esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction Interval Adjustment for Load-Forecasting using Machine Learning
## 4 Experiments and Results
### Prediction Intervals Evaluation Metrics
#### 4.1.1 PICP (Prediction Interval Coverage Probability)  
The PICP is the rate of real values that lies within the prediction interval. The PICP is estimated using the following equation:  
\[PICP=\frac{1}{w}\cdot\sum_{g=1}^{w}\theta_{g} \tag{12}\]  
where \(w\) is the number of observations and \(\theta_{g}\) is defined by the following equation:  
\[\theta_{g}=\left\{\begin{array}{ll}1,&\text{if}\quad V_{t}(p,h)<U(p,h)\text { and }V_{t}(p,h)>L(p,h)\\ 0,&\text{Otherwise}\end{array}\right. \tag{13}\]  
where \(U(p,h)=\hat{V}_{t}(p,h)+z_{\delta}\sqrt{Var[\epsilon(p,h)]}\) and \(L(p,h)=\hat{V}_{t}(p,h)-z_{\delta}\sqrt{Var[\epsilon(p,h)]}\).
# Active Gait Retraining with Lower Limb Exoskeleton Based on Robust Force Control
## 5 Experimental and Simulation Results
### Experimental Results  
The rehabilitation exoskeleton was tested on a healthy subject of 31 years with a weight of 50 kg and a height of 1.6 m.  
The experiment consists of the following steps: First, the exoskeleton is fitted to the subject. Second, the exoskeleton is turned on, and the speed and amplitude of the steps are adjusted to a slow walk, which is consistent with the rehabilitation exercises, while the subject is asked to try to walk gently, simulating muscle weakness.  
The result of the internal control loop is shown in Figure 11, where the desired torque for the hip (Tau_d:Hip on the graph) is represented by the black line, the torque of the hip spring (Tau_s:Hip on the graph) is represented by the dotted magenta line, the desired torque for the knee (Tau_d:Knee on the graph) is represented by the blue line, and the torque of the knee spring (Tau_s:Knee on the graph) is depicted by the red dotted line. In this figure, it can be observed that torque tracking is almost perfect for the hip, while it presents some differences for the knee.  
Figure 12 shows the output torque (\(\tau_{m}\)) of the force control for both the hip and knee. This figure shows that the output signal is chatter-free because the control law was obtained by the use of modified sliding modes.  
Figure 13 shows the trajectory tracking of the hip joint for the five running cycles; the black line represents the desired trajectory (x_d_; x_d in the graph), the red line represents the  
Figure 11: Torque tracking. _Tau_d:Hip_ is the desired torque for the hip, _Tau_s:Hip_ is the torque of the hip spring, _Tau_d:Knee_ is the desired torque for the knee, and _Tau_s:Knee_ is the torque of the knee spring.  
Figure 12: Motor torque. _Tau_m:Hip_ is the controller output torque for the hip, and _Tau_m:Knee_ is the controller output torque for the knee.  
trajectory running the exoskeleton (\(x_{e}\); x_e in the graph), and the blue dotted line represents the trajectory of the actuator (\(x_{m}\); x_m in the graph). It can be observed that the exoskeleton trajectory follows the desired trajectory.  
Figure 14 shows the trajectory tracking from the knee joint; as in Figure 7, the black line represents the desired trajectory (\(x_{d}\); x_d in the graph), the red line represents the trajectory running the exoskeleton (\(x_{e}\); x_e in the graph), and the blue dotted line represents the trajectory of the actuator (\(x_{m}\); x_m in the graph). The exoskeleton follows the desired trajectory.  
Table 3 shows the Mean Squared Error (MSE) and Mean Absolute Error (MAE) observed in the tracking of the desired trajectories for both the hip and knee.
# A New Approach to Detect Hand-Drawn Dashed Lines in Engineering Sketches
## 4 Non-Solid Line Clustering
### Chaining Dashed Strokes
Hence, we measure both the angles and the step, as depicted in Figure 11:  
Angles are measured by simply selecting the greater one, and comparing against the threshold as described in Algorithm 5:
```
1:if (max(Angle1, Angle2) > ThresholdAngle[i]) then
2: stroke[i] is discarded
3:end if
```  
**Algorithm 5**Angle measurement  
The algorithm discards stroke[i] if the maximum angle exceeds the threshold. For valid orientations, their merit (_meritOffsetAngle_) is calculated by the ramp function described in Figure 10.  
