esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Isolation of Cytok Compounds and Cell Proliferation Analysis  
The aerial parts of the plant material including leaves and thorns of _S. spinosum_ were air-dried, ground to a powder and firstly extracted with petroleum ether. The defatted leftover was then re-extracted with 70% aqueous ethanol. The ethanol extract was further fractionated by being partitioned with ethyl acetate and 1-butanol, and all the obtained fractions PF (petroleum ether fraction), AF (ethyl acetate fraction), BF (1-butanol fraction), and WF (water fraction) were evaluated for their cytotoxic activity by MTT and spectrophotometric assays.  
The average IC\({}_{50}\) values for MCF-7 were found to be 22.0 \(\pm\) 6.5, while for both HeLa and Caco-2 cell lines the IC\({}_{50}\) was above 1000 \(\upmu\)g/mL (Table 1).  
In the present study, further experiments were carried out on the AF extract in order to separate its cytotoxic components, being a column chromatography performed, resulting in the isolation of 23 sub-fractions that were studied concerning its effects on cell proliferation. Two fractions (Fr.18 and Fr.20) showed higher potential of cytotoxic activity than the others, Fr. 18 in all tested cells. The most cytotoxic-active fraction (Fr.18, respectively M8) showed the average IC\({}_{50}\) values against HeLa, MCF-7, and Caco-2 cell lines as followed: 20.7 \(\pm\) 2.5, 5.9 \(\pm\) 1.1 and 56.0 \(\pm\) 4.2 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, respectively. This fraction was subsequently purified and the first sub-fraction, designated as M8R, showed IC\({}_{50}\) values against HeLa, MCF-7, Caco-2, and MDA cell lines as followed: 5.8 \(\pm\) 1.1, 2.2 \(\pm\) 0.8, 9.5 \(\pm\) 1.4, and 62.5 \(\pm\) 3.8 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, respectively (Table 1).
## 2 Introduction and Application of Modeling
### MLP and LSTM Network Model  
The MLP and LSTM networks were used to predict the hourly flow. The MLP network has an input layer, output layer, and several hidden layers, and is a kind of multi-layer feed-forward network based on the backpropagation algorithm during training as shown in Figure 6a. As shown in Figure 6b, LSTM networks are a type of recurrent neural network capable of learning order dependence in sequence prediction problems. A common LSTM unit is composed of a cell, an input gate, an output gate, and a forget gate. The parameters of the LSTM model were 168 units and 300 epochs. In addition, the activation function was considered as the rectified linear unit.  
Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-squared were considered for the performance evaluation:  
\[\text{MAPE}=\frac{1}{N}\sum_{i=1}^{N}\biggl{|}\frac{\textit{Observed }\textit{MNF}_{i}-\textit{Predicted }\textit{MNF}_{i}}{\textit{Observed }\textit{MNF}_{i}}\biggr{|}\times 100 \tag{1}\]  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\lvert\textit{Observed }\textit{MNF}_{i}-\textit{Predicted }\textit{MNF}_{i}\rvert \tag{2}\]  
Figure 5: Flow chart of MNF prediction model.  
Figure 6: Schematic structure: (**a**) MLP neural network and (**b**) LSTM network.  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(\text{Observed }MNF_{i}-\text{Predicted }MNF_{i})^{2}} \tag{3}\]
# Heat Release Rates of Straight Soybean and Diesel Oil Blends in a Compression Ignition Engine
###### Abstract  
Straight soybean and diesel oil blends are proposed as alternatives for electricity generation in isolated regions. The compression ignition engine is considered the prime mover and has the attractive potential to distribute electricity generation for supply in isolated regions, as well as for small applications demanded by the agro-industrial sector. The heat release rate evaluation of straight soybean blends is the main focus of this paper. A single-cylinder compression ignition engine with a nominal power of 14.7 kW/2200 rpm fueled with blends of 50% and 80% \(v/v\) straight soybean oil with commercial diesel oil was tested on a dynamometer bench. The heat release rate and ignition delay were determined from in-cylinder pressure measurements using zero-dimensional modeling. The experimental results showed a promising performance and coherent behavior with the physicochemical fuel properties and load conditions tested. The highest fraction of vegetable oil led to a combustion delay, characterized by high diffusive and residual combustion phases, although the fuel oxygen content favored the combustion. Finally, this work allowed observation of the development of the heat release rate of straight soybean blends in a diesel engine, understanding the influence of the fuel properties and in-cylinder gas properties on the combustion process.  
