esa-sceva/satcom-chunk-collection huggingface dataset:
# Reshaping the Digital Twin Construct with Levels of Digital Twinning (LoDT)
## 3 Digital Twinning throughout the Life Cycle of Physical Assets  
### Uses and Life Cycle Phases of Physical Assets  
In general, physical infrastructure assets undergo a life cycle comprising four phases: planning and design, construction, utilisation, and end-of-life. These phases exhibit distinct characteristics such as the nature of activities undertaken, duration, stakeholders involved, and impacts on third parties. According to these characteristics, various purposes [44] are defined. These uses are the basis for defining the requirements in the digital twinning of physical assets.  
Across these phases, the asset undergoes several changes in its physicalness. This is a crucial point, considering that, according to prominent models and the architecture presented herein, the presence of a physical entity is a necessary condition to the concept of a DT. Hence, the absence of a constructed physical asset (or the process leading to the creation of one) and the absence of an automatic data flow invalidates the very definition of a DT. In phases where the entity lacks physical materiality, and thus the establishment of an automatic data connection with the digital space is not feasible, its representation solely exists in the digital space. In the digital space, data and models representing these entities are integrated, forming what is known as a digital model [45].  
The knowledge transfer between the aforementioned life cycle phases--facilitated by the so-called \"digital thread\" [46]--has a significant impact on the value derived from assets. Information loss during the transition between phases in the life cycle of physical assets is a prevalent issue in the construction industry, which is generally characterised by low productivity [47] and significant process fragmentation [48]. A more traditional and innovation-resistant culture in the sector [15] and the absence of open and trustworthy information across the value chain are some of the identified reasons for this problem [49]. Poor interoperability across life cycle phases can result in network, restarts, and the risk of data loss. These issues take on a special dimension when numerous stakeholders are involved, asset life cycles are long, and organisations retain information in silos, which are characteristics easily found in the infrastructure sector.
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 3 Materials and Methods
### Single Forecast  
The ARIMA model, or autoregressive integral sliding average model, as a classic model in traditional time series forecasting methods, captures the dynamics of time series data by analyzing their autocorrelation. The ARIMA model has a wide range of applications in the field of price forecasting, especially in handling linear data and revealing seasonality and trends in data. LSTM and GRUs are two classic models in the field of machine learning methods; they belong to the variants of recurrent neural networks (RNNs), which are recurrent neural network structures in deep learning that are specialized in capturing nonlinear relationships and temporal dynamics in data and are particularly suitable for processing and predicting long-term dependence in time series data. LSTM avoids the gradient vanishing problem of traditional RNNs in long sequences by introducing a memory unit, whereas GRUs act as a simplified variant of LSTM, reducing the complexity of the model while maintaining the performance. Due to the differences between LSTM and GRUs in terms of information flow and control mechanisms during computation, the selection of base models by incorporating them simultaneously into the combined model can capture the intrinsic features of the data in a more comprehensive way, thus significantly enhancing the generalization ability of the model.  
Therefore, this study adopts these three classical single prediction models as the source for generating the base model in the combined model, which can take advantage of the fact that the combined model has both statistical and data-driven models, capturing a wider range of data features and patterns and improving the accuracy and reliability of prediction. The specific operational steps and details of each model have been described in detail in Appendix A.  
Meanwhile, in order to verify the applicability and effectiveness of the decomposition denoising technique combined with a single forecasting model on the large scallion price series, this study forms and compares the three single models, based on the adoption or non-adoption of the three decomposition techniques, in a variety of integrated models, so as to evaluate the differences in the accuracy and directional smoothness of the forecasts of the different decomposition denoising techniques when they are applied to the different models and to provide references and bases for subsequent studies.
