esa-sceva/satcom-chunk-collection huggingface dataset:
# Valorization of Waste Tires by Pyrolysis and Activation Processes
## 2 Materials and Methods
### Feedstock Preparation and Characterization  
Prior to pyrolysis, the waste tires (untreated and acid-treated waste tire) were subjected to proximal analysis, elemental analysis, heating value tests, and thermogravimetric analysis. Metal- and textile-free waste tire powder was obtained from Lehigh Technologies,Inc. (Georgia, USA) with a particle size range of 75-125 \(\upmu\)m. The chemical activation process was based on an adapted version of the methodology suggested by [PERSON] et al. [26]: tire powder was impregnated with H\({}_{2}\)SO\({}_{4}\) 98% \(w/w\) for 14 h at 70 \({}^{\circ}\)C, in a volume tire/H\({}_{2}\)SO\({}_{4}\) ratio of 1:3 to ensure complete impregnation. Then, the sample was filtered under vacuum and washed with distilled water to achieve neutral pH, followed by final drying at 105 \({}^{\circ}\)C for 24 h. Moisture content was determined by the ISO 5068:1983 method, ash content by ASTM D3172-13, volatile matter by ISO 562-1981, and fixed carbon was determined by difference. An elemental analyzer (Leco, model CHN628, St. Joseph, MI, USA) was utilized to determine carbon, hydrogen, and nitrogen content according to ASTM D5373-08, while sulfur determination was done using the S-628 module of the same analyzer according to ASTM D4239. The heating value was obtained using a Parr Calorimeter 1341 (Parr Instruments, Moline, IL, USA) and a thermogravimetric analysis was performed using a thermogravimetric analyzer (Linseis, model STA PT1600, Selb, Germany). For this last analysis, approximately 10 mg of sample was heated at a rate of 20 \({}^{\circ}\)C/min until a temperature of 850 \({}^{\circ}\)C was reached, which was maintained for 1 h under N\({}_{2}\) atmosphere.
## 4 Discussion
### Challenges and Limitations  
The driving cycle has to be known in advance to establish the optimal energy management strategy with dynamic programming, which cannot be implemented in real vehicle. Other work focuses on the prediction of real-life driving cycle in order to adapt dynamic programming solutions to real-time control strategies [42]. Other approaches such as minimum Pontryaguin principle could be investigated and adapted in real time. However, one purpose of this algorithm is to ascertain the potential of reduction of pollutant emissions for conventional vehicles. It provides a reference of comparison to evaluate other sub optimal real-time strategies.  
The generic functions developed in this paper have been derived from a specific data set from a particular engine. Nevertheless, for similar engine architectures, one can expect trends to be the same, although quantitatively there may be variations. The general optimization methodology and results presented here should therefore apply for SI engines with equivalent features. The units considered for pollutant models are concentrations, so an increase in the mass flow will naturally result in pollutants increase. Engine models introduce a displacement volume parameter, which can be used for scaling purposes. Next project will include the latest technologies for direct injection spark ignition engines (DISI) and corresponding control parameters in the optimization process (turbo charging, variable valve lifting,...). The models will be evolving with these new technologies.  
A new objective will consist of adapting the algorithm to hybrid vehicles. Indeed, one can wonder if the additional degree of freedom brought by hybridization would change the engine management. From this perspective, a new state variable that represents the battery state of charge has to be introduced in the algorithm. We get three dimensions for the states (time, state of charge and catalyst temperature) and three dimensions for the control variables (intake pressure, spark advance and equivalence ratio). This is very challenging regarding to the use of computer's memory and calculation time.
## 3 Results and Discussions
### Wetting and Drying Cycles  
A series of tests with wetting and drying cycles for the untreated, 1%, and 2% XG-treated specimens were conducted. Figure 9 represents the results of the wetting and drying cycle tests. The UCS at cycle zero represents the compressive strength of each sample under optimum curing time.  
