esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Our tracking results provide a more complete understanding of the 3-dimensional habitat use of immature white sharks in eastern Australian and New Zealand waters. Owing to the very fine-scale PSAT sampling interval used and the recovery of four archival tags, we were able to establish so far unreported consistent vertical diel patterns among tracked individuals. To determine whether the observed patterns are characteristic of all age classes within the EA white shark population, tagging of adult EA white sharks with high-sampling interval PSATs will be required. Overall, our results provide a sound basis for future studies disentangling the drivers behind the observed movement patterns, which will be critical for future conservation and management efforts.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/biology11121689/s1](https://www.mdpi.com/article/10.3390/biology11121689/s1)]([https://www.mdpi.com/article/10.3390/biology11121689/s1](https://www.mdpi.com/article/10.3390/biology11121689/s1)), Figure S1: PSAT attachment methods; Figure S2: Spatial scale of individual movements by month; Figure S3: Depth occupation of four sharks from recovered PSAT data; Figure S4: Deep dive locations (depth > 200 m). Figure S5: Frequency of deep dive initiation times. Figure S6: Drive profiles.  
**Author Contributions:** Conceptualization, J.L.Y.S. and P.A.B.; methodology, J.L.Y.S., P.A.B. and C.H.L.; validation, J.L.Y.S., C.H.L., A.M. and P.A.B.; formal analysis, J.L.Y.S. and C.H.L.; investigation, J.L.Y.S., P.A.B., C.H.L. and A.M.; resources, P.A.B.; data curation, J.L.Y.S., C.H.L. and P.A.B.; writing--original draft preparation, J.L.Y.S.; writing--review and editing, C.H.L., A.M. and P.A.B.; project administration, P.A.B.; funding acquisition, P.A.B. and J.L.Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Primary project funding and support was provided by the New South Wales Department of Primary Industries (NSW DPI) through the Shark Management Strategy. J.L.Y.S. received financial support through the German Academic Exchange Service (DAAD), NSW DPI and the Marine Ecology Research Centre, Southern Cross University (SCU).  
**Institutional Review Board Statement:** This study was conducted under New South Wales Department of Primary Industries Scientific Research (P01/0059[A]), Marine Parks (P16/0145-1.1) and Animal Care and Ethics (07/08) permits.  
**Data Availability Statement:** The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.  
**Acknowledgments:** We are grateful to [PERSON], [PERSON] and the Shark Management Strategy teams for infrastructure support and help in the field.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 5 Conclusions  
1. The influence coefficient method is the common method for rotor online dynamic balance. The experiments proved that the precision of influence coefficient can be affected by transformation of speed, service time, environment condition, temperature field, abrasion, and lubrication condition of the supporting components, signal measurement and calculation accuracy etc. The accuracy of the influence coefficient has a big effect on the accuracy of online dynamic balance.
2. The accuracy of the influence coefficient can be improved by the self-adaptive method proposed in this paper. The experiments have proved that the balance accuracy can be improved and the trial weight times can be reduced.
3. In theory, we do not need to know the real influence coefficient when the gain scheduling method is applied in online dynamic balance, but the gain scheduling control method belongs to the open loop control and no feedback and compensation mechanism for the inaccurate gain. Based on the self-adaptive method of the influence coefficient, a self-adaptive method for gain parameters is proposed in the paper. The experiments prove that the balance accuracy can be improved based on the gain parameters self-adaptive method, and the residual unbalance vibration differences between the testing points can be reduced by adjusting the parameters of the weighting matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**The Balance Speed: 5200 rpm**} & \multicolumn{2}{c}{**The Balance Speed: 5600 rpm**} \\  & \multicolumn{2}{c}{**The Initial Unbalance Vibration:**} & \multicolumn{2}{c}{**The Initial Unbalance Vibration:**} \\  & \(\vec{V_{01}}=16.42\upmu\)m\(\angle-10.3^{\circ}\) & \(\vec{V_{01}}=21.65\upmu\)m\(\angle-14.1^{\circ}\) \\
**Testing Times** & \(\vec{V_{02}}=32.32\upmu\)m\(\angle 37.2^{\circ}\) & \(\vec{V_{02}}=36.96\upmu\)m\(\angle 31.9^{\circ}\) \\ \cline{2-5}  & \multicolumn{2}{c}{**The Residual Unbalance Vectors (g-cm\(\angle^{\circ}\))**} & \multicolumn{2}{c}{**The Residual Unbalance Vibration Vector (\(\upmu\)m\(\angle^{\circ}\))**} \\ \hline \multirow{2}{*}{1} & 218.07\(\angle\)\(-72.2\) & 6.82\(\angle\)\(-69.9\) & 240.02\(\angle\)\(-99.2\) & 8.46\(\angle\)\(-99.9\) \\  & 136.9\(\angle\)1  
**Author Contributions:** Conceptualization, S.Z. and Y. W.; Methodology, S.Z. and Z.Z.; Software, S.Z.; Validation, S.Z., Y.W. and Z.Z.; Formal Analysis, Y.W. and Z.Z.; Investigation, S.Z. and Z.Z.; Resources, S.Z., Y.W. and Z.Z.; Data Curation, S.Z. and Z.Z.; Writing-Original Draft Preparation, S.Z.; Writing-Review & Editing, S.Z., Y.W. and Z.Z.; Visualization, S.Z; Supervision, S.Z.; Project Administration, S.Z., Y.W. and Z.Z.; Funding Acquisition, S.Z. and Z.Z.  
