esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Improved Active Disturbance Rejection Control Strategy Design
### Design of an Auto Disturbance Rejection Controller
#### 3.1.3 Design of the NLSEF  
The traditional proportional-integral-differential (PID) control method struggles to fulfill the system's performance requirements solely by summing up the errors from individual elements. It also faces challenges in balancing between the parameters of speed and overshoot. The nonlinear state error feedback (NLSEF) law replaces the linear combination form with a nonlinear structure. This enhances the information processing efficiency and improves the system's performance to some extent. Additionally, dynamic compensation linearization is achieved through the compensation of the extended state observer. In the NLSEF, the system's state error compares the output \(z_{i}(i=1,2,\ldots,n)\) of the extended state observer with the output \(v_{i}(i=1,2,\ldots,n)\) of the tracking differentiator to determine the error amount:  
\[\varepsilon_{i}=v_{i}-z_{i}(i=1,2,\cdots n) \tag{21}\]The \(\theta_{e}\) tracking signal of the TD is different from the observation signal \(\theta_{e}\) in the observer, and then the linear PID combination is changed into the form of a series controller. The corresponding form of the NLSEF is  
\[\left\{\begin{array}{l}e_{2}=v_{1}-z_{1}\\ e_{3}=v_{2}-z_{2}\\ i_{0}=\beta_{1}\text{fal}(e_{2},\alpha_{2},\delta)+\beta_{2}\text{fal}(e_{3}, \alpha_{3},\delta)\\ i_{q}^{*}=i_{q0}-1/b\cdot z_{3}\end{array}\right. \tag{22}\]  
In Formula (22), \(i_{q0}\) is the pre-set current control quantity of the q axis; \(i_{q}^{*}\) is the final control quantity after the q axis plus the feedforward disturbance.
# Recent Developments in Plasmonic Sensors of Phenol and Its Derivatives
## 4 Localized Surface Plasmon Resonance Phenomenon-Based Optical Sensor for Phenolic Compounds
Many scientists have investigated the optimized fabrication of new plasmonic nanomaterials, demonstrating unique features such as high and excellent plasmon enhancement as well as assistance for photoelectrochemical technologies. For example, there are several reports on Au-decorated La\({}_{2}\)Ti\({}_{2}\)O\({}_{7}\)/rGO-based plasmon enhancement-assisted photoelectrochemical monitoring of bisphenol A [94], SPR of GNP-activated g-C\({}_{3}\)N\({}_{4}\) nanosheet-based photoelectrochemical determination of bisphenol A [95], and LSPR using carbon dot-functionalized GNPs for sensing of dopamine [96].
# Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine
## 7 Discussion  
The proposed sentimental analysis system for high education students using Arabic text can provide valuable insights into the perceptions and opinions of students at PSAU. The analysis of student reviews, classified as positive, negative, or neutral, can help identify areas of improvement in the educational process. This information can be used to make necessary adjustments to the curriculum and teaching methods to enhance the educational experience for students. Additionally, the system can help identify specific issues that students may be facing and provide targeted solutions to address these issues. By providing a more detailed understanding of student perceptions and opinions, the proposed system can help to improve the overall quality of education at Prince Sattam University. Furthermore, the system can also help in improving the quality of teaching by providing teachers with feedback on their performance, which can help them to make necessary adjustments and improve their teaching methods.  
Compared to the literature, our work enhanced sentiment analysis literature by providing improved analytical precision, surpassing prior research outcomes. It captures an extensive array of sentiments, going beyond traditional emotional categories, and broadens the scope of analysis to include a diverse range of text data, with a particular focus on overcoming the intricacies of the Arabic language. The SVM-SAA-SCR technique offers marked advancements, such as enhanced performance indicators, expanded sentiment identification, and increased flexibility for various complex Arabic datasets. Finally, we draw the reader's attention that this work sought to validate the suggested system with a well-known tool such as CAMeLBERT through a comparison based on accuracy and results similarity. This comparison has shown a very slight difference between both systems and promising results for SVM-SAA-SCR.
