esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Temporal Autoformer
#### 3.4.2 The Overall Architecture of Improved Autoformer  
The architecture of improved temporal autoformer is shown in Figure 6[23]. It is obvious that the temporal autoformer adopts the encoder-decoder structure. For the encoder, the inputs are the processed features in the past \(l\) time steps \(\mathcal{X}_{en}\in R^{B_{X}l\times d_{model}}\). For  
Figure 5: Multi-scale trend part extraction.  
the decoder, the inputs contain both the seasonal and trend-cyclical parts of the processed features \(\mathcal{X}_{ens}\), \(\mathcal{X}_{des}\in R^{B\times\left(\frac{l}{2}+O\right)\times d_{model}}\). \(O\) represents the future length. The details are:  
\[\mathcal{X}_{ent}\]  
, \[\mathcal{X}_{ens} = \text{MultiSeriesdecomp}(\mathcal{X}_{en},K)\] \[\mathcal{X}_{des} = \text{Concat}(\mathcal{X}_{ens},\mathcal{X}_{0})\] (11) \[\mathcal{X}_{det} = \text{Concat}(\mathcal{X}_{ent},\mathcal{X}_{Mean})\]  
where \(\mathcal{X}_{0}\in R^{O\times d_{model}}\) are the placeholders filled with 0 and \(\mathcal{X}_{Mean}\in R^{O\times d_{model}}\) are the placeholders populated with the mean values of \(\mathcal{X}_{en}\).  
Both encoder and decoder consist of Auto-Correlation, multi-scale series decomposition, and feed forward networks. Auto-Correlation mechanism employs the connection of sub-series exhibiting similar characteristics through the application of time delay aggregation. The encoder only retains the seasonal part after multi-scale series decomposition. The equation denoting the formulation of the \(l\)-th encoder layer can be expressed as \(\mathcal{X}_{en}^{l}=Encoder(\mathcal{X}_{en}^{l-1})\), where N represents the total number of encoder layers. The decoder is composed of an accumulation structure to handle multi-scale trend-cyclical components, as well as a stacked Auto-Correlation mechanism to address seasonal components. Extracting temporal trend features at different scales through automatic multi-scale series decomposition, the model can obtain more comprehensive temporal information, then strengthen the ability of encoders and decoders to extract temporal dependencies of different scales and achieve multi-scale temporal feature fusion. Assuming the existence of \(M\) decoder layers, the formulation of the \(l\)-th decoder layer can be succinctly summarized by \(\mathcal{X}_{de}^{M},\mathcal{T}_{de}^{M}=Decoder(\mathcal{X}_{de}^{l-1}, \mathcal{X}_{en}^{N})\). Then, the output of the decoder can be obtained by:  
\[\mathcal{X}_{o}=\mathcal{W}_{\mathcal{S}}*\mathcal{X}_{de}^{M}+\mathcal{W}_{ \mathcal{T}}*\mathcal{T}_{de}^{M} \tag{12}\]  
where \(\mathcal{W}_{\mathcal{S}}\) and \(\mathcal{W}_{\mathcal{T}}\) is the weight matrix for seasonal and trend-cyclical components, respectively.  
Figure 6: Improved temporal autformer architecture.
## 4 Results and Discussion
### Long-Run and Short-Run Evidence  
The results of the long-run and short-run evidence interpreted with the residual diagnostic test are presented in Table 7, below. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Null** & **Trace Test** & **P-value** & **Null Hypothesis** & **Maximum Eigenvalue** & **P-value** \\ \hline r \(\leq\) 0 & 232.7068 * & 0.0000 & r \(\leq\) 0 & 77.65021 * & 0.0000 \\ \hline r \(\leq\) 1 & 155.0566 * & 0.0002 & r \(\leq\) 1 & 48.54521 & 0.0278 \\ \hline r \(\leq\) 2 & 106.5114 * & 0.0074 & r \(\leq\) 2 & 31.58198 & 0.3264 \\ \hline r \(\leq\) 3 & 74.92944 * & 0.0184 & r \(\leq\) 3 & 33.87687 & 0.1492 \\ \hline r \(\leq\) 4 & 45.33503 & 0.0846 & r \(\leq\) 4 & 27.58434 & 0.0798 \\ \hline r \(\leq\) 5 & 19.38669 & 0.4654 & r \(\leq\) 5 & 21.13162 & 0.5728 \\ \hline r \(\leq\) 6 & 7.639644 & 0.5047 & r \(\leq\) 6 & 14.26460 & 0.8568 \\ \hline \hline \end{tabular} Note: r show the cointegrating equation numbers; * denotes hypothesis rejection at the 0.05 level.  
