esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, the internal deformation of section coal pillar is monitored by fiber grating sensing technology, and a prediction model of section coal pillar deformation is constructed by combining machine learning. The model is applicable to the conditions of shallow buried close coal seam section coal pillar stability monitoring, working face mining on section coal pillar internal deformation monitoring and broken ring prediction. In addition, the model combines two noise reduction algorithms, which helps to improve the prediction accuracy of the model. The accuracy and reliability of the DBO-LSTM-BP prediction model is confirmed by comparing it with the measured data in the field. The main findings of this study are summarized as follows:  
1. Background and Monitoring: Using the 22,206 and 22,208 working faces of the Daliuta Huojitu Well as the background, embedded optical fiber grating sensors were introduced to achieve the real-time monitoring of internal deformation strain changes in the coal pillar section.
2. Stress Influence and Deformation Trends: As the working face advanced beneath the overlying residual inclined coal pillar, the area of concentrated stress influence further shifted towards the inclined coal pillar side. The overall impact area exhibited an approximately \"diamond-shaped\" influence, with the width of the peak strain region initially increasing and then decreasing.
3. Effectiveness of Denoising Algorithm: Using the EEMD-DE-SVD denoising algorithm, the Signal-to-Noise Ratio (SNR) increased by an average of 0.8 dB to 2.3 dB, and the Root Mean Square Error (RMSE) decreased by an average of 1.02 to 2.33, validating the effectiveness of the EEMD-DE-SVD denoising algorithm.
4. Prediction Model Accuracy: The EEMD-DE-SVD-DBO-LSTM-BP model was used to predict optical fiber grating strain signals. According to the results, the prediction accuracy of the EEMD-DE-SVD-DBO-LSTM-BP model ranged from 88% to 99%, demonstrating the effectiveness of this denoising prediction model.  
Conceptualization, D.Z. and J.Y.; methodology Y.W. and D.G.; data curation, J.C. and D.Z.; writing--original draft preparation, Y.W. and J.C.; writing--review and editing, D.Z. All authors have read and agreed to the published version of the manuscript.  
The work was supported by financial support from the National Natural Science Foundation of China (NSFC) (grant number: 52474147), Shaanxi Province Qin Chuangyuan \"Scientists+Engineers\" Team Building Project (grant number: 2023 KJ-144), and Xi'an Qin Chuangyuan \"Scientists+Engineers\" Team Building Project (grant number: 23 KGDW0027-2022).  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the China Energy Shenhua Shendong Coal Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 6 Strengths, Limitations, and Future Research Directions
### Strengths
#### 6.1.3 Performance Optimization  
CNNs can be effectively compressed using techniques such as pruning, quantization, and knowledge distillation, reducing model size and improving inference efficiency without significant loss of performance. They are also well-suited for hardware optimization, with specialized hardware accelerators (e.g., GPUs, TPUs) designed to perform convolutional operations efficiently, leading to optimized performance in terms of speed and energy consumption.  
ViTs can efficiently scale to handle high-resolution images or large-scale datasets because they operate on the entire image at once and do not require processing of local receptive fields at multiple spatial scales, potentially resulting in improved performance in terms of scalability.  
Transfer Learning for pre-train on large-scale datasets and fine-tune on smaller datasets can potentially lead to improved performance with limited available data and can be used with both architectures.
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 2 The Proposed Method  
In this section, a detailed description of the uncertainty analysis of 3D line reconstruction is presented. It is a wise choice to use different representations according to stages of line reconstruction. In this work, the Plucker line representation is applied in projection and triangulation. A 3D line is computed in Plucker coordinates and then converted into the proposed line representation for uncertainty estimation and analysis. Spatial line representation and triangulation in Plucker coordinates are introduced in Section 2.1. A minimal spatial line representation is introduced in Section 2.2. The relationship between the proposed line representation and the Plucker line representation is introduced in Section 2.3. The uncertainty estimation and visualization of two-view line reconstruction are described in Section 2.4. Based on the two-view triangulation, Section 2.5 introduces the uncertainty update of multi-view 3D line reconstruction.