## 5 LoRaWAN Simulation Tools
### LoRaSIM  
LoRaSim has been developed based on SimPy as a discreet event simulator using Python to simulate, investigate, and analyze the LoRaWAN network scalability and collision functionality [41]. LoRaSim includes many Python scripts, with the base of them being loraDir.py, loraDirMulBs.py, directionalLoraIntf.py, oneDirectionalLoraIntf.py, LoRaWAN.py, and LoRaEnrgssim.py. The first script is for a lone gateway simulation, while loraDirMulBs.py is utilized to emulate several gateways (up to 24). DirectionalLoraIntf can emulate devices that are equipped with directional antennae and many networks, whereas oneDirectionalLoraIntf.py is for emulating gateways with directional antennae and many networks. A radio propagation model is implemented in LoRaSim, depending on the well-known long-distance path loss model. The radio transceiver sensitivity at room temperature concerning various LoRa SF and BW settings is estimated. It also considers many related parameters, such as the thermal noise power across, receiver bandwidth, noise figure, and SNR. Several packages are required for running LoRaSim smoothly, such as matplotlib, SimPy, and NumPy. LoRaSim offers a plotted view of deployments with no graphical interface, as shown in Figure 9. In contrast, it can show a data plot when users execute graphical code. Many improvements for LoRaSim were proposed to make it multipurpose [60; 61; 62; 63; 36] and support the downlink due to the original version supporting only the uplink. Thus, it can test scalability and energy consumption, as well as other performance metrics. Much of the information is seen on the command line and exported to File-Name.dat, which can show its content data using other graphical programs such as Gnuplot, seaborn, Mplot, or others.
## 4 ML-Based Resource Allocation
### Reinforcement Learning Approach
As the objective of the agent is to learn the strategy for maximizing the rewards, the action is selected based on QoS, i.e., SINR threshold, RB availability, and data rates. The actions that meet the constraints are rewarded otherwise negatively rewarded. To meet the objectives of C2, C5, and C7 constraints and maximize the throughput, the agent automatically learns and adapts to the newer situation under dynamic network conditions. Algorithm 1 represents the resource allocation and power control in a D2D-enabled cellular network using an RL-based algorithm. Q-learning is a classical algorithm to learn state-action value functions in RL. However, for large state-action spaces, the environment is complex and dynamic. In such a situation, a Q-learning algorithm cannot use the Q-table to execute and store all state-action values. DRL is one of the algorithms which is used to overcome this issue. ```
1 Input: D2D-enabled HetNet cellular network environment parameters and minimum SINR requirements for D2D pairs, SUEs, and CUEs;
2 Output: Effective resource allocation with power control using RL algorithm in D2D-enabled HetNets according to optimal allocation policy \(\pi^{*}(s_{t})=\arg\max_{a}Q(s_{t},a_{t})\);
3 Initialization: Initialize the storing Q-value function for the state-action pairs;
4 Initialization: For all available actions in the selected current state \(s_{t}\), select the action \(a_{t}\) for maximizing the Q-value function according to the \(\epsilon-greedy\) strategy to obtain \(r_{t}\);
5 for each \(s\in S\), each \(a\in A\)do
6 forepisode \(e\) = 1,2,3,...,\(E\)do
7 Initialize the current state \(s_{t}\) at time t in the D2D-enable HetNets Cellular network environment;
8 forsteps \(t\) = 1,2,3,...,\(T\)do
9 The agent (D2D pairs) takes action \(a_{t}\) based on \(\epsilon-greedy\) strategy to obtain to obtain \(r_{t}\);
10 Execute action \(a_{t}\) and move into the next state \(s_{t+1}\) of the environment;
11 In memory D, store the experience \((s_{t},a_{t},r_{t},s_{t+1})\). 12 Update the target network by (Equation (22))
13 Update state-action as \(s_{t}\gets s_{t+1}\) and \(a_{t}\gets a_{t+1}\) until all \(Q(s_{t},a_{t})\) converge
14 End for
15 End for
16 End for
```  
**Algorithm 1**RL-based resource allocation
# Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal
## 1 Introduction
### Our Contribution  
In this paper, we propose a burst detection algorithm for DUW TDMA signals in satellite communications. The main contributions of this paper are shown in the following:  
(1) We analyze the principle of eliminating frequency offset and phase offset by traditional dual correlation (TDC) detection algorithm, which detects centralized UW, and apply it to DUW detection.  