**Acknowledgments:** This work was supported by the National Natural Science Foundation of China (No. 51605332), the science and technology funds from Tianjin Municipal Education Commission (No. 2017 KJ110).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Regional Climate Change in Southeast Mexico-Yucatan Peninsula, Central America and the Caribbean
## 2 Materials and Methods
### Data
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Latitude** & **Longitude** & **Country/State** & **Close City** \\ \hline \multicolumn{5}{c}{**Southeast Mexico-Yucatan Peninsula**} \\ \hline
1 & \(16.75^{\circ}\) & \(-92.25^{\circ}\) & Chiapas & San Cristobal \\ \hline
2 & \(17.75^{\circ}\) & \(-92.25^{\circ}\) & Tabasco & Villahermosa \\ \hline
3 & \(18.75^{\circ}\) & \(-89.25^{\circ}\) & Campeche & Champion \\ \hline
4 & \(20.25^{\circ}\) & \(-87.75^{\circ}\) & Quintana Roo & Felipe Carrillo Puerto \\ \hline
5 & \(21.25^{\circ}\) & \(-88.75^{\circ}\) & Yucatan & Buctzotz \\ \hline \multicolumn{5}{c}{**Central America**} \\ \hline
6 & \(9.25^{\circ}\) & \(-79.25^{\circ}\) & Panama & Panama City \\ \hline
7 & \(9.75^{\circ}\) & \(-84.25^{\circ}\) & Costa Rica & San Jose \\ \hline
8 & \(12.25^{\circ}\) & \(-86.25^{\circ}\) & Nicaragua & Managua \\ \hline
9 & \(13.75^{\circ}\) & \(-89.25^{\circ}\) & El Salvador & San Salvador \\ \hline
10 & \(14.25^{\circ}\) & \(-87.25^{\circ}\) & Honduras & Tegucigalpa \\ \hline
11 & \(14.25^{\circ}\) & \(-90.25^{\circ}\) & Guatema & Guatema \\ \hline
12 & \(17.25^{\circ}\) & \(-88.75^{\circ}\) & Belize & Belmopan \\ \hline \multicolumn{5}{c}{**Caribbean**} \\ \hline
13 & \(22.75^{\circ}\) & \(-81.75^{\circ}\) & Cuba & Limonar \\ \hline
14 & \(19.25^{\circ}\) & \(-72.75^{\circ}\) & Haiti & Arbonite \\ \hline
15 & \(19.25^{\circ}\) & \(-69.75^{\circ}\) & Dominican & Maria Trinidad Sánchez \\ \hline
16 & \(18.25^{\circ}\) & \(-77.25^{\circ}\) & Jamaica & St. Ann Parish \\ \hline
17 & \(18.25^{\circ}\) & \(-65.75^{\circ}\) & Puerto Rico & San Juan \\ \hline \end{tabular}
\end{table} 
Table 2: Geographic coordinates of 17 points of the study region. Figure 2: Spatial distribution of the analyzed points (in blue). Southeast Mexico, Central America, and the Caribbean are in green color. Note. The numbers correspond to the study points in Table 2.
