esa-sceva/satcom-chunk-collection huggingface dataset:
# Plasmonics in the Ultraviolet with Aluminum, Gallium, Magnesium and Rhodium
## 2 High Oxidation Tendency: Aluminum and Magnesium
### Magnesium  
Magnesium nanoparticles also present a strong plasmonic response in the UV, even stronger than Al. The efficiency of them is higher than those of Al for the same wavelength range. However,the oxidation of Mg NPs is more aggressive. The native oxide MgO forms without stopping upon exposure to air as oxygen rapidly diffuses through porous oxide into the metal [15; 23]. This process leads to a complete destruction of the plasmonic effects on Mg NPs, unless they are manipulated under controlled atmospheres [49]. In order to prevent their oxidation and preserve their UV plasmonic performance, a coating procedure with gallium has been recently proposed [50].  
A very interesting fact about Mg is that it can absorb up to 7.6 wt % of hydrogen gas [51]. This, together with its abundance and low cost, makes Mg a very attractive material for hydrogen storage and hydrogen sensing purposes in the looming hydrogen economy [52; 53]. Recently, it has been experimentally demonstrated how the plasmonic response of Mg NPs (more precisely, nanodisks fabricated by colloidal hole-mask lithography) can be switched from \"on\" to \"off\" states by exposing them to molecular hydrogen (H\({}_{2}\)). Mg upon exposure to H\({}_{2}\) turns into MgH\({}_{2}\), which, as a dielectric material, does not support plasmonic resonances. The hydrogenation process is reversible by exposure to oxygen, turning MgH\({}_{2}\) into metallic Mg again [49]. This feature makes Mg very appealing for creating new active plasmonic devices. For example, this metal-dielectric transition has been experimentally used in the construction of an hybrid plasmonic metamolecule with hydrogen-regulated chiroptical response [54].  
In the field of bio-sensing, the modification of the ultraviolet (UV) fluorescence decay rate of p-terphenyl dye molecules by Mg nanoapertures in free solution has been reported by [PERSON] et al. [45]. Mg nanoapertures exhibit a lifetime reduction of up to \(\approx\)7.2\(\times\). This value is higher than that reported for Al (\(\approx\)5.3\(\times\)). Single Mg nanohelices with strong chiroptical response have also been reported as plasmonic sensors for molecular detection [55].
# Proximity-Based Adaptive Indoor Positioning Method Using Received Signal Strength Indicator
## 3 Proposed Indoor Positioning Method  
In this paper, we propose the PAP algorithm for RSSI-based indoor positioning. The proposed method is structured into three steps: RSSI error correction, adaptive location estimation, and post-processing. The RSSI error correction step removes outliers and corrects noise in collected RSSIs. The adaptive location estimation step selects either a conventional trilateration or MP technique based on distances from APs for a positioning method. The position is determined through only one AP if an RSSI received from this AP is higher than a certain threshold \(T_{p}\). Location outliers are removed in the post-processing step by excluding estimated coordinates outside designated positioning regions. Then, positioning results are corrected by the moving average filter, which collects the estimated coordinates and obtains the average coordinates in a window. Figure 1 illustrates the flow for the proposed method.
# Recent Progress in On-Chip Erbium-Based Light Sources
## 3 Gain Medium Based on Different Host Materials
### Er Silicate
(**a**) Schematic of the pump-probe measurement. (**b**) Side view of the coupling configuration. The tapered fibers are placed in direct contact with the two end-facets of the nanowire at a small angle. (**c**) Top-view microscope image of the coupling system [39]. For slot waveguide structure, as shown in Figure 4c, the Er/Yb silicate film was deposited on the Si film and after that, poly-Si was deposited and etched into strip waveguide [62]. The TM mode of the optical filed could be well confined in the gain medium in the slot due to the continuity of normal electric displacement at the interface. Therefore, the overlap factor of pump and signal light in the gain medium was high, which is beneficial to improve the conversion efficiency of the pump light and the optical gain of the waveguide amplifier. Finally, a 1.7 dB signal enhancement was observed at 1.53 \(\upmu\)m.  
