esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 A Hairy Root Platform for the Production of Bioactive Abietane Diterpenes
### Elicitation with Methyl jasmonate and Its Analogue Coronatine
#### 5.1.2 Coronatine as Alternative Elicitor of the Synthesis of Abietane Diterpenes  
Coronatine (Cor) is a structural analogue of the bioactive conjugate of the JA-leu, the active intracellular form of JA, capable of mediating various aspects of bacterial virulence. It causes the stomata to reopen to facilitate bacterial invasion, their growth in the apoplast, and the induction of disease symptoms [62]. There are several features that make Cor preferable to MJ as elicitor agents in plant cell/HR cultures: it is a more stable molecule, and does not need to be chemically converted to JA-Ile to bind the COI1 receptor [63]. _S. sclarea_ HRs  
Figure 5: Effects of MJ or Cor elicitation (7 or 28 days) on the final biomass (**a**) and on the total abietane diterpene production in _S. sclarea_ hairy roots (**b**). The asterisks denote a significant difference between elicited and control hairy roots (_p_ < 0.001) according to Student’s t test. Data are from [PERSON] et al. 2017.  
elicited continuously for 7 or 28 days with Cor (0.1 uM) showed a significant transcriptional activation of different genes encoding biosynthetic enzymes of the MEP-pathway, although to a lesser extent compared to the MJ treatment. However, similarly to MJ treatment, the transcriptional gene activation was transient, peaking at 24 h from the beginning of the Cor elicitation [51]. Consistent with this coordinated transcription of biosynthetic genes, the total amount of abietane diterpenes was significantly enhanced in Cor-elicited _S. sclarea_ HRs. After 7 days compared to the untreated HRs (14.50 \(\pm\) 0.31 mg L\({}^{-1}\)), a significant increase was observed in total abietane diterpene production (78.56 \(\pm\) 1.12 mg L\({}^{-1}\), approximatively a 5-fold increase). After 28 days of Cor elicitation, the content of aithopinone in _S. sclarea_ HRs increased further (103.32 \(\pm\) 2.10 mg L\({}^{-1}\), approximatively a 24-fold increase over the basal content of control hairy roots (4.40 \(\pm\) 0.13 mg L\({}^{-1}\)). Very low concentrations (0.1 uM) of Cor for 28 days allowed to extract 410.97 \(\pm\) 2.50 mg L\({}^{-1}\) of abietane diterpenes, determining a 28-fold increase compared to the content of unelicited HRs, significantly higher than the increase observed after a long-term elicitation with MJ (Figure 5b). This was due mainly to a negligible negative effect caused by Cor treatment on the final HR biomass (Figure 5a) [51].
## 3 Results and Discussion
### Algorithm Tuning and Validation
#### 3.2.2 Algorithm Validation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & \(\lambda\) & **N** & \(\mathbf{r}^{2}\) & _RMSE_ & _MAPD_ & \%_Bias_ \\ \hline  & **[nm]** & & & [m\({}^{-1}\)] & [\%] & [\%] \\ \hline  & \multicolumn{5}{c}{**MODIS-Aqua**} \\ \hline
**MLR** & **275** & **186** & **0.47** & **2.499** & **33** & \(\mathbf{-17}\) \\
**MLR** & **380** & **188** & **0.45** & **0.117** & **54** & \(\mathbf{-1}\) \\
**MLR** & **412** & **762** & **0.33** & **0.068** & **33** & \(\mathbf{-10}\) \\ RFTB & 275 & 191 & 0.50 & 3.100 & 78 & 37 \\ RFTB & 380 & 243 & 0.47 & 0.400 & 102 & 28 \\ RFTB & 412 & 6820 & 0.30 & 0.100 & 52 & 18 \\ Shan11 & 350 & 237 & 0.45 & 1.669 & 134 & 108 \\ Shan11 & 412 & 7748 & 0.28 & 0.287 & 92 & 72 \\ TS11 & 412 & 7299 & 0.39 & 0.201 & 102 & 86 \\ GIGP & 412 & 6116 & 0.30 & 0.077 & 40 & \(-12\) \\ QAA & 412 & 6133 & 0.14 & 0.137 & 59 & 18 \\ \hline  & \multicolumn{5}{c}{**MODIS-Terra**} \\ \hline
**MLR** & **275** & **291** & **0.43** & **2.746** & **48** & \(\mathbf{-26}\) \\
**MLR** & **380** & **326** & **0.32** & **0.337** & **45** & \(\mathbf{-29}\) \\
**MLR** & **412** & **10612** & **0.19** & **0.081** & **35** & \(\mathbf{-1}\) \\ RFTB & 275 & 171 & 0.35 & 2.950 & 72 & 22 \\ RFTB & 380 & 269 & 0.35 & 0.380 & 125 & 21 \\ RFTB & 412 & 6962 & 0.20 & 0.110 & 63 & 34 \\ Shan11 & 350 & 345 & 0.35 & 1.308 & 97 & 75 \\ Shan11 & 412 & 10734 & 0.16 & 0.311 & 129 & 108 \\ TS11 & 412 & 9607 & 0.20 & 0.202 & 109 & 87 \\ GIGP & 412 & 7976 & 0.12 & 0.108 & 51 & 11 \\ QAA & 412 & 8048 & 0.05 & 