Keywords:straight soybean oil; biofuels; heat release rate; physicochemical fuel properties; distributed electricity generation +
Footnote †: journal: Applied Sciences
# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
## 2 Methodology
### Multi-Feature Attention Module  
In semantic segmentation, the most frequently used upsampling method is to directly use uppooling or deconvolution to gradually restore the same pixel as the input image. However, this method may simply restore the pixel to the original size, but it is not very helpful for pixel positioning and pixel identification. The intra-class inconsistency that often appears in semantic segmentation is mainly the lack of contextual information, which is also included in the segmentation of remote sensing images.  
Under normal circumstances, the main feature of the decoding module is to fix the pixel positioning of the category. However, most of these types of methods usually use complex decoding modules, so they consume a lot of computing resources [32]. The attention mechanism is usually used to redistribute the weights of feature maps to strengthen semantic information, which can effectively enhance the model's ability to capture target information [33; 34]. SENet [35] is the most representative introduction of the attention mechanism into convolutional neural networks in recent years. It implements a channel dimension attention mechanism by introducing a learning parameter in the convolution process, which can adaptively adjust the weight of the feature mapping channel during the training process. The MFA module proposed in this work starts from the high-level and low-level feature information in the upsampling process. On the one hand, in the process of gradually restoring images, it can automatically learn to pay attention to the structures of buildings of different shapes and sizes in remote sensing images while highlighting the salient features that are useful for the task. On the other hand, the module more effectively deploys feature maps of different scales, and high-level features provide guidance information for low-level features.  
Figure 4 shows the MFA module designed in this paper. Considering that the low-level features have more pixels, the 2 \(\times\) 2 convolution is performed on the low-level features to reduce the feature map channel and feature map size of the neural network, thereby reducing the amount of calculation caused by the upsampling process. At the same time, it also prepares for feature fusion with low-level features. The context information generated from high-level features is feature-fused with low-level features through 1 \(\times\) 1 convolution. The fused features are subjected to 1 \(\times\) 1 convolution and nonlinear Relu function, and  
Figure 3: FPP Module.  
then upsampling is performed once, and the obtained feature information is multiplied by the original low-level features.
Monolithically integrated semiconductor lasers are more cost-effective, energy efficient, wavelength tuneable and easier to integrate than ECLs. Considering the global trend towards photonic integrated circuits (PICs), they constitute a key component in optical coherent transceivers targeting low cost, low foot-print, high flexibility and low power consumption. To leverage these benefits, vendors and operators are already proposing broadcasting metro network solutions that make use of PIC based coherent technologies and simplify the network architecture [9; 10]. Given the data traffic increase in metropolitan area networks, and according to latest market reports [11], this means that we are about to experience a tenfold demand for such transceivers by 2019.  
In this paper, we investigate the performance of coherent systems using semiconductor lasers as the optical source in the transmitter. Following our previous research on this topic [12; 13; 14; 15] where the main subject of matter was its interplay with feedback based CPRs, this paper provides a detailed and concise analysis focusing on one of the most popular CPRs; the blind phase search (BPS). In Section 2, we present a frequency noise model that takes into account carrier induced frequency noise and flicker noise, and numerically calculate the laser linewidth while varying different parameters of the model. In Section 3, we investigate the impact of carrier induced frequency noise on system performance using simulations. In Section 4, we depict the setup and explain the experiments that validate our simulation results. In Section 5, we propose the \"Effective Linewidth\"; an alternative to laser linewidth that solves the non-Lorentzian assumption and is able to predict system performance. Finally, conclusions are presented in Section 6.
