esa-sceva/satcom-chunk-collection huggingface dataset:
# Upper Body Pose Estimation Using Deep Learning for a Virtual Reality Avatar
## 2 Related Works
[23] propose a body pose estimation approach using a regression model based on a deep neural network (DNN) with images as the input. [PERSON] et al. [24] embed a kinematic model into the deep learning architecture to impose kinematic constraints. They also propose a deep regression architecture combining a traditional convolutional neural network (CNN) for supervised learning and an auto-encoder that implicitly encodes 3D dependencies between body parts. Recent studies have introduced a Transformer model, a sequence-to-sequence encoder-decoder model, to vision problems [25; 26], and pose regression problems [27; 28; 29]. [PERSON] et al. [27] propose a transformer network, Transpose, which estimates 2D pose from images. [PERSON] et al. [28] combine CNNs with transformer networks in their method METRO (Mesh Transformer) to reconstruct the 3D pose and mesh vertices from a single image. [PERSON] et al. [29] present a spatial-temporal transformer called PoseFormer to exploit the keypoint correlation of the human joints for each frame and the temporal correlations and to produce temporally coherent 3D human poses from videos. In addition, researchers have used deep learning for multi-person pose estimation from an image-based standpoint. OpenPose [30] is one of the most popular bottom-up approaches for multi-person human pose estimation. DeepCut [31] is another bottom-up approach for multi-person human pose estimation, and AlphaPose, based on the regional multi-person pose estimation (RMPE) framework [32], is a popular top-down method of pose estimation. Whereas the datasets for these methods include images, this study investigates estimating the human upper body pose using a numerical dataset. Moreover, researchers have used motion capture in data-driven deep learning methods. [PERSON] et al. [33] formulate pose estimation as a DNN regression problem by localizing the body's joint positions from images. The data-driven clarifier is based on motion capture data the researchers collect to identify a solution similar to the current pose [34; 35; 36]. This study proposes a pose regression model to predict human upper body joint poses using deep learning. We then apply our model in a VR environment to evaluate our results in real time.
## 1 Introduction
These drawbacks have prompted scientists to investigate the feasibility of using alternative, less harmful cooling methods. Some of those include cold compressed air cooling (CCA) and minimum quantity lubrication (MQL). [PERSON] et al. [45] used vegetable-based cutting fluids produced from sunflower oil in his experiments. Drilling AISI 304 using this new oil mixture showed that the feed rate is the most influential cutting parameter on thrust force. [PERSON] et al. [46] studied the influence of cutting environments on tool life and geometry accuracy of drilled PM steel alloy. Using both CCA and MQL cooling techniques, considerable improvements were noted, such as lower cutting forces, torque and tool wear. As noted by [PERSON] et al. [47], a major issue affecting the implementation of cooling techniques in drilling is the inaccessibility of the cutting zone by the cooling medium. Comparing tool wear of uncoated and TiN-coated HSS drills, lower wear was noted using coated tools. Generally, as foam porosity increases, the thermal conductivity of the workpiece reduces, leading to higher tool wear. Applying coolant to the cutting zone can greatly diminish the negative thermal properties of the porous material. From this brief literature overview, it is clear that the machinability of aluminum foams produced from scrapped milled chips has yet to be researched. Therefore, the goal of this paper will be to evaluate the influence of drilling parameters and cutting environment on thrust force, deviation of the hole, and tool wear during the drilling of closed cell aluminum A380 foam produced from scrap aluminum alloy. Along with using an environmentally friendly alternative to powder metallurgy foams, multiple cooling methods such as cutting fluid, CCA and MQL will be assessed. It is hoped that this research could greatly contribute to expanding the applicability of aluminum foams with a focus on minimizing processing costs, as well as the impact on the environment that conventional machining has. The main goal of this paper is to determine the influence of drilling parameters and cutting  
Figure 1: Sustainability aspects associated with the implementation of cutting fluids. environment on thrust force, deviation of hole and drill wear during the machining of closed cell aluminum A380 foam.
# Automatic Ontology-Based Model Evolution for Learning Changes in Dynamic Environments
## 4 Proof of Concept and Case Study
### Proof of Concept  
A proof of concept was implemented to verify the feasibility of the presented approach. In our proof of concept implementation, an application for assisting engineers was used to evolve users' initial ontology-based models regarding arisen changes in the users' surrounding environments at runtime. The presented application consisted of two distinct layers, called frontend and backend. The frontend layer consisted of an Angular-based web application for engineers, while the backend layer dealt with the automatic ontology-based model evolution. This layer was accessible from the frontend layer via RESTful Web services. All of the approach's modules were implemented as RESTful Web services, developed using Spring Boot and Java technologies. We adopted Spring Boot as the basic framework to simplify the work, since it is an open-source Java-based framework that makes the development of RESTful Web services simple.  
