esa-sceva/satcom-chunk-collection huggingface dataset:
# Vibration Reduction on Circular Disks with Vibroacoustic Metamaterials
## 2 Numerical Design of the Circular Disk with a Vibroacoustic Metamaterial
### Numerical Model of the Disk  
To design the VAMM for integration into the circular disk, an experimentally validated numerical model of the disk is necessary. For this, a finite element (FE) model of the disk is elaborated in Ansys(r) Workbench. The model consists of 10,004 hexahedron elements of type SOLSH190 and is depicted in Figure 4a. To determine the material properties as well as the effective clamping diameter, an experimental characterization of the disk clamped at a diameter of 61 mm is performed. For this, the disk is excited by an impulse hammer at a distance of 137 mm away from the center of the disk. The resulting vibrations are measured at 116 points with an LDV. The location of measurement points can be seen in Figure 4a where the numerical model is also evaluated. Since this section focuses on the numerical design of the VAMM test structure, the test setup is not shown here. It is described later in Section 3.1 together with the setup for measuring the disk in the rotating state.  
Figure 3: Circular disk with diameters \(d_{\mathrm{o}}\) = 300 mm and \(d_{\mathrm{i}}\) = 30 mm, and thickness \(t\) = 2 mm.  
Figure 2: Example of a Campbell diagram (**a**). Solid lines: stationary observer; dashed lines: rotating observer. Mode shapes for circular disks (**b**). Source: compare [28].  
Figure 4b shows the experimentally and numerically obtained transfer functions in terms of accelerance, averaged over the 116 evaluation points as shown in Figure 4a. The transfer functions indicate a good agreement between measurement and simulation. To parameterize the model, a [PERSON]'s modulus of \(E=205\) GPa, a density \(\rho=7850\) kg/m\({}^{3}\) and a [PERSON]'s ratio of \(\
u=0.3\) were chosen. Since the clamping of the disk in the experiment with a flange of a diameter of 61 mm is not ideal, the effective clamping diameter is updated to fit the measurement data. A good correlation is found for a diameter of 58.5 mm. Since the circular disk is made from steel, damping is omitted in the simulation. The width of the peaks in the simulated and measured data matches well. This confirms the assumption of the omitted damping in the simulation.
## 3 Multi-Hop IoT Systems for Patient Monitoring
### Interoperability and Standardization in the IoT  
Interoperability and standardization are foundational principles in the IoT, playing a pivotal role in ensuring seamless communication and functionality across a diverse array of devices and systems. These principles are particularly crucial as IoT ecosystems continue to grow and encompass various industries, including healthcare, smart cities, industrial automation, and more.  
Interoperability is the linchpin of effective communication in the vast and interconnected landscape of the IoT. In the realm of health monitoring, interoperability ensures that devices, irrespective of their manufacturers, can communicate seamlessly. This is achieved through the adoption of standardized communications protocols such as Message Queuing Telemetry Transport (MQTT), Constrained Application Protocol (CoAP), and Hypertext Transfer Protocol (HTTP). These protocols facilitate a universal language for devices, promoting a harmonious environment where disparate components work together cohesively [40].  
Standardization is the cornerstone that supports the edifice of interoperability. It involves the establishment of common frameworks, protocols, and specifications. Organizations such as the Internet Engineering Task Force (IETF), the Institute of Electrical and Electronics Engineers (IEEE), and the International Organization for Standardization (ISO) contribute to the development of these standards. Through industry-wide collaboration, stakeholders define and adhere to a set of norms that ensure uniformity across diverse IoT devices and platforms [41].  
In the context of health monitoring within the IoT, interoperability allows for the integration of an extensive array of sensors, wearables, and medical devices into a cohesive network. Standardized data formats and communication protocols are crucial in this scenario. They ensure that information flows seamlessly between devices, maintaining data consistency and integrity. This interoperability is fundamental to creating a unified and comprehensive view of health data, allowing for more accurate diagnostics and personalized healthcare solutions.  
