esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Object and Methodology  
The purpose of this research was to determine the relationship between forming pressure and other parameters, including the binder pressure (Q), thickness of workpiece (s\({}_{0}\)), and the ratio of stress (\(\sigma\)). Thus, a mathematical model of the forming pressure is given in Equation (1).  
\[\mathrm{P}\ =\ \mathrm{f}(\mathrm{Q},s_{0},\ \sigma) \tag{1}\]  
Binder pressure is one of the key factors in the hydrostatic forming process. It is both effective in preventing coronary instability and also maintaining the forming pressure during the forming process. Thus, it was selected as a factor for research in this paper. Based on the diameter of a product, the binder pressure range was from 80 bar to 115 bar.  
The initial blank thicknesses of each material was 0.8 mm, 1.0 mm, and 1.2 mm, with the same diameter of 110 mm, as shown in Figure 2. This is a popular thickness range used in thin-shell forming technology.  
Materials selected for the survey were steel DC04, copper CDA260, and sus 304. The ultimate strength, yield stress, and degree of tensile strain of each material represent that material's ability to deform. Here, to consider the mathematical equation, two parameters--the ultimate strength and yield stress--are used to represent the material under investigation. The ultimate strength and the yield stress of each material were determined and are presented in Table 1.  
In this study, the ratio of stress of these materials was selected as a representative parameter for each material. This is defined as the ratio between ultimate strength (\(\sigma_{\text{m}}\)) and yield stress (\(\sigma_{\text{f}}\)), as shown in Equation (2).  
\[\sigma=\frac{\sigma_{\text{m}}}{\sigma_{\text{f}}} \tag{2}\]  
The selected product was a low, cylindrical cup. The product was shaped according to the size of the die shown in Figure 3. According to the hydrostatic forming mechanism, the most difficult position to shape was the bottom radius of the die. The bottom radius of the product was highly dependent on the strength of the forming liquid pressure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Ultimate Strength** & **Yield Stress** & **Ratio of Stress** \\  & \(\sigma_{\text{m}}\) **(MPa)** & \(\sigma_{\text{f}}\) **(MPa)** & \(\sigma\) \\ \hline Cu & 420 & 320 & 1.3 \\ DC04 & 415 & 220 & 1.9 \\ SUS 304 & 520 & 205 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of materials.  
Figure 3: Die profile.  
Figure 2: Experimental workpieces.  
The method chosen in this study was empirical planning. With the subjects and the research conditions presented above, an orthogonal second order design for three variables was considered to be most suitable to solve the issue.
# Interactive Exploration of Virtual Heritage by Means of Natural Gestures
## 3 Materials and Methods
### User Study Design  
The deployment of the systems in the Callatis Museum of Mangalia was followed by making it available to be readily used by museum visitors. Our goal was to investigate their impressions after interacting with digital cultural heritage assets, both artifacts and architectural edifice, by means of hand gestures.  
Museum visitors were provided with descriptive user manuals to help them get familiarized with the conceptual framework of the system. The members of the stuff performed short demonstrations of the usage, in order to showcase the basic functionality of the systems. The participants were also presented with possible interactions with the system by means of a video which was playing in a loop on large screens inside the museum.  
The intention was to have users perform freely tasks, without any help, then have them assess the usability of the applications. We did not impose a time limit for the interaction, nor a rigid set of tasks to be performed. At this point, we were interested in assessing the perceived usefulness of the system and the willingness of casual/regular museum visitors to use gesture-based VR technology.  
Users were presented with a questionnaire at the end of their visit. The survey was created by the software development team and refined after being analyzed by museum employees at the Callatis Museum in Mangalia.  
The items in the questionnaire were organized in three sections: first, demographic information is collected as variables on nominal scale, then, two sections dedicated to measuring usability, respectively, the degree of utility of the application and satisfaction of the users, composed of items measured on a five-point Likert scale, ranging from _Strongly disagree_ (1) to _Strongly agree_ (5) with the affirmation in the item. During the study, the survey was provided for users on paper, and it was also made available for museum visitors online, using the Google Forms platform (Google Forms platform, [[https://www.google.com/forms/about/](https://www.google.com/forms/about/)]([https://www.google.com/forms/about/](https://www.google.com/forms/about/)) (accessed on 10 February 2022)), which provides a user-friendly interface and easy access to collected survey data. The survey is presented as Supplementary Materials for this paper.  
Figure 13: ISVS-B’s state machine describing internal states of ISVS-B according to the user’s right-hand location and gesture.
