esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Random Forests and ROPE for VF-ABM Parameter Optimization  
Common sensitivity analysis methods (e.g., One Parameter At a Time (OPAT), Fourier Amplitude Sensitivity Test (FAST) etc.) and calibration algorithms (e.g., Genetic Algorithm (GA), Particle Swamp Optimization (PSO), Bayesian approach etc.) are not optimal for large scale ABM as in our case herein. We thus used a new approach utilizing Random Forests and ROPE for sensitivity analysis and model calibration respectively. The execution of Random Forests does not require a large number of samples for running sensitivity analysis. This feature makes Random Forests less computationally expensive and more reliable for simulating long time scale models as compared to other sensitivity analysis methods [62; 63; 64; 65]. For example, the estimated number of samples required for Fourier Amplitude Sensitivity Test (FAST) is 128 \(\times\) parameter number \(\char 62\) 2. That is, a total of 5,766,549 samples for the case of VF-ABM. An estimated time of running FAST is seven years with the computer node of two NVIDIA Tesla P100 12 GB GPUs and two Intel E5-2683 v4 CPUs [60; 61]. In contrast, Random Forests uses a bootstrapping method for sampling and required only 5,000 samples for VF-ABM, making the sensitivity analysis much feasible [62; 63; 64; 112].  
Among the 24 parameter subjected to ABM calibration (Table 5), they were mostly related to cytokine synthesis, cell activation, ECM synthesis and cell recruitment (sprouting amount). In our VF-ABM, many biological activities of neutrophils, macrophages and fibroblasts are controlled by parameters of sprouting frequency and amount as well as cytokine synthesis. Specifically, parameters of sprouting frequency and amount were used to abstract the migration of neutrophils and macrophages to vocal fold vasculatures and transmigration from capillary to mucosal tissue as well as the recruitment and proliferation of fibroblasts in VF-ABM. Two sprouting-related parameters, namely parameter 155 (WhSproutAmount4) and parameter 156 (WhSproutAmount5) were used to control the amount of neutrophils. Three sprouting-related parameters (parameter 159 (WhSproutAmount8), 160 (WhSproutAmount9) and 161 (WhSproutAmount10) were used to control the amount of macrophages. Four sprouting-related parameters (parameter 150 (WhSproutFreq5), 162 (WhSproutAmount11), 163 (WhSproutAmount12) and 164 (WhSproutAmount13) were used to control the amount of fibroblasts. Further, the sprouting-related parameters for fibroblasts were linked to cytokines, growth factors and ECM contents in VF-ABM. Sprouting-related parameters are thus critical to initiate and sustain cell activities of neutrophils, macrophages and fibroblasts during tissue repair. The ABM outputs of cell numbers are mostly determined by the sprouting amount and sprouting frequency of these three cell types. As cell numbers were used as inputs for calibration, it is reasonable that the parameters of sprouting amount and frequency have the strongest influence on outputs of cell numbers in VF-ABM.
