esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Deep Eutectic Solvents on Extraction of Polyphenols from Grape Seeds and Skin
## 2 Materials and Methods
### Determination of Total Polyphenol Content (TPC)  
Total polyphenol content of extracts was determined using the rapid microtiter plate _Folin-Ciocalteu_ method [28]. Briefly, 10 \(\mu\)L of diluted samples and serial standard solutions (gallic acid) were loaded on a 96-well microtiter plate (MTP). The repeated volumes of _Folin-Ciocalteu_ reagent diluted 10 times (100 \(\mu\)L), and 1 M Na\({}_{2}\)CO\({}_{3}\) (80 \(\mu\)L) were transferred to wells. After an hour of incubation at room temperature in the dark, the absorbance of blue coloration was measured at 630 nm against a blank sample on an MTP reader (BIOTEK, USA, ELx800 Absorbance Microplate Reader). The results were expressed as mg Gallic Acid Equivalents (GAE) per gram of dry weight (mg GAE g\({}^{-1}\) DW). Calibration curve (y = 0.0028x + 0.0087), prepared for the working solutions of Gallic Acid in the concentration range of 10 to 80 mg L\({}^{-1}\), showed good linearity (r\({}^{2}\) = 0.9983). Limit of detection was 1.341 mg L\({}^{-1}\), and limit of quantification was 4.470 mg L\({}^{-1}\). Spectrophotometric analyses were done in triplicate.
# Detecting the Speed Change Intention from EEG Signals:
## 2 Materials and Methods
### Experimental Procedure Training  
In this study, two protocols were used. In E1 (Figure 2a), data were recorded from five users in which EEG was collected in states of monotonous walk and change intention to subsequently analyze them in offline and pseudo online analysis. A second experiment E2, see (Figure 2b), was also recorded for the same two states while exoskeleton was controlled in closed-loop. E1 protocol was also used to obtain the training trials of the EEG classification model that would be used to control the exoskeleton in closed-loop. During these training trials the exoskeleton was in passive (non-assistance) mode.  
Figure 1: Experimental setup for E1 and E2 experiments. (**a**) E1: The subjects use the EEG acquisition equipment and IMUs. (**b**) E2: The subjects use in addition the H3 exoskeleton. During both experiments, subjects walk during 10–12 s at a reduced constant speed and then, at their own will, increase or decrease their current speed. After that, they continued walking for 2–3 s before stopping. This repetition was performed several times. This figure shows the approximate areas (between red lines) of the different phases of the protocol in both walking directions of the experimental room. Additionally, in E2 protocol, the exoskeleton was in passive mode for the model training while in the test phase was controlled in closed-loop.  
In the training phase for both E1 and E2 the users performed the following process for each trial. First 15 s were used for \(H^{\infty}\) convergence [19]. Then, they started walking at a constant speed during 10-12 s till a voluntarily increase or decrease of speed within a specified area (as a guideline) was performed, then speed was maintained between 4 and 6 s. The first two training trials were used to set the speed. This helps the subject to learn how to perform the increment within the delimited (but diffuse) area in a wide interval. It was emphasized that the subject did not think beforehand to make the change and that the view was always set forward. This was repeated 8 more times. During all the trials, a technical assistant followed the subject from behind with the acquisition equipment on a cart (Figure 1). As a result of the 10 trials, 80 speed changes were registered, with approximately an statistical distribution of 50% increases and 50% decreases.
## 5 Experimental Results
### Result Analysis  
The proposed algorithm, as explained in Section 3, was verified by actual driving data based on offline simulation of the expressway. Figure 6 shows an algorithm verification scene during the simulation. The map and measurement information are shown in the legend. The lane is represented by an x-shaped marker, and the guardrail is also expressed by a square-shaped marker. The results of each position correction resulting from ICP map-matching are shown in red. The black dots express lane information of the HD map, and thick grey dots indicate the guardrail point clouds of the HD map. The global position of the proposed localization result is shown as a red car, and the raw data of GPS with simple dead reckoning and the DGPS (RT2002)-based reference global vehicle position are depicted as a black vehicle and a blue vehicle, respectively, in Figure 6.  
