esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 2 Integrated Bearing Dynamic Performance Analysis
### Influence of Structural Parameters on Vibration in Integrated Bearings
#### 2.2.1 Influence of Inner Groove Curvature Radius Coefficients on Vibration Value  
When the outer groove curvature radius coefficient is set at 0.529 and the cage pocket clearance is 0.051 mm, the relationship between the inner raceway curvature radius coefficient and bearing vibration in integrated bearings is shown in Figure 10. As shown in the figure, with an increase in the inner raceway curvature radius coefficient, both the vibration acceleration level and vibration velocity of the integrated bearings initially decrease and then increase. The optimal vibration characteristics are observed when the inner raceway curvature radius coefficient is in the range of 0.52 to 0.53.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 5 TERI-MAC Design  
In this section, we first present the design of the TERI-MAC protocol in detail. The primary objective of TERI-MAC is to improve the network's energy efficiency and channel utilization in UWSNs. In TERI-MAC, the receiver needs to know the propagation delay to its neighbors, which could be measured through the classic _two-way_ handshake approach that was tested in a sea experiment [31].
## 3 Intelligent Contact Tracing Based on Transmission Characteristics
### Trajectory-Based Outdoor GPS Contact Tracing  
Considering that transmission of the virus is related to human activity patterns [23], we focus on the trajectory using GPS data for outdoor contact tracing. We clarify the distinction between casual contact and close contact using both distance and the similarity of the trajectory vector between the confirmed case and potential contact, which factors in accompanying movement. Unlike recent GPS-based contact tracing methods that primarily track current location history, our trajectory-based approach leverages vectors from specific time periods alongside confirmed case data. This reduces the rate of false detections by accounting for temporal dynamics [15].  
Figure 3 shows the distance and similarity between individuals used for the trajectory-based outdoor GPS contact tracing. The trajectories of each individual are denoted by dotted lines, and the points \(G_{u}\), \(G_{v_{1}}\), and \(G_{v_{2}}\) represent the positions of the confirmed patient and the potential contacts, respectively, at time \(t\). \(D(u,v_{1})\) means the distance between the patient and the first potential contact. \(S_{current}(u,v_{1})\) signifies their similarity at the current time. We minimize GPS errors by applying the extended Kalman filter (EKF) [24] to the GPS data (\(G_{t}\)) of confirmed cases and potential contacts, considering the error range, which is the propagation characteristic of GPS. Next, the estimated distance between the confirmed person and potential contact is calculated using the filtered GPS data. We define a casual contact when the calculated distance is within the maximum infection range (\(R_{max}\)) for COVID-19, and when the cosine similarity, calculated using the trajectory vector (\(\vec{T}_{t}\)) of the two individuals, is greater than the threshold value (\(S_{max}\)). If the number of casual contacts during \(t_{min ExposurePeriod}\) exceeds a threshold value for outdoors (\(O_{threshold}\)), it is judged as an exposed case.  
Figure 2: Exposed case detection process of space-based indoor Wi-Fi contact tracing.
# Explorative Supercooling Technology for Prevention of Freeze Damages in Vaccines
## 3 Results and Discussion
A TT vaccine was supercooled for 7 days to test if the vaccine's physical characteristics could be maintained for an extended period. In Figure 6, the supercooled was successfully preserved by OMF treatment without any phase transition for 7 days. A shake test was conducted and compared the supercooled vaccine with the control vaccine (Frozen (\(-14\)\({}^{\circ}\)C)). The test results indicated a significant difference in the sedimentation rates between the supercooled and frozen vaccines. In general, TT vaccines adsorbed on aluminum adjuvants are stable during the controlled cold supply chain. However, they may change their appearance and lose potency when frozen because the adjuvant gel structure is destroyed by freezing. Figure 4: Snapshots of supercooled and control vaccines after shake tests. Test vials (**a,b**) were stored frozen at \(-20\)\({}^{\circ}\)C and \(-14\)\({}^{\circ}\)C (without OMF treatment) for 24 h, and then thawed before commencing the shake tests. Vial (**c**) was refrigerated at \(7\)\({}^{\circ}\)C and vial (**d**) was subject to supercooling treatment for 24 h.  
