esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Stability of Er:YAG Laser Non-Surgical Periodontial Treatment
## 3 Results

Table 4: Comparison of probing pocket depth between both treatment groups up to 1 year. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**CAL**} & \multicolumn{3}{c}{**Control Group**} & \multicolumn{3}{c}{**Laser Group**} & \\ \cline{2-9}  & **N** & \(\mathbf{\bar{x}}\) & **S\({}_{\mathbf{X}}\)** & **N** & \(\mathbf{\bar{x}}\) & **S\({}_{\mathbf{X}}\)** & **Difference** & **t** & \(\boldsymbol{p}\)-Value** \\ \hline
**Initial visit** & 527 & 4.67 & 0.74 & 572 & 4.71 & 0.78 & 0.05 & 1.075 & 0.28 \\ \hline
**1 month** & 527 & 3.57 & 1.23 & 572 & 3.35 & 1.16 & 0.22 & 3.108 & _0.00_ \\ \hline
**3 months** & 527 & 3.10 & 1.25 & 572 & 3.06 & 1.20 & 0.05 & 0.617 & 0.54 \\ \hline
**6 months** & 527 & 3.15 & 1.20 & 572 & 3.06 & 1.23 & 0.09 & 1.203 & 0.23 \\ \hline
**12 months** & 527 & 3.25 & 1.09 & 572 & 3.12 & 1.14 & 0.13 & 1.962 & _0.05_ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of clinical attachment level between both groups for the whole study period. Table 7 presents the intergroup comparison of plaque-present surfaces in all the visits tested. Plaque-free sites are approximately 24% in the initial visit for both groups. The achieved differences between both groups at the first, third and twelfth month after treatment are in the range between 4 and 5%, and these differences are significant (\(p\) < 0.05). The achieved reduction as a long-term effect (reporting at 12 months) in both studied groups demonstrates a decrease in the presence of plaque by more than 2 times, and a significantly greater reduction is achieved in the laser-instructed group (\(p\) < 0.05).
## 5 Discussion
### Towards the Acceleration Process
#### 5.1.2. Estimation Based on Hardware Platforms  
[PERSON] et al. [16] presented a GPU-based motion compensation optimization of a VVC decoder. This study was chosen for estimation analysis over heterogeneous platforms because it performed acceleration of a VTM6.1 decoder using an architecture similar to that stated in Section 3. Hither, all the decoder blocks, including entropy decoding, intra prediction, inverse quantization/transformation, and in-loop filter, were executed on a CPU, and the execution of the motion compensation module was migrated to a GPU. This implementation achieved a 16-fold times acceleration compared with the performance on a CPU using RA sequences of an A-Class resolution similar to those mentioned in Section 4.1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Reference** & **ED** & **TX** & **IP** & **EP** & **DBF** & **SAO** & **ALF** & **OT** \\ \hline \multirow{3}{*}{AI} & our HGPP & 19 & 9 & 23 & 0 & 32 & 3 & 7 & 7 \\  & our EGPP & 18 & 8 & 22 & 0 & 24 & 1 & 19 & 8 \\  & [5] & 27 & 10 & 12 & 0 & 17 & 3 & 13 & 18 \\ \hline \multirow{4}{*}{RA} & our HGPP & 9 & 5 & 6 & 33 & 26 & 2 & 11 & 8 \\  & our EGPP & 8 & 4 & 3 & 35 & 16 & 1 & 27 & 6 \\  & [17] & 3 & 2 & 12 & 41 & 19 & 3 & 19 & - \\  & [16] & 7 & 7 & 2 & 39 & 8 & 1 & 31 & 5 \\  & [5] & 9 & 3 & 2 & 33 & 17 & 3 & 13 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of the computational load distribution (in %) among functional blocks for different VVC decoder implementations.  
In addition, [PERSON] et al. migrated the new VVC adaptive multiple transform (AMT) over an EGPU in [19]. In this implementation, the 2D transform was split into one 1D vertical transform and one 1D horizontal transform. On the other hand, the execution order was a vertical transform followed by a horizontal transform. This study achieved an 11-fold improvement in the TX module time. Therefore, the aforementioned improvements of 16% and 11% acceleration for the EP and TX, respectively, were applied to the results obtained in Section 5.1.1.
