esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Crack Detection on Cultural Heritage Surfaces
## 4 Discussion
### Limitations and Future Work  
This study integrates the deep learning models GoogLeNet and SegNet to enhance the efficiency of crack detection. Additionally, laser projections onto the wall are used to measure crack length. Based on the hardware configuration described in Section 2.4, training the GoogleNet model on 66 images for 125 iterations required approximately 2 min, resulting in a trained model size of approximately 60 MB. In comparison, training the SegNet model on 300 images for 75,000 iterations took approximately 185 min, yielding a model size of around 12 MB. During inference, each image can be processed in less than 0.1 s for both GoogleNet classification and SegNet segmentation. Even on a standard laptop,the total processing time remains under 1 s per image, demonstrating the efficiency and practicality of the proposed workflow.  
However, certain limitations remain in its application. First, the two laser beams must be projected parallel to the wall and captured from a direct frontal view. If the laser beams are not parallel, an incorrect scale is obtained, leading to significant errors in crack length estimation. Similarly, if the image is captured at an angle, the incorrect scale results in an underestimation of crack length.  
Furthermore, this study focuses on three types of wall surfaces, including plain walls, weathered walls, and brick walls, for deep learning-based crack detection. In practical applications, however, cultural heritage structures span various historical periods, encompassing a much broader range of wall materials than those used in this study. As the diversity of training images increases, a larger dataset is required for deep learning model training, which may slightly reduce accuracy.  
Additionally, the SegNet model has limitations in detecting thin linear features, a common challenge among deep learning models [63; 64; 65]. To address this, the labeled training images in this study not only mark the crack regions but also include surrounding areas as part of the crack [66]. Consequently, while crack length can be measured in subsequent analyses, crack width cannot be accurately determined.  
Future research could explore mounting laser projectors on unmanned aerial vehicles (UAVs) to capture cracks at various heights on building exteriors, thereby enhancing the versatility of this technique. Additionally, for deep learning-based crack detection, image segmentation methods could be employed to increase the proportion of crack regions within segmented images, potentially improving SegNet's detection accuracy. Once segmentation is completed, the segmented images could be merged back to their original size to facilitate the measurement of both crack width and length.
## 3 Results
### Effects of CWP on Concrete Microstructure
#### 3.4.2 X-Ray Diffraction (XRD)  
XRD analysis by [PERSON] et al. [52], as presented in Figure 15, showed that CWP in cement paste reduced the CH content due to its pozzolanic reaction, forming additional C-S-H gel. The peaks for portlandite diminished with an increase in the curing days of concrete, indicating that CWP consumed CH and contributed to the pozzolanic reaction.  
[PERSON] et al. [56] observed XRD spectra of cement with CWP replacements displayed increased quartz peaks, particularly at 20-40% CWP substitution. These higher quartz levels, originating from CWP, were accompanied by reduced CH peaks over time, signifying that CWP enhanced the hydration process and aided in creating a denser microstructure.  
XRD analysis by [PERSON] et al. [84] revealed that CWP contributed to the formation of crystalline phases such as quartz and calcite, while portlandite levels decreased due to pozzolanic reactions. Despite its finer particle size, CWP exhibited slower pozzolanic activity than waste brick powder, which was attributed to potential recrystallisation during production and the dilution effect from cement substitution. This initially led to reduced hydration products with strong mechanical properties. However, over time, CWP concrete demonstrated significant improvements in mechanical properties, likely due to delayed pozzolanic activity and the gradual release of water from its structure, which promoted continued hydration.  
XRD analysis by [68] confirmed the formation of calcium-silicate-aluminate hydrates in CWP-blended samples, where a reduction in CH was noted at 20-40% CWP replacement. The complex hydrates contributed to microstructural enhancement and reduced the porosity.