## 3 Results and Discussion
### Effect of Co-Existing Ions on Adsorption  
The effluents coming out from industries contain several types of ions. These ions affect the surface properties of adsorbent materials. Sodium sulfate was chosen as an electrolyte to find the ionic strength effect on the removal of Cr(VI) and Ni(II) by Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/Fe\({}_{3}\)O\({}_{4}\) composites from aqueous environments. The concentration of both Cr(VI) and Ni(II) were kept constant (50 mg:L\({}^{-1}\) ) at a 20 mL volume. The pH ofthe Cr(VI) solution was adjusted to 2-4 while that of Ni(II) was adjusted to 7.5. Figure 9 shows the continuous decrease in the removal of both Cr(VI) and Ni(II) as the ionic strength increased from 0.01 to 0.1 then became constant [77]. In an acidic environment all active sites, like the oxygen of Fe\({}_{3}\)O\({}_{4}\), the nitrogen present in amine, and the imine group of PANI and composites, become protonated. Under this condition chromium as well as sulfate ions also become hydrated as:  
\[\begin{split}\text{SO}_{4}{}^{2-}+\text{H}^{+}& \longrightarrow\text{HSO}_{4}^{-}\\ \text{Cr}_{2}\text{O}_{7}{}^{2-}+\text{H}^{+}& \longrightarrow\text{HCr}_{2}\text{O}_{7}^{-}\end{split}\]  
These HSO\({}_{4}\)\({}^{-}\) and HCr\({}_{2}\)O\({}_{7}\)\({}^{-}\) compete with each other for positively charged active sites present on the surface of adsorbents. HSO\({}_{4}\)\({}^{-}\), due to its smaller size, interacts faster with active sites of adsorbents as compared to HCr\({}_{2}\)O\({}_{7}\)\({}^{-}\), thus causing a decrease in the adsorption of HCr\({}_{2}\)O\({}_{7}\)\({}^{-}\). These results are supported by previously reported work which explained the effects of chloride and sulfate ions on the adsorption of chromium, nitrate, and phosphate ions [78]. Adsorption of Ni(II) was high at pH 7.5. At this pH, all the active sites present on the surface of all adsorbents become deprotonated and behave as negatively charged sites due to presence of a lone pair of electrons, hence sulfate ions have no effect on adsorption of Ni(II) ions. But under this condition, Na\({}^{+}\) ions start to compete with Ni(II) ions thus causing a decrease in its adsorption. Similar results have also been shown by already reported work [79]. From Figure 9, one can also deduce that initially the decrease in adsorption is fast, but as the ionic strength became 0.1, the decrease became relatively less and became nearly constant. This may be due to filling of all active sites by electrolyte ions as well pollutants.
# GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
## 3 Experimental Analysis and Method
### Data Acquisition
While the vehicle was moving and the GNSS signal was available, the position, speed, heading angle, pitch angle, and three-axis acceleration data were collected simultaneously at a frequency of 50 Hz by two integrated navigation systems installed on the vehicle. The data collected by the self-developed integrated navigation system were then used to build a training set for the LightGBM model, which was used to predict vehicle position in the event of a GNSS outage. Furthermore, to closely match all realistic driving scenarios encountered by a typical land vehicle, the data acquisition board of the self-developed integrated navigation system was manually disconnected from the satellite antenna in several scenarios. These scenarios included uphill straight, uphill bend, downhill straight, downhill bend, and two flat straight segments, which simulated six GNSS outage situations encountered by the vehicle, with each outage lasting 20 s. When simulating an outage in the self-developed integrated navigation system, only the INS system can properly output data, as the input of the LightGBM model for vehicle position prediction is under GNSS outage. Because the outage would not occur in regular operation when a Novatel pwrpaK7-e1 integrated navigation system was employed, it provided accurate vehicle position data throughout, which were used as a baseline reference against the predicted output of the LightGBM machine learning model.