## 3 Extraction of Echo Envelope Fluctuation Features
### Experiment Study of Benchmark Model and Rock  
#### 3.2.1 Experiment Configuration  
For comparison with the simulation results, broadband acoustic scattering testing was conducted with the scaled benchmark model and with rock. Figure 5 shows the experiment configuration: Figure 5a is the schematic diagram of the experimental layout, Figure 5b is the photo of the sea experiment, and Figure 5c is the photo of the rock that was tested.  
The target and monostatic sonar were mounted on the experimental ship, and the target was mounted by two thin ropes on the rotator, by which the azimuth was changed from 0 degree to 180 degrees. The depth of the target and monostatic sonar was 5 m deep in the water, and the range between the target and the sonar was 21 m, which satisfies the testing requirements for far field acoustic scattering. The transmitting beam was maintained on the benchmark to diminish interference from reverberation.  
A continuous wave (CW) signal and a linear frequency modulation wave signal were te ted; the parameters were as listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Signal Form** & **Frequency** & **Pulse Length** & **Target** \\ \hline CW & 30 kHz, 60 kHz & 1 ms, 3 ms, 10 ms & Benchmark model \\ LFM & 20–40 kHz, 40–80 kHz & 1 ms, 3 ms, 10 ms & Benchmark model \\  & 43–47 kHz & 1 ms & Rock \\  & 71–74 kHz & 1 ms & Rock \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the incident signal.  
Figure 5: Configuration of the benchmark sea experiment. (**a**) The schematic diagram of the experimental layout; (**b**) photo of the sea experiment; (**c**) photo of the rock that was tested.
# Asynchronous Flipped Grant-Free SCMA for Satellite-Based Internet of Things Communication Networks
## 6 Conclusions  
A novel uplink grant-free asynchronous SCMA scheme dubbed AF-SCMA for satellite-based IoT networks has been introduced together with its key characteristics in this paper. With the concept of flipped diversity, a packet is transmitted with its flipped replica together. By removing the interference chunks in a zigzag pattern, a specific packet can probably be decoded successfully, although it collides with other packets encoded with the same codebooks. The theoretical performance of AF-SCMA has been analyzed and derived in an iteration way in detail. Simulation results show that the analytical performance of AF-SCMA is well matched with that of simulation in terms of throughput and PLR. For the target \(PLR=10^{-3}\), AF-SCMA can achieve a higher throughput compared with S-SCMA and A-SCMA (without flipped replica) and it can benefit from the power unbalance, which can further boost the throughput of AF-SCMA. Moreover, a larger codebook set can also improve the performance of AF-SCMA. However, SCMA codebook design is a tough and complicated issue which is subject to future research. Finally, the design of the AF-SCMA pilot sequence is investigated. According to the simulation results, ZC sequences can operate and can be detected well as a pilot sequence with a reasonable length. Above all, the proposed AF-SCMA can reduce the negative effects of SCMA codebook collisions and provides remarkable performance with acceptable \(\left[E_{b}/N_{0}\right]_{D}\), which is applicable to the future satellite-based IoT networks, especially for a massive machine-type communication scenario.  
Figure 6: Pilot sequence detection performance for AF-SCMA (4, 6) with different ZC sequence length of \(N_{ZC}=67,127\) and \(257\), \(\left[E_{b}/N_{0}\right]_{D}=13\) dB, without power unbalance and frequency offset.  
**Author Contributions:** [PERSON] proposed the basic framework of the research scenario. [PERSON] put forward the innovative idea of AF-SCMA and modeled the problem. In addition, [PERSON] did the simulations and wrote the paper. [PERSON] gave some suggestions on the mathematical model and formula derivation.  
