esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Work
### Future work  
In real applications of acoustic target tracking, accuracy and precision of target tracking results are two important QoS metrics from view point of the application layer and end user. These metrics are closely related to the time synchronization and localization precision of sensor network. In this paper we discussed about one source of errors that greatly decreases the accuracy of target tracking results. It is difficult to recognize and prove the existence of this source of errors by considering localization and time synchronization errors. Therefore we carried out our studies reported in this paper by assuming very low time synchronization and localization errors.  
In real applications, an end user may require different levels of QoS metrics based on the state of the system. If the end user wants to have control of the accuracy and precision of target tracking results, it is necessary to know the factors that influence the accuracy and precision of target tracking results and also know the relationship between influencing factors and the accuracy and precision of target tracking results. Knowing these, it will be possible for a middleware to guarantee the required level of accuracy and precision of target tracking errors by adjusting the time synchronization and localization precision parameters. To achieve this, the error propagation of our proposed method is currently under further study.
###### Abstract  
Meteorological time series, such as rainfall data, show spatiotemporal characteristics and are often faced with the problem of containing missing values. Discarding missing values or modeling data with missing values causes negative impacts on the accuracy of the final predictions. Hence, accurately estimating missing values by considering the spatiotemporal variations in data has become a crucial step in eco-hydrorological modeling. The multi-layer perceptron (MLP) is a promising tool for modeling temporal variation, while spatial kriging (SK) is a promising tool for capturing spatial variations. Therefore, in this study, we propose a novel hybrid approach combining the multi-layer perceptron method and spatial kriging to impute missing values in rainfall data. The proposed approach was tested using spatiotemporal data collected from a set of nearby rainfall gauging stations in the Ratnapura area, Sri Lanka. Missing values are present in collected rainfall data consecutively for a considerably longer period. This pattern has scattered among stations discontinuously over five years. The proposed hybrid model captures the temporal variability and spatial variability of the rainfall data through MLP and SK, respectively. It integrates predictions obtained through both MLP and SK with a novel optimal weight allocation method. The performance of the model was compared with individual approaches, MLP, SK, and spatiotemporal kriging. The results indicate that the novel hybrid approach outperforms spatiotemporal kriging and the other two pure approaches.  
machine learning; spatial kriging; optimal weight allocation; spatiotemporal data 2020 14 999 1
## 4 Discussion
DT exhibited earlier leaf yellowing under salt stress, while LQ maintained strong growth vigor, indicating that LQ was more adaptable under salt stress. Further analysis revealed that _A.__mongholicus_ responded to the salt-stressed environment through three mechanisms: an enzyme resistance system, osmoregulatory substances, and a non-enzymatic resistance system. Notably, in the secondary metabolic pathway, the salt-tolerant germplasm accumulated significant levels of typical metabolites such as apigenin, neohesperidin, L-phenylalanine, succin acid, dopamine, and 3-(4-hydroxyphenyl)pyruvate acid. The accumulation of these metabolites aligned with the performance of salt-tolerant varieties in the biomass data, further supporting their adaptive advantage under salt stress. In addition, combined metabolomic and transcriptomic analyses revealed the regulation of characteristic metabolic pathways, such as the phenylalanine metabolism pathway and the isoquinoline alkaloid synthesis pathway, suggesting that these pathways could play important roles in salt tolerance. Although there was insufficient experimental verification of the antioxidant effects of L-phenylalanine and 3-(4-hydroxyphenyl)pyruvate under salt stress, these compounds, as key intermediates in the phenylalanine metabolism and isoquinoline alkaloid synthesis pathways, may indirectly enhance plant salt tolerance by regulating antioxidant enzyme activities and boosting ROS scavenging ability, among other mechanisms. L-phenylalanine, as a precursor of flavonoids and other antioxidants, may help plants respond to salt stress by enhancing their antioxidant system, while 3-(4-hydroxyphenyl)pyruvate acid may regulate the antioxidant and stress response of plants by participating in the synthesis of isoquinoline alkaloids. By comparing two germplasms with different salt tolerance performances in this study, we further elucidated the distinct performance of _A.__mongholicus_ under salt stress. This provided foundational data for the future development of selection and breeding strategies and also enriched the transcriptomic information of _A.__mongholicus_. Future studies could further investigate the changes in salt tolerance mechanisms of _A.__mongholicus_ under varying salt concentrations and stress durations. Specifically, the effect of salt on plants under varying stress conditions does not follow a simple linear relationship; rather, it exhibits certain complexities. Additionally, a comprehensive analysis of phenotypes, metabolites, and gene expression in _A.__mongholicus_ germplasm under varying salt stress conditions could provide a clearer understanding of the mechanisms of flavonoid biosynthesis, phenylalanine metabolism, and isoquinoline alkaloid synthesis in response to salt stress. Future research could also investigate key genes and regulatory factors associated with salt tolerance through genomics and transcriptomics, which would provide robust support for the development of molecular markers for salt tolerance and the breeding of salt-resistant plants.
