esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Sound Field Rendering Processor
## 5 Conclusions  
Real-time sound rendering is computation-intensive. The output sound quality and system scalability are two concerning issues in the design of a real-time sound rendering system by hardware. In this research, a real-time sound rendering processor with the hybrid architecture was investigated and implemented to speed up computation and improve the sample rate of the output sound. While rendering sound in a small shoe-box room with dimensions of 1.28 m \(\times\) 1.28 m \(\times\) 0.64 m, the proposed processor performs sound rendering at real-time, while the offline computer-based software simulation takes about 12.70 min. Compared with the FPGA-based sound rendering system with the time-sharing architecture, our processor achieves 256 times increase in computation speed and improvement in the sample rate of the output sound.  
Furthermore, owing to the limited hardware resources inside a single chip, multiple chips are usually required to carry out rendering for a large sound space. To make the system easily extendable, interfaces were provided in the proposed sound rendering processor for system scalability. Through the interfaces, multiple processors are easily connected to each other to extend the rendered sound space. Although the current interface achieves good performance in data transmission and system scalability, the data transmission speed is limited due to data width. In future work, the serial advanced technology attachment (SATA) interface will be investigated and applied in the processor to enhance the data transfer speed.  
This work was supported in part by the Strategic Information and Communications R&D Promotion Programme (SCOPE), Ministry of Internal Affairs and Communications. Thanks for Professor [PERSON]'s valuable discussion on the system parallelization by using OpenMP. [PERSON] and [PERSON] specified the system architecture and designed the system. [PERSON] and [PERSON] discussed and derived the algorithm, [PERSON], [PERSON], [PERSON], and [PERSON] conceived the system solution and helped debug prototype system. [PERSON] and [PERSON] wrote and revised the paper.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Kinetic Model Development and Determination of Kinetic Parameters
#### 2.5.2 Product Formation Kinetics  
For product formation, the Luedeking-Piret (LP) model (Equation (9)) is the most commonly used kinetic model [35]. The LP model was implemented to fit the experimental ethanol production data in biofilm reactors with Media A-E under optimized conditions. As shown in Equation (9), the rate of product formation depends on \(X\) and \(dX\)/\(dt\) in a linear fashion.  
\[\frac{dP}{dt}\ =\ a\frac{dX}{dt}+\beta X \tag{9}\]  
where \(a\) and \(\beta\) are the product formation constants that may vary with the fermentation condition. If \(a\
eq 0\) and \(\beta=0\), product formation is growth associated. If \(a\
eq 0\) and \(\beta\
eq 0\), product formation ismixed-growth-associated. If \(a=0\) and \(\beta\
eq 0\), product formation is non-growth associated [36]. The \(\beta\) can be easily calculated from the data at the stationary phase (Equation (10)). At stationary phase, \(dX/dt=0\) and \(X=X_{max}\).  
\[\beta\ =\ \frac{(dP/dt)_{stationary}}{X_{max}} \tag{10}\]  
The product (\(P\)) is obtained as a function of time from Equation (9) as follows (Equation (11)).  
\[dP\ =\ adX+\beta\int X(t)d(t). \tag{11}\]  
Using Equation (7) for \(X_{t}\), when Equation (11) is integrated by \(P=P_{0}\) initial condition at \(t=0\), the change of product concentration with time can be determined by the following equation (Equation (12)).  
\[P\ =\ P_{0}+aX_{0}\Bigg{(}\frac{e^{\mu_{max}t}}{1-\frac{X_{0}}{X_{max}}(1-e^{\mu_ {max}t})}-1\Bigg{)}+\beta\frac{X_{max}}{\mu_{max}}ln\Big{(}1-\frac{X_{0}}{X_{ max}}\Big{(}1-e^{\mu_{max}t}\Big{)}\Bigg{)} \tag{12}\]  
Equation (12) can be also written as follows (Equation (13)).  
\[P\ =\ P_{0}+aA(t)+\beta B(t) \tag{13}\]  
where \(A(t)\ =\ X_{0}\Bigg{(}\frac{e^{\mu_{max}t}}{1-\frac{X_{0}}{X_{max}}(1-e^{\mu_{ max}t})}-1\Bigg{)}\) and \(B(t)\ =\ \frac{X_{max}}{\mu_{max}}ln\Big{(}1-\frac{X_{0}}{X_{max}}\Big{(}1-e^{\mu_{ max}t}\Big{)}\Big{)}\).  
