esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relationship between Various Driving Factors and NPP
#### 3.2.2 Relationship between Climate Driving Factors and NPP Change  
According to the results of the Pearson correlation analysis of NPP, NPP was positively correlated with annual average temperature (\(p<0.001\)) and annual precipitation (\(p<0.001\)), and it was negatively correlated with sunshine hours (\(p<0.001\)) in the north-central Yangtze River Delta (Figure 9). The NPP was negatively correlated with annual precipitation and positively correlated with sunshine hours in the southern Yangtze River Delta, which was partially positively and partially negatively correlated with annual average temperature.  
In particular, the average Pearson correlation coefficient between NPP and annual average temperature in the research region was \(-0.0037\), the area of the positively correlated area accounts for \(51.12\%\), the area of the negatively correlated area accounts for \(48.88\%\), and the ratio of positively and negatively correlated area was close to \(1\):\(1\) (Figure 9a). The average Pearson correlation coefficient between NPP and annual precipitation was \(0.18\), and the area of positively correlated areas accounted for \(78.88\%\) and \(21.12\%\) were negatively correlated areas, where annual precipitation generally contributed to the growth of NPP (Figure 9b). The average Pearson correlation coefficient between NPP and sunshine hours was \(-0.06\), with \(37.27\%\) of the area positively correlated and \(62.73\%\) of the area negatively correlated (Figure 9c). Overall, NPP in the research region showed a strong  
Figure 8: Relationship between elevation and slope classification and NPP.  
Figure 7: \(q\)-value of each driving factor. “**” represents passing the \(0.001\) significance test and “**” represents passing the \(0.05\) significance test. “TEM” represents the average annual temperature, “PRE” represents the total annual precipitation, “TSH” represents the total annual sunshine hours.  
positive correlation with annual precipitation, and annual precipitation contributed to an increase in NPP, except for the southern region of the study area.
## 3 Results  
### Preparation of Poly(VBC-co-DVB-co-STY)HIPEs  
Poly(VBC-co-DVB-co-STY)HIPEs were prepared with varied ratios of VBC, DVB, and STY monomers (Figure 1). Using free radical polymerisation initiated by AlBN, polyHIPE monoliths with three different crosslinking degrees were synthesized. Firstly, the influence of initial DVB-caused crosslinking, on the degree of hypercrosslinking, was evaluated. For this purpose, 2, 5 and 10 mol.% of DVB were used as initial crosslinking degree. Rather low degrees of initial crosslinking was used as it was expected that the rigidity of the polymer network would have a substantial diminishing effect on the post polymerisation hypercrosslinking process. Most examples from the literature describe a Friedel-Crafts type of hypercrosslinking performed on poly(4-vinylbenzyl chloride) with a low degree of crosslinking, up to 5 mol.% [4; 6; 45; 46], while much less is known on this type of hypercrosslinking of networks with higher degree of crosslinking. Furthermore, we were interested in how the concentration of the hypercrosslinking monomer, in our case 4-vinylbenzyl chloride, influences the hypercrosslinking process. As the process involves a reaction between the neighboring functional groups, it is expected that the distribution of functional groups within the polymer network would have a significant effect on the formation of crosslinks during the hypercrosslinking process. Therefore, the experimental design included variation of both initial crosslinking degree (DVB content) and \"dilution\" of functional groups i.e., the ratio of functional to \"diluting\" monomer (styrene was used for this purpose). It was assumed that all added DVB to the monomer mixture was crosslinking as the highest addition of DVB was 10 mol.%. Three series of experiments were performed, one for each initial crosslinking degree (Table 1). In addition to DVB, STY was added to each batch (from 5 to 65 mol.% of monomers, Table 1), and the rest was VBC. Chloromethyl functional groups in the monomeric system was diluted with the addition of STY to obtain different VBC contents at the same initial crosslinking degrees, which proved to be an important factor in further hypercrosslinking studies of synthesized polyHIPEs.  
The kinetic stability of the emulsion is extremely important for the formation of polyHIPE monoliths and was achieved with the addition of 20 wt.% of the surfactant Span 80 (HLB = 4.3) from the oleate family of surfactants, which is suitable for reducing the interfacial tension in w/o emulsions. With the addition of surfactant, stable HIPEs and polyHIPEs were obtained with typical interconnecting open cell polyHIPE morphology and average diameter of primary pores between 5 and 10 \(\upmu\)m.
