esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Tear Samples Collection and Analysis of Metabolic Protein Concentrations  
A total of 2 uL tears were collected at the same time period of day (between 3 PM-4 PM) from each subject (1 uL in V1 and 1 uL in V2). An unstimulated, basal tear sample was collected using a disposable, sterile microcapillary tube (Drummond Scientific, Broomall, PA 19008, USA) from the lateral annulus of the eye, with extreme care not to touch the conjunctiva to avoid tear reflex collection. Subjects were allowed to blink normally during tear collection. If any significant reflex tearing during the collection were observed, the sample would have to be discarded and 5 min were allowed for tear normalization before obtaining the sample again. The collected tears were expelled immediately from microcapillary tubes into a lo-bind sterile Eppendorf tube (Sigma, Gillingham, Dorset, UK) and diluted 1:10 in 9 uL of ice-cold assay buffer (Merck Millipore, Watford, UK). This low volume sample has previously been shown to be sufficient for cytokine analysis, when performing a low volume protocol, which uses only 10 uL volumes of samples and standards, instead of the 25 uL used in regular protocols [2; 4; 40; 41]. The tears diluted with assay buffer in a lo-bind Eppendorf tube were kept in a box with ice for no more 2 h and then centrifuged using the SciSpin Micro 24R (SciQuip, Shrewsbury, UK) at 8000 rpm for 30 s at 4 \({}^{\circ}\)C before transferring to a \(-\)80 \({}^{\circ}\)C freezer. A panel of 5 metabolic proteins (insulin, leptin, active ghrelin, C-peptide and GIP) were simultaneously assayed in normal tear samples using the Human Metabolic Hormone Magnetic Bead Panel (Millipore, Merck Millipore, UK). The procedure was performed according to the manufacturer instructions and following an adapted protocol for low sample volumes, as previously described [42]. Final concentrations were read using Bio-Plex software (Bio-Plex Manager 6.1 Software, Bio-Rad, Hercules, CA 94547, USA) in a Luminex 200 machine (Luminex Corp, Austin, TX, USA). The minimum detectable concentrations (MinDC in pg/mL), were leptin = 41, insulin = 87, GIP = 0.6, active ghrelin = 13 and C-peptide = 9.5.
###### Abstract  
Frequency multipliers and mixers based on Schottky barrier diodes (SBDs) are widely used in terahertz (THz) imaging applications. However, they still face obstacles, such as poor performance consistency caused by discrete flip-chip diodes, as well as low efficiency and large receiving noise temperature. It is very hard to meet the requirement of multiple channels in THz imaging array. In order to solve this problem, 12-\(\mu\)m-thick gallium arsenide (GaAs) monolithic integrated technology was adopted. In the process, the diode chip shared the same GaAs substrate with the transmission line, and the diode's pads were seamlessly connected to the transmission line without using silver glue. A three-dimensional (3D) electromagnetic (EM) model of the diode chip was established in Ansys High Frequency Structure Simulator (HFSS) to accurately characterize the parasitic parameters. Based on the model, by quantitatively analyzing the influence of the surface channel width and the diode anode junction area on the best efficiency, the final parameters and dimensions of the diode were further optimized and determined. Finally, three 0.34 THz triplers and subharmonic mixers (SHMs) were manufactured, assembled, and measured for demonstration, all of which comprised a waveguide housing, a GaAs circuit integrated with diodes, and other external connectors. Experimental results show that all the triplers and SHMs had great performance consistency. Typically, when the input power was 100 mW, the output power of the THz tripler was greater than 1 mW in the frequency range of 324 GHz to 352 GHz, and a peak efficiency of 6.8% was achieved at 338 GHz. The THz SHM exhibited quite a low double sideband (DSB) noise temperature of 900-1500 K and a DSB conversion loss of 6.9-9 dB over the frequency range of 325-352 GHz. It is indicated that the GaAs monolithic integrated process, diodes modeling, and circuits simulation method in this paper provide an effective way to design THz frequency multiplier and mixer circuits.  
