esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dosimetric Analysis of Reduction Cardiac Dose with Lead Shielding in Breast Cancer Radiotherapy
## 2 Materials and Methods
### VMAT Treatment Planning  
Coplanar VMAT plans were implemented using the singular value decomposition for dose optimization and the adaptive collapsed cone convolution algorithm for dose calculation in the Pinnacle Treatment Planning System. The dose grid size was 3 mm, and the prescription dose was 50 Gy in 25 fractions to the left breast PTV. The field design of the VMAT plan consisted of two partial arcs: one was the clockwise partial arc, with the gantry rotated from 300\({}^{\circ}\) to 110\({}^{\circ}\); the other was the counterclockwise arc, with the gantry rotated from 110\({}^{\circ}\) to 300\({}^{\circ}\), as shown in Figure 3. The multi-leaf collimator (MLC) had a 10 mm high-resolution lead width at the isocenter position.  
Figure 1: Treatment planning using Tangential 3D conformal radiotherapy (3D-CRT) (**a**) Whole breast irradiation scheme (2 fields). (**b**) Dose distribution image. Yellow planning targeting area, purple tumor bed.  
Figure 2: Treatment planning using intensity-modulated radiotherapy (IMRT) (**a**) Whole breast irradiation scheme. (6 fields). (**b**) Dose distribution image. Yellow planning targeting area, purple tumor bed.  
The primary aim of the treatment plan was to achieve 100% of the PTV with 95% of the prescribed dose to ensure dose coverage of the target volume. The PTV was prescribed to 50 Gy (D\({}_{50}\)%) and the optimization constraint ensured that the 95% isodose line  
Encompassed 95% of PTV (V95% \(\geq\) 47.5 Gy). The planning objectives for OARs were V\({}_{5\text{Gy}}\) < 30%, V\({}_{15\text{Gy}}\) < 10% and V\({}_{30\text{Gy}}\) < 1% for the heart.  
Figures of merits such as the conformity index (CI) and homogeneity index (HI) were calculated to evaluate the quality of the treatment plan. The CI is used to evaluate the conformal coverage of the PTV by the isodose volume prescribed in the treatment plan, and is defined as follows:  
\[\text{CI}=\text{V}_{\text{TV}}\times\text{V}_{\text{PTV}}/\text{TV}^{2}\]  
where V\({}_{\text{TV}}\) is the volume of the prescription isodose, V\({}_{\text{PTV}}\) is the volume of the PTV, and TV\({}_{\text{PV}}\) is the volume of V\({}_{\text{PTV}}\) within V\({}_{\text{TV}}\). The closer the CI value is to 1, the better the conformal coverage. The HI, as suggested in the ICRU 83 report (ICRU, 2010), can be expressed as follows:  
\[\text{HI}=(\text{D}_{2\%}-\text{D}_{98\%})/\text{D}_{50\%}\]  
where D\({}_{2\%}\), D\({}_{50\%}\) and D\({}_{98\%}\) represent the near-maximum absorbed dose, the median absorbed dose and the near-minimum absorbed dose, respectively. D\({}_{2\%}\), D\({}_{50\%}\) and D\({}_{98\%}\) are the doses covering 2%, 50% and 98% of the target volume, respectively. The HI value represents the dose homogeneity within the target volume. The smaller and closer the value of HI to 0, more superior the homogeneity, while the larger and closer the value to 1, more inferior the homogeneity.
# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 3 Materials and Methods
### Pose Estimation and Fall Detection Algorithm
#### 3.4.2 Fall Detection Algorithm
When a fall occurs, there is a rapid change in body position, so we calculated vertical speed using the movement of the shoulders, torso, or feet. If the vertical speed exceeds a specific threshold, it indicates a fall, as opposed to someone slowly lying down. This way, the algorithm can detect sudden drops, which are characteristic of falls, rather than more controlled movements like lying down. The speed of key body points is calculated by measuring the displacement between their positions in consecutive frames. Another way to distinguish between falls and intentional lying down is to measure the angles between important body parts. When someone falls, their torso and legs frequently become more parallel to the ground, resulting in acute angles. In contrast, lying down involves a smoother, more controlled transition in body posture. A threshold of 45 degrees is used in the code. If the angle between the torso and legs drops below this value, it indicates that the person's body is approaching a horizontal position, which is typical of a fall [58]. For lying down, the transition is slower, and the body takes time to align in this way. The alert system is a component designed to notify caregivers or family members immediately when a fall is detected. This mechanism ensures prompt intervention, which can be vital in preventing further injury or complications for the individual who has fallen. Our system includes an alert mechanism using the Telegram Bot API. When a fall is detected, the system sends an alert message to a predefined chat ID (Figure 3).
