esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment Result and Analysis
### The Working Mechanism of Contrast Detection Channel
By comparing the STMD output results after thresholding, we found that the false positive responses generated by the background were effectively suppressed,  
Figure 12: (**a–c**) Motion trajectories tracked by the \(\mathcal{CI}\)STMD model without the incorporation of a contrast information detection channel, under various thresholds \(\beta\) of \(10,50\), and \(100\), respectively. (**d–f**) Motion trajectories tracked by the \(\mathcal{CI}\)STMD model with the integration of a contrast information detection channel, under various thresholds \(\beta\) of \(10,50\), and \(100\), and a fixed contrast information standard deviation threshold \(\gamma\) of \(0.0025\). while the responses of small targets were retained. The results from the above experiments indicate that an appropriate threshold allows us to differentiate small moving objects from background fake features. Figure 14: The contrast information variances under different time. (**a**) The contrast information variances of the small target. (**b**) The contrast information variances of the fake feature. (**c**,**d**) Output signal after applying a contrast threshold. Figure 13: (**a**–**c**) Motion trajectories tracked by three different models: ESTMD, DSTMD, and \(\mathcal{F}\)STMD. (**d**–**f**) The trajectories of movement, as sensed by the \(\mathcal{CISTMD}\) approach, are investigated across a range of contrast information variance parameters \(\gamma\), at \(0.001,0.002\), and \(0.0025\).
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 5 The Methodology
### Sentinel-1 Data Selection and Pre-Processing  
The main features of the used Sentinel-1A and 1B have already been mentioned. By exploiting the GEE platform, IW GRD scenes with VH polarization were selected for the areas under investigation. Temporal selection was performed by focusing on the month/s of event/s analyzed in a multi-temporal framework. To ensure the reliability of our results, we implemented a filtering process to exclude poor-quality images from the collection. This step was crucial to avoid potential inaccuracies and inconsistencies in the data used in the analysis. A total of 475 images stratified based on the calendar month of event occurrence were analyzed.  
Several pre-processing steps were automatically executed within the GEE platform to enhance SAR image quality. These steps include thermal noise removal, data calibration, multi-looking, and range Doppler terrain correction. However, to further improve SAR data quality, additional pre-processing steps are necessary. The Committee on Earth Observation Society (CEOS) Analysis-Ready-Data for Land (CARD4L) recommends several supplementary pre-processing techniques, including border noise removal, speckle filtering, and radiometric terrain correction [52]. For speckle filtering, we employed the Refined Lee filter [53], which was originally introduced by [54], and we used this filter at a kernel size of 7 \(\times\) 7. To address the influence of topography on SAR backscatter, we implemented radiometric terrain correction based on the approach proposed by [PERSON] and [PERSON] [55], as implemented by [56; 57]. This approach incorporates an angular-based correction that considers the angular relationships between the SAR image and terrain geometry. We utilized the SRTM dataset at a resolution of 1 arc-second (30 m), which is readily available in GEE [58]. The latter was replaced by our DEM with a higher resolution of 10 m, where it was available. The primary objectives of these pre-processing steps are to mask and exclude pixels located in the active layover and shadow areas of the image [55; 59]. Following the  
Figure 2: SAR-RST-FLOOD approach flowchart.  
completion of the pre-processing steps, we converted the output to a logarithmic scale of dB. This conversion facilitates clearer visualization and enhances image interpretability.