## 3 CNQF Architecture
### Context Management and Adaptation Subsystem  
As shown in Figure 1, CNQF provides context-aware functionality through its Context Management and Adaptation Subsystem (CAS). CAS consists of distributed Context Acquisition Function blocks (CAF) instantiated in each access network. The CAFs are PDPs that execute context-aware or  
Figure 1: Converged Networks QoS Management Framework (CNQF) subsystems and operational entities in a converged network scenario.  
context-driven policies within the CNQF system. Each CAF elements has associated Adaptation Servers (ADs) which are function blocks that configure/reconfigure PEPs directly affected by context-driven policy decisions in the CAF. See [35] for further discussion on context-aware QoS control with CAS.
# Short-Term Prediction of COVID-19 Cases Using Machine Learning Models
## 5 Conclusions and Future Work
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Round** & **Method** & **RMSE** & **MAE** & **R2** & **Method** & **RMSE** & **MAE** & **R2** \\ \hline \multicolumn{8}{c}{**Confirmed Cases**} & \multicolumn{8}{c}{**Death Cases**} \\ \hline
1 st Round & Prophet & 3.5808 & 2.7740 & \(-0.5786\) & Prophet & 0.5837 & 0.4524 & \(-1.7821\) \\
2 nd Round & SVR & 24.8737 & 22.8440 & 0.8043 & SVR & 2.4589 & 2.2902 & 0.7660 \\
3 rd Round & PR & 55.1576 & 49.7568 & 0.9676 & Poly-MLP & 2.3675 & 2.1407 & 0.9394 \\
4 th Round & PR & 63.7025 & 57.3959 & 0.9928 & Poly-MLP & 5.8317 & 4.9376 & 0.9072 \\
5 th Round & Prophet & 36.9007 & 31.2751 & 0.9985 & Prophet & 1.3350 & 1.0758 & 0.9877 \\
6 th Round & Prophet & 10.9600 & 9.0326 & 0.9999 & Prophet & 0.7154 & 0.6148 & 0.9870 \\
7 th Round & Prophet & 38.8742 & 34.5744 & 0.9995 & Prophet & 1.5454 & 1.4078 & 0.9956 \\
8 th Round & Prophet & 51.7929 & 46.3280 & 0.9996 & Prophet & 1.4812 & 1.1691 & 0.9982 \\
9 th Round & Prophet & 117.2432 & 98.1062 & 0.9987 & Prophet & 0.8894 & 0.6813 & 0.9996 \\
10 th Round & Prophet & 155.8378 & 131.5769 & 0.9990 & Prophet & 2.2835 & 1.8957 & 0.9987 \\
31 st Round & Prophet & 6.22\(\times 10^{-11}\) & 4.99\(\times 10^{-11}\) & 1.0000 & Prophet & 9.27\(\times 10^{-12}\) & 8.71\(\times 10^{-12}\) & 1.0000 \\
32 nd Round & Prophet & 1.89\(\times 10^{-10}\) & 1.50\(\times 10^{-10}\) & 1.0000 & Prophet & 4.86\(\times 10^{-13}\) & 2.60\(\times 10^{-13}\) & 1.0000 \\
33 rd Round & [PERSON] & 7.93\(\times 10^{-11}\) & 7.48\(\times 10^{-11}\) & 1.0000 & [PERSON] & 4.86\(\times 10^{-13}\) & 2.60\(\times 10^{-13}\) & 1.0000 \\
34 th Round & [PERSON] & 3.11\(\times 10^{-09}\) & 2.63\(\times 10^{-09}\) & 1.0000 & [PERSON] & 1.33\(\times 10^{-12}\) & 1.17\(\times 10^{-12}\) & 1.0000 \\
35 th Round & [PERSON] & 4.40\(\times 10^{-11}\) & 3.33\(\times 10^{-11}\) & 1.0000 & [PERSON] & 8.21\(\times 10^{-11}\) & 7.04\(\times 10^{-11}\) & 1.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Verification of Experimental Results for Infected and Fatality Cases.
