esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The PPO/TAIC-based polymer composites exhibited excellent dielectric properties which are suitable for high-frequency applications. In this work, the composition-process -structure-property relationship of PPO/TAIC-based polymer composites with ultralow dielectric loss was studied. Special attention was focused on the effects of peroxide initiator concentration upon the dielectric properties. For prepreg samples, the plasticizing effect of TAIC played a key role which caused a decrease in the dielectric constant with the initiator content, while it did not affect dielectric loss much. When the samples were cured at high temperatures, more mechanisms affecting polarizability developed with the advancement of the reaction, including some factors that contributed to lower polarizability, such as increased crosslink density, free volume and quantities of bulky pendant groups, and reduced plasticizing effect, etc., as well as some factors that promoted polarizability such as enhanced polarity, diminished molecular symmetry, branching, and hydroxyl impurities, etc. As a result of competition, the latter mechanisms dominated the trends and both the dielectric constant and dielectric loss of the cured samples increased significantly with higher initiator concentration compared to the corresponding prepreg samples.  
Conceptualization, K.W., Z.L., Q.L. and X.Y.; methodology, X.Y.; validation, K.W. and Z.L.; formal analysis, X.Y., Z.F., D.L., Y.M. and C.L.; investigation, X.Y. and Z.F.; data curation, X.Y.; writing--original draft preparation, X.Y.; writing--review and editing, K.W.; visualization, X.Y.; supervision, K.W. and Z.L.; project administration, K.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Department of Science and Technology of Guangdong Province (Project No.2020B010175001).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors sincerely acknowledge the Department of Science and Technology of Guangdong Province (Project No. 2020B010175001) for the financial support. The authors would like to thank [PERSON] from Sun Yat-Sen University for assistance with positron annihilation lifetime spectroscopy measurement.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline
**Prepreg Sample** & **Residual Rate of** \\  & **–C=C– in TAIC** \\ \hline P/T-0.25-L & 56.06\% \\ P/T-0.75-L & 52.62\% \\ P/T-1.50-L & 45.94\% \\ \hline \end{tabular}
\end{table}
Table 8: Residual rate of –C=C– in TAIC of different prepreg samples.
## 3 Preliminaries, Models, and Definitions
### Definitions  
#### 3.3.1 Network Coverage  
An underwater 3D space (target coverage area) is usually represented by a cuboid or cube. This space is divided into a number of small cubic grids whose sides have the length \(w\)[18]. The center of each cubic grid called the grid point, which is regarded as the representative of the grid. Each grid point has its own coordinates. A typical underwater 3D space is illustrated in Figure 1.  
Network coverage, which reflects the degree an underwater sensor network covers a monitoring or target area, is a primary criterion parameter in the evaluation of a node-deployment algorithm. This parameter is defined as the ratio of the number of grid points covered by UWSNs to the total number of grid points in the target area. Network coverage is denoted by \(r_{cov}\), whose formula is as follows:  
\[r_{\text{cov}}=\frac{N_{\text{cov}}}{N_{total}} \tag{7}\]  
where \(N_{cov}\) is the number of grid points covered by the active nodes and \(N_{total}\) is the total number of grid points in the target area.
## 3 Results  
### Material Characterization  
#### 3.1.1 Zeta Potential  
The zeta potential assessment performed on the test _E. coli_ strain UCFL-94, raw, and activated hydrochar used in the column experiments showed negative values in the pH range of 6.6-6.8 (Figure 1). The zeta potential of raw hydrochar was slightly increased by KOH activation from \(-15\) mV to \(-13\) mV. Also, a negative zeta potential of \(-11\) mV was measured for the _E. coli_ UCFL-94 suspension.