# Improved Trimming Ant Colony Optimization Algorithm for Mobile Robot Path Planning
## 5 Experimental Simulation and Results
### Verification of the Superiority of the ITACO Algorithm  
To verify the superiority of the ITACO algorithm, this paper conducts comparative experiments between the ITACO algorithm and two other improved ant colony algorithms--the AOA algorithm proposed in reference [27] and the AMACA algorithm proposed in reference [28]. The experiments are conducted under the same conditions, using a 20 \(\times\) 20 grid map with identical obstacle configurations in terms of quantity and placement. The starting point is set at the bottom-right corner, and the destination is set at the top-left corner. To ensure the reliability and statistical significance of the results, we ran each algorithm independently 30 times under the same experimental conditions.  
The experimental data are presented in Table 5, and the optimal path planning results and convergence curves of comparison of ITACO with AOA and AMACA are shown in Figure 5.  
From Table 5, it can be observed that the path length planned by the ITACO algorithm is 27.5471, which is 9.34% shorter than the 30.3848 path length achieved by the AOA algorithm and 1.76% shorter than the 28.0416 path length achieved by the AMACA algorithm.  
In terms of convergence speed, from Figure 5, the AOA algorithm requires 21 iterations to converge, whereas the ITACO algorithm converges in only 2 iterations. Although  
Figure 5: Comparison between ITACO, AOA, and AMACA algorithms on 20 \(\times\) 20 grid map. (**a**) Path planning results for ITACO, AOA, and AMACA; (**b**) Convergence curves for ITACO, AOA, and AMACA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Path Length** & **Improvement (\%)** & **Iterations** \\ \hline ITACO & 27.5471 & & 2 \\ AOA & 30.3848 & 9.34\% & 21 \\ AMACA & 28.0416 & 1.76\% & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of experimental results.  
the AMACA algorithm converges within two iterations as well, the ITACO algorithm demonstrates superior path optimization results compared to AMACA.  
The rapid decline in the initial iterations of the ITACO and AMACA convergence curve in Figure 5b is followed by a stabilization phase. This behavior stems from the algorithms' strategies, which efficiently eliminate suboptimal paths and minimize oscillations in later stages to ensure stable convergence.
# Effect of Incorporating Waste Limestone Powder into Solid Waste Cemented Paste Backfill Material
## 3 Results
### SEM Image Quantitative Analysis  
SEM images of each group were binarized for quantitative analysis of pore topography data. SEM images (500\(\times\)) were binarized by using the FRACLAB toolbox in MATLAB. By setting the nearest contrast threshold, NMR porosity was used to generate a binary image together with parallel color inversion. Figure 8 shows two samples that contrasted original SEM images and its binarized images. By implementing count instruction, the pixel count and the unit count of the bright area of binary images were calculated, followed by the calculation of porosity, and then the comparison with NMR porosity, as shown in Table 3. The size of the SEM image was 779,264 pixel units with a total size of 1024 \(\times\) 761, in which the scale bar was 100 um length and 185 pixel units. Finally, a single 500\(\times\) SEM image area was calculated to be 227,710 um\({}^{2}\).  
Figure 8: SEM binary image (500\(\times\)) contrasts of (**a**) A4 and (**b**) B4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & **Porosity** & **Calculated Porosity** & **Pixel Area** & **Counting Unit** & **Calculated Average** \\  & **(\%)** & **(\%)** & **Pixel Area** & **Counting Unit** & **Pore Radius (μm)** \\ \hline A1 & 1.99 & 2.17 & 16,900 & 662 & 1.54 \\ A2 & 2.39 & 2.53 & 19,679 & 708 & 1.61 \\ A3 & 2.69 & 2.63 & 20,491 & 738 & 1.61 \\ A4 & 2.51 & 2.50 & 19,518 & 670 & 1.65 \\ B1 & 14.13 & 14.12 & 110,065 & 1903 & 2.32 \\ B2 & 13.41 & 12.97 & 101,102 & 2209 & 2.06 \\ B3 & 13.37 & 13.97 & 108,841 & 1992 & 2.25 \\ B4 & 13.12 & 14.09 & 109,802 & 2705 & 1.94 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Binary parameter statistics.  
