esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Literature Review
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Protocol** & **Entergy-efficient** & **Position of Base Station** & **Number of Cluster Nodes** & **Number of CH** & **Cluster Method** & **Mobility** \\ \hline LEACH [1] & Yes & Outside & Unpredictable & Uncertain & Distributed & Static \\ I-LEACH [5] & Yes & Outside & Unpredictable & Certain & Distributed & Static \\ CPCP [6] & Yes & Centre & Unpredictable & Uncertain & Distributed & Static \\ CACP [19] & Yes & Outside & Unpredictable & Uncertain & Distributed & Static \\ LEACH-C [7] & Yes & Outside & Unpredictable & Certain & Centralized & Static \\ FBR [8] & Yes & Outside & Unpredictable & Uncertain & Distributed & Static \\ DEECC [9] & Yes & Outside & Unpredictable & Uncertain & Distributed & Static \\ ECDC [11] & Yes & Outside & Unpredictable & Uncertain & Distributed & Static \\ UCAR [12] & Yes & Inside & Unpredictable & Uncertain & Distributed & Static \\ \hline \end{tabular}
\end{table} 
Table 2: Comparability based on certain metrics. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Protocol** & **Published Year** & **Type of Mode** & **Approach** & **Sensing Model** & **Drawbacks** \\ \hline DEECIC [9] & 2012 & Homogenous & Distributed & Disc & \begin{tabular}{c} Clustering algorithms that are \\ more energy-efficient than \\ those that are more coverage \\ efficient. \\ \end{tabular} \\ \hline ECDC [11] & 2014 & Heterogeneous & Distributed & Disc & \
\begin{tabular}{c} Routing requires \\ retransmission of control \\ packets, consuming additional \\ energy. \\ \end{tabular} \\ \hline CUCA [15] & 2017 & Homogenous & Distributed & Disc & Useful for small networks only. \\ \hline DECAR [12] & 2014 & Homogenous & Distributed & Disc &
\begin{tabular}{c} Send aggregate data to sink is \\ not possible in a huge network. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Protocol** & **Network Size in m\({}^{2}\)** & **Number of Nodes** & **Location Aware** & **Deployment Model** & **Coverage Type** & \begin{tabular}{c} **Residuary** \\ **Energy** \\ **Involved** \\ \end{tabular} \\ \hline LEACH [1] & 100 \(\times\) 100 & 100 & Yes & Random & No & No \\ I-LEACH [5] & 100 \(\times\) 100 & 100 & Yes & Random & No & Yes \\ CPCP [6] & 200 \(\times\) 200 & 400 & Yes & \begin{tabular}{c} Random and \\ non-uniform \\ \end{tabular} & Area & No \\ CACP [19] & 120 \(\times\) 120 & 1000 & Yes & \begin{tabular}{c} Random and \\ uniform \\ \end{tabular} & Area & Yes \\ LEACH-C [7] & 100 \(\times\) 100 & 100 & Yes & Random & Yes & Yes \\ FBR [8] & 150 \(\times\) 150 & 1000 & Yes & Random & No \\ DEECIC [9] & 100 \(\times\) 100 & 100 & No & Random & Bounded Area & Yes \\ ECDC [11] & 200 \(\times\) 200 & 100–200 & Yes & \
\begin{tabular}{c} Random and \\ non-uniform \\ \end{tabular} & Area and point & Yes \\ CUCA [15] & 40 \(\times\) 40 & 60–100 & Yes & Not discussed & Area & No \\ DECAR [12] & 40 \(\times\) 40 & 100 & Yes &
\begin{tabular}{c} Random and \\ Grid \\ \end{tabular} & Area & Yes \\ \hline \end{tabular}
\end{table} 
Table 3: Comparability based on physical metrics.
# Effect of Stirrup on Bond Strength Degradation in Concrete Cracked by Expansion Agent Filled Pipes
## 1 Introduction
Furthermore, the test results show that the deterioration of the bond strength was more severe in the \"Side-split\" cracked specimen than in the \"Single-split\" one, as can be seen in Figure 2.  
