esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of Test
### Layout of Measuring Sensors  
For all specimens except specimens CP-4 and CP-5, the buried depth, \(L_{t}\) was 3.10 m and 0.4 m height of the specimens left unburied, as shown in Figure 5. A total of 20 strain gauges, 16 soil pressure cells and 13 linear variable displacement transducers (LVDTs) were used for these specimens to measure their strain distributions, soil pressures and lateral displacements, respectively. The strain gauges were symmetrically attached to both sides of the piles and labeled as S1 to S20 as illustrated in Figure 5a. The first two soil pressure cells were attached to the pile at the buried depth of 175 mm below the soil surface, and rest were spaced at 350 mm interval along the height of the pile and labeled as T1 to T16 as shown in Figure 5b. Figure 5c shows LVDTs locations of D1 to D13. The LVDT D1 was located at the top of the specimen and the remaining LVDTs were placed from the soil surface to the bottom of the specimen at 200 mm spacing.  
Figure 4: The soil filling process. (**a**) Installation of a specimen. (**b**) Placement and details of a specimen. (**c**) Details of the pile base. (**d**) Fully filled soil box.  
While for CP-4 and CP-5 specimen, the buried depth \(L_{t}\) was 2.79 and 3.41 m, respectively and 0.4 m height of these specimens left unburied. The arrangement of the three different sensors for CP-4 and CP-5 specimens is shown in Figure 6. Similar to other specimens, the strain gauges and soil pressure cells of CP-4 and CP-5 specimens were arranged symmetrically in the front and back of the pile, while the LVDTs were arranged at one side. The displacement measurement proposed by the reference [28] for buried piles was used in the test.  
Figure 5: Sensor layouts of specimens (unit: mm). (**a**) Strain gauges details. (**b**) Soil pressure cells details. (**c**) linear variable displacement transducers (LVDTs) details.  
Figure 6: Sensor layouts of specimens (unit: mm). (**a**) CP-4. (**b**) CP-5.
# Flexible Fashion Product Retrieval Using Multimodality-Based Deep Learning
## 3 Proposed Multimodality-Based Deep Learning for Fashion Product Retrieval  
The proposed multimodality-based deep learning method consists of an image encoder, a query encoder, and a multimodal classifier as shown in Figure 2.  
The image encoder is used to extract the features of apparel images for effective searching. We added an elementwise (or pointwise) convolution of the mobile-net to the VGG-16 structure to improve the computational efficiency [28]. The CNN model is modified to remove the fully connected(FC)-layer and to store the vectorized image data in the database before the FC layer is reached.  
The query encoder inputs the words of the query into the LSTM model [29] one-by-one. Generally, words are expressed via vector embedding. However, in the proposed method, the number of words is small, because a set of keywords is used as a query. Therefore, a one-hot vector is used rather than a word embedding. After all the words of the query are vectorized, the vector stored in the cell is used as the input to the multimodal classifier for training and testing. The structure of the query form and its learning are presented in Section 3.1. Three different types of queries are supported for flexible and user-centric search: Boolean, category, and pseudo-SQL.  
The multimodal classifier is a decision maker that determines whether a specific image in the DB is the image desired by the user (corresponding to the user query) by combining vectors obtained from the image encoder and the query encoder. The result is either \"true\" or \"false\". As a result, the output of the multimodal classifier is a two-dimensional vector obtained via the softmax function or logistic regression that outputs the matching probability for a pair of the encoded query and the encoded image.  
Table 1 explains more details on the deep learning architecture of the proposed approach. It shows the input and output dimensions, the number of batch size (B), and deep learning methods. A customized VGG16 is mainly used for the image encoder, and the LSTM is used for the query encoder  
Figure 2: Proposed deep learning network for efficient fashion product retrieval. Feature vectors of product images created by the deep CNN and query vectors generated by LSTM-based one-hot encoding are then combined together to semantically train the model with fashion products and classify them.
## 6 Conclusions  
With about seven weeks BeiDou data of the BETS network, BeiDou POD is carried out using the three-day solution strategy. The results are assessed by the orbit differences over the overlapped time span of the adjacent three-day solutions.  
