esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
## 5 Parametric Study
### Influence of Hole Diameter  
The influence of the hole diameter on the pull-out behavior of notched perfobond shear connectors is shown in Figure 14. In this group of specimens, the cut ratio \(c_{w}/d_{p}\) was kept as 0.5. When the hole diameter was increased from 40 mm to 50 mm, 60 mm, 70 mm, and 80 mm, the pull-out resistance of the notched perfobond shear connector was increased by 5%, 18%, 29%, and 18%, respectively. It was revealed that the increase of the hole diameter between 40 mm and 70 mm led to an increase in the pull-out behavior of the notched perfobond shear connector. The main reason was that the pull-out failure mode was directly related to the shear of the concrete dowel in the hole, whose resistance was increased by increasing the hole diameter. When the hole diameter was greater than 70 mm, the pull-out resistance tended to increase more slowly. This was possibly because the restraint effect of the perforating rebar reduced with a large concrete dowel in the hole of notched perfobond shear connectors.
# Smart Road Traffic Accidents Reduction Strategy Based on Intelligent Transportation Systems (TARS)
## 4 Performance Evaluation
### Performance Evaluation Metrics
#### 4.2.2 Message Loss Ratio (MLR)  
The MLR shows the percentage of lost packets that are dropped during the transmission. The following formula is used to calculate the MLR.  
MLR = 100 \(-\) (Total No. of Received Messages/Total No. of Sent Messages) \(\times\) 100
## 1 Introduction
Thus, less time is required for a route discovery process which leads to reduced end-to-end delay for data transmission between the source and destination nodes. In contrast, this periodic exchange of routing information and route request packets in the route discovery process leads to high control packet overhead throughout the network. On the other hand, the reactive routing protocols are also known as on-demand routing protocols due to their on-demand manner for the route selection process. Some good examples of reactive routing protocols are dynamic source routing (DSR) [30] and ad hoc on-demand distance vector (AODV) [31]. Nodes running the reactive routing protocols do not need to maintain any prior routing information but exchange routing information of the network only when there is a need for communication. Thus, source node based on reactive routing protocols simply discovers routes in an on-demand manner to establish a connection to a destination node; hence, few control packets overhead are generated for the maintenance of network topology information. Conversely, during the route discovery initiation process in reactive protocols, all nodes exchange topology information with one other, which typically involves flooding of packets in the network. Then, the reactive protocol takes time to gather and analyze the network topology information. This process tends to prolong the end-to-end delay for data transmission between source and destination nodes [32]. Hybrid routing protocols such as zone routing protocol (ZRP) [33] and Secure Link State routing (SISP) [34] handle routing activity by dividing the network into zones while combining the best features of reactive and proactive routing protocols to make a timely and informed routing decision. For instance, in the event that a node wants to transmit packets to a destination node within a certain geographical zone of its immediate neighboring network, it will choose the proactive approach to handle this task. On the other hand, if the destination node is outside the geographical zone of its immediate neighbor, it will rather use the reactive routing protocols. However, this approach increases the complexity, computational cost, and energy consumption of mobile nodes [35] during the route selection process in the hybrid routing protocol, which degrades the network performance due to repeatedly changing network zones and constant switching between routing protocols.
## Abstract  
Background: The phenomenon of vapor lock, characterized by the formation of gas bubbles, poses challenges in achieving effective cleaning and debridement during endodontic treatments. This study aimed to evaluate the presence of vapor lock and to quantify its volume in the root canal system. Methods: Ten single-rooted teeth were selected, and their root canals were shaped using the Protaper Next system and irrigated with 5% NaOCl and 17% EDTA. Then, the canals were irrigated with a solution made of contrast medium (8 M cesium chloride solution) mixed in equal proportions with 5% sodium hypochlorite. CBCT scans were performed to analyze the presence and volume of bubbles in different canal thirds. Statistical analysis was conducted to compare the number of bubbles and the percentage of space occupied by them. Results: The results demonstrated the presence of vapor lock in all treated teeth, with a higher concentration of gas bubbles in the apical canal third. The formation of gas bubbles hindered the cleaning and debridement procedures, particularly in the apical region. Conclusions: This study provides evidence of the occurrence of vapor lock and highlights the importance of enhancing the cleaning phase in endodontic treatments to minimize bubble formation or eliminate them once formed. These findings contribute to a better understanding of the challenges posed by vapor lock and suggest avenues for optimizing endodontic procedures.  
