esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Heavy Metal Removal  
#### 3.2.1 Iron Removal Rate  
Figure 4 shows the iron removal efficiencies over 90 days for kaolin and nanomaterial-added kaolin samples compacted using standard methods. The initial iron concentration in the leachate was measured as 59.6 mg/L. On the 30 th day, the lowest iron removal rate was 68.65% for kaolin, while the iron removal rates of nanomaterial-added kaolin samples varied between 70.09% and 80.63%. Iron removal rates continued to increase at a decreasing rate until the 60 th day, and the iron removal rate of all kaolin samples reached a maximum on the 60 th day. While the removal rate for the kaolin sample was 87.23% on the 60 th day, the iron removal efficiencies of the nanomaterial-added kaolin samples ranged from 90.15% to 93.33%. As the ratio of nanomaterial in kaolin increased, the rate of iron removal also increased. On the 60 th day, the iron concentration values were 7.61 mg/L for kaolin, and 5.63 mg/L, 4.21 mg/L and 3.97 mg/L for 5% iron oxide, 5% aluminum oxide and 5% Oltu clay added kaolin, respectively. The results showed that the nanomaterial performance in  
Figure 3: Average permeability of kaolin with different nanomaterial content.  
kaolin, in terms of increased iron removal rate, was Oltu clay > aluminum oxide > iron oxide, respectively.  
Over 90 days, the kaolin + 5% Oltu clay sample showed the best performance in terms of iron removal. The large specific surface area of the nanomaterials plays an important role in increasing the heavy metal adsorption capacity from water [38; 39]. The smaller size and higher surface area of Oltu clay compared with aluminum oxide and iron oxide enabled the Oltu-clay-added kaolin samples to retain more iron in the leachate.  
On the 90 th day, the iron removal efficiencies of kaolin and kaolin/nanomaterial samples decreased slightly, and the iron concentration at the outlet increased. The decrease in the iron removal rate can be explained by the loss of heavy metal retention as a result of the saturation of the surface area of kaolin and nanomaterials with heavy metals and the separation of the heavy metals from the surface [40; 41].  
Iron was retained in higher amounts in all kaolin samples than other heavy metals in the leachate. This is related to the high concentration of iron in leachate compared to other metals. Moreover, the reaction of oxygen with iron in leachate and between the cavities of compressed kaolin samples in reactors causes the formation of iron hydroxide flocs, which contribute to the adsorption of iron [42].
## 3 Reliability Models of WSNs
### Tooling
#### 3.6.1 Proposed Extensions  
The tooling developed for evaluating the power consumption (see Figure 2) was extended to enable us to evaluate the reliability of WSNs. This environment is shown in Figure 16 and consists of three tools: _editor_, _translator_ and _evaluator_.  
The _editor_ is a graphical tool that allows to implement a WSN application and create a WSN topology (e.g., by choosing the number and location of the nodes, radio range, packet size, and communication protocols). Additionally, the user defines some parameters (e.g., stop criteria, application code and WSN topology) to evaluate the power consumption of the WSN via the _editor_. However, the evaluation of the WSN reliability needs further parameters, which were added in the  
\begin{table}
\begin{tabular}{c c c c} \hline
**Protocol** & **Communication** & **Multipath** & **Number of Hops to the Sink Node** \\ \hline FLOODING & broadcast & YES & Undefined \\ DIRECT & unicast & NO & 1 \\ LEACH & unicast & NO & 1 or 2 \\ \hline \end{tabular}
\end{table}
Table 1: Comparative elements of routing protocols.  
Figure 15: Region Model created when WSN uses the LEACH protocol.  
editor_. The user can define a region in a WSN and set needed properties about reliability (e.g., link reliability). As before the reliability extension, the _editor_ still uses the _translator_ and _evaluator_ to create and evaluate, respectively, the WSN power consumption and reliability.  
The _translator_, as mentioned in Section 2.2, generates power consumption models of applications and networks. With the proposed extension, the _translator_ also generates reliability models in RBD. The reliability of links and nodes is expressed by RBD basic blocks (see Section 3.2) automatically generated from both the WSN topology and information defined by the user in the _editor_. Meanwhile, the _translator_ also generates the Path model using the defined routing protocols (see Section 3.3) and creates Region model (see Section 3.4) by composing these Paths.  
