esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Reconstruction Method for Irregularly Sampled Observation Sequences for Digital Twin
###### Abstract  
Various uncertainties such as communication delay, packet loss and disconnection in the Industrial Internet, as well as the asynchronous sampling of sensors, can cause irregularity, sparsity, and misalignment of sampling sequences, and thereby seriously affect the training and prediction performance of a digital twin model. Sequence reconstruction is an effective way to deal with the above problems, but if the measurement data become sparse or contain significant noise due to packet loss and electromagnetic interference, existing methods struggle to achieve ideal results. Therefore, a novel variational autoencoder model based on a parallel reference network and neural controlled differential equation (PRN-NCDE) is proposed in this article to solve the problem of reconstructing irregular series under sparse measurements and high noise levels. First, a multi-channel self-attention module is established, which can not only analyze the position and feature information of the sampled data to improve the reconstruction accuracy under sparse measurements, but also effectively tackle the misalignment and irregularity of the observation sequence through multi-channel and mask mechanisms. Second, to improve the accuracy of sequence reconstruction under large noise levels, a PRN is established to obtain reference features, which are weighted and fused with the features of observed data. Third, we use the NCDE to construct a decoder that can combine the control input of the system to predict the output values to solve the problem of sequence reconstruction in a controlled system. Finally, a weighted loss function is constructed to better train the network parameters of the model. This article takes the furnace of the boiler system in a coal-fired power plant as the test object to verify the effectiveness and fitting accuracy of the proposed PRN-NCDE model compared to the existing methods for a controlled system under sparse measurements and large noise levels. Simulation results show that the proposed PRN-NCDE model can improve the estimation accuracy by more than 50% and 70% compared with the recurrent neural network-NCDE (RNN-NCDE) under different sampling numbers and noise levels, and by more than 80% and 60% compared with the recurrent neural network-NODE (RNN-NODE).  
digital twin; sequence reconstruction; neural controlled differential equation; self-attention; feature fusion; coal-fired power plant +
Footnote †: journal: applied sciences
# Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods
## 1 Introduction
### Biomedical Background
#### 1.1.1 Electrodiagnostics of Facial Nerve Palsy  
Electrophysiological tests are mainly used to determine the severity and prognosis of a peripheral facial nerve lesion [17].  
Electroneurography objectively records the amplitude of electrically evoked muscle action potentials. It analyses the evoked compound muscle action potential (CMAP) of a specific facial muscle after transcutaneous stimulation of the main trunk of the facial nerve [18]. The main trunk is stimulated supramaximally at its exit from the stylomastoid foramen with a bipolar stimulator or stimulating electrodes. The CMAP is recorded using a bipolar pair of surface electrodes placed on the target muscle. Important test between 72 h and 21 days after onset, interpretation of result in comparison to needle electromyography (nEMG) result [19]. Nerve injury is expressed as percentage of function relative to the normal side [16].  
Electromyography (EMG) measures volutional responses of the facial muscles without electrostimulation. A facial motoneuron unit consists of a facial motoneuron and all muscle fibers innervated by this motoneuron. Needle EMG (nEMG) is the method used to analyse a facial motor unit action potential (MUAP) recorded from a needle electrode inserted in the facial muscle. This examination is important 2-3 weeks after onset of the palsy, because pathologic activity can occur in case of facial nerve degeneration. In the later time course, nEMG is important to detect reinnervation potentials as signs of reinnervation of the facial muscles [18]. Surface electromyography (sEMG) works with voluntary activity of the facial muscles and not with external stimulation. The recording field is more superficial and larger than when using nEMG. sEMG is not used for prognostication. Multichannel sEMG is important if the interplay of different facial muscles should be analysed.  
Blink-reflex testing is a test that allows stimulation of the facial nerve proximal to the lesion site. Testing is electrostimulation of the supraorbital branch of the trigeminal nerve (V1) and simultaneous sEMG recording from the orbicularis oculi muscle on both sides. Standard blink testing involves electrical stimulation of the supraorbital nerve on the affected side combined with a 2-channel simultaneous sEMG recording from both orbicularis oculi muscles. The exit of the supraorbital nerve in the supraorbital foramen is palpated on the rim of the orbit [20]. It may be most helpful if facial nerve damage is suspected to occur within the brainstem [18].