To measure the step, the following calculations are performed. First, the maximum allowable step is calculated as a percentage (specified by ThresholdStep[i] in Equation (7)) of the length of the shortest segment (selected between stroke[i] and the chain). \[OffsetStep[i]=Min(|stroke[i]|,\ |chain|)\cdot ThresholdStep[i] \tag{7}\]  
Figure 11: Offset measurement for close and distant dashes. \[Threshold\Angle[i]=IncreasedTol[i]\textbf{-MaxDashAngle}\]0 Orientation merit ramp function. \[Threshold\Angle[i]=IncreasedTol[i]\textbf{-MaxDashAngle}\]1 Different orientation between chain and dash. Second, the two distances of the tips that delimit the gap (A1 and B0 in the example of Figure 12) to the line defined by the other segment are calculated. The greater distance is selected as the \"step\" and compared to the offset (Algorithm 6). The algorithm compares the maximum distance between the tips of the gap to the offset step. For the valid steps, their merit (_meritOffsetStep_) is calculated as a ramp function that equals 1 for a null step, and 0 for greater than OffsetStep[i] (Figure 13). The merit of the whole offset (_meritOffset_) is calculated by balancing both meritOffsetAngle and meritOffsetStep, as follows:  
\[Threshold\Angle[i]=IncreasedTol[i]\textbf{-MaxDashAngle}\]2  
We note that both meritOffsetAngle and meritOffsetStep must not be null to obtain the candidate dash accepted as such. _MeritDash_ is finally calculated for each candidate dash, thus allowing the candidate dash with the highest merit to be added to the chain:  
\[Threshold\Angle[i]=IncreasedTol[i]\textbf{-MaxDashAngle}\]3  
where _DashBalanceStepAngle_ is an adjustable parameter (default is 50%).
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 2 Research Methods and Data
### Data
#### 2.2.2 Permeability Coefficient  
The permeability coefficient has a significant effect on the groundwater flow and the movement of soil particles within the ground [14,15]. Changes in the permeability coefficient can influence stress distribution within the ground, potentially leading to ground subsidence [16]. Therefore, the permeability coefficient was included as a factor influencing the ground subsidence risk. The permeability coefficient in the target area was in the range of 7.37 \(\times\) 10\({}^{-5}\) to 5.62 \(\times\) 10\({}^{-4}\). The permeability coefficients for the target area were derived from the drilling survey data. The Kriging method was used to represent the overall permeability coefficient of the area using the respective values for each location. The permeability coefficients exhibited variability even within a 500 m \(\times\) 500 m grid, and the median value of the grid was selected and extracted as the representative value.
