esa-sceva/satcom-chunk-collection huggingface dataset:
# Unravelling the Environmental Application of Biochar as Low-Cost Biosorbent: A Review
###### Abstract  
In this age, a key target for enhancing the competitiveness of the chemical, environmental and biotechnology industries is to manufacture high-value products more efficiently and especially with significantly reduced environmental impact. Under this premise, the conversion of biomass waste to a high-value added product, biochar, is an interesting approach under the circular economy principles. Thus, the improvements in the biochar production and its new and innovative uses are hot points of interest, which are the focus of vast efforts of the scientific community. Biochar has been recognized as a material of great potential, and its use as an adsorbent is becoming a reliable strategy for the removal of pollutants of different streams, according to its high adsorption capacity and potential to eliminate recalcitrant compounds. In this review, a succinct overview of current actions developed to improve the adsorption capability of biochar, mainly of heavy metal and organic pollutants (dyes, pharmaceuticals and personal care products), is summarized and discussed, and the principal adsorption mechanisms are described. The feedstock and the production procedure are revealed as key factors that provide the appropriate physicochemical characteristics for the good performance of biochar as an adsorbent. In addition, the modification of the biochar by the different described approaches proved their feasibility and became a good strategy for the design of selective adsorbents. In the last part of this review, the novel prospects in the regeneration of the biochar are presented in order to achieve a clean technology for alleviating the water pollution challenge.  
adsorption; biochar; micropollutants; heavy metals; biomass; water pollution +
Footnote †: journal: Applied Sciences
# Specific Modeling Issues on an Adaptive Winglet Skeleton
## 2. Criticalities and Approach
The stress maps of the different cases show a concentration on the central part of the investigated zone, with a uniform distribution on the transition between the central zone and the rest of the model. This in some way resizes any mesh boundary effect and focuses the attention on the central part, affected by the discontinuity of stiffness. Another interesting aspect is the impact of both the in-plane element dimension onto the stress. The cases in Figure 6b-d, in fact, show a linear trend punctuated by progressive halving of both the thickness gradient and the in-plane normal directions. This suggests that both the thickness variation and the element size play a role on the observed behavior. The high level of stress, jointly to the uncertainty related to the just mentioned trend with the refinement of the mesh has led to the adoption of a design solution to mitigate the local accumulation of stress. As already discussed, the investigated load case determines the inward bending of the winglet, with a consequent compression of the top skin along the span direction. A secondary effect is the bending of the skin also in chordwise direction, between the rear and the middle spars. Considering that the stiffness is higher in the spanwise direction for the overlap of the cap and of the skin laminate and that a sudden variation of rigidity occurs upstream the rear spar, solutions altering the chordwise stiffness look more appropriate. In line with this consideration, a \"T\" shaped beam bonded on the inner surface of the skin and linking the rear to the middle spar has been investigated. The layout considered is illustrated in Figure 8.  
The solution has proved to reduce of about 37% the stress level, thus definitely mitigating the local criticality. A comparison of the initial and final configuration is presented in Figure 9, that highlights a better situation not only on the rear spar but also on the middle spar. Figure 8: Layout of the chordwise beam to increase the rigidity on the critical zone.
## 2 Results
### Functionality of the Gene RdINV and Heterologous Protein Size Analyses  
The DNA sequence characterized from _R. dainensis_, containing or not the two intronic sequences (constructions RdINV-plB4 and cRdINV-plB4, respectively), was included in _Pichia pastoris_. Yeast transformants were cultivated in BMG medium and the protein expression induced with methanol as referenced [34]. The \(\beta\)-tructofuranosidase activity was only detected in transformants carrying construction cRdINV-plB4. Figure 2 shows data obtained with one of the selected clones. Both activity levels and expression of an extracellular protein of \(\sim\)170 kDa increased, as did the induction time. Maxima levels of activity (\(\sim\)25 U/mL; \(\sim\)55 \(\mu\)g/mL) were detected in yeast culture filtrates after 96 h of methanol induction. As expected, no activity was detected in transformants, including the empty plasmid plB4, providing a direct evidence that gene _RdINV_ truly directs the synthesis of the \(\beta\)-tructofuranosidase activity.  
