esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application to Virtual Fit Analysis Method to Pilot Oxygen Mask Design
### Design of Pilot Oxygen Mask by Virtual Fit Analysis
 \tag{1}\]  
where, \(i=\) vertical location, \(i=\) 0 to 100,  
\(j=\) face number,  
\(HD_{ij}=\) horizontal distance of mask boundary to the surface of face \(j\) at vertical location i, \(LL_{i}=\) lower limit at vertical location \(i\), and  
\(UL_{i}=\) upper limit at vertical location \(i\). The accommodation percentages of the revised mask boundaries derived through the virtual fit analysis method for the four sizing categories were found 4.8-88.7% higher than those of the existing boundary shapes over the face sections for KAF pilots. The accommodation percentages at the nasal side, cheek, and chin areas showed noticeable improvements for proper fit of the mask to the face (Figure 10). All the accommodation percentage differences between the existing and revised masks except two conditions (SN and MW at the nasal root) were found significant by the paired \(t-\)test at \(\alpha=0.05\). Figure 9: Virtual fit analysis results of the existing and alternative designs of pilot oxygen mask boundary (size: small narrow; red lines: the lower and upper limits of appropriate fit; blue lines: plots of the horizontal distances between the mask boundary and face surfaces). (**a**) Existing design; (**b**) alternative design.
# Recent Progress in Synthesis and Photonic Applications of Two-Dimensional Bismuthene
## 4 Photonic Applications Based on 2D Bismuth
### Nonlinear Photonic Devices
In addition, the authors also indicated that the modulation depth can be adjusted by regulating the energy of the switched light, which is suitable for the fabrication of nonlinear photonics devices, such as the passively Q-switched and light modulators. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**2D Materials** & \(\lambda_{c}\) **[nm]** & \begin{tabular}{c} **Pulse Width** \\ **[fs]** \\ \end{tabular} & \
\begin{tabular}{c} **Output Power** \\ **[mW]** \\ \end{tabular} &
\begin{tabular}{c} **Single Pulse** \\ **Energy [n]** \\ \end{tabular} & \(\mathbf{f_{wp}}\) **[MHz]** & **Refs** \\ \hline Bismuthene & 1561 & 193 & 5.6 & & 8.85 & [16] \\ Bismuthene & 1557.5 & 621.5 & 122.1 & & 22.74 & [23] \\ Bismuthene & 2789 & 645 & & & [70] \\ Bismuthene & 1065 & 2.56 \(\times\) 10\({}^{5}\) & & 590 & 0.2237 & [74] \\ Antmomene & 1557.7 & 552 & 0.66 & 6.43 \(\times\) 10\({}^{-2}\) & -25 & [65] \\ BP & 1532 & 940 & 5.6 & & 4.69 & \\ MoS\({}_{2}\) & 1042.6 & 6.56 \(\times\) 10\({}^{5}\) & 2.37 & 0.35 & 6.74 & [75] \\ MoTe\({}_{2}\) & 1559.5 & 229 & 57 & 2.14 & 26.601 & [76] \\ WS\({}_{2}\) & 1561 & 246 & 18 & 0.178 & 80 & [77] \\ SnS\({}_{2}\) & 1562 & 623 & 1.2 & 4.09 \(\times\) 10\({}^{-2}\) & 29.33 & [78] \\ MoSe\({}_{2}\) & 1340 & 4.2 \(\times\) 10\({}^{5}\) & 52.6 & & 238 & [72] \\ ReS\({}_{2}\) & 1300 & 4.03 \(\times\) 10\({}^{5}\) & & 420 & 0.214 & [73] \\ BiSe\({}_{2}\) & 1571 & 579 & 1.56 & 0.112 & 12.54 & [79] \\ TiS\({}_{2}\) & 1544.5 & 402 & & -5.7 & [80] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: FOMs of lasers based on bismuthene and other 2D materials. Figure 4: Schematic diagram and key parameters of the bismuthene–based nonlinear devices. (**a**) Schematic figure showing the bismuthene band structure in the trap state [68]. (**b**) Schematic figure of the passively Q–switched laser with the bismuthene saturable absorber [70]. (**c**) Optical spectrum of the continuous-wave laser and the passively Q–switched laser, respectively [71]. (**d**) Q–switched pulses recorded at 400 ns/div and 20 μs/div [74]. (**e**) Schematic graphic of the FWM setup based on bismuthene-coated microfiber [25]. (**f**) The formation process of the diffraction rings of the 633 nm laser corresponding with the 532 nm laser excitation [26].
