esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparison between Fuzzy Logic Controller (FLC) and PI Controller-Based Energy Management System
Figure 16: 3D outputs of proposed FLC: (**a**) 3D output of proposed FLC with SOC battery and error current as inputs (**b**) 3D output of proposed FLC with speed and error current as inputs. Figure 14: Boost Converter Output Voltage and Current using FLC. Figure 15: Boost converter output voltage and current using PI controller. The stability of our proposed fuzzy logic algorithm mainly depends upon the control measures. Three main control measures have been used to achieve the stability of the proposed fuzzy algorithm, which are the integral squared error (ISE), integral absolute error (IAE), and integral time-weighted absolute error (ITAE). These measures can be calculated from the following equations:  
\[ISE = \int\epsilon^{2}dt \tag{34}\]  
\[ISE = \int\epsilon^{2}dt \tag{35}\]  
\[ITAE = \int t|\epsilon|dt \tag{36}\]  
While the main purpose of ISE is to integrate the square of the error over the specified time (or until the time when responses settle). ISE reduces the large errors while tolerating the smaller ones. The main function of IAE is to integrate the absolute error over the specific time during which the responses settle. The response of IAE is slower than ISE, but by utilizing this, oscillations are minimized. Finally, ITAE integrates the absolute error multiplied by the time over time. This tuning method is very good as it produces systems that settle much faster than the other two tuning methods. After applying these tuning methods, we have achieved values of these terms for PI controller and fuzzy logic. A detailed comparison is made for the stability analysis between PI and fuzzy logic using these values. This comparison is carried out when the battery's SOC is 45% and 95%, respectively. The comparison is shown in Figure 13.0  
As can be seen from Table 9, the ISE value for FLC is 0.41 when the battery's SOC is 45%, which is better than the ISE value of the PI controller. Similarly, the IAE and ITAE values for FLC are better than the PI controller response. Now, when the battery's SOC is 95%, then similar criteria will be applied for comparison. The ISE, IAE, and ITAE values of FLC are better than the values of the PI controller response. Hence, the FLC is a more stable system than the PI controller.
## 2 Experiments and Results
### Picosecond Amplifier System at 1 kHz
#### 2.1.1 Picosecond Oscillator  
The seed pulse was provided by a diode-pumped picosecond oscillator designed as shown in Figure 1. The laser crystal was an a-cut, 0.5 at. % Nd-doped YVO\({}_{4}\) crystal with a size of 3 \(\times\) 3 \(\times\) 5 mm\({}^{3}\). The surfaces of the crystal were antireflection-coated at 808 nm and around 1 \(\upmu\)m \((T\)\(>\) 99%). For heat dissipation, the crystal was wrapped with indium foil and mounted tightly in a water-cooled copper heat sink, where the water temperature was maintained at 18 \({}^{\circ}\)C. The pump was a fiber-coupled diode laser with a 50 \(\upmu\)m core diameter (BWT, Beijing, China) and 2 W output power at 808 nm. With a semiconductor saturable absorption mirror (SESAM), the oscillator ran in a CW mode-locking state and provided about 400 mW average power at a repetition rate of 80 MHz as shown in Figure 2a. The stability of the output power was measured to be \(<\)0.7% rms as shown in Figure 2b. By controlling the dispersion of the cavity with Gires-Tournois interferometer (GTI) mirrors, the pulse duration could be changed from 10 ps to 15 ps. Figure 3 shows the intensity autocorrelation traces and spectra (AQ6315A, Ando Inc., Tokyo, Japan) of the mode-locked pulses with 10 ps and 15 ps, if sech\({}^{2}\)-pulse shapes were assumed, measured by a commercial intensity autocorrelator (Femtochrome, FR-103 MN). Considering the amplification efficiency and to avoid damaging optics, a 15 ps pulse was used as the seed for the