# Effect of Hydrogen Ion Presence in Adsorbent and Solution to Enhance Phosphate Adsorption
###### Abstract  
In this paper, the effect of hydrogen ions on the adsorption onto granular activated carbon (GAC) with the inorganic contaminant phosphate, which exists as a form of four species depending on the solution pH, is investigated. Various batch isotherm and kinetic experiments were conducted in an initial pH 4 as an acid, a pH 7 as neutral, and a pH 9 solution as a base for the GAC conditioned with deionized water and hydrochloric acid, referred to as GAC and GACA, respectively. The physical properties, such as the total surface area, pore volume, pore size distribution, and weight of the element, obtained from Brunauer-Emmett-Teller (BET) and scanning electron microscopy coupled with energy-dispersive X-ray spectrometry (SEM-EDX) represent no significant differences. However, the hydrochloric acid (HCI) condition results in an alteration of the pH of the point of zero charge from 4.5 to 6.0. The optimized initial pH was determined as being acid for the GAC and as being neutral for the GACA. According to the Langmuir isotherm, the relatively high \(Q_{m}\) was obtained as being acid for the GAC and clearly distinguishes the pH effect as being the base for the GACA. An attempt was made to assess the adsorption mechanism using the pseudo-first-order (FPO), the pseudo-second-order (PSO), and the intraparticle diffusion models. The higher R\({}^{2}\) for the PSO in the entire pH range indicated that chemisorption was predominant for phosphate adsorption, and the pH did not change the adsorption mechanism. A prolonged Bed Volume (BV) for the GACA demonstrated that the hydrogen ions on the surface of the GAC enhanced phosphate adsorption.  
adsorption; isotherm; kinetic; GAC; pH Article
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## 2 Literature Review
### RPL Rank Attack
The sink node in ARM is defined as a centralized module, while other nodes are defined as a distributed module. The centralized module works in the DIO message analysis and decision-making participation. On the other hand, the distributed module works by alerting the sink nodes regarding any changes that happened in the destination nodes. The evaluation results indicate that ARM safeguards the RPL network with a high accuracy rate. The researchers in [13] presented a Sink-Based Intrusion Detection System (SBIDS) to detect rank attacks in the RPL network. This system works by the rule of comparing the Node Current Rank (NCR) with the Node Parent Rank (NPR) and checking the minimum rank between their siblings. The evaluation results of SBIDS showed that it was effective in detecting rank attacks.
## 3 Existence of Synchronous Motion
### Synchronization Criterion  
By differentiating Equation (16), the \(\ddot{x}\), \(\ddot{y}\), \(\dot{\psi}\) and \(\ddot{\psi}\) can be determined. Introducing these derivatives into the last four formulae of Equation (9), and integrating these formulae over \(\varphi=0\sim 2\pi\), the averaged balance equations of an exciter and three CRs are given by  
\[T_{\rm o1}=T_{\rm e01}-f_{1}\omega_{\rm m}=T_{\rm u}(\chi_{\rm f1}+\chi_{\rm a 1}) \tag{18}\]  