Finally, while the _evaluator_ assessed only the power consumption (Sensor Node, Application and Network models) using CPN Tools [17, 18, 19], with the proposed extension, it can evaluate the Region models created by the translator. Additionally, it was necessary to use another software (Mercury) [28], because the CPN Tools only deal with CPN models (and Region models are created in RBD).
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Roles and Importance of Components and Parameters
#### 4.3.4 Long-Term Applications  
Over the course of their lifespan, civil infrastructure systems (CIS) vary in condition. It is difficult to evaluate different real conditions and execute performance evaluations since continuous monitoring is needed to trace the reaction throughout time [74]. Supertall structures have been constructed all over the world in recent decades to suit the economic and social demands of communities, particularly in Asia. The Shanghai Tower, whichstands 632 m tall in Shanghai, China, is a supertall structure. Given its supertall height and complicated structural layout, the project requires a thorough detailed analysis to determine its structural performance against dead loads, severe winds, earthquakes, and temperatures [75]. For effective monitoring of the skyscraper throughout both construction and operation, a complete building performance monitoring system with over 400 sensors was constructed. The monitoring system's primary characteristics are as follows: the direct determination of wind loads on building facades using 27 wind pressure sensors; the measurement of structural inclination and the redefinition of structural sway at various heights using 27 wind pressure sensors; and the simultaneous placement of sensors and data measurement devices with structural construction to document input data. To capture the early structural responses, sensors and data collecting devices were placed along with the structure. All data are transmitted to the service stage following construction. The life-cycle monitoring and assessment of the structure may be carried out from the point of its \"birth\" thanks to the integration of the in-construction and in-service monitoring systems.
## 5 Constrained Weighted Least-Squares
The minimum of Equation (22) is obtained by differentiating \(L(\mathbf{\theta},\lambda)\) with respect to \((\mathbf{\theta},\lambda)\), as follows  
\[\frac{\partial L(\mathbf{\theta},\lambda)}{\partial\mathbf{\theta}^{T}} =\Big{(}\mathbf{A}^{T}\mathbf{W}\mathbf{A}+\lambda\mathbf{S}\Big{)}\mathbf{ \theta}-\mathbf{A}^{T}\mathbf{W}\mathbf{b}=\mathbf{0}, \tag{23}\] \[\frac{\partial L(\mathbf{\theta},\lambda)}{\partial\lambda} =\mathbf{\theta}^{T}\mathbf{S}\mathbf{\theta}=0. \tag{24}\]  
Solving the Equation (23) for \(\mathbf{\theta}\), the following CWLS solution, denoted by \(\widehat{\mathbf{\theta}}_{\text{CWLS}}\), is obtained  
\[\mathbf{\theta}_{\text{CWLS}}=\Big{(}\mathbf{A}^{T}\mathbf{W}\mathbf{A}+\lambda \mathbf{S}\Big{)}^{-1}\mathbf{A}^{T}\mathbf{W}\mathbf{b}. \tag{25}\]  
where the value of \(\lambda\) is unknown, and can be determined as a root of the 4 th-order polynomial, which is obtained by substituting Equation (25) into Equation (24) (see Appendix B), given as  
\[\sum_{i=1}^{3}\frac{\alpha_{i}\beta_{i}}{(\zeta_{i}+\lambda)^{2}}=0, \tag{26}\]  
where \(\{\alpha_{i}\}\), \(\{\beta_{i}\}\) and \(\{\zeta_{i}\},\ \forall i=1,2,3\), are given in Appendix B. Then, the procedure based on CWLS method is  
1. Initialize elements of matrix of weights as \(\mathbf{W}=\mathbf{I}\). 2. Using the standard root finding algorithm determine all real-valued roots of the Equation (26). 3. Substitute obtained \(\lambda\) in previous step into Equation (25), and determine all sub estimates of \(\hat{\mathbf{\theta}}_{\text{CWLS}}\) for which the \(J(\mathbf{\theta})\)is minimal. 4. Using the Equation (20) construct the weighting matrix \(\mathbf{W}\). 5. Repeat steps 2-4 until \(\hat{\mathbf{\theta}}_{\text{CWLS}}\) satisfies the stopping criterion.