## 5 Result and Discussion
In roll and pitch motion, the PD controller is dominated with 0.0003% in both motions, while the PID controller produces the worst overshoot with 1.1111% and 1.1391%, respectively. The IGWO PD2-LQR controller is the best for yaw and altitude motions, with 0.0018% and 0.0000%. The PID controller produced the highest overshoot (0.5967%) in yaw motion and the P-LQR controller (8.0415%) in altitude motion. Note that for the first two motions the IGWO PD2-LQR controller has the second-best performance after the PD controller. It is worth mentioning that if we made a comparison, not as a percentage, we find that the value is minimal. Thus, we can neglect those values and assume that both controllers have the same performance. The rest of the controllers range from 0.1% to 1.0%. As for the steady-state error, the PD controller is dominated in all four motions with a 0.0000 error, while the LQR controller is the worst with 0.0054 in roll and pitch motions and 0.0057 in yaw motion. In altitude motion, the PD2-LQR controller produces the highest error with 0.0039. However, the value of steady-state error for all the controllers is minimal, less than 0.005. Therefore, these values can be neglected and can be approximated to be \(\sim\)0. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sampling**} & \multicolumn{3}{c}{**Rise Time, s**} & \multicolumn{3}{c}{**Selling Time, s**} & \multicolumn{3}{c}{**Overshoot, \%**} & \multicolumn{3}{c}{**Steady-State Error**} & \multicolumn{3}{c}{**RMSE**} \\ \cline{2-19}  & **Sampling  
which can be considered small. Thus, the RMSE of the IGWO PD2-LQR controller is still acceptable. Next, if we compare GWO PD2-LQR and IGWO PD2-LQR controller performance, we can see that the IGWO PD2-LQR controller mostly performed better than the GWO PD2-LQR controller. These two controllers' value shows less variance since both controllers use an optimization algorithm in obtaining the controller's parameters. However, the IGWO PD2-LQR controller is still showing a significant improvement compared to the GWO PD2-LQR controller. In terms of the rise time and settling time, the IGWO PD2-LQR controller is dominated for roll, pitch, and yaw motions, while the GWO PD2-LQR controller is the best in altitude motion. The percentage overshoot of the IGWO PD2-LQR controller is the best in roll and pitch motions, while the GWO PD2-LQR controller is best in yaw motion. Nevertheless, both controllers produce zero percent overshoot in altitude motion. In steady-state error, the IGWO PD2-LQR controller is better in all four motions. IGWO PD2-LQR controller dominates RMSE, roll, pitch, and yaw motion while the GWO PD2-LQR controller dominates in altitude motion.
## 4 UAV Data Acquisition and Preprocessing
### Flight Control Strategy
#### 4.1.2 Ground Sampling  
The Ground Sampling Distance (_GSD_) is a crucial parameter in remote sensing and image processing. It quantifies the distance between the individual pixels in an image and the ground truth, which directly affects the accuracy of geospatial measurements for  
Figure 5: Diagram of the main flight parameters (**a**,**c**) modified from [16]). (**a**) flight height (**H**); (**b**) ground sampling distance (_GSD_); and (**c**) flight velocity (\(v\)).  
cracks. (i) DJI can officially provide GSD values that are applicable to a wide range of focal lengths [16]. The most commonly observed GSD value, typically associated with a 24 mm focal length, is calculated as H/55. (ii) Alternatively, _GSD_ can be derived directly from the diagram in Figure 5b, using Equation (2):  
\[GSD\approx(\mu*H)/f \tag{2}\]  
where _GSD_ represents the ground sampling distance of a flight, and its unit is cm/pixel; \(\mu\) is the image pixel calibration size (\(\upmu\)m), which can be officially provided by DJI. Take DJI Mini 2 as an example, where \(\upmu\) is given as 4.4 \(\upmu\)m. If the flight height is 22.5 m, thereby _GSD_ can be computed as 0.4125 cm/pixel.
## 5 Conclusions and Future Work  
In this paper, we proposed a syntactic and semantic enhanced multi-lay graph attention neural network (SSEMGAT) to solve the problem of introducing noise in dependent trees in sentences without obvious syntactic structure. Given the inherent defects in dependent trees, we introduced the composition tree structure, which can obtain more field-of-view information at the causal level, and we enhanced the syntactic features by merging syntactic information at different levels. The multi-head attention mechanism may misjudge the sentiment polarity due to the noise introduced by the interference of irrelevant words, so we construct local attention and global attention of specific aspects based on the attention mechanism to assign the attention weight between aspect and context. Facing feature information with a rich hierarchy, we used the multi-layer stacked graph attention module to aggregate different hierarchical information separately and used attention to give higher weight to the information most relevant to the feature. Finally, the extracted syntactic and semantic features are fused with the pre-training knowledge to obtain the most specific aspect of rich hierarchical feature information to achieve aspect sentiment classification.  
