esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this paper, a combined approach of principal component analysis with extreme learning machines is investigated to predict boiler output power in a thermal power plant. The electrical output of a boiler in the operating system requires knowledge of 232 operating parameters. Principal component analysis is performed to reduce the dimensionality of the input dataset. These principal components form the input matrix for the extreme learning machine. Total inputs are divided into two parts--training and testing datasets. To validate the effectiveness of the combined approach of PCA-ELM, it is compared with ELM in terms of its testing accuracy and simulation time. Further, the ELM model is designed with various activation functions and several hidden neurons, and the results for simulation time and testing accuracy are compared. In summary, according to the predictions made, the following conclusions can be drawn:  
* To predict the boiler output power in a thermal power plant, PCA-ELM is superior to ELM in predicting accuracy and learning speed. This is because with PCA, the dimension of the inputs is reduced while maintaining the properties of the data points using variance, thereby reducing the computation time.
* In terms of prediction precision, PCA-ELM shows superior performance over ELM because of the eigenvalue decomposition technique for the input data covariance matrix. In this way, correlated input parameters are carefully converted into linearly uncorrelated ones. Thus, this shows the necessity for dimensionality reduction methods to enhance forecasting.
* The generalization ability of the ELM is corroborated with changes in ELM parameters' activation function and the number of hidden nodes. In a comparison of PCA-ELM models with a hyperbolic tangent function and those with a sigmoid function, the latter shows better performance in terms of errors. However, the performance of the hyperbolic tangent function deteriorates with the increasing number of hidden neurons.  
This paper primarily studies the forecasting of boiler output based on thermal power plant operating parameters. It can further be extended to the optimization of the input parameters. For the fact of serious air pollution caused by thermal plants, these outcomes may also be considered in the production planning. Hence there are numerous other ways to incorporate this study.  
Conceptualization, K.K.D., C.R.R. and P.S.V.; methodology, O.C.S.; software, M.A.; validation, K.K.D., P.S.V., C.R.R., O.C.S., M.A., Y.A. and B.A.; formal analysis, O.C.S.; investigation, K.K.D.; resources, C.R.R.; data curation, P.S.V.; writing--original draft preparation, K.K.D. and C.R.R.; writing--review and editing, O.C.S. and P.S.V.; visualization, C.R.R.R; supervision, O.C.S.; project administration, P.S.V.; funding acquisition, M.A., Y.A. and B.A. All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the financial support received from Taif University Researchers Supporting Project Number (TURSP-2020/278), Taif University, Taif, Saudi Arabia.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
Optimizing control rate parameters is one of the key technologies in motor control systems. To address the issues of weak robustness and slow response speed in traditional adaptive control strategies, an adaptive control system based on sliding mode control is proposed to enhance the overall performance of permanent magnet synchronous motors. The Non-dominated Sorting Genetic Algorithm II and Multi-objective Particle Swarm Optimization are employed to effectively optimize control parameters, thereby mitigating motor torque and speed overshoot. A Partial Sample Shannon Entropy Evaluation method, leveraging entropy theory in conjunction with the Z-score method, is introduced to facilitate the feedback regulation of the optimization process by assessing motor output torque. Simulation results confirm that the proposed control strategy, in combination with the optimized control rate parameters, leads to substantial improvements in motor performance. Compared to traditional adaptive control strategies, the proposed approach improves the motor's steady-state response speed by 42% and reduces rotor error during system fluctuations by 23%, significantly enhancing the motor's response speed and robustness. Following parameter optimization, speed and torque overshoot are reduced by 38% and 10%, respectively, resulting in a significant improvement in the stability and precision of the motor control system.  
permanent magnet synchronous motor; optimization design; adaptive sliding mode control; entropy +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Main Implications and Future Perspectives  
#### 3.5.1 Main Implications  
Enhancing Sustainability in the Agri-Food Sector:  
The extraction of bioactive compounds like polyphenols from agricultural waste, such as grape marc, underscores the potential to reduce the environmental impacts associated with waste disposal. By valorising these by-products, the agri-food sector can contribute to a circular economy, where waste is minimised and transformed into valuable products.  
