esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### Structure Elucidation of Compound **1**  
Compound **1** was obtained as a colorless oil, with a molecular ion peak [M]\({}^{-}\) at \(m/z\) 180.0788 in the HRESIMS spectrum, corresponding to an elemental formula of C\({}_{10}\)H\({}_{12}\)O\({}_{3}\). The UV absorption maxima at 230 and 254 nm indicated the presence of aromatic ring and conjugated enone group [24]. The \({}^{1}\)H- and \({}^{13}\)C-NMR spectra of **1** were similar to those of isoegomaketton [25], except for the absence of signals for a methine group and the presence of an oxygenated tertiary carbon signal at \(\delta_{\rm C}\) 71.3 (C-9). The \({}^{1}\)H- and \({}^{13}\)C-NMR spectra of **1** displayed signals for an aromatic system at \(\delta_{\rm H}\) 7.46 (1H, s, H-2)/\(\delta_{\rm C}\) 144.4 (C-2), 6.84 (1H, s, H-3)/109.1 (C-3), and 8.81 (1H, s, H-5)/147.6 (C-5), and a conjugated carbonyl carbon at \(\delta_{\rm C}\) 184.6 (C-6), suggesting that **1** has a 3-furylketone, supported by the \({}^{1}\)H-\({}^{13}\)C HMBC correlations of H-2/C-4, C-5, H-4/C-2, C-5, and H-5/C-2, C-3, C-4 (Figure 3). The \({}^{1}\)H NMR signals for two uncoupled methyl groups at \(\delta_{\rm H}\) 1.40 (6H, s, H-10 and H-11), a _trans_-olefinic group at 6.77 (1H, d, \(J\) = 15.3 Hz, H-7) and 7.08 (1H, d, \(J\) = 15.3 Hz, H-8), and the \({}^{13}\)C-NMR signal for  
Figure 1: _Perilla fufufescens_ var. _crispa_ (Lamiaceae): **(a)** A mutant cultivar, _P. fufescens_ var. _crispa_ (cv. Antisperill; PFCA), which developed by 200 Gy gamma irradiation from a labeled Cobalt (\({}^{60}\)Co) source on seeds of an original cultivar; **(b)** an original cultivar (PFC) that has red-purple leaves.  
Figure 2: Chemical structures of compounds from the SC-CO\({}_{2}\) extracts of PFCA and PFC.  
an oxygenated tertiary carbon at \(\delta_{\rm C}\) 71.3 (C-9) showed the presence of an isoprenyl alcohol group, positioned at the C-6 as evidenced by the \({}^{1}\)H-\({}^{13}\)C HMBC correlations of H-7/C-6, C-9 and H-8/C-6, C-9 (Figure 3). Further detailed analysis of \({}^{1}\)H-\({}^{1}\)H COSY, \({}^{1}\)H-\({}^{13}\)C HSQC, and \({}^{1}\)H-\({}^{13}\)C HMBC NMR data allowed unambiguous assignments for all of the \({}^{1}\)H- and \({}^{13}\)C-NMR signals of **1** (Figures S1\(-\)S5). Therefore, the structure of **1** was determined as (\(2E\))-1-(3-furanyl)-4-hydroxy-4-methyl-2-penten-1-one and this compound was named as 9-hydroxy-isoegomaketone.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 2 Related Works
### Deep Learning-Based Point Cloud Completion Methods
#### 2.2.3 Point-Based Method
VAE and DDPM are typically trained on complete 3D objects to determine the model's weights for generating the latent representations of the corresponding point cloud data. [PERSON] et al. [12] proposed the HyperPocket variational autoencoder architecture, which uses the hypernetwork paradigm to fill the missing regions of the point cloud with different variants produced by the underlying representations. VRCNet [39] uses dual-path units and VAE-based relationship enhancement modules for probabilistic modeling of point clouds. [PERSON] and [PERSON] et al. [14] proposed a point cloud generation diffusion probability model that transforms the noise distribution resulting from multiple noisy additions to the point cloud into a target distribution Markov chain. The neural network learns this process to obtain optimal parameters for generating point clouds from Gaussian noise that conforms to the target data distribution. However, the diffusion model is a stochastic process and may result in partial deviations between the finally generated point cloud and the actual shape.
