esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of a Digital Twin System for Log Rotary Cutting Optimization
## 3 Results  
This article uses Unity3D software as a 3D visualization system platform which interacts with physical layer devices in real-time through data interfaces. It uses particle effects to render physical layer point cloud data and dynamically visualize the collection process. The XCharts plugin is used to visualize physical layer data as dynamic charts. The platform is integrated with the digitally twinned visualization system for optimizing log rotary cutting, providing service layer 3D reconstruction and rotary cutting simulation functions.  
The main interface of the visualization platform has multiple functions, including data collection, centering and cutting analysis, and rotary cutting simulation, all of which are driven by UI buttons. The data collection module controls the log-centering machine to collect data and displays the 3D point cloud effect in real-time on the platform. The right side of the interface shows the working status information of the log-centering machine, including the communication status, sensor data, and motor speed, as shown in Figure 8.  
The log centering analysis module focuses on reconstructing the three-dimensional point cloud information of the log and solving for the maximum inscribed cylinder. The information panel on the right side of the interface displays the detected log feature information, including the log grading based on curvature, the centroid offset, and the lowest point position of the log. If the log is deemed unacceptable based on the grading, it can be saved at the lowest point position. The log curvature is calculated by dividing the arch height by the length, as per the national standard, as shown in Figure 9.  
The rotary cutting analysis module is used to confirm the machining parameters, including the motor speed and the feed rate of the rotary blade, based on a kinematic model established using the innermost circumscribed cylinder of the log and the preset thickness of the veneer. The module employs the three-dimensional model of the log and the preset equipment model to simulate and verify the machining parameters obtained in the simulation module, as shown in Figure 10.  
Figure 8: Data collection interface.
## 3 Methodology
### Remote-Sensing Information Encoder  
The RSI encoder is designed to learn spatial and temporal features from remote-sensing data. As shown in the top diagram of Figure 1, it is mainly composed of a two-dimensional convolution layer, a wavelet transform block, and a residual block. Given the historical \(L_{\text{in}}\) steps of cloud images \(\mathbf{X}_{\text{RS}}\in\mathbb{R}^{L_{\text{in}}\times H\times W\times C_{ \text{RS}}}\) as the input of the RSI encoder, the procedure is  
\[\begin{split}&\mathbf{I}_{1}=\text{Conv2D}(\mathbf{X}_{\text{RS}}), \\ &\mathbf{I}_{\text{LL}},\mathbf{I}_{\text{HL}},\mathbf{I}_{\text{ HH}}=\text{WTBlock}(\mathbf{I}_{1}),\\ &\mathbf{I}_{2}=\text{Conv2D}(\text{ResBlock}(\mathbf{I}_{1})),\\ &\mathbf{I}_{3}=\text{ConvFusion}(\text{Concat}([\mathbf{I}_{2}, \mathbf{I}_{\text{LL}},\mathbf{I}_{\text{LH}}+\mathbf{I}_{\text{HL}}+\mathbf{ I}_{\text{HH}}])),\\ &\mathbf{Z}_{\text{RS}}=\text{Linear}(\text{Flatten}(\text{ReLU }(\mathbf{I}_{3}))),\end{split} \tag{9}\]  
where ConvFusion is \(1\times 1\) 2D convolution to integrate frequency components with image features; \(\mathbf{Z}_{\text{RS}}\in\mathbb{R}^{L_{\text{in}}\times D}\) is the output of the RSI encoder.
## 4 Conclusions  
In summary, we proposed and numerically studied a parallel optoelectronic RC system. We first proved that the proposed scheme enhanced the internal dynamics more than a typical single-loop RC. In addition, we numerically analyzed several typical influencing factors to optimize the performance of the proposed RC. The simulation results demonstrated that the proposed system has the potential to obtain lower BERs for nonlinear channel equalization. Furthermore, the proposed optoelectronic RC scheme is less complex than other ANN schemes and is easy to implement in hardware, so that in the future, channel equalization in communication systems can be realized at the cost of smaller chip resources or power consumption. Altogether, this makes the DPol-MZM-based parallel RC an appealing tool for dealing with nonlinear distortion problems in communication systems.  
