esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Acquisition and Preprocessing
### Data Cleaning  
Abnormalities in the acquisition of parameters, delivery of data, and external environmental fluctuations can result in the occurrence of missing data and obvious outliers in the obtained water quality parameter data. Consequently, it is essential that data cleansing is in place prior to model building. In this paper, we identified and removed outliers from the raw data using the box plot technique. When a data value exceeded the maximum value or was lower than the minimum value, it was regarded as an outlier. In the original dataset arranged in ascending order, the lower quartile (Q1) and upper quartile (Q3) correspond to the 25 th and 75 th percentiles, respectively, while the median is denoted as Q2. Figure 3 and Table 1 provide further details on this methodology.  
In order to maintain the integrity and validity of the data, we employed the box plot technique to identify and eliminate six obvious outliers in the dissolved oxygen dataset utilized in this investigation, and these outliers were eliminated in order to provide a quality dataset for the subsequent training of the model. The specific elimination process is illustrated in Figure 4, with the red dots representing the screened out obvious outliers.  
Once the values were eliminated using the box plot, the empty data points were replaced with interpolated values, following the equation presented as Formula (1):  
\[x_{k}=x_{h}+\frac{(x_{b}-x_{h})(k-h)}{b-k} \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Standard of Judgment** & **Verdict** \\ \hline x \(>\) Q3 + 1.5 (Q3 \(-\) Q1) or x \(<\) Q1 \(-\) 1.5 (Q3 \(-\) Q1) & Outlier \\ x \(>\) Q3 + 3 (Q3 \(-\) Q1) or x \(<\) Q1 \(-\) 3 (Q3 \(-\) Q1) & Extreme \\ \hline \hline \end{tabular}
\end{table}
Table 1: Methods of identifying outliers.  
Figure 3: Structure of box plot.  
where \(x_{k}\) denotes the missing data to be filled in, \(x_{h}\) denotes the data at the previous sampling point, and \(x_{b}\) denotes the data at the subsequent sampling point. \(k\), \(h\), and \(b\) denote the locations of the sampling points corresponding to the data. The results of 900 sets of DO data after data cleaning are shown in Figure 5.  
Figure 4: Box plot of DO data.  
Figure 5: Preprocessed dissolved oxygen data.
## 4 Results and Discussion
### Mass Loss due to Aging  
Figure 8 illustrates the impact of nanomaterials on the thermal aging characteristics of asphalt binders, as determined through the Rolling Thin Film Oven (RTFO) test. The graph reveals a discernible trend where the inclusion of nanomaterials, specifically NT, exhibits a potential for reducing mass loss, which is indicative of enhanced thermal stability. Initially, the addition of NT appears to mitigate the aging process, with mass loss decreasing to 0.234% at a 2% dosage, representing a reduction of 12% from the control. Even at an 8% inclusion, the mass loss for NT is 0.259%, which is only marginally lower than the control, suggesting that NT may act as an anti-aging agent, hinting at a complex interaction between NT and the binder as concentrations increase.  
Conversely, binders amended with NA and NS demonstrate a clear and direct increase in mass loss as the nanomaterial content escalates. NA sees a marked rise from the baseline mass loss to 0.414% at an 8% addition, translating to a significant increase of 55.64%. NS exhibits an even more pronounced uptick, with the mass loss reaching 0.558% at an 8% inclusion, representing an increase of 109.77% compared to the control. These results suggest that NA and NS, perhaps due to their specific surface interactions and higher binder affinity, contribute to a more substantial release of volatile compounds, thereby more intensely affecting the binder's thermal stability compared to NT.  
When ranking the nanomaterials concerning aging resistance, NT appears to offer the best performance at low to moderate concentrations. In contrast, NA and NS, while enhancing certain binder properties, seem to decrease aging resistance. Despite the variations in mass loss with different nanomaterial contents, it is noteworthy that all modified binders remain within the acceptable limit of less than a 1% mass loss as stipulated by [41] for RTFO aged asphalt. This confirms that, within the investigated range, the nanomaterials do not compromise the asphalt's resistance to aging, thereby maintaining the prescribed standards for durability and performance.  
Figure 8: Effect of nanomaterials on mass loss due to RTFO.
## 2 Research and Development Trends
### CPSS for Public Transportation  
The current study on traffic CPS does not consider the behaviors of travelers, which leads to incomplete research. Therefore, the effective combination of the human society and physical system through CPSS is expected to achieve sufficient management for complex public transportation systems [15; 16].  