# Influence of Dielectric Plate Parameters on the Reflection Coefficient of a Planar Aperture Antenna
## 3 Numerical Results
### The Influence of the Structural-Geometric Parameters of the Dielectric on \(S_{11}\)  
As already mentioned, the structural-geometric parameters of the dielectric include the thickness d in mm, dielectric plate dimensions h\({}_{1}\times\) h\({}_{2}\), and the density \(\rho\) of the dielectric plate in kg\(\cdot\)m\({}^{-3}\).  
Figure 6: (**a**) Block diagram of the geometry of the problem; (**b**) HFSS model of an aperture antenna fed by a circular waveguide placed on a grounded planar surface with dielectric plate.
## 1 Introduction
[16] proposed that an area of further investigation in their global assessment of IWV from a satellite-based sounder would be whether a sampling effect might at least partly explain their observed biases in extreme dry and wet regimes between satellite and ground-based IWV products. In [PERSON] et al. [17] the representativeness of a single IWV observation from a satellite-based spectrometer at the satellite overpass time on a sun synchronous orbit is quantified making use of a 2-hourly IWV data set from Global Navigation Satellite Systems (GNSS) stations, showing that the single IWV value is generally slightly lower than the daily mean. Sampling issues are also touched on in [PERSON] et al. [18], where the spatio-temporal variability of IWV on small spatial and temporal scales is assessed with two months of data from the High Definition Clouds and Precipitation for advancing Climate Prediction (HD(CP)\({}^{2}\)) Observational Prototype Experiment (HOPE) [19]. By comparing IWV distributions computed for several ground-based and space-born remote sensing instrument measurements, the impact of sampling on the IWV values could be observed. The aim of this study is to assess quality and sampling issues of integrated water vapor products from multiple satellite sources that show different orbital and instrument characteristics such as spectral channels and spatial resolution and employ different retrieval algorithms. The dense German GNSS network consisting of nearly 300 Global Positioning System (GPS) stations within Germany (note that from now on it will be referred to GPS) serves as a reference. Their ground-based IWV product with a high temporal resolution of 15 min is hardly affected by clouds and precipitation and therefore is well suited to address instrument sampling and limitations, e.g., clear-sky cases. By focusing on Germany with its structured orography and different land surfaces our study addresses the quality of satellite-derived IWV statistics and climatologies over complex land surfaces. This study is set up such that three main topics are addressed;  
* How well do individual IWV satellite measurements agree with local measurements over Germany? * To which degree does temporal averaging provide robust IWV climatologies? * What is the impact of temporal as well as satellite retrieval-specific sampling on aggregated IWV data as well as IWV frequency distributions? In Section 2 the various remote sensing instruments and corresponding IWV products used in this study, are introduced. The set-up of the study and the matching of the various satellite and GPS IWV products are described in Section 3. Results on the comparison of the satellite IWV products to the GPS IWV measurements, both on pixel-basis and for aggregated data, as well as the impact of sampling effects on IWV aggregations and distributions with corresponding statistics, are presented in Section 4. A Discussion and Outlook are given in Section 5.
###### Abstract  
This paper addresses the intricate challenge posed by remote handling (RH) operations in facilities with operational lifespans surpassing 30 years. The extended RH task horizon necessitates a forward-looking strategy to accommodate the continuous evolution of RH equipment. Confronted with diverse and evolving hardware interfaces, a critical requirement emerges for a flexible and adaptive software architecture based on changing situations and past experiences. The paper explores the inherent challenges associated with sustaining and upgrading RH equipment within an extended operational context. In response to this challenge, a groundbreaking, flexible, and maintainable human-machine interface (HMI) architecture named MAMIC is designed, guaranteeing seamless integration with a diverse range of RH equipment developed over the years. Embracing a modular and extensible design, the MAMIC architecture facilitates the effortless incorporation of new equipment without compromising system integrity. Moreover, by adopting this approach, nuclear facilities can proactively steer the evolution of RH equipment, guaranteeing sustained performance and compliance throughout the extended operational lifecycle. The proposed adaptive architecture provides a scalable and future-proof solution, addressing the dynamic landscape of remote handling technology for decades.  