When \([P-P_{0}-\beta B(t)]\) is plotted versus \(A(t)\), the growth-dependent product formation constant (\(a\)) can be found from the slope of the plot (Figure 1B,E,H,K,N).
# Mobility-Multihoming Duality
## 2 Combined Mobility and Multihoming  
The key point to note in considering related work is that at the time of writing, there are no combined mobility/multihoming solutions, so our proposed solution is unique. LISP has work in progress for providing mobility and multihoming together [2], but it is an extension to LISP multihomimg. For Shim6, a possible combined multihoming and mobility solution was described [3], but it was never implemented. More details for both LISP and Shim6 are shown below.  
In this Section, we present a critical examination of related work before presenting our approach in the next Section. In our comparison of other possible solutions, we focus on those that have presence within the Internet Engineering Task Force (IETF). Such solutions are, arguably, the most likely to yield practical, deployable solutions at the time of writing.  
Also, in our consideration of a Future Internet scenario (a near-future viewpoint) we focus on IPv6 as the network (layer 3) protocol of choice.  
A key component in enabling any mobility and multihoming mechanism is the way that addressing and address bindings are used, and so this features in our discussion.
## 2 Materials and Methods  
Creating multicriteria prediction neural models was performed by collecting data in the period 2008-2015 of winter wheat from cultivation area located in Poland, in the central and south-western part of Greater Poland, with emphasis on districts of Poznan (52\({}^{\circ}\)24'29.759\" N 16\({}^{\circ}\)56'0.672\" E), Koscian (52\({}^{\circ}\)5'10.77\" N 16\({}^{\circ}\)38'41.998\" E), and Gostyru (51\({}^{\circ}\)53'5.762\" N 17\({}^{\circ}\)0'47.829\" E; Figure 1). For the construction and verification of model, data from 301 fields were collected (Table 1). The following information constituted a database for the creation of predictive neural models with two sets, A and B. Set A (255 fields) makes up the data for model building from 2008-2014, whereas set B (45 fields) includes only 2015, which was not used in the model, but was used for model validation.  
Meteorological data for the research area, such as air temperature, precipitation, and period, were collected from Davis stationary and mobile meteorological stations close to the research area--Kornik, Gola, Turew, Piotrowo, and Stary Golebin.  
Neural predictive models took into account three dates in a calendar year: 15 April, 31 May, and 30 June. The models were named in the following way: QQWW15_4 (QuantitativeQualitativeWinterWheat15_April), QQWW31_5, and QQWW30_6. The independent variables that influence crop yields could be easily accessed by agricultural producers (Table 2).  
The following model of winter wheat yield prediction makes it possible to forecast and simulate expected yields directly before harvesting, in the current agricultural year.  
The data collected to build and verify the correctness of the models were the basis for the development of software, commissioned by farms located in Poland (Greater Poland), using fields covered by their own research. Specific prediction dates used in the models, i.e., 15.04, 31.05, and 30.06, were proposed by agronomists working in the area. The use of unambiguous calendar year dates is more practical from the point of view of agricultural production. The proposed prediction dates make it much easier to monitor winter wheat yielding. Moreover, they are selected in such a way as to reflect as much as possible the key stages of vegetation in typical years, i.e., 15.04--the shooting phase, 31.05--the earning phase, and 30.06--the beginning of full maturity.
# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 4 Experimental Evaluation
### Experiment 1  
The first experiment in this study employed the stochastic differential model, as described in Section 3.1. The primary objective of this model was to identify the onset of seismic events within an arbitrary time-series dataset. To do that, the two-fold synchronized dataset was fed to the stochastic differential model. Then, we extracted the actual starting point of every earthquake event and compared it to the true labels held on the two-fold synchronized dataset files. Finally, we had to evaluate the results and extract the metrics of the results.  
The results obtained from this experiment can be seen in Table 3 and Figure 8. The metrics received from the first experiment show us a moderate performance of the model; the accuracy and precision reached 46% while the F1 score was 63%. Also, the AUC curve (shown in Figure 9) was 50% and the recall was at 100%.  
Figure 8: _Cont._  
Figure 7: AUC for Model 2.
