esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### qRT-PCR Conditions  
The SYBR @ Premix Ex TaqMan reagent kit was used to detect the expression of key _CsAAAT_ genes during the processing of oolong tea. The specific primers for the target gene designed using DNAMAN are shown in Table S1, and the specificity of the qRT-PCR amplicons was confirmed via sequencing. _CsGAPDH_ (KA295375.1) was used as the internal reference gene. The total volume of the reaction system was 200 \(\mu\)L, including 10.0 \(\mu\)L of SYBR Green Master Mix, 0.4 \(\mu\)L each of forward and reverse primers (the concentration of all primers was 10 \(\mu\)mO-/\(\mu\)L), 2.0 \(\mu\)L of cDNA template, 0.4 \(\mu\)L of passive reference dyes, and 6.8 \(\mu\)L of sterile deionized water. The qRT-PCR conditions were 30 s at 95 \({}^{\circ}\)C, and the amplification program conditions were as follows: 94 \({}^{\circ}\)C for 30 s, 94 \({}^{\circ}\)C for 5 s, and 60 \({}^{\circ}\)C for 30 s, performing 40 cycles, followed by 60 \({}^{\circ}\)C to 95 \({}^{\circ}\)C melting curve detection (Figure S1), and a final step of 10 min at 72 \({}^{\circ}\)C for extension. After that, ABI QuantStudio 3 was used to measure the expression of various target genes in the samples, and the 2\({}^{\Delta\Delta\Delta\mathrm{C}}\) method was applied to calculate the relative expression levels of relevant genes [36]. Fresh tea leaves (F) were used as the control tissue, where the relative expression was set to 1.0 (2\({}^{\circ}\)0).
## 6 Calculation of Parameters for Mudslide Mitigation
### Mudslide Impact Pressure  
Mudslide impact force is a critical parameter in the design of mudslide mitigation structures. The overall impact pressure exerted by a mudslide can be calculated using Equation (15), as specified in the \"Mudslide Disaster Prevention and Control Engineering Design Specification\" (DZ/T0239-2004) [33]:  
\[P=\lambda\frac{\gamma_{c}}{g}Vc^{2}\sin\alpha \tag{15}\]  
where P represents the overall mudslide impact pressure (kN); \(\lambda\) denotes the building shape coefficient, taken as 1.33 for rectangular structures; \(\gamma_{c}\) indicates the mudslide specific weight (kN/m\({}^{3}\)); \(Vc\) is the average mudslide velocity across the channel cross-section (m/s); and \(\alpha\) represents the angle between the impacted surface of the structure and the direction of mudslide impact, assumed to be 90\({}^{\circ}\) in this study. The calculated overall mudslide impact pressures for each section are presented in Table 5.
# Computing the Gromov-Wasserstein Distance between Two Surface Meshes Using Optimal Transport
## 5 Computational Experiments
### How Round Is Calmodulin?  
A visual inspection of Figure 5 indicates that the ligand-bound conformation of calmodulin is more compact that its ligand-free conformation. To quantify this idea of \"compactness\" we use two independent measures of the surface of the proteins:  
1. The _sphericity_\(S\) of a surface \(F\) quantifies how well it encloses volume. It is expressed as the surface area of an equivalent sphere (i.e., with the same volume \(V\) as the volume enclosed by \(F\)) divided by the surface area \(A\) of \(F\): \[S=\pi^{1/3}(6V)^{2/3}/A.\] (31) The sphericity is at most one, and equals one only for the round sphere,
2. The GW distance between the surface of the protein and the surface of a round sphere.  
We computed these two measures on all 51 conformations of the trajectory described in the previous subsection. Note that to compare the surfaces of the different conformations of calmodulin to the round sphere, we need a triangular mesh on the surface of that sphere. We generated this mesh by placing \(N=1000\) points uniformly on the sphere and generating a triangulation from these points. We used the Matlab package \"Uniform sampling of the sphere\" available from [52] to position the points and QHull [53] to generate the triangulation.  
We compared the 51 sampled meshes representing the 51 conformations of calmodulin (see above for details) with the mesh representing the surface of the sphere using FreeGW. Results of these calculations are compared to the corresponding sphericity of the meshes computed using Equation (31) in Figure 6. The GW distances and the sphericity are (anti) correlated (correlation coefficient: \(-0.8\)): as the sphericity increases, the level of correspondence between the protein surface and the sphere increases, and the GW distance decreases. We observe an inflection point for the sphericity along the trajectory at the 40 th conformation: the GW distance shows a similar inflection point at the same conformation. This indicates that the GW distance between a protein represented with its surface and the sphere has value as a tool to assess the compactness of that protein.