Keywords:terahertz (THz); GaAs monolithic integrated circuits; tripler; subharmonic mixer (SHM); Schottky barrier diodes (SBDs) +
Footnote †: journal: Applied Sci. 2020, 10, 7924; doi:10.3390/app10217924
# Effect of the Light Environment on Image-Based SPAD Value Prediction of Radish Leaves
## 2 Materials and Methods
### Photographic Environment and Data Acquisition Methods  
The images were taken using a box-type device with a height of 900 mm, width of 460 mm, and depth of 460 mm, constructed on a flat room floor. A digital camera (DC-GX7 MK3, Panasonic Corp., Tokyo, Japan) was fixed on a tripod so that the camera could capture images of leaves from above for expected applications in image-based SPAD value predictions in an open field using a UAV or the use of a fixed-point camera for the greenhouse. The camera parameters were F-value 7.1, ISO 200, and focal length 12 mm. In this photographic environment, the color temperature and light intensity can be controlled by switching the lighting using a dimmer. In this study, halogen bulbs (color temperature: 3000 K) and LED bulbs (color temperature: 5000 K) were used, with nine levels of illuminance for the halogen bulbs (3, 50, 100, 300, 500, 800, 1000, 2000, and 2700 lx) and nine levels of illuminance for the LED bulbs (120, 300, 500, 800, 1000, 2000, 3000, 4000, and 5440 lx). Figure 1 shows a schematic diagram of the experimental device to capture images of radish leaves.  
In this study, leaf patches of radishes were prepared, and the color information was obtained for an image of a leaf patch with a circularity greater than 0.85 (Figure 1). The minimum, mean, median, and maximum values of the RGB, HSL, and HSV color systems (see Section 2.3 for detail) were calculated for each leaf patch. The RGB, HSL, and HSV values of the center of gravity of the leaf patches were calculated for each leaf patch and used as input variables in the Random Forests analysis (described in Section 2.4), together with the color temperature and illuminance of the photographic environment, to predict the SPAD values.
# Damping Ratio of Sand Containing Fine Particles in Cyclic Triaxial Liquefaction Tests
###### Abstract  
Sand liquefaction triggered by earthquakes is a devastating geological disaster and has emerged as an engaging topic in earthquake engineering. With an enhanced understanding of pure sand liquefaction promoted by laboratory research, there is a growing concern, following filed investigations, over the influence of fine particles on the liquefaction potential of sand containing inclusions. Efforts have been devoted to clarifying the significance of certain physical indicators (e.g., plasticity index, particle shape and gradation characteristics), and fruitful conclusions can be found in the published literature. However, the relationship between the content of fine particles and the cyclic degradation in liquefaction process seems still unclear. To fill this knowledge gap, three sets of cyclic triaxial tests were performed on various sand-fines mixtures with the dry damping method. The experimental results revealed that (i) fine particles provided a negative contribution to the global soil structure; (ii) however, the damping ratio measured from the obtained stress-strain loops manifested its independence from the fines content during cyclic degradation. In this paper, we propose a shearing mechanism on the microscopic scale to explain the above contrasting observations. For a given soil fabric, the fine particles around sand-to-sand contact points probably break strong force chains, intensifying the threat of liquefaction. By contrast, these fines play the same role in favouring relative sliding between sand grains during both the loading and unloading stages. As the maximum stored energy and the energy loss per cycle are amplified with the same scaling factor, the damping ratio, defined as the ratio between them, should display a macroscopic invariance in triaxial tests.  
sand liquefaction; cyclic degradation; damping ratio; cyclic triaxial test 202310390/app13084833 Article
# Advanced Brain Tumor Segmentation Using SAM2-UNet
## 5 Experiments and Results
### Discerning the Inner Workings of the Proposed SAM2-UNet Architecture
#### 5.1.2 Dice Coefficient (DICE)  
The Dice coefficient is a simple and intuitive metric that measures the overlap between predicted and actual regions and makes interpretion very easy. It is useful for imbalanced datasets, as it gives equal importance to both false positives and false negatives. This metric is widely used in fields like biomedical imaging due to its sensitivity in detecting small structures. It is effective for both binary and multi-class segmentation tasks [22].  
