esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockage Effects in Wind Tunnel Tests for Tall Buildings with Surrounding Buildings
## 2 Wind Tunnel Tests  
### Wind Tunnel Tests  
The wind tunnel tests were conducted in the boundary layer wind tunnel of Wuhan University, China, where the cross-section of the wind tunnel is 3.2 m wide \(\times\) 2.1 m high. The aerodynamic contour of the wind tunnel is illustrated in Figure 2.  
The target pressure-measuring model is a square-shaped cylinder with an aspect ratio of 4. To avoid the influence of the scale ratio of the wind field on the test data, the wind field was set as a smooth uniform flow. The research object of this paper was the mean wind pressure coefficient and drag coefficient, so research results in uniform flow are also applicable to the turbulent field in the atmospheric boundary layer. For the isolated pressure model, wind tunnel tests of three scale ratios were carried out in this study. For the grouped building models, three scale ratios, eight relative positions, and two relative heights of the pressure model and the surrounding model were considered in this study. A total of 51 test conditions were carried out in this paper. The test models were defined as a large model, medium model, and small model, respectively, according to the scale ratio from large to small. If it is assumed that the real size of the tall building is 240 m high and 60 m wide, the scale ratios of the large model, medium model, and small model were 1/200, 1/300 and 1/600, respectively. Figure 3 shows the layout of the pressure points on the target pressure-measuring model. Figure 4 shows the layout of the grouped tall buildings.  
Figure 1: The same blocking ratio with a different layout of the surrounding buildings.  
Figure 2: The aerodynamic contour of the wind tunnel.  
Table 1 shows the geometric dimensions of the isolated and group models with different scale ratios in the wind tunnel. Table 2 shows the projection blockage ratio of each test condition, where the equation of the projection blockage ratio is:  
\[R=\frac{S}{A} \tag{1}\]  
where \(S\) is the sum of the projected areas of all building models and \(A\) is the cross-sectional area of the wind tunnel.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Small Model (1/600)** & **Medium Model (1/300)** & **Large Model (1/200)** \\ \hline Pressure model & 0.4 \(\times\) 0.1 m & 0.8 \(\times\) 0.2 m & 1.2 \(\times\) 0.3 m \\ Surrounding model a & 0.2 \(\times\) 0.1 m & 0.4 \(\times\) 0.2 m & 0.6 \(\times\) 0.3 m \\ Surrounding model b & 0.4 \(\times\) 0.1 m & 0.8 \(\times\) 0.2 m & 1.2 \(\times\) 0.3 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: The geometry size of the test models.  
Figure 4: The layout of the real grouped tall buildings.  
Figure 3: The layout of the pressure points of the model.  
In Table 2, \(H\) represents the height of the pressure model, which means that the surrounding models of test conditions 1a-8a are half the height of the target pressure-measuring model, and the surrounding model of test conditions 1b-8b is equal to the height of the target pressure-measuring model. Figure 5 shows the photos of the wind tunnel test model in this study.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 4 Multi-Object Detection for Inland Ships
### Small Object Detection Anchor  
In the ordinary YOLOv5 detection network, there are three rows of anchors parameters; each row has six values, and the values are (10, 13, 16, 30, 33, 23), (30, 61, 62, 45, 59, 119), (116, 90, 156, 198, 373, 326). Each row represents a different feature map of the application; among them, the sizes of the three different feature maps are 80 \(\times\) 80, 40 \(\times\) 40, and 20 \(\times\) 20, respectively. In the object detection task, detecting small targets on a large feature map is generally desirable. Because the large feature map contains more small target information, the anchor value on the large feature map is usually set to a small value, while the value on the small feature map is set to a large value to detect large objects. Due to the large number of buoys in the ship dataset, the buoys are likely to occupy a very small proportion of the image in the distant images due to the different image sizes in the ship detection. The labeling of small target data is often inaccurate, and the overall detection effect is significantly lower than other types of ships. In this paper, a smaller anchor is selected in the detection network. Thus, the corresponding larger feature map can be obtained to detect smaller targets. We add a smaller anchor (5, 6, 7, 9, 12, 10), thus adding a feature map of 160 \(\times\) 160 to improve the detection ability of buoys in object detection.
## 2 Mathematical Formulation of OPF Problem
### Cost Function Formulation for SPV and Wind Turbine  
RES involves no running costs, unlike fossil fuel based thermal generation plants. RES per unit price is determined by either reimbursement cost to the initial expenditure or renovation and maintenance cost in case of possession by independent systems operators (ISO). RES may be operated and owned by private sectors; in such cases, ISO must pay a per unit price proportionate to the scheduled output energy of these plants. Direct cost of \(j\)th wind energy plants is mathematically modeled as a function of scheduled output power:  
\[C_{wj}=g_{wj}\times P_{w-sch,j} \tag{5}\]  
where \(P_{w-sch,j}\) represents the schedule output power for \(j\)th wind turbine and \(g_{wj}\) represents the direct cost coefficient, linked with the same wind turbine.  
