esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Principles of PD Source Localization  
Various methods have been explored by many researchers to measure PD based on electrical and non-electrical phenomena [8]. The detecting signal emission based on the fact can be successfully identified, which appears to be the origin of acoustic waves of PD [7; 18].  
Figure 1 shows the relationship between the PD source and the AE sensor. Figure 1a shows the sound wave travel time is the passed time while the acoustic PD signal disseminates from its discharge location to an AE sensor. The PD signal disseminated was symmetrical in that the signal impacted the tank wall or case at the outside of the transformer. The tank wall of transformer was an AE sensor installation meant to receive signals from PD signals. Figure 1b shows the various parameters characterizing PD wave propagation paths. Nevertheless, a number of different dissemination paths could exist for sound waves traveling from the PD source to the sensor [7], as can be seen by Figure 1. The determination of the time of arrivals (TOAs) from the PD source to a number of AE sensors was used to be an acoustic emission (AE) measurement method to locate the PD source. The acoustic signal traveling distance from a PD source to an AE sensor as follows the Equation (1) [18]. All acoustic waves spread through the internal structure of a high voltage device until reaching the external surface.  
\[\text{distance}(\text{m})=\text{velocity}\Big{(}\frac{\text{m}}{\text{s}} \Big{)}\times\text{traveltime}(\text{s}) \tag{1}\]  
The total sound wave travelling time to a sensor can be computed as Equation (2) and the sound wave travel time is the time elapsed while an acoustic PD signal prop-agates from its discharge location to an AE sensor, which can be calculated from the following Equation (3) [7].  
\[t=\sqrt{\frac{X_{i}^{2}+X_{h}^{2}}{V_{air}}}+\frac{X_{s}-X_{i}}{V_{metal}} \tag{2}\]  
\[t=\frac{X_{s}}{v_{metal}} \tag{3}\]  
For the direct path, in which the sound wave only travels before reaching AE sensor, the elapsed time can be calculated from the following Equation (4).  
\[t=\sqrt{\frac{X_{i}^{2}+X_{w}^{2}}{v_{air}}} \tag{4}\]  
According to above, the acoustic signal travels considerable distance \(X_{w}\) before reaching transformer tank wall. In addition, the shortest traveling time can be completely achieved when the sound wave entering to the metal tank together with a critical incident angle. The critical incident angle (\(\upalpha\)) can be following Equation (5).  
\[\upalpha=\sin^{-1}\left(\frac{v_{air}}{v_{metal}}\right) \tag{5}\]  
Figure 1: The relationship between PD source and AE sensor. (**a**) Radical PD sound wave propagation patterns. (**b**) various parameters characterizing PD wave propagation paths [14].  
In order to obtain the corresponding wave traveling time, it can be calculated from the following Equation (6).  
\[t=\frac{X_{w}}{V_{air}\ \cos\theta}+\frac{X_{S}-X_{W}\ \tan\ \theta}{V_{metal}} \tag{6}\]
## 4 Results and Discussion
### Permeability and Water Absorption  
Permeability is an important internal factor affecting concrete durability so that, by decreasing permeability, multiple properties of concrete such as resistance to harsh environments improve. Figure 13 shows the water absorption results, representing permeability, of this study. For all specimens, the water absorption at 30 min and 24 h slightly decreased by increasing age, which is related to the evolution of internal pores. After 28 days of curing, chemical reactions are still uncompleted, which leads to less connection of internal pores so that, by increasing the curing time, a finer pore structure in the cement paste is achieved [69; 70].  
Figure 12: Water absorption of specimens in tap water and exposed to freeze–thaw cycles, at 28 days.  
According to Figure 13, at 28 days, the 30 min and 24 hours' water absorption of specimens increased with increasing waste content. At 120 days of age, the water absorption of specimens with 5 and 10% replacement was similar to that of control samples, while for more than 10% replacement, the water absorption had a rising trend. So, it can be said that, for higher than 10% replacement ratios, an increment in interconnected pores and cavities is the main cause of the higher water absorption values. [PERSON] et al. [71] and [PERSON] et al. [72] reported a similar behaviour. Generally, any decrease in permeability and water absorption can be mainly due to the pore structure modification effects of the wider grain size distribution of SCMs, especially in the interfacial transition zone (ITZ) [33]. However, in this study, increasing the RM content negatively affected the water absorption, as the existence of many pits and folds on the surface of deactivated RM grains results in higher water absorption capacity [14,16].