Figure 5: Microscopic views of supercooled (\(-14\)\({}^{\circ}\)C) and other control vaccines (frozen (\(-20\)\({}^{\circ}\)C), frozen (\(-14\)\({}^{\circ}\)C), and refrigerated (\(-20\)\({}^{\circ}\)C)) after shake tests. Scale bars indicate 100 \(\upmu\)m and 20 \(\upmu\)m for magnification 10\(\times\) and 40\(\times\), respectively. Real efficacy data are often difficult to obtain, as each product has its particular threshold for freeze damage. As such, there may be a difference between exposure to freezing temperatures and freezing a vaccine sample sufficiently to destroy its potency. Furthermore, the shake test's inability to distinguish between exposure to freezing temperatures and loss of potency due to freezing may create a lack of trust in its veracity. However, as seen with all the control samples in this study's different trials, both short- and long-term exposure to subzero temperatures resulted in freezing, proving these temperatures were well below the freezing point. Additionally, the WHO assures that freezing at these temperatures would have, without doubt, resulted in a loss of potency for aluminum adjuvant TT vaccines [3]. On the other hand, the supercooled vaccines treated by OMF for 7 days passed the shake test and did not experience any ice crystal nucleation. Therefore, it is reasonable to conclude that the developed supercooling technology can ensure the physical stability of freeze-sensitive vaccines during storage and transportation for prolonged periods of time, perhaps paving the way to improve the potency.
## 6 Results and Analysis
### Development of Landslide Susceptibility Map  
Mapping the area with landslide potential in the Mu Cang Chai District was carried out using all models and the final maps are shown in Figures 6-9, individually. The distribution of pixels were also carried out and is shown in Figures 6-9. The results showed that most of the pixels in the study area were distributed in the very low class (39.19%), followed by the very high class (23.40%), high class (16.86%), low class (10.35%), and moderate class (10.21%), respectively, whereas the landslide pixels were found mainly in the very high class (82.86%), followed by the high class (8.87%), moderate class (4.03%), very low class (2.42%), and low class (2.02%), respectively. Figure 10 shows the frequency ratio analysis of the four machine learning methods for landslide spatial prediction in the study area. Analysis of the findings indicated that the produced map was highly appropriate as most landslide pixels were found in the very high and high classes.  
Figure 6: Landslide Susceptibility Map (LSM) of the study area based on the MBNBT model.  
Figure 7: Landslide Susceptibility Map (LSM) of the study area based on the MLPN model.  
Figure 8: Landslide Susceptibility Map (LSM) of the study area based on NBT model.
## Materials and Methods
### Batch Adsorption Experiments  
Isothermal adsorption experiments of NO3-N on adsorbents were performed when 0.05 g resin was introduced to each 150 mL conical flasks containing 50 mL of NO3-N in solution (10 to 60 mg/L concentration). To investigate the adsorption kinetics, 0.2 g resin was immersed into 200 mL NO3-N solution with the initial concentration of 60 mg/L, and samples were withdrawn at different time intervals. The pH (4.0-10.0) with 50 mg/L NO3-N solution in the presence of Cl- (200 mg/L) was adjusted using 0.1 mol/L HCl or 0.1 mol/L NaOH solution, respectively, as required. A competing anion (SO42- or Cl-) was added to the NO3-N solution to study the effect of negative ions on adsorption capacities. The static tests of D311 and AEE-3 were conducted in a bi-solute system (HA/NO3-, SDBS/NO3- or TA/NO3-) for four adsorption-desorption cycles. Analyses of NO3-N were obtained by measuring the UV1800 spectrophotometer at a wavelength of 220 nm. The adsorption amount \(Q_{\mathrm{e}}\) (mg/g) was calculated using Equation (1):  
\[Q_{e}\ =\ \frac{(C_{0}-C_{\mathrm{e}})V}{W} \tag{1}\]  
where \(C_{0}\) and \(C_{\mathrm{e}}\) are the initial and equilibrium concentrations (mg/L) of NO3-N, \(V\) is the volume of solution (L) and \(W\) represents the weight of dry resin (g).