## 2 Supermarket Refrigeration Systems
### Typical Decentralized Control Structure  
The \(T_{air}\) in the display cases is controlled by a hysteresis controller (1) that opens and closes the inlet valve.  
\[u_{disp,i}(k)=\left\{\begin{array}{cl}0&\text{if }T_{air,i}>T_{air,i}^{max}\\ 1&\text{if }T_{air,i}<T_{air,i}^{min}\\ u_{disp,i}(k-1)&\text{if }T_{air,i}^{min}<T_{air,i}<T_{air,i}^{max}.\end{array}\right. \tag{1}\]  
The suction pressure controller is normally a Proportional Integral controller (PI-controller) with a dead band (\(\pm DB\)) around the reference of the suction pressure (2) where \(e(t)\) is the control error and \(u_{PI}\) is output from the PI-controller. If the compressor capacity is a discrete value, then the applied control signal \(u_{comp,i}\) (\(i=1,\,2,\,\ldots,\,nc\)) is obtained as follows:  
\[u_{PI}(t)=K_{p}e(t)+\int\frac{e(t)}{K_{i}}dt\;\;\;e(t)=\left\{ \begin{array}{cl}P_{suc}-P_{suc}^{ref}&\text{if }|e(t)|>DB\\ 0&\text{otherwise,}\end{array}\right. \tag{2}\] \[\left(u_{comp,1},u_{comp,2},\ldots,u_{comp,nc}\right)=\left\{ \begin{array}{cl}(0,0,\ldots,0)&\text{if }u_{PI}<C_{comp,1}/2\\ (1,0,\ldots,0)&\text{if }\left\{\begin{array}{cl}u_{PI}\geq C_{comp,1}/2\\ u_{PI}<C_{comp,1}+C_{comp,2}/2\end{array}\right.\\ \vdots&\vdots\\ (1,1,\ldots,1)&\text{if }u_{PI}\geq C_{comp,nc}/2,\end{array}\right.\]  
where \(C_{comp,i}\) is the \(i^{\prime}\)th compressor capacity (\(\sum_{i=1}^{nc}C_{comp,i}=100\)) and \(nc\) is the total number of compressors installed. The dead band ensures that the integration is stopped as long as the suction pressure is within the dead band. When the pressure exceeds the upper bound, the integration is started and one or more additional compressors are turned on to eventually reduce the pressure, and vice-versa when the pressure falls below the lower bound. Hence, moderate changes in the suction pressure and small biases from the reference do not initiate a compressor switching.  
In the supermarket, many of the display cases are similar in design and work under uniform conditions. As a result, the inlet valves of the display cases are switched with very similar switching frequencies. The valves therefore tend to synchronize, leading to wide oscillations of suction pressure and temperatures, with the consequence that many of the controlled variables are out of control, and the compressors wear out due to frequent starts/stops and synchronization.
## 2 Materials and Methods
### Analysis of the Imposed Current and Ultraviolet Light Effects  
To assess the responses to the imposed current and UV light effects, a factorial experimental design was employed (Table 1), involving two input variables: (A) the imposition of 0.2, 1, and 2 mA \(\times\) cm\({}^{-2}\) of electric current and no current (control), and (B) with and without exposure to UV light. It is important to note that the control experiments without UV light exposure were conducted under identical conditions to the experimental runs with UV light, except for the UV light source. This ensured that all parameters, such as the dye concentration, solution volume, and experimental setup, remained consistent across both sets of experiments. The output variables assessed included dye degradation, cell potential, obtained mass, and pH. To minimize experimental errors, all experiments were conducted randomly and in duplicate. Multivariate analyses were performed using the NCSS 2007 computational program. The factorial design and selected variables aimed to comprehensively understand the impact of the imposed current and UV light on the photocatalytic system's performance, ensuring the reliability of the results through careful control of variables.
