esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Probabilistic Joint Importance-Based Retrofit Strategy
### Importance of Individual Bridge  
Feature selection is a classic problem in statistics, machine learning, data mining among other research fields to solve large-scale computing problems. The purpose of feature selection is to screen out a feature subset that is most relevant to the target variable, and the process is similar to the selection of a bridge set that is most important for the seismic risk of transportation network. In this paper, an information-based feature selection approach is used to solve the problem of bridge retrofit strategy.  
In information theory, the total amount of information in a variable can be measured by the level of uncertainty in its data. The information entropy is a commonly used metric, calculated as:  
\[H(v)=-\int p(v)\log p(v)dv \tag{6}\]  
where \(p(v)\) is the PDF of variable \(v\). \(H(v)\) can be understood as the expectation of the logarithm of \(p(v)\), and larger value represents more information contained in the variable. Subsequently, the interdependence between two variables can be described as the amount of their common information, named mutual information or information gain. In practice, mutual information is often used to measure the reduced uncertainty of target variable under a given feature, and larger value indicates stronger dependence of target variable on the feature. Herein, the concept of mutual information is extended to the risk mitigation problem illustrated in Section 2. The mutual information between the damage states of a bridge (\(\lambda_{i}\)) and the post-earthquake functionality status of the network (\(\varphi\)) is written as:  
\[\begin{array}{ll}MI(\lambda_{i};\varphi)&=-\iint p(\lambda_{i},\varphi)\log \left[\frac{p(\lambda_{i},\varphi)}{p(\lambda_{i})p(\varphi)}\right]d\lambda_ {i}d\varphi\\ &=-\sum\limits_{j=1}^{n_{x}}\sum\limits_{\varphi=0}^{1}p(\lambda_{i}=DS_{j}, \varphi)\log\left[\frac{p(\lambda_{i}=DS_{j};\varphi)}{p(\lambda_{i}=DS_{j})p( \varphi)}\right]\end{array} \tag{7}\]  
where \(p(\lambda_{i},\varphi)\) is the joint PDF of \(\lambda_{i}\) and \(\varphi\), considering \(\lambda_{i}=[DS_{1},DS_{2},\ldots,DS_{n_{x}}]\) and \(\varphi=[0,1]\). Essentially, \(MI\) is the mathematical expectation of the logarithmic difference between \(p(\lambda_{i},\varphi)\) and \(p(\lambda_{i})\cdot p(\varphi)\). When \(\lambda_{i}\) and \(\varphi\) are completely independent, there is \(p(\lambda_{i},\varphi)=p(\lambda_{i})\cdot p(\varphi)\) and \(MI(\lambda_{i};\varphi)=0\). With the increase of their dependence, the value of \(MI(\lambda_{i};\varphi)\) goes up but always satisfies \(MI(\lambda_{i};\varphi)\leq\min[H(\lambda_{i}),H(\varphi)]\). According to the values of \(MI\), we can rank the importance of bridges that affect the seismic risk of the network. Under the same reinforcement condition, retrofitting a more important bridge could bring more benefit for risk mitigation.
# A Comparative Study of SOx, NOx, PM2.5 and PM10 in the UK and Poland from 1970 to 2020
## 2 Characteristics and Impacts of Air Pollution
### The Main Sources of Sulphur Oxides, Nitrogen Oxides, and Particulate Matter  
#### 2.2.1 Sulphur Oxides  
Although oxides of sulphur can enter the atmosphere through natural sources such as volcanic eruptions (which emit huge quantities of this substance), soil erosion, or fires, the air registers significantly more hazardous and elevated concentrations of sulphur oxides due to human activity. It can be seen from Figure 1[7] that the predominant source of SO\({}_{2}\) in the UK was the energy industry until 2018. In 2019 and 2020, however, the highest level of SO\({}_{2}\) emission (28% and 32%, respectively) emanated from domestic combustion.  
Sulphur is abundant in fossil fuels such as coal, natural gas, and oil, and in renewable sources such as biomass. Upon the combustion of these raw materials, the sulphur present reacts with oxygen (from air) to form sulphur oxides, predominantly in the form of SO\({}_{2}\).
