esa-sceva/satcom-chunk-collection huggingface dataset:
# Microalgal Proteins and Bioactives for Food, Feed, and Other Applications
## 4 Legislation Governing The Use of microalgae
### European Regulation on Marketing of Microalgae for Food
#### 4.1.2 Regulation on Novel Foods and Novel Food Ingredients  
Novel Foods and Novel Food Ingredients regulation is applied to foods and food ingredients that were not consumed on a significant level in Europe before May 1997. This concept includes, for example, microalgae oils rich in omega-3 fatty acids, which have been introduced to the market recently and thus fall under this regulation, despite the consumption of omega-3 fatty acids having a long history. Another example of a product regulated as novel food is a blue colorant extracted from _Arthospira_, despite _Arthrospina_ itself not being considered as a novel food and being consumed for several centuries. The risk assessment process leading to the commercialization of novel food product is usually time-consuming and expensive [16]. _Arthrospira_ and _Chlorella_ are not included in the novel food list, because they are not considered as novel and have a designation GRAS (generally recognized as safe) [143].  
The main principle of this regulation is to ensure food safety for consumers, so that a product is not dangerous or nutritionally disadvantageous and is labelled properly. When companies intend to introduce a novel food or novel food ingredient to the market, firstly they must present the scientific information and a safety assessment report to a national authority for authorization. The process of authorization involves conditions of use, designation of novel food or food ingredient, specification and labelling requirements. After, the Commission asks the Standing Committee on Food Chain and Animal Health for its opinion, and, if the novel food or food ingredient is likely to have an effect on public health, it also asks the EFSA Scientific Committee for Food.  
When the applicant considers its food or food ingredient as'substantially equivalent' to a similar product which is already marketed in EU, the process can be simplified to a procedure called 'notification' [16].
## 2 Materials and Methods  
C\({}_{32}\)H\({}_{66}\), hexane, ethanol, and acetone with a nominal purity of 98% were purchased from Aldrich and used without purification. C\({}_{32}\)H\({}_{66}\) was dissolved into hexane under magnetic stirring, to keep its concentration varying from 0.05% w/v to 0.5% w/v. Fresh n-type Si wafers with a length/width of 10 \(\times\) 10 mm\({}^{2}\) and a SiO\({}_{2}\) layer about 1-2 nm in height were obtained by ultrasonic cleaning in ethanol, acetone for 30 min each, and then drying at 60 \({}^{\circ}\)C. A 50 \(\upmu\)L solution was casted on a Si wafer surface, spun at 5000 rpm until it transformed into a film, and the final C\({}_{32}\)H\({}_{66}\) nanofilm was obtained after drying at 60 \({}^{\circ}\)C for 4 h.  
Cross-linking experiments were conducted on a home-made electron cyclotron resonance ECR-enhanced microwave plasma reactor, which uses extraction ions to fill a space fully with H\({}_{2}\) gas to collide and exchange its kinetic energy to hydrogen. This process may produce H with some certain kinetic energy and/or H\({}_{2}\) having more kinetic energy. The energetic hydrogen breaks C-H bonds and then all C radicals will produce cross-linking. The bombardment fluence of hyperthermal hydrogen on the samples was estimated by the extraction voltage (as primary H\({}^{+}\) kinetic energy) and collision numbers, which depend on the distance between extraction grids, sample surface, and the pressure, for a given extraction voltage. One of the bombarded C\({}_{32}\)H\({}_{66}\) samples was soaked, washed by hexane, and then dried with compressed N\({}_{2}\). All samples were sent to XPS and AFM characterization ex situ. Direct evidence of the cross-linking was to be identified if there was any C\({}_{32}\)H\({}_{66}\) film remaining in the surface after washing by hexane.  
XPS was carried out with a Kratos Axis Ultra spectrometer using a monochromatic Al K\(\alpha\) source (8 mA, 14 kV). High-resolution spectra of the core level were recorded using a 0.05 eV step, 20 eV pass energy, and an analysis area of 300 \(\upmu\)m \(\times\) 700 \(\upmu\)m, while survey spectra were recorded at a 0.7 eV step, 160 eV pass energy, and analysis area of 300 \(\upmu\)m \(\times\) 700 \(\upmu\)m. AFM topographic images were obtained in an area of 5 \(\upmu\)m \(\times\) 5 \(\upmu\)m using a Park Systems (Model: XE-100) operated in the noncontact mode. We used a noncontact cantilever with a nominal spring constant of 40 N/m and the tip was pyramidal in shape with a radius of 10 nm at the apex.  