## 3 Classification
### Mode of Communication
#### 3.3.1 Direct  
_Direct_ mode of communication involves vehicles and VRUs communicating with each other _directly_ i.e., without any intermediate entity. References [3; 4; 15; 21; 28; 39] are some examples of such direct mode that use various technologies for communication. This may be the fastest mode of communication among all three modes due to its ability to establish direct communication. This mode may be best suited for safety applications due to lower latency in communication. However, it requires that all devices be equipped with same type of communication technology. This may pose deployment challenges. It also requires devices to process the received safety messages locally which may require high computing power. Also, due to its direct nature, the range of communication is limited by the underlying technology.
# Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics
## 3 SGP Motion Model Implementation
### Vehicle Parameter Estimation
As in the process applied with the mobile robot, we filtered the estimated baselines excluding the data for the motion of the rear wheels, in which \((\Delta\phi_{r}-\Delta\phi_{l})<0.1[rad]\), describing \(\Delta\phi_{b,r}\) and \(\Delta\phi_{b,l}\) the rear wheel movement. Additionally, we removed unrealistic baselines that ranged above 110% and below 90% of the baseline median. Due to a higher noise level in the baseline values, absolute thresholding was not applicable. For the virtual front wheel radii estimation, we used test input data from the simulation, having previously defined a driving scenario with a straight road so that the back wheels and front wheels could rotate in a comparable manner similarly to the virtual front wheel. Based on this we defined the following Equations (9) and (10). \[\Delta\phi_{v}r_{v}=\frac{(\Delta\phi_{f,r}+\Delta\phi_{f,l}) \cdot\tilde{r}_{f}}{2}=\rho=\frac{(\Delta\phi_{r}+\Delta\phi_{l})\cdot\tilde{ r}_{b}}{2} \tag{9}\] \[r_{v}=\frac{(\Delta\phi_{r}+\Delta\phi_{l})\cdot\tilde{r}_{b}}{2 \Delta\phi_{v}}=\frac{(\Delta\phi_{r}+\Delta\phi_{l})\cdot\tilde{r}_{b}}{2 \frac{(\Delta\phi_{f,r}+\Delta\phi_{f,l})}{2}}\sim\mathcal{N} \tag{10}\]  
in which we used the rear wheel movement \(\Delta\phi_{r,l}\), the front wheel movement \(\Delta\phi_{f,l}\) and \(\Delta\phi_{f,r}\) including the front wheel radius \(\tilde{r}_{f}\) and the estimated rear wheel radius \(\tilde{r}_{b}\sim\mathcal{N}\). Note that the front and rear wheel mean radii are not equal. For each element in the test input, we performed the radii estimation for the radius from Equation (10). The resulting set of Gaussian distributions are combined using the basic properties of Gaussian distributions. Since the sGP models estimate the movement of the vehicle for \(P\), the complete description of the rear kinematics of the Ackermann steering is enough to estimate the vehicle's behavior. Since the kinematic constraints connect the virtual wheels and rear wheels through the ICR, we could implement a motion model for a Bayes filter without estimating \(T\). This is different to the bicycle model described in [46], where the kinematic model is reduced to a virtual rear as well as a front wheel. Since all the motion parameters refer to the center of the rear baseline value, we can estimate \(\Delta X\), \(\Delta Y\) and \(\Delta\theta\) without \(T\). However, we estimate \(T\) in order to have a more complete insight into the models. We calculated \(T\) using the test wheel motion, estimated baseline \(B\) and the distance \(R\) to the ICR. We estimated \(T\) through \(\tilde{v}_{t}=\vec{\omega}_{t}\times\vec{k}\), where \(\vec{k}\) describes distances and includes \(T\). This calculation includes the factor \(\frac{\tilde{r}_{b}}{r_{v}}\).