Figure 1: Importance of bond in truss mechanism: (**a**) truss mechanism; (**b**) equilibrium force at the circled point in (**a**); (**c**) equilibrium affected by bond degradation due to induced corrosion crack. On the other hand, the pattern of the induced crack in tested specimens should represent as closely as possible the conditions found in structural RC members. Therefore, to appropriately model the damage, in the previous study [27], a specimen was built to monitor the internal cracking condition due to the volumetric expansion of the rebar. Figure 3 shows the RC beam when EAFPs replaced the corner longitudinal rebars to simulate the induced corrosion cracks. Hours after filling the expansion agent, longitudinal cracks running parallel to the reinforcing bars are marked in blue, and the value on the figure shows the maximum crack width measured in each section, as shown in Figure 4a,c. To observe the internal cracking condition, the monitor specimens were saved in half. Figure 4b,d show the detail of internal cracking. Side-splitting cracks mainly occurred around the main rebar due to the expansion of EAFP. With EAFP, it is possible to control induced crack patterns to reproduce closely the damage in actual structural members. Figure 3: Rebar arrangement of monitor specimen: (**a**) short specimen; (**b**) long specimen. Figure 2: Effect of induced cracked pattern on bond deterioration [26]: (**a**) degradation of bond due to induced cracks; (**b**) induced crack pattern. Furthermore, stirrups are capital for better structural performance in engineering practice. While most previous studies employed specimens without stirrups, researchers highlighted their positive role in inhibiting bond strength degradation [21; 28; 29; 30; 31]. This indicates a need to understand the effect of stirrup on bond degradation of cracked concrete. Therefore, this study quantitively investigated the effect of stirrup on bond deterioration due to side split induced corrosion cracks. The corrosion damage (induced cracks) simulated by an EAFP focuses on the more fundamental effect of the cracking itself. Then, three series of the cracked specimen with different stirrup ratios are subjected to a pull-out test. Finally, an empirical model built from the result is compared with corroded specimen data in the literature.
# Improving the Performance of Turbo-Coded Systems under Suzuki Fading Channels
## 3 Noise Distribution  
### AWGN Distribution  
The Gaussian distribution can be written as [15; 16]:  
\[p(w)=\frac{1}{\sqrt{2\pi\sigma_{w}^{2}}}\exp(-\frac{(w-\mu_{w})^{2}}{2\sigma_{w} ^{2}}), \tag{3}\]  
where, \(\sigma_{w}^{2}\) and \(\mu_{w}\) are the variance and the statistical mean of Gaussian random variable \(w\). The complex AWGN RV is represented using \(\mathbf{w}=\mathbf{w}^{I}+\mathbf{w}^{Q}\), where superscripts \(I\) and \(Q\) are denoted for the in-phase and the quadrature part of the complex AWGN signal.
## 4 Experimental Validation and Discussion  
### Experimental Setup  
In this section, the BRB fault data issued from IMs was utilized to evaluate the performance of the CMS-CWT algorithm. The IM test rig was used to collect the vibration signals from a faulty motor with 1 BRB fault and a baseline (health) motor. Figure 8 shows the test rig applied to the experimental study. Meanwhile, the motors were also tested under a series of load conditions to evaluate the load dependency of the proposed approach. The system consists of a drive motor, a coupling, a DC load generator, an encoder, a thermocouple, a horizontal accelerometer, and a vertical accelerometer. The tested motor is a 2-pole pairs IM with rated speed of 1500 rpm. As illustrated in Figure 9, the fault case is a motor with 1 BRB which was created by drilling a hole into one bar up to its full depth. Vibration signals of the testing motor were measured by a vertical and a horizontal accelerometer. The encoder mounted at the free end of the motor was utilized to measure motor speed and slip for the calculation of fault frequency.  
To examine the performance of proposed method in detecting BRB faults, the vibration signals were measured under different operating conditions under 0%, 20%, 40% to 80% of the full operating load and at the rated frequency. All the measurements were sampled with a high-speed data acquisition system at a sampling rate of 96 kHz per channel. A data length of 20 s was acquired in order to achieve a good analysis in evaluating CMS-CWT method.  
Figure 7: Three-dimensional vibration spectrum analysed by (**a**) CMS, (**b**) FSC, and (**c**) CMS-CWT with \(Fs\) = 10,000 Hz.  
In addition, the Hamming window with the length of \(2^{8}\) and the step size of 35% of the window length (\(R=89\)) was utilized for the signal processing. As a result, the maximum cycle frequency that could be analyzed by the CMS and the FSC was approximately 375 Hz and 539 Hz, respectively; thus the loop frequency analysis range was enough to support for the analysis of BRB faults.
