esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Factors Driving the Spatiotemporal Changes in NPP  
This study found that the spatiotemporal changes in the NPP of vegetation in Inner Mongolia are driven by multiple factors such as climate change, human activities, and regional differences. Previous studies showed that climate change directly affects the increase in NPP through spatial differences in temperature and precipitation [8; 22]. The rapid growth of NPP in areas with abundant precipitation indicates that water supply is the main factor limiting plant growth, while temperature changes have a more significant impact in the cold temperate coniferous forest area [9; 25]. In recent years, the frequent occurrence of extreme climate events such as drought and extreme precipitation has had a profound impact on the short-term fluctuations of NPP [26; 27].  
Human activities, especially alterations in land use and land cover patterns, can exert significant impacts on NPP [8; 22]. In some cases, these anthropogenic influences may even surpass the effects of climate change in shaping NPP dynamics [22]. In China, positive national policy interventions such as returning farmland to forests and grassland restoration have significantly increased vegetation coverage and thus improved NPP. In this research, regions with high NPP values were predominantly observed in the eastern areas characterized by extensive forest coverage, largely attributable to effective national environmental policies. However, due to natural conditions such as drought and poor soil, progress in ecological restoration in some regions is relatively slow, indicating that the effectiveness of human activities is largely constrained by regional environmental conditions [28]. In addition, rapid urbanization and land use changes may lead to a  
Figure 6: Correlation between climatic variables and NPP. (**a**) Precipitation and NPP; (**b**) temperature and NPP. In this study, \(R\) refers to the correlation coefficient.  
decrease in NPP in certain regions, especially when farmland is converted to construction land, resulting in a significant reduction in plant community productivity and carbon sequestration capacity [29].  
Regional differences in NPP are closely reflected in the diversity of land use patterns, such as relatively small fluctuations in NPP in forest and grassland systems, while farmland exhibits higher spatiotemporal variability due to the influence of tillage and fertilization [30]. A particularly illustrative case is the accelerated NPP growth in croplands within China's karst regions, a trend not mirrored in other land cover types [22]. In this research, the strong positive sustainability of NPP in the eastern region in Inner Mongolia indicates that the ecosystem in this area has greater potential for improvement; however, the trend in NPP in the western region is relatively stable, with spatial variations in NPP likely modulated by landscape heterogeneity [15].
# Measurement of Dielectric Properties of Thin Materials for Radomes Using Waveguide Cavities
## 2 Theory and Materials
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material Name** & **Simple Name** & **Thickness** \\  & & **(mm)** \\ \hline PTFE Coated Glass Fabric * [26] & PTFE Fabric & 0.15 \\ Dyneema composite fabric hybrid * [27] & Dyneema & 0.1 \\ Polyester, PU-Coated, impregnated, flame retardant, & Polyester & 0.19 \\
240 g/sqm [25] & Ecopak EPX70 RS, Ripstop, & Ecopak & 0.15 \\ recycling-backpack-laminate, 171 g/sqm [25] & Ecopak & 0.15 \\ Etaproof 200, waterproof cotton, 200 g/sqm, 2 nd & Etaproof & 0.27 \\ Condura, 500 den, coated [25] & Cordura & 0.36 \\ N-Shell, z-liner pocket lining, 70 g/sqm [25] & N-Shell & 0.3 \\
3-layer-laminate, robust, mini-ripstop, 170 g/sqm [25] & 3-LL & 0.3 \\
2-layer-laminate, soft, slightly elastic, 105 g/sqm [25] & 2-LL & 0.2 \\ \hline \hline \end{tabular}  
* Reference materials that have been used as radomes in previous projects.  
\end{table} 
Table 2: Materials being investigated, their shortened names for ease of reading, and their thicknesses [25; 26; 27]. but is a useful feature. On the other hand, being water- and windproof is essential for a radome material. These features, alongside the loss tangent and permittivity values, assist with determining which materials could be appropriate options for radomes. Therefore, in the following section, the simplified equations method is used to determine the unknown dielectric properties of the materials being investigated to establish their appropriateness for radome applications.