## 2 Experimental Details
### Characterization  
Density was measured at room temperature by the Archimedes' method with an electronic densimeter (Qualitest USA, MD-300S). The amorphous nature of glasses was evaluated by means of X-ray diffractometer (X'pert Pro, Panalytical) with CuK\(\alpha\) (=1.542 A) used as source. Differential scanning calorimetry (DSC 204 F1, NETZSCH) was used for thermal behavior of the glass samples. The measurement was carried out in the range of 25-550 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min and under N\({}_{2}\) atmosphere. Thermomechanical analysis (TMA) was carried out using a dilatometer (DIL402F3, NETZSCH) in the range of 25-450 \({}^{\circ}\)C, with a heating rate of 5 \({}^{\circ}\)C/min under N\({}_{2}\) atmosphere. Glass samples with a dimension of 5 \(\times\) 5 \(\times\) 8 mm\({}^{3}\) were used for the measurement. Raman data were acquired in the range from 50 to 1600 cm\({}^{-1}\), with 0.5 cm\({}^{-1}\) resolution by the spectrometer ([PERSON], France). A diode laser operating at 515 nm was used as an excitation source. The X-ray photoelectron spectroscopy analysis was carried out by a spectrometer (NEXSA, Thermo Fisher Scientific., Waltham, MA, USA). The product of Gaussian and Lorentzian functions was used for the curve fitting.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Glasses** & **V\({}_{2}\)O\({}_{5}\)** & **ZnO** & **BaO** & **Al(PO\({}_{3}\))\({}_{3}\)** & **B\({}_{2}\)O\({}_{3}\)** \\ \hline VZBB & 70 & 5 & 22 & 0 & 3 \\ AP2.5 & 67.5 & 5 & 22 & 2.5 & 3 \\ AP5.0 & 65 & 5 & 22 & 5 & 3 \\ AP7.5 & 62.5 & 5 & 22 & 7.5 & 3 \\ AP10.0 & 60 & 5 & 22 & 10 & 3 \\ AP12.5 & 57.5 & 5 & 22 & 12.5 & 3 \\ AP15.0 & 55 & 5 & 22 & 15 & 3 \\ \hline
**Glasses** & **V\({}_{2}\)O\({}_{5}\)** & **ZnO** & **BaO** & **Mg(PO\({}_{3}\))\({}_{2}\)** & **B\({}_{2}\)O\({}_{3}\)** \\ \hline MP2.5 & 67.5 & 5 & 22 & 2.5 & 3 \\ MP5.0 & 65 & 5 & 22 & 5 & 3 \\ MP7.5 & 62.5 & 5 & 22 & 7.5 & 3 \\ MP10.0 & 60 & 5 & 22 & 10 & 3 \\ MP12.5 & 57.5 & 5 & 22 & 12.5 & 3 \\ MP15.0 & 55 & 5 & 22 & 15 & 3 \\ \hline
**Glasses** & **V\({}_{2}\)O\({}_{5}\)** & **ZnO** & **BaO** & **NaPO\({}_{3}\)** & **B\({}_{2}\)O\({}_{3}\)** \\ \hline NP2.5 & 67.5 & 5 & 22 & 2.5 & 3 \\ NP5.0 & 65 & 5 & 22 & 5 & 3 \\ NP7.5 & 62.5 & 5 & 22 & 7.5 & 3 \\ NP10.0 & 60 & 5 & 22 & 10 & 3 \\ NP12.5 & 57.5 & 5 & 22 & 12.5 & 3 \\ NP15.0 & 55 & 5 & 22 & 15 & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the present glasses, (Al(PO\({}_{3}\))\({}_{3}\) = xAP, Mg(PO\({}_{3}\))\({}_{2}\) = xMP and NaPO\({}_{3}\) = xNP (where x = 2.5, 7.5, 10, 12.5, 15) (mol%).  
Figure 1: Thermal profile of (70-x)V\({}_{2}\)O\({}_{5}\)-5 ZnO-22 BaO-3B\({}_{2}\)O\({}_{3}\)-xM(PO\({}_{3}\))\({}_{n}\) glass system (mol%) (where M = Al, Mg, Na, n = 3, 2, 1, respectively, mol%), and inset depicts photographs of glass samples.
## 2 Latest Developments
This work revealed that the performance of the IMERG final product showed the best results in the areas of higher annual average rainfall in terms of statistical indices (i.e., FAR, POD and CSI). From Table 1 it can be obtained that the GPM satellite born precipitation data are available as near-real-time data available with a 4-h latency from acquisition and post-real-time research data, which are processed within 2.5 months.
