esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
GPS-equipped vehicles are an effective approach for acquiring urban population movement patterns. Attempts have been made in the present study in order to identify the population displacement pattern of the study region using taxis' origin and destination data, and then model the parameters affecting the population displacement pattern and provide an ultimate model in order to predict pick-up and drop-off locations. In this way, the passenger pick-up and drop-off locations have been identified in order to obtain the population movement pattern. In this study, [PERSON]'s I index was used to measure the spatial autocorrelation, and hot spot analysis was used to analyze spatial patterns of pick-up and drop-off locations. Effective parameters modeling was performed using the Poisson regression. The results of the spatiotemporal distribution map for pick-up and drop-off locations indicated a similarity in patterns and equal results for some locations. Results also indicated different features of spatial distribution during different hours of the day. Spatial autocorrelation analysis results indicated a low probability of randomness in the general spatial distribution of the locations. The result of modeling the parameters shows the positive effect of the parameters on the pattern of population movement, and according to the \(p\)-value of 0.000, Poisson regression is significant for the pick-up and drop-off locations. The modeling results also highlighted the importance of movement patterns in recognizing urban hot spots, which is valuable for policymakers and urban planners.  
population movement patterns; Moran's I index; spatial autocorrelation; hot spots; spatio-temporal distribution; Getis-Ord-Gi; Poisson regression Article  
Discovering Intra-Urban Population Movement Pattern Using Taxis' Origin and Destination Data and Modeling the Parameters Affecting Population Distribution [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Discovering Intra-Urban Population Movement Pattern Using Taxis' Origin and Destination Data and Modeling the Parameters Affecting Population Distribution. _Appl. Sci._**2021**, _11_, 5987. [[https://doi.org/10.3390/app1135987](https://doi.org/10.3390/app1135987)]([https://doi.org/10.3390/app1135987](https://doi.org/10.3390/app1135987))
## 3 Results  
### Curing Performance of Zeolite Powder Synergized with CaO on Pollutants in PG  
As shown in Figure 2, the raw PG material used in this study contained approximately 0.3894 wt.% soluble phosphorus impurities. This high concentration of soluble phosphorus is problematic, as it can negatively impact the properties of PG-based products, such as the strength and durability, and poses a potential environmental risk due to leaching. The use of zeolite powder as a physical adsorbent for soluble phosphorus impurities in PG has proven to be highly effective. Zeolite, with its well-developed nanoscale pore structure and large specific surface area, adsorbs soluble phosphorus, significantly reducing its concentration. Following the treatment with zeolite powder, the soluble phosphorus content in the PG decreased to 0.3432 wt.%. A further improvement in the removal of soluble phosphorus impurities was achieved through the chemical modification of PG with CaO. When the PG was treated with both zeolite powder and CaO, the soluble phosphorus content was reduced to below the detection limit, demonstrating the combined effectiveness of physical adsorption and chemical modification.  
In addition to phosphorus, PG also contains a considerable amount of soluble fluorine impurities, with an initial concentration of approximately 0.3659 wt.%. Fluorine compounds, particularly soluble fluoride, are known to be environmentally hazardous and can lead to soil and water contamination if not properly treated. Zeolite powder was again employed to adsorb the soluble fluorine impurities present in PG, achieving a reduction in  
Figure 2: Comparison of soluble phosphorus and fluorine impurity contents in PG before and after treatment with zeolite powder and CaO.  
fluorine content to 0.2857 wt.%. Although this represents a reduction in fluorine concentration, the removal efficiency was not as high as that achieved for phosphorus. To further reduce the fluorine content, the PG was subjected to chemical modification with CaO. The application of CaO alone reduced the soluble fluorine content to 0.0365 wt.%. When zeolite powder and CaO were simultaneously used in a synergistic treatment process, both physical adsorption and chemical modification contributed to a more substantial reduction in soluble fluorine content. This combined treatment reduced the fluorine concentration to 0.0361 wt.%, a significant improvement over either treatment applied individually. These results demonstrate that the physical adsorption of pollutants, when coupled with chemical modification, offers a highly effective method for reducing both soluble phosphorus and fluorine impurities in PG. The combination of zeolite powder and CaO treatment provides a robust, synergistic approach to improving the environmental safety and resource potential of PG.