## 5 Experiment Results and Discussion
### Experiment Description
Each participant was assigned a unique username, which relates to the user interaction data collected during the play of SG1. By this username, the settings for each participant for SG2 were then retrieved. Participants also used this username when filling out the questionnaire. The purpose of the first user testing activity is to collect data on user interactions and ratings for the interaction mechanisms. SG1 tracks user interaction data and sends them to the database (LRS) in real-time (as described in Section 4.2.1). The selected set of interaction data is then used to calculate the accessibility settings in the data processing part of the system (as described in Section 4.2.4). After that, SG2 retrieves the settings and adapts the game accordingly (as described in Section 4.2.5). During user testing, devices were connected to the Internet to store and retrieve the data. The questionnaire aims to collect qualitative data related to SG2 to evaluate its usability/accessibility, satisfaction with interactions and general impression of the game. Additionally, tasks with assigned interaction mechanisms are also assessed to compare the ease of use to the first game where users experienced both interaction mechanisms. When it was easier for the participant, the questionnaire was accessed on paper. In addition to questions about the usability, accessibility and user experience of SG2, the questionnaire contained questions to collect basic demographic data about the user. A complete questionnaire with questions and possible options for answering is available in Appendix B. Table 7 shows the distribution of participants by age group and disability type, i.e., the absolute and relative frequency of occurrence of a particular age group and disability. Among participants with disabilities, the most represented is the category of participants with multiple disabilities in which all participants have a physical disability in combination with other types of disabilities, i.e., cognitive and neurological disorders/specific learning disabilities, visual impairments and auditory impairments. Although 19 participants answered that they had some type of visual impairment in the questionnaire, we did not consider those participants whose answers did not fall under the definition of visual impairment as defined by the World Health Organization (WHO) [76]. For this reason, the recommended number of participants in usability testing according to [PERSON] [75] was not met for this category, which represents one of the limitations of this study.
###### Abstract  
**Objective**: The objective of this study was to evaluate the effect of different surface treatment methods on the shear bond strength (SBS) of metal brackets bonded to two types of CAD/CAM composite restorations: milled and 3D-printed. **Materials and Methods**: A total of 160 flat-shaped specimens (10 \(\times\) 10 \(\times\) 2 mm\({}^{3}\)) were prepared from four different CAD/CAM composites; two milled (Lava Ultimate(tm) [LU] and Grandio(tm) [GR]) and two 3D-printed (Crownet(tm) [CT] and C&B Permanent(tm) [CB]). These specimens underwent thermocycling (5000 cycles at 5-55 \({}^{\circ}\)C), then were categorized based on the surface treatment into four groups (\(n\) = 10): Group C (control, no surface treatment), Group HF (treated with 9.6% hydrofluoric acid), Group DB (mechanical roughening by a diamond bur), and Group SB (sandblasting using aluminum oxide). Metal brackets were bonded using Transbond XT Primer and universal adhesive, stored in artificial saliva for 24 h, then thermocycled again. Shear bond strength (SBS) was tested using a universal testing machine until bracket debonding occurred. The adhesive remnant index (ARI) was assessed using a stereomicroscope to quantify the residual adhesive following debonding. **Result**: Regarding material, GR and LU restorations had significantly higher SBS values compared to CT and CB, ranging from 13.90 MPa to 20.35 MPa. Regarding surface treatment, SB and HF groups showed significantly higher SBS values. The ARI scores showed different adhesive modes of failure, with higher instances of scores 0 and 1, which indicate no or minimal adhesive remaining. **Conclusions**: Both milled and 3D-printed materials had adequate SBS for clinical use, with milled materials showing superior results. Surface treatments like sandblasting and HF significantly improved bond strength, with adhesive failure being common.  
