esa-sceva/satcom-chunk-collection huggingface dataset:
# Coupled D33 Mode-Based High Performing Bio-Inspired Piezoelectric MEMS Directional Microphone
## 1 Introduction
Therefore, the less effective area in comparison with the entire microphone size leaves challenges in terms of realizing high acoustic functionalities, such as sensitivity, SNR and self-noise, which are important at the application end. This paper reports on a different way of using the D33 mode, the coupled D33 mode, that senses with higher electrical sensitivity in comparison to the regular D33 mode. The higher electrical sensitivity positively biases the acoustic sensitivity and enhances the SNR and self-noise. Section 2 describes a comparative study between the proposed and regular D33 modes. Then, in the same section, a parametric sweep study is adopted to select the best IDT parameters for realizing a bio-inspired MEMS directional microphone. With satisfactory results, N-unit cells are fabricated to create a piezoelectric MEMS directional microphone inspired by the ears of the fly _Ormia archaea_, as presented in Section 3. In the same section, the designed bio-inspired directional microphone is simulated in terms of the acoustic-piezoelectric interaction with the COMSOL Multiphysics software. The simulated output voltage by applying 1 Pa of sound pressure at 1 kHz frequency is compared with the measured sensitivity. The deviation is found to be 6.8% between the simulated and measured sensitivities. Besides the sensitivity, Section 4 describes how the experimental measurements were performed. Furthermore, the same section describes the directionality, SNR and self-noise. In comparison with other works utilizing D33 modes, this work shows significant improvements.
# A New Concept Compliant Platform with Spatial Mobility and Remote Actuation
## 4 Applications  
Originally, the main activities for the invention herein presented were intended to be operated in the laparoscopic environment. Figure 7 shows a possible end-effector that could be mounted on the platform. With reference to the labels used in Figure 7, the endoluminal catheter (a) carries the base platform (b), which supports three Type (c) legs on which the mobile platform (d) is mounted. The mobile platform surface holds the surgical means (f) that can vary according to the type of required operation. Other miniaturized instruments can be mounted on the controlled platform, such as vision means (e) that allow the surgeon to observe the coronal cavity wherein the device is inserted. Moreover, a communication duct (g) was designed, allowing the surgeon to access the region to be operated. For this reason, the mobile and base platforms were also provided with an opening that connects the duct (g) with the tubular duct (a). With reference to Figure 7, it should be also noted that the surgical device was comprised of a shaped surface (f) that made it easier to act as a guiding element inside the cavity during the use of the device.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Case 1** & & **Case 2** & & **Case 3** \\ \hline
**Input** & **Output** & **Input** & **Output** & **Input** & **Output** \\ \hline \(\alpha_{1}=0\) & \(u_{x}=8\cdot 10^{-3}\) mm & \(\alpha_{1}=20^{\circ}\) & \(u_{x}=-8\cdot 10^{-3}\) mm & \(\alpha_{1}=0^{\circ}\) & \(u_{x}=-2.4\) mm \\ \(\alpha_{2}=0\) & \(u_{y}=2\cdot 10^{-2}\) mm & \(\alpha_{2}=20^{\circ}\) & \(u_{y}=-1\cdot 10^{-2}\) mm & \(\alpha_{2}=5^{\circ}\) & \(u_{y}=0.1\) mm \\ \(\alpha_{3}=0\) & \(u_{z}=-1.17\) mm & \(\alpha_{3}=20^{\circ}\) & \(u_{z}=0.43\) mm & \(\alpha_{3}=20^{\circ}\) & \(u_{z}=-6\cdot 10^{-2}\) mm \\ \(d_{1}=0.5\) mm & \(\theta_{x}=8\cdot 10^{-2}\,^{\circ}\) & \(d_{1}=0\) mm & \(\theta_{x}=2\cdot 10^{-2}\,^{\circ}\) & \(d_{1}=1\) mm & \(\theta_{x}=-1.7^{\circ}\) \\ \(d_{2}=0.5\) mm & \(\theta_{y}=4\cdot 10^{-2}\,^{\circ}\) & \(d_{2}=0\) mm & \(\theta_{y}=2\cdot 10^{-2}\,^{\circ}\) & \(d_{2}=0\) mm & \(\theta_{y}=-5\cdot 10^{-2}\,^{\circ}\) \\ \(d_{3}=0.5\) mm & \(\theta_{z}=9\cdot 10^{-2}\,^{\circ}\) & \(d_{3}=0\) mm & \(\theta_{z}=8\cdot 10^{-4}\,^{\circ}\) & \(d_{3}=0\) mm & \(\theta_{z}=1.1^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input rotations and displacements and their effect on the platform for the three analyzed cases.  
