esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Using vibration signals for bearing fault diagnosis can generally achieve good diagnostic results. However, it is not suitable for practical industrial applications due to the restricted installation and high cost of vibration sensors. Therefore, the easily obtainable motor current signal (MCS) has received widespread attention in recent years. Meanwhile, traditional fault diagnosis methods cannot meet the diagnostic accuracy requirements because of the low signal-to-noise ratio (SNR) of the MCS. Committed to achieving bearing fault diagnosis through MCS, a rolling bearing fault diagnosis method, ISCV-ViT, based on the MCS and the Vision Transformer (ViT) model, is proposed. In particular, a signal processing method based on the instantaneous square current value (ISCV) is proposed to process the MCS directly obtained through a frequency converter into time-domain images. Then, the ViT model is applied for bearing fault diagnosis. Finally, experimental verification is carried out based on the public bearing dataset of Paderborn University (PU) and the bearing dataset of Shenzhen Technology University (SZTU). The analysis of the experimental results demonstrates that the average accuracy of the ISCV-ViT for the two datasets is up to 96.60% and 94.87%, respectively.  
bearing fault diagnosis; signature processing; deep learning; vision transformer; motor current signal analysis +
Footnote †: journal: Applied Sciences
## 2 Array Antenna Angle-of-Arrival Positioning Algorithm
The target parameter is obtained by searching the peak value of the spectral function by parameters, which can be expressed as:  
\[[\hat{\theta},\hat{\phi},\hat{\gamma},\hat{\eta}]=\text{argmax}Pmusic(\theta,\phi,\gamma,\eta) \tag{14}\]  
Since the above process is a four-dimensional search and requires a large amount of calculation, to reduce the amount of calculation, B is expressed in the following form by combining Equation (14) and the properties of the Hardmard product:  
\[\mathbf{a}(\theta,\phi,\gamma,\eta)=\mathbf{p}(\theta,\phi,\gamma,\eta)ea(\theta,\phi )=\mathbf{\Lambda}_{s}(\theta,\phi)\mathbf{P}(\theta,\phi)\mathbf{\omega}(\gamma,\eta) \tag{15}\]  
where \(\mathbf{\Lambda}_{s}(\theta,\phi)\) denotes a diagonal matrix consisting of \(\mathbf{a}(\theta,\phi)\), so \(Pmusic(\theta,\phi,\gamma,\eta)\) can be expressed as follows:  
\[ \begin{array}{cc}Pmusic(\theta,\phi,\gamma,\eta)&=\frac{1}{\omega^{H}( \gamma,\eta)\mathbf{P}^{H}(\theta,\phi)\mathbf{\Lambda}_{s}^{H}(\theta,\phi)\mathbf{Q}_{ music}\mathbf{\Lambda}_{s}(\theta,\phi)\mathbf{P}(\theta,\phi)\mathbf{\omega}(\gamma,\eta)}\\ &=\frac{1}{\omega^{H}(\gamma,\eta)\mathbf{D}(\theta,\phi)\mathbf{\omega}(\gamma,\eta )}\end{array}  \tag{16}\]  
where \(\mathbf{D}(\theta,\phi)=\mathbf{P}^{H}(\theta,\phi)\mathbf{\Lambda}_{s}^{H}(\theta,\phi) \mathbf{Q}_{music}\mathbf{\Lambda}_{s}(\theta,\phi)\mathbf{P}(\theta,\phi)\). To maximize \(Pmusic(\theta,\phi,\gamma,\eta)\), \(\mathbf{D}(\theta,\phi)\) should be as small as possible, and parameter search can be achieved as follows:  
\[[\hat{\theta},\hat{\phi}]=\text{argmin}\lambda_{\text{min}}(\mathbf{D}(\theta, \phi)) \tag{17}\]  
where \(\lambda_{\text{min}}(\mathbf{D}(\theta,\phi))\) represents the minimum eigenvalue of \(\mathbf{D}(\theta,\phi)\). Assuming that the vertical distance between the source and base station is \(h\), the coordinates of the source location can be obtained by combining Figure 2 as follows:  
\[[x,z]=[h\tan\theta\cos\varphi,h\tan\theta\sin\varphi] \tag{18}\]
## 3 Results and Discussion
### Validation of the HPLC Analytical Method Performance
#### 3.2.3 Precision of the Test Method  
Under the conditions of high-performance liquid chromatography described in Section 3.1, we analyzed HNS at concentrations of 10, 20, and 30 mg/L, and measured the peak areas. Concentrations were calculated using Equation (1). Standard deviation (SD) and relative standard deviation (RSD) were calculated according to Equations (4) and (5), respectively. RSD was used to assess the intra-day and inter-day precision of the method. We conducted six replicate analyses of HNS at each concentration on the same day and repeated these experiments over three consecutive days. As shown in Table 6, the intra-day RSDs for all concentrations did not exceed 1.1%, and the inter-day RSDs did not exceed 1.2%. These results indicate that the method provides good precision.  
