esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study was performed to confirm the correlation between gas flow and the LIBS spectral signal, by measuring changes in the plasma signal according to the gas flow. In the LIBS system, the shape of the plasma generated by the laser changed with the gas flow, as did the strength of the signal detected by the detector. This signal change can be used to determine the gas flow rate. In addition, based on the gas flow rate, the LIBS signal can be accurately calibrated. The change in plasma signal according to the observation direction was predicted by modeling the view factor and wave equation. Although modeling requires simplified assumptions, the trend of the signal change according to deformation of the plasma was confirmed. In the simulation, the LIBS spectral intensity changed according to the increase in gas flow rate, and the largest signal amplitude was seen when the angles of the plasma and signal detector coincided. In the experiment, the LIBS signal change was measured on a Mg alloy surface in the presence of gas flow. Meaningful LIBS plasma signals were observed at a flow rate of 300-370 cm\({}^{3}\)/s. In this range, the intensity of the plasma signal increased with increasing flow rate. At flow rates above this range, the signal was inconsistent, which was inferred to be a result of instability of the plasma due to the  
Figure 14: LIBS intensity by flow rate for Ar, N\({}_{2}\), and He gases.  
Figure 15: Measurement sensitivity of LIBS signal intensity for Ar, N\({}_{2}\), and He gases.  
flow and reduction of the view factor. In the experiment with three different gas flows, the sensitivity of the LIBS signal to the flow rate change was highest in the order of He, N\({}_{2}\), and Ar. In addition, the measured flow rate change for He gas showed the highest similarity to the theoretical calculation.  
This study showed that the change in LIBS signal can be measured in the presence of gas flow. The results could be used to calibrate the LIBS signal measured in similar environments. In addition, our measurement method could be used to indirectly predict the characteristics of gas flow based on changes in the plasma signal.  
Conceptualization and supervision, J.K.; experiment and analysis, S.Y.; evaluation, H.-W.C.; writing--review and editing S.Y., H.-W.C. and J.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research Program by the SeoulTech (Seoul National University of Science and Technology).  
Not applicable.  
This study was supported by the Research Program funded by the SeoulTech (Seoul National University of Science and Technology).  
The authors declare no conflict of interest.
## 2 Challenges in Underwater Communications
### Limited Bandwidth  
The harsh underwater medium allows only specific frequencies in the acoustic spectrum to carry information [12]. This restricts the available bandwidth, which in turn, puts restrictions on the design of the acoustic systems. Consequently, routing protocols have to take into account the limited range of available frequencies to select the routing paths and deliver data packets to the final destination. The convergence (transmission range) of an underwater application is inversely proportional to its bandwidth, as shown in Table 1[1].
# Use of Surface Acoustic Waves for Crack Detection on Railway Track Components--Laboratory Tests
## 1 Introduction
### Surface Acoustic Waves for Crack Detection  
In this paper, the term surface acoustic wave (SAW) refers to ultrasonic waves that propagate along surfaces similarly to Rayleigh waves, with the difference that the propagation medium is not an elastic half-space but a body with geometrical dimensions much larger than the wavelength. These waves may also be termed quasi-Rayleigh waves. A promising method for permanent monitoring of components with regard to surface cracks is to employ surface acoustic waves for crack detection and characterization [3]. To this end, surface acoustic waves are excited at one point of the structure, and the transmitted waves are detected at another point. The detected signal changes if a crack grows between the excitation and the detection points, since the crack acts as an obstacle in the wave path [3; 4]. Possible techniques for the excitation of surface acoustic waves are electromagnetic acoustic transducers (EMAT) [5], piezoelectric transducers equipped with wedge or comb adaptors [6], or pulsed laser beams [3]. [PERSON] et al. recently published a paper dealing with the location of surface and sub-surface defects in rail tracks, using Rayleigh waves excited and measured via a non-contacting approach [7]. The main differences between their work and the work presented here will be discussed in the corresponding section on specimen and measurement setup. The goal of the present work is to investigate the feasibility of the SAW-based approach using mobile transducer equipment and wave propagation simulations, to pave the way for the design of a permanent installation.