## 2 Materials and Methods
### Ground Motion Prediction Equations (GMPEs)  
Ground motion modelling is one of the most vital factors in seismic hazard assessment. Therefore, the selection of empirically derived GMPEs as a means of estimating ground motion measures is a critical task. For the present study, the maximum expected ground motion, expressed in terms of PGA, PGV, and Sa, was computed using GMPEs developed for the Greek territory. It should be noted that different GMPEs were utilized for crustal and slab area source models. Specifically for PGA, the GMPEs proposed by [PERSON] et al. [76] (MARG2002), [PERSON] et al. [77] (SKAR2003), [PERSON] [78] (DANTS2007), [PERSON] [79] (SAKK2016), and [PERSON] et al. [80] (CHOU2018) were considered for both the ESHM13 and ESHM20 crustal seismotectonic models. [PERSON] [79] does not provide a GMPE formulation for PGV. In addition, the model from SKAR2003 was replaced with its updated version for PGV [77] (SKAR2007). Concerning the slab source models, the GMPEs of [PERSON] et al. [81] (SKAR2013) were employed for PGA and PGV. Given that area source zones were adopted in this study, only epicentral or hypocentral distances were considered. Thus, more recent GMPEs (e.g., [82]) that use different distance metrics were not taken into account. In addition to PGA and PGV, Sa values for various periods (T) were calculated for Athens, Methane, and the capitals of the islands of Salamina, Aegina, Poros, Hydra, Spetses, Kythira, and Antikythira using DANTS2007, which was optimized from data of crustal earthquakes. All GMPEs (except for MARG2002) include a term related to the type of focal mechanism. Given that most seismic zones include various focal mechanism types, their respective participation rates were calculated for each zone belonging to each adopted model, resulting in weighted GMPEs. The focal mechanism catalog that was utilized was proposed by [PERSON] and [PERSON] [23].
# A New Method for Detecting Weld Stability Based on Color Digital Holography
## 4 Conclusions  
In summary, the article successfully designed an optical system for detecting the weld stability based on color digital holography technology. Regarding the roughness  
Figure 8: Stability evaluation of butt-jointed aluminum alloy plate. (**a**) x-direction deformation field; (**b**) y-direction deformation field; (**c**) z-direction deformation field; (**d**) the center section line; (**e**) the variance curve.  
Figure 9: Stability evaluation of the defective butt-jointed aluminum alloy plate. (**a**) x-direction deformation field; (**b**) y-direction deformation field; (**c**) z-direction deformation field; (**d**) the center section line; (**e**) the variance curve.  
characteristics of the aluminum alloy plate surface, the 4f-CycleGAN algorithm based on deep learning has the maximum sum of PSNR and has the best denoising results for the speckle noise. The three-dimensional deformation fields of aluminum alloy plates with lap-jointed, butt-jointed, and defective butt-jointed were calculated, and the corresponding coefficients of variation were 0.0335, 0.1240, and 0.3965 respectively, indicating that the stability of the lap-jointed and butt-jointed aluminum alloy plates is good while the stability of the defective butt-jointed aluminum alloy plate is poor. The optical system can achieve non-contact, non-destructive, dynamic, and full field testing, as well as detect the weld stability of the workpiece in service. The research results of the paper would provide a new strategy for detecting the weld stability. There are potential application prospects in fields such as architecture, bridges, roads, and aerospace.  
Conceptualization and writing--original draft, Q.L.; writing--review and editing, G.H.; funding acquisition and validation, H.X.; conceptualization, R.W.; writing--review and editing W.Z.; funding acquisition, J.G.; software, Q.F.; software, C.G.; funding acquisition and writing--review and editing, Q.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (62165007), the National Natural Science Foundation of China (11862008) and the National Natural Science Foundation of China (62065010).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available because the data also form part of an ongoing study.  
The authors declare no conflicts of interest.
## 3 Results
### Performance Analysis
#### 3.3.5 Confusion Matrices for the Models with the Two Feature Matrices  
The confusion matrix was calculated for each of the models implemented in order to see the rates of true positives, true negatives, false positives, and false negatives. Figure 6 shows the values corresponding to the models trained with the mode matrix for all years. Figure 7 shows the results of the models trained with the second mode matrix every 3 years.  
