esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Since the point cloud data from LiDAR has begun to be used to obtain railway information, the LiDAR technology has been gradually extended from qualitative analysis to quantitative analysis. As an important branch of LiDAR technology applied in railways, existing railway reconstruction based on point cloud data has formed many calculation models and methods. Currently, highly dense and high-precision LiDAR equipped with a sensor is becoming more and more common, making it possible to reconstruct existing railways accurately. To improve the technical maturity of existing railway reconstruction practices and to promote their business applications, it is necessary to obtain the accurate reconstruction track parameters. The key to obtaining these parameters is determining the objective function, constraint condition, and computational method. A feasible operational basis can then be provided for obtaining the track reconstruction parameters.  
In this study, a method for existing railway reconstruction with constrained optimization based on point cloud data is presented. Based on the intelligent algorithm theory, the concept of the PSO is introduced, and the method of omnidirectional searching is presented. After the point cloud data of the centerline was obtained, the objective function with the constraint condition was established and combined with railway survey technology. For single and continuous curves that contain multiple basic curve units, the complexity of the calculating time of reconstruction is analyzed. Due to the low demand for initial parameters of the proposed method, identifying track plane elements does not need to be very precise at the beginning. Using design data, measurement data, and artificial data as inputs, this study analyzed the feasibility, suitability, robustness, and practicality of the proposed method. The results show that the method can obtain reconstruction parameters and is applicable to engineering in practice.  
Moreover, vehicle-borne laser 3D scanning technology overcomes the problems such as the low flexibility, low efficiency, and complexity of the traditional method, and it reduces operation time in the railway where the train is running to ensure accuracy and safety and enhance the efficiency  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Elements** & **Average Relative Error** \\ \hline Circular curve radius & 1.2\% \\ First transition curve & 2.3\% \\ Second transition curve & 5.4\% \\ First tangent & 1.8\% \\ Second tangent & 0.8\% \\ Average & 2.3\% \\ Full curve & 1.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average relative error of three units of the track curve.  
and reliability of the existing railway reconstruction. Additionally, in terms of reconstruction, the parameters of point cloud data mainly differ from GNSS data in the means of obtaining the data. Therefore, the above method is also applicable to GNSS data. Furthermore, a future application scenario would likely shift from railways to highways.
## 5 Case Study  
### Construction of the DBN Structure  
This paper uses 1050 records of emergency events on the outer ring road of Shanghai from 3 January 2018 to 28 December 2019, as the data source. Following the methodology outlined in Section 3.1, the incident data is preprocessed, and the resulting data is then imported into GeNie software for DBN modeling [52]. A hierarchical Bayesian network structure, illustrated in Figure 12, is established as the initial model structure in GeNie 3.0 software.  
The initial hierarchical network structure nodes are divided, as shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dimensions** & **Factors** & **Elements** & **Features of Time-Varying (Dynamic/Static)** \\ \hline \multirow{8}{*}{Pressure resilience} & Exposure to presecure & Exposure to meteorology & S \\  & Exposure to pressure & Exposure to road type & S \\  & Exposure to traffic flow & D \\  & Diversity of pressure & Diversity of accident types & S \\  & Diversity of vehicle types & S \\  & Uncertainty of pressure & Uncertainty of settered objects & S \\  & Uncertainty of fine & S \\  & Hazardous to facility losses & S \\  & Hazardous to the vehicle involved & S \\  & Hazardous to facility losses & S \\  & Robustness of road width & S \\  & Robustness of road maintenance & S \\  & Robustness of states & Robustness of pavement performance & S \\ State resilience & & Robustness of lane access & D \\  & & Robustness of facility functions & S \\  & Redundancy of states & Redundancy of road network connectivity & S \\  & Response awareness & Redundancy of design traffic capacity & S \\ Response resilience & Rapidity of response & Independence of response and disposal & S \\  & Rapidity of response & Availability of rescue resources & S \\ Response resilience & Resourcefulness of response & Availability of fraction resources & S \\  & Availability of firefighting resources & S \\  & Responsive learnability & Emergency review capabilities & S \\ \hline \hline \end{tabular}
\end{table}
Table 5: Time-varying features of road operational resilience elements.  