Interoperability and standardization are pivotal in ensuring the scalability of IoT ecosystems. As IoT devices proliferate in number and variety, adherence to established standards becomes imperative. This ensures that new devices can seamlessly integrate into existing networks, promoting a future-ready infrastructure. Scalability is essential for accommodating the continuous influx of emerging technologies and innovations in health monitoring, providing a flexible framework that can evolve with the dynamic landscape of the H-IoT [42].
# Coordinated Control Strategy of CU-MTDC under Abnormal Conditions Considering Power Supply Security
## 5 Conclusions  
Considering the influence of power supply security of urban power systems and offshore wind farm integration, a coordinated control strategy of coastal urban multi-terminal DC (CU-MTDC) application under abnormal conditions is proposed in this paper. Based on the priority control idea, the calculation and control process are very simple. The proposed control strategy dynamically schedules the number of the regulation converters according to the DC unbalanced power value and the offshore wind power influence, instead of all the converters involved in control. The effectiveness of the proposed control strategy is verified by simulation in PSCAD/EMTDC. The simulation results indicate that the influence range of DC faults on the urban power grid can be limited in a small area, and the transmission security of the important power supply area is guaranteed with the proposed method. In addition, some converters in CU-MTDC could still maintain a fixed power output under the contingencies with the proposed method. Since they can keep the promissory power transmission, the economic benefits could be brought to system operators.  
Owing to the fact that the complexity of the control increases with the number of converters involved in the regulation, there are several promising directions for CU-MTDC future research. Future work should focus on how to reduce the control complexity when the number of the regulated converter stations is too large. Moreover, how to introduce the reactive power control function of VSC into the coordinated control strategy to improve the power supply security and voltage security also has been planned.  
Conceptualization, Z.W., K.L., and J.L.; writing-original draft preparation, Z.W., K.L. and K.S.; writing--review and editing, J.W. and Z.L.; visualization, K.S. and Z.W; supervision, K.L. funding acquisition, K.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC), grant number 51777116.  
Not applicable.
## 3 Experimental Results
### Comparison of Multi-Focus Image (i.e., Flowerpot)
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c} \hline \hline
**Evaluation** & **PA-DCCCNN** & **NSC-PCNN** & **CNN** & **LC-MWN** & **GPDF** & **DCT-CV** & **IM** & **CF** & **BF** & **QT** & **DSHIFT** & **MST-SR** & **SSDI** & **CSR** & **BRW-TS** & **Proposed Method** \\ \hline SSM & 0.872 & 0.860 & 0.859 & 0.856 & 0.859 & 0.854 & 0.859 & 0.864 & 0.857 & 0.856 & 0.866 & 0.866 & 0.859 & 0.861 & 0.859 & 0.858 \\ Qatar & 0.898 & 0.881 & 0.886 & 0.882 & 0.885 & 0.857 & 0.883 & 0.887 & 0.883 & 0.883 & 0.883 & 0.883 & 0.891 & 0.885 & 0.883 & 0.885 & **0.891** \\ CC & 0.974 & 0.972 & 0.949 & 0.949 & 0.949 & 0.946 & 0.949 & 0.949 & 0.949 & 0.949 & 0.949 & 0.953 & 0.949 & 0.949 & 0.949 & **0.949** \\ \(AC\) & 15.120 & 14.419 & 14.629 & 14.888 & 14.699 & 14.723 & 14.686 & 14.524 & 14.744 & 14.843 & 14.846 & 14.835 & 14.749 & 13.849 & 14.703 & 15.623 \\ \(G_{b}\) & 0.834 & 0.867 & 0.831 & 0.827 & 0.831 & 0.822 & 0.827 & 0.832 & 0.827 & 0.828 & 0.828 & 0.825 & 0.835 & 0.830 & 0.829 & 0.829 \\ \(B_{b}\) & 7.227 & 7.30 & 7.226 & 7.229 & 7.227 & 7.212 & 7.227 & 7.224 & 7.224 & 7.225 & 7.225 & 7.225 & 7.225 & 7.226 & 7.226 & 7.226 \\ \hline \hline \end{tabular}
\end{table} 
Table 38: Comparative Analysis of quantitative measures (average value) ([21]).
