esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration of Rotating and Revolving Planet Rings with Discrete and Partially Distributed Stiffness
## 2 Analytical Model  
The schematic of a planetary gear system and the vibration model of a planet ring are shown in Figure 1a. The rotating speed and revolving speed of the planet ring are \(\omega\) and \(\Omega\), respectively, and the ratio of \(\Omega\) and \(\omega\) is \(i_{0}=\Omega/\omega\). The stationary basis is {i, j, k}; the origin \(O_{1}\) of {e\({}_{1}\), e\({}_{2}\), e\({}_{3}\)} basis is set to the center of the ring, where the direction of \({\bf e_{1}}\) is along the extension of \(O_{0}O_{1}\). The basis {e\({}_{1}\)\({}^{\prime}\), e\({}_{2}\)\({}^{\prime}\), e\({}_{3}\)\({}^{\prime}\)} is a local coordinate system with the origin \(O_{2}\) on the middle surface, and e\({}_{1}\)\({}^{\prime}\) is along the radial direction of the ring. \(u(\theta\theta,\,t)\) and \(v(\theta,\,t)\) are radial and tangential deformations, respectively. \(M\) is a material point on the ring and \(\phi\) is the relative angle of \({\bf e_{1}}^{\prime}\) to the basis \({\bf e_{1}}\), where \(\phi=\omega t+\theta,\,\theta\) is the specified angular coordinate of the point \(M\) around the planet \(\phi\)ring. \(R_{0}\) is the revolving radii, and \(R_{1}\) is the radii of the middle surface. The density of the ring is \(p\), its elastic modulus is \(E\), and its section area is \(A\) with width \(B\) and thickness \(h\). Moreover, \(k_{m1}\) and \(k_{m2}\) are mesh stiffnesses between planet ring with sun gear and ring gear, respectively; \(\alpha\) is the pressure angle of \(k_{mi}\) on the ring, and apparently the mesh stiffnesses will affect both the radial and tangential deformations. When a planetary gear system runs, the contact zone between planet ring and rolling bearing usually concentrates on a part of the whole circle contact area [28] because of mesh forces. Hence, the planet ring is supported in the radial direction by discrete partially distributed stiffnesses, as shown in Figure 1b, and each spring denotes the position of a roller in the bearing and its position to \({\bf e_{1}}\) is \(\theta_{bi}\). The support stiffness of the roller is \(k_{bi}\), and the values of all \(k_{bi}\) are assumed to be the same. The positions of \(k_{m1}\) and \(k_{m2}\) to \({\bf e_{1}}\) are \(\theta_{mi}\) (\(i=1,2\)). Because the planet ring rotates, \(\theta_{bi}\) and \(\theta_{mi}\) are time dependent, and \(\theta_{bi}=\theta_{bi0}-\omega t\), \(\theta_{mi}=\theta_{mi0}\omega t\), respectively, where \(\theta_{bi0}\) and \(\theta_{mi0}\) are the original angles to \({\bf e_{1}}\). \(\theta_{mi0}\) is determined by the geometry of mesh pairs in the planetary gear system, and \(\theta_{bi0}\) is determined by the selected bearing. The velocity vector of the point \(M\) on the ring is obtained by the following kinetic formula [29]:  
\[ \begin{array}{ll}{\bf v}_{M}&={\bf v}_{O_{1}}+\Omega{\bf e_{3}}\times{\bf r }_{M}+{\bf v}_{M,r}\\ &=R_{0}\Omega{\bf e_{2}}+\Omega{\bf e_{3}}\times\left(R_{1}\cos\phi{\bf e_{1} }+R_{1}\sin\phi{\bf e_{2}}+u{\bf e_{1}^{\prime}}+v{\bf e_{2}^{\prime}}\right) +{\bf v}_{M,r}\end{array}
# Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification
## 3 Deep Learning Methods for Aspect Level Sentiment Classification
### Recent Trends in Aspect Level Sentiment Classification
There are limited hybrid methods that have incorporated syntactical knowledge along with deep neural networks using Graph Convolutional networks (GCN) [33; 34; 35]. Another way of handling the syntactical information is with the usage of Graph Attention Network (GAT). [PERSON] et al. [36] and [PERSON] et al. [37] have leveraged Graph Attention networks for ALSC task. The GAT method proposed by [PERSON] et al. [37] uses typed dependencies (i.e., dependency label or relation) for enhanced performance. The authors also utilized BERT (Bidirectional Encoder Representations from Transformers) [38] model to generate contextual embeddings. BERT is a pre-trained language model that has gained popularity in many NLP tasks, including ALSC. There are few attempts in literature exploring BERT for ALSC. The incorporation of BERT with the deep learning methods has shown promising results. [PERSON] et al. [31] leveraged BERT in embedding layer and encoding layer to generate contextual embeddings. [PERSON] et al. [39] utilized BERT embeddings as input to their proposed deep neural network. [PERSON] et al. [40] used a pre-trained BERT model directly for prediction. Figure 4 summarizes the 35 deep learning methods statistically tested for significant performance in this study across multiple datasets of different domains. For a fair comparison, the methods based on domain-specific corpus or domain-specific embeddings are not dealt with in this study.