# Gaussian Process Surrogates for Modeling Uncertainties in a Use Case of Forging Superalloys
## 1 Introduction
While in maximization and minimization problems aleatoric and epistemic uncertainties can often be treated in the same way, in most cases robust results can be obtained by distinguishing between these two sources of uncertainty [30]. We refer to robust results when mean predictions are associated with low aleatoric uncertainty. There is already considerable related work in reduced-order surrogate modeling and optimization using GP regression surrogates. However, to the best of our knowledge, we could not identify related work for solving inverse problems in which aleatoric and epistemic uncertainties are treated differently. Optimization approaches for solving inverse problems usually use only epistemic uncertainty. When epistemic and aleatoric uncertainties are taken into account, they are often simply combined, resulting in the potential loss of important information. To sum up, we identify the following drawbacks:  
* Related work shows that mainly epistemic uncertainty is used for prediction or optimization with GP regression. * In research using aleatoric and epistemic uncertainties, they are not considered separately when solving inverse problems. As a response, we present the following main contributions of our research to tackle the identified drawbacks of related work:  
1. We present a GP based surrogate that models (a) the mean result, (b) the aleatoric and (c) the epistemic uncertainty of a manufacturing process outcome. 2. We utilize aleatoric and epistemic uncertainties in solving inverse problems for robust optimization results. With the proposed surrogate model and novel multi-objective optimization strategy, we pave the way for surrogate modeling and inverse problem-solving for practical applications that make use of explicit modeling of sources of uncertainties. Our findings are validated on a typical hot metal forming manufacturing process: preforming an Inconel 625 superalloy billet on a forging press. This paper is structured as follows. In Section 2, we present the proposed surrogate model, providing an introduction to GP regression in Section 2.1 and describe the GP based parts of our surrogate model in Sections 2.2 and 2.3. The data generation of aleatoric uncertainty for our surrogate model approach is presented in Section 2.4. Section 3 deals with optimization, where we outline active learning in Section 3.1 and solving inverse problems in Section 3.2. In Section 4 we present the studied use case, preforming an Inconel 625 superalloy billet on a forging press, where we give insights on the design of the forging aggregate characteristics in Section 4.1 and all information regarding the corresponding FEM simulation in Section 4.2. Section 5 shows the results, which are discussed in Section 6. In Section 7, we present the conclusion of our work and an outlook for the future.
## 3 Proposed Scheme
### Data Embedding  
In this section, we provide a detailed step-by-step description of the process of hiding data in the encrypted AMBTC image.  
Figure 2: Block diagram of the proposed scheme.  
**Input**: An encrypted AMBTC cover image \(I^{\prime}\) and secret data \(S=(s_{1},s_{2},\ldots,s_{n})\).  
**Output**: An encrypted marked image \(I^{\prime\prime}\).  
**Step 1:**: Read a \(trio(Q_{1},Q_{2},M^{\prime})\) from the encrypted AMBTC image \(I^{\prime}\), where \(Q_{1}\) and \(Q_{2}\) are the quantization levels and \(M^{\prime}\) is a bitmap \(M^{\prime}=(m_{1},m_{2},\ldots,m_{15},m_{16})\).
**Step 2:**: The exclusive-or operation between the secret message bits \(s_{i}\) and the pseudorandom bits \(r_{i}\) are calculated using Equation (13), where \(r\) is determined via DK using a standard stream cipher.  
\[s_{i}^{\prime}=s_{i}\oplus r_{i} \tag{13}\]
**Step 3:**: A bitmap \(M^{\prime}=\{m_{1},m_{2},\ldots,m_{15}\}\) is assigned to codeword \(c\). For codeword \(c\), the syndrome \(\eta\) is obtained by employing Equation (14).  
\[\eta=(c\cdot H^{T})\bmod 2 \tag{14}\]
**Step 4:**: Exclusive-or operation is performed on the syndrome and the encrypted bits. That is, \(\hat{\eta}=\eta\oplus s_{i+4}^{\prime}\). If syndrome \(\hat{\eta}=0\), this means no operation. Otherwise, flip the \(\hat{\eta}\)-th coordinate \(c_{\hat{\eta}}\) of \(c\). For image restoration, the rows and columns of the changed location \(c_{\hat{\eta}}\) are obtained using Equation (15), where \(rc\) is the value of the row and column, and 4 bit information of \(rc\) is added to the last bit of \(M^{\prime}\). The \(rc\) needs to be converted to binary before it is added last to \(M^{\prime}\). In the formula, \(cnt\) is first initialized to 0 and then the formula is applied.  
\[rc=\sum_{i=1}^{4}\sum_{j=1}^{4}\begin{cases}(i\mid\mid j),\ if(\hat{\eta}=cnt),\\ +cnt\end{cases} \tag{15}\]
**Step 5:**: After the operation, the codeword \(c\) is assigned to \(M_{1}^{15}\), i.e., \(M_{1}^{\prime 15}=c\). Add payload coordinates \(rc\) after bitmap \(M^{\prime}\). That is, \(M^{\prime}=M^{\prime}\mid\mid rc\).