\[T_{\rm o2}=T_{\rm e02}-f_{\rm R1}\eta_{m}\eta_{r}^{2}T_{\rm u}(2+\eta_{m}W_{\rm c 01})=T_{\rm u}(\chi_{\rm f2}+\chi_{\rm a2}) \tag{19}\]  
\[T_{\rm o3}=T_{\rm e03}-f_{\rm R2}\eta_{m}\eta_{r}^{2}T_{\rm u}(2+\eta_{m}W_{\rm c 01})=T_{\rm u}(\chi_{\rm f3}+\chi_{\rm a3}) \tag{20}\]  
\[T_{\rm o4}=T_{\rm e04}-f_{\rm R3}\eta_{m}\eta_{r}^{2}T_{\rm u}(2+\eta_{m}W_{\rm c 01})=T_{\rm u}(\chi_{\rm f4}+\chi_{\rm a4}) \tag{21}\]  
With \(T_{\rm oi}\) (\(i=1\), 2, 3, 4) denoting the output electromagnetic torque of motor \(i\) (motors 2, 3 and 4 are three fictional motors, i.e., \(T_{\rm e02}=T_{\rm e03}=T_{\rm e04}=0\)). Explicit expressions for the coefficients in Equations (18)-(21) are provided in Appendix A. From Equations (18)-(21), calculating the difference of \(T_{\rm o1}\) and \(T_{\rm o2}\), \(T_{\rm o2}\) and \(T_{\rm o3}\), \(T_{\rm o3}\) and \(T_{\rm o4}\), we obtain  
\[\Delta T_{\rm o12}=T_{\rm o1}-T_{\rm o2}=T_{\rm u}[(\chi_{\rm f1}+\chi_{\rm a 1})-(\chi_{\rm f2}+\chi_{\rm a2})] \tag{22}\]  
\[\Delta T_{\rm o23}=T_{\rm o2}-T_{\rm o3}=T_{\rm u}[(\chi_{\rm f2}+\chi_{\rm a 2})-(\chi_{\rm f3}+\chi_{\rm a3})] \tag{23}\]  
\[\Delta T_{\rm o34}=T_{\rm o3}-T_{\rm o4}=T_{\rm u}[(\chi_{\rm f3}+\chi_{\rm a 3})-(\chi_{\rm f4}+\chi_{\rm a4})] \tag{24}\]  
Additionally, calculating the sum of Equations (22) and (23), Equations (23) and (24), Equations (22)-(24), we can also obtain the differences of the output electromagnetic torque between motors 1 and 3 (\(\Delta T_{\rm o13}\)), motors 2 and 4 (\(\Delta T_{\rm o42}\)), motors 1 and 4 (\(\Delta T_{\rm o14}\)). Consequently, Equations (22)-(24) can describe the coupling characteristics of the exciter and three CRs. Then, Equations (22)-(24) are put into a new arrangement as follows:  
\[\frac{\Delta T_{\rm o12}}{T_{\rm u}}-W_{\rm s01}+\eta_{m}^{2}\eta_{r}^{2}W_{ \rm s02}=T_{\rm c12}(\overline{\kappa}_{1},\ \overline{\kappa}_{2},\ \overline{\kappa}_{3}) \tag{25}\]  
\[\frac{\Delta T_{\rm o23}}{T_{\rm u}}=T_{\rm c23}(\overline{\kappa}_{1},\ \overline{\kappa}_{2},\ \overline{\kappa}_{3}) \tag{26}\]  
\[\frac{\Delta T_{\rm o34}}{T_{\rm u}}=T_{\rm c34}(\overline{\kappa}_{1},\ \overline{\kappa}_{2},\ \overline{\kappa}_{3}) \tag{27}\]  
The left side of Equations (25)-(27) stands for the differences of the dimensionless residual torques between two motors, while \(T_{\rm cij}(\overline{\kappa}_{1},\ \overline{\kappa}_{2},\ \overline{\kappa}_{3})\) from the right side is described as the dimensionless coupling torque.
## 2 Materials and Methods  
### Materials  
For the experiments, fly ash from a coal-fired power plant (Skawina, Poland) was used as a supplier of aluminosilicate precursors. Fly ash particles are driven out of the boiler with flue gases and extracted via electrostatic precipitators or other ways of filtering [39]. The composition of fly ash varies strongly depending on the type of coal that is being burned, as well as its elemental and oxide composition and physical properties [40]. Because of this, the raw material was analyzed as the first step. The results are presented in Section 3.1. The loss on irrigation of the fly ash is 2.44%.  