To accomplish the backend layer implementation, we made use of Apache Lucene [25] to select the most useful semi-structured data source that could answer the changes occurring in the user's surrounding environment. We opted for Apache Lucene, as it is a powerful information retrieval tool that provides Java-based indexing and search technology. For the data source format unification, the set of mapping rules described in Table 2 was written in Java to transform the tabular data to XML data. In addition, a common JSON parsing API named Jackson, which converts JSON to XML data, and Trang API, which produces XML schema from an XML data input, were used. For the learning phase, an XML-Schema Object Model (XSOM) parser was utilized to parse the generated XML schema and inspect the elements and attributes in it. Moreover, the set of mapping rules defined in Table 3, together with the Jena API, were integrated to build the local ontology and then fill it via concepts, relations, properties, and individuals. Then, for the refinement phase as well as the evolution module, we used the extended Java WordNet Library (extJWNL), DBpedia, and Matcher Java regex classes.
# User-Information-Aware D2D Multicast File Distribution Mechanism
## 6 Cluster Head Selection Mechanism
### D2D Multicast Utility Metric
As mentioned, social relationships among users, which can be described the different levels of trust, familiarity, and demand similarity among users and so on, affect the stability of D2D links. In order to accurately measure the transmission performance of the D2D multicast communication system, the D2D multicast utility metric \(C_{i}^{e}\) is proposed in this paper, as shown in (22):  
\[C_{i}^{e}=B\biggl{(}\sum_{j\
eq i}^{|U_{C}|}s_{i,j}\biggr{)}\log_{2}\Bigl{(}1+ SINR_{i}^{min}\Bigr{)} \tag{22}\]  
where \(s_{i,j}\) is the strength of social relationship between user \(u_{i}\) and user \(u_{j}\), its value can be obtained from the social relationship strength matrix \(\mathbf{S}\). The D2D multicast utility metric \(C_{i}^{e}\) reflects the average throughput that the system can achieve when considering the instability of the D2D links. In the calculation of utility metric \(C_{i}^{e}\), both the social relationship strength information and channel quality information between users are taken into account, which makes it closer to the actual communication environment than the traditional throughput model.
###### Abstract
Moreover, Figure 1 also describes multiple CWIs with different JSRs added to the modulated signal (QPSK), where the amplitude of the CWI on top of the original signal is significantly affected by JSR. Figure 2 illustrates the impact of the received PS signal with various \(E_{b}/N_{o}\) parameters on interference clarity. As can be seen, as \(E_{b}/N_{o}\) decreases, the noise level increases; however, in comparison with the signal's power level, this does not affect the location and visibility of the CWI peaks, thus indicating that JSR is the primary effective parameter for interference detection. Figure 1: The effect of varying the JSR parameters on the received PS signal. Adaptive notch filters (ANFs) were used to eliminate or reduce low and strong RFI impacts. ANF methods can suppress CW and other kinds of NB interference. In a typical IIR NF, a zero is placed on a unit circle with a phase equal to the frequency of the instantaneous jammer, so as to completely remove the jamming However, precisely because the jamming is thus completely eliminated, this method generates a significant amount of self-noise [13], as briefly described in [14]. ANFs, for their part, create self-noise, which significantly reduces the receiver's SNR in this situation [15]; therefore, the depth of ANFs' excision NFs is adjusted to JSR specifications. In the case of high-power jammers, a deep notch is required for perfect jamming removal. Many papers have been written on the rejection or mitigation of NB interference using various suppression filters; however, most such works emphasize the suppression of CWI and MCWI, while few studies appear to focus on adjusting the depth of the NF based on an estimation of the power of the interference in suppressing CWI and MCWI. This paper proposes a technique for suppressing CWI and MCWI based on an adaptive direct IIR NF, focusing on adjusting the depth of the NF. The proposed method overcomes the main drawbacks of ANF while removing interference; it avoids the self-noise effect of the NF by adjusting and controlling the depth of the NF using the ANF \(H_{d2}(z)\). The main contribution of this work is to propose a new class of adaptive algorithms to control the update filter coefficient and adjust the depth of NFs with respect to estimating the power of interference using a second adaptive IIR NF based on a direct-form structure. The structure of this paper is as follows: Section 2 reviews previous research. The signal model of the received signal in the presence of interference is described in Section 3. The proposed suppression system model, its adaptation algorithm, and the optimal notch depth are described in Section 4. Section 5 presents the proposed performance based on simulation results, and the paper is concluded in Section 6.
