esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations  
Some limitations still need to be dealt with in future efforts. Firstly, because of frequent clouds in Southern China, the Sentinel-2 images were seasonally composited, meaning that they may have missed key stages of the soil surface. Denser optical time series (e.g., by the fusion of multisource satellite data) are worth looking into for improving soil pH prediction accuracy [62; 63]. Secondly, the current soil pH values are related to cropping history, suggesting that long-term cropping system data could be more effective in DSM [64]. However, limited by crop rotation samples and remote sensing images (the number and quality of Sentinel images in previous years cannot be guaranteed), we only produced and used crop rotation maps for two years. This failed to cover the long-term dynamics of the cropping systems. Thirdly, some other variables associated with farming management could be considered to characterize human activities in soil pH prediction, especially N fertilization, which is closely related to soil pH change. In a different study mapping soil pH for the Netherlands, agricultural management type, ammonia and nitrogen emissions, manure application, and water drainage classes were included, obtaining good results [60]. Unfortunately, we were unable to obtain spatial data on those farming practices. Statistical data cannot reveal spatial differences in small regions, while satellite remote sensing is quite challenging to use for accurately mapping N fertilization and other practices. Finally, our experiment was conducted in a small region, with special characteristics in terms of its climate and cropping practices. Cropping practices in other regions and their influence on the soil pH are different, which may change the effectiveness of grouping by crop rotation in predicting the soil pH.
###### Abstract  
The aim of this study was to explore the underlying age-related differences in dynamic motor control during different step ascent conditions using muscle synergy analysis. Eleven older women (67.0 \(y\)\(\pm\) 2.5) and ten young women (22.5 \(y\)\(\pm\) 1.6) performed stepping in forward and lateral directions at step heights of 10, 20 and 30 cm. Surface electromyography was obtained from 10 lower limb and torso muscles. Non-negative matrix factorization was used to identify sets of (\(n\)) synergies across age groups and stepping conditions. In addition, variance accounted for (VAF) by the detected number of synergies was compared to assess complexity of motor control. Finally, correlation coefficients of muscle weightings and between-subject variability of the temporal activation patterns were calculated and compared between age groups and stepping conditions. Four synergies accounted for \(>\)85% VAF across age groups and stepping conditions. Age and step height showed a significant negative correlation with VAF during forward stepping but not lateral stepping, with lower VAF indicating higher synergy complexity. Muscle weightings showed higher similarity across step heights in older compared to young women. Neuromuscular control of young and community-dwelling older women could not be differentiated based on the number of synergies extracted. Additional analyses of synergy structure and complexity revealed subtle age- and step-height-related differences, indicating that older women rely on more complex neuromuscular control strategies.  
EMG; muscle synergies; forward stepping; lateral stepping; aging; neural control +
Footnote †: journal: Applied Sciences
# Study on Degradation of Benzothiazolium-Based Ionic Liquids by UV-H\({}_{2}\)O\({}_{2}\)
## 3 Results
### Effect of Temperature on Degradation  
On the basis of the Vant Hoff rule, the higher the temperature is, the higher the reaction rate will be. In this paper, the activity of hydroxyl radicals does increase with the rise of temperature, but hydrogen peroxide, which produces hydroxyl radicals, decomposes into oxygen and water more quickly, resulting in a decrease in the concentration of hydroxyl radicals. The degradation of 0.1 mM [C4 Bth]PF6 was investigated in the condition of 100 mM H2O2 and pH 5.06 for different temperatures 25 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, 45 \({}^{\circ}\)C, 55 \({}^{\circ}\)C, and 65 \({}^{\circ}\)C. The experimental results are shown in Figure 4. The IL degradation yield increases with the increase of temperature when the system temperature is lower than 45 \({}^{\circ}\)C. When the temperature is greater than 45 \({}^{\circ}\)C, the situation is reversed.
