esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
Hence, the values of both R\({}_{1}\) and R\({}_{2}\) decreased in the presence of carbon materials. In the presence of both Ag NPs and carbon materials, the values of R\({}_{1}\) (4.88 \(\Omega\)) and R\({}_{2}\) (24.55 \(\Omega\)) were the lowest. In this case, more electrons were generated and better transported by a combination of plasmonics and the \(\pi\)-\(\pi\) conjugation affecting the FTO/TiO\({}_{2}\) and dye-adsorbed TiO\({}_{2}\)/electrolyte interfaces. Therefore, the values of R\({}_{1}\) and R\({}_{2}\) were reduced and the parameters were determined by EIS, as shown in Table S1. Figure 7: Electrochemical impedance spectroscopy (EIS) data of DSSCs based on: (a) unmodified open-ended TiO\({}_{2}\) NTAs, (b) embedded Ag NPs, (c) applied with carbon materials, and (d) with both Ag NPs and carbon materials. The incident photon-to-current efficiency (IPCE) of DSSCs based on the open-ended TiO\({}_{2}\) NTAs is shown in Figure 8. Plasmon is a type of quasiparticle consisting of free electrons collectively vibrating within the metal. At the interface of a metal with a negative dielectric constant and a medium with a positive dielectric constant, surface plasmon resonance (SPR) combines a spreading electromagnetic wave (from visible to near-infrared frequency) with the plasmon. This combination generates plasmon-polariton, which leads to optical absorption; a strong electric field is also generated in some parts. During SPR, the light energy accumulates on the metal nanoparticle surface, and optical control is possible in the frequency range below the optical diffraction limit. Therefore, the intensity of DSSC based on open-ended TiO\({}_{2}\) NTAs embedded with Ag NPs was higher than without embedded Ag NPs. This may mean that more electrons were generated by the plasmonic activities, which increased the short circuit current. The current intensity in the DSSCs based on open-ended TiO\({}_{2}\) NTAs with carbon materials was also higher than in those without. This may mean that electrons were better transported by \(\pi\)-\(\pi\) conjugation, which also increased the short circuit current. Moreover, the current intensity was the strongest in the presence of both Ag NPs and carbon materials. In this case, electrons were generated in large quantities and were better transported by plasmonic activities and \(\pi\)-\(\pi\) conjugation.
## 3 Experimental Results
### Generalization Verification  
In an ideal situation, the image after the principal component extraction would be fed to the DeepLab v3+ neural network, and the spatial feature information would be obtained after training. However, in real scenes, many data to be classified are unlabeled, which requires that the trained neural network can also extract spatial feature from unseen images. Therefore, two labeled pictures in the HyRANK dataset are used to verify the generalization of the feature extraction network. Among them, the picture Loukia is used as a known dataset, and the picture Dioni is used as an unknown dataset. After feature extraction and fusion, the classification results are obtained. Table 6 represents the classification result of the known dataset Loukia and the unknown dataset Dioni using different classifiers.  
The results show that the classification accuracy of Loukia is relatively mediocre. The reason is that the same feature category is scattered and each area is small. Different types of feature categories are mixed together, which brings great difficulty to the classification. Apart from this, the difference in spectral characteristics between different types of ground features is not obvious enough, which is also a reason for the poor classification.  
However, although Dioni is a new dataset for the feature extraction network, the framework can still perform the classification task excellently. Facing the same environmental conditions, the framework has a strong classification ability for hyperspectral data collected by the same sensor. Therefore, the framework has strong generalization ability and application prospects.
