esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Shafts play a key role in the operation of mining plants. They connect underground excavations with the surface and provide the ability to transport people, equipment, and raw materials. The nature of the dynamic interaction of a conveyance moving at a significant speed along deformed guide rails is complex, and the method of assessing the interaction of hoisting conveyances with shaft steelwork, despite ongoing research, still requires further understanding and improvement. Misalignments of the guide rails and conveyance movements transverse to the shaft axis induce impact (guiding) forces, which are the key design parameters of shaft steelwork. The reliable assessment of guiding forces allows the design of safe and economical steelworks and the assessment of their structural safety during operation under deformations and corrosive deterioration. Determining the value of guiding forces requires their field measurements or the use of approximate empirical formulas. Both methods have their limitations--measurement is expensive and interferes with normal shaft operation, while empirical formulas are subject to high error due to the lack of consideration of many structural details specific to each shaft that significantly affect the behavior of the system. This study presents a new method for using a relatively simple-to-implement measurement of hoisting conveyance acceleration to assess guiding forces. A finite element model of the skip and steelwork was built, and simulations of the conveyance interaction with the structure were carried out. A strong relationship between the sliding plate's impact point location and the guiding force was found. Extreme values of the guiding force were observed in the vicinity of the button connection. The study showed that reducing the skip load mass does not affect the force value. Simplified methods of calculating the moments of inertia of the hoisting conveyance significantly overestimate the code-based values of the guiding forces. The presented method considers the actual stiffness and mass distribution of hoisting conveyance and, therefore, allows for a more accurate estimation of the guiding forces and the transport of larger loads. This data-driven approach allows for the continuous monitoring of the guiding forces, the adjustments of the hoisting parameters, the rational planning of repairs, and a reduction in the replacement of corroded shaft steelwork.  
shaft transportation; monitoring and diagnostics; shaft steelwork; accelerometric tests; guiding force; dynamic simulation +
Footnote †: journal: Applied sciences
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 4 Design of the TSR-DARDA Scheme
### Tsr-Darda Scheme
#### 4.2.1 Circular Tier and Cell Partitioning of the Surface of the Half Sphere
The number of cells partitioned in the tier \(T_{k-1}\) is:  
Figure 4.4  
where, \(\gamma_{k-1\_n}=R\cos\frac{d_{k}+d_{k-1}}{R}\). Since \(\gamma_{k-1\_w}=\gamma_{k\_n}\), \(h_{k-1\_w}=h_{k\_n}\), \(h_{k-1\_n}=R-R\sin\frac{d_{k}+d_{k-1}}{R}\). The area of each cell is calculated by:  
\[ \begin{split} s_{cell\_k-1}&=\frac{r}{2\pi\gamma _{k-1\_n}}(2\pi\gamma_{k-1\_w}\cdot h_{k-1\_w}-2\pi\gamma_{k-1\_n}\cdot h_{k-1 \_n})\\ &=\frac{r}{\cos\frac{d_{k}+d_{k-1}}{R}}(R\cos\frac{dr}{R}\cdot(1- \sin\frac{dr}{R})-R\cos\frac{d_{k}+d_{k-1}}{R}\cdot(1-\sin\frac{d_{k}+d_{k-1}} {R}))\end{split}  \tag{14}\]  
So, we can get the number of nodes in each cell is \(\xi_{k-1}=\rho\cdot s_{cell\_k-1}\) and the time required for collecting nodes in the tier to finish data aggregation is \(\tau_{k-1\_n}=3\xi_{k-1}(1+\mu)\). Considering the constraint that the tier width could meet the goal that when collecting nodes in the outer layer finish transmission to the parent collecting nodes in the inner layer and the collecting nodes in the inner layer just finish data aggregation, we have the following equation:  
\[\tau_{k-1\_n}=\tau_{k} \tag{15}\]  
After simplification, it becomes:  
\[\xi_{k-1}=\xi_{k}+1 \tag{16}\]  
Therefore, the tier width \(d_{k-1}\) can be obtained. If the procedure is repeated, we can obtain the width of each tier \(\{d_{k},d_{k-1},\,d_{k-2},\,\ldots,\,d_{2},\,d_{1}\}\) from the outmost tier to the innermost tier under the condition \(d_{k}=\delta r\) and \(d_{1}=r\) which denotes the hotpots.