## 2 Materials and Methods
### Cpe  
Based on the permutation entropy and IOTA, [PERSON] et al. proposed the CPE to analyze the information interactions between financial time series [39]. The implementation process is as follows:  
1. For two time series with the same length \(\{x(t)\}\) and \(\{y(t)\}\), \(t=[1,2,\ldots,N]\), their state vectors \(X_{t}=[x_{t},x_{t+r},x_{t+2r},\ldots,x_{t+(d-1)\tau}]\) and \(Y_{t}=[y_{t},y_{t+r},y_{t+2r},\ldots,y_{t+(d-1)\tau}]\), \(t\in[1,2,\ldots N-(d-1)\tau]\), are obtained through the phase space reconstruction procedure using the delay parameter \(\tau\) and the embedding dimension \(d\).
2. Performing nondecreasing sort on state vector \(X_{t}\), and obtaining its position index \(\pi_{X}\). Rearranging the state vector \(Y_{t}\) with the position index \(\pi_{X}\) as the standard, and the result is recorded as \(G_{t}=Y_{t}(\pi_{x})\).
3. Based on the principle of IOTA, the monotonicity is quantified by counting the number of intersection points of the horizontal lines which are drawn from each data point of \(G_{t}\) and \(G_{t}\) itself. The intersections number of the kth state vector is calculated using the following equation:\[k_{t}=\sum\limits_{i=1}^{d-2}\sum\limits_{j=i+1}^{d-1}\Theta[(G_{t}(j+1)-G_{t}(i)) (G_{t}(i)-G_{t}(j))] \tag{1}\]  
where \(\Theta[x]\) is the Heaviside function:  
\[\Theta[x]=\left\{\begin{array}{ll}1,&x>0\\ 0,&x\leq 0\end{array}\right. \tag{2}\]
4. According to this method, all state vectors of the time series are traversed, and the number of the intersections of each state vector can be expressed as a unique integer \(z\), \(z\in[0,R]\), \(R=(d-1)(d-2)/2\) is the maximum possible number of intersections. For all the \(R+1\) possible values for the integer \(z_{i},i=0,1,\ldots,R\) of intersection points \(k_{t}\) in each state vectors, its probability can be obtained by \[p(z_{i})=\frac{\#\{k_{t}|k_{t}=z_{i}\}}{N-(d-1)\tau}\] (3) where \(1\leq t\leq N-(d-1)\tau\), \(0\leq i\leq R\), \(\#\) is the number of elements in the set. Then, after obtaining the probability distribution set \(P=\{p(z_{i}),i=1,\ldots,R\}\), CPE is defined as: \[H_{x\to y}(d,\tau)=-\sum\limits_{i=0}^{R}p(z_{i})\log_{2}p(z_{i})\] (4)  
According to the above definition, the greater the coupling strength between the two time series, the smaller the CPE. For two random time series, the entropy value reaches the theoretical maximum \(\log_{2}(R+1)\).
# Noise Impact Analysis of School Environments Based on the Deployment of IoT Sensor Nodes
## 2 Methods
### Evaluation of Low-Cost Acoustic Level Sensor  
According to the work performed by the authors in [6; 7; 8; 9], low-cost sound sensors require calibration and continuous monitoring for the accuracy of their measurements [11; 21]. Therefore, between the Sen0232 [22] and the Sen12642 [23] acoustic sensors, the SEN0232 sensor [11] was utilized due to its accuracy and its simplicity of integration [22]. Specifically, it outputs an analog voltage value that can be processed by the microprocessor and consequently be converted to a dBA. On the contrary, the Sen12642 sensor does not support dBA-associated measurements; however, it provides a proportional value that does not correspond to a specific intensity value, and this could potentially be performed afterwards with the application of an appropriate algorithm implemented in SW. Subsequently, while Sen0232 can be used as is, Sen12642 required further evaluation. It should be noted that Sen12642 is a more cost-effective solution. The SEN0232 sensor utilizes 3 pins: one for the power supply connection, another for connecting it to the ground, and the third to an analog input pin of the boards described in Section 2.3 below. The measuring range of this acoustic signal sensor ranges from 30 dBA to 130 dBA, with a measurement error of \(\pm 1.5\) dB and a frequency response ranging from 31.5 Hz to 8.5 KHz. The school environment is not a heavy industrial environment as described in the WHO finding, and consequently, the  
Figure 4: Number Convention of the IoT sensors based on their locations.  