The UCS of pure soil gradually decreased with increasing wetting/drying cycles. The biopolymer concentration has a significant impact on soil compressive strength during wetting/drying cycles. Due to biopolymer degradation, the 1% XG-treated sample experienced a sharp UCS reduction at the second cycle, and then the soil strength slightly decreased by increasing the wetting/drying cycles. A relatively significant deterioration in soil strength with increasing cycles was seen for higher concentrations of XG. Despite UCS reduction in all specimens, the biopolymer-treated samples exhibited higher soil strength than the pure soil samples and leveled off after a certain number of cycles. The plotted lines are the fitting curves for each soil reference.  
Figure 8: Results of UU triaxial tests; failure envelope curves.  
\begin{table}
\begin{tabular}{c c c} \hline
**Soil Reference** & **Cohesion, c (kPa)** & **Internal Friction Angle, \(\phi\) (\({}^{\circ}\))** \\ \hline Pure soil & 280 & 41.0 \\
1\% XG & 580 & 36.6 \\
2\% XG & 970 & 25.9 \\ \hline \end{tabular}
\end{table}
Table 3: Results of UU triaxial tests; internal friction angle and cohesion of pure soil and XG-treated soil.  
The obtained results prove the effectiveness of XG biopolymer in reducing soil strength loss during cyclic wetting/drying.
## 4 Discussion of CPMA Application
### Data Collection Visualization in CPMA  
As stated in Section 3, the proposed CPMA was evaluated by applying two types of sensors developed in other studies. The evaluation arrangement is shown in Figure 10. Different physical phenomena provide impulses for each sensor. A modal shaker was used as a vibration behavior maker to read the accelerometer, and arbitrary pressure was applied to the piezo sensor to increase its voltage reading.  
Figure 10: Evaluation arrangement.  
Figure 9: Website appearance of CPMA.  
During evaluation, the duration of time and latency of data transmission depend on the Internet connection because it relates to the process of uploading images captured from the main UI. The four images had file sizes ranging from 6 kB to 50 kB. With the relatively small size and stable Internet connection, the upload process and its appearance on the website took approximately 4 s. Figures 11 and 12 show the results of reading the data and their appearance on the main UI and the CPMA website accessed from three different types of devices. Subsequently, the synchronization/uploading process can be performed in two ways: only after the end of data processing (the disconnect button was pressed) or continuously at a given time. However, when the process was carried out continuously, the data processing on the UI crashed when synchronization occurred. This disturbance caused the data retrieval process from the serial port to stop for 90-290 ms, as shown by the results of data recording with a synchronization process every 5 s in Figure 13. Therefore, this continuous synchronization feature is optimally applied when the monitoring process is only performed to display the monitoring data and not to record data for further processing (to be exported to the CSV).  
Figure 11: Main UI data visualization.  
Figure 12: CPMA website data visualization.  
Furthermore, the message-sender feature can be applied using the process and results shown in Figure 14. With this feature, it is understood that an operator at the machining site can receive a message from the user/manager of the CPMA at another location.
# Research on Grooved Concrete pavement Based on the Durability of Its Anti-Skid Performance
## 3 Results and Conclusions
### Results of Anti-Skid Tests
#### 3.1.2 Anti-Skid Results for LT Schemes  
Table 12 illustrates the skid resistance performance results for LT samples, and Table 13 displays the range analysis of LT samples. kij in Table 13 refers to the average value of the sum of \(\mu\) in the j column under the kij level; Rj refers to the difference between the maximum value and minimum value of kij, kij, kij, kij, and kij in the j column, namely, range. The size of range represents the different effects of each factor on the \(\mu\) value. Figure 8 shows the range analysis chart of the six factors.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample No.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \(\mu\) & 0.63 & 0.58 & 0.63 & 0.56 & 0.82 & 0.46 & 0.57 & 0.65 & 0.61 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of skid-resistant performance for L samples.  
Figure 7: Photo after spraying the composite curing agent after grooving.  
The range analysis of LT samples shown in Table 13 and Figure 8 indicates that T spacing is the key factor impacting the anti-skid performance of concrete pavement, followed by L spacing, L width, T width, L depth, and T depth. The groove volume within a certain scope is the primary factor affecting \(\mu\). The results of mathematical calculations and experimental findings are slightly different, but remain consistent throughout the geometric analysis. Finally, the test results show that LT samples 19 (5, 5, 30, 6, 4, 10 mm) and 22 (6, 3, 15, 6, 5, 24 mm) have the greatest skid resistance.
