esa-sceva/satcom-chunk-collection huggingface dataset:
# Development and Evaluation of Light-Weight Active Noise Cancellation Earphones
## 1 Introduction  
Portable devices like smart phones and MP3 players with wearable headphones or earphones are widely used by every age group from youth to the elderly. These portable devices bring convenience to people for communication and entertainment in any environment, for example in public buildings and on transportation [1]. Unfortunately, the performance of these devices will be degraded by annoying environmental noise. The acoustic noise problem has become more serious as increased numbers of equipment such as engines, blowers, fans, air-conditioners, and compressors are used in many installations, air planes, automobiles, etc. [2].  
In general, there are two ways to reduce the environmental noise for portable devices: passive and active. The passive method uses thick acoustic earmuffs to cover the ear completely to block the environmental noise. However, they are relatively large, bulky, costly, and ineffective at low frequency range. The active method uses the ANC system to cancel the unwanted noise based on the principle of superposition [3]. Specifically, an anti-noise of equal amplitude and opposite phase is generated and actively combined with the primary noise, thus resulting in the cancellation of both noises.  
Several studies were published on the application of ANC headphones [4; 5; 6; 7; 8; 9; 10; 11]. In addition, several ANC headphones are commercially available and have been carefully evaluated [12]. Basically, there are three types of ANC headphones on the market. The first type of headphone (type A) has earmuffs that sit on top of the ears. The second type (type B) completely encloses the ears. These two types of ANC headphones are over-ear headphones and rely on large and bulky passive earmuffs clamping on the ears to reduce high frequency noise, thus they are not portable while performing outdoor activities. Furthermore, these passive headphones are not comfortable to wear due to the pressure experienced on the side of the head restricting blood flow and hence producing discomfort over time [13]. Users experience mild headaches, feel headphones pushing the ears against the head (benign paroxysmal positional vertigo [14]), and feel clamping pressureon their eardrum. The last type is an in-ear ANC earphone (type C) which was developed recently. Therefore, light-weight ANC earphones are the future trend; they are more comfortable and they are efficient for users to reduce environmental noise.  
As shown in Figure 1, this paper develops three different light-weight ANC earphones: in-ear, eard, and clip phones, and compares their noise reduction with commercially available ANC headphones (types A, B and C) of a leading brand company. The rest of the paper is organized as follows. Section II introduces the ANC algorithms used for the earphones and proposes using natural sound for modeling the required secondary path. Section III presents real-time experimental results and compares and analyzes their performance.
## 5 Discussion
### Meteorological Data  
The final limitation of this research arises from using the low-spatial-resolution, site-observed meteorological data to represent the entire study area, which is a common limitation in almost all modeling approaches [8; 54]. Generally, the representativeness of weather data is sufficient in flat and homogeneous regions. However, it must be carefully considered for the representativeness of the observation data in topographically irregular and complex areas. Nonetheless, this problem could be largely attenuated by the development of extrapolation or interpolation techniques for meteorological data [76]. Remote sensing techniques might also potentially be used to acquire meteorological variables at higher spatial resolution [77].  
Figure 13: Comparison between the Copernicus FPAR and our estimated FPAR: (**a**) In mid-March; (**b**) In mid-April; and (**c**) In mid-May.
## 9 Performance Evaluators
### Performance of \(E^{2}jsl\) with CRLB  
We have compared the performance of the proposed \(E^{2}jsl\) with CRLB. As mentioned earlier that we are considering the static anchor nodes which are already localized and synchronized and moving sensor node that needs to be localized and synchronized. It is given by Figures 14-16 that the estimation error of location is taken as a function of \(\frac{1}{\sigma^{2}}\) for the proposed method and CRLB. According to Figure 14, we can say that \(E^{2}jsl\) proves to be more accurate in terms of location because its performance falls on the CRLB which is obvious. Similarly, the performance of \(E^{2}jsl\) in comparing CRLB with estimation error of the clock parameter as a function of variance is sufficiently accurate according to Figure 15 for clock skew and Figure 16 for clock offset respectively. As both RMSE of clock parameters touches CRLB. So, we conclude that the proposed estimator achieves more accurate synchronization and location.  
Figure 14: RMSE of location estimates vs. \(1/\sigma^{2}e\).  