## 3 Results and Discussions
Furthermore, the effect of a smaller target was investigated (see SI) and we found that even a nodule of 2.5 mm in radius could be identified in the confocal images at a depth of 4 cm and at 0.6 GHz. The \(R\) value, in this case, was 23.3 against 26.3 for the 5 mm target radius. This shows that even if the wavelength at 0.6 GHz is 6.7 cm (see Figure 1) since the target was located in the near-field of the antennas it could still be localized. To further investigate the performance of this localization method, some simulations were repeated for the best conditions, i.e., 0.6 GHz and \(5\times 5\) antenna distribution, up to 10 cm of depth. Figure 9 reports these results which show how the ratio \(R\) increased up to more than 35 at 8 cm of the target depth and sharply decreased for further depths. This demonstrates how this localization could theoretically work for very deep nodules. Figure 8: Ratio \(R\) between the mean intensity at the expected target position and the rest of the map for \(3\times 3\) (**a**), \(4\times 4\) (**b**), and \(5\times 5\) (**c**) antennas distributions.
## 3 Proposed Methodological Approach  
As emerged from the previous literature review, MCDM integration is a commonly adopted practice to support decision-making in the renewable energy sector with a particular focus on the solar energy field and is evidently considered a strategic approach in contemporary research. However, to the best of the authors' knowledge, despite having been successfully implemented in other application fields [31], an integration between DEMATEL and ELECTRE TRI has not been proposed before for the implementation of data-driven measures aimed at optimizing supply chain management in the solar industry. We aim to address this gap by providing a framework capable of offering meaningful advantages when dealing with complex decision-making problems, as explained in the following.  
* DEMATEL helps in understanding the interrelations among criteria, providing insights into the structure of the decision problem [32]. This understanding enhances the formulation of criteria weights, which in turn improves ELECTRE TRI accuracy.
* The hybrid approach enables a more robust sensitivity analysis by considering both causal relationships among criteria (from DEMATEL) and the trade-offs between alternatives (from ELECTRE TRI) [31]. This comprehensive analysis enhances decision-makers' confidence in the chosen solution.
* Integrating DEMATEL with ELECTRE TRI enhances decision transparency by offering a structured framework for identifying influential criteria [33] and assessing the performance of a great volume of alternatives [34]. This stimulates stakeholder confidence and facilitates effective communication throughout the decision-making process.
* Compared to integrating other MCDM methods, combining DEMATEL and ELECTRE TRI may offer a deeper coverage of decision aspects. DEMATEL captures the causal relationships among criteria [32], providing a comprehensive understanding of the decision structure, while ELECTRE TRI evaluates alternatives based on outranking relations [31]. This dual perspective ensures a thorough exploration of the decision space, leading to well-informed and robust decisions.  
The next subsections briefly provide relevant methodological details.
## 5 Conclusions
### Implications for Theory and Practice
#### 5.1.2 Practical Implications  
Firstly, we have improved the efficiency of product innovation. Using the FOS and BERTopic, the product innovation opportunity identification method proposed in the thesis can help companies and research institutions to more accurately identify innovation opportunities in invalid patents. This helps to improve the efficiency of product innovation, reduce waste of resources and promote the competitiveness and market position of firms.  
Secondly, we facilitate technology transfer and transformation. By identifying innovation opportunities in invalid patents, the findings of the thesis can help the implementation of technology transfer and transformation. The technological resources contained in invalid patents can be reused to facilitate technology transfer and transformation, promoting the industrialisation and commercialisation of technological achievements.  
Again, we provide decision support tools: the product innovation opportunity identification method proposed in the thesis can be used as a decision support tool to help companies and research institutions make more informed choices in product development and innovation investment decisions. This helps to reduce innovation risk and improve the accuracy and success of decisions.  
In addition to the above implications, the method proposed in this paper can also help small and micro enterprises that lack the resources to develop new technologies or large enterprises that wish to develop higher level technologies first and thus capture the market. Researchers can use this approach to discover technologies or methods in other fields from invalid patents and generate new ideas to solve their problems.  
The constructed pathways thus provide companies with precise insight into trends in embodied technology areas and optimise the allocation of resources for technological innovation as a scientific basis for decision-making.