Similarly, a direct cost assigned to \(k\)th SPV sources producing \(P_{s-sch,k}\) scheduled power is:  
\[C_{s,k}=h_{k}\times P_{s-sch,k} \tag{6}\]  
where \(h_{k}\) represents the direct cost coefficient of the \(k\)th SPV source.
## 1 Introduction
However, these methods continue to exhibit deficiencies in terms of feedback delay and parameter coupling conflict, and they fail to leverage the full potential of multi-source sensor information (e.g., IMU (inertial measurement unit), foot-end force) to achieve closed-loop control. To address the aforementioned issues, a sensor-embedded CPG stability enhancement algorithm is proposed. This study has achieved breakthroughs in three key areas: First, we innovatively propose a sensor-embedded CPG control framework that integrates IMU and foot-end force feedback directly into the rhythm generation layer for real-time stability adjustment. Second, at the theoretical level, we establish a stability criterion for the CPG-body system based on an augmented Lyapunov function, while developing a dual adaptive mechanism combining force-amplitude adaptation and IMU-driven asymmetric coupling to address terrain variations. Finally, experimental validation shows that in motion control on complex terrains, this algorithm performs excellently. The center-of-mass fluctuations are strictly confined to 59.3-65% of the permissible values, and the displacement standard deviation is as low as 0.002006-0.003467 m. Moreover, stable rhythmic motion can be restored in about 2 s. This fully verifies that the algorithm can achieve high-precision motion control on complex terrains, demonstrating strong stability and rapid recovery capability.
# A Proposal for a Standardized Approach to Inducing Low Energy Availability in Athletes
## 3 Results
### High-EEE Protocol  
The first protocol, the high-EEE protocol, is set in a way of increasing EEE without any changes in EI and FFM (Figure 1). A subject is measured during the baseline period of 7 days to determine real-life EEE, EI and current FFM as described in the methods for measuring EA [17]. Subject's baseline EA value is calculated. Then, an increased EEE is calculated based on a new, lower EA value:  
\[\text{EEE}=\text{EI}-\text{EA}\text{ }^{*}\text{ FFM}\]  
When a daily EEE value has been calculated, this must be transformed into actual training plan comprised of duration and intensity. As we know, different training intensities influence metabolism differently; we propose baseline training characteristics to be repeated in longer duration in more training units. This means a subject would have more identical training units from baseline that would be scattered across a day. Some studies increased EEE by simply setting a calculated duration of an activity at 70% of maximal  
Figure 1: A schematic representation of high-EEE protocol. EI remains identical, while EEE is increased in a way to reach a favorable EA.  
oxygen consumption to achieve the desired EA [5; 18; 19]. This is appropriate for sedentary, normally active population. However, for athletes such an approach might not represent an ideal scenario of increased training volume. In reality, increases in training load are very rarely done in this way. Their training units are more frequently increased in volume and intensity based on their training characteristic and not merely at 70% maximal oxygen consumption. We suggest using actual athlete's training characteristics from baseline measurements and repeating them in more training units across the week might more accurately represent reality.  
Identical EI can be achieved when baseline EI is simply repeated in the same way. Participant must strictly follow their well-recorded diary log for the same time period or for multiplied time period in case of longer LEA duration. For the participant, following their eating protocol represents smaller burden than following a new diet regime. However, as time passes in the protocol, appetite reduction/increase becomes progressively hard to withstand. It is crucial that an athlete strictly follows their eating plan. In this way, the risk of different macronutrient ratio and different energy value is eliminated.  
To ensure the constant value of FFM we propose inducing LEA shortly after measuring baseline values. We know that body composition does not change in matter of days but in longer time periods. If the protocol would induce LEA in more than 2 weeks, we propose FFM would be measured in every 7 days after 2 weeks of measurements. In this way, correction of EEE change would be made to fit the EA goals accordingly.  
Other internal and external factors influencing high-EEE protocol are represented in Table 1.
# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 3 Methodology
### Real-Time Processing
OpenCV's role in framing, preprocessing, and initial part identification lays the groundwork for YOLOv8 to execute its specialized task of high-precision object detection. OpenCV not only facilitates the extraction of frames but also enhances the quality of input data for YOLOv8, optimizing the latter's performance. Together, they form a robust pipeline wherein OpenCV's capabilities seamlessly complement the strengths of YOLOv8, collectively contributing to the methodology's efficacy in identifying parts, detecting defects, and providing real-time feedback to inspectors. In parallel, a secure HTTPS protocol establishes a connection to Firebase, which is the backbone for real-time database management. It logs server actions, including video processing results and relevant metadata, ensuring a persistent state of operation and historical data tracking. The Unity engine plays an essential role in the visualization of results. Unity sends requests to Firebase to fetch the predictive classification results and generates a holographic display based on this information. An 'OK' hologram is displayed to the operator if the assembly is correct. Should there be any discrepancies, a 'NOT OK' hologram appears, alerting the operator to the specific issue. The selection of Unity [56; 57; 58; 59] for visualization in the MR-based quality control system is underpinned by its versatile capabilities, seamless integration, and ability to elevate the overall user experience. As a robust cross-platform game engine, Unity accommodates the dynamic integration of predictive classification results, enabling the generation of holographic displays that signify the correctness or issues with assemblies. This adaptability ensures that the system's visualization component can effectively communicate real-time quality control results to operators, contributing to a comprehensive and responsive user interface. Unity's ease of integration with Firebase is a pivotal factor, streamlining the process of fetching predictive classification results. This efficient data flow enhances the system's ability to display timely and accurate holographic representations. This seamless integration facilitates a smooth communication flow between the analysis components of the system and the visualization module, ensuring that operators receive instant feedback on the quality of assemblies. This entire cycle, from the capture of real objects to the visualization of the hologram, is designed to operate in real-time, minimizing any delay that could impact the efficiency of the assembly process. The integration of real-time streaming, advanced image processing, secure data management, and an interactive holographic display exemplifies the convergence of cutting-edge technologies to enhance the precision and reliability of manufacturing quality control.
# Spatiotemporal Feature Enhancement for Lip-Reading: A Survey
## 3 Spatiotemporal Feature Enhancement Methods
### Spatiotemporal Feature Enhancement Based on Spatial Features
#### 3.1.1 Spatial Feature Enhancement Based on ResNet
The experiment demonstrated that StiRes3D successfully mitigated the information loss caused by using separable convolution as an approximation for standard 3D convolution. This provides a plug-and-play solution for spatiotemporal feature extraction. For extracting spatiotemporal information from input images consisting of multiple consecutive frames, 64-core 3D convolutional layers were embedded in ResNet by utilizing multiple convolutional neural networks and spatial attention modules; subtle variations in mouth shapes for phonetically similar words were detected, significantly enhancing the spatiotemporal feature extraction capability [54]. Further, to enhance spatiotemporal features [71], fine-grained global collaborative features (FGS) were introduced based on 3D convolution, in which the global feature learning module is for capturing the overall characteristics of the lips, whereas the local feature learning module was leveraged to learn both coarse-grained and fine-grained correlations among the features. Additionally, diffusion and fusion techniques were applied to improve the collaboration between the local and global features, strengthening the model's ability to capture critical visual information, and allowing global features to address the fine-grained requirements of lip-reading tasks more effectively. Generally, deep learning-based lip-reading methods adopt a spatiotemporal sequence connection approach, where spatial features are first extracted, followed by global temporal modeling. Notably, these methods often fail to capture sufficient spatial information. For this reason, [PERSON] et al. [72] proposed the Dual-Stream Spatiotemporal Separation Network (DSSN) based on ResNet with 3D convolution to extract features and retain richer spatiotemporal representations from videos. This end-to-end dual-tower architecture separately models temporal and spatial information, and integrates spatial features from the front-end network with temporal features from the back end network through collaborative learning, overcoming, to an extent, the limitations of existing lip-reading methods in fully extracting and utilizing spatial information.
# Seismic Design Procedure for Low-Rise Cold-Formed Steel-Special Bolded Moment Frames
## 5 Nonlinear Analysis  
### Archetype Frames  
The proposed detailed connections are intended for use in middle-storey low-rise buildings. For the archetype frames, two-storey and three-storey buildings were designed. The configuration of the frame models is shown in Figure 19. The sizes of the members are summarised in Table 4. The steel properties assumed for the column and beam were \(f_{y}\) = 317 MPa and \(f_{y}\) = 379 MPa, respectively. Fibre elements were used for the beams and columns in the modelling, whereas bolted connections were modelled as rotational springs [5]. High-strength bolts (with a strength grade of 10.9) were assumed to be used with snugly tightened joints. To model the rotational spring characteristics, the bolt diameter was assumed to be 20 mm, and the bolt pre-load for the snugly tightened condition was assumed to be equal to 60.0 kN (see Section 4.3). The layout of the bolts at the connections was selected from the tables in the study by [PERSON] and [PERSON] [3]. The archetype beam-to-column connections followed the design procedure proposed in Section 2; therefore, inelastic behaviour was not expected to happen in the connecting plate, and it was eliminated in the modelling. The mass was lumped at the intersection of the beam and column, corresponding to the tributary area of the floor. Due to the limitations of the connection configuration, the perimeter frame system commonly used in EU and US practices was assumed. The weights used in the preliminary design were assumed to be 4.5 kN/m\({}^{2}\) and 3.6 kN/m\({}^{2}\) for regular floors and roofs, respectively. The global mechanism [18] was the target limit state; the beams used at the roof level were smaller than those at the regular floor level. The roof beam strength was assumed to be about 0.8 times that of the regular beams in the preliminary design. The member selection was conducted via trial and error until the global mechanism was ensured. The equivalent lateral force procedure from ASCE 7 [2] was used to design the frames. The seismic design factors (SDFs) are summarised in Table 5. These values were the same as those of \"steel special moment frames\". An open-source program, \"OpenSees\", was utilised to conduct the numerical simulations [19].  