## 5 Conclusions  
In this study, a biomimetic glue, polydopamine, is used to firmly bind TiO\({}_{2}\) nanoparticles on the surface of a TFC membrane, and this polydopamine-TiO\({}_{2}\)-modified membrane shows a remarkable anti-fouling performance (i.e., low static BSA surface adhesion, low additional membrane resistance, and less relative flux decline), when compared to the neat membrane and the membranes modified by polydopamine or TiO\({}_{2}\). When filtrating 0.5 g.L\({}^{-1}\) of BSA solution for 8 h in dark conditions, the flux of the membrane modified by polydopamine-TiO\({}_{2}\) stabilizes at around 95% of its pure water flux value after 30 min of filtration. The good anti-fouling performance of the polydopamine-TiO\({}_{2}\) modified membrane is attributed to: (1) the improved hydrophilicity; (2) the relatively uniform coverage of TiO\({}_{2}\) nanoparticles on the membrane surface; and (3) the special relatively regular nanosized papillae surface structures. In addition to the good anti-fouling performance, the polydopamine-TiO\({}_{2}\) modified membrane can also maintain a similar filtration performance to the initial neat membrane in dark conditions. However, for the membranes only modified by polydopamine or TiO\({}_{2}\), the fluxes all decrease because of the additional hydraulic resistance.  
Since photocatalysts of polydopamine-modified TiO\({}_{2}\) nanoparticles can be induced by visible light, the effect of visible light photocatalysis on the filtration and performance was also investigated. However, the results show that the visible light photocatalysis of the polydopamine-modified TiO\({}_{2}\) can degrade the surface structures of the membranes. Due to these advantages and drawbacks, it is suggested that this polydopamine binding method should be applied in dark conditions, in order to achieve the best, long-term anti-fouling performance. If the function of photocatalysis is required, an anti-oxidizing reagent should be added to the polydopamine solution or to the membranes, which still needs to be systemically investigated.  
[PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] reviewed and revised the paper.  
The authors declare no conflict of interest.
## 2 General Description of the Technology
### Injection Technology  
Injection technology allows you to inject polyurethane resin into the soil at different depths through small perforations, causing minimal disturbance to structures and the overlying ground in order to solve problems related to the capacity of the ground under foundations. The treated soil is consolidated in a vertical or sub-vertical direction thanks to a succession of low pressure injections of the resin under the foundation. Once the resin injected into the soil expands, the soil interface can be re-established at different depths and in areas where the admissible stress values are low (see Figure 2). A better load distribution is thus achieved and the tension peaks under the foundation are limited.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Soil Type** & **Elastic Modulus (MPa)** & **Elastic Modulus of Resin (MPa)** \\ \hline Sand & 10 to 25 & \\ Medium sand compactness & 15 to 30 & \\ Dense sand compactness & 35 to 55 & \\ Sand and gravel & 70 to 180 & 10 to 80 \\ Medium consistency clay & 5 to 10 & \\ Hard clay & 10 to 25 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elastic modulus in different types of soil.  
Figure 1: Images obtained with the electron microscope of the Geoplus expanding polyurethane resin without confinement (density equal to 37 kg/m\({}^{3}\)). (**a**) Enlarged \(\times\) 100; (**b**) Enlarged \(\times\) 200.
# Virtual View Synthesis Based on Asymmetric Bidirectional DIBR for 3D Video and Free Viewpoint Video
## 3 Experimental Results and Discussion  
In our experiments, the proposed method is run on a PC with Intel Core i5 (3.7 GHz) and 32 GB memory. Five public multiview video-plus-depth (MVD) sequences (\"Ballet\" and \"Breakdancers\" [33], \"Dancer\" [34], \"Shark\" [35], \"Kendo\" [36]) and six public still image-plus-depth sequences from the Middlebury Stereo Data Sets [37] are used to evaluate the performance of the proposed method. The parameters of the data sets are shown in Table 1. All camera parameters of the data sets are known, including internal and external parameters. For MVD sequences, the synthesized view is named after the tested sequence and projection information. For example, the image warped from view 4 and view 2 to view 3 of Ballet is named as BA3.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 5 MASU Empirical Evaluation
### Evaluation Results  
The performance of the Sensing Tier, for both prototypes, was evaluated in terms of resource consumption and considering heterogeneous devices. In contrast to the simulation tests, the prototype evaluation considered devices with diverse capabilities. In order to do that, we deployed the prototype in different Android devices. We used smartphones and tablets with different OS versions and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Implementation Aspect** & **Prototype I** & **Prototype II** \\ \hline Implementation Type & Embedded into Unity & Native Android application \\ Network infrastructure & AP & T2T \\ Mechanism for the P2P network creation & AllJoyn & AllJoyn \\ Location of the Manager role & In the first node that joins & In the node that acts as AP \\ Data gathering method & Alljoyn & CoAP \\ Data message type & TCP & UDP unicast \\ \hline \hline \end{tabular}
\end{table}
Table 5: Implementation details of the two prototypes.  