Development of Value-Added Products:  
This study demonstrates the feasibility of turning grape marc, a by-product, into a source of high-value antioxidants. This could lead to the creation of nutraceuticalals, functional foods, or natural food preservatives, opening up new markets and revenue streams for the agri-food industry. This powdered formula rich in polyphenols also offers a promising ingredient for the food and cosmetic industries, where antioxidant properties are highly valued.  
Optimised Extraction Methods:  
The comparison of different solvents for extracting polyphenols reveals that hydroalcoholic solutions are more effective than water alone in both extracting higher concentrations of phenols and increasing antioxidant activity. This insight is crucial for refining extraction processes and improving the efficiency of bioactive compound recovery, potentially leading to better yields and a more cost-effective operation in commercial settings.  
Pilot Plant Insights:  
The laboratory-scale experiment, followed by the demonstration at the pilot plant, provides essential data on how large-scale operations can be scaled up. The pilot plant's operational stages--extraction, separation, and drying--offer valuable information for optimising these processes to maximise polyphenol recovery. Such insights are critical for transitioning from laboratory experiments to industrial applications, making the process more viable and scalable.
# A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites
## 2 Materials and Methods
### Debiasing Data  
In biological experiments, RNA cleavage enzymes are commonly utilized to isolate specific RNA sequences. The method often results in many positive samples with identical cleavage sites, which means that the nucleotides at the beginning and end of the RNA viewpoint fragments are identical. In the positive samples, the viewpoints usually begin with \"G\" and end with \"G\". However, machine-learning models tend to emphasize RNA fragment boundaries more during feature extraction, leading to biased results. Therefore, removing the bias in the data can ensure the prediction results' authenticity. RPI-Net suggests solving the problem by replacing boundary nucleotides with random nucleotides. However, we found that this method can lead to the loss of information in the combined sequence and increase the uncertainty of the prediction results. Notably, the sequence context has a vital role in selecting binding sites. Therefore, this research proposes a debiasing method that incorporates boundary contexts. In other words, the areas three nucleotides ahead and behind of the central nucleotide served as the context. The mean values of the context features replaced the feature of the central nucleotide. This method involved replacing the nucleotides in a window of size 3 centered at the \"G\" preceding the viewpoint and in a window of size 3 centered at the end of the viewpoint. It could maximize the integrity of the combined information. Given an RNA sequence \(S\), the feature calculation method for its boundary nucleotides is as follows.  
\[\text{F}_{\text{i}}=\frac{1}{6}\sum_{\text{j}=-3}^{3}\text{F}_{\text{i+j}} \tag{14}\]  
where F represents the feature matrix of the RNA sequences, i represents the position of the nucleotide, and j represents the position of the context of nucleotide \(i\).
## 6 Applications of UAV Computing
### Agriculture  
Drones have been used to scan big agricultural fields to discover illnesses early on that jeopardize plant quality. Using image processing and AI classification based on IoT architecture and real-time data capture, the deployed system can forecast agricultural illnesses in real-time [165]. The authors of [166] presented an architecture that combines UAVs, Wireless Sensor Networks (WSNs), and IoT to deliver precision ecological agriculture. Another architecture has been developed to analyze pictures taken using UAV aerial multispectral sensors using the DL technique for crop mapping monitoring and disease evaluation based on satellite data [166]. In addition, in [167; 168], UAVs played a vital role in increasing the productivity of farming activities. Furthermore, UAVs play an exciting role in agricultural monitoring by enabling various previously available tasks to laborers and former [169; 170].