## 5 Conclusions  
An analysis of the hydrological and meteorological conditions in the southern part of the Baltic Sea for the purpose of bunkering LNG reveals important factors that affect the safety and efficiency of LNG operations. Wind speed, sea state, visibility, air temperature, and other weather conditions are crucial for ensuring a safe working environment in the Baltic ports. In the Baltic Sea, where the maximum mean wind speed is 15-20 m/s and the sea state is 3-4 on the Beaufort scale, LNG operations can easily comply with the IMO IGF Code and ISO 20519 standards and other port guidelines [152]. Wind speed can strongly affect the stability of ships, and high seas, especially during storms, may pose a hazard to harbor infrastructure and cargo handling equipment. Visibility within a range of 500 m to 1 km meets the requirements for the safety of operations; however, according to the regulations specified in the IMO IGF Code and port regulations, operations are prohibited in fog and at a visibility of less than 500 m. Air temperatures, which may drop to \(-\)20 \({}^{\circ}\)C in winter, meet the requirements for LNG handling; however, extremely low temperatures may affect the performance of cryogenic equipment and cause the freezing of valves. High air humidity, although below 90%, may cause the icing of equipment and hinder its operation in low temperatures. Sea currents in the Baltic Sea of a maximum speed of 1.5 km do not pose any serious risk, but they may nevertheless affect the stability of ships during maneuvers. The salinity of the Baltic, at a mean value of 7 PSU, is considerably lower than that of oceans and may affect the properties of water and the equipment used in LNG bunkering. Variable sunshine duration, depending on the season, and waves of 0.5-2 m in normal conditions and even 4-7 m in storms, also affect the stability of ships and the safety of LNG operations. These conditions call for the deployment of suitable monitoring systems and systems securing port infrastructure, including anti-icing systems, which are required especially in winter.  
To sum up, the hydrological and meteorological conditions in the southern Baltic Sea meet the requirements prescribed by international law. Nevertheless, considering their variability, special focus must be placed on the monitoring of weather parameters and their impact in order to ensure safe and streamlined LNG operations in Baltic ports.  
Conceptualization, E.O.; methodology, E.O. and J.F.; investigation, E.O., J.F., M.Z. and B.R.; resources, E.O., J.F., M.P., M.Z. and B.R.; data curation, E.O., J.F., M.P., M.Z. and B.R.; writing--original draft preparation, E.O., J.F., M.P., M.Z. and B.R.; writing--review and editing, E.O., J.F., M.P., M.Z. and B.R.; visualization, E.O., J.F., M.P., M.Z. and B.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# An Improved YOLOX Model and Domain Transfer Strategy for Nighttime Pedestrian and Vehicle Detection
## 2 Related Work
### Low-Light Detection
#### 2.2.2 Low-Illumination Enhancement and Restoration Methods  
When faced with a low-light image, the first thing that comes to mind is the illumination enhancement and restoration of the low-light image to support subsequent tasks. Low-light vision tasks recover image detail and correct color shifts based on the human visual experience. Early attempts relied on Retinex theory approaches [47; 48; 49] or histogram equalization (HE)-based approaches [50; 51]. Nowadays, Convolutional Neural Networks (CNN)-based methods and Generative Adversarial Nets (GAN)-based methods have significantly improved in this task thanks to the development of deep learning. The authors of [52] combined the Retinex theory with a deep network for low-light image enhancement. The authors of [53] used an unsupervised GAN to solve this problem. A self-supervised learning strategy for images with abnormal illumination was recently proposed by the authors of [54]. Existing low-light image enhancement methods mainly focus on low contrast to increase visibility while the high noise is usually addressed with a post-processing module. To solve this problem, The authors of [55] suggest a unique technique for improving low-light images based on simultaneous illumination and noise adjustment with unpaired data. A Structure- and Texture-Aware Network (STAN) for low-light image enhancement was proposed by the authors of [56] based on the observation that the representations of structure and texture are highly separated in the frequency spectrum. STAN is made up of a structure sub-network and a texture sub-network. The methods mentioned above have worked well for low-light enhancement, but it is usually not the best performance to use the enhanced images directly in existing object detection networks.