# Use of Binary Classification in Non-Invasive Load Monitoring
## 2 NILM--Key Elements
### Research Methodology
The study was carried out with data collected from five homes and uploaded to the public REDD (Reference Energy Disaggregation Data) database. This included the identification of the following seven appliances: cooker, water heater, oven, electronics, waste grinder, washer-dryer, and microwave. To evaluate the performance of the kNN method properly, the dataset was divided into a training part (containing 90% of the data) and a test part (containing 10% of the data). The kNN algorithm identified four appliances without error; only for electronics was the identification at 66.7%. The accuracy coefficient (6) covering the identification accuracy of all devices achieved a value of 92.6%. In this paper, the authors used a global approach and a wide range of potential identification features, which were subjected to analytical selection. The approach presented, concerning the use of a wide set of identification features acquired under a variety of conditions, indicates a potential solution to identification feature selection problems. In [11], a new approach in which a threshold was used to identify the state of devices on the basis of energy consumption readings was introduced. The feedforward neural-network-based NILM outperformed the traditional logistic regression by 5.78%, whereas the use of the threshold improved the accuracy by an additional 19.1%. The overall performance improvement over logistic regression was 25%. The authors of [13] proposed a neural NILM algorithm using a convolutional neural network, which simultaneously detects and classifies events while minimizing the computation time. The applied technique transforms current signals into images processed by a CNN, enabling device recognition under various conditions. The results on the BLUED dataset achieved an F1 score of 99.8%, outperforming the other solutions. In [4], the authors developed a comprehensive NIALM laboratory for testing devices and artificial intelligence methods. The results indicate that the frequency analysis of the EMI (electromagnetic interference) is not feasible with the European power grid. A technique was introduced that utilizes high-frequency signals for device identification, involving the introduction of disturbances to analyze system responses. In [16], the authors proposed a non-intrusive load monitoring method based on random forest to determine energy consumption by building subsystems. Three feature selection methods were applied and compared to achieve an accurate NILM based on the weather parameters, wavelet analysis, and principal component analysis, ensuring the most accurate results. The method achieved RMS errors below 46.4 kW and relative errors below 12.7%. The NILM allows for the disaggregation of energy consumption signals into individual devices, leading to more conscious energy use and savings. In this study, the K-nearest neighbors algorithm was used for the classification of energy signals. The REDD dataset was used, achieving signal separation for K = 5 after appropriate data processing.
## 3 Comparison to Previous Work and Additional Theoretical Outcomes
### FS-Ordinal versus SVM-Ordinal Structures
In this particular example, the ordinal structure would be found if the statement, \(\mu_{1,2}<\mu_{1,3}\), was true. Note that, based on an _eye test_ associated with Figure 4, the relation, \(\mu_{1,2}<\mu_{1,3}\), is more _intuitive_ than the relation \(\mu_{1,2}=\mu_{1,3}\). This simple example shows us why it was important to introduce the concept of LSM mappings in combination with the generalised definition of FS-ordinal class structures provided in this work. Figure 4: Example of an ordinal-structured 3-class toy data set with class order \(\omega_{1}\prec\omega_{2}\prec\omega_{3}\) (This figure is adapted from our previous work [21]). Note that the main condition in Theorem 1 requires that the values of \(\mu_{1,2},\mu_{1,3}\), and \(\mu_{2,3}\) are pairwise distinct. The discussion of the current example showed the importance of this condition. In the current example, we had the following relations, \(\mu_{1,2}=\mu_{1,3}\) and \(\mu_{2,3}<\mu_{1,2},\mu_{1,3}\). Moreover, this observation leads to a short and simple extension of Theorem 1, which we summarise in Corollary 1. Note that in contrast to the current example, in Corollary 1, we assume that the unique value (e.g., \(\mu_{2,3}\)) is _greater_ than the two equal values (e.g., \(\mu_{1,2}\) and \(\mu_{1,3}\)). **Corollary 1** (Extension of Theorem 1). : _Let \(X_{\Omega}\subset\mathbb{R}^{d}\times\{\omega_{1},\omega_{2},\omega_{3}\},d \in\mathbb{N}\), be a \(d\)-dimensional labelled data set, which constitutes a \(3\)-class classification task. Moreover, let the corresponding PSM, \(M\), be defined as follows, for some LSM, \(\mu\in\mathcal{M}^{d}\),_  
\[M^{(id)}= \begin{pmatrix}0&e&f\\ e&0&g\\ f&g&0\end{pmatrix} ,\quad e,f,g>0.\]  
_Furthermore, let two of the three values, \(e,f,g\), be equal and smaller than the remaining one. More precisely, let one of the following three statements be true:_  
\[(i) (e=f)\wedge(g>e=f)\] \[(ii) (e=g)\wedge(f>e=g)\] \[(iii) (f=g)\wedge(e>f=g)\]  
_Then, there exists a permutation \(\tau\in\mathcal{T}^{3}\), such that \(X_{\Omega}\) constitutes an FS-ordinal classification task specific to \((\mu,\pm\tau)\)._  
The proof of Corollary 1 is provided in the Appendix A. Note that it can be analogously shown that in the case of obtaining two equal values that are greater than the third one, e.g., \((e=f)\wedge(g<e=f)\), in combination with some LSM \(\mu\), always leads to a violation of Definition 3, i.e., to the observation that the current task is not FS-ordinal specific to mapping \(\mu\).