# Evaluating DL Model Scaling Trade-Offs During Inference via an Empirical Benchmark Analysis
## 6 Evaluation
### Computational Overhead of Inference  
The plots in Figure 4 depict the effect of model scaling on the computational overhead imposed on the underlying model serving as infrastructure during runtime inference for each of the referenced DL use cases.  
From these plots, it is immediately evident that when the model complexity increases, so does the computational overhead. Moreover, the model complexity and the computational overhead present an almost linear relationship. For example, in the BERT case (Figure 4a), the coefficient of determination (R squared) is \(R^{2}=0.913\), and for the EfficientNet use case (Figure 4b), \(R^{2}=0.984\). These value indicate a well-fit linear relationship. Furthermore, for the MLP-Regression case (Figure 4c), we observe two distinct phases of linearity, the second of which presents a higher linear coefficient that the first phase.
# Ground Based Hyperspectral Imaging to Characterize Canopy-Level Photosynthetic Activities
## Results
### Estimated Maximal Fluorescence  
Generally, the control groups were well (\(R^{2}\) = 0.85) fitted by the exponential model, whereas the Diuron-treated groups showed large variations in the model that best fit the SIF response to PAR (Figure 4). As exponential models showed a strong relationship between SIF and PAR in control groups, it was reasonable to use the y-intercepts of the models as the maximal fluorescence intensities. In contrast, the three models for the Diuron-treated groups provided different goodness-of-fit and maximal fluorescence values. Regarding goodness-of-fit, the quadratic model provided the best estimation of the maximal fluorescence, followed by the linear model and average model. This was possibly because the level of NPQ in Diuron-treated plots would have been higher under the highest light intensities where the most damage would have been expected. Although a previous study showed a linear relationship between chlorophyll fluorescence and PAR under high light intensities (over 2800 \(\mu\) mol/m\({}^{2}\)/s) [45], no strong linear relation was observed between SIF and PAR under solar illumination (up to 2000 \(\mu\) mol/m\({}^{2}\)/s) in the present study. In particular, the linear model provided a reduced goodness-of-fit than the quadratic model, suggesting that the linear model might not be optimal for estimating the maximal fluorescence for the Diuron-treated groups. However, there was a reasonable consideration for using the average model, despite the worst goodness-of-fit. No obvious trend was identified between SIF and PAR values in the Diuron-treated groups, and thus variations among data points could be considered random measurement errors. Granting that, it was acceptable to use the mean value as the group measurement (and thus the maximal fluorescence of that group), thereby reducing the measurement error. Nonetheless, the three models provided different values for maximal fluorescence intensity, and would have had different effects on the successive data processing.
## 3 The Dynamic Reliability Evaluation and Critical Component Identification Method for Power System
### The Critical Component Identification Model  
After finishing the power system's reliability evaluation, the dynamic reliability levels of all critical system states can be obtained. Therefore, the critical components of the power system can be quickly identified with the aid of reliability tracking, which provides a basis for reliability-oriented power system dispatch and emergency reinforcement of the reliability of the power system. Reliability tracking requires assigning reliability levels to each component and quantifying the number of reliability levels that each component should contain.  
This paper utilises the proportional allocation method for tracking reliability. The method is predicated on two fundamental principles:  
1. The unreliability of the system is attributable to the faulty components; the normal operation components do not contribute to the allocation of system reliability levels.
2. Component allocation is proportional to the system reliability levels.  
Based on these two principles, the change in reliability levels caused by the second-order failure event \(k\) should be shared by the fault components \(x_{1}\) and \(x_{2}\).  
\[f(k\to 1)=\frac{f(x_{1})}{f(x_{1})+f(x_{2})}f(k) \tag{41}\]  
\[f(k\to 2)=\frac{f(x_{2})}{f(x_{1})+f(x_{2})}f(k) \tag{42}\]  
where \(f(k\to 1)\) and \(f(k\to 2)\) represent the reliability levels that should be allocated to the two failure components. Equation (40) depicts the calculation of component reliability parameters.  
\[f(x_{i})=\frac{\lambda_{i}}{\lambda_{i}+\mu_{i}} \tag{43}\]  
where \(\lambda_{i}\) is the failure rate of component \(i\); \(\mu_{i}\) is the repair rate of component \(i\).