\[\text{SD}=\sqrt{\frac{\sum_{i=1}^{n}(x_{i}-\overline{x})^{2}}{n-1}} \tag{4}\]  
where \(x_{i}\) is each individual measurement; \(\overline{x}\) is the mean of the measurements; \(n\) is the number of measurements.  
\[\text{RSD}=\left(\frac{\text{SD}}{\overline{x}}\right)\times 100\% \tag{5}\]  
where \(\overline{x}\) is the mean of the measurements.
# Quality of Service Criteria in Integrated Passenger Transport Systems: An Overview
## 4 Public Passenger Transport Service Quality  
The term \"quality\" has a relative meaning. It is used in numerous ways and there is no clear definition. According to the International Organization for Standardization, \"Quality is the degree to which a set of inherent characteristics meets the requirements\" [19]. For consumers, the association lies primarily with the utility or the price of the product. On the other hand, providers relate it to the design and manufacturing elements involved in production. The quality of a product or service is determined by the relationship between the requirements and needs of the customer and their realisation by the provider [20].  
\"Quality of transport\" refers to the overall standard of transport services, whether they are public or private, aimed at the movement of individuals, goods or services. This concept includes a range of elements that influence the efficiency, safety, reliability and convenience of transport. Public transport services are usually operated by local government authorities or transport providers (operators) and are designed to provide passengers (customers) with affordable, convenient and reliable transport options. However, an increase in supply (qualitatively or quantitatively) will not automatically lead to a corresponding increase in demand and satisfaction [21]. Upgrading service quality is the key element in increasing the number of customers in PPT [22].  
The quality of public transport can be analysed from two different points of view: (1) the service provider's and (2) the passengers' (in a broader sense: users') points of view [23]. It can vary significantly from one city or area to another and even within the region. Different modes of public transport may exhibit varying levels of quality. It is important to identify which are the attributes perceived as relevant by the customers.  
One case study [23] measured service quality variations in a set of integrated policies implemented in the Campania region (Italy) going under the name of the Regional Metro System (RMS). One of the conclusions is that the quality of public transport depends on several service criteria, some of which are quantitative. (e.g., average travel time and its reliability, transit waiting time, monetary costs), while others are qualitative, whose effects on user behaviour are more difficult to assess (e.g., riding comfort, information, personal security).  
Improving the quality of public transport often requires ongoing investment, strategic planning and collaboration between governments, transport authorities and public transport operators. Gathering feedback from passengers and conducting regular assessmentscan help identify areas for improvement and guide efforts to enhance the system's quality. Nowadays, quality has a growing importance in the field of transport [24].
## 2 Statistical Methodology
### Representative Variables Selection  
We now present how each representative variable is extracted out of a CORE-cluster \(S_{\xi,\tau}\). As mentioned in Section 2.2, the pertinent relations between two variables \(X^{i}\) and \(X^{j}\) may be lost in \(w_{i,j}\), since \(n\ll p\) and recovered by their coherence \(c(X^{i},X^{j})\) using other relations. In this example, the similarity \(s\) captures true positive and false negative relations and the notion of coherence makes robust the detection of relations. For the same reasons, it may however also capture false positive relations, so the CORE-clusters may contain undesirable variables. The variables captured by CORE-clusters using false positive relations should however be located at the cluster boundaries if the data are not too noisy. False positive connections are indeed less common and on average weaker than true positive connections in this case.  
In order to limit the impact of the false positive relations, we then define the representative variables as the CORE-cluster centers. More specifically, each representative variable minimizes an average distance with the other variables of a CORE-cluster \(S_{\xi,\tau}\). Instead of using distances based on the maximum capacity Equation (2), we use a more standard notion of distance calculated as sums of weighted edges traversed by the optimal paths. This limits the phenomenon of having multiple variables with the same optimal distance due to the min-max strategy. The graph weights \(w_{i,j}\), which represent a similarity level, must however be converted into distances, which can be simply done by using \(d_{i,j}=1/\left(w_{i,j}+\epsilon\right)\), where \(\epsilon>0\). The representative variable of a CORE-cluster \(S_{\xi,\tau}\) is then the one that has the lowest average distance to the other variables of \(S_{\xi,\tau}\). The impact of selecting the representative variables as CORE-cluster centers is discussed Appendix A.