Wettability of the surfaces was evaluated through contact angle measurement using a Rame-hart 100 goniometer. Drops (10 \(\upmu\)L) of deionized water were placed into the surfaces via a micrometer syringe (Gilmont Instrument). At least three measurements on each of the two identically treated samples were taken and averaged.
## 3 Results and Discussion
### Effect on the Physical Characteristics
#### 3.1.3 Effect on P-Wave Velocity  
The physical parameter P-wave velocity was sensitive to the micro-morphology in the medium, so it can be used as an appropriate indicator to examine the damage on the microscopic scale [24]. In this study, the P-wave velocity was used to understand the degree of cracking with F-T action. Figure 8 showed the average P-wave velocity with the different number of F-T cycles and reflected that the F-T action significantly affected the P-wave velocity of the samples. From that, the P-wave velocity showed a decreasing trend under F-T action, with the average P-wave velocity decreasing by 5.8% after 30 F-T cycles. With increased crack density, however, the P-waves reflected, refracted, diffracted, or scattered through cracks in the medium, and the corresponding P-wave velocity slowed down [25]. Therefore, the variation in Figure 8 signified that plenty of micro-cracks have formed inside the tested samples. In addition, the decrease in P-wave velocity also indicated the reduction in the microstructural density of the tested samples that were subjected to F-T action.  
Figure 6: The average porosity and loss rate with cycles in tested sandstone.  
Figure 7: SEM micrographs of sandstone samples under F-T actions. Orange frame 1: pore; Red frame 1: intergranular fracture; Red frame 2: transgranular fracture.
## 4 Results
### Unimodal-Based DL Experiments  
Five pretrained CNNs--MobileNet, DenseNet121, InceptionV3, VGG16, and VGG19--were evaluated on unimodal fingerprint (FP) and finger vein (FV) recognition tasks using the NUPT-FPV dataset, which was split into 80% training and 20% testing. As shown in Tables 6 and 7, MobileNet achieved the highest accuracy in both modalities, with 97.47% for FP and 97.54% for FV. DenseNet121 followed with 95.57% (FP) and 96.62% (FV), while InceptionV3 recorded 92.41% (FP) and 90.79% (FV). VGG16 and VGG19 yielded considerably lower accuracies, particularly on FV, with VGG16 achieving only 59.14%.  
MobileNet was chosen for multimodal fusion due to its strong precision, recall, and F1-scores across both FP and FV modalities, along with low error rates (e.g., FAR = 0.0002 and FRR = 0.0291 for FV). Its lightweight design further supports deployment in real-time and resource-limited biometric systems.
# An Interference Cancellation Scheme for High Reliability Based on MIMO Systems
###### Abstract  
This article proposes a new interference cancellation scheme in a half-duplex based two-path relay system. In the conventional two-path relay system, inter-relay-interference (IRI) which severely degrades the error performances at a destination occurs because a source and a relay transmit signals simultaneously at a specific time. The proposed scheme removes the IRI at a relay for higher signal-to-interference plus noise ratio (SINR) to receive interference free signal at a destination, unlike the conventional relay system, which removes IRI at a destination. To handle the IRI, the proposed scheme uses multiple-input multiple-output (MIMO) signal detection at the relays and it makes low-complexity signal processing at a destination which is a usually mobile user. At the relays, the proposed scheme uses the low-complexity QR decomposition-\(M\) algorithm (QRD-\(M\)) to optimally remove the IRI. Also, for obtaining diversity gain, the proposed scheme uses cyclic delay diversity (CDD) to transmit the signals at a source and the relays. In simulation results, the error performance for the proposed scheme is better when the distance between one relay and another relay is low unlike the conventional scheme because the QRD-\(M\) detects received signal in order of higher post signal-to-noise ratio (SNR).  
Keywords:IRI; relay; CDD; QRD-\(M\) +
Footnote †: journal: Applied
# Rheological Performance of Bio-Char Modified Asphal with Different Particle Sizes
## 5 Results and Discussions
### Bending Beam Rheometer Test
#### 5.4.2 Low Critical Temperature Analysis  
The low critical temperature of asphalt was determined on the basis of the Superpave TM specification, in which the creep stiffness is 300 MPa and the m-value is 0.3 [36]. The low critical temperatures of all asphalts are shown in Figure 6.  