## 3 Results: Summarized Characteristics of Triggering Scenarios and Probed Feature Validation
### Micro-Component Geometric Feature Measurement  
Two kinds of micro-component geometric features, namely hole profile and depth, were studied to validate the performance of the micro-probing structure. Precision geometric measurement technologies have been under development for decades. In the case of micro-components with high aspect ratios, high optical-reflection and high curvature surfaces, however, micro-CMMs are still more reliable than an optical metrology device, particularly because these optical metrology devices are still incapable of precisely measuring the geometry profile of an inside micro-hole with a diameter of less than 0.2 mm. Nevertheless, these micro-holes with high aspect ratios are widely applied to engine nozzles, printer nozzles and wire drawing dies utilized in the textile industry. In this study, therefore, the inside geometry profile of the micro-hole was measured by the proposed tri-switches probing structure. The experiment drove the probe to collect hits across numerous depths in discrete movements with an upward increment of 5 \(\upmu\)m. The profile detecting results were consistent with the inner geometry of the micro-hole according to the section observation of the optical metrology device, as shown in Figure 12.  
The depth of micro-holes in this study was measured by the tri-switches probing head. The depth of micro-holes drilled by a \(\mathcal{O}\)0.3 mm micro drilling tool could be easily measured by a spherical stylus tip with sphere diameter of \(\mathcal{O}\)120 \(\upmu\)m, as shown in Figure 13.  
Figure 11: Gauge blocks mounted on the granite platform.  
Figure 12: Consistency of measuring the inner profile of a micro-hole: the proposed vs. optical device.  
High-accuracy performance in the vertical direction of the tri-switches probing head was also validated with this experimental result, which involved 30 repeated measurements of the depth of a micro-hole. The average depth was estimated 222.82 \(\pm\) 0.20 \(\upmu\)m with 5% risk. However, compared to the results from using an optical microscope (the within uncertainty was close to 20 \(\upmu\)m), as shown in Figure 14, the differential value was as much as 15.43 \(\upmu\)m. It might be due to the lip clearance angle of the micro drilling tool. Because the inside bottom of the micro-hole was not leveled, measurement results by these two devices tended to be inconsistent. Otherwise, the difference between them could be more ideal. Therefore, the height feature that was measured by this micro-CMM system maintained more favorable reproducibility than the optical metrology device.
## 3 Nonthermal Methods
### Ultrasound Method  
Ultrasound is another efficient and environmentally friendly nonthermal technology, which is essential in the food industry to meet consumer demands for safe and healthy food and an innovative method for food preservation and safety. Ultrasound shows great potential in the decontamination of food pathogens, such as _L. monocytogenes_, _Salmonella_, and _Staphylococcus_ spp. However, to optimize this technology, parameters such as frequency, intensity, and treatment time must be adjusted according to each type of food and microorganism involved [68].  
The use of high-power ultrasound (20-100 kHz) causes the destruction of microorganisms by cavitation, a process in which gas bubbles formed in liquids collapse, generating shock waves that destroy the cell walls of microorganisms and modify their DNA. A study demonstrated the effectiveness of ultrasound in reducing _L. monocytogenes_ biofilm on stainless steel surfaces, with a decrease of 3.8 log cfu/mL. Also, combining ultrasound with ozonation led to a reduction of 7.31 log cfu/mL, with no recoverable cells after treatment [10].  
The use of ultrasound in dairy products has increased due to its effects on (a) the thermal stability of whey proteins, (b) the gelation of caseins, (c) the viscosity of the dairy product, (d) the homogenization of milk, and (e) the reduction of the freezing time of ice cream [69].  
Recent studies suggest that ultrasound, combined with other technologies within the framework of the \"multiple hurdle technology\", improves the efficiency of the microbial inactivation process compared to the use of a single technology. This requires further research on the mechanisms of inactivation and the synergistic effects of different technologies. Thus, ultrasound becomes a promising method of nonthermal sterilization, capable of improving food safety while preserving the quality of the finished product [70].