\end{table} 
Table 6: Johansen cointegration test results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**F-Statistic** & **Significance** **Levels** & **Lower Bound** & **Upper Bound** & **Status** \\ \hline
3.870465 & 10 percent & 2.03 & 3.13 & \\ \cline{2-4}
5 percent & 2.32 & 3.50 & Co-integrated \\ \cline{2-4}
1 percent & 2.96 & 4.26 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Autoregressive Distributed Lag (ARDL) bounds test for cointegration results. Table 7 represents the results of the long-run and short-run evidence in Panel A and Panel B. Focusing on the long-run evidence with elasticity in the Panel A variables, the results indicate that cropped area, energy use, fertilizer offtake, GDP per capita and water availability have a significant association with CO2 emission, having p-values of 0.0148, 0.0031, 0.0003, 0.0010 and 0.0099, respectively. The long-run analysis results conclude that a 1% increase in all variables--including cropped area, energy use, fertilizer offtake, GDP per capita and water availability has a positive correlation, with CO2 emission increases of 0.07%, 0.82%, 0.60%, 0.21% and 0.70%, respectively. In the long-run evidence, the analysis results regarding CO2 emission and cropped area, energy use, fertilizer offtake, GDP per capita and water availability have a positive and significant association. The analysis results also show that improved seed distribution and total food grains have a negative linkage to CO2 emission in Pakistan. In South Asia, Pakistan has a dominant role and is greatly affected by a number of
# Research on Amphibious Multi-Rotor UAV Out-of-Water Control Based on ADRC
## 2 Dynamic and Kinematic Modeling
### Thrust (Moment) \(\tau_{p}\)  
The amphibious multi-rotor vehicle relies on its propulsion system for both underwater and aerial movements, as shown in Figure 3, for the location distribution of its propellers and the steering of its motors. The arrows in the figure point to the direction of rotation of the motor, and the direction of thrust generated is parallel to the \(z\) axis.  
It is assumed that the propellers are fully submerged in a certain medium when the vehicle is operating, and the effects of factors such as fluid flow are ignored. Air propellers have also been shown to be effective in water [35]. The propeller thrust for any operating condition can be estimated using the empirical equation [36], then the thrust \(F_{ji}\) is  
\[F_{ji}=K_{F_{ji}}\rho_{i}n^{2}D_{i}^{4}(i,j=a,w) \tag{7}\]  
In the above equation, \(K_{F_{ij}}\) denotes the thrust coefficient of the propeller [37], where the subscripts \(j\) and \(i\) denote the type of propeller and the type of medium it is in, respectively. For example, \(K_{F_{env}}\) denotes the propeller thrust coefficient used during the transition from air to water for an amphibious multi-rotor UAV; \(n\) denotes the propeller speed, \(D_{j}\) denotes the diameter of different types of propellers.  
Figure 3: Distribution of motors.  
The propeller generates thrust through the rotating fluid, so the drive motor must generate enough torque to achieve rotational motion. This paper uses DC brushless motors, and their required drive torque can be calculated by the following empirical [17] formula:  
\[T_{ji}=K_{T_{ji}}\rho_{i}n^{2}D_{i}^{5}(i,j=a,w) \tag{8}\]  
In this equation, \(K_{T_{ji}}\) denotes the propeller moment coefficient [37]. Both thrust and moment coefficients are related to the geometric parameters of the propeller and the properties of the working medium and can be approximated empirically instead of in the actual modeling calculations.  
Let the propeller speed of the \(i\) propeller be \(n_{i}\), and the six propeller speeds be \(n_{1},n_{2},n_{3},n_{4},n_{5},n_{6}\), then the thrust generated by the vehicle propeller system and its moment can be obtained as  
\[\tau_{P}=\left[\begin{array}{c}X_{T}=0\\ Y_{T}=0\\ \sum\limits_{m=1}^{6}f_{m}\\ l\cos 60^{\circ}[(f_{1}+f_{3})-(f_{4}+f_{6})]+l(f_{2}-f_{5})\\ l\sin 60^{\circ}[(f_{1}+f_{6})-(f_{3}+f_{4})]\\ (T_{1}+T_{3}+T_{5})-(T_{2}+T_{4}+T_{6})\end{array}\right] \tag{9}\]  
In this equation, \(i=a,w\), \(f_{m}=K_{Fa,w}\rho_{i}{n_{m}}^{2}D_{a}^{4}\), \(T_{m}=K_{Ta,w}\rho_{i}{n_{m}}^{2}D_{a}\)5, and \((m=1,2,3,4,5,6)\), where \(a,w\) represent the thrust (moment) coefficients used in the air and underwater, respectively, and \(l\) represent the distance from the motor to the origin \(o\) of the motion coordinate system, \(l=0.42m\).