**Funding:** This research was supported by the National Nature Science Foundation of China (NSFC) under the Grant No. 61601045 and 61372111.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Mathematical Model
### Synchronous Generator  
A three-phase AC synchronous generator is used in this hydraulic wind turbine to output electrical energy. According to Park transformation and [PERSON]'s law, voltage balance equations of the rotor and stator in dq0 coordinates are computed as follow [28]:  
\[U_{sd}=\frac{d\psi_{sd}}{dt}-\omega_{e}\psi_{sq}-R_{a}i_{sd} \tag{20}\]  
\[U_{sq}=\frac{d\psi_{sq}}{dt}+\omega_{e}\psi_{sd}-R_{a}i_{sq} \tag{21}\]  
\[0=\frac{d\psi_{dd}}{dt}+R_{dd}i_{dd} \tag{22}\]\[0=\frac{d\psi_{dq}}{dt}+R_{dq}i_{dq} \tag{23}\]  
where \(U_{sd}\) and \(i_{sd}\) represent the stator voltage and current on the Park's \(d\) axis. \(U_{sq}\) and \(i_{sq}\) represent the stator voltage and current on the Park's \(q\) axis. \(R_{a}\), \(R_{dd}\), and \(R_{dq}\) represent the stator winding resistance and the damper winding's resistance. \(\psi_{sd}\) and \(\psi_{sq}\) are the stator windings flux linkage. \(\psi_{dd}\) and \(\psi_{dq}\) are the damper windings flux linkage. \(i_{dd}\) and \(i_{dq}\) are the stator currents of the damper windings.  
The flux linkage equation of all winding is constituted as follows. The electromagnetic torque \(\Gamma\) is computed by Equation (29):  
\[\psi_{sd}=L_{sd}i_{sd}+\sqrt{\frac{3}{2}}M_{s}f\dot{i}_{fl}+\sqrt{\frac{3}{2}}M _{sd}i_{dd} \tag{24}\]  
\[\psi_{sq}=L_{sq}i_{sq}+\sqrt{\frac{3}{2}}M_{sq}i_{dq} \tag{25}\]  
\[\psi_{fl}=L_{f}i_{fl}+\sqrt{\frac{3}{2}}M_{sf}i_{sd}+M_{fd}i_{dd} \tag{26}\]  
\[\psi_{dd}=L_{dd}i_{dd}+\sqrt{\frac{3}{2}}M_{sd}i_{sd}+M_{fd}i_{fl} \tag{27}\]  
\[\psi_{dq}=L_{dq}i_{dq}+\sqrt{\frac{3}{2}}M_{sq}i_{sq} \tag{28}\]  
\[\Gamma=p(\psi_{sd}i_{sq}-\psi_{sq}i_{sd}) \tag{29}\]  
where \(M_{gf}\), \(M_{sd}\), \(M_{sq}\), and \(M_{fd}\) represent the mutual inductance coefficient between the two windings. \(L_{sd}\), \(L_{sq}\), \(L_{f}\), \(L_{dd}\), and \(L_{dq}\) are the inductance coefficient of all windings. \(p\) represents the pole pairs.
## 4 Discussion
### The Change of Crown CO\({}_{2}\) Exchange with Environmental Variables  
There are several meteorological factors that affect _GPP_, \(R\), and _Ne_, the most essential of them are \(T_{air}\), _PAR_, and _VPD_. Temperature mainly affects the activities of various enzymes in plant's photosynthesis, especially a series of enzymatic reactions in the dark reaction of photosynthesis [62]. The increasing _VPD_ signifies a growing moisture deficit in the atmosphere that results in the increased transpiration by plants. Transpiration and photosynthesis are two closely coupled processes regulated by stomata [11]. Affected by \(T_{air}\), _PAR_, and _VPD_, the _GPP_ and _Ne_ in CK treatment showed double peak phenomenon in each month, while that in LN and MN treatments only in August and September, and HN in June and August showed that phenomenon (Figure 4a). From Figure 9a-e, we find the carbon uptake decreased with increasing _VPD_ during the growing season, probably because _VPD_ beyond a threshold must have decreased the activity of photosynthesis-related enzymes and induced restricted opening of stomata for the conservation of moisture, [63]. However, the absolute value of \(Ne_{max}\) in nitrogen addition treatments were higher than that of the controls (Figure 9f). These phenomena indicated that nitrogen addition slows down the phenomenon of photosynthesis midday depression.  
We also found crown carbon fixation increased in the early and middle of the growing season (Figure 5), which probably because that temperature was suitable for plant growth, thereby increasing photosynthesis, maintaining and building their own material increased, so did leaf, branch, and trunk respiration, resulting in an increase in overall crown respiration, which reached a peak at the end of July and early August. At the end of the growing season, photosynthesis of trees decreased when the temperature dropped, and the physiological metabolic activities could only maintain the survival needs, while the branch and trunk respiration also decreased correspondingly (Figure 5). In addition, the reduction of the demand for ATP and carbon skeleton for leaf growth and photosynthetic photosynthetic synthesis with seasonal vegetation senescence occurred, ultimately leading to reducing the respiration rate. Due to the value of _GPP_ declining sharply and being less than \(R\), the value of _Ne_ changed from negative value to positive value, indicating the crown shifted from carbon sink to carbon source under control, low, and medium nitrogen treatments at the end of September (25 September). However, the time when crown shifted from carbon sink to carbon source under HN occurred 3 days later (28 September), probably because the growing season cumulative _GPP_ was highest under HN (Figure 5).
