esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Flotation Deinking Performance: A Comparative Analysis of Machine Learning Techniques
###### Abstract  
Flotation deinking is one of the most widely used techniques for the separation of ink particles from cellulose fibers during the process of paper recycling. It is a complex process influenced by a variety of factors, and is difficult to represent and usually results in models that are inconvenient to implement and/or interpret. In this paper, a comprehensive study of several machine learning methods for the prediction of flotation deinking performance is carried out, including support vector regression, regression tree ensembles (random forests and boosting) and Gaussian process regression. The prediction relies on the development of a limited dataset that assumes representative data samples obtained under a variety of laboratory conditions, including different reagents, pH values and flotation residence times. The results obtained in this paper confirm that the machine learning methods enable the accurate prediction of flotation deinking performance even when the dataset used for training the model is limited, thus enabling the determination of optimal conditions for the paper recycling process, with only minimal costs and effort. Considering the low complexity of the Gaussian process regression compared to the aforementioned ensemble models, it should be emphasized that the Gaussian process regression gave the best performance in estimating fiber recovery (R\({}^{2}\) = 97.77%) and a reasonable performance in estimating the toner recovery (R\({}^{2}\) = 86.31%).  
deinking; flotation; paper recycling; machine learning; support vector regression
## 2 Methods
### Nearest Neighbor (NN) with Ant Colony Optimization (ACO)  
In order to minimize computation time and boost the scheduling result of a total system resource set \(N=\{N_{1}\ldots N_{m}\}\) and a finite set of tasks \(T=\{T_{1}\ldots T_{j}\}\), it is important to choose the right job scheduling order through an approval procedure, \(Pre-approve\_Tasks()\). The local search phase through the nearest neighbor operator constructs the problem graph and determines the beginning node of ant. The proposed method assumes that the job scheduling mechanism is analogous to the ant foraging process. According to the system resource node's hardware performance parameters and the system average load gap, the pheromone is updated, and the estimated time to execute all tasks is kept to a minimum. The execution time is the amount of time required by a task to complete its execution. We define \(C_{i}\) to be the execution time of task \(i\). The parameters \(t_{in}\) and \(t_{out}\) represent respectively the arrival time of task \(i\) for processing and the complete processing time of task \(i\). The general formula for an execution time for a task \(i\) is defined by Equation (1):  
\[C_{i}=t_{out}-t_{in} \tag{1}\]  
The estimated total execution time of \(n\) tasks is represented by Equation (2):  
\[C_{n}=\sum_{i=1}^{n}C_{i} \tag{2}\]  
The heuristic function of the ant colony optimization algorithm is calculated using the estimated completion time. Moreover, to assign a task, a resource node with a strong pheromone concentration (high efficiency and low load), maximum remaining memory, and a short completion time is chosen.  
The aim of the proposed task scheduling method, nearest-ant colony optimization (ACO-NN) is to assign each task of the set \(T\) to the resource set \(N\) on the basis of maintaining load balance and increasing the efficiency of the system's task scheduling. This hybrid algorithm combined ACO with NN to get an efficient and feasible optimization method. Figure 1 displays the flowchart of the specific steps for ACO-NN in distributed systems based on load balancing, while the generic pseudocode of this proposed method is presented in Algorithm 1.  
Figure 1: The flow chart of nearest-neighbour ant colony optimization task scheduling technique.
## 2 Materials and Methods
### Characterization Methods  
ZetaSizer Nano ZS was used for the detection of aggregates and measurement of small samples permitting the evaluation of particle and molecule size, translational diffusion, electrophoretic mobility, and zeta potential of particles at high and low concentrations. The zeta potential analyzer uses electrophoretic light scattering for particles, molecules and surfaces, measuring range for sizes from 0.3 nm to 10 \(\upmu\)m.  