A number of processing scenarios are identified and data are processed to investigate the impact of tracking networks, by involving MEOs and by introducing integer ambiguity resolution for possible improvement on POD of the current BeiDou regional system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Satellite** & **Solution** & **Along** & **Cross** & **Radial** & **3D** \\ \hline \multirow{3}{*}{**IGSO**} & Free & 24 & 15 & 7 & 29 \\  & Fixed & 14 & 13 & 7 & 20 \\ \cline{2-6}  & Free & 45 & 13 & 12 & 48 \\ \cline{2-6}  & Fixed & 41 & 14 & 12 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Orbit RMS for IGSO and MEO satellites of free and fixed solutions, unit is cm.  
From the post-fit observation residuals, BeiDou has similar phase accuracy as GPS and Galileo but a slightly larger range noise. In the tracking geometry investigation, extending the Chinese network to Australia brings rather small improvement on GEOs, whereas adding the United Arab Emirates station DHAB to the west of the Chinese network along-track RMS of C03 and C05 on the same side are reduced from several meters to decimeter level, but not for C01 and C04 on the eastern side as they are not observed from the newly added stations. Further improvement is also achieved if more western stations are included. From these results, we can conclude that deploying tracking stations on the eastern side, for example in New Zealand and/or in Hawaii will significantly reduce along-track RMS of C01 and C04.  
Moreover, including the current two MEOs C11 and C12 brings further improvement on IGSO orbits by up to 10%, but no improvement on GEOs. Further tests should be carried out if more MEO satellites are available and involved.  
Performing ambiguity-fixing to all satellites brings almost no improvement on the orbit quality. However, if only ambiguities of IGSOs and MEOs are considered, the along-track RMS is reduced. In general, ambiguity-fixing does not show a significant contribution as for GPS. This may be improved after a more stable and accurate free solution being achieved by a stronger tracking geometry.
## 2 Aviation Fuel Emissions
### Comprehensive Comparison of Emissions Performance between SAFs and Conventional Aviation Fuels  
This study conducts a comparative analysis of CO\({}_{2}\)/NO\({}_{x}\)/SO\({}_{2}\)/CO/PM/UHC emissions between traditional aviation fuels and SAFs. Overall, SAFs exhibit significantly lower emissions of SO\({}_{2}\) and PM from aviation engine exhaust compared with traditional fuels. SAFs offer no obvious advantages in CO\({}_{2}\), CO, NO\({}_{x}\) and UHC emissions compared with conventional petroleum-based aviation fuels during engine combustion. However, from an LCA perspective, SAFs demonstrate a significant advantage in CO\({}_{2}\) emissions, thus positioning them as a key strategy for addressing carbon emissions challenges in the aviation industry. CO, NO\({}_{x}\) and UHC emissions are determined by both the nature of the fuel and the operating conditions of the engine, which would explain the controversy over the strength of the two fuels in terms of the emissions of these pollutants. The comprehensive comparison of emission performance between SAFs and traditional aviation fuels is presented below. From a combustion and emission perspective, the difference in CO\({}_{2}\) generation between SAFs and traditional aviation fuels is negligible. However, from an LCA perspective, SAFs derived from different processes and feedstocks, such as FT, HEFA, ATJ, and pyrolysis, can significantly reduce GHG emissions in the aviation industry by 41-89%. Due to the direct correlation between SO\({}_{2}\) emissions and sulfur content in fuels, SAFs have extremely low sulfur content, enabling pure FT fuel to reduce SO\({}_{2}\) emissions from pure JP-8 by 92%. Blending various types of SAFs with traditional aviation fuels can also reduce SO\({}_{2}\) emissions to varying degrees, depending on the blending ratio. The aviation industry's PM emissions are closely related to sulfur and aromatic compound content in fuels, showcasing the advantages of SAFs. Compared with Jet A-1, fuels such as 100% CH-SKA, 50% HEFA-SPK, and 100% FT-SPK can reduce EI-PM by 7-25%, 40-60%, and 70-95%, respectively, while ATJ-SPK significantly reduces total particle number emissions by up to 97%. In contrast, NO\({}_{x}\) emissions mostly originate from thermal drive, as engine
# Satellite Climate Data Records: Development, Applications, and Societal Benefits
## 2 CDRs and Development
### International Coordination  
The CEOS/CGMS Working Group on Climate (WGCimate [35]) was established in 2010 and is a joint group including Committee on Earth Observation Satellite (CEOS) Agencies and the Coordination Group for Meteorological Satellite (CGMS). The WGCimate coordinates and encourages collaborative activities between the world's major space agencies in the area of climate monitoring with the overarching goal to improve the systematic availability of Climate Data Records through the coordinated implementation and further development of a global architecture for climate monitoring from space. WGCimate facilitates the implementation and exploitation of ECV time-series through coordination of the existing and substantial activities undertaken by CEOS Agencies and via  
Figure 2: An example of a climate data record (CDR) production chain: (**a**) Fundamental CDR (AVHRR reflectance at 0.63 \(\upmu\)m channel); (**b**) CDR (AVHRR cloud fraction); (**c**) Thematic CDR (AVHRR aerosol optical thickness (AOT)); and (**d**) Climate Information Record (AVHRR AOT trend).  