**Step 6:**: Go to _Step 1_ to continue the embedding processes until all messages have been embedded in the encrypted cover image.  
If this process is repeatedly applied as often as the size of the image, marked images \(1^{\prime\prime}\) are obtained. Using Example 3 below, we briefly reviewed the data hiding process.  
**Example 3**.: _As given in Figure 3, when the bitmap block \(M^{\prime}\) = (1010010110100101) and the secret bit \(s\) = (1010010110100101) is first constructed for data hiding. Next, the syndrome \(\eta\) is calculated. That is, \(\eta=H\cdot c^{T}\) = (0000). Then, an exclusive-or operation is performed on the syndrome and the secret bit. The result is the decimal number 10. 4-bit data hiding is completed by inverting the number at position 10 of the bitmap. To restore the block, it is necessary to store the payload for the 10 th position. The data hiding process is completed by adding (0110) at the end of the bitmap \(M^{\prime}\)._  
Figure 3: Block diagram of the data embedding example.
# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
###### Abstract  
Chlorophyll content in wheat leaves reflects its growth and nutritional status, which can be used as a health index for field management. In order to evaluate the potential of hyperspectral data to estimate the chlorophyll content in wheat leaves, this study focused on the leaves of wheat at the flag-picking stage, flowering stage, grain-filling stage, and maturity stage. Based on the framework of five vegetation indexes, the spectral index was constructed by using the combination of 400-1000 nm bands. The correlation between the constructed spectral index and the measured chlorophyll value was analyzed, and the optimal spectral index was screened using the correlation coefficient. Based on the optimal spectral index, polynomial regression, random forest, decision tree, and artificial neural network were used to establish the estimation model for chlorophyll value, and the optimal model for estimating the chlorophyll value of wheat leaves was selected through model evaluation. The results showed that the five optimal spectral indices at the four growth stages were primarily composed of the red band, red edge band, and near-infrared band. The five optimal spectral indices during the grain-filling stage had the highest correlation with the chlorophyll value, and the absolute value of the correlation coefficient was greater than 0.73. The accuracy of the estimation model established in the four growth stages was different, with the estimation accuracy of the flag stage being the best, showing an R\({}^{2}\) and RMSE of 0.79 and 2.63, respectively. These results indicate that hyperspectral data are suitable for estimating the chlorophyll value of wheat leaves, and the polynomial regression model of the flag-picking period can be used as the optimal model for estimating the chlorophyll value of wheat leaves.  
winter wheat; chlorophyll content; hyperspectral 2024202414703.
## 5 Discussion
### AI and Machine Learning in Shoreline Monitoring  
Integrating ML and AI methods in coastal monitoring is a quantum leap in effectively processing and analyzing large volumes of data [33]. These technologies can fully automate processes requiring human labor, such as segmenting and detecting shorelines from satellite imagery. For example, the ensemble deep learning model WaterNet has exhibited extremely high accuracy, reaching 99.79% in shoreline segmentation. This high accuracy enables shoreline dynamic analysis almost in real-time, which is vital for quick responses to environmental changes and long-term planning of coasts. Machine learning models predicting the position of shorelines well into the future also help decision-makers prepare and adapt to the oncoming impacts of climate change.