# Roller-Integrated Acoustic Wave Detection Technique for Rockfill Materials
## 2 Methodologies
### Technical Implementation  
In this study, a roller-integrated acoustic wave detection technique was developed that includes two parts, detection and signal analysis. The schematic diagram of this technique is shown in Figure 9. The detection principle is explained above. This technique has the characteristics of non-contact, high efficiency, high precision, continuousness, and convenience. It is suitable for rockfill materials.  
As shown in Figure 9, the detection device (2) includes an acoustic field microphone (3), a microphone mounting device (10), a signal conditioning module (4), a data acquisition module (5), an analysis processing module (6), a display (7), and a GPS receiver (9). Moreover, the system also includes a vibratory roller (1) and an onboard battery (11). The sound field microphone (3) is mainly utilized to receive acoustic field time-domain signals. The signal conditioning module (4) is utilized to convert the signal received by the sound field microphone (3) into a standard signal by means of amplification and filtering. Then the standard signal can be collected by the data acquisition module (5), which simultaneously acquires the spatial signal associated with the roller position provided by the GPS receiver (9). Subsequently, two signals are transmitted to the analysis processing module (6) through wired communication. The analysis processing module (6) is utilized to implement the filtering, spectrum analysis, and logarithmic processing of the acoustic signal, obtain SHA of  
Figure 8: Relation curve between dry density of gravel materials and SCV based on A-model. SCV: sound compaction value.  
Figure 9: Schematic diagram of acoustic wave detection technique.  
the effective acoustic signal, calculate the SCV values multiplied by the calibration coefficient in real time, deal with the spatial signals related to the roller position by the differential algorithm, and calculate the current position coordinate of vibration roller 1 (centimeter level). The display (7) is utilized to display the real-time compaction nephogram with the spatial location characteristic and display other relevant information on the spot, such as the driving speed, compaction monitoring information, number of compaction times, and so on. The GPS receiver (9) is utilized to acquire spatial signal information relating to the roller position. The RTK-GPS receiver, satellite, and GPS base station fix the position of the roller. The microphone installation device (10) is utilized to fix the acoustic field microphone (3). Vibration isolation of the contact parts between the acoustic field microphone (3) and the installation device (10), as well as between the installation device (10) and the vibratory roller (1), is implemented by soft rubber. The onboard battery (11) provides power support for the entire continuous compaction control acoustic wave detection system.
# Assessment of Appropriate Geometry for Thermally Efficient CO\({}_{2}\) Adsorption Beds
## 3 Results and Discussion
### Temperature Profiles during Adsorption and Regeneration Processes  
Temperature values increase in the bed during the adsorption process due to the exothermic phenomenon of the adsorption. An increment in bed temperature has a negative impact on the adsorption capacity, so a bed design that can minimize increases in temperature values is desirable. Figure 7a shows that the outlet temperature of the triangular cross-sectional bed has the lowest temperature values. Its maximum temperature (during the adsorption process) is about 327 K, which is lower than that of the circular bed by 6 K. Figure 7b also emphasizes the merits of the triangular bed in reaching higher temperatures quickly during the desorption process. The triangular cross-section has three corners that raise the bed temperature closer to the surfaces than the core. The core of the circular cross-sectional bed is wider, so the heating process takes time since the thermal conductivity of adsorbent materials is very low (\(\sim\)0.3 W/m\(\cdot\)K).