(2) We analyze that DUW naturally has two different kinds of time delays, namely the delay within the group and the delay between the two groups. We combine them with TDC and propose a DUW dual correlation detection algorithm (the \"DUWDC\" described below). In addition, its advantage is that the change of DUW distribution structure will not affect its detection performance.  
(3) We carry out massive experiments to see the effects of frequency offset, signal to noise ratio, different distribution structures of DUW on detection performance. Simulation results show that DUWDC has good stability, high detection accuracy and good anti-frequency offset performance.  
The rest of this article is arranged as follows: Section 2 is the system model, Section 3 is the algorithm description and Section 4 is the performance analysis. Section 5 is the simulation experiments and Section 6 is the conclusion.
# Solid-Phase Microextraction Arrow for the Sampling of Volatile Organic Compounds in Milk Samples
## 3 Results and Discussion
### Evaluation of the Conventional SPME Procedure  
For the accurate comparison of the SPME Arrow technique with the conventional SPME technique, the main factors affecting the latter technique were also evaluated. Regarding the extraction step, the effects of fiber type (PDMS fiber 100 \(\upmu\)m, CAR/PDMS 75 \(\upmu\)m, DVB/PDMS 65 \(\upmu\)m) sample volume (1-10 mL), extraction time (5-80 min), extraction temperature (25-60 \({}^{\circ}\)C), salt addition (0-20% _w/w_ of NaCl), and stirring rate (0-1000 rpm) were investigated. Regarding the thermal desorption step, desorption time (1-5 min) and desorption temperature (220-300 \({}^{\circ}\)C) were investigated. The results for sample volume, extraction temperature, extraction time, salt addition and stirring rate evaluation are shown in Figures S1-S5 (Supplementary Materials).  
For fiber selection, although similar chromatograms were obtained for CAR/PDMS 75 \(\upmu\)m, DVB/PDMS 65 \(\upmu\)m, the CAR/PDMS 75 \(\upmu\)m fiber was finally chosen since its use provide direct comparison of the sorptive phase of SPME Arrow technique with the conventional SPME technique. Regarding extraction time and temperature, equilibrium was reached after 60 min at 50 \({}^{\circ}\)C, similarly with the SPME Arrow method. Moreover, salt addition was not found to assist the SPME process and, therefore, no salt addition was chosen. The optimum stirring rate was found to be 1000 rpm. This finding is also similar to the SPME Arrow method; however, the conventional SPME method was influenced more by this factor since the peak areas for the examined analytes and total VOCs  
Figure 5: Evaluation of the salt addition for SPME Arrow.  
became 1.3-5 times higher than without stirring, while for SPME Arrow method the peak areas for the examined analytes and total VOCs became 1.3-1.7 times higher than without stirring. Finally, for the conventional SPME method, the optimum sample volume was found to be 3 mL, instead of 5 mL which was optimum for the SPME Arrow method. This can be attributed to the lower amount of extraction phase of conventional SPME fibers that lead to fiber saturation with higher sample volume. Regarding the desorption conditions, also in the case of conventional CAR/PDMS SPME fiber, efficient analyte desorption was observed at 230 \({}^{\circ}\)C and 1 min was sufficient to desorb them, and the fiber remained for a total time of 5 min for cleaning.
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 4 Case Study
### Power Station Investigation  
The power station under investigation is a natural gas-fired, combined cycle power plant located in the southern coastal plain of Israel, near the city of Ashkelon. The plant has a total generating capacity of 860 MW and plays a critical role in supplying electricity to the national grid. In normal status, the power plant is expected to produce more than 800 Megawatts for the electricity grid and an additional up to 60 Megawatts for self-use. The power station site covers a total area of 90,000 square meters and includes various facilities and infrastructure, such as natural gas receiving and processing facilities, water treatment and cooling systems, electrical switchgear and transmission infrastructure, control rooms and administrative buildings, and maintenance workshops and storage areas. The construction of the power station was completed in 2014, following a three-year construction period. The total estimated construction cost of the project was approximately USD 1.2 billion.  
