esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Statistical KNN-Based Passengers Mobility Prediction  
A non-parametric KNN classifier segregates a model in which the alteration of both distance metrics and the number of nearest neighbours are done simultaneously for optimum results and comprehensive comparison. The reason for simultaneous parameters settings is that the KNN classifier stores training data which can be easily tuned and modelled to compute resubstitution predictions. Alternatively, the model can be classified to train new observations using the predict method. The following modified equation is to be considered [19]:  
\[p(y=c|x\subset D,K)=\frac{1}{K}\sum_{i\in\text{N}_{\text{K}}(x,D)}I(y_{\text{i} }=c), \tag{1}\]  
where \(p\) is probability of classes \(x_{\text{i}}\), which depends on peak time \(x_{1}\) and off-peak time \(x_{2}\) test inputs within our framework; \(N_{\text{K}}(y,D)\) are the \(K\) nearest points to an integer \(y\) in our dataset \(D\); and \(I(e)\) is an indicator function with \(e\) equal to 0 for false and equal to 1 for true. This method provides flexibility whether learning could be instance-based or memory-based. Commonly, Euclidean distance metric is used to set the parameters of KNN classifier; however, we use _Mahalanobis_ distance metric to manipulate our dataset bounds due to the optimal classification results evaluation. This simply \"looks at\" the \(K\) points in the training set that are nearest to the test input \(x_{i}\). We input peak and off-peak bus passenger ridership data with multiple values of \(K\). KNN classifiers work fine with low inputs; however, they do not function appropriately with the inputs of high dimensions. Computation with _Mahalanobis_ distance metric uses a positive definite covariance matrix \(C\) between each pair of elements of a given random vector. The default value of the matrix \(C\) is the sample covariance matrix of \(X\), as computed by \(\text{nancov}(X)\). The following modified equation is to be considered [19; 20]:  
\[d_{(i,j)}(x,\mu)=\sqrt{\big{(}x-\mu\big{)}^{T}\Sigma^{-1}\big{(}x-\mu\big{)}}\ \, \tag{2}\]  
where \(d_{(i,j)}(x,\mu)\) is the distance between a data vector \(x\) and the mean vector \(\mu\) with \((i,j)=0,1,2,\ldots\). The _Mahalanobis_ distance metric can be thought of as a vector distance that uses a \(\Sigma^{-1}\) norm, is a stretching factor on the space and is the inverse of variance-covariance matrix \(\Sigma\) between \(x\) and \(y\)[20]. The number of nearest neighbours in \(X\) used to classify each point during prediction is specified as a positive integer value which can be less than the number of rows in the training data. Note that, for an identity covariance matrix \(\Sigma_{i}=I\), the _Mahalanobis_ distance becomes the familiar Euclidean distance.
## 5 Conclusions  
This study addresses the thermal management problem of the power electronic system HSLO for a multi-chip integrated circuit in a space telescope and proposes an automatic optimization system based on multi-software integration. This system not only has the capability to optimize the heat source layout, but also achieves a highly automated optimization process. Compared to traditional empirical layouts, IOS has significantly improved temperature extremes and temperature uniformity. Specifically, the temperature difference decreased by 76.36% and the temperature uniformity increased by 79.60%. These results indicate that IOS effectively enhances the temperature distribution uniformity of the multi-chip PCB substrate, improves the temperature field, and provides a solid foundation for subsequent thermal design.  
In addition, for some related tasks that need optimization during the design simulation phase, they often resort to mechanical repetition of simulations to verify the feasibility of theories. In such conditions, MSIIOS can achieve higher efficiency, especially when using this method in the traditional thermal design phase of space telescopes, it can greatly increase efficiency, and reduce labor costs. For different requirements, MSIIOS can make corresponding adjustments, such as replacing optimization algorithms with fitting algorithms, sampling algorithms, etc.  
In the end, the system can reduce labor costs and increase efficiency. However, the adjustment costs depend on the application scenario. Using multiple software solutions together may cause problems when the scenario changes. Indeed, this is an area that requires improvement in future research efforts.  