The HPLC (high pressure liquid chromatography) method was used in order to determine the amount of the erythromycincin from the obtained samples. The stationary phase used a column at 70 \({}^{\circ}\)C, and the mobile phase consisted of a mixture of acetonitrile-0.2 M ammonium acetate-methanol-water, in a 450:100:100:350 volume ratio, adjusted at pH 7.0. The UV detection of column effluent was registered at \(\lambda\) = 215 nm. The injected volume was 100 \(\upmu\)L for the test solutions consisting of erythromycincin propionate-loaded polymers, the reference solution containing standard erythromycincin A, and the reference solution containing erythromycincin B CRS and erythromycincin C CRS, in equal amounts, diluted to 50 mL with hydrolysis solution of dibasic potassium hydrogen phosphate (pH 8.0).  
In vitro release study of erythromycincin from PLA/PLGA nanoparticles was carried out using a Cary 60 UV-Vis--Agilent spectrometer (Agilent, Santa Clara, CA, USA), into a phosphate buffer solution at a pH value of 7.5. A well-established amount of each sample was suspended in 5 mL PBS in a centrifuge tube and ultrasound for a short time. The samples were then incubated at 37 \({}^{\circ}\)C, and, at set times, the samples were centrifuged. The supernatant was then collected, and the nano-powders were reconstituted by the addition of fresh PBS. The percentage of antibiotic release was represented as a function of contact time.  
Morphological features of erythromycincin-loaded both PLA and PLGA nanoparticles were analyzed by using a TESCAN scanning electron microscope (Brno, Czech Republic).  
Another important characterization of drug-loaded polymer is represented by thermal analytical study. The Thermogravimetric Analyzer, Discovery TGA 5500 (TAInstruments, New Castle, DE, USA), monitors the stability of a sample (polymers, organic and inorganic compounds) in terms of weight variation versus time/temperature, in a controlled atmosphere, giving information about temperature (range: room temperature \(\div\) 1000 \({}^{\circ}\)C), evaluation of the degradation temperatures, steps of degradation, and determination of weight loss at a certain temperature under specific conditions.  
An Alpha Bruker FT-IR spectrometer (Bremen, Germany), spectral range 4000-400 cm\({}^{-1}\), and 4 cm\({}^{-1}\) resolution, permits evaluation of interaction ways between the antibiotic and the two polymers.
## 2 Materials and Methods
### Data processing and Analysis  
An outline of data processing and analysis steps is shown in Figure 3. The 11 indicators had different dimensions, and indicator data were, therefore, normalized prior to analysis. Indicator data from 200 peaches were grouped and normalized using the \(Z\)-score normalization method in SPSS (IBM ver. 25.0), resulting in dimensionless datasets with an average of zero and a standard deviation of one [31; 32]. Data points outside the range of \((-3,3)\) were considered to be outliers, and data from 10 samples were excluded to ensure a normal distribution \((-3\sigma,3\sigma)\) of the processed data. Normalized data from 11 indicators measured in 190 peaches were then renumbered for subsequent analyses. Based on correlation analysis, dielectric properties appeared to best characterize and predict values of the other indicators in peaches (see below).  
PCA and k-means clustering analysis were used to develop an evaluation and grading method for fresh peach quality. A principal component is a new indicator that cannot be directly measured by experiment. The content of each principal component can be defined by a component score (\(F_{\text{nl}}\)) obtained from the following formula:  
\[F_{nl}=\sum_{i=1}^{11}e_{ij}Z_{ni}, \tag{2}\]  
Figure 2: Contact probe and measurement of dielectric properties in peaches.  
where \(Z_{\text{ni}}\) is the normalized indicator value of sample, \(F_{\text{nj}}\) is the score of the \(j\)-th principal component for the \(n\)-th peach, and \(e_{\text{ji}}\) is the load of \(i\)-th original indicator in the \(j\)-th principal component. Each indicator has a different weight for evaluating the quality of a sample. The final composite score requires a linear weighted summation. The variance contribution rate can be used as the weight value, representing the extent to which the indicator data reflect the overall data. The comprehensive score can be obtained from the following formula:  
\[W_{n}=\sum_{j=1}^{5}F_{nj}\eta_{j}, \tag{3}\]  
where \(W_{n}\) represents the composite score of \(n\)-th peach, and \(nj\) represents the variance of the \(j\)-th principal component.
