esa-sceva/satcom-chunk-collection huggingface dataset:
# Chitosan Nanocomposite Coatings for Food, Paints, and Water Treatment Applications
## 5 Conclusions and Future Perspectives  
The present review reported on the current and most important topics regarding the development of chitosan-based nanocomposite coating formulation for food preservation, water treatment, and anti-corrosive and self-healing paint applications. Over the past several decades, chitosan has emerged as a promising biomaterial for various industrial applications including food, pharma, paint, cosmetic, and textile industries, due to their natural origin, structural similarity to glycosaminoglycan's (naturally presented in native tissues), biocompatibility, biodegradability, low toxicity, antimicrobial, and antifouling activities. Biopolymer-based edible films and coatings play a significant role in prolonging the post-harvest life and improving the quality of fresh fruits and vegetables. It protects the fresh fruits and vegetables from damages or deteriorations during storage after harvesting by reducing the rate of moisture and gas transfer, by delaying respiration, senescence, etc., and by maintaining structural integrity or firmness and sensory attributes. Technology advancement enabling the preparation of multifunctional chitosan-based coatings or films could be promising to be used as packaging materials for food applications. Chitosan-based coatings containing active anticorrosive agent with self-healing ability have great potential for application in paint industries due to its good film-forming properties, superior adhesion abilities to substrates, antifouling properties, and the ability to reversibly form complexes with potential corrosion inhibitors. Chitosan and its nanocomposites have been used for removing dyes from textile wastewater. It has also found application in the prevention of biofouling from submerged surfaces to membranes. More practical applications of chitosan nanocomposites with metal oxides and clay will emerge in the near future as the world is grappling to reduce synthetic polymer waste in our ecosystems.  
Conceptualization, J.D. and S.D.; writing--original draft preparation, S.K., F.Y., S.D.; writing--review and editing, S.K., F.Y., S.D., J.D.  
S.K. is thankful to Department of Biotechnology, Government of India for providing financial support (sanction letter vide no. BT/20/NE/2011) to pursue post-doctoral research at Functional Materials, KTH Royal Institute of Technology, Stockholm, Sweden through \"Biotechnology Overseas Associateship Award for NER Scientists\". S.D. acknowledge financial support by TRC grant RC/AGR/FISH/16/01.  
The authors declare no conflicts of interest.
## 5 Models Based on Canonical Piece Wise Linearization  
The DPD linearization has been achieved with Volterra based methods as discussed in Section 3. However, \"out of the box\" approach which can achieve better performance is always interesting addition to the topic. Recently, it was shown in [26; 27; 28] that out of all the possible architectures, CPWL method outperforms the other models such as memory polynomial (MP) and generalized memory polynomial (GMP). CPWL is an obvious choice due to the performance enhancement that it brings, however it has a lot of complexity and overheads. The CPWL model can be expressed as [28]:  
\[\begin{array}{l}y(n)=\sum_{m=0}^{M}\sum_{k=0}^{K}\sum_{l=1}^{L}c_{m,k,l}^{(1 )}\big{|}x(n-k)|^{2}-\beta_{l}\big{|}x(n-m-k)\\ +\sum_{m=1}^{M}\sum_{k=0}^{K}\sum_{l=1}^{L}c_{m,k,l}^{(2)}\big{|}|x(n-k)|^{2}- \beta_{l}\big{|}x^{2}(  
Here, the baseband input is represented by \(x(n)\) and the output baseband signal is represented by \(y(n)\), \(K\) is the FIR length filter, \(M\) is represented by memory depth, \(L\) is the number of partitions in the CPWL, \(\beta_{l}\) shows the threshold while \(c_{m,k,l}^{(1)}\), \(c_{m,k,l}^{(2)}\),\(c_{m,k,l}^{(3)}\),\(c_{m,k,l}^{(4)}\) presents the model coefficients.  
In the Equation (1), there are many orders of multiplications and additions which will add a lot of overhead in terms of complexity and utilization if hardware resources during DPD implementation, the most important of them is dedicated hardware adders and multipliers.