## 3 Results and Discussion
### FTIR Spectral Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Wavenumber (cm\({}^{-1}\))** & **Correspondence** & **Reference** \\ \hline
977 & C–C backbone stretching vibration of & \\  & crystalline PHBV & [38,39] \\
1054 & O–C–C stretching & [40] \\
1099 & asymmetric O–C–C stretching & [41] \\
1129 & symmetric C–O–C stretching of & [41] \\  & amorphous PHBV & [41] \\
1179 & asymmetric C–O–C stretching of & [42] \\  & amorphous PHBV & [42] \\
1226 & symmetric C–O stretching of crystalline PHBV & [38] \\
1261 & symmetric C–O stretching of aliphatic esters & [41] \\
1275 & symmetric C–O stretching of carbonyl group of & [39,40,43] \\  & crystalline PHBV & [39,40,43] \\
1379 & symmetric C–H bending vibration of & [40] \\  & methyl groups & [40] \\
1452 & asymmetric C–H stretching and bending & \\  & vibrations of methyl and methylene groups & [39,40,43] \\
1720 & symmetric C=O stretching of crystalline PHBV & [6,38,40] \\
2933 & symmetric vibration of C–H of methylene groups & [39,40,43] \\
2975 & asymmetric vibration of C–H of methyl groups & [39,40,43] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Assignments of PHBV IR bands.
## 4 Discussion
### Discussion of Linear and Informer Results  
In the context of time-series forecasting, the order of the sequence often holds significant importance. We contend that despite the incorporation of positional and temporal embeddings, the approach employed by Informer still exhibits a deficiency in preserving temporal information. Although Linear is characterized by its simplicity, it possesses several noteworthy features. Notably, the effectiveness of dependency capture improves as the path length decreases. This capability allows Linear to effectively capture both short-term and long-term temporal relationships. The Linear model is a linear model that consists of up to two linear layers. As a result, it exhibits advantages such as reduced memory usage, its ability to work with fewer parameters, and faster inference speed compared to existing Informers. After the completion of the training process, it is possible to visually examine the weights associated with the seasonal and trend branches. This analysis can provide valuable insights into the predicted values. Furthermore, it is worth noting that the Linear approach can be acquired effortlessly without the need for tuning the hyperparameters of the model. In terms of performance, it is observed that the average decrease achieved by the Linear method surpasses that of the Informer-based method across all scenarios.
## 6 Analysis and Control of FESS
### System Configuration and Mathematical Model
However, in the proposed system, the position loop provides the position angle information, which is required to generate waveforms and calculate the feedback \(d\)-\(q\) axis currents to control the flux and torque of FESS independently. To determine the parameters of the PI controller, the mathematical equations of PMSM are derived in the rotor \(d\)-\(q\) reference frame as presented in Equations (29-31) [61]. Transforming the three-phase voltage and current equations of the PMSM into two-phase \(d\)-\(q\) system eases the analysis and control of the machine. Conversion of a three-phase system to a two coordinate \(d\)-\(q\) reference frame system is performed using [PERSON]'s and [PERSON]'s transform [73]. This allows determination of the controller constants in terms of machine parameters. Detailed description of the PI controller design for PMSM is described in [74-76]. \[V_{q}=R_{s}I_{q}+L_{q}\frac{dI_{q}}{dt}+\omega_{e}L_{d}I_{d}+\omega_{e}\lambda _{m} \tag{29}\]  
\[V_{d}=R_{s}I_{d}+L_{d}\frac{dI_{d}}{dt}-\omega_{e}L_{q}I_{q} \tag{30}\]  
Similarly, the torque developed by the motor is given as:  
\[T_{e}=\ \frac{3}{2}\frac{P}{2}\lambda_{m}I_{q}+\left(L_{d}-L_{q}\right)I_{d}I_{q}) \tag{31}\]  
The mechanical torque is represented by:  
\[T_{e}=J\frac{d\omega_{m}}{dt}+B\omega_{m}+T_{L} \tag{32}\]  
where, \(I_{d}\) = Direct axis stator current in ampere (A); \(I_{q}\) = Quadrature axis stator current in ampere (A); \(R_{s}\) = Stator resistance in ohms (\(\Omega\)); \(L_{d}\) = Direct axis inductance in henry (H); \(L_{q}\) = Quadrature axis inductance in henry (H); \(J\) = Moment of inertia in kg\(\cdot\)m\({}^{2}\); \(B\) = Friction of viscous in N\(\cdot\)m/rad/s; \(P\) = Number of poles; \(\lambda_{m}\) = Rotor flux constant in V/rad/s; \(\omega_{e}\) = Rotor's electrical speed in rad/s; \(T_{L}\) = Load torque in N\(\cdot\)m; \(T_{e}\) = Electromagnetic torque in N\(\cdot\)m; \(\omega_{m}\) = Rotor's mechanical speed in rad/s. The model of the PMSM-Flywheel system is implemented with the help of Equations (1)-(6) of the flywheel, and Equations (29)-(32) for a surface mounted PMSM using the control method mention above. A detailed description and different controlling techniques of PMSM are discussed in references [77-79]. Figure 3: General cascaded proportional-integral (PI) control structure of an electric motor drive.