In the field of CPSS for transportation, knowledge-based methods remain popular. [PERSON] et al. developed a personalized and interactive transportation service mobile application based on the support of European Union \"FP7 MobiS\" project [17]. [PERSON] et al. used a knowledge-based method to estimate automatic passenger flow [18]. For the first time, considering social factors, the authors conducted a special analysis and research of the traffic CPSS [19]. [PERSON] et al. discussed the acquisition, cleaning, and fusion techniques of various big data, and the integration, extraction, and application of real-time transportation conditions based on natural language and text [20]. [PERSON] et al. used mobile data, bus cards, and other social signals to collect transportation demand, forecast transportation congestion and found the root reasons, and then provided the best transportation path and other guidance [21]. [PERSON] et al. captured social network structures, lifetime events, short-term travel, and activity planning from multi-source data collections [22]. [PERSON] et al. addressed tight coupling between the performance of vehicle-to-vehicle communications and the performance of cooperative ITSs' safety applications [23]. [PERSON] et al. proposed a CPSS-based parallel driving framework, which consists of connected vehicles with different levels of automation and necessitates a unified approach for future smart and safe driving [24].  
From the research overview, CPSS in transportation is becoming the forefront of theoretical research, and is the application basis in urban public transportation. Researching the CPSS in public transportation is necessary for human traveling behaviors, and can effect, or even dominate the performance of an urban public transportation system.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 4 A Resilient Platform Approach  
We implemented a software platform (see Figure 1) to leverage the construction of resilient applications based on the microservices architecture defined in [58]. This platform offers developers and users the mechanisms needed to build and use resilient services through standardized interfaces. With this in mind, our platform is deployed on a Kubernetes [59] orchestration cluster, which provides auto-scaling, load balancing, service discovery, and self-healing, among other features. Our resilient service platform consists of the following components:  
* API gateway node: This contains a service that allows the platform to receive data from external sources, such as user devices or access gateways. JSON was chosen as the data representation format in order to provide a standardized mechanism to send data to the platform
* Master node: This contains the control plane components needed to manage the Kubernetes cluster. It provides the Kubernetes API and allows for scheduling and cluster data management.
* Worker nodes: These host the pods, which group the containers and other resources used by applications. The master node manages the pods and worker nodes
* Access gateway: This allows IoT nodes to send data to the platform. For this work, we used a LoRaWAN gateway.
* Broker: This implements a publish/subscribe communication mechanism between the platform microservices. This functionality was implemented using Zookeeper and Kafka frameworks.
* ResCity Database: This provides storage services to the platform microservices.  
Figure 1: Platform for provisioning resilient services.
## 5 Conclusions  
In this work, we propose a novel spatial temporal self-attention augmented graph convolutional clustering networks for skeleton-based video anomaly detection tasks by employing the SAA-STGCAE to extract features and embedded clustering. We proved that the SAA-Graph can achieve a more flexible and dynamic representation between skeletons while overcoming the locality of graph convolution. This data-driven approach increases the flexibility of the graph convolutional network and brings more versatility to adapt to various data samples. To the best of our knowledge, we are the first to consider using self-attention for video anomaly detection tasks as an enhancement of spatial temporal graph convolution to capture global features. Our proposed model achieves the excellent performance on both two anomaly detection datasets, ShanghaiTech Campus and CUHK Avenue. Future work includes detecting abnormal phenomena between humans and  
Figure 8: Normality Score of video clips from ShanghaiTech dataset. Regularity score is normalized to [0, 1] and the red areas in the figure represent anomalies.  
Figure 9: The failure cases on the ShanghaiTech Campus dataset. (**a**) Vehicle appears in the clip, which is not processed by our method; (**b**) Abnormal motion tracking skeleton may be lost when obstructed by obstacles; (**c**) False negative situation is a misjudgment by a slow cyclist.  
human-object interactions, enhancing skeleton features with appearance features, and looking for a fully self-attentional solution, which leads to improved network performance and reduces the number of parameters.  
Project administration, L.S.; Conceptualization, C.L. and R.F.; Methodology, R.F. and C.L.; Formal analysis, R.F., Y.L. and Y.G.; Funding acquisition, L.S., C.L. and Y.G.; Resources, Y.L.; Validation, Y.G.; Writing, R.F. and C.L.; Writing--review and editing, W.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China under grant of 2018 YFC0824402 and 2020 YFB1712401, and was supported in part by the Nature Science Foundation of China (62006210).  
Not applicable.  
The authors declare no conflict of interest.