# An Exploration into Damage Repair and Manufacturing Technology of Photomask Glass Substrates
## 6 Conclusions  
In this study, the feasibility of the high-efficiency array-type magnetorheological polishing technology combined with the removal function rotation angle technology in manufacturing and repairing photomask glass substrates was explored. Moreover, a new technological route related to photomask quartz glass substrates was proposed. In addition, a surface quality detection device was used to detect the surface defects, and the HF acid etching method was used to etch the photomask quartz glass substrates after CMP. Based on that, the polished hydrolysis layer can be eliminated, and much damage and many defects were found on the subsurface. Moreover, the surface was modified by the array-type magnetorheological device developed by our laboratory, and the experimental results of surface-shape errors demonstrated the effectiveness of surface shape modification. Furthermore, the removal function rotation angle was used to uniformly remove the subsurface damage and the surface mid-frequency ripple errors caused by surface shape modification. A surface quality detection device and white light interferometer were employed to observe the evolution of the surface scratches caused by CMP. This confirmed the effectiveness of the removal function rotation angle in removing the damage and the mid-frequency ripple errors. Finally, much surface damage can be eliminated and there was no mid-frequency ripple error on the surface. These techniques can meet the performance requirements of photomask glass substrates and realize the rapid damage removal and high-quality manufacture of photomask glass substrates.  
Conceptualization, G.T.; data curation, F.S.; formal analysis, B.W. and S.G.; methodology, B.W. and C.S.; supervision, G.T. and S.G.; validation, B.W. and S.G.; writing--original draft, B.W. and F.S.; writing--review & editing, B.W. and G.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** National Key R&D Program of China (No. 2021 YFC2202403) [PERSON], National Key R&D Program of China (No. 2020 YFB2007504) [PERSON], National Natural Science Foundation of China (U1801259) [PERSON], Strategic Priority Research Program of the Chinese Academy of Sciences (No. XD25020317) [PERSON], National Natural Science Foundation of China (62175259) [PERSON].  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data also forms part of an ongoing study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Materials and Methods
### Data Forwarding Method
#### 3.3.3 Learning to Forward
Broadcasting and listening in neighborhood, sensor nodes iteratively acquire and update their knowledge over time. Function \(r(p_{j,t+1},v_{k})\) in Equation (15) is approximated via Equation (13) based on the localization and neighborhood at time slot \(t\) The \(Q\)-values can be updated as shown in Equation (15),  
\[Q(p_{i,t},v_{j})=r(p_{i,t},v_{j})+\gamma\sum_{k\in H(j,t),k\
eq i,j,k\
eq i}Pr _{j,k}(t+1)r(p_{j,t+1},v_{k}) \tag{15}\]  
where \(r(p_{j,t+1},v_{k})\) is the reward of \(v_{j}\) transmitting packet \(p_{j,t+1}\) to \(v_{k}\) at time slot \((t+1)\), and \(r(p_{j,t+1},v_{k})\) is approximated via Equation (13) based on the localization and neighborhood at time slot \(t\)Additionally, \(Pr_{j,k}(t+1)\) represents the probability of transmission from \(v_{j}\) to \(v_{k}\) and \(\gamma\) is the learning factor. In the learning process, sensor nodes calculate the reward of each potential relay node and update the \(Q\)-value. Finally, sensor nodes acquire the \(Q\)-table by which the most appropriate relay node can be determined. ```
