esa-sceva/satcom-chunk-collection huggingface dataset:
# Some Practical Acoustic Design and Typical Control Strategies for Multichannel Active Noise Control
## 2 Practical Acoustic Design
### ANC in the Opening of Buildings  
Another problem that is worthy of study is the noise control at openings of an enclosure [19; 20; 21; 22; 23; 24; 25; 26]. It has been found that more error sensors usually produce a better noise control performance. In [23], a multichannel ANC system designed for a window with natural ventilation is proposed. This system was tested on a full-size window, where 16 control channels are evenly distributed across the window opening. It showed that the window installed with this ANC system could attenuate noise as effectively as the fully-glazed window while natural ventilation is still maintained.  
In another study [24], a boundary arrangement of error microphones was investigated. Compared to the abovementioned distributed arrangement [23], the boundary arrangement allows better natural ventilation and lighting. It was found that a double-layer error microphone arrangement over the boundary could achieve a better performance than a single-layer one. The experiments were carried out in an anechoic chamber. A cabinet with an opening was used as a room model with a window. The opening was embedded on a baffle as shown in Figure 5a. An ANC system with 32 channels was mounted in the room model. Three configurations of error microphones were used, i.e., evenly distributed, single-layer, and double-layer as shown in Figure 5b-d. There are some interesting findings in the experiments and computer simulations with the same setting. The performance is summarized in Table 2. First, it was found from computer simulations that the upper-limit frequency of the enclosure could be increased by adding more layers of error microphones at the edge. Secondly, the double layer strategy had higher NR than that of the single layer one. This conclusion provides useful guidance for ANC acoustic arrangement.  
Figure 5: (Color online) Photos of the experiment setup: (**a**) a panoramic view of the anechoic chamber, (**b**) evenly distributed error microphones, (**c**) single layer error microphones at the edge, and (**d**) double-layer error microphones at the edge. Figures are cited from [PERSON]’s 2019 paper [24].
## 1 Introduction  
Object grasping is a fundamental task in robotics with a wide range of practical applications, including loading, unloading, and arranging objects. Traditionally, geometry-based methods are used to solve this task. These methods analyze a three-dimensional model of the object to determine the optimal grip position. Although these methods are effective in fixed settings and for known objects, they require a priori knowledge and cannot be easily adapted to dynamic environments [1].  
To overcome the limitations of geometry-based methods, data-driven approaches for object grasping with robotic arms have been introduced. These methods, which typically use convolutional neural networks (CNNs), process images from two-dimensional or three-dimensional cameras for classification, object identification, segmentation, and grip position estimation [2; 3; 4; 5; 6; 7]. However, challenges arise when the image background is cluttered or objects overlap, which makes it difficult to understand objects in complex contexts [8]. Cluttered environments are shown in Figure 1.  
Significant progress has been made in this field with the use of deep reinforcement learning (DRL) algorithms, as exemplified by iconic applications such as Google's Deep-Mind AlphaGo and OpenAI Five [9; 10]. DRL combines CNN for image feature extraction with Q-learning techniques for decision-making. Previous studies have shown that DRL is able to learn and make better decisions over time without the need for explicit modeling of the environment [11].  
In the context of robotic arm control, DRL has been used to improve the effectiveness of pre-grasping tasks--the process by which a robotic arm pushes an object to create space before grasping it. Although this method has shown potential, studies have shown that some methods still face challenges in terms of success rates and training time required [12; 13; 14].  
This study introduces significant novelty in robotic arm control by directly integrating image masking techniques with deep reinforcement learning (DRL) algorithms. While many previous studies have explored the use of DRL for object grasping tasks, this study stands out by adopting an image masking approach to address the challenges in cluttered environments. This integration allows the robot to more effectively explore confined areas, reducing interference caused by complex backgrounds and overlapping objects. Furthermore, this study shows that image masking techniques can significantly reduce training time and improve the success rate of object grasping. With these adjustments, the robotic arm can better adapt to situations that cannot be handled by conventional DRL methods. These findings underline the innovative capabilities of the proposed approach in improving training efficiency and performance in more complex and dynamic industrial scenarios.  