## 3 Results and Discussion
### Optimal Waveband Selection Using ANOVA Analysis Results
#### 3.2.1 Development of Single-Waveband Algorithm (SWA)  
Residue Detection Algorithm for Honeydew  
A single-waveband algorithm (SWA) was developed to detect honeybee residue on the surface of 2B-finished SSS (Table 1). Figure S1a shows F-value of the one-way ANOVA for classifying 2B finished SS surface and honeybee residues. The resultant optimal wavebands for the original juice, and the 1:5, 1:10, and 1:20 dilution samples were close to 480 nm, and the threshold values for detecting the residues using the selected wavebands were 1983, 827, 545, and 362, respectively (Figure S1). The calibration accuracies were 92.92%, 91.56%, 86.86%, and 86.33%, and the validation accuracies were 92.94%, 91.45%, 86.99%, and 83.99%, respectively. The optimal wavebands for the 1:50 and 1:100 dilution samples were 684.4 nm, and the threshold values were 1742 and 1563, respectively. The calibration accuracies were 75.78% and 69.08%, and the validation accuracies were 70.46% and 67.25%, respectively. The 480-nm and 684-nm wavebands are related to carotenoids and chlorophyll, respectively [35; 36; 37]. For the SWA developed to detect honeybee residue on the surface of #4-finished SSS, the optimal waveband for the original juice was 508 nm, the threshold value was 1857, the calibration accuracy was 92.55%, and the validation accuracy was 92.54% (Figure S2a). The optimal wavebands for the 1:5, 1:10, 1:20, and 1:50 dilution samples were close to 480 nm, and the threshold values were 688, 429, 459, and 388, respectively. The calibration accuracies were 92.25%, 90.91%, 90.05%, and 69.73%, and the validation accuracies were 92.19%, 91.91%, 89.88%, and 63.42%, respectively. The optimal waveband for the original juice was 660 nm, the threshold value was 483, the calibration accuracy was 68.70%, and the validation accuracy was 65.64%. The 480-nm and 684-nm wavebands were related to carotenoids and chlorophyll-_a_, respectively [35; 36; 37].
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 6 Monitoring Phenomena Other than Rain
### Dsd  
[PERSON] et al. [99] used two or three configured microwave link instruments to estimate the three parameters of a gamma DSD model; the DSD retrieval performance using different microwave link combinations of frequency and polarization are analyzed. The experimental results show that the DSD retrieval based on microwave link is accurate under ideal conditions, and the accuracy and successful rate in practical situations are highly dependent on the stability of the power level and the precision of the instrument. [PERSON] et al. [100] proposed a method for retrieving path-averaged DSD parameters using dual-frequency and dual-polarization joint microwave links. The DSD parameters are obtained based on the Levenberg-Marquardt optimization algorithm, and this method can be used as an effective supplement to traditional DSD monitoring systems, such as disrodmeter.
## 2 Materials and Methods
### Measurements of Light Intensity and Canopy Photosynthesis  
A closed growth chamber (\(100\times 80\times 50\) cm) was used to measure the light intensity distribution and whole canopy photosynthetic rate (Figure 1B). An acryl plate was used for the ceiling of the growth chamber for light penetration, and the inner surface was covered with a uniform black board to normalize the reflected light. Two LED plates (80 \(\times\) 16 \(\times\) 2 cm, Seoul Semiconductor Co., Ansan, Korea) were positioned on the growth chamber with an 8:2 ratio of red and blue LEDs. For precise simulation setting, the datum point for measuring PPFD was fixed in the central position of the bed. The plants were arranged in 3 \(\times\) 3 isotropic form with a high planting density (HD) of 25 plants m\({}^{-2}\) and a low planting density (LD) of 16 plants m\({}^{-2}\).  
For alternatives for light interception that cannot actually be measured, light intensities at several points were used as an indirect index to describe the accuracy of estimated canopy light interception. PPFD was set at 200 umol m\({}^{-2}\) s\({}^{-1}\), and light intensity was measured by a light meter (LI-250A, LI-COR, Lincoln, NE, USA) in both the empty and canopy-arranged growth chamber at fixed points. To investigate whether the lighting power and distribution were well set in the simulation, light intensities were first compared in an empty chamber without plant models at 16 different positions with 3 height-levels from the floor. With the lettuce plants in the chamber, light intensities were only measured on the floor because the measurement through the plant canopy can cause disaccord with simulation by touching.  
The net photosynthetic rate (P\({}_{n}\)) of the whole canopy was measured by a gas analyzer (LI-840A, LI-COR, Lincoln, NE, USA) connected to the growth chamber. To obtain the P\({}_{n}\), the growth chamber was enclosed, the change in CO\({}_{2}\) concentration was monitored every second from 800 to 400 umol mol\({}^{-1}\), and the difference in CO\({}_{2}\) concentration averaged over 3 min was used to calculate the whole canopy photosynthetic rate. PPFD was set at 100, 200, and 300 umol m\({}^{-2}\) s\({}^{-1}\). The temperature was set to 22 \({}^{\circ}\)C, and the range of relative air humidity was 60%-80% in the growth chamber. Air leakage from the growth chamber was measured at CO\({}_{2}\) concentrations above 1000 umol mol\({}^{-1}\), and the number of air exchanges was 0.0016 h\({}^{-1}\), which was used to calibrate the photosynthetic rate.  