Then, dynamic nodes such as RESILIENCE, pressure resilience, state resilience, and response resilience are associated with their own nodes in the previous time slice according to the node-relationship analysis, as shown in Figure 13.  
Meanwhile, based on the processed data source, the network structure learning is completed with the Greedy Thick Thinning algorithm (algorithm parameters). Max Parent Count = 10 to establish the connection between elemental nodes in the same layer and form the final DBN structure as in Figure 14.  
Figure 12: Initial hierarchical Bayesian network structure in GeNie.
## 2 Background and Related Works  
### Sentiment Analysis  
Sentiment analysis is a field of natural language processing that develops mechanisms to analyse the polarity of the opinion expressed in a sentence (or even a part of sentence) automatically. Sentiment polarity is the most well-studied sub-problem of sentiment analysis. It may be conceptualised as a multi-class text classification problem, in which the goal is to determine whether the overall opinion expressed in a given text is positive, negative or even neutral. There are many variants of this task. One is to classify a text according to a rating scale, e.g., 1 = \"worst\" to 5 = \"best\". We may consider different levels of analysis, from the document level to the sentence level. It is also possible to formulate the problem as a regression problem. The goal, in this case, is to predict the intensity of the sentiment that represents better the mental state of the author [12]. The intensity is a real value in the range from 0 (negative) to 1 (positive). Since it is a text classification problem, we can apply any existing supervised learning method, e.g., naive Bayes or Support Vector Machines [13; 14; 15].
## 2 Materials and Methods
### DTI Imaging Acquisition and Analysis  
Images were acquired using a 3.0T GE Healthcare whole-body high-speed imaging device equipped for echo planar imaging (GE Discovery MR750w 3.0T). Then, MRI scans were performed at the Affiliated Hospital of Yangzhou University. Participants were laid in a supine position and sedated during the examination. Head stabilization was achieved using foam pads, and any form of noise was reduced using the 29 dB-rating earplugs. The DTI protocol was TR = 16,500 ms, TE = 96.2 ms, flip angle = 90\({}^{\circ}\), field of view (FOV) = 224 \(\times\) 224 mm, acquisition matrix size = 112 \(\times\) 112, 70 interleaved slices, voxel size = 2 \(\times\) 2 \(\times\) 2 mm\({}^{3}\), 3 B0 images, 30 diffusion-weighted images, and b value = 1000 s/mm\({}^{2}\).  
Thereafter, processing of the diffusion MRI dataset was implemented using the \"Pipeline for Analyzing Brain Diffusion Images (PANDA) ([[http://www.nitrc.org/projects/panda/](http://www.nitrc.org/projects/panda/)]([http://www.nitrc.org/projects/panda/](http://www.nitrc.org/projects/panda/)), accessed on 29 September 2020)\" MATLAB toolbox [61]. The main procedures included an initial pre-processing and computation of DTI metrics for statistical analysis, where local diffusion homogeneity (LDH) = 7 voxels, smooth: normalizing resolution = 2 mm, and smoothing kernel = 6 mm.  
The preprocess steps included conversion of DICOM files, estimation of brain mask, cropping raw images, correcting for eddy-current effects, and calculating diffusion tensor metrics. Then, we normalized the diffusion metrics into the MNI space through atlas-based analysis, after which we calculated regional diffusion metrics by averaging the values within each region of the JHU ICBM-DTI-81 atlas, which showed 50 brain regions [62; 63; 64; 65]. Furthermore, the mean FA value of each region was extracted for statistical analysis. All procedures were fully automated and completed by PANDA.