1:for each \(v_{i}\in V\)do
2:if\(\exists s_{m}\in S(i,t)\)then
3:\(p_{i,t}=\arg\max_{p_{i,t}\in P_{i,t}}Vol(p_{i,t})\)
4:\(a_{i}(t)=(p_{i,t},s_{m})\)
5:else
6:for each \(p_{i,t}\in P_{i,t}\)do
7:for each \(v_{j}\in H(i,t)\)do
8:for each \(v_{k}\in H(j,t)\) and \(k\
eq i\)do
9:\(Q(p_{i,t},v_{j})=r(p_{i,t},j)+\gamma\sum_{k\in H(j,t),k\
eq j,k\
eq i}Pr_{j, k}(t+1)r(p_{j,t+1},k)\)
10:endfor
11:endfor
12:endfor
13:\((p_{i,t},v_{j})=\arg\max_{a_{i}(t)\in A}Q(p_{i,t},v_{j})\)
14:\(a_{i}(t)=(p_{i,t},v_{j})\)
15:endif
16:return\(a_{i}(t)\)
17:endfor
```  
**Algorithm 2** RelaySelect(\(S(i,t),H(i,t),P(i,t)\)). In our method, each sensor node has to ascertain its neighborhood and then selects the relay node in its neighborhood. Specifically, we have to execute two rounds of calculation for each sensor node in each time slot: (1) the determination of neighbor nodes within the sensor's communication range; (2) the selection of the neighbor node with highest \(Q\)-value. In the first round of calculation, it takes a complexity of \(O(\frac{N(N-1)}{2})\) to calculate the distances between sensor nodes. In the second round, the complexity depends on the size of the neighborhood of sensor nodes. In the most complicated case, all the sensor nodes in the same neighborhood, i.e., \(\forall j\
eq i,v_{j}\in H_{i}\), we need to calculate \((N-1)\) times of \(Q\)-value of the neighbor nodes of \(v_{i}\). Therefore, it takes a complexity of \(O(N(N-1))\) at most to select relay nodes of all the sensor nodes. Since the number of time slots is constant, the complexity of our method can be ascertained as \(O(N^{2})\).
## 2 Wind Turbine Model Description  
The model of a small turbine of 7 kW is used. The equations of the model are summarized in Equations (1)-(6). The development of these equations can be found in [PERSON] et al. [18].  
\[\dot{l}_{a}=\frac{1}{L_{a}}\Big{(}K_{g}\cdot K_{\phi}\cdot w-(R_{a}+R_{L})I_{a }\Big{)}, \tag{1}\]  
\[\lambda_{i}=\Bigg{[}\Bigg{(}\frac{1}{\lambda+\varsigma_{8}}\Bigg{)}-\bigg{(} \frac{\varsigma_{9}}{\theta^{3}+1}\Bigg{)}\Bigg{]}^{-1}, \tag{2}\]  
\[C_{p}(\lambda_{i},\theta)=c_{1}\Bigg{[}\frac{C_{2}}{\lambda_{i}}-c_{3}\theta -c_{4}\theta^{c_{5}}-c_{6}\Bigg{]}e^{-\frac{\varsigma_{7}}{\lambda_{i}}}, \tag{3}\]  
\[\dot{w}=\frac{1}{2\cdot J\cdot w}\Big{(}C_{p}(\lambda_{i},\theta)\cdot p\pi R ^{2}\cdot v^{3}\Big{)}-\frac{1}{J}\Big{(}K_{g}\cdot K_{\phi}\cdot I_{a}+K_{f} w\Big{)}, \tag{4}\]  
\[\ddot{\theta}=\frac{1}{T_{\theta}}\Big{[}K_{\theta}\Big{(}\theta_{ref}-\theta \Big{)}-\dot{\theta}\Big{]}, \tag{5}\]  
\[P_{out}=R_{L}\cdot I_{a}^{2} \tag{6}\]  
where \(L_{a}\) is the armature inductance (H), \(K_{g}\) is a dimensionless constant of the generator, \(K_{\phi}\) is the magnetic flow coupling constant (V-s/rad), \(R_{a}\) is the armature resistance (\(\Omega\)), \(R_{L}\) is the resistance of the load (\(\Omega\)), considered in the study as purely resistive, \(w\) is the angular rotor speed (rad/s), \(I_{a}\) is the armature current (A), the values of the coefficients \(c_{1}\) to \(c_{9}\) depend on the characteristics of the wind turbine, \(J\) is the rotational inertia (kg m\({}^{2}\)), \(R\) is the radius or blade length (m), \(\rho\) is the air density (kg/m\({}^{3}\)), \(v\) is wind speed (m/s), \(K_{f}\) is the friction coefficient (N m/rad/s), \(\theta_{ref}\) is the reference for the pitch (rad), and \(\theta\) is the pitch (rad).  