## 3 Size Effects on Mechanical Properties of Solder Joints
### Tensile Behavior of Solder Joint
[51] performed finite element simulations to investigate the size dependency of solder joints formed only by IMC, and found that the stress concentration was more significant due to the absence of the'relief' effect of bulk solder joints (the'relief' effect refers to the ductility of the solder alloy, which can blunt the crack front and relieve the stress singularity). [PERSON] et al. [52] investigated the solder volume, aging, and reflow effects on the shear strength of Sn3.0 Ag0.5 Cu/Cu solder joints, and they found that reflow times had a tiny influence on the shear strength because reflow mainly increased the interfacial IMCs but did not alter the microstructure in solder layers. [PERSON] et al. [53] used the lap-shear specimen to measure the shear strength of Cu/Sn/Cu and FeNi/Sn/FeNi solder joints. In their experiment, Sn grain size was observed to enlarge with the decreasing of solder thickness, thus explaining the unusual relationship between shear strength and solder thickness. Figure 8: Size effects on tensile strengths of solder joints characterized by V/A, and shear and compression strengths of the solder joints. Shear strengths of different kinds of solder joints are summarized in Table 2. Compared with the tensile strengths in Figure 8, most of the reviewed data in Table 2 show milder decreasing tendencies with the increase of the V/A ratio, except the data from [PERSON] et al. 's study [50] which indicated a different mechanism of mechanical constraint between shear and tensile specimens. Of course, the deformation mechanisms between shear and tensile specimens are different, and thus different stresses and displacement singularities at the joint corners are found. With the theoretical analysis on the singularities of solder joint corners, the constraint differences between tensile and shear specimens can be expected to be revealed. Size effects on the strength of solder joints are not always \"smaller-being-stronger\". Instead, under certain circumstances, the \"smaller-being-weaker\" phenomenon can occur as well. [PERSON] et al. [54] reported an unusual size effect on the strength of Sn/Pb solder joints with fine lamellar phases, in which both micropillars compression tests and dislocation modeling were employed to analyze the decreasing strength with the specimen size. It was reported that there were fewer interphase boundaries in smaller sized specimens, which blocked the dislocation and thus led to the lower mechanical strength.
# Mars to Earth Data Downloading: A Directory Synchronization Approach
## 2 DTNbox
### Multilateral Synchronizations  
These are created by extending bilateral synchronizations to other nodes. For example, by extending the previous bilateral synchronization involving A and B to C, we can obtain the tri-lateral synchronization shown in Figure 2. The extension can be either bidirectional or unidirectional. We will see a possible use for this feature at the end of the experiment section.  
More generally, as a result of multiple extensions, a tree of multilateral synchronizations (bidirectional or unidirectional), can be established, as that shown in Figure 3. To avoid loops DTNbox sets automatic constraints on possible extensions.  
Figure 1: Bilateral synchronizations: (**a**) bidirectional and (**b**) unidirectional.  
Figure 2: Multilateral synchronization by extension from a paired node.
# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
## 5 Experiment
### Similarity Measurement  
In order to verify the effect of weight adjustment in the similarity measurement, we compared the spp1, spp12, and spp124 methods with these added weight adjustments which were denoted as spp1+, spp12+, spp124+. The former directly uses Equation (3) to calculate similarity, and the latter uses Equation (4). In order to calculate \(\delta_{i}\) from Equation (5), we selected 100,000 RGB images from the Matterport3D data set and calculated the average response of each node of the SoftMax layer as \(h_{i}\). Since two branches are identical and share their parameters, only one branch needed to be calculated. The tunable parameter was set as \(u=0.5,\delta=0.1\). The results of data_1 and data_2 are shown in Figures 9 and 10, respectively.  
At a high recall rate, spp1+, spp12+ and spp124+ have higher precision than spp1, spp12 and spp124. When considering feature distinguishability, the visual loop closure detection algorithm reduced the probability of false positives and improved precision.
## 3 Results
### Performance of PPI Prediction
#### 3.4.2 Performance Comparison between the RP Classifier and the SVM Model  
Many machine learning techniques and algorithms are employed to predict PPIs. We compared our RP model with the state-of-the-art SVM model. During the experiment, we extracted the feature values by the same method to ensure fairness. We used the LIBSVM toolbox on [[http://www.csie.ntu.edu.tw/~cjlin/libsvm/](http://www.csie.ntu.edu.tw/~cjlin/libsvm/)]([http://www.csie.ntu.edu.tw/~cjlin/libsvm/](http://www.csie.ntu.edu.tw/~cjlin/libsvm/)). The radial basis function (RBF) kernel was applied in our experiments. The method of a gridding search was employed to optimize two kernel parameters: C and \(g\).  