# Studies on Influence of Chromium Layer on Inner Surface of Steel Tube on Heat Transfer
## 3 Numerical Simulation and Results Analysis
### Temperature Distribution in the Tube for a Series of Seven Heat Impulses  
As the wall temperature increases, the heat flux density on the inner surface of the tube changes as a function of time for the first and subsequent heat impulses. The temperature distribution \(T_{i}(t,r_{in},z)\) of the tube's inner surface for different chromium layer thicknesses at the six chosen zones, S4, S10, S16, S21, S25 and S30, for the series of seven heat impulses is shown in Figure 14.  
Figure 14: Temperature distribution \(T_{i}(t,r_{in},z)\) of the tube’s inner surface with different chromium layer thicknesses at the 6 zones S4, S10, S16, S21, S25 and S30, for the sequence of seven heat impulses.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & 0 \(\upmu\)**Cr** & 10 \(\upmu\)**Cr** & 30 \(\upmu\)**Cr** & 200 \(\upmu\)**Cr** \\ \hline highest peak temperature [\({}^{\circ}\)C] & 712.8 & 699.0 & 674.5 & 569.0 \\ \hline lowest temperature of the peak base [\({}^{\circ}\)C] & 224.1 & 226.1 & 229.8 & 250.1 \\ \hline \end{tabular}
\end{table}
Table 3: The highest peak temperature and the lowest peak base temperature as a function of the chromium layer thickness at the zone S30 for a single heat impulse.  
As the thickness of the chromium layer increases, the highest peak temperature decreases, while the lowest peak base temperature increases. Such a heat transfer mechanism is related to the high thermal conductivity of chromium, which is almost three times greater than that of the 30 HN2 MFA steel--Figures 5 and 6. With each subsequent heat impulse, the lowest peak base temperature of the chrome-plated inner surface of the tube increases relative to the same temperature of the non-chrome-plated inner surface.
# Recognition and Location Algorithm for Pallets in Warehouses Using RGB-D Sensor
## 2 Methods
### Classifier Training  
#### 2.3.1 Analysis and Feature Selection  
The choice of features has a considerable impact on the speed and accuracy of an algorithm. Due to real-time and pixel-level accuracy requirements, the features should be easily extracted, pixel-level, and distinguishable. Three different types of pallets are presented in Figure 3. As mentioned above, the objects to be recognized are pallets, goods, and the ground/shelf. The most common features can be divided into three parts: shape, texture-based features, and color. The features are next discussed with reference to the real situation.  
(1) It is difficult to apply the same shape feature to represent all pallets and quickly extract information because a pallet's shape and size are variable from changing angles.  
(2) Texture-based features are generally local features that use a statistical vector to express the gray change in the cell of each pixel, such as histogram of oriented gradient (HOG), local binary patterns (LBP), and scale-invariant feature transform (SIFT). These features are widely used in complex object recognition tasks [44; 45]. However, pallets are often composed of plastic with a smooth and flat surface. Occasionally, texture information may not be enough for the algorithm to segment the target and background, and more time is needed for classification due to expensive computation.  
(3) Because a source image is recorded in the RGB channel format, it can be transformed into HSI color space to help easily segment a target with little computational cost. As shown in Figure 2, the color feature can fit most situations. Therefore, effective color components R, G, B, H, and S were selected as the input features to train the classifiers in this study.  