## 1 Introduction
From a mathematical perspective, these models further divide into continuous, discontinuous, and empirical formulations, each with distinct mechanistic interpretations. Discontinuous criteria, while adept at distinguishing shear failure from bedding plane slippage, face limitations in transitional failure regimes. [PERSON] et al. [8] identified such transitional behavior in layered organic-rich shales, where failure surfaces alternate between matrix rupture and bedding plane detachment, leading to significant predictive deviations in discontinuous models. This underscores the necessity of aligning criteria selection with material-specific failure mechanisms, particularly in geomechanical applications like drilling and wellbore stability. To evaluate predictive efficacy, this study examines three representative models: the Single Plane of Weakness (SPW) model--a discontinuous, shoulder-type formulation; the Patchy Plane of Weakness (PPW) model--a hybrid discontinuous-undulatory approach accounting for localized bedding defects; and [PERSON]'s continuous U-type criterion. Leveraging triaxial strength data from 12 lithologically diverse laminated rocks, a comprehensive assessment was conducted using least-squares regression, with predictive accuracy quantified through the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R\({}^{2}\)). This analysis not only elucidates the interplay between anisotropy intensity and shale strength variability but also identifies optimal criteria for specific geological scenarios, bridging theoretical formulations with practical engineering applications.
# Developments, Applications, and Innovations in Agricultural Sciences and Biotechnologies
## 7 Alleviating Abiotic Stress in Crops
### Mitigating Calcium Nitrate Stress in Chinese Flowering Cabbage  
Excessive calcium nitrate in soil is a common issue in intensive agricultural systems, leading to oxidative stress, disrupted photosynthesis, and reduced yields [25]. The study on Chinese flowering cabbage demonstrates how fulvic acid (FA), a plant growth regulator derived from humic substances, alleviates calcium nitrate (Ca(NO3)2) stress in cabbage seedlings (Contribution 7). This study underscores the potential of FA as a sustainable and cost-effective solution to mitigate nutrient toxicity in high-intensity farming systems:  
Fulvic acid (FA) in Chinese Flowering Cabbage: the application of fulvic acid improved photosynthetic capacity and antioxidant activity, alleviating the effects of calcium nitrate stress.  
Exogenous ABA in _Sophora vicifolia_: the use of abscisic acid (ABA) enhanced germination rates and antioxidant enzyme activities, improving drought tolerance.  
Photosynthetic recovery: FA restored the photosynthetic capacity of stressed plants by enhancing net photosynthesis, stomatal conductance, and transpiration rates.  
Oxidative stress reduction: FA significantly reduced the accumulation of reactive oxygen species (ROS) such as hydrogen peroxide (H2O2) and superoxide radicals (O2\({}^{-}\)), as well as malondialdehyde(MDA), a marker of lipid peroxidation.  
Antioxidant enzyme activity: the application of FA increased the activity of key antioxidant enzymes, including superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD), thereby enhancing the plant's stress resilience.  
Nitrate regulation: FA modulated the expression of nitrate transporters (BCNRT1.1 and BCNRT1.5), limiting nitrate accumulation in shoot tissues and improving nutrient balance.
## 3 Low-Strain Integrity Testing
### Vibration Wave Method  
Conventional hand-held hammer impacts often produce compressive stress waves with inadequate signal-to-noise ratios and inconsistent force characteristics (amplitude and duration) [78]. To address these limitations, vibratory excitation has emerged as an effective alternative. [PERSON] and [PERSON] [79] systematically characterized measurable parameters from velocity response plots of vibrator-excited cylindrical concrete piles. [PERSON] [80] advanced this approach by developing a low-cost portable vibrator that generates repeatable, controlled sinusoidal excitations, overcoming the limitations of manual hammer impacts. The vibrator was utilized to generate sinusoidal signals for pile excitation in PIT, and the impulse response function (IRF) was extracted from the deconvolution of the pile response from the sinusoidal swept frequency excitation. Defects and pile tips can be identified from the IRF curves. Typical impulse response functions are shown in Figure 7. [PERSON] [81] developed a novel nonlinear non-smooth analysis method for deep foundation PIT utilizing higher-order frequency response functions (FRF). Controlled unsteady excitation is provided by the vibrator during testing. A new FRF is realized based on the higher-order spectrum (HOS) of pile vibration, and an adaptive unsteady-state transformation is implemented for the FRF. Finally, qualitative NDT of pile damage severity is performed based on the high-order FRF. The proposed new technique is more effective than the conventional low-strain integrity testing technique for pile assessment.  