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 4 Matrix-Based Data Relay Algorithm
### Benef of Broadcasting  
The benefit of broadcasting one piece of data \(d_{j}\) is the sum of increased expected utility of all receivers. By multiplying Equation (1) by \((1-D_{v_{k},d_{j}})\), the increased expected utility of \(v_{k}\) receiving \(d_{j}\) can be calculated in a unified manner by matrix computation.  
\[\begin{split}\Delta EU(d_{j},L_{k})=&(1-D_{v_{k}, d_{j}})\cdot\sum_{d_{l}\in L_{k}}U_{d_{h},d_{l}}\\ =&(1-D_{v_{k},d_{j}})\cdot\sum_{d_{l}\in L_{i}}D_{v _{k},d_{l}}\cdot U_{d_{h},d_{l}}\\ =&(1-D_{v_{k},d_{h}})\cdot(\overrightarrow{D_{v_{k},*}}\cdot\overrightarrow{U_{*,d_{h}}})\end{split} \tag{9}\]  
The benefit of broadcasting \(d_{h}\) is the sum of increased information utility of sensors that can receive this data. However, for \(v_{i}\), it does not know which sensor exactly receives this piece of data broadcast. Only the connection probability stored in matrix \(C\) indicates the probability of receiving this data. Therefore, the benefit of \(v_{i}\) broadcasting \(d_{h}\) can be computed as:  
\[Benefit(v_{i},d_{h})=\sum_{v_{k}\in N_{i}}C_{v_{i},v_{k}}\cdot\Delta EU(d_{h}, L_{k})= C\cdot((\overrightarrow{\Lambda}-\overrightarrow{D_{*,d_{k}}})\circ(D \cdot\overrightarrow{U_{*,d_{k}}})) \tag{10}\]  
where \(D_{v_{k},*}\) is a row vector of matrix \(D\), \(\overrightarrow{\Lambda}=[1]_{|N_{i}|\times 1}\) is a column vector of ones, the operator \"\(\circ\)\" is the Hadamard product that takes two matrices with identical dimensions but only produces their corresponding elements. In matrix representation, one sensor can compute the benefit of broadcasting any of its data by a single matrix computation:  
\[B= C\cdot((\Gamma-D)\circ(D\cdot U)) \tag{11}\]  
where \(B=[Benefit(v_{i},d_{h})]_{1\times|L_{i}|}\), and \(\Gamma=[1]_{|N_{i}|\times|L_{i}|}\).
## 2 Materials and Methods
### Characterization Techniques  
#### 2.3.1 Structure Study  
The parent CHA and copper ion-exchanged CHA (Cu-CHA) zeolite catalysts were characterized by several characterization techniques. To study the catalysts' crystalline phases, X-ray diffraction (XRD) was carried out on a Rigaku Miniflex diffractometer equipped with Cu K\(\alpha\) radiation, \(\lambda\) = 0.15406 nm. The XRD patterns were recorded over 20 = 5-50\({}^{\circ}\) with a scan speed of 3\({}^{\circ}\) per min and step size of 0.02\({}^{\circ}\). Nuclear Magnetic Resonance (NMR) spectroscopy was used for molecular identity and structure. The coordination of aluminum of CHA and Cu-CHA zeolites catalysts was investigated using \({}^{27}\)Al solid-state Nuclear Magnetic Resonance (MAS NMR) using Varian 500 MHz spectroscopy. The \({}^{27}\)Al NMR spectra were acquired via a single pulse experiment with a 30\({}^{\circ}\) pulse and a sample spinning frequency of 13 kHz. A combined relaxation delay and acquisition time of 1.1 s was used. A total of 2000 scans were accumulated for each spectrum. External referencing was established using an Al(NO\({}_{3}\))\({}_{3}\) solution (0 ppm). The TESCAN instrument was used to conduct elemental and morphology analysis through scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) by utilizing secondary electrons.
## 2 System and Channel Model  
The schematic of the multi-hop parallel hybrid FSO/RF cooperation system is shown in Figure 1. For a multi-hop parallel hybrid system with \((N,M)\) structure, the source digital signal has \(N+1\) paths and each cooperation path has \(M\) hops from the source node to the destination node--that is, there is a direct path from the source node to the destination node and \(N\) cooperation paths, where each cooperation path has \(M\) hops branches (include \(M-1\) relays in each path).  
In each one-hop link or direct link, the source digital signal is divided into two identical signals after subcarrier modulation. These two modulated signals are loaded onto the carriers by FSO and RF transmitters, respectively. In each regenerative repeater, the signal with the maximum SNR from the two received signals is selected for demodulation and regeneration relay without using any forward error correction (FEC). Only one relay is allowed to process the received signals at one time based on the symbol-wise DF relaying method. Based on the best path selection scheme, a cooperative path is chosen to implement the transmission of the digital signal from the source node to the destination node.