# Narrowband Internet of Things (NB-IoT):
## 5 Open Research Questions and Discussion
### Radio Resource Management  
#### 5.2.1 Tones Allocation.  
As seen in the literature, most of the articles consider single-tone allocation for the simplicity in the simulation, thus, multi-tone allocation is not well studied. This causes a knowledge gap in the effectiveness of different tone-allocation possibilities. Moreover, for guard-band, in-band and standalone it is still not clear about the respective performance metrics that could be achieved in terms of throughput, coverage range, interference robustness etc. This restricts to a certain extent the optimal choice of deployment for a large number of devices with the required performance. Furthermore, different frame structures, especially for TDD configurations, are not discussed even though NB-IoT is required to support TDD. Therefore, optimal resource use techniques must be proposed that incorporate repetition, mobility, tones allocation, etc. for efficient spectrum usage.
###### Abstract  
This study represents a pioneering initiative in the printing industry, especially in Poland, which assessed the environmental impacts and eco-efficiency of proof printing through the life cycle assessment (LCA) methodology. The process of proof printing on a target substrate was compared with the traditional hard proofing process, which requires trial printing in production conditions. The analysis adhered to the ISO 14040 and 14044 standards, assessing greenhouse gas (GHG) emissions, raw material use (e.g., plastics, water), and environmental toxicity. The innovative proofing on the target substrate process exhibits a lower environmental impact, as confirmed by the GHG emissions and plastic and water demand of the process. The GHG emissions were reduced from 2610 kg of CO2e to 68.4 kg of CO2e per functional unit (FU). The water demand for the proofing on the target substrate process was 40 times lower, and the plastic demand was also 40 times lower, decreasing to 20 kg per FU. The toxicity impact of the method based on the proofing system on the target substrate on living organisms is more than six times lower than that of the conventional method. The proof printing on the target substrate process offers an environmentally friendly alternative to the traditional hard proofing process, with lower GHG emissions and a lower environmental impact.  
life cycle assessment; proof printing; printing products; greenhouse gas emissions; sustainability; surface design Article
# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 5 Performance Evaluation
### Experimental Results and Evaluation Analysis
#### 5.2.1 Failure Prediction Accuracy  
A fault-prediction model was used to predict the fault conditions of edge devices at the edge end. A fault-prediction model with high precision can effectively avoid container migration caused by misjudgment, thus avoiding large migration costs and load-balance fluctuations caused by invalid migration. Therefore, the analysis shows that the accuracy of the fault-prediction model has a significant impact on the determination of the subsequent migration strategies. The higher the accuracy of the fault-prediction model, the more the service reliability will be improved in the edge environment, and the higher the possibility of avoiding service failure. In our experiments, accuracy is the percentage of correctly predicted faulty nodes compared to all predicted faulty nodes. Recall is the percentage of correctly identified faulty nodes compared to all actual faulty nodes.  
Figure 3 shows the experimental results of LSTM and GRU. As shown in the figure, the GRU model performed better than the LSTM model in terms of both precision and recall. The GRU model has a precision of 0.94 and a recall of 0.85. Analysis of the experimental results suggests that this difference is because the dataset used in this study was small, and the GRU model is a simplification of the LSTM model, which may perform better when there is less data. Considering the historical data collection of edge devices in practical situations, it is challenging to collect a large amount of data for training under normal circumstances. Therefore, using the GRU model, which performed better on the dataset used in this study for the fault prediction of edge devices, can avoid unnecessary migrationcosts caused by prediction failures when both the precision and recall rates are relatively high. If the prediction is successful, service reliability can be guaranteed through container migration.
