esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This paper presents a method for determining the dynamic response of systems with viscoelastic damping elements, where parameters are uncertain. It is assumed that uncertain parameters are described using interval numbers, meaning that the lower and upper bounds of the parameter are known. The presented method enables the consideration of uncertainties related to both the construction parameters and the damping elements, with a focus on demonstrating its applicability to parameters of viscoelastic material.  
To determine the system's dynamic response, interval analysis was applied. However, direct application can lead to the significant overestimation of results. To reduce this overestimation, equations of motion were formulated using the element-by-element technique, and a transformation was applied, ensuring uncertainties related to the same parameter are considered only once. This dual approach effectively reduces overestimation, as demonstrated in the provided examples. Additionally, all results were compared with those from the vertex method, known for its accuracy but being computationally intensive. The method used here involves solving linear interval equations via the fixed-point iteration method, utilizing [PERSON]'s fixed-point theorem. It has been shown to yield satisfactory results at a considerably lower computational cost than the vertex method. However, it was observed that in cases of larger parameter variability (e.g., 10%), this iterative method may not always converge, necessitating the monitoring of convergence during computations. Nevertheless, as demonstrated in the examples with variability of 5%, the method produces accurate results and can effectively estimate the lower and upper bounds of the dynamic response of structures.  
This method was applied for the first time to systems with viscoelastic elements described by both classical and fractional rheological models. Formulations were also proposed for both the Kelvin and Maxwell models. The Maxwell model, to be applicable with the presented method, requires a formulation with internal variables. The proposed approach is general and can be applied to more complex models, such as the Zener model or the generalized Maxwell model.  
In future research, further reductions in overestimations, particularly in resonance zones, and improvements in method convergence in the case of larger uncertainties are planned. Additionally, the method is envisaged to be applied to the analysis of beams and plates with viscoelastic elements, which could have significant practical implications.  
Figure 27: Graphs of \(H_{44}\): (**a**) real part; (**b**) imaginary part.  
**Funding:** This research was funded by the research project of Poznan University of Technology, grant number 0411/SBAD/0010.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this article and codes used are available upon request to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Multi-Granularity Semantic Collaborative Reasoning Network for Visual Dialog
## 4 Experimental Setup and Metrics  
### Datasets  
We conducted experiments on the VisDial v1.0 dataset [4] to verify our proposed model. The train, validation, and test splits contained 1.23 M, 20 K, and 44 K dialog rounds, respectively. For the train and validation splits, each image had a 10-round dialog, whereas in the test split, a random set of question-answer pairs and a current question were presented alongside each image. The train split included 123K images taken from the MS-COCO dataset [27], and 2 K and 8 K images from Flickr were used for the validation and test splits, respectively. Additionally, each question followed a list of 100 candidate answer options, one of which was the ground truth.
# A Lightweight Graph Neural Network Algorithm for Action Recognition Based on Self-Distillation
## 5 Discussion and Conclusions  
This paper proposes the use of self-distillation (BYOT) for creating a lightweight HAR GCN model (ST-GCN). If taking the second block of ST-GCN 12 as the compressed result, compared with the heaviest backbone ST-GCN 20, the compression rate is 80%. The features from the lightweight ST-GCN are denser than those for the ST-GCN trained with pure action labels, and the features from deeper blocks are denser than the features from shallower blocks.  
In the future, work will be carried out to improve the proposed idea. Considering the generalization, the self-disllation (BYOT) will be tested on more action datasets [28] and more popular skeleton-graph-GNNs [2]. Furthermore, the current idea is limited by the cost of manual labelling. Trying to optimize the self-distillation under self-supervision will decrease the cost because it trains the backbone without action labels. Moreover, the compressed lightweight ST-GCN can be used on other applications, such as real-time anomaly detection.  
[PERSON] carried out the work and drafted the manuscript; [PERSON] supervised the work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the China Scholarship Council (CSC) csc201903170208 and the Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN-2020-05095.  