X.H.: conceptualization, methodology, supervision, writing--original draft, writing--review & editing, formal analysis, data curation. J.H.: conceptualization, formal analysis, writing--original draft, writing--review & editing. Y.G.: conceptualization, formal analysis, writing--review & editing. L.G.: funding acquisition, investigation, project administration, resources, supervision, writing-review & editing. K.H. writing--review & editing, supervision. J.Z.: conceptualization. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Youth Innovation Promotion Association, Chinese Academy of Sciences, (No. 2015173) and the National Natural Science Foundation of China, (61605203).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.  
Figure 11: The temperature cloud diagram of PCB board: (a) \(\mathbf{T_{max}}\) index is optimal; (b) \(\mathbf{T_{avg}}\) index is optimal; (c) empirical layout.
# Numerical and Experimental Investigations of a Micromixer with Chicane Mixing Geometry
## Materials and Methods  
### Micromixer Description  
The micromixer LTF-MS, with a complex structure of \"chicanes\" or an \"S-form\" channel, made by Little Things Factory GmbH (Elsoff, Germany), was chosen for this study. The investigated micromixer contains a Y-mixer and S-type microchannel (or \"chicane\" type), the latter includes specially designed meander elements (here and further, the term \"meander\" is associated with the repeated part of the \"chicanes\" channel). The micromixer is shown in Figure 1a.  
The micromixer represents a plate made entirely of borosilicate glass (Borofloat(r) 33) with three connectors. The glass material makes it possible to run experiments and allows easy optical analysis using colored flows, fluorescence methods, or to indicate reactions [3]. Small dimensions of the micromixer lead to high values of heat-dissipation, so we can ignore the temperate gradient within the flow for the purposes of this study, and an isothermal model can be applied.  
The understudied micromixer consists of two pre-inlet channels, the Y-mixer, micromixing channel with meander elements and long outlet channel. The pre-inlet channels have a diameter of 1 mm and are used as pre-tempering units. Their material and small diameter provide intensive heat transfer. The Y-mixer has a 90\({}^{\circ}\) angle between elbows and the same 1 mm diameter. The mixing channel contains 22 meander elements with an alternate direction of curvature. A more detailed sketch is represented in Figure 1b.
## 1 Introduction  
Automatic License Plate Recognition (ALPR) is a computer vision technology that plays a crucial role in intelligent transportation systems and vehicle management systems [1, 2]. With recent technological advancements, ALPR systems have significantly improved in performance and are being utilized in various fields, including management, road surveillance, traffic law enforcement, and automatic toll collection [3, 4, 5]. In real-world environments, license plate images experience severe geometric distortion depending on vehicle parking angles and camera installation positions. In parking lots and on roads, geometrically distorted license plate images result in decreased text recognition accuracy due to significant deformation [6, 7, 8]. To overcome these issues, the accurate correction of distorted license plates in ALPR systems is essential for improving recognition accuracy, and various approaches are being actively developed [9, 10, 11, 12]. Currently proposed distortion correction techniques process single input images one at a time. This single-model approach has limitations in handling various distortion patterns of license plates [13, 14, 15]. Particularly in real environments, license plates appear with various distortions depending on angles and vehicle positions, creating challenges that standard convolutional networks with fixed receptive fields struggle to process effectively [16, 17].  
To address these issues, this paper proposes an ensemble technique based on a Deformable Convolutional Network (DCN) [18] that incorporates a Corner Enhancement Module (CEM) and processes detected license plate images in parallel using versions adjusted to different aspect ratios (2:1, 1.5:1) along with the original. Experimental results showed that this method demonstrates superior performance compared to existing commercial ALPR systems, even for license plates with severe perspective distortion, and maintains stable performance under real environmental conditions.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 6 Local Experiments Performance Evaluation
### Summary of Results  
This section summarizes the results obtained across all experiments and which are represented in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test** & & **AVG** & **STDEV** & **MAX** & **MIN** & **MED** & **IC** \\ \hline
1 ms & TTC & 65.62 & 0.76 & 67.10 & 64.67 & 65.61 & 0.47 \\ \hline
10 ms & TTC & 60.50 & 3.61 & 66.78 & 56.71 & 59.79 & 2.24 \\ \hline
1000 ms & TTC & 56.30 & 7.22 & 72.09 & 47.42 & 55.60 & 4.48 \\ \hline \hline \end{tabular}
\end{table}
Table 17: OPC UA results for topology 2 and variable message size.  