strong collaboration with other CEOS Working Groups and Virtual Constellations. It also identifies multi-agency implementation teams for each product, reviews their actions, and ensures that a coherent implementation plan exists for each and every product. In defining these implementation plans, WGCimate fully accounts for other pertinent international initiatives, such as Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM [36]). Within SCOPE-CM, the contributing organizations coordinate their scientific and technical development activities and cooperate on the basis of shared and distributed responsibilities for the generation of global products.
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 4 Progress of CO\({}_{2}\) Utilization in China
### Development Status of CO\({}_{2}\) Geological Utilization in China
#### 4.3.1. \(\mathrm{CO}_{2}\)-Enhanced Oil Recovery (\(\mathrm{CO}_{2}\)-EOR) in China
In addition, Sinopec Shengli Oilfield and Qilu Petrochemical cooperate to carry out China's first million-tonne CO\({}_{2}\)-EOR project, which was officially completed and put into production in August 2022 [83; 138]. In this project, Qilu Petrochemical is responsible for CO\({}_{2}\) capture and transport, while Shengli Oilfield is in charge of CO\({}_{2}\) injection into oil reservoirs for oil production. CO\({}_{2}\) produced in the coal gasification process of the fertilizer plant is captured and transported by tanker truck; 73 injection wells and 166 production wells are deployed; and CO\({}_{2}\) is injected into the ultra-low permeability reservoirs of Shengli Oilfield. It is expected to inject a cumulative amount of more than 10 million tonnes of CO\({}_{2}\) over the course of 15 years [138], to achieve the purpose of increasing oil production by 3 million tonnes [83] and enhancing oil recovery by 12% [139].
## 2 Experimental Program
### Description of the Experimental Method  
#### 2.2.1 Digital Image Correlation (DIC) Method and Instrumentation  
DIC is a computer vision-based data analysis program that uses mathematically related methods to analyze digital images of specimens undergoing deformation and has been widely used in various applications. Compared with the conventional technology, DIC has the advantage of continuously measuring spatial displacement and full field strain [34; 35]. It was necessary to prepare the concrete specimens before the DIC testing. Firstly, dirt and dust was removed from the surface of the specimens. Additionally, speckle marks were sprayed onto the outer surface of the concrete specimens, as shown in Figure 4a. The camera was calibrated before the start of the strain measurement test. \(90\times 72\) mm calibration plate was used in this test.  