## 5 Conclusions  
In this study, nine _McOSCs_ were identified from bitter gourd, and their gene structures were analyzed. These results provide the basis for further research on amino acid substitutions that can significantly alter OSC products. The nine genes were expressed at different levels in different tissues, suggesting that different characteristic triterpenoids might be biosynthesized in different tissues. _McOSC7_ was successfully overexpressed in a bitter gourd hairy system, where it promoted the accumulation of camaldulenic acid, enoxolone, and quinovic acid (Figure 5). Thus, our results demonstrate that the overexpression of an _OSC_ gene can improve the pharmacological value of bitter gourd. Further research is required to explore the specific synthesis mechanisms of ursane- and oleanane-type triterpenoids in bitter gourd.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3](https://www.mdpi.com/article/10.3)]([https://www.mdpi.com/article/10.3](https://www.mdpi.com/article/10.3)) 390/ijms23010196/s1.  
**Author Contributions:** Conceptualization, [PERSON] ([PERSON]) and Y.Y. ([PERSON]); methodology, Y.L. and Y.H.; validation, [PERSON] ([PERSON]), Y.Y. ([PERSON]) and Y.Y. ([PERSON]); formal analysis, [PERSON] and [PERSON]. ([PERSON]); software, [PERSON]; data curation, [PERSON] and [PERSON]([PERSON]); writing--original draft preparation, [PERSON] and [PERSON]([PERSON]); writing--review and editing, [PERSON]; visualization, [PERSON] and [PERSON]; supervision, [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]) and [PERSON] ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the NSFC (Grant No. 31670338), Southeast Asia Biodiversity Research Institute, Chinese Academy of Science (Grant No.Y4 ZK111B01), the Yunnan Fundamental Research Projects (Grant No. 202101 AT070190), Digitalization, Development and Application of Biotic Resource (Grant No. 202002 AA100007), Poverty Alleviation through Science and Technology Projects of Chinese Academy of Sciences (Grant No. KFJ-FP-201905), and Technology Transfer into Yunnan Project (Grant No. 202003 AD150005).  
Figure 5: Effect of bitter gourd _McOSC7_ on triterpene synthesis. Overexpression of _McOSC7_ in hairy roots increased the contents of camaldulenic acid, enoxolone, and quinovic acid. Dotted arrows represent possible intermediates; solid arrows represent detected final products.
## 2 Conceptual Model for Coordinated Automatic Generation Control in a Multimachine Smart System
### Hierarchical Control System Based on Nonlinear Model Predictive Control (NLMPC)  
The control strategy is structured into a hierarchical system, with different levels of control tailored to specific aspects of the power system:  
* Primary Level: This level focuses on the real-time monitoring and control of power flow and generation costs. It ensures that the system operates within optimal parameters at all times.
* Secondary Level: This level manages the technical aspects of the system, such as oscillations, losses, and voltage fluctuations. It ensures that the system remains stable and efficient from a technical standpoint.
* Tertiary Level: This level involves the continuous adaptation and optimization of system resources. It dynamically adjusts generation and load balancing strategies to maintain overall system efficiency and effectiveness.
# Experimental Investigation and Mechanism of Fly Ash/Slag-Based Geopolymer-Stabilized Soft Soil+
## 3 Response Surface Analysis
### Compared with the Mechanical Properties of Cement-Stabilized Soil  
Under the same conditions, take 20% geopolymer- and cement-stabilized soft soil, respectively. The cement is P.O. 42.5 ordinary Portland cement with a water-cement ratio of 0.5. The mix ratio of geopolymer stabilized soil is the best mix ratio of the above orthogonal test. A comparative analysis of the effect of geopolymer and cement in strengthening soft soil was conducted. Figure 4 is a comparison chart of the compressive strength of cement-soil test block and geopolymer-soil test block at different ages. It can be seen that the unconfined compressive strength of cement-soil and geopolymer soil increases with the increase of curing time. Under the same conditions, the compressive strength of cement-stabilized soft soil is higher than that of fly-ash-slag based geopolymer stabilized soft soil. From 0 to 28 days, the compressive strength of cement-soil increased rapidly, and its strength reached 2.87 MPa at 28 days. However, from 28 to 90 days, the growth rate of the compressive strength of cement-soil slowed down, which was lower than the growth rate of geopolymer-stabilized soil [32]. The geopolymer-stabilized soil reached 3.21 MPa at 90 d.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Number**} & \multirow{2}{*}{**A**} & \multirow{2}{*}{**B**} & \multirow{2}{*}{**C**} & \multicolumn{2}{c}{**UCS/kPa**} \\ \cline{3-6}  & & & & 7 **d** & **28 d** \\ \hline