vapor lock; root canal; air entrapment; irrigation; sodium hypochlorite; gas bubbles; micro-CBCT
# CSEM Optimization Using the Correspondence Principle
## 2 Methodology
### Adjoint Source  
The source injected at a receiver during adjoint modeling in the fictitious time domain must satisfy the following equation:  
\[\Delta E^{*}(\mathbf{x}_{r},\omega)=-\sqrt{\frac{-2\omega_{0}}{i \omega}}\int_{0}^{T}dt^{\prime}J^{\prime}(\mathbf{x}_{r},t^{\prime})e^{- \sqrt{\omega\omega_{0}}t^{\prime}}e^{i\sqrt{\omega\omega_{0}}t^{\prime}}. \tag{39}\]  
Obtaining the adjoint source (in fictitious time domain) from \(\Delta E^{*}(\mathbf{x}_{r},\omega)\) is an under-determined problem, which allows a high degree of freedom in the choice of \(J^{\prime}(\mathbf{x}_{r},t^{\prime})\)[13]. For example, one can assume an estimated pulse, \(\hat{J}^{\prime}(\mathbf{x}_{r},t^{\prime})\), formed from the sum of pulses corresponding to the second derivative of a Gaussian:  
\[\hat{J}^{\prime}(\mathbf{x}_{r},t^{\prime})=\sum_{m=1}^{N_{\omega }}A_{m}\partial_{t^{\prime}}^{2}\Gamma(t^{\prime}-\tau_{m}), \tag{40}\] \[\Gamma(t^{\prime}-\tau_{m})=\sqrt{\frac{\beta_{m}}{\tau}}e^{- \beta_{m}(t^{\prime}-\tau_{m})^{2}}, \tag{41}\]  
where \(N_{\omega}\) is the number of frequencies, and \(\beta_{m}=\pi^{3}/\tau_{m}^{2}\).  
With this choice, the adjoint source depends on the \(2N_{\omega}\) parameters, \(A_{m}\) and \(\tau_{m}\), determined by minimizing the functional defined by the difference between the spectra of the residuals and the sources (calculated from Equation (40)) in the least-squares sense.  
Following another strategy, we chose to estimate the adjoint source by solving the linear system corresponding to the discrete form of Equation (39), with equations given as follows:  
\[\Delta E^{*}(\mathbf{x}_{r},\omega_{q})=\sum_{n=1}^{N_{t}}A_{qn}f^{ \prime}(\mathbf{x}_{r},t_{n}^{\prime})\,, \tag{42}\] \[A_{qn}=-\Delta t^{\prime}\sqrt{\frac{-2\omega_{0}}{i\omega_{q}}}e^ {-\sqrt{\omega_{q}\omega_{q}}t_{n}^{\prime}}e^{i\sqrt{\omega_{q}\omega_{q}}t_{ n}^{\prime}}\,, \tag{43}\]  
where \(q\) is the index associated with the frequencies, i.e., \(q\in\{1,\cdots,N_{\omega}\}\), and \(N_{t}\) is the number of time samples. To stabilize the system solution, we added the minimum norm regularization with coefficient \(\lambda\)[25], and we also used preconditioning via reparameterization:  
\[\mathbf{J}^{\prime}=\mathbf{P}\mathbf{m}, \tag{44}\]  
where the preconditioning operator \(\mathbf{P}\) has the form of a Ricker pulse with peak frequency \(f_{p}\), expressed as follows:  
\[P_{nk}=\Big{(}1-2\pi^{2}f_{p}^{2}(t_{n}-t_{k})^{2}\Big{)}e^{-\pi^{2}f_{p}^{2} (t_{n}-t_{k})^{2}}. \tag{45}\]  
Finally, we obtain the adjoint source (in the fictitious time domain) by solving the linear system:  
\[\bigg{(}\begin{array}{c}\mathbf{AP}\\ \lambda\mathbf{I}\end{array}\bigg{)}\mathbf{m}=\bigg{(}\begin{array}{c}\Delta \mathbf{E}^{*}\\ \mathbf{0}\end{array}\bigg{)}. \tag{46}\]  
We obtain the solution through the conjugate gradient algorithm [19].