**Data Availability Statement:** The dataset NTU RGB+D was released by \"NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis\", which can be downloaded from [[https://rosel.ntu.edu.sg/dataset/actionRecognition/](https://rosel.ntu.edu.sg/dataset/actionRecognition/)]([https://rosel.ntu.edu.sg/dataset/actionRecognition/](https://rosel.ntu.edu.sg/dataset/actionRecognition/)) and are accessible from 1 January 2017.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funding agency had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
Figure 11: The features from (**a**) the 1 st block; (**b**) the 2 nd block; (**c**) the 3 rd block and (**d**) the overall output are projected by the 20-nearest-neighbor-graph. The features are trained by ST-GCN 8 with _pos_ as input data, performed on NTU xsub. The overall output includes the features from the output of the whole backbone. Although many classes are mixed together, classes 41 (green) and 42 (pink) can be linearly separated. They are falling and staggering, which are anomalies compared with the other six daily actions.
# X-ray Diffraction Data Analysis by Machine Learning Methods--A Review
## 4 Applications of Machine Learning in XRD Data Analysis
### Challenges and Limitations of Machine Learning  
Overall, machine learning is an effective tool for XRD data analysis, but it also faces several challenges and limitations that must be addressed to make the technique more reliable and ubiquitous.  
Data quality: ML models generally require large, diverse, and high-quality datasets for effective training. Obtaining enough labeled XRD data for specific materials or conditions can be time consuming and expensive. Additionally, the noise and artifacts present in experimental XRD data can decrease the performance of ML models. To mitigate data scarcity and diversity, data augmentation [93], transfer learning [114], or domain adaptation might be employed [115]. Additionally, preprocessing steps, such as noise filtering and background subtraction, help mitigate the impact of noise in the data.  
Interpretability: ML algorithms can lack transparency in their decision-making process, making it difficult to interpret and explain the underlying reasons behind predictionsmade by ML models, potentially hindering their use in scientific research. Efforts are being made to develop more transparent ML models through techniques such as feature visualization and saliency maps. Furthermore, hybrid approaches that combine ML with more traditional methods can leverage the advantages of ML while providing more interpretable results [116].  
Generalizability: ML models may fail to generalize to new materials not seen by the model during training; accurate predictions across different crystal structures, lattice parameters and phase compositions require robust ML models. While generalization can be improved by the selection of appropriate features, data representation, and model architecture, the acquisition of datasets spanning the full range of possible variations is the best option for tackling generalization issues [103].  
Model robustness refers to the ability of ML models to perform well under different conditions and input data quality. Overfitting negatively impacts robustness and occurs when ML models adjust to the training data instead of uncovering the meaningful patterns. To prevent overfitting and improve the model's robustness, researchers can employ regularization techniques to prevent extreme parameter values, cross validation to assess the model's performance on multiple subsets of the data, and early stopping of the model's training when the validation set's performance starts to degrade [117].
## 3 Generator Side Converter Control
Figure 5 gives a configuration of (7) in MATLAB/ SIMULINK. The generator speed (\(\omega_{g}\)) equal rotational speed (\(\omega_{r}\)) where there is no gearbox between wind turbine and PMSG. * Determine the reference power as shown in Figure 6, which describes the relation between the generator reference power and the generator speed, using (8). \[C_{p}(\lambda) = \frac{60.04-4.69\lambda}{\lambda}e^{\left(\frac{-21+0.735\lambda} {\lambda}\right)}+\frac{0.0068\lambda}{1-0.035\lambda} \tag{2}\]2 (8)  
\[C_{p}(\lambda) = \frac{60.04-4.69\lambda}{\lambda}e^{\left(\frac{-21+0.735\lambda} {\lambda}\right)}+\frac{0.0068\lambda}{1-0.035\lambda} \tag{2}\]3 The relation among mechanical powers generated and rotor speeds for different wind speeds. To describe the control function, considering \[C_{p}(\lambda) = \frac{60.04-4.69\lambda}{\lambda}e^{\left(\frac{-21+0.735\lambda} {\lambda}\right)}+\frac{0.0068\lambda}{1-0.035\lambda} \tag{2}\]4 If the PMSG operating at point \"a\" and wind speed increases from \(\mathrm{u}_{w\mathit{l}}\) to \(\mathrm{u}_{w\mathit{2}}\) (point \"b\"), the additional power causes the PMSG to accelerate also causes difference between reference DC current and measured DC current, this difference operate to adjust the duty cycle of DC-DC boost converter to achieve optimum speed of the PMSG \(\omega_{g}\) which attain maximum power extraction. Finally, the generator will reach the point \"c\" where the difference between reference DC current and measured DC current is within the bandwidth of the hysteresis current controller. A similar situation occurs when the wind speed decreases. In this proposed strategy, there is no need to measure wind and generator speed using mechanical sensors which lead to inaccurate measurement.