The number of true positives and true negatives shows that the random forest has the best performance, since it has the highest values in these two cases, with this behavior being evident in both feature matrices. At the same time, the random forest has the lowest false positives and false negatives in the two matrices. However, the results of the mode matrix every three years are slightly better than those of the first matrix in all three models. In conclusion, it is clear that creating a matrix that maintains a temporal change in the data allows the models to predict a higher number of true positives and true negatives while keeping the number of false positives and false negatives to a minimum.  
Table 12 shows the variables that have the greatest influence on the prediction of the random forest model using Gini importance or mean decrease in impurity. Gini importance  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **Random Forest** & **SVM** & **Logistic Regression** \\ \hline Accuracy (mean) & 0.89 (Train); 0.88 (Test) & 0.88 (Train); 0.88 (Test) & 0.86 (Train); 0.85 (Test) \\ F1 Score (mean) & 0.85 (Train); 0.85 (Test) & 0.83 (Train); 0.82 (Test) & 0.80 (Train); 0.80 (Test) \\ Recall (mean) & 0.80 (Train); 0.80 (Test) & 0.78 (Train); 0.77 (Test) & 0.77 (Train); 0.77 (Test) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Performance for the models after PCA. Values correspond to the model created with the mode matrix every 3 years by variable.  
Figure 6: Confusion matrices for the models that receive the mode matrix of all years as input.  
Figure 7: Confusion matrices for models receiving the mode matrix every 3 years as input.  
\"indicates how often a particular feature \(\theta\) was selected for a split, and how large its overall discriminative value was for the classification problem under study\" [35].
# Histopathology and Genetic Biomarkers of Choroidal Melanoma
## 5 Genetic Profile
The relatively recent introduction of immune checkpoint inhibitors (ICIs) has represented a turning point in the treatment of metastatic cutaneous melanoma. However, metastatic CM is extremely resistant to ICIs [36]. The mechanisms by which CM cells develop resistance to ICIs are still largely unknown. It is believed that one of these potential mechanisms would be the induction of an immune-suppressive CM microenvironment through the upregulation of immunosuppressive molecules and downregulation of activated T cells. [PERSON] et al. [37] demonstrated that one of the mechanisms leading to poor T cell immune reaction in CM hepatic metastases is tryptophan 2,3-dioxygenase (TDO)-mediated tryptophan (TRP) catabolism, since the liver is rich in TDO. TRP is essential for T cell and NK cell functions and it has been shown that its catabolites act as suppressor molecules for T cell and NK cell proliferation [38]. [PERSON] et al. [37] found a high expression of TDO protein in both CM liver metastasis tissue and metastatic CM cell lines; interestingly, TDO was diffusely expressed by both normal hepatocytes and neoplastic cells, suggesting a strong role for TDO in the development of ICI resistance. \begin{table}
\begin{tabular}{l l} \hline \hline
**Gene Symbol** & **Gene Name** \\ \hline \multicolumn{3}{l}{**UpRegulated in Class 2 Uveal Melanomas**} \\ \hline CDH1 & E-cadherin \\ \hline ECM1 & Extracellular matrix protein 1 \\ \hline HTR2B & 5-Hydroxyryptamine (serotonin) receptor 2B \\ \hline RAB31 RAB31 & member of the RAS oncogene family \\ \hline \multicolumn{3}{l}{**DownRegulated in Class 2 Uveal Melanomas**} \\ \hline EIF1B & Eukaryotic translation initiation factor 1B \\ \hline FXR1 & Fragile X mental retardation, autosomal homolog 1 \\ \hline ID2 & Inhibitor of DNA binding 2 \\ \hline LMCD1 & LIM and cysteine-rich domains 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Genes included in the 15-gene expression profile.