## 5 Discussion of Test Results  
### Post-Processed Data  
The acceleration data were recorded with a sampling frequency of 128 Hz, over a typical duration of 2 min. An example of accelerations and power spectral density functions is proposed in Figure 8, for the vertical and transverse directions. All the collected records were post-processed via the SMIT Toolsuite [48], based on the ERA-OKID-OO approach [49; 50].  
In doing so, the vertical accelerations were specifically investigated, being of primary interest for the examined system, with respect to mostly negligible longitudinal/transverse dynamic effects (i.e., Figure 8c,d).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Scenario**} & \multicolumn{2}{c}{**Occupants**} & \multicolumn{2}{c}{**Standing**} & \multicolumn{2}{c}{**Walking**} & \multicolumn{2}{c}{**Period**} \\ \cline{2-10}  & **Total \(p\)** & **Density \(p\)/m\({}^{2}\)** & **Number** & **Position** & **Notes** & **Number** & **Position** & **Notes** & \\ \hline CN\#1 & 1 & 0.228 & - & - & - & 1 & Center & Jump & P\#1 \\ \hline CN\#2 & 1 & 0.228 & - & - & - & 1 & Center & Jump & P\#1 \\ \hline CN\#3 & 1 & 0.228 & - & - & - & 1 & Center & Jump & P\#1 \\ \hline CN\#4 & 1 & 0.228 & - & - & - & 1 & Center & Jump & P\#1 \\ \hline CN\#5 & 2 & 0.457 & 1 & Center & - & 1 & Overall & Normal & P\#1 \\ \hline CN\#6 & 3 & 0.686 & 1 & Center & - & 2 & Overall & Normal & P\#1 \\ \hline CN\#7 & 3 & 0.686 & 1 & Center & - & 2 & Overall & Normal & P\#1 \\ \hline CN\#8 & 4 & 0.914 & 1 & Center & - & 3 & Overall & Normal & P\#1 \\ \hline CN\#9 & 4 & 0.914 & 3 & Center & - & 1 & Overall & Normal & P\#1 \\ \hline CN\#10 & 6 & 1.372 & 3 & Center & - & 3 & Overall & Normal & P\#1 \\ \hline CN\#11 & 6 & 1.372 & 3 & Center & - & 3 & Overall & Normal & P\#1 \\ \hline CN\#12 & 0 & - & - & - & - & - & - & - & P\#2 \\ \hline CN\#14 & 1 & 0.228 & - & - & - & 1 & Overall & Normal & P\#2 \\ to CN\#19 & 2 & 0.457 & - & - & - & 2 & Overall & Normal & P\#2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Examined test scenarios for the system object of analysis (Figure 6).