The \(\beta\)-tructofuranosidase purified from _R. dainensis_ and _P. pastoris_ showed apparently the same molecular mass, \(\sim\)170 kDa (Figure 3a), which is far from the theoretical \(\sim\)70 kDa calculated on the basis of the protein RdINV deduced sequence. Treatment of the heterologous protein with PNGase F led a band of \(\sim\)144 kDa, which implied that \(\sim\)15.3% of the protein mass was due to N-glycosylation (Figure 3b). Similar results were previously obtained with the enzyme expressed in _R. dainensis_[26]. No change in the electrophoretic mobility of the protein was obtained after using a mixture of O-glycosidase and neuroaminidase (data not shown), but the treatment with \(\alpha\)-(1-2,3,6) mannosidase resulted in two bands of \(\sim\)105 and \(\sim\)80 kDa (Figure 3b, lanes 6-8). The sequential digestion with PNGase F first and then with mannosidase produced the same two-band pattern (Figure 3c). All these data pointed to glycosylation constituting almost 53% of the total protein molecular mass.  
Figure 1: Structural alignment of family 32 of the glycosyl hydrolases (GH32) catalytic domain from yeasts and fungi. Predicted catalytic domain of RdINV (178–496 residues) was superimposed to the _S. cerevisiae_ invertase (ScINV, PDB code 4 EQV), _Sch. cocidentalis_\(\beta\)-rructofuranosidase (SoFfase, PDB code 3 KF5), _A. avamori_ exoinulinase (AaEI, PDB code 1 YM9) and _X. dendorhous_\(\beta\)-rructofuranosidase (XdINV, PDB code 5 ANN) using ENDscript server. The black squares indicate amino acid similarity as calculated by MSAProbs. Secondary structure elements suggested by DSSP program are shown as squiggles for \(\alpha\)-helix, arrows labeled \(\beta\) for \(\beta\)-strands, and strict \(\alpha\)- and \(\beta\)-turns depicted by TTT and TT letters, respectively. \(\beta\)-strands (A–D) of each blade (1–5) of the \(\beta\)-propeller were depicted. Catalytic residues are highlighted with black asterisks.
## 3 Drone Routing Algorithms
### Charging
#### 3.2.3 Delivery Destination Clustering (DDC)  
In DDC [17], the authors utilized the k-means clustering method to optimally cluster the delivery destinations and allocate one CS at each centroid of the cluster. The delivery coordinates are generated based on the frequently visited customers' locations. This process also helps to find the optimal number and locations of CSs, which can extend the drone flight time to cover multiple customers in a single mission. Nevertheless, due to the constrained flight time a large cluster may not be covered by a single CS located at centroid of the cluster. Considering this real-life scenario, DDC also considers necessity of placing additional CSs within a large cluster to successfully execute the mission.
## 2 Materials and Methods
### Estimation of the Number of Signals
#### 2.3.3 Determination of the DL Factor  
Selection of the DL factor is an important aspect of the estimation of \(M\). In this study, we used two DL factors that were dependent on the received signal intensity. A schematic illustration of these two factors is shown in Figure 2.  
The DL factor is given by  
\[\eta(\omega)=\eta_{1}(\omega)\delta_{1}(\omega)+\eta_{2}\delta_{2} \tag{11}\]  
\[\delta_{1}(\omega)=\text{tr}\{\mathbf{R}(\omega)\} \tag{12}\]  
\[\delta_{2}=\frac{1}{\omega_{2}-\omega_{1}}\int_{\omega_{1}}^{\omega_{2}}\text{ tr}\{\mathbf{R}(\omega)\}\text{d}\omega \tag{13}\]  
where \(\eta_{1}\) and \(\eta_{2}\) are the two component DL factors. \(\eta_{2}\delta_{2}\) is used for stabilization and is called the constant DL in Figure 2 because, at low signal intensities, \(\eta_{1}(\omega)\delta_{1}(\omega)\), which is called the frequency-dependent DL in Figure 2, approaches zero, and the estimates would thus be unstable.  
To determine \(\eta_{1}(\omega)\) for the wavenumber estimation procedure, we assumed that the optimal \(\eta_{1}(\omega)\) is common within a specific frequency range. This assumption was made because, as mentioned below, \(M^{\prime}\) shows a step-like change with respect to \(\eta_{1}\). The optimal \(\eta_{1}\) that gives the optimum \(M\) value has a range and is not a critical value.  