## 2 Biosynthesis and Precursors of Terpenoids
### Monoterpenoid Chemical Compounds  
Monoterpenoids are substances that can be found in essential oils derived from different plants, including vegetables, fruits, herbs, and spices [41; 42; 43]. Monoterpenes are C10 molecules that can be acyclic, monocyclic, or bicyclic. Monoterpene synthase uses GPP (geranyl pyrophosphate) as a substrate to produce them. Additionally, GPP is also a substrate for the production of GGPP (geranyl-geranyl pyrophosphate) and FPP (farnesyl pyrophosphate), two important substances in animal, plant, and yeast cell metabolism [22]. Monoterpenoid compounds are classified as acyclic (e.g., linalool, geraniol, \(\beta\)-myrcene, (+)-citronellol, nerol), monocyclic (thymol, (\(-\))-menthol, limonene, eugenol, \(\gamma\)-terpinene, terpione, and piperitone), bicyclic (\(\alpha\)-pinene, (\(-\))-\(\beta\)-pinene, camphene, sabinene, myrtenol, (+)-camphor, (\(-\))-bornel, (+)-cis-verbenol, \(\Delta\)3-terpinene, eucalypt, sabinene hydrate, and fenchone), and others (\(\alpha\)-phellandrene, \(\rho\)-cynene, ocimene, fenchol, (\(-\))-isopulegol terpinen-4-ol, \(\alpha\)-terpineol, (+)-dihydrocarvone, pulegone, carvone, geranyl acetate, metholisoner, and safranal) [44]. Monoterpenoids are found mostly in the taxonomic groups Asteraceae, Apiaceae, Verbenaceae, Poaceae, Myrtaceae, Lamiaceae, Pinaceae, Rutaceae, Lauraceae, and Cannabaceae. The following monoterpenoid compounds are significant in plant species: \(\alpha\)- and \(\beta\)-pinene (_Pinus palustris_), \(\delta\)-3-care, \(\alpha\)-phellandrene, and myrcene (_Lippia ctiridora_) are found as complex mixtures in most essential oils, particularly in those extracted from plant leaves, while seed and flower oils contain more specialized monoterpenes and present fruits or flowery odors. Linalool has two stereoisomers are present: (\(R\))-(\(-\))-linalool and (\(S\))-(+)-linalool. \(S\)-linalool is found in major constituents of the essential oils of coriander (_Coriandrum sativum_ L.) seed, palmarosa (_Cymbopogon matrinit_ var martini (Roxb.) [PERSON]), and sweet orange (_Citrus sinensis_ [PERSON]) flowers. Meanwhile, (\(R\))-Linalool is present in Ho oils from _Cinnamoun camphora_, rosewood oil, lavender (_Lavandula officinalis_ [PERSON]), laurel (_Laurus nobilis_), and sweet basil (_Ocimum basilicum_) [2]. D-carvone from caraway (_Carum carvi_), with its spicy and bread-like fragrance; mentol is derived from wild mint (_Mentha arensis_) and has a strong minty aroma; D-limonene from citrus species with a fresh orange peel odor; citral from lemongrass (_Cymbopogon citratus_) having a fresh lemon peel odor; Eucalyptusptol, also known as 1,8-cineole from eucalyptus (_Eucalyptus globulus_) having a camphoraceous cool odor. Menthone and isomenthone are components of essential oils such as pennyroyal, peppermint, _Pelargonium geraniums_, and others. Thymol (_Thymus vulgaris_); camphor derived from camphor tee (_Cinnamouna camphora_) [45].
## 3 Methodology
### Siamese Region Proposal Network (Siameserpn) Architecture
#### 3.1.2 Region Proposal Network  
The region proposal network (RPN) is first proposed in Faster-RCNN [21], which can extract precise candidate regions quickly and efficiently. Therefore, it makes proposal generation very effective while achieving high accuracy.  
In this subnetwork, RPN consists of two parts: pairwise correlation and supervision. The supervision consists of two branches: regression and classification. The regression is to generate the candidate regions, and the classification is to distinguish the foreground and background of input images. If \(n\) anchors are automatically generated, RPN needs \(2n\) representation channels for the classification task and \(4n\) presentation channels for the regression task. Therefore, the pairwise correlation needs to divide \(M(z)\) into two datasets \([M(z)]_{cls}\) and \([M(z)]_{rpg}\), and divide \(M(x)\) into \([M(x)]_{cls}\) and \([M(x)]_{rpg}\). Then, \([M(z)]\) is used as the correlation kernel to compare similarity with \([M(x)]\). Finally, the regression correlation \(C_{rpg}\) and classification correlation \(C_{cls}\) are computed through Equation (2) [22]:  
\[C_{rpg}=[M(z)]_{rpg}\ ^{*}[M(x)]_{rpg},\,C_{cls}=[M(z)]_{cls}\ ^{*}[M(x)]_{cls} \tag{2}\]  
where \([M(z)]_{rpg}\) and \([M(z)]_{cls}\) are used as convolutional kernels, and the sign * is the convolution operation.  