Figure 7a shows the distance error of the localization result based on reference DGPS (Oxts-RT3002) output over time. Figure 7b shows changes of localization errors on the longitudinal direction and Figure 7c,d expresses errors on lateral and yaw angles over time for the partial section, including the junction, plotted with 3-sigma bounds (blue dashed lines) to show how the filter is well-tuned in order to estimate the vehicle's position and posture. The four error calculations happen during a 570-s time period. For some sections that have no environment measurements or were failed to map-matching properly, the accumulated positioning error increases slightly until the proper map-matched correction is obtained. The performance of the proposed positioning algorithm enables lateral steering control to be applied safely.  
Figure 6: Algorithm verification scene (vehicle Color: red—localization result, black—Low-cost GPS, blue—DGPS (RT3002) for reference). (**a**) Lane + guardrail matching (junction (JC): entry). (**b**) Guardrail matching only (no lane; JC: middle).
# A New Multi-Objective Unit Commitment Model
## 4 Numerical Examples
### Comparing with Other Methods  
One important contribution of this paper is that the strategy of finding the compromise solution is directly optimized in the objective function in (11), which avoids looking for other non-dominated solutions as described in Section 2.3. The MOUC solution in this paper is compared with other two MOUC methods namely weighted-sum [37] and NBI [38; 39]. In addition, the results and computation times have been shown in Figure 5 and Table 1 respectively.  
In conventional methods, the Pareto front is calculated by weighted-sum and NBI, then the best solution with respect to (11) is selected among the Pareto front. Hence, in order to find a better compromise solution, a denser Pareto front is required. In Figure 5, for weighted-sum and NBI, we use each algorithm to calculate 30 non-dominated solutions to obtain the Pareto front. The initialization strategy of this paper can make the solution of our method better than the result of weighted-sum, but not as good as that of NBI. However, as the iterative process progresses, our method ultimately finds solutions that are better than the NBI method. Besides, in the proposed problem formulation, the UC problem only needs to be solved for one time, hence, the efficiency outperforms weighted sum and NBI as seen in Table 1.
# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
## 2 State of the Art
### IEEE 802.15.4
#### 2.2.1 IEEE 802.15.4 Specification
Different modes for the CCA are available (1 to 4), each has its own way to determine the availability of the channel. The CCA mode 4 reports always an idle channel and is used in low duty cycle applications. It implies the usage of a modified version of the PCA backoff algorithm [14]. The latest version of IEEE 802.15.4 uses a special extension called deterministic and synchronous multi-channel extension (DSME) that extends the usage of GTS to all network devices by using multiple channels for slot allocation in dense networks. This extension increases the complexity of slots coordination and complicates the process of avoiding inconsistencies in slots schedules [14].
## 2 Literature Review  
### Externally-Bonded FRP (EB-FRP)  
Local retrofitting of reinforced concrete (RC) members with externally applied FRP composites is an extremely popular solution, the suitability of which is largely justified by its minimal weight, superior tensile strength, resistance to corrosion, and negligible impact on the mass or stiffness of the structure. FRP laminates are brittle materials with a linear-elastic, stress-strain relationship to failure. Compared to Grade 60 reinforcing steel, the stiffness of FRP is less than half (e.g., Young's modulus of 13,900 ksi for carbon FRP vs. 29,000 ksi for reinforcing steel), and its ultimate tensile strength in the direction of the fibers is more than 50% larger (e.g., 143 ksi vs. 90 ksi for steel). The effectiveness of EB-FRP materials in increasing the strength and/or ductility of a structural member is proportional to the level of deformation (strain) that they can develop, which has been shown to be heavily dependent on the mode of failure [8]. EB-FRP materials often fail at stress levels significantly lower than their tensile capacity. This occurs for two main reasons. First, the bond between the FRP and the concrete substrate is weak compared to the FRP's tensile capacity, making EB-FRP materials highly susceptible to premature debonding. This debonding is often followed by immediate and brittle failure due to the sudden load increase in the internal steel reinforcement. Second, the brittle nature of FRP materials makes them highly sensitive to stress concentrations. This sensitivity can lead to premature fracture due to bends (e.g., at corners), surface irregularities, or uneven stress distribution caused by the anchorage system. As a result, EB-FRP design guidelines prescribe strict limits on the maximum strain to be used when computing FRP strength contributions. ACI 440.2R-17 [9], the leading EB-FRP design and construction guideline in the United States, imposes a maximum effective strain value in the FRP of 0.4% for shear applications, about 40% of its typical ultimate (rupture) tensile strain. Prevention of stress concentrations is tackled with guidelines on surface preparation and corner rounding (minimum corner radius of 0.5 in [13 mm] on corners to be wrapped with FRP).