# Evolution of Flow and Streaming in Exponential Variable Cross-Section Resonators
## 4 Results and Discussion
### Validation of the Model  
To verify the present GKS model and the relevant code, the numerical results from this work were compared with values predicted by using the Galerkin method. Figure 4 shows the curves of the pressure waves during two acoustic periods, calculated from these two methods. The pressure, \(P\), in Figure 4 is \(P_{0}+p\), where \(p\) is the oscillated pressure. The non-dimensional parameter \(t\Omega/2\pi\) expresses the acoustic cycle time under steady state, which is the same as that in Figure 2 of Reference [19]. The solid line denotes the results from the GKS model with shape parameter \(m=2.0\), and the circular symbol represents the results obtained with the Galerkin method, from Reference [19], with flare constant \(\alpha=4.0\). Here, a shape parameter, \(m=2.0\), is corresponding to a flare constant, \(\alpha=4.0\), and the non-dimensional velocity amplitude of the piston is \(U^{\pi}=5\times 10^{-4}\). The physical model and parameters settings are the same as in the literature [19], for convenient comparison. A slight discrepancy can be observed between the two waveforms, but overall, they seem to be in good agreement. The possible cause for the discrepancy may come from the different governing equations. The results from the Galerkin method are based on a one-dimensional wave equation, while the present results from the GKS are two-dimensional.  
Figure 4: Pressure waves during two acoustic periods, obtained from two different methods.  
Figure 3: Schematic of the ghost mesh on the boundary of the exponential-shaped resonator.
# Comparative Analysis of Urban and Rural Ecosystems in the Context of Suitability for Honeybees
## 3 Results
### Multivariate Correlation by Means of PCA  
The first principal component (PC1) explains 28.3% of the total variance, while the second component (PC2) accounts for 13.8%. The PCA revealed five principal components (PCs), which collectively explained 71.4% of the observed variance. The variables B1, B2, BC0, BC1, and SC1 exhibited strong positive loadings on PC1, indicating their significant contribution to this component. Olndex and HY exhibited strong positive loadings on PC2, suggesting that they play a key role in distinguishing variance along this dimension. Some variables (e.g., BC1, SC3) contributed to both PC1 and PC2, indicating that they influence multiple aspects of variance in the dataset (Figure 3 and Supplementary Figure S4).  
Clustering was performed using the K-Means method with five clusters and convex hulls. The average silhouette score was 0.39, indicating slightly better cluster separation compared to the previous three-cluster partitioning (Figure 4). The variables that most effectively differentiate the clusters using the Random Forest classifier have been identified. The most significant features have been ranked according to their contribution to cluster differentiation (see Table 7).  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Grup of Colonies**} & \multirow{2}{*}{**N**} & \multicolumn{3}{c}{**Trait *} \\ \cline{3-5}  & & **BC0 (dm2)** & **BC1 (dm2)** & **BGD (\%)** \\ \hline BCO \textless{} Median & 79 & 17.9 & 48.6 & 284 \\ BCO \textgreater{} Median & 81 & 39.9 & 68.5 & 176 \\ \hline \end{tabular}
* Abbreviations explained in Table 1.  
\end{table}
Table 6: The mean values of the traits in the groups of colonies were classified according to BC0 values relative to the median BC0, which is 28.4 dm2.  
Figure 3: The PCA loadings plot demonstrates the contribution of individual variables to the first two principal components (PC1 and PC2). The direction and magnitude of each variable’s influence on these components is indicated by the arrows.  
In addition to the numerical values, the cluster results were significantly influenced by the independent variables Year (\(\chi^{2}(3,\,200)\) = 14.28, \(p\) < 0.001) and Location (\(\chi^{2}(6,\,68)\) = 163.27, \(p\) < 0.001). These findings suggest that there is a statistically significant relationship between year, location, and cluster formation. However, no statistically significant association was observed between the type of location and cluster division (\(\chi^{2}(2,\,68)\) = 8.77, \(p\) = 0.067).
# Dynamic Identification of Tensile Force in Tie-Rods by Interferometric Radar Measurements
Therefore, tensile force identification theories requiring only the knowledge of natural frequencies as the input data can be applied when interferometric radar measurements of vibrations of the tie-rod are made. Thus, to exploit the advantages of the radar interferometry for the estimation of tensile force in tie-rod, the present paper first considers the choice of an appropriate tensile force identification theory for reprocessing radar data among the theories available in the literature (Section 2). In Section 3, the radar interferometry for dynamic structural measurements is presented. Then, in Section 4, the experimental setup for dynamic laboratory experiments on a tie-rod under known tensile forces is described. Furthermore, in Section 5.1, a modified version of the theoretical reference model for tensile force identification is proposed, aimed at better describing the structural conditions actually realized in the experiments. The experimental identification of the natural frequencies of a tie-rod is reported in Section 5.2, where a comparison between accelerometric and interferometric radar evaluations is made. Finally, in Section 5.3, the computational approach for determining the values of the tensile force starting from interferometric radar measurements is described. The tensile forces estimated from dynamic identification are then compared with the actual values. The obtained results indicate that an accurate estimation of tensile force in tie-rods through radar interferometry is possible provided that in the experiments, a corner reflector is installed on the tie-rod, and a suitable tensile force identification theory is employed. In particular, since the installation of the corner reflector is easier than that of accelerometers, the proposed approach might be convenient in practical applications.