Figure 6: (a) Platform displacement along the \(z\)-axis, positive direction (Case 2); (b) platform pose for a generic actuation scheme (Case 3).
## 4 Discussions
### Derivation of an Optimum CaO Content Range  
Figure 19 shows the optimum CaO content range for ensuring an adequate durability performance, which was derived for FA by considering all of the contents mentioned above. For concrete replaced with FA to ensure an adequate durability properties, it is desirable to reduce the carbonation and chloride penetration depth while maximizing the relative dynamic elasticity modulus and weight reduction rate when mixing concrete.  
As can be seen from Figure 19, the optimum CaO content of FA was found to be between 39% and 48%. This corresponds to a FA mixing rate range between 21.7% and 37.7%. In this range, the compressive strength value at the age of 28 days was found to be between 25.1 and 31.0 MPa, which exceeded the target compressive strength (f\({}_{\text{ck}}\) = 24 MPa). For reference, according to a previous  
Figure 17: Results of the (**a**) compressive strength and (**b**) compressive strength development rate according to the varied CaO content.  
Figure 18: Results of the compressive strength in terms of the CaO/SO\({}_{3}\) rate.  
study by [PERSON] [26], the optimum CaO content of GGBFS used to satisfy all of the four durability test conditions was found to be between 53% and 56%. This corresponds to a GGBFS mixing rate range between 27.5% and 47.1%. Therefore, the optimum mixing rate of FA was evaluated to be slightly lower than that of GGBFS.
## 3 Results  
### Land-Cover Change  
Classification of land cover using MODIS data in South Africa for the selected time slices is shown in Figure 4. MODIS class 3 (Deciduous Needleleaf Forests) is not included because this class does not appear in any year at either national or provincial level. Nationally, land cover is dominated by grasslands (average 45.3323%) and open shrublands (average 38.5289%) followed by croplands (average 5.3821%) and savanna (average 4.1185%) (Figure 5). Based on the classification of these land-cover classes at different time slices, trajectories of changes over time can be identified at a national scale (Table 2). Some classes (e.g., water bodies, savanna) show variability with no clear net changes, whereas other classes show more consistent patterns of increases (e.g., deciduous broadleaf forests, croplands) or decreases (e.g., evergreen broadleaf forests, grasslands). Land-cover values for 2015 may also reflect the strong El Nino event in that year, which impacted many different ecosystems in South Africa (e.g., [61]) because these events are associated with decreased regional rainfall and often drought conditions. Several broad patterns can be identified:  
Figure 3: Map of the nine provinces of South Africa which, from west to east, are: NC = Northern Cape, WC = Western Cape, NW = North West, FS = Free State, EC = Eastern Cape, KZN = KwaZulu-Natal, GT = Gauteng, MP = Mpumalanga, LIM = Limpopo. Also shown are Zim = Zimbabwe, Moz = Mozambique, eSW = eSwatini (Swarzland). * Water bodies show a declining trend throughout;
* Forests of different sorts increase;
* Open shrubland and grassland decrease;
* Croplands increase;
* Urban areas increase. Figure 4: Land-cover maps for South Africa for 2001, 2005, 2010, 2015 and 2019. Figure 5: Comparison between the percentage of land-cover classes at a national scale in 2001, 2005, 2010, 2015 and 2019. Despite these changes, variability between successive time slices of different land-cover types at a national scale has a range of only \(\pm\)0.89-1.68%, yielding interannual values of \(\pm\)0.16-0.42% but with an average of 0.24% (Figure 5). In detail, aggregated changes in land-cover type are reflected at a regional level, at the scale of individual provinces (Table 3), but these changes may be amplified because of the different proportions of certain land-cover classes that are present in different regions. For example, grasslands make up an average of 91.068% of the land area of Limpopo Province but only 13.581% in Northern Cape Province. This means that any changes in climatic or environmental conditions affecting grasslands would have the greatest impacts in the former and not the latter region. As a result, different provinces show different trajectories of change and with respect to specific land-cover types (Table 3).
## 3 Pulse-Compression-Based Phased Array Setup and Imaging Procedures
Since the  
Figure 1: Schematic representation of the pulse-compression-based phased-array (PuC-PA) ultrasonic testing system: coded signals are generated in a personal computer and fed to the system for signal generation and acquisition; the ultrasonic signals are transmitted and received with separate transducers in a pitch–catch configuration, and the acquired and digitalized signals are sent back to the computer for processing and imaging purposes. transducers used with the PuC and pulse echo systems were different, with different element sizes, the number of elements was chosen so as to achieve the same overall aperture of the transducer in the two cases. In TFM imaging, the inspection plane/volume to be visualized is subdivided into a grid of pixels/voxels and the UT beam is virtually focused on each point of the grid, both in transmission and reception, by processing the FMC. It is important to mention that for the PuC-PA system where separate transducers are used, the imaging plane is considered in between and equidistant from the transmission and reception transducers; see diagram in Figure 2.
