esa-sceva/satcom-chunk-collection huggingface dataset:
# Performability Evaluation of Autonomous Underwater Vehicles Using Phased Fault Tree Analysis
## 3 Performability Evaluation Method Based on Phased Fault Trees
### System Details
#### 4.1.3 Communication Unit  
The AUV uses a variety of communication methods, including satellite communication, WiFi, and RF on the surface, as well as underwater acoustic signals to communicate with the mission controller of the USV. The communication unit also estimates location using the GPS, a USBL with the USV, a CTD (conductivity, temperature, depth) sensor, an inertial measurement unit, a Doppler velocity log, and a water depth sensor. Periodically, each sensor initializes itself to fix errors that arise during mission execution. Defects in the acoustic elements may occur in underwater acoustic systems, which are installed on underwater communication nodes for wireless communication [76]. There is also a chance of a ground fault [82]. A number of failures can happen, including water leaks, satellite communication and RF connection loss [83], communication modem failure, antenna seal damage, GPS antenna failure [80], GPS communication issues [79], and communication -modem failure. Failures like reflection in the water or a loss of connection to the USV station could occur with the USBL [84].
## 6 Experimental Verification
### Comparison of Experimental Results
#### 6.3.3 Inertial Stability Accuracy  
In order to compare the inertial stability accuracy of the three components by PI and PI + SAKF, make the swing table rotate in a sinusoid reference angle with an amplitude of 1\({}^{\circ}\) and a frequency of 1 Hz. Meanwhile, set the inertial reference speed \(\omega_{\mathrm{r}}\) to 0 for all three components. Then subtract the swing table angle from each component angle to obtain the residual motion of the OESs.  
The residual angle of the OESs by two control methods, and the angle of the swing table are shown in Figure 10.  
The comparison results of the stability accuracy by PI and PI + SAKF are shown in Table 4. It can be seen from Table 4 that, comparing with the PI method, the PI + SAKF can significantly reduce the error magnitude of all three OESs under the same disturbance conditions, which indicates that the proposed control scheme can achieve effective improvement of stability accuracy in a wide range of OESs.  
Figure 10: Measured angular position of (**a**) the direct drive component, (**c**) harmonic driving component, and (**e**) RV driving component, and residual motion angle of (**b**) the direct drive component, (**d**) harmonic driving component, and (**f**) RV driving component by PI and PI +SAKF, with the swing table rotating at sinusoidal 1\({}^{\circ}\)/1 Hz and the inertial reference speed \(\omega_{\mathrm{r}}\) = 0.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 4 Data Rate Optimization
### **P4** Optimization  
For a given transmitting power and the current slice state, the RB allocation question can be formulated as  
\[\textbf{P4:}\textbf{maximize}\ (\textbf{R}_{k})=\sum_{b\in B} \boldsymbol{\varTheta}\textbf{B}_{kb}\frac{\sum_{b\in B}a_{kb}(t)}{M}\times \log_{2}\left(1+\frac{\textbf{P}\textbf{h}_{kb}(t)}{\sigma_{k}^{2}(t)}\right) \tag{23}\] \[s.t.\ \ \textbf{R}_{k}\leq\textbf{R^{\prime}}_{k}\] (24) \[\sum_{b\in B}B_{kb}\leq B_{b}\] (25) \[\sum_{i=1}^{3}\sum_{j=1}^{N}A_{ij}(t)\leq C_{ch}\] (26) \[T_{ij}\in\{1,2,3\},1\leq i\leq 3,\ 1\leq j\leq N\] (27) \[A_{ij}(t)\in[0,1],1\leq i\leq 3,1\leq j\leq N \tag{28}\]  
where \(\boldsymbol{\varTheta}\) is a matrix of \(\chi_{kb}\).  
We design an optimization Algorithm 1 for the MS to find out the proper slices named FPCB (Fixed Power Change the Block).  