In future research, we will continue to apply the model to different domains to verify the generalization performance and observe the model's performance in multilingual datasets. Current research still has challenges in mining deeper correlation information between syntax and semantics, and we will further develop methods that can dig deeper into the correlation between them.  
Conceptualization, X.X. and A.W.; methodology, X.X.; software, J.H.; validation, X.X.; formal analysis, X.X., A.W. and Z.K.; investigation, J.H.; data curation, A.W.; writing --original draft preparation, X.X. and A.W.; writing--review and editing, X.X.; supervision, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Autonomous Region Natural Science Foundation Joint Fund Project, Research on Xinjiang Tourism Sentiment Analysis Technology Based on Deep Learning, under Grant 2021D01C081.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Bayesian Estimation under the SEL, GEL, and QEL Functions
### Bayesian Estimation under the Non-Informative Prior  
The non-informative prior utilized is the quasi-density prior. It has broad applications, such as its employment in [21] for the calculation of the Bayesian and Bayesian shrinkage estimators. The quasi-density prior of \(\beta\) is defined as  
\[\pi_{2}(\beta)=\frac{1}{\beta^{m}},m>0,\beta>0. \tag{26}\]  
Based on the quasi-density prior, the posterior distribution function follows \(Gamma\Big{(}n-m+1,\frac{1}{S}\Big{)}\). Due to the positivity of the hyperparameters of the Gamma distribution, it is important to ensure that \(n-m+1\) is greater than 0. The Bayesian estimations of the parameter \(\beta\) are given as  
\[\hat{\beta}_{SEL}=E(\beta|\underline{X})=\frac{n-m+1}{S}, \tag{27}\]  
\[\hat{\beta}_{GEL}=\left[E\big{(}\beta^{-p}|\underline{X}\big{)}\right]^{-\frac {1}{p}}=\frac{1}{S}\bigg{[}\frac{\Gamma(n-m-p+1)}{\Gamma(n-m+1)}\bigg{]}^{- \frac{1}{p}}, \tag{28}\]  
\[\hat{\beta}_{QEL}=\bigg{[}\frac{E\big{(}\beta^{q}|\underline{X}\big{)}}{E( \beta^{-q}|\underline{X}\big{)}}\bigg{]}^{\frac{1}{2q}}=\frac{1}{S}\bigg{[} \frac{\Gamma(n-m+q+1)}{\Gamma(n-m-q+1)}\bigg{]}^{\frac{1}{2q}}. \tag{29}\]  
The Bayesian estimators of \(R(t)\) are expressed as follows  
\[\hat{R}_{SEL}(t)=E(R(t)|\underline{X})=\bigg{[}\frac{S}{S+\ln(1+t^{\alpha})} \bigg{]}^{n-m+1}, \tag{30}\]  
\[\hat{R}_{GEL}(t)=\left[E\Big{(}R(t)^{-p}|\underline{X}\Big{)}\right]^{-\frac {1}{p}}=\left[1-\frac{p\ln(1+t^{\alpha})}{S}\right]^{\frac{n-m+1}{p}}, \tag{31}\]  
\[\hat{R}_{QEL}(t)=\left[\frac{E\big{(}R(t)^{q}|\underline{X}\big{)}}{E\Big{(}R (t)^{-q}|\underline{X}\Big{)}}\right]^{\frac{1}{2q}}=\left[\frac{S-q\ln(1+t^{ \alpha})}{S+q\ln(1+t^{\alpha})}\right]^{\frac{n-m+1}{2q}}. \tag{32}\]
# A 1064 nm Dispersive Raman Spectral Imaging System for Food Safety and Quality Evaluation
## 2 Raman Spectral Imaging System
### Spatial Resolution  
The spatial resolution of the 1064 nm Raman spectral imaging system was evaluated using a standard resolution glass test chart (Edmund Optics Inc., Barrington, NJ, USA). The test chart was placed on the top of a Petri-dish containing naphthalene. The 1064 nm laser source illuminated the test chart. A step size of 0.1 mm was used to scan X and Y directions. Figure 3 shows the single band image of the resolution test chart. The laser spot on the white space penetrated through the glass test chart to the naphthalene, while the illumination on the black dots suppressed the Raman spectra of naphthalene, creating a contrast between white space and the black dot. In the Figure 3, the small inner dots have the diameter of 0.25 mm with 0.5 mm distance between each dot. The outer larger dots have the diameter of 0.5 mm with 1 mm dot spacing. Both the dots and their dot spacing can be distinguished in the image due to the small step size used to scan the chart. The step size used to scan the X and Y direction is the spatial resolution of the Raman image. Figure 3 shows that the system can achieve the spatial resolution of 0.1 mm.