Figure 3: Location of the IoT sensors.  
technical specifications of the SEN0232 meet the characteristics of our ecosystem, and it is an adequate and cost-effective solution to bolster the noise tracking.
## 2 Methodologies
### Versatility of GAN-SDAE System  
The usefulness of the GAN-SDAE system was confirmed by the classic bearing fault diagnosis. However, in the actual industry, gear fault diagnosis is also common. To verify the applicability of GAN-SDAE, we applied this system to gear fault diagnosis. The gear fault signals were obtained  
Figure 19: The synthetic standard uncertainty, \(u_{\text{C}}\).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **MAE** & **MSE** & **MAD** & **RMSE** \\ \hline Dataset A & 0.0649 & 0.0051 & 0.0289 & 0.0716 \\ Dataset B & 0.0892 & 0.0108 & 0.0412 & 0.1041 \\ Dataset C & 0.0476 & 0.0048 & 0.0466 & 0.0690 \\ Dataset D & 0.0927 & 0.0121 & 0.0473 & 0.1100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: GAN-SDAE system evaluation indexes.  
Figure 18: The results of comparing GAN-SDAE with the methods.  
by the drive-train dynamic simulator (DDS). The different types of faults in the gears are shown in Table 4 [50].  
The process of data augmentation for the gears was similar to the bearing experiment. The number of samples in the training set increased from 900 to 1200, which achieved the purpose of expanding the training samples. Visual analysis of categories Label 1 to Label 5 based on t-SNE is shown in Figure 20. It is evident that the t-SNE for the GAN-SDAE grouped the same gear fault types together and accurately distinguished five gear types. The cluster analysis for the gears had the same effect as in the bearing experiment and these results confirmed that the gear-fault features extracted by the GAN-SDAE were separable.  
A comparison of gear fault diagnosis was conducted using the same comparison method as in Experiment A, including SVM, BPN, DAE, SDAE, and GAN-SDAE. The experimental results of the different methods are shown in Figure 21.  
The results indicate that both SVM and BPN were less than 80% accurate. The accuracy of the DAE fault diagnosis was 80.23%, and the effect is common. The accuracy of the SDAE was 86.93%, and data augmentation by GAN increased this result to 90.35% for the proposed system. The \(u_{\text{C}}\) of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault type** & **Description** & **Label** \\ \hline Normal & The gear works normally & 1 \\ Chipped & Crack in the gear teeth & 2 \\ Miss & Missing a tooth in the gear & 3 \\ Root & Crack in the root of gear teeth & 4 \\ Surface & Wear in the surface of gear & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Description of gear fault types.  
Figure 20: Feature visualization through t-SNE.  
GAN-SDAE for gears is shown in Figure 22. With above 40 repetitive experiments, \(u_{\text{C}}\) was below 0.6. This result reflects the stability of the GAN-SDAE system for gear fault diagnosis.
# THBase: A Coprocessor-Based Scheme for Big Trajectory Data Management
## 6 Query Processing Module
### Spatiotemporal Range Query  
A spatiotemporal range query retrieves a set of trajectories according to a time range \(\mathit{tr}_{st}\) and a spatial range \(\mathit{sr}_{st}\).  
Because spatiotemporal range query involves multiple trajectories, to improve query efficiency, we implement the corresponding parallel query algorithm based on Endpoint Coprocessor. Compared with MapReduce framework, Endpoint is more customizable, with higher query precision, and the format conversion overhead between Region and MapReduce Partitioner is avoided. According to the introduction of T-table and L-index, tRegion and its L-index coreside on the same RegionServer, and the data of the same MO is distributed in the same tRegion. Therefore, when processing a query, each Endpoint-based task can obtain all the index and trajectory data to be processed from the RegionServer where it runs on, and has a high locality level. When performing parallelism, except for the node where the client is located, there is no need to communicate across nodes, thus effectively controlling communication overhead and improving execution efficiency.  