emote handling: robotic; intelligent human-machine interface; software architecture evolution; fusion energy; modular architecture; software lifecycle
## 2 Literature Review
### Scheduling of Bottling Operations
#### 2.1.1 Physicochemical Properties and Their Relationship with Filtration  
The efficiency of wine filtration is significantly influenced by its physicochemical properties, including viscosity, density, and surface tension. [PERSON] et al. noted that an increase in viscosity leads to greater flow resistance through porous media, which requires higher pressures to maintain an adequate filtration rate [31]. This phenomenon is particularly relevant in continuous filtration applications, where variations in wine composition directly impact both the duration and quality of the filtration process. [PERSON] et al. further explain that surface tension and density influence the formation of filter cakes that accumulate on the membrane surface [32]. These layers of particles affect permeability and increase the frequency of required maintenance, highlighting the importance of controlling the physicochemical properties of wine to enhance process efficiency and minimize membrane fouling. Additionally, recent studies have explored in greater depth the relationship between fluid dynamics and filtration performance. [PERSON] et al. demonstrated that compounds such as ethanol, dry extract, and glycerol significantly affect wine viscosity, influencing its behavior when flowing through filtering media [33]. Moreover, [PERSON] and [PERSON] analyzed how surface tension and density determine bubble stability, a phenomenon relevant to the liquid-gas interface in filtration systems [34]. Finally, [PERSON] and [PERSON] described how surface-tension gradients generate capillary flows in complex liquids such as wine, which can be extrapolated to the formation of deposits on membranes during filtration [35].
## 4 Experiment Study  
### Experiment Setup  
#### 4.1.1 Dataset  
The agile satellite scheduling problem (AEOSSP) is an arduous combinatorial optimization and complex engineering problem. The observation time window of each task and the acquisition of various parameters such as pitch value at each moment within the time window require complex dynamic equation calculations. Considering the lack of a benchmark for the AEOSSP and the requirements of the model training dataset, we designed simulation scenarios and generated a dataset based on [PERSON]'s study [1]. Specifically, we applied the regional observation target used in [1] as our task to be scheduled, and all targets were spread according to a uniform random distribution. On the simulation day, we set eight orbits from the satellite and defined the O-ith to distinguish the ith orbit. The scene where the target can be observed on the first orbit (O-1) was considered the training dataset. A total of 200,000 scenes were generated as the training dataset. The validation dataset consisted of 1000 scenes generated independently.  
The maximum pitch, roll, and yaw angles of the satellite were 45\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\), respectively. The scheduling time range was from 00:00:00 to 24:00:00 on a certain day, a total of 24 h. The six orbital parameters of the satellite were the semimajor axis (_a_), eccentricity (_e_), inclination (_i_), perigee angle (\(\omega\)), right ascension of ascending node (\(\Omega\)), and angle of horizontal approach (\(m\)). The satellite orbit parameter setting is shown in the following Table 2.
# A Survey on Depth Ambiguity of 3D Human Pose Estimation
## 2 Status of Research
### Temporal Consistency Constraints
#### 2.2.1 Method Content  
[PERSON] et al. [17] address the interframe incoherence and independence by propagating joint position information between frames. The model introduces temporal smoothness loss to constrain the temporal consistency, preventing excessive changes between two frames. Their skip connection learns the difference between 3D poses in different moments, making it easier to disambiguate. Due to the advantages of processing multiple sequences in parallel, lower computational complexity, and fewer model parameters, the temporal convolutional network (TCN) is often used to process coherent sports information in image sequences. [PERSON] et al. [10] employ TCN to conduct dilated temporal convolutions capturing long-term information, as shown in Figure 3. [PERSON] et al. [18] solve ambiguity by proposing a spatiotemporal graph convolutional networks (ST-GCN) to combine spatial configurations and temporal consistencies. [PERSON] et al. [19] introduce dynamic spatial graph (DSG) and dynamic temporal graph (DTG) convolution to calculate the spatiotemporal relationship between human joints. Their dynamical graph network can identify the key points with motion consistency to reduce the ambiguity when lifting a 2D pose to a 3D pose. Due to the fact that human bone lengths remain consistent across video, [PERSON] et al. [20] predict the bone lengths in a specific frame of a video to alleviate the ambiguity of 3D pose estimation. In addition, the visibility scores of 2D key points are utilized in this method as additional knowledge.