## 2 Data and Model Experiment  
### Observation Data  
Air quality monitoring data, including the hourly concentrations of pollutants (CO, SO\({}_{2}\), PM\({}_{2.5}\), PM\({}_{10}\), NO\({}_{2}\), O\({}_{3}\)) and daily AQI (Air Quality Index), were collected from Guangzhou Environmental Monitoring Centre data network ([[http://210.72.1.216:8080/gzaqi_new/RealTimeDate.html](http://210.72.1.216:8080/gzaqi_new/RealTimeDate.html)]([http://210.72.1.216:8080/gzaqi_new/RealTimeDate.html](http://210.72.1.216:8080/gzaqi_new/RealTimeDate.html))). Hourly and daily meteorological data [wind speed, wind direction, relative humidity (RH), temperature, satellite cloud images, etc.] were downloaded from the Guangzhou meteorological data network ([[http://data.tqby.com.cn/weather/index.jsp](http://data.tqby.com.cn/weather/index.jsp)]([http://data.tqby.com.cn/weather/index.jsp](http://data.tqby.com.cn/weather/index.jsp))). These websites are regularly maintained by the government, and the data are released to the public. The data mentioned above were used to analyse the air quality status and pollution episodes in 2015, and to evaluate the WRF-Chem model's performance.
## 2 Materials and Methods
### Mesh Sensitivity and FE Model Validation  
The mesh influence on the FEA simulations is minimized through a mesh sensitivity analysis. The choice of parameters, including the mesh generation, is very sensitive to the computational time and accuracy of explicit finite element simulations. Therefore, initial validation through a series of simulations is required to balance computation time and result accuracy. The setup of the mesh sensitivity analysis is shown in Figure 3. A rigid floor was assigned with a fixed body support, and a fruit modeling pear was analyzed for a drop-impact simulation. A standard earth gravity of 9.81 m/s\({}^{2}\) was defined for all bodies in the negative Y axis. The dimensions and properties of the pear were modeled based on the literature and compared to references of FEA and experimental testing. The pear model was rounded at the contact point to minimize convergence issues due to a perceived initial numerical gap in cases of lower-order elements for mesh bodies [30]. An initial drop height condition was also defined for the pear to minimize contact instability further, leading to a divergent contact force. A total of 21 simulation scenarios were set up and performed in this study for the pear impact test at different impact platforms, impact heights, and packaging sizes. The pear fruit was modeled with solid elements. Nine different mesh sizes were selected, varying from 10 mm to 1 mm, with a total run time of 0.01 s. Due to the nature of the current rounded simulation, real pears may exhibit different rebound and damage properties as they have more of a 'teardrop' than a rounded shape. Nevertheless, the current idealized model was adopted for the initial study as it had good agreement with the literature where spherical representations were found not to have significant differences compared to real shapes in cooling studies [31].  
Figure 3: Simulation Setup.
# Microencapsulation of Natural Food Antimicrobials: Methods and Applications
## 2 Encapsulation Methods
### Molecular Inclusion
Spray drying and precipitation methods have been compared for the encapsulation of antimicrobial lemongrass volatile oil by \(\beta\)-CD and hydroxypropyl-\(\beta\)-CD [14]. An increase in citral content, defined as the major lemongrass oil compound, was obtained for \(\beta\)-CD microparticles (inclusion \(>\) 50%) compared to HP-\(\beta\)-CD microparticles (inclusion \(<\) 30%) due to the high affinity of volatile oil to \(\beta\)-CD as an encapsulating material. A high encapsulation yield (81%) was obtained for \(\beta\)-CD microparticles produced by the precipitation technique in comparison to both \(\beta\)-CD and HP-\(\beta\)-CD spray-dried microparticles (33% and 28%, respectively) [14]. Based on these results, using \(\beta\)-CD and precipitation techniques for the encapsulation of volatile compounds could be considered as easy and inexpensive processes carried out at low temperatures. The difference in entrapment efficiency of EOs between kneading and freeze-drying methods applied for CD-EO inclusion complexes is attributed to the high volatilization rate of EOs during the complexation process and drying step in the kneading technique. The entrapment efficiency of microencapsulated carvacrol in HP-\(\beta\)-CD by both kneading and freeze-drying methods was lower for the kneading method as a result of vaporization of volatile compounds during the heating, prolonged complexation time and drying step [12]. The same results were obtained for thymol and thyme oil microencapsulation in \(\beta\)-CD inclusion complexes [13]. The entrapment efficiency of thymol and thyme oil in \(\beta\)-CD inclusion complexes by freeze drying was higher than that obtained with the kneading method (\(p<\) 0.05). In addition, the EE of thymol oil in all \(\beta\)-CD microparticles was lower than thymol due to the presence of different compounds in thyme oil competing for inclusion reactions with \(\beta\)-CD [13]. Eugenol (EG) is a heat-sensitive EO with excellent antibacterial activity that cannot be directly used in the preparation of food products requiring a thermal process. However, the thermal stability of EG molecules could be increased by encapsulation in \(\beta\)-CD [11], and better inclusion of EG in \(\beta\)-CD microcapsules occurred in neutral and weakly acidic media (pH 6.0). The \(\beta\)-CD derivatives (2,6-di-\(\beta\)-CD (DM- \(\beta\)-CD), mono [2-O-(2-hydroxyethyl)]-\(\beta\)-CD (HE-\(\beta\)-CD) and mono [2-O-(2 hydroxypropyl)]-\(\beta\)-CD (HP-\(\beta\)-CD)) showed stronger binding affinity to EG than \(\beta\)-CD [15]. However, in another research work, more efficient EG encapsulation in \(\beta\)-CD than its derivative 2-HP-\(\beta\)-CD was found to be attributed to the probable interruption of EG encapsulation in the 2-HP-\(\beta\)-CD molecule cavity by a hydroxypropyl group side chain [26].