## 2 Materials and Methods
### Estimation of Solar and Extraterrestrial Irradiances on a Horizontal Surface  
The horizontal beam irradiance (\(B\)) can be calculated from the normal beam irradiance (\(B_{n}\)) from the following equation [26]:  
\[B=B_{n}*\cos\theta_{z} \tag{1}\]  
where \(\theta_{z}\) is the solar zenith angle (SZA) which is given by:  
\[\cos\theta_{z}=\cos\phi*\cos\delta*\cos\omega+\sin\phi*\sin\delta \tag{2}\]  
where, \(\phi\) is the latitude of the location, \(\delta\) is the solar declination angle (deg) and \(\omega\) is the hour angle (deg). The solar declination is estimated by the following equation:  
\[\delta=23.45*\sin[360*(284+d_{n})/365] \tag{3}\]  
where \(d_{n}\) is the day number of the year. Then, the hourly global irradiance (\(G\)) is the sum of diffuse and direct horizontal irradiance:  
\[G=D+B \tag{4}\]  
The irradiance falling on a plain at normal incidence at the top of the atmosphere (\(G_{0n}\)) can be estimated from the following equation:  
\[G_{0n}=G_{sc}*\left[1+0.033*\cos(360*d_{n}/365)\right]\left(\mathrm{W/m^{2}}\right) \tag{5}\]  
where \(G_{sc}\) is the solar constant (1367 \(\mathrm{W/m^{2}}\)). According to [PERSON] (2020) [5] the respective solar constant for PAR is 534 \(\mathrm{W/m^{2}}\).  
Then, the irradiance on a horizontal plain at the top of the atmosphere can be estimated by the following equation:  
\[G_{0}=G_{0n}*\cos\theta_{z}=G_{0n}*\left(\cos\phi*\cos\delta*\cos\omega+\sin \phi*\sin\delta\right)\left(\mathrm{W/m^{2}}\right) \tag{6}\]  
The daily total global irradiation which is obtained by a horizontal plain at the top of the atmosphere (\(G_{0d}\)) is given by the following equation:  
\[G_{0d}=\left(24*3.6/\pi\right)*G_{0n}*\left[\left(\cos\phi*\cos\delta*\sin \omega_{s}+\left(\pi*\omega_{s}/180\right)*\sin\phi*\sin\delta\right)\right] \left(\mathrm{kJ/m^{2}}\right) \tag{7}\]  
where \(\omega_{s}\) is the sunset hour angle and is given by:  
\[\omega_{s}=\cos^{-1}(-\tan\phi*\tan\delta) \tag{8}\]  
The daily sums of global and horizontal beam radiation are obtained from the hourly values. Furthermore, the astronomical day length (\(S_{0d}\)) which is the computed time during which the center of the solar disk is above an altitude of zero degrees (without allowance for atmospheric refraction) is given by:  
\[S_{0d}=(1/7.5)*\cos^{-1}(-\tan\phi*\tan\delta)\left(\mathrm{h}\right) \tag{9}\]  
The clear sky irradiance was estimated using the [PERSON] (1945) [27] model which is a function of the solar zenith angle. This model was tested by [PERSON] et al. (2007) [28] in Israel showing high performance. The equation involved in the said model is shown below:  
\[G_{c}=1098*\cos\theta_{z}*\exp(-0.057/\cos\theta_{z}) \tag{10}\]  
Almost a similar equation was obtained using the maximum values of global irradiances of the study station:  
\[G_{c}=2170.5*\left(\cos\theta_{z}\right)^{-0.175}*\exp(-0.578/\cos\theta_{z}) \tag{11}\]
## 2 Materials and Methods
### Transcript Profiling
#### 2.8.3 Quantitative Real-Time (q-)RT-PCR  
An equal amount of RNA was reverse transcribed into cDNA using the PrimeScript(tm) RT Reagent Kit, by following the manufacturer's protocol (Takara, Shiga, Japan). Prior to q-RT-PCR transcript profiling, several previously described housekeeping genes were tested for their stability in the designed experiment ([PERSON], 2018). Optimal reference genes were selected using geNorm software ([PERSON], 2018). q-RT-PCR primers for reference genes and target genes were designed using Primer Express software (Applied Biosystems, Foster City, CA, USA). For all investigated tissues and conditions, _Scg_-Act and _Scg_-Ef1a were selected by the geNorm software as having the most stable expression and were used as reference genes throughout this study. Primer sets were validated by designing relative standard curves with a serial ten-fold dilution of a calibrator cDNA sample. Efficiency of q-RT-PCR and correlation coefficient (R2) were measured for each primer pair. Primers for q-RT-PCR are given in Table S2. All PCR reactions were performed in duplicate in 96-well plates on a StepOne System (ABI Prism, Applied Biosystems, Foster City, CA, USA). Each reaction contained 5 \(\upmu\)L Fast Sybr Green, 0.5 \(\upmu\)L of Forward and Reverse primers (10 \(\upmu\)M), and 4 \(\upmu\)L of cDNA. For all q-RT-PCR reactions, the following thermal cycling profile was used: 50 \({}^{\circ}\)C for 2 min, 95 \({}^{\circ}\)C for 10 min, followed by 40 cycles of 95 \({}^{\circ}\)C for 15 s and 60 \({}^{\circ}\)C for 60 s. Finally, a melting curve analysis was performed to check for primer dimers. For all transcripts, only a single melting peak was found during the dissociation protocol. Additionally, PCR products were run on a 1.2% agarose gel containing GelRed(tm) (Biotium). After electrophoresis only a single band was observed, which was cloned (TOPO(r) TA cloning kit for sequencing, Invitrogen) and sequenced (Sanger sequencing, LGC Genomics, Berlin, Germany) to confirm target specificity. All q-RT-PCR results were normalized to the transcript levels of the selected reference genes and calculated relative to the transcript level in a calibrator sample according to the comparative Ct method ([PERSON], 2018). GraphPad Prism 5 (GraphPad Software, San Diego, CA, USA) was used to test the statistical significance of differences in gene expression levels.