Figure 13: Relationship between number of iterations and clock offset RMSE (ns)\({}^{2}\).
## 5 Computational Experiments
### Computational Efficiency  
In this section, the computation time for the proposed method for static problems is evaluated. The calculation time transition was measured as the number of nodes \(N\) in the layout or the number of AGVs \(A\) increased. The problem environment and initial conditions were randomly generated based on the number of nodes and AGVs in the layout. Route planning with CBS [14] was also performed in the same problem environment for performance evaluation.  
Figure 17 shows the transition of calculation time for route planning as the number of nodes \(N\) in the layout increases, and Figure 18 shows the transition of computation time as the number of AGVs \(A\) increases. Figure 17 shows that the computation time increases linearly with the number of nodes \(N\) in the layout. The computational complexity is evaluated as \(\mathcal{O}(N)\). On the other hand, Figure 18 shows that the computation time increases linearly as the number of AGVs \(A\) increases. The computational complexity is evaluated as \(\mathcal{O}(A)\). It is superior to CBS, which increases computation time exponentially with the number of nodes \(N\) or AGVs \(A\) in the layout.  
Figure 16: Routing result for two AGVs derived by the proposed method at each iteration. \((1),(2),\cdots,(t)\) indicates the route plan pf AGV1, and \(1,2,\cdots,\{t\}\) indicates the route plan of AGV2.
## 2 Design of Broadband CP Antenna
### Parameter Analysis
#### 2.2.2 Width of the Extended Feedline (g\({}_{4}\))  
Another important parameter is the width of feedline, which is also cleared from design stages. At optimal value of g\({}_{4}\) = 0.5 mm, antenna offers satisfactory results which are also discussed above. If the value of g\({}_{4}\) is increased by 0.25 mm and fixed on 0.75 mm, the return loss is affected along with a slight effect on bandwidth. At g\({}_{4}\) = 0.75 mm, the antenna offers a return loss \(<\)\(-\)15 dB. On the other side, if the value reduced from optimal value and was fixed on g\({}_{4}\) = 0.25 mm, the return loss is compromised and reaches to \(<\)\(-\)11 dB, as given in Figure 4.
## 4 Results
### Sabratha and Metaponto Case Studies
#### 4.2.2 Results
Figure 15: _Cont._  
The multi-temporal data set of Figure 17 centers on palaeochannel B, whose visibility changes seasonally depending on the moisture content and vegetation cover. The palaeochannel could be observed thanks to damp marks in the summer scenes shown in Figure 17a-c. On the other hand, crop marks are visible in the spring picture acquired in 2013 (Figure 17d). Also in this case, the palaeochannel feature is clearly visible in the COSMO-SkyMed Spotlight image (Figure 18). Moreover, a significant improvement was obtained by using the same enhancement approach applied to palaeochannel A.  
Lastly, the multi-temporal image set of Figure 19 centers on an ancient road in the urban fabric of Metapontum. In this case, the soil marks of this feature are more visible from the Google Earth scenes than from the unprocessed and processed COSMO-SkyMed Spotlight images (Figure 20). Figure 16: Metapontum, palaeochannel A. (**a**) COSMO-SkyMed Spotlight image; (**b**) SUM\({}^{\prime}\)2 of enhanced Lee filter result. Figure 15: Metapontum, palaeochannel A (see red box with label pc_A in Figure 14): multi-temporal image set available from Google Earth. In this case, the palaeochannel feature is not curvilinear thus making morphological identification difficult. In detail, Google Earth scenes were acquired on: (**a**) 22 September 2004; (**b**) 8 June 2006; (**c**) 11 August 2006; (**d**) 8 May 2013. Figure 17: Metapontum (Southern Italy): multi-temporal data set available from Google Earth of palaeochannel B (see pc_B in Figure 14). The visibility of the palaeochannel B changes seasonally depending on moisture content and vegetation cover. It could be observed thanks to damp marks in the summer scenes shown in Figure 17a–c. Crop marks are visible in the spring picture acquired in 2013 (Figure 17d). Figure 14.0 Metapontum, palaeochannel B. In the COSMO-SkyMed Spotlight image the palaeochannel feature is clearly visible. A significant improvement was achieved by using the enhancement approach described in Section 4.2.