For the Yeast PPI dataset, we use the optimized parameters \(C=0.3\) and \(g=1\). The obtained results as shown in Table 5. For Human and _H. pylori_ PPI datasets, the optimized penalty parameters are 0.06 and 0.5, and the kernel function parameters are 2 and 0.3, respectively.  
Figure 3: ROC curves of our method on the _H. pylori_ dataset. The curve provides Sensitivity (TP Rate) against 1-Specificity (FP Rate).  
Figure 2: ROC curves of our method on the Human dataset. The curve provides Sensitivity (TP Rate) against 1-Specificity (FP Rate).  
When we predicted the PPIs by applying the SVM classifier on the Yeast dataset, we obtained an average _Acc_, _PE_, _Sen_, and _MCC_ of 95.06%, 95.35%, 94.76% and 90.60%, respectively. Compared with the SVM classifier, the accuracy of our method is higher by about 0.58%. When we predicted the Human PPI dataset, the results based on the SVM classifier of the average accuracy, precision, sensitivity, _MCC_, respectively, were 95.19%, 94.91%, 95.04% and 90.84%, respectively. Compared with the SVM classifier, the accuracy of our method was higher by about 1.40%. On the _H. pylori_ dataset, the SVM classifier achieved an 85.05% average accuracy and a 91.92% precision, with 76.80% sensitivity and 74.27% _MCC_. Compared with the SVM classifier, the accuracy of our method was higher by about 2.57%.  
Based on data in Table 5, the average accuracy, precision, and sensitivity values of our proposed model are much higher than averages attained by the SVM approach. The higher the standard deviation, the more unstable the algorithm is. Furthermore, we plotted the ROC curves on the three datasets by applying the SVM classifier as shown in Figures 4-6. It can be seen that the AUC areas yielded by our method are higher than those of the SVM classifier.
# Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search
## 2 Classical and Robust Approaches
### Least Trimmed Squares with Redundancy Constraint (LTS-RC)  
The LTS-RC extends LTS by adding a constraint to the summation of residuals. This estimator ensures that each parameter remains associated with at least two observations represented in the summation by their corresponding residuals. This measure guarantees observation redundancy [20].  
For the adjustment of geodetic networks established only with GNSS baseline vectors as observations, the algorithm maintains at least two residuals associated with each parameter, guaranteeing redundancy. In estimation problems where a parameter depends on more than one type of observation, however, the algorithm must be adapted [39]. This is to ensure that each different type of observation is present at least twice.
# Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot
## Materials and Methods
### Machine Learning-Based Robot Dynamic Model
#### 3.2.4 Dataset Collection  
From the inverse dynamics equation described in the previous section, it can be seen that the torques of the six-axis robotic arm are interdependent. Therefore, in order to establish a machine learning model, it is necessary to collect the dynamic information of each axis, including the position, angular velocity, angular acceleration, and motor output torque of the arm during trajectory motion. The angular acceleration is not directly obtainable data and needs to be obtained by differentiating the angular velocity. In order to incorporate diversity in the training data of trajectories, this paper will utilize a random trajectory generation algorithm to enable random arm movements within the workspace. By adjusting the trajectory movement time to increase velocity and acceleration changes, the training data will become more varied. Finally, the high data exchange rate feature of the EtherCAT communication protocol can be utilized to extract the dynamic parameters of each axis.  
The algorithm will generate the new target point within the work at random and plan the trajectories through the cubic polynomials in the joint space, to make the dataset represent the features in each posture and motion adequately. It is important to check whether the operation is in working space for all the points in the trajectory after designing the training trajectory to ensure that the arm will not collide during the movement.  
From the inverse dynamics equation described in the previous section, it can be seen that the torques of the six-axis robotic arm are interdependent. Therefore, the position, angular velocity, and angular acceleration of each axis are important parameters for training the dynamic model. In order to align the trajectory with the diversity of training data, the movement time for each segment of the trajectory was set to 3 s, 5 s, and 8 s. This experiment collected a total of 20 million data points for the angle and angular velocity of each axis.
# MEMS Vaporizing Liquid Microthruster: A Comprehensive Review
## 4 Instability Control into VLMs
### Flow Boiling Instabilities in Microchannels
#### 4.1.1 Bubble Clogging, Explosive Boiling and Flow Reversal  
Bubbles clogging in the confined channels is one of the main sources of the inception of severe flow boiling issues. The overall process initiates from the nucleation of tiny bubbles at the various locations of the channels. Most of these bubbles condense; other ones detach from the channel wall and start to grow. If these bubbles manage to smoothly pass through the whole channels, then the flow will not be disturbed. However, if one bubble grows up to the channel size, then it blocks the passages of the incoming flow. Soon after, they become a vapor slug appearing as an elongated bubble. Typically, bubble clogging significantly depends on flow conditions, i.e., subcooled or saturated boiling and flow parameters such as mass flux, operation pressure, and inlet condition of the coolant.  