0.200 & 91 & 52 \\ \hline  & \multicolumn{5}{c}{**SeaWiFS**} \\ \hline
**MLR** & **275** & **342** & **0.25** & **2.976** & **49** & \(\mathbf{-57}\) \\
**MLR** & **380** & **418** & **0.32** & **0.318** & **58** & \(\mathbf{-53}\) \\
**MLR** & **412** & **10233** & **0.10** & **0.081** & **47** & **23** \\ RFTB & 275 & 199 & 0.38 & 2.660 & 90 & 40 \\ RFTB & 380 & 423 & 0.36 & 0.370 & 209 & 41 \\ RFTB & 412 & 11594 & 0.06 & 0.110 & 55 & 27 \\ Shan11 & 350 & 444 & 0.51 & 1.425 & 108 & 86 \\ Shan11 & 412 & 10451 & 0.26 & 0.229 & 100 CDOM spectral slope was only retrievable with empirical approaches. MLR and RFTB performed comparably to each other, although RFTB was not tested at \(S_{g}\)(412-555). In the application of retrieval algorithms for \(S_{g}\) below (Section 3.3), the MLR is used mainly for its simplicity, but we would expect RFTB retrievals to yield nearly equally accurate results. Shan11 and TS11 performed poorly (Table 7; MLR highlighted in bold). Correlations between modeled and measured CDOM slope were weak in the UVA and VIS, but as the dynamic range of the field data is quite low (Figure 2), error and bias were still low in the retrievals (Table 7). In all sensors and bands for the MLR and RFTB, \(S_{g}\) tends to be slightly overestimated in waters with low \(S_{g}\), and slightly underestimated in waters with high \(S_{g}\), indicating the weak sensitivity of these empirical approaches also reflected in the low correlation coefficients.
# Micro-Raman Spectroscopy Investigation of Crystalline Phases in EAF Slag
## 1 Introduction
The incident photons will interact with the system, and the amount of energy change by a photon is characteristic of the nature of each bond present. Not all vibrations will be observable with Raman spectroscopy, but sufficient information can usually be identified to enable the characterisation of the phases of the sample [10]. Raman spectroscopy is a powerful technique that has many advantages over other common analytical techniques. It offers very detailed sample characterisation by probing individual chemical bond vibrations. As a result, a Raman spectrum is information rich, and contains data relating to the specific chemical structure of the material being analysed. It can be used to fully characterise the sample composition, and a fast phase identification is possible with databases. Beyond general material identification and characterisation, Raman spectroscopy also probes more subtle chemical effects, such as crystallinity, polymorphism, phase, intrinsic stress/strain and hydrogen bonding. Raman spectroscopy is a very fast technique, typically requiring just a few seconds to obtain a good quality spectrum. It is a real non-destructive technique, since it is non-contact, non-destructive, and requires no sample preparation. For these reasons, it is suitable for true in situ analysis [11]. The recent advancement of technology in laser and spectrometer miniaturisation and the improved filters for laser light rejection and fibre-optic probes has allowed the development of portable Raman spectroscopy systems. There are other spectroscopic techniques complementary to Raman, such as Fourier transform infrared (FT-IR) and near-infrared (NIR) spectroscopy. However, because of the high sampling rate and spectral specificity, it is an invaluable tool for understanding the impact of process changes on the process and product. Some examples in the scientific literature have already demonstrated the advantages of the characterisation of EAF slag by using Raman spectroscopy [8]. The aim of this work is the evaluation of Raman spectroscopy in the characterisation of EAF slag to demonstrate the possible use of Raman spectroscopy for fast and reliable phase identification and quality control. Among the various types of Raman spectroscopy, micro-Raman techniques make it possible to perform the analysis of areas of a few square microns, while portable Raman usually hasa spot of some millimetres. As a consequence, the spectra obtained by portable Raman are usually the sum of the contribution of several phases. As a preliminary work, we perform the micro Raman analysis of a well-crystallised EAF slag sample to properly collect the spectrum of each phase.