Figure 7: Impulse response function of pile subjected to sine-sweep excitation: (**a**) intact pile; (**b**) defective pile [80].
# Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas
## 2 Materials and Methods
### Forecasting Algorithm for Aqi  
The research builds a tailored predictive model based on _AQI_ features after determining the most accurate model for predicting _AQI_ (MA-AQI). This model was designed to refine _AQI_ predictions, improving accuracy and adapting the prediction framework to specific dataset characteristics or external influencing factors like seasonal variations and vehicular traffic patterns. The MA-AQI model corresponds to the value prediction scenario. In this scenario, the predicted _AQI_ value requires all the values for the input features. However, the value for the input features is not available, so the model is available only for real-time readings and _AQI_ prediction.  
The _AQI_ forecasting (AQIF) model incorporates additional features represented by the data series. The historical dataset enables the model to analyze trends and variations in vehicular traffic intensity in conjunction with weather conditions. This behavior affects accurate _AQI_ forecasting in an urban environment like Bronx County.  
The AQIF uses the ForecastBySsa algorithm, described in the paper by [PERSON] et al. [95]. The analyzed parameters conceptually depicted in Figure 7 are as follows [96]:  
* The input column contains the historical _AQI_ values used to train the model. The accuracy of predictions relies on the quality and quantity of the historical data between 1 January 2020 and 31 December 2023;* The windowSize determines how many past observations are considered to predict future values. Multiple tests were provided for this parameter to obtain the best value for the accuracy of the forecasting;
* The horizon parameter specifies how many future days the model will forecast. This value is set to 5 days;
* The output column contains the forecasted _AQI_ values for the next five days, predicted after the model is trained. These values are displayed in the proposed environmental monitoring and public health application.  
The AQIF model's evaluation included the same performance metrics as the previously mentioned ML models, \(R^{2}\), \(MAE\), \(MAPE\), and \(MedAE\).
## 6 One vs. More than One Element per Member
### Global Response Parameters, Two Elements per Beam (\(2E\))  
To make the comparison for inter-story shears, the parameter \(R_{V1}\), defined by Equation (7) is used. In such an equation, \(V_{1}\) and \(V_{2}\) symbolize the shears for models \(1E\) and \(2E\), respectively. As in many other discussions, only the results of \(R_{V1}\), averaged over all the seismic records, are shown.  
\[R_{V1}=\frac{V_{1}}{V_{2}} \tag{7}\]  
Figure 10 illustrates the mean values of \(R_{V1}\) for all the models. It can be seen that, on an average basis, the values essentially equal unity for the 3-story building, implying that there are no differences between the shears of Model \(1E\) and those of Model \(2E\). For the 9-story model, in contrast, the average \(R_{V1}\) values significantly increase with respect to those of the 3-story model. The values increase through the height of the model; the largest observed value is 1.21 implying that the shears can be overestimated by up to 21% if just one element per beam is used in the structural modeling. For the case of the taller model (Model 3), the average values of \(R_{V1}\), similar to those of Model 2, in general increase through the height of the model, but the values, although still significant, are smaller than those of Model 2, the maximum average value is observed to be 1.10. Even though they are not shown, overestimations of up to 33% can be seen individually for some seismic records.  
The responses for lateral drifts are also calculated and contrasted for the _1E_ and _2E_ structural representations. The parameter used to make the comparison is \(R_{D1}\), but the results are not shown. It is mentioned, however, that the average values of \(R_{D1}\) are essentially equal to unity for any model and seismic intensity indicating that the lateral drifts are precisely calculated if one element per beam is considered.