# Quantification of Microplastics in Plastic-Bottled Chinese Baijiu Using Micro-FTIR in Imaging Mode
## 5 Conclusions  
In conclusion, this study employed micro-Fourier transform infrared microscopy to quantify microplastics in Chinese baijiu and evaluated their content using the Pearson correlation coefficient. The results verified the occurrence of microplastics in Chinese baijiustored in plastic bottles, which could pose a possible threat to public health. Cellulose and PA were the frequently found microplastic types. The mean concentration found was 436 particles/500 mL, with a range spanning from 172 to 944 particles/500 mL. Possible contamination sources encompass raw materials, the atmosphere, and equipment or containers releasing microplastics.  
In contrast to reports on bottled water, microplastic concentrations observed in this analysis appear to be lower; however, comprehensive adverse health outcomes linked to microplastics in food and beverages are yet to be fully determined. Further research is required to provide essential information for consumers, regulators, and the food industry, facilitating a more comprehensive comprehension of the sources and potential impacts of microplastics in food and beverages. To tackle the risks posed by microplastics, we would like to conduct additional cellular experiments to investigate the impact of these minuscule plastic particles on human health. Furthermore, endeavors should be made to devise tactics that curtail the release of microplastics into the environment and guarantee the safety of the food supply chain.  
X.Z.: Conceptualization, Investigation, Data curation, Original draft preparation. J.W.: Reviewing and Editing. H.L.: Methodology, Formal analysis, Investigation. Q.W.: Data curation. J.R. and S.T.: Resources. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the Zhejiang Market Regulatory Administration Science and Technology Program (Grant number: 20190101, ZC2021A015).  
Not applicable.  
The authors gratefully acknowledge the financial support provided by the Zhejiang Market Regulation Administration Science and Technology Programme Project.  
The authors declare no conflict of interest.  
Microplastics (MPs); Fourier transform infrared spectroscopy (FTIR); Polyamide (PA); Polyethylene terephthalate (PET); Polypropylene (PP); Polyvinyl chloride (PVC); Polyethylene (PE); European Food Safety Authority (EFSA); Focal plane array (FPA); Polyurethane (PU); Polystyrene (PS).
## 2 Theory  
### Auto-Regressive Model  
Suppose that the system model of the random sequence \(x(n)\) is a linear difference equation:  
\[x(n)=\sum_{i=0}^{q}b_{i}w(n-i)-\sum_{j=0}^{q}a_{j}x(n-j)) \tag{1}\]  
where \(w(n)\) is the white Gaussian noise sequence; \(a_{j}\) and \(b_{i}\) are constants.  
The \(z\) transformation of Equation (1) is as follows:  
\[\sum_{j=0}^{q}a_{j}X(z)z^{-j}=\sum_{i=0}^{q}b_{i}W(z)z^{-i} \tag{2}\]  
Therefore, the transfer function of the system is:  
\[H(z)=\frac{X(z)}{W(z)}=\frac{B(z)}{A(z)} \tag{3}\]  
where the expressions of \(A(z)\) and \(B(z)\) are:  
\[A(z)=\sum_{j=0}^{q}a_{j}z^{-j} \tag{4}\]  
\[B(z)=\sum_{i=0}^{q}b_{i}z^{-i} \tag{5}\]  
Assuming that the power spectral density of the white noise is \(p_{w}(z)=\sigma_{w}^{2}\), the output power spectral density is:  
\[p_{x}(z)=\sigma_{w}^{2}\frac{B(z)B(z^{-1})}{A(z)A(z^{-1})} \tag{6}\]  
where \(z=e^{iw}\), and Equation (6) can be substituted into:  
\[p_{x}(z)=\sigma_{w}^{2}\left|\frac{B(e^{iw})}{A\left(e^{iw}\right)}\right|^{2} \tag{7}\]According to Equation (1), when \(i>0\) and \(b_{i}=0\), the difference equation of the system can be changed to:  
\[x(n)=w(n)-\sum_{j=0}^{q}a_{j}x(n-j) \tag{8}\]  
Equation (8) is an auto-regressive (AR) model, where \(a_{j}\) is the parameters of AR model; \(w(n)\) is the white Gaussian noise with mean variance of \(\sigma_{w}^{2}\); \(q\) is the order of AR model [18]. And its output power spectrum is:  
\[p_{x}(w)=\frac{\sigma_{w}^{2}}{\left|1+\sum_{k=0}^{q}a_{k}e^{-jwk}\right|^{2}} \tag{9}\]  
where Akaike information criterion (AIC) is used to determine the order of AR model; Yule-Walker method is used to estimate the parameters of AR model [19]. After calculating \(q\) and \(a_{k}\), the power spectrum of the time series can be estimated by calculation.