The binarized images had a clearer pore structure than the original images and were easy to measure, as shown in Figure 8. Among them, A4 (Figure 8a) had a uniform pore distribution with most of the pore diameters less than 5 \(\upmu\)m and maintaining a compact structure. In a similar way, it was analyzed that B4 had more pores than A4, while for B4 (Figure 8b) a large number of pores had sizes over 10 or even 20 \(\upmu\)m.  
Table 3 shows that porosity was calculated by using the image processing method of Figure 8. With the help of stereology principles, the calculated porosities of groups A1, A2, B3, and B4 were slightly larger than NMR porosity. The other four cases were reversed. Although there were differences between calculated porosity and NMR porosity, the values were almost the same. The calculated porosity, number of holes (counting units), and the hole radius of group B were significantly greater than in group A. The data shown in Table 3 followed that the average pore radius changed as a result of adding limestone powder. The average pore radius in group A was basically unchanged, but in group B the average pore size first decreased, then increased, but then finally decreased significantly.
## 2 Materials and Methods
### Characterization of the Study Area  
The region surrounding the Alegria WTP has several potential demands for industrial water to be supplied with reclaimed water [11; 12; 25; 33]. More specifically, the water demands of several concrete production plants stand out [6; 25], there being at least four CMPs located very close to the Alegria WTP: SUPERMIX CONCRETO, TOPMIX, POLIMIX CONCRETO, and CONCREVIT (Figure 1).  
WTP Alegria is the biggest wastewater treatment plant in the state of RJ and treats about 1.5 m\({}^{3}\)/s of raw sewage at the secondary level, through the activated sludge process with extended aeration, which gives the system greater reliability, achieving, on average a biochemical oxygen demand concentration of 25 mg/L and a total suspended solids concentration of 44 mg/L [34].  
The WRP of the Alegria WTP (Figure 2a) is fed by part of the effluent flow from the secondary treatment, and basically consists of an in-line filtration stage, followed by disinfection using sodium hypochlorite. The entire system is inserted in a container (Figure 2b), and the reclaimed water is then pumped for loading water tank trucks (Figure 2c) [1]. The WRP has an installed capacity to supply 720 m\({}^{3}\)/day of reclaimed water, i.e., less than 1% of the sewage flow treated at the WTP [12]. The system was built to provide reclaimed water for the Porto Maravilha construction works, which was one of the major projects in the city's preparation program to host the 2016 Olympic Games in Rio de Janeiro [2], but it is currently inoperative [10]. Thus, all treated effluent from the Alegria WTP is currently being discharged into the Cunha Channel, which flows into Guanabara Bay (Figure 2d).  
Figure 1: Location of the Alegria WTP/and potential consumers for reclaimed water, including the four CMPs in the study region (Caju and surroundings) listed by this research.  
Figure 2: Details of the reclaimed water production system of the Alegria WTP. (**a**) A container that houses the reclaimed water production system; (**b**) details of the filtration and disinfection system; (**c**) water tanker truck loading/supply system, and (**d**) the outflow channel for the discharge of treated effluent into the Cunha Channel.
# Enhancement of Feed Source through Three Dimensional Printing
## 5 Conclusions  
This manuscript proposed a 3D printable WBP structure which improves the broadside directivity and gain of the feed source. The proposed five-layer surface was numerically studied through CST-MWS and is able to generate a more uniform phase distribution for the 10 to 15 GHz frequency range. In design frequency, lower side lobe levels of \(-17.5\) dB and \(-15.5\) dB, respectively, in E- and H- planes were obtained. Moreover, an improvement of 2.5 dBi in broadside directivity and gain was observed with better radiation patterns. The overall system was 448 g in weight and the fabricated WBP was 294 g. This signifies the light weight condition of the overall system. The measured return loss was less than 2.2, which shows matching behavior over the operational frequency band. Possible future work could include approaches that will be implemented to realize a 3D-printable beam-steerable surface. This work shows the full phase requirement of 360\({}^{\circ}\), which can be further extended to be implemented in the beam deviation phenomenon. There is no use of dielectrics, which reduces the cost and weight of the overall system. The next possible applications and challenges could be observed in high power systems and radiation patterns synthesis in near field regions. This research has great potential for radar and wireless communication systems due to its capability for greater bandwidth and improved electromagnetic waves transmission.  