# Sparse Low-Rank Based Signal Analysis Method for Bearing Fault Feature Extraction
## 6 Conclusions  
According to the low-rank characteristic and sparsity of fault features in the time-frequency domain, this paper proposes a novel method FISLRA based on a low-rank sparse matrix estimation for the diagnosis of REBs. Concretely, a sparse and low-rank model is formulated in the time-frequency domain. Then, a fast-converging algorithm is derived based on ADMM to solve the formulated model. Moreover, to further highlight the periodical transients, a CKT scheme proposed in this paper is also incorporated to solve the proposed low-rank spare model. With the proposed method, the weak fault features indicating the occurrence of the bearing are successfully extracted. Moreover, the effectiveness and advantages of the FISLRA are validated in detecting the bearing fault by comparing it with TSLRM and SK. In short, FISLRA can be seen as an enhanced method of the TSLRM method. Compared with the TSLRM method which is susceptible to HCs, FISLRA can effectively abstract the fault features from a noisy signal even if disturbed by HCs. However, the setting of the parameters in the FISLRA method is worthy of deeper research. In the field of bearing fault diagnosis, low-rank sparse matrix estimation is currently in the early stages of research, and the setting of parameters is still an open topic that requires further research. Therefore, we will conduct research on parameter settings in future research work. Moreover, we have found through preliminary research that the gearbox fault features also exhibit a low-rank property and sparsity in the time-frequency domain. Therefore, we will conduct further research on the gearbox fault feature based on low-rank sparse matrix estimation in the future.  
**Author Contributions:** [PERSON] proposed the method FISLRA. [PERSON] and [PERSON] analyzed the signal and wrote the draft manuscript, [PERSON] and [PERSON] provided some valuable advice. All authors have read and agreed to the published version of this manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China grant number 51575424, the National Key Research and Development Program of China grant number 2019 YFB1311903 and the Joint Foundation of the Ministry of Education grant number 6141A02022113. And the APC was funded by the National Natural Science Foundation of China grant number 51575424.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# E-Sail Three-Dimensional Interplanetary Transfer with Fixed Pitch Angle
## 3 Description of the Mission Scenarios and Notes on the Trajectory Design
Note that the case of \(a_{c}=1\) mm/s\({}^{2}\) is consistent with the scenario in which the generic transfer is performed by using an E-sail with a sort of \"canonical value\" of the characteristic acceleration, as defined by [PERSON] [29] for a photonic solar sail-based transfer. The results of numerical simulations are illustrated in the next section.
## 5 Stressing State Analysis of CFST Arch Models
### Change Characteristics Reflected by Strain/Stress Fields of Arch Models
The stress fields of the arch models can be gained to receive a more specific stress description of steel tubes and core concrete on cross-sections. As shown in Figure 11, stress fields of typical sections  
Figure 10: Strain fields of two arch models around characteristic loads \(Q\): (**a**) Section 11 of arch-A; (**b**) Section 13 of arch-B. Figure 9: Strain fields of section 11 of arch-A around characteristic point \(P_{\text{A}}\). are plotted. In order to clearly demonstrate stress changes of steel tube, its thickness was approximately increased without following the actual proportion of steel tube and concrete. The solid lines in steel tubes and dashed lines in core concrete signify that the stress was approaching zero. Because of the variation of section size, these lines did not overlap, but were parallel with each other. In this regard, the rationality of NSF method is shown. Considering diverse stress levels of steel and concrete, their stress color legends are listed separately in figures. For section 11 of arch-A in Figure 11a, both concrete and steel tube underwent compressing stress before 180 kN with very small tensile stress. Obvious tensile area existed at 190 kN. The steel tube, especially, showed great tensile stress values considering that the concrete material was hardly capable of tension. The maximum tensile value, 226 MPa, was at the upper-right corner of the steel tube. Meanwhile, compressive stress born by core concrete also increased rapidly compared with the previous stable stress state. At this stage after the failure load, the mechanical behavior of both concrete and steel tube had a qualitative development. Section 13 of arch-B in Figure 11b depicts the different stressing features. Like the strain fields, the stress of the lower-left corner of the section showed obvious changes in values from 85 kN to 90 kN, while the tensile stress of the right side maintained small values. By delineating strain/stress fields of typical sections of the two arch models, their change characteristics around failure loads can be found. Generally, owing to their different instability modes, the strain/stress fields of arch-A represent a more recognizable variation after the failure load, which can be easily distinguished from the changes of isopleth curve shapes or values in strain/stress fields.
# A Faster Algorithm for Reducing the Computational Complexity of Convolutional Neural Networks
## 2 Related Work  
### Convolutional Neural Networks  
Machine-learning has produced impressive results in many signal processing applications [20; 21]. Convolutional neural networks extend the machine-learning capabilities of neural networks by introducing convolutional layers to the network. Convolutional neural networks are mainly used in image processing. Figure 1 shows the structure of a classical convolutional neural network, LeNet. It consists of two convolutional layers, two subsampling layers and three fully connected layers. Usually, the computation of the convolutional layers occupies most of the network.  