## 3 Result
### Spatial and Temporal Expression Analysis of Terpene-Related Genes  
The spatial and temporal expression analysis of terpene-related genes, including 1-deoxy-D-xylulose 5-phosphate reductioisomerase (_DXR_), 1-deoxy-d-xylulose-5-phosphate synthase (_DXS_), 4-hydroxy-3-methylbut-2-enyl-diphosphate reductase (_HDR_), geranyl diphosphate synthase (_GPPS_), (3S)-linalool/(E)-nerolidol synthase (_CSLinNer_) and carotenoid cleavage dioxygenase (_CCD_), was determined by qRT-PCR (Figure 5). By comparing _DXR_ and _CSLinNer_ expressions over the ripening period, we found that the expression levels of _CSLinNer_ in four treatments were completely consistent with _DXR_ in every period. For example, _DXR_ expression in the F group was the lowest of the four treatments at 56 DAA, which was also true for _CSLinNer_. _CCD_ is the key gene of carotenoid cleavage, leading to the production of several aroma-active norisoprenoids [46]. However, we found that there was no obvious expression trend for _CCD_ during the ripening period in any of the four treatment groups.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Treatments** & **Aldehydes** & **Acids** & **Esters** & **Alcohols** & **Terpenses** & **Total** \\ \hline CK & 149.9252 & 4.4743 & 54.7845 & 31.4012 & 26.6212 & 267.2065 \\ GF & 209.5317 & 4.8707 & 56.2325 & 42.3891 & 29.6508 & 342.6749 \\ G & 176.4324 & 2.4130 & 46.0202 & 26.0214 & 20.5888 & 271.4758 \\ F & 143.2634 & 4.4158 & 59.4534 & 49.9531 & 21.0618 & 278.1475 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Total concentrations (\(\mu\)g/L) of each aroma compound in grapes at harvest from plants subject to the four different treatments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Treatments** & **Aldehydes** & **Acids** & **Esters** & **Alcohols** & **Terpenses** & **Total** \\ \hline CK & 6 & 3 & 8 & 10 & 8 & 35 \\ GF & 6 & 3 & 10 & 10 & 11 & 40 \\ G & 6 & 2 & 7 & 9 & 9 & 33 \\ F & 6 & 3 & 8 & 10 & 8 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The effect of the different treatments on the number of volatile aroma compounds in the ‘Hanxiangmi’ table grape at harvest.  
By [PERSON]'s correlation analysis, we found that only the expression level of _VvDXS_ was significantly correlated with linalool and terpene concentrations in four treatments throughout the entire ripening period (Table 5), which indicated that _VvDXS_ may play important roles in terpene biosynthesis. Interestingly, the expression levels of _DXR_, _GPPS_ and _CCD_ in CK were higher than that in G, F and GF treatments at harvest (77 DAA), which indicated that girdling and foliar fertilization with K inhibited the expression of these genes.