various kinds of CPU, battery and sensor chips. The features of the different types of devices used are detailed in Table 6. This device heterogeneity allows us to gain some insights about the changes in the performance of the framework across devices.  
The evaluation considered number of tests where some of devices sensed the environment and shared the obtained data, and others only received the sensed information. The duration of all the tests was 20 min, and in every test the battery of all the devices had the same charge level (100%). The tests performed consider an outdoor space, where the devices are moving arbitrarily at walking speed.  
In the evaluation of the Prototype I, the devices remained within the coverage zone of a fixed AP. By contrast, in the evaluation of the Prototype II, the devices are never outside the coverage zone of the device that acts as AP or their mutual coverage zone. As a result, both types of tests do not consider dynamic network topologies or diverse mobility patterns.
## 4 Results
### Classification Results Using CCA Fusion  
In the past chapter, we discussed the proposed methodology that the features extracted by CNN-1 and CNN-2 are concatenated into a single enhanced vector through Canonical Correlation Analysis (CCA) Fusion. After that, the fused vectors of enhanced features were passed out to the classifiers to classify the input images. Bagging Ensemble, Total Boost Ensemble, Fine K-Nearest Neighbour (FK-NN), RUSBoost, Coarse K-Nearest Neighbour (Coarse K-NN), and Support Vector Machine (SVM) are some of the classifiers; used in the proposed methodology. We have implemented multiple classifiers of machine learning in this step. The reason was to minimize the whole system's execution time as much as possible.
## 2 Materials and Methods
### Suppixel Positioning Method  
The final object position in the _KCF_ algorithm is obtained by estimating the maximum value position of the correlation response patch. However, the maximum position calculation may incorporate errors that accumulate frame by frame as the object moves, imposing the tracking bounding box to drift or even losing the object. Thus, we develop a subpixel positioning method that obtains the maximum value position in the correlation response more accurately and detects the position of the peak value in the response patch. Our method's operating principle is that according to a set of discrete values, the coordinate \(m\) is the observed position of the extreme value \(f(m)\) with left and right neighboring positions be \(f(m-1)\) and \(f(m+1)\), respectively, and the true extreme value is \(m+\beta\) with \(\beta\) the estimated position offset.  
The correlation response is illustrated in Figure 5, where P\({}_{3}\) is the peak response point and P\({}_{2}\), P\({}_{3}\), P\({}_{4}\), P\({}_{5}\) are the points around the peak point. For example, we consider the horizontal coordinate \(x\) of the peak position to illustrate how to determine a more accurate position. Assuming that the observed peak position in the current frame is with a response value \(f(x)\) and the true peak position in the current frame is \(x+\varepsilon\) with a response value \(f(x+\varepsilon)\) where the \(\varepsilon\) is the offset value, [PERSON]'s formula can approximate the peak point of the correlation response:  
\[f(x+\varepsilon)=f(x)+f^{\prime}(x)\varepsilon+\frac{1}{2}f^{\prime\prime}(x) \varepsilon^{2}+\left(\varepsilon^{3}\right) \tag{9}\]  
The derivative at the peak of the continuous response is zero and is obtained by neglecting the highest order term, which can be expressed as  
\[f^{\prime}(x+\varepsilon)=f^{\prime}(x)+f^{\prime\prime}(x)\varepsilon+\frac {1}{2}f^{\prime\prime}(x)\varepsilon^{2} \tag{10}\]  
By solving the quadratic equations utilizing a small step size per move in adjacent frames, the solution of Equation (10) can be obtained as:  
\[\varepsilon=\frac{-f^{\prime\prime}(x)+\sqrt{f^{\prime\prime}(x)^{2}-2f^{ \prime\prime}(x)f^{\prime}(x)}}{f^{\prime\prime}(x)} \tag{11}\]  
\[f^{\prime}(x)=\frac{f(x+1)-f(x-1)}{2} \tag{12}\]  
\[f^{\prime\prime}(x)=\left[f^{\prime}(x+1)-f^{\prime}(x)\right]-\left[f^{ \prime}(x)-f^{\prime}(x-1)\right] \tag{13}\]  
\[f^{\prime\prime}(x)=\left[f^{\prime\prime}(x+1)-f^{\prime\prime}(x)\right]- \left[f^{\prime\prime}(x)-f^{\prime\prime}(x-1)\right] \tag{14}\]  
The true peak position can be written as  
\[\left\{\begin{array}{l}x_{true}=x+\varepsilon_{x}\\ y_{true}=y+\varepsilon_{y}\end{array}\right. \tag{15}\]  
where \(\varepsilon_{x},\varepsilon_{y}\) are the offset values in the \(x\) and \(y\) direction, respectively, and \(x_{true},y_{true}\) are the true position of the peak.  
