esa-sceva/satcom-chunk-collection huggingface dataset:
# Vertical Dynamic Response Prediction of the Electromagnetic Levitation Systems
## 1 Introduction
[5] established a two-input two-output (TITO) vertical dynamic model of the electromagnet module and the track and analyzed the difference of dynamic response of the levitation system under the excitation of track irregularity. He proposed an adaptive control method to suppress the vibration of the rear levitation gap under various track irregularities by simulation. [PERSON] et al. [21] analyzed the problem that the deviation range of the rear levitation gap exceeds the safe range when the low-speed maglev train passes the track joints. He used a magnetic flux feedback algorithm to realize the decoupling of the module levitation system. Simulation and experiment results show that this decoupling method has a good effect on restraining the gap fluctuation. [PERSON] et al. [22] used the feedback linearization method to solve the problem described in [21]. Furthermore, [PERSON] et al. [23] and [PERSON] et al. [24] proposed a scheme to move the sensor position. All these researches have positive effects on improving the stability of the levitation system under the track irregularity. In this paper, a SITO (single-input two-output) dynamic model of the electromagnet module and the track is established. By using the discrete frequency method, the amplitude spectrums of levitation gaps at different speeds and different track wavelengths are obtained, which can fully reflect the vertical dynamics of the levitation system. The validity of the dynamic model is verified by the numerical simulation. The remainder of this paper is organized as follows: In Section 2, the vertical dynamic model of the electromagnetic levitation system with the track is presented. In Section 3, the dynamic response of the levitation gap under the track irregularity is investigated. In Section 4, numerical simulation verification is presented. Section 5 gives a brief conclusion of this work.
# Applicability of Infrared Thermography for the Detection of Phase Transitions in Metal Alloys
## 2 Materials and Methods
### DSC Validation  
The phase transition detection made with the IRT method is compared with DSC measurements made with a DSC 131 provided by SETARAM (KEP Technologies, Caluire-et-Cuire, France). For each sample, two heating/cooling samples are performed: one fast cycle at a 5 \({}^{\circ}\)C/min rate to ensure a good thermal contact between the sample and the crucible and to delete any previous thermal history of the samples; one slow cycle at a 1 \({}^{\circ}\)C/min matching the heating rate of the IRT experiment. Prior to the measurements, the DSC has been calibrated with Gallium (Purity: 99.999%), Indium (Purity: 99.995%), Tin (Purity: 99.999%), and Lead (Purity: 99.999%) in order to validate the measurement accuracy in the 29-330 \({}^{\circ}\)C range. Consequently, the temperatures of the phase transitions were measured with an uncertainty of \(\pm\)0.5 \({}^{\circ}\)C. The DSC is cooled with a ventilation system at room temperature meaning that temperatures below 20 \({}^{\circ}\)C cannot be reached. Therefore, transitions occurring below 20 \({}^{\circ}\)C for the samples cannot be retrieved with DSC and be extracted from literature in the following results. In addition, the interpretation of the DSC curve is made according to the guidelines from [19] stating the transitions temperatures are associated to onset temperatures when applicable and to peak temperatures otherwise.
## 3 Data and Methods
### Construction of the Three-Dimensional Model  
We first used ArcGIS 10.7 software to process the profiles, crustal velocity model, surface traces of active faults, historical earthquake catalogues [19], and 14,381 relocated seismic events in the Linfen Basin from 1 January 1981 to 1 June 2018 (Figure 3) into the Universal Transverse Mercator (UTM) coordinate system and converted the data into a format compatible with the Skua GOCAD (2017) 3D modelling software. We then imported these data into the SKUA-GOCAD 3D modelling platform to construct a 3D workspace for the Linfen Basin (Figure 4). The P-wave velocity data were converted to a 3D seismic cube (also known as a 'voxel'); this 'voxel' was divided into 100 grids in three directions (X, Y, Z). The 3D working area was subjected to high-density grid refinement, in which 24 E-W projection profiles were created to represent the geometric features of the faults accurately. The projection profiles were spaced 6 km apart. Subsequently, a 3D seismic source model (Figure 10) was built using the discrete smooth interpolation in GoCAD though the \"make surface\" step, which was constructed to analyse the seismogenic structures and seismic environments of large historical earthquakes in the Linfen Basin.