The implementation of Endpoint includes the steps of extending the CoprocessorProtol interface, implementing the interface function of Endpoint, extending the abstract class BaseEndpoint-Coprocessor and specifying the calling mode of the client [25]. Here, the interface function of Endpoint is mainly introduced.  
The processing flow is shown in Algorithm 2, where lines 2 to 5 describe interface functions of Endpoint in each tRegion. In the query process of each Endpoint, we first query L-index to get an ordered set of rowkeys \(\mathit{ors}_{\mathit{st}}\) by \(\mathit{tr}_{\mathit{st}}\) and \(\mathit{sr}_{\mathit{st}}\) (see Section 5.2 for the query process in L-index) (line 2). Then, we use \(\mathit{ors}_{\mathit{st}}\) to access the corresponding tRegion, and filter the query results by \(\mathit{tr}_{\mathit{st}}\) and \(\mathit{sr}_{\mathit{st}}\) to generate trajectory points \(\mathit{ps}_{\mathit{st}}\) (line 3). After that, \(\mathit{ps}_{\mathit{st}}\) are merged into complete trajectories \(\mathrm{Ts}_{\mathit{st}}\) as the results of tRegion (line 4). Finally, we summarize the results of all tRegions and then return them to users (line 6).  
```
Input: Query time range \(\mathit{tr}_{\mathit{st}}\), Query spatial range \(\mathit{sr}_{\mathit{st}}\); Output: A set of Trajectories;
1 for each \(\mathit{tRegion}\)do in parallel
2\(\mathit{ors}_{\mathit{st}}\)\(\leftarrow\)L-index.getRowkeys(\(\mathit{tr}_{\mathit{st}},\mathit{sr}_{\mathit{st}}\));
3\(\mathit{ps}_{\mathit{st}}\)\(\leftarrow\)tRegion.get(\(\mathit{ors}_{\mathit{st}}\)).filter(\(\mathit{tr}_{\mathit{st}},\mathit{sr}_{\mathit{st}}\));
4\(\mathit{Ts}_{\mathit{st}}\)\(\leftarrow\)ps\({}_{\mathit{st}}\). mergeByMOID();
5 end parallel
6 returnclient.collect(\(\mathit{Ts}_{\mathit{st}}\));
```  
**Algorithm 2** Spatiotemporal Range Query
## 1 Introduction
### Proposed Optimization Methodology and Performance Validation
A statistical analysis was performed in order to evaluate the solution quality, repeatability, and processing times of each algorithm. Additionally, the adapted version of a 69-node system was used to model a more dynamic seven-day operation scenario. Daily demand and renewable generation profiles were generated using artificial neural networks (ANNs), allowing for a realistic representation of temporal fluctuations.
# Stability Analysis of Multi-Agent Tracking Systems with Quasi-Cyclic Switching Topologies
## 3 Controller Design and Stability Analysis
### Stability Analysis
\begin{split} V(t)&\leq e^{2a(N_{\sigma}(t_{0},t)+1)} \\ & e^{-2(\lambda N_{s}\left[\frac{N_{\sigma}^{+}(t_{0},t)}{N_{\rm ss }}\right]-\lambda^{\bigstar}N_{s})\tau_{min}^{s}}\\ &\quad+2(\bar{\lambda}N_{\rm ss}\left[\frac{N_{\sigma}^{+}(t_{0},t)}{N_{\rm ss}}\right]+\lambda^{\star}N_{\rm ss})\tau_{max}^{s}\\ &\leq Ce^{2((\bar{\lambda}\left[\frac{N_{\sigma}^{+}(t_{0},t)}{N_ {\rm ss}}\right]+\lambda^{\star})N_{\rm ss}\tau_{max}^{ss}-(\bar{\lambda}\left[ \frac{N_{\sigma}^{-}(t_{0},t)}{N_{\rm ss}}\right]-\lambda^{\bigstar})N_{s}\tau _{min}^{s})}\\ &\leq Ce^{2(((\bar{\lambda}+\gamma)\left[\frac{N_{\sigma}^{+}(t_ {0},t)}{N_{\rm ss}}\right]+\lambda^{\star})N_{\rm ss}\tau_{max}^{s}}\\ &\quad-((\bar{\lambda}-\gamma)\left[\frac{N_{\sigma}^{-}(t_{0},t) }{N_{\rm ss}}\right]-\lambda^{\bigstar})N_{\rm ss}\tau_{min}^{s}\\ &\quad-\gamma(\left[\frac{N_{\sigma}^{+}(t_{0},t)}{N_{\rm ss}} \right]N_{\rm ss}\tau_{max}^{ss}+\left[\frac{N_{\sigma}^{-}(t_{0},t)}{N_{\rm ss }}\right]N_{\tau_{min}^{s}}))\end{split}  \tag{17}\]  
where \(C=e^{2a(N_{\sigma}(t_{0},t)+1)}\). From the condition in Theorem 1, it can be derived that  
\[ \begin{split}&((\bar{\lambda}+\gamma)\left[\frac{N_{\sigma}^{+}(t_ {0},t)}{N_{\rm ss}}\right]+\lambda^{\star})N_{\rm ss}\tau_{max}^{s}\\ &-((\bar{\lambda}-\gamma)\left[\frac{N_{\sigma}^{-}(t_{0},t)}{N_ {\rm ss}}\right]-\lambda^{\bigstar})N_{\rm ss}\tau_{min}^{s}\leq 0\end{split}  \tag{18}\]  
Substituting (18) into (17), we have  
\[ \begin{split} V(t)&\leq Ce^{-2\gamma(\alpha N_{ \sigma}^{+}(t_{0},t)\tau_{max}^{ss}+\beta N_{\sigma}^{-}(t_{0},t)\tau_{min}^{ s})}\\ &\leq Ce^{-2\gamma^{\prime}(N_{\sigma}^{+}(t_{0},t)\tau_{max}^{ \rm ss}+N_{\sigma}^{-}(t_{0},t)\tau_{min}^{s})}\\ &\leq Ce^{-2\gamma^{\prime}(T^{+}(t_{0},t)+(1-\eta)T^{-}(t_{0},t ))}\\ &\leq Ce^{-2\gamma^{\prime}(T^{+}(t_{0},t)+T^{-}(t_{0},t)-\eta T^{ -}(t_{0},t))}\\ &\leq Ce^{-2\gamma^{\prime}(t-t_{0}-\eta T^{-}(t_{0},t))}\end{split}  \tag{19}\]  
where \(\gamma>0\), \(\alpha\in(0,1]\), \(\beta\in(0,1]\), \(0<\gamma^{\prime}\leq\gamma min\{\alpha,\beta\}\), \(\eta\in(0,1]\). There must be a positive number \(\Gamma\) satisfying \(2\gamma^{\prime}(1-\eta)<\Gamma<2\gamma^{\prime}\), namely, \(2\gamma min\{\alpha,\beta\}(1-\eta)<\Gamma<2\gamma min\{\alpha,\beta\}\) so that  
\[V(t)\leq Ce^{-\Gamma(t-t_{0}))}V(t_{0}) \tag{20}\]  
which implies the exponential stability of the tracking error (7). Therefore, with the proposed tracking strategy, the tracking error between each tracking agent and target agent is 0 when \(t\rightarrow\infty\) even if unstable subsystems exist. Thus, the proof is complete.
# Improving Real-Time Position Estimation Using Correlated Noise Models +
## 3 Position and Uncertainty Estimation Methods
### Kalman Filter with Higher Order Noise
#### 3.2.1 Uncorrelated Gaussian Noise  
For a GPS measurement with iid Gaussian noise (both in different coordinates and in time), the system state is represented by one position coordinate for each spatial direction considered (latitude, longitude and altitude). We, therefore, treat the coordinates separately: \(\mathbf{x}=\mathbf{x}\); hence, the forward map, \(\mathbf{f}\), is the identity with vanishing process noise. The measurement noise is considered to be additive and uncorrelated: \(\mathbf{g}(\mathbf{x})=\mathbf{x}+\mathbf{e}\), with \(\mathbf{\varepsilon}\sim\mathcal{N}(0,\mathbf{\Sigma}_{v})\), and \(\mathbf{\Sigma}_{v}\) is associated with a stationary OU distribution \(\mathbf{\Sigma}_{v}=\sigma^{2}/\theta\). Here, \(\sigma^{2}\) and \(\theta\) are the same as for the non-learning method above.  