# A Fast, Decentralized, Self-Aligned Carrier Method for Multicellular Converters +
###### Abstract  
This paper proposes a fast, decentralized method for self-aligning the carriers of a multiphase/multilevel converter operating on the basis of phase-shifted pulse width modulation or level-shifted pulse width modulation. In the proposed method, each cell of the converter synchronizes and updates simultaneously its own carrier angle or carrier level based on the information shared with its neighboring cell, such as its angle/level, its index number, and the total number of activated cells of the converter. Different from the conventional decentralized method (with basic and modified updating rules), which requires some conditions in terms of cell number and initial carrier angles to start up and operate properly, the proposed method can be applied to the system with any number of cells and does not require special conditions of initial carrier angles. Further, while the conventional method needs an iteration process to adjust the inter-carrier phase-shifts and can be applied only to a multiphase converter which uses phase-shifted pulse width modulation, the proposed method offers an accurate and fast alignment of phases (for phase-shifted pulse width modulation) or levels (for level-shifted pulse width modulation) and thus can be applied to both multiphase and multilevel converter types. The simulations and the experimental results are presented in detail to show the validity and the effectiveness of the proposed methods. Further, thorough simulations on multiphase converters with different number of cells also show that the proposed method is much faster than the conventional method in both configuration and reconfiguration processes, especially in case the system has a large number of cells.  
multicellular converter; multilevel/multiphase converter; decentralized control; phase shifted pulsewidth modulation (PWM); level-shifted PWM; carrier waveform; self-aligned carrier; interleaved carrier; dynamic reconfiguration +
Footnote †: This paper is an extended version of our paper published in [Conference Name]: 2020 IEEE International Conference on Industrial Technology (ICIT) [Location]: Buenos Aires, Argentina, Argentina [Date]: 26-28 February 2020.
# Content-Adaptive and Attention-Based Network for Hand Gesture Recognition
## 4 Experiment and Results  
### Datasets  
The performance of the proposed network was evaluated using two publicly available datasets, namely the NVGesture dataset [42] and the EgoGesture dataset [43; 44]. Figure 4 presents some sample frames captured from these two datasets. The NVGesture dataset is a large dataset of 25 gesture types recorded by multiple sensors and viewpoints, each of which is used for human-computer interfaces. It contains a total of 1532 dynamic hand gesture videos, each containing only one gesture. We followed [42] and split the data with a ratio of 5:2:3, resulting in 750 training, 300 validation, and 482 testing videos, respectively.  
Figure 4: Some frames captured from two datasets: (**a**) NVGesture dataset; (**b**) EgoGesture dataset.  
The EgoGesture dataset is a large-scale dataset for egocentric hand gesture recognition, consisting of 83 classes collected from four indoor and two outdoor scenes. The dataset has a total of 24,161 gesture samples recorded from 50 different subjects. The dataset was grouped in a 6:2:2 ratio, resulting in 14,416 training samples, 4768 validation samples, and 4977 testing samples, respectively. The details of the datasets used in our experiments are summarized in Table 2.
# Beam Dynamics Design of a Multi-Ion RFQ for Medical Application
## 3 Beam Dynamics Design
### Design Considerations
In RFQ beam dynamics design, the ability to suppress space charge effects is typically evaluated by examining the reduction in transmission efficiency as the beam current increases. If the degree of efficiency degradation decreases with increasing beam current, it indicates a stronger capability to mitigate space charge effects. Therefore, it demonstrates better compatibility when the RFQ accelerates ions that are more sensitive to space charge effects. Considering the ion species that the RFQ is designed to accommodate and their sensitivity to space charge effects, the transmission efficiency for 1 mA He\({}^{2+}\) should be no less than 95%. **Compact structure**: During the design of medical accelerators, compactness is one of the key priorities. A compact structure not only reduces manufacturing and installation costs but also reduces space requirements, making it convenient to use in medical environments. In this study, to balance cost and space constraints, the RFQ length is limited to less than 2.8 m.
* **Operational stability**: Due to the limitations of actual experimental conditions, the operating state of each accelerator is often not completely ideal. Therefore, it is crucial for the RFQ to have sufficient error tolerance. Considering input beam errors, the RFQ should exhibit adequate tolerance to ensure that the beam transmission efficiency remains above 95%. Under the influence of mechanical manufacturing errors, the emittance growth of the RFQ is required to be kept below 5%, and the extra beam loss should be less than 10%. Finally, the requirements for the multi-ion RFQ are summarized in Table 1.