## 2 Description of the Origami Twisted Tower
### Kinematics of the Twisted Tower
#### 2.2.2 Bending Motion  
For the convenience of analyzing the bending motion of the one-layer twisted tower, the bending motion can be considered as two synchronized parts: one is the inclination between the top and bottom plates, and the other is the rotation angle around the central axis. Figure 6a shows the side view of the bending motion for the i-th layer. Specifically, \(\phi_{i}\) is the inclination angle, \(d_{i}\) is the distance between the center points of the top and bottom plates, and \(h_{i}\) is the distance between the highest point of the top plate and the corresponding point of the bottom plate [32]. Figure 6b is the side view with the maximum bending angle. The maximum bending angle \(\phi_{i}^{max}\) can be represented as follows [33]:  
\[\phi_{i}^{max}=2\sin^{-1}((h_{i}-\varepsilon_{d})/4R) \tag{6}\]  
Figure 4: The geometric model of the axial motion of a two-layer twisted tower: (**a**) the skeleton model of a two-layer twisted tower; (**b**) the trajectory of the apex with axial motion in top view.  
Figure 5: Definition of offset angle \(\alpha\): (**a**) clockwise twisting; (**b**) counterclockwise twisting.  
where \(\varepsilon_{d}\) is the thickness of the plate and R is the radius of the circumscribed circle. The radius R can be represented as follows:  
\[R=\left(L+\sqrt{2}L/\sin(\pi/8)\right)/2 \tag{7}\]  
The distance \(d_{i}\) between the two center points can be represented as follows:  
\[d_{i}=h_{i}-2 Rsin(\phi_{i}/2) \tag{8}\]  
As discussed earlier, the axial extension/contraction motion of the twisted tower can be uniquely determined by the offset angle \(\alpha\). The bending angle \(\phi\) can also be uniquely determined by the offset angles \(\alpha_{1}\) and \(\alpha_{2}\) of a pair of link plates, as shown in Figure 7. The projection of the bottom octagon surface is represented by the line \(A^{\prime}B^{\prime}\). The upper octagon shows the position of each point after the bending motion, and the dashed line represents the direction in which the bending motion occurs. It can be considered that the height of point A is the same as that of point C, while the height of point B is the same as that of point D. \(A^{\prime}A\) and \(B^{\prime}B\) are the two sides corresponding to the two link plates. The angle between \(A^{\prime}A\) and the bottom plane is \(\alpha_{1}\), while that between \(B^{\prime}B\) and the bottom plane is \(\alpha_{2}\). The height difference between points A and B can be obtained as \(\Delta\)h, and the radius of the inscribed circle of the octagon is r. Thus, the bending angle \(\phi\) can be calculated as follows:  
\[\phi=\sin^{-1}(\Delta h/2r) \tag{9}\]  
Figure 6: Schematic of bending motion: (**a**) side view with bending angle \(\phi_{i}\); (**b**) side view with maximum bending angle \(\phi_{i}^{max}\).  
Figure 7: Side view of a bending one-layer twisted tower with offset angles \(\alpha_{1}\) and \(\alpha_{2}\).
###### Abstract  
In vitro cultured 3D models of CRC have been demonstrated to hold considerable worth in drug discovery, drug resistance analysis, and in studying cell-cell and cell-matrix interactions that occur in the tumor microenvironment. The 3D models resemble the in vivo physiological microenvironment by replicating the cell type composition and tissue architecture. Molecularly imprinted polymers (MIPs) have been investigated for use instead of antibodies against small non-immunogenic structures, such as sialic acid (SA). Glycco-conjugates including SA are present on all cells, and often deregulated on cancer cells. Here, we present a novel approach for targeting and detecting colorectal cancer cells (CRC) by using in vitro cultured HT29 3D spheroids co-cultured in vitro with either fluorescent MIPs targeting SA, SA-MIPs, or the two lectins targeting SA, MAL I, and SNA. Both formaldehyde-fixed and viable HT29 3D spheroids with or without SA-MIPs were imaged in 3D by confocal microscopy. The results revealed a preserved cell morphology and viability of the HT29 3D spheroids co-cultured in vitro with SA-MIPs. However, the lectins MAL I and SNA targeting the \(\alpha\)-2,3 or \(\alpha\)-2,6 SA glycosidic linkages, respectively, affected the cell viability when co-cultured with the viable HT29 3D spheroids, and no living cells could be detected. Here, we have shown that the SA-MIPs could be used as a safe and low-cost diagnostic tool for targeting and detecting cancer cells in a physiologically relevant 3D cancer model in vitro.  