## 3 Optimization Problem Characterization  
The key objective here is to maximize the sum rates for user devices in the MISO-NOMA cell. Sum rate maximization is considered based on optimizing the power coefficients for each user terminal in compliance with the status of the channel between each user and the BS. In downlink MISO-NOMA, the objective function or the sum rates for \(M\) user devices can be formulated as [3; 22]:  
\[R_{sum}=\sum_{i=1}^{M}\log_{2}\Bigg{(}1+\frac{|\mathbf{h}_{i1}+\mathbf{h}_{i2}|^{2}P_ {t}\eta_{i}}{|\mathbf{h}_{i1}+\mathbf{h}_{i2}|^{2}P_{t}\sum_{j=1}^{i-1}\eta_{j}+\sigma ^{2}}\Bigg{)} \tag{16}\]In the optimization problem, the constraints can be presented as follows:
## 6 Conclusions  
The feature-level fusion of multi-source remote sensing images has shown their potential in many fields. In this study, to explore land use/land cover (LULC) classification in mountain area, a novel feature-level fusion framework, in which two different cloudiness amounts of the Landsat operational land imager (OLI) images, and an fully polarized Advanced Land Observing Satellite-2 (ALOS-2) image was applied for conducting the experiments. Comparing the accuracy of several types of remote sensing images, the results demonstrated that the feature fusion data performed better than that from single remote sensing data source, and achieved the best results with an OA of 87% and a Kappa of 84.5%. These findings not only can promote the use of multi-source remote sensing images whose features have been fused but also emphasize that SAR datasets are important to effective observing the earth's surface. However, it should be pointed out that the features were manually prepared in the present study, which is a limitation for rapid LULC classification mapping. An automatic approach should be considered to extract features from multi-source imagery. It is also possible to directly use an existing features database if available.  
Due to the particularity of SAR data, the geometric deformation of microwave images was different from optical remote sensing images. Although the terrain correction for ALOS-2 data was based on the RD localization model, the terrain corrected image is not quantitatively evaluated, and it is impossible to determine how much the contribution of the terrain correction is. For instance, this study only analyzes the fusion of Landsat-8 OLI and SAR images. Therefore, some additional datasets could be applied to improve the classification accuracy if it is available.  
This work aims to provide a reference for selecting the suitable data and classification method for LULC classification in cloudy mountain areas. The findings of this paper indicates that when in cloudy mountain areas, the fusion data should be preferred, during the period of low cloudiness, the Landsat OLI data should be selected, when no optical remote sensing data are available, and the fully polarized ALOS-2 data are an appropriate substitute.  
**Author Contributions:** [PERSON] and [PERSON] assisted with the study design and the interpretation of the results; [PERSON] designed and wrote the paper; [PERSON], [PERSON] and [PERSON] developed the algorithm and drafted the preliminary version of this paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China (grant number 2016 YFB0501403).  
**Conflicts of Interest:** The authors declare that they have no conflicts of interest.
# One-Dimensional Consolidation Properties of Soft Clay under Multi-Stage Loading
## 3 Test Results and Discussion
### Variation Law of Consolidation Degree  
According to the measured displacement during the test, the degree of consolidation can be calculated:  
\[U_{t}=\frac{s_{ct}}{s_{c}} \tag{12}\]  
where \(s_{ct}\) is the deformation value measured at a certain time, and \(s_{c}\) is the final deformation value measured in the consolidation test. Figure 4 illustrates the changes in the degree of consolidation over time under both single-stage loading and multi-stage loading conditions. Upon reaching a loading of 200 kPa, the specimen experiences rapid and significant deformation. Within 60 min, the degree of consolidation exceeds 95%, and the settlement rate is less than 0.01 mm per hour after 240 min. This signifies the completion of primary consolidation and the beginning of the secondary consolidation stage. In the case of specimens subjected to multi-stage loading, each stage of the load application triggers immediate large deformations, and the degree of consolidation increases non-linearly with each subsequent load. The number of stages directly impacts the duration of primary consolidation, with a higher number of stages leading to an extended consolidation period.  
Figure 4: Variation in \(U_{t}\) with \(t\) for multi-stage loading.  
The rate of consolidation for single-stage loading is generally faster than that for multi-stage loading. As a result, when using multi-stage preloading for foundation treatment, it is recommended to reduce the number of preloading stages and expedite the consolidation rate of the foundation soil to meet the required strength.  