## 2 Materials and Methods  
### Mueller Matrix Polar Decomposition (MMPD)  
The Mueller matrix polar decomposition method proposed by [PERSON] and [PERSON] describes the process of interaction between light and medium by decomposing a Mueller matrix into three sub-matrices with clear physical meanings: diattenuation (\(M_{\rm D}\)), retardance (\(M_{R}\)) and depolarization (\(M_{\Delta}\)), as shown as Equation (1) [12].  
\[M=M_{\Delta}M_{R}M_{D} \tag{1}\]  
Based on Equation (1), a group of MMPD parameters \(D\), \(R\) and \(\Delta\) can be calculated by Equations (2)-(4), representing diattenuation, retardance and depolarization properties of the samples, respectively. It should be noted that the lower case \(m_{ij}\) (\(ij=1\), \(2\), \(3\), \(4\)) in Equation (2) represents the corresponding Mueller matrix elements before performing any decomposition, while the lower case \(m_{\Delta}\) in Equation (4) is the \(3\times 3\) depolarization sub-matrix from \(M_{\Delta}\).  
\[D=\frac{1}{m_{11}}\sqrt{m_{12}^{2}+m_{13}^{2}+m_{14}^{2}} \tag{2}\]  
\[R=\cos^{-1}[\frac{tr(M_{\Delta})}{2}-1] \tag{3}\]  
\[\Delta=1-\frac{|tr(m_{\Delta})-1|}{3} \tag{4}\]  
The parameters \(\delta\) and \(\theta\) derived from the retardance matrix \(M_{R}\) are usually used to describe the value and fast axis orientation of linear birefringence induced by fibrous tissue structures [13]. Since the birefringent fibrous structures including collagen, elastics, and muscle fibers that are prevalent in tissues, these two MMPD parameters can provide important information of the distribution behaviors of fibrous structures during many pathological processes [15; 16; 17; 18; 19; 20; 25; 26; 27; 28]. It should be noted that compared with the original orientation parameters \(\theta\), a modified MMPD parameter \(\theta_{e}\) proposed in our recent study has been demonstrated as a more suitable indicator for describing the orientation of complicated tissue samples containing layered fibrous structures [26]. The parameters \(\delta\) and \(\theta_{e}\) can be calculated according to Equations (5)-(8) [13; 26]. The \(M_{R}\)(_i_j_) (_i_j_ = 2, 3) in Equation (5) represents the corresponding elements of the 4 \(\times\) 4 retardance matrix \(M_{R}\), while \(R\) is the total retardance, \(\varepsilon_{ijk}\) is the Levi-Civita permutation symbol, \(\delta_{ij}\) is the Kronecker delta, and \(m_{R}\) is the 3 \(\times\) 3 retardance sub-matrix striking out the first row and the first column of \(M_{R}\).  