## 4 Calculation Results and Experimental Validation  
### Optimal Solution Based on Improved Genetic Algorithm  
In accordance with the constraints of the experimental conditions, this paper aims to verify the reliability of the proposed parameter optimization method for seismic isolation bearings. In the example presented in this paper, the mass of the isolated object is set to 320 kg, which means that each seismic isolation bearing supports a vertical load of 80 kg. For practical engineering structures, such as small bridges, where each bearing supports a vertical load ranging from 300 kN to 10,000 kN [29], the optimization results can be obtained either by applying the optimization method presented in this paper or by using dimensional analysis and dynamic equation methods to derive the optimization results for the scaled model.  
The total width of the three-dimensional seismic isolation bearing is designed to be 150 mm, and the total height is designed to be 54 mm. The basic computational parameters for the genetic algorithm are set as follows: the number of small habitats is 5, the number of individuals in each population is 20, the crossover probability is 0.8, the mutation probability is 0.05, and the maximum number of generations is set to 6.  
Through optimization using the improved genetic algorithm, after six generations of iteration, the maximum acceleration-transmission ratio at the top center of the isolated structure was reduced from 138.1% to 46.0%. The optimal design parameters for the hollow cross-section of the three-dimensional seismic isolation bearing at this point are \(h_{1}=10\) mm, \(h_{2}=15\) mm, \(l=24\) mm, and \(r=16\) mm. The optimization iteration process of the genetic algorithm is shown in Figure 6.  
Figure 5: Design process diagram of the three-dimensional isolation device.  
Figure 6: Genetic algorithm optimization iteration process.
## 5 Discussion  
### Errors Induced by Landsat Albedo Estimation and Spatial Discrepancy  
The Landsat albedo estimation algorithm has been validated on a variety of sensors and land cover types [15; 16; 44; 58]. In this study, it was first calibrated using field observations. The errors induced by the estimation algorithm were hence eliminated.  
In most cases, the sites were not located at the center of a MODIS pixel. Comparing the MODIS product with field observations or high-resolution data at the site tends to induce errors due to spatial discrepancy. In this study, high-resolution data were first calibrated with the field observations. When evaluating the spatial representativeness of the MODIS product, Landsat pixels located at the center of the MODIS pixels were selected, so the analysis focused on this area could guarantee the spatial agreement to the greatest extent.  
\begin{table}
\begin{tabular}{c c c c} \hline
**LC** & **Land Cover Type** & \(\mathbf{R^{2}}\) & **RMSE** & **BIAS** \\ \hline
2 & Evergreen Broadleaf forest & 0.688 & 0.016 & 0.014 \\
4 & Deciduous Broadleaf forest & 0.863 & 0.018 & 0.013 \\
5 & Mixed forest & 0.926 & 0.026 & 0.014 \\
7 & Open shrulands & 0.947 & 0.018 & 0.014 \\
8 & Woody savannas & 0.944 & 0.030 & 0.017 \\
10 & Grasslands & 0.941 & 0.021 & 0.009 \\
12 & Croplands & 0.965 & 0.023 & 0.003 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison in terms of accuracy between TM and MODIS for different land types.  
Figure 4: \(R^{2}\) variation with aggregation scale.
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 2 Methods
### CoordConv Layer  
CoordConv is an innovative convolutional variant that integrates spatial coordinates as additional channels, facilitating the direct interpretation of feature locations within a network [39]. PCCAU-Net judiciously adopts this technique as a cornerstone within its deep learning pipeline. Traditional convolutional operations prioritize translational invariance, serving as a rudimentary stage for feature abstraction [40]. However, this approach can sometimes act as a constraint when parsing high-resolution images replete with geographical information. Intricacies like topographic variances, road layouts, and other geographical features are deeply intertwined with their spatial coordinates. PCCAU-Net quickly surpassed this limitation by embedding spatial awareness into its architecture by implementing CoordConv. Importantly, this is not a mere capitulation to technological trends but an acknowledgment of its substantive contributions. In the initial stage of the model, CoordConv is invoked five times in sequence--first targeting the original size remote sensing imagery, and subsequently being deployed across four pyramid levels (P1, P2, P3, and P4). This design assures that, from the outset, location information at each spatial scale is deeply explored and integrated. This forward-thinking approach confers heightened acuity to PCCAU-Net during feature extraction, enabling a more intuitive grasp of spatial interrelations between entities beyond mere inference. This capability becomes particularly salient in decoding complex terrains, especially when certain spatial arrangements among features carry explicit geographical meanings. Overall, PCCAU-Net's strategic employment of CoordConv marks a paradigmatic shift in our handling of high-resolution remote sensing data, aiming for a more precise and intuitive extraction of spatial information. The schematic representation of the CoordConv structure is illustrated in Figure 2.  
Figure 2: Schematic diagram of CoordConv operation details, where Input represents the input image, and x and y represent additional coordinate channels.