# Reduced Complexity Sequential Digital Predistortion Technique for 5G Applications
## 4 Complexity Reduction through Coarse Delay Alignment  
In order to further reduce the computational complexity associated with the identification of the memory polynomial coefficients, coarse delay alignment algorithm was used to time-align the input and output waveforms prior to the identification of the memory polynomial predistortion function. The main reason is that fine-delay alignment is a computationally extensive process. In fact, delay alignment with a resolution \(\frac{T_{s}^{2}}{R}\) where \(T_{s}\) is the sampling period of the two signals to be time-aligned typically requires up-sampling the signals by a factor \(R\), performing delay estimation and alignment often using cross-correlation technique, and then down-sampling the waveforms by a factor of \(R\) back to their original sampling rate. Therefore, using a delay resolution equal to the signal's sampling rate \((R=1)\) will eliminate the need for the signal up-sampling and down-sampling processes in the delay estimation and alignment and hence reduce the computational complexity associated with this process.  
For the experimental validation of the coarse delay alignment, the identification of the LUT sub-function was not modified, and a fine delay alignment was used in that step. However, a coarse delay alignment algorithm was adopted for the MP DPD identification.  
To assess the impact of the delay misalignment caused by the limited delay resolution of the coarse alignment, three delay resolutions were considered. These are 1 ns, 5 ns and 10 ns, which correspond to \(0.1T_{s}\), \(0.5T_{s}\) and \(T_{s}\), where \(T_{s}\) is the sampling period of the 100 MSA/s input signal. Moreover, a reference memory polynomial DPD derived from accurate delay estimation and alignment was used as a benchmark.  
The effects of the coarse delay alignment are depicted in the plots of Figure 11. For conciseness, this figure only includes the results corresponding to 50 MSA/s and 25 MSA/s rates in the feedback path. Comparable results have been obtained for the 40 MSA/s and 30 MSA/s cases. The results of Figure 11 show that the impact of limited delay resolution is slightly more pronounced for the signal acquisition performed at 25 MSA/s than the one performed at 50 MSA/s. Most importantly, these results clearly demonstrate that a delay resolution of up to 10 ns, which corresponds to one sample, does not have a significant impact on the sequential DPD performance. Hence, delay estimation and alignment for the MP DPD identification can be performed with low complexity algorithms that do not require sub-sample resolution without compromising the performance of the S-DPD.  
Figure 10: Spectra at the output of the amplifier linearized using the sequential DPD for various feedback sampling rates.
# Lip2 Speech: Lightweight Multi-Speaker Speech Reconstruction with Gabor Features
## 2 Literature Review
### Reconstructed Speech with Multiple Speakers  
Regarding speaker-dependent models, [PERSON] et al. [26] introduced an exemplar-based strategy for multi-speaker reconstruction. They used a CNN to extract high-level acoustic features from visual frames, aiding the estimation of target spectrograms through an audio dictionary. [PERSON] et al. [23] proposed a GAN for directly estimating speech signals from mouth-focused video frames. While this enabled intelligible speech synthesis in a speaker-independent context, the PESQ score reached only 1.24 on unseen speakers with the GRID dataset. This is arguably due to the model's generation of raw waveforms, for which appropriate loss functions were challenging to ascertain.  
[PERSON] et al. [18] introduced a video-to-speech architecture to enhance previous multi-speaker speech reconstruction methods. They integrated additional speaker-related input, including discrete identity or speaker embeddings, to separate linguistic content and speaker identity. Adversarial losses were introduced to segregate identity from video embeddings. Their model used both video and a specified identity as input, producing utterances in the chosen identity while preserving content intelligibility (WER 42.7%) and showcasing effective speaker control (EER 7.3%) across synthesised audio from videos of 14 diverse speakers. Similarly, [PERSON] et al. [19] proposed a multi-speaker face-to-speech waveform generation model, employing a GAN alongside linguistic and speaker characteristic features. Their approach enabled the direct conversion of face images into speech waveforms through end-to-end training. Linguistic features were extracted from lip movements via a lip-reading model, and speaker characteristics were predicted by face encoders through cross-modal learning. This model achieved MOS scores of 3.74 and 3.87 for seen and unseen data with four speakers from the GRID dataset. [PERSON] et al. [20] proposed a multi-speaker VTS system based on cross-modal knowledge transfer from voice conversion (VC). Utilising vector quantization with contrastive predictive coding (VQCPC) for VC's content encoding, discrete phoneme-like acoustic units were derived. Their systemachieved PESQ scores of 1.816 and 1.417 on seen and unseen speaker tests with GRID speakers. While they achieved multi-speaker speech reconstruction to a certain extent, these studies all necessitate additional information such as speech or output text supplementary information to reconstruct speech from multiple speakers. In the absence of explicit speaker details, distinguishing and reconstructing speech from different speakers becomes challenging for these models. Moreover, their intricate structures hinder lightweight deployment and implementation on portable devices.