# IoT Open-Source Architecture for the Maintenance of Building Facilities
###### Abstract  
The introduction of the Internet of Things (IoT) in the construction industry is evolving facility maintenance (FM) towards predictive maintenance development. Predictive maintenance of building facilities requires continuously updated data on construction components to be acquired through integrated sensors. The main challenges in developing predictive maintenance tools for building facilities is IoT integration, IoT data visualization on the building 3D model and implementation of maintenance management system on the IoT and building information modeling (BIM). The current 3D building models do not fully interact with IoT building facilities data. Data integration in BIM is challenging. The research aims to integrate IoT alert systems with BIM models to monitor building facilities during the operational phase and to visualize building facilities' conditions virtually. To provide efficient maintenance services for building facilities this research proposes an integration of a digital framework based on IoT and BIM platforms. Sensors applied in the building systems and IoT technology on a cloud platform with opensource tools and standards enable monitoring of real-time operation and detecting of different kinds of faults in case of malfunction or failure, therefore sending alerts to facility managers and operators. Proposed preventive maintenance methodology applied on a proof-of-concept heating, ventilation and air conditioning (HVAC) plant adopts open source IoT sensor networks. The results show that the integrated IoT and BIM dashboard framework and implemented building structures preventive maintenance methodology are applicable and promising. The automated system architecture of building facilities is intended to provide a reliable and practical tool for real-time data acquisition. Analysis and 3D visualization to support intelligent monitoring of the indoor condition in buildings will enable the facility managers to make faster and better decisions and to improve building facilities' real time monitoring with fallouts on the maintenance timeliness.  
digital twin; facility management; building information modeling; HVAC; fan coil; Internet of Things; predictive maintenance; fault detection; smart building +
Footnote †: journal: Applied Sciences
## 2 Results and Discussions  
### Photocatalytic Efficiency (PE) Results  
#### 2.1.1 Spectrophotometric Colorimetry (SPC) of Surface-Polluted Samples  
Figure 2 shows the PE of the samples over time, as measured using SPC. The PE results and their respective standard deviations are provided in Supplementary Material Table S1.  
All samples showed the progressive degradation of RhB under UV-Vis irradiation. However, the treated samples (G_T16 and W_T16) exhibited significantly higher PE compared to their respective references (G_Ref and W_Ref), confirming the effectiveness of TiO\({}_{2}\) functionalization for self-cleaning properties.  
The W_T16 sample reached a PE of 31% at the end of the irradiation period, slightly higher than the G_T16 sample, which reached 29%. This difference might have been related to the difference in the light absorption characteristics between the white and gray samples. Darker surfaces, such as the gray samples, absorb more light, reducing the generation of electron-hole pairs and, consequently, the PE, while light surfaces, such as the white samples, reflect more light, increasing the radiation available to the photocatalyst and promoting a greater efficiency [30].  
Additionally, other factors could influence the observed behavior, such as surface roughness and porosity. Rougher surfaces, such as the gray substrate, tend to retain more TiO\({}_{2}\). However, the photocatalytic activity is more effective at moderate roughness with controlled TiO\({}_{2}\) content [12]. On very rough surfaces, some of the deposited TiO\({}_{2}\) may be blocked from the light, limiting its activation and reducing the overall efficiency.  
Our previous study [31] thoroughly investigated the surface morphology of uncoated and functionalized samples of the same substrate, including cross-sectional scanning electron microscopy (SEM) analysis to assess coating deposition. The results provided insight into the structural characteristics of the substrate, such as the fact that uncoated samples showed a morphology with a rough texture and visible pores. On the other hand, TiO\({}_{2}\)-coated samples showed a reduction in surface porosity, concluding that the deposition of the TiO\({}_{2}\) dispersion minimized roughness and resulted in a more uniform morphology. Examples of SEM representations are presented in Supplementary Materials (Figures S3 and S4).