## 4 Performance Evaluation and Discussion
### Model Establishment
#### 4.1.3 Parameters Selection  
As discussed in Section 3.2, the parameters \(\{\alpha_{P},\alpha_{R},\alpha_{RP}\}\) indicate how memory factors impact the perceived video quality over time. The larger \(\{\alpha_{P},\alpha_{R},\alpha_{RP}\}\) are, the easier it is for the user to forget. According to the authors of [16; 17], the effects of primacy and recency gradually decrease within 15-20 s. Therefore, in this study, the deteriorating time was set to 15 s. Since the user usually recalls unpleasant events when providing a QoE score, the effect of repetition is larger and remains longer than primacy and recency. As a result, the values of \(\alpha_{RP}\) must be smaller than \(\alpha_{P}\) and \(\alpha_{R}\). In this study, the effect of repetition remained within 30 s. The optimal remaining times of repetition effect will be analyzed in our future works. The function _solve_ in MATLAB [47] was employed to compute the parameters \(\alpha_{P}\), \(\alpha_{R}\), and \(\alpha_{RP}\) according to Equations (3)-(5), respectively. Consequently, the obtained values of parameters \(\{\alpha_{P},\alpha_{R},\alpha_{RP}\}\) are shown in Table 1.  
Thereby, the parameters \(\{\beta_{1},\beta_{2},\beta_{3}\}\) and \(\{\lambda_{1},\lambda_{2}\}\) of weight memory and the proposed cumulative QoE model could then be estimated. Considering a streaming session with a video in the training set of \(L\) seconds, the cumulative QoE from the beginning to the end of the streaming session was calculated as follows:  
\[\begin{split} CQ_{L}&=\lambda_{1}\left(Q_{L}\times W _{L}^{T}\right)+\lambda_{2}DoI\\ &=\lambda_{1}\sum_{i=0}^{L}w_{i}q_{i}+\lambda_{2}DoI\\ &=\lambda_{1}\sum_{i=0}^{L}\left(\beta_{1}f_{P}(i)+\beta_{2}f_{R }(i)+\beta_{3}f_{RP}(i)\right)q_{i}+\lambda_{2}DoI\end{split} \tag{8}\]  
where \(Q_{L}\) is the vector of instantaneous QoE \((q_{0},q_{1},\ldots,q_{L})\) and \(W_{L}\) is the memory weight vector \((w_{0},w_{1},\ldots,w_{L})\).  
As mentioned in Section 3.4, the cumulative QoE at the end of the session \(CQ_{L}\) was also considered as the overall QoE. Therefore, we first needed to minimize the least square error:  
\[\mathbf{J}=\left\|CQ_{L}-Q_{overall}\right\|^{2} \tag{9}\]  
where \(Q_{overall}\) is the subjective overall user's QoE obtained from the database. A curve fitting was performed using _lsqcurvefit_ in MATLAB [47] with 28 training videos to obtain the memory weight parameters \(\{\beta_{1},\beta_{2},\beta_{3}\}\) and the cumulative QoE parameters \(\{\lambda_{1},\lambda_{2}\}\). The numerical values of those parameters are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{\alpha_{P}}\) & \(\mathbf{\alpha_{R}}\) & \(\mathbf{\alpha_{RP}}\) \\ \hline
0.6807 & 0.6807 & 0.3404 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the primacy and recency effect, forgetting curve and repetition.  
Figure 7: Some examples of instantaneous QoE prediction performance obtained from the LSTM-QoE model on different test videos of the database.