# Enhancing Image Dehazing with a Multi-DCP Approach with Adaptive Airlight and Gamma Correction
## 2 Related Work  
### Markov-Random-Field-Based Approach  
[PERSON] [1] utilizes Markov random fields to enhance image contrast and adjust the changes in airlight based on distance. It is applicable to both color and grayscale images and effectively processes without the need for geometric information. While this method has shown significant visibility improvement in real outdoor images, it may cause issues like halo artifacts in areas with depth discontinuities.
## 5 Experimental Results
### Training and Validation Time Analysis  
In this experiment, we compare the computation time including training and validation time among VGG, CGFace, ADA-CGFace, and XGB-CGFace. The results in Figure 11 prove that VGG requires 4800 s, CGFace takes the least time at 1500 s, while ADA-CGFace and XGB-CGFace take longer time at 2400 s and 2380 s, respectively. IF-CGFace models (including ADA-CGFace and XGB-CGFace) tend to take longer time because the features from CGFace must be extracted first and these features are used to train AdaBoost and XGB classifiers, so it will take more time to process. However, compared to CGFace model, IF-CGFace models take a little more time but the model performance is improved significantly.
## 2 Methodology  
### Volumetric Method  
One of the simplest techniques for predicting structural integrity is the volumetric method. The implementation of this technique depends on the concept of the notch stress intensity factor, which is built on the idea that the failure mechanism necessitates a physical volume [31]. This idea is explained by the fact that the kind of loading, the geometry of the configuration, and the scale effect determine the failure stress. The maximum surface stress known by the hot spot stress value cannot describe the effect of these criteria on the fracture resistance. We must incorporate the stresses and stress gradient of all surrounding points on the volume of the failure mechanism. Similar to the shapes of plastic zones, the bulk is assumed quasi-cylindrical. The diameter of this cylinder is called <<distance effective>>; in this region, the tensile stress can be approximated. Therefore, the amount of this stress is built on two parameters: The effective stress \(\sigma_{eff}\) and effective distance \(X_{eff}\). The figure below explains the parameters of this technique. The bi-logarithmic distribution of elastic-plastic stresses along the ligament shows three different regions; the first region (I) shows the increase in stress \(\sigma_{yy}\) up to a maximum value; then, in the second region (II), there is a decrease until the elastoplastic state occurs. Region (III) indicates the linear behaviour of the curve shown in Figure 1. The projection of the minimum amount of the relative stress gradient \(\chi\) gives distance and effective stress. The relationship of the relative stress gradient is:  
\[\chi(\mathrm{r})\ =\ \frac{1}{\sigma_{yy}(r)}\ \frac{\partial\sigma_{yy}(r)}{ \partial r} \tag{1}\]  
where \(\sigma_{yy}(r)\) indicates the crack opening stress and \(\chi(\mathrm{r})\) indicates the relative stress gradient.  
The mean quantity of the stress distribution through the effective distance represents the effective stress. Indeed, to improve the numerical result of this stress, as well as to incorporate the value of the stress gradient related to the load mode and the configuration, the stress is multiplied by a weight function:  
\[\sigma_{eff}=\frac{1}{X_{eff}}\int_{0}^{X_{eff}}\sigma_{yy}(\mathrm{r})\times \varphi(\mathrm{r})\mathrm{d}\mathrm{r} \tag{2}\]  
where \(\sigma_{eff}\) represents the effective stress, \(X_{eff}\) represents the effective distance, and \(\varphi\) represents the weight function.  
The calculation of these parameters using this approach gives us the stress intensity factor:  
\[k\ =\ \sigma_{eff}\ \sqrt{2\pi.X_{eff}} \tag{3}\]
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 3 Experimental Results and Comparisons of Various Algorithms
### Experimental Setup  
Experiments with real human data as the test set were conducted using Python 3.8.11 with PyTorch 1.8.1 (a machine learning framework) for training and validation. The hardware setup included an Intel i7-13700 processor (Intel Corporation, 2200 Mission College Blvd., Santa Clara, CA, USA), 128 GB of RAM, and an NVIDIA RTX 4090 GPU (24 GB VRAM). Detailed training parameters are provided in Table 4. The loss function utilized was CrossEntropyLoss, widely adopted for classification tasks, with a batch size of eight to ensure stable gradient updates. The total number of training epochs was set to 150, with an initial learning rate (LR) of \(1~{}\times~{}10^{-4}\). A CosineAnnealingLR scheduler was employed to dynamically adjust the learning rate, improving model convergence efficiency.