## 3 Experimental Approach
### Data Collection
#### 3.4.1 Off-Hours Events; Plug Loads  
Data for the off-hours plug event were collected and analysed in the second semester of 2018 in all eight zones of the southern triangle. The events were calibrated with regard to what was considered as a natural baseline off-hour consumption in the same period. Whether this baseline relates to a true plug load need or to some equipment systematically left ON during the off hours is for the facility management to evaluate. Our event was set to capture only the wastage that was tracked in excess of this baseline, which was considered as normal.  
The figure below illustrates the findings from the second zone of the ground floor of the South Triangle. The study period was 180 days; from 1.7.2018-31.12.2018. As shown on the graph, there were some periods (e.g., 01-16.10.2018) where energy metering was interrupted. Especially in the case of energy metering, such interruptions occur quite often in the study building and did not have any impact on the validity of the data analysis, as they were fully accounted for in the calculations. Whenever an energy meter fails to report its value, a NULL value is registered in the database. Such values are then used to restrict the time domain of valid measurements.  
The baseline off-hour consumption threshold was found to be around 700 W as shown in Figure 2. Therefore, consumption up to that level was considered intentional. The event triggered on approximately 40 instances. The event represented an excess of around 12% with regard to the baseline figure. Appropriate user notifications were sent out to the zone users, registered to the particular event, every time the event triggered (a daily resolution was set).  
A similar off-hours analysis was made in several more zones of the South Triangle, revealing similar patterns for the particular event.
## 2 System and Theoretical Model  
In order to analyze the impact of coronal plasma on optical links, the Figure 1 shows the geometric diagram for deep space communication during superior conjunction. Here, the distance between the Earth and the Sun is \(L_{se}\) and the distance between the probe and the Sun is \(L_{sp}\). Note that, \(z\) denotes the signal propagation distance, \(r\) is the distance between the Sun and the communication link, which is usually in units of the solar radius \(R_{sun}\). Therefore, both the Sun-Earth-Probe (SEP) angle \(\alpha\) and the Sun-Probe-Earth (SPE) angle \(\beta\) are directly related to \(r\). According to the triangle relationship, the link distance can be written as:  
\[L_{link}=L_{se}\cos\alpha+L_{sp}\cos\beta. \tag{1}\]
## 2 Shaking Table Test with Scaled Model and Verification Analysis
### Shaking Table Test with Scaled Model  
In order to verify the similarity between the fluid-structure interaction analysis applying the SPH method and the scaled model test, an acrylic tank was constructed with the shape and specifications shown in Figure 1. The dimensions of the tank were 500 (width), 400 (height), and 10 (thickness), and the heights of the fluid inside the tank were 125 and 200 mm, which were 25% and 40% of the width of the tank, respectively. The shaking table used in the test loaded the seismic load on to the scaled model through displacement control. The shaking table experiment was performed using the time-displacement of the real earthquake shown in Figure 2a, and the time-acceleration acting on the scaled model was measured using an accelerometer attached to the bottom of the shaking table. The measured time-acceleration curve is shown in Figure 2b.  
When the same seismic load was applied, as the ratio of the width of the fluid storage structure and the height of the internal fluid changes, the maximum sloshing height, free-water surface shape, and time-sloshing height of the fluid were experimentally confirmed, and the results are shown in Figure 3 and Table 1. When the ratio of the fluid height and the width of the fluid storage structure (h/B) was 0.4, the time for the maximum sloshing height to occur was 6.000 s, and the measured maximum and minimum sloshing heights of the fluid were 308.0 and 168.0 mm. Considering the initial fluid height of 200 mm, the maximum fluid height rose to 154% of the initial fluid height, and the minimum  
Figure 1: Scaled model and specification.  
Figure 2: Input time-history curve and measured time-acceleration curve.  
height fell to 84% of the initial fluid height. In addition, when h/B was 0.25, the time for the maximum sloshing height to occur was 6.020 s, and the maximum and the minimum sloshing heights of the fluid generated at this time were measured as 247.0 and 95.0 mm. Considering the initial fluid height of 125 mm, the maximum sway height of the fluid rose to 198% and the minimum sway height fell to 76%. From these experimental results, it can be seen that as h/B increases, the maximum and the minimum sloshing heights of the fluid tend to decrease.