# Reduced Reference Quality Assessment for Image Retargeting by Earth Mover's Distance
## 3 Quality Assessment Using EMD
### Global EMD Based on Saliency Features  
Although the image patches are well-evaluated for local quality, the quality of the image includes not only local quality but also global quality. Previous studies show that people can use the eye tracking data to promote the effect of IQA metrics [17], while humans are the ultimate evaluators of image quality, so it is reasonable to introduce principles of human visual saliency features into image evaluation as the global measurement. This can make objective image evaluation more consistent with subjective image evaluation.  
In [PERSON]'s model [18], color, intensity, and orientation are extracted for visual saliency features. Since human vision is sensitive to image texture information, texture feature is added in this section. In this section, we extract ten visual saliency features from reference image, including two color contrasts (red-green and blue-yellow), two intensity contrasts (light to dark and dark to light), four orientation features \((0^{\text{o}},45^{\text{o}},90^{\text{o}},135^{\text{o}})\), and two texture features (original and extended LBP) [19].  
In the reference image, we extract ten saliency features, and we turn each feature map into a histogram. We transfer all the histograms. In the retargeted image, we also extract ten saliency features and turn each feature map into a histogram. Since the center of the image is the center of human vision, we make the center of each feature map as the centerof the histogram. Therefore, we obtain global image quality score using EMD (GEMD) by average strategy:  
\[\text{GEMD}=\frac{1}{N}\sum_{j=1}^{N}\text{EMD}(IH^{ref}_{f,j},IH^{ret}_{f,j}), \tag{5}\]  
where \(N\) is the total number of saliency features, and \(IH^{ref}_{f,j}\) and \(IH^{ret}_{f,j}\) are the \(j\)-th saliency features normalized weighted histogram for reference and retargeted images, respectively.
# Energy Generation and Attenuation of Blast-Induced Seismic Waves under In Situ Stress Conditions
## 2 Theoretical Analysis of Blasting Seismic Energy Generation and Attenuation under In Situ Stress
### Seismic Energy Generated in Rock Blasting under In Situ Stress
With regard to cylindrical wave propagation in a plane, the initial seismic energy on the elastic boundary is:  
\[E_{\rm s0}=2\pi r_{\rm f}\rho\bigg{(}c_{\rm P}\int_{0}^{\infty}{{{{v_{1}}^{2}} dt}}+c_{\rm s}\int_{0}^{\infty}{{{{v_{2}}^{2}}dt}}\bigg{)} \tag{6}\]  
The ratio of the initial seismic energy on the elastic boundary to the total explosive energy is defined as the blasting seismic efficiency \(\eta\)[35],  
\[\eta=E_{\rm s0}/E_{\rm e} \tag{7}\]  
A series of measurements show that the seismic efficiency \(\eta\) is in the range of 1-12% [33]. This is a small percentage. However, this energy has the most extensive impact on the blasting site. Therefore, the seismic efficiency of blasting attracts considerable attention, and many control methods are proposed to minimize blast-induced seismic waves. From Equations (1)-(7), it is seen that the seismic energy generated in rock blasting depends not only on the weight and properties of the used explosive (\(Q_{\rm e}\) and \(W_{\rm e}\)) but also on the rock properties (\(\rho\), \(c_{\rm P}\), and \(c_{\rm s}\)) and the size of the elastic boundary (\(r_{\rm f}\)). For rock blasting in deep depths, many experimental and numerical results have demonstrated that the presence of in situ stress would affect the acoustic velocity in the rock medium and the size and shape of the fractured zone. [PERSON] et al. [36] reported that the wave velocity along the specimen axis is positively related to the confining pressure. [PERSON] et al. [37] further pointed out that the longitudinal wave velocity of the specimen increases with confining pressure only at the pre-peak phase of the stress-strain relationship. The experimental results of [PERSON] and [PERSON] [38] reveal that the longitudinal and transverse wave velocities of the limestone core specimens exponentially increase with confining pressure below a critical value. However, beyond the critical confining pressure, the increasing trend in the wave velocities becomes linear. In addition, some researches show that in a specimen with preload compressive stress, the blast-induced fractured zone gradually shrinks as the compressive stress increases, and the major cracks align the loading direction of the compressive stress [39; 40; 41; 42]. From the above analysis, it can be seen that for blasting in stressed rock masses, the change of the acoustic velocity and the fractured zone will inevitably affect the energy conversion from explosive energy to seismic energy.