Conceptualization, X.F. and L.Z.; methodology, L.Z. and X.P.; software, X.F. and L.Z.; validation, X.F.; formal analysis, X.Y.; investigation, X.F. and L.Z.; resources, L.Z., X.Y. and X.P.; data curation, X.F.; writing--original draft preparation, X.F.; writing--review and editing, L.Z., X.Y., X.G. and X.P.; visualization, X.F. and L.Z.; supervision, L.Z., X.Y. and X.P.; project administration, L.Z.; funding acquisition, L.Z., X.Y. and X.P. All authors have read and agreed to the published version of the manuscript.  
The National Key Research and Development Program of China (2020 YFB1805700), in part by the Natural National Science Foundation of China (61771424 and 62101483), the Natural Science Foundation of Zhejiang Province (LZ18F010001 and LQ21F010015), the Swedish Research Council (2019-05197_VR), State Key Laboratory of Advanced Optical Communication Systems and Networks of Shanghai Jiao Tong University, the Fundamental Research Funds for the Central Universities (2020 QNA5012), and Zhejiang Lab (2020 LC0 AD01).  
Not applicable.  
Figure 10: Transmission performance curve: (**a–c**) The time-domain scatter graph of PAM4 signals (**a**) corresponding to the case without RC; (**b**) corresponding to the case with the single-loop RC; (**c**) corresponding to the case with the proposed double-loop RC.
# Magnetic Nanomaterials for Magnetically-Aided Drug Delivery and Hyperthermia
## 11 Magnetic Nanoparticles in Preclinical Studies
Histidine replacement at the surface of the MMSNs activates the nano-assembly and facilitates the release of the drug molecules. These bi-functional nanoplatforms are very useful in biomedical applications as they contain both drug delivery and cell-imaging abilities [101]. [PERSON] and co-workers have developed three paclitaxel-conjugated nanoparticles using Fe\({}_{3}\)O\({}_{4}\) shells (Scheme 2) and gold cores (not shown). Fe\({}_{3}\)O\({}_{4}\) nanoparticles were conjugated with paclitaxel that was attached by means of a poly(ethylene glycol) (PEG) spacer through a phosphodiester moiety at the (C-2')-OH position. In the presence of phosphodiesterase, these nanoparticles release paclitaxel. These nanoparticles have magnetic tracking capability. Different methods were discussed to synthesize Au-NPs in order to produce both hydrophilic and hydrophobic paclitaxel conjugates [102]. Figure 10: \"In-vivo accumulation of Fe\({}_{3}\)O\({}_{4}\)-MSN at tumor site. (**a**) In-vivo T2 weighted MR images (upper) and color mapped (lower) images of tumor site before and 3 h after intravenous injection of Fe\({}_{3}\)O\({}_{4}\)-MSN (arrows indicate tumor site). (**b**) Confocal laser scanning microscopic images of sectioned tumor tissue harvested 24 h after injection. Left: Red fluorescence showing Fe\({}_{3}\)O\({}_{4}\)-MSN internalized cells. Right: Merged image with DAPI stained nuclei (blue) (scale bar) 10 μm.” [100], with permission from [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON], Uniform mesoporous dye-doped silica nanoparticles decorated with multiple magnetite nanocrystals for simultaneous enhanced magnetic resonance imaging, fluorescence imaging, and drug delivery. _JACS_**2009**, _132_ (2), 552-557. Copyright American Chemical Society 2009. [PERSON] and co-workers found that the gold nanorods modified with carboxylated bovine serum albumin showed both anti-tumor and anti-angiogenesis effects under near-infrared laser irradiation. Magnetic resonance imaging (MRI) and near-infrared fluorescence (NIRF) imaging showed that the Den-RGD (nanoprobe, Dendrimer-arginine-glycine-aspartic acid) visualized the tumors with high target-to-background ratio, and the ability to evaluate the therapeutic response by monitoring the tumor neovascularization [103]. [PERSON] and co-workers have synthesized dual stimuli pH and temperature-responsive magnetic nanohydrogels based on poly(_N_-isopropylacrylamide)-chitosan for thermo-chemotherapy [104]. They have incorporated Fe\({}_{3}\)O\({}_{4}\) magnetic nanoparticles into hydrogels to achieve temperature optimized magnetic nanohydrogel (MNHG) for magnetic hyperthermia with lower critical solution temperature which is less than 42 \({}^{\circ}\)C [104].