## Appendix B
### Nearest-Neighbor Method  
The approach is based on the space-time-magnitude distance metric among two earthquakes given by [PERSON] and [PERSON] [22]:  
\[\eta_{ij}=(t_{j}-t_{i})r_{ij}^{d_{f}}10^{-bm_{i}}, \tag{10}\]  
where \(r_{ij}\) is the epicentral distance between events \(i\) and \(j\), \(d_{f}\) is the spatial fractal dimension and \(b\) is the component of the Gutenberg-Richter distribution. Each event \(j\) is connected to its nearest neighbor \(i=argmin_{it_{i}j>t_{i}}\eta_{ij}\) if their distance, \(\eta_{j}\), is lower than a predefined threshold \(\eta_{0}\). The earthquake catalog is then partitioned on distinct clusters, each containing at least one event. For the selection of the threshold value, \(\eta_{0}\), the logarithm of the nearest neighbor distance \(\eta^{*}=\{\eta_{j}\}_{j=1,...,N}\) is considered, where \(N\) the number of events. It follows an 1D Gaussian distribution with two components, which is essentially a mixture model of two Gaussian densities with parameters \(N(\mu_{1},\sigma_{1})\), \(N(\mu_{2},\sigma_{2})\) and \(a_{1}\), \(a_{2}\) weights, respectively. Then, the intersection of the two functional forms gives the threshold value.  
There are only two free parameters, the fractal dimension \(d_{f}\) and \(b\) value, which are considered equal to \(d_{f}=1.51\) and \(b=1.0\), respectively. The logarithm of the separation distance is equal to \(\log\eta_{0}=-5.04\), based on the intersection of the two modes in the 1D density distribution of distances (Figure 11).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**PS** & \(\tau_{min}\) & \(\tau_{max}\) & \(p_{1}\) & \(x_{k}\) & \(x_{meff}\) & \(r_{fact}\) \\ \hline RB1 & 1 & 10 & 0.95 & 0.5 & 2.5 & 10 \\ RB2 & 1 & 10 & 0.95 & 0.5 & 2.5 & 20 \\ RB3 & 0.5 & 20 & 0.95 & 0.5 & 2.5 & 20 \\ \hline \end{tabular}
\end{table}
Table 11: Input parameters for the Reasenberg clustering algorithm. The first row corresponds to the standard parameter set [12].
# Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product
## 2 Data
### Satellite Data Sets
#### 2.1.2 GEOV1 FCover Product  
The GEOV1 FCover product has been available since 1999 from the Copernicus Land Monitoring Services [29]. The product is provided in a Plate Carree projection at 1/112\({}^{\circ}\) spatial resolution and a 10-day frequency. The GEOV1 FCover product was derived from SPOT/VEGFATION sensor data using back-propagation neural networks. The CYCLOPES FCover product was scaled to train the back-propagation neural networks with the SPOT/VEGFATION top-of-canopy directionally-normalized reflectance values over the BELMANIP (Benchmark Land Multisite Analysis and Intercomparison of Products) network of sites [7]. The calibrated neural networks were used to generate the GEOV1 FCover product from SPOT/VEGFATION top-of-canopy directionally-normalized reflectance data.