## 1 Introduction
The details are as follows: Firstly, a label system for evaluating electricity sale packages and customer clustering is utilized to identify similar customers to a new customer. Secondly, a set of similar customers is identified, and N similar customers are selected as experimental customers. Their decision-making information is aggregated using the Choquet integral to construct a customer group decision-making matrix. Next, to account for customers' psychological risk preferences, the Prospect Theory is integrated into the preference difference function of the classical SIR method, resulting in the degree of prospect strengths and weaknesses. Building on this, and addressing the challenges of attribute correlation and weight determination, a model is constructed using Choquet integral and comprehensive attribute weights. This model ranks electricity sale packages based on the degree of prospect strengths and weaknesses, capturing the differences between various schemes through the prospect strengths and weaknesses flow. The ranking of packages for recommendation is then derived from this flow. Finally, a case analysis is conducted with customers in a western Zhejiang region in China to verify the accuracy and effectiveness of the proposed recommendation method. In light of the aforementioned background and analysis, the objective of this paper is to refine the challenge of recommending electricity sale packages within the context of actual multi-attribute group decision-making into a coherent theoretical framework. The primary contributions of this paper are delineated as follows: In the comparison of electricity sale package schemes, this paper integrates the Prospect Theory into the calculation of the prospect strengths and weaknesses degree within the classical SIR method. This integration solves the limitation that Prospect Theory ignores the objective uncertainty of customers' consumption habits and budget. Consequently, this approach facilitates a more nuanced portrayal of the distinctions among various electricity sale packages. Addressing the intricacies surrounding the correlations among electricity sale package attributes and the determination of their weights, this paper employs the Choquet integral to calculate the correlation coefficients of these attributes. Furthermore, leveraging the probabilistic language deviation function, it determines the attribute weights of electricity sale packages, thus elucidating the differences among the attributes of various packages.
# Robot Joint Vibration Suppression Method Based on Improved ADRC
## 2 Mathematical Model of Robot Joint Module
### Structure of Harmonic Reducer
\begin{aligned} m_{f}\ddot{x}_{f}+c_{fx}\dot{x}_{f}+k_{fx}x_ {f}=k_{wfx}(t)(x_{w}-x_{f})+c_{fx}(\dot{x}_{w}-\dot{x}_{f})\\ +k_{fc}(t)(d_{fc1}-d_{fc2})\cos(\omega_{w}t-\alpha_{fc})+c_{fc}(\dot{d}_{fc1 }-\dot{d}_{fc2})\cos(\omega_{w}t-\alpha_{fc})\\ m_{f}\ddot{y}_{f}+c_{ry}\dot{y}_{f}+k_{ry}y_{f}=k_{wfty}(t)(y_{w}-y_{f})+ c_{fg}(\dot{y}_{w}-\dot{y}_{f})\\ +k_{fc}(t)(d_{fc1}-d_{fc2})\sin(\omega_{w}t-\alpha_{fc})+c_{fc}(\dot{d}_{fc1 }-\dot{d}_{fc2})\sin(\omega_{w}t-\alpha_{fc})\\ m_{f}\ddot{\theta}_{f}+c_{ft}\dot{\theta}_{f}+k_{ft}\theta_{f}=k_{wft}(t )(\theta_{w}-\theta_{f})+c_{ft}(\dot{\theta}_{w}-\dot{\theta}_{f})\\ -k_{fc}(t)(d_{fc1}+d_{fc2})-c_{fc}(\dot{d}_{fc1}+\dot{d}_{fc2}) \end{aligned} \right. \tag{5}\]  
where \(m_{f}\) is the mass of the flexible gear, \(c_{fx}\), \(c_{gy}\), and \(c_{ft}\) are the damping of the flexible gear in the x, y, and rotation directions, \(k_{fx}\), \(k_{yy}\), and \(k_{ft}\) are the stiffness of the flexible gear in the x, y, and rotation directions, \(\alpha_{fc}\) is the relative displacement angle of the gear mesh, \(\omega_{n}\) is the rotational speed of the PMSM, \(d_{fc1}\) and \(d_{fc2}\) are the displacement values on the equivalent action of the gear, and the expressions are  
\[\left\{ \begin{aligned} d_{fc1}=-(x_{f}-x_{c})\cos(\omega_{n}t- \alpha_{fc})-(y_{f}-y_{c})\sin(\omega_{n}t-\alpha_{fc})\\ +(\theta_{r}-\theta_{c})c_{ft}+e_{fc1}(t)\\ d_{fc2}=(x_{f}-x_{c})\cos(\omega_{n}t-\alpha_{fc})+(y_{f}-y_{c}) \sin(\omega_{n}t-\alpha_{fc})\\ +(\theta_{r}-\theta_{c})c_{ft}+e_{fc2}(t)\end{aligned} \right. \tag{6}\]  