Convolutional layers extract features from the input feature maps via different kernels. Suppose there are Q input feature maps of size Mx \(\times\) Nx and R output feature maps of size My \(\times\) Ny. The size of the convolutional kernel is Mw \(\times\) Nw. The computation of the output in a single layer is given by the equation  
\[y_{r,x,y}=\sum_{q=1}^{Q}\sum_{u=1}^{Mw}\sum_{v=1}^{Nw}w_{r,q,u,v}x_{q,x+u,y+v}, \tag{1}\]  
where X is the input feature map, Y is the output feature map, and W is the kernel. The subscripts x and y indicate the position of the pixel in the feature map. The subscripts u and v indicate the position of the parameter in the kernel. Equation (1) can be rewritten as Equation (2).  
\[y_{r}=\sum_{q=1}^{Q}w_{r,q}*x_{q} \tag{2}\]  
Suppose there are P images that are sent together to the neural network, which means the batch size is P. Then the output Y in Equation (2) can be expressed by Equation (3).  
\[y_{r,p}=\sum_{q=1}^{Q}w_{r,q}*x_{q,p} \tag{3}\]  
Figure 1: The architecture of LeNet.  
If we regard the \(y_{r,p}\), \(w_{r,q}\) and \(x_{q,p}\) as the elements of the matrices \(Y\), \(W\) and \(X\), respectively, the output can be expressed as the convolution matrix in Equation (4).  
\[Y=W*X \tag{4}\]  
\[Y=\left[\begin{array}{ccc}Y_{1,1}&\cdots&Y_{1,P}\\ \vdots&\ddots&\vdots\\ Y_{R,1}&\cdots&Y_{R,P}\end{array}\right]W=\left[\begin{array}{ccc}w_{1,1}& \cdots&w_{1,Q}\\ \vdots&\ddots&\vdots\\ w_{R,1}&\cdots&w_{R,Q}\end{array}\right]X=\left[\begin{array}{ccc}x_{1,1}& \cdots&x_{1,P}\\ \vdots&\ddots&\vdots\\ x_{Q,1}&\cdots&x_{Q,P}\end{array}\right] \tag{5}\]  
Matrix \(Y\) and matrix \(X\) are special matrices of feature maps. Matrix \(W\) is a special matrix of kernels. This convolutional matrix provides a new view of the computation of the output \(Y\).
# An Efficient Neural Network for Shape from Focus with Weight Passing Method
## 6 Discussion  
The presented method is one of the paradigms of 3D shape recovery techniques known as SFF. Three-dimensional shapes have possible potentials for various applications including motion reconstruction [30; 31; 32], 3D object retrieval [33], 3D cameras, manufacture of TFT-LCD color filter, and measurement of surface roughness, medical examinations, microelectronics, and focus variation for 3D surface. The proposed method is a simplified version of the previous well-known methods based on neural network. From the experimental results, it is clear that the proposed model has considerably reduced the complexity without a loss in accuracy. It is expected that if the PCA is used to reduce the dimensionality to select the surface locally, it can give marginally better accurate depth than the NN.Planar. However, the additional computational complexity by PCA will rise proportional to the total iteration numbers. It would be interesting to do a separate study that can make fair comparisons among the methods based on different dimensionality reduction techniques for three shape refinement through the SFF in term of complexity and accuracy.  
In literature, a number of machine learning techniques such as convolutional neural network (CNN) [34; 35], deep convolutional neural network (DCNN) [36; 37] recurrent neural network (RNN) [38], graphical model [39], have been proposed. Among these, CNN includes the fully connected layers which connect each neuron in a layer to all neurons in the next layer. It is similar to the feedforward artificial neural network. Therefore, WP method can be applied with the same settings by using CNN for 3D imaging applications to reduce local minimization. Similarly, the other well-known techniques are also applicable with modifications in the weight update procedures.  
In machine learning techniques, the restricted Boltzmann machine [24; 25; 26] are considered powerful methods to pre-process the input data to expedite the learning process. In our future work, the restricted Boltzmann machines will be used, instead of reducing the dimensionality of the data, in refining the depth maps in SFF. It is expected that RBM will provide depth maps efficiently with higher accuracy. In addition, it would be interesting to do a separate study for comparing the performances of different machine learning-based methods in 3D shape recovery in SFF in terms of efficacy and accuracy.
## 2 Transcriptional Regulation in Integrons  
The expression of gene cassettes inserted in integrons is under a tight and complex regulation, which may be modulated at both transcriptional and translational levels by different mechanisms. At means of transcription control, the following aspects influencing gene cassette expression will be discussed in detail below: the activity and strength of the common cassette promoter regions (Pc and P2); the position effect imposed by the physical distance of gene cassettes relative to Pc; the role of cassette-borne promoters and other additional transcriptional units. All of them may affect the amount of mRNA produced by each gene cassette inserted in the integron and, therefore, in the case of RIs, the antibiotic susceptibility of the host strains [3].