# LoRaWAN Communication Protocols: A Comprehensive Survey under an Energy Efficiency Perspective
## 2 Background Knowledge
### MAC Layer  
End-device access to LoRaWAN is based on pure-ALOHA, which is a multiple and random-access protocol for data transmission through a common medium [32]. In more detail, each time an end-device has data to transmit, a packet is sent without any prior link coordination for scheduling transmissions or prior medium access sensing/control for other ongoing transmissions from IoT end-devices. This aspect leads to an increased number of collisions, which significantly contributes to packet loss, especially in large-scale networks. LoRaWAN MAC frame format is presented in Figure 4. Specifically, payload in PHY layer (PHYPayload) consists of a one-byte MAC header (MHDR), the payload of the MAC layer and a four-bytes message integrity code (MIC). Note that MHDR specify message type (MType) and the major version (Major) of frame format of the LoRaWAN specification. LoRaWAN provides eight different MAC message types as shown in Table 3.  
LoRaWAN includes confirmed message transmission in order to improve transmission reliability by using acknowledgement messages (ACK) to guarantee data reception. Confirmed message transmission, as in the case of downlink traffic, impose limitations to network capacity, constituting downlink communication efficiency and scalability an important need for LoRaWAN. In [33], the authors perform an extensive evaluation of LoRaWAN performance in small and large-scale networks. The authors analyze several aspects, including coverage, traffic characteristics, packet loss, signal quality, and LoRa parameter distributions.  
Figure 4: LoRaWAN MAC frame format.
# Modeling Wicking Fabric Inhibition Effect on Frost Heave
## 4 Numerical Simulations with Coupled THM Analysis  
The assumption of two-dimensional plane strain was made, and the numerical simulations were performed under various test conditions, including WWF and NWF models, with different frost-sensitive soils, cooling rates (\(U\)), and GWLs (\(I\)). Moreover, assuming that the bottom of the soil sample is the origin and upwards is positive, \(l\) is the distance from the origin to the upper surface of the saturated zone in the soil. The size of the sample, the boundary conditions, and the mesh of the two-dimensional model using nine-node quadrilateral elements are presented in Figure 5. It is noted that the preliminary examinations of the mesh sensitivity showed no significant difference in the simulation resultsby the mesh density when using the mesh shown in Figure 4 and a mesh finer than that shown in Figure 5. For this reason, the mesh in Figure 4 was used in this study to ensure the convergence of the results while maintaining the accuracy of the calculations and taking up fewer computational resources. However, the water content near the WF varies significantly during the freezing process. Thus, in the WWF model, a refined grid was adopted near the WF. In this study, the observation points were placed at the same locations as the measured sensors in the model test, for the consistency between the numerical simulation and the test results. There are five observation points located at the height (\(d\)) of 25 mm, 75 mm, 125 mm, 175 mm, and 225 mm away from the origin. The dimensions of the soil column are 250 mm \(\times\) 150 mm (H \(\times\) W), which are similar to the actual model test. It is noted that the bottom of the soil is set at a baseline (0 mm), and the value (\(d\)) is considered upward as positive.  