where \(e_{fc1}(t)\) and \(e_{fc2}(t)\) are the displacement error forces of gear meshing. The dynamic equation of a rigid gear can be described as  
\[\left\{ \begin{aligned} m_{c}\ddot{x}_{c}+c_{cx}\dot{x}_{c}+k_{cx}x_ {c}=-k_{fc}(t)\cos(\omega_{n}t-\alpha_{fc})(d_{fc1}-d_{fc2})\\ -c_{fc}\cos(\omega_{n}t-\alpha_{fc})(\dot{d}_{fc1}-\dot{d}_{fc1}) \\ m_{c}\ddot{y}_{c}+c_{cy}\dot{y}_{c}+k_{cy}y_{c}=-k_{fc}(t)\sin( \omega_{n}t-\alpha_{fc})(d_{fc1}-d_{fc2})\\ -c_{fc}\sin(\omega_{n}t-\alpha_{fc})(\dot{d}_{fc1}-\dot{d}_{fc1}) \\ m_{c}\ddot{\theta}_{c}+c_{ct}\dot{\theta}_{c}+k_{ct}\theta_{c}=T_{ out}(t)+(d_{fc1}+d_{fc2})k_{fc}+(\dot{d}_{fc1}+\dot{d}_{fc2})c_{fc}\end{aligned} \right. \tag{7}\]  
where \(m_{c}\) is the mass of the rigid gear, \(c_{cx}\), \(c_{cy}\), and \(c_{ct}\) are the damping of the rigid gear in the x, y, and rotation directions, respectively, and \(k_{cx}\), \(k_{cy}\), and \(k_{ct}\) are the stiffness of the rigid gear in the x, y, and rotation directions, respectively.
## 3 Results and Discussion
### Effects of Operating Conditions on Reaction Responses
#### 3.4.5 Response Optimization  
Finally, the Minitab Response Optimizer tool helps to obtain the best possible result for phenol conversion (\(Y_{1}\)) and hydrogen yield (\(Y_{2}\)). Moreover, it can help solve problems involving several design variables and one or more responses simultaneously. It also has an interactive graphic mode that helps in investigating local behaviors around an optimized solution. By using a response optimizer, the optimal conditions for the suggested reduced model (Equations (8) and (9)) were found to be 800 \({}^{\circ}\)C, 0.16 mL/min feed flow rate, 0.3 g catalyst weight, 0 Ni/Co ratio, and 10 wt % of phenol concentration in order to obtain 94.98% of \(Y_{1}\) and 67.4% of \(Y_{2}\) responses (Figure 15a,b, respectively).  
Figure 14: Contour plot for significant interactions on \(Y_{2}\): (**a**) \(AE\); (**b**) \(BE\); (**c**) \(CE\); (**d**) \(DE\).  
Figure 13: Surface plot for significant interactions on \(Y_{2}\): (**a**) \(AE\); (**b**) \(BE\); (**c**) \(CE\); (**d**) \(DE\).
## 3 Optimization Problem Formulation
### The Algorithm  
The reliability indicators (1)-(3) are calculated using a tree-based representation of the distribution system, establishing parent-child relationships between network sections. This allows for the precise identification of consumers affected by faults, as well as the sections located upstream and downstream of the fault location. To account for uncertainty in component reliability data (failure rate and repair time) and demand variability, an MCS is integrated into the optimization framework. This ensures a probabilistic assessment of investment decisions, reflecting real-world operating conditions.  
Figure 2 shows the flow chart of the algorithm. Below is a step-by-step description of the enhanced probabilistic algorithm for selecting investment options that maximize reliability improvements, outlined as follows:  
* Data input and parameter initialization.
* Load network topology and consumer data, including the number of customers, their loads, and prices for unserved energy.
* Incorporate uncertainty in component reliability data, defining probability distributions for failure rate and repair time.
* Incorporate demand uncertainty, defining probabilistic distributions for consumer load fluctuations over time.
* Define a set of potential investment options (e.g., component replacements, network reinforcements, automation upgrades) and their associated costs.
* Set MCS parameters--the number of iterations (\(N\)), which in this study is set to 2500.
* Construct a tree-based network representation, establishing parent-child relationships between sections to facilitate fault impact assessment.
* Initiate the MCS and set the iteration counter to 1.
* Randomly generate values for failure rates, repair times, and consumer demand.
* Calculate the reliability indicators (_SAIDI_, _ENS_, and _COST_) for the current MCS iteration for the baseline scenario.
* Calculate the expected reduction in reliability indicators for each investment option relative to the baseline scenario.
* Increase the iteration count by 1.  