In summary, the proposed method obtains an approximate offset value by calculating the peak position with subpixel positioning and utilizing the Taylor formula. This strategy reduces the object's drift and enables accurate object tracking.
## Materials and Methods  
### Wind Tunnel Measurements  
#### 2.1.1 The Spray Drift Device  
The wind tunnel could generate wind at 2-52 m/s airspeeds, which is located at the National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology (NPAAC) in South China Agricultural University (Figure 1a,b). For the purpose of ensuring the safety test of the UAV and keeping the aircraft in a cross-wind environment, a test stand as the UAV platform was placed at the end of the DC wind tunnel whose cross-section size was 2.0 m \(\times\) 1.1 m (width \(\times\) height) (Figure 1a). The distance between the nozzle nearest to the wind tunnel and the end face of the wind tunnel was 400 mm (Figure 1b). The wind tunnel and the UAV test platform constituted the drift test unit which can produce the predetermined conditions. The combination of a hot wire anemometer and wind tunnel governor was to make the wind speed at the nozzles on the UAV test platform reach the expected wind speeds of 2, 4, and 6 m/s. For the flight parameters and spraying parameters of the aircraft, the following settings were carried out in this test: The arm length of the four-rotor model was 700 mm and the 36 \(\times\) 12.8 inches (diameter \(\times\) pitch) rotor was chosen. According to the flight attitude of the UAV during routine operation, an angle of 5 degrees for the windward angle of the UAV rotor along the vertical direction of the wind produced by the wind tunnel was set. Therefore, the rotation rate of the front rotors was 1800 rpm and that of the rear rotors was 2200 rpm based on a flight speed of 3-5 m/s. Four 110015 VP fan nozzles (LICHENG Co., LTD, Ningbo, China) were installed directly under the rotors. The height of the nozzles from the ground was 1800-1900 mm due to the horizontal inclination of the aircraft. At the same time, the nozzles were vertically 200-210 mm away from the top of the wind tunnel to ensure that the nozzles were at the rated airflow with low turbulence (Figure 1b). The spray system was supplied by a hydraulic pump with a pressure capacity of 10 bar, which provided 2, 3, 4, and 5 bar pressures for this test. Before the spraying application, all clean sampling lines were arranged by well-trained operators who wore new disposable nitrile gloves to avoid cross-contamination. Then, the rotors of the UAV test platform were activated. After the rotors rotated steadily, the liquid supply switch of the spray system was turned on and maintained for 10 \(s\) to prevent oversaturation of the sampling lines. All tests were repeated three times.
# A Posteriori Detection of Numerical Locking in \(hpq\)-Adaptive Finite Element Analysis
## 4 Verification and Utilization of the Proposed Tools
### Model Problems  
It is well known from the literature presented in the state-of-the-art section that in the case of the bending-dominated plates, the shear locking may appear, depending on the plate thickness and the applied discretization. In the case of the bending-dominated shells, the shear-membrane locking is possible. On the other hand, in the case of the membrane-dominated shells, the locking phenomena do not appear. This is the reason for our choice of three model problems introduced below.  