# Validation of Omega Subunit of RNA Polymerase as a Functional Entity
## 3 RNA Polymerase Machinery as a Whole  
The first indication of RNAP activity in the cell came from [PERSON] and [PERSON]'s mammalian enzyme system in 1959 [4]. A literal explosion of data within a short period of this discovery followed [5; 6; 7; 8; 9; 10]. Almost immediately, and simultaneously, several laboratories identified similar enzyme activities from various sources, which encompassed bacteria, plants, and animals. These initial investigations also revealed many salient features of RNAP, like dependence on all the four NTPs [11], the requirement of divalent cation [11], the role of DNA as a template in this reaction. The next decade of RNAP research was characterized by a flurry of activity, the main thrust of which was directed at evolving improved methodologies to obtain a pure form of the active protein. It is well beyond the scope of this article to explain all the techniques developed and improvements achieved, but the rapidity and vigor with which progress was made in this direction are well documented. One such study was the development of a protocol by [PERSON] and [PERSON] to purify bacterial RNA polymerase using Polymin P precipitation and DNA-cellulose chromatography [12]. It revealed that the RNA polymerase is a multi-subunit enzyme complex. The enzyme purified using the above-mentioned protocol showed the presence of 4 bands of varying mobilities in SDS-PAGE as well as on the urea-PAGE. These were designated as \(\beta^{\prime}\), \(\beta\), \(\alpha\), and \(\omega\) in the order of decreasing weight [13]. However, the \(\omega\) protein was obtained in such small amounts that it was not considered as a valid subunit of the bacterial RNA polymerase. It did not appear to be necessary for activity, though it was noted that the protein was associated with most enzyme preparations in a stoichiometric ratio.
# Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
## 2 Related Work
### Survival Prediction and Hazard Function  
Survival prediction is a branch of statistics that deals with time-to-event data, that is, data where the objective variable is the time until the event occurs. The effectiveness of the strategy has been confirmed across various domains, such as credit score analysis [19] and customer analysis [20]. In digital advertising, survival prediction helps to predict the conversion rate [21], user experiences after clicking on an ad [22], the winning bid price [23], and the failure rate of the reserve price [24].  
The main feature of the survival prediction task is to correctly model censored data such that no event occurs during the observation period. However, in the ad discontinuation task, such data are scarce because the operator manually determines the time of discontinuation (Although there are some cases where the serving volume is low, they are  
\begin{table}
\begin{tabular}{l l} \hline \hline
**The Process for Ad Creative Operations** & **Machine Learning Methods to Support the Operation Process** \\ \hline (1) Creating and submitting to the ad platform & [PERSON] et al. [10], [PERSON] et al. [1], [PERSON] et al. [11], [PERSON] et al. [2] \\ (2) Serving to users on the platform & [PERSON] et al. [12], [PERSON] et al. [14], [PERSON] et al. [3], [PERSON] et al. [5] \\ (3) Discontinuuing when the ads become less effective & **Our study** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of machine learning methods to support the process for ad creatives.  
very few). When such censoring data are scant or nonexistent, [PERSON] et al. [25] reported that the survival prediction task can be regarded as equivalent in terms of formulation to the classification task. Although it is debatable whether survival prediction is suitable for ad discontinuation tasks, we assume that the properties of ad discontinuation have some similarities with those of survival prediction tasks. Thus, we attempt to utilize the hazard function, which is an important idea in survival prediction.  
The hazard function can model the main variable of interest as the time until an event. This function has been used to represent the decaying survival probability, and deep learning-based survival prediction models have been developed by training a loss function based on the function [26; 27; 28]. We considered that the function is suitable for modeling the discontinuation probability of exponentially decaying ad creatives (described in detail in Section 3). Through training based on the hazard function with a deep learning model, we expect to develop a better prediction model that will help ad operators with discontinuation.
## 4 Discussion
### Intenannual Variation of Phenological Parameters Based on SIF and NDVI  
Vegetation phenology was estimated using SIF and NDVI products in Northeastern China, and interannual changes were compared (Figure 13). The results suggested that there were significant differences between the phenological dates estimated by SIF and MODIS NDVI. The SOS\({}_{\text{SIF}}\) showed a weak advanced trend, and the SOS\({}_{\text{NDVI}}\) showed a significant delayed trend. The EOS\({}_{\text{SIF}}\) showed a delay over time, which was consistent with the EOS\({}_{\text{NDVI}}\) trend, but the latter was more significant, and the EOS ended later. The changes in the SOS and EOS led to the prolongation of the LOS\({}_{\text{SIF}}\) and the significant advance of the LOS\({}_{\text{NDVI}}\). However, the LOS\({}_{\text{SIF}}\) was still shorter than the LOS\({}_{\text{NDVI}}\). Both the POP\({}_{\text{SIF}}\) and the POP\({}_{\text{NDVI}}\) had a downward trend, and the fluctuation state was first up and then down. The differences may be due to the large differences in seasonal cycles between leaf indices and photosynthesis [37]. Furthermore, the NDVI signal was easily affected by weather, such as clouds and snowfall, and the SIF signal was not affected by these factors [38], resulting in a large gap between the phenology estimated by SIF and NDVI.  