* If the counter is less than or equal to the set number of iterations \(N\), go to Step 4; otherwise, go to Step 9.
* Compute the average reduction in reliability indicators across all Monte Carlo iterations to obtain an expected improvement for each investment option.
* Formulate the optimization problem under the given total investment budget constraint. Select investment options that contribute the most to the reduction in reliability indicators.
* Display the optimal set of investments and conclude the algorithm.
# Role of Irisin in Endocrine and Metabolic Disorders--Possible New Therapeutic Agent?
## 9 Obesity  
Irisin serves as a mediator for the beneficial metabolic effects of physical exercises, by regulating thermogenesis and inhibiting adipogenesis in the adipose tissue [2].  
The data regarding the relationship between irisin and adiposity are conflicting, with some authors reporting positive outcomes and others showing negative correlations [2].  
Irisin concentrations in obesity are influenced by caloric restriction diets, with high levels of irisin after early intervention. This fact may be associated with greater reductions in blood glucose levels and insulin concentrations. Irisin also increases energy expenditure and, as a result, high levels of triglycerides start providing energy substrates for the organism. When the irisin levels are increased by metabolic abnormalities, weight loss leads to low levels of irisin; thus, the result will be an improved metabolic profile [38].  
There are doubts whether higher or lower FNDC5/irisin levels are related to metabolic homeostasis--mainly in patients with metabolic risk--due to excessive body adipose tissue. The results of a cluster study have shown that obese middle-aged men with higher irisin levels exhibit a better metabolic profile, along with a lower risk of T2 DM development.  
Some anthropometric/body composition variables, as well as the risk of T2 DM, have been inversely related to FNDC5/irisin levels [46].  
In obese children, the levels of irisin are higher than in underweight and normal weight children. Irisin has also been positively correlated with adiposity markers (BMI) and negatively correlated with body muscle mass and physical activity. There is no proof of any association between irisin and cardio-metabolic markers in children [47].  
In obese patients, irisin has been significantly correlated with the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) index. Subjects with initial high levels of irisin showed significant improvements in body composition, lipid profile, and fasting glucose after weight loss. These findings suggest that increased irisin exerts beneficial regulatory effects on fat, glycemic control, and insulin resistance [48]. Due to the fact that body weight reduction and exercise influence the kinetics of irisin [48], it may represent a new therapeutic approach for the treatment of obesity [48].  
The irisin profile in several endocrine and metabolic disorders are summarized in Table 1.
## 4 Experiment Verification  
### Fault Simulation and Sample Acquisition  
The fault injection method is to artificially bring a demand fault to the system model, causing the system to malfunction or fail under certain operating conditions, and to observe the response of the system, thereby providing the tester with results-related testing process [24].  
Fault diagnosis is based on the fault sample. Since the fault data of the relay valve is limited, a leakage fault simulation test platform (Figure 5) based on the structure and working principle of the relay valve is developed in this paper. This test simulates the internal and external leakage of the relay valve The test data under normal and leakage conditions are collected as sample data.  
Figure 4: Leakage diagnosis process based on support vector machine (SVM) Method.  
Figure 5: Relay valve leakage simulation test platform.  
Table 3 lists the relay valve fault injection methods for internal and external leakage.  
The \"relay valve leakage simulation test platform\" is mainly composed of the brake control device, the acquisition device, and the throttle valve leakage fault simulation device. The \"normal\" test is carried out 5 times, and the internal and external \"leakage\" simulation tests are carried out 10 times, respectively. The CV1 and BC pressure sensors collect the input and output pressure data of the relay valve. The sampling rate of the acquisition device is set to 1000 Hz to ensure that the sampling accuracy is sufficiently high to avoid influencing the leakage state.
###### Abstract  
Wildfires in Mediterranean regions have become a serious problem, and it is currently the main cause of forest loss. Numerous prediction methods have been applied worldwide to estimate future fire activity and area burned in order to provide a stable basis for future allocation of fire-fighting resources. The present study investigated the performance of an artificial neural network (ANN) in burned area size prediction and to assess the evolution of future wildfires and the area concerned under climate change in southern Spain. The study area comprised 39.41 km\({}^{2}\) of land burned from 2000 to 2014. ANNs were used in two subsequential phases: classifying the size of the wildfires and predicting the burned surface for fires larger than 30,000 m\({}^{2}\). Matrix of confusion and 10-fold cross-validations were used to evaluate ANN classification and mean absolute deviation, root mean square error, mean absolute percent error and bias, which were the metrics used for burned area prediction. The success rate achieved was above 60-70% depending on the zone. An average temperature increase of 3 \({}^{\circ}\)C and a 20% increase in wind speed during 2071-2100 results in a significant increase of the number of fires, up to triple the current figure, resulting in seven times the average yearly burned surface depending on the zone and the climate change scenario.  