River sand (IVERSO, Iwiny, Poland) is used as aggregate in the geopolymer mix. Due to the mechanical requirements of the print head, it had to be ensured by sieving that the grain size of the sand was not larger than 2 mm. River sand is often used in geopolymers context as a fine aggregate for enhancement of mechanical properties [41].  
To achieve properties such as rapid curing, low flow of the paste after extrusion, and the desired rheology, several additives are investigated (Table 2). Attempts were made to isolate their effects individually or in combination.
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 1 Introduction
In previous work [4; 5], it was shown that the (thick) ellipsoidal calculus mentioned above can be used to quantify worst-case bounds of certain confidence levels for the state variables during state prediction and innovation stages if uncertainty can be described by means of normally distributed probability density functions. Then, a comparison of the inner and outer ellipsoidal boundaries--resulting from a mapping of covariance ellipsoids--provides a computationally efficient option for robustifying extended Kalman filter techniques by accounting for the influence of linearization errors during the computation of the involved covariance matrices. For the application of nonlinear stochastic filtering approaches in the frame of underwater navigation, the reader is referred to [17] and the references therein. In this paper, we restrict ourselves to the pure set-based uncertainty representation by means of ellipsoidal state enclosures. A comparison with stochastic estimation schemes--based on the unscented Kalman filter (UKF)--is studied separately in an ongoing research activity. To validate the ellipsoidal state estimation algorithm in this paper, we consider the temporally synchronized motion of two autonomous DDboat robots for which GPS-based position measurements and compass-based heading information are available, in addition to an uncertainty model for the impact of external disturbances and the imperfectly known actuator behavior of both boats. The major contribution of this paper is an experimentally driven uncertainty modeling procedure for autonomous surface vessels that comprises an observer-based a posteriori quantification of disturbances and modeling errors and the description of an interface of this uncertainty representation with a set-based ellipsoidal state estimation scheme. Using this approach, uncertainties in the resulting system trajectories can be enclosed in a reliable manner so that tasks such as guaranteed collision avoidance can be solved systematically. This paper is structured as follows. Section 2 describes the operating conditions in which the DDboats were used to perform the trajectory tracking experiment that serves as a basis for the validation of the localization algorithm in this paper. Moreover, suitable dynamic system models are derived in this section. The ellipsoidal state estimation algorithm is derived in Section 3, before uncertainty representations for the DDboats are derived in further detail in Section 4. The obtained ellipsoidal estimation results are presented in Section 5. Finally, conclusions and an outlook on possible future work are given in Section 6.
# Optical OFDM Error Floor Estimation by Means of OTDR Enhanced by Front-End Optical Preamplifier
###### Abstract  
Optical time-domain reflectometer (OTDR) enables simple identification and localization of a plethora of refractive and reflective events on a fiber link, including splices, connectors and breaks, and measuring insertion/return loss. Specifically, large enough OTDR dynamic range (DR) and thus high signal-to-noise-ratio (SNR) enable clear far-end visibility of longer fibers. We point out here that, under such conditions, the optical bit-error-rate (BER) floor is dominantly determined by reflective events that introduce significant return loss. This complements the OTDR legacy tests by appropriate optical BER floor estimation in the field. As high SNR implies inter-symbol interference as dominating error generating mechanism, we could apply the classical time-dispersion channel model for the optical BER floor determined by the root-mean-square (rms) delay spread of the actual fiber channel power-delay profile. However, as the high-SNR condition is not always fulfilled mostly due to insufficient DR, we propose here inserting a low-noise optical preamplifier as the OTDR front-end to reduce noise floor and amplify the backscattered signal. In order to verify the model for the exemplar test situation, we measured BER on the same fiber link to find very good matching between the measured BER floor values and the ones predicted from the OTDR trace.  