The state variables of the control system are the current in the armature and the angular speed of the rotor, \([I_{a},w]\). On the other hand, the manipulated or control input variable is the pitch angle,\(\theta_{ref}\), and the controlled variable is the output power, \(P_{out}\), unlike other works where the rotor speed is the controlled variable.  
The RL controller proposed in this paper is applied to generate a pitch reference signal, \(\theta_{ref}\), in order to stabilize the output power, \(P_{out}\), of the wind turbine around its rated value. Equations (1)-(6) are used to simulate the behavior of the wind turbine and thus allow us to evaluate the performance of the controller.  
The values of the parameters used during the simulations (Table 1) are taken from [PERSON] et al. [37].
## 2 Methods  
The flow of grasping mode recognition for the manipulator based on sEMG signals is illustrated in Figure 1. Arm sEMG signals are collected by a wireless 8-channel sEMG armband, and the sEMG data are transmitted to the computer in real-time via Bluetooth. The computer, through software, communicates with the Bluetooth serial port and obtains the current sEMG data according to the serial communication protocol. Subsequently, a deep-learning model for predicting the manipulator's grasping mode is trained using sEMG data. The trained model is then integrated with the manipulator environment to achieve control of the manipulator's grasping mode, which has multiple degrees of freedom.
# Real-Time Interaction for 3D Pixel Human in Virtual Environment
## 2 Related Works
### Pose Estimation  
Pose estimation is a method for predicting and tracking the location of a person or group of people and obtaining 2D or 3D landmarks. Recently proposed 3D monocular pose-estimation methods, such as those in [29; 30], can perform three-dimensional human pose estimation with high accuracy and robustness, but because these methods use the Human3.6M dataset proposed by [PERSON] et al. [31], they all estimate a relatively small number of joints. In particular, the results predicted by these two methods do not contain certain joints in the human face and hands that are required by our method to generate 3D avatars. Therefore, they are not applicable. Additionally, although we only use a few joints in the face, arms, and hands, in the future, when expanding our method to whole-body 3D avatars, a higher number of human joints will yield better results and be more scalable.  
MediaPipe Pose [32; 33] uses a blazePose [34] detector and the pose landmark model proposed by [PERSON] et al. [35]. It is a relatively accurate real-time 3D pose estimation library that can obtain the 2D and 3D coordinates of 33 key points. In this study, we used 3D pose coordinates from MediaPipe Pose to convert 2D pixel human arms into 3D. Additionally, the MediaPipe Pose only recognizes the pose data of the object with the highest probability of being a human, which helps us convert only the users participating in a video conference into three dimensions while ignoring other objects.
# Urban PM2.5 Concentration Prediction via Attention-Based CNN-LSTM
## 3 Data and Method
### Spatiotemporal Correlation Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Station** & **S1** & **S2** & **S3** & **S4** & **S5** & **S6** & **S7** & **S8** & **S9** \\ \hline S1 & 1 & 0.95 & 0.2 & 0.69 & 0.8 & 0.95 & 0.86 & 0.95 & 0.72 \\ \hline S2 & 0.95 & 1 & 0.24 & 0.76 & 0.86 & 0.82 & 0.82 & 0.95 & 0.77 \\ \hline S3 & 0.2 & 0.24 & 1 & 0.29 & 0.57 & 0.08 & 0.53 & 0.37 & 0.4 \\ \hline S4 & 0.69 & 0.76 & 0.29 & 1 & 0.87 & 0.51 & 0.76 & 0.8 & 0.96 \\ \hline S5 & 0.8 & 0.86 & 0.57 & 0.87 & 1 & 0.62 & 0.93 & 0.93 & 0.9 \\ \hline S6 & 0.95 & 0.82 & 0.08 & 0.51 & 0.62 & 1 & 0.78 & 0.84 & 0.57 \\ \hline S7 & 0.86 & 0.82 & 0.53 & 0.76 & 0.93 & 0.78 & 1 & 0.94 & 0.85 \\ \hline S8 & 0.95 & 0.95 & 0.37 & 0.8 & 0.93 & 0.84 & 0.94 & 1 & 0.83 \\ \hline S9 & 0.72 & 0.77 & 0.4 & 0.96 & 0.9 & 0.57 & 0.85 & 0.83 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlation coefficients of PM\({}_{2.5}\) among all monitoring stations. Figure 6: Comparison of the autocorrelation coefficients at different time lags for different stations. paper can capture the spatiotemporal relations of the variation in PM\({}_{2.5}\) concentrations. The attention mechanism is introduced in the proposed method to weigh the past states, which helps to measure the importance of past states at different times, as modeled using the LSTM, for PM\({}_{2.5}\) concentrations.