## 2 Methods
### Graph Neural Network (GNN) Approach
#### 2.3.2 Graph Neural Network Architecture  
The GNN model consisted of two graph convolution layers, followed by a pooling layer to generate graph-level representations. Node features (**X**) and edge indices (**E**) were used to update node embeddings. The node embedding update at layer \(l\) was performed using a graph convolution operation, as shown in Equation (2):  
\[\mathbf{H}^{(l+1)}=\sigma\Big{(}\mathbf{D}^{-1/2}\mathbf{A}\mathbf{D}^{-1/2} \mathbf{H}^{(l)}\mathbf{W}^{(l)}\Big{)}, \tag{2}\]  
where \(\mathbf{D}\) is the degree matrix, \(\mathbf{A}\) is the adjacency matrix, \(\mathbf{H}^{(l)}\) represents the node embeddings at layer \(l\), \(\mathbf{W}^{(l)}\) is the trainable weight matrix for layer \(l\), and \(\sigma\) is the activation function (ReLU).  
The final graph representation was obtained by applying mean pooling to the node embeddings from the last graph convolution layer. This step aggregated the information from all nodes to form a single vector representation of the graph, as shown in Equation (3):  
\[\mathbf{H}_{\text{graph}}=\frac{1}{N}\sum_{i=1}^{N}\mathbf{H}_{i}, \tag{3}\]  
where \(N\) is the number of nodes in the graph, and \(\mathbf{H}_{i}\) is the embedding of node \(i\).
## 4 Discussion
### Soil Lead  
#### 4.2.1 Soil Lead Concentrations in Absence of Plant Growth  
Amending soils with biochar and compost had no effect on Pb availability at the beginning of the experiment, which was unexpected. Although their influence can vary based on factors like application rate, source materials, and soil type, both are generally known for their capacity to immobilize soil contaminants [37; 43; 52]. Organic matter interacts with heavy metal ions in soil through three key mechanisms: sorption to surfaces; precipitation with organic acids and other elements; and complexation with precipitates [7; 43; 90; 97]. Biochar's effectiveness in reducing available heavy metals is often attributed to its large surface area, highly microporous structure, and active organic functional groups, in addition to generally high pH and CEC [37]. Further, biochar contains (hydr)oxides and carbonate phases that cause heavy metals to precipitate [98]. In contrast, compost is more easily decomposable and contains a greater proportion of humic substances, which are responsible for many chemical reactions in soil due to their ability to complex with metal ions, minerals, and hydro(oxides) [8; 19; 49].  
The significant increase in soil pH brought about by the addition of biochar was also expected to correspond more closely with decreased Pb availability. In their meta-analysis on the effects of biochar on metal availability, [PERSON] et al. (2018) noted that increases in soil pH cause metal precipitation, decrease metal availability, and promote adsorption onto soil because of increases in pH-dependent negative charges [43]. Our research did suggest a weak negative correlation with pH in general across all treatments, however the difference in pH between BIOC and the other treatments did not appear to affect significant differences in Pb availability. It is likely that the magnitude of that effect in terms of immobilization would have been more pronounced in an acidic soil than the native alkaline soil used for this experiment [99].  
Soil available Pb was probably influenced more directly by native soil properties than the amendments. Calcareous soil is high in CaCO\({}_{3}\) and the alkaline pH coupled with carbonate buffering can result in the formation of metal-carbonate precipitate complexes that decrease metal availability [100]. Further, Fe and Pb behave similarly in soils due to commonalities in electronegativity and covalent index (thus the strong correlation observed in this study), and Pb is also known to form complexes with Fe hydro(oxides), particularly in high pH soils [101]. This may explain some of the reductions seen in both Pb and Fe over time.
## 2 Methods and Data
### Evaluation of the Predictions  
In particular, the predictions of the real data from Section 2.5 are interpreted according to established relationships known in the characterization of porous material.  
Based on the work of [PERSON] and [PERSON] (2016), the Kozeny-Carman trend was already successfully used by [PERSON] et al. (2016) in the description of porous material.  