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
## 2 Orthogonal Waveforms with Nonlinear Phase Function
The variation of this number as a function of the OLFM waveform time-bandwidth product, and as a function of both the OLFM waveform duration \(T\) and bandwidth \(B\) is demonstrated in Figure 1a,b, respectively. Figure 1: Number \(M\) of approximate orthogonal linear frequency-modulated (OLFM) waveforms in Equation (4) for varying (**a**) time-bandwidth product \(TB\); and (**b**) both duration \(T\) and bandwidth \(B\).
## 4 Experiments
### Experiments about Influencing Factors
#### 4.2.5 Result regarding Overlapping and Discussion  
In Section 3, we deem that random mapping reduces the dependence on time-steps by forcing the memory of the previous prediction outputs, but it also weakens the information transmission ability of the hidden layer. This is why the classification accuracy of RN-RNN is more unstable than that of RNN when suffering from SNR variation, length change, or data missing, as shown in Figures 9-11. However, we still believe that random mapping is an excellent solution to alleviate the problem of gradient vanishing and exploding, especially when reconsidering the potential information in hidden layer.  
We use a simple and effective trick to improve the information transmission capability of RN-RNN in the hidden layer that is overlapping. It refers to the overlap between two adjacent input sequences, and aims to put more emphasis on the input data, which can be transmitted in the hidden layer further. Overlapping can improve the integrity of information with no prior information.  
Figure 11: Comparison of several improved classifiers, including the full structure BICORN-RNN, to standard RNN with different missing data ratios.  
To analyze the information transmission capability in the hidden layer more clearly, we employ contrast experiments with vary accuracies in overlapping ratio, as shown in Figure 12. It is noteworthy that overlapping will inevitably increase the cost of computation. Both time and accuracy should be considered comprehensively, as timeliness is another important measure in our work.  
A certain overlapping ratio of RNN and RN-RNN can improve the accuracy significantly, up to about 20% (the maximal accuracy of RN-RNN can be 90% with 25% overlapping ratio), while there is no prominent effect of overlapping in the other three methods: up to about 10% (the maximal accuracy of BICORN-RNN can be 95% with 65% overlapping ratio). However, it should not be overlooked that the extra calculation is obviously due to a high overlapping ratio. It also suggests that it would be beneficial to choose an RN-RNN with a precise overlapping ratio to ensure that a 90% accuracy is reached, while maintaining an acceptable real-time performance. The reason for the better accuracy of the RN-RNN is that overlapping compensates for the loss of information in hidden layer caused by random mapping.
## Results and Discussion
### Physicochemical Parameter Values of Portuguese Honey Samples
Free acidity of honey is owed to its organic acid content in terms of gluconic, formic, malic, succinic, oxalic, butyric, citric, 2,3-dihydroxybutanediolic, pyroglutamic, lactic, benzoic, maleic, isobutyric, pyruvic, \(\alpha\)-ketoglutaric and glycolic acids. Despite the fact that organic acids are found in minor proportions in honey (0.17-1.17 g/100 g), these play an important role in the developed aroma and flavour of honey, since the majority of the aforementioned acids are present in honey in the form of volatile esters. On the other hand, storage temperature, processing conditions and the nectar source may influence organic acid content of honey. In addition, an increased acidity may serve as an indicator of honey fermentation and transformation of alcohol into organic acid [35]. The free acidity values for the three Portuguese honey types studied were below the limit of 50 meq/kg set by the European directive relating to honey [1]. The highest free acidity values (meq/kg) were recorded for heather honeys. In a previous work carried out on Portuguese heather honeys free acidity ranged between 10.5-38.1 meq/kg [15]. [PERSON] et al. [32] reported average \(\pm\) standard deviation values of free acidity equal to 27.00 \(\pm\) 5.00 for _Eucalyptus_ spp. honeys purchased from local markets in Portugal. Vitamin C recorded the highest value (mg/100 g) in heather honey. Even though honey is not a rich source of vitamin C compared to fruits like orange, lemon and so forth, hence, vitamin C contributes to the total antioxidant activity of honey and more specifically to that of the water soluble antioxidants that are present [28]. Previous studies in the literature on Portuguese honeys (rosemary, viper's bugloss, heather) have reported values in the range of ca. 14.00-14.58 mg/100 g. The highest value in vitamin C was recorded for heather honey from the region of Portela in Northeast Portugal [14]. What is in full agreement with the results of the present study is that vitamin C was affected by honey botanical origin.