# Effects of Process Parameters on the Quality of Suantang Beef
## 3 Results and Discussion
### Quality Analysis of ST Beef and Boiled Beef
#### 3.6.2 Protein Composition and SDS-PAGE of ST Beef and Boiled Beef  
The effects of ST processing on the contents of water-soluble protein, salt-soluble protein, and insoluble protein in beef are shown in Table 5. There was no significant difference in the total protein content between ST beef and boiled beef. However, it is worth noting that beef's water-soluble protein and salt-soluble protein decreased after ST cooking. This phenomenon may be due to the decrease of pH and cathepsin L activity in beef after ST processing, which made the protein in ST beef have better cohesion so that the protein cannot be extracted completely when using buffer solution [21]. This result corresponds to the measured texture result. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Testing Indicators** & **Factors** & **Sum of Square** & **Df** & **Mean Square** & **F-Value** & **Significance** \\ \hline \multirow{4}{*}{Sensory evaluation} & Salt addition & 0.848 & 2 & 0.424 & 10.822 & **\#** \\  & Cooking time & 1.503 & 2 & 0.752 & 19.174 & **\#** \\  & Meat thickness & 0.662 & 2 & 0.331 & 8.449 & **\#** \\  & Beef–ST ratio & 0.082 & 2 & 0.041 & 1.043 & \\  & Error & 0.706 & 18 & 0.039 & & \\ \hline \multirow{4}{*}{Turnout rate} & Salt addition & 478.969 & 2 & 239.485 & 507.957 & **\#** \\  & Cooking time & 263.091 & 2 & 131.545 & 279.013 & **\#** \\  & Meat thickness & 117.247 & 2 & 58.624 & 124.343 & **\#** \\  & Beef–ST ratio & 50.449 & 2 & 25.224 & 53.502 & **\#** \\  & Error & 8.486 & 18 & 0.471 & & \\ \hline \hline \end{tabular} Note: significance: *, \(p<0.05\), significant.  
\end{table} 
Table 4: Analysis of variance of orthogonal test results. SDS-PAGE electrophoresis of water-soluble, salt-soluble, and insoluble proteins in ST beef and boiled beef is shown in Figure 4. The water-soluble protein of beef changed obviously after being cooked by ST. The band range of 135-180 kDa molecular weight in lane 1 becomes shallow. That was maybe an incomplete extraction process, causing a band concentration to decrease; b and c bands did not appear after ST cooking, which may be that the ST prompts endogenous proteases to hydrolyze this protein. After extraction, the concentration of salt-soluble protein was low. The band is shallow, while the concentration of insoluble protein is high and the band deep, which may be due to the degradation of meat protein, protease, and peptidase, and some form large aggregates with a decrease in pH value [46]. The results of SDS-PAGE electrophoresis were consistent with those of protein content determination. Figure 4: Effect of ST on the protein composition of beef: 1, 2 swimming lanes are ST beef and boiled beef water-soluble protein; 3, 4 swimming lanes are ST beef and boiled beef salt-soluble protein; 5, 6 swimming lanes are ST beef and boiled beef insoluble protein. Different letters denote different proteins.
# Biomolecules from Microalgae and Cyanobacteria: Applications and Market Survey
## 2 Microalgae
### Compositions and Technologies for Photoautotrophic Production of Microalgae and Cyanobacteria
#### 2.3.1 Amino acids, Peptides, and Proteins  
Microalgae and Cyanobacteria are considered to be rich and natural sources of vegetable proteins, mainly used as functional ingredients [56]. Indeed, they can produce proteins from 6% to 80% of their dry mass, depending on the species [57], e.g., _Chlorella vulgaris_ (Chlorophyta) (51-58% DW), _Arthospira maxima_ (Cyanobacteria) (60-71% DW), _Isochrysis galhana_ (Haptophyta) (50-56% DW), or _Porphyridium purpureum_ (Rhodophyta) (28-39% DW). Moreover, bioactive peptides are convenient high added-value compounds for humans due to their therapeutic potential in the treatment of various diseases, in addition to providing nutritional benefits [56].  