# Study on the Vibration Effects of Cyclic Blasting on Bridge Structures under Construction
## 3 Numerical Simulation and Analysis of Blasting Vibration
### Comparative Analysis of Numerical Simulation Results
#### 3.3.3 Analysis of Simulated Blasting Vibration Velocity of the Bridge  
Figures 12 and 13 depict the numerical simulation values for the bridge piers and bridge deck, respectively. Examination of the peak vibration velocity waveform simulated by FLAC3D reveals that the highest simulated peak vibration velocity occurs at Bridge Deck Measurement Point 1, reaching 0.250 cm/s, whereas for the bridge pile simulation, it is 0.095 cm/s, as depicted in Figure 14. The small discrepancy between the maximum peak vibration velocity and the actual measured value suggests that the numerical simulation results are acceptable. In comparison with the measured value, the peak vibration of the simulated value occurs around 0.2 s.  
FLAC3D simulation predicts that the maximum peak vibration velocity at measuring points 6-10 in the middle of the bridge column is 0.209 cm/s. Consequently, the peak vibration velocity induced by tunnel blasting is highest at 0.235 cm/s on the bridge floor, followed by 0.209 cm/s on the bridge column, and 0.095 cm/s on the bridge pier.  
Figure 14: Simulation of the peak vibration velocity at the bridge pile measuring points.  
Figure 12: Simulation of peak vibration velocity at pier measurement points.  
Figure 13: Simulation of peak vibration velocity at measurement point on the bridge deck.  
Considering that the vibration velocity of bridge measuring points typically ranges between 0.05 and 0.25 cm/s, it could potentially exert a minor influence on the concrete that has not fully solidified within a short timeframe. For bridge concrete with a strength exceeding 95%, the permissible vibration limit is less than 2 cm/s within 6 h, and less than 6 cm/s after 72 h. In the context of this project, with the tunnel blast site located 43 m from the bridge, the highest vibration intensity resulting from blasting construction on the bridge measures 0.235 cm/s, comfortably below the vibration safety threshold of 2 cm/s. Thus, the vibration impact of blasting construction on the reinforced concrete materials of the bridge remains within the safety standard.
## Materials and Methods
### Attention-Based Feature-Fusion VGG19
#### 2.1.1 Baseline VGG Network  
VGG19 is a deep neural network architecture that researchers developed from the Visual Geometry Group (VGG) at the University of Oxford in 2014 [32].  
The VGG19 architecture is a deep convolutional neural network with 19 layers, hence the name VGG19. The VGG19 architecture has 19 layers, including 16 convolutional layers and 3 fully connected layers. The first 13 layers are convolutional, and the remaining 6 are fully connected. The convolutional layers have a fixed kernel size of 3 \(\times\) 3, and they use a stride of 1 and 0 padding to keep the spatial resolution of the feature maps the same. The max-pooling layers have a fixed kernel size of 2x2 and a stride of 2, which reduces the spatial resolution by half.  
The VGG19 architecture has been used for many image recognition and classification tasks, including object recognition, scene classification, and image segmentation. It has achieved state-of-the-art performances on several benchmark datasets, including the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [38] and the CIFAR-10 [39] and CIFAR-100 [40] datasets.  
When fine-tuning with the VGG19 architecture, the first few convolutional layers are typically frozen, while the later ones are fine-tuned. The intuition behind this approach is that the earlier layers learn lower-level features that are more general. In contrast, the later layers learn higher-level features that are more specific to the task.  
The present study employs VGG19 and allows for domain adaption via retraining some of the model's layers. In this way, the model retains sound feature-extracting knowledge from the ImageNet database domain and can extract powerful low-level features common in every image domain. Higher medical-specific features are expected to be mined by the deep convolutional layers of the network, which are trainable. With this conception, the total trainable parameters of the network are approximately 5 million.
###### Abstract  
In wideband radar systems, the performance of motion parameters estimation can significantly affect the performance of object detection and the quality of inverse synthetic aperture radar (ISAR) imaging. Although the traditional motion parameters estimation methods can reduce the range migration (RM) and Doppler frequency migration (DFM) effects in ISAR imaging, the computational complexity is high. In this paper, we propose a new fast non-parameter-searching method for motion parameters estimation based on the cross-correlation of adjacent echoes (CCAE) for wideband LFM signals. A cross-correlation operation is carried out for two adjacent echo signals, then the motion parameters can be calculated by estimating the frequency of the correlation result. The proposed CCAE method can be applied directly to the stretching system, which is commonly adopted in wideband radar systems. Simulation results demonstrate that the new method can achieve better estimation performances, with much lower computational cost, compared with existing methods. The experimental results on real radar datasets are also evaluated to verify the effectiveness and superiority of the proposed method compared to the state-of-the-art existing methods.  