## 3 Results
### Surface Roughness
Figure 2A represents mean Ra values of four groups according to the laser types irradiated. The control group was the specimen without laser irradiation, and its Ra value was measured to be \(0.177\pm 0.034\)\(\upmu\)m. Mean Ra values of the experimental groups after laser irradiation were measured to be \(0.178\pm 0.023\)\(\upmu\)m for the K2 mobile group, \(0.208\pm 0.020\)\(\upmu\)m for the Epic \(10^{\text{TM}}\) group, and \(0.156\pm 0.020\)\(\upmu\)m for the Saeshin diode laser group, respectively. Mean Ra values were higher in the K2 and Epic \(10^{\text{TM}}\) groups and lower in the Saeshin diode laser group than that of the control group. However, the difference in mean Ra values was not statistically significant between any two groups. Figure 2B represents mean Ra values of four groups based on the output power. Mean Ra values were measured to be \(0.158\pm 0.021\)\(\upmu\)m for 1.0 W group, \(0.178\pm 0.021\)\(\upmu\)m for 2.0 W group, and \(0.205\pm 0.020\)\(\upmu\)m for 3.0 W group, respectively. In the 1.0 W group, mean Ra values were lower than those of the control group. In the 2.0 and 3.0 W laser groups, mean Ra values were higher than those of the control group. As the output values increased, the average Ra values also tended to increase. However, the difference in mean Ra values was not statistically significant between any two groups (\(p>0.05\)).
## 1 Introduction
Furthermore, [PERSON] et al. [49] used deep learning to classify ore and waste in a three-dimensional ore body model and to analyze the optimal pit limit. Deep learning models have also been developed to detect micro-seismic events [50], evaluate the road roof status [51], and quickly recognize mine water inrush to prevent underground safety incidents in the production stage [52]. Moreover, deep learning has been used to develop a truck fuel consumption prediction system [53], coal cutting pattern recognition system [54], automatic iron ore quality control system [55], and zinc ore recovery prediction system [56]. However, at present, no studies exist on methods for using deep learning to predict major production index values by learning the characteristics of truck haulage systems from the big data of underground mining sites. The aim of this study is to present a new method that uses a DNN model to predict the ore production and crusher utilization of an underground mine truck haulage system. An underground limestone mine equipped with an information & communications technology (ICT)-based mine safety management system was selected as the study area. Big data collected for 1 month in October 2018 were processed to create the DNN model training data. The numbers of hidden layers and hidden layer nodes of the DNN model were set to various values, and the coefficient of determination and mean absolute percentage error (MAPE) of the training and test data were analyzed to select the optimal DNN model. The completely trained DNN model was used to predict the ore production and crusher utilization for 10 days, starting on 1 November 2018, and the results were compared with the values observed on site.
# Hybrid Arctic Puffin Algorithm for Solving Design Optimization Problems
## 6 Results and Discussion
### JADE APO Histogram Analysis  
As illustrated in Figure 11, the histogram analysis of final fitness values for functions F1-F12 using the JADE dynamic Arctic Parameter Optimization (APO) algorithm reveals distinct distributions, showcasing the variability in optimization outcomes for each function. For F1, the fitness values are highly concentrated around 300, with a prominent probability peak, indicating consistent optimization performance. In contrast, F2 demonstrates a bimodal distribution with peaks near 400 and 410, reflecting variability in the optimization process. The fitness values for F3 exhibit a skewed distribution leaning toward 600, whereas F4 presents a more symmetric distribution centered around 810. The histogram for F5 demonstrates a narrow peak around 900, indicating highly consistent optimization results. F6 shows a predominant peak at 1800 with a few higher values, reflecting occasional deviations in the optimization outcome. F8 has a broad distribution with a peak around 2210, indicating variability in the final fitness values. The distribution for F9 is extremely narrow around 2530, similar to F5, reflecting consistent optimization results. Finally, F11 and F12 show broader distributions centered around 2600 and 2864 respectively, indicating some variability in the final fitness values.  
Figure 11: Histogram analysis of CEC2022 (F1–F12).