When the bubble growth occurs in a very short time (below 1 s), it is well known as rapid bubble growth: it activates other flow boiling instabilities early, such as the parallel channel instability with a premature critical heat flux (CHF) occurrence, or explosive boiling phenomena with periodic wetting and dry-out, as observed in [31]. Figure 8 describes the establishment process of the explosive boiling flow regime.  
Reverse flow or backflow is also triggered by rapid bubble growth. As previously discussed, rapid bubble growth causes the rapid elongation of the upstream and downstream two-phase flow interface in the respective directions. The upstream movement of the vapor slug gives rise to a reverse flow. It is a highly undesired and dangerous phenomenon that deteriorates the heat exchange between solid walls and the flow, causing the failure of the device due to excessive thermal stress.
###### Abstract  
The coupling of photosynthesis and transpiration in plant leaves forms the basis of carbon-water coupling in terrestrial ecosystems. Previous studies have attributed the coupling of leaf photosynthesis and transpiration to joint stomata control, but they lack analyses of the coupling mechanism. In this study, winter wheat (_Trticulum aestivum_ L.) was selected as a plant material on the North China Plain. Under the conditions of warming and drought stress, the photosynthetic rate (An), transpiration rate (Tr), water pressure saturation (VPD), and leaf temperature (T1) of wheat were recorded on clear days at the jointing, flowering, and grain-filling stages from 9:00 to 12:00 a.m. Then, the measured values were fitted to the simulated values obtained using the Ball-Berry and Penman-Monteith models. The results showed that the stomatal size, stomatal conductance, An, and Tr of winter wheat leaves were decreased by warming, drought stress, and their synergistic effects. Based on the Ball-Berry model, different fitting effects were observed in the treatments of adequate water supply with warming (R-g), water deficit with warming (R-d), adequate water supply without warming (N-g), and water deficit without warming (N-d). The R\({}^{2}\) values of the R-g, R-d, N-g, and N-d treatments were 0.962, 0.958, 0.964, and 0.943, respectively. The Tr values were fitted based on the Penman-Monteith model. In the R-g, R-d, N-g, and N-d treatments, the R2 values of the R-g, R-d, N-g, and N-d treatments were 0.923, 0.849, 0.934, and 0.919, respectively. In conclusion, both warming and water deficit reduce stomatal conductance, An, Tr, and the coupling effect of photosynthesis and transpiration.  
warming; drought stress; winter wheat; stomatal conductance; photosynthetic rate; transpiration rate +
Footnote †: journal: Applied Sciences
# Improved Simplified Engineering Fault Displacement Hazard Evaluation Method for On-Fault Sites
## 2 Development of the Improved, Simplified Hazard Evaluation Method
### The Improved Hazard Evaluation Procedure  
Based on the considerations above, the simplified engineering surface displacement hazard evaluation method in [5] could be improved by better estimating the probability that a rupture crosses the site and the displacement distribution along the rupture.  
Figure 4: Distribution of displacements versus relative coordinate \(x_{i}/FL\) for a magnitude of 6.5 and the relative site locations \(x_{s}/FL\) equal to 0.5, 0.38, 0.3, 0.2 and 0.1 separately. Numbers 1–5 indicate the expected displacements. The red dotted line is the average displacement given by Equation (4). The black dotted line is the displacement value used in [4]. The black dashed line is the maximum displacement according to the bilinear distribution.  
The surface displacement hazard is calculated for the fault with an accurately mapped length of 27.9 km for the sites at \(x_{s}=0.1\times FL\), \(x_{s}=0.3\times F\), and \(x_{s}=0.5\times FL\).  
The steps of an improved hazard evaluation procedure are as follows.  
