained almost even along the experimental section.
3. The change in the cooling water inlet temperature from about 8-9 \({}^{\circ}\)C to about 14-15 \({}^{\circ}\)C created an insignificant impact on the Nu\({}_{\text{t}}\) distribution along the tube for Re\({}_{\text{in}}\)\(\approx\) 9500, and the results showed that stronger and almost even condensation in this case took place from the beginning until almost the middle of the test section. Further on, Nu\({}_{\text{t}}\) rapidly decreased.
4. The change in the cooling water inlet temperature from about 8-9 \({}^{\circ}\)C to about 14-15 \({}^{\circ}\)C had more impact on Nu\({}_{\text{t}}\) along the test section for Re\({}_{\text{in}}\)\(\approx\) 21,000. The results also showed that more even condensation along the whole test section was achieved at a lower cooling water inlet temperature.
5. Condensation efficiencies for both analyzed cooling water flow rates are similar, i.e., about 54-58% for a lower flue gas Re\({}_{\text{in}}\) number and about 42% for a higher flue gas Re\({}_{\text{in}}\) number. For all analyzed regimes, at a lower cooling water inlet temperature, the condensation efficiency is about 6-10% higher than at a higher cooling water inlet temperature.  
Figure 16: Comparison of heat balance for Re\({}_{\text{in}}\)\(\approx\) 9500 at different flue gas and cooling water inlet temperatures.  
**Author Contributions:** Conceptualization, R.P.; methodology, A.S. and P.P.; formal analysis, A.S. and V.K.; experimental investigation, A.S. and V.K.; resources, A.S., R.P., G.M. and E.P.; writing--original draft preparation, A.S.; writing--review and editing, P.P., H.J., G.M. and E.P.; supervision, R.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research Council of Lithuania (LMTLT), grant number S-MIP-20-30.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Results
### AR and VR  
To validate the AR and VR environments with the DT, an interaction is carried out with the elements of the manufacturing cell for the supervision, simulation, and control of certain process parameters. The system was implemented on the Unity platform where it works with the real-time data that arrive from the process; Meta 2 glasses for AR and HTC VIVE glasses for VR are used, which allow for interaction and visualization with the physical environment in real time. The AR application allows additional information to be observed so that the operator can interact with the virtual environment and allows for control of certain parameters from the glasses, enhancing the experience. In the application with VR, the operator can control and monitor the physical machine remotely through a virtual reality helmet, which provides greater mobility at work and, at the same time, maintains an immersive experience as seen in Figure 9.  
Both AR and VR allow users to visualize 3D environments in an immersive way as seen in the previous figure, thus allowing fora better understanding of the process and optimization thanks to the fact that complex processes can be simulated with data obtained in real time and errors can be identified and corrected more quickly as observed in Figure 8.  
Among the benefits that were evidenced by integrating AR and VR technologies in conjunction with a DT are (1) an immersive visualization and control system in real time with which users can interact and have a deeper understanding of the production line for a more accurate decision-making, (2) simulation of different scenarios and possible conditions with the real data obtained from the flexible production line, thus testing different configurations and possible evaluation of the results, and (3) optimization of the process; thanks to the benefits mentioned in (1) and (2), it is possible to achieve faster identification of errors and make more accurate configurations by knowing and understanding the process in a more real and interactive way. In addition, one of the  
Figure 8: Analysis of response to errors caused by the user.  
Figure 9: Physical and virtual environment with AR and VR.  
advantages of using these technologies in the DT is that it allows for the control and visualization of the process remotely and allows for an improvement in the customer experience because it is possible to customize products according to each need without increasing production costs and with friendly systems for operators and customers. It can also be used for the training and education of operators both in industry and in the classroom where these environments allow for greater interaction and simulation with real parameters, which favors learning in technical areas.
###### Abstract  
This study estimates the strength of a special mixture of high-strength concrete (HSC) with admixtures for use in a nuclear power plant (NPP). Nuclear power plant structures need a HSC with some additional qualities to operate the safe options. For this purpose, the experimented concrete was specially designed to fulfill the required qualities of NPP. For gaining these desirable qualities, it needs to monitor the concrete strength development process. Here, the PZT materials were used as sensors to acquire data by measuring the electromechanical impedance (EMI), and then cross correlation (CC) was calculated to look at changes according to strength development. Data were measured for 28 days, and over this period concrete can gain up to 96% of its design strength. This technique is based on a single sensor. After casting concrete, the PZT material starts vibrating as an actuator to produce vibrations. At the same time, it also works as a sensor to measure the dynamic response of the structure to the vibrations. With strength development, the resonant frequencies of the EMI start changing. To estimate the strength development, a fuzzy logic tool was used to analyze the parameters, allowing for us to estimate and predict the concrete strength. For cross-checking, the estimated strength was compared with the actual strength of concrete; this was determined by examining cuboid cores taken from specimens during experiments at the 1 st, 3 rd, 7 th, 14 th, and 28 th days. According to the results, this approach of strength estimation and monitoring the strength development is useful for forecasting the stability of structures.  
concrete strength estimation; structural health monitoring; piezoelectric sensor; concrete early age strength; EMI (electromechanical impedance) +
Footnote †: journal: Applied Sciences
# Cracks in Arch Dams: An Overview of Documented Instances
## 2 Observational Data/Material: Summary of Case Studies
### Multiple-Arches Dams  
_Daniel-Johnson_ dam (1970): During its construction in 1969, several types of cracks appeared in almost all the arches of this dam. The dam developed oblique cracks on the downstream surface and plunge cracks in the upstream heel. For this case, epoxy resin injections were performed; however, water infiltration continued despite the treatment.  