Smooth \(L_{1}\) loss function with normalized coordinates and cross-entropy loss function are used for regression and classification, respectively. If \(P_{x}\), \(P_{y}\), \(P_{w}\), \(P_{h}\) represent the coordinates of the center point, the width and the height of predicted bounding boxes, and \(T_{x}\), \(T_{y}\), \(T_{w}\), \(T_{h}\) represent those parameters of the ground truth. Then normalized distances between the prediction and ground truth can be computed. RPN is optimized by minimizing the loss function in Equation (3) [22]:  
\[Loss=L_{cls}+\lambda L_{rpg} \tag{3}\]  
where \(\lambda\) is the hyper-parameter to balance the performance of regression and classification. \(L_{rpg}\) is the loss function for regression, and \(L_{cls}\) is loss function for classification.
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 1 Introduction
utilization and operational efficiency in remote calibration systems, there is an urgent need to develop advanced and efficient scheduling mechanisms. Motivated by these challenges, this paper investigates methodologies for synchronizing task scheduling, an extension of the traditional Parallel Machine Scheduling Problem (PMS). The goal of synchronizing task scheduling is to efficiently divide tasks between two types of devices while making sure that processes are in sync by sending data in real time. It extends PMS by incorporating the critical requirement of process synchronization. This paper builds upon this foundation to address two primary issues in distributed remote calibration systems. The first issue is the substantial variability in calibration demands arising from the complexity and diversity of equipment, data types, and the geographical distribution of detection points and laboratories. The second issue involves the need for synchronized interactions between detection points and central laboratories, complicating scheduling strategies that depend on the specific requirements of each calibration task. In summary, this paper makes the following contributions:  
1. We introduce a multi-point collaborative distributed remote calibration model to address the growing demands of remote sensing devices, focusing on current and voltage sensors. By looking at how resources behave in remote calibration situations, we change the way resource modeling works to focus on two important parts: the calibration expert as a scarce resource and the standard device. 2. We decompose the synchronized task scheduling problem into two interrelated components: device-based optimization and sequence-based optimization. To address these challenges, we develop an innovative hybrid genetic algorithm that seamlessly integrates these components to ensure efficient and flexible utilization of resources. 3. Experiments validate the proposed method, showcasing its superior performance in resource allocation and scheduling flexibility. Extensive experiments with varying tasks, servers, and clients confirm its adaptability and efficiency over established algorithms such as Longest Task First (LTF), Shortest Task First (STF), and the Dynamic Synchronous Scheduling (DSS) algorithm. The rest of this article is structured as follows. Section 2 provides an overview of related work. In Section 3, the mathematical modeling framework for remote sensor calibration is explained, and the proposed algorithm is introduced. In Section 4, the performance of the proposed scheme is evaluated by comparing it to well-known baseline algorithms. Finally, Section 5 offers a concise conclusion summarizing the key findings of the study.
## 3 Materials and Methods
### Static Detection
#### 3.3.2 Selection and Deletion of Prediction Boxes  
In deep learning object detection algorithms, many prediction boxes of different sizes are generated for the same detected object, and the best prediction box is retained by the algorithm as the final detection box. The non-maximum suppression (NMS) algorithm [39], which sorts the prediction boxes of a certain category by their confidence values, obtains the best position of the object by calculating the intersection and merge ratio (IoU) screen, but the detection box with the second-highest confidence may be mistakenly deleted due to the large overlap area with the box that has the highest confidence. The improved algorithm (Distance-IoU-NMS, DIoU-NMS) used in YOLOv4 can solve the problem of slow convergence regarding the loss function of the detection frames, but missed detections may occur when detecting objects that are in close proximity and obscure each other.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Data Set** & **Number** \\ \hline Training set & 834 \\ Test set & 104 \\ Validation set & 104 \\ Total & 1042 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data set of off-line training of detection model.  