# Effect of Fuel Composition on Carbon Black Formation Pathways
## 3 Results and Discussion
### Lattice Fringe Analysis  
Lattice fringes, formed by the constructive interference of the transmitted and diffracted beams in a TEM provide a reconstruction of the lamellae structure and can be used to quantify the nanostructure of flame formed carbons or carbon black and differentiate between materials based on their lamellae arrangement. For the CT- and FCC-CBs, lattice fringe analysis has further resolved, spatially, nanostructure differences between the particle's shell and core lamellae. Fringe length and tortuosity are quantified from HRTEM micrographs and compared in Figure 4.  
CT-CB: Figure 4A shows differences in fringe lengths of the lamellae that comprise the particle's core and the particle's shell. Lamellae that comprise the particle's shell have longer (higher % > 1 nm in length) fringes relative to the lamellae that make up its core as seen by TEM and indicated here by the positive %'s for longer fringe lengths.  
FCC-CB: Figure 4B shows a different plot of fringe lengths for FCC-CB's shell and core lamellae. One striking feature of this graph is the similarity between parameters for the lamellae that comprise the particle's shell and core, indicating radial uniformity across the particle.
# Coloured Petri Nets-Based Modeling and Validation of Insulin Infusion Pump Systems
## 4 Model-Based Approach
### Second System Refinement  
The second refinement of the reference model of insulin infusion pump systems provides an extended version of the first system refinement, including time constraints and real data type values. This refinement is composed of the same higher-level modules of the first refinement; however, the specification improves the reference model with a more detailed representation of insulin infusion pump systems to comply with stringent regulatory requirements (e.g., the correct rate of infusion) with real and timed data types.  
The main modification comprises battery verification, administration control, and insulin infusion, using the timed data types called UNITITMED, REALETMP, and DATATEPMP. These data types allow manufacturers to handle timed unit data types, timed real data types, and an integer and real values product. In this refinement, the battery verification is guided by controlling the frequency of verification, conducted each 2-time unit, instead of releasing the next verification as soon as the current verification is conducted. The administration control is similar to the standard and personalized infusion modes. The reference model refinements are available for readers who wish to analyze and re-use the specifications [28], along with an example of an ACES-based assurance case template. Figure 8 describes the second model refinement for the standard infusion that shows the administration control, starting from the Prepare Partial Application transition to the Finish Total App transition.  
A user-defined administration rate guides the administration control, and thus, considering an insulin dosage, it does not mean that the pump applies the dosage at once. The system split the dosage up to schedule the infusion based on the administration rate, and the infusion depends on the number of insulin units allowed. For example, the model represents the administration control of basal insulin using the TABA place, which maintains basal insulin administration time. Once the system conducts the basal insulin infusion, the Release Basal transition fires, get the next insulin infusion time from the TABA place, and configures the next basal dosage. Additionally, the system sends the next time to conduct the insulin infusion to the TABA place, enabling the correct configuration for the basal insulin infusion.  
The second refinement maintains the same parameters used for the first refinement. However, except for parameter QTDBASAL, parameters are assigned with the real data types. The refinement also includes six new input parameters (i.e., DADMPD, TADMPD, TADMBPD, DADMPS, TADMPS, and TADMBPS) related to the administration rate and time.