From Figure 6, it could be seen that 4% BS had better low critical temperature than 4% GS. The 2% BL, 2% BS, and 4% BS had better low critical temperatures which were no higher than \(-\)18 \({}^{\circ}\)C. The bio-char content should have been less than 4% because the low critical temperatures of bio-char modified asphalts increased with the added bio-char, and the low critical temperatures of 8% BL and 8% BS were much higher than \(-\)18 \({}^{\circ}\)C. Furthermore, the low critical temperatures of 2% BS and 4% BS were lower than those of 2% BL and 4% BL. Therefore, the smaller bio-char particles size of less than 75 \(\upmu\)m were recommended to be used as the modifier.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Asphalt Binder** & **Time (s)** & **Deflection (mm)** & **Stiffness (mPa)** & **m-Value** &
\begin{tabular}{c} **Remarks (for Stiffness** \\ **and m-Value)** \\ \end{tabular} \\ \hline Control & 60.0 & 0.446 & 179 & 0.31 & Pass the specification \\
4\% GS & 60.0 & 0.422 & 187 & 0.30 & Pass the specification \\
2\% BL & 60.0 & 0.447 & 177 & 0.30 & Pass the specification \\
4\% BL & 60.0 & 0.329 & 243 & 0.29 & Fail the specification \\
8\% BL & 60.0 & 0.328 & 245 & 0.28 & Fail the specification \\
2\% BS & 60.0 & 0.378 & 213 & 0.30 & Pass the specification \\
4\% BS & 60.0 & 0.308 & 262 & 0.30 & Pass the specification \\
8\% BS & 60.0 & 0.284 & 284 & 0.28 & Fail the specification \\ \hline \hline \end{tabular}
\end{table}
Table 4: The Bending Beam Rheometer (BBR) results of control, graphite modified and bio-char modified asphalts at \(-\)18\({}^{\circ}\)C.
# Study on the Effect of Vibrating Process on the Compactness of Slipform Concrete
## 4 Optimal Vibrating Parameter Equation and Paving Speed Equation
### Paving Speed Equation  
This study laid out its vibrating bar based on the GOMACO GP-2400 slipform paver (Figure 13a). When the paver was working, the vibrating bar was rotated from horizontal to downward at an angle of \(\theta\) to obtain a better-vibrating effect (Figure 13b). The actual length of the vibrating bar was L. Assuming that the propagation direction of the vibration wave impact generated at any point of the vibrating bar is perpendicular to the vibrating bar, the length of the vibrating effect of the vibrating bar at any point in the corresponding vibrating area after the \(\theta\), angle rotation of the vibrating bar downward is \(L/\cos\theta\). The paving speed \(V\) of the slipform paver was deduced from Equation (3) (Equation (4)).  
\[V=\frac{60L}{Kt\cdot cos\theta} \tag{4}\]  
where \(L\) is the length of the Concrete vibrating rod (m), \(\theta\) is the angle between the pounding bar and the horizontal direction, and \(V\) is the vibrator frequency corresponding to the small ball experiment sliding die paving speed (m/min).  
The equation for the paver travel speed \(V\) and the vibration viscosity coefficient \(\eta\) were derived (Equation (5)) by substituting \(t\) in Equation (3) with the vibration viscosity coefficient \(\eta\).  
\[V=\frac{4696.482L(\rho_{C}-0.0858)}{KH\eta\cdot cos\theta} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Vibrating Frequency** & \(T\) & \(t\) & \(K\) = \(t_{y}t\) \\ \hline
6000 r/min & 70–90 s & 45.7 & \(1.53\leq K\leq 1.97\) \\
8000 r/min & 60–80 s & 31.7 & \(1.61\leq K\leq 2.14\) \\
10,000 r/min & 50–70 s & 30.2 & \(1.66\leq K\leq 2.32\) \\ \hline \end{tabular}
\end{table}
Table 6: Ranges of vibrating coefficient \(K\) values for different vibrating parameters.  
\begin{table}
\begin{tabular}{c c c} \hline
**Performance** & \multicolumn{3}{c}{**Optimum Vibrating Time (s)**} \\
**Requirements** & **6000 r/min** & **8000 r/min** & **10,000 r/min** \\ \hline Compressive strength & \(50\leq t\leq 90\) & \(40\leq t\leq 80\) & \(40\leq t\leq 70\) \\ Macroporosity & \(70\leq t\) & \(60\leq t\) & \(50\leq t\) \\ \hline \end{tabular}
\end{table}
Table 5: Optimal vibrating time for concrete with different bounding frequencies under the mold.
# ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization
## 4 Performance Evaluation
### Discussion  
The exceptional performance of TDDRMF, achieved with only 5% training data, highlights its suitability for dynamic microservice environments. Our experiments demonstrate that TDDRMF effectively captures both short-term and long-horizon dynamics while maintaining low computational overhead. In single-step forecasting, the incorporation of temporal regularization enables the model to learn multi-scale patterns--short-run trends and periodic behaviors--resulting in an RMSE of 0.3353, moderately higher than LSTM, yet with training and inference times reduced by orders of magnitude. For 10-step horizons, the nuclear-norm constraint further refines the representation of microservice dependencies, driving an 18.5% RMSE improvement over TAMF while reducing the training time from 12.95 s to 2.24 s. These results highlight how the synergy of low-rank approximation and global convex optimization can rival deep architectures in accuracy while vastly improving efficiency.  
Under cold-start conditions, TDDRMF maintains robust performance, remarkably outperforming ARIMA and TAMF in stability and precision. The ability of low-rank regularization to suppress noise in sparse samples, coupled with the dynamic error-detection mechanism that selectively triggers updates, enables dependancy predictions even when historical data are scarce.  
Finally, the error threshold \(\epsilon\) in our dynamic update scheme governs a critical trade-off between accuracy and efficiency. A larger \(\epsilon\) reduces update frequency--thereby lowering overall training cost--but risks missing gradual workload shifts and elevating RMSE, whereas a smaller \(\epsilon\) offers marginal RMSE gains at the expense of frequent retraining and high computational burden. We thus tune \(\epsilon\) via grid search for the value at which RMSE improvements plateau while the update overhead remains moderate. This sensitivity analysis confirms that \(\epsilon\) is a key lever for practitioners to balance real-time responsiveness against resource constraints. However, increasing the data volume may decrease the model's prediction accuracy and stability. To address this challenge, our model can be enhanced by incremental learning, dynamic data adjustment, and the integration with sliding window methods, for better handling long-term prediction tasks.
## 4 Results and Analysis
### Simulation Results of Arch Culvert under Periodic Vehicle Load
#### 4.2.3 Settlement of Pavement of Steel Corrugated Pipe Arch Culvert  
Figure 30a shows the change in pavement settlement over time. The settlement difference between ZD1 and ZD4 that reached the maximum was about 3.3 mm at PB255. For PB0-PB255, both the settlement value and settlement rate of ZD1 were lower than those of ZD4. After that, the settlement rates of ZD1 and ZD4 tended to decrease. But the settlement rate of ZD4 decreased significantly and was lower than that of ZD1. At PB700, the two curves are close to intersecting, and the settlement is approximately \(-18\) mm. ZD1 and ZD4 represent the pavement settlement above the steel corrugated pipe vault and the soil between the arches, respectively. ZD4 is always lower than ZD1 before PB700, which may be due to the larger thickness of the backfill soil between the arches and the slightly larger settlement caused by compression deformation. To explore the stability value of the pavement settlement, a quadratic polynomial fitting was performed on the two curves. The data after PB255 (760 s) were used to improve the rationality of fitting. The fitting curves and corresponding polynomials are shown in Figure 30b. The figure shows that ZD1 and ZD4 are predicted to be stable at PB1500 when the settlements are \(-23\) mm and \(-27\) mm, respectively, with a difference of 4 mm.  
Figure 30: Settlement of road surface and vault: (**a**) Numerical simulation; (**b**) Polynomial fitting.  
Figure 29: Mises stress curve.  
Combined with the above analysis, the settlement rates of ZD1 and ZD4 decreased at approximately PB255. However, this settlement rate caused by the actual daily traffic volume is less than the calculated value. According to the simulation, the time required for structural deformation of the new steel corrugated pipe arch culvert bridge is quite short. Therefore, the foundation design and reinforcement scheme are safe for the engineering project.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 3 Data Description and Analysis
### Feature Comparison, Profiling and Analysis
#### 3.3.1 Feature Comparison  
First, a pair-plot is performed to carry out a correlation of the parameters. Distinct characteristics in each of the data sets [47] based on the status of the driver can be observed. The comparison between characteristics of the \"lane detection\" set is carried out as shown below.  
1. As can be seen in Figure 1, there is a distinct relation between the \"aggressive\" and \"drowsy\" target variables with the inclination of the car to the lane centre. It can be noticed that there is an unexpected positioning of the car in the lane center for \"drowsy\" cases.
2. The car orientation relative to the lane curvature seems to be opposite for several \"aggressive\" and \"drowsy\" cases. Given the number of cases, the clear distinction between the two target variables gives explicit information to work with.
3. Next, regarding the comparison between the status and width of the road, while it is clear that the number of cases of drivers being aggressive seems to increase with either narrow roads or--even more so--wide roads, the number of cases showing an increase among drowsy drivers is relatively significant.  