## 2 Background
### WBASN Standards
These PHYs provide features such as good throughput, power efficiency and different data rates. The IEEE 802.15.4e standard provides an extension to the MAC layer functionality of IEEE 802.15.4 to make it more capable for industrial, commercial and medical applications by using the concept of multi-channels. (3) ZigBee  
The ZigBee standard operates under the umbrella of the ZigBee Alliance, which consists of more than seventy members from the communication industry. ZigBee is a wireless mesh network standard with the characteristics of low power, low cost, energy efficiency and limited latency, which makes a strong case for its use in industrial and medical applications. ZigBee chips are integrated with microcontrollers, and they operate under various ISM frequencies bands, including 2.4 GHz, 784 MHz (China), 868 MHz (Europe) and 915 MHz (the USA and Australia), with data rates from 20 to 250 Kbps. ZigBee works on the network layer by supporting star, tree, and mesh topologies. It works under the same guidelines of IEEE 802.15.4, i.e., a central coordinator as a controlling entity. ZigBee is developed over the physical layer and MAC mechanisms of the IEEE standard 802.15.4 and adds four additional key components, i.e., the network layer, the application layer, manufacturer-defined application objects and ZigBee device objects. ON World, a famous technology research firm, published a report that ZigBee's share of IEEE 802.15.4-based IoT applications is growing day by day and that, by 2020, ZigBee will be used in up to 80% of IEEE 802.15.4-based IoT devices [67]. Table 5 provides a comparative analysis of available wireless technologies for WBASNs.
## 3 Results and Discussion
### Dataset  
This study used the FMS dataset proposed by [PERSON] et al. [29]. This dataset was collected from 2 Azure Kinect depth cameras and covered 45 subjects between the ages of 18 and 59 years. The dataset consists of functional movement data, which are divided into left and right side movements, including deep squat, hurdle step, in-line lunge, shoulder mobility, active straight leg raise, trunk stability push-up, and rotary stability. In order to improve the accuracy and stability of the data, the researcher used two depth cameras with different viewpoints to collect the movement data of the subjects. The dataset contains both skeletal data and image information.  
The Azure Kinect depth sensor provides better quality and accuracy of data compared to depth cameras, such as Kinect V1, Kinect V2, and Real Sense; thus, it offers the possibility of machine learning and other methods for tasks such as human motion recognition and movement assessment [30; 31; 32; 33; 34]. In addition, the dataset not only provides strong support for functional movement assessment and rehabilitation training, but also provides technical support and data sources for research and applications in the fields of intelligent fitness and virtual reality.  
The 3D skeleton data acquired by the frontal depth camera were used in this experiment. The score distribution of each movement is shown in Figure 4a.  
As shown in Figure 4a, for example, the number of 2 score is much larger than the number of 1 and 3 scores in m11, due to the uneven distribution of FMS scores, which may decrease the performance of some machine learning models. Although the model has a high overall accuracy rate, 1 point and 3 points have low accuracy rates. Therefore, in order to avoid this situation, the problem of unequal distribution of each movement in the dataset needs to be addressed. This experiment used the Borderline SMOTE oversampling algorithm, which is a variant of SMOTE algorithm [35]. This algorithm synthesizes new samples using only the minority boundary samples and considers the category information of the neighbor samples, avoiding the poor classification results caused by the overlapping phenomenon in the traditional SMOTE algorithm. The Borderline SMOTE method divides the minority samples into three categories (Safe, Danger, and Noise) and oversamples only the minority samples of the Danger category.  
After Borderline SMOTE pre-processing, the expert score uniform distribution of FMS movements is also realized, as shown in Figure 4b. However, the distribution of m13 is still uneven. In order to avoid impacts on the experimental results, m13 was not tested in the subsequent experiments. The movements in the dataset are divided into the left side and the right side except for m01, m02, and m11, both sides have the same movement type and movement repetitions. In order to facilitate the targeted analysis and processing of the movement data, we only analyzed the movements on the left side of the body. In summary, the movements used for this experiment included m01, m03, m05, m07, m09, m11, m12, and m14.