At the start of the data processing, we initialise the prior distribution with state mean \(\mathbf{\mu}_{0}\) and covariance \(\mathbf{K}_{0}\). We chose \(\mathbf{\mu}_{0}\) to be \(5\,\mathrm{m}\) from the \"true\" position, and the variance was taken to be \(K_{xx,0}=20\,\mathrm{m}^{2}\). This choice was motivated by the fact that for a Gaussian prior distribution, the true mean value lies near the edge of the central 90% probability region, since \(\sqrt{K_{xx,0}}\approx 4.47\,\mathrm{m}\). An inference algorithm, we expect, would lead to a narrower posterior distribution around the true value.  
The above procedure is used generally if a model diverges from the \"true\" value even if evolved by the exact dynamics. In such a case, some additive process noise is incorporated into the inference process. For the iid Gaussian model, the usage of such complementary process noise is equivalent to a Brownian motion around the \"true\" position. In order to find an optimal value for the process noise, we vary its magnitude and carry out inference with these Brownian models. Although these Brownian models contain correlated noise, their performance is inferior to a well-constructed OU model.
# Peeling Damage Recognition Method for Corn Ear Harvest Using RGB Image
## 1 Introduction
employed a camera to acquire RGB images of ears on conveyor belt, and then proposed a method to distinguish broken ear based on YOLO [6]. The information of ear broken can be used to adjust header speed. Kernel damage is also attracting research interests, which means kernels are cracked during harvesting. [PERSON] et al. utilized on-board hardware operation and parallel statistical look-up table mapping to extract images of kernels, based on which the image processing algorithm is developed to distinguish whole and damaged kernels [28]. Another damage form is ear peeling damage, the primary characteristic of which is the kernel loss from ears. Unfortunately, to date, the automatic detection of ear peeling damage has not been reported. The recognition method is the basis of online detection of ear peeling damage. Mechanical peeling leads to random dropping of kernels from ears. Hence, comparing to kernel damage, ear damage usually exhibits large region and random distribution. These characteristics make it necessary and possible to be recognized by RGB images. However, conventional image processing methods, such as image segmentation [29; 30], are difficult to be applied to recognize the damage in corn ear. This is because the objective images of snapped ears always contain great amount of features. Next, we provide an example to illustrate this issue. As presented in Figure 1(a), we selected a typical objective image during snapping as the original image in this example with the spatial resolution of  
Figure 1: Actual scenes of corn ear harvest in China. \(700\times 700\), containing intact ears, damaged ears, impurities, and backgrounds. The well-known image segmentation method, thresholding, was used for the objective image [31; 32; 33; 34]. The red, green, and blue bands were employed for the test, respectively. The result is shown in Figure 2. The image is divided into numerous of parts, and the region of damage is almost impossible to be recognized, regardless of the used band. In this study, we aimed to develop a peeling damage recognition method based on RGB image. Different from conventional methods, the proposed method contains two steps. First, we recognize the regions of corn kernels by using a novel dictionary-learning-based method. Second, we recognize the ear damage in the regions outside of the kernel regions by using a novel thresholding method. For both two steps, we also develop the method based on the concept of image corrosion and expansion for post-processing of recognized results. The practicality and accuracy of the proposed method was verified through experiments on both single ear recognition and multiple ears recognition. This paper is organized as follows. Section 2 introduces the the materials used in this study and the methods to recognize corn kernel region and ear damage region. Sections 3 and 4 presents the experimental results and discussions, respectively. Section 5 concludes this paper.