# Prediction of Remaining Fatigue Life of In-Service Bridge Cranes
## 4 Fatigue Residual Life Prediction of Bridge Crane
### Residual Fatigue Life Prediction Process  
The remaining fatigue life prediction process of the bridge crane is shown in Figure 7, and the location of the fatigue failure point is first determined by static structural analysis. The three-parameter Weibull distribution model is established by the characteristic parameter samples of this location, and the rainflow counting method is applied to count them cyclically to obtain the stress spectrum. Finally, the stress spectrum obtained from simulation and test is brought into the constant-amplitude and variable-amplitude load remaining life prediction formula to obtain the remaining fatigue life of the bridge crane.
## 3 Results and Discussion  
### Geological Setting  
The field data were recorded over a barchan dune near Dammam city (26\({}^{\circ}\)20\({}^{\prime}\)57.2\({}^{\prime\prime}\) N 49\({}^{\circ}\)49\({}^{\prime}\)51.4\({}^{\prime\prime}\) E), Saudi Arabia (Figure 3a,b). The thickness of the dune ranges from 0 to 7 m and it overlays a flat Sabkha layer. One borehole was drilled up to 10 m deep at the midslope of the dune, and was used as ground truth and for result confirmation. A complete core was extracted from the top 3.5 m, then a core sample of only 0.7 m was extracted at each 1.5 m depth range. The well penetrated the entire dune, as well as the top 3 m of the underlying sabkha.  
Based on the samples extracted from the borehole, the dominant mineral in the dune is quartz. We also found small amounts of feldspar and traces of calcite. The sabkha layer contains quartz, feldspar, calcite, and significant amounts of halite and gypsum. The top 2.5 m of the sabkha was composed of fine-grained to medium-grained sand, while the bottom 0.5 m contained muddy sand with a noticeable fraction of fine grains. Sediments in the top 7.6 m of the drilled interval showed partial saturation, whereas the bottom 2.4 m was fully water saturated.  
Figure 3: (**a**) Satellite view showing Saudi Arabia and the study area marked with red stars; (**b**) image showing the study area with the seismic line (red line); and (**c**) the seismic line including 12 receivers (blue triangles) and two sources (red stars) with their actual positions and elevations using D-GPS data.
# Benefit Analysis of Grid-Connected Floating Photovoltaic System on the Hydropower Reservoir
## 2 Environmental, Technical, Financial Specifications of the Plant
### Environmental Impact Assessment
#### 2.3.2 Impact of System Components on the Natural Environment  
PV Panel  
The level of environmental impact during the operating period is negligible and is globally defined as a form of clean energy. After an operating period, large volumes of PV panels can be collected and recycled.  
Positive impact during the operating period: the installation of solar cells has the effect of reducing water evaporation, reducing solar energy absorption, tends to decrease water temperature, increase dissolved oxygen concentration in water, and has good impact on the activities of fishery in the lake.  
Substation  
110 kV substations using oil-immersed transformers can be a source of water pollution, fire, and explosion hazards, threatening human life and property safety.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & & **Eaton** & & \multicolumn{2}{c}{**SMA**} & **Schneider** & **ABB** \\ \cline{2-7}  & **1670** & **2000** & **2200** & **1000** & **2500** & **680** & **1000** \\ \hline AC capacity (kW) at pf = 1 (50 \({}^{\circ}\)C) & 1666 & 2000 & 2200 & 900 & 2250 & 680 & 1000 \\ AC capacity (kVA) (50 \({}^{\circ}\)C) & 1831 & 2000 & 2200 & 900 & 2250 & 680 & 1000 \\ DC voltage (V) & 550–1000 & 550–1000 & 605–1000 & 625–850 & 850–1425 & 550–885 & 600–850 \\ DC max current (A) & 3100 & 3700 & 3700 & 1635 & 3000 & 1280 & 1710 \\ AC voltage (V) & 356 & 384 & 423 & 365–465 & 440–660 & 380 & 400 \\ AC max current (A) & 2707 & 3000 & 3000 & 1568 & 2624 & 1040 & 1445 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main parameters of some types of inverters in the world.  
Power Line  
Power lines are at risk of electrical safety for plants and animals when there is a ground fault, short circuit, or a violation of the grid safety corridor.