Figure 3: Pallet examples in warehouse. (**a**) Example a. (**b**) Example b. (**c**) Example c.
# Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve +
## 3 Failure Type Prediction Method Using Physical Indices and Data Features
### Failure Type Prediction Using Random Forest  
Random forest classifier is an ensemble algorithm based on bagging. On one hand, as a \"forest\" that builds multiple decision trees to vote for making decisions, it can effectively improve the classification accuracy of new samples and get better performances than just single classifier. On the other hand, compared with other ensemble algorithms, the random forest does not need to perform feature selection and is not sensitive to abnormal samples. Therefore, we choose the random forest to predict the failure type of high-speed solenoid valves in this paper. The method using the random forest to predict the failure type is shown in Figure 7.  
We randomly select \(n\) samples from the generated data set and randomly select \(k\) features from the comprehensive feature vector to construct a decision tree. Then, the \(m\) decision trees obtained by repeating the above process \(m\) times form a decision forest. The prediction and classification results are determined by majority voting.  
The random forest samples both the training samples and the features, which fully guarantees the independence between each tree constructed and makes the voting results more accurate. Its randomness is reflected in that the training samples of each tree are random, and the splitting properties of each node in the tree are also randomly selected. With these two random factors, even if each decision tree is not pruned, the random forest will not produce overfitting.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CFV** & **New Valve** & **Valve with Aged Coil** & **Valve with Dried Lubricant** \\ \hline \(\mathrm{i_{1}}\) (A) & 0.2864 & 0.1926 & 0.3254 \\ \hline \(\mathrm{i_{2}}\) (A) & 0.6422 & 0.5101 & 0.6188 \\ \hline \(\mathrm{t_{1}}\) (ms) & 3.31 & 4.06 & 4.37 \\ \hline \(\mathrm{t_{2}}\)
# Radioactivity in Soils of Kosovo and Radiological Implications
## 2 Materials and Methods
### Activity Concentration (A)  
Each of the soil samples reaching the balance were placed on the detector and counted for a period of 60.000 s. The obtained gamma spectra were analyzed using data acquisition and analysis program named Gamma Vision [19]. The activity concentration of each sample was subtracted from the activity concentration of an empty plastic container to remove the contribution of the background radiation. To calculate the activity concentrations of the soil samples by specific radionuclide (\(A\)), we used Equation (1):  
\[A(\mathrm{Bq}/\mathrm{kg})=\frac{C}{\varepsilon\times P\times m\times t} \tag{1}\]  
where \(C\) is the net count (area) under the corresponding peak, \(\varepsilon\) is the detector efficiency at the corresponding peak energy, \(P\) is the absolute transition probability of the specific \(\gamma\)-ray at the corresponding peak energy, m is the mass of the sample (kg), and \(t\) is the counting time in seconds.  
To measure the activity concentration of \({}^{226}\)Ra, \({}^{232}\)Th, \({}^{40}\)K, and \({}^{137}\)Cs on soil samples, we used standard methodology, based on \(\gamma\)-ray energies of specific elements. This procedure has been described elsewhere [20,21].  
To calculate the uncertainty of the activity concentration (\(\Delta A\)), we used Equation (2) [22].  
\[\Delta A=A\sqrt{\left(\frac{\Delta C}{C}\right)^{2}+\left(\frac{\Delta \varepsilon}{\varepsilon}\right)^{2}+\left(\frac{\Delta P}{P}\right)^{2}+ \left(\frac{\Delta m}{m}\right)^{2}} \tag{2}\]  
where \(\Delta C\) is the uncertainty of count rate, \(\Delta P\) is the uncertainty of emission probability found in the nuclear data tables, \(\Delta\varepsilon\) is the uncertainty of efficiency, and \(\Delta m\) is the uncertainty of weighing.  
To calculate the minimum detectable activity for each radionuclide, we used Equation (3) [23]:  
\[\mathrm{MDA}(\mathrm{Bq}/\mathrm{kg})=\frac{1.645\sqrt{B}}{\varepsilon\times P \times m\times t} \tag{3}\]  
where 1.645 is the statistical coverage factor (confidence level 95%), and \(B\) is the background for the related region of a specific radionuclide.  