3D culture; colorectal cancer; molecularly imprinted polymers; sialic acid; spheroids +
Footnote †: journal: Medical Cancer Cell Spheroids Co-Cultured with Molecularly Imprinted Fluorescent Particles Targeting Sialic Acid Show Preserved Cell Viability.
# A Joint Denoising Learning Model for Weight Update Space-Time Diversity Method +
## 5 Evaluation and Result Analysis
### Simulation Results
Thus, methods such as \"Wiener+Ours2\", \"KBM-ANC+Ours2\", \"CNN+Ours2\", and \"Ours1+Ours2\" also exist. \"Ours1+Ours2\" denotes the two-stage processing method proposed in this paper. Each of the above experiments was repeated 200 times at \(\theta 1=30\lx@math@degree\) and an SNR of \([-20,10]\) dB. Figure 19a shows that the stability of resistance to MAI increases after replacing the second stage with the proposed STD method, and the best results are obtained with the two-stage method proposed in this paper. The same advantage is also reflected in the BER comparison in Figure 19b. The high or low BER value directly reflects MAI's good or bad suppression. The lower BER value indicates better communication quality and a lower impact on MAI. The above results show that the two-stage method proposed in this paper has better resistance to MAI.
# Liquid-Crystal-on-Silicon for Augmented Reality Displays
## 3 Fringing Field Effect and Novel Solution
### Fabrication Method  
The pretilt angle pattern can be realized by a photoalignment layer, which provides inhomogeneous anchoring energy distribution. Two methods have been proposed to realize the spatial variant pretilt angle control by [PERSON]'s group. In 2011, the stack of a horizontal alignment polymer on a vertical alignment layer was reported [80]. By controlling the dosage of UV light exposure, the degree of polymerization can be adjusted spatially. After rinsing away the unexposed area, the ratio of polymerized homogeneous alignment domain and exposed vertical alignment domain coordinately determine the local LC pretilt angle. In 2013, [PERSON] et al. reported a new alignment material which enables direct UV patterning on a pretilt angle [81]. Based on the above-mentioned methods, a two-step manufacturing process can generate FFE-opposing features (Figure 10): (1) A uniform pretilt angle \(\alpha_{1}\) is produced on the polyimide (PI) alignment layer on ITO glass by uniform UV irradiation; (2) A second photomask for selective UV exposure is added to differentiate the pretilt angle \(\alpha_{i}\) in inner zone and \(\alpha_{o}\) in outer zone. If the alignment layer is replaced by silicon oxide [82], an e-beam treatment can be adopted for the patterning.  
For infrared spatial light modulators, the patterned pretilt angle and anchoring energy can also be achieved on the polymer-stabilized liquid crystal (PSLC) [83; 84]. First, an LC-monomer mixed precursor is prepared and filled in the LCoS panel. Second, a photomask with gradient optical density is required to generate spatially variant UV light intensity. The UV light irradiated on the LC layer is more intense in the center region than at the pixel edge, therefore, in the center, it generates a higher degree of polymerization. Through this spatially variant polymerization process, an inhomogeneous pretilt angle and anchoring energy distribution can be formed, as Figure 11 illustrates.  
Figure 9: (**a**) The pretilt angle dependent phase retardations. (**b**) Spatial phase retardation uniformity at voltage-off state.