# A Knowledge Graph Embedding Approach for Polypharmacy Side Effects Prediction
## 2 Methods
### Model Architecture  
Figure 2 shows the overview of the proposed model for multidrug side effects prediction. This model is mainly divided into two parts: embedding and prediction modules. For a given multidrug combination, the initial embedding vectors of its constituent drugs are given as input to the embedding module. Here, the multidrug combination includes a drug pair as well as single drug to consistently control single and multidrug inputs, i.e., a single drug is considered a multidrug combination consisting of one drug. Theoretically, all multidrug input data can be fed into the embedding module without limiting the number of drugs constituting the multidrug combination. However, owing to the limitations of the present labeled data, we could test only multidrug combinations comprising up to two drugs. As the output, one unified embedding vector was produced for a given multidrug combination, regardless of the number of constituent drugs. The unified embedding vector of a multidrug combination reflects its associated side effect information and connectivity strengths from its constituent drugs to related proteins. This embedding vector is regarded as a multidrug embedding vector.  
The detailed description of the embedding module is as follows. It consists of a fully connected neural network with 1 hidden layer, where the hidden layer has 384 dimensions. Given a multidrug, the output vectors of the hidden layer for constituent drugs were summed to combine individual drug characteristics. The activation function of the hidden layer was LeakyReLU [28], whereas that of the output layer was hyperbolic tangent. The  
Figure 1: An illustration of the core idea of this study based on a toy example. The embedding vectors of single drugs, multidrug combinations, and proteins are constructed such that they exist in the same latent space. A multidrug is embedded in the position where the properties of its constituent drugs are superimposed.  
summed vector was then normalized to 1. Herein, all constituent drugs of a given multidrug do not have separate learning parameters; instead, they share the same parameters.  
The prediction module receives a multidrug embedding vector as Input and produces prediction scores for targeted side effects as output. This module is a fully connected layer without hidden nodes, where a sigmoid function was used as the activation function. Compared with the embedding module, the prediction module has a simple structure because our model was designed to learn drug side effect information more in the embedding module than in the prediction module. This allows interpretability of side effects using the embedding vectors.
## 4 Numerical Model
### Influence of Water-Cement Ratio
#### 4.5.2 Vault Displacement Analysis  
Figure 21 shows the displacement curve of seven monitoring points near the arch crown. Monitoring point 4 was the central point of the vault, monitoring points 1-3 were on the left side of the central point of the vault and monitoring points 5-7 were on the right side of the central point of the vault. When \(\eta\) = 0.90, 0.95, and 1.00, the maximum displacement occurred at the monitoring point 3, and when \(\eta\) = 0.75, 0.80, 0.85, the maximum displacement occurred at the center point of the vault, indicating that with the increase of slurry water-cement ratio, the area where the maximum displacement occurred gradually moved to the side of the left partition wall. When the water cement ratio was small (\(\eta\) = 0.75, 0.80, 0.85), the displacements of the symmetrical monitoring points with the same height on both sides of the vault were basically the same.  
Figure 22 shows the relationship between the maximum displacement of monitoring points on the vault and the water-cement ratio. When the slurry water-cement ratio \(\eta\) = 0.75, 0.80, 0.85, 0.90, 0.95, and 1.00, the maximum displacements of the vault were 21.7 mm, 22.9 mm, 25.8 mm, 33.9 mm, 70.8 mm, and 139.8 mm, respectively. Compared with the simulation results of the previous water-cement ratio condition, the variation range of the maximum displacement of the vault was 1.2 mm, 2.9 mm, 8.1 mm, 36.9 mm, and 69 mm. With the increase of water-cement ratio, the maximum displacement of the vault increased and the increasing range was also increased, indicating that when the water cement ratio was large (\(\eta\) = 0.85, 0.90, 0.95, 1.00), reducing the slurry water-cement ratio obtained obvious reinforcement effects. When the slurry water-cement ratio was 0.80 or 0.85, the maximum displacement of vault caused by excavation was small and the reduction range was large, and the reinforcement effect was better and the benefit was higher.  
Figure 21: Displacement of monitoring points near the vault of left tunnel.