# Design and Implementation of Virtual Security Function Based on Multiple Enclaves
## 6 Result
### Security Analysis  
Putting VNFs into a single enclave that affects the scalability of NFVs, because users cannot scale in/out a single VNF. In our design, the sensitive modules of a VNF are put into different enclaves as elements of Click. The system can freely combine these security modules according to user requirements, and increase the scalability of the system while protecting its running state security.  
The security of our system is protected by Intel SGX technology. The code, sensitive data, and system configuration of elements in VNF cannot be stolen or modified. In our experiments, various IDS security rules are put into enclaves for protection. Network packets are encrypted during the communication between multiple enclaves and are decrypted only inside enclaves. Therefore, the attackers can only capture the encrypted traffic, but cannot obtain the content of the packets, and cannot obtain and modify the IDS rules and detection results. The system is divided into two parts: the trusted part and the untrusted part. The trusted part stores IDS-related sensitive data, states, and policies in isolated and secure EPC memory. The code and data in the trusted part cannot be accessed by the operating system, drivers, BIOS, or VMM. The security of the system depends on the SGX hardware. Meanwhile, the untrusted part provides the necessary system call interfaces for the trusted part and the transmission of the packets between them.  
In addition, the transmission of information between multiple enclaves is a very important security issue. In this architecture, multiple enclaves must first pass SGX's local  
Figure 8: CPU usage of the system at different packet sizes.  
attestation to ensure that they each run on a trusted platform and establish a secure channel. Thereafter, multiple enclaves must pass this channel for information transmission, ensuring the security of information transmission.
# Recommendation Algorithm for Multi-Task Learning with Directed Graph Convolutional Networks
## 5 Conclusions  
The current recommendation models all have the defects of cold start and single recommendation items more or less. This paper mainly studies a multi-task learning recommendation model with a directed graph convolution network (MTL-DGCNR) for the recommendation field of e-commerce. This model integrates related technologies such as a directed graph convolutional network, a soft attention mechanism, a TransH algorithm, and multi-task learning. The working principle of MTL-DGCNR is expounded and the superiority of this model is verified experimentally. However, there are still some deficiencies in this model, and further research is planned in two aspects in the future.  
(1) In the multi-task learning mode, the data preprocessing stage not only needs to build the graph structure data of the user-commodity interaction information, but also needs to build the knowledge map of the user and the commodity. Therefore, a relatively large amount of work and parameters are undoubtedly increased in the preparatory stage. Even if multi-task learning proves to be effective in improving the performance of the model, the complexity of the model is still high. It is hoped that the complexity and parameter quantity of the model can be reduced through more in-depth research without reducing the performance of the model.  
(2) In data sets with dense user behaviors or many types of user operation behaviors, the workload of the MTL-DGCNR model in processing each node and each directed edge will become larger, and the speed of model convergence may also be affected accordingly. If the gated graph sequence neural networks (GGNN) are used as the main recommendation task, and the gating mechanism is used to relatively reduce the influence of the front hidden nodes on the back hidden nodes, the convergence and performance of the model should be better. However, corresponding algorithm improvements are needed to adapt to the processing of directed graph structured data.  
Conceptualization, L.Y. and J.L.; methodology, L.Y.; software, J.L.; validation, LY., H.C. and J.L.; formal analysis, G.Z.; investigation, G.Z.; resources, H.C.; data curation, J.L.; writing-original draft preparation, J.L.; writing-review and editing, L.Y.; visualization, J.L.; supervision, L.Y.; project administration, W.D.; funding acquisition, L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant number U2133205 and 61771087, the Natural Science Foundation of Sichuan Province under Grant 2022 NSFSC0536, the Traction Power State Key Laboratory of Southwest Jiaotong University under Grant TPL203, the Research Foundation for Civil Aviation University of China under Grant 3122022 PT02 and 2020 KYQD123.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Mathematical Model
### Objective Function
#### 4.3.2. AGV Energy Consumption Objective  
In the SACOP problem, the total driving energy consumption of AGVs is mainly affected by two non-negligible factors: the total length of the AGV path and the number of path conflicts. Under the condition of constant speed and conflict-free driving, the energy consumption of AGV is directly proportional to the path length. However, there are often collision conflicts between the multi-AGV driving paths determined by direct solutions. Considering that the AGV operating space in the SACOP problem is a free rectangular plane, rather than the aisle environment with finite degrees of freedom in traditional problems, adopting a speed control strategy to avoid conflicts is the most economical and feasible method. By adding a mirrored acceleration-deceleration process at possible conflict points, collisions can be avoided while also avoiding any impact on the system scheduling plan. Under a unified speed control strategy, the speed adjustment energy consumption of AGVs to avoid conflicts can be seen as directly proportional to the number of conflicts.  