m pacs: 15 April 2017
## 3 Methodology
### Artificial Neural Network (ANN) Model  
An artificial neural network (ANN) model is defined as interconnected mathematical systems of neurons that interchange communication among themselves [60; 61; 62]. The ANN is a computational model employed to capture the hidden relationship between a set of numerical input data to a set of numerical target data. Basically, the ANN model (depicted in Figure 3) comprises three layers, namely input, hidden, and output layers, with sufficient number of neurons, \(H_{i}\), \(H_{h}\) and \(H_{o}\), respectively. In practice,  
* the input layer receives the \(i\)-th data point (\(\overrightarrow{x}_{i}\)) that comprises the dust accumulation in terms of exposure days (\(D_{i}\)) and the average daily ambient temperature values (\(T_{i}\)) (i.e., \(H_{i}=2\)), \(\overrightarrow{x}_{i}=\left[D_{i},T_{i}\right],\ i=1,\ldots,n\);* the hidden layer manipulates them through the so-called hidden neuron activation function, \(G()\), to define the output of each \(h\)-th hidden neuron, \(h=1,\ldots,H_{h}\), based on the received inputs;
* the output layer receives the processed information and provides the \(i\)-th daily system conversion efficiency (i.e., \(H_{o}=1\)) by the following equation [62]: \[\eta_{i}=\sum_{h=1}^{H_{h}}\overrightarrow{\beta}_{h}G\left(\overrightarrow{w }_{h}\overrightarrow{x}_{i}+b_{h}\right),\] (3) where \(\overrightarrow{w}_{h}\) (the input weights vector that connects the inputs to each \(h\)-th hidden neuron), \(b_{h}\) (the input bias of each \(h\)-th hidden neuron), and \(\overrightarrow{\beta}_{h}\) (the output weights vector that connects the outcomes of each \(h\)-th hidden neuron to the output neuron, \(h=1,\ldots,H_{h}\)) are the internal parameters of the ANN model, and \(G()\) is the hidden neuron activation function.  
During the training phase, the internal parameters of the ANN model are usually defined randomly, then updated iteratively by resorting to the error back-propagation (BP) algorithm. Thus, setting the internal parameters of the ANN model requires a large computational effort to ultimately define the optimal internal parameters that lead to the accurate daily system conversion efficiency predictions [62, 63].  
In this work, the ANN model is an optimized version of that developed in [54] for the same purpose. The ANN proposed in [54] is a two hidden layer system composed of 10 and 4 hidden neurons. However, in this work, to further enhance the daily system conversion efficiency predictions, the ANN model is here optimized in terms of the following variables: (i) the number of hidden neurons, \(H_{h}\), and (ii) the neuron activation function. To this end, possible candidates for the number of hidden neurons, \(H_{h}\), and the hidden neuron activation functions are considered for identifying the optimum architecture of the ANN model by evaluating the prediction performance of the ANN architecture candidates in terms of the standard performance metrics in the literature (Section 3.4), as we shall see in Section 4.
## 6 Open Challenges, Future Prospects and Research Directions
### ML-Driven Predictive Adaptation  
The integration of ML and AI techniques represents another promising direction. These technologies can be used to develop predictive adaptation methods that preemptively mitigate the effects of adverse conditions. In this context, ML techniques hold significant promise for enhancing adaptive transmission in FSO systems. By leveraging vast amounts of historical and real-time data, ML models can predict atmospheric conditions and channel states, allowing FSO systems to preemptively adjust their transmission parameters. Predictive adaptation allows the system to prepare for potential disruptions before they occur and can mitigate adverse effects such as signal fading and scattering, ensuring a more stable and reliable communication link.  
ML-Aided Optimization and Resource Allocation  
ML algorithms can also optimize resource allocation within FSO networks. By analyzing usage patterns and channel conditions, these algorithms can allocate bandwidth and power resources more efficiently, enhancing overall network performance. For example, during periods of peak demand, the system could dynamically allocate more resources to critical communication links, ensuring consistent service quality.