## 3 GNSS Spoofing Signal Power Control Algorithm  
### Constraint Analysis  
In a spoofing scenario, the variance \(\overline{\sigma}_{\overline{\
u}}^{2}\) of the general ambient noise power \(N_{0}\), coherent integration time \(T_{c}=NT_{s}\), and \(I/Q\) branch normalized spreading code remains unchanged. For spoofer, total power \(\sum\limits_{h=\mu}P_{h}^{a}\) of each channel is uncontrollable. Only spoofing signal \(P_{m}^{s}\) and total power \(\sum\limits_{m=\mu}P_{m}^{s}\) are designed to satisfy the following basic conditions: 1 The maximum signal-to-noise ratio \(SNR_{max}^{s}\) of each branch spoofing signal is lower than the receiver signal-to-noise ratio detection threshold \(SNR_{thres}\) (or the maximum signal-to-noise ratio \(SNR_{max}^{a}\) of authentic satellite signal); 2 the minimum signal-to-noise ratio \(SNR_{min}^{s}\) of each branch spoofing signal is higher than the average signal-to-noise ratio \(SNR_{ave}^{a}\) of the authentic satellite signal; 3 the power of the spoofing signal of each branch corresponding satellite elevation angle matching; 4 if the target receiver is dynamic, spoofing signal power should be adjusted according to the signal power transmission loss model; 5 the relative acquisition probability of each spoofing signal satisfies \(P_{i}>0.5\). When the single-channel spoofing signal is relatively close to the probability of acquisition \(P_{i}>0.5\), the ability of the spoofing signal to be preferentially acquired is explained. The 5 th constraint here means that we usually think that when the single-channel spoofing signal is relatively close to the probability of acquisition \(P_{i}>0.5\), it indicates that for the receiver, the acquisition probability of spoofing signal is greater than the acquisition probability of authentic signal, so the spoofing signal will be acquired preferentially, which is also a condition for successful spoofing.  
The above constraint formula is expressed as:  
\[\left\{\begin{array}{l}SNR_{max}^{s}<SNR_{thres}\\ SNR_{min}^{s}>SNR_{ave}^{a}\\ P_{m}^{s}\approx k_{1}+\frac{k_{2}}{\cos(\alpha+\theta)}\\ P_{m}^{s}=P_{T}+G_{T}+G_{R}+20\lg(\lambda/(4\,\pi d))\\ P_{i}>0.5\end{array}\right. \tag{19}\]
# A Low-Cost Electronic System for Human-Body Communication
## 3 System Description
### Preliminaries  
In general, devices on the body communicate wirelessly through radio frequency (RF) technology. However, they suffer from high attenuation of signals provided by the body tissue [19; 20]. We consider that galvanic coupling can provide power efficiency and security for communication beyond a significant reduction in vulnerability to attacks [15; 21]. In addition, it enables implementing sensor devices without using RF components and antennas [22]. These advantages make galvanic coupling useful for many applications, mainly in the healthcare field.  
Our goal in using signal transmission through an HBC structure is to guarantee confidentiality, integrity, and data availability. Furthermore, the developed galvanic communication platform comprises low-cost, miniaturized hardware that can be linked to vital signs monitoring devices _in loco_ and is non-invasive.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 4 Adaptive Channel Allocation Algorithm
### The Adaptive Channel Allocation
#### 4.2.1 Decision Function  
The decision function is presented by the following steps.  
1. _We begin by calculating the traffic of GW's links, where these links are assigned the same channel_ \(p\)_,_ \(TGW_{p}\)_: \[TGW_{p}=\sum_{l_{gi}\in GWL,ch_{gi}=p}t_{gi},\] (8) where_ \(GWL\) _is the group of links adjacent to the GW, and_ \(ch_{gi}\) _is the channel assigned to_ \(l_{gi}\)_._
2. _We compute the decision factor :_ \[\max_{p,q}|\frac{TGW_{p}}{TGW_{q}}-1|\geq\lambda,\] (9)  
_where the parameter_ \(\lambda\) _represents the uneven limit of the data transmission utilization for each channel. The parameter_ \(\lambda\) _ought to be given experimentally by considering the network topology, the traffic design, and the quantity of NICs distributed to GW. Both_ \(p\) _and_ \(q\) _are defined as any pair of the channels allocated for links in_ \(GWL\)_._The decision function (DF) is defined as follows:  
\[DF=\begin{cases}\text{Yes},&\max_{p,q}|\frac{TGW_{p}}{TGW_{q}}-1|\geq\lambda,\\ No,&\text{otherwise}.\end{cases} \tag{10}\]  
According to the result of the decision function, the channel re-allocation is utilized by using the same steps in Section 4.1, if the value is \"Yes\"; or if its value is \"No\", it continues using the last channel allocation.