## 2 Keratin-Based Nanoparticles
### Human Hair Keratin-Based Nanoparticles
Of note, in the liver puncture experiment, approximately 300% and 400% reductions in blood loss were observed for the application of keratin extracts and KNPs, respectively (Figure 6A). Overall, the hemostatic time of keratin extracts and KNPs was shorter than control groups, and KNPs showed an improved effect as compared to keratin powder both in liver puncture and tail amputation (Figure 6B). Chlorhexidine (CHX) is widely used to treat and prevent skin and mucosal infections; in dentistry, it has been recognized as a gold standard against plaque and gingivitis for three decades [44]. Due to its cationic charge, [PERSON] and coworkers used CHX as a drug model for the preparation of CHX-KNPs (KCNPs) by exploiting the natural electrostatic interaction between the negatively charged protein and the cationic drug by using the drug induced ionic gelation technique as well as with different ratios of keratin and CHX, e.g., 9/1, 7/3, and 5/5 [16]. For their purpose, the authors extracted keratin from human hair using the reduction strategy, allowing them to isolate a protein characterized by two major clusters of bands: the 25 and 40 kDa. The nanoparticles' stability was evaluated over time, showing that KCNPs at the ratio of 9/1 are stable even when standing for the longest period. Release experiments, performed under different conditions, showed that only the 8% of CHX was released within 100 h in basic environment, probably due to the enhanced negative charges of the KCNPs that tightly adsorb CHX. Conversely, about 39.5% of CHX was released within 240 h at pH 5. Since an antibacterial material requires low toxicity to normal cells, the cytotoxicity of KCNPs towards fibroblasts was evaluated, showing that, at a lower concentration (2.5 mg/mL of KCNPs, i.e., 0.2 mg/mL of CHX), the cell viability was reduced to 66% as compared to the FBS control, indicating that CHX cytotoxicity is enhanced when complexed with keratin. Besides, antibacterial analyses confirmed the effectiveness of KCNPs and their superior activity against Gram-positive microbes as opposed to Gram-negative ones. Despite being promising, these data account for a greater side toxicity of KPNPs, which might reduce their system applicability as antibacterial agents.
## 4 Microbial Fermentation for Commercial Production of C\({}_{30}\) Caroenoids
### Growth Factor Effects on C\({}_{30}\) Carotenoid Production  
The main regulatory factors for carotenoid production in nonphotosynthetic bacteria are temperature, aeration, agitation, and culture media composition. Conversely, carotenoid synthesis by phototrophic bacteria is modulated by oxygen and light, with the bacteria producing less oxidized forms of carotenoids in oxygen-deprived environments and synthesizing ketocarotenoids in oxygenated environments [73; 74; 75]. Modifications in the quantity and quality of these factors have various effects on different species of microorganisms when initiating carotenoid production. _Rhodotorula glutinis_ DFR-PDY, a yeast strain, produces higher carotenoid yields when using fructose as a carbon source, but exhibits greater cell growth in galactose-supplemented media [76]. Although the lactic acid bacteria (LAB) _L. plantarum_ subsp. _plantarum_ KCCP112267 showed maximum cell growth in maltose, it produced the greatest C\({}_{30}\) carotenoid 4,4\({}^{\prime}\)-diaponeurosporene yield in lactose-supplemented media. Nitrogen sources also play a role in microbial growth and carotenoid yield, with beef extract being optimal for both cell growth and 4,4\({}^{\prime}\)-diaponeurosporene production [77; 31; 78].  
In lieu of conventional supplementation, the use of waste products for fermentation in carotenoid production is cost-effective and sustainable. Fermentation can be carried out in a solid state (SSF), or via submerged fermentation (SmF). IN SSF, the substrate consumption rate is slower, and fermentation requires a longer time and less moisture (such as in wheat bran and fruit pulp). In SmF, substrate consumption occurs rapidly, and the resupply of substrates, which include soluble sugars, molasses, and fruit juice, must be constant [79].
# Improved Sea Ice Image Segmentation Using U\({}^{2}\)-Net and Dataset Augmentation
## 4 Discussion
### Sc-U\({}^{2}\)-Net Network  
In the comparison experiments of U\({}^{2}\)-Net and SC-U\({}^{2}\)-Net, we used the same training set to train U\({}^{2}\)-Net and SC-U\({}^{2}\)-Net, so as to exclude the accuracy improvement brought by the increase of dataset, and the experiments show that when using the same training and test sets, the IoU, F1-score, and recall of SC-U\({}^{2}\)-Net were higher than those of U\({}^{2}\)-Net. SC-U\({}^{2}\)-Net is more effective for sea ice segmentation on remote sensing images. We also compared U\({}^{2}\)-Net (Training set 1) (U\({}^{2}\)-Net trained without data augmentation) and SC-U\({}^{2}\)-Net (Training set 5) (SC-U\({}^{2}\)-Net trained with data augmentation), and the results show that the simultaneous use of data augmentation and network improvement can improve the accuracy and generalization of the model. SC-U\({}^{2}\)-Net was able to segment the narrowly shaped sea ice with a smaller area, and the segmentation results are basically consistent with the labeled images. U\({}^{2}\)-Net is effective in segmenting sea ice over a large area on the sea surface, while narrowly shaped sea ice extending into the land interior is ignored.  