# Influence of Drilling Along Pilot Holes on Drilling Performance and Effective Rock Strength +
## 6 Discussion  
Drilling efficiency benefits from larger pilot holes for several important reasons. Reducing the amount of rock the drill bit must cut through lowers overall resistance and makes it easier for the bit to penetrate, while using less energy to remove rock. Greater bit stability from larger pilot holes also helps to maintain a constant drilling pressure and direction by minimizing wobble and deviation. Lower energy consumption and more effective cutting are the results of this stability. Furthermore, bigger pilot holes maximize the area in contact with the rock for the drill bit, improving cutting effectiveness and lowering the Mechanical Specific Energy (MSE) needed for drilling. Additionally, they  
Figure 14: Illustrates the drill ability strength of the rock with WOB results (in N-m) for HO-1.5, HO-1.0, HO-0.5, and FF configurations for gabbro.  
Figure 15: Illustrates the drill ability strength of the rock with ROP results (in N-m) for HO-1.5, HO-1.0, HO-0.5, and FF configurations for gabbro.  
help remove debris more effectively, avoid clogging, and lessen bit wear and damage. This enhanced debris removal further increases drilling efficiency. Larger pilot holes also minimize mechanical stress on the drill bit and other equipment during drilling, which reduces wear and tear, extending equipment lifespan and lowering maintenance costs. Significant energy savings are achieved through decreased rock resistance, increased bit stability, improved cutting efficiency, better debris removal, and reduced equipment wear. These savings translate into lower operating costs and better drilling performance overall.  
This study provides a valuable understanding of the effects of pilot holes on drilling performance and effective rock strength. However, several limitations need to be acknowledged. The experiments were conducted using a single rock type (high-strength gabbro), which may not fully represent the variety of geological formations encountered in real-world scenarios. Additionally, while the Large-scale Drilling Simulator (LDS) closely replicates field conditions, it cannot fully capture the complexity of downhole environments, including variations in pressure, temperature, and fluid interactions. Furthermore, the analysis focused on a limited range of pilot hole diameters, excluding configurations, such as varying depths or asymmetrical geometries, that could provide deeper insights. Tool wear and long-term effects were not considered, leaving gaps in evaluating the sustainability and cost-effectiveness of drilling practices over extended periods. Finally, the role of drilling fluids, essential for cooling, lubrication, and debris removal, was not thoroughly analyzed. These factors indicate areas where additional study is needed to improve the understanding and applicability of the results.