## 3 Results  
### Release Delivery of VEGF and MCP-1  
The VEGF and MCP-1 loaded CP-1 microspheres, VEGF loaded CP-1 microspheres, MCP-1 loaded CP-1 microspheres, VEGF loaded CP-2 microspheres, and MCP-1 loaded microspheres were prepared by a double emulsion method. SEM images showed that growth factors loaded microspheres were homogeneous and the diameters for the different kind of microspheres were 0.7 \(\pm\) 0.12 \(\upmu\)m (Figure S1). Then, the growth factor encapsulation efficiency (\(E_{encaps}\)) was measured using ELISA kits, as shown in Figure S2. For dual VEGF and MCP-1 loaded into CP-1 or CP-2 microspheres, the \(E_{encaps}\) of MCP-1 was higher than that of VEGF, and for a single growth factor loaded into CP-1 or CP-2 microspheres, the \(E_{encaps}\) of MCP-1 was also higher than that of VEGF. According to the \(E_{encaps}\) of VEGF and MCP-1, and the biodegradable characteristics of linear PLGA-mPEG block copolymer microspheres, three delivery systems were constructed by allocating different types of microspheres into star-shaped PLGA-mPEG hydrogels (Figure 1) [19]. Figure 1a showed that VEGF and MCP-1 had a similar release profile from the composite hydrogel in 4 weeks, proved that VEGF and MCP-1 were simultaneously released from the hydrogel. However, the cumulative MCP-1 release in delivery system II increased fast to 49.3 \(\pm\) 4.7% within the first 9 days compared to the delivery system I (36.7 \(\pm\) 3.1%), exhibiting an initial burst release behavior (Figure 1b). For delivery system III, the cumulative MCP-1 release decreased to 16.8 \(\pm\) 3.9%, presenting a delayed release behavior (Figure 1c). Meanwhile, VEGF performed a delay-release profile in delivery system II, which was similar to release profile of MCP-1 in delivery system III.
## 2 Data and Methods
### Study Area and Data
#### 2.1.3 Ocean Numerical Models and Reference MDT  
In order to make the comparison between the MDT solutions and the empirical MDTs, several ocean numerical models; i.e., Simple Ocean Data Assimilation 3 (SODA3), Ocean and Sea-Ice Reanalysis System (ORASS), Copernicus, and geodetic model CNES-CLS13 MDT were used in this study. The spatial resolutions of the SODA3, ORASS5, and Copernicus ocean models were all \(1/4^{\circ}\), but the reference time of these MDTs was different; detailed information is listed in Table 2. Based on Sea Level Anomalies (SLA) data provided by Aviso ([[ftp://ftp-access.aviso.altimetry.fr/climatology](ftp://ftp-access.aviso.altimetry.fr/climatology)]([ftp://ftp-access.aviso.altimetry.fr/climatology](ftp://ftp-access.aviso.altimetry.fr/climatology)) (accessed on 19 May 2020)), the reference time of these MDTs was unified to the same time period [21].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Geoid Model** & **Maximum d/o** & **Assignulated GOCE Data** \\ \hline DIRR4 & 260 & Nov. 2009–Aug. 2012 \\ DIRR5 & 300 & Nov. 2009–Oct. 2013 \\ DIRR6 & 300 & Oct. 2009–Oct. 2013 \\ TIMR4 & 250 & Nov. 2009–Jun. 2012 \\ TIMR5 & 280 & Nov. 2009–Oct. 2013 \\ TIMR6 & 300 & Oct. 2009–Oct. 2013 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the multigeneration GOCE-derived GGMs.  
Figure 2: The error degree variance in geoid height calculated with different GOCE-derived GGMs.
# Multi-Sensor Data Fusion Method Based on Self-Attention Mechanism
## 2 Model  
Multi-sensor data fusion is a critical field aimed at utilizing information from multiple sensor systems to provide more accurate and complete outputs using data-fusion techniques. In this context, the proposed fusion model shown in Figure 1 integrates data from a binocular vision measurement system, laser tracking system, and depth camera system to generate high-precision point clouds as the output. The process of acquiring a point cloud using multiple sensors often involves the challenging task of achieving a unified coordinate system, which requires complex treatments such as coordinate transformation and registration. However, the proposed multi-sensor data fusion method does not require a unified coordinate system, simplifying the data preprocessing steps and providing a more efficient solution.  
The workflow of the multi-sensor data fusion model begins by scanning the target object using a binocular vision measurement system. This step captures two types of point cloud data: low precision and high precision. A low-precision point cloud provides the overall structural information of the target object to the network; however, its accuracy is limited by noise and distortion. A high-precision point cloud serves as labeled data to guide the learning and optimization of the network to achieve the desired output. Subsequently, a laser tracking system is employed to measure the control points on the target object. The laser tracking system provides highly accurate position information for the high-precision control points used to calibrate and enhance the point cloud. Simultaneously, the depth camera system captures both RGB and depth images of the target object. RGB images provide color and texture information about the target object, while depth images provide distance and depth information. The image data provide richer visual information to the model, enabling a better understanding of the appearance and shape features of the target object.  