We vary \(\eta_{1}(\omega)\) and select the minimum value of \(\eta_{1}\) that gives \(M^{\prime}(\omega)\) such that it satisfies the following condition:  
\[\text{max}\{M^{\prime}(\omega)\}<M_{\text{th}}\text{, with }(\omega-\omega_{ \text{w}}\leq\omega\leq\omega+\omega_{\text{w}}) \tag{14}\]  
where \(\omega_{\text{w}}\) is the width of the frequency window that is used for stable estimation and \(M_{\text{th}}\) is a threshold \(M\) value that is sufficiently large for wavenumber estimation.
## 2 Detection System Architecture and Signal Characteristics
### Geomagnetic Signal Characteristics Analysis  
The geomagnetic disturbance signal caused by a vehicle is shown in Figure 2. From Figure 2, we can see that the area of front and rear wheels has the strongest magnetic disturbance signal. However, there is a magnetic signal blind zone between the front and rear wheel, which may cause misjudgment of vehicle status.  
Figure 2: The magnetic signal blind zone problem of high chassis vehicles.
## 4 Simulation Experiment and Results Analysis
### Experimental Analysis and Summary
#### 4.3.2 Comparison Experiment of Scheduling Length of Algorithms under Different Task Numbers  
The experiment mainly verifies whether the scheduling result of HSP-QGA is still the optimal value under different task numbers (number of tasks: 20, 40, 60, and 80). In order to eliminate the influence of CCR values on the experimental results, smaller and larger CCR values were set up to carry out the experiments. Other parameters are uniformly set: the number of DAGs is 1, and the number of processors is 4.  
Figure 10a shows the scheduling length of each algorithm when CCR = 0.1 and the number of tasks is 20, 40, 60, and 80, respectively. The results showed that the HSP-QGA reduced scheduling length by an average of 12.38% (standard deviation reduced by 10.6%) compared to the PA-CGA algorithm, 14.86% (standard deviation reduced by 18.9%) compared to the ACOQ algorithm and 20.69% (standard deviation reduced by 23.1%) compared the ACO algorithm. Figure 10b shows the scheduling length of each algorithm under different task numbers when CCR = 4. The HSP-QGA reduced scheduling length by an average of 20.47% (standard deviation reduced by 14.7%) compared to the PA-CGA algorithm, 17.61% (standard deviation reduced by 16.7%) compared to the ACOQ algorithm, and 24.80% (standard deviation reduced by 14.2%) compared to the ACO algorithm. The experimental results show that the HSP-QGA has a smaller scheduling length, whether in the case of CCR less than 1 or CCR greater than 1.
## 5 Simulation and Discussion  
To verify the reliability and effectiveness of the proposed hierarchical ABS control strategy, an overall control strategy model was built in MATLAB/Simulink. The general design of the ABS control system is shown in Figure 5. Under emergency braking conditions, the road surface identification outputs the target slip ratio \(\lambda_{\text{ref}}\). The ABS controller based on the ADRC algorithm is used to track the target slip ratio, and the output current controls the EMB actuator. The vehicle simulation conditions are set to emergency braking scenarios on different road surfaces with an initial braking speed of 20 m/s, and white noise is added to simulate real-world disturbances. The performance of the longitudinal vehicle
## 5 Experiments and Results
### Key-Based Security
#### 5.3.2 Key-Based Multiplicative Strategy  
Using the multiplicative strategy, the whole audio set was watermarked utilizing a key \(k_{1}\). Then, data was extracted utilizing a key, \(k_{2}\), with \(k_{1}\
eq k_{2}\). The \(\alpha\) value was set to 1.  
Figure 12 shows the BER results and the BER distribution for the multiplicative strategy.  
The BER mean, median, and variance were calculated as 0.4993, 0.5000, and 0.0016, respectively. Both mean and median were equal (or very close) to 0.5.  
A Wilcoxon rank-sum test was carried out to determine if both methods showed the same behavior. The test rejection of the null hypothesis of equal median at the default 5% significance level (\(p=5.9499e-75\)) indicated that the multiplicative strategy was better than additive strategy, as the median and mean in the multiplicative strategy were closer to equal to 0.5, the ideal BER value for an incorrect key.  
Figure 11: Bit Error Rate (BER) results for watermarking using the additive strategy and incorrect key. (**a**) BER results. (**b**) BER distribution.