Conceptualization, S.S.; methodology, S.S.; software, S.S.; validation, S.S.; formal analysis; investigation, S.S.; resources, S.S.; data curation, S.S.; writing--original draft preparation, S.S.; writing--review and editing, S.S.; visualization, S.S.; supervision, S.M.A., M.A. and K.P.E.; project administration, S.S.; funding acquisition, S.S., S.M.A. and M.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Australian Research Council (ARC) and in part by the Macquarie University International Research Excellence Scholarship (iMQRES), 45035091. We are thankful to University of Technology Sydney (UTS), Faculty of Engineering and Information Technology Blue Sky Research Grant 2023 for supporting us.  
Could be provided upon request.  
We want to extend thankfulness to Commonwealth Scientific and Industrial Research Organisation (CSIRO), Marsfield, New South Wales (NSW), Australia, and Macquarie University and University of Technology Sydney (UTS) for providing support for measurement of antenna prototype.  
The authors declare no conflict of interest.
## 4 Numerical Examples
### Clamped Circular Plate Subjected to Uniform Load  
A clamped circular plate subjected to a uniformly distributed load \(q\) is taken into consideration in this section. The thickness of the plate is \(h\). The radius of the plate is \(r=100\). A quarter of the plate with symmetry conditions on axes \(x\) and \(y\) is modeled in Figure 15. The displacement boundary conditions of the present theory along the clamped edges in local coordinates are \(\overline{\theta}_{s}=0,\ \overline{\theta}_{n}=0\), and \(w=0\). Divisions of 32, 128, 512, and 2048 triangular elements are employed for the convergence studies. Typical meshes of 512 and 2048 triangular elements for the circular plate are shown in Figure 16. The results for normalized deflection \(W_{0}=W_{c}/\frac{q\pi^{A}}{100D_{h}}\) of the clamped circular plate are listed in Table 6, where \(W_{c}\) is the central deflection of the circular plate. Again, an excellent agreement between the present solutions and the reference solutions is observed for this problem.  
Figure 15: Model of the circular plate.
## 3 Experimental Results and Analysis
### Analysis of Dynamic Peak Strain Law  
Figure 8 shows the variation of the dynamic peak strain of sandstone specimens under different axial pressures due to the coupling effect of temperature-water coupling.  
Figure 8 shows that the dynamic peak strain of the sandstone sample increases with increasing axial pressure. The peak dynamic strain is 5.99 \(\times\) 10\({}^{-3}\) at no axial pressure, 5.98 \(\times\) 10\({}^{-3}\) at 0.5 MPa axial pressure, and 8.03 \(\times\) 10\({}^{-3}\) at 4 MPa axial pressure, which is an increase of 2.05 \(\times\) 10\({}^{-3}\). This increase can be attributed to the internal structure of the sandstone sample being damaged when subjected to axial pressure, which reduces the dynamic compressive strength of the sandstone specimen and increases the dynamic peak strain. The dynamic compressive strength of the sandstone sample decreases and the dynamic peak strain increases. As the axial pressure increases, the internal damage to the sandstone specimen continues to accumulate, the dynamic compressive strength of the sample continues to decrease, and the dynamic peak strain continues to increase. As the axial pressure continues to increase, the internal structural damage of the sample is aggravated, and the internal cracks are interconnected, making it easier for the specimen to fracture, which is then manifested as a rapid decrease in dynamic compressive strength and a rapid increase in dynamic peak strain. The dynamic peak strain of the sandstone specimen with the increase in axial pressure of the temperature-water-coupled effect shows a quadratic growth trend. The fitting function for this trend is shown in Equation (2). Here, the correlation coefficient is 0.9804, which is obvious:  
\[\varepsilon(P)=6.161-0.249P+0.181P^{2}\ \ (R^{2}=0.9804) \tag{2}\]  
where \(\varepsilon(P)\) is the dynamic strain of the sandstone sample.