Besides their high protein content, microalgae and Cyanobacteria are also known for their high amino acid (Aas) content, in which the more abundant are aspartate and glutamate [39]. According to [PERSON] et al. [58], microalgae contain essential amino acids compared to egg and soybean. Microalgae and Cyanobacteria, such as those belonging to the species _Nostoc sphaeroides_, _Arthospira platensis_ (Cyanobacteria), and _Chlorella vulgaris_ (Chlorophyta), show a large variability in protein digestibility, with values of 82%, 81%, and 76%, respectively [59]. Aminograms of _Arthospira_ extracts with high protein digestibility (83-90%) have highlighted all essential amino acids, particularly, leucine, valine, and isoleucine [60]. The increased digestibility of Cyanobacteria is explained by their thinner cell wall. [PERSON] et al. [61] reported that other species, such as _Nannochloropsis oceanica_ (Eustigmatophyceaeae) and _Chlorella sorkiniana_ (Chlorophyta), may exhibit low digestibility rates of 50% and 55%, respectively, due to the rigidity and thickness of their cell walls, which are considered digestibility barriers.  
Cyanobacteria represent an inexhaustible source of proteins and various compounds with promising antiviral activities. Cyanovirin-N is an 11 kDa protein produced by _Nostoc ellipsoidorum_ that displays viricidal activity against several viruses and, notably, the human immunodeficiency virus (HIV). Other microbial metabolites were equally reported to exhibit antiviral activities, such as Scytovirin, a single 95-amino acids protein, which was isolated from the Cyanobacteria _Syttonema varium_, with potent antiviral activity against HIV.
# Reallocating Charging Loads of Electric Vehicles in Distribution Networks
## 1 Introduction
The proposed control algorithm reduced the total cost of energy generation based on dynamic energy prices, including power losses. EV users were prioritized according to their preferred charging time periods. It was ensured that voltage profiles cannot exceed their limits when EVs were charged using the on-line fuzzy coordination algorithm [18]. In one study [19], EV charging loads were formulated considering the present and the expected constraints of a real network. A linear model was developed based on the dynamic nature of EV charging loads along with their arrival and departure times. Meanwhile, phase unbalances, voltage magnitudes and limitations of the distribution cables and transformers were considered in the proposed charging method [19]. Numerical optimization techniques have been reviewed for optimal scheduling of EV charging loads, discussing dynamic programming, linear programming, and non-linear programming [23; 24]. Meta-heuristic methods have also been presented to illustrate multi-objective scheduling problems with probabilistic charging and discharging of EVs [23; 24]. In this paper, the proposed control algorithm reallocates EV charging loads in advance based on the short-term load forecasting. It is assumed that the proposed control algorithm receives the short-term load forecasting from DNOs. However, the performance of the proposed control algorithm is tested with the real daily profiles of EV charging loads acquired from the smart meters during trials of the Customer-Led Network Revolution (CLNR) project. These profiles were used to synthesize stochastic charging durations for each EV with one-minute time resolution considering unbalanced EV charging loads across the three phases. Therefore, the proposed control algorithm can be used in the real-time monitoring applications of smart grids [25]. Distribution networks will evolve into smart grids using two-way communication technologies with smart meters and measuring sensors [26; 27]. The results of the proposed methodology can be interpreted into lookup tables to assist DNOs in creating introductory assessments of the hosting capacity of their existing network components, as illustrated in another paper [28]. The main contributions of this paper are outlined as follows. It develops the centralized control algorithm to reallocate EV charging loads with one-minute time resolution considering the predefined constraints. This algorithm can be easily integrated into existing power system control paradigms. It implements non-iterative unbalanced optimal power flow calculations for the centralized control algorithm. The rest of this paper is structured as follows. The centralized control algorithm is described in Section 2. A non-iterative unbalanced power flow solver is implemented in Section 3. The studied network is described in Section 4 along with profiles of the synthesized EV charging loads and residential loads. Results and conclusions are discussed in Sections 5 and 6 respectively.