# Adsorption of Emerging Pollutant by Pecan Shell-Based Biosorbent
## 2 Materials and Methods
### Batch Adsorption  
The adsorption assays were conducted in 250 mL conical flasks containing 100 mL of CPX solution (10 mg L\({}^{-1}\)) and 200 mg of PSB. Although the concentration of CPS used does not mimic the real concentrations of the antibiotic usually identified in environmental matrices (\(\upmu\)g L\({}^{-1}\) to ng L\({}^{-1}\)), this value was used in order to adjust the parameters in the adsorption process, thus, the possibility of utilizing PSB for the removal of CPX could be assessed, before proceeding to tests with lower concentrations and closer to reality The mixture was constantly stirred at 170 rpm on an orbital table (Tecnal TE-140, Piracicaba, Sao Paulo, Brazil) for a predetermined time. The parameters studied were pH (3-10), contact time (5 to 360 min), initial CPX concentration (2-10 mg L\({}^{-1}\)), and solution temperature (10-50 \({}^{\circ}\)C). The studies were carried out at an initial pH of 6 at room temperature (23 \(\pm\) 2 \({}^{\circ}\)C). After the predefined contact time, the samples were filtered with a 22 \(\upmu\)m membrane, and the final CPX concentration was determined using a molecular absorption spectrophotometer in ultraviolet-visible (Thermo Scientific, USA) at a wavelength of 272 nm.  
The values of adsorption capacity (\(q_{e}\), mg g\({}^{-1}\)) of the PSB at equilibrium and the removal percentage (\(\%\)) of ciprofloxacin by the pecan shell biosorbent were obtained using the following equations:  
\[q_{e}=\frac{(C_{0}-C_{e})V}{w} \tag{2}\]  
\[\%\ Removal=\frac{C_{0}-C_{e}}{C_{0}}\times 100 \tag{3}\]  
where \(C_{0}\) and \(C_{e}\) (mg L\({}^{-1}\)) are the initial concentration and the CPX equilibrium in the solution, respectively. \(W\) (g) is the mass of PSB added to the solution, and \(V\) (L) is the solution volume.  
To understand the process' thermodynamics, the parameters Gibbs Free Energy variation (\(\Delta\)G, kJ mol\({}^{-1}\)), entropy variation (\(\Delta\)S, kJ mol\({}^{-1}\)), and enthalpy variation (\(\Delta\)H, kJ mol\({}^{-1}\)) were calculated according to the following equations:  
\[lnK_{d}=\frac{\Delta H}{RT}+\frac{\Delta S}{R} \tag{4}\]  
\[\Delta G=\Delta H-T\Delta S \tag{5}\]  
where \(R\) is the universal gas constant (8.314 J mol\({}^{-1}\) K\({}^{-1}\)), \(T\) is the temperature of the solution (K), and \(K_{d}\) is the thermodynamic equilibrium constant (dimensionless), which can be obtained through the ratio between the CPX concentration at equilibrium and the adsorption capacity at equilibrium [14]. The adsorption capacity was estimated using the linearized form of equilibrium (isothermal) and kinetic models, as Table 1 shows.
## 2 Literature Review
### Panel-Forming Methods  
Panel forming methods are different depending on the materials used. In case of glasses, FRP or GFRC, molds are necessary to make variously shaped panels. Melted glass, FRP, and GFRC are then put into the molds to form the panel shapes desired. Materials such as polystyrene foam, Styrofoam, wood, or metal can be used as the molds depending on the strength required and cost [16].  
Molding to pour and solidify material are not appropriate for aluminum or metal panel fabrication. There are diverse methods of forming sheet metal panels including bending, die forming, single-point forming, dieless forming, multipoint forming, hydroforming, and explosive forming [7]. While the choice of the method depends mainly on the cost, curvature of the panels, and manufacturing speed, bending is generally used to fabricate curved surfaces due to its economy. Bending is achieved by roller bending, press bending, or stretch bending [17]. Roller bending involves the procedure of pushing a material between a fixed roller and a curved-surface-forming roller to make a product with the desired curvature. This method is the most conventional fabrication method used for one-way curved panels. A press bending machine pushes and bends a sheet metal panel. Roller bending as well as press bending are typically used to generate a uniformly bent profile and are not suitable for making a bent shape with variable curvatures or double-curved panels. Differently from roller or press bending, stretch bending can be used to create a non-uniform one-way bent profile. Like press bending, stretch bending is not suitable for creating double-curved panels [7].  
In accordance with whether heat is provided during bending, bending is classified into cold bending and heat bending [18]. Cold bending has been very common since it is cost effective and workable on site. With cold bending, curved surfaces are produced by forcefully bending and forming flat metal panels along the curvature desired at field temperature without heating. This study utilized cold bending method when fabricating aluminum mock-up panels.