```
0:\(A\), \(P\), \(B_{kb}\), \(P\) (This is Inputs)
0:\(B\), \(A\) (This is Outputs)
1: Set the initiate solution \(B^{(0)},P,A\)
2:while\(A\) is not full do
3: calculate the capacity of each RB
4:for each \(n\in[1,length(A)]\)do
5:if\(A_{n}\) is not full then
6:for each \(i\in[1,length(A_{n})]\)do
7: allocate this slice to the MS
8: calculate the rate of this slice in the block n
9:\(R=R+R_{i}\)
10:endfor
11:else
12: jump to the next RB
13:endif
14: Compare which of the block has a higher rate and select it as \(B\)
15: Try other kind of \(B\)
16: Renew the channel allocation \(A\)
17:endfor
18:endwhile
19:return Outputs \(B\), \(A\)
```  
**Algorithm 1** Algorithm of FPCB (**P4**)
# Video Human Action Recognition Based on Motion-Tempo Learning and Feedback Attention
## 3 The Proposed Critical Action Information Extraction Network
### Multi-Granularity Adaptive Fusion Module (MgAFM)
#### 3.1.2 Adaptive Fusion Module (AFM)  
The AFM adaptively fuses motion features from sub-actions at different tempos to capture the varying dynamics. As shown in Figure 4, the displacement tensor resulting from MEM processing is denoted as \(\mathbf{D}_{1},\mathbf{D}_{2},\ldots,\mathbf{D}_{n}\). Initially, the displacement tensors from the \(n\) branches, each corresponding to different motion tempos sub-actions, are connected and passed through average pooling to obtain a global representation. This global representation is then fed into MLP and sigmoid function to compute the adaptive weight vector:  
\[\mathbf{D}_{c}=[\mathbf{D}_{1};\mathbf{D}_{2};\cdots;\mathbf{D}_{n}]\in R^{3n \times H\times W} \tag{5}\]  
\[\mathbf{W}_{c}=\sigma(\mathrm{MLP}(\mathrm{AvgPool}(\mathbf{D}_{c})))\in R^{ 3n\times 1\times 1} \tag{6}\]  
where \(\sigma\) denotes the sigmoid function and \(\mathbf{D}_{c}\) represents the features concatenated along the channel dimension. Secondly, the concatenated \(\mathbf{D}_{c}\) is element-wise multiplied with the weight vector \(\mathbf{W}_{c}\) to yield the feature vector \(\mathbf{D}_{f}\):  
\[\mathbf{D}_{f}=\mathbf{D}_{c}\odot\mathbf{W}_{c}\in R^{3n\times H\times W} \tag{7}\]  
where \(\odot\) denotes the element-wise multiplication. Finally, we use three \(1\times 3\times 3\) separable convolutions to convert the displacement tensor \(\mathbf{D}_{f}\) into features with the same dimensions as the original RGB features.
# An Efficient Distributed Coverage Hole Detection Protocol for Wireless Sensor Networks
## 1 Introduction
Ignoring the detection of holes affects the efficiency of geographic routing, data congestion and the excessive energy consumption of the hole boundary nodes. Additionally, for information flow, the hole could also affect the overall capacity of the network. Thus, the identification of the holes in sensor networks is of primary interest, because their presence often has a physical correspondence and may also map to one of the special events that are being monitored by the sensor networks. Depending on the application environments and the level of information constraints, algorithms for identifying various coverage holes in the sensor networks can be generally classified into three categories: the computational geometry approach, the statistical approach and the topological method. In this paper, energy-efficient coverage hole detection algorithms are developed for wireless sensor networks, which can detect the presence of coverage holes distributively. The proposed hole detection methods can be achieved by considering the theoretical analysis of the point of intersection of the sensing discs of the sensors. First, the set of nodes that encloses a coverage hole is identified. This is done by taking location information of the nodes, their sensing coverage overlap and the circumference of the non-overlapping region of the sensors. Then, the coverage hole is detected by collaborating with the one-hop neighbors of the nodes that enclose the hole. The rest of this paper is organized as follows. Related work is presented in Section 2. Section 3 describes the system model for detecting the holes in the WSNs. The proposed hole detection protocol is given in Section 4. The performance evaluation of the proposed protocol is done in Section 5, and concluding remarks are made in Section 6.