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## Materials and Methods
### Statistical Analysis  
The fit of the phenology model was verified by examining the adjusted coefficient of determination (\(R^{2}\)). Analyses were then formulated to test hypotheses using the phenology model parameters. SOS, EOS, and LOS derived from cumulative daily GPP were compared by site using multivariate analysis of variance (MANOVA). Site differences in the multivariate response of these parameter estimates were evaluated with Pillai's Trace, Wilks' Lambda, Hotelling's Trace, and Roy's Largest Root. The \(p\)-value, \(F\)-value and \(R^{2}\) were used to evaluate the strength of the univariate relationships between each of these phenological parameters by site [48]. As all sites had prescribed fire applied during the dormant season in 2009, 2011, 2013, 2015 and 2017, we also compared the SOS, EOS, LOS, \(t_{PRD}\) and \(k_{PRR}\) in fire years versus non-fire years across the three sites.  
To evaluate the weather system as a potential phenology driver, we computed the Spearman correlation coefficient to quantify the effects of air temperature, precipitation, and radiation (PAR, \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)) anomalies on ecosystem-scale phenological processes by modeling SOS, EOS, and LOS, at inter-annual scale by site. We also examined relationships between SOS, EOS, and LOS and values of these climate variables in the month, 3 months prior to spring and months of late autumn (March and January-March, October to November, respectively) [46]. We evaluated the effects of air temperature and precipitation anomalies on SOP and POR using ANCOVA by site at a monthly scale. Model input values of air temperature and PAR were average values, and precipitation was input as a cumulative value. In order to understand the mechanisms driving phenological processes of the study landscape, we analyzed the relationship between SOS and EOS with LOS by site using linear regression analysis. This illuminated the impact of spring phenology and autumn phenology date on the LOS of the study sites. Statistical analyses and visualization were processed in OriginPro 9.1 (OriginLab Corporation, Northampton, MA, USA) and SPSS statistics version 17.0 (SPSS Inc., Chicago, IL, USA). Model assumptions of normality and homoscedasticity were visually evaluated.
## 2 Localization Algorithm based on Spring Model (LASM)
### System model
#### 2.1.2 Localization process  
Figure 2 shows the explanation of the localization process based on the spring model. In process I, sensor nodes are deployed in an area. The circles denote the nodes and the solid lines denote that these two nodes are within the radio range. In process II, the wireless sensor network is represented by the spring system. The sensor nodes are represented by the particles. The distances between neighbor nodes are represented by the springs. As shown in process III, the anchor nodes have their actual absolute positions, so the anchor particles (which represent anchor nodes) are fixed in their absolute positions. The blind particles (which represent blind nodes) don't know their positions, so they are drawn to random positions. After this, the springs between these blind particles are stretched or compressed, which makes the total forces of the blind particles not equal to zero. Therefore, these blind particles will move according to (4). In process IV, these blind particles go back to the stable positions where they are before. The positions can be obtained using (4). These positions are what we need to localized.  
Here something needs to be declared: each spring is a virtual one. The position and velocity of each particle is also changed virtually. In fact, the position of each blind node is not change.
## 2 Results and Discussion
Figure 1: The preliminary heatmap analysis encompassing separately all \(119\) VOCs identified in apples, performed with old and commercial cultivars. The figure presents the results after gradation analysis. A statistical analysis was conducted to identify a set of compounds that could be considered characteristic of a given cultivar. The 'Gala' cultivar was found to have a notable abundance of 2,3-Dihydrofuranone, Estragole, and Hex-1-en-5-yl acetate and an absence of Propyl butyrate, Oct-1-en-3-ol, Butyl 2-methylpropanoate, and Hept-3-en-6-ol. The 'Melrose' cultivar was characterized by a high content of Propyl butyrate, Butyl 2-methylbutyrate, Hex-2-enal, 2-Methylbutyl acetate, and Hexyl hexanoate and an absence of Pent-1-en-3-one, 6-Methylhept-5-en-2-ol, Benzaldehyde, Hexyl octanoate, Butyl 2-methylbutyrate, Hex-2-enal, 2-Methylbutyl 2-methylpropanoate, and Hept-3-en-6-ol. The 'Melrose' cultivar was characterized by a high content of Propyl butyrate, Butyl 2-methylbutyrate, Hex-2-enal, 2-Methylbutyl 2-methylpropanoate, and Hexyl hexanoate, and Hexyl 2-methyl  