The first problem concerns a bending-dominated square plate. The plate longitudinal dimensions are equal to \(l=3.1415\times 10^{-2}\) m, while its basic thickness is \(t=0.03\times 10^{-2}\) m. A symmetric quarter of the plate can be seen in the Section 5.2.1. The plate is clamped along its edges. The vertical surface traction of the value equal to \(p=4.0\times 10^{6}\) N/m\({}^{2}\) is applied to the upper surface of the plate. The traction acts downwards. The plate, as well as the next two shell structures, is made of steel. For this material the [PERSON]'s modulus is \(E=2.1\times 10^{11}\) N/m\({}^{2}\), while the [PERSON]'s ratio equals \(\
u=0.3\).  
The second model problem is a bending-dominated half-cylindrical shell. Its length is equal to \(l=3.1415\times 10^{-2}\) m, while its semi-circle circumference is \(\pi R\approx 3.1415\times 10^{-2}\) m, where \(R=1.0\times 10^{-2}\) m is the radius of the shell middle surface. The shell thickness is \(t=0.03\times 10^{-2}\) m. A symmetric quarter of the shell is displayed in Section 5.2.3. The shell is clamped along its straight edges, and the curved edges are free. The shell is loaded with the vertical traction of the value \(p=4.0\times 10^{6}\) N/m\({}^{2}\), directed downwards. The third example is a symmetric half of a membrane-dominated cylindrical shell. The shell dimensions are analogous as for the bending-dominated shell, i.e., \(l=3.1415\times 10^{-2}\) m, \(\pi R\approx 3.1415\times 10^{-2}\) m, with \(R=1.0\times 10^{-2}\) m, and \(t=0.03\times 10^{-2}\) m. A symmetric octant of the shell is presented in Section 5.2.4. The symmetry boundary conditions are applied along the straight edges and one (left) curved edge of the octant, while there are no rotations along the other (right) curved edge. The shell is loaded with the internal pressure \(p=4.0\times 10^{6}\) N/m\({}^{2}\) acting outwards.
# Performance-Based Evaluation of Courtyard Design in China's Cold-Winter Hot-Summer Climate Regions
## 2 Method for Courtyard Design Optimization
### Courtyard Design Optimization
#### 2.3.3 Adjusting Ecological Buffer Spaces  
The micro-climate regulation function of ecological buffer space is that (1) a climatic buffer area with a gradient difference is set up between the building and its surroundings so that the impact of various extreme climatic effects on the indoor environment can be reduced to a certain extent; and (2) the exchange of climatic factors between the outside and the inside of the building can be regulated. This climatic-critical space can not only meet users' various demands of physiological comfort, but also promote the adaptability of the human body to climatic changes.  
The design of a suitable ecological buffer space is significant in the control of solar radiation. In summer, the ecological buffer space is open to the outdoor environment. Owing to its sheltering effect on solar radiation, the initial indoor air temperature is lower than the outdoor temperature. When the outdoor temperature rises, the air flow rate will increase due to the thermal convection between the internal and external air, thus, cooling the external wall of the indoor spaces. On the other hand, rooms are connected through the ecological buffer space, so that each indoor space reduces in size and becomes a \"shade island\"; the indoor area that needs to be conditioned is reduced considerably. In winter, owing to the low solar altitude, sunshine can directly reach the internal and external walls. If the traditional ecological buffer space is improved by closing it with transparent and thermally insulating materials (like double glazing), the solar radiation produces a greenhouse effect. The enclosed ecological buffer space is heated, thus, increasing temperatures of the internal building surfaces and reducing the heating load.  
To evaluate the effect of the ecological buffer space, this study considered the effects of its treatment under different circumstances in winter and summer on building energy consumption. The initial design gave little consideration to the micro-climate regulation function of the ecological buffer space of courtyards, and lacked a variability consciousness to the boundary treatment of courtyard. Also, the initial design completely mixed the buffer spaces and the indoor spaces, and segmented indoors and outdoors with a glass curtain wall so that the micro-climate regulation function of courtyards could not be used effectively. In the improved design, buffer spaces from the initial design were adjusted, and the boundary was clarified using buffer spaces (i.e., room and corridor, balcony, and sunshade terrace on the plane), so that the ecological buffer spaces could have a certain spatial form. Also, a buffer space was added for the south-facing rooms, which is opened in summer and closed in winter, as shown in Figure 5.  
Figure 5: The ecological buffer spaces: Initial design (**left**), improved design (**right**).