## 5 Results and Discussion
### Stage II
#### 5.3.2 More Demanding LiDAR Configuration  
Figure 7 presents the results of simultaneous latency and throughput measurements using the more demanding LiDAR configuration. As in the previous case, only average values are shown in the figure for better readability; the corresponding standard deviations (SD) are provided in Table 9, which also breaks down the results by attenuation level and includes the compliance with latency and throughput requirements, similarly to the previous table. Unlike the experiment with the less demanding LiDAR setup, here, we observe somewhat different phenomena. First, for each level of additional attenuation, the configurations optimized for minimal latency performed significantly worse, which was expected, as the selected LiDAR setup deliberately exceeded their capacity. The observed behavior stems from packet losses caused by link saturation. Therefore, we will focus on the configurations optimized for high throughput. Figure 7: Results from simultaneous latency and throughput measurements for selected base station configurations with the more demanding LiDAR setup—Stage II. Table 9 presents the results of the four configurations. At 0 dB of additional attenuation, all of them successfully transmitted all LiDAR-generated traffic without packet loss and maintained latency clearly below 50 ms. This meets not only the requirements shown for P3 from Table 2 for human-operated drone control with a video stream but also the thresholds for cooperative driving (P2). It is also worth noting that RTT was measured, while most recommendations refer to end-to-end latency. In this scenario, the best performance in terms of throughput and RTT was achieved by Configuration 4 (Auto CSI, TRS, SSB, SR Period = 1, USS), but only in ideal radio conditions without additional attenuation. When the attenuation was increased to 10 dB, a significant performance degradation was expected. However, configuration 4 once again delivered the best results. Nevertheless, all configurations remained within the specified latency and throughput recommendations. This could again be a result of Uplink Power Control, which was described in the previous section. Another important factor influencing this outcome is the relationship between signal quality and the Modulation and Coding Scheme (MCS). As attenuation increases, the signal-to-noise ratio (SNR) decreases, which can reduce the achievable MCS and thereby limit throughput or increase latency. Figure 8 illustrates this effect for Configuration 1, which used Auto CSI, TRS, and SSB. It demonstrates a clear non-linear relationship between attenuation and both SNR and MCS under the more demanding LiDAR configuration.
# Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models
## 4 Methodology of the Proposed Three-Layer Granule Models
### Granulating in the Input Space
#### 4.1.1 Construction of Information Granules (IGs)  
The process of granulating is described as follows. Assuming a cluster resulting from DBSCAN, several data points are selected from the cluster as its descriptors firstly. If the cluster has \(N\) data points, the selected data points are formed as a set of prototypes, \(Z\), expressed in (3):  
\[Z=\{z_{1},z_{2},\cdots,z_{c}\} \tag{3}\]  
where \(z_{i}\) represents the prototype of the \(i\)th cluster; \(c\) is the number of selected data points, and usually set as a relatively small value for the consideration of efficiency.  
Then, based on the selected prototypes, data points can be grouped into different IGs, which are constructed centering on those prototypes. To realize IG construction, the membership of data is a useful tool for expressing data's belonging to IGs, e.g., the fuzzy membership definition in (4). Assuming a data point, \(x_{k}\in X_{h}\), where \(X_{h}\) is the \(h\)th cluster in DBSCAN clustering results, its membership function to IG is defined as (4):  
\[u_{i}(k)=\frac{1}{\sum\limits_{j=1}^{c}\left(\frac{\left\|x_{k}-z_{j}\right\| }{\left\|x_{k}-z_{j}\right\|}\right)^{\frac{2}{n-1}}} \tag{4}\]  
where \(u_{i}(k)\) is the value of membership expressing how close \(x_{k}\) is to the \(i\)th granule; \(m\) is the fuzzification parameter, which is required to be larger than 1. Considering the prototypes are directly regarded as cores of granules, \(m=2\) is generally determined [20].  