Figure 14: Training loss graph depicts how the training loss was minimized over 30 epochs, wherein the model was iterated 250 times for each epoch. The training loss was minimized to 0.4% on the final epoch.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **mDice** & **mIoU** & **MAE** \\ \hline SAM2-Unet & 0.771 & 0.569 & 0.046 \\ U-Net & 0.563 & 0.392 & 0.178 \\ U-Net++ & 0.610 & 0.439 & 0.167 \\ Mask R-CNN & 0.746 & 0.571 & 0.094 \\ DeepLabV3 & 0.709 & 0.549 & 0.062 \\ ResUNet & 0.591 & 0.419 & 0.183 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the SAM2-UNet model across various metrics compared to other image segmentation models.  
\[\text{Dice}=\frac{1}{N}\sum_{i=1}^{N}\frac{2|A\cap B|}{|A|+|B|}\]
# A New Device to Test the Bending Resistance of Mechanical Endodontic Instruments
## 4 Discussion
Throughout the years, this test has also been used to analyze the bending ability of NTR instruments, without considering the structural differences between the two instruments. Moreover, the development of the ISO test was dependent on the lack of anatomical knowledge. Nowadays, with the spread of CBCT evaluation, the apical part of the root is not the only curved one. It is possible to see several curvatures at different portions of the root canal system [12; 13; 14; 15]. Therefore, the bending properties of the coronal part of the instrument, despite it not being subjected to the same amount of stress as the apical one [21], should be investigated as well. The concept of minimally invasive dentistry has led to clinicians approaching root canal treatment in a conservative way in terms of root canal shaping and the access cavity. According to [PERSON] et al. [22], the more conservative the access cavity, the more difficult and angulated the access to the canal. A traditional access cavity removes the amount of dentin necessary to access the orifices of the canals directly. On the contrary, a conservative one exploits the flexural features of the heat-treated rotary instruments to enter the canal in a tilted way. Therefore, the comparison between a traditional access cavity and a conservative one is not only conducted in terms of the percentage of dentin spared but also of the slope of insertion of the instrument. It is not possible anymore to rely on a test such as ISO 3630-1 due to anatomical reasons, metallurgic reasons, and different clinical approaches. For these reasons, evaluations of the bending ability of an instrument should consider the whole instrument. The present testing machine provides the correct amount of information to do so. Thanks to this new testing machine, it may be possible to precisely investigate the bending behavior of the whole rotary instrument, providing more reliable and realistic information about the flexibility of the instrument.
## 5 Challenges and Future Research Directions
### Future Research Directions
#### 5.2.3 Mobile Edge Computing  
Mobile edge computing is one of the key technologies for 5G and future networks. By performing computation and processing at edge nodes near users, it avoids transmitting all data to the cloud, thus reducing latency, improving efficiency, and saving bandwidth resources [11]. The flexible self-organizing capabilities of UAVs allow edge computing nodes to be deployed in remote or disaster areas, providing stable computing services to underserved regions. The current research status and trends in the integration of MEC and SAGINs focus primarily on the following: (1) resource allocation and scheduling [101; 105; 124], (2) network performance optimization (e.g., latency [68; 132; 134] and throughput [117]), (3) energy efficiency [115; 119; 139], (4) security control [107], and (5) joint optimization [120; 169]. Some of these studies target specific scenarios and design localized parameter optimization strategies. However, such approaches may face adaptability and efficiency challenges as the network scale expands or the state dynamically changes. Future research directions will emphasize standardization and unification, aiming to construct a standardized MEC-SAGIN integrated network architecture to enhance interoperability and deployment efficiency. With the application of artificial intelligence, dynamic optimization methods based on online learning and intelligent inference are expected to become mainstream, driving the development of stable and efficient joint optimization strategies. Transitioning from local optimization to system-level global collaborative optimization will be key to meeting the demands of complex scenarios, thereby improving network performance and scalability.
## 3 Theory and Method
### The Antenna Beamwidth  
The GMR antenna beamwidth measurement is important for reliable and accurate antenna temperature measurement but is usually carried out in a microwave anechoic chamber. Measurement using an anechoic chamber is complex and expensive, and especially this cannot be used to monitor the antenna pointing of a radiometer in operational field applications after installation. Existing research shows that the sun can be used to not only calibrate radar antenna pointing but also measure antenna patterns. The use of the solar radiation for checking the antenna specifications has been a well-established method in the radar field. [12; 13; 20; 21]. Therefore, we suggest using the sun method in our study in order to improve the accuracy of a ground-based GMR observation by automatically checking the antenna alignment and system stability.  