For the testing of generalized strain at the crack tip, the speckles on the surfaces of the specimens were identified by DIC technology. The displacement field of the specimens was obtained by image  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{3}{c}{**Specimen Size/mm**} & \multicolumn{3}{c}{**Crack Size/mm**} & \multirow{2}{*}{**Moisture**} & \multirow{2}{*}{**Test Box**} \\ \cline{2-2} \cline{5-8}  & **Length** & **Width** & **Height** & **Length** & **Width** & **Height** & **Content/\%** & **Temperature/\({}^{o}\)C** \\ \hline A1 & 300 & 150 & 150 & 150 & 1 & 75 & 100 & \(-\)20 \\ A2 & 300 & 150 & 150 & 150 & 0.6 & 75 & 100 & \(-\)20 \\ A3 & 3  
matching before and after deformation. The correlation coefficient \(C\) related to the image gray level is expressed as follows:  
\[C=\frac{\sum\sum\left[f(x,y)-\overline{f}\right]\bullet\left[g(x^{\prime},y^{ \prime})-\overline{g}\right]}{\sqrt{\sum\sum\left[f(x,y)-\overline{f}\right]^{2 }\bullet\sum\sum\left[g(x^{\prime},y^{\prime})-\overline{g}\right]^{2}}} \tag{1}\]  
where \(f(x,y)\) is the gray level of the reference coordinate points; \(\overline{f}\) is the average gray level of the reference sub-domain; \(g(x^{\prime},y^{\prime})\) is the gray level of the coordinate points (\(x^{\prime},y^{\prime}\)) on the target map; and \(\overline{g}\) is the average gray level of the target sub-region. By matching points in the calculating area of the two images before and after deformation, the change in the position of the relevant points and the change in the displacement field distribution in the measuring range can be obtained. The displacement field was derived using the least squares method, and thus the generalized strain field distribution in the measuring area was obtained.
## 4 Case Study and Hybrid Modelling
### Design of Experiment and Simulation Results
Finally, only for the Scenarios 2 and 3, the model has been used to evaluate the system's availability, the mean waiting time in queue and the mean number of jobs in queue for different level of service utilization \(r=\) [0.5; 0.6; 0.7; 0.8; 0.9; 0.95; 0.99]. As shown in Table 6, this subset of scenarios confirms that the performances of the queueing system decrease when operational conditions and aging are considered, and this is more emphasized in Scenario 3 (W\({}_{\mathrm{i}}\): Mean waiting time in the queue; A\({}_{\mathrm{i}}\): Availability of the machine tool; L\({}_{\mathrm{i}}\): Mean items in the queueing system). Figure 8: The Monte Carlo simulation process of progressive mean waiting in queue (W).
## 3 Experiments and Results
### Experiment 2: Questionnaire Survey
#### 3.2.2 Results  
The questions and their answer rates are shown in Figure 13. The red and blue sides represent positive and negative responses, respectively, and the gray sections are neutral responses. Under the visual+force condition, more than half of the answers to all questions were positive. Figure 14 shows the responses to the questions on immersion and illusion intensity under the three conditions, and Table 1 summarizes the statistical results analyzed by one-way ANOVA and the multiple comparisons made by the Tukey-Kramer method at the 0.05 significance level. The immersion responses were slightly higher under the conventional and visual+force conditions than under the visual condition, but the difference was not statistically significant. In contrast, the illusion intensity was significantly different between the visual+force condition and the visual condition.  
Figure 13: Questions and responses in the questionnaire (all responses are expressed as percentages of participants who selected the specific response).
## 2 Theoretical Background
### Comparing Human and CNN Attention Maps  
#### 2.2.1 Overview of Findings  
Typically, the similarity between human and CNN attention maps is quite low [10; 47; 48; 49; 50]. Human attention tends to be more selective and focused on specific areas, while CNN attention is more diffuse and distributed [50; 51]. Some studies found that CNN put more weight on context than humans [9; 11]. For instance, CNN attention maps may highlight the mere presence of body parts (e.g., fingers or lips) to classify skin diseases [45]. Accordingly, the areas attended by humans are more discriminative and diagnostic [8].  
Aside from these general observations, human-CNN similarity depends on _technological factors_. First, it varies with the network [9; 10; 11; 52]. For instance, similarity is higher for deeper networks with more layers [9] and for networks that process information more like humans, for instance via biologically plausible receptive fields [10] or human-inspired attention mechanisms [47; 53]. A second technological influence on human-CNN similarity is the XAI method used to elicit CNN attention maps [8; 49; 51; 54]. This is not surprising, given the immense variety in the outputs of different XAI methods: some highlight edges while others highlight broad regions and some provide pixelated or patch-like segments while others provide smooth and gradual heatmaps. Finally, the similarity may also depend on an interaction of neural network and XAI method [55]: a particular network may appear more similar to humans than another network when explained by one XAI method but less similar to humans when explained by another XAI method.  