Keywords:wildfire; ANN; climate change; confusion matrix; k-fold cross-validation; Spain +
Footnote †: journal: Applied Sciences
## 3 Key Technologies in Trust Management for WSNs
### Trust Evaluation Methods
#### Success-failure methods
However, the computational complexity of the entropy-based method is higher than that of the Bayesian beta method. A form of direct trust evaluation can be used to counter a Sybil attack. In Sybil, the attacker forges multiple new identities that appear as new nodes. As WSNs are open by nature and normally have a high node failure rate, the addition of new nodes is a common occurrence. Therefore, it is not easy to determine whether new nodes are fake or legitimate entities. To mitigate the risk, [PERSON] et al. [5] proposed to initialize all new nodes (physical or virtual) as 'untrustworthy'. The trust value of a legitimate new node will increase as the node successfully participates in transactions.
## 5 ENO Controller Design
### Controller Gain Tuning
#### 5.1.1 Variable PVC Model  
The second major step of tuning was performed using a PVC model that varied based on a solar diurnal cycle that included standard noise. GPS fixes were acquired during the day, which was dictated by the PVC model. Battery capacity remained at 100 \(mA-hr\) from the previous tuning step. \(K_{D1}\) was re-evaluated for values of 500, 1000, and 2000 respectively. A \(K_{D1}\) value of 2000 was selected because GPS fix interval and SOC had the smallest amplitude of oscillation.  
Reducing fluctuation in data collection rate near the SOC set point was handled by an additional set of gains, where selection of which gain set to use was determined by a gain scheduler. Tuning of the gains were performed by first setting gain values for data collection consistency (\(K_{P2}\), \(K_{D2}\), and \(K_{I2}\)) equal to the first \(K_{P1}\), \(K_{D1}\), and \(K_{I1}\). Simulations were performed for 30 days, battery capacity was 800 \(mA-hr\), and the PVC model was variable. Plot (c) of Figure 9 shows GPS fix interval for \(K_{D2}\) evaluated at 1000, 1500, and 2000, where \(K_{P2}\) was previously selected as 0.1.  
Figure 8: Constant PVC fix interval results with tuned controller. (**a**) SOC with respect to time; and (**b**) GPS fix interval.  
GPS fix interval settled to 38 \(\pm\) 2 s after 28 days for \(K_{D2}\) assigned to 1000. Similarly, a GPS fix interval of 38 \(\pm\) 2 s was achieved after 22 days for \(K_{D2}\) set to 1500. Gain values of both 1000 and 1500 had GPS fix interval variation between 30 to 60 s prior to reaching a steady value. Ultimately the original value of 2000 was selected for \(K_{D2}\), the same value as \(K_{D1}\), because a steady GPS fix interval was achieved in 6 days compared to 22 and 28 days for gain values of 1000 and 1500.
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
###### Abstract  
High frequency wireless communication aims to provide ultra high-speed transmissions for various application scenarios. The waveform design for high frequency communication is challenging due to the requirements for high spectrum efficiency, as well as good hardware compatibility. With high flexibility and low peak-to-average power ratio (PAPR), discrete Fourier transformation spreading-based orthogonal frequency division multiplexing (DFT-s-OFDM) can be a promising candidate waveform. To further enhance the spectral efficiency, we integrate faster-than-Nyquist (FTN) signaling in DFT-s-OFDM, and find that the PAPR performance can also be improved. While FTN can introduce increased inter-symbol interference (ISI), in this paper, we deploy an isotropic orthogonal transform algorithm (IOTA) filter for FTN-enhanced DFT-s-OFDM, where the compact time-frequency structure of the IOTA filter can significantly reduce the ISI. Simulation results show that the proposed waveform is capable of achieving good performance in PAPR, bit error rate (BER) and throughput, simultaneously, with 3.5 dB gain in PAPR and 50% gain in throughput.  
waveform; DFT-s-OFDM; FTN; IOTA filter; PAPR; high frequency Article  
1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], N. Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering.  
_Sensors_**2022**, 22, 4907. [[https://doi.org/10.3390/s22134907](https://doi.org/10.3390/s22134907)]([https://doi.org/10.3390/s22134907](https://doi.org/10.3390/s22134907))