Figure 6: Full set of topology 2 results.  
The main aspects to highlight are:  
* MQTT behavior is highly variable for different IAT and for different message sizes. In some experiments, the resulting TTC is high for smaller IATs, and on other experiments, becomes larger for higher IATs. What would be expected would be to see an increase of TTC with an increase in IAT. This situation was observed several times, and we believe it is due to the fact that there is no queuing support in MQTT. The broker has to handle very different message sizes.
* CoAP presents more stability for the variation of low/medium IATs and message size. However, for the case of large IAT, the resulting TTC is significantly higher in comparison to other CoAP TTCs, as well as to the overall results obtained for the other 2 protocols.
* OPC UA is less sensitive to variations of the IAT. The fundamental reason for the operation of OPC UA is, however, not clear to us, and requires further investigation to understand this stability whether it is derived from the selection of scenario parameters, or really derived from the communication semantics of OPC UA. In regards to the impact of the message size, OPC UA shows more sensitivity to variable message sizes. Moreover, when compared to MQTT and to CoAP, the resulting TTC is overall higher, even though the difference is in milliseconds.
* The lowest TTC is achieved by CoAP, for the case of an IAT of 1 ms (topology 2, fixed message size). We believe this happens due to the reliability exponential backoff mechanism of CoAP, which is beneficial for frequent messages.
* When the IAT is 1000 ms, MQTT and OPC UA result in similar TTCs, while CoAP shows significantly higher TTCs. Although the IAT impacts the TTC computation, the CoAP behavior seems to be derived from the fact that with larger intervals between messages, the CoAP establishment process has implications on the TTC. In other words, CoAP seems to suit best scenarios where messages are more frequent.
* In regards to the change of topology, all protocols experience some impact, as expected. However, the impact is not significant. The difference observed for all protocols in terms of differences of TTC is in the order of milliseconds.
* Across the different experiments, the overall TTC is low and suitable for critical IoT environments.  
Figure 7: TTC achieved by MQTT, CoAP and OPC UA across all experiments, for an IAT of 1,10 and 1000 milliseconds.
## 2 Related Work
### Machine-Learning-Based Methods  
Machine-learning-based methods [7; 8] utilize algorithms, such as Support Vector Machines (SVMs) and random forests, to study traffic features. Earlier researchers used load data and statistical features [9; 10], both of which are appropriate for the identification of specific scenes in complex traffic. [PERSON] et al. [11] improved on the traditional SVM method and proposed an SVM network optimization method by calculating the feature weights and parameter values of each individual SVM binary classifier. Despite their good performance in non-encrypted traffic identification, these methods still face challenges in encrypted traffic identification due to the lack of clear feature patterns. Consequently, traditional traffic identification methods often perform poorly in encrypted traffic recognition.
###### Abstract  
Landslides are destructive geological hazards that occur all over the world. Due to the periodic regulation of reservoir water level, a large number of landslides occur in the Three Gorges Reservoir area (TGRA). The main objective of this study was to explore the preference of machine learning models for landslide susceptibility mapping in the TGRA. The Wushan segment of TGRA was selected as a case study. At first, 165 landslides were identified and a total of 14 landslide causal factors were constructed from different data sources. Multicollinearity analysis and information gain ratio (IGR) model were applied to select landslide causal factors. Subsequently, the landslide susceptibility mapping using the calculated results of four models, namely, support vector machines (SVM), artificial neural networks (ANN), classification and regression tree (CART), and logistic regression (LR). The accuracy of these four maps were evaluated using the receive operating characteristic (ROC) and the accuracy statistic. Results revealed that eliminating the inconsequential factors can perhaps improve the accuracy of landslide susceptibility modelling, and the SVM model had the best performance in this study, providing strong technical support for landslide susceptibility modelling in TGRA.  