# A Review of Pedestrian Indoor Positioning Systems for Mass Market Applications
## 3 Inertial Based Systems
### Step and Heading Systems
The fusion of both measurements has shown relatively good accuracy in [54] as both measurement errors are complementary, that is, the gyroscope produce high accurate measurements in the short term and the magnetometer gives low accurate measurements but stable in time. An example of a step and heading system is found in [42], where the authors design a system for hand held smartphones. Similarly, in [41] a SHS for smartphones placed in the pocket of the user is designed where the steps are detected using the gyroscope signal. A comparison of the performance of different systems using low cost sensors is presented in [43]. Despite the improvements of the SHS in the reduction of the drift, it is still present and therefore these kind of systems cannot be applied for a long period of time without any correcting strategy. Inertial based systems are completely scalable in terms of size and number of users as there is no need of deploying infrastructure in the environment. Furthermore, nowadays smartphones already include an embedded IMU and thus the cost of the system is minimum. However, as has been said, it is important to take into account the accuracy reduction produced by the inertial drift.
# Artificial Intelligence Software Adoption in Manufacturing Companies
## 3 Methodology
### Logistic Regression  
To analyze relationships between the dependent variable \"AI usage\" and each independent variable, logistic regression was used. This statistical method is particularly suitable for modeling binary outcomes and allows us to assess the impact of individual predictors on the probability of AI adoption. [PERSON] [68] describes the process of this regression as follows.  
Let Y be a binary or dichotomous variable with values 0 or 1. The probability that the variable Y takes the value 1, depending on the k explanatory variables Xi, i = 1,2,..., k is given by the following nonlinear equation:  
\[P(Y=1)=p=\frac{e^{\beta^{\prime}\chi}}{1+e^{\beta^{\prime}\chi}}=\frac{1}{1+e^ {-\beta^{\prime}\chi}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Category** & **Variable Name** & **Variable Description** \\ \hline Dependent & AI usage & Yes \\  & & No \\ \hline \hline \multirow{6}{*}{Independent} & \multirow{2}{*}{Company size} & Large (\(>\)250 employees) \\  & & Medium (50–249 employees) \\  & & Small (20–49 employees) \\ \cline{2-3}  & \multirow{2}{*}{Role} & OEM producer \\  & & Supplier \\  & & Both \\ \cline{2-3}  & & High \\ \cline{2-3}  & \multirow{2}{*}{Technology intensity} & Medium-high \\  & & Medium-low \\  & & Low \\ \hline \multirow{6}{*}{Industry 4.0 readiness} & Level 5—Very high users \\  & & Level 4—High users \\ \cline{1-1}  & & Level 3—Advanced users \\ \cline{1-1}  & & Level 2—Advanced beginners \\ \cline{1-1}  & & Level 1—Beginners \\ \cline{1-1}  & & Level 0—Non-users \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables included in this study.  
where  
* B is the transposed vector of regression coefficients, \(\beta^{\prime}=(\beta_{0},\beta_{1},\beta_{2},...,\beta_{k})\)
* X is the vector of k explanatory variables.  
The above equation represents the logistic cumulative distribution function, and the model is called the logistic regression model ([PERSON] and [PERSON], 2012). From Equation (1), it follows:  
\[\underset{\beta^{\prime}X\rightarrow\infty}{lim}P(Y=1)=1\]  
\[\underset{\beta^{\prime}X\rightarrow\infty}{lim}P(Y=1)=0\]  
Equation (1) can also be written in the following form:  
\[\frac{p}{1-p}=e^{\beta^{\prime}\mathrm{X}} \tag{2}\]  
where the left-hand side represents the odds:  
\[\frac{p}{1-p}=odds\ (Y=1|\ X_{1},X_{2},...,X_{k})\]  
If we take the logarithm of Equation (2), we get:  
\[ln\frac{p}{1-p}=\beta^{\prime}\mathrm{X}=\beta_{0}+\beta_{1}X_{1}+\beta_{2}X_ {2}+...+\beta_{k}X_{k} \tag{3}\]  
Equation (3) represents the logistic regression model. It is evident from Equation (3) that the relationship between the logarithm of the odds and the independent variables is linear.