## 2 Problem Statement
Thus, the main challenge is the deployment and runtime management since the involved domains may not only be geographically far from data centers interconnected over a Wide Area Network (WAN) infrastructure, but they may belong to different administrative domains (multi-domain) [28]. Note that the term _multi-domain_ may be applied to the same provider or different providers, but the term _multi-provider_ necessarily refers to multi-domain. Current providers can implement slicing without 5G, but this is likely to become much more prevalent with 5G and its emerging specifications, which require partitioning of data,  
Figure 1: Network Slicing in 5G Networks. Based on [22]. control, and management planes to separate the environments to be created. Thus, serving individual customers or providing specific services, giving Internet Service Providers the opportunity to more easily support multi-tenancy, specific customers, and use cases to meet each slice's exclusive Service Level Agreements (SLAs). Additional efforts are needed to develop algorithms and methods capable of managing resources and of truly providing advanced, efficient, and reliable 5G services with appropriate Quality of Service (QoS) for end-users [30]. In 5G, QoS must be E2E, i.e., established between a sole Service Provider (SP) and the end-user [23; 31]. Regarding the concept of QoS, there is also that of experience (QoE), which is related to users' perceptions of their experience of a given service. The better the compliance with the QoS parameters, the better the perception of QoE. [32]. It will also be a challenge to meet the E2E response time for slicing the network and establishing the connection according to the needs demanded by each user. Thus, our proposed Orchestrator, called 5G-Horizontal (5G-H), is horizontal since it must establish and maintain the NS that meets the requirements of the target service at the lowest cost during the service's life cycle while considering all the infrastructure available in the region, regardless of the infrastructure and networks (specific providers), working in operating time, and aiming solely at the requirements of the services demanded. Further research should mitigate the challenges in Computer Engineering about aspects of dynamic orchestration in operating time, Distributed Cloud Computing, Distributed Big Data, and management algorithms with Artificial Intelligence (AI), which are all vitally related to orchestration in 5G [33; 34].
## 4 Experimental Results
### Model Verification
#### 4.1.4 The Impact of Overpass Time Difference on Model Estimation  
Since the HY-2A scatterometer and SSMIS radiometer have different overpass times when collecting the dataset used in this study, it needs to further analyze its impact on the estimated TC intensity. We calculated the RMSEs between the MSW that was estimated by the proposed model and the CMA best track data at 5 min intervals for the overpass time difference (see Figure 8).  
As shown in Figure 8, the RMSEs did not increase as the time difference became larger. Most of the RMSEs were in the range of 3.5 to 4 m/s. The smallest RMSE occurred at the 55 min time difference bin, and it was probably due to the small sample size of the high wind conditions. This results indicate that the data matching scheme used in this study was effective; the errors introduced by these time differences had no significant effect on the estimated MSW.
# Prediction of Machine Failure in Industry 4.0: A Hybrid CNN-LSTM Framework
## 7 Lessons Learned  
During the course of this study we came across the following difficulties:  
_Data Interoperability:_ Currently, data are not shared across machines in a production line. Industry 4.0 is now working on system integration to make data more inter-operable so that better decisions may be made to optimize the production process. Where elements of the knowledge wrapped in the data are made available, we believe data integration can improve machine learning results.  
_Match and Validate Data:_ It can take a long time to come up with rules to match data received from multiple sources. This becomes progressively difficult as the number of sources grows. Machine learning models can be taught to learn the rules and forecast fresh data matches. There is no limit to the amount of data that may be used, and more data are actually beneficial in fine-tuning the model.  