# Solar Sail-Based Mars-Synchronous Displaced Orbits for Remote Sensing Applications
## 3 Numerical Results  
Using the mathematical model described in the previous section, the performance required by the ideal solar sail is evaluated as a function of the desired characteristics of the heliocentric, circular DNKO. In this context, Figure 4 shows the required values of \(\{\alpha,\ \beta\}\) as a function of \(\{\rho/a,\ H/a\}\).  
In particular, Figure 4a is consistent with a low-performance solar sail (i.e., \(\beta\leq 0.05\)), while Figure 4b is consistent with a medium/high-performance solar sail (i.e., \(\beta\geq 0.05\)). For comparison with the performance of actually designed solar sails, the obtained lightness number is just under 0.01 for the LightSail 2 [44] (i.e., \(\beta\simeq 0.0098\)), Near-Earth Asteroid Scout (NEA Scout) [45; 46] (i.e., \(\beta\simeq 0.0093\)), and Advanced Composite Solar Sail System (ACS3) [47] (i.e., \(\beta\simeq 0.0077\)) missions. Therefore, based on the classification made in this paper, the aforementioned first-generation missions fall in the group of low-performance solar sails.  
Note that the gray areas in Figure 4 correspond to the envelope of all the spheres of influence of Mars during its revolution around the Sun. In fact, the SOI of Mars is a spherical region that moves in the interplanetary space following the Red Planet and with a variable radius depending on the Sun-Mars distance. We can indeed express the radius \(R_{\text{SOI}}\) of Mars SOI as  
\[R_{\text{SOI}}=\rho_{\mathcal{G}^{\prime}}\left(\frac{\mu_{\mathcal{G}^{\prime }}}{\mu_{\odot}}\right)^{2/5} \tag{10}\]  
Figure 4: Required values of \(\{\alpha,\ \beta\}\) as a function of \(\{\rho/a,\ H/a\}\). The gray areas correspond to the envelope of the SOI of Mars. (**a**) Low-performance ideal solar sails. (**b**) Medium/high-performance ideal solar sails.  
where \(\mu_{G}\simeq 42,828\,\,\,\text{km}^{3}/\text{s}^{2}\) represents the gravitational parameter of Mars and  
\[\rho_{G^{\prime}}=\frac{a\left(1-e^{2}\right)}{1+e\,\cos\
u} \tag{11}\]  
is the Sun-Mars distance, ranging from a minimum of 1.3814 au to a maximum of 1.6660 au, as a function of the planet true anomaly \(\
u\) (i.e., the angle between axis \(p\) and the current Sun-Mars line).  
For example, consider the two scenarios indicated in Figure 5. In particular, scenario \(A\) is obtained with \(\rho=0.998840\,a\) and \(H=0.01\,a\) (from which \(\beta\simeq 0.1\)), while scenario \(B\) is obtained with \(\rho=0.999423\,a\) and \(H=0.005\,a\) (from which \(\beta\simeq 0.05\)). Note that \(\alpha\simeq 71\) deg in both cases. These two possible cases (the former consistent with a high-performance solar sail and the latter with a medium-performance solar sail) are used as examples in the next sections.
## 5 Conclusions  
Modern surveying methods such as laser scanning and photogrammetry make it possible to create nearly flawless and highly detailed digital 3D representations of objects of all forms and sizes, which allows the preservation of the objects of cultural or technical heritage in the digital form for future generations. The level of preserved detail may, however, become an obstacle for such preservation as, naturally, it increases the data size.  
For this reason, we proposed a novel method for the progressive dilution of such point clouds, primarily intended for creating digital twins of historical buildings (but other applications are also possible). This algorithm is based on the determination of the flatness in the area around each point in the cloud (this is determined by fitting the plane through the spherical surroundings of the point and calculating the deviation of these points from the plane; where the surfaces are planar, this deviation would be minimum, while on curved/rugged surfaces, this deviation is large). Depending on this flatness and user-defined parameters of minimum and maximum dilution, the algorithm then dilutes flatter areas more than highly detailed ones, which brings a notable reduction in the data size compared to the uniform dilution, providing comparable detail. The advantages of this algorithm include a significant point cloud reduction with minimum loss of detail (demonstrated both numerically using RMSD and visually), easy implementation in free software CloudCompare, and rapidity of processing. The only limitation we are aware of is the need for good preparation of the input data and cleaning it from noise and/or data artifacts.  