## 4 Results
### Run-Time Complexities  
The results of the time complexity experiment are shown in Figure 13. Each run-time result was the total time for all subject trials. As the total run time of the cross-channel DWRPCA and RPCA algorithms was substantially lower compared to other algorithms, the \(y\)-axis is demonstrated on a log scale. The run-time result of the cross-channel DWRPCA does not include the CNN model training time, since the model no longer needs to be updated when constructed for a specific sensor type. Owing to the dynamic adjustment of weighting factors \(\omega\), the computational time complexity of the cross-channel DWRPCA was strongly reduced and was even slightly lower than the conventional RPCA algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{1}{c}{\(p\)**-Value**} & **Wavelet** & **VMD** & **RPCA** & **WRPCA** & **Our Proposed** \\ \hline \multirow{8}{*}{TAE (s)} & \multirow{4}{*}{\(t_{1}\)} & Wavelet & & & & \\  & & VMD & 0.0295 * & & & & \\  & & RPCA & 0.0000 * & 0.0000 * & & & \\  & & WRPCA & 0.0000 * & 0.0000 * & 1.0000 & & \\ \cline{2-6}  & & **Proposed method** & 0.0000 * & 0.0000 * & 1.0000 & & \\ \cline{2-6}  & & **Wavelet** & & & & & \\  & & WRDCA & 0.0000 * & 0.0000 * & 0.9684 & & \\  & & **Proposed method** & 0.0000 * & 0.0000 * & 0.9684 & 1.0000 & \\ \hline \multirow{8}{*}{ARE (\%)} & \multirow{4}{*}{\(h_{1}\)} & Wavelet & & & & & \\  & & VMD & 0.0000 * & & & & \\  & & RPCA & 0.0000 * & 0.0000 * & & & \\  & & **Proposed method** & 0.0000 * & 0.0000 * & 0.0000 * & 0.2086 & \\ \cline{2-6}  & & Wavelet & & & & & \\  & & VMD & 0.0954 & & & & \\  & & RPCA & 0.0000 * & 0.0000 * & & & \\  & & WRPCA & 0.0000 * & 0.0000 * & 0.4464 & & \\  & & **Proposed method** & 0.0000 * & 0.0000 * & 0.5155 & 0.9114 & \\ \cline{2-6}  & & Wavelet & & & & & \\  & & VMD & 0.2420 & & & & \\  & & RPCA & 0.0000 * & 0.0000 * & & & \\  & & WRPCA & 0.0000 * & 0.0000 * & 0.7563 & & \\  & & **Proposed method** & 0.0000 * & 0.0000 * & 0.8065 & 0.9462 & \\ \hline \end{tabular}  
* * \(p\) < 0.05, significant difference.  
\end{table}
Table 2: \(p\)-Values of TAEs and AREs of key physiological points from the paired \(t\)-test.  
Figure 12: Power spectral density of reference noise and extracted noise. (**a**) Reference noise; (**b**) wavelet transform; (**c**) VMD; (**d**) RPCA; (**e**) WRPCA; (**f**) cross-channel DWRPCA (proposed method). The \(y\)-axis is in a log scale.
## 5 Conclusions and Recommendations  
This research aimed to develop the UTM monitoring application based on network Remote ID communication, which included a conflict detection algorithm using an inverted teardrop detection shape. Several flight tests were conducted to evaluate the UTM monitoring application using some small fixed-wing UAVs in rural and suburban areas. The flight test results were analyzed in terms of the communication latency, flight parameters, and their effects on the UTM monitoring performance using statistical and random forest machine learning algorithms. The conclusions and recommendations of this research are as follows.
# Working State of ECC Link Slabs Used in Continuous Bridge Decks
## 1 Introduction
It also indicated that those top surface strains of link slab were significantly below the ultimate strain of ECC materials so that ample strain capacity was reserved for the volumetric change induced by temperature variation [21]. Moreover, non-corrosive materials should be ideal alternatives considering the risk of steel reinforcement corrosion. Fiber reinforced polymer (FRP) composites of light weight and high strength have matched the diverse contributions of reinforcing steel to bridge deck slabs [22; 23; 24; 25]. A large-scale laboratory testing study was carried out to assess the performance of FRP composites reinforced ECC link slabs in bridge deck systems [26]. Similar to existing design concepts and analysis theories, this experimental study simply relied on conventional semi-empirical parameters. The complex structural working state of loaded ECC link slab has not been understood, so it is difficult to achieve an accurate evaluation of structural performance. The drawbacks of traditional structural design concepts and analysis theory occasionally restrict the application of ECC link slabs in bridge engineering. With these considerations, the primary objective of this investigation is to gain insight into the working state and the structural failure mechanism of ECC link slab from an innovative point of view. Unlike the existing studies, the behavior characteristics before ultimate failure are of greater concern in this investigation as some unseen features could exist in the whole working state. Since the strains collected from ECC link slabs and reinforcing materials are discontinuous, another task of this investigation is to spatially expand the experimental data. Thus, these continuous data can be used for the mathematical modeling of ECC link slabs' working states. With the increase in load, the working state of ECC link slab experiences quantitative change and the accumulation of which naturally produces the qualitative change. The important information carried by the qualitative change points at the mutated structural performance of ECC link slabs which has not been emphasized before. All these efforts constitute useful supplements to current structural design concepts and analysis theories for ECC link slabs.