# Model of Multi-Branch Trees for Efficient Resource Allocation
## 3 Example  
In this section, we show an example of our method for easily understanding our method as defined in Section 2. We consider an organization whose hierarchy is shown as Figure 1. In the first step, every evaluator selects projects that one must assess. All labels of the links in Figure 1 are projects each evaluator selects. In the second step, the relative assessment values are determined. In the example, the following values are determined.  
\[\begin{split}& v(0,1),v(0,9),v(1,1),v(1,3),v(2,5),v(2,7),v(3,1),v(3,2 ),v(4,3),\\ & v(4,4),v(5,5),v(6,6),v(6,8),v(7,6),v(7,7),v(8,8),v(8,9),v(8,10).\end{split} \tag{6}\]  
Note that these values are relative for each evaluator, and thus, \(v(5,5)\) is meaningless because \(E_{5}\) has one project to assess.  
While this model is not a tournament model, the left subtree of the root node can be regarded as a tournament model because \(E_{1}\) satisfies the tournament condition and \(E_{0}\) selects \(P_{1}\), which is selected by \(E_{1}\). Thus, we can calculate \(w(P_{4})\) by using a tournament model.  
According to the tournament model, \(w^{\prime}(P_{4})\) is calculated as \[w^{\prime}(P_{4}) = v\Big{(}0,p(4,l(4,0))\Big{)}\prod_{h=1}^{l(4,0)-1}\frac{v\Big{(}e(4,h ),p(4,h)\Big{)}}{v\Big{(}e(4,h),p(4,h+1)\Big{)}} \tag{7}\] \[= v\Big{(}0,p(4,3)\Big{)}\prod_{h=1}^{2}\frac{v\Big{(}e(4,h),p(4,h) \Big{)}}{v\Big{(}e(4,h),p(4,h+1)\Big{)}}\] \[= v\Big{(}0,p(4,3)\Big{)}\frac{v\Big{(}e(4,1),p(4,1)\Big{)}}{v \Big{(}e(4,1),p(4,2)\Big{)}}\frac{v\Big{(}e(4,2),p(4,2)\Big{)}}{v\Big{(}e(4,2 ),p(4,3)\Big{)}}\] \[= v(0,1)\frac{v(1,3)}{v(1,1)}\frac{v(4,4)}{v(4,3)}.\]  
This is because we have \(l(4,0)=3\), \(p(4,1)=4\), \(p(4,2)=3\), \(p(4,3)=1\), \(e(4,1)=4\) and \(e(4,2)=1\). The next example of the calculation is on \(P_{5}\). We calculate it by using the generalized model.  
\[v(0,5) = v\Big{(}0,p(5,l(5,0))\Big{)}\cdot\frac{v\Big{(}e(5,l(5,0)-1),5 \Big{)}}{v\Big{(}e(5,l(5,0)-1),p(5,l(5,0))\Big{)}} \tag{8}\] \[= v\Big{(}0,p(5,3)\Big{)}\cdot\frac{v\Big{(}e(5,2),5\Big{)}}{v \Big{(}e(5,2),p(5,3)\Big{)}}\] \[= v(0,9)\frac{v(2,5)}{v(2,9)}\] \[= v(0,9)\frac{v(2,5)}{v(2,7)\frac{v(6,9)}{v(6,7)}}\] \[= v(0,9)\frac{v(2,5)}{v(2,7)\frac{v(6,8)\frac{v(8,9)}{v(8,8)}}{v( 6,6)\frac{v(7,7)}{v(7,6)}}}\] \[= v(0,9)\frac{v(8,8)}{v(8,9)}\frac{v(6,6)}{v(6,8)}\frac{v(7,7)}{v (7,6)}\frac{v(2,5)}{v(2,7)}.\]  
This is because we have \(l(5,0)=3\), \(p(5,3)=9\), and \(e(5,2)=2\).