## 2 Materials and Methods
### Specim Restoration and Study Groups (Color Matching)  
The prepared teeth were further divided into two groups based on the type of restorative materials used for restoring class 1 cavities. Half of the prepared teeth (40) were restored with a single-shade (OM) (single-shade universal composite, Tokuyama Dental, Encinitas, CA, USA) (test group) and the other half were restored with a conventional resin-based composite (Filtek Z-350 (FT), 3M technology, Minnesota, MN, USA) (control group). Based on the combinations of tooth shades, restorative materials, and photopolymerization, the specimens were divided into 3 groups.  
Group 1. Filtek (FT)--Teeth restored (photopolymerized/light cured) with Filtek Z-350 resin-based composite. This group consisted of four subgroups with four different acrylic tooth shades (B1, B2, A3, and C3). All teeth were restored with the corresponding color resin material (Filtek).  
Group 2. OM-C--Teeth restored (photopolymerized/light cured) with OM resin-based composite. This group consisted of four subgroups with four different acrylic tooth shades (B1, B2, A3, and C3). All teeth were restored with the same shade of OM resin material.  
The same restored tooth specimens were included in groups 2 and 3. Specimens in group 3 were initially uncured (OM-UC) and assessed for color or shade, then were later photopolymerized (OM-C) prior to post-curing color assessment.  
Cleaned cavities were air-dried and restored in two increments measuring 2 mm deep and 2 mm wide for both materials. Resin-based materials were dispensed in a dark container and packed with a plastic instrument and condenser. The restoration material was standardized for each cavity, as the cavity dimensions were similar and the material placement teeth were same for all specimens. In addition, a single operator performed all restorations (KA). Excess material was removed and margins were adapted with a plastic instrument, while the occlusal surface was flat. A mylar strip was covered with a glass  
Figure 1: Prepared acrylic molar tooth used for restoration.  
slide and the restorative material was photocured for 40 s from the occlusal aspect. A 20 s polymerization exposure process was also performed on buccal and lingual surfaces. Polymerization was performed using an LED light curing unit (3M Elipar Deep Cure-S LED, Minnesota, MN, USA) with a light intensity of 400 mW/cm\({}^{2}\) in a uniform, continuous curing mode (Figure 2).  
Baseline tooth colors were assessed for different shaded acrylic teeth on the occlusal surface prior to restoration with different resin-based materials. For group 1 (FT), color was assessed after restoration in four different colored teeth. However, in teeth restored with OM, the color was assessed prior to light curing (group 3--OM-UC) and after light curing (group 2--OM-C).
## 4 Discussion  
### Mechanics of Tooth Movement  
The mesial end of the CMA was fixed to the canine so as not to rotate, but the distal end was connected to the molar with a rotatable ball-and-socket joint. When retraction force was applied to the canine from the mandibular dentition, the CMA and the canine tipped or rotated clockwise in a body around the distal end (Figure 2). In addition, the canine extruded due to the vertical component of the retraction force. The molar and premolars also tipped and moved distally, but hardly extruded because the vertical force did not act on them. As a result of these tooth movements, the canine protruded from the dentition.  
In clinical settings, the protrusion of the canine from the dentition is an undesirable movement except in cases where the canine is depressed before treatment. On the other hand, in the case where the maxillary dentition is distalized with conventional Class II intermaximillary elastics, the anterior teeth are extruded, but the canine protrusion does not occur [14]. The CMA is not the best device to use in all cases and should be selected depending on the individual patient.  
In the occlusal plane, the rotation of the canine was small due to constraints by the CMA. On the other hand, since the molar was connected to the CMA with the rotatable joint, the rotational angle of the molar was greater than that of the canine. This rotation resulted in some distal movement of the buccal cusp of the molar, which would contribute to the improvement of Class II malocclusion [6; 7]. It has also been reported that the buccal cusp of the molar is often rotated mesially in malocclusions [15]. In such cases, the distal rotation of the molar will be effective in correcting the malocclusion.  
The mandibular dentition, which is placed in an aligner or fixed to a lingual arch, moves mesially and rotates clockwise by the mesial retraction force applied to the mandibular first molar from the maxillary canine [16]. This movement corrects the Class II occlusal relationship between the upper and lower molars. The mandibular incisors are intruded by the rotation of the mandibular dentition, while the maxillary incisors do not move, as shown in Figure 2, which may cause an open bite.