esa-sceva/satcom-chunk-collection huggingface dataset:
# Gridshells in Recent Research--A Systematic Mapping Study
## 3 Gridshells in Recent Research
### Which Types of Gridshells, Which Materials, and Which Parts of the Gridshells Are Investigated?
#### 3.4.1 Gridshell Material  
The gridshell material facet was mapped in the six categories, _timber_, _steel_, _aluminium_, _composite_, _other_ and _not mentioned_, as described in Section 1.1. The material facet describes the member material. As seen in Figure 13, the material is typically _not mentioned_ (206) in the studies. _Steel_ (51) was, therefore, the most used material, followed by _timber_ (28), _aluminium_ (21), _composite_ (17) and _others_ (4). Regarding material, classification on the node-material was also done, but this resulted in even fewer articles where the material could be determined, and this facet was therefore excluded. When node material was determined, it was also almost always the same as the member material, with the exception of timber gridshells with metal nodes.  
As seen in Figure 12, _steel_ gridshells are typically classified as _discrete_. Regarding gridshell parts, studies on _steel_ gridshells focus on either the _node_ (18), _all parts_ (13) or the _global shape_ (10). Examples of _steel_ studies are [41; 42; 43; 44]. _Timber_ gridshells are typically classified as _bending active_. Regarding gridshell parts, studies on _timber_ gridshells focus on either _all parts_ (19) or _nodes_ (4). Examples of _timber_ studies are [23; 36; 45; 46]. _Aluminium_ gridshells are typically classified as _discrete_. Regarding gridshell parts, studies on _aluminium_ gridshells focus typically on the _node_ (10). Examples of _aluminium_ studies are [30; 47; 48]. _Composite_ gridshells are almost only classified as _bending-active_. Regarding gridshell parts, studies on _composite_ gridshells typically focus on _all parts_ (10). Examples of _composite_ studies are [27; 49; 50]. _Others_ are very few with no clear trends, except that they focus on _all parts_ (4). Examples of _other_ studies are [51; 52; 53]. Examples of studies where the material is not mentioned include [17; 18; 20]
## 2 Design and Configuration of DPMS
### Signal Processing and Filter Design  
The measured data from the confocal sensor can be analyzed in three parts including vibration signal, rotation signal, and height data. Since the disturbance signal will couple with the real surface height data, the real surface features are separated by the signal processing. The rotation signal and system vibration can be separated by FFT method in this system. The rotating speed is defined in the beginning of the experiments. The vibration from the structure can be filtered by determining the mechanical frequency of the swing arm. The working frequency can be analyzed by rotating the arm independently on a ceramic platen. The frequency data are shown in Figure 3. The IIR filter is used to eliminate the influences of vibration and disturbances. Then the measured signal is analyzed and presented as Figure 4a. The comparison of processed and original signals is shown in Figure 4b,c. The original height data are combined with arm tilting, pad waviness, and pad. As the signal is analyzed by the designed algorithm, the measurement signal can be extracted and calculated.  
Figure 2: Schematic of scanning locus of confocal sensor.
# ZnO/Ag Nanocomposites with Enhanced Antimicrobial Activity
## 3 Results and Discussion
### Characterization of ZnO/Ag Nanocomposites
#### 3.1.4 Raman Spectroscopy  
Spectra measured under the 325 nm excitation wavelength are presented in Figure 5, which shows the typical spectra of ZnO compounds under the resonant conditions, with an intense LO peak (sum of two modes with A1 (LO) and E1 (LO) symmetry) and its second-order 2 LO peak [29]. In the average spectra of each sample, additional small intensity peaks can be identified (see the vertical dashed lines in Figure 6). All of them are related to the fundamental or previously observed multiphonon modes of the ZnO compound [30]. No additional peaks/bands that can be associated with the presence of Ag NPs in the powders can be observed in the spectra that were measured.  
Furthermore, the homogeneity of the samples and possible correlation of the Ag atomic percentage with the intensity of additional peaks/bands were accomplished. For this different reference, the LO peak in the spectra was selected, measured under 325 nm excitation. This peak is separated from the additional bands and makes it possible to define the homogeneity or correlate the spectra evolution with the Ag content. The homogeneity of the powders was assessed by calculating the relative integrated intensity of the 2 LO peak in the spectra measured under 325 nm excitation. The ratio of intensities between the 2 LO and LO peaks mainly changes due to the band gap variation in the ZnO compound, which changes the proximity to the resonant conditions [31; 32]. As seen from the box plot in the left panel of Figure 6, the mean value of the relative integrated intensity of the 2 LO peak is quite similar in all the samples, except for sample Zn4. Also, the homogeneity of the samples is different and is can be seen to be quite good for Zn2, with Zn3 being the worst. No correlation of the mean value or the inhomogeneity of the sample with the amount of Ag concentration was observed, assuming a gradual increase in the Ag NPs in the nanocomposites from sample Zn1 to sample Zn6.  