Based on the above gain medium, the research group designed a silicon-based on-chip waveguide laser of sub-kHz narrow linewidth and high output power, as shown in Figure 5a [63]. The resonant cavity of the waveguide laser consists DFB cavity, pump-resonant external distributed Bragg reflector (DBR) cavity, signal-resonant external DFB cavity. The DFB cavity is used to obtain lasing at 1535 nm, the pump-resonant external DBR cavity is used to improve pump absorption efficiency and the signal-resonant external DFB cavity is used to provide additional optical feedback. Based on the above structure, the output power and efficiency of the laser can be improved, and the threshold and linewidth of the laser can be reduced. In addition, the Si\({}_{3}\)N\({}_{4}\) films are added to reduce the propagation loss of the waveguide laser, as shown in Figure 5b. Over 90 mW saturated output power is achieved at 1535 nm with a maximum power conversion efficiency of 66%. The threshold of the waveguide laser is 22 mW and the linewidth is approximately 755 Hz.
# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 3 Sensor Operating Modes and Energy Consumption Measurements
### Consumption Profiles in Active and Idle Mode
#### 3.3.1 Connection Event  
During the BLE connection event, devices exchange all the data that was prepared during the last time between two consecutive connection events. The number of messages exchanged during one connection interval can differ, but for the given ECG sensor, most connection events exchange the same amount of data. One example of a BLE connection event current consumption profile recording is presented in Figure 4. The current profile starts by the BLE radio wake-up procedure between \(0.0\,\mathrm{ms}\) and \(1.5\,\mathrm{ms}\), followed by an RX event from \(1.5\,\mathrm{ms}\) to approximately \(1.8\,\mathrm{ms}\). Then, a TX event follows, which lasts until approximately \(2.75\,\mathrm{ms}\), continuing with the BLE stack processing, and ending with the transition of the BLE radio back into sleep mode.  
Figure 4 shows current profiles for 4 different TX power settings: \(0\,\mathrm{dBm}\), \(-6\,\mathrm{dBm}\), \(-12\,\mathrm{dBm}\) and \(-18\,\mathrm{dBm}\). We can observe that the energy consumption during a connection event for different TX power settings is different, which enables the designers to extend the autonomy of the sensor in closer operating ranges by reducing the transmitter power settings. The results of the energy consumption measurements for different TX power settings are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**TX Power Setting [dBm]** & \(e_{com}\) [u]] & **std\((e_{com})\) [μ]** \\ \hline \(0\) & \(73.01\) & \(0.65\) \\ \hline \(-6\) & \(68.92\) & \(0.77\) \\ \hline \(-12\) & \(66.50\) & \(1.00\) \\ \hline \(-18\) & \(65.48\) & \(0.47\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Energy consumption of BLE connection events for different TX power settings.  
Figure 3: Current consumption profile recording in an active mode (i.e., while streaming ECG samples).
## 3 Results and Discussion
### Analysis of Influencing Factors on Carbon Emission Intensity  
#### 3.2.1 Processing Scale  
Figure 5 illustrates the distribution of carbon emission intensity across STPs of varying processing scales. Small-scale STPs (<1 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d) exhibited significantly higher carbon emission intensity (0.582 kg CO\({}_{2}\)-eq/m\({}^{3}\)) compared to larger STPs (0.392 kg CO\({}_{2}\)-eq/m\({}^{3}\)), with a 48.5% increase attributed to low operational efficiency of equipment and high energy consumption per unit of water treated. The average carbon emission intensities for STPs with processing scales of 1.0 \(\times\) 10\({}^{4}\)-5.0 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d, 5.0 \(\times\) 10\({}^{4}\)-10.0 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d, and 10.0 \(\times\) 10\({}^{4}\)-20.0 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d were 0.408, 0.392, and 0.397 kg CO\({}_{2}\)-eq/m\({}^{3}\), respectively. This indicates a decreasing trend in carbon emission intensity with increasing processing scale, which was most pronounced for scales below 5.0 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d. However, the impact of scale effect on carbon emission intensity became insignificant when the processing scale exceeded 5.0 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d. Small-scale STPs (<1 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d) had a power consumption per ton of water that was 59.1% higher than the average of the 98 STPs surveyed. This elevated energy consumption directly contributed to their significantly higher carbon emission intensity. The operational efficiency of equipment and facilities was lower in small-scale STPs and improved with increasing processing scale. However, beyond a processing scale of 1.0 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d, further increases in scale had minimal impact on improving equipment operational efficiency. Specifically, the power consumption per ton of water for STPs with processing scales of 1.0 \(\times\) 10\({}^{4}\)-5.0 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d, 5.0 \(\times\) 10\({}^{4}\)-10.0 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d, and 10.0 \(\times\) 10\({}^{4}\)-20.0 \(\times\) 10\({}^{4}\) m\({}^{3}\)/d was 0.289, 0.302, and 0.306 kW\(\cdot\)h/m\({}^{3}\), respectively, showing no significant reduction.  