Considering the growth of moso bamboo shoots, a soft DIoU-NMS algorithm (Soft-DIoU-NMS) was used in this study. The algorithm sorts the prediction boxes according to the confidence degree and selects the prediction box with the highest score as the benchmark. Then, the first linear attenuation calculation is carried out to determine the confidence degree of other prediction boxes. The prediction box with the highest confidence degree is retained, and the second highest confidence degree is taken as the benchmark. In this way, the confidence level remains unchanged after processing. Finally, comprehensive culling is performed to obtain the optimal effect of the detection box. The highest confidence level is used as the benchmark. The complexity of the improved algorithm in Equation (2) is almost unchanged compared with that of the normal NMS algorithm, and the implementations are equally simple.  
\[\begin{split}\text{S}_{i}=\text{S}_{i},\text{IoU}-R_{DIoU}(\mu,B _{i})<\varepsilon\\ \text{S}_{i}=\text{S}_{i}(1-\text{IoU}(\mu,B_{i})),\text{IoU}-R_{DIoU }(\mu,B_{i})>\varepsilon\end{split} \tag{2}\]  
In Equation (2), \(\text{S}_{i}\) is the confidence score of the current category, \(R_{DIoU}\) is the penalty term of the DIoU loss function, \(B_{i}\) represents all detection boxes being compared in the current category, \(\mu\) represents the detection box with the highest confidence among all prediction boxes, and \(\varepsilon\) is generally taken as 0.5.
# Modeling of Characteristics of Complex Microstructure and Heterogeneity at the Core Scale
## 4 Discussion  
In core experiments, repeated pressurization and displacement can damage the core, so the experiments that can be conducted on each core are limited. Additionally, using multiple cores makes it impossible to discuss the impact of a single parameter on the results. The superiority of simulations lies in their repeatability and the ability to analyze any parameter individually. The key to the accuracy of the simulation is whether the model can capture the main characteristics of the core. The pore structure within rock cores is generally considered the primary factor affecting the attenuation and dispersion of waves. In addition, the description of the rock matrix is also one of the important factors influencing wave velocity. This paper proposes a comprehensive model that can flexibly adapt to each different rock core, making core analysis more accurate, comprehensive, and economical. This model can be applied to different types of rock and various types of fluid contained in the pores. Different types of rocks are simulated by considering the distribution of heterogeneous bodies and the distribution of pore structures obtained by inversion of pressure experiments. Depending on the simulation requirements, different fluids can consider different viscous forces, inertial forces, and even body forces.  
In the theoretical part of the new model in the study, in addition to the basic parameters of the core, the parameters that control the characteristics of the core include the autocorrelation lengths a and b, the rounding coefficient n, the angle \(\theta\) in the random medium model, and the compliant porosity \(\phi_{c}\) in the multiple fracture squirt flow model. The above parameters set in the simulation are obtained through empirical estimation. The advantage of this method is that it can quickly obtain the parameters used for simulation based on the specific conditions of the core, and it has good accuracy. However, it has the drawbacks of relying on experience and lacking programmability. To address this drawback, the subsequent work involves using artificial intelligence to obtain the aforementioned parameters from the core samples and applying them in the simulation, which will further enhance the accuracy and standardization of the simulation.
## 2 Materials and Methods
### Data Extraction and Quality Assessment  
The following items were extracted from all included studies using a pre-specified data extraction form in Microsoft Excel: first author, year of publication, participants' characteristics, intervention and comparator details, outcome measures and conflict of interest. Data extraction was performed by one reviewer and checked by another, and the possible discrepancies were resolved through discussion. The risk of bias in the included studies was assessed using version 2 of the Cochrane risk-of-bias tool for randomized trials (ROB-2) [23]. Quality assessment was undertaken by two independent reviewers, and disagreements were solved by discussion and consensus or after consulting a third reviewer.
# A Spatial 4-DOF Laser Collimation Measurement System
## 5 Technical Approach in the Article  
The paper first carried out a theoretical calculation of resolution and range, divided into x/y straightness and x/y direction angle to determine the optical path design and electronic circuit design, respectively. Then, the system test optical path was built, before optical path debugging was carried out, preliminary tests were conducted, and laser spot images were collected. Then, the algorithm was determined and test data were collected. Thirdly, an error analysis was conducted. Then, further calibration and stability tests were conducted, before the results were finally analyzed (Figure 18).  