## 4 Processing Methods  
Multichannel Analysis of Surface Waves [10] provided information only on the Ferrara site, as no dispersive event could be identified in the Kastela dataset, which was probably due the presence of tubes or other voids just below the road.  
In Figure 2a, we show the frequency-wavenumber (f-k) spectrum of the Ferrara dataset, from which we picked the most energetic linear (i.e., dispersive) event to compute the dispersion curve shown in Figure 2b. The frequency range is from 3.5 to 15 Hz, giving a maximum wavelength of 58 m, i.e., a penetration depth of 29 m (\(\lambda/2\), where \(\lambda\) is the wavelength). Finally, we inverted such a dispersion curve using the neighborhood algorithm in the GeoPSy software [27]. The initial model consists of three layers + Halfspace, the properties of which are described in Table 2. One million profiles were computed; in Figure 2c, we show the 500 best--that is, those generating a synthetic dispersion curve having the lowest misfit with respect to the picked one.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Line Name** & **First Rec. N** & **First Rec. E** & **Last Rec. N** & **Last Rec. E** & **UTM Zone** \\ \hline Ferrara Line 1 & 4964800 & 713828 & 4964503 & 713850 & 32 \\ \hline Ferrara Line 2 & 4964552 & 712300 & 4964258 & 712347 & 32 \\ \hline Kastela Line 1 & 4822696 & 611466 & 4822770 & 611167 & 33 \\ \hline Kastela Line 2 & 4822643 & 612232 & 4822660 & 611943 & 33 \\ \hline Kastela Line 3 & 4822683 & 611984 & 4822703 & 611821 & 33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: UTM coordinates of the first and last receiver of each acquired seismic line.  
We performed first break tomography on all the lines for all of the wavefields. Before this, some pre-processing was performed. Specifically, to increase the signal-to-noise ratio, for each shot, we applied a predictive deconvolution to the uncorrelated signals as in [28]. Furthermore, to remove random noise, we stacked in the time domain the seismograms relative to the same shot point.  
The first breaks were manually picked on the seismograms. Examples of first-break picking can be seen in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Layer Number** & **Thickness** & **Vs** & **Density** \\ \hline
1 & 3–15 m & 100–200 m/s & \\ \hline
2 & 3–15 m & 120–220 m/s & \\ \hline
3 & 3–15 m & 140–240 m/s & \\ \hline Halfspace & & 150–250 m/s & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the initial model used for the inversion of the surface waves.  
Figure 2: (a) f-k spectrum. (b) Dispersion curve. (c) The 1000 best fitting S-wave velocity profiles. The color of the lines represents the misfit, as indicated in the color bar.  
Then, the travel times were inverted with Cat3D software [29], which is a tomographic package developed at the National Institute of Oceanography and Applied Geophysics-OGS that uses a ray-tracing algorithm based on a minimum time principle and SIRT (Simultaneous Iterative Reconstruction Technique) as an inversion method [30].
## 3 Results and Analysis  
### Overlapping-Droplet-Classification-Test Validation and Analysis  
#### 3.1.1 Bayesian Optimisation-Based Hyperparameter Optimisation  
Based on the experimentally collected fog-droplet-image dataset, this study used the XGBoost algorithm to construct a triple classification model for strongly overlapping fog droplets with a single concave point, parallel overlapping fog droplets, and non-parallel overlapping fog droplets, and selected the droplet morphological features (eight dimensions) as the classification driving variables. The training and test sets were randomly divided according to the ratio of 4:1, and 12,000 overlapping-droplet training sets and more than 3000 overlapping-droplet test sets were obtained, which were used for model training and generalisation performance evaluation, respectively.  
The hyperparameter system in XGBoost controls the model performance through multi-dimensions, in which the tree structure parameters (e.g., max_depth and gamma) determine the model complexity, the regularisation parameters (lambda, alpha, colsample_bytree, and subsample) inhibit overfitting, and the learning process parameters (n_estimators and learning_rate) regulate the training dynamics. Traditional hyperparameter optimisation methods such as grid search and random search suffer from low efficiency and tend to fall into local optimums, while Bayesian optimisation achieves intelligent search by constructing a probabilistic agent model: it dynamically selects the evaluation points based on a priori knowledge, which improves the sample utilisation rate while maintaining the search speed, avoids the trap of local optimums, and possesses the overfitting-resistant property. The optimal configuration of XGBoost hyperparameters is in contrast to the tuning results that are shown in Table 5, which contains the initial value, search space, and final tuning results of each hyperparameter.  