The position of the sun can be accurately calculated according to literature [22; 23]. The sun can be assumed to be in the far-field region and a point source for the antenna, and can be treated as a homogeneous disk with an approximate angular diameter of 0.53\({}^{\circ}\). During the observing of the sun, the received solar power as a function of the radial distance to the antenna beam axis is approximated by a Gaussian function while the GMR antenna is scanning in azimuth and elevation [12; 24]. Assuming a uniform disk for the sun and the simplest reasonable model of the actual solar power and antenna power pattern can be parameterized by  
\[\Delta T_{sun}(x,y)=A_{d}G(x,y), \tag{6}\]  
\[G(x,y)=\exp[-4\ln 2\Bigg{(}\frac{x^{2}}{\theta_{H}^{2}}+\frac{y^{2}}{\theta_{E }^{2}}\Bigg{)}], \tag{7}\]  
where \(A_{d}\) is the TB increment of the antenna beam pointing to the center of the sun and it is the peak solar power. \(\theta_{H}\) and \(\theta_{E}\) are the half-power beamwidth of the H-plane (azimuthscanning) and E-plane (elevation scanning), respectively. \(x\) and \(y\) are the solar azimuth and elevation angle radial distance from the beam center, respectively. Thus, the sun data can be easily fitted to this equation by the least-square method.  
When the antenna scans the sun, a rotation of the antenna in azimuth with constant elevation is not a great circle in the sky-sphere, and there are some distortions in the scanning path. The rotation azimuth angle will turn out greater than the true azimuth angle in the sky-sphere and it has to be calibrated. There is an extreme case when the azimuth of the antenna is rotated at an elevation angle of 90\({}^{\circ}\), but the antenna beam will not move in the sky. [PERSON] and [PERSON] provided an accurate method to calibrate the angle distortion [21], we calculated the distortion with their method and the result is shown in Figure 3. It can be seen that the distortion is small for low elevation and it is more obvious with a large rotation angle of the antenna in azimuth.
# Feature Point Identification in Filtel Weld Joints Using an Improved CPDA Method
## 2 Feature Point Recognition Model of Structured Laser Light in Fillet Welding  
The developed three-dimension vision sensing system with structured laser light is shown in Figure 1a. The system consists of a CCD (charge-coupled-device) camera, a laser line projector, and an image processing unit. Figure 1b shows the developed 3D sensing system integrated on the robotic arm and Figure 1c is one of the obtained images of the structure-light laser stripe in the fillet welding. In this situation, the line-shaped structured laser is projected onto the surface of the fillet weld seam, which deformed the straight laser light into a twisted fringe pattern. The deformed fringe pattern is then captured with the CCD camera. As shown in Figure 1d, the interest here is to find the corner feature point \(P(x,y)\) in the image coordinate system \(\{\bf I\}\) as it indicates the location of the weld seam. According to the pin-hole model, the corresponding 3D position (\(x_{w}\), \(y_{w}\), \(z_{w}\)) of the weld seam can be obtained in each time constant by combining the camera model \(\{\bf C\}\) with the ray-plane equation of the structured laser line using the triangulation model in Equation (1). As the 3D sensing system is mounted on the end-effector \(\{\bf Z\}\), the 3D spatial coordinate (\(x_{b},y_{b},z_{b}\)) in base coordinate system \(\{\bf B\}\) for a welding robot can be reconstructed with the given relation between the robot arms. Therefore, the key issues are the accurate extraction of the contour feature, which the projected laser deformed as a curve; and the robust identification of feature point \(P\), which is the corner point on the projected laser line using image processing algorithm. Effective identification of these features is the key and the first step in enabling automatic robot welding for fillet weld seams.  
Figure 1: Laser structured-light 3D sensing system. (**a**) Schematic diagram of structure-light and CCD camera in fillet welding; (**b**) developed system on robot arm; (**c**) the obtained laser stripe image from the CCD camera; (**d**) the 3D reconstruction relation; (**e**) diagram of triangulation reconstruction.  
\[\begin{bmatrix}x_{w}\\ y_{w}\\ z_{w}\end{bmatrix}=\frac{L}{f\cos\theta-x}\begin{bmatrix}x\\ y\\ f\end{bmatrix} \tag{1}\]  
where, in Equation (1), (\(x\), \(y\)) are coordinates of the identified feature corner points of fillet welding in the image plane. (\(x_{w},y_{w},z_{w}\)) represent the 3D position of fillet weld seam. \(f\) is the focal length for the CCD camera. \(L\) is the distance between the CCD camera and the laser projector. The value of \(f\) and \(L\) can be determined in the laser ray-plane calibration [31].