Figure 11: The spatial distribution of the month with the maximum correlation between pre-season climate and phenological metrics in Northeastern China. (**a**) Month with maximum correlation between pre-season climate and SOS based on SIF and NDVI; (**b**) month with maximum correlation between pre-season climate and EOS based on SIF and NDVI; (**c**) month with maximum correlation between pre-season climate and LOS based on SIF and NDVI; (**d**) month with maximum correlation between pre-season climate and POP based on SIF and NDVI.  
Figure 12: Time series of multi-year monthly mean for each vegetation type. (**a**) Time series of CV based on SIF and NDVI; (**b**) time series of GL based on SIF and NDVI; (**c**) time series of CV based on SIF and NDVI; (**d**) time series of CV based on SIF and NDVI; (**e**) time series of CV based on SIF and NDVI; (**f**) time series of CV based on SIF and NDVI; (**g**) time series of CV based on SIF and NDVI; (**h**) time series of CV based on SIF and NDVI.  
Despite there being significant differences in time series between SIF and NDVI products, we also found some similarities in spatial patterns (Figure 3), which proved that SIF was reliable for monitoring vegetation phenology. Therefore, both SIF and NDVI products can be used to track temporal changes in vegetation phenology over the past decades.
## 3 Related Works
However, LESCA's potential applications for secure high-speed communication are limited. Finally, a speech encryption algorithm is presented in [33]. The proposed algorithm pre-processes the original speech by deleting the unvoiced bits of the signal in order to choose the necessary data for encryption. The algorithm uses PRNG based on two 256-bit shift registers. One of the registers is linear, while the other is nonlinear. This allows the algorithm to expand its key space, which makes it secure against brute-force attacks. For statistical analysis, correlation coefficient, SNR and PSNR analysis are performed. The experimental results show that the generated keystream has high sensitivity, reduced correlation and a uniform histogram. However, the research does not provide sufficient analysis of the performance of the proposed cipher. Table 2 presents a summary of the comparison of related works presented in this study. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Work** & **Method(s)** & **Limitation** \\ \hline
[6] & Self-shrinking generator method & Efficient as hardware implementation, but it is not suitable for applications that require high-speed encryption rates \\ \hline
[15] & Chaotic system in conjunction with two NFSRs & Low encryption speed, which makes it unsuitable for high-speed encryption-based applications. \\ \hline
[17] & Round function and permutation table & Limited potential application for secure high-speed communication. \\ \hline
[24] & And2 LPSR, Geffe and majority-based & Performance is affected by the kind of error control code, nonlinear combinational functions and the degree of the generator polynomial \\ \hline
[25] & Deep learning-based key generation network & Lack of performance analysis to examine the algorithm’s efficiency \\ \hline
[26] & 2D discrete wavelet transforms and the Arnold mapping algorithm & Lack of performance analysis to examine the algorithm’s efficiency \\ \hline
[27] & Fixed-point chaotic maps & Low performance and lack of sufficient statistical testing. \\ \hline
[28] & Two-dimensional coupled map lattice & Low encryption speed, which makes it unsuitable for high-speed encryption-based applications. \\ \hline
[29] & Dynamic key-dependent structure & Lack of performance analysis to examine the algorithm’s efficiency. \\ \hline
[30] & Chaos-based cryptosystem & Suitable for highly secure applications that do not require high throughput rates. \\ \hline
[31] & LFSR and a genetic algorithm & Lack of performance analysis to examine the algorithm’s efficiency \\ \hline
[32] & Analogue–digital hybrid chaotic system & Lack of performance analysis to examine the algorithm’s efficiency \\ \hline
[33] & PRNG based on two 256-bit shift registers & Lack of performance analysis to examine the algorithm’s efficiency \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of related works.
## 4 Simulation Results and Analysis
### Validation of the DLL Module  
In the DLL module, the time-domain waveform of the data code obtained after despreading is shown in Figure 17. In the DLL, once tracking is completed, the output signal from the acquisition branch's IAD1 is processed to retrieve the demodulated data code after accumulating a number of times equal to the sampling points in a data code. As a result, the demodulated data code in the DLL module exhibits a one-data-period delay relative to the original data code, while maintaining consistency in fluctuations.  