There is a strong correlation between the average degree of consolidation of soft soil and the consolidation stress under multi-stage loading. A regression formula has been developed to determine the design value of the vertical degree of consolidation based on the load value. After comparing several commonly used fitting formulas, it was found that \(U_{t}\) and \(P\) have a logarithmic relationship. The fitting formula and the correlation coefficient value, \(R^{2}\), are presented in Figure 5. The empirical relationship between the average vertical degree of consolidation and consolidation stress of soft marine soil in northern China can be expressed as follows:  
\[U_{t} =M\ln P+N \tag{13}\]  
where \(M\) = 53.38, \(N\) = \(-\)181.755, \(M\), and \(N\) are the undetermined coefficients. The values for soft soil in different places can be calculated using the least-square method based on the test results. The empirical formula can be used to determine the degree of vertical consolidation of soft soil under various loads, which serves as the foundation for calculating the settlement of multi-stage loading.
## 2 Materials and Methods
### Operational Requirements  
In addition to assessing the quantitative skill of the product, we wanted to evaluate how NUCAPS can be implemented into NHC and Ocean Prediction Center (OPC) operations. The NHC provides guidance for tropical cyclones across the entire North Atlantic. For remnants or pre-cursors of tropical cyclones, the Tropical Analysis and Forecast Branch (TAFB) of NHC provides guidance south of 31\({}^{\circ}\)N while OPC provides guidance north of 31\({}^{\circ}\)N. Both centers work closely to inform the public, other forecast offices, and management agencies using text and graphical guidance relating to tropical cyclones and disturbances. To introduce NUCAPS to the operational process, we consider the (i) accuracy for monitoring and forecasting, (ii) quality control, (iii) latency, (iv) spatial coverage, and (v) display within forecaster software systems. The methods in the preceding sections help assess (i) and (ii).  
Product latency is defined as the time difference from when an observation is made to when it arrives in front of the forecaster. Latency is critical to forecasting, as the atmosphere can rapidly change and forecasters must quickly and accurately synthesize model, in situ, and satellite observations to disseminate watches and warnings to the public.  
In operational forecasting, the MW and IR sounding data are downlinked at the command and data acquisition (CDA) sites in Svalbard and McMurdo. The MW and IR sounder data from NOAA-20 are then ingested by the NUCAPS algorithm, and the resulting retrieval products are delivered to forecasters within \(\approx\)60 min using the SBN. The direct broadcast data stream is an alternative data access route, where a ground receiver station can capture MW and IR sounder data within its line of sight [27; 28]. Using direct broadcast, IR and MW sounding observations have a lower latency (\(\approx\)20 min) but with regional coverage.  
NHC and OPC display satellite data using the NCEP AWIPS (N-AWIPS) system, which was first deployed in 1994. Both centers are migrating to the newer AWIPS-II architecture. NUCAPS has been extensively evaluated for display in the AWIPS-II through the Hazardous Weather Testbed (HWT) [1; 29]. However, because NUCAPS is not displayable in N-AWIPS, we evaluated alternative display options during the campaign.  
NUCAPS was delivered as both a graphical skew-T and a gridded \"plan view\" format [29]. We chose to distribute the graphics using a web-based display because it is easily accessible and does not take up additional space on a crowded AWIPS display. Forecasters at times must work remotely, and web tools enable access outside of AWIPS. Display in the AWIPS-II remains important, as it allows for overlay and comparison with other products, but it was not assessed in this study.
# Underwater Glider Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis
## 3 General Operation States of Underwater Gliders
### Inflection State  
In Figure 4, the schematic diagram of the vehicle in the inflection state or neutral buoyancy state is shown. This state is the transition between the descent and ascent states, where the VBS increases the buoyancy force \(F_{B}\) to balance the weight of the vehicle, obtaining a net buoyancy force that is equal to zero or neutral buoyancy (\(B=F_{B}-\omega\cong 0\)), slowing down the vehicle and reducing the hydrodynamic reactions until it reaches the state of rest (\(U\cong 0\)). This state is commonly used to obtain the reference point of the VBS (\(V_{d}=0\)) in order to estimate the budget and the velocities of the vehicle, as well as to adjust the flight parameters during the mission. In practice, the underwater gliders are subjected to a process of ballasting before a mission to estimate the point of neutral buoyancy through prediction tools that come with the commercial vehicles.  