# An Image Compensation-Based Range-Doppler Model for SAR High-Precision Positioning
## 1 Introduction
### Refinement of Geometric Model Parameters
The application of SAR data processing technologies, including high-precision GNSS orbit-aided imaging [32; 33] and motion compensation [34; 35], enables the refinement of a select few crucial parameters of the rigorous model, such as initial slant range and azimuth time, significantly enhancing the positioning accuracy of imagery [36; 37]. The systematic influence of these parameters on image errors underscores a strong consistency in geometric positioning [38]. The utilization of SAR high-precision positioning under conditions of sparse control points, particularly with the implementation of image space-corrected RFM, further reinforces this perspective [20]. Considering the inherent complexities anddifficulties of implementation in the classical refinement of the R-D model with GCPs, we raise a question: is it possible to achieve high-precision positioning for the R-D model, in order to allow it to be combined with an image-based compensation function? This idea is similar to the approach used in the RFM. If feasible, it could greatly simplify the refinement process for the R-D model. In addition, the RFM is primarily designed for spaceborne images and has not been effectively applied to airborne images (until now). In contrast, the R-D model with image compensation may be easily extended to airborne SAR images. To the best of our knowledge, there is still no relevant work on the abovementioned aspects. In this regard, similar to RFM with image space compensation, the strategy for improving the positioning accuracy of satellite-borne SAR images [20; 29], we developed an image compensation-based R-D (ICRD) model for SAR high-precision positioning and conducted a variety of experiments on both spaceborne and airborne SAR images to validate its accuracy. That means we attempt to achieve high-precision positioning of the R-D model by adding a correction function in the image space instead of optimizing imaging and geometric parameters. The effectiveness of this method will reduce the complexity and professionalism of the R-D model, and provide a consistent processing method for aerospace SAR high-precision positioning. The outline of this paper is as follows. A short overview of SAR image geometric models and model refinement is provided in Section 1; some limitations related to model refinement are also discussed in this section. In Section 2, an ICRD model for SAR high-precision positioning is proposed, and image compensation using polynomial functions is detailed. In the next section, we report on the comparative experiments we conducted, and the accuracy of the model is validated. We discuss some important issues associated with the proposed model in Section 4, and the last section contains our conclusions.
## 6 JCS Sidelink Performance Evaluation Using CCM-SPS  
In this section, key performance metrics such as CRLB, PRR, and UD were evaluated under varying vehicle density and packet size scenarios, and comprehensive experimental discussions were conducted. Among them, CRLB is a sensing performance metric that affects target detection accuracy, thereby influencing autonomous driving decision-making; PRR is a communication reliability metric that impacts communication quality; and UD is a latency metric that affects the real-time performance of intelligent transportation systems.
## 4 Results from the Literature Review
### Autonomous Buses  
Autonomous buses are designed to operate independently, relying on advanced technologies, such as sensors, cameras, radars, and artificial intelligence (AI), to navigate and transport passengers safely. As part of the broader category of autonomous vehicles (AVs), these buses are usually electric, fostering a shift toward more sustainable and efficient public transport [278]. One major advantage of autonomous buses is the cost savings from eliminating driver wages. These savings can benefit both transit operators and passengers, potentially lowering ticket prices--an especially valuable aspect in the face of economic challenges and the need for resilience during global crises [279; 280]. Autonomous buses also contribute to lower greenhouse gas emissions by optimizing driving patterns, such as braking and acceleration, and utilizing electric powertrains [281], which are more environmentally friendly. This shift could help combat climate change by reducing the carbon footprint of public transport systems [282; 283]. In the context of epidemic risks, autonomous buses offer further advantages. They can transport infected patients to healthcare facilities with minimized human interaction or deliver samples and test kits to laboratories [45]. Research also supports the use of autonomous shuttles during fluctuations in passenger demand, highlighting their superior energy efficiency and cost-effectiveness under stringent health and safety protocols [284].