## 3 Results and Discussion
### Training Performance of SVM and Comparison between the EMG and GRF Features
This potentially provides a feasible technique for wearable assist systems to better adapt to different environments. In this work, due to the limited access to resources such as hospitals and experimental sites, only healthy subjects and three major terrains are involved for validating our proposed technique. Although in some relevant studies, the same or similar formulas were used for proving the concept, we still think that applying the technique to people in rehabilitation processes in more complex terrain scenarios would increase the meaningfulness of the work. For example, in [30], both the elderly and young subjects were recruited to estimate the slope angle using their walking data. The experimental data showed that the elderly people applied different muscle recruitment strategies to the young subjects. Specifically, the elderly tended to increase the activation in proximal hip extensors and decrease the activation in distal knee extensors compared to the younger subjects. Nevertheless, the results in [30] demonstrated that the machine learning-based technique would adapt to such differences when \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Actual Terrains**Prediction** & **Uphill** & **Level** & **Downhill** \\ \hline
**Uphill** & 98.59\% \(\pm\) 1.15\% & 1.41\% \(\pm\) 1.15\% & 0\% \(\pm\) 0\% \\
**Level** & 0.86\% \(\pm\) 1.54\% & 96.87\% \(\pm\) 1.62\% & 2.27\% \(\pm\) 1.52\% \\
**Downhill** & 0.35\% \(\pm\) 0.73\% & 5.06\% \(\pm\) 3.25\% & 94.59\% \(\pm\) 3.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix for the classification results of the SVM using both the EMG and GRF features. The element is presented as average \(\pm\) SD. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & [20] & [21] & [22] & [32] & **This Work** \\ \hline \multirow{3}{*}{**Input**} & Interaction force, & \multirow{3}{*}{7 EMG} & \multirow{3}{*}{12 EMG} & 9 EMG & 2 EMG \\  & 4 EMG1  & 4 GRF2  & 2 GRF \\  & Position sensor & \multirow{3}{*}{2 Accelerometers} & \multirow{3}{*}{12 GRF} & 9 EMG & 2 GRF \\
**Methodology** & BLDA & SVM and LDA & Muscle Synergies & SVM and LDA & SVM \\
**Overall** & & 67.1\% (only EMG) & & \\
**Accuracy** & & 95.2\% (only Accelerometers) & & 97.7\% & 96.8\% \\ \hline \hline \end{tabular}  
* \({}^{1}\) = 4 EMG” is an abbreviation for the EMG of four muscles used as the input signal. 2 \"4 GRF\" is an abbreviation for the GRF of four areas on the foot used as the input signal.  
\end{table} 
Table 7: Comparisons of classification accuracy with existing work utilizing EMG signals. adequate experiment data were collected and used as training samples, producing similar estimation performances in both sets of participants (estimation errors are both below 3 degrees). Hence, in our future research plan, we aim to enlarge our training data volume by taking more types of terrains and elderly people into consideration by collaborating with associated research institutions.
# Reasoning about Quantum Information: An Overview of Quantum Dynamic Logic
## 2 The Logic of Quantum Actions
### Syntax of Quantum Dynamic Logic
Finally, we can define the _tautological proposition_ ('top') \(\top:=p\vee\
eg p\) and the _contradictory proposition_ (\"bottom\") \(\bot:=p\wedge p\), where \(p\) is the first atomic sentence in our list \(\Omega\). **Abbreviation: quantum implication as a dynamic operator. ** Applying the weakest precondition operator \([\pi]\psi\) to the quantum test \(\pi:=\varphi\)? of a formula \(\varphi\), we can define a _quantum implication_ as an abbreviation in our logic  
\[\varphi\stackrel{{ S}}{{\rightarrow}}\psi:=[\varphi?]\psi\]  
The notation is due to the fact that quantum implication is also known as 'Sasaki Hook' in quantum logic. Our definition has the advantage that it makes clear that this is not a static conditional, but a _dynamic_ operator, whose semantics can best be described in terms of successful tests [2]: a system satisfies \(\varphi\stackrel{{ S}}{{\rightarrow}}\psi\) if after any successful test of property \(\varphi\), it will necessarily satisfy property \(\psi\). This dynamic view of the main quantum implication can be traced back to the analysis of the Sasaki hook as a Stalnaker conditional presented in [28; 29], which is a view that is also further reflected upon in, e.g., [30; 31]. **Abbreviations: quantum negation and quantum disjunction. ** The dynamic modality \([\pi]\varphi\) also allows us to define the notion of _orthocomplement_ (also known as 'quantum negation') in our language as follows:  
\[\sim\varphi:=[\varphi?]\bot\]  
This characterization of orthocomplement indicates that it is a strong, 'dynamic' form of negation, capturing the _impossibility_ of successfully testing \(\varphi\). In standard Hilbert space terms, \(\sim\varphi\) means that the system is in a state that is orthogonal to (the linear subspace generated by the set of states satisfying) \(\varphi\), so that any test of \(\varphi\) will surely yield a negative answer. We can also define the _quantum disjunction_\(\sqcup\) of properties as the De Morgan dual via the orthocomplement of the classical conjunction, i.e., as  
\[\varphi\sqcup\psi:=\sim\left(\sim\varphi\wedge\sim\psi\right)\]  
Intuitively, \(\varphi\sqcup\psi\) says that the system is either in a \(\varphi\) state, or in a \(\psi\) state, or in a _superposition_ of \(\varphi\)-states and \(\psi\)-states.