In the inflection state, the components that are in contact with the water are subjected to compression by the hydrostatic pressure and, therefore, to a reduction in the displaced volume that needs to be compensated by the VBS system in the operation depth. The volume reduction depends on the mechanics of the materials and the design of each component. On the other hand, the stratification in the ocean can compensate for the decrease of volume given the increment of density of the saltwater with respect to the operation depth [15]. For example, the vehicle Seaglider is designed with a special pressure hull, which compensates itself for the volume reduction by the hydrostatic pressure with the increment of density, generating a buoyancy force \(F_{B}\) similar to that found at the surface, but at 1000 msw depth [11].  
The calculation of the volume compensation at the inflection point of the vehicle is outside the scope of this article, however, it is recommended to take it into account as part of the comprehensive study of a glider to be considered in the VBS budget.  
Thus, neglecting the compensation of VBS by the compression of the pressure hull, the reference volume \(V_{0}\) for the neutral buoyancy state is defined in Equation (1):  
\[F_{B}=V_{0}\rho_{d}g=mg\quad\rightarrow\quad V_{0}=m/\rho_{d}. \tag{1}\]
## 2 Methodological and Operational Framework for CDTs
### Deploying the CDT Operational Model in Connected Agile Supply Networks
#### 4.2.3 System Modelling  
Once we identify the problem and the case, we need to understand how the collaborative supply chain processes work, where the needs for CDTs are and understand the different capabilities/needs in relation to the CDT enablers described in Section 2.3. After that, we will be able to configure the cognition operational model process (presented in Figure 2).  
More particularly, the things to address are:  
* Operational/process modelling: Create the process workflows and map the stakeholders, roles, inputs and outputs.
* Define the needs for CDTs: Depending on the challenge, the workflows we need to understand which asset/process or even entity in the supply chain has to be modelled as CDT. We might come with a 1-to-1 relationship between CDT and entity or a group of inter-connected CDTs.
* Elaborate on the CDT enablers: For each of the CDT identified, we need to understand how the enablers apply. This is a time-consuming issue since all actors have to agree on the information to be monitored, governance issues, cognition levels of autonomy per CDT and other parameters.
* Understand and model information needs: The focus is on the information to be exchanged/collected which will be further used in the analytics/cognition models.
* Deploy and train the necessary cognition/analytics models: Different strategies are required to train different types of models. To ensure the quality of the service exposing them, special attention must be put on monitoring concept drift, and models' performance over time.
* Deploy the necessary optimization algorithms for decision making.  
Figure 4: CDT deployment methodology.  
In this phase we might result in some improvements on the scope and prioritization of the processes/actors.  
After the modelling phase, we move to the design and operation which is the actual implementation of the CDTs operation. Here, we create all inter-connectivity services (data collectors, data exchange services, integration with existing stakeholder's backend systems) and necessary cognition/optimization services and visualizations. Initial deployment is done at the different stakeholders and a first operational trial is done for testing.  
The trial phase can be on selected CDTs and a specific scenario. The main goal phase is to refine and fix the models and enablers (configured in the modelling phase) and the overall CDT performance.
# Non-Cutting Moving Toolpath Optimization with Elitist Non-Dominated Sorting Genetic Algorithm-II
## 2 Path Optimization Problem Description
### Path Smoothness  
The smoothness of a path states how curved the path is. \(A\) smoother path results in lower energy consumption for the motion trajectory. The smoothness of a curve is described by three consecutive points, \(p_{i}\), \(p_{i+1}\), and \(p_{i+2}\), using the angle between the two lines formed by these points. The smoothness of the path is quantified via calculating the average turning angle, which can be computed using (6), where the angle between two successive lines defines a corner. This equation uses the cosine rule to determine the angle. The cosine of the angle is computed by taking the dot product of the vectors \(p_{i+1}-p_{i}\) and \(p_{i+2}-p_{i+1}\), and then dividing this by the product of the magnitudes of these vectors. This relates the geometric layout of the points to their angular relationship.  