## 1 Introduction
### Mechanisms of Abnormal Impacts of Extreme Meteorological Factors on Power Grid Equipment  
Extreme weather conditions, particularly high wind speeds during tropical cyclones, can disrupt power grid equipment, including wind farms and transmission lines. High wind speeds first activate turbine protection mechanisms, disconnecting turbines from normal operation and resulting in wind power fluctuations [23]. Additionally, high wind speeds can impose uneven stress on transmission towers, causing irreversible fatigue damage and potential collapse. Tropical cyclones may also apply excessive wind pressure on overhead lines, leading to breakage. As tropical cyclones move, the changing relative positions of extreme weather factors and grid equipment create time-varying weather impacts and corresponding equipment anomalies. Based on the Rankine vortex wind field model [24], assuming that the distance between grid equipment \(i\) and the center of the tropical cyclone at time period \(j\) is \(d_{i,j}\), the maximum wind speed of the cyclone is \(v_{\max,j}\), and the radius of maximum wind speed is \(D_{\max,j}\), then the wind speed perceived by equipment \(i\) at time period \(j\), denoted as \(v_{i,j}\), can be expressed as follows:  
\[v_{i,j}=\begin{cases}\frac{d_{i,j}}{D_{\max,j}}v_{\max,j}&d_{i,j}\in\left[0,D_ {\max,j}\right)\\ \frac{D_{\max,j}}{d_{i,j}}v_{\max,j}&d_{i,j}\in\left[D_{\max,j},\infty\right) \end{cases} \tag{1}\]  
In the equation, the distance \(d_{i,j}\) between equipment \(i\) and the center of the tropical cyclone can be expressed as follows:  
\[d_{i,j}=\sqrt{\left(x_{i}-x_{0,j}\right)^{2}+\left(y_{i}-y_{0,j}\right)^{2}} \tag{2}\]  
In the equation, \(\left(x_{i},y_{i}\right)\) represents the coordinates of equipment \(i\), and \(\left(x_{0,j},y_{0,j}\right)\) represents the coordinates of the tropical cyclone center during time period \(j\).  
Figure 1: Risk assessment research framework considering the spatiotemporal evolution of power grid anomalies under tropical cyclones.
## 6 Numerical Results  
In this section, we evaluate the PBA using numerical experiments, comparing the method with the MILP approach, which consists of solving MILP model (2)-(24) using a MILP solver. Next, we analyze the impact of several network parameters, including the URLLC traffic ratio, PP capacity available, network slice prioritization policy, numerology applied, and network size on the packet-switched Xhaul network performance. The primary performance metrics used in the analysis are the objective function value (\(z\)), i.e., the number of required (active) PPs in the network and latencies of flows.
## 3 Results and Discussion  
_The Individual Free and Bound Phenolic Compound Contents in Raw Materials and Uncooked and Cooked Pasta Samples_  
The free (FP) and bound (BP) phenolic contents of the raw ingredients and pasta samples analysed in this study are comprehensively presented in Tables 2-5. These tables provide detailed data for the four types of pasta formulations: pasta made with 100% semolina, pasta made with 100% whole-wheat semolina, pasta made with 96% semolina and 4% of tomato powder, and pasta made with 97% semolina and 3% spinach powder, respectively. Figures S1-S4 in the Supplementary Materials show the trend of the percentage content of the different phenolic classes throughout the production and cooking processes of the different pasta samples. In accordance with the existing literature, phenolic acids and flavones were the two main classes of phenolic compounds in semolina [28; 29; 30]. As reported in Table 2, bound ferulic acid was the predominant component in semolina (71 mg/kg dw), followed by its bound isomer (22.4 mg/kg dw), and represented more than 80% of the total phenolic content. Free phenols were also detected, although in lower concentrations, with \(p\)-hydroxybenzoic acid and ferulic acid being the most abundant (7.7 and 2.2 mg/kg dw, respectively). These data are supported by various studies that confirmed the prevalence of these compounds in semolina [28; 31]. Other phenolic acids were identified, in a range from 0.2 to 2 mg/kg dw, and vanillin, \(p\)-coumaric, vanillic acid, and diferulic acid were detected both in the free and bound form (Table 2). Flavones were also detected in trace amounts, represented by two free apigenin derivatives, with a total content of 1.4 mg/kg dw. The total phenolic content in the semolina amounted to 113.2 mg/kg dw, consisting of 12% free phenols and 88% bound phenols. The uncooked P-S sample presented a similar trend observed for the semolina (S). Ferulic acid and its isomer were still the main bound phenolic acids, and \(p\)-hydroxybenzoic acid the main free one, with values similar to the S sample (71.4, 18.2, and 7.8 mg/kg dw, respectively). Most of the other identified phenolic acids and flavones presented a slight decrease compared to S, and this reduction is likely due to the high temperatures applied during the extrusion and drying of the pasta, as already reported in other studies [26]. However, no significant (\(p<0.05\)) reduction (5.3%) in the total phenolic content was observed compared to the raw semolina, and the free and bound total phenols presented the same contribution (12.3% and 87.7%, respectively), suggesting a small effect of the extrusion and drying steps on these compounds.