To sum up, the minimum AGV energy consumption objective function \(F_{2}\) can be expressed as Equation (11).  
\[F_{2}=\min\sum_{n=1}^{N}\left(l_{n}+\beta C_{n}\right) \tag{11}\]  
\(l_{n}\) represents the total length of the driving path of AGV \(n\), \(C_{n}\) represents the number of conflicts that occurred by AGV \(n\), and \(\beta\) is the conversion coefficient related to the system, which can be measured experimentally for specific systems.
# Production of a Human Metabolite of Atorvastatinin by Bacterial CYP102A1 Peroxygenase
## 2 Materials and Methods
### Atorvastatin Hydroxylation's Kinetic Parameters and Total Turnover Numbers  
For determining the kinetic parameters of CYP102A1 mutants, the reaction mixtures included an atorvastatin substrate at a concentration of 5-200 \(\mathrm{\SIUnitSymbolMicro u}\)M in 100 mM potassium phosphate buffer (pH 7.4) and 0.20 \(\mathrm{\SIUnitSymbolMicro u}\)M CYP102A1. The reaction mixtures were preincubated for 5 min at 37 \({}^{\circ}\)C. An aliquot of 10 mM H\({}_{2}\)O\({}_{2}\) initiated the reactions, which were performed for 10 min at 37 \({}^{\circ}\)C.  
To determine total turnover numbers (TTNs) of CYP102A1 mutants, the reaction mixtures included atorvastatin substrate at a concentration of 200 \(\mathrm{\SIUnitSymbolMicro u}\)M in 100 mM potassium phosphate buffer (pH 7.4) and 0.20 \(\mathrm{\SIUnitSymbolMicro u}\)M CYP102A1. The reaction mixtures were preincubated for 5 min at 37 \({}^{\circ}\)C. An aliquot of 10 mM H\({}_{2}\)O\({}_{2}\) initiated the reactions, which were performed for 30 s, 1, 2, 3, 4, 5, 10, 20, and 30 min at 37 \({}^{\circ}\)C.  
The kinetic parameters of CYP102A1 catalyzed reactions supported by NADPH were determined by the reaction, including 5-500 \(\mathrm{\SIUnitSymbolMicro u}\)M of atorvastatin in 100 mM potassium phosphate buffer (pH 7.4) and 0.20 \(\mathrm{\SIUnitSymbolMicro u}\)M enzymes. The NADPH-generating systems were added to the initial reaction and the reaction mixtures were incubated at 37 \({}^{\circ}\)C for 30 min. The products were extracted with ethyl acetate and analyzed by HPLC as described above. The kinetic parameters were analyzed by GraphPad Prism software (GraphPad Software, San Diego, CA, USA).  
Comparison of Atorvastatin Hydroxylation Activity Supported by External Addition of Hydrogen Peroxide and In Situ Hydrogen Peroxide Generation  
For the reactions with externally added hydrogen peroxide, the reaction mixtures included 2 mL total volume including 200 uM atorvastatinatin in a potassium phosphate buffer (100 mM, pH 7.4) and 0.20 uM CYP102A1. The reaction mixtures were pre-incubated for 5 min at 37 \({}^{\circ}\)C. An aliquot of 10 mM H\({}_{2}\)O\({}_{2}\) initiated the reactions.  
For the reactions with in situ hydrogen peroxide production, the reaction mixtures included 2 mL total volume including 200 uM atorvastatinatin, 0.20 uM CYP102A1,4 g/L of glucose, and 10 U/mL of glucose oxidase in a potassium phosphate buffer (100 mM, pH 7.4).  
The reactions were performed at 37 \({}^{\circ}\)C at 2, 5, 10, 20, 30, 60, and 120 min point time, aliquots of 250 uL reaction mixture were taken and the reactions were stopped by adding 600 uL of ice-cold ethyl acetate for 4-OH atorvastatinatin measurement.