2-methylpropanoate, Octanoic acid, Hept-3-en-6-ol, 2,3-Dihydrofuranone, 2-Ethylhexanol, and Hex-1-en-5-yl acetate. The 'Red Prince' cultivar was found to have a notable abundance of Hexyl 2-methylbutyrate, Butyl propanoate, and Hexyl acetate and an absence of Propyl butyrate, Benzaldehyde, Butyl 2-methylpropanoate, Octanoic acid, Hept-3-en-6-ol, 2-Ethylhexanol, and Hex-1-en-5-yl acetate. The following characteristics are unique to 'Lobo': a high content of Heptan-2-ol and an absence of Pent-1-en-3-one, Hexyl octanoate, Butyl 2-methylpropanoate, and Hex-1-en-5-yl acetate. The 'Ligol' cultivar includes a high content of Hept-2-enal, Heptan-2-ol, Farnezen (sum of isomers), Hexyl butyrate, and Hexyl octanoate and does not include Pent-1-en-3-one, Propyl butyrate, Butyl 2-methylbutyrate, 2-Methylbutyl acetate, Propyl acetate, and Hex-1-en-5-yl acetate. The 'Sampion' cultivar was found to have a notable abundance of Pent-1-en-3-one, Hept-2-enal, Pentanol, Butyl 2-methylpropanoate, and Hex-1-en-5-yl acetate and an absence of Butyl 2-methylbutyrate, Heptan-2-ol, 2-Methylbutyl acetate, Propyl acetate, and Hept-3-en-6-ol. The aforementioned groups of compounds were initially classified in accordance with the specified cultivars. It can be assumed that similar fingerprint analyses can also be performed in other fruits with complex VOC profiles, such as pears [21; 27], plums [28; 29], or quinces [30], and they can probably be used to distinguish between varieties. More and more attention is being paid to the authentication of the origin of products. In the case of awarded Protected Designation of Origin or Protected Geographical Indication labels, it is particularly important to be able to reliably and confidently determine the characteristic features of agricultural products on the basis of which labels were awarded. The developed method works very well in the case of apples, and we can conclude that it will be equally effective in the case of other fruits with a complex VOC composition, as well as many other agricultural and food products.
## 3 Results and Discussion
#### 3.1.2 Organic Acids  
Organic acids play a major role in defining fruit taste in addition to their action as an acidulant preservative to inhibit the growth of microorganisms and improve fruits shelf life [35]. Comparable organic acids level was detected in all examined apricot fruits, with malic acid (peak 8) as major component, that exhibited higher levels in fruits (21.9-29.8 mg/g) than seed kernels (0.15-2.4 mg/g). No significant difference was observed in malic acid level among the different fruit types. Malic acid is a common acid in fruits and vegetables [36] that exhibit significant antioxidant activity [37], asides from acting as a bioavailability enhancer of minerals such as iron [36]. next to malic acid, tglic acid (peak 1) was also detected in all apricot fruits and seed kernels with no significant variance among them. Whether apricot seed can present a source of tglic acid as in croton oil with potential industrial applications as flavoring agent [38] needs further investigations.  
Other acids detected at lower levels include fumaric (peak 6) and quininic acids (peak 10) in all apricot fruits and seed kernels. Organic acids profiling in Egyptian apricot are in accordance to those reported in literature regarding the presence of malic acid as major form [27]. However, no studies reported the existence of tglic acid in apricot fruits and seeds. Various studies reported the presence of other acids detected in this study including isocitric acid, succinic acid, fumaric acid, shikimic acid [20]. Levels of organic acids and sugars play an important impact on fruit flavor and quality [39]. Apricot fruit is highly appreciated by consumers, as fruit exhibits a perfect balance between sugars and organic acids, combined with a strong and rich flavor and aroma [40].
# Microalgae as Potential Sources of Bioactive Compounds for Functional Foods and Pharmaceuticals
## 2 Materials and Methods
### Evaluation of Bioactivities
#### 2.5.3 Anti-Tumoral Activity  
Roswell Park Memorial Institute medium culture media was supplemented with L-glutamine (2 Mm), penicillin (50 U/mL), 10% fetal bovine serum, and streptomycin (50 \(\upmu\)g/mL) to provide best conditions for cultivation of human hepatocellular carcinoma cell line (HepG2) (ATCC\({}^{\circledR}\) HB-8065 TM) and human monocytic cell line THP-1 (ATCC\({}^{\circledR}\) TB-202). S17 cell line (a cell line derived from murine bone marrow non-cancerous cells) was kindly provided by Dr. [PERSON] (CBMR, Ualg) and utilized to evaluate selectivity between cancer and non-cancer cells. S17 cells were grown in [PERSON]'s Modified Eagle, supplemented as described above. All lines were incubated in a dynamic climate chamber (Binder, Tuttlingen, Germany) set on proving an humidified atmosphere at 37 \({}^{\circ}\)C and 5.0% CO\({}_{2}\).  