The system overstrength factor, \(\Omega\), for each archetype model was computed with the following formula:  
\[\Omega=V_{\text{max}}/V_{\text{DBE}} \tag{6}\]  
As summarised in Table 6, the system overstrength factor, \(\Omega\), ranged from 2.45 to 3.55, with an average of 2.84. This value was slightly smaller than the assumed design value of 3.0. Moreover, it can be recognised that the conservative mechanism was only reached when the roof drift ratio went beyond 4.0%. From these results, it can be concluded that the design system overstrength factor of \(\Omega_{0}\) = 3.0 was an appropriate value.
# Preparation and Properties of Polyurethane Composite Foams with Silica-Based Fillers
## 3 Results and Discussion
Figure 5: SEM images of neat PU foam without additive (**a**) and PU composite foams with silica filler by sol-gel reaction at different composition of without surface modification at different aerogel compositions of (**b**) 0.5 wt%, (**c**) 1 wt%, (**d**) 1.5 wt%, and (**e**) 3 wt%. The mechanical properties of polyurethane composite foams are presented in Figure 7. In the case of adding silica aerogel, the physical properties are improved when a small amount (0.5 wt%) of the aerogel is added. However, if the added amount is further increased, the compressive strength decreases and eventually shows properties similar to those of the original PU foam. The effects on the physical properties becomes more evident when silica nanoparticles are added into polyurethane foam (see Figure 7b). The compressive strength increased the most when 1 wt% of silica nanoparticles were added, and the compressive strength decreased when 3 wt% was added. On the other hand, when polysiloxane is added through sol-gel reaction to produce PU composite foam, the compressive strength increases at very small amount, but it is rather reduced at higher compositions. All these results are thought to be correlated with the distribution of additives in the PU cell wall. All types of fillers exhibit the effect of reducing the cell size, but the additives partially aggregate in the cell wall at higher content, eventually leading to a defect that reduces the mechanical properties. The thermal conductivity of the PU composite foam containing silica aerogels and silica nanoparticles was measured and summarized in Figure 8. As expected, the PU composite foam to which silica aerogel is added exhibits the lowest thermal conductivity. Looking at the composition effect, the thermal conductivity initially decreases, but it rather increases with the composition by the amount of fillers further increasing. This behavior shows the same trend as the change in pore size. In the case of the same fillers added, the thermal conductivity is strongly affected by the cell structure, and it can be seen that the thermal conductivity decreases as the cell size decreases. The same results were observed for PU composites with silica nanoparticles added. Figure 6: Distribution of mean cell size of PU and PU composite foams with three different methods. The error bars correspond to the standard deviation of cell size in the foams. Figure 7: Compressive stress of PU foam and PU composite foams with different composition of (**a**) silica aerogels, (**b**) silica nanoparticles, and (**c**) silica produced by sol-gel reaction.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 2 Probability Distributions
### Frechet Distribution (F3)  
The distribution represents a particular case of the GEV distribution when the shape parameter is positive. It is also known as the Type III extreme value distribution [11; 17; 18].  
\[\mathrm{f(x)}=\frac{\alpha}{\beta}\cdot\left(\frac{\mathrm{x}-\gamma}{\beta} \right)^{-\alpha-1}\cdot\exp\left(-\left(\frac{\mathrm{x}-\gamma}{\beta} \right)^{-\alpha}\right) \tag{14}\]  
\[\mathrm{F(x)}=1-\exp\left(-\left(\frac{\mathrm{x}-\gamma}{\beta}\right)^{- \alpha}\right) \tag{15}\]  
\[\mathrm{x(p)}=\gamma+\beta\cdot\left(-\ln(1-\mathrm{p})\right)^{-1/\alpha} \tag{16}\]  
where \(\alpha,\beta,\gamma\) are the shape, the scale and the position parameters; \(\alpha,\beta>0,-\infty<\gamma<\infty,\mathrm{x}>\gamma\).