## 2 Materials and Methods  
### Patient Inclusion and Data Acquisition  
The data collection was carried out in a prospective fashion, according to a predefined image acquisition protocol, in the Maastricht University Medical Center+ (MUMC+) and the Catharina Hospital Eindhoven (CZE), both in the Netherlands, and the Queen Alexandra Hospital in Portsmouth, United Kingdom. The training dataset consisted of a total of 468 patients, with 2319 pre-malignant polyps and 420 benign, with CRPs of all sizes, from diminutive polyps to large polyps. The dataset includes polyps acquired in WL, BLI, LCI, and I-Scan (HDWL; Mode 1, 2, and 3) modalities. Using I-Scan data adds robustness to the algorithm, as all modes have almost similar visual properties as the three other modalities. The test set was restricted to 60 patients to match with the exact same patients analyzed by endoscopists (further explained in Section 2.6). All the test set was acquired from CZE, with 45 patients identified as pre-malignant polyps and 15 benign. For each test patient, a single image of the polyp was acquired at different time steps with three different modalities--WL, BLI, and LCI--adding to a total of 180 polyp images. Figure 1 summarizes the data collection described above. All collected data was made fully anonymous prior to the study.  
Figure 1: Data collection diagram. Patients with carcinoma were excluded. The dataset contained representatives of several modalities, White Light (WL) and High Definition White Light (HDWL), Blue Laser Imaging (BLI), Linked Color Imaging (LCI).
## 5 Realistic Modeling of Resource Constraints
### Battery Consumption Modeling  
Battery cost is one of the principal deterrents for participation in FL. The estimated battery usage per device type is summarized in Table 3. For each client \(i\), we model the relative battery drop as follows:  
\[\Delta B_{i}=\begin{cases}\frac{B_{\text{self}}^{i}}{B_{\text{total}}^{i}}&\text {if selected for training and communication}\\ \frac{B_{\text{self}}^{i}}{B_{\text{total}}^{i}}&\text{otherwise}\end{cases}\]  
where \(B_{\text{self}}^{i}\) is the battery consumed when participating in a round, \(B_{\text{self}}^{i}\) is the idle battery consumption, and \(B_{\text{total}}^{i}\) denotes the current battery level.  
This battery model is embedded into the client's payoff function through a cost term \(\lambda_{B}\cdot\Delta B_{i}\), allowing the system to naturally favor battery-efficient participation patterns.
# Ozone Generation by Surface Dielectric Barrier Discharge
## 3 Results and Discussion
### Discharge Power Measurements  
Typical Lissajous curves representing the relation between the transported charge and the instantaneous voltage in one full voltage cycle, for seven values of applied peak-to-peak voltage, are shown in Figure 3 (since the Lissajous curves for all three generators look similar, in Figure 3 we showed only the case for the smooth-edge generator). The area of a quasi-parallellogram limited by the Lissajous curve represents the energy dissipated in the SDBD discharge in each voltage cycle. As expected, this area increases with the applied voltage.  
We numerically calculated the area of each Lissajous figure by multiplying it by the frequency of the voltage waveform to obtain the active discharge power as a function of the applied voltage, which increases with the voltage for all three types of generators, as can be seen in Figure 4. This is consistent with the research by [PERSON] et al. [59], who experimentally studied SDBD airflow actuators and observed that the active power of the discharge behaves like a power function of an input voltage. The data points in Figure 4 almost overlap for the smooth-edge and serrated generators (with a difference smaller than 5%), while the active power for the thin-wire generator is significantly lower (on average about 25% smaller than the other two). These results are consistent with the results of the current measurements discussed in Section 3.1, where we observed a reduction of the number of current pulses for the thin-wire electrode in comparison to the other two electrode types. In our studies, a maximal active power of 4.82 W was recorded for the smooth-edge electrode generator, 4.67 W for the serrated electrode generator and 4.08 W for the thin-wire-electrode generator for the applied voltage of 21.6 kV, which are typical power levels for SDBD actuators of this size. A similar active power of about 4 W was recently registered by [PERSON] et al. [60] for their SDBD plasma actuator with a 70 mm smooth-edge electrode, with a thin dielectric barrier made of polyamide and an applied voltage of 14 kV (peak-to-peak).  
Figure 3: Typical Lissajous curves measured for the generator with the smooth-edge electrode and various applied (peak-to-peak) voltages.