# Automatic Blob Detection for Dental Caries
## 4 Conclusions  
There is a huge potential for use of dental radiography and, in particular, for work focused on caries detection. Blob detection was used in this research to detect all caries candidates, which also included false positives. The use of image analysis techniques was encouraged to filter out detected unwanted regions, such as edges from the caries regions. A threshold property value was then introduced to aid in the elimination of false positives from the selected regions to leave the detected caries candidates. The proposed caries detection method was able to administer favourable results compared to other detection models with an accuracy of 97%.  
The introduction of blob detection offers a more robust and automatic way to detect caries on dental radiographs compared to other detection methods. The proposed approach presents an avenue for future research using unsupervised models from its impressive success rate with supervised models. More dental radiographic images are needed to improve the training and learning process to produce even better results. Figure 6 shows both the training and validation accuracy graphs of the proposed method. Weight regularization methods are also encouraged to reduce overfitting.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Detection Methods** & **Dataset Images** & **Accuracy** \\ \hline Caries detection in panoramic images [23] & 1392 & 98.0\% \\ Dental classification for periapical images [22] & 78 & 82.5\% \\ Textural classification of digital images [21] & 64 & 96.88\% \\ PaxNet [11] & 470 & 86.05\% \\ ICDAS [12] & 620 & 92.37\% \\ GLCM [13] & 240 & 80\% \\ Deep learning [16] & 304 & 64.14\% \\
**Proposed approach** & 11,114 & **97.0**\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of various caries detection methods.
# Effect of Dry-Wet Cycle Periods on Properties of Concrete under Sulfate Attack
## 2 Experimental Program
### Testing Procedure
#### 2.4.1 Flexural Strength  
According to GB/T 50081-2019 [27], the flexural strength was tested using a universal testing machine. The placement of the specimen is shown in Figure 4. The pressure-bearing surface of the specimen was the side, and the distance between the supports was 300 mm. The specimen was loaded at a rate of 0.06 MPa/s until broken, and then the failure pressure and fracture position were recorded. The flexural strength was calculated using Equation (1).  
\[f_{\text{t}}=0.85\frac{Fl}{bh^{2}} \tag{1}\]  
where \(f_{\text{t}}\) = the flexural strength of the concrete (MPa), \(F\) = the failure load of the specimen (N), \(l\) = the span between pillars (mm), \(b\) = the specimen width (mm), and \(h\) = the height of the specimen (mm).  
Figure 3: Wetting and drying process. (**a**) Wetting process; (**b**) drying process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Code** & **Dry–Wet Cycle Period (Days)** & **Dry–Wet Ratio** \\ \hline T3 & 3 & \\ T7 & 7 & \\ T14 & 14 & 3:1 \\ T21 & 21 & \\ \hline \hline \end{tabular} Note: T7 means that the drying–wetting cycle period was 7 days, in which natural drying lasted 126 h and wetting lasted 42 h.  
\end{table}
Table 3: Dry–wet cycle design.  
The flexural strength of the sulfate-eroded concrete specimen over time was evaluated with the relative flexural strength coefficient \(R_{\text{f}}\), as follows:  
\[R_{\text{f}}=\frac{f_{\text{t}}^{\text{n}}}{f_{\text{t}}^{\text{0}}} \tag{2}\]  
where \({f_{\text{t}}}^{\text{0}}\) and \({f_{\text{t}}}^{\text{n}}\) are the flexural strength after 0 and t dry-wet cycles, respectively.
# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
## 5 Prospects
### Inverse Halftoning Technology for Color Image Distortion  
The existing color inverse halftoning technology is to obtain the inverse halftone image through the inverse halftone processing of each channel, resulting in poor visual quality in color performance. Therefore, to construct the relationship between the color channel and the specific color space model is an important aspect of the research on color image inverse halftoning. To address this issue, we can add color difference information between the restored continuous-tone image and the original continuous-tone image into the loss function. On the other hand, we can add a sub-network to restore the color information. The experimental results of the method in reference [20] demonstrated that networks at different stages tend to recover different aspects of image information. On the basis of this idea, in the following study we will design a deep network with three sub-networks. The first sub-network is employed to remove halftone noises and restore the initial continuous-tone image, the second sub-network is designed to enhance detail and texture information, and the last sub-network is applied to adjust color performance. At the same time, the corresponding loss functions will be set for each sub-network and the whole deep network, in which color difference loss function will be considered emphatically.
# Towards Autonomous Bridge Inspection: Sensor Mounting Using Aerial Manipulators
## 6 Blob Detection
The constant velocity model for each axis is given as follows:  
\[\mathbf{x}_{k+1}=F_{k}\mathbf{x}_{k}+\mathbf{w}_{k}\,,\qquad F_{k}= \begin{bmatrix} 1&\text{T}_{s}\\ 0&1\end{bmatrix} , \tag{23}\]  
where \(\text{T}_{s}\) is the discretization step, \(\mathbf{x}_{k}\in\mathbb{R}^{2}\) is the state vector containing the position and velocity along the corresponding axis and \(\mathbf{w}_{k}\in\mathbb{R}^{2}\) is the process noise. The observation model along a single axis is given as follows:  
\[\text{z}_{k}=H_{x}\mathbf{x}_{k}+\text{v}_{k}\,,\qquad H_{k}= \begin{bmatrix}1 &0\end{bmatrix} , \tag{24}\]  
where \(\text{z}_{k}\in\mathbb{R}\) is the position observation along the corresponding axis and \(\text{v}_{k}\in\mathbb{R}\) is the measurement noise. If the detector is unable to provide measurements and the pose tracker removes the pose from the tracking set, the linear Kalman filter is still able to provide blob position estimates. Experimental validation of the described methods is performed in an indoor Optitrack environment with an Intel Realsense D435 RGB-D camera. To ensure ground truth is available for detection validation, reflective markers are attached to both the camera and the blob. In order to determine the transformation between the camera optical frame and the reflective markers attached to the camera, an optimization-based calibration approach is used as described in [32]. Results are shown in Figures 7 and 8. The experiments are performed with the UAV in constant motion while looking at the general direction of the painted blob. Figure 7 shows a relative difference between the ground truth UAV motion in the world frame and the UAV motion as observed from the detected blob frame. Figure 8 presents the comparison of ground truth and detected blob positions expressed in the world frame. It is important to note that camera calibration errors can manifest themselves as static offsets between the detected and ground truth blob positions in Figure 8. However, in this case, the visual detection provided a reliable blob tracking results which is a direct consequence of careful camera calibration. Figure 8: This figure shows the comparison of the ground truth blob position and the blob position estimate expressed in the Optitrack world frame. The root mean squared error across all three axes is 4.6 cm. Figure 7: This figure shows the comparison of normalized motion of the UAV body frame as observed from the Optitrack world frame and as observed from the detected blob frame labeled \(\mathbf{p}_{B}^{W}[k]-\mathbf{p}_{B}^{W}[0]\) and \(\mathbf{p}_{B}^{blob}[k]-\mathbf{p}_{B}^{blob}[0]\), respectively.