_Kromme-Churchill_ dam (1943): In 1957, wide horizontal cracks were detected in the buttresses and arches, some of which extended to the upward face. Between 1987 and 1990, a total of 20 concrete samples were collected. In 1963, reinforced concrete extensions were attached to the downstream face to strengthen the buttresses. In addition, upstream cracks were treated with mortar injections.
## 2 Materials and Methods
### Antibacterial Activity of TiO\({}_{2}\) NPs  
The antibacterial activity of nanoparticles (NPs) was tested using the liquid medium method with different species of pathogens such as _Escherichia coli_, _Pseudomonas aeruginosa_, _Proteus vulgaris_, and _Staphylococcus aureus_. The tested bacteria cells were prepared from (0.5) McFarland turbidity standard (5 \(\times\) 10\({}^{7}\) cell mL\({}^{-1}\)). The inhabitation rate of the bacteria was studied by inoculating the broth with 0.2 and 0.5 mL of bacterial strains following the addition of TiO\({}_{2}\) NPs at various concentrations of 400, 600, and 1000 ug mL\({}^{-1}\) with and without amoxicillin (30 ug mL\({}^{-1}\)). The testing tubes were incubated for 24 h at 37 \({}^{\circ}\)C. The growth of the bacteria was estimated by its optical density (O.D.) at a wavelength of 600 nm, while its inhibition ratios or efficiency (%) was determined using the following equation [26].  
\[\mathrm{Inhibition\ of\ Efficiency\ (\%)}=\frac{(\mathrm{Control\ O.D.\ Test\ O.D.})}{ \mathrm{Control\ O.D}}\times 100 \tag{2}\]  
Figure 1: Schematic illustration of the experimental set-up of TiO\({}_{2}\) NPs suspension preparation at ambient temperature.  
The effect of TiO\({}_{2}\) NPs with amoxicillinin on the _E. coli_ and _S. aureus_ bacteria bacteria was assessed using the agar diffusion method. This process involves spreading a lawn of bacterial culture on the nutrient agar plates and wait for a few minutes. Thereafter, wells of 8 mm in size were punched into the agar using the head of sterile micropipette tips and poured a mixing solution of TiO\({}_{2}\) NPs and amoxicillin into the wells. The inhibitory potency was monitored after the incubation process to assess the mixture efficiency by measured inhibition zone diameters from different directions more than once using a ruler.
# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 3 Materials and Methods
### Network Architecture
#### 3.2.1 Encoder  
As shown in Figure 3, the encoder consists of three inputs, two double convolution layers, two channel attention modules and seven downsampling layers. The numbers in the figure are the number of channels after the feature map passes through the network layer.  
On the input side, the 9 bands with different features are categorized into three parts as the input to the network. The visible light bands (Band 2, Band 3 and Band 4) with a resolution of 10 m and the near-infrared band (Band 8) have richer ground detail information. They are the main parts for feature extraction. All other five bands have a resolution of 20 m. The ground detail information in clear cases is relatively poor.  
Figure 2: A Sentinel-2 foggy and fog-free image pair.  
Band 5, Band 6 and Band 7 are Red Edge bands, which are mainly used to observe the mutation properties of vegetation reflectance in remote sensing applications. From Table 1, it can be observed that the wavelengths of Band 5, Band 6 and Band 7 have a small difference from the wavelengths of Band 8, as well as a small difference in the penetrating capability. Therefore, the features extracted from Band 5, Band 6 and Band 7 are fused with the shallow features of the main network to standardize and correct the features extracted by the main network.  
Band 11 and Band 12 have relatively longer wavelengths and stronger penetration power (as shown in Figure 1). The features extracted from Band 11 and Band 12 are fused with the deeper downsampling features. Meanwhile, the features extracted from Band 11 and Band 12 are fused with the features of the upsampling stage in the decoder. The feature extracted from the infrared band (less interfered with haze), together with the learning of deep high-order features and the learning of shallow spatial detail features, make the final restored results closer to the real situation.  
For feature extraction, the double convolution network group is used for initial feature extraction for the input image. As illustrated in Figure 4a, it includes two sets of \(3\times 3\) convolutions. The double convolution layer can be formulated as Formula (2)  
\[F_{c}=\delta(BN(Conv(\delta(BN(Conv(F_{i})))))) \tag{2}\]  
where \(F_{i}\) is the input image data or feature map. \(F_{c}\) is the feature map after feature extraction from double convolution. \(Conv\) and \(BN\) are \(3\times 3\) convolution and Batch Normalization. \(\delta\) is the Leaky ReLU function. In the downsampling module, as shown in Figure 4b, the input features are subjected to maximum pooling of stride = 2. Then, downsampling and feature extraction are implemented through the double convolutional network.  
Figure 3: The architecture of the proposed network.