## 3 Field Measurement
### Wheel Load Measurement Results  
According to the measurement, the wheel loads during train operation at each point are as shown in Figure 9. Peaks in the graph are load values generated when the wheels pass through the strain gauge attached to the rail, and oscillations around zeros are electrical signals and noise components of the strain gauge. An average train speed was measured at 291.4 km/h.  
The dynamic wheel load from the earthwork start section (Point 1) to the deflection section (Point 2) was reduced by 8%, and a maximum wheel unloading ratio for static wheel load (85 kN) was 0.16. The train wheel kept maintained contact with the rail when the train passed the settled section, but the car body was moving upward due to an inertia force, causing the wheel-suspension spring expansion. As a result of those behaviors, the wheel loads at Point 2 were decreased.  
At Points 3 and 4 where rail deflection was restored, the wheel loads appeared to be similar and were increased by 26% compared to Point 2. Since the separation distance between the vehicle and the wheel was reduced, the impact load was generated by a body force of the car body and the restoring force of the axle spring (Figure 10).  
The impact occurred 49 to 58 m from the start point of the earthwork section, all of which are summarized in Figure 11.  
The measured results show that the increased wheel loads from the impact load and decreased wheel loads from track irregularities are within an acceptable loading range (wheel unloading ratio \(\leq 0.8\)) as defined by the Korean Standards for Railway Vehicle Safety Criteria [17]. However, continuously applied impact loads may cause rail and track damage.  
Figure 10: Impact load diagram in field measurements.  
Figure 9: Results of wheel load measured at each point. (**a**) Point 1; (**b**) Point 2; (**c**) Point 3 and 4.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reinforcement Learning Energy-Efficient Routing Protocols (RL-Based E2 RPs)
#### 5.4.3. Q-learning-Based Energy-Efficient Routing Protocol (QL-E2 RP)  
The author of [157] proposes an E2 RP for UWSNs based on the Q-learning technique named (QL-E2 RP). The main aim of the proposed routing protocol is to provide and explore the efficient assets in hierarchically clustered networks. After the initial round of collecting communicating data packets, the Q-learning technique is continued after the BS is aware of the routing timeliness and energy consumption for processing the communicating data packets. The Q-value in this routing protocol is calculated, which is based on the transmission delay and remaining energy. In contrast, a regulatory element that balances the network's transmission delay and energy usage decides the reward. When the network regulatory element is set to zero, the aforementioned routing protocol only considers the residual energy. In addition, when the network regulatory element is set to one, then the focus of the aforementioned routing protocol is only to reduce the transmission delay of the network. According to [158] the proposed routing protocol achieves great performance in terms of network throughput, energy efficiency and also increases the lifetime of the network.
# Wideband Vibro-Acoustic Modulation for Crack Detection in Wind Turbine Blades
## 2 VAM Model for Detecting Cracks in WT Blades
### Description of the Proposed Model for WT Blade Crack Detection
The small non-linear parameters \(\alpha\) and \(\beta\) represent the shift in the resonance frequency and changes in attenuation upon the application of the rotational load. It is assumed that these parameters can be estimated based on known information about blade resonance frequency and damping variation due to the presence of a crack.
## 5 Simulation Results
### Testing of the Proposed Control Strategy
#### 5.2.3 Case 3: Tracking Performance on Time-Varying Power Load Demands  
Renewable energy sources such as wind power and photovoltaic power have time-varying characteristics. When they are connected to a micro-grid with a MGT-CCP system, it is necessary to adjust the power output of gas turbine to maintain the stability of the micro-grid. Therefore, Case 3 tests the controller's ability to track time-varying power load demands. Figure 15 is a block diagram of the MGT-CCP system connected to renewable energy.  