1 & 1 & 1 & 1 & 41.05 & 127.5 \\
2 & 2 & 2 & 2 & 224.47 & 396.1 \\
3 & 3 & 3 & 3 & 437.33 & 1214.8 \\
4 & 1 & 2 & 3 & 920.68 & 2000 \\
5 & 2 & 3 & 1 & 33.79 & 66.4 \\
6 & 3 & 1 & 2 & 183.27 & 337.4 \\
7 & 1 & 3 & 2 & 209.81 & 360.71 \\
8 & 2 & 1 & 3 & 493.95 & 1509.9 \\
9 & 3 & 2 & 1 & 18.98 & 39.8 \\ \hline K1 & 1171.54 & 718.27 & 93.82 & & \\ K2 & 752.21 & 1164.13 & 617.55 & & \\ K3 & 639.58 & 680.93 & 1851.96 & & \\ \(k1\) & 390.51 & 239.42 & 31.27 & 7-day Compressive strength \\ \(k2\) & 250.74 & 388.04 & 205.85 & & \\ \(k3\) & 213.19 & 226.98 & 617.32 & & \\ R (range) & 531.96 & 483.2 & 1758.14 & & \\ \hline K1 & 2488.21 & 1974.80 & 233.70 & & \\ K2 & 1972.40 & 2435.90 & 1094.21 & & \\ K3 & 1592.00 & 1641.91 & 472.70 & & \\ \(k1\) & 829.40 & 658.27 & 77.90 & 28-day Compressive strength \\ \(k2\) & 657.47 & 811.97 & 364.74 & & \\ \(k3\) & 530.67 & 547.30 & 1574.90 & & \\ R (range) & 896.21 & 793.99 & 4491.00 & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Orthogonal test analysis table.  
It can be seen from Figure 5 that the cohesion of the cement-stabilized soil is greater than that of the geopolymer-stabilized soil, and the internal friction angle of the cement-stabilized soil is smaller than that of the geopolymer-stabilized soil. It can be seen that both cement and geopolymer can significantly improve the compressive and shear strength of soft soil and can play a good role in improving the mechanical properties of soft soil. It can far meet the strength requirements of mixing piles in practical projects.
## 4 Implications and Future Work  
The coal pillar bump and abrupt collapse of roadway rib has been a major problem faced by coal mining practitioners for several decades. These violent failures are a result of the long-term accumulation of mechanical damage caused by overburden pressure. However, due to the invisibility of mechanical damage, there is a lack of an effective method to quantify the damage accumulation and predict the subsequent failure. So far, the most common method for failure forecasting is still based on the long-term measurement of coal surface deformation. This method is costly, time-consuming, and imprecise. Moreover, there is not a convincing theory relating the surface deformation to the mechanical damage, because the damage is the cumulative destruction of bonds in the rock's microstructure [52]. Therefore, it cannot be directly measured by macroscopic scale tests. In view of this situation, we devote this study to explore the applicability of the ultrasonic technique in the investigation of the inner structural deformation of coal under creep compression. The implications of this study for practitioners can be summarised as follows: (1) The long-term coal creep is a multiple structural deformation involving of particle dislocation, pore closure, matrix cracking, compaction of clay mineral, and so forth. It continuously alters the coal structure, resulting in the fluctuations of ultrasonic velocity and associated dynamic modulus. Therefore, the capacity of ultrasonic velocity parameter for damage detection is greatly weakened. (2) The acoustic attenuation parameter can make a comprehensive evaluation of the structural deformation, reflecting the competition between compaction and cracking during the creep process. (3) The coal is characterised by a strong inhomogeneity, showing a high sample-to-sample scatter in mechanical properties. Conventional test methods that are successful in the characterisation of other rock types cannot be used for coal. Therefore, the non-destructive testing technique which can achieve a repetitive measurement in a single sample is much suitable.  
The unsolved problems of this study are (1) the relationship between the inhomogeneity of coal and the explosive failure mode is still remained unknown and (2) the influences of compaction and micro-cracking on acoustic attenuation have not been quantified. These problems will be investigated in the future work by enlarging the sample size and introducing the CT scanning technique.