## 3 Research Framework of Internet of Emergency Services
### Real-Time Data Collection and Analysis
#### 3.3.2 Artificial Intelligence Frameworks and Models  
Artificial Intelligence (AI) frameworks and models are increasingly being applied in emergency response and management to support decision making and enhance operational efficiency [10,33,97]. Here, we will explore some of the AI frameworks and models commonly used in emergency response and management, as well as some specific examples. One of the most widely used AI frameworks in emergency management is machine learning, which involves the use of algorithms to automatically learn patterns in data and make predictions or decisions based on those patterns. Machine learning can be applied in a variety of emergency management scenarios, including predicting the severity of natural disasters, identifying potential terrorist threats, accident detection [98], panic detection [23], evacuation route planning [60], detecting the spread of infectious diseases, and prioritising the patients in case of emergency [99]. Neural networks can be used to classify emergency situations based on their severity and urgency, or to identify the most effective response strategies based on past emergency response data, or for localization [100]. For example, researchers at the University of California, Berkeley, developed a machine learning model that uses data from past hurricanes to predict the likelihood and severity of future hurricanes. The model takes into account a variety of factors, including wind speed, rainfall, and atmospheric pressure, and can be used to help emergency responders prepare for and respond to future hurricanes.  
Another AI framework commonly used in emergency management is natural language processing (NLP), which involves the use of algorithms to analyze and understand human language. NLP can be used to analyze social media posts and news articles to identify emerging threats or trends, or to analyze emergency call transcripts to identify key information and prioritize response efforts [9]. For example, researchers at the University of Edinburgh developed a system called the Emergency Social Media Aggregator (ESMA) that uses NLP to analyze social media posts during natural disasters, terrorist attacks, and other emergencies. The system can identify key information, such as the location and severity of the emergency, and can be used to help emergency responders prioritize response efforts.  
In conclusion, AI frameworks and models are increasingly being used in emergency response and management to support decision making and enhance operational efficiency (see Table 6). Machine learning, natural language processing, and neural networks are some of the most commonly used AI frameworks and models in emergency management, and specific examples of their application range from predicting the impact of natural disasters to analyzing social media posts during emergencies.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 4 Chitosan
### Parameters Influencing the Antibacterial Properties of Chitosan
#### 4.1.3 Molecular Weight  
High-molecular-weight chitosan, low-molecular-weight chitosan, and oligochitosan (short-chain chitosan) are three distinguished types of chitosan [81]. [PERSON] et al. [82] recently showed that lowering MW increased antibacterial efficacy for Gram-negative bacteria, while the reverse impact was found for Gram-positive bacteria. Several researchers observed the discrepancy in antibacterial activity, and it was postulated that high-molecular-weight chitosan cannot pass through the microbial membrane, meaning it accumulates on the cell surface, which alters the cell permeability by blocking nutrient transport and results in cell lysis [83; 84; 85; 86]. Dissociated chitosan molecules in solution with a smaller molecular weight may, on the other hand, bind to DNA and suppress mRNA production by penetrating the nucleus of bacteria [87; 88; 89; 90].
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 1 Introduction
When traditional power grid infrastructures gradually adapt themselves to smart grid and microgrid concepts, they start to grow their dependencies on emerging technologies, such as smart technology, cyber-physical systems (CPSs), artificial intelligence, cyber security, edge computing, big data analytics, etc. [3]. The cyber security issues that existed in IT systems will remain in microgrids; what is more, some new cyber attacks appear during the microgrid operations. This work will introduce the application and enabling techniques in microgrids, including distributed energy resources (DERs), a distributed management system (DMS), a power control system, advance metering infrastructure (AMI), energy scheduling, and dynamic pricing [4]. Because cyber-physical attacks can happen at any point through the information chain, this survey takes Secure Smart Grid Standard NISTIR 7628 [5] as a high-level security analysis framework and the National Infrastructure Advisory Council's report: A Framework for Establishing Critical Infrastructure Resilience Goals (used as NIAC in this survey) [6], as a high-level resilience analysis framework, which details the operation actors from the ICT architecture and will be explained in the next section [3]. The main contribution of this work can be summarised as follows:  
(1) We introduced the common cyber-physical attacks, communication protocols, and research testbed from the smart grid research and demonstrated various secure countermeasures and resilient technologies through four stages: securing communication channels, state estimation, detecting malicious attacks, and contingency responses for mitigation. (2) This work proposed secure and resilient frameworks by considering cyber security measurements, redundancy and resilience, real-time monitoring, and situational awareness. (3) This work investigated the specific challenges related to resilience in smart grids and microgrids, including grid disruptions, distributed energy resources (DERs) integration, security concerns, communication, and control systems. The paper outlines a comprehensive framework for enhancing the security and resilience of smart grids, structured around critical analyses and proposed solutions spanning multiple dimensions. Section 2 lays the foundation with an exploration of secure and resilient frameworks. In Section 3, various cyber-physical attacks and real-world attack scenarios are introduced based on the frameworks. Section 4 delves into an array of defence and mitigation mechanisms, including the development of secure communication channels, advanced state estimation, robust attack detection strategies, comprehensive contingency responses, and the integration of testbeds within the contexts established in Section 2. Lastly, Section 5 confronts the challenges inherent in implementing these frameworks, proposing potential solutions that span the spectrum of security and resilience in smart grids.