# Higher-Order Belief Propagation Correction Decoder for Polar Codes
## 4 Numerical Results
Furthermore, the average decoding latency in the MBPC decoder defined by the clock cycles can be expressed by Equation (7) and compared with that of the CA-SCL decoder. \[L_{ave}=I_{ave}\times 2\log N, \tag{7}\]  
where the \(I_{ave}\) represents the average number of iterations. Without considering the consumption of CRC, the decoding clock cycles of the CA-SCL decoder can be represented by the conventional SCL decoder in [27] as \(L_{SCL}=2N+K-2\). Observed from Table 1, the MBPC-1 decoder has a low latency overhead at all simulated points, while the MBPC-2 decoder shows a lower decoding latency than that of the CA-SCL decoder in medium to high SNR regions. Figure 4: The BLER performance comparisons in the polar code with length \(N=2048\), \(R=1/2\), and \(I_{max}=200\). A 24-bit CRC is used.
# River Discharge and Saltwater Intrusion Level Study of Yangtze River Estuary, China
## 2 Data and Methodology
### Numerical Model  
In the current study, a MIKE21 model [17] developed by the DHI Group was applied to simulate the salinity level changes in the YRE. The model was based on a flexible unstructured mesh approach, and had been previously used for applications in coastal and estuarine environments [18]. The system was based on the numerical solution of the 2D incompressible Reynolds averaged Navier-Stokes equations, with assumptions made for the Boussinesq and hydrostatic pressures. The spatial discretization of the governing equations were performed using an element-centered finite volume method. The spatial domain was discretized by the subdivision of the continuum into non-overlapping elements. In the 2D model, the elements could be triangular or quadrilateral [19].
## 3 Materials and Methods
### SUSAN Corner Point Inspection  
The simple, fast, efficient, and noise-resistant SUSAN operator was first proposed by Smith and Brady in 1997 [PERSON] and Brady in 1997 Smith and Brady (1997). The operator uses a circular template with diameter D as shown in Figure 2a to move around the image. The center of the template is called the \"nucleus\". The difference between the gray value of the pixel point inside the template and the gray value of the center of the template is determined by whether the gray difference is less than the threshold t. If the pixel point is determined to be in the same region as the center of the template, the region is called the nucleus similarity region (Univalue Segment Assimilating Nucleus). The SUSAN operator determines whether the pixel at the nucleusposition is a corner point by determining the area of the nucleus similarity region. Here, Figure 2b shows the USAN area schematic.  
The formula used to determine whether a pixel point belongs to the USAN zone is:  
\[C(x,y)=\left\{\begin{array}{l}1,|I(x,y)-I(x_{0},y_{0})|\leq t\\ 0,|I(x,y)-I(x_{0},y_{0})|>t\end{array}\right., \tag{1}\]  
In Equation (1), \(I(x,y)\) is the gray value of the pixel points other than the kernel, and \(I(x_{0},y_{0})\) is the gray value of the pixel points corresponding to the kernel. The more accurate Equation (2) is generally used in the actual calculation instead.  
\[C(x,y)=e^{-(\frac{I(x,y)-I(x_{0},y_{0})}{t})}, \tag{2}\]  
The USAN area at pixel point (\(x_{0},y_{0}\)) in the image is calculated by Equation (3):  
\[n(x_{0},y_{0})=\sum_{i=1}^{n}C(x_{i},y_{i}), \tag{3}\]  
where \(n\) is the number of non-nuclear pixels in the circular template. If the USAN value of a pixel point is less than a specific threshold, that point is considered as the initial corner point. The initial response function value for the initial focus is calculated using Equation (4):  
\[R(x_{0},y_{0})=\left\{\begin{array}{l}g-n(x_{0},y_{0}),\quad n(x_{0},y_{0}) <g\\ 0,\quad\quad\quad\quad\quad\quad\quad\quad\quad n(x_{0},y_{0})\geq g\end{array} \right., \tag{4}\]  
\(g\) is the geometric threshold for noise suppression. If \(R(x_{i},y_{i})\) is the maximum value in the domain, then \((x_{i},y_{i})\) is the corner point.