Compared with U\({}^{2}\)-Net, the improved SC-U\({}^{2}\)-Net was able to pay more attention to the details of the target and segmented the discontinuous sea ice better.
## 4 Verification of Repair Effect
### Preparation and Treatment of Specim
#### 4.1.3 High-Temperature Treatment  
The concrete specimens were divided into five groups: ambient temperature, 300 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C, respectively. A thermocouple was embedded in the center of one specimen in each group. A muffle furnace was used to heat the specimens. The temperature increasing rate was set as 10 \({}^{\circ}\)C/min. The temperature in the center of the specimens could be obtained by a temperature inspection instrument outside the muffle furnace. When the temperature shown on the temperature inspection instrument reached 300 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C, respectively, the center of the specimens reached the corresponding temperature, the temperature was maintained for 30 min, then the muffle furnace was turned off, the furnace door was opened for natural cooling, as shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test Items** & **Apparent Density/(kg/m3)** & **Bulk Density/(kg/m3)** & **Voidage/\%** & **Mud Content/\%** & **Clay Content/\%** & **Needle or Plate Content/\%** & **Crushing Value/\%** \\ \hline Technical standard & \(\geq\)2600 & - & \(\leq\)45.0 & \(\leq\)1.0 & \(\leq\)0.5 & \(\leq\)15 & \(\leq\)10 \\ Result & 2680 & 1760 & 34.60 & 0.68 & 0.10 & 7 & 9 \\ \hline \hline \end{tabular}  
**Note:** The gravel used in this study follows JGJ 52-2006 [31].  
\end{table}
Table 7: Performance of gravel.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test Items** & **Apparent Density/(kg/m3)** & **Bulk Density/(kg/m3)** & **Methylene Blue** & **Powder Content/\%** & **Voidage/\%** & **Mass Loss/\%** & **Fineness Modulus** \\ \hline Technical standard & \(\geq\)2500 & \(\geq\)1400 & \(<\)1.4 & \(\leq\)10.0 & \(\leq\)44.0 & \(\leq\)8 & 2.3–3.0 \\ Result & 2750 & 1673 & 1.2 & 7.61 & 38.44 & 6.00 & 2.86 \\ \hline \hline \end{tabular}  
**Note:** The gravel used in this study follows JGJ 52-2006 [31].  
\end{table}
Table 8: Performance of sand.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Items** & **Water Reducing Rate/\%** & **Density/(g/mL)** & **Solid Content/\%** & **Fluidity of Content/mm** & **PHI Chloride Content/\%** & **Alkali Content/\%** \\ \hline Technical standard & \(\geq\)25 & 1.09 \(\pm\) 0.02 & 22 \(\pm\) 2 & \(\geq\)250 (w/b = 0.29) & 6–8 & \(\leq\)0.02 & \(\leq\)0.2 \\ \hline Result & 20 & 1.1 & 21 & 265 & 8 & 0.01 & 0.09 \\ \hline \hline \end{tabular}  
**Note:** The superplasticizer used in this study follows JGJ 56-84 [32].  
\end{table}
Table 9: Performance of superplasticizer.
## 6 Results and Discussions
### Diagnosis of Combined BRB and 5%, 7%, and 10% of ITSC Faults  
The first experiment was implemented to diagnose one BRB, two BRB, and 5% of ITSC faults. The second experiment was executed for the diagnosis of one BRB, two BRB, and 7% of ITSC faults. The third experiment was performed to diagnose one BRB, two BRB, and 10% of ITSC faults. The SC-IM is operated at No load. The RMSE is developed to evaluate the performance of ANFIS as given in Table 6.  
It was proved that the ANFIS based DWT achieves better performance in diagnosing combined BRB faults and 5%, 7%, and 10% of ITSC faults. The index of faults generated from the FLC model for diagnosis of different BRB and 5%, 7%, and 10% of ITSC faults is shown in Figure 15.