## 2 Experimental Programme  
### Materials  
The basic materials used in this study include Portland cement (CEM 52.5 N), spherical Fe\({}_{3}\)O\({}_{4}\) nanoparticles (98% purity), a polycarboxylate ether superplasticiser (PCE), a cellulose-based viscosity-modifying admixture (VMA), and fine sand. The cement had a median particle size of approximately 7.2 \(\upmu\)m and specific gravity of 3.2. The Fe\({}_{3}\)O\({}_{4}\) nanoparticles used in the study had a particle size of 100 nm and specific gravity of 4.95, as stated by the manufacturer. The sand was a very fine quartz sand with median particle size of 170 \(\upmu\)m and specific gravity of 2.65. A very fine sand was selected for testing to remain within the limit of maximum particle sizes permissible to conduct rheology experiments within the confined geometry of the plate-plate rheometer. The gap between the shearing plates should be at least 5-10 times the maximum particle size in the mixture [20]. Table 1 shows the chemical composition and Figure 1 shows the particle size distribution of the cement powder and sand.  
The magnetic properties of the cement and magnetic nanoparticles (MNPs) were measured by a vibrating sample magnetometer (VSM) at 25 \({}^{\circ}\)C. The magnetisation curves of the materials are plotted in Figure 2, and the magnetic parameters are summarised in Table 2.  
Five mixtures were prepared to study the effect of volume concentration of fine aggregates on the magnetorheological response and confined flow under magnetic field.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Material** & \begin{tabular}{c} **Saturation Magnetisation** \\ **(\(M_{s}\), emu/g)** \\ \end{tabular} & \
\begin{tabular}{c} **Remnant Magnetisation** \\ **(\(M_{r}\), emu/g)** \\ \end{tabular} &
\begin{tabular}{c} **Coercive Field** \\ **(\(H_{c}\), Oe)** \\ \end{tabular} \\ \hline Cement & 0.59 & 0.05 & 70.72 \\ MNP & 77.56 & 10.23 & 108.27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Magnetic properties of cement and Fe3O4 nanoparticles (MNP) used in the study.  
Figure 1: Particle size distribution of (**left**) cement powder; (**right**) sand.  
Figure 2: Magnetisation curves of (**left**) Portland cement, (**right**) MNPs.  
The water to cement ratio of the paste and the volume of MNPs used was kept constant. The quantity of superplasticiser, and in the instance of the reference paste the quantity of VMA, was adjusted to attain a mini slump spread diameter of 255 \(\pm\) 5 mm. The sand amount was added to the paste in 10% volumetric replacements of the total volume of paste. Mixtures M0, M1, M2, M3, and M4 thus have 0%, 10%, 20%, 30%, and 40% total volume of sand. This is visually represented in Figure 3, and the mixture proportions of the pastes are shown in Table 3.
## 2 Materials and Methods  
The proposed identification method makes use of trunk test data taken from the literature in conjunction with numerical modeling. The data consist of the experimental trunk stiffness measured along different directions for the sample population who participated in the original work [18]. A numerical biomechanical model [10] of the trunk is used to calculate the trunk stiffnesses depending on the muscle tones taken as input parameters. The identification procedure solves the boundaries of the domain of feasible tone values by matching the tone-dependent model stiffness with the experimental stiffness taken as a reference. In accordance with the common practice in biomechanical modeling, the muscle tone has been represented by its ratio to the maximum active force of the muscle, called tone ratio (RMTT) [2]. In the following, the RMTTs will be represented by a vector in a space denoted T.  
Details on the experimental protocol relative to the multidirectional trunk test, and the relevant ethical and safety considerations, are given in [18]. A numerical musculoskeletal trunk model [10] is used to calculate the upper and lower bounds of the trunk flexural stiffnesses in five different directions. The domain of possible tone values, or equivalent RMTTs, is narrowed down by locating the experimental trunk stiffness [18] relative to those bounds.  
The identification methodology is presented according to either of the following two simplifying hypotheses. In the first, the RMTT is assumed to be identical for all muscles. In the second, a more realistic hypothesis is adopted, where three classes of muscles are distinguished, each sharing a common RMTT value. One class comprises all the external muscles, whereas the internal muscles are divided into two classes: thin and thick muscles.