## 3 Results and Discussion

Table 11: Optimum cutting parameters and output responses. \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline  & \multicolumn{2}{c}{**Output Response**} & \multicolumn{2}{c}{**Error Rate by RSM Result**} \\ \cline{2-5}  & **Temperature** & **Power Consumption** & **Temperature** & **Power Consumption** \\ \hline \hline Second-order regression model predictions & 384.14 & 93.745 & 0.5\% & 5.5\% \\ \hline Confirm experiment with FEM analysis & 400.92 & 98.66 & 4.9\% & 11.0\% \\ \hline Validation experiment in real experimental environment & 394.73 & 94.66 & 3.3\% & 6.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 12: A comparison of the temperature and power consumption values for the optimum cutting parameters. When Table 12 is examined, it can be seen that there is a 0.5% error rate between the minimum temperature values obtained from the RSM and the regression model, and a 5.5% error between the power consumption values. The confirmation experiment using the FEM analysis shows that there is an error rate of 4.9% between the temperature values and of 11% between the power consumption values. In the turning test carried out in the real experimental environment, the temperature and power consumption in the cutting zone were measured as 394.73 \({}^{\circ}\)C and 94.66 W, respectively. When these values are compared with the results obtained from RSM, it can be seen that there were error rates of 3.3% in temperature and 6.6% in power consumption. According to these results, the second-order regression model for the temperature and power consumption had less errors. In the turning experiments, the temperature and power consumption in the cutting zone were measured at 394.73 \({}^{\circ}\)C and 94.66 W, respectively. Accordingly, it was noted that the second-order regression analysis for temperature and power consumption gave better results than the RSM results. When the results of the turning experiments in the real environment and FEM analyses were compared, it was seen that the temperature and power consumption results from the FEM analyses were higher than the turning experiments in the real environment.
## 2 Ground Motion Intensity Measures
### Spectral Shape IMs
To account for it, [PERSON] and [PERSON] [35] proposed a root mean square-based IM, demonstrating that choosing optimal periods ensures efficiency regardless of the number of stories or ductility demand [35]. \[IM_{Bianchini}=\Big{(}S_{a}\Big{(}T_{f}\Big{)}\ _{\ldots}\ S_{a}(T_{n})\Big{)}^{ \frac{1}{n}} \tag{7}\]  
\[IM_{[PERSON]}\ =\Big{(}S_{a}\Big{(}\beta T_{f}\Big{)}\cdot S_{a}\Big{(}\beta T _{f}+\delta T\Big{)}\ _{\ldots}\ S_{a}\Big{(}\beta T_{f}+n\delta T\Big{)}\Big{)}^{ \frac{1}{n}} \tag{8}\]  
Some studies integrated spectral values over a range of periods to better account for spectral shape. [PERSON] [36] proposed integration of a pseudo-spectrum velocity curve over the integration limits 0.1 and 2.5 s (Housner Intensity \(HI\), Equation (9)). [PERSON] et al. [37] proposed a similar IM using the same periods of vibration as [PERSON] [36] but using the velocity response spectrum (velocity spectrum intensity \(VSI\), Equation (10)). The measure has been suggested for assessing earthquake responses for earth and rockfill dams. [PERSON] et al. [37] also introduced the acceleration spectral intensity (\(ASI\)), as they considered the information related to the acceleration spectrum more important for the seismic response and design of short-period dams (Equation (11)). \[HI=\int_{0.1}^{2.5}PS_{v}(T,\xi)dT \tag{9}\]  
\[VSI=\int_{0.1}^{2.5}S_{v}(T,\xi)dT \tag{10}\]  
\[ASI=\int_{0.1}^{0.5}S_{a}(T,\xi)dT \tag{11}\]  
These IMs do not consider structural damage in the form of period elongation, and hence, several modifications were proposed to equations \(VSI\) and \(ASI\) by changing the integration limits. [PERSON] [38] replaced the integration limits in \(VSI\) by \(T_{f}-t_{1}\) to \(T_{f}+t_{2}\), where \(t_{1}\) and \(t_{2}\) are period widths that are structure specific. [PERSON] [39] replaced the integration limits with \(T_{y}\) to 2 \(T_{y}\), where \(T_{y}\) is the yield period determined from pushover analysis. [PERSON] [40] replaced the upper integration limit in [PERSON] [39] with the hardening period of the structure. There is a need for this IM when strong ground motions from larger events or from site-amplified motions extend the natural period of building beyond the yield period.