# Research on Plant Disease and Pest Diagnosis Model Based on Generalized Stochastic Petri Net
###### Abstract  
With the advancement of modern agricultural technology and the expansion of large-scale production, this article aims to solve the difficulties in plant disease and pest control through the application of artificial intelligence and automation technology, and provide accurate disease and pest warning mechanisms. This study first conducted a detailed identification and classification of plant disease and pest warning mechanisms, and established a dynamic model of disease and pests based on the environmental factors and symptoms of affected areas. On this basis, using the isomorphism relationship between generalized stochastic Petri nets and Markov chains, a plant disease and pest diagnosis model based on generalized stochastic Petri nets and an equivalent Markov chain model were constructed. The simulation results show that different combinations of infection rates have a significant impact on the probability of meeting treatment standards, with the combination of moderate and severe infection rates having the greatest impact on the probability of meeting treatment standards, while the impact of mild infection rates is relatively small. By comprehensively analyzing the interaction between mild, moderate, and severe infection rates, the critical zone surface under different disease and pest warning thresholds was obtained. Through actual data verification, the generalized stochastic Petri net model can effectively quantify the dynamic characteristics of disease and pest propagation. Combined with the equivalent analysis of Markov chains, it can provide key thresholds and decision support for disease and pest warning. This method provides a theoretical basis for automated monitoring and precise control of pests and diseases in large-scale agricultural planting, and it has high practical application value.  
diseases and pests; early warning mechanism; generalized stochastic Petri net; Markov chain; botany +
Footnote †: journal: Applied sciences  
0000-0001-5000-5000-0000][PERSON]  
0000-0001-5000-5000-0000][PERSON]
## 4 Initial Trajectory Generation
### B-Spline-Based Trajectory Generation
\[\frac{dc(q(t))}{du} =\frac{1}{(\Delta t)}b_{1}M_{d}\mathbf{v_{i}}^{T},\quad b_{1}=[0,1,u,\ldots,u^{d-1}]\in\mathbb{R}^{d+1}, \tag{31}\] \[\frac{d^{2}c(q(t))}{d^{2}u} =\frac{1}{(\Delta t^{2})}b_{2}M_{d}\mathbf{v_{i}}^{T},\quad b_{2}= [0,0,1,u,\ldots,u^{d-2}]\in\mathbb{R}^{d+1}.\]  
The explicit form of estimation of velocity and acceleration of a given time index is calculated as follows:  
\[\frac{dc(q(t))}{du} =d\cdot\frac{p_{c}(i+1)-p_{c}(i)}{p_{k}(i+d+1)-p_{k}(i+1)}, \tag{32}\] \[\frac{d^{2}c(q(t))}{d^{2}u} =\] \[(d^{2}-d)\cdot(\frac{p_{c}(i+2)-p_{c}(i+1)}{p_{k}(i+d+2)-p_{k}(i+2 )}-\frac{p_{c}(i+1)-p_{c}(i)}{p_{k}(i+d+1)-p_{k}(i+1)}).\]  
In most of the situations, initial control points are generated, as explained in Section 3. Such methods may or may not be smooth enough for initial trajectory generation. There are various ways to generate intermediate waypoints to improve the quality of the trajectory using B-splines. For example, the initial trajectory was constructed using cubic B-Spline in [62]. Such a capability is mainly due to B-spline's properties. It is particularly continuity and convex-hall properties that make B-spline trajectory generation such a robust technique.