In addition, the accuracy of the measurements was obtained from the reference standard range of 95 to 98% for all radionuclides. The certified soil reference material (IAEA-375) checked the measurement system for accuracy with a matrix similar to the samples.
## 1 Introduction  
Flavor is one of the dominant factors determining the quality of meat and consumer purchasing decisions [1]. Although the flavor of raw meat is slight, it is also an indicator for consumers to evaluate meat freshness and nutrition-related attributes [2]. Meanwhile, some volatile and non-volatile precursors are always produced during refrigeration of raw meat and will determine the cooking quality and sensory attributes of meat products [3]. Therefore, the control and improvement of the volatile and non-volatile compounds in raw meat is a hot topic globally. Many factors influence the volatile and non-volatile compounds in raw meat, such as breeds, gender, age, slaughtering methods, aging time, packaging methods, and storage conditions [4]. Aging after 4 \({}^{\circ}\)C chilling is the most popular way to preserve freshness and tenderization of raw meat. Some recent studies reported that compared with traditional 4 \({}^{\circ}\)C chilling, fast chilling sped up the reduction of carcass's temperature and reduced purge losses and cooking losses [5; 6]. Rapid chilling of pre-rigor carcass significantly slowed the post-mortem process [7], reduced the rate of pH decline, and inhibited glycolysis [8; 9]. Meanwhile, chilling method affected the degree of lipid oxidation and protein degradation, to produce different volatile and non-volatile compounds such as free fatty acid, oxidative free radical, peptides, and amino acids, which would contribute to cooked meat aroma [1]. Therefore, several researchers raised the possibility that changing the rate of carcass chilling after slaughter may affect meat volatile and non-volatile compounds [10; 11]. However, studies only showed that accelerated chilling can improve the physical and chemical properties of meat due to the reduced post-mortem metabolic rate [7; 12], the volatile and non-volatile compounds in raw meat cut from the chilled carcass with different chilling rates during refrigeration were not well understood.  
The aims of this study were to (1) classify raw meat at different chilling rates and refrigeration times based on analysis of volatile and non-volatile compounds; (2) identify the key and differential volatile and non-volatile compounds in response to different chilling rates, and (3) investigate the evolution of key volatile and non-volatile compounds at different chilling rates during refrigeration.
# Go Wider: An Efficient Neural Network for Point Cloud Analysis via Group Convolutions
## 2 Related Work
### Deep Learning Methods Directly on an Unstructured Point Cloud  
_PointNet_[10] takes the lead in treating point clouds as a set of unordered points and applying stacked Multi-Layer-Perceptron (MLP) to learn individual point features. The global feature is finally obtained by a symmetric function (e.g., max pooling). However, local features that are beneficial to better understanding a geometric shape are not considered in this approach. In order to improve the performance and overcome this problem, _PointNet++_[11] constructs a hierarchical neural network that applies recursively _PointNet_ on the local features that combine the sampled points with the corresponding neighbouring points. _DGCNN_[12] applies _PointNet_ on the edge features concatenating each point and its edges connecting the corresponding point and its neighbouring pairs. _PointCNN_[14] transforms a given irregular point cloud to a latent canonical order by learning a \(\chi\)-convolutional operator, after which classic 2D CNNs are used for local feature extraction. [15] attempts to learn a customized convolutional weight from the geometric local relation for a shape-aware representation.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 2 Related Technology and Works
### Related Works on the Coexistence of LTE and Wi-Fi
For performance evaluation, it uses the ns3 to simulate LAA and Wi-Fi coexistence scenarios. The results show that it can achieve fairness between LTE-U and Wi-Fi users in a small cell environment. In [3], the four main functionalities of LAA, i.e., CS, LBT, DTX, and TPC, are investigated. Q-learning mechanisms are used for carrier selection that takes DTX or both DTX and TPC into account to provide an efficient coexistence scheme. Using the Markov model, [PERSON] et al. [18] model and analyze the coexistence of LTE and Wi-Fi, including throughput, access probability, and collision probability. Our study and [18] share the observation of the change in throughput as the number of STA varies. In [19], an optimal detector for detecting Wi-Fi APs is designed based on second-order statistics (SOS) of orthogonal frequency-division multiplexing (OFDM) signals and using the singular value decomposition (SVD) method. Theoretical expressions are derived for the detection and the false alarm probabilities. The flexible carrier sensing adaptive transmission (CSAT) framework and algorithm are designed for spectrum access and sharing. In [20], a coexistence model of LTE and Wi-Fi with two virtual zones is suggested. The inner zone is the secondary zone, which represents Wi-Fi with an unlicensed spectrum and the outer zone is the primary zone, which represents LTE with a licensed spectrum. The numerical solution of the model is presented using MOSEL-2 simulation and the mathematical solution is derived to validate the model. A threshold minimum bit rate established the user admission. [PERSON] et al. [21] address the detection of selfish behaviors in Wi-Fi/LTE coexistence environments, such as the tamping of the back-off mechanism, traffic class parameters, the clear channel access (CCA) threshold, and others. Their approach applies correlation-based signal detection to accurately infer the operational parameters of LTE transmissions without decoding. The researches mentioned above have their contributions. However, many of the proposed mechanisms sacrifice Wi-Fi users when facing a dilemma in improving fairness or increasing the throughput.