Therefore, the present study proposed a method that integrates image masking to overcome the problem of sparse rewards during training. When image masks are applied, a robot explores over a limited area, resulting in a higher learning rate.
## 5 Experimental Validation
### Experimental Results for the VSI  
Figure 21a-c show the voltage and current waveforms of the unity-powered utility grid when DC power is fed by the VSI in the uniform, right, middle, and left shaded P-V curves. These waveforms are directly interfaced with the scaled-down power grid because they do not show unexpected ripple components and THD.  
The sinusoidal grid current and voltage waveforms are taken from the SSC by neglecting the transient period of the system. Figure 21a shows the grid current (Ig) is in phase with grid voltage (Vg), maintaining unity power factor and supplying power to the grid with 3.56 A at uniform weather condition. Figure 21b shows the output power of the VSI reduces at shading pattern 1, because of the current decreases by 1.17 A, and maintaining the grid voltage of 59.7 V. Similarly, Figure 21c-d show the grid currents and voltages are 59.3 V and 58.7 V, and 1.29 A and 1.07 A, respectively, under the shading patterns 2 and 3. The experimental and simulation outcomes of the VSI are verified because the same phase and frequency of the grid parameters are obtained from both comparative studies.  
Figure 21: Experimental results of the proposed VSI: (**a**) uniform weather; (**b**) shading pattern 1; (**c**) shading pattern 2; and (**d**) shading pattern 3.
## 1 Introduction
These studies simulated the propagation of AE signals in thin-walled objects from various sources with different orientations and simulated signals were analyzed by means of wavelet transform. The disadvantage of the FEM method is the time needed to create the model and set its parameters, as well as the availability of expensive specialized software. To decrease the computational efforts, several numerical methods can be used for acoustic wave propagation modeling, such as the finite difference equation [7], the semi-analytical finite element [8], boundary elements [9], global matrix approaches [10], and spectral element approach [11]. Significant progress in the development of guided wave methods has been made by the [PERSON] group. Along with analytical [12] and numerical [13] methods of analysis, the authors have offered a new approach based on the application of piezoelectric wafer active sensors (PWAS) [14]. PWAS can be used both for receiving and emitting. By using two or more PWASs, it is possible to calculate the characteristics of the received signal with the known characteristics of the emitted signal for areas of the structure with and without damage [15]. To ensure the modeling procedure in the analytical mode, the modal analysis method was used as the basis for calculating the transfer function of the waveguide. Modal analysis is an analytical method based on the decomposition of the actions applied to the waveguide into a set of eigenmodes of the waveguide [16; 17]. Modal analysis is a mathematically accurate method that provides, in an analytical form, an integral equation for the field of elastic strains in a waveguide and naturally includes the modal expansion of waves, thereby offering a clear understanding of the physics of waveguide behavior. The modeling method represented in this paper is carried out in three steps:  
1. The \(\delta\)-function is used as the emitted signal corresponding to the elementary act of AE. Then, using the modal analysis, the waveform of the signal is calculated at a distance \(l\) from the emission point. 2. The signal attenuation in the waveguide is calculated analytically in the form of a frequency-dependent coefficient, which is calculated separately for each mode and for each thickness of the object. 3. The AE sensor is modeled by means of its transfer function. Finally, the transfer functions corresponding to dispersive propagation, attenuation and sensing are multiplied, thereby forming a single transfer function of the waveguide. Next, the impulse response of the waveguide is calculated by the transfer function discretization method. Test results of the structure's wall thickness and distances between the defect and sensor are used as the parameters of an impulse response.