As shown in Figure 17, the data code after despreading matches the original data code, except for a one-data-period delay. This confirms the correctness of the spreading method and transmission process. Therefore, the despreading functionality in the designed DLL module is correct.
###### Abstract  
The growing demand for hydrocarbons has driven the exploration of riskier prospects in depths, pressures, and temperatures. Substantial volumes of hydrocarbons lie within deep formations, classified as high pressure, high temperature (HPHT) zone. This study aims to delineate hydrocarbon potential in the HPHT zone of the Malay Basin through the integrated application of rock physics analysis, pre-stack seismic inversion, and artificial neural network (ANN). The zones of interest lie within Sepat Field, located offshore Peninsula Malaysia, focusing on the HPHT area in Group H. The rock physics technique involves the cross-plotting of rock properties, which helps to differentiate the lithology of sand and shale and discriminates the fluid into water and hydrocarbon. The P-impedance, S-impedance, Vp/Vs ratio, density, scaled inverse quality factor of P (SQp), and scaled inverse quality factor of S (SQs) volumes are generated from pre-stack seismic inversion of 3D seismic data. The obtained volumes demonstrate spatial variations of values within the zone of interest, indicating hydrocarbon accumulation. Furthermore, the ANN model is successfully trained, tested, and validated using 3D elastic properties as input, to predict porosity volume. Finally, the trained neural network is applied to the entire reservoir volume to attain a 3D porosity model. The results reveal that rock physics study, pre-stack seismic inversion, and ANN approach helps to recognize reservoir rock and fluids in the HPHT zone.  
rock physics; inversion; artificial neural network; Malay Basin +
Footnote †: journal: Applied Sciences
## 4 Discussion  
Many different factors affect the appearance of agricultural crops and cause changes in the reflectance signal of the canopy. Reasons can be manifold. There are naturally occurring processes, such as ripening or senescence but also plant diseases, plant stress like drought or salt stress as well, as treatment with herbicides such as glyphosate or weather events such as hall damage. An early differentiation between these very different processes that is quick and easy to perform and to process would be useful for the right action to be taken. Remote sensing techniques can be used for different purposes such as evaluation of weed infestation, nutrient deficiency, diseases or drift from herbicide treatment [28; 29; 30]. Therefore, the question arises if it may also be an appropriate method to detect vegetation dieback caused by glyphosate application.  
In the present study, the effect of glyphosate application on spectral vegetation indices was investigated as a specific colouring was observed under field conditions (Figure 1) which was attributed to glyphosate. The observed chlorosis implies that a loss of photosynthetic pigments could be the main reason causing the discolouring of the plant cover but other factors may be relevant as well.
# Optical accelerometers for Detecting Low-Frequency Micro-Vibrations
###### Abstract  
Optical accelerometers are high-precision inertial sensors that use optical measurement technology to achieve high-precision and electromagnetic interference-resistant acceleration measurements. With the intensive research and development of optical accelerometers in recent years, their applications in inertial navigation, structural health monitoring, precision vibration isolation systems, wind turbine fault monitoring, earthquake monitoring, and other low-frequency vibration detection have flourished. Optical accelerometers have various schemes; however, their characteristics vary considerably due to different optical modulation schemes. This study aims to address the lack of systematic evaluation of currently available low-frequency optical accelerometers. Optical accelerometers can be classified into four categories in accordance with their optical modulation schemes: optical path-, optical intensity-, optical phase-, and optical wave-length-modulated accelerometers. The typical performance, advantages and disadvantages, and possible application scenarios of various optical accelerometers are summarized. This study also presents the current status and trends of low-frequency optical accelerometers in consideration of the growing demand for high-precision, low-frequency acceleration measurements.  
accelerometer; optical sensing systems; optical modulation; accelerometer review +
Footnote †: journal: Applied Physics Letters  
Compared with electrical sensing, optical sensing improves the sensitivity of accelerometers by an order of magnitude and enables accurate detection of micro-vibrations [7]. Various optical accelerometers have been reported in the past decades, yet a systematic review of low-frequency optical accelerometers has remained elusive. In this review, we focus on the measurement principle and performance of state-of-the-art optical accelerometers and divide them into several categories in accordance with their optical modulation scheme. By using typical demonstrations as examples, merits and disadvantages are presented here, providing a big picture of optical accelerometers, along with their application prospects and development tendencies.