Considering that there exists a case that a point is equal to the prototype, namely \(x_{k}=z_{r}\), where \(z_{r}\) is a prototype, the function in (4) will not be able to compute because of \(\mid\mid x_{k}-z_{r}\mid\mid=0\). To address this problem, a modification on (4) is defined as below:  
\[u_{i}(k)=\begin{cases}0,&x_{k}=z_{r},z_{r}=z_{i}\\ 1/\sum\limits_{j=1}^{c}\left(\frac{\left\|x_{k}-z_{j}\right\|}{\left\|x_{k}-z_ {j}\right\|}\right)^{2/(m-1)},&x_{k}\
eq z_{r}\\ 1,&x_{k}=z_{r},\;x_{r}\
eq z_{i}\end{cases} \tag{5}\]  
The new function degenerates to distinct membership as \(x_{k}=z_{r}\). When \(z_{r}=z_{i}\), the membership, \(u_{i}(k)\), can be directly set as 1. When \(z_{r}\
eq z_{i}\), implying this point overlaps other prototype points, the membership can be set as \(u_{i}(k)=0\) and \(u_{r}(k)=1\). Then, after the re-calculation on the membership, data points can be partitioned into different groups centering on prototypes, and each group is regarded as a fuzzy granule.
# A Comprehensive Review of Open Caisson Modeling Technology: Current Practices and Future Prospects
## 3 Advances in Physical Modeling Method
### Classification of Physical Modeling Based on Gravitational Acceleration  
Based on gravitational acceleration, g, physical modeling can be divided into two groups: n-g (centrifuge) and 1-g (laboratory on a small size and filed on a full scale). Centrifuge tests (n-g modeling) increased the effective gravitational acceleration to n times Earth's gravity by spinning a model at high speeds. By improving stress conditions in small-scale models, this technique made it possible to simulate full-scale behavior realistically. By comparison, 1-g modeling comprises small-scale laboratory testing and full-scale field investigations and works under typical Earth gravity. Laboratory models at 1-g were cost-effective and widely used for fundamental studies, while field tests provided real-world validation of geotechnical principles. In physical modeling, scaling laws based on dimensional analysis and the governing equations of engineering problems were very important to replicate the prototype conditions. Scaling factors such as geometry, stress, time, and others played significant role to ensure similitude between model and prototype [56], validating that model behavior faithfully reflected the prototype behavior [57]. Variability in model and prototype behaviors encountered due to scaling process arose from various factors such as grain size of soil mass relative to model size [58], time scaling affected pore pressure reduction and consolidation processes [59] and size and capacity of model box resulting in incomplete similitude [60]. Overall, 1-g models had limitations in replicating the in situ stress conditions of prototype and therefore suitable for problems or processes that were not dependent on stress [61] as scale effects were significant for phenomenon influenced by stress levels [62]. However, centrifuge models can effectively replicate in situ stress state of prototype [57] and provided high level of control due to capability of gravity manipulation [60]. The comparison between 1-g and n-g modeling used for open caisson excavation studies including experimental setup, scale effects, and experimental constraints is discussed in detail by reviewing existing case studies. Table 1 provides a summary of the physical modeling approaches in this section, along with information on ground conditions, shaft geometries, and modeling techniques.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Description** & **Ground Conditions** & **Construction Method** & **Shaft Geometry** & **Type of Model Test** \\  & & & **(H \(\times\) D) m** & \\ \hline Model caisson sinking & Dry Sand & \
\begin{tabular}{c} Model caisson sinking \\ Internal soil excavation \\ \end{tabular} & 20 \(\times\) 32 & 1-g model test \\ \hline Model caisson sinking & Sandy soil &
\begin{tabular}{c} Model caisson sinking, \\ Internal soil excavation \\ \end{tabular} & \multicolumn{1}{c}{