As a critical infrastructure asset, the power station is subject to stringent safety, security, and environmental regulations. Additionally, regular maintenance and inspection activities are carried out to maintain the reliability and performance of the power station's components and systems. Given its strategic importance, the power station has been designed and constructed in accordance with the Israeli building code (SI 413) and other relevant standards for critical infrastructure protection. However, the plant's location in aseismically active region necessitates a comprehensive assessment of its seismic resilience and the development of appropriate mitigation strategies to ensure its continued operation and minimize the impact of potential seismic events.
# Design of a Piezoelectrically Actuated Ultrananocrystalline Diamond (UNCD) Microantilever Biosensor
## 2 Materials and Methods
### Beam Eigenfrequency Matching  
The purpose of optimizing the microcantilever beam is to standardize the design process. This step ensures that the initial beam length is determined using consistent criteria for both Si- and UNCD-based microcantilevers. The simulations involve an eigenfrequency optimization, where Equation (6) is used as the objective function [15].  
\[\left(f_{n,0}-f_{r}\right)^{2} \tag{6}\]  
Equation (6) represents a user-defined objective function employed within the COMSOL Multiphysics Version 4 optimization module. Here, \(f_{r}\) refers to the frequency used as a variable in the simulations. This objective function is commonly applied for eigenfrequency tuning.  
This objective function (Equation (6)) represents the square of the relative error. The optimization was based on the minimization of the relative error using the BOBYQA (Bound Optimization By Quadratic Approximation) algorithm.
## 3 System Architecture
### Overall Scheduling Process  
Figure 7 shows the overall scheduling: Assume that each computing cluster has enough service types, the computing cluster information has been exchanged between network nodes, and the control plane has also sent the corresponding flow table to the data plane. When a user generates a service request packet with specific requirements, the scheduling and matching process begins after the packet reaches the network node. If the scheduling is successful at the current network node, the packet is directly forwarded to the gateway of the target computing cluster; if the match fails, the requirement constraints are appropriately relaxed and matched again. If it still fails and the current node is not a top-level node, the packet is forwarded to the upper-level master node for re-matching. If the current node is a top-level node, the scheduling fails. Upon the packet's arrival at the gateway of the target computing resource management gateway, either local secondary scheduling can be performed, or a random selection of the target computing cluster can be made. Once the computing cluster has processed the request, the result is returned to the user.  
Unlike other schemes that can only select the optimal node, this node uses the SDN data plane's state area to store candidate computing clusters. Based on the service's success rate level, the restriction for selecting clusters can be relaxed. For example, when the success rate requirement level is 4, any one of the top-four computing clusters can be selected. This approach reduces the load pressure on the optimal cluster compared to selecting only the best cluster and increases the probability of selecting other computing clusters, thereby improving load balance and resource utilization. In this sense, the network node acts as a load balancer at the network layer, balancing the load among computing clusters while scheduling. At the same time, using the success rate level in the request as a selection parameter instead of a matching field can greatly reduce the size of the flow table and further improve the scalability of the system.  
Figure 7: Overall scheduling process.
## 2 Background and Research Gaps
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Literature Gap** & **Goal of This Article** & **Impact on Industry 4.0** \\ \hline Digital Transformation Framework & Develop a comprehensive framework guiding digital transformation in manufacturing, tailored to Industry 4.0. & Provides a roadmap for digital transition in line with emerging industrial standards. \\ \hline Human-Centric Design Integration & Prioritize the integration of human factors in system design, enhancing the worker experience and ergonomic aspects. & Improves worker satisfaction and productivity, reducing risks of automation-related alienation. \\ \hline Discrete-Event Simulation Application & Utilize DES to create a digital replica of the existing process, enabling detailed analysis and scenario testing. & Facilitates informed decision-making through accurate simulation and analysis. \\ \hline Innovative Manufacturing Approaches & Design and validate innovative manufacturing approaches through what-if scenarios, leading to smart manufacturing solutions. & Enhances efficiency and adaptability in manufacturing processes. \\ \hline Scalability Across Sectors & Demonstrate a scalable model that can be applied in various sectors, not limited to manufacturing. & Offers versatile solutions adaptable to different industries, increasing the reach of Industry 4.0 principles. \\ \hline Balancing Technology and Humanity & Emphasize the importance of harmonizing technological advancements with human elements, ensuring a synergistic growth. & Ensures that technological progress supports and enhances human roles and experiences. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Progresses beyond the state of the art. applications tailored to SMEs. These limitations underline the need for more concrete and replicable approaches. This study addresses these gaps by proposing an operational DES-based model, validated through a real case study, to support both strategic and operational decision-making in the digitalization process.