Figure 9: Segmentation process for parallel overlapping droplets. Where (**a**) is Binary Image Extraction; (**b**) shows Convex Hull Construction; (**c**) shows Concave Points Detection; (**d**) shows Centre of mass calculation; (**e**) is Parallel Segmentation Line Generation.
## 5 Results
### User Spacecraft Orbit Determination Results
#### 5.2.1 Case #1  
Table 3 exhibits the maximum error observed in the user spacecraft's trajectory relative to the operational solution. The associated discrepancies in the TDRS orbits leveraged to derive these outcomes are displayed in Table 2. As several TDRS satellites were employed in generating the user spacecraft's trajectory, the total cumulative TDRS orbit position and velocity discrepancies indicated in Table 3 were computed as follows:  
\[TDRS_{position\_error}=\sqrt{\sum_{i=6}^{12}{(\Delta x^{2}+\Delta y^{2}+\Delta z ^{2})}_{TDRS_{i}}} \tag{2}\]  
Figure 12: The FDF estimated TT&C measurement bias of all TDRS satellites over the past 4+ years.  
\[TDRS_{velocity\_error}=\sqrt{\sum_{i=6}^{12}{(\Delta\dot{x}^{2}+\Delta\dot{y}^{2}+ \Delta\dot{z}^{2})}_{TDRS_{i}}} \tag{3}\]  
where \(TDRS_{position\_error}\) and \(TDRS_{velocity\_error}\) is the total cumulative error in position and velocity of the TDRS constellation's orbits, respectively. The maximum positional errors for each TDRS in the constellation are represented by \(\Delta x\), \(\Delta y\), and \(\Delta z\), while the maximum errors in velocity are denoted by \(\Delta\dot{x}\), \(\Delta\dot{y}\), and \(\Delta\dot{z}\) The variable \(i\) identifies the specific TDRS number, where \(i\) can take the values 6, 7, 10, 11, or 12.  
According to the findings for all user spacecraft, discrepancies were observed in position and velocity measurements. Among them, LANDSAT-9 recorded a maximum difference of about 50 m in position and 45 mm/s in velocity. In contrast, IXPE showed a considerably lower error with only 16 m and 16 mm/s in position and velocity, respectively.  
If accurate accounting of TT&C biases is performed, any discrepancies in the TDRS orbit due to TT&C biases would not have a significant impact on the user spacecraft orbit. Moreover, incorporating more data types or an additional DOWD pass could further enhance a spacecraft's orbit accuracy.
## 2 Background
### Named Data Networking  
NDN presents a high-level overview of the concept and associated approaches, in which data are identified based on substance or content, rather than on geographical location. Distributed computing is in high demand, as new technologies, such as big data, data mining, and AI, emerge. Increasing numbers of articles are devoted to NDN. The next-generation Internet is built on the foundation of NDN, which is a typical representation and implementation of content-centric networking (CCN) [12]. A new architecture that can allow large-scale deployment of content-based distribution is needed with the increasing use of the Internet, which has shown the limitations of the TCP/IP protocol stack. NDN has emerged as a game-changing alternative approach and a possible option for next-generation Internet architecture. NDN combines several aspects for routing, security, and mobility.  
As far as we know, very little research has considered multi-metric limitations on routing techniques. Several methods have been proposed for routing NDN, one of which is the ant colony optimization algorithm (ACO). The ACO has a better function than other traditional NDN routing techniques, in terms of bandwidth, cost, and delay [23]. As shown in Figure 2 the forwarding strategy (FS) is part of routing in NDN, to process content transmission in NDN communications. Another method, known as ant-colony  
degree into account, decreasing the average number of hit hops, the frequency of cache replenishment, and the throughput of content hits [27].  