## 2 MBR Types and Configuration
### Aerobic Membrane bioreactors in OMPs Removal  
Membrane bioreactors (MBRs) are nowadays very popular in sewage treatment and among industries for water reclamation due to their best permeate quality, less space requirement and reduced sludge management cost than activated sludge processes [60; 61]. MBR is capable to reject bacteria and suspended solids, produce high purity permeate and flexible enough to operate with inflow variations. In comparison to activated sludge process MBR produces permeate with very low organics and accomplish reduction in OMPs at great extent [34; 62; 63]. The mechanisms for eliminating OMPs by MBR are complex and include volatilization, size exclusion, electrostatic repulsion or adsorption [39; 64]. Further, OMP removal depends on physicochemical peculiarities of OMPs, membrane characteristics such as pore diameter, molecular weight cut-off (MWCO) and zeta potential, membrane-solute interactions and feed properties [64]. The microfiltration/ultrafiltration membrane of an MBR can retain all suspended solids leading to higher mixed liquor suspended solids (MLSS) concentration, long sludge retention time (SRT). This system also provides the opportunity to develop different bacterial consortia thereby producing pure permeates while efficiently accomplishing the removal of moderately biodegradable OMPs [14; 27]. MF/UF-MBR are also more capable of rejecting OMPs and viruses than activated sludge processes [65; 66]. [PERSON] et al. (2013) have suggested that in MBRs to obtain pure water quality and to alleviate membrane fouling several operating parameters need to be optimized such as hydraulic retention time (HRT), solid retention time (SRT), mix liquor suspended solid (MLSS), food to microorganism (F/M) ratio, transmembrane pressure (TMP) and flux (J). Furthermore, fouling factors including membrane synthesis and morphology (types, orientation and physical properties), biomass characteristics and MBR operation (HRT, SRT, etc.) need to be taken into consideration because they are the major factors that affect MBR process [54]. In Table 1, a summary of reports for OMP removal employing MBRs is listed. It is clear that MBRs can more efficiently remove OMPs than conventional biologic treatment. MBRs achieved around 100% removal of PPCPs such as salicylic acid and propylparaben. Further, MBRs also can remove OMPs like beta blockers at 70-80% and atenol can be removed by up to 97% [67]. The authors report that membrane bioreactor-reverse osmosis (MBR-RO) successfully removed >99% of azithromycin, clarithromycin, erythromycinomycin, ofloxacin, sulfamethoxazole, diazepam, lorazepam, famotidine, ranitidine and clopidogrel [67]. In another study, [PERSON] et al. (2015a) observed that moving bed bioreactor-membrane bioreactor (MBBR-MBR) combined process was effective in OMP removal. However, low removal efficiency is reported for ketoprofen (16.2%), carbamazepine (30.1%), primidone (31.9%), bisphenol A (34.5%) and estriol (39.9%). Nonetheless, the same study found that hybrid MBBR-MBR system could effectively remove most of the selected micropollutants [68].
# Anomaly Detection for IOT Systems Using Active Learning
## 5 Results  
Model assessment is a pivotal stage in machine learning since it evaluates the performance of a trained model on novel and unexplored data. The subsequent list of the customary evaluation metrics can be used for anomaly identification.  
Precision refers to the proportion of accurate optimistic predictions made by a model that are true positives, meaning it correctly identifies anomalies. A model with a high accuracy score suggests a minimal occurrence of false positives.  
Recall  
The true positive rate is the percentage of real anomalies in the dataset that were found. A model with few false negatives has good recall accuracy. Table 6 presents the evaluation metrics pertaining to our suggested model.  
* F1-Score  
These are two requirements balanced by the harmonic mean of accuracy and recall. A well-balanced model with great accuracy and recall has a high F1-score.  
* Receiver Operating Characteristic (ROC) Curve  
Image 13 shows the relationship between the genuine positive rate (recall) and false positive rate (1-specificity) at different thresholds. This illustration is frequently referred to as a ROC curve. One measure used to evaluate a model's overall performance is the AUC score. A rating of 1 signifies optimal categorization performance, whereas a score of 0.5 denotes random guessing.
# Temporal Encoding to Reject Background Signals
## 2 Results
### Experimental Demonstration  
The scheme detailed above was realised using a GaN violet emitting (405 nm), micro-LED device as the transmitter and a silicon Single Photon Avalanche Detector (SPAD) as the receiver. The LED chip was bonded to a custom CMOS driver allowing short pulse operation, with durations of 5 ns. The LED wavelength of 405 nm was initially used as the devices showed improved pulsed performance in this configuration over other wavelength counterparts. Data signal modulation was applied as a slower on-off keying of the short pulse train. Figure 2a shows a measured pair of pulses from the micro-LED of 5 ns duration and with a relative delay of 40 ns. A variable neutral density filter was placed between the emitter and detector to control the received power at the SPAD. A schematic of the measurement setup is shown in Figure 2b. Full details of the devices and electronic drivers are given in Section 4.1.
## 4 Discussion
These technologies can improve navigation and enhance the real-time communication of traffic conditions and operational advisories, potentially mitigating some of the challenges identified in our study. For example, V2X could enable more accurate and timely gap acceptance decisions by providing drivers with enhanced situational awareness and predictive data on vehicle movements and speeds. Future studies should explore how the implementation of C-ITS can positively impact traffic efficiency and safety, particularly in complex driving environments where traditional navigational aids falter. Such research could provide valuable insights into the potential for these technologies to significantly improve driving conditions.