Figure 1: A schematic diagram of the overall processes studied—the reconstruction process of 3D-scanned plant models (3D-SPMs) (**A**), the evaluation of ray-tracing simulation compared with actual measurement in a growth chamber with LED lighting (**B**), and a case study concerning the effect of variation in the light environment on light interception, photosynthesis, and light use efficiency (LUE) (**C**).
# Modular Combined DC-DC Autotransformer for Offshore Wind Power Integration with DC Collection
## 2 MCAT Topology and Its Operation Principles
### Operation Principles of the MCAT  
Neglecting transmission losses, the rated DC power \(P_{\rm N}\) transmitted from the high-voltage DC grid to the low-voltage one is described as:  
\[P_{\rm N}=E_{\rm high}I_{\rm dchigh}=E_{\rm low}I_{\rm dclow} \tag{1}\]  
where \(E_{\rm high}\) and \(E_{\rm low}\) are DC voltages of high-voltage and low-voltage DC grids, respectively (\(E_{\rm high}>E_{\rm low}\)); \(I_{\rm dcligh}\) and \(I_{\rm dclow}\) are DC currents of high-voltage and low-voltage DC grids, respectively. The positive direction of currents and voltages are shown in Figure 2, and the DC voltage ratio \(k_{\rm dc}\) (\(k_{\rm dc}>\) 1) is defined as:  
\[k_{\rm dc}=\frac{E_{\rm high}}{E_{\rm low}} \tag{2}\]  
The transmission power of each MMC in HVM and LVM is described as:  
\[\left\{\begin{array}{l}P_{\rm MMCH,\it n}=\frac{(E_{\rm high}-E_{\rm low}) \,\lambda_{\rm dchigh}}{2N}=\frac{P_{\rm N}}{2N}\left(1-\frac{1}{k_{\rm dc}} \right)\\ P_{\rm MMCL,\it m}=\frac{E_{\rm low}(\lambda_{\rm dchigh}-\lambda_{\rm dchigh} )}{M}=\frac{P_{\rm N}}{M}\left(1-\frac{1}{k_{\rm dc}}\right)\end{array}\right. \tag{3}\]  
where \(P_{\rm MMCH,\it n}\) and \(P_{\rm MMCL,\it m}\) are the transmission power of the \(n\)th MMC in HVM and the \(m\)th MMC in LVM, respectively (\(n=1,\,2,\,\ldots,\,2N,\,\it m=1,\,2,\,\ldots,\,M\)).  
\(P_{\rm N}\) is transmitted through two different paths. One path is HVM-AC bus-LVM, this part of \(P_{\rm N}\) is delivered through the DC-AC-DC conversion and named \(P_{\rm dc-ac-dc}\). The other part of \(P_{\rm N}\) flows from the high-voltage DC grid to the low-voltage DC grid directly and is named \(P_{\rm dir}\). \(P_{\rm dc-ac-dc}\) and \(P_{\rm dir}\) are calculated as (4) and (5):  
\[P_{\rm dc-ac-dc}=\sum_{m=1}^{M}P_{\rm MMCL,\it m}=\sum_{n=1}^{2N}P_{\rm MMCH, \it n}=P_{\rm N}(1-\frac{1}{k_{\rm dc}}) \tag{4}\]  
\[P_{\rm dir}=E_{\rm low}I_{\rm dchigh}=E_{\rm low}\frac{P_{\rm N}}{E_{\rm high }}=\frac{P_{\rm N}}{k_{\rm dc}} \tag{5}\]  
\(P_{\rm MCAT}\) is the total capacity of the MCAT and is calculated as (6):  
\[P_{\rm MCAT}=\sum_{m=1}^{M}P_{\rm MMCL,\it m}+\sum_{n=1}^{2N}P_{\rm MMCH,\it n }=2P_{\rm N}\left(1-\frac{1}{k_{\rm dc}}\right) \tag{6}\]  
Comparing (6) with (5), \(P_{\rm MCAT}\) depends only on \(P_{\rm dc-ac-dc}\).