## 3 Results
#### Imaging Findings
For mass lesions, the main features observed in the BRCA1 and ATM groups included an irregular shape; BRCA1 carriers tended to exhibit a more circumscribed margin in comparison with the ATM group, without showing a significant difference (\(p\) = 0.427). For non-mass lesions, a segmental distribution and clumped enhancement were the dominant MRI features observed in the groups; no significant difference was observed between the groups. As for as the kinetic curves, all eight cases (100%) of ADH detected via breast MRI showed a high enhancing curve (type 2-3), as well as the 83.3% of LN lesions and the single RS being recognized by MRI. The two FEA findings showed a type 1 and type 2 curve, respectively. A significant difference was found in regard to the kinetic curves of the B3 lesions (\(p\) = 0.009). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mutation Group** & **Mass Enhancement** & **Non-Mass Enhancement** & **Negative** \\ \hline BRCA1 & 6 (60\%) & 4 (40\%) & 2 (16.7\%) \\ \hline BRCA2 & 2 (66.7\%) & 1 (33.3\%) & \\ \hline ATM & 3 (75\%) & 1 (25\%) & 2 (33.3\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MRI appearance of B3 lesions in the three groups of mutations analyzed. **Scheme 4. ** The graphic shows the distribution of MRI enhancement findings in the different types of B3 lesions analyzed. By comparing the MRI findings with the final post-operative histological examination, the DWI of the lesions, the mass to non-mass enhancement and MRI margins were significantly correlated with the risk of carcinoma, with a \(p\) values of 0.028, 0.034 and 0.002, respectively. Figure 1 shows an example of an ADH with mammographic evidence of a cluster of pleomorphic calcifications in the internal quadrants of the left breast and an NME on MRI. Figure 1: 45-year-old woman with BRCA1 and 2 mutations and mammographic evidence of a cluster of pleomorphic calcifications in the internal quadrants of the left breast (**a,b**). No sonographic findings were reported. MRI revealed an irregular area of non-mass enhancement (**c**). Stereotactic biopsy confirmed lesion as an atypical ductal hyperplasia. The arrow explains the location of microcalcifications.
# Selection of Grid Road Networks Based on Raster Data
## 2 Related Work
Furthermore, the existing vector-based selection methods cannot directly handle raster-format networks, and converting vector data into raster format may result in a loss of data accuracy or introduce errors;
3. There is no effective road selection method that considers multiple attributes, such as the road network structure, width, and connectivity, simultaneously. To address these challenges, our study contributes a novel approach for selecting road networks from unstructured raster datasets. The proposed method automatically identifies the road importance based on the width attributes, enabling the automatic recognition of road grade information and ensuring the retention of primary roads to the greatest extent, thereby producing more reliable selection results without the need for the manual input of road grade information. Additionally, it simplifies road networks by maintaining good connectivity, removing redundant secondary roads and isolated segments, and preserving both the overall road network structure and the local distribution density. This enables the more reliable and practical generalization of road networks from unstructured raster datasets.
## 3 Results and Discussion
### Fatty Acid Composition
 \\ \hline Snow White 2021 & 31.10 & 8.33 & 0.12 & 0.05 & 2.37 & 13.84 & 74.34 & 0.58 & 0.18 & 0.10 & 0.04 & 0.05 \\ Snow White 2022 & 22.0 & 8.63 & 0.14 & 0.06 & 2.60 & 13.18 & 74.28 & 0.72 & 0.20 & 0.09 & 0.03 & 0.06 \\ Albakomp 2021 & 27.50 &Recently, several research groups have analyzed poppy seed samples of various origins for oil content and fatty acid composition [11; 25; 26; 27; 28; 29; 30; 31; 32; 33]. According to them, the oil content of the seeds is varying between 27.71 and 52.40%, depending on the variety, location, and extraction technology. Our data fit into the lower level of this range. Based on the above references, the dominant fatty acid in poppy oil is linoleic acid, the ratio of which varies between 53% and 76% of the oil. Among the saturated fatty acids, palmitic acid (hexadecanoic acid) and stearic acid (octadecanoic acid) are present in 8-19% and 2-4%, respectively. In addition to these, it also contains oleic acid (13-25%) and a lower amount of linolenic acid (0.24-1.32%). Thus, the detected fatty acid composition of the experimental varieties is in accordance with the literature, defining linoleic, oleic, and palmitic acids as the most abundant fatty acids in poppy oil [15; 25; 26; 27; 28; 29; 30; 31; 32; 33]. The measured percentages of linoleic acid fits into the upper range of the international data. The finding of [PERSON] and [PERSON] [25], [PERSON] et al. [26], and [PERSON] at al. [30] that the white-seeded variety is characterized by increased levels of palmitic acid and linoleic acid, as well as a lower content of stearic, oleic, and linolenic acid compared to the blue poppy, could not be proved in our study. As these authors investigated Turkish and Slovakian accessions while ours were of Hungarian origin (except the Czech 'Orel'), it seems to be more likely that the oil spectrum is associated with genotype independent from the seed colour.