Conceptualization, M.S.; methodology, M.S.; software, M.S.; validation, R.U. and T.K.; formal analysis, M.S. and R.U.; investigation, T.K.; writing--original draft preparation, M.S.; writing--review and editing, R.U. and T.K.; visualization, M.S.; funding acquisition, R.U. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Grant Agency of CTU in Prague--grant number SGS22/046/OHK1/17/11, \"Optimization of acquisition and processing of 3D data for purpose of engineering surveying, geodesy in underground spaces and 3D scanning\", and by the Technology Agency of the Czech Republic--grant number CK03000168, \"Intelligent methods of digital data acquisition and analysis for bridge inspections\".  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Vocal Fold Cell Phenotyping
#### 2.1.2. Vocal Fold Cell Isolation  
The mucosa of both sides of vocal folds were dissected and separated from the underlying thyroarytenoid muscles under a stereoscope. Vocal fold mucosal samples within an experimental group (e.g., 1 day after injury) and cytometric panel (see below) were pooled and subjected to single cell isolation. The strategy of sample pooling has been used for rat vocal fold cell and protein analysis considering the small size of rat vocal folds [66; 67]. Samples were dispersed into single cell suspensions using digestion, centrifugation and filtration steps [66]. First, dissected mucosa were placed in conical panels covered in aluminum foil and then incubated in Ca/Mg free DPBS solution (Mediatech, USA, Cat#: 21-031-CV) with 0.05% collagenase (Gibco, USA, Cat#: 17018-029) /0.001% DNAse I (Sigma Aldrich, USA, Cat#: D4513) for 20 min at 37 \({}^{\circ}\)C to dissolve the extracellular matrix for cell release. The samples were thoroughly mixed and incubated again in 37 \({}^{\circ}\)C water bath. Once samples were visually confirmed to contain no large chunks of tissue, the solution was filtered with 40 nm cell strainers. Filtered samples were centrifuged at 290g for five minutes at 4 \({}^{\circ}\)C. After aspirating the supernatant, remaining cells were suspended in HBSS (Corning, USA, Cat#: 21-022-CV) from 1M HEPES solution with 2% fetal calf serum (HyClone, Canada, Cat#: SH30071.03) and 10 nM HEPES (diluted from 1M HEPES solution, Corning, USA, Cat#: 25-060-CI). Resulted single cell suspension samples were transferred to flow cytometry polystyrene tubes and centrifuged at 201g for 10 min at 4 \({}^{\circ}\)C. After aspiration and suspension, the total cell number was determined with trypan blue staining and a hemacytometer. The number of isolated vocal fold cells ranged between \(2.15\times 10^{5}\) cells and \(2.02\times 10^{6}\) cells across time points.
# Point Cloud Upsampling Algorithm: A Systematic Review
## 4 Deep Learning-Based Point Cloud Upsampling
### Other Methods of Point Cloud Upsampling Models  
In addition to the mainstream upsampling models mentioned above, other techniques can further improve point cloud upsampling models.  
[PERSON] et al. (2016) proposed a point cloud upsampling model that uses the entire object model as the input and can learn potential features in the point cloud belonging to different object categories. They studied the effects of random downsampling and curvature-based downsampling, in addition to upsampling at different magnifications. Similarly, this model also has some limitations. It cannot effectively process defective point cloud data because it learns the entire object's features, which limits its application to low-resolution inputs.  
[PERSON] et al. [49] proposed a network structure that can learn point cloud normals and color features. Its network structure is constructed as a variant of PU-Net, and other features and coordinates of the point cloud are used as model inputs. Although the network's sampling effect is not outstanding, adding the normal and color of the point cloud to the model for training can retain features other than the shape, which is a research direction having high potential.  