Figure 5: The relationship between scale and carbon emission intensity. (**a**) Distribution of carbon emission intensity in STPs of varying scales. (**b**) Distribution of power consumption per ton of water in STPs of varying scales.
## 3 Results
### Verification Using Graphical User Interface (GUI) and Programmable Logic Controller (PLC)  
The learning model, established using the visualization technique and transfer learning process, was implemented on the actual conveyor belt to sort the defective products, and the results were verified. As shown in Figure 8, the conveyor belt was composed of the input, the camera, and the sorting areas. The bolt heads were transported by the conveyor belt, and sensors were installed at certain locations to acquire images when the bolts were sensed. Conveyor belt transportation and stoppage, as well as the signal sensors input and output, were implemented by the logic configuration of XEC DR32H, a PLC product by LS Electric.  
When the bolt, transported by the conveyor belt, approached the sensors, the cameras acquired images, which were then stored on a personal computer (PC) connected to the PLC. Once the image data were confirmed, the images from all the cameras were entered into the learned model to produce corresponding values. If the system determined that the product was defective, the Modbus TCP method was used to transmit the designated input value within the PLC. The PLC, in turn, used the input signals to operate the sorting mechanism and classify the products.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Defective (NG) & 0.96 & 0.98 & 0.97 & 66 \\ Non-defective (OK) & 0.98 & 0.95 & 0.97 & 66 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Validation results of algorithm for screening coating defects.  
To help the user compare the real-time image with the resulting values, a GUI was configured, as shown in Figure 9, where \"OK\" and \"NG\" denote non-defective and defective products, respectively. Using the camera and PLC setting buttons, the connection was confirmed and, thereafter, the model setting button brought up the learning model. A start button triggered the sorting mechanism of the coated bolts. The sensor values were obtained while the bolts were being transported via the conveyor belt; images from the four cameras were obtained as shown in Figure 9, and the sorting results were produced within 0.9 s. Through this process, the previously learned model was verified; the system was proven to be applicable to actual environments. This system could make the sorting process for bolts of varying colors and sizes more efficient.
# Log Volume Measurement and Counting Based on Improved Cascade Mask R-CNN and Deep SORT
## 6 Limitations and Future Scope  
In this paper, we present the improved Cascade Mask R-CNN for detecting log ends. To enhance the detection ability of small log ends, we performed attention computation on the multi-scale features of ResNet output, optimized the redundant information of features, and finally significantly enhanced the detection ability of small log ends. At the same time, we tracked and counted the log counts using Deep SORT. Finally, the volume of the logs was estimated. We provided new ideas for the detection and counting of large log stacks, and although our method has achieved better results, there are still some shortcomings that we need to continue to study in the future. Our shortcomings and future research directions are shown below:  
1. The existing model has a poor ability to detect the messy log images. In the future, we will continue to increase the log image dataset to increase the generalization of the model.
2. We will reduce computational overhead and memory usage through model pruning and optimization techniques to enable real-time processing and deployment on resource-constrained edge devices.