## 3 Materials and Methods
### Measurement Equipment  
The measurements of the E-field strength (magnitude and distribution) were carried out using the LSProbe 1.2 Field Probe System [48]. The Field Probe System consists of an isotropic E-field probe connected through a fiber optic cable to a Computer Interface (CI-250). CI-250 was used to connect the isotropic field probe to a laptop. During all measurements, a continuous record of x-, y- and \(z\)-axis E-field values directly on the computer hard disk was performed. The E-field strength range of LSProbe 1.2 is from 0.1 V/m to 1 kV/m, in the frequency range of 0.3 to 6 GHz, and from 1 V/m to 1 kV/m, in the frequency range of 6 to 8.2 GHz.
## 3 Results and Discussion
### Validation of ANN Models  
This study trained and validated several ANN models with two hidden layers by using the training and the test sets, respectively. The number of classifications output by the ANN models depended on the input datasets. The maximum number of classifications was four under this study. The ANN models could only classify D01, D02, D07, and D08, when the number of divisions in FTIR spectra ranged between four and sixteen. More datasets and preprocessing of FTIR spectroscopy may increase the number of classifications; this will require further work. For the four datasets D01, D02, D07, and D08, the accuracy of the ANN models varied with data size and wavenumber range, as given in Table 2. Table 2 lists the names of the divisions and the corresponding wavenumber range, as well as the accuracy of the ANN model. The ANN model registered 100% accuracy for the seven divisions of P2/4, P3/8, P7/8, P1/16, P2/16, P3/16, and P4/16.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name of Division** & **Range of Wavenumber** & **Accuracy of the ANN** & **Characteristic Peaks** \\  & **(cm\({}^{-1}\))** & **Model (\%)** & **(cm\({}^{-1}\))** \\ \hline  & & & 757 cm\({}^{-1}\) \\
**P1/4** & 650–1487 & 75\% & 870 cm\({}^{-1}\) \\  & & & 956 cm\({}^{-1}\) \\
**P2/4** & 1487–2324 & 100\% & 1184 cm\({}^{-1}\) \\
**P3/4** & 2324–3160 & 60\% & 2946–2998 cm\({}^{-1}\) \\
**P4/4** & 3160–4000 & 75\% & 3501 cm\({}^{-1}\) \\  & & & 3656 cm\({}^{-1}\) \\  & & & 757 cm\({}^{-1}\) \\
**P1/8** & 650–1068 & 75\% & 870 cm\({}^{-1}\) \\  & & & 956 cm\({}^{-1}\) \\
**P2/8** & 1068–1487 & 50\% & 1184 cm\({}^{-1}\) \\
**P3/8** & 1487–1905 & 100\% & 1757 cm\({}^{-1}\) \\
**P4/8** & 1905–2324 & 55\% & \\
**P5/8** & 2324–2724 & 30\% & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of the artificial neural network (ANN) model according to the range of wavenumber.  
Figure 7: The number of divisions for FTIR spectra as input datasets for ANNs.  
Moreover, the last column presents several characteristic peaks of neat PLA, which are described in Table 3. When the number of divisions of the FTIR spectra is four or eight, the accuracies of the ANN models for the divisions P2/4, P3/8, and P7/8 are consistent with the characteristic peaks of 1184 cm\({}^{-1}\), 1757 cm\({}^{-1}\), and 3501 cm\({}^{-1}\), respectively. When the number of divisions of the FTIR spectra is 16, the accuracies of the ANN models for the divisions P1/16, P2/16, and P3/16 correspond to the characteristic peaks of 757 cm\({}^{-1}\), 870 cm\({}^{-1}\), 956 cm\({}^{-1}\), and 1184 cm\({}^{-1}\). Therefore, (-C-C-) stretching in the crystalline and the amorphous phases, (C-CH3) stretching, (C-O-C) asymmetric stretching, carbonyl group (C=O) stretching, and stretching of the carboxylic acid terminal group appear to be good classifiers for the ANN models used in this study.