## 3 The Vectorized Earth Observation Retrieval Classifier  
The Vectorized Earth Observation Retrieval (VEOR) classifier stems from the Probabilistic Cloud Mask (PCM) algorithm proposed by [69], which was further reimplemented for the detection of low stratiform clouds [70]. Following this, the VEOR prototype was used for the derivation of cloud climatology over Central Europe from Advanced Very High Resolution Radiometer (AVHRR) imagery [71]. Its usability was also assessed for the generation of the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) climate data records [72]. Ultimately, the VEOR methodology was improved within the current study, especially concerning classification accuracy, self-optimization and numerical performance. There are two common concepts behind all of these algorithm versions. The first is related to the computation of the multidimensional classification probability histogram, which is flattened to a vector form during algorithm training (this is the origin of the vectorized adjective in the VEOR name), and the second concept is related to the computation of an index that assigns a unique identifier (ID) to each element in the flattened probability vector. The vector with ID is generated by placing values of input classification features transformed to 8-bit integers on consecutive bit ranges, within a 64-bit integer, by means of bitwise operators (bitwise_shift and bitwise_or). This second vector is analogous to the quality flag products usually associated with Earth observation (EO) products (e.g., MYD35 cloud mask), where different ancillary information is provided. Thus, after VEOR training, two look-up vectors (LUV) are derived: one with a classification probability and the second with sorted ID. The classification of new input data involves the computation of a new ID vector that is itself located within the sorted LUV by means of a fast binary search algorithm [73] in order to retrieve the classification probability. Comprehensive explanation of the VEOR algorithm for a simplified two-dimensional (2D) synthetic dataset is provided in the following subsections.
## 3 Methodology of the Resampling Strategy and Variable Circular Correlation Time  
### Principle of the Resampling Strategy  
Generally, the original signal can be reconstructed from the sampled signal without loss of signal quality if the sampling frequency is higher than twice the upper cutoff frequency of the original signal. However, this principle is not suitable for signal acquisition of multimode multiband GNSS receivers. These receivers can receive multi-frequency satellite signals at the same time. Therefore, a much higher sampling frequency is required to sample the received broadband signal. Obviously,  
Figure 1: The signal processing framework of GNSS receivers. The modules marked as red are the improved strategies of this paper, including the resampling strategy, variable circular correlation time and acquisition with pilot channel.  
it is rather aimless, low-efficiency, and of low quality to acquire a specific frequency signal by sampling the received broadband signal in this way. Moreover, the higher sampling frequency results in much more computation and time consumption for signal acquisition, because the data size of the sampled signal is much larger. To address this dilemma of balance between the sampling frequency and the greatly increasing computation, we propose the resampling strategy based on the bandpass sampling theory [23].  
**Theorem 1**.: _Bandpass Sampling Theory: Assume that the frequency band of a time-continuous signal \(x(t)\) is located at \([-f_{u},-f_{l}]\ \cup\ [f_{l},\ f_{u}]\), where \(f_{l}\) and \(f_{u}\) are the lower and upper cutoff frequencies, respectively. \(B\) denotes the bandwidth of the signal \(x(t)\), \(B=f_{u}-f_{l}\). In order to reconstruct the original signal \(x(t)\) without spectrum aliasing from the sampled sequence \(X[k]\), the acceptable sampling frequency \(f_{s}\) should be in the range of_  
\[\frac{2f_{u}}{n}\leq f_{s}\leq\frac{2f_{l}}{n-1},\ n\in[1,\ N] \tag{2}\]  
_where \(N=\mathcal{Z}^{-}(f_{u}/B)\) denotes the maximum integer no more than \(f_{u}/B\). Note that, \(n=1\) just makes sense for the case that \(f_{s}\in[2f_{u},\ +\infty)\)._  
The relationship between \(f_{s}\) and \(f_{u}\) is illustrated in Figure 2. In order to avoid spectrum aliasing when reconstructing the original signal, the sampling frequency \(f_{s}\) should be chosen from cyan areas, not gray ones. In Figure 2, the available range of the sampling frequency \(f_{s}\) consists of \(N\) non-overlapping cyan sections, denoting as \(\Phi_{n}=[2f_{u}/n,\ 2f_{l}/(n-1)],\ (n=1,2,\ldots,N)\) and \(f_{s}\ \in\ \cup_{n=1}^{N}\Phi_{n}\). It is realizable for the sampling frequency to be much lower, such as \([2B,\ 4B]\), instead of the conventional range (\(f_{s}\geq 2f_{u}\), the cyan \(n=1\)). The parameters, \(f_{su}\), \(f_{sl}\), and \(f_{ds}\) will be discussed for the proposed resampling strategy later.