## 4 Test Results and Insights
### Perceived Physical and Cognitive Load  
Figure 11 shows the results of the Raw NASA-TLX, used to address the perceived physical and mental demand that the different interfaces required. With Stratos, the user felt more frustration and effort in completing the tasks than the touch counterpart and felt a higher physical load when using the Stratos interface. After observing the users, we learned they needed to raise their hands 20 cm above the device to interact with it. This differs from the trackpad, where users keep their hands on the armest. The users also confirmed this consideration in the interview after the test. Although Stratos had a slightly higher mental load demand, the user felt more performative. However, for all the survey data, a significant difference (\(\alpha\) = 0.05) between the two groups cannot be confirmed for all the scales (see Table 4). Even here, the Mann-Whitney U test was used instead of the student's Independent Samples \(T\)-test due to the non-normality of the data sample.  
\begin{table}
\begin{tabular}{c c c} \hline
**NASA-TLX Subscale** & **W** & \(p\) \\ \hline Mental Demand & 70 & 0.138 \\ Physical Demand & 64 & 0.291 \\ Temporal Demand & 38 & 0.378 \\ Performance & 51.5 & 0.939 \\ Effort & 66 & 0.237 \\ Frustration & 73.5 & 0.080 \\ \hline \end{tabular} No value is significant (\(p<0.05\)).  
\end{table}
Table 4: Mann-Whitney U test for the Raw NASA-TLX.  
Figure 10: Boxplot of the occurrences where the users looked directly at the input interface.  
\begin{table}
\begin{tabular}{c c c} \hline
**Task** & **W** & \(p\) \\ \hline Start & 52.5 & 0.821 \\ Music & 45.5 & 0.762 \\ Cell & 52.5 & 0.880 \\ \(\star\) File explorer & 29.5 & 0.374 \\ POI & 49.0 & 0.971 \\ \hline \end{tabular} No value is significant (\(p<0.05\)). \(\star\) Failed attempts are not used in the calculation.  
\end{table}
Table 3: Mann-Whitney U test for the task time.
# The Role of Fungi in Food Production and Processing
## 3 Future Insights: Current Trends, Prospects, and Potential Directions
### Future Prospects and Potential Directions for Further Exploration
#### 3.2.3 Advanced Approaches to Mitigating Post-Harvest Diseases and Food Waste  
Meanwhile, post-harvest diseases induced by fungi remain one of the primary issues leading to food waste. Current research efforts are focusing mostly on advanced approaches to mitigate these losses, including investigation of utilizing various biocontrol agents derived from fungi to address post-harvest crop diseases and increase the life of agricultural products. The use of antagonistic yeasts has developed as a potential method to reduce the fruit spoilage caused by mycotoxin-producing fungi [164]. Extensive research in this field has focused on the enzymes of various antagonistic yeasts due to their remarkable ability to adsorb and biodegradable mycotoxins. Antagonistic yeasts show very good results in the biodetoxification of products derived from fruits. In recent years, competitive fungi have been increasingly used instead of chemical methods. In this way, the reduced growth of saprophytic fungi and degradation of mycotoxins is achieved [186]. As a result, antagonistic yeasts and their antifungal mechanisms have gained significant recognition. Integrated disease management approaches can enhance the biocontrol activity either synergistically or additively. To address potential variations in the efficacy of antagonistic yeasts, they can be utilized in synergy with other yeasts, bacteria, bioagents, and physical processes. Various combinations of processes and agents have been explored to enhance the efficiency of antagonistic yeasts. UV-C irradiation, for instance, has been tested as a method to prevent fungal diseases in fruits [187]. A study by [PERSON] et al. [188] has shown that UV-C irradiation improves the quality of fruits by increasing the activity of antioxidant enzymesand reducing mycotoxins. Another study, where a combination of UV-C treatment with _Pichia ecembensis_ as an antagonistic yeast was used, indicated the control of post-harvest decay in melons [187]. Consequently, further research is necessary to understand the biochemical mechanisms underlying the detoxification pathways and evaluate the by-products of detoxification. This deeper understanding will inform more effective strategies for food preservation and safety [186]. Concurrently, research is underway to explore how fungi can be utilized for reducing food waste by decomposing the inedible parts of crops. Efforts are being made to develop novel food products from recycled stale bread and brewing residues, thereby promoting circular economy models [189].