## 3 Results and Discussion
### CNN Recognition Results for Different Domain Features  
This paper uses this classifier for comparative analysis from the perspective of feature composition, respectively, with the division of the sample dataset remaining unchanged, to further illustrate the accuracy of multi-domain features combined with CNN for recognizing foundation pile defects. Table 10 displays the accuracy results for CNN model recognition in the time, frequency, and time-frequency domains, respectively.  
The analysis of Table 10 reveals that the test accuracy achieved by the suggested method is 97.78%, which is significantly higher than any single-domain characteristics. This suggests that multi-domain features offer more excellent feature information in CNN recognition than single-domain features.
# Study on the Stability and Control of Gob-Side Entry Retaining in Paste Backfill Working Face
## 5 Results of Numerical Simulation
### Discussion  
Based on the above research findings, the stability mechanisms of surrounding rock in GER with paste backfill are further summarized and concluded as follows:  
1. At a low backfill rate (65%), the surrounding rock deformation is severe, and the roof exhibits significant asymmetric subsidence. As the backfill rate increases, the deformation gradually decreases. At a backfill rate of 95%, the overall deformation of the roadway is controlled at a relatively low level, indicating that a high backfill rate effectively improves the stability of the surrounding rock.
2. At a low backfill rate, the backfill material's bearing capacity on the roof is weak, resulting in a large movement space for the roof of the gob and severe mine pressure manifestation. As the backfill rate increases, the bearing capacity of the backfill material is enhanced, leading to a reduction in the manifestation of mining pressure. This further proves the effectiveness of a high backfill rate in controlling mine pressure manifestation.
3. As the mining face advances, the solidification and strength of the slurry backfill material gradually increase, enhancing its ability to support the overlying strata. The activity of the overlying strata tends to stabilize, and the stress in the mined-out area gradually becomes steady, thus ensuring the stability of the roadway.  
Figure 12: Distribution law of vertical stress in the filling area with different pushing distances.
# Activation of Persulfate for Groundwater Remediation:
## 2 Activation Mechanism of Persulfate for ISCO Application
### Alkaline Activation
However, due to the natural buffering capacity of the water and soil as well as the sulfuric acid generated from persulfate reactions, high base conditions require a large amount of alkali to be injected in field practices [41]. [PERSON] (2011) demonstrated that the most proper molar ratio of alkaline to persulfate may be 2:1 when alkaline-activated persulfate is used [28; 34]. Moreover, the elevated base levels may change the soil dispersion, resulting in a decrease in remediation efficiency [34; 69]. Therefore, base activation is often coupled with other activation methods, which can significantly enhance the remediation of organic contaminated soil. [PERSON] (2018) used zerovalent iron (nZVI) and H\({}_{2}\)O\({}_{2}\) combined with alkaline to activate persulfate and had a considerable removal efficiency of phenol [59]. Similar trend was observed when alkaline was coupled with heat, Fe\({}^{2+}\) and ultrasonic activation [70]. Only 30.8% of BDE was degraded by persulfate at ambient temperature (25 \({}^{\circ}\)C), while 46.6% of BDE was removed when the temperature was raised to 40 \({}^{\circ}\)C. Alkaline activation with the addition of Fe\({}^{2+}\) and with the assistance of an ultrasonic application also enhanced the degradation of BDE, with removal efficiencies of 51.5% and 49.2%, respectively [70]. When coupled with other technologies to activate persulfate, the role of the alkaline environment in the degradation process is altered in the system. [PERSON] et al. (2022) combined alkaline with heat-activated persulfate to degrade TCA and TCE, and demonstrated that the main role of alkaline was to hydrolyze the chlorine alkanes rather than activate the persulfate in a base-heat combined activation system [71].