# Review of Image-Based 3D Reconstruction of Building for Automated Construction Progress Monitoring
## 4 Knowledge Gaps and Challenges
### Non-Automated Image-to-BIM Registration  
Schedule deviation is derived from comparing the as-planned model and the as-built model, and this process is based on their registration. In recent years, several research contributions have been presented that address the registration of the images/point clouds and the corresponding BIM models. It is expected to realize fully automatic construction process monitoring with fast information feedback. However, most practices either depend on manual intervention for the registration or work automatically under severe constraints [4]. According to the type of data collected, the challenges faced by automatic registration are analyzed below.  
1. Time-lapse images or videos from fixed camera: Since the cameras are fixed, it is convenient to manually register each camera only once. However, the scenes captured by this method are so limited that it is only suitable for shooting large-scale scenes. Therefore, it is necessary to equip multiple high-resolution cameras at the construction site. Even so, they are still severely affected by lighting conditions and there are still many unavoidable occlusions [33].
2. Unordered images or videos: To avoid occlusion, some scholars proposed to free the camera and use unordered images that collected by field personal. If the camera is not calibrated, and the position and orientation are unknown, registration is almost impossible. Many researchers took video clips to generate partial point clouds, and then integrate different parts to form a global 3D model. However, each part needs to record the initial state of the camera and be registered. Usually, many clips need to be taken to cover all the details of a building, which is very troublesome [35; 59].
3. Image sequences taken with UAVs: Images sequences are usually taken by camera-equipped UAVs and come with GPS coordinates and camera orientation which can used to align the point clouds and BIM. Ideally, only the starting position needs to be registered and all architectural details could be captured at once. However, the effect was not good in practice due to the inaccurate GPS coordinates especial in the vertical axis. The longer the flight path means the greater the accumulated deviation, and it is difficult to build accurate point clouds.  
In fact, based on existing technology, these challenges can be attributed to finding a balance between automation and accuracy. Because the higher the degree of automation, the less chance of manual parameters to correct errors. Therefore, in-depth exploration of algorithms is needed in the future to find reasonable solutions.
## 4 Results and Discussion  
### Monte Carlo Simulation  
There are many stochastic methods for probabilistic slope analysis in previous studies, but the Monte Carlo simulation is still the most common technique. Even though a large amount of computing time is required to use this technology, the mathematical formulation of the Monte Carlo method is relatively simple.  
During the process of probabilistic analysis, the finite element method with strength reduction technology is repeated enough times to reach a stable value. Figure 5 shows the convergence of the estimated mean value and standard deviation of FOS from 2000 Monte Carlo simulations for the test embankment slope. The result converges to a stable value when the number of Monte Carlo simulations reaches about 1000 from Figure 5. The 1000 random field will be generated during the 1000 Monte Carlo simulations; then, 1000 FOSs could be obtained based on the mean value, standard deviation and the spatial correlated structural of the strength parameters. The average of the 1000 FOSs will be used as the result to evaluate the stability. Indeed, there is still an error compared with the true solution; however, it is acceptable for geotechnical engineering. Therefore, all the results of FOS are obtained by performing Monte Carlo simulations 1000 times, for the balance of computing time and numerical error. These results are used to describe the probabilistic stability of the slope.
# Dynamic Graph Attention Network for Skeleton-Based Action Recognition
## 4 Experiments
### Ablation Studies
#### 4.4.2 Importance of Partition Strategy  
To assess the effectiveness of the proposed node-partition bias strategy introduced into the attention module, we design three comparative schemes: (a) **no partition**, which removes all partition-based biasing; (b) **empirical partition**, which relies on prior knowledge of human skeleton structure to divide joints into several predefined body parts, injecting corresponding biases during attention computation; and (c) **learned partition**, where partitions are adaptively learned during training, allowing flexible node grouping.  