## 3 Research Design
### Relevant Technological Concepts
#### 3.3.4 Text-Based Recommendation Using NLP  
NLP is being used and has been used for text-based recommendation, e.g., [PERSON] et al. [1] have built a system which helps researchers to decide to which journals they should send their manuscripts based on past publications of the journals. [PERSON] et al. [1] tested a hybrid model based on chi-square feature selection and softmax regression, yielding an accuracy of 61.37%.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 3 Proposed Target Speech Enhancement Method
### Enhancing Target Speech Using Estimated Mask  
With the estimated mask in Equation (39) and the output of the FBF spatial filter \(Y_{m}(\tau,\omega)\) in Equation (42), it is straightforward to compute the enhanced target speech as follows:  
\[Y_{s}(\tau,\omega)=Y_{m}(\tau,\omega)\times mask(\tau,\omega) \tag{43}\]  
where \(Y_{s}(\tau,\omega)\) is then the spectra of the enhanced speech or an approximation of the target speech.  
For presentation completeness, our proposed speech enhancement algorithm is termed as an AVS-SMASK algorithm, which is summarized in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline (1) & Segment the output data captured by the \(u\)-sensor, \(v\)-sensor, \(w\)-sensor, and \(o\)-sensor of the AVS unit by the N-length Hamming window; \\ (2) & Calculate the STFT of the segments: \(X_{u}(\tau,\omega)\), \(X_{v}(\tau,\omega)\), \(X_{w}(\tau,\omega)\) and \(X_{o}(\tau,\omega)\); \\ (3) & Calculate the ISDR vector \(\mathbf{I}_{\text{isdr}}=[I_{uv},I_{uv}]\) by Equations (14)–(16); \\ (4) & Obtain the valid \(\mathbf{I}_{\text{isdr}}\) according to the known direction of arrival (DOA) (\(\theta_{s}\phi_{s}\)) and the summary of \\ (5) & Utilize the valid \(\mathbf{I}_{\text{isdr}}\) to estimate the DOA (\(\hat{\theta}_{s}\), \(\hat{\phi}_{s}\)) of the target speech for each time-frequency (TF) point; \\ (6) & Determine TF points belong to the target speech by Equations (36) and (37); \\ (7) & Calculate the nonlinear soft TF mask: mask(\(\tau,\omega\)) by Equation (39); \\ (8) & Calculate the output of the FBF \(Y_{m}(\tau,\omega)\) by Equation (40); \\ (9) & Compute the enhanced speech spectrogram by Equation (43); \\ (10) & Get the enhanced speech signal \(y\)(n) by ISTFT. \\ \hline \end{tabular}
\end{table}
Table 2: The pseudo-code of our proposed AVS-SMASK algorithm.
## 6 Conclusions  
This study employed a mixed-methods approach to conduct a comprehensive analysis of the primary barriers to adopting AIVGTs in the video production industry. By deeply understanding the intricate interactions between the industry and these emerging AI tools, we refined valuable insights, offering practical cases and data for further exploration. Our research identified eight major adoption barriers: innovation, market demand, technological maturity, cross-disciplinary collaboration, ethics and privacy, public acceptance, data security and copyright, and global versus local perspectives.  
The quantitative analysis underscored the significant impact of these factors on the industry's adoption of AIVGTs, particularly emphasizing the importance of technological maturity, global and local applications, data security and copyright, and market demand. These findings provide valuable insights for industry practitioners and serve as essential references for the strategic planning of AI tool production companies. This enables them to effectively address and overcome the barriers to AIVGT adoption and promote the healthy development of AI technology in video production.  
For instance, critical measures include enhancing the technological maturity of AI video generation tools, especially in areas such as content personalization, creative diversity, and natural language processing. Additionally, devising more detailed marketing strategies and establishing collaborative platforms among content creators, technology developers, academic researchers, and industry experts are crucial steps. Ensuring the legality and ethics of AI video content to mitigate legal risks, along with establishing user feedback mechanisms to continuously collect suggestions from various stakeholders, are also essential actions.  
Furthermore, we acknowledge the limitations of our study, such as the scope of the sample and the choice of research methods, which may have influenced the results. Therefore, we stress the importance of future research, which should encompass a broader industry context, various scales and types of video production practices, and attitudes toward the adoption of AI video generation tools in different cultures and regions.  