## Appendix A Conversion of the Actual Geographic Coordinate System to the Kernel Coordinate System  
Assuming that the geographic coordinates of two points obtained through GPS are \((\mathrm{Lat}_{\mathrm{A}},\mathrm{Lung}_{\mathrm{A}})\) and \((\mathrm{Lat}_{\mathrm{B}},\mathrm{Lung}_{\mathrm{B}})\), respectively, then (A1) can be used to calculate the distance \(S\) between the two points [30]  
\[S=2\sin^{-1}\sqrt{\sin^{2}(\frac{a}{2})+\cos(\mathrm{Lat}_{1})\times\cos( \mathrm{Lat}_{2})\times\sin^{2}(\frac{b}{2})}\times 6371\times 10^{3}\;( \mathrm{m}),\] (A1)  
where \(a=\mathrm{Lat}_{\mathrm{A}}-\mathrm{Lat}_{\mathrm{B}}\) and \(b=\mathrm{Lung}_{\mathrm{A}}-\mathrm{Lung}_{\mathrm{B}}\).  
As shown in Figure A1, when a train enters a kernel, assume that the geographic coordinates of the starting point are \(P_{\mathrm{entry}}=(\mathrm{Lat}_{\mathrm{entry}},\mathrm{Lung}_{\mathrm{ entry}})\) and the geographic coordinates of an SBS are \(P_{i}=(\mathrm{Lat}_{i},\mathrm{Lung}_{i})\). Projecting the path obtained by connecting points \(P_{\mathrm{entry}}\) and \(P_{i}\) onto the right-hand side boundary of the kernel would yield \(P_{\mathrm{p}}=(\mathrm{Lat}_{\mathrm{entry}},\mathrm{Lung}_{i})\). Let \(S_{\mathrm{e}i}\), \(S_{\mathrm{ep}}\), and \(S_{\mathrm{ip}}\) represent, respectively, the actual geographic distance between \(P_{\mathrm{entry}}\) and \(P_{i}\), the actual geographic distance between \(P_{\mathrm{entry}}\) and \(P_{\mathrm{p}}\), and the actual geographic distance between \(P_{i}\) and \(P_{\mathrm{p}}\). Then, \(S_{\mathrm{e}i}\), \(S_{\mathrm{ep}}\), and \(S_{\mathrm{ip}}\) can be derived from (A1). Let \(S_{\mathrm{e}i,\mathrm{kernel}}\) represent the length of \(S_{\mathrm{e}i}\) after kernel coordinate conversion. As defined earlier in Figure 4, the relationship between the unit coordinate length of a kernel and its actual geographic length is one coordinate unit: \(\sqrt{2}R\) m. Therefore, \(S_{\mathrm{e}i,\mathrm{kernel}}\) can be expressed as  
\[S_{\mathrm{e}i,\mathrm{kernel}}=\frac{S_{\mathrm{e}i}}{\sqrt{2}R}.\] (A2)  
Let \(\theta\) represent the angle between the path of the line connecting \(P_{\mathrm{entry}}\) and \(P_{i}\) and the path of the line connecting \(P_{i}\) and \(P_{\mathrm{p}}\), which is given by  
\[\theta=\tan^{-1}\frac{S_{\mathrm{ep}}}{S_{\mathrm{ip}}}.\] (A3)  
As was defined earlier, \((X_{\mathrm{entry}},Y_{\mathrm{entry}})\) represents the kernel coordinates of the starting point when a train enters the kernel. The kernel coordinates of the SBS \((X_{i},Y_{i})\) can be expressed as  
\[X_{i} =X_{\mathrm{entry}}+S_{\mathrm{e}i,\mathrm{kernel}}\cos\theta,\] (A4) \[Y_{i} =Y_{\mathrm{entry}}+S_{\mathrm{e}i,\mathrm{kernel}}\sin\theta.\] (A5)  
The coordinates of any SBS on the kernel can be derived through the preceding equations (i.e., (A1)-(A5)).
## 2 Experimental Investigation
### Pull-Out Specim Preparation  
The direct pull-out test was carried out in accordance with the ACI 440.3R-04 and CSA-S806-02 standard guidelines. A 150 mm cubic mould was used to prepare the pull-out specimens. The moulds were prepared by partitioning rectangular steel moulds with plywood formwork supported by clamps, as shown in Figure 3.  
\begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Mix Proportion (Kg/m3) [2]**} \\ \hline Fly Ash & 480 \\ \hline Slag & 360 \\ \hline Micro Fly Ash & 120 \\ \hline Sodium Metasilicate (Anhy.) Alkali Activator & 96 \\ \hline Fine Aggregates & 763 \\ \hline Coarse Aggregates & 677 \\ \hline \multicolumn{2}{c}{**Properties of Fresh SCGC**} \\ \hline Slump Flow (mm) & 670 \\ \hline T500 (sec) & 4.10 \\ \hline \multicolumn{2}{c}{**Properties of Hardened SCGC**} \\ \hline Compressive Strength, at 28 th Day (MPa) & 40 \\ \hline Indirect Tensile Strength, at 28 th Day (MPa) & 3 \\ \hline Modulus of Elasticity, at 28 th Day (GPa) & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mix proportion and properties of self-compacting geopolymer concrete.  
Figure 2: Slump flow of SCGC.  
Prior to the preparation of the cubic moulds, the basalt FRP bars were marked and fixed in the steel socket using a strong adhesive named HILTI RE500 for better grip in the socket, unlike the conventional cement grout method. The use of an epoxy adhesive of this kind will prevent the failure of FRP bars due to the gripping force of the clamps in the universal testing machine during testing [3]. The basalt FRP bars gripped in the steel socket tubes were left to dry for one week. The embedment length, also known as the bonded length or bond length, was determined with respect to the reinforcing bar's diameter. The three embedment lengths used in this study were the 5 d\({}_{\text{b}}\), 10 d\({}_{\text{b}}\), and 15 d\({}_{\text{b}}\) lengths. The embedment length of 5 d\({}_{\text{b}}\) corresponds to a bonded length equal to 5 times the diameter of the bar. Later, the embedment lengths were marked on the basalt FRP bars, and the unbonded lengths were taped using foams as bond breakers and made ready for casting in the cubic moulds (Figure 4).  
A day before casting, the steel cubic moulds were cleaned, and a natural release agent called LanoForm was applied. Later, the prepared basalt FRP and steel bars were placed at the bottom of the mould and clamped on either side of the mould to prevent slippage from the centre of the mould. On the day of casting, the freshly prepared SCGC was poured, with the pull-out bars in the centre of the mould (Figure 5).  
Figure 4: Pull-out bars taped with bond breaker foams before casting.  
Figure 3: Moulds for pull-out specimens.  
The pull-out specimens were then demoulded and subjected to ambient curing for 28 days by leaving them in the laboratory at a room temperature of 23 \(\pm\) 2 \({}^{\circ}\)C and at a relative humidity of 50%. The demoulded specimens on the day of testing are shown in Figure 6 below:
# Path Planning for Conformal Antenna Surface Detection Based on Improved Genetic Algorithm
## 5 Conclusions  
To address the shortcomings of the TGA when solving the path planning problem for conformal antenna surface detection, this paper proposes an improved genetic algorithm combining the historical optimal population, which improves several key steps of the genetic algorithm. Simulation experiments showed that the algorithm not only solved medium- and small-scale two-dimensional standard TSPs better, but also that it was successfully applied to the detection path planning problem of the conformal antenna.  
Figure 16: Optimal detection paths for conformal antenna derived by CHOP-IGA.  
Figure 17: The test platform.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Time Spent on Scanning/s** & **Improvements in Efficiency** \\ \hline Original path & 230.18 & 48.44\% \\ Optimized path & 118.67 & 48.44\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of scanning time.  
Further improvements in this paper include the following: the current algorithm only includes mutation operation for the historical optimal population to reach a better solution, and it is necessary to consider how to more fully utilize the information of the good gene fragments contained in the historical optimal population; the current algorithm performs well in solving medium- and small-sized problems, and how to solve larger-scale problems is also a key question; at present, in the path planning problem for conformal antenna surface detection, only a single constraint is considered, and how to construct a path planning problem with multiple constraints needs to be considered.  