All of these data, including the low-precision point cloud, high-precision control points, RGB images, and depth images, were preprocessed accordingly and input into the constructed convolutional neural network with self-attention (CNN-SA) multi-modal data fusion network. The purpose of this network is to learn how to effectively fuse multi-modal data from different sensor systems, with high-precision point clouds as the output. By training the neural network, the model learns the correlations and weights between the data, thereby maximizing the utilization of the information provided by each sensor system.  
The multi-sensor data fusion model overcomes the limitations of a single-sensor system and maximizes the quality of the point clouds. The multi-modal data fusion network, which is the core component of the multi-sensor data fusion model, plays an important role in integrating and extracting information from different modalities, and the various constituent modules in the network will be introduced in detail in the next section.
## B. Distance Estimation Error in RSS-Based Localization  
Assuming that the measurement of the received signal strength \(P_{RX}\) is affected by an additive Gaussian random noise with variance \(\sigma_{RSS}^{2}\), the variance of the RSS measurement can be reduced if \(n\) measurements are averaged. In this case, the averaged measured value of the RSS can be expressed as:  
\[\tilde{P}_{RXav}=P_{RX}+\mathcal{N}\left(0,\frac{\sigma_{RSS}}{\sqrt{n}}\right) \tag{27}\]  
Note that this variance \(\sigma_{RSS}^{2}\) coincides with the variance of the log-normal shadowing \(\sigma^{2}\) if the environment is dynamic or the nodes are moving, whereas in a completely static situation, it will be lower than \(\sigma^{2}\), as explained in Section 3.2.
## 3 Architecture of the Platform and Related Software and Hardware Aspects
### Core Facilities for Nodal Interactions and Ad Hoc Networking  
#### 3.1.1 The Localization Process  
The localization of each mobile node relies on GPS input and involves a transformation of coordinates and a parametrizable smoothing of the results.  
In general, there are two types of coordinate systems used in a Geographic Information System (GIS): The first type is a global, spherical coordinate system expressed in terms of the latitude (\(\varphi\)) and longitude (\(\lambda\)). (In three-dimensional mappings, this representation additionally includes an altitude/elevation component.) The second type is a projected coordinate system. All systems of this type, such as Universal Transverse Mercator (UTM), Albers Equal-Area, or Robinson, among others, provide mechanisms to project maps of local areas on the Earth's spherical surface onto a two-dimensional Cartesian coordinate plane by transforming the global coordinates of the Earth model \(\varphi\) and \(\lambda\) to planar Cartesian coordinates x and y.  
In the MANET platform, each mobile node is equipped with its own GPS device, which provides measurements of the node's location expressed in spherical coordinates (\(\varphi,\lambda\)). These are subsequently transformed to Easting (E) and Northing (N) geographic Cartesian coordinates using the \"Albers Equal-Area Conic Projection\" method [36; 37; 38], which is a conic, equal-area map projection that uses two standard parallels to reduce some of the distortion introduced by projections with one standard parallel. Although this method still suffers from some distortion of shapes and linear scales, the distortion is minimized in the region between the standard parallels.  
GPS measurements are repeatedly acquired and transformed by each node periodically (with a frequency that can be tailored), thus enabling updates of the node's current position (expressed in Cartesian coordinates) and velocity. However, instead of using the most recent values directly, these values are fed into a circular buffer, which acts as a sliding window of observations. Whenever the current position and velocity of the node are required, the relevant information is obtained by least squares-based smoothing exercised on the values currently stored in the buffer. This smoothing helps to alleviate anomalies due to measurement errors or random perturbations induced by the real-world environment. The size of the buffer can be parametrized, thus controlling the degree of the smoothing effect.  
We note that it is possible to bypass the aforementioned GPS-based localization process, simply by making the localization module return location and velocity values produced by simulation software, according to an appropriate mobility model. This alternative mode of operation can be applied to some or all of the mobile nodes in the MANET. In this  
Figure 2: Architecture of the platform.  
mode, the mobility of the affected nodes is emulated, and there is no need to attach them to robotic devices.