###### Abstract  
The occurrence of geological events such as landslides is one of the main causes of damage along linear infrastructures: Damage to transport infrastructures, as roads, bridges, and railways, can restrict their optimal functions and contribute to traffic accidents. The frequent and accurate monitoring of slope instability phenomena and of their interaction with existing man-made infrastructures plays a key role in risk prevention and mitigation activities. In this way, the use of high-resolution X-band synthetic aperture radar (SAR) data, characterized by short revisiting times, has demonstrated to be a powerful tool for a periodical noninvasive monitoring of ground motion and superstructure stability, aimed at improving the efficiency of inspection, repairing, and rehabilitation efforts. In the present work, we suggest a semiautomatic GIS approach, which, by using satellite radar interferometry data and results of geomorphological field survey integrated in a qualitative vulnerability matrix, allows to identify sections with different levels of damage susceptibility, where detailed conventional in situ measurements are required for further analysis. The procedure has been tested to investigate landslide-induced effects on a linear infrastructure in Campania Region (Italy), the Provincial Road \"P.R. 264\", which is affected, along its linear development, by several slope instabilities. COSMO-SkyMed interferometric products, as indicator of ground kinematics, and results of in situ damage survey, as indicator of consequences, have been merged in a qualitative \(4\times 4\) matrix, thus obtaining a vulnerability zoning map along a linear infrastructure in January 2015. Furthermore, an updating of landslide inventory map is provided: In addition to 24 official landslides pre-mapped in 2012, 30 new events have been identified, and corresponding intensity and state of activity has been detected.  
damage; differential SAR interferometry; landslides; linear infrastructures; vulnerability
## 5 Conclusions  
In this paper, a new dual-band balanced bandpass filter (DB-B-BPF) with inherent common-mode rejection is presented. The proposed structure is based on the use of two pairs of magnetically coupled open-loop resonators implemented in multilayer technology. The lower differential-mode (DM)passband is created by the middle layer resonators, while the upper DM passband is associated with the top layer resonators. By using this configuration, it is possible to design the two DM passbands with independent control of bandwidth and matching level. At the same time, very good band-to-band isolation and common-mode rejection is achieved. To demonstrate the usefulness of this proposal, an example of a filter operating for GPS and Wi-Fi applications (1.58 GHz and 2.4 GHz) was designed, fabricated and measured. Experiments show a Common-Mode Rejection Ratio (CMRR) better than 38 dB and 31.1 dB for the lower and upper DM passbands, respectively. Measured DM insertion loss for both passbands is 0.92/1.94 dB and the obtained 3-dB bandwidth is 8.86/5.69 %.  
[PERSON], [PERSON] and [PERSON] designed, simulated, manufactured and measured the filter prototype. [PERSON], [PERSON] and [PERSON] supervised the whole study. All the authors participated in writing the paper and in revising the article. All authors have read and agreed to the published version of the manuscript.  
This work has been funded by the Spanish AEI Ministry of Science, Innovation and Universities and EU Feder Funds (project TEC2017-84724-P). [PERSON] acknowledges financial support of a research scholarship of the Spanish Ministry of Education (reference PRE2018-085677). The authors declare no conflict of interest
## 3 Results
### Changes in Antioxidant Activity, Content of Secondary Metabolites and Minerals in Harvested Seeds
In summary, the largest effects on buckwheat seed mineral content were observed on the amounts of Fe and Zn, but the effects were opposite for the two cultivars-CP7, EMF10  
Figure 6: Mineral content in the harvested seeds of two buckwheat cultivars. (**a**) ‘VB Vokiai’ seeds; (**b**) ‘VB Nojai’ seeds. The amount of K, Ca, Mg, Na is presented in % of dry weight, the amount of Fe, Cu and Zn-in \(\upmu\)g per g. The means \(\pm\) standard errors of three replicates are presented (\(n=3\)). Different lowercase letters indicate significant differences (\(p<0.05\); [PERSON]’s LSD test). and EMF15 significantly increased Fe and decreased Zn content in 'VB Vokiai' seeds, while in seeds of 'VB Nojai' CP7 decreased Fe content and EMF treatments increased Zn content. The results of HPLC analysis of the amounts of secondary metabolites rutin, quercetin and D-fagomine in buckwheat are presented in Figure 7.  