d ARTIC 2024 14 9530. [[https://doi.org/10.3390/app14209530](https://doi.org/10.3390/app14209530)]([https://doi.org/10.3390/app14209530](https://doi.org/10.3390/app14209530))
# Enhancing the Accuracy of Advanced High Temperature Mechanical Testing through Thermography
## 4 Discussion
The accuracy of thermography and pyrometry persisted in comparison to thermocouples upon the HE23 coated and ceramic surfaces at the three locations compared across the chosen 25 mm specimen gauge length during isothermal conditions, Figure 12. Accuracy was maintained for over 50 h to within \(\pm\)2 \({}^{\circ}\)C. However, during dynamic thermal cycling significant inaccuracies were found between the techniques employed, Figure 13. Readings from the pyrometer are inaccurate, suggesting the wavelength emitted from the RLF is significantly obscuring its accuracy during dynamic cycling. At first glance spot welded thermocouple and thermography readings appear in symmetry whilst it is clear that the tie-on thermocouple substantially over and undershoots the control waveform. This behaviour was noted in previous work [11], the small mass of the thermocouples gives rise to rapid temperature changes. thermocouples appear to absorb heat energy during heating stages of the cycle and shed heat during the cooling stage. This gives rise to different ramp rates and, directly compared to thermography, inaccurate temperature readings. This effect also seems to appear on closer inspection of the symmetry between readings from thermography control and the spot-welded thermocouple, during the heating and cooling stages of dynamic cycles, Figure 14. Accuracy then recovers to within \(\pm\)2 \({}^{\circ}\)C during isothermal stages of the cycle, again consistent with previous work, [11; 13]. Consistent with previous work [11; 13], thermocouples on the specimen gauge section have shown to generate significant complications, Figure 8. In this investigation thermocouples have shown to induce thermal hot spots, insulating the test piece surface beneath them as well as distorting thermography readings. In previous work thermocouples have also been shown to cause significant inaccuracies due to shadowing effects, [11; 13]. Not only does the non-invasive thermography technique avoid such complications but could also provide additional benefits by locating any hot/cold spots and connecting them to sites of crack initiation or the onset of failure. Historic optical methods to measure crack growth provide a simple approach to crack monitoring, however the sensitivity is significantly reduced in comparison to DCPD methods, and also may be restricted by the heating apparatus being utilised. Thermography has the potential to control test temperature as well as providing an accurate means of crack growth monitoring.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 4 Speech Enhancement Experiment
### Spectrogram Evaluation  
We also analyzed the spectrograms of enhanced speech produced by the proposed and the competitive methods to visually quantify the level of _residual_ noise as well as _distortion_. For this purpose, we generated a noisy speech data set by corrupting the utterance sp05 with 5 dB _bubble_ (non-stationary) and 5 dB _f16_ (colored) noises.
# A Novel Bionic Structure Inspired by Lufta Sponge and Its Cushion Properties
## 1 Introduction
Based on the principle of symmetry and self-similarity using fractal theory, a bionic thin-walled tubular structure is designed. The parametric design combined with FEA, energy absorption of fractal structures. [PERSON] et al. [18] established a thin-walled buffer structure model based on the Koch fractal structure, mixed basic Koch fractal structures with circles and hexagons, and designed a variety of mixed fractal structures. Each mixed fractal structure was designed by repeating the basic fractal element. The result shows that hybrid Koch absorbers have higher crashworthiness. Fractal structures tend to have a high self-similarity and symmetry [19], which is consistent with the observed features in natural luffa sponge and can be used to guide structural design. The current study of luffa sponge focuses onits mechanical properties. [PERSON] et al. [20] studied the compression behavior of luffa-filled tubes and regard luffa sponge as a part. However, they did not mention the contribution of the I layer. By observing the fiber texture of the I layer, it can be found  
Figure 1: The structure of luffa sponge: (**a**) Nature luffa; (**b**) Four main parts of luffa sponge; O (out layer), M (middle layer), I (inner layer) and C (core layer); (**c**) Three main directions of luffa sponge. that the fibers were arranged along the axis, and a few fibers were arranged along the circumferential direction. The O layer of fiber is mainly in accordance with the circumferential arrangement. The M layer fibers lie in between and grow in both directions. [PERSON] et al. [21] separately extracted four different structural parts and single luffa fibers of columnar luffa samples, and studied their structure and mechanical properties. The results show that Young's modulus and breaking strength of single fiber of luffa sponge are equivalent to wood fiber, which are about 2.3 GPa and 103 MPa respectively, and the average mechanical strength of the inner section of luffa sponge are about 1.6 times of the core part. Inspired by the excellent mechanical structure of the I layer, this paper aims to prepare a bionic structure with excellent cushioning properties by studying the hierarchical structure of a luffa sponge. The main flow chart of this paper is shown in Figure 2. Firstly, the morphology of luffa sponge is observed and determines the main cushioning section. Then, a three-dimensional model is built according to the feature structure by the fractal theory and Python programming, and prepared by 3D printing. Then, FEA and quasi-static compression experiments are carried out to obtain the cushion properties. Finally, an optimization method is adopted to find the best design parameter to improve energy absorption.
# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 2 Background
### Gradient Boosting  
Boosting is a well-known weighted ensemble method in which each base algorithm is sequentially implemented. The weights are modified and updated to place a greater emphasis on training tuples, especially those that were misclassified by the previous classifier. Gradient boosting is a subset of boosting where the loss function is minimized using DT as a weak learner, as well as gradient descent. Additionally, the loss function is employed to enhance the performance of the learner in subsequent iterations. Typically, the relative weights of the previous misclassified tuples are modified in each iteration of the boosting classifier. As a result, the boosting classifier, which is based on the reinforcement learning principle [7], is extremely robust. In this work, we focus on the use of gradient boosting in decision trees.  
Various problems, such as regression, multiclass classification, and ranking, can be resolved using gradient boosting [11]. Gradient boosting outperforms other classification algorithms with regard to processing categorical features (i.e., a distinct set of incomparable values) while maintaining high accuracy. Categorical features in gradient boosting are handled at the preprocessing phase, which entails substituting one or more numerical values for the original categorical variables. In addition, missing value support, ordered boosting implementation, ease of using graphics processing unit (GPU) training, and visualization are significant characteristics of gradient boosting. It also enables the estimation of leaf values using random permutations while selecting the tree structure (i.e., binary decision tree) to avoid the overfitting associated with the gradient algorithms.
## 2 The Conventional Language Identification Approaches
### Drawbacks of the Conventional Language Identification Approaches  
Although the conventional approaches have been successfully applied to LID tasks, they have limitations in multi-lingual ASR systems in which the LID module needs to be directly conducted on a user device. First, in the i-vector-based approach, GMM supervector, and TV-Matrix should be trained with hyperparameters according to the number of GMM mixtures and a dimension of the i-vector, as demonstrated in Equation (1). For example, if the dimension of the i-vector is defined by \(D\) and the dimension of the mean supervector from UBM refers to \(M\), the size of the TV-Matrix should be \(M\)\(X\)\(D\). As the number of languages increases, a higher dimension of i-vector is required, thus increasing the computation time.  
Next, in the DNN-based approaches, the feedforward neural network generally uses a number of hidden layers for reliable training, and thus estimation of the DNN feature requires a large amount of processing time and hardware resources. In consideration of computation complexity, both the i-vector and DNN approaches may not be suitable for LID modules employed in user devices with low hardware capacity.  
Another drawback of the conventional approaches is considered in terms of language-discriminative features. The approaches have a strong expectation that features derived from i-vector or DNN-BN convey language-discriminative information. However, the approaches initially process language-independent acoustic features such as MFCC or Short-Time Fourier Transform(STFT) to derive the feature. Hence, the output of i-vector or DNN-BN may have insufficient language information.  
To overcome the limitations of the conventional approaches, this study proposes a new LID approach based on language-discriminative features called rhythm metrics that were originally introduced in linguistics studies.