Figure 4: Absorption spectra of ZnO/Ag Nanocomposites: Zn1, Zn2, Zn3, Zn4, Zn5, and Zn6.
###### Abstract  
Mosaic speech is degraded speech that is segmented into time \(\times\) frequency blocks. Earlier research with Japanese mosaic speech has shown that its intelligibility is almost perfect for mosaic block durations (MBD) up to 40 ms. The purpose of the present study was to investigate the intelligibility of English mosaic speech, and whether its intelligibility would vary if it was compressed in time, preserved, or stretched in time. Furthermore, we investigated whether intelligibility differed between native and non-native speakers of English. English (\(n=19\)), Indonesian (\(n=19\)), and Chinese (\(n=20\)) listeners participated in an experiment, in which the mosaic speech stimuli were presented, and they had to type what they had heard. The results showed that compressing or stretching the English mosaic speech resulted in similar trends in intelligibility among the three language groups, with some exceptions. Generally, the intelligibility for MBDs of 20 and 40 ms after preserving/stretching was higher, and decreased beyond MBDs of 80 ms after stretching. Compression also lowered intelligibility. This suggests that humans can extract new information from individual speech segments of about 40 ms, but that there is a limit to the amount of linguistic information that can be conveyed within a block of about 40 ms or below.  
mosaic speech; temporal resolution; speech intelligibility; compressed speech; stretched speech +
Footnote †: journal: Applied Soft. 2020, 10, 6920; doi:10.3390/app10196920
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 5 Simulation Results and Analysis
### Performance Metrics  
#### 5.3.1 PSNR  
Peak Signal-to-Noise Ratio (PSNR) is an index used to measure the quality of images or signals. It is usually used to evaluate the similarity between an image and the original image, especially in the field of image compression and reconstruction. The higher the PSNR value, the higher the similarity between the two images, the better the quality. The calculation formula is  
\[PSNR=10\cdot log_{10}(\frac{max^{2}}{MSE}), \tag{15}\]  
where \(max\) represents the maximum possible value of the pixel value, usually 255 in image processing, and \(MSE\) represents the mean square error.
## 3 Materials and Methods
### Use Case: Dynamic Pointcloud Generation with OpenDroneMap  
After testing the different architectures, we used the best option for a specific use case: pointcloud processing. Pointcloud processing and management have specific requirements for computing. Similarly to [23], we found that a good software option for processing is Open Drone Map (ODM) [24]. This is an open framework used to generate and process maps and pointclouds from Unmanned Aerial Vehicles (UAVs) [25].  
In this use case, we will demonstrate how Docker and Kubernetes dynamically manage and scale pointcloud generation. Drone images are transmitted to a Raspberry Pi for preprocessing, which then requests a GPU-accelerated transformation matrix from an Edge server to align spectral bands. Kubernetes orchestrates Docker containers in the Cloud, distributing the computational load for efficient pointcloud generation. The system architecture is shown in Figure 2.  
The workflow starts with the drone capturing images and transmitting them to a Raspberry Pi, which preprocesses the images for pointcloud generation. Upon receiving the first image, the Raspberry Pi requests a transformation matrix from the Edge server to align the images from different spectral bands. The Edge server, using GPU acceleration, computes the matrix, ensuring all images share the same perspective and reducing computation time.  
After obtaining the transformation matrix, the Raspberry Pi extracts metadata from the images to apply vignetting corrections. It then aligns the images using the transformation matrix, ensuring consistency across all spectral bands. Once the images are preprocessed and aligned, the Raspberry Pi sends them to the Cloud environment for further processing. In the Cloud, Kubernetes orchestrates the deployment of ODM instances within Dockercontainers. Each instance is responsible for processing a specific spectral index, such as the NDVI, or band.  
The system scales dynamically by adjusting the number of ODM worker instances based on the number of spectral indices to be computed in parallel. This scalability is achieved through Kubernetes' ability to manage resources and deploy additional instances as needed. A Watcher Service operates within the Cloud environment to monitor the availability of ODM instances. It ensures that new instances are registered with ClusterODM, which functions as a load balancer, distributing tasks among the available workers.  
By distributing computational tasks across multiple ODM workers, the system significantly accelerates the pointcloud generation process. The parallel processing of different spectral indices reduces the time required to obtain results. This architecture demonstrates the effectiveness of combining Kubernetes and Docker to manage complex workloads in a Cloud environment.