Another aspect of NDN that is being discussed in current research is handover performance. Network mobility (NeMO) [28] has solved the mobility issues on NDN, but no detailed discussion is available on evaluation performance, i.e., signaling cost and handover latency. Other methods have been proposed, e.g., using IPv6 on mobility and applying SINEMO architecture [29]. Although some problems with the NeMO method have been solved, problems such as low efficiency and delay caused by triangular routing, high handoff cost and latency, and a high level of packet droplet and signaling overhead have not been addressed.
## 2 Materials and Methods  
### Isolation of Causal Agent of Bacterial Blight  
The sampling was carried out on 50 plants of _Phaseolus vulgaris_ (var. \"Flor de Mayo M38\") showing symptoms of _Xanthomonas_ sp. (bacterial blight) in San Diego Ecatepec, Amozoc, Puebla, Mexico (19\({}^{\circ}\)02\({}^{\prime}\)46.6\" N 98\({}^{\circ}\)05\({}^{\prime}\)15.6\" W). Necrotic leaves with irregular leaf spots, surrounded by yellowing and chlorosis, were collected during the months of May to September 2019. The geographical area in which the samples were obtained presented weather (cwb) with an average annual rainfall of 1500 mm and an altitude of 2320 masl [13]. The samples were kept in plastic bags and transported at 4 \({}^{\circ}\)C to the laboratory.  
Small pieces of tissue (1 cm\({}^{2}\)) were disinfected with 1% sodium hypochlorite for 3 min and washed with sterile distilled water. The isolation was done by means of direct seeding of the affected tissue in intuutritious agar (peptone 5 g/L, meat extract 4 g/L, agar 15 g/L), seeding four pieces per Petri dish (90 \(\times\) 15 mm). These were sealed and incubated to 36 \({}^{\circ}\)C for 72 h. Subsequently, bacterial transfers were made to obtain pure colonies and were subjected to Gram staining [6]. Only the Gram (-) isolates were purified in YDC medium (agar, yeast extract, dextrose and calcium carbonate) and BK (Middlebrook) at 36 \({}^{\circ}\)C for 48 h. Preliminary identification was carried out using the morphological characteristics of the colonies and biochemical characterization [14]. The samples were preserved in 20% glycerol.
## 3 Results and Discussion
### Physicochemical Properties of Emulsions
#### 3.1.2 Storage Stability of Produced Formulations  
The DLS parameters changes in emulsions during the storage are given in Figures 1, S1 and S2. Considering the stability of the investigated samples over 14 days, we found that the quality of each sample had decreased (Figure 1). This included an increase in sizes of droplets, PDI, and ZP values for each sample. However, the addition of extracts to the emulsion system prevented rapid degradation of the emulsion, as indicated by lower dynamics of Z-ave and increasing PDI values, especially for acetone-extract-doped samples. Such results agree with those reported earlier [26], as well as with the oxidative tests presented in this study. The presence of GA in emulsion does not always cause positive outcomes. In the presence of GA in most samples, the parameters describing the quality of the emulsion were less favorable compared with those without added GA. Only the reference sample was a clear exception, for which the GA presence did not cause a significant drop in quality factor for fresh samples. This effect was clearly visible for the samples after 7 days of storage, and partially after 14 days. For that time, only Z-ave increased. For the samples with one of the following added extracts, ethanolic, acetone, or water, the positive effect of GA addition on ZP values could be observed. The effect remained throughout the whole storage time (14 days); however, it was not accompanied by either a decrease in droplet size or PDI. Therefore, it can be concluded that even when GA positively influences the ZP value, it is not enough to prevent the degradation of the emulsion.  
Analyzing the direct size distribution (Figures S1 and S2), as a function of time and the presence of GA, we can conclude that both of those factors affect both populations within the 100-1000 nm range. In general, both fractions tend to separate with the storage time, and the addition of GA does not prevent this separation, which confirms the changes in PDI presented in Figure 1. The effect of size separations is the most pronounced for the emulsion system without any additives, and the formulation containing acetone extract is almost insensitive to both factors: GA addition and storage time.  
For each sample, on the 7 th and 14 th day of storage, the microscopic images are presented in Figure S3 and Figure S4, respectively. The conclusions support the results of the DLS analysis that the presence of GA does not affect the large-sized droplets.  
Concluding this part of the study, we claim that for produced formulations, GA does not act synergistically with any of the used extracts. The globally observed interaction produces no effect or, for selected formulations, is rather anti-synergistic. It also shows that the size of observed droplets is mainly determined by the type of extract.