Keywords:landslides; susceptibility mapping; support vector machines; Three Gorges Reservoir area (TGRA) +
Footnote †: journal: Applied Sciences
## 3 Results
### Femoral Anteversion and Internal Rotation  
Several studies highlighted the relationship between femoral anteversion and internal hip rotation:  
[PERSON] et al. (2021) [48] showed that increased femoral anteversion altered muscle lever arms and led to compensatory mechanisms, including increased internal rotation during gait. This internal rotation compensated for altered hip joint contact forces and stabilized joint movement.  
[PERSON] et al. (2018) [49] confirmed that subjects with increased femoral anteversion displayed greater internal rotation during both static and dynamic conditions. This was particularly noticeable during gait, where internal hip rotation ranged from 2.8\({}^{\circ}\) to 11.8\({}^{\circ}\) throughout different phases.
# Smartwatch-Based Monitoring and Alert System for Factory Operators Using Public Cloud Services
## 4 Results
### Presenting Detailed Use Cases
#### 4.2.5 Managing Tasks  
The smartwatch application is designed to display an employee's task list, organizing tasks into urgent and non-urgent categories and allowing the user to update task statuses directly on the watch, as shown in Figure 10. This interface connects to a REST API to retrieve and update tasks within the EmployeesTasks table. When an employee changes the status of a task on the smartwatch, a refreshUI message is sent through the WebSocket, which notifies the web application to reflect these updates in real time on the supervisor's interface.  
On the supervisor's side, the web application provides a dedicated page, shown in Figure 11, for managing tasks. Here, supervisors can add, update, or delete tasks by sending POST, PUT, or DELETE requests to a designated endpoint on the application server. They can also retrieve the latest task list using a GET request. When the supervisor accesses this page, a WebSocket connection is established, with a listener set up to receive message events. Whenever a task is modified, either by the supervisor on the web app or by the employee on the smartwatch, a refreshUI message is sent through the WebSocket. This triggers both the web interface and the smartwatch to fetch the updated task list, ensuring that data remains synchronized in real time.  
Figure 11: Managing tasks page for a supervisor.  
Figure 10: Tasks interface on smartwatch.  
This bidirectional communication ensures that any changes in the task list--whether performed by the supervisor or the employee--are immediately reflected on both devices. As a result, the smartwatch app and web app remain in sync, offering a seamless experience for task management and status updates across the two platforms.  
Figure 12 demonstrates the bidirectional communication flow, showing how a task created by the supervisor is subsequently updated by the employee.
## 3 SPSA Learning-Based Three-Dimensional Fuzzy Modeling  
### Modeling Methodology  
As for an unknown DPS system, space base functions are frequently calculated by the KL decomposition technique. However, implementing the KL decomposition technique is  
Figure 1: Framework of three-dimensional fuzzy model (three-dimensional abbreviated as 3D).  
bound to result in the simplification of models. The three-dimensional fuzzy model has the innate ability to amalgamate space-time separation and space-time reconstruction into three-dimensional fuzzy rules and thereafter avoids the model reduction.  
In this study, a novel SPSA learning-based three-dimensional fuzzy model methodology is proposed, the scheme of which is shown in Figure 2.  
The modeling methodology is divided into two parts, namely, learning for preceding components of three-dimensional fuzzy rules and learning for the resulting components of three-dimensional fuzzy rules. In the first part, by using AP clustering, the preceding components of three-dimensional fuzzy rules are learned. In the second part, the Fourier function is initially selected as the space base function as the resulting components of the three-dimensional fuzzy rule. Then, the SPSA method is used to determine the coefficients of the Fourier space base function adaptively. Three distinct characteristics are evident in the SPSA learning-based three-dimensional fuzzy modeling method as given below:  
1. [label=(0)]
2. AP clustering is employed to manage a collection of spatiotemporal data and extract the preceding components of three-dimensional fuzzy rules in an adaptable manner, without the need to define the number of clusters.