In sum, previous studies have provided a rather nuanced picture of the technological influences on human-CNN similarity. Conversely, they have rarely investigated any human-related influences or factors that affect human attention maps. In the present study, we investigated two of these factors: the tasks used to elicit human attention maps and the images to be categorized. Before specifying our research questions, we will give an impression of the variability in these factors across previous studies.
# Review of Propulsion System Design Strategies for Unmanned Aerial Vehicles
## 4 Discussion
### Literature Analysis
#### 4.1.1 UAV Types  
Considering the UAV type, the focus of most of the studies was divided between fixed-wing UAVs and multirotor vehicles. Within the latter sub-category, fourteen papers explicitly referred to quadrotor systems [16; 24; 31; 32; 34; 35; 43; 44; 50; 79; 83; 90; 105; 106], two to hexarotor UAVs [64; 133], one to bicopters [85] and one to octocopter drones [108], respectively. Several papers did not specifically state a UAV type target in terms of architecture configuration, whereas others focused more properly on the drone characteristics, as the specific performance that the UAV needed to assure, or the kind of mission in which the system was expected to be engaged [36; 40; 41; 42]. Apart from the fixed-wing and multirotor UAVs, five papers described flapping-wing drones [60; 94; 110; 113; 114], paying particular attention to the design of the flapping system itself.  
The current taxonomy highlights the architectural configurations of UAVs, therefore allowing capturing possible differences among design approaches, which emphasize the characteristics of the drones connected to geometric- and dynamic-related factors. An alternative taxonomy could investigate the UAV size, for instance to compare the number of papers devoted to each size, as an indicator of the academic interest in the topic, market shares of the products in the corresponding categories, as an indicator of the impact of research, or the number of filed patents referring to the different UAV size, as an indicator of the business interest.
# Study on Creep Behavior of Silty Clay Based on Fractal Derivative
## 5 Discussions  
The classical Burgers model and fractional Burgers model were improved by introducing the fractal dashpot, and the creep characteristics of the soft soil sampled from a riverside in Jiangsu were analyzed in combination with the test results. The proposed fractal Burgers model can accurately describe the creep characteristics of the soil, which verifies the applicability and convenience of describing the viscoelastic plastic characteristics of the soil with a fractal dashpot. However, the fractal derivative creep model mentioned in this paper is only a mathematical expression based on the fractal derivative, which is a preliminary result of the integer derivative of time [27]. As a relatively new component, the exact physical significance of fractal dashpot and the physical mechanisms of describing the model are still questions that need to be further studied.  
The complete curve of the classical creep test consists of three stages: instantaneous creep, steady-state creep, and accelerated creep [32]. In the actual project, the failure deviatoric stress state usually does not occur, so the deviatoric stress state of the creep test is lower than the failure stress, and the creep curve mainly shows the creep characteristics of the first two stages [35; 39]. By carrying out the failure deviatoric stress creep test of the soil, the characteristics of the three stages of creep can be further understood by considering the failure process [33]. In addition, the current traditional creep model is established under a specified constant stress level and confining pressure, and the model parameters are determined by fitting the test data. However, the laboratory test samples are limited, and the influence of confining pressure and stress at any state on the creep model parameters are not quantified. How to deduce the creep model parameters related to any stress and confining pressure state through the limited data of the laboratory test will have higher practical research significance and value.  
The laboratory creep test of the soil is generally carried out in a limited period of time (ranging from a few days to several months). The proposed creep model can well characterize the results of the short-term creep test, but the service period of the actual project is nearly 100 years, which belongs to long-term creep, and the geological conditions at the project site are complex--whether the proposed model can accurately characterize the long-term creep behavior and how to speculate the long-term creep behavior in the actual project through the laboratory test is a problem that needs future study. At present, there is an adopted method to build a numerical simulation model through the laboratory test and predict the long-term creep of the actual project [46], but more engineering measurement cases are still needed to verify it.