# Global GNSS-RO Electron Density in the Lower Ionosphere
## 3 Results
### Magnetic-Field Dependence
Magnetic field-line pitch angles (a.k.a., dip angles) are contoured at an interval of 20\({}^{\circ}\). The pitch angles on the cylindrical-projection map (**left**) are derived from the magnetic field at a 100 km altitude, whereas the ones on the azimuthal polar maps are from the field at one Earth radius above the surface. The longitude–latitude grid size is 8\({}^{\circ}\)\(\times\) 4\({}^{\circ}\) for these maps.
## 6 Analysis of Results
### Energy Optimization
#### 6.3.1 Constraints  
\[E\_node\leq E\_initial \tag{20}\]  
\[Forwarder(HT_{r}^{n})\leq MiniForwarder(minHT_{n=1}^{N}) \tag{21}\]  
\[Tr_{n}\leq Tr_{max}\,\forall\,n\,\epsilon\,N \tag{22}\]  
The purpose of the objective function is to reduce total energy consumption. One of the constraints is that the energy provided to each node should be less than the initial energy as stated in Equation (20). The second constraint is that the forwarder of each source node is selected on the value of holding time and is designed in such a way that the node with maximum residual energy will have higher chances of selection. The last constraint is that the transmission range of each node should be less than the maximum required transmission range.
# A Novel Preprocessing Method for Dynamic Point-Cloud Compression
## 3 Proposed Method  
The computing system of this study leverages a pre-processing process to resolve problems appearing in the data, so that redundant information can be efficiently extracted from dynamic point-clouds. Figure 1 shows the entire compression process using the divider algorithm that can remove redundant information, including the proposed pre-processing procedure.  
Figure 1 shows the whole system of the proposed method. The suggested pre-processing process have been added prior to the divider process. The pre-processing process converts the existing point-cloud to have a voxel-based data structure.
# Linear Vibration Analysis of Shells Using a Seven-Parameter Spectral/_hp_ Finite Element Model
## 6 Numerical Results
### Numeric Validation
#### 6.3.2 Cylinder  
Cylinders with linear thickness variation, and subjected to CC and CF boundary conditions are studied for different radius to thickness ratios, in order to determine a valid work range for the present formulation. For these geometries, the radius-thickness ratio is calculated using the minimum thickness, and the natural frequencies are represented by the dimensionless parameter \(\Theta\), which is obtained by [50]  
\[\Theta=2\pi fr\left[\frac{\rho\left(1-\
u^{2}\right)}{E}\right]^{1/2}. \tag{46}\]  
The number of spectral \(p\) shell elements used for this geometry is shown in Tables below the label **7-PL**. In all the cases, elements with a polynomial degree \(p=8\) were used.  
Table 18 presents the natural frequencies for a cylinder with both edges clamped and for different \(r/h\) ratios. The ratio length to radius remains fixed and its value is \(L/r=2\). From these results, a raise in the parameter \(\Theta\) can be observed as the shell becomes thicker. The highest error values are obtained when thicker shells are analyzed (\(r/h=5\)), having a maximum error of 6.0%.  
Table 19 presents the dimensionless parameter \(\Theta\) for a cantilevered cylinder with linear thickness variation, where the edge with maximum thickness is clamped. In this case, for a fixed aspect ratio \(L/r\), the natural frequencies rise for thick shells, and the relative errors also increase.
# Dynamics Modelling and Simulation for Deployment Characteristics of Mesh Reflector Antennas
###### Abstract  
The dynamics of mesh reflectors are characterized by large deformation when antennas undergo a large motion in the deployment process. In this work, absolute node coordinate formulation (ANCF), with the merit of precise description of large deformation, is employed to consider the flexibility of the reflector net. From a practical perspective, the effect of the torsional spring is incorporated into the dynamic governing equations by an equivalent mechanical model. Deployment simulation of the mesh reflector antennas is performed by a numerical procedure. It can be found that with the action of a degenerative driven force caused by the friction effect, the deployed process of antennas exhibits a character of asynchronous deployment. Additionally, a significant increase in tension during the final period of deployment has an intense influence on smooth deployment.  
mesh reflector antenna; large deformation; absolute node coordinate formulation (ANCF); torsional spring; dynamics characteristics +
Footnote †: journal: Applied Sciences
###### Abstract  
A control method combining Backpropagation (BP) neural network and Adaptive Linear Active Disturbance Rejection Control (ALADRC) is proposed for the attitude control problem of quadrotor aircraft. The proposed controller can observe and compensate the total disturbance in the working process of the quadrotor system. At the same time, it has a good ability to suppress the disturbance of the quadrotor load mass change. In addition, adaptive control and BP neural network are used to adjust the controller parameters in real time to solve the problem of difficult parameter tuning. Finally, the stability of the system is proved based on Lyapunov theory. The simulation results show that the quadrotor system can still track the altitude and attitude angle commands stably in the presence of disturbances, and has strong adaptability to load mass changes, so that the quadrotor can still complete the given tasks in the presence of multi-source disturbances.  