## 5 Conclusions  
This paper proposed the CEER for UWSNs. The proposed routing protocol significantly improved the PDR, end-to-end delay, energy consumption, stability period, and transmission loss. CEER uses the sink mobility technique for energy consumption, and for the network's reliability, it uses the cooperative method for data transferring. The simulation results show that the proposed scheme is efficient from the current routing protocols in evaluated performance metrics. The results also indicate that CEER is better up to 25%, 25%, 200%, and 28% in terms of energy consumption, PDR, end-to-end delay, and transmission as compared to the EELRP, respectively, and 25%, 20%, 25%, and 28% as compared to the EEDORVA, respectively.  
Conceptualization, H.H.; Formal analysis, A.Z. and H.H.; Funding acquisition, M.T.B.O.; Investigation, A.Z.; Methodology, M.T.B.O.; Project administration, T.R. and M.T.B.O.; Resources, I.K.; Software, I.K.; Supervision, T.R.; Writing-original draft, I.A.; Writing-review & editing, I.A. All authors have read and agreed to the published version of the manuscript.  
The researchers would like to thank the Deanship of Scientific Research, Qassim University, for funding the publication of this project.  
Not applicable.  
Figure 8: Transmission loss of proposed and EELRP and EEDORVA.
## 4 Results and Discussion
### Cytotoxicity Test  
Evaluation of toxicity of the TM-PEC films and TM-nanoemulsion was performed by MTT assay, and the results are presented in Figure 6. After 24 h, extracts of both TM-PEC films and TM-nanoemulsion showed 102-104 and 84% metabolically active cells in comparison with cells without the medicine (71%-74%, negative controls), respectively. By increasing the contact time to  
Figure 5: pH Sensitivity of TM-PEC films.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Tensile Stress (MPa)** & **\% Strain** & **Young’s Modulus (kPa)** \\ \hline TM-PEC \%0.4 & 0.94 \(\pm\) 0.08 & 9.72 \(\pm\) 1.27 & 106.77 \(\pm\) 23.57 \\ TM-PEC \%1 & 2.24 \(\pm\) 0.22 & 8.52 \(\pm\) 1.62 & 265.87 \(\pm\) 32.50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical properties of the PEC films.  
48 h, cell viability of TM-nanoemulsion reached 90% and remained relatively constant (103%) for TM-PEC %1 film. The TM-PEC %0.4 film had a lower level of cell viability after 48 h. This can be explained by the effect of some thyme oil ingredient, such as thymol and carvacrol, on cell growth and proliferation [63; 64]. Carvacrol is a new and natural anticancer drug for human OSCC (oral squamous cell carcinoma). It could suppress human OSCC cancer cells' proliferation by a series of biological pathways including cell cycle regulation, apoptosis, tumorigenesis, adhesion, and invasion [63]. Additionally, the MTT assay and colony formation assays illustrated the function of carvacrol in cell growth and proliferation [63]. Thymol is considered as an oxygenated compound and the role of O\({}_{2}\) in stimulating cell proliferation has been described in many literatures [65; 66], this may explain the role of thymol in stimulating the growth of fibroblast [67]. It can be hypothesized that the incorporation of thymol into the dressings might have improved the collagenization by modulating fibroblasts growth [64].  
It can be concluded from the results of MTT assay that the TM-PEC 1% was not cytotoxic and TM-nanoemulsion and TM-PEC 0.4% had low-toxicity in the term of fibroblast cells; therefore, the films could be a good candidate for _in vivo_ assay.