The initial boundary conditions of the NWF model were set as follows. The base surface was fixed vertically, and its lateral boundaries were fixed horizontally in the mechanical field. The hydrostatic pressures were applied to the base surface to maintain a constant GWL. Impermeable boundaries were applied to the lateral sides and top surface in the hydraulic field. The adiabatic boundary condition was applied on the two lateral sides, while on the top and bottom surfaces, the constant thermal boundary condition was applied. Except for the suction boundary caused by the WF, the boundary conditions for the WWF model were the same as those for the NWF model. As shown in Figure 4b, the evaporation model (Equation (22)), working as a line source, was applied to the WF to simulate the drainage process caused by the WF. The water absorption process occurs in this scenario at the WF buried within the specimen.
## 4 Experiments
### Experimental Results
#### 4.2.2 Qualitative Results  
We show some qualitative comparisons of real-world sparse light fields (LFs) between our approach and other cutting-edge techniques in Figure 5. Scenes 3 and 4 feature fewer occlusions and simpler textures, whereas the publicly accessible real-world CD scene and synthetic scenes 1 and 2, which have significant and complex occlusions, are compared. Occlusion artifacts are preserved, and hazy outputs are produced around occlusions by DeOccNet [54]. This restriction shows that it cannot precisely remove occlusions and restore distinct features. In comparison to DeOccNet, ISTY [58] shows higher performance, handling occlusions better in the sparse, dense LFs. But there remains space for improvement, as indicated by the final images' perceived occlusion artifacts. In the sparse LF dataset, the strategy that we propose shows strong de-occlusion performance through using information about occluded objects from various viewpoints in addition to effectively differentiating occlusions from background parts. In comparison to both DeOccNet and ISTY, our approach performs better because of its sophisticated feature extraction and integration strategies, which also lead to fewer occlusion artifacts and more precise reconstructions.  
Figure 5: Qualitative comparisons of the sparse LF dataset. Selected areas of the outputs are highlighted with red boxes for detailed comparison. Our method reconstructs sharper occlusion-free CV images by effectively utilizing occluded background information from other views.  
Figure 6's dense LF dataset illustrates how DeOccNet [54] struggles to handle dense and complicated occlusions, as seen by the outputs' frequent blurriness and retention of notable occlusion artifacts. When it comes to complex, multi-disparity occlusions, ISTY [58] performs better than DeOccNet and our approach, producing clearer outputs and fewer occlusion artifacts. Nevertheless, residual artifacts are still a problem for all of them. Although it falls short of ISTY, our suggested approach performs competitively in the dense LF dataset. This is mainly because our method does not include inpainting techniques, which are especially useful for dense LFs where inpainting expertise may greatly help reconstructing scenes without occlusion.
# Optical Sensing Technologies for Cryo-Tank Composite Structural Element Analysis and Maintenance
## 3 Test Article Description
### Optical Technologies
#### 3.1.2 Distributed Fiber Optic Sensor  
By contrast, the physical principle at the basis of the distributed sensing is the Rayleigh scattering, which originates from the interaction between the electromagnetic wave propagating in the fiber core and the intrinsic silica impurities. The Rayleigh backscatter profile of a certain optical fiber is correlated with its specific heterogeneous reflective index, distributed randomly along its length (called fiber signature). After an external stimulus (such as strain), the backscatter pattern exhibits a spectral shift that may be used to estimate the occurred deformation field along the fiber itself by a comparison with the reference (original) state. The space resolution of such information (\(\Delta Z\)) is related to the optical frequency sweep range of the tunable laser source (\(\Delta\)F) exciting the fiber, as given by Equation (3), where \(c\) is the speed of light and \(n\) is the fiber refractive index:  
\[\Delta\;Z=c/2n\Delta F \tag{3}\]  
In this application, the distributed fiber is again displayed along the main direction of the plate. Strain data are logged during the test execution by the Luna OdisiB interrogator.