# An Efficient QC-LDPC Decoder Architecture for 5G-NR Wireless Communication Standards Targeting FPGA
## 2 Materials and Methods  
Initially, the structure was constructed in Simulink using fundamental blocks to create a basic model consisting of 280 bits as a codeword. Subsequently, this design was transcribed into hardware description language (VHDL) and tested using ModelSim SE 10.6 software, focusing on a single frame. The development of this architecture paved the way for enhanced versions, as elaborated in the results section. A co-simulation process was undertaken to evaluate the decoder's performance-based on signal-to-noise ratio (SNR) and bit error rate (BER). The co-simulation process utilizes two software components: MATLAB/Simulink r2022b handles all the blocks of the communication system, incorporating the AWGN model for flexibility and future modifications, whereas the HDL simulator (such as Vivado, ModelSim, or Quartus) describes the decoding process.
# Comparative Study of Indoor Propagation Model Below and Above 6 GHz for 5G Wireless Networks
## 7 Conclusions  
This paper has presented the comparative propagation characteristics for the 5G channel at two different frequency bands. Two models have been proposed to study the loss due to the diffraction from wall edge and the loss of high frequency band. The wideband measurements were conducted at 3.5 GHz and 28 GHz using a 5G channel sounder with a high chip rate of 1000 Mcps. The 5G channel parameters for path loss, excess delay and power delay profile were calculated. The signal loss due to the edge shadow and high frequency was investigated. The path loss exponent values for the LOS scenario were 1.6 and 1.3 at 3.5 and 28 GHz, respectively. However, the received power was dropped in the NLOS scenario, where the PLE values were 2.7 and 3.6 at 3.5 GHz and 28 GHz, respectively. The 3 GPP models i.e., FI and ABG provided reliable performance for path loss for both single and multi-frequency models in LOS scenario. Based on the proposed models, the average diffraction loss values were 11.11 dB and 23.37 dB at 3.5 GHz and 28 GHz respectively. The loss due to frequency, termed frequency drop was 19.73 dB for the LOS scenario and 32.00 for the NLOS scenario. The RMS delay spread values were less than 8 ns and 12 ns at both frequency bands, for the LOS and NLOS scenarios, respectively. These results indicate that the 5G channel has good performance in term of path loss with very low delay spread. The findings in this study are useful to test and implement for real environment and gives a sight for the next-generation IoT based smart city 5G network. This also implies that the future 5G wireless networks can support a high data rate with low latency using high directive antenna to provide high gain power with small cell size.  
Conceptualization, A.M.A.-S., T.A.R. and T.A.-H.; methodology, A.M.A.-S., T.A.R. and T.A.-H.; A.D., and M.N.H.; software validation, A.M.A.-S., T.A.R., T.A.-H., A.D., M.N.H., M.H.A., K.D. and M.A.; formal analysis, A.M.A.-S., T.A.R. and T.A.-H.; investigation, A.M.A.-S., T.A.R. and T.A.-H.; resources A.M.A.-S., T.A.R. and T.A.-H.; data curation, A.M.A.-S., T.A.R., T.A.-H., A.D. and M.N.H.; writing--original draft preparation, A.M.A.-S.; writing--review and editing, A.M.A.-S., T.A.R., T.A.-H., A.D., M.N.H., M.H.Z., K.D. and M.A.; visualization, A.M.A.-S., T.A.R. and T.A.-H.; supervision, T.A.R.; project administration, A.M.A.-S., T.A.R. and T.A.-H.; funding acquisition, T.A.R. and T.A.-H.  
This research was funded by Research Management Centre (RMC), Universiti Teknologi Malaysia (UTM) and School of Science and Technology, Nottingham Trent University.  