In summary, this in-depth exploration of our study uncovers the multifaceted barriers encountered when adopting artificial intelligence video generation tools in the video produc  
* 15. What technological advances or market changes are likely to affect the development and adoption of AI video generation tools? Open questions.
* 16. What advice would you give to your peers who are considering using AI video generation tools?
* 17. What are your expectations or changes you would like to see in the future development of AI video generation tools?
## 4 Results and Discussion
To illustrate this further, it can be noted that it takes much more time to detect the voxel regions in the Old Hospital and St Mary's Church scenes, as they have more inlier voxels than the other two scenes, as detailed in Table 1. Hence, for computational efficiency, in future studies, we will consider dilated convolution [35] to reduce the number of weight parameters the decoder in AdapNet requires. Finally, from Table 4, it can be noted that point detection, in general, takes longer with Case 2 than with Case 1. This is because the increased number of voxels typically means that more inlier 3D points and interest points are detected in Case 2 than in Case 1. As such, more interest points are considered for bi-cubic interpolation in SuperPoint. Similarly, point description and description matching take longer with the King's College and St Mary's Church scenes, since they have more inlier 3D points per voxel on average than the other two scenes, and more candidate pairs require consideration. To examine further performance of our model, we include the mean and standard deviation of the estimated camera pose results in Table 5. Some papers [15; 18; 21] have reported the localization rate, defined by computing the percentage of images localized within a given camera pose error threshold (for example, with translation and rotation errors smaller or equal to 0.25 m and 2 degrees). Tables 5 and 6 show that the result errors in Case 2 are more stable than those in Case 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **King’s College** & **Old Hospital** & **Shop Facade** & **St Mary’s Church** \\ \hline \multirow{4}{*}{Case 1} & \multirow{4}{*}{Voxel Region Detection} & 27.8 & 31.0 & 27.1 & 35.1 \\  & Point & 5.4 & 4.4 & 4.0 & 6.5 \\  & Detection/Description & & & & \\  & Description Matching & 18.1 & 5.2 & 4.9 & 16.1 \\  & PnP & 0.7 & 0.9 & 0.7 & 0.9 \\  & Total Computation Time & 52.0 & 41.5 & 36.7 & 58.6 \\ \hline \multirow{4}{*}{Case 2} & \multirow{4}{*}{Voxel Region Detection} & 31.3 & 64.2 & 55.2 & 85.0 \\  & Point & 6.2 & 4.6 & 4.2 & 8.0 \\ \cline{1-1}  & Detection/Description & & & & \\ \cline{1-1}  & Description Matching & 11.9 & 4.9 & 4.1 & 12.9 \\ \cline{1-1}  & PnP & 0.6 & 0.7 & 0.6 & 0.9 \\ \cline{1-1}  & Total Computation Time & 50.0 & 74.4 & 64.1 & 106.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of computation time (ms) required for individual procedures in camera pose estimation.
## 2 Materials and Methods
### Texture Feature Extraction
#### 2.3.3 Higher-Order Spectra, Entropy Features, and Hu's Moments  
Higher-order spectra (HOS) invariants were first extracted from each ROI, which retains both phase and magnitude information of the image [32]. The normalized bispectrum and phase entropies are calculated from bispectrum plots for every \(20^{\circ}\) (i.e., 9 directions, from \(0^{\circ}\) to \(160^{\circ}\)), generating a total of 45 features since each bispectrum plot can be described with 5 features [32]. To further analyze and measure pixel variations, the energy, Shannon [33], Renyi [34], Fuzzy [35], Kapur [36], Yager [37], Vajda [38], and maximum entropy [39] were calculated on each ROI. Finally, 7 Hu's moments which represent the invariant patterns of the image were extracted [40].
## 4 Experimentation and Results  
In this section, we illustrate our results after implementing Algorithms 1 and 2 using SciPy which has been discussed in Section 3. To evaluate our proposed algorithms, we used three different datasets, as follows:  
* **BCG Strains Dataset**: Finding a cure for COVID-19 will be a long-term and difficult process, and research into new vaccines can take several years. Researchers have suggested that an old vaccine BCG can cause a boost to the immune system, and this could be the reason for the relatively low COVID-19-attributed death rates in some countries. A number of clinical trials with BCG have started already, but the results will not be available for many months. The studies of the BCG vaccine relationship to COVID-19 have used data from the BCG World Atlas project, which started nearly a decade ago. The Atlas is neither perfect nor complete, but it is the only available database of this kind. There are various BCG Strains and according to some studies they are not the same and the impact that the vaccine causes might vary. There is a recent hypothesis that COVID-19 mortality may depend on which strain has been used in a given country. The BCG Strains Dataset that we used for this study is a.csv file consisting of 46 rows and 52 columns.