[PERSON] et al. [50] proposed a sequential point cloud upsampling framework to generate fine-grained and temporally consistent upsampling results for dynamic point cloud sequences. They extract features from multiple low-resolution point clouds (such as previous/current/subsequent frame) and fuse the features to perform an upsampling operation. The model can capture multi-scale information of dynamic sequences and improve the upsampling effect. This model also has significant limitations, including requiring a continuous point cloud input and consuming a large quantity of computing resources.
## 2 Materials and Methods
### Description of Florescence Spectral Information  
#### 2.4.1 Florescence Ratio Reflectance, Difference Reflectance and Their First Derivative Reflectance  
The leaves of rice plants grow completely before flowering, making the leaf area index (LAI) basically stable for about two weeks. This means that the green structure of the rice canopy can remain stable during flowering. From the start of flowering, white florets were constantly adding to the unchangeable green background until the full flowering stage, and then diminished till the end of flowering. It could be presumed that the change of canopy reflectance before and after flowering was mainly caused by the change of florets. Therefore, if canopy reflectance before flowering (green background reflectance) and during the flowering stage (florets + green background reflectance) could be obtained, the rice flowering information can be characterized by comparing the difference of canopy reflectance before and after flowering. Based on this mechanism, the idea of florescence ratio reflectance, difference reflectance and their first derivative transformations were proposed in the study. They were expected to enhance the difference in canopy reflectance between before and after flowering. The florescence ratio reflectance (\(R_{r\_F(l)}\)) and florescence difference reflectance (\(R_{d\_F(l)}\)) were calculated as:  
\[R_{r\_F(l)}\ =\ \frac{R_{F(l)}}{R_{0\_FI~{}(i)}} \tag{5}\]  
\[R_{d\_F(i)}\ =\ R_{F(l)}-R_{0\_FI~{}(i)} \tag{6}\]where \(R_{F\left(i\right)}\) is the canopy spectral reflectance at wavelength \(i\) during the flowering stage, and \(R_{0,F\left(i\right)}\) is the canopy spectral reflectance of the exact day before flowering at wavelength \(i\).  
Derivative spectral transformation can effectively eliminate the interference of background to target reflectance [41]. Here, the first derivative florescence ratio reflectance (\(R^{\prime}_{r,F\left(i\right)}\)) and the first derivative florescence difference reflectance (\(R^{\prime}_{d,F\left(i\right)}\)) were calculated using florescence ratio reflectance and difference reflectance. They were expressed as:  
\[R^{\prime}_{r\_F\left(i\right)} = \frac{R_{r\_F\left(i+1\right)}-R_{r\_F\left(i-1\right)}}{\Delta\gamma} \tag{7}\] \[R^{\prime}_{d,F\left(i\right)} = \frac{R_{d,F\left(i+1\right)}-R_{d,F\left(i-1\right)}}{\Delta\gamma} \tag{8}\]  
where \(R_{r,F\left(i+1\right)}\) and \(R_{r,F\left(i-1\right)}\) are the florescence ratio reflectance at wavelength \(i+1\) and \(i-1\). \(R_{d,F\left(i+1\right)}\) and \(R_{d,F\left(i-1\right)}\) are the florescence difference reflectance at wavelength \(i+1\) and \(i-1\). \(\Delta\gamma\) is wavelength difference between wavelength \(i+1\) and wavelength \(i-1\).
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 4 Joining Procedure and Time Synchronization Problems
### Exchange of RPL Control Messages  
Once the incoming device is synchronized by TSCH, it can follow the same pattern of active and inactive slots within the slotframe. However, the node does not yet know about the topology of the network, as this requires it to receive an RPL DIO message. The node that is trying to join the topology will begin a process of sending DIS messages requesting the information necessary to join the topology. These messages will be transmitted with a fixed period that is determined by the DIS Transmission Time (DISTT). In contrast, the nodes that are already connected to the network are sending DIO messages using the Trickle Timer mechanism that was described in the previous sections.  
Figure 4 represents the RPL message exchange mechanism after synchronization. It can be seen how the DIS messages that are received by nodes that are already part of the network restart the Trickle Timer, forcing DIO messages to be sent more frequently. The connection to the RPL DODAG only occurs when the new nodes receive the DIO messages with the topological information.