We would like to thank the Research Management Centre (RMC) at Universiti Teknologi Malaysia(UTM) for funding this work (Vot 04E21) jointly with the NICS group at Nottingham Trent University, Nottingham, United Kingdom.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Description of Variables and Signal Processing
Furthermore, as previously delineated, mean values over a predefined temporal span were calculated, along with the standard coherence and the Global Coherence Index. Coherent states are associated with the stability of the system, the correlation measures, and efficient energy utilization within a body signal [34]. These concepts are intrinsically linked to frequency domain analyses; hence, entropy and spectral centroid measures were also implemented for each of the aforementioned variables due to their relevance to information quality, order, and energy distribution within the power spectrum. Lastly, zero-crossing analysis, widely used in biosignal processing, was used because of its effec \begin{table}
\begin{tabular}{l c c} \hline \hline
**Type of Feature** & **Features Names** & **Description** \\ \hline Main signals & HRV, FC, TTP, RES & \
\begin{tabular}{c} Sampled at 128 Hz with a \\ 3 min recording duration \\ Coherence values between 0 \\ and 1 \\ Coherence values between 0 \\ Coherence values between 0 \\ and 1 \\ Heart rate mean values \\ between 50 and 120 ppm, \\ Variability values \textgreater{} 600 ms, \\ transit time values \textgreater{} 500 ms \\ and less than 620 ms \\ Average standard deviation of \\ HRV and TTP and \\ mean-related deviation of \\ ECG and PPG \\ Number of zero-crossing on \\ each signal \\ Shannon entropy values of \\ each signal. Examined in a \\ bandwidth of 0–0.4 Hz. \\ Spectral centroid \\ \end{tabular} \\
\begin{tabular}{l c} \hline \hline
**Type of Feature** & **Features Names** & **Description** \\ \hline Main signals & HRV, FC, TTP, RES &
\begin{tabular}{c} Sampled at 128 Hz with a \\ 3 min recording duration \\ Coherence values between 0 \\ and 1 \\ Coherence values between 0 \\ and 1 \\ Heart rate mean values \\ between 50 and 120 ppm, \\ Variability values \textgreater{} 600 ms, \\ transit time values \textgreater{} 500 ms \\ and less than 620 ms \\ Average standard deviation of \\ HRV and TTP and \\ mean-related deviation of \\ ECG and PPG \\ Number of zero-crossing on \\ each signal \\ Shannon entropy values of \\ each signal. Examined in a \\ bandwidth of 0–0.4 Hz. \\ Spectral centroid values of \\ each signal. Examined in a \\ bandwidth of 0–0.4 Hz. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of all features implemented in the classification exercise. tiveness in identifying key frequency components in random signals and revealing change factors [35]. As observed, the feature selection process was guided by a central concept, yet it was ultimately directed towards the objective of classification using all of these features. The goal was to explore which coherence-related variables could be effective in classifying depression, given their association with the chaotic and stabilizing properties stated in the main concept.
## 2 Monosaccharide Composition, Combinations, and Bioactivities of Mushroom Polysaccharides
### Monosaccharide Composition and Combinations  
Monosaccharide compositions of PSs produced by Ascomycetes differ from those of PSs produced by Basidiomycetes. In Ascomycetes, the PSs are mainly hetero-PSs, and Glc, Man, Gal are often presented (Table 1). In a study of _Cordyceps_ (_Ophiocordyceps_) _sinensis_ PSs, [PERSON] et al. reviewed that monosaccharide composition usually consisted of Glc, Man, and Gal in various molar ratios [4], and the latest research of _Cordyceps sinensis_ also found the similar result [18; 19]. In contrast, intracellular PSs (IPSs) from mycelia contain various polyglucans as well as Glc [20; 21]. Compositions of other _Cordyceps_ species were generally similar to those of _C. sinensis_[22; 23; 24; 25], although some other monosaccharides, such as Rha, Xyl, Fru and Ara, were present in _C. ciacadae_ and _C. kyushuensis_[26; 27]. In _Morchella esculent_, another well-known ascomycete, the PSs contain four or more monosaccharides [28; 29; 30]. [PERSON] et al. isolated 52 PSs from fermentation systems and fruiting bodies of _Tuber_ species, and found that they were all composed mainly of Man, Glc, and Gal, suggesting that _Tuber_ PSs have identical chemical compositions [31]. On the other hand, two fractions of _T. huidongense_ PSs were extracted from fruiting bodies, and Glc was the only monosaccharide detected in THWP-2 [32]. In _T. rufum_ PS, Fuc was present in addition to Glc and Gal [33]. In comparison with Ascomycetes, PSs of Basidiomycetes are more complex, particularly in regard to monosaccharide composition and molar ratios of hetero-PSs. Many reviews have focused on chemical characterization of PSs from Basidiomycetes [1; 2; 3]. However, current knowledge of monosaccharide composition/combinations is not systematic. Table 2 summarizes results of studies during the past few decades of PSs from _Ganoderma_ species, including monosaccharide compositions and molar ratios. The monosaccharide composition of PS from fruiting bodies, mycelia, or fermentation broth of a same fungal strain showed different. To facilitate interpretation of PS properties, [PERSON] et al. have reviewed different PSs separately according to the main chain, and found most of the main chains consist solely of Glc, Man, or Gal; however, some PSs contain multiple main chains composed of two or more different monosaccharides in varying combinations, making description difficult [5].