\[\delta=\cos^{-1}\biggl{\{}\sqrt{\left[M_{R}(2,2)+M_{R}(3,3)\right]^{2}+\left[ M_{R}(3,2)-M_{R}(2,3)\right]^{2}}-1\biggr{\}} \tag{5}\]  
\[\theta_{e}=\frac{1}{2}\tan^{-1}(\frac{a_{2}}{a_{1}}) \tag{6}\]  
\[\left(m_{R}\right)_{ij}=\delta_{ij}\cos R+a_{i}a_{j}(1-\cos R)+\sum_{k=1}^{3} \varepsilon_{ijk}a_{k}\sin R(i,j=1,2,3) \tag{7}\]  
\[a_{i}=\frac{1}{2\sin R}\sum_{i,j=1}^{3}\varepsilon_{ijk}(m_{R})_{jk} \tag{8}\]
## 3 Monitoring and Analysis of Subsidence in Shield Tunneling Under-Passing the Bridge and Culvert
### Monitoring and Analysis of Ground Subsidence  
Based on the geotechnical and field investigations, five excavation faces with different soft-rock height ratios of the left line tunnel were selected for monitoring to understand the relationship between the soft-rock height ratio of the excavation face and the ground subsidence; these excavation faces were all under the culvert. The ground monitoring  
Figure 8: Subsidence change observation.  
Figure 10: Transverse layout of surface monitoring points.  
Figure 9: Longitudinal layout of surface monitoring points.  
points above the vault of these excavated faces were numbered as ZD134, ZD133, ZD131, ZD129, and ZD128, and the corresponding soft-rock height ratios of the excavated faces were 0, 0.20, 0.50, 0.80, and 1.00, respectively. Through the statistical analysis of the measured data of the ground surface settlement monitoring point above the excavation faces, the change law of the ground surface settlement of the transverse and longitudinal measuring points under the same shield tunneling measures was obtained.
## 5 Experimental Evaluation
### Simulation Results
#### 5.2.5 Performance of DMD-JD Detection Algorithm in Multipath Channel under QPSK Modulation  
As shown in Figure 15, the FTN detection algorithm under the condition of QPSK (4 QAM) modulation and TU channel compression factor \(\tau\) = 0.8 is compared. It can be seen that the DMD-JD algorithm can also be improved by about 2 dB under QPSK (4 QAM) modulation.  
Figure 14: Under the TU channel moving speed of 60 km/h, compare the performance of unidirectional LSTM (Uni-LSTM) and bidirectional LSTM (Bi-LSTM).  
Figure 15: FTN detection performance of different algorithms is obtained by using QPSK (4 QAM) modulation, MMSE equalization and compression factor \(\tau\) = 0.8 in TU channel.
## 3 Results and Discussion  
### Fho-A  
To determine the impact of the concentration of alginate in solution on FHO-A synthesis, the concentration of alginate as TOC was measured and the amount of alginate as mass (g) was calculated before and after synthesis of FHO-A. Table 1 shows the amount or concentration of alginate when added at 60, 120, and 180 mg/L. The difference between alginate concentrations before and after synthesis can be considered to be total amount of coated alginate on FHO. After synthesis of FHO-A, the concentration of alginate was 5.4, 4.16, and 38.2 mg/L for A-60, A-120, and A-180, namely 91.0%, 96.5%, and 78.6% of initial alginate were used in synthesis, respectively. As such, the highest coating efficiency was accomplished at A-120, while the highest alginate concentration was obtained on A-180. Ideally, the conditions were used to generate A-180 provide maximum Cu(II) removal, but in practice, the excess alginate concentration in solution required a longer solid-liquid separation time due to the increase in viscosity and the suspension of FHO-A particles. Biopolymers, such as starch, have been utilized as stabilizers [22]. Therefore, the following experiment was carried out using FHO-A prepared with A-120 except for the batch removal efficiency tests.  
The stability of FHO-A was also considered by measuring the concentration of Fe and alginate in solution. The concentration of Fe in a form Fe(III) was ignorable even in the acid condition, indicating that the particle of HFO-A was stable due to the interaction between Fe(III) and the carboxyl group of alginate.