## 4 Case Study
### Managerial Implications  
The results obtained and their interpretations provide hospital managers with relevant managerial implications which are not immediately available considering the classical statistical methods. Indeed, network analysis permits to highlight hidden patterns of cause and effect in a very sophisticated way, providing managers with a further point of view on their baseline of data.  
The findings obtained in the case study were also debated with the hospital managers, the Brest Unit managers, the surgeons, and the rest of the staff (i.e., anaestologists, scrub nurses, and other assistant nurses). This discussion permitted a sound interpretation of the results and, most of all, an evaluation of the best potential solutions to improve teamworking of surgical teams.  
The two main managerial indications deriving from it are the following.  
First, health managers should favour a greater rotation of the surgery team composition avoiding, voluntarily or not, to form \"sub-groups\" of practitioners that work continuously together. This solution provides surgical teams with the ability to face a wider spectrum of potential problems and the ability to maintain a higher level of attention. These indications may be valid not just for breast surgery but, more in general, for routine surgeries.  
Moreover, Breast Unit managers should pay attention to the task assignment during the activity planning of the unit in order to better balance surgery and non-surgical activities (e.g., diagnostic visits, ward activities, check-up visits, etc.) for each doctor, promoting variety, e.g., turnovers. Thanks to a more appropriate surgical workload, the doctors should be less subject to the stress induced by physical and psychological overload conditions, improving the final performance of surgeries.  
Although the analysis approach can be applied in many different health contexts, the managerial implications described here tend to be valid only for the breast surgery and, more generally, for the routine surgery.
## 4 Experimental Results
### Quality Assessment Metrics  
Novel view synthesis methods based on NeRF are usually evaluated via the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Given the synthesized new view, \(I\) and the ground truth image, \(G\), the PSNR is  
\[\text{PSNR}(I,G)=10\lg{(\frac{\text{MAX}(I)}{\text{MSE}(I,G)})}^{2}. \tag{15}\]  
The SSIM describes the structural similarity between the images and quantifies this similarity by introducing brightness and contrast. The SSIM ranges from 0 to 1, with larger values indicating greater image similarity. The SSIM is calculated as follows:  
\[\text{SSIM}(I,G)=\frac{(2\mu_{I}\mu_{G}+C_{1})(2\sigma_{IG}+C_{2})}{(\mu_{I}^{ 2}+\mu_{G}^{2}+C_{1})(\sigma_{I}^{2}+\sigma_{G}^{2}+C_{2})}, \tag{16}\]  
where \(\mu_{I}\) and \(\mu_{G}\) are the means of \(I\) and \(G\), which indicate the brightness, and the contrast is estimated according to the variances \(\sigma_{I}\) and \(\sigma_{G}\); \(\sigma_{IG}\) is the covariance of \(I\) and \(G\), and it is used to estimate the structural similarity; and \(C_{1}\) and \(C_{2}\) are two constants, which were set as 0.01 and 0.02, respectively.  
In addition to the aforementioned radiometric evaluation metrics, we employed the mean absolute error (MAE) to assess the accuracy of the generated DSM. The MAE measures the quality of the geometric information reconstruction. Given the generated DSM (\(X_{i}\), \(Y_{i}\), \(\hat{Z_{i}}\)) and the light detection and ranging (LiDAR) DSM (\(X_{i}\), \(Y_{i}\), \(Z_{i}\)), the MAE was calculated as follows:  
\[\text{MAE}=\frac{1}{hw}\sum_{i=1}^{h}\sum_{j=1}^{w}|\hat{Z_{i}}-Z_{i}|. \tag{17}\]