As illustrated in Figure 3, Figure 3a presents the final adopted partition strategy (proposed partition), Figure 3b shows the learned partition, and Figure 3c depicts the empirical partition strategy, which yields relatively inferior performance. As shown in Table 5, the proposed partition achieves the highest accuracy. Compared with the empirical partition, the proposed partition assigns the shoulders and hips to the corresponding arm and leg regions, which enhances the representation of motion coordination and benefits action recognition. In contrast, the learned partition lacks explicit prior constraints, making it more susceptible to data noise during training. This often leads to ambiguous partition boundaries or incorrect semantic grouping, thereby limiting the model's potential for performance improvement. These observations confirm that introducing a partition bias effectively enables the model to focus specifically on dynamic interactions between distinct body parts, thus enhancing performance. Different partition implementations offer varying trade-offs between accuracy and flexibility, and thus their selection should depend on the specific application scenario and data characteristics.
# Effects of Ethanol-Diesel on the Combustion and Emissions from a Diesel Engine at a Low Idle Speed
###### Abstract  
In this study, detailed experiments were conducted on the combustion and exhaust characteristics of ethanol-diesel blended fuels. The four-stroke four-cylinder common-rail direct injection diesel engine was used. The experiment was carried out at 750 rpm at a low speed idle, and a 40 Nm engine load was applied to simulate the operation of the accessories during the low idle operation of the actual vehicles. The test fuels were four types of ethanol-blended fuel. The ethanol blending ratios were 0% (DE_0) for pure diesel, and 3% (DE_3), 5% (DE_5) and 10% (DE_10) for 3%, 5% and 10% ethanol mixtures (by vol.%). Blending ethanol with diesel fuel increased the maximum combustion pressure by up to 4.1% compared with that of pure diesel fuel, and the maximum heat release rate increased by 13.5%. The brake specific fuel consumption (BSFC) increased, up to 5.9%, as the ethanol blending ratio increased, while the brake thermal efficiency (BTE) for diesel-ethanol blended fuels remained low, and was maintained at 23.8%. The coefficient of variation (COV) of the indicated mean effective pressure (IMEP) was consistently lower than 1% when ethanol was blended. The blending of ethanol increased the ignition delay from a 12.0 degree crank angle (\({}^{\circ}\)CA) at DE_0 to 13.7 \({}^{\circ}\)CA at DE_10, and the combustion duration was reduced from 21.5 \({}^{\circ}\)CA at DE_0 to 20.8 \({}^{\circ}\)CA at DE_10. When ethanol blending was applied, nitrogen oxides (NOx) reduced to 93.5% of the level of pure diesel fuel, the soot opacity decreased from 5.3% to 3% at DE_0, and carbon monoxide increased (CO) by 27.4% at DE_10 compared with DE_0. The presence of hydrocarbon (HC) decreased to 50% of the level of pure diesel fuel, but increased with a further increase in the ethanol blending ratio. The mean size of the soot particulates was reduced by 26.7%, from 33.9 nm for pure diesel fuel, DE_0, to 24.8 nm for DE_10.  
ethanol-diesel; combustion; emission; low idle speed Article
## 5 Discussion
### Theoretical and Practical Implications  
Based on the VPi4 index, SMEs can be divided into four groups. The practical benefits include the possibility of comparing the company with others, both based on the classic VPi4 index and in terms of classification into one of four groups of clusters. Managers of SMEs can compare their own enterprises with those of competitors and understand which technologies may be suitable for further deployment in relation to the competitive advantage. The enterprise can better analyze the current weaknesses and strengths of technical factors. In practice, it is very difficult to determine the level of technology in an enterprise, especially in SMEs, which have limited capacities and funding opportunities. Thanks toour model, managers will get a better overview of the current situation, as they can use our model in planning and strategy development for the future.  