In Vitro Cytotoxic Activity  
A trypsin/EDTA solution (1 mL) was added to the cell suspension for plating adherent cells (HepG2 and S-17) and incubated according to previously mentioned conditions. To assess for cytotoxic activity, seeded cells were incubated with the extracts or fractions at doses varying from 3.9 to 125 \(\upmu\)g/mL for 48 h under the conditions described above.  
Cell viability after 48 h of extract incubation was assessed by the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide) assay. Two hours before the end of the incubation period, 20 \(\upmu\)L of MTT (5 mg/mL in PBS) was added to each well and further incubated for 2 h. After incubation, the purple formazan crystals (produced from cleavage of MTT by mitochondrial dehydrogenase) were dissolved in DMSO. Absorbance was measured at 590 nm (Biotech 4 Multi-Detection, USA). As a positive control, cells were treated with a chemotherapeutic drug (etoposide), at the same concentrations as the extracts. The assay was also performed on DMSO (0.6% (_v/v_)), to assess a possible cell viability reduction due to the solvent. Results were expressed in terms of cell viability percentage and half maximal inhibitory concentration values (IC\({}_{50}\)-in \(\upmu\)g/mL). Selectivity index was determined by dividing the cell viability of non-tumoral cells (S17) by the viability of tumoral cells (HepG2 or THP-1) after being exposed to the same concentration of extracts or fractions.
## 4 Conclusions  
The study applied the UV laser to sintering NiO thin film sensors to achieve efficient a manufacturing process involving small batch sizes. Compared to the furnace curing process, laser sintering reduces hours of furnace treatment time to a few seconds. The 355 nm wavelength UV laser was applied in this study. In this study, the 2-W laser output could provide the lowest average of resistance results compared to the other laser energy output settings. The small variations of resistance for 2-W laser output indicated the sintering process was stable. In addition to the resistance results, the optimal laser parameters setting was also determined by the highest sensitivity. The corresponding process parameters were 2-W laser output, 150 mm/s, and 90 Hz. Compared to the previous studies involving the furnace curing process in the literature, the resistance of laser-sintered sensors decreased up to 96%, which allowed the NiO thin films thermistor to be easily operated.  
SEM and confocal microscopy were used to investigate the relationship between the surface morphology and electrical properties of NiO thin films under different laser outputs. For the 1.5-W laser output, the surface of the thin films retained the shape of the nanoparticle agglomeration. It was indicated that the energy absorption was insufficient. As to 2.5-W, cracks were observed on the surface. Although the thin-film surfaces were even, the resistances were high due to the cracks. To figure out the effect of cracks on resistance, XRD analysis was performed. It was found that the crystal plane distances were not affected by recrystallization but the cracks. Based on the analysis, there were obvious regional compressive stains before the appearance of cracks, and the uneven shrinking strains caused the cracks on the surface as energy irradiation increased.  
**Author Contributions:** Formal analysis, Y.-T.C., K.-Y.H. and C.-H.C.; resources, W.-T.H.; writing--original draft preparation, Y.-T.C.; writing--review and editing, Y.-T.C., H.-T.Y., W.-T.H. and K.-M.L.; supervision, H.-T.Y., W.-T.H. and K.-M.L.; funding acquisition, W.-T.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by [Ministry of Science and Technology] grant number [MOST 110-2622-E-492-016].  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study due to REASON (please provide a detailed justification).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 20: (a) 2-W laser output causes regional compression strain; (b) excess reginal strains result in significant cracks, which were created by 2.5-W laser output.
## 5 WBAN and the Communications Protocol Stack
### Physical Layer  
Communications in and around the human body highlight a unique set of challenges that are not present in other types of networks. At the physical layer, protocols need to take account of how the body reacts to RF communications and the complications therein. The characteristics of RF signals at the physical layer can be separated into signal propagation effects in and along the human body and how body movement has an impact on the transmission of RF signals.