\[A[p_{i},p_{i+1},p_{i+2}]=\pi-\cos^{-1}\biggl{(}\frac{(x_{i+1}-x_{i})(x_{i+2}-x _{i+1})+(y_{i+1}-y_{i})(y_{i+2}-y_{i+1})}{D(p_{i},p_{i+1}).D(p_{i+1},p_{i+2})} \biggr{)} \tag{6}\]  
In this expression, \(p_{i}=(x_{i},y_{i})\) represents the \(i\)th point in the curve, and \(D(p_{i},p_{i+1})\) and \(D(p_{i+1},p_{i+2})\) denote the distance between points \(p_{i}\) and \(p_{i+1}\) and \(p_{i+1}\) and \(p_{i+2}\), respectively. In addition, \(\cos^{-1}\) is the inverse cosine function. The overall smoothness of the sequence is then calculated as the average of these angles:  
\[S(p)=\frac{1}{n}\sum_{i=0}^{n-1}\{A[p_{i},p_{i+1},p_{i+2}]\} \tag{7}\]  
The smoother the curve, the closer the angle will be to \(180^{\circ}\) (straight line). Here, the smoothness is improved by minimizing the difference in the arithmetic mean between the angles and \(180^{\circ}\) for the \(n\) number of traveling points, which is given as \(S(p)\). Smaller average angles imply sharper turns and less smooth paths, while larger average angles indicate smoother transitions between points. This measure can be used to optimize paths for applications requiring minimal sharp turns, which is beneficial in machining.  
Figure 1: Toolpath planning: (**a**) The original toolpath, (**b**) the newly generated toolpath considering the path length optimization.  
A smoother path can be achieved through the path smoothness objective function. In this function, the largest angle of rotation between two sequential lines on the path is reduced. Two sequential lines are removed to create a new line, thereby reducing the maximum angle on the path. Figure 2 shows the concept of path smoothness in toolpath planning. On the left-hand side, the original toolpath planning is illustrated, while, on the right-hand side, two consecutive lines, where the largest angle of rotation is positioned, are removed to create a new line, represented by dashed lines. It demonstrates the newly generated toolpath.
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 3 Elastography Techniques
### B: Shear Wave Imaging
#### 3.2.3 Point Shear Wave Elastography (pSWE)  
Point shear wave elastography (pSWE), developed in 2008, is similarly dependent on the dynamic stress ARFI but instead focuses on a single focal location, unlike 2D-SWE, which uses multiple focal locations. While 1D-TE primarily serves liver applications, pSWE offers numerous advantages that could potentially displace 1D-TE in shear wave imaging. Unlike 1D-TE, pSWE utilizes B-mode ultrasound to directly visualize and select the region of interest, whereas 1D-TE is heavily dependent on A-mode ultrasound. pSWE can easily be performed on a conventional ultrasound machine as well, with no need for any special probe or machinery, but it does not have the ability to showcase images of tissue stiffness [52; 53; 54; 55; 56; 57; 58]. See Figure 2.  
Figure 2: Methods for ultrasound elastography. By the physical quantity that was measured, the following USE methods can be divided into two groups: shear wave imaging (**right**) and strain imaging (**left**). A “humping” transducer at the tissue’s surface causes dynamic mechanically induced compression to produce shear waves, quasi-static mechanically induced displacement via active external compression or passively induced physiologic motion, and dynamic ultrasound-induced tissue displacement and shear waves via acoustic radiation force impulse excitation.  
Furthermore, we can estimate tissue stiffness using ultrasound and MR elastography techniques, which measure shear wave speed. The documentation of different stiffness metrics may vary depending on the employed methodology. The frequently reported parameters and their respective units are shear wave velocity in meters per second, the magnitude of the complex shear modulus in kilopascals (referred to as \"shear stiffness\" in literature), and Young's elastic modulus in kilopascals (referred to as \"elasticity\" in medical literature). The absence of consistency in the documented parameters hinders the ability to make comparisons between different procedures. These elastography techniques offer the capability to assess factors beyond stiffness, such as shear wave attenuation and tissue viscosity. However, it is important to note that these additional parameters are still being investigated and have not been reported in clinical settings [40; 59].