## 1 Introduction
The T-Map model is a widely used method which uses a hypothetical Euclidean point-space of a convex polyhedron and is completely compatible with ASME Y 14.5-1994 standards. Its shape, dimension and internal subset reflect TF type, size, shape and all possible variations of pose [17]. The variation range of each TF which is gained by the T-Map model through the area coordinate system is also called the convex hull. Points in the convex hull have a one-to-one mapping relation with all possible error variations of TFs in the tolerance zone. T-Map uses the Minkowski mum algorithm to calculate the ultimate variation of the target feature. This algorithm scans vertexes and boundaries of the convex hull along the dimension chain of TFs one by one in a recursive manner, finally getting the convex hull which describes variations of the target feature. This is a model that calculates the boundaries of target feature variation by geometric operation. The calculation load of Minkowski sum shows exponential growth with the increase in component links in the dimension chain. The calculation load for complicated dimension chains is extremely good. As the T-Map method does not suggest an analytical model between the convex hull vertex of the target feature and the convex hull vertex of each TF on the dimension chain, deviation data expressed by the convex hull vertex of each TF and any point within the convex hull cannot be used in product performance analysis. Therefore, based on the T-Map, the area coordinate system was used in this paper to study a shaft-hole relative pose description method with the existence of tolerance and clearance fit. The mapping relationship between the area coordinate system and Cartesian coordinate system was further established for each TF on the dimension chain. A tolerance analysis model based on tracking local coordinate systems [18], as proposed previously, was combined to obtain analytic expression of pose decoupling of the axis TF as expressed by the area coordinate system. This method not only accurately obtained the ultimate error of the target feature through permutation and combination of ultimate area coordinate values, with a greatly reduced calculated quantity when compared with the T-Map method, but also obtained the sole deviation value of TF on arbitrary DOF within the tolerance domain by changing the corresponding area coordinate value. Therefore, it provides the foundations for accurate analysis of product performance.
## 3 Results
### Objective-Subjective Correlation Analysis  
The correlation between performance metrics and perceived visual comfort for the sample in June (\(N\) = 46) shows that, in total, eight subjective variables including satisfaction with 'Glare sun', daylight (both annual and at the moment: 'A-Natural' and 'Natural' variables, respectively), 'A-View', total light at the desk ('A-Light desk' and 'Light desk'), and lighting quality ('A-Light quality' and 'Light quality') had a moderate (0.3 \(<r\) < 0.5, \(p\) < 0.001) relationship with at least one of the simulated performance metrics. All variables except glare had positive correlations with user satisfaction. Note that it is shown as negative numbers since user satisfaction was defined in inverse order, with 1 being the highest satisfaction level and 7 being the lowest satisfaction level.  
The variable 'Glare sun' had the highest correlation with point-in-time horizontal illuminance (momentary) at task area (E\({}_{\text{h-task}}\)) for mean values under the clear sky condition (\(r\) = 0.38, \(p\) < 0.01) and the intermediate sky condition (\(r\) = 0.33, \(p\) < 0.01).  
Satisfaction with daylight for both the annual and momentary situations ('A-Natural' and 'Natural') showed a significant correlation with E\({}_{\text{h-room}}\), ASE, and DGP. The highest correlations for E\({}_{\text{h-room}}\) with 'A-Natural' were for the mean value of illuminance under clear sky and intermediate sky conditions (\(r\) = \(-\)0.30, \(p\) < 0.01), both classified as moderate correlations. Additionally, 'Natural' and 'A-Natural' showed a moderate correlation with DGP (\(r\) = \(-\)0.41, \(p\) < 0.01).  
Since the view to the outside is often inextricably linked to daylight, it was included in the analysis as well. The 'A-View' variable correlated with vertical illuminance (E\({}_{\text{v-eye}}\)), horizontal illuminance at task area (E\({}_{\text{h-task}}\)), and horizontal illuminance for the room area (E\({}_{\text{h-room}}\)) as well as with DA, sDA, and ASE. The highest correlation was found for the mean value of E\({}_{\text{h-room}}\) under 'clear' sky conditions (\(r\) = \(-\)0.46, \(p\) < 0.01).  
'A-Light desk' and 'Light desk' showed correlations with the horizontal illuminance at task area (E\({}_{\text{h-task}}\)) and room area (E\({}_{\text{h-room}}\)) as well as with DA and sDA. Additionally, 'Light desk' correlated with vertical illuminance (E\({}_{\text{v-eye}}\)) and 'A-Light desk' with ASE. The highest correlation for 'A-Light desk' was found for the minimum value of E\({}_{\text{h-room}}\) under intermediate sky conditions (\(r\) = \(-\)0.47, \(p\) < 0.01). The highest correlation for 'Light desk' was found for the minimum value of E\({}_{\text{h-room}}\) under 'intermediate' sky conditions (\(r\) = \(-\)0.47, \(p\) < 0.01).  
Finally, 'Light quality' and 'A-Light quality' had significant correlations with E\({}_{\text{h-room}}\), sDA, and ASE. The highest correlation for 'Light quality' was found for the minimum value of E\({}_{\text{h-room}}\) under 'clear' sky conditions (\(r\) = \(-\)0.36, \(p\) < 0.01).