3. To further improve the robustness and accuracy of log end detection and counting, considering integrate data from other sensors (e.g., LiDAR, thermal imaging) could be beneficial.
# Using Dynamo for Automatic Reconstruction of BIM Elements from Point Clouds
## 1 Introduction
The current mainstream BIM software, Autodesk Revit and ArchiCAD, integrate with the visual programming language tools Dynamo and Grasshopper, respectively, which are used to develop codes and algorithms for automating tasks in BIM projects. There are good examples of works that use other software and other approaches to computer programming to automate point cloud modeling [15; 38; 39; 40; 41], and there are several authors who use Dynamo in their studies for complementary tasks in modeling and in the BIM workflow [42; 43; 44; 45; 46; 47]. However, there is a lack of studies that used Dynamo as the main tool to automatically model the geometry from point clouds. In exploring the advancements and applications of 3D laser scanning and digital photogrammetry in the AEC industry, this paper acknowledges a significant gap in the automation of BIM from point clouds. Despite the rapid adoption of scan-to-BIM technologies, professionals in the field continue to face challenges in terms of efficiency, cost, and the manual labor involved, which hampers accuracy and increases project timelines [15; 19]. This research was motivated by the pressing need to address these issues through the development of automated tools that facilitate the conversion of raw data into functional BIM elements. This research aimed to develop automation approaches for modeling architectural elements from point cloud data with Autodesk Revit 2020 and Dynamo [41; 42]. The use of Dynamo in the creation of automation algorithms for Revit brings some advantages, such as the compatibility between the two tools, since Dynamo is a native tool of Revit, the possibility of using and creating the BIM elements of Revit in their correct categories and types without the need to use generic 3D elements, the lack of need to use other external software to complement the work, and the possibility of sharing the algorithm with other users who use the same tools so they can also test them. Our contributions are crucial for advancing BIM technologies. Firstly, we introduce novel algorithms developed in Dynamo for the automated modeling of architectural elements from point clouds, significantly reducing manual input and associated errors. Secondly, we validated these algorithms against traditional methods through a comprehensive case study of the Old Lifeguard Station of Fuseta, showcasing not only improved accuracy but also considerable time savings. Lastly, our approach leads the way in incorporating these automated solutions into mainstream BIM software, making it easier for others to adopt throughout the industry. By detailing these contributions, this article fills a critical void in current research, setting a new benchmark for future developments in scan-to-BIM processes.
## 2 Study Sites and Database Description
### French Study Site  
A study site located in the Occitanie region of France (centered on 3.80\({}^{\circ}\)E and 43.67\({}^{\circ}\)N, Figure 1) was chosen to validate our approach for the operational mapping of soil moisture. Relatively flat, it is composed mainly of forest, vineyard, grasslands, and agricultural fields (mainly wheat). The climate of the study site is Mediterranean with a rainy season between mid-October and March and an average cumulative rainfall of approximately 750 mm. The average air temperature varies between 2.9 \({}^{\circ}\)C and 29.3 \({}^{\circ}\)C. The top soil texture of the agricultural fields is a loam. Twenty-three reference plots with areas between 0.6 and 9 ha were selected over our study site, with 10 grasslands and 13 plots of wheat.  
Twenty-five SAR images acquired by Sentinel-1 and 11 optical images acquired by Sentinel-2 were used in this study. In addition, in situ campaigns were conducted simultaneously with SAR acquisitions to collect the in situ measurements of soil moisture in our reference plots.
## 2 Numerical Methods
### Numerical Domain Grid and Boundary Conditions  
The geometric computational model of the single-hole gas film studied in this work is shown in Figure 1. The computational domain consists of the main flow channel, the cooling hole channel and the coolant channel.  
In this section, the following boundary conditions were assumed: a cooling hole diameter of 3 mm, an inlet velocity of 30 m/s for the mainstream, an operating pressure of 1 MPa, and a coolant flow rate of 0.001 kg/s, to obtain a specific blowing ratio of 2. The temperature was set to 353.15 K for the coolant and 363.15 K for the main gas.  