## 3 Simulation Results and Discussion
### The Growth of the Faceted Interface  
To examine the thermal gradient in silicon, we compare our simulated temperature distributions using CA-LBM with those obtained by the analytical solution in [5] for different growth velocity. Figure 4 shows the thermal fields of the Si crystal and melt during crystal growth for \(v_{e}=106\), 153, and 218 \(\upmu\)m/s. The growth velocity is controlled by changing the initial undercooling. The initial undercooling of the three different growth velocity is 0.8 K, 1 K and 1.2 K respectively. As shown in Figure 4, as the growth velocity increases, the absolute value of thermal gradients at both sides of the interface increase as well. The temperature gradient in Si melt is negative and it is necessary to produce faceted interface. When growth velocity is slow, the temperature gradient is positive. Hence, the morphology of the interface remains planar. On the other hand, when growth velocity is high, the temperature gradient in the Si melt becomes negative and the perturbation introduced into the planar interface is amplified leading to interface instability and forms zigzag facets.  
Furthermore, to examine the facet formation mechanism in more detail, we extracted a slice from the columnar crystals for discussion. Figure 5 shows the simulation results for \(q(\phi)\) with \(\lambda=3\)  
Figure 4: Temperature fields of Si crystal and melt during crystallization for \(v_{e}=106\)\(\upmu\)m/s, 153 \(\upmu\)m/s, and 218 \(\upmu\)m/s.  
Figure 3: Evolutions of interface morphologies for \(v_{e}=241\)\(\upmu\)m/s in the domain of 613 \(\upmu\)m \(\times\) 500 \(\upmu\)m : (a) t = 0 s; (b) t = 0.7 s; (c) t = 0.9 s.  
and \(\delta=0.2\) in the domain of 613 \(\upmu\)m \(\times\) 500 \(\upmu\)m. Figure 5a represents the position and shape of the solid-liquid interface at every 0.2 s. The growth velocity of the solid-liquid interface is 153 \(\upmu\)m/s. A wavy perturbation was introduced into the interface and the perturbation grew and resulted in zigzag facets. The interface is still rounded up to 0.6 s, and facets become clear after 0.6 s. Two small facets merged into a bigger one. The undercooling of facet B is higher than that of facet A, leading to the faster growth velocity of facet B than facet A. Hence facet B can catch up with facet A and unite B to form facet C which is shown inside the small box in Figure 5a. We can further examine the temperature field at 3 s from Figure 5b. As shown, temperature rises at the solid-liquid interface due to the latent heat. The latent heat diffuses mainly toward undercooling liquid. Undercooling is higher at the peaks than that at the concave. The greater interface undercooling, the faster growth velocity of faceted interface. The interface undercooling is about 0.58 K.