# Energy-Balanced Routing Algorithm Based on Ant Colony Optimization for Mobile Ad Hoc Networks
## 3 IEMACO Routing Algorithm
### Protocol Design
```
0: phr-tab, \(N_{\mathit{dest}}\), \(N_{\mathit{src}}\), \(N_{\mathit{current}}\), \(N_{\mathit{neigh}}\)
0:\(P_{\mathit{optimal}}\)  Initialize the network by using Equations (9) and (11) if There is a path \(P_{\mathit{local}}\) to \(N_{\mathit{src}}\)then return\(P_{\mathit{optimal}}=P_{\mathit{local}}\) else\(P_{\mathit{robust}}\)= generation of FANT by \(N_{\mathit{src}}\) endif if\(P_{\mathit{robust}}\
eq 0\)then  Update pheromones in phr-tab by using Equation (12)  Update phr-tab = phr-tab (\(N_{\mathit{current}}\), \(N_{\mathit{neigh}}\))  st-rt-tab \(\leftarrow\) st-rt-tab(\(N_{\mathit{current}}\)) endif if FANT = \(N_{\mathit{dest}}\)then  Generation of BANT by \(N_{\mathit{dest}}\)  Update phr-tab and return \(P_{\mathit{robust}}\) endif if Next hop is not reachable then  link-disconn =detection and maintenance of link  disconnections during traversal of the ants endif if link-disconn= 0 then  Select \(P_{\mathit{optimal}}\) between \(N_{\mathit{src}}\) and \(N_{\mathit{dest}}\) \(P_{\mathit{optimal}}=P_{\mathit{robust}}\) else  Restart the generation of FANT by \(N_{\mathit{src}}\) endif return\(P_{\mathit{optimal}}\)
```  
**Algorithm 1** IEMACO protocol  
We next discuss the space and time complexity of the IEMACO algorithm. Without loss of generality, we assume the number of nodes in the network is \(n\) and the number of ants in each iteration is \(m\). In Reference [32], the space complexity of the basic ant colony algorithm has been analyzed  
Figure 1: Flow of the route discovery. and is proved to be \(O(n*n)+O(n*m)\). For the improved ant colony algorithms, when the scale of the problem is the same, its space complexity is similar to that of basic ACO. For AntHocMMP and IEMACO, more two-dimensional matrices are needed to store the energy cost than the basic ACO. However, the space complexity of AntHocMMP and IEMACO is the same as that of basic ACO, which is \(O(n*n)+O(n*m)\). The time complexity of each iteration has been analyzed in literature [33]. For AntHocMMp and IEMACO, the time complexity of each step in the algorithm implementation process can be obtained by Table 2. If the algorithm circulates \(N_{c}\) times, the time complexity of the AntHocMMP algorithm is \(O(N_{c}*n^{2}*m)\) which is same with the basic ACO. Since the IEMACO algorithm introduces the acceleration convergence mechanism, the time complexity is reduced to \(O(log(N_{c})*n^{2}*m)\).
## 4 Inverse Problems
### Fluid Mechanics and FSI  
Cardiovascular catheterization pressure measurement is considered the reference standard on patient-specific hemodynamics, as it constitutes a direct assessment of pressure and dimensions within the blood vessels or the cardiac cavities using high-accuracy transducers. When available, most inverse models of computational fluid dynamics use a least-square-error of the time-dependent pressure function as the optimization target, while image-based flow data is used as boundary conditions [103; 118; 119]. Models incorporating FSI can instead use the pulse wave velocity as an optimization target that accounts for both the hemodynamics and elastic properties of the vessel [120]. The carotid-femoral pulse wave velocity is considered the gold standard for systemic arterial stiffness assessment, which is calculated as the patient-specific distance between the carotid and femoral artery and the time delay between the pressure wave measured at those locations. Local estimations of pulse wave velocity can also be obtained from invasive catheterization and by flow-to-area ratios from doppler ultrasound or phase-contrast MRI [121]. However, CFD cardiovascular modeling often relies on rigid wall simplification which significantly reduces the computational cost of the forward problem. Furthermore, cardiac catheterization is an invasive procedure and may not be available, so target pressure data is either non-available or non-reproducible owing to model limitations. In these cases, either least-square-error of nodal velocity between simulation results and 4D flow MRI assessment or branch flow distributions have been used as target functions [122].