In the control, 'VB Vokiai' seeds contained less rutin and quercetin (by 17% and 13%, respectively) but 10% more D-fagomine compared to 'VB Nojai' seeds (Figure 7). The amount of rutin in 'VB Nojai' seeds was reduced by 11% in EMF10 group; CP5 reduced rutin amount (by 10%), but EMF15 slightly increased it (by 5%). Changes in quercetin levels were also cultivar-dependent. In 'VB Nojai' seeds, quercetin levels were reduced in CP7 and EMF15 groups (by 26% and 25%, respectively), but increased by 19% in the CP5 group. In 'VB Vokiai' seeds CP7, EMF10 and EMF15 increased quercetin 31%, 5% and 45%, respectively. The amount of D-fagomine in 'VB Nojai' seeds was unaffected by the treatments, while in 'VB Vokiai' seeds, CP7 increased it by 12%. The amount of secondary metabolites per plant was calculated by multiplying the average weight of seeds per plant (Figure 4) by the average amount of secondary metabolite per weight unit (Figure 7). Secondary metabolite production per plant found to be most significantly increased by seed treatment with CP5 in 'VB Nojai' seed rutin content per one plant increased 1.9, quercetin 2.3, D-fagomine 1.9 times. EMF15 treatment was the most efficient for 'VB Vokiai': rutin content per plant increased 1.6 times, quercetin content 2.3, D-fagomine content 1.6 times (although the total production of seed D-fagomine was the highest in the CP5 group, which increased 1.8 times compared to control seeds). In comparison to rutin and D-fagomine, treatments were more potent in changing the content of quercetin.
# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 2 Preliminaries  
The task-scheduling plan of a radio- and television-transmission station for daily execution of the transmission task is called the Schedule. According to the Chinese radio and television industry standard [39; 40], the operation diagram is defined as follows:  
**Definition 1**.: _Schedule: a table that specifies the broadcasting tasks undertaken by the transmitter in a day according to the sequence of program broadcasting time._  
The mathematical model of the Schedule is a two-dimensional table; the horizontal axis represents the time, and the vertical axis represents the equipment code. The DSTT model can be described as the problem of filling the transmission task into the Schedule and maximizing the comprehensive-evaluation function. The DSTT model framework is shown in Figure 1.  
The transmission-task sequence and the transmission-effect evaluation value matrix are the input data, and the maximum value of the evaluation function is taken as the fitness. Using intelligent-algorithm calculations, a Schedule meeting the requirements is obtained.
# Small-Scale Zero-Shot Collision Localization for Robots Using RL-CNN
## 3 Dataset Description
### Segment and Labeling Scheme  
As shown in Figure 2, the collected data contain 3-axis acceleration and 3-axis angular velocity. The sampling frequency is 1500 Hz. The data of different axes are synchronized in time. In our previous work, we proved that when the sampling frequency is 1500 Hz, setting the effective period to 267 ms can ensure that the collected vibration signals contain sufficient contact information. Thus, the effective period is set to 267 ms. In addition, to capture the transient characteristics of the collision without introducing too much irrelevant information, we set the pre-collision period to 20 ms. In the training and testing processes, the effective period of the signal is used as the input for the proposed method.  
In terms of labeling, the actual physics of collisions should be considered. As shown in Figure 3, when the compensator is not collinear with the center line of the charger, the compensator will rotate in the same direction regardless of whether the collision occurs on the upper region or the lower region of the charging port. This will cause the vibration signals corresponding to the collision points of the upper and lower regions to be similar to some extent, and the purpose of the proposed two-stage method is to isolate this similarity to improve the positioning accuracy of the zero-shot collision points. For this consideration, in the first stage, data can be first labeled as U or L based on whether the collision point is in the upper or lower region. In the second stage, in order to predict which radial region the collision point will occur in, the data are labeled as R1 to R8, which is in the same order  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Number of Zero-Shot Collision Points** & **Number of Collision Points** \\ \hline Case 1 & 949 & 4704 \\ Case 2 & 944 & 4709 \\ Case 3 & 946 & 4707 \\ Case 4 & 943 & 4710 \\ Case 5 & 930 & 4723 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The total number of zero-shot collision points and collision points in different cases.  
Figure 1: The distribution of zero-shot collision points and collision points.  
as shown in Figure 1. At this point, labels based on different annotation methods were established.