# Efficient Tensor Sensing for RF Tomographic Imaging on GPUs
## 6 Results and Analysis  
### Running Time of Synthetic Data  
Figure 4(a) shows that the running time of the CPU tensor sensing and two GPU tensor sensing implementations (unoptimized and optimized ones) for \(\mathcal{X}\) of size \(n\times n\times 6\) of rank one, where \(n\) varies from 40-120 at a step of 20. The sampling rate was set to 20%, and both CPU and GPU tensor sensing performed five iterations for completion. The detailed time value is listed in Table 2. While \(M=N_{1}\times N_{2}\times N_{3}\times\) sampling rate and \(\mathcal{A}\in\mathbb{R}^{M\times N_{1}N_{2}N_{3}}\), the scale of the main operation matrix \(\mathcal{A}\) increased at a rate of four times as the increase rate of \(n\).  
We can see that the running time of the CPU tensor sensing was polynomial with the increase of \(n\), while the running time of the GPU tensor sensing was linearly growing. The unoptimized and optimized GPU tensor sensing achieved an average of 4.24\(\times\) and 44.79\(\times\) and up to 9.77\(\times\) and 84.70\(\times\) speedups, respectively. This illustrates the effectiveness of the optimization methods proposed in Section 4.2. When the tensor size was small, the data transfer occupied a major portion of the entire execution time of the unoptimized GPU tensor sensing. As a result, its performance was even lower than the CPU tensor sensing.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 4 Experiments and Results
### Effect of Text Transcription Errors on Performances  
In practice, audio transcriptions typically contain errors. For instance, words can contain typos, i.e., one or several characters might have been added, removed, or altered, or entire words could be added or removed. In this section,, we investigate the effect of transcription errors on the alignment accuracy obtained for Chapter 10 and Playlist 50.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline \multirow{2}{*}{**Chapters**} & **Duration** & \multirow{2}{*}{**\# Words**} & \multirow{2}{*}{**\# Pivots**} & \multirow{2}{*}{**MAAE [ms]**} & \multirow{2}{*}{**Q50\% [ms]**} & \multirow{2}{*}{**Q95\% [ms]**} & \multirow{2}{*}{**Q99\% [ms]**} & \multirow{2}{*}{**PCO [\%]**} \\  & **(hh:mm:ss)** & & & & & & & & \\ \hline \multirow{2}{*}{10 (baseline)} & 00:20:26 & 2672 & 0 & 51 & 46 & 118 & 145 & 100 \\
7-10 & 01:23:25 & 11,322 & 4 & 52 & 46 & 117 & 147 & 100 \\
10–13 & 01:17:36 & 10,762 & 4 & 51 & 46 & 117 & 147 & 100 \\
7–13 & 02:20:35 & 19,411 & 6 & 52 & 46 & 118 & 147 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Absolute alignment error on Chapter 10 for alignments of several chapters.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & & & & & & & \\ \hline \multirow{2}{*}{**Tracks**} & **Duration** & \multirow{2}{*}{**\# Words**} & \multirow{2}{*}{**\# Pivots**} & \multirow{2}{*}{**MAAE [ms]**} & \multirow{2}{*}{**Q50\% [ms]**} & \multirow{2}{*}{**Q95\% [ms]**} & \multirow{2}{*}{**Q99\% [ms]**} & \multirow{2}{*}{**PCO [\%]**} \\  & **(hh:mm:ss)** & & & & & & & \\ \hline \multirow{2}{*}{P50 (baseline)} & 02:51:43 & 18,904 & 8 & 120 & 40 & 300 & 1221 & 94.9 \\
50 + P50 & 05:34:32 & 34,990 & 12 & 121 & 40 & 302 & 1223 & 94.9 \\
50 + 50 & 05:29:53 & 34,261 & 12 & 121 & 40 & 302 & 1222 & 94.8 \\
50 + P50 + 50 & 08:12:42 & 51,157 & 16 & 122 & 42 & 302 & 1223 & 94.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Absolute alignment error on Playlist 50 (P50) for alignments with 50 to 100 extra songs.