# Electric-Field-Coupled Resonator Antenna for 5G Applications
###### Abstract
A rectangle-shaped complementary split-ring resonator is connected inside a ring monopole to get multiband characteristics as well as a compact structure [17]. A triangle-shaped structure is engraved with two rectangle-shaped slots and a triangular split-ring resonator to achieve a multiband and compact antenna. This antenna exhibits poor impedance matching at low frequency as well as poor bandwidth and gain [18]. In all the literature mentioned here, compactness is achieved by using the substrates of high dielectric values. According to the half-wavelength principle of resonance, materials of higher dielectric constants will cause small dimensions, but they will account for more dielectric loss. However, for the application of 5G, the communication needs to be more efficient, which requires the utilization of low dielectric constant materials in antenna design. For this reason, the proposed structure used the substrate of a low dielectric constant, where achieving compact dimension is a challenge. However, there are some special cases where the substrate is chosen depending on the demand of the application. For instance, reference [8] used Rogers 6010, which is suitable for microelectronics, and reference [9] used Rogers ULTRALAM, which is suitable for implantable device applications as it is biocompatible. In addition, all of them except reference [3] give narrow bandwidth at the low-frequency band. So, compact and wideband antenna design for the latest generation of technology needs to utilize the materials of low dielectric constants to ensure low loss communication. In this paper, an electric-field-coupled resonator is used as the main radiator of the antenna rather than partially loaded with different shaped patches. With these types of resonators, compactness can be achieved by using the substrates of low dielectric constants. This is possible due to the fact that the resonance of these structures depends on the loop inductance and gap capacitance. So, the requirement of the half-wavelength principle does not need to be fulfilled [19; 20]. However, the drawback is that it shows very poor performance, i.e., narrow bandwidth and low gain. Here, the patch and the ground plane are modified to obtain enhanced impedance bandwidth, and parasitic elements are added to increase the gain without changing the dimension of the antenna.
## 2 Frequency Regulation Model of DFIG
### De-Loading Strategy for FR Reserve Capacity  
#### 2.2.1 Normal Operation of DFIG  
For the normal operation of DFIG, in order to improve the power output efficiency of WT, the maximum power point tracking (MPPT) control strategy is adopted to achieve maximum capture of wind energy [17]. The rotor speed of the DFIG normally operates in the range of 0.7-1.2 p.u., and the safety range can reach 0.6-1.3 p.u. under transient dynamic disturbances [5]. In the MPPT state, the rotor speed of DFIG is adjusted dynamically to be the optimal rotor speed \(\omega_{opt}\), so as to achieve the maximum power output \(P_{MPPT}(v_{w})\) under different WSs, as shown in Figure 2a. If the maximum power reaches the rated power \(P_{e}\), the corresponding optimal speed \(\omega_{opt}\) will go beyond the normal operation range; thus, the pitch angle will be increased to constrain rotor speed within its limit, keeping DFIG's power output at the rated power, as shown in Figure 2b, where \(v_{e}\) is the rated WS corresponding to \(P_{e}\).
# A Survey on Machine Learning Approaches for Personalized Coaching with Human Digital Twins
## 2 Motivation  
### The Virtual Fitness Coach, an Example of a Personalized Application  
The purpose of the VFC platform is to provide coaching to individuals who work sedentary jobs, promoting a healthy lifestyle and physical activity during the workday. Based on step data collected using wearable devices, the platform predicts whether a daily goal will be achieved and then sends personalized coaching if necessary. The evaluation of step data can be performed using personalized models that have been trained with individual data or generalized models trained with data from all participants.  
As with other examples in this domain, the extent to which the application goal is achieved, that is, the quality of the coaching, strongly depends on the way the data is processed and the accuracy of the predictions made, i.e., the quality of the coaching stands or falls with the performance of the machine learning implementations. Inappropriate or poorly timed interventions will cause the participant to lose interest in using the platform [19; 20].