## 2 Theory and Method
### Theory of the PSO-GD Algorithm
#### 2.2.2 Experimental Function Testing  
To evaluate the PSO-GD algorithm, the convergence of a potential function \(U\) on the \(x\)-\(y\) plane is first tested. The equation for \(U\) is as follows:  
\[U=\sum_{i=1}^{n}\left(-2\pi\sigma_{1i}\sigma_{2i}\sqrt{1-\rho_{i}^{2}}\right) ^{-1}\exp\left[\frac{-1}{2(1-\rho_{i}^{2})}\left(\frac{(x-\mu_{1i})^{2}}{\sigma _{1i}^{2}}+\frac{2\rho(x-\mu_{1i})(y-\mu_{2i})}{\sigma_{1i}\sigma_{2i}}+\frac {(y-\mu_{2i})^{2}}{\sigma_{2i}^{2}}\right)\right] \tag{27}\]  
Figure 1: Schematic diagram of the \(\alpha\)-trimmed constraint (n = 5, \(\alpha\) = 0.25).  
Table 1 summarizes the parametric information for _U._ Figure 3 shows the distribution pattern of the fields, which is characterized by two local minima in the upper half and a global minimum in the lower right corner. The GD, PSO, and PSO-GD algorithms are respectively employed to locate the optimum solution. The optimization capacity of each algorithm is subsequently evaluated based on its optimization result and path. Observation of the optimization results in Figure 3 reveals the following behavior: The PSO and PSO-GD algorithms converge on the global minimum, while the GD algorithm falls into a local minimum. Because the GD algorithm is designed to solve convex optimization problems, its optimization result for a nonconvex optimization problem primarily depends on the selected initial model. The GD algorithm is susceptible to converging on a local minimum near the initial model. In terms of the optimization path, the PSO algorithm searches for an optimum solution in a larger area, which involves a more complex path. When the PSO algorithm eventually converges on the global minimum, its search efficiency considerably decreases as the model approaches the optimum solution. For the PSO-GD algorithm, the addition of the GD information to the model-correction term, to some extent, improves its convergence efficiency near the optimum solution. Therefore, the new algorithm is more effective at executing a global search in the parametric space.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**n** & \(\mu_{1}\) & \(\mu_{2}\) & \(\sigma_{1}\) & \(\sigma_{2}\) & \(\rho\) \\ \hline
1 & 50 & 25 & 15 & 10 & 0.2 \\
2 & 60 & 20 & 10 & 20 & 0 \\
3 & 75 & 75 & 10 & 12 & 0.5 \\
4 & 10 & 50 & 60 & 70 & 0.1 \\
5 & 25 & 66 & 15 & 12 & \(-0.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the potential function.  
Figure 2: Flowchart of the PSO-GD algorithm.
## 3 Results
### Comprehensive Information on the SD Profile
The seismicity and the dominant lithology also have qualitative descriptions. Figure 4: Comparison of comprehensive information on geophysics, geological structure, and other aspects along the SD profile in Mongolia. (**a**) The decomposition characteristics of the lithospheric magnetic field. The pink curve, black curve, and red curve represent the superficial surface part f\({}_{\rm s}\), middle layer part f\({}_{\rm m}\), and basement part f\({}_{\rm b}\), respectively. (**b**) The solid circle displays the size and depth of earthquakes within 100 km of the SD profile: The solid red circles, solid pink circles, and solid blue circles represent earthquakes with magnitude 6 or higher, earthquakes with magnitude 5–6, and earthquakes with magnitude 3–5, respectively. The red triangles indicate cities. (**c**) The lithology, magnetic susceptibility, and remanent magnetization of rock samples. The pink and blue columns represent the magnetic susceptibility and remanent magnetization values of the rock samples, respectively. The red column, green column, and yellow column represent magmatic rocks, sedimentary rocks, and metamorphic rocks, respectively.