## 3 Test Results and Analysis
### Analysis of Measured Curves
#### 3.3.2 Load-Displacement Curves of SRRAC Columns  
From Figure 9, it can be seen that with the increase of temperature, the load-displacement curve of SRRAC columns tends to be flat, and the peak point gradually decreases and shifts to the right. Loading process of some specimens includes elastic stage, stiffness strengthening stage, elastic-plastic stage, descending stage, and residual stage. The characteristics of each stage are as follows:  
1. Elastic stage: Both the section steel and the concrete are in the elastic deformation range, and the deformation of the two is coordinated, and no crack is generated on the concrete surface.
2. Elastic-plastic stage: After the load exceeds the elastic limit, due to the occurrence and continuous development of cracks on the concrete surface, the section steel and the longitudinal steel bar gradually yield, and the load-displacement changes nonlinearly. At this stage, the bond crack begins to appear, that is, the bond and slip of the steel and concrete are more obvious.
3. Stiffness strengthening section: For specimens with a temperature greater than 600 \({}^{\circ}\)C, the load-displacement curves of specimens will show a significant increase in curvature before the elastic limit is reached, that is, the stiffness strengthening stage. This is because after the higher temperature, the free water in the internal void of the concrete evaporates, making the concrete loose. During the loading process, as the load increases, the loose concrete is gradually compacted, thus the stiffness of the specimens is improved.
4. Descending section: After the limit point is exceeded, the bearing capacity of the specimens is obviously reduced due to the gradual withdrawal of the protective layer concrete. The decreasing extent is related to the temperature. The higher the temperature is, the smaller the decreasing amplitude is.
5. Residual stage: After entering this stage, with the increase of displacement, the decrease of load tends to be gentle, the concrete at longitudinal reinforcement and protective layer has basically withdrawn from work, and the residual strength is provided by section steel and concrete in core area.  
Figure 9: Load-displacement curves of SRRAC columns. (**a**) r = 0%; (**b**) r = 30%; (**c**) r = 70%; (**d**) r = 100%.
## 3 Results and Discussion
### Production of an Amazake-like Beverage from Bacillus amyloliquefaciens
The rate of saccharification from starch hydrolysis is governed both by the enzymatic activity and initial concentration of the starch substrate, the latter of which was equal in both of our amazakes [14]. The higher RS content in AAO indicates that the saccharification efficiency is higher for _A. oryzae_. However, given that sugar metabolism involves many pathways with complex interactions [32], it is challenging to know the exact reasons for the changes in the RS values. Previous studies have found that the cooperation between enzymes such as amyloid enzymes and reductases allow microorganisms, particularly _A. oryzae_, to effectively metabolize carbohydrates [22]. Further investigations are necessary to determine the specific sugars produced during both saccharification processes. Our findings emphasize _A. oryzae_ as the optimal microbial inoculum source for maximal saccharification efficiency under these conditions (i.e., same temperature and ratio of ingredients) (\(p\) < 0.01). However, it is noteworthy that saccharification with _B. amyloliquefaciens_ remains feasible, although with potentially different kinetics. Figure 2: (**A**) Reducing sugar content, (**B**) pH, (**C**) \(\alpha\)-amylase activity, and (**D**) protease activity values measured during fermentation for amazake samples produced with _A. oryzae_ at 55 \({}^{\circ}\)C, _B. amyloliquefaciens_ at 55 \({}^{\circ}\)C, and _B. amyloliquefaciens_ at 70 \({}^{\circ}\)C. Three fermentation replicates were performed for each condition. Results are presented as means \(\pm\) standard deviations. Unlike _A. oryzae, B. amyloliquefaciens_ secretes thermal-resistant amylases [14]. Hence, we conducted additional saccharification experiments with ABA at 70 \({}^{\circ}\)C, and were able to show a significantly higher RS content compared to the fermentation at 55 \({}^{\circ}\)C (\(p\) < 0.05). Similarly to the 55 \({}^{\circ}\)C ABA, the ABA produced at 70 \({}^{\circ}\)C exhibited rapid RS production within the initial 8 h, followed by a plateau between 8 and 16 h. These results suggest that higher temperatures enhance the saccharification process in ABA, potentially due to increased amylase activity or reduced bacterial proliferation by _B. amyloliquefaciens_[33]. Further investigations employing varying saccharification temperatures are necessary to validate these findings. Amazake production at higher temperatures is an exciting prospect because it could prevent the proliferation of food-borne pathogens associated with lower temperatures. It opens avenues for secondary fermentations utilizing the fermented sugars produced with _B. amyloliquefaciens_.