\[\kappa=\frac{\varepsilon}{K_{\rm c}}\bigg{(}\frac{V_{p}}{S_{p}}\bigg{)}^{2} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Series** & **No.** & **Comp. \%** & **No. of Images** & **Dimensions** \\ \hline  & 6 & 18 & 40 & 673 \(\times\) 687 \\  & 7 & 19 & 21 & 685 \(\times\) 680 \\  & 8 & 21 & 40 & 688 \(\times\) 677 \\  & 9 & 24 & 40 & 670 \(\times\) 685 \\  & 10 & 29 & 40 & 670 \(\times\) 685 \\ \hline R3 & 1 & 7 & 40 & 760 \(\times\) 310 \\  & 2 & 10 & 40 & 760 \(\times\) 310 \\  & 3 & 11 & 40 & 760 \(\times\) 310 \\  & 4 & 14 & 40 & 760 \(\times\) 310 \\  & 5 & 18 & 40 & 760 \(\times\) 310 \\  & 6 & 19 & 40 & 760 \(\times\) 310 \\  & 7 & 24 & 40 & 760 \(\times\) 310 \\  & 8 & 28 & 40 & 760 \(\times\) 310 \\  & 9 & 30 & 40 & 760 \(\times\) 310 \\  & 10 & 31 & 40 & 760 \(\times\) 310 \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 6: Compressed images, taken from a Nano CT Zeiss Xradia Versa 420; (**A**) schematic arrangement in the compression device with GDLs on the channel (R2) and block sides (R3), with BW image samples (not to scale); (**B**) sample image from a nano CT Zeiss Xradia Versa 420 (German punctuation in the image).  
With the Kozeny-Carman Equation (3), the permeability \(\kappa\) of a porous material is related to morphological characteristics of the micro-structure, namely the total volume \(V_{p}\) and the inner surface of the solid structure \(S_{p}\). \(K_{c}\) is the Kozeny constant, representing the shape of the micro-structure. Furthermore, it is known that the product of the permeability \(\kappa\) and tortuosity \(\tau\) is related to the fraction of the total volume and the inner surface:  
\[\kappa\cdot\tau\sim\varepsilon\cdot\left(\frac{V_{p}}{S_{p}}\right)^{2} \tag{4}\]  
as long as the shape of the micro-structure does not change under compression. That allows a Kozeny-Carman (KC) trend to be defined.  
\[\kappa\cdot\tau\big{|}_{x\%}=\left.\kappa\cdot\tau\right|_{\text{ref}}\cdot \varepsilon\left(\frac{V_{p}}{S_{p}}\right)^{2}\bigg{|}_{x\%}\big{/}\varepsilon \left(\frac{V_{p}}{S_{p}}\right)^{2}\bigg{|}_{\text{ref}} \tag{5}\]  
The subscript'ref' in Equation (5) denotes a reference value of compression. In [26], a value of 0% was used. With this relationship, the consistency of \(\kappa\cdot\tau\), calculated for compressed material (\(x\%\)), can be justified. The training data (from [26]) consist of ensembles of 25 stochastic micro-structures for each pair of binder type and compression level. When the Kozeny-Carman trend (Equation (5)) was applied to the LB simulations, the trend was applied with'ref' being the average values of 25 realizations of 0% compression for each binder type.
# Review of Artificial Intelligence Adversarial Attack and Defense Technologies
## 5 Defense Strategy
### Using Auxiliary Tool
#### 5.3.1 Defense-GAN  
[PERSON] et al. [54] proposed a mechanism applicable to both white-box and black-box attacks to reduce the efficiency of adversarial perturbance. This method utilizes the power of generative adversarial network [55], and the main idea is to \"project\" input images onto the range of the generator \(G\) by minimizing the reconstruction error \(\left\|G\left(z\right)-x_{2}^{2}\right\|\), prior to feeding the image \(x\) to the classifier. Therefore, compared with the adversarial samples, the legitimate samples are closer to the range of \(G\), thus greatly reducing the potential adversarial perturbance. The process of the defense-GAN mechanism is shown in Figure 17. Although defensive-GAN has been proved to be quite effective in defense against attacks, its success depends on GAN's expressiveness and generative ability. In addition, the training of GAN is challenging, i.e., without proper training, the performance of defense-GAN will obviously decline.