# Fault Prediction of Mechanical Equipment Based on Hilbert-Full-Vector Spectrum and TCDAN
## 2 Fault Feature Extraction
### Full-Vector Spectrum  
Suppose \(\{H_{i}\}\) and \(\{V_{i}\}\) are discrete sequences in the horizontal and vertical directions, respectively. To further improve the computational efficiency, the sequences \(\{H_{i}\}\) and \(\{V_{i}\}\) constitute complex sequences:  
\[\{z_{i}\}=\{H_{i}\}+j\{V_{i}\}(i=1,2,\ldots,\mathrm{N}/2-1) \tag{4}\]  
Figure 2: The processing flow of fault feature extraction: (**a**) The processing flow of the traditional Hilbert-envelope spectrum; (**b**)The processing flow of the Hilbert–full-vector spectrum.  
By Fourier transform, it becomes:  
\[\{Z_{i}\}=\{Z_{Ri}\}+\{Z_{li}\} \tag{5}\]  
where \(Z_{Ri}\), \(Z_{li}\) are the real and imaginary parts of \(Z_{i}\), respectively.  
According to the definition of full-vector spectrum theory [31; 32], the major half-axis of the elliptic trajectory of the rotor under a certain harmonic is called the principal vector of the harmonic, which is expressed by \(R_{ai}\). The minor semi-axis is called the auxiliary oscillator vector under the harmonic, denoted by \(R_{bi}\), and thus the following can be obtained:  
\[\begin{array}{ll}R_{ai}&=\frac{1}{2N}\left[\left|Z_{i}\right|+\left|Z_{(N-l) }\right|\right]\\ R_{bi}&=\frac{1}{2N}\left[\left|Z_{i}\right|+\left|Z_{(N-l)}\right|\right]\\ \tan 2\alpha_{i}&=\frac{Z_{li}Z_{R(N-l)}-Z_{Ri}Z_{l(N-l)}}{Z_{Ri}Z_{R(N-l)}+Z_{ li}Z_{l(N-i)}}\\ \tan\phi_{i}&=\frac{Z_{li}+Z_{(N-l)}}{Z_{Ri}+Z_{R(N-i)}}\\ (i&=1,2,\cdots,N/2-1)\end{array} \tag{6}\]  
where \(\alpha_{i}\) is the angle between the principal oscillator vector and the horizontal direction, and \(\phi_{i}\) is the phase angle when the axis moves along the elliptic trajectory.
## 4 Validation
### Validation Results  
#### 4.3.1 Real World vs. Simulated Number of Port Visits  
Figure 7a presents the real-world number of ship arrivals per destination port, the corresponding simulated values for 35 individual simulation runs, and the mean of the simulated values. As per the figure, the simulated values agree well with the data. This indicates that the traffic flows within the simulation model follow the data and that the simulated ship transit times are in the same range as, or lower than, the real-world transit times.  
Figure 6: Probability density function (PDF) of IB transfer times determined based on simulated data obtained from Winter Navigation Motorways of the Sea II (WINMOS II) [26].
## 2 Materials and Methods  
### Design  
A federated system was designed where terminology bundles, which are offered through a simple and secure programmatic interface, can be pulled by the partners and provided as an institution-internal SPARQL endpoint. These bundles were created though a continuous integration/continuous deployment (CI/CD) pipeline (see Figure 2). This pipeline simplifies the provisioning of these bundles. The central component of the SPHN DCC Terminology Service consists of the following two main parts, as depicted in Figure 2: the terminology server, where the source files and ready-to-use terminologies are stored in the object storage, and second, the CI/CD pipeline. The terminology server is an object storage and was implemented using the MinIO object storage. For the CI/CD pipeline, GitLab runners were chosen. The terminology server consists of the following two storage areas, called buckets: \"Sources\" and \"Ontologies\". The CI/CD pipeline picks up the sources and applies the converters to the source files, tests the conversions and repacks the bundles according to the specifications. The bundle is signed and then made available in the ontologies area. From this area, other processes pick up the bundles to make the content available in the BioMedIT research environments, hospitals, or the BioMedIT Portal, which provides a download area for individual users. Both storage areas have an archive; therefore, old data can be accessed when needed.  
Figure 2: Design of the SPHN DCC Terminology Service. The terminology service consists of the terminology server and the GitLab runner pipeline. Termologies are converted from their source format through the CI/CD pipeline into SPHN-compliant versions in the RDF (Resource Description Framework) standard.