## 5 ACTOR Design
### UCB for Non-Stationary Bandits (ACTOR-D)  
The basic version of UCB assumes that the distribution of rewards for each arm (transmission power) is stationary in time. However, that is rarely the case, and these distributions are subject to severe change due to evolving environments and mobility [37]. To handle non-stationary rewards, [PERSON] [18] proposes a variant of UCB called the Discounted UCB.  
The Discounted UCB alters the calculation of the first term in the UCB index by applying an adjustable discount factor (\(\lambda\)) to the older observations, giving more importance to the new ones. Equation (9) determines how the discounted UCB calculates rewards.  
\[\bar{X}_{t}(a)=\frac{\bar{X}_{t-1}(a)\cdot(RewardScale-\lambda)+r\cdot\lambda}{ RewardScale} \tag{9}\]  
_RewardScale_ is the maximum possible value for the rewards (100). For tuning \(\lambda\), a simple choice is to resemble the weight that Contiki-NG RPL assigns to the new packet's ETX in the routing [38]. Since there is no need to keep track of previous rewards, the Discounted UCB occupies a smaller space in the memory. Given the memory limitations, we chose the Discounted UCB algorithm to be integrated with ACTOR.  
The use of bandit models in embedded systems has been minimal due to practical limitations such as missing floating point operations and a small memory capacity. To tackle this challenge, fixed-point arithmetic has been proposed in the literature. [PERSON] et al. [36] propose methods to efficiently calculate two variants of the UCB algorithm (namely the Discounted UCB and the Sliding Window UCB) for the channel selection problem in IEEE 802.15.4. Through Monte Carlo simulations, they also showed that the accuracy loss in their method is negligible.  
ACTOR requires efficient algorithms to determine the natural logarithm and square root. The natural logarithm is easily convertible to base-2 since \(\log_{e}(x)=\log_{2}(x)/\log_{2}(e)\). Then, the binary logarithms can be calculated via repeated squaring and dividing [39].  
For the implementation of the square root, efficient algorithms exist. The simplest algorithm consists of a linear search that starts checking all the second power of all integers starting from 2 and returns the maximum integer that has a square that is smaller than the input. A more efficient solution improves this search to be binary. All of these methods work fine to determine the UCB.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 2 Analysis of the Current Situation and Limitations of Bottom-Up Monitoring Methods  
The bottom-up method is a terrestrial ecosystem-based approach that estimates carbon emissions by multiplying data on biomass burning activities (fire points, vegetation indices, biomass, etc.) by emission factors for different types of fires. This method remains the most prevalent approach for estimating GHG emissions, as adopted by the Intergovernmental Panel on Climate Change (IPCC) and numerous national governments.
# Galile Acid Induces S and G2 Phase Arrest and Apoptosis in Human Ovarian Cancer Cells In Vitro
## 1 Introduction  
Ovarian cancer (OC) is among the top gynecologic cancers in the US with a death tally of 13,940 in the past year alone [1]. With each passing year, 239,000 new cases emerge worldwide, along with 152,000 fatalities [2]. In other words, \(\geq\)2/3 of the ovarian cancer patients will die from the disease. Unfortunately, this statistic has remained the same over the last 30 years. One possible reason may be the fact that, in only 17% of the cases, OCs are detected while it is localized in the ovaries. In a vast majority (\(>\)62%) of cases, the disease is diagnosed after the malignancy has spread to distant locations [3]. Despite advancements in OC therapy, including surgery and platinum-based chemotherapy, the 5- and 10-year survival periods of late stage cancer patients are only 32% and 15%, respectively [4]. OC is very sensitive to chemotherapy. However, after an initial good response to treatment, patients often relapse, requiring a multidrug approach, which introduces numerous adverse effects [5, 6, 7]. Therefore, it is crucial and urgent to develop new therapeutics for OC treatment and prevention, possibly from natural compounds, so as to manage OC.  