Backpropagation neural network; attitude control; total disturbance; adaptive control; parameter tuning
## 3 Datasets and Angular Features
### Unification of Input Data Size  
When the calculated angle features are extracted from consecutive frames, they form a two-dimensional array. As the length of the video affects the length of this two-dimensional array, interpolation methods are used in this study to standardize the input data size. The ROIAlign (region of interest align) method [17], which is used in object recognition, is applied to interpolate the targeted area (region of interest) of the hand more accurately. By employing this approach, videos of different durations can be transformed into sequences with fixed frames, and it is also possible to standardize the size of the input 2D matrix data.  
In our experiments, videos of approximately 1 to 2 s were extended to 100 frames using the ROIAlign method. From these 100 frames, a fixed number of frames was randomly extracted while maintaining intervals within 10 frames. As a result, we obtained 54 time-sequential data points for each class. The video data, comprising six videos with three recordings each from both male and female participants, resulted in 54 time-sequential data points per video, amounting to 324 feature vectors. With 46 characters, this led to a total of 14,904 feature vectors.  
The feature vectors obtain values ranging from \(-1\) to 1 for calculations using the cosine formula. However, as the ViT is originally a model suited for image-based learning, these values were converted to a pixel-value range of 0 to 255 using the following Equation (2):  
\[x_{new}=(x_{old}+1)\times 125 \tag{2}\]  
Based on the calculations of angular features, three datasets were created. These three datasets differ in the features they use, as shown in Table 2. Dataset 1 focused solely on the joint angles (Feature 1), comprising 20 dimensions. Dataset 2 concentrated on the overall tilt of the hand (Feature 2), also encompassing 20 dimensions. Finally, Dataset 3 combined Features 1 and 2, resulting in a total of 40 dimensions.
## 2 Research Question and Research Approach
Therefore, the central research question can be formulated as: _In what way can the IFM Framework and graph-based design languages be combined to achieve holistic digital design processes and traceability of design solutions between functional and product structural level design?_  
The general underlying concepts of the presented research are the concept of function modeling, i.e., an abstract description of the intentions of a technical system; the concept of the use of a function modeling framework, i.e., a conscious interconnection of the different information entities describing the function of a technical system; the concept of graph-based design languages, i.e., a knowledge representation of a technical system in the form of a graph; and the concept of a design compiler, i.e., a software component that is able to generate a design graph from knowledge presented in the form of vocabulary and grammar. The central methodological research approach is the analysis of the possibilities and limitations of two frameworks (the integrated function modeling framework and an engineering framework based on GBDLs), an identification of common and different entities, the development of connection possibilities and an evaluation of the resulting combined framework. It is important to note that the main emphasis of the research described in this paper is on the methodical and structural aspects of the integration of the IFM framework into an engineering framework based on GBDLs--a larger share of the research is prescriptive. The sample product multicopter was used for the development of these aspects to analyze the applicability and merit--a descriptive research step--and to explain the different integration issues and solutions. Multicopters are unmanned aerial vehicles (UAVs) with an immense application potential due to their capability to hover at a certain position and to start from nearly any surface. Central parameters concerning the design of a multicopter are the topology (quadrocopter, hexacopter, octocopter, etc. ), the flying and lifting performance, the manufacturing cost and its structural integrity [34]. An important issue is that the manufacturing of multicopters and additive manufacturing in combination with topology optimization has led to rigid, lightweight structures [35]. The design of a multicopter is always a multi-domain problem, as the controllability and trajectory generation are directly connected with the flight performance that is a consequence of the design of the multicopter [36].
# Microseismic Data-Direct Velocity Modeling Method Based on a Modified Attention U-Net Architecture
## 3 Numerical Tests and Results
### Feature Datasets  
#### 3.2.1 Single-Event Microseismic Records  
Based on the completed labeled dataset, we used finite-difference forward modeling to simulate microseismic records corresponding to the features. The detectors were positioned at a depth of 10 m with a horizontal spacing of 10 m, resulting in a total of 192 detectors. The sampling interval was set to 1 ms, and the sampling duration was 1.28 s, incorporating fully matched absorbing boundaries. For the simulation of microseismic signals, we employed a Ricker wavelet with a dominant frequency of 30 Hz as the source function. This source function was applied to each individual underground source location within the velocity model, specifically those situated below 500 m in depth. The forward modeling process yielded a set of single-event microseismic records, with dimensions of 1280 \(\times\) 192. To align with the dimensions of the labeled dataset, these records were down-sampled as previously described in Section 2.4. The records were down-sampled to dimensions of 128 \(\times\) 192.  
Figure 3: The generated multi-layer random stratigraphic models.