4.1 Characterisation of the Fault Activity, i.e., the Definitions of \(\alpha(\mathrm{m}_{0})\) and \(\mathrm{f}(\mathrm{m})\)  
In the case of the Paks site, the Gutenberg\(-\)Richter relation associated with the fault and developed based on seismotectonic source models has the following parameters: \(\alpha(m_{0})=0.81\), \(m_{0}=3.5\), the maximum magnitude, \(m_{u}=6.7\), \(\beta=2.455\) and  
\[\alpha(m)=\alpha(m_{0})\times\frac{exp(-\beta m)-exp(-\beta m_{u})}{\exp-\beta m _{0})-exp(-\beta m_{u}(} \tag{7}\]  
\(m_{u}=6.7\) is in line with the fault length magnitude correlation of [36]. However, in the case of the Paks fault, the maximum magnitude that could be obtained using the area of the fault plane indicates that the value of 6.7 is an overestimation (the fault length is 27.9 km, and the seismogenic depth is 9 to 12 km).
## 1 Introduction  
AR affects nearly 30% of the world's population and is becoming a worldwide health problem with an increasing incidence every year [1; 2]. It is a hypersensitivity reaction that is characterized by sneezing, itching of the nose, and rhinorrhea subsequent to exposure to certain allergens [3]. These symptoms develop due to the secretion of inflammatory mediators from mast cells, eosinophils (EOS), and basophils (BASO) [4]. Moreover, Th-2-related signals, including immunoglobulin E (IgE) and interleukin 4 (IL-4), trigger inflammatory reactions and are recognized as critical mediators of allergic rhinitis [5]. On exposure to allergens, immune cells such as eosinophils, basophils, and mast cells (MCs) secrete secondary signaling molecules including IgE, IL-4, and tumor necrosis factor (TNF), which cause differentiation of naive CD4\({}^{*}\) T cells into Th2 cells that in turn trigger acute allergic responses like allergic rhinitis [6; 7].  
Recently, [PERSON] et al. reported that Th-2 cells are stimulated by the epithelial-derived cytokines IL-25, IL-33, and thymic stromal lymphopoieetin (TSLP), the levels of which are regulated by the activity of protein arginine methyltransferase 1(PRMT1). This causes the activation of B cells or EOS and the subsequent development of allergic rhinitis. They further demonstrated that the elevated secretion of epithelial cytokines, including IL-25, IL-33, and TSLP, is essential for Th-2 cell differentiation. Thus, previous studies have established an association between the epithelium mediators IL-25, IL-33, and TSL, as well as IgE and IL-4 Th-2 signaling molecules with allergic rhinitis [8].  
_Cinanomom cassia_ has traditionally been used in Asian cultures for its beneficiary effect on dysppesia, peptic ulcers, and ischemic brain injury [9]. _Artemisa annua_ has also been reported to possess anti-cancer, anti-adipogenic, anti-osteoporotic, and anti-malarial properties [10; 11]. Several studies have reported that _Cinnamomum cassia_ and _Artemisa annua_ have a preventive effect on allergic rhinitis in human clinical trials via their anti-inflammatory effects [12; 13]. However, there are no reports on the ameliorative effect of the combination of these herbs, CIAR (_Cinnamomum cassia_ and _Artemisa annua_), on Th-2 signaling mediated inflammatory responses.  
Therefore, this study aims to investigate the anti-allergic effect of CIAR in the OVA-induced rhinitis mouse model by analyzing the improvement in the levels of Th-2 signal molecules in serum and nasal fluid.
## 2 Related Work
### The Design of Simulation Approaches for Railway Network Disturbance Events
```
Input: Original network G_original, damaged network G_damaged, recovery strategy, number of nodes to recover m Output: Partially recovered network G_recovered Function SIMULATE_RECOVERY(G_original, G_damaged, recovery_strategy, m): G_recovered \(\leftarrow\) G_damaged.copy() removed_nodes \(\leftarrow\) G_original.nodes() - G_damaged.nodes() if recovery_strategy == \"betweenness_based\":  Calculate betweenness centrality for all removed nodes in original network  Sort removed nodes in descending order of betweenness centrality  nodes_to_recover \(\leftarrow\) top m high-betweenness nodes from removed_nodes else if recovery_strategy == \"degree_based\":  Calculate degree for all removed nodes in original network  Sort removed nodes in descending order of degree  nodes_to_recover \(\leftarrow\) top m high-degree nodes from removed_nodes else if recovery_strategy == \"random\":  Randomly select m nodes from removed_nodes  nodes_to_recover \(\leftarrow\) these randomly selected nodes for each node v in nodes_to_recover:  Add node v to G_recovered  for each edge (v, u) connected to v in original network:  if u exists in G_recovered:  Add edge (v, u) to G_recovered  return G_recovered
```  
**Algorithm 3** Network Recovery Strategy Algorithm Pseudocode.