## 1 Introduction
The original SUNSPACE was synthesized by using silica fume (SF). This by-product is a fine powder (with particle size ranging from 20 to 500 nm) derived from ferro-silicon and silicon metal alloy processing. It is commonly used in construction materials, for example, cement, concrete, bricks or ceramics. It is also demonstrated that SF has good performance as a heavy metals stabilizer [24]. Recently, it has been used in the synthesis of porous material to PM capture [19; 20]. The color of silica can change from light to dark grey as a function of the manufacturing process, considering for example, the composition of starting materials, the combustion temperature and the obtained final products [25]. Silica fume used in SUNSPACE synthesis is dark grey. The dark color represents an esthetic limit if the idea is to apply SUNSPACE as plaster on wall or as roofiles. To overcome this problem, another industrial by-product, bottom ash (BA), was recently proposed for material synthesis and is mainly used with the goal of obtaining a light-colored material [20]. BA is the residue of municipal solid waste incineration (MSWI). It is estimated that BA represents 90% of solid residues generated in incineration plants [21]. Twenty million tons of BA are produced every year in Europe [26]. BA is not considered hazardous waste (2008/98/CE); it contains heavy metal with low volatilization. [PERSON] et al. revealed that fine fraction of BA (lower than 300 microns) is characterized by a higher leaching concentration of heavy metals, such as Pb and Zn, compared to coarse fraction (more than 1400 microns) [27]. The chemical composition of these ashes (high amount of Al, Si, Ca and Fe) makes them suitable mainly for cement production [28]. Recent work shows the possibility to use BA as a stabilizer agent for fly ash derived from the same incineration process [29]. BA is also used as adsorbent material due to high porosity for heavy metal wastewater treatments [30]. Moreover, another improvement has been introduced in this work. For the first time, hydrogen peroxide was used instead of sodium bicarbonate as a pore former to avoid thermal treatment. This allows for the simplification of the application of materials as plaster and then increases the material sustainability. Structural, morphological, colorimetric and porosimetric analyses of all SUNSPACE typologies were performed. Additionally, adsorption tests were made to compare the ability of all samples to trap PM.
# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 5 Performance Evaluation
### Slide Behavior with Different Mobility Models  
In this section, we investigate the performance of SLIDE after changing the mobility model. We compare the performance of SLIDE under three different mobility models. First, we study the effect of the simulation parameters (broadcast cycle \(T_{b}\), velocity \(v\), communication range \(\sigma\), look ahead time \(T_{l}\), and the protected zone \(R\)). Second, we compare the scalability. Finally, we study the effect of the mobility model parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **GM** & **EGM** \\ \hline alpha \(\alpha\) & 0.86 & 0.86 \\ Speed standard deviation & 0.1 & 0.1 \\ Pitch standard deviation & 10 & 0 (fixed altitude) \\ Angle standard deviation & 10 & - \\ Direction standard deviation & - & 2.49 \\ Time step (updateInterval) & 0.5 & 0.5 \\ Margin & 25 m for \(v=3\) \& 40 m for \(v=5\) & 25 m for \(v=3\) \& 40 m for \(v=5\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters for GM and EGM.  
Figure 4: Samples of the generated paths using GM.  
Figure 5: Samples of the generated paths using EGM.