## 3 Results  
### Clinical Evaluation Results  
The clinical evaluation results are summarized in Table 2. Group 1 (only 3-D-PFTM) comprised 15 patients (seven men, eight women), and the average age was 64.5 years (33-82 years). Implant placement was performed at 17 sites, and the average implant stability quotient (ISQ) measured during the second surgery was 78.9 \(\pm\) 3.8. Mesh exposure occurred in two patients, one site for each of them, and the mesh exposure rate was 11.8%. Group 2 (3-D-PFTM + cross-linked collagen membrane) comprised 56 patients (19 men, 37 women), with an average age of 56.5 years (21-81 years). Implant placement was performed at 72 sites, and the average ISQ value measured during the second surgery was 79.9 \(\pm\) 6.8. Mesh exposure occurred in three patients, one site for each of them, and the mesh exposure rate was 4.2%. Group 3 (3-D-PFTM + non-cross-linked collagen membrane) comprised 29 patients (13 men, 16 women) with an average age of 54.4 years (20-80 years). Implant placement was performed at 40 sites, and the average ISQ value measured during the second surgery was 77.0 \(\pm\) 7.4. Mesh exposure occurred in two patients, and the mesh exposure rate was 5.0%. The ISQ values of all three groups were 60 or higher, which indicates good implant osseointegration. A 2-fold decrease in the exposure rate was observed in the group that additionally used the resolvable collagen membrane than in the group that used only the 3-D preformed Ti mesh; using the cross-linked or non-cross-linked resorbable collagen membrane had no significant effect. One hundred patients in all three groups were followed up for at least 6 months after prosthetic restoration, and a total of 129 implants had a 100% survival rate. In addition, good secondary soft tissue healing was observed after 2 weeks of chlorhexidine gargle in all seven cases with mesh exposure, and there was no significant effect on prosthetic restoration and implant survival in the future. Late infection was also observed in two patients, but it was controlled with a 1-week antibiotic treatment and chlorhexidine gargle; they also recovered without a significant impact on implant survival. No other complications were observed. When the  
Figure 2: Method of radiographic analysis. (**A**) A line parallel to the long major axis of the implant is drawn, as well as a perpendicular line extending bucucally at the level of the implant platform. (**B**) Table 3. D-PFTM, three-dimensional preformed titanium mesh; BA, bone augmentation immediately after surgery; HG, hard tissue gain after 6 months of healing.  
3-D preformed Ti mesh was removed for the second implant surgery after 6 months of healing, a layer of soft tissue was observed directly below the mesh.
## 7 Conclusions  
In this paper, through the vibration and noise measurement, the vibration acceleration spectrum of the compressor surface, the vibration acceleration frequency spectrum of the four positions on the support plate connected to the compressor, the vibration acceleration spectrum of the outer wall on the back of the refrigerator, and the 1/3-octave frequency spectrum of the radiated sound from the outer wall on the back of the refrigerator were obtained. In addition, the main vibration sources that cause the radiated noise of the refrigerator back wall were determined using the coherence theory analysis. By aiming at the key component of the support plate, a new solution was proposed to embed 2-D ABHs on the support plate to suppress the transmission of compressor vibration to the refrigerator body, thereby reducing the vibration of the refrigerator body and the sound radiated by the back wall. The concentration and manipulation effects of the 2-D ABH on flexural waves are verified by numerical simulation of flexural wave propagation in the time domain. FEM models of the original support plate and the damping ABH support plate were established to investigate the comprehensive effect of the 2-D ABHs and the  
Figure 19: Acceleration response spectrum of the right monitoring point.  
Figure 18: Acceleration response spectrum of the middle monitoring point.  
damping layers on the vibration characteristics of the support plate through vibration mode and dynamic response analysis. The main research results are as follows:  
1. Suppressing the transmission of the support plate to the vibration of the refrigerator compressor can significantly reduce the vibration of the refrigerator body and the radiation sound of the back wall of the refrigerator;
2. In the range of 2 kHz to 4 kHz, most of the vibration energy is concentrated by the ABH regions of the damping ABH support plate, and the vibration acceleration of the rest of the damping ABH support plate is significantly reduced;
3. The 2-D damping ABHs can suppress the vibrations generated by the compressor at 1390.6 Hz, 2087.5 Hz, and 2260.9 Hz from being transmitted to the refrigerator body through the support plate, thereby suppressing the radiated sound of the back wall of the refrigerator at frequencies of 1250 Hz, 2000 Hz, and 2500 Hz.  
Conceptualization, X.D.; methodology, X.D.; software, X.D.; validation, X.D. and Q.F.; formal analysis, X.D.; investigation, X.D.; writing--original draft preparation, X.D.; writing--review and editing, X.D., J.Z. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2019 YFB2006402.  
Not applicable.  
All data included in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.