## 5 Experiments and Discussions
### Datasets  
The experiment utilized \(11\) real network datasets, including four labeled real network datasets and seven unlabeled real network datasets. The labeled real network datasets consist of the Karate network [41], Dolphins network [42], Polbooks network [43], and Football network [1]. The unlabeled real network datasets include the Lesmis network [44], Jazz network [45], Email network [46], Netscience network [47], Power Grid network [48], Facebook network [49], and PGP network [50]. The basic information is presented in Table 2.  
Likewise, to expand experiments, the LFR benchmark is used as synthetic networks [51]. We generate different scale networks on the LFR test network model for experiments. The specific parameter settings of the LFR network are shown in Table 3, where \(\overline{d}\) is average degree, \(d_{\text{max}}\) denotes maximum degree, min\(c\) represents minimum community size, max\(c\) is maximum community size, and \(tau1\) and \(tau2\) are the parameters for power law distribution. \(\mu\) is the mixed parameter. The larger the mixed parameter, the more difficult the community division.
## 6 Data Availability  
All presented surface area time series and water level time series from satellite altimetry used for the validation as well as results for many additional targets are freely available in the DAHITI at [[http://dahiti.dgfi.tum.de](http://dahiti.dgfi.tum.de)]([http://dahiti.dgfi.tum.de](http://dahiti.dgfi.tum.de)). The supplementary material of this paper also contains results of all 32 study areas where surface area time series, used optical data, long-term water probability maps and scatter plots of the validation are presented in detail.  
Supplementary Materials are available online at [[http://www.mdpi.com/2072-4292/11/9/1010/s1](http://www.mdpi.com/2072-4292/11/9/1010/s1)]([http://www.mdpi.com/2072-4292/11/9/1010/s1](http://www.mdpi.com/2072-4292/11/9/1010/s1)).  
[PERSON] developed a first version of the approach in his bachelor thesis. [PERSON] improved the land-water classification, developed the gap-filling approach, performed the validation and wrote the paper. [PERSON] contributed to the manuscript writing and helped with the discussions of the applied methods and results.  
This research received no external funding. The APC was funded by the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program.  
We thank the Agencia Nacional de Aguas (ANA); US Geological Survey (USGS) and Bavarian Environmental Agency (BEA) for providing the in situ data. We thank three anonymous reviewers for their valuable comments that helped to improve the manuscript.  
The authors declare no conflict of interest.
# Influence of Cracking on the Durability of Reinforced Concrete with Carbon Nanotubes
## 3 Results and Discussion  
### Fresh-State Properties, Hardened Density and Compressive Strength  
The average slump, fresh density (\(\rho_{\text{f}}\)), hardened density (\(\rho_{\text{28d}}\)), and compressive strength (\(f_{\text{cm,28d}}\)) at 28 days are presented in Table 4, as well as the percentage difference related to reference PC concrete (\(\Delta\)). Due to the small percentage of CNT (0.1 wt% of cement paste), the workability was similar in reinforced (CNTRC) and plain concrete (PC). It has been reported that the incorporation of CNT tends to decrease the workability of cement pastes, due to the large CNT surface area, which promotes significant surface interactions with the mix [41; 42]. However, the slump was slightly higher in concrete with CNT. Similar behavior was reported by [PERSON] et al. [43] in cement pastes, attributed to the good dispersion of CNT and high effectiveness of the dispersant.  
No significant differences of fresh and hardened density were found between PC and CNTRC. The level of compactness attained in both concretes was similar and an eventual increase of air-entrainment caused by the dispersion surfactant of CNT was not confirmed.  
The compressive strength was improved about 10% with the incorporation of 0.1% CNT. A greater efficiency (nearly 20%) was reported in a previous study [17] involving concrete reinforced with the same type of CNT and similar concrete composition (equal w/c). This suggests that a lower level of CNT dispersion could have been attained in this study. Nevertheless, it is shown that CNT contributed to the improvement of compressive strength, due to their well-known filler, nucleation, and bridging effects [30; 41; 44]. On the one hand, the CNT are able to fill the small pores up to 50 nm, increasing the microstructure density [30; 45], and also acting as nucleation sites for the growth of more evenly distributed hydration products [24]. On the other hand, as later shown in Section 3.5, the CNT may bridge the hydration products and promote the load transfer between small microcracks [17]. This mitigates the development of macroracks, which are the main cause of mechanical failure and durability decay.