Next, a comparison between the characteristics of the \"traffic detection\" set is presented.  
1. As can be seen in Figure 2, the distance from the nearest car does not appear to affect the drowsy cases, which can be explained from the even spread. However, the cases of aggressive driving are split between those with little distance, causing drivers to possibly make dangerous maneuvers, and those having a significant distance between cars, encouraging them to race.
2. The above characteristics can also be observed with cases where the time of impact between the car of concern and the car in front is considered. With a reduced time of impact, drivers seem to opt for the same choice of driving normally.
3. As the number of vehicles increases, the likelihood of drivers going at a controlled, normal pace seems to be implied from the relation between the number of vehicles and the type of driving.
4. The GPS speed can be most easily interpreted, with an extremely high pace more often than not suggesting aggressive driving. The roads and their corresponding speed limits may be something to be considered in the future with respect to this parameter.  
Figure 1: Feature comparison of “lane detection” dataset.
# An Innovative Low-Cost Equipment for Electro-Concentration of Microalgal Biomass
## 4 Discussion  
For the proper concentration of algal biomass, mixing of the media is required to improve cell-electrode interaction, which in turn increases the floc formation [16]. Other authors, such as [17], found that this variable did not affect the efficiency of algal harvesting. In the present work, we found that mixing affected the concentration of algal biomass positively, without a high increase in temperature. The only variable that did not affect it (either positively or negatively) was time. However, longer times (>30 min) could eventually increase energy consumption. Through the development of this work, an electrical current of 50 W was used. [16; 18; 19; 20] found that at a higher voltage (>30 V), the time was reduced (<10 min). This occurs due to an increase in the number of free ions from the sacrificial electrode, which accelerates the shift of cell-surface charges, allowing for faster flocculation. Another variable that directly affected the efficiency was the distance between the electrodes. All the experiments performed employed a length of 5 mm, which, according to [16], is an optimal distance to increase the efficiency of the process and reduce the time. According to [21], distances less than 10 mm affect the formation of gas bubbles around the electrode, and more considerable distances can negatively affect the overall efficiency and increase energy consumption. Optimization by Response Surface Methodology can lead to the best results [22; 23; 24]  
Another crucial factor is the electrode material. Table 3 presents a short review of different materials employed for the harvesting of algae and cyanobacteria. The most common material is aluminum. Other materials, such as graphite, require the addition of electrolytes or chemical flocculants, such as Al\({}_{2}\)(SO\({}_{4}\))\({}_{3}\) or chitosan, to achieve higher efficiencies (>92%) [4; 16]. Figure 4: Mass loss of the electrodes (**a**) and efficiency of electroflotation by the reuse of electrodes (**b**).
## 2 Data and Methods
### Ecolight Simulation Data
\(r_{rs}\)(\(\lambda\), z) is the ratio of upwelling radiance (\(L_{u}\)(\(\lambda\), z)) to downwelling plane irradiance (\(E_{d}\)(\(\lambda\), z)) at wavelength \(\lambda\) nm and water depth z m in the water column:  
\[r_{rs}\)(\(\lambda\), z)=\frac{L_{u}(\(\lambda\), z)}{E_{d}(\(\lambda\), z)}. \tag{4}\]  
\(K_{x}\)(\(\lambda\), z), particularly \(K_{d}\)(\(\lambda\), z), \(K_{u}\)(\(\lambda\), z), and \(K_{Lu}\)(\(\lambda\), z), represents the diffuse attenuation coefficients of \(E_{d}\), upwelling plane irradiance (\(E_{u}\)(\(\lambda\), z)), and \(L_{u}\), respectively. \(K_{x}\)(\(\lambda\), z) is defined as follows [23]:  
\[K_{d}(\lambda,z)=-\frac{d\ln E_{d}(\lambda, z)}{dz}=-\frac{1}{E_{d}(\lambda,z)}\frac{dE_{d}(\lambda, z)}{dz}, \tag{5}\] \[K_{u}(\lambda,z)=-\frac{d\ln E_{u}(\lambda,z)}{dz}=-\frac{1}{E_{u }(\lambda,z)}\frac{dE_{u}(\lambda,z)}{dz},\] (6) \[K_{Lu}(\lambda,z)=-\frac{d\ln L_{u}(\lambda,z)}{dz}=-\frac{1}{L_{ u}(\lambda,z)}\frac{dL_{u}(\lambda,z)}{dz}. \tag{7}\]Although it is defined differently in practice, \(K_{x}(\lambda,z)\) often has nearly the same values in vertically homogeneous waters except near the surface, and it asymptotically approaches the same value at great depth.