## 3 Results
#### 3.1.2 Case Study in the Year 2003
However, when an intense belt of relativistic electrons existed in the lower magnetosphere in November 2003-December 2005 (after a giant magnetic storm in late October 2003), three such events occurred around the globe relatively rapidly (slightly more than 3 months). These were M8.1 (north of Macquarie Island on 23 December 2004), M9.1 (Sumatra, the Andaman Islands on 26 December 2004), and M8.6 (northern Sumatra on 28 March 2005). Figure 6: Histograms of the monthly number of earthquakes of M \(\geq\) 4.5 occurring near the footprints of geomagnetic lines L = 2.0–2.5 from January to December of 2003. Figure 7: Histograms of the global seismic energy released into the five 26-month intervals: July 1999\(-\)August 2001, September 2001\(-\)October 2003, November 2003\(-\)December 2005, January 2006\(-\)February 2008, and March 2008\(-\)April 2010, based on the USGS seismological catalog for M \(\geq\) 4.5. The results in Figures 2-7 clearly show that the geomagnetic field lines, when they are filled with high-energy particles, may take part in seismic processes (earthquakes tend to occur near the footprints of these magnetic lines). Here, we need to mention another result showing that the magnetic field lines are somehow involved in the earthquake preparation process [48]. For example, Figure 8 shows the geomagnetic field lines connecting 38 geomagnetically conjugated points at the Earth's surface in the southern and northern hemispheres. In the southern hemisphere, the coordinates of 38 points were determined, distributed relatively evenly along one of the seismic belts (the boundary of the Antarctic lithospheric plate). By using the GEOPACK computational package [33], we calculated the \(L\)-value (McIlwain parameter) for each of the 38 points, and the coordinates of a magnetically conjugate point in the northern hemisphere were determined. Figure 8 shows that the Antarctic lithospheric plate border, which is traced by the earthquake epicenters, is in magnetic conjugation with the zone at the junction ofogenic and platform structures in the northern hemisphere. The effect of geomagnetic conjugation is most clearly manifested between the southern boundary of the Nazca tectonic Plate and the northern boundaries of the Cocos and Caribbean tectonic plates. It is well known that many processes in the upper geospheres (ionosphere, magnetosphere) are magnetically conjugated (occur at the same geomagnetic L-line); the same is evidently true for the solid Earth.
## 3 Results and Discussion
### Spatial-Temporal Patterns of GPP
From the simulated presentation, deciduous stands had a higher average annual GPP (482.81 gC m\({}^{2}\)/year in 2013 and 455.79 gC m\({}^{2}\)/year in 2014) compared to conifer forests (363.01 gC m\({}^{2}\)/year in 2013 and 343.11 gC m\({}^{2}\)/year in 2014) even if the distribution of conifer stands were mainly located in the outlet of the watershed with lower altitude, warmer climate and more adequate soil water resource. That is because deciduous stands had a higher stomatal conductance (g\({}_{\text{s,max}}\) = 5 mm/s) and leaf N content (N\({}_{\text{max}}\) = 1.8%) than that of conifers (g\({}_{\text{s,max}}\) = 1.6 mm/s; N\({}_{\text{max}}\) = 1.6%). Additionally,  
Figure 9: (a) Time-series comparison of modeled and observed gross primary productivity (GPP) for 2013 and 2014 in the Arou footprint area. (b) Combined comparison between simulated and measured GPP for 2013 and 2014.  
confer canopy was more densely clumped than deciduous stands, with a clumping index of 0.5 and 0.8, respectively. It would decrease the ratio of sunlit leaves in the conifer canopy. Grassland had the most wide distribution within the Upper Heihe and also had the most spatially heterogeneous annual GPP, which ranged from 1.76 to 2119.18 gC m\({}^{2}\)/year in 2013 and 1.75 to 2174.46 gC m\({}^{2}\)/year in 2014. The spatial variability of grassland GPP had a high congruency with LAI, which reveals that the leaf abundance also took a significant contribution of grass photosynthetic efficiency, because it controlled vegetation surface energy balance through the interception of water and solar energy. The distribution of GPP depended on complex interaction effects between topographic conditions, plant function types, vegetation biomass, regional meteorology, soil moisture, temperature and N dynamics in the model. The quantitative relationships and feedbacks between these spatial factors should be further explored. Theoretically, the model could run at any spatial resolution depending on the input dataset availability, while the lack of high-resolution soil types and LAI limited the ability of spatial patterns simulation. To fit the fusion of multi-source datasets, the spatial downscaling of DEM and land cover from 30 m to 100 m would also damage the original data qualities and further decrease the ability of model to explain GPP distribution due to the high correlation between these two factors and photosynthesis.