_Modelling:_ When compared to using a single ML model, combining multiple ML models can yield superior predictions. However, developing the hybrid objective function for optimization is difficult given the complex nature of time-series data such as missing entries and dynamic periodicity. For example, classification and anomaly detection techniques can be coupled to keep classification model precision while retaining anomaly detection benefits. PdM can be used on equipment or systems that do not have a huge dataset in this fashion.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **MAE** & **RMSE** & **R-Squared Accuracy** \\ \hline CNN & 32.985 & 45.012 & 0.932 \\ LSTM & 30.125 & 42.98 & 0.952 \\ CNN-LSTM & 27.021 & 38.701 & 0.968 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different evaluation indexes for three methods.  
Figure 11: Results for predicted and real values for the hybrid CNN-LSTM model.  
Figure 10: Results for predicted and real values for LSTM.
# Failure Mechanism and Structural Analysis of Chain Slings with Non-Standard Connections
## 5 Guidelines for On-Site Implementation  
Based on the findings of this study, guidelines for the management and safe usage of chain slings in industrial field applications are proposed. Prior to using a chain sling, it is essential to perform direct dimensional inspections of key parameters such as the nominal diameter (d) and pitch (p). Deformed chains--specifically, those with more than a 10% reduction in diameter or more than a 5% increase in pitch--must be identified and discarded.  
In accordance with international standards such as ISO 16872 and EN 818-4, chain slings must be assembled using appropriate rigging accessories such as shackles and master links. The practice of directly wrapping the chain around eyebolts or similar structures should be strictly prohibited. Furthermore, attention must be paid to the direction of chain contact and the state of load distribution to prevent the occurrence of unnecessary bending loads or torsional stresses on the chain.  
Regular maintenance and the establishment of replacement intervals are also crucial. Chain slings must be subjected to periodic detailed inspections depending on the frequency of use, working environment, and loading conditions, and should be replaced as necessary under a structured management system. If any external abnormalities--such as cracks,  
Figure 24: Results of chain fracture in Case 3. The red circle indicates the location of the chain fracture.  
Figure 25: Results of chain fracture: (**a**) Detailed view of Case 4; (**b**) Detailed view of Case 5. The red circle indicates the location of the chain fracture.  
deformation, or corrosion--are detected, the sling must be taken out of service immediately, and further inspection should be carried out.  
In addition, regular safety training must be provided to workers regarding proper usage, inspection methods, and appropriate measures to take when defects are discovered. Pre-use inspections, including an assessment of the condition of the chain sling, must be conducted before each operation to prevent potential accidents.
## 3 Background
### Recurrent Neural Network (RNN)  
Recurrent Neural Networks (RNNs) are a sophisticated class of artificial neural networks uniquely designed to process sequences of data by leveraging their inherent ability to maintain a'memory' of previous inputs. This capability distinguishes RNNs from traditional neural networks, which treat each input independently, without regard for order or sequence. The core idea behind RNNs is their internal state, or memory, which captures information about what has been processed so far, allowing them to exhibit dynamic temporal behavior. This makes them exceptionally well-suited for applications involving sequential data, such as natural language processing, speech recognition, and time series prediction. RNNs operate by looping through each element in a sequence, updating their internal state based on both the current input and the previously acquired knowledge. This process enables them to make informed predictions about future elements in the sequence: essentially, learning patterns and dependencies within the data. For instance, in language modeling, an RNN can predict the next word in a sentence based on the words it has seen so far, capturing the grammatical and contextual nuances of the language.  
Despite their powerful capabilities, RNNs are not without challenges. One of the main issues they encounter is the difficulty in learning long-term dependencies, known as the vanishing and exploding gradient problems. These problems arise due to the nature of backpropagation through time (BPTT), the algorithm used for training RNNs, which can lead to gradients becoming too small or too large, making it hard for the RNN to learn correlations between distant elements in a sequence. To overcome these challenges, several variants of RNNs have been developed, including Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU). These architectures introduce mechanisms to better control the flow of information, allowing them to retain important long-term dependencies while forgetting irrelevant data, thereby mitigating the issues of vanishing and exploding gradients. LSTMs, for example, incorporate gates that regulate the addition and removal of information to the cell state, making them highly effective for tasks requiring the understanding of long-range temporal dependencies. In recent years, RNNs and their variants have been at the heart of numerous breakthroughs in fields requiring the analysis of sequential data. From generating coherent text in natural language generation tasks to providing real-time translations in machine translation systems, and even enabling sophisticated voice recognition and synthesis in virtual assistants, RNNs have demonstrated their versatility and power. As the research continues to evolve, it islikely that we will see further advancements in RNN architectures and their applications, solidifying their role as a cornerstone of sequential data analysis in artificial intelligence.