# Using Hybrid LSTM Neural Networks to Detect Anomalies in the Fiber Tube Manufacturing Process
###### Abstract  
The production process of tubes for fiber optic cables is a complex process, where proper execution is crucial to the quality of the final product. This process has a complex state vector whose structure and coordinates dynamically change during the tube extrusion process. Small fluctuations in process parameters, such as temperature, extrusion pressure, production speed, and optical fiber tension, affect the optical attenuation of the final product. Such defects necessitate the withdrawal of the product. Due to the high number of process coordinates and the technological inability to automatically label those segments of the production process that cause anomalies in the final product, the authors used data clustering methods to create a training set that enabled the use of neural tools for anomaly detection. The system proposed in the main part of the paper includes a hybrid Long short-term memory (LSTM) network model, which is fed with data streams recorded on the tube extrusion production line. The input module, which performs preprocessing of input data, conducts multiresolution analysis of recorded process parameters, and recommends the process state's belonging to a set of classes describing individual production anomalies to appropriate LSTM network modules. The learning process of the three-channel network allowed effective recognition of five classes of the monitored tube production process. The fit level of the proposed network model reached R2 values of \(\geq\)0.85.  
Keywords:optical tubes extrusion process; data clustering; LSTM networks; anomalies detection
## 3 Results and Discussion
### Maximum Liquid Length  
Spray liquid length is the maximum axial penetration of the liquid phase fuel. Previous studies have demonstrated that a shorter spray liquid length leads to a better air-fuel mixing, because the fuel vaporization is completed before the fuel reaches the combustion region [32]. On the other hand, too long liquid length leads to fuel wall impingement on the cylinder, which could produce large soot emission with reduced engine efficiency [33]. Figure 4 shows the liquid length (LL) at 800 K and 900 K for the tested fuels. The injection pressure is 1500 bar and the oxygen concentration is 15%. The LL is constant in time; therefore, the values correspond to an average between 800 \(\upmu\)s and 4000 \(\upmu\)s. From this figure, it can be seen that the spray liquid length of OME\({}_{1}\) is shorter, which could be due to the lower distillation temperature of OME\({}_{1}\) (37.40 \({}^{\circ}\)C) compared to the other fuels. Additionally, as [PERSON] indicated in a previous study [34], low viscosities and high densities lead to shorter liquid lengths. This could explain why OME\({}_{x}\) (\(v\) = 1.082 mm\({}^{2}\)/s; \(\rho\) = 1057.1 kg/m\({}^{3}\)) shows shorter LL than diesel and HVO (\(v\) = 2.7 mm\({}^{2}\)/s; \(\rho\) = 779.1 kg/m\({}^{3}\)).
## 2 Materials and Methods  
### Study Protocol and Eligibility Criteria  
The study protocol was prepared following the Preferred Reporting Items Systematic Review and Meta-Analyses Statement [13; 14; 15] and registered in PROSPERO (Prospective Register of Systematic Reviews).  
The focused question of the present review was: \"What are the hemostatic measures adopted in cases of bleeding following dental extractions in patients treated with oral anticoagulants?\"  
The following PICO (Patient, Intervention, Comparison, Outcome) was established for article inclusion:  
(P) Type of participants: patients treated with oral anticoagulants  
(I) Type of interventions: dental extraction, either single or multiple  
(C) Comparison between interventions: any type of hemostatic measure compared with other treatments/placebo/no treatment  
(O) Type of outcome measures: bleeding control following dental extraction.  
The inclusion criteria were:  
* Patients treated with oral anticoagulants
* Patients undergoing dental extractions
* One of the following study designs: observational studies, case-control studies, randomized controlled trials and interventional studies
* A minimum number of 10 participants in the study
* English language. The exclusion criteria were:
* Patients not treated with oral anticoagulants
* Patients undergoing oral surgical interventions other than tooth extraction
* One of the following study designs: systematic reviews and review articles, case reports and case series
* Less than 10 participants in the study
* Articles not written in English.  
No time limitations were set.