OTDR; BER floor; dynamic range; optical amplifier 2021  
222 September 2021  
30 October 2021  
2
## 3 Results  
### Analysis of nrDNA-ITS Sequences  
To analyze the variable sequences of the medicinal plants used in Adenophorae Radix and the related adulterants, we first amplified the nrDNA-ITS regions of the 24 original plant samples listed in Table 1, successfully amplifying the \(\sim\)800 bp fragments. The sequences of all the PCR amplicons were determined using both T7 and SP6 primers after cloning into the pGEM-T Easy Vector system, and they were registered in GenBank with the accession numbers indicated in Materials and Methods. The length of the entire nrDNA-ITS region was 793-795 in three _Adenophora_ species, 743 bp in _C. lanccolata_ and _C. pilosula_, and 689 bp in _G. littoralis_. They aligned to a length of 799 bp (Table 2). The sequence variability and species-specific sequence variants of the two ITS regions were analyzed separately. The intra- and inter-species variability of the ITS1 region was 0.0000-0.0205% \(\pm\) 0.0104% and 0.2760% \(\pm\) 0.2279% to 0.5810% \(\pm\) 0.0122%, respectively, and that of ITS2 was 0.0000-0.0198% \(\pm\) 0.0114% and 0.2657% \(\pm\) 0.2132% to 0.5717% \(\pm\) 0.0133%, respectively (Table 2). _A. stricta_ had the most diverged ITS1 and ITS2 sequences within the species (intra-specific), whereas _G. littoralis_ had the most variability among the species (inter-specific) (Table 2). The intra- and inter-species variability was similar between the ITS1 and ITS2 regions, but the species-specific nucleotide substitutions, which are useful for SCAR primer design, were more numerous in ITS1, especially between _C. lanccolata_ and _C. pilosula_ (Figure S1 and data S1). Therefore, we used the ITS1 region for the further development of SCAR markers for Adenophorae Radix and its adulterants.  
To analyze the phylogenetic relationships among the four herbal medicines and related plant species, we inferred a phylogenetic tree based on the NJ method using full-length nrDNA-ITS sequences. As a result, twenty-four samples were clustered into four groups with 100% bootstrap values consisting of monophyletic Adenophorae Radix, Cdonopsis Lanceolatae Radix, Codonopsis Pilosulae Radix, and Glehniae Radix groups (Figure S2). At the species levels, _C. lanccolata_ was genetically closer to _C. pilosula_ than to the other species and _G. littoralis_ was most distant from the other species. Furthermore, _Adenophora_ species, _A. stricta_, _A. triphylla_, and _A. triphylla_ var. _japonica_, did not cluster depending on species (Figure S2). These phylogenetic results confirmed that the four herbal medicines can be distinguished using the sequence variability of nrDNA-ITS regions.
## 3 Results
### Synthesis of 4(3H)-quinazolinone
#### 3.1.3. Synthesis of 2-disubstitutedmethyl-3-p-substitutedphenyl-4(3_h_)-quinazolinone (3a-s)  
A total of 19 2-disubstitutedmethyl-3-p-substitutedphenyl-4(3_H_)-quinazolinone derivatives were successfully synthesized, purified (purity \(\geq\)95%), and their properties demonstrated, which were named as 3a-s (Figure 3, Table 3).  
\begin{table}
\begin{tabular}{p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt} p{12.8 pt}} \hline \hline
**X, R1, R2** & **Name/Symbol** & **Efficient energy** & **Efficiories** & **Character-uistic** & **Melting Point (°C)** & **Thin-Layer Chromatopa-bR/B** & **1H-NMR (DMSO, 500 MHz)** & **IR** (cm\({}^{-1}\)) & **MS** \\ \hline \multirow{4}{*}{\(\chi=\mathrm{F}\) R1 = Br, R2 = Tri} & 2-(bromo-(1H-triazazol-1-