Gallic acid (3,4,5-trihydroxybenzoic acid, GA) is the principal polyphenol in tea, grapes, berries and other fruits, and wine. Multiple reports have suggested GA to have antibacterial, antiviral, and anti-inflammatory properties. Its anti-tumor activity has been reported in breast [8], lung [9], oral [10], prostate [11] and cervical cancer cells [12].  
In prior studies, we demonstrated that GA produced the largest suppression of human OC cells, among eight natural phenols used in traditional Chinese medicine (TCM) [13]. More specifically, we revealed that GA selectively inhibited two separate cell lines of OC, but not normal ovarian cells (IOSE 364 cells) [14]. Moreover, GA ceased VEGF secretion and reduced angiogenesis via the PTEN-AKT-HIF-1a-VEGF signaling network in a concentration-dependent manner. In animal experimentation, GA was shown to reduce tumor development by 50%, as evidenced by diminished vascularity, necrosis/fibrosis, neoplastic stromal retraction, and apoptosis [15]. Apoptosis is the main physiological phenomenon of natural compounds inhibiting the development of tumor cells. However, there is no report on GA-induced apoptosis of OC cells and its underlying mechanism. In this research, the suppressive role of GA on OC proliferation, apoptosis and cell cycle was explored. OVCAR-3s and A2780/ CP70s are two platinum-resistant ovarian cancer cell lines. OVCAR-3s has a wild-type p53 gene sequence while A2780/ CP70s has a point mutation in the p53 gene [16]. Particularly, this study focused on the GA-mediated apoptosis of OVCAR-3 cells.
## 1 Introduction  
Magnetic nanoparticles are widely used as multifunctional materials for magnetic separation, catalysis, MRI contrast agents and vectorization. They can be embedded in polymers such as PDMS to form functional elastomers, which combine the properties of the matrix and the nanoparticles [1, 2]. Incorporating well-dispersed nanoparticles into a polymeric matrix is, however, not an easy task [3, 4, 5, 6]. When nanoparticles are mixed with polymer melts or polymer solutions, depletion of the free chains in close vicinity of one another [7] can occur. This is mainly the case when the nanoparticle surface is not efficiently stabilized. As a result, nanoparticles must be compatibilized with the selected polymer matrix, for instance by grafting a polymer layer onto the surface [8; 9; 10]. The length of the grafted polymer also has an important role [4; 7; 11; 12]. In the case of a curable polymer matrix such as PDMS, obtaining very fine dispersions are rendered more difficult due to the polymer's low surface energy. Different strategies were used to overcome this issue [2; 9; 13; 14; 15]. The use of oleic acid is a good pathway to disperse nanoparticles into this hydrophobic polymer [16; 17; 18]. However, a long mixing time with different solvents is needed. Other results showed that grafting from or grafting onto methods can be done with success to overcome aggregation [19; 20; 21]. In this case, small quantities of functionalized nanoparticles are obtained, mainly because the grafting method starts from diluted dispersions.  
Here we propose a versatile and easy method to compatibilize nanoparticles with a PDMS matrix. First, polyacrylic acid polymer (2,100 g/mol, noted PAA2k) is selected to stabilize iron oxide nanoparticles in water [22; 23] at pH from 4 to 11. We already demonstrated that this coating provides both stability and post functionalization to prepare controlled aggregates, either spherical [24] or elongated [25; 26]. In a second step, aminopropylmethylsiloxane-_co_-dimethylsiloxane block copolymer (PDMS-_co_-APMS) is used to add a hydrophobic coating to the NPPAA (Figure 1). These nanoparticles (called NPPDMS, for nanoparticles coated first with PAA2K, then with PDMS-_co_-APMS) are easily dispersed into a PDMS matrix without adding solvent. The corona thickness is directly governed by the molecular weight of the PDMS copolymer. Contrary to grafting onto or grafting from methods, this synthesis pathway gives rise to large numbers of functional nanoparticles in a few hours.