Conceptualization, W.T. and C.W.; validation, W.T. and C.W.; investigation, Y.D., K.L., Z.W. and X.D.; writing--original draft preparation, Y.D., K.L. and Z.W.; writing--review and editing, W.T., C.W. and X.D.; supervision, W.T., C.W. and X.D.; project administration, X.D. and C.D.; funding acquisition, X.D. and C.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National key R & D program of China (No. 2020 YFB170300) and the National Defense Basic Scientific Research Program of China (No. JCKY2019210B004).  
Not applicable.  
Data are contained within the article. The TSPLIB library used in the article is a publicly accessible library that can be accessed and downloaded at [[http://comoptifi.uni-heidelberg.de/software/TSPLIB95/](http://comoptifi.uni-heidelberg.de/software/TSPLIB95/)]([http://comoptifi.uni-heidelberg.de/software/TSPLIB95/](http://comoptifi.uni-heidelberg.de/software/TSPLIB95/)), accessed on 18 July 2023.  
The authors would like to acknowledge the financial support from the National key R & D program of China (No. 2020 YFB170300) and the National Defense Basic Scientific Research Program of China (No. JCKY2019210B004).  
The authors declare no conflict of interest.
# Optimizing Crop Yield and Reducing Energy Consumption in Greenhouse Control Using PSO-MPC Algorithm
###### Abstract  
In this study, we present a novel smart greenhouse control algorithm that optimizes crop yield while minimizing energy consumption costs. To achieve this, we relied on both a greenhouse climate model and a greenhouse crop yield model. Our approach involves applying the model predictive control (MPC) method, which utilizes the particle swarm optimization (PSO) algorithm to identify optimal controllable parameters such as heating, lighting, ventilation levels. The objective of the optimization is to maximize crop yield while minimizing energy consumption costs. We demonstrate the superiority of our proposed control algorithm in terms of performance and energy efficiency compared to the traditional control algorithm. The effectiveness of the PSO-based optimization strategy for finding optimal controllable parameters for MPC control is also demonstrated, outperforming the traditional genetic algorithm optimization. This study provides a promising approach to smart greenhouse control with the potential for increasing crop yield while minimizing energy costs.  
g remarkRemark  
greenhouse control; model predictive control; particle swarm optimization; greenhouse climate model; biophysical model
## 5 Results and Discussion
### Simulation of Flyrock Trajectories
However, in the particular blast, the actual range of the farthest flyrock fragment identified and measured using GPS coordinates was around 112 m. Moreover, it is worth noting that the accuracy of simulation should not be assessed by comparing the predicted range with the actual range of flyrock measured at the blast site due to two major reasons: first, the farthest fragment identified might not be that which is sampled in the demonstration data; and second, the simulation experiment has been conducted with certain assumptions regarding the launch angle, launch velocity, fragment shape, and drag coefficient, which are not necessarily the same in actual conditions. From this point of view, the over-prediction of range obtained from the simulation is fairly acceptable for ensuring the safety of men, materials, and equipment by delimiting the danger zone of the blast before executing it.
## 4 Results and Discussion  
Using (29), the channel representation was implemented in Matlab Simulink to develop the channel emulator. Separate Matlab functions were used to generate programmable path amplitudes \(|\mathbf{M}_{\mathbf{q}}|\), delays \(\tau_{\mathbf{q}}\), phase shifts \(\phi_{l,q}\), the Doppler shift, and Doppler frequencies. A block diagram of the Simulink platform for the channel emulator is shown in Figure 5. The Simulink platform starts by incorporating the formulated Milne oscillator in (14) and (15) to generate the path amplitudes, path delays, and phase shifts. The platform also generates  
Figure 4: Sample histograms of Doppler frequencies, evolution of path amplitudes over 3 s, Doppler power spectrum, and corresponding temporal coherence generated using (18), (21), (26), and (29).  
Doppler shifts and frequencies using Matlab functions (M-functions) based on (25) and (26). Outputs of the Milne oscillator block and Doppler blocks are fed to the block of M-functions used for generating channel samples based on (27). The channel samples are then fed to the channel gain generator block to formulate the channel taps based on (28). The taps are then multiplied with the path delays obtained from the Milne oscillator-based model. We kept an additional (optional) noise model, which can be added for more realistic modelling. Multiple blocks are presented for each component to represent the generation of multiple channel taps.