## 4 Results and Analysis  
### Velocity of the Wide-Area POT Glacier in Svalbard  
Using the proposed wide-area glacier velocity POT monitoring method to process the Sentinel-1 data set collected from seven maps, the displacement of Svalbard glaciers along the satellite range and azimuth direction were obtained in each map range, and we performed a vector synthesis of the displacement in the two directions in the process of solving the flow velocity. The two-dimensional plane flow velocity of the island-wide glacier was solved and calculated, as shown in Figure 4a. The overall consistency of the wide-area glacier flow velocity monitoring results was good, and a total of 25 unstable displacements of larger glaciers were detected. The flow velocity of each position of the glacier is generally different, and in general, the flow velocity gradually increases from the upstream to the downstream, and the flow velocity is the largest in the tail. The glacier with the highest flow rate in Svalbard in 2021 was located in the third Basin of Ostfinna, with a peak flow rate of 6.18 m/day, followed by the Negree glacier in the central-eastern part of Svalbard, with a peak flow rate of 4.50 m/day. In addition, there are a number of small glaciers scattered throughout the island, and the flow velocity magnitude is relatively small, at generally less than 1 m/day.  
Figure 4b shows the slope of Svalbard. According to terrain slope and flow velocity information, glacier motion can be divided into four types, namely, gravity flow, extrusion flow, blocked gravity flow, and blocked extrusion flow. Gravity flow refers to the flow caused by the component force of glacier gravity along the slope, which is greater than the resistance of the glacier bottom to the glacier. It is the basic movement form of mountain glaciers with large slopes. When the movement is preceded by protruding bedrock or large, slow-moving ice blocks, the shear stress system of the glacier becomes press-forward and press-backward, where many thrust faults and complex folds occur, which block the gravity flow. Extrusion flow is the movement of glaciers caused by unbalanced forces due to the uneven thickness of glaciers. Similarly, when confronted with a protruding bedrock or a large, slow-moving ice block in front of it, it creates a blocking extrusion flow similar to blocking gravity flow. Table 4 shows the main parameters of the 25 detected glaciers, including information on glacier nature, length, area, peak flow velocity, and flow direction.  
Figure 3: Results of the first (**a-1–a-3,b-1–b-3**) and second (**c-1–c-3,d-1–d-3**) image pairs along the range and azimuth under different cross-correlation windows in the pre-experiment.  
Among them, the flow direction refers to the position of the end point of the glacier relative to the starting point.
## 4 EMD and DCNN Partial Discharge Type Recognition  
### EMD and DCNN Partial Discharge Type Identification Process  
The flow chart of EMD and DCNN partial discharge type recognition is shown in Figure 12.  
1. The original partial discharge signal x (t) of the three defects is obtained by collecting the partial discharge signal of the T-head cable. The original partial discharge signal x (t) is decomposed by EMD after noise reduction, and the intrinsic mode IMFi (t) component of the original partial discharge signal x (t) of the three defects is obtained.
2. The intrinsic mode IMFi (t) component separates the training samples and the test samples at a ratio of 8:2, and the DCNN network trains the training samples. Through the training of sample training, the T-type terminal partial discharge type recognition classifier is obtained.
3. The test sample set is used to test the T-type terminal partial discharge type recognition classifier, test the validity and correctness of the model, and adjust the parameters. The final model is applied to the identification of the T-type terminal partial discharge type, and the corresponding recognition results are obtained.
# Attentional Keypoint Detection on Point Clouds for 3D Object Part Segmentation
## 4 Experiments and Discussions on Part Segmentation
### Ablation Study and Qualitative Results  
In this section, we conduct ablative studies to demonstrate the effectiveness of three proposed modules of our AKDNet on the ShapeNet-Part dataset.  
**Effectiveness of Feature Extension Module:** To demonstrate the effectiveness of the feature extension module, we provide the visualization result of ShapeNet-Part dataset with and without this model based on PointNet++ (see Figure 4). From the comparison between Figure 4a,b, we can observe that, through the feature extension module, our method achieved more reasonable results, especially for certain parts located within the interior regions of the shape.  
In more detail, the feature extension module allows the model to capture more detailed and refined features from the point cloud, thereby leading to a better representation of the shape's structure. This is particularly beneficial for parts of the shape that are internal or less prominent, which might be challenging to accurately capture using conventional methods. The improved feature representation results in more accurate and coherent segmentation, especially in the interior regions of the shape.