In this section, the computational model was partitioned into a hybrid \"hex-core\" grid using ANSYS Fluent meshing. A prism boundary layer was added near the bottom wall of the main flow region and around the walls of cooling holes. Specifying 5 layers of boundary layer grids, expanding the grid by a factor of 1.1, and specifying the total height of the grid ensured that the y+ values at various positions on the bottom wall were less than 1. To accurately capture the fine flow details in the areas where the cooling jets mix with the main flow and dissipate energy, BOI-body meshed grids were used around and downstream of the cooling holes. The total number of grids used was approximately 15 million, as shown in Figure 2 (see Figure 3 for detailed information on the mesh in the center section and the boundary layers). The minimum mesh orthogonal quality was higher than 0.5. For the simulations using large-eddy simulation (LES) for the flow in the cooling holes, a time step size of \(\Delta\)t = 0.03 D/U (i.e., 3 \(\times\) 10\({}^{-6}\) s) was assumed; a flow cycle (the time required for the main flow to pass over a plate) corresponded to 30 D/U = 0.003 s.  
Figure 1: Schematic representation of cooling hole calculation domain.  
The structure of shaped film cooling holes chosen in this article was designed primarily with respect to the lobed mixing nozzle [28] previously used by the author for mixing at high temperatures and high pressure differential. The specific details of the design are shown in Figure 4.  
Figure 4: Schematic diagram of the lobe-shaped cooling holes.  
Figure 3: Detailed diagram of the grid boundary layer.  
Figure 2: Schematic diagram of the computational domain grid.
## 2 Motion States of Freestanding Package
### Package Rocks Around Point 2 or Detaches from Point 2  
After the package collides with the vehicle at point 2, if point 1 detaches from the vehicle, there are two potential cases:  
Case 1: \(\dot{g}_{N1}^{+}>0\), if \(\varepsilon_{N2}=0\); then, \(\dot{g}_{N2}^{+}=0\), and the package rocks around point 2 after collision.  
Case 2: \(\dot{g}_{N1}^{+}>0\), if \(\varepsilon_{N2}>0\); then, \(\dot{g}_{N2}^{+}>0\), and the package detaches from the vehicle at point 1 and point 2.  
These two cases are shown in Figure 2, and the velocity jumps in the vertical direction at points 1 and 2 are:  
\[\begin{split} v_{N1}&=\dot{g}_{N1}^{+}+\varepsilon_{ N1}\dot{g}_{N1}^{-}=\dot{g}_{N1}^{+}>0\\ v_{N2}&=\dot{g}_{N2}^{+}+\varepsilon_{N2}\ddot{g}_{N 2}^{-}=0\\ \end{split} \tag{18}\]  
In these two cases, since \(\Lambda_{N1}=0\), by substituting Equation (18) into Equation (9) and considering Equation (12), we obtain:  
\[\begin{split}\Lambda_{N2}&=m\ddot{g}_{N2}^{-}(1+ \varepsilon_{N2})\Big{(}\tfrac{3\sin^{2}\alpha}{4}-1\Big{)}\\ \tfrac{\dot{g}_{N1}^{+}}{\dot{g}_{N2}}&=(1+ \varepsilon_{N2})\Big{(}\tfrac{3\sin^{2}\alpha}{2}-1\Big{)}\\ \end{split} \tag{19}\]  
By substituting Equation (19) into Equation (7), the tangential force at point 2, \(\Lambda_{N2}\), can be determined when the package rocks around point 2:  
\[\Lambda_{T2}=\frac{3}{8}m\ddot{g}_{N2}^{-}(1+\varepsilon_{N2})\sin(2\alpha) \tag{20}\]Therefore, to prevent the package from sliding, the friction force generated by the material between the rocking package and the vehicle must exceed \(\Lambda_{T2}\).  
After the package collides with the vehicle, if the package rocks around point 2 or detaches from the vehicle, \(\hat{S}_{N1}^{+}>0\). From this, we can derive the necessary condition for the occurrence of these two scenarios:  
\[\frac{h}{b}\leq\frac{1}{\sqrt{2}} \tag{21}\]