## 5 PM-DQN Based Joint Routing and Resource Allocation Algorithm
### PM-DQN-Based Joint Routing and Resource Allocation Algorithm
```
1:Input: mini-batch \(k\), step-size \(\eta\), replay period \(C\) and memory size \(N\)
2: Initialize replay memory \(D=\phi\) and the number of replayed experience \(K_{0}=0\)
3: Initialize \(Q\) function with random weights \(\theta\) and target \(\hat{Q}\) function with weights \(\theta^{-}=\theta\)
4:For\(\textit{epsoide}=1\), \(M\)do
5: Initialize state \(s_{1}=\{n_{1},\ \psi_{1}\}\)
6:For\(t=1\), \(T\)do
7: Node \(n_{i}\) selects next relay \(n_{j}\) according to \(a_{t}\) which is obtained from the former stage
8: Observe reward \(r_{t-1}\) and generate the newest transition \((s_{t-1},a_{t-1},r_{t-1},s_{t},\delta_{t-1})\)
9: Node \(n_{0}\) samples mini-batch of transitions \((s_{k},a_{k},r_{t-1},s_{k+1},\delta_{k})\) to update weights
10: Set \(y_{k}=\left\{ \begin{array}{c}-m(m>0)\quad\text{if the selected action is unavailable}\\ r_{k}+\gamma\text{max}_{\sigma}\hat{Q}(s_{k+1},a^{\prime};\theta^{-})\quad \text{otherwise}\end{array} \right.\)
11: Perform gradient descent on \((y_{k}-Q(s_{k},a_{k};\theta_{i}))^{2}\) and select action \(a_{t+1}\) of the next relay \(n_{j}\) according to (19)
12: Execute action \(a_{t}=\left\{n_{j},c_{i},p_{\psi_{j}}\right\}\): Transmit data to \(n_{j}\) through \(c_{i}\); meanwhile transmit \(a_{t+1}\) to \(n_{j}\) and send \((s_{t-1},a_{t-1},r_{t-1},s_{t},\delta_{t-1})\) back to \(n_{0}\)
13: Do Prioritized Memories Algorithm in \(n_{0}\)
14: Every \(C\) steps reset \(\hat{Q}=Q\)
15:End For
16:End For
```  
**Algorithm 2** PM-DQN Based Joint Routing and Resource Allocation Algorithm
# Bricks Using Clay Mixed with Powder and Ashes from Lignocellulosic Biomass: A Review
## 4 Investigation of Hybrid Bricks by Blending Clay with Ash from Lignocellulosic Biomass Combustion
### Hybrid-Fired Bricks by Blending Clay with Lignocellulosic Biomass Ash
[32] showed that the addition of only 2% of RHA to clay was sufficient to improve the mechanical properties of clay bricks. As for [64] 5% of RHA introduced in clay could produce high-quality clay bricks. In addition, Ref. [65] investigated the viability of using waste biomass combustion ash, such as (RHA) or plank wood ash (WA), with contents ranging from 10% to 30%, in the production of clay bricks. Two different firing temperatures, 900 \({}^{\circ}\)C and 1000 \({}^{\circ}\)C were tested to determine the effect of temperature. According to the results, 10 wt.% RHA and 20 wt.% WA, as well as a firing temperature of 1000 \({}^{\circ}\)C, provided the best mechanical, physical, and thermal properties. Ref. [66] investigated the viability of reusing waste sugarcane bagasse ash (K) for use as a secondary raw material during the production of clay-fired bricks. The replacement rate of natural clay was limited to 20% by weight in 5% steps. According to the findings, sugarcane bagasse ash waste (K) can be safely and sustainably used in clay bricks when its weight is less than 10%. Refs. [67; 68] and studied the mechanical, physical, and thermal performance of new forms of clay bricks by adding various dosages of 5%, 10%, and 15% of Rice husk ash RHA and sugar bagasse ash (SBA). According to [67], bricks containing up to 5% RHA and SBA can be used in the building and construction industry without causing any issues. Additionally, by adding these ashes, the matrix becomes more porous, making the bricks lighter. However, according to [68], using SBA and RHA up to 15% by weight of clay when making fired clay bricks not only helps to reduce landfill waste but also promotes the development of environmentally friendly and energy-efficient buildings. Palm waste has also begun to be utilized in the ceramic industry. Indeed, [PERSON]. [69; 70] studied the feasibility of using palm oil fuel ash (POFA) in the manufacture of fired clay bricks as an alternative to minimize the disposal of this waste in landfills. For [70], 0%, 1%, 5%, 10%, 20% and 30% of POFA waste were introduced in order to design clay bricks fired at 1050 \({}^{\circ}\)C. The results show that incorporating this waste up to 5% was effective in producing strong, light, affordable, and environmentally friendly clay bricks. Ref. [69] examined the possibility of incorporating POFA waste with high percentages of 5%, 10%, 15% and 20%, respectively, into clay bricks under real industrial conditions. Contrary to [70], it was proved that the use of OPFA up to 5% as a partial replacement of clay on a large scale is not only useful to minimize the environmental impacts related to the disposal of this waste but also to reduce the excessive use of clay in the ceramic sector.