Developed classification of enterprises extends the possibilities of VPi4 index especially for SMEs. SMEs are specific in terms of I4 technology implementation. The proposed categorization of enterprises into four groups also offers easy comparison of enterprises. Due to the limited capital and the necessity to choose only one of many possible technologies, the implementation process of I4 in SMEs is not usually gradual by individual index levels (from first to second level and above). Small- and medium-sized enterprises often have different needs when introducing new technologies. Their preferences are evidenced by clustering.  
The results point out which Industry 4.0 technologies are most often used by small- and medium-sized enterprises. The results confirmed that these are mainly technologies at the first level, i.e., analysis, collecting data, cloud storage (and for them having skilled people). With the exception of I4 technological enterprises group and partly I4 advances enterprises group, small- and medium-sized enterprises do not use a higher level of VPi4 index technologies (virtual reality, data sharing, and learning software) or special applications, such as 3D printing, drones, or nanotechnology. From this perspective, the paper extends knowledge about the use of technology in SMEs.  
Another theoretical contribution is confirmation of the hypothesis of different approaches to technology between SMEs and large enterprises. The differences between SMEs and large enterprises show that large enterprises have a higher level of Industry 4.0 implementation. On the other hand, SMEs have the advantage of implementing new technologies due to greater flexibility; more involved, empowered employees; less bureaucracy and coordination due to the usually leaner organizational structure; and the ability to produce customized products and close relationship with customers. These benefits should be used by SMEs to increase value-added services and tailored manufacturing processes.
# Transmission Efficiency of Cycloid-Pinion System Considering the Assembly Dimensional Chain
## 6 Conclusions  
In this work, on the basis of considering the assembly dimensional chain, the transmission efficiency of the two structures with and without the pinion sleeve is analyzed. Specifically, the following five aspects are included:  
A more concise and accurate model of the cycloid tooth profile is proposed. Specifically, the slightest improvement over the inversion model is 27.75801%. The computation time is at least 3.33333% better than the velocity instantaneous center method model.  
The variation pattern of the number of teeth engaged under the coupling effect of the assembly and meshing clearance is obtained. The target optimal solution is set in the area around the point (26.5, 24.5, 9) in Figure 7.  
The way in which the different parts fit together and the corresponding upper and lower tolerances are determined to obtain a dimensional tolerance chain. Based on the extreme value method, the fit clearances of the two different structures are determined. The difference between the two shows a similar periodically varying step function.  
The structure with a pin tooth sleeve corresponds to a smoother change in the coefficient of friction, indicating smoother operation of the whole system. In the same time interval, the friction without the pin tooth sleeve dissipates more power and fluctuates more sharply. The increase in input speed increases the frictional losses. However, the addition of a pin tooth sleeve can slow down the increase in losses. Within the normal range of machined surface quality, roughness has a small effect on the loss of friction efficiency.  
The results of the variation of the contact forces for the two structures are obtained by ADAMS. The addition of the pin tooth sleeve makes the whole system run more smoothly.  
Conceptualization, N.J., S.W., and J.Z.; methodology, N.J. and J.Z.; software, N.J. and A.Y.; validation, N.J. and W.Z.; formal analysis, N.J.; investigation, N.J.; resources, N.J.; data curation, N.J.; writing--original draft preparation, N.J. and J.Z.; writing--review and editing, N.J.; visualization, N.J. and S.W.; supervision, J.Z.; project administration, S.W. All authors have read and agreed to the published version of the manuscript.  
The work was funded in part by Hubei Province Technology Innovation Special Fund (No. 2019 AAA069).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Not applicable.  
Figure 18: First-order derivative of the meshing force curve.
# Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations
## 2 Data and Methods
### Root Mean Square Error (RMSE)  
In the theory of mathematical statistics, residuals and variances are often used to evaluate the degree of fitting and regression of the probability density function (PDF). In this study, RMSE is analyzed to systematically evaluate the difference between the measured and theoretical distributions.  
The RMSE is given as  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}} \tag{14}\]where \(y_{i}\) is the observation, \(\hat{y}_{i}\) is the estimation, and _RMSE_ is a widely used metric to evaluate the difference between the estimation and the real value.