Assuming the MGT-CCP system is operating at operating point #3, the power required by the user is a fixed value and the power from the renewable sources is shown in Figure 16. In order to verify the effectiveness of the proposed method in depth, in Figure 16, the power changes rapidly in the first 3000 s, and the power changes slowly in the next 3000 s. The results are given in Figure 17 and Table 4. The simulation results show that the proposed method can track the electric load command better, the tracking speed is faster and the dynamic deviation is smaller. In addition, because of the implementation of coordination, the cold-water temperature deviates from the set value by a smaller amount. The power tracking performance under the IAE indicator increased by 62%, and the cold load tracking performance increased by 9%.  
Figure 14: Case 2: performance of unknown input disturbance rejection (Solid in blue: proposed method; dot-dashed in red: PID; dot in green: reference). (**a,b**) Output variables; (**c,d**) Manipulated variables.  
Figure 15: The block diagram of the MGT-CCP system connected to renewable energy.  
Figure 16: Power from renewable sources.  
Figure 17: _Cont._  
Figure 17: Case 3: Tracking performance on time-varying power load demands (Solid in blue: proposed method; dot-dashed in red: PID; dot in green: reference). (**a**) Sum of power output; (**b**,**c**) Output variables; (**d**,**e**) Manipulated variables.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **IAESum of power output** & **IAETemperature** \\ \hline PID & 4053 & 22 \\ Proposed method & 1525 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance indices for Figure 17.
## 2 Statistical Analysis for the Determination of Operating Parameter Requirements
### Overview of Operational Actions and Mechanical Responses  
Table 3 lists the six most-representative operational actions selected as input variables for the analysis to identify the correlations between operational actions and mechanical responses. The main three operational actions associated with speed parameters (cutterhead rotation, shield jack, and screw conveyor rotation speeds) are the key drivers for adjusting the mechanical responses. The other three operational actions (opening ratio of screw conveyor gate, number of shield jacks, and number of cutterhead motors) tend to remain constant except in exceptional circumstances. Their values are fixed at the beginning of excavation. Table 3 lists the 10 mechanical responses selected from the many that are available on the control panels of various TBMs.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Estimated Operating** & **Number of Data Used** & **Equation** & \(\mathbf{R^{2}}\) \\
**Parameter Requirement** & & & \\ \hline \(y_{1}\) & 20 & \(y_{1}=7.58g_{1}-0.10g_{2}-2.18g_{3}-8.18a_{1}+\) & 0.68 \\ \(y_{2}\) & 18 & \(y_{2}=5.66g_{3}+2.18g_{2}-7.34g_{3}-9.06a_{1}+\) & 0.81 \\ \(y_{3}\) & 14 & \(y_{3}=-32.64g_{1}-17.16g_{2}-6.44g_{3}-\) & 0.90 \\ \(y_{4}\) & 13 & \(y_{4}=-51.89g_{1}-44.26g_{2}-40.28g_{3}-\) & 0.90 \\ \(y_{4}\) & 13 & \(7.83a_{2}-11.70a_{4}+197.66\) & 0.90 \\ \hline \end{tabular}
\end{table}
Table 2: Summary of regression equations estimating operating parameters for soil excavation.
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 5 Transport Management with SDN  
As shown in Figure 5, data traffic management depends on traffic measurements, which influence the characteristics and functionalities of the traffic flows.  
Despite the many measurements in network traffic problems, all of these need to be managed according to the specific situation and conditions. In these problems, F5 TS provides many facilities to manage network traffic with SDN technology and concepts. Traffic management depends on the traffic flow measurements considered between the nodes, which could be anywhere within the entire network. This ensures that all network components are in working condition and that the network is available to all accessing users. Further, this maintains the network performance through the SDN-based MA, which reduces the overall implementation complexity and improves the performance. The management of traffic varies according to many factors, such as applications, services, and environments. Further, measurements of 5G-based traffic depend on emerging technologies.