# Grid-Connected PV Systems Controlled by Sliding via Wireless Communication
## 3 The DC/DC Converter and DC/AC Converter Controllers
### DC/AC Converter PI Controller  
The inverter must inject nearly sinusoidal current and the current must be co-linear with the grid voltage. Thus, the power quality is guaranteed, reducing the harmonic distortion. In this work, a PI controller is used because is easy to implement and it works appropriately. Figure 6 depicts the PI control scheme, where there are two control loops. The inner control loop is for the current and it compares the grid reference current with the measured current obtained through the reactance whereas the outer loop is for the voltage and it compares the buck-boost converter output voltage (or the inverter input voltage) with the reference voltage to remain this voltage almost constant. Thus, the PI control achieves the adequate duty cycle to make the inverter injects the proper current to the grid.
# Insight on Extraction and Preservation of Biological Activity of Cereal \(\beta\)-D-Glucans
## 1 Introduction
This is likely due to their ability to effectively reduce postprandial glycemic response, as well as to improve long-term blood cholesterol levels, a known risk factor for cardiovascular disease [11]. Based on the scientific evidence gathered over the past decades, both the Food and Drug Administration (FDA) and European Food Safety Authority (EFSA) have allowed the reporting of health claims regarding the cholesterol lowering and glycemic response control exhibited by \(\beta\)-D-glucans from barley and oats. According to the EFSA's opinion, it is scientifically proven that 3 g of oat or barley \(\beta\)-D-glucan per day contributes to maintaining average blood cholesterol concentrations [12; 13]. Concerning the hypoglycemic effect, the EFSA has positively evaluated the health claim for \(\beta\)-D-glucans derived from barley or oats; it has been proven that 4 g of \(\beta\)-D-glucans from oats or barley for every 30 g of carbohydrates available in a quantified portion within a meal would contribute to a reduction in the increase in postprandial blood glucose [12]. Similarly, in 1997, the FDA authorized, in the United States, health claims relating to the lowering of serum cholesterol and a reduction in coronary heart disease for \(\beta\)-D-glucans from barley and oats, if containing in a minimum of 0.75 g per serving of reference food for a daily intake of minimum 3 g [14]. In addition, recent studies have indicated cereal \(\beta\)-D-glucans as bioactive carbohydrates with potential prebiotics capable of promoting abundant and stable beneficial microbial flora with high biodiversity. Human digestive enzymes do not hydrolyze dietary fiber, but gut bacteria can act on it by releasing Short-Chain Fatty Acids (SCFAs) and other metabolites [15]. Several studies have also shown that increased SCFA levels are positively correlated with higher insulin sensitivity, reduced serum cholesterol concentrations, weight control, and reduced inflammation, all of which can minimize the risk of developing metabolic diseases [16]. The origin, molecular weight (MW), and structural properties of \(\beta\)-D-glucans all directly impact their functional characteristics, both on the technological and the health side. In the food industry, most extraction, purification, processing, and preservation procedures have a significant impact on the MW and structural/conformational characteristics of cereal \(\beta\)-D-glucans, which results in their lower MW and loss of viscosity [7; 10]. Based on these considerations, this review aims to conduct a comparative analysis of existing \(\beta\)-D-glucan extraction methods, indicating how these methods affect the biological and functional structures and properties of cereal \(\beta\)-D-glucans.
# Numerical Evaluation of Ultrasonic Detection of Solid Occlusions in CSP Molten Salt Piping
## 3 Materials and Methods  
The methodologies employed in this study combined the characterization of solar salt properties with advanced numerical simulations, to evaluate ultrasonic wave propagation and its interaction with solid inclusions. The study specifically addressed the dual-phase nature of solar salt--liquid and solid--due to its critical role in thermal energy storage and transfer within concentrating solar power (CSP) systems. By leveraging numerical techniques, the investigation provided insights into the behavior of ultrasonic signals under different thermal conditions, enabling the assessment of solidification processes and the potential for non-invasive detection of solid inclusions. The following sections detail the material properties and the numerical framework adopted for this analysis.