## 2 Materials and Methods
### Method
#### 2.3.4 Machine Learning Methods  
Partial least-square regression (PLSR) is a data modeling method that draws on concepts from multiple linear regression analysis, canonical correlation analysis, and principal component analysis. This method focuses on the linear relationship between the independent and dependent variables, and hence can solve the multicollinearity problem and guarantee model stability [16]. Support vector machines (SVMs) [17] can find the optimal hyperplane which can maximally separate multidimensional samples of two different classes. For two-dimensional and three-dimensional sample spaces, the optimal hyperplane is a straight line and a plane, respectively, while for the higher dimensional n-dimensional sample space, the optimal hyperplane is abstractly defined as an (n \(-\) 1)-dimensional space. For linearly separable problems, finding the optimal hyperplane can be transformed into solving a quadratic programming problem. For nonlinearly separable problems, the kernel trick can be applied to project the input features into a higher dimensional space where the problem becomes linearly separable. Random forests (RF) use random resampling to generate multiple sample sets from the original sample set. A tree classifier is generated based on each sample set, and then the final classification output is made according to an ensemble of multiple tree classifiers. Each sample set is used to train a single classification tree [18]. At the nodes of the tree, M features are randomly selected from m features (where m > M). According to the minimum node impurity principle, one of the m features is selected for branch growth, and the tree is grown to minimize the impurity of each node. Multiple linear regression (MLR) is a statistical regression method, with advantages of simplicity, low computational complexity, and high fitting accuracy [19]. Assuming that the dependent random variable \(y\) is affected by k independent variables \(x_{1},x_{2},\cdots,x_{\text{k}}\), it can be expressed as (Equation (3)):  
\[y=\beta_{0}+\beta_{1}x_{1}+\beta_{2}x_{2}+\cdots+\beta_{\text{k}}x_{\text{k}}+\varepsilon \tag{3}\]  
where \(\beta_{0},\beta_{1},\beta_{2},\cdots,\beta_{\text{k}}\) are the model regression coefficients and \(\varepsilon\) is a random error. The regression coefficient can be obtained using the least-square method.
## 2 Background
### Facility Location Problem
#### 2.1.2 Clustering and k-Means Procedure  
Clustering is the classification of similar objects, data, or individuals into different groups. Objects, data, or individuals in the same group have greater similarity and have the smallest similarity in different categories. This analysis is an unsupervised learning technique that offers different views on the inherent structure of a given dataset [43; 44; 45]. Additionally, according to [PERSON] et al. [46], clustering algorithms can be used as regionalization techniques. According to [PERSON] and [PERSON] [47], there are different methods of clustering, which can be categorized into the hierarchical approach or in the partition approach. In the hierarchical approach, a nested series of partitions is produced, and in the partition approach, only one partition is obtained. The advantage of the partitional methods is realized in applications with large datasets; that is, when the construction of a dendrogram is computationally prohibitive in the hierarchical approach. Additionally, unlike hierarchical methods, in which clusters are not revisited after being built, reallocation algorithms can gradually improve clusters' partitional methods. With proper data, this results in high-quality clusters. Regarding the partition approach, the squared error criterion is the most frequently used criterion, and the k-means is the simplest and most commonly used algorithm with this criterion. The k-means algorithm, first introduced by [PERSON] [48], starts with a random initial partition, and with each iteration, patterns are reassigned to the clusters based on the similarity between the pattern and the cluster centers. The interactions occur until a convergence criterion is met or the square error stops decreasing significantly after a few interactions. This reasoning comprises the following steps in Algorithm 1[47]:  
```
Initialize k-means with random values  While (a given number of iterations 'i' < = 'I') {  Select 'k' cluster centers coinciding with ((k randomly chosen patterns) or (k randomly defined points));  Assign each pattern to the closest cluster center;  For each 'k' new clusters:  Recalculate (new centroids) = mean of all points assigned to that cluster;  Recalculate new centroids;  Iterate through items:  Find the mean closest to the item;  Assign item to mean;  Update mean; }  End when convergence criterion 'i' < = 'I' is met.
# CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments
###### Abstract  
In complex environments, path planning for mobile robots faces challenges such as insensitivity to the environment, low efficiency, and poor path quality with the rapidly-exploring random tree (RRT) algorithm. We propose a novel algorithm, the complex environments rapidly-exploring random tree (CERRT), to address these issues. The CERRT algorithm builds upon the RRT approach and incorporates two key components: a pre-allocated extension node method and a vertex death mechanism. These enhancements aim to improve vertex utilization and overcome the problem of becoming trapped in concave regions, a limitation of traditional algorithms. Additionally, the CERRT algorithm integrates environment awareness at collision points, enabling rapid identification and navigation through narrow passages using local simple sampling techniques. We also introduce the bidirectional shrinking optimization strategy (BSOS) based on the pruning optimization strategy (POS) to further enhance the quality of path solutions. Extensive simulations demonstrate that the CERRT algorithm outperforms the RRT and RRV algorithms in various complex environments, such as mazes and narrow passages. It exhibits shorter running times and generates higher-quality paths, making it a promising approach for mobile robot path planning in challenging environments.  
path planning; RRT; path optimization; complex environments Article