3. The Fourier space base functions are investigated in three-dimensional fuzzy modeling.
4. SPSA algorithm is used to acquire the optimal parameters of Fourier space base functions.
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 1 Introduction
The principal contributions of this paper are outlined as follows:  
* To address the challenges of high-order nonlinear modeling in AC microgrids, this paper employs a dynamic linearization approach to construct a time-varying linear model of the microgrid. This model exclusively uses input-output data and does not need any extra model information, which improves the model's applicability and flexibility, and enhances the system's robustness. * To address the issues of sensor saturation and disturbances encountered during the secondary voltage restoration in AC microgrids, the paper introduces a distributed adaptive iterative learning control approach that relies on a dynamic linearization model. The algorithm combines the dynamic linearization model of microgrids with iterative learning, utilizing input data from neighboring DREGUs and adjusting the pinning errors. It effectively overcomes issues related to sensor saturation and measurement disturbances, ensuring that the voltage accurately returns to the predetermined reference values. The rest of this paper is structured as follows. Section 2 offers the required introduction and formulation of the problem. Section 3 describes the dynamic linearization model for AC microgrids and presents the design of a distributed model-free adaptive secondary voltage controller, including evidence of the algorithm's convergence. Section 4 presents case studies to validate the performance of the proposed method. Finally, Section 5 summarizes the paper and explores potential avenues for future research.
# Effect of Hydrophobic Silica Nanochannel Structure on the Running Speed of a Colloidal Damper
## 4 Experiments and Result Analysis
### Experimental Results and Comparative Analysis
#### 4.2.1 Effect of Number of Functional Groups of Grafted Molecules on the Running Speed of the CD  
Figures 4 and 8 show the results of fractal percolation model velocity calculations and the experimental results of CD operation speed for silica gels with different numbers of grafted molecules (10-100 C4, 10-100 C8, and 10-100 C18), respectively.  
It can be determined from Figure 4 that the seepage velocity of hydrophobic silica nanochannels was related to the pressure difference: the larger the pressure difference, the faster the seepage velocity. The seepage velocity of nanochannels was also related to the number of grafted molecules on the surface of the silica gel. Theoretically, under the initial conditions of no residual water in the channel, when the pressure difference was constant, there were a greater number of grafted molecules and the seepage velocity was higher. With increasing pressure difference, the difference tended to be more obvious. Owing to the influence of hydrophobic layer thickness, the pore sizes of 10-100 C4, 10-100 C8, and 10-100 C18 decreased; in turn, the hydrophobicity was also enhanced, and the fluid flow velocity was increased [24]. At the same time, because of the different contact angles, the pressure difference when infiltrating nanochannels was also different, which led to the difference in seepage velocity [25].  
Comparing Figures 4 and 8, it was found that the fractal seepage model velocity of water in nanochannels under constant pressure difference \(\Delta p\) was consistent with that of the CD under initial loading, and it also showed a good fractal characterization effect. However, it was inconsistent with the change trend of the CD running speed after continuous loading.  
Figure 8: Experimental values of the operating speed of 10-100 CM silica gel CDs.  
Figure 9: Experimental values of the operating speed of 10-2\(r_{a}\) C18 silica gel CDs.  
Relative to those in 10-100 C8 and 10-100 C18, the seepage velocity of water in 10-100 C4 was greater, which is contrary to the theory. This is attributable to the change in the effective pore size of silica gel particles because of the retention of some water in nanochannels after continuous loading. Because the hydrophobicity of 10-100 C8 nanochannels and 10-100 C18 nanochannels is stronger than that of 10-100 C4 nanochannels, the residual water in the 10-100 C4 nanochannels will be relatively small, but under the influence of residual water, the effective pore volume and porosity of the 10-100 C4 nano channels will change more, making the seepage velocity faster.