# Seismic Periodic Noise Attenuation Based on Sparse Representation Using a Noise Dictionary
## 2 Method
### Noise Period Scanning  
The waveform of periodic noise is similar in a single trace. Based on the similarity, we scan the noise period from ambient noise, which exists in the records before the first arrivals. First, given the scanned period interval \([T_{\text{min}},T_{\text{max}}]\), the noise period \(T\) is changed from the initial scanned period \(T_{\text{min}}\) to the final scanned period \(T_{\text{max}}\). The data \(S_{j}\) on the single trace \(h_{j}(j=1,2,\ldots,m)\) are split into many time windows \(s_{i}(i=1,2,\ldots,n)\), denoted as  
\[S_{j}=[s_{1},s_{2},\ldots,s_{n}] \tag{1}\]  
where the length of time window \(s_{i}\) is equal to the scanned period \(T\). The size of \(S_{j}\) is \(N\times 1\) (\(N\geq nT\)). Next, the correlation coefficients for the adjacent two time windows \(s_{i}\) and \(s_{i+1}\) are calculated:  
\[\text{Corr}(s_{i},s_{i+1})= \frac{\text{Cov}(s_{i},s_{i+1})}{\sqrt{\text{Var}(s_{i})}\sqrt{ \text{Var}(s_{i+1})}} \tag{2}\]  
where \(\text{Cov}(s_{i},s_{i+1})\) is the covariance of the two time windows and \(\text{Var}(s)\) is the variance of \(s\). For accuracy, we average the correlation coefficients to obtain a coefficient to evaluate the similarity of all time windows.  
\[C(T)= \frac{1}{n-1}\sum_{i=1}^{n-1}\text{Corr}(s_{i},s_{i+1}) \tag{3}\]  
Then, the scanned period \(T\) is increased and the former procedures are repeated until all periods in the interval \([T_{\text{min}},T_{\text{max}}]\) are scanned. The period which matches the maximum value of the correlation coefficients is our estimated period:  
\[\widetilde{T}=\underset{T}{\text{argmax}}\{C(T)\} \tag{4}\]  
where \(\widetilde{T}\) is the period of periodic noise on the trace \(h_{j}\).
## 2 Image Pre-Processing
### Two-Dimensional Dual Tree Complex Wavelet Transform (2D-DTCWT)
#### 2.2.1 Framework of DTCWT  
Wavelet transform has been exploited with great success across many applications in both the scientific and engineering fields. In the theory of wavelet transform, a continuous signal \(x(t)\) of finite energy can be decomposed in terms of wavelets and scaling functions via  
\[x(t)=\sum_{n=-\infty}^{\infty}c(n)\phi(t-n)+\sum_{j=0}^{\infty}\sum_{n=-\infty }^{\infty}d(j,n)2^{j/2}\psi(2^{j}t-n), \tag{5}\]  
where \(\phi(t)\) is the scaling function and \(\psi(t)\) is the wavelet function. The scaling coefficient series \(c(n)\) and wavelet coefficient series \(d_{j}(n)\) are computed via the inner products  
\[c(n)=\int_{-\infty}^{\infty}x(t)\phi(t-n)dt \tag{6}\]  
\[d_{j}(n)=2^{j/2}\int_{-\infty}^{\infty}x(t)\psi(2^{j}t-n)dt. \tag{7}\]  
Discrete wavelet transform (DWT) is the fast implementation of the continuous wavelet transform. Although DWT has many advantages, there are still some fundamental problems such as oscillations, shift variance, aliasing, and lack of directionality. Inspirted by Fourier transform, complex wavelet transform (CWT) is proposed with a complex-valued scaling function and complex-valued wavelet function, shown as  
\[\psi_{\mathbb{C}}(t)=\psi^{\mathbb{R}e}(t)+j\cdot\psi^{\mathbb{S}m}(t). \tag{8}\]  
The filter-bank topology of DTCWT is shown in Figure 4, where the wavelet functions in 'Tree \(\Re e\)' and in 'Tree \(\Im m\)' form an approximate Hilbert transform pair:  
\[\psi^{\mathbb{R}e}(t)\approx Hilbert\Big{[}\psi^{\mathbb{S}m}(t)\Big{]}, \tag{9}\]  
where \(Hilbert[\cdot]\) denotes the Hilbert transform operator.  
In the time domain, there is an equivalent expression,  
\[h_{1}^{\mathbb{S}m}(n)=h_{1}^{\mathbb{R}e}(n-0.5), \tag{10}\]