## 3 Establishment of Integrated Navigation Model
### Measurement Equation of Integrated Navigation System  
The observation equations of INS and LEO based on the pseudorange and pseudorange rate are as follows [6]:  
\[\left\{\begin{array}{c}Y_{\rho}=R_{\rho}X+W_{\rho}\\ \dot{Y}_{\rho}=\dot{R}_{\rho}X+\dot{W}_{\rho}\end{array}\right.. \tag{45}\]  
After sorting, the measurement equations for the tight combination of INS and LEO can be obtained as follows:  
\[Y(t)=R(t)X(t)+W(t)=\left[\begin{array}[]
## 5 Conclusions  
The main objective of this study is to investigate the behavior of very soft soil and constructability of two ground improvement methods, i.e., forced replacement method and deep mixing method. For this purpose, the large deformation finite element (LDFE) analyses using Coupled Eulerian-Lagrangian technique are implemented. The results of the CEL modelling are verified by comparison between those and the field investigations with settlement, height, and range of soil heave. Additionally, to examine the constructability and economics of each method, comprehensive reviews are performed. The following conclusions could be drawn from the present study:  
* The CEL technique is successfully applied to solve the constructability problems of sites on very soft soil. The applicability of the CEL modelling is verified by comparison between those and the field investigations with settlement, height, and range of soil heave for the forced replacement method. Furthermore, the replacement shape can be predict with varying the embankment geometries such as the height, width, and angle of embankment, therefore, it can be extremely useful to estimate the reasonable quantity of soil for the replacement.
* For the deep mixing method, the pile equipment could not be self-standing, also, in cases of pile jacking and driving, the pile equipment during pile installation settle and overturn, because of being not enough bearing capacity of pile equipment to resist overturning. In addition, the vertical displacements settlement at each edge occurs during the pile installation, especially, the vertical displacements in the front of the pile equipment are larger than those of the rear. The vertical displacements during the pile installation are larger than those in the case of self-standing of pile equipment. It is indicated that the behavior of pile equipment is influenced by the pile installation loading, such as pile jacking and driving.
* Two ground improvement methods can improve a certain soil strength, however, there are the risks for constructability during constructions, such as pile installation, also the soil conditions composed of very deep soft soil in this site is unfavorable to economic feasibility.
* The behavior of very soft soil and constructability with methods can be investigated using CEL technique, which would be useful tools for comprehensive reviews in preliminary design.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. 2016R1A6A3A03010454) for [PERSON]. [PERSON] and [PERSON] would like to thank the National Research Foundation of Korea (NRF) (Grant No. 2011-0030040) for supporting this research work.  
[PERSON] performed the numerical studies and wrote the draft of the paper; [PERSON] designed this research and assisted with the writing of the paper; [PERSON] analyzed the numerical data and assisted with the numerical analyses.  
The authors declare no conflict of interest.
# Massive Data Capture Approach for Modeling Existing Building Stocks
## 2 Methodology
### S2--Data Processing
#### 2.2.2. S2.2--Image Extraction and Filtering  
This second processing sub-step involves obtaining the final images. The main novelty in this project is the extraction of selected frames from video recording, considering their quality and the necessary optical corrections. It involves optical calibration, as wide-angle lenses or other cameras that are normally used in UAVs, such as GoPro, have distortions that need to be corrected [62].  
1. Camera calibration, which is usually performed using the OpenCV technique [63]: This function requires some correspondences between environment points and their projection in the camera image from different viewpoints. In general, these correspondences are obtained from the corners of Charuco chessboard patterns. The procedure requires pictures to be taken of the Charuco calibration board [64], covering the entire surface of the sensor, to train a computer vision model and obtain the camera calibration profile. Once the camera's intrinsic parameters and distortion coefficients have been obtained, the frames will be compatible with each other. These parameters remain fixed unless the camera optic is modified; thus, camera calibration only needs to be performed once.
2. Filtering and frame extraction: Once the camera has been calibrated, unusable frames must be extracted and filtered from the video recording of the building. To automate this process, an algorithm developed by the authors has been used, based on the Open Source Computer Vision Library (OpenCV library) [65]. The algorithm iterates through all the frames of the videos and computes a blur quantification from the Open CV library. Once the quality value has reached the desired threshold level, the frame is extracted and named by ordered number, which can be correlated to a time stamp and a space position. Finally, the frames are filtered by name using logic gates, depending on the coverage required, and applied using the position/relation calculated using drone velocity and the video frame name.
3. Frame calibration: To generate the final images, the extracted frames are processed through an optical calibration algorithm, utilizing the identified optical profile, and final pictures are obtained. This order of processing only computes calibration of the frames that are needed, which ensures fast results and computing optimization.  
The main novelty of this step is its compatibility and scalability to larger datasets from previous projects. The outcome of this step is the final images.