# Dynamic Response Analysis of Soil around Curve Section Tunnel under Train Vibration Load
###### Abstract  
To investigate the changes in soil dynamic response around the tunnel periphery under the vibration loads generated by train operation on curved sections, field measurements were carried out to observe void water pressure, water level, and settlement on the ground. Additionally, simulation modeling using MIDAS was employed to simulate and analyze the soil's dynamic response under the impact of train loads. Based on the track stress diagram combined with the axle weight of the train, the transverse and vertical loads of the track are calculated. The corresponding parameters are entered into the train dynamic load table in the MIDAS/GTS NX dynamic analysis module, so as to simulate the vibration loads of the tunnel. The results show that the application of vibration load is an important reason for the change in pore water pressure during train operation. During the initial stages of train operation, the pore water pressure exhibits a significant increase, followed by a gradual decrease over time. The overall variation follows a seasonal pattern, with the pore pressure increasing as the depth of burial increases. The response of the soil around the tunnel to the vibration of the train is closely related to the location. The closer to the tunnel, the more sensitive the soil is to the vibration of the train, and the greater the amplitude and rate of pore pressure change and vertical deformation in the soil. The variation trend of groundwater level in soil is basically consistent with that of pore pressure, and the groundwater level is proportional to the depth. The dynamic response of the soil at the bottom of the curved tunnel decreases with the increase in the turning radius. The main influence range of the dynamic response is 0-15 m at the bottom of the tunnel. The excess pore water pressure generated by the soil gradually dissipates, and it can be predicted that the vibration of the train will not cause deformation damage to the surrounding soil.  
curve tunnel; vibration load; dynamic response; tunnel settlement +
Footnote †: journal: Applied Sciences
## 2 Methodology
### Sample Collection  
During the drilling of five wells, 14 columnar sediment samples with a diameter of 70 mm and height of 200 mm were collected using a stainless-steel soil sampler. The riverbed sediment samples were collected at the depths of 10-30 cm and 40-60 cm below the riverbed at WR0. Considering the depth distribution of the groundwater table in the riparian zone, the riparian sediment samples were individually collected at the depth of 40-120 cm below the surface at WB1, at the depth of 50-130 cm below the surface at WB2, at the depth of 100-180 cm below the surface at WB3, and at the depth of 120-200 cm below the surface at WB4. The three sediment samples were collected from each riparian borehole at equal intervals. All sediment samples were stored in the separate bags in a cooling box keeping the temperature below 5 \({}^{\circ}\)C and then taken back to the lab for testing within 24 h. Sediment samples collected were divided into two aliquots: one for determining  
Figure 2: The transect for monitoring and sampling: (**a**) the top view; (**b**) the cross-section.  
the contents of total nitrogen (TN) and total carbon (TC), and the other for analyzing permeability coefficient (K), particle size, and sediment type.  
Water samples were collected from the river and the four wells using a liquid sampling tube with a total capacity of 500 mL. Water sampling was carried out at 2 h intervals from 6:00 a.m. to 6:00 p.m. on any particular day of 21-23 October 2021. The water sampling points were 50 cm below the free water table. Figure 2b presents the positions of the sediment and water sampling points. The tube was cleaned with deionized water before sampling to avoid cross-contamination between water samples [38]. Water discharged from the tube was stored in both polyethylene vials of 200 mL and amber glass vials of 20 mL. The former vials were used for the concentration analysis of inorganic nitrogen (NH\({}_{4}^{+}\), NO\({}_{3}^{-}\), and NO\({}_{2}^{-}\)), while the latter vials were used for the DOC determination. All water samples were stored in the cooling box and subsequently taken back to the lab for testing within 24 h. Each water sample was filtered through a 0.45 \(\upmu\)m filter membrane before testing.
# Tissue Engineering Strategies for Retina Regeneration
## 4 Biomaterials and Scaffolds Used for Tissue Engineering
### Co-Graft of RPE and Retinal Organoid  
During advanced stages of AMD, when both PR and RPE are lost, RPE replacement alone may not rescue vision. Retinal repair at this stage requires transplantation of both tissues. Designing better models of photoreceptor-RPE interaction for transplantation is an important goal that needs an urgent solution, for the treatment of advanced geographic atrophy. Retinal organoids (ROs) are a considerable source of photoreceptor precursor cells, but they lack a continuous and mature layer of RPE [119]. Using a co-graft made of RO sheet and RPE is beneficial since it can address both the lack of photoreceptors and RPE. In our lab, a composite graft made of RO sheets and polarized RPE sheets cultured over parylene is used as a composite implant to determine its potential to repair retina and rescue vision(unpublished data) in preclinical animal models of retinal degeneration (Figure 3).  
Figure 2: Diagram illustrating the concept of temperature-dependent cell-sheet detachment. (**a**) Preparation of thermoresponsive polymer; (**b**) Cell seeding into the polymer at a temperature below 32 \({}^{\circ}\)C (**c**) Schematic diagrams for the interactions of the thermoresponsive surface with the cells growing on it (**d**) Cell sheet detachment from the thermoresponsive cell culture dish, where the cell sheet retains the extracellular matrix and cell-cell junctions.