* **NNDSS Dataset**: To protect Americans from serious diseases, the National Notifiable Diseases Surveillance System (NNDSS) helps public health control and prevent about 120 diseases. These diseases are important to monitor nationwide and include infectious diseases such as Zika and foodborne outbreaks such as E. coli. CDC receives these data through NNDSS which also supports the COVID-19 response. The NNDSS Dataset that we used for this study is a.csv file consisting of 1961 rows and 31 columns and provisional cases of notifiable diseases are displayed for the United States, U.S. territories, and Non-U.S. residents.
* **Provisional Counts of Death Dataset**: This dataset is also taken from CDC and is a.csv file consists of 4318 rows and 34 columns. The number of deaths reported in this table is the total number of deaths received and coded as of the date of analysis. Data for 2019 and 2020 are provisional and may be incomplete because of the lag in time between when the death occurred and when the death certificate is submitted to National Center for Health Statistics (NCHS).  
The results of applying the Algorithm 1 on the mentioned datasets are depicted in the following sections.
# A Comparative Study of the CMT+P Process on 316L Stainless Steel Additive Manufacturing
## 4 Results and Analysis
### Fracture Morphology  
The micro morphology of the tensile fracture of the deposited samples is shown in Figure 15. A large number of equiaxed dimples were found on the fracture surfaces of all specimens. The dimples of the horizontal tensile specimens were shallower, on the contrary, those were deeper on the vertical tensile specimens, and obvious tearing edges were found around the dimples, indicating that the material broke after full plastic deformation and good plasticity of deposited specimens, and the vertical tensile specimens had better plasticity and ductility than the horizontal tensile specimens [35]. The main reason may be that the columnar crystals grew along the direction of construction during deposition, which made them the main carrier of the stress in the vertical direction, while the lateral boundary of the columnar crystals in horizontal direction opposite, which showed that the plastic deformation ability in the grain was better than that in the grain boundary. At the same time, it can also be observed that the orientation of dimples on the fracture surface of the vertical specimen were consistent, which fully showed that the tensile load on these specimens was mainly carried by the columnar crystals. It was also shown in the figure that there were traces of cleavage fracture in the horizontal tensile fracture of sample #1, which indicated mixed fracture mode of toughness and brittleness fracture in the sample. while no cleavage surface appeared in the other samples, which indicated that deposition of low line energy led to the formation of brittle phase and the decline of mechanical property. At the same time, it can be observed in the figure that with the increase the line energy, the number of the second phase spherical particles in the fracture surface decreased gradually. This may be due to the higher heat input, which made the solute atoms out of austenite grains during the growth process, reduced the saturation of solid solution, and thus reduced the precipitation during the aging process.  
Figure 14: SEM photos of morphology of cross section in the middle region of sample #4.  
Figure 15: Microscopic fracture morphologies of tensile specimens in horizontal and vertical directions: (a) #1-H2, #2-H2, #3-H2, and #4-H2 respectively and (b) #1-V2, #2-V2, #3-V2, and #4-V2 respectively.
## 5 Analysis and Evaluation
### Throughput  
According to a previous study [22], a resolution node should be able to manage up to 66,500 query requests per second with an average of 2 get requests/s per user, and a single resolution node can handle more than 8300 users with a single storage unit. The GET request is the same as _QUERY_ requests in this paper. In ENRS, we deploy nearly 70 HMs for 1600 user nodes according to the latency constraints, which are shown in Figure 6. We assume that the user nodes are in the magnitude of \(10^{6}\), thus more than 1400 HMs are needed according to Figure 6. The length of the _QUERY_ message in ENRS is 248 bits. Thus, the name resolution traffic processed by ENRS can be up to \(1400\times 66\),\(500\times 248\) bit \(\approx\) 21 GB/s, which can effectively reduce the control-plane traffic of the backbone network.