Figure 17: Results of the stability experiments: (a) X straightness SD = 1.11 \(\mu\)m; (b) Y straightness; (c) pitch SD = 0.91 arcsec; (d) yaw SD = 0.90 arcsec.
## 6 Results Validation
### Numerical Evaluation  
The Classification module provides critical functionality for the Sehaa system (see Section 4.4). The classification is accomplished in two levels. The first level is to distinguish the health-related tweets from the unrelated ones, while the second level is to classify the related tweets into awareness or afflicted tweets (see Table 2). At each level, the classification relies on machine learning algorithms.  
In order to select the most efficient algorithm, we used different machine learning algorithms in the Spark ML packages, using Python with different feature extraction techniques to train the classification models. We split the manually labeled data into training sets (60%) and testing sets (40%). We built different model pipelines and trained these models using the Naive Bayes (NB) and logistic regression (LR) algorithms. To find the best algorithm, we numerically evaluated them using the testing data set using the well-known evaluation criteria Accuracy and F1-score (see Section 4.5). These scores for the first-level and second-level classifiers are plotted in Figures 11 and 12, respectively.  
Figure 11 shows that Naive Bayes with Trigram feature extraction provided the highest Accuracy (78.2%) compared to any other combinations of feature extraction techniques with Naive Bayes or Logistic Regression. However, Logistic Regression with Trigram feature extraction provided the highest F1-score among all the classification and feature extraction methods. We had selected Naive Bayes with Trigram (due to the higher Accuracy score) for the results presented in the previous sections.  
Figure 12 shows the results for the second-level classifier. Note that it provides higher accuracies than the first-level classifier. For both Accuracy (86.7%) and F1-score (85.6%), Logistic Regression with HashingTF provided the best results, and therefore it was selected for the second-level classification.  
Figure 10: Sehaa: the ratio of awareness tweets to the afflicted cases.
# Key Barriers to Personalized Learning in Times of Artificial Intelligence: A Literature Review
## 4 Discussion
### Implications for Technology Developers  
Technology developers play a crucial role in the advancement of AI-driven PL systems, as they are responsible for creating tools that are not only innovative but also accessible and pedagogically sound. The study highlights that, while AI offers advanced personalization capabilities, significant technological and ethical challenges persist, including user-friendliness, transparency, and data privacy concerns. These issues must be addressed to ensure that AI systems can be effectively adopted within educational settings without creating additional barriers for educators and students.  
To enhance the usability of AI-driven PL tools, developers should prioritize user-centered design principles that consider the needs of both educators and learners. Ensuring that AI models offer clear explanations of their recommendations will improve transparency and foster greater trust in their use. Furthermore, strong privacy protections must be embedded within AI systems to safeguard student data and mitigate algorithmic biases that may disproportionately affect certain learner groups. Additionally, interdisciplinary collaboration between educators, policymakers, and developers is essential to creating AI-driven PL systems that are not only technologically advanced but also aligned with pedagogical objectives.  
As a final insight, it is noteworthy to mention that the implementation of AI-driven personalized learning requires coordinated efforts among educators, policymakers, and technology developers. Educators must be empowered with the right skills and support structures, policymakers need to create enabling environments, and technology developers should ensure that AI tools are ethical, transparent, and user-friendly. While AI holds great promise for enhancing the personalization of learning experiences, significant challenges remain in terms of technological infrastructure, pedagogical adaptability, and psychological acceptance. The findings suggest that the effective implementation of AI-driven PL necessitates a nuanced approach that carefully balances technological innovation with pedagogical best practices. As institutions continue to explore the integration of AI in education, it is imperative to consider the diverse needs of learners and the structural constraints within educational systems. Future research should focus on long-term studies that assess the impact of AI-driven PL on student outcomes and engagement, as well as the development of AI systems designed to address the pedagogical and psychological barriers identified in this review. Ethical considerations, particularly regarding data privacy and algorithmic bias, must also remain central to discussions on the future of AI in education. Ultimately, a multidisciplinary and context-sensitive approach will be necessary to harness the full potential of AI-driven personalized learning in diverse educational settings.