esa-sceva/satcom-chunk-collection huggingface dataset:
# A Combined Elevation Angle and C/N0 Weighting Method for GNSS PPP on Xiaomi MI8 Smartphones
## 3 Combined Elevation Angle and C/N0 Weighting Method
\begin{array}{l}\sigma_{L1}^{2}=\left|\sigma_{C/N0,\ L1}^{2}-\sigma_{C/ N0\_cal}^{2}\right|\times\frac{\sigma_{\text{ele\_MAX}}^{2}-\sigma_{\text{ele\_MIN}}^{2}}{ \sigma_{C/N0\_MAX,\ L1}^{2}-\sigma_{\text{C/N0\_MIN,\ L1}}^{2}}+\sigma_{de}^{2} \\ \sigma_{LS}^{2}=\sigma_{C/N0,\ L5}^{2}\times\frac{\sigma_{\text{ele\_MAX}}^{2}- \sigma_{\text{ele\_MIN}}^{2}}{\sigma_{C/N0\_MAX,\ L5}^{2}-\sigma_{\text{C/N0 \_MIN,\ L5}}^{2}}\end{array} \right. \tag{8}\]  
where \(\sigma_{L1}^{2}\) and \(\sigma_{LS}^{2}\) are the observation variances at the L1/G1/B1/E1 frequencies and the L5/E5a frequencies, respectively; \(C/N0\) and \(C/N0\_cal\) are the measured C/N0 and fitted C/N0, respectively; C/N0_MIN and C/N0_MAX are the minimum and maximum C/N0 values, which are set to 25 dB-Hz and 45 dB-Hz at the L1/G1/B1/E1 frequencies, and 20 dB-Hz and 40 dB-Hz at the L5/E5a frequencies, respectively and ele_MIN and ele_MAX are the minimum and maximum elevation angles, which are set to 10\({}^{\circ}\) and 90\({}^{\circ}\), respectively. The observation weight value is an inverse ratio of the observation variance. Once the observation variance is determined following Equation (8), the observation weight can be acquired.
## 3 Day-Ahead Distributionally Robust Optimization Model
### Model Solving  
The original problem is divided into a master problem (MP) and a subproblem (SP), and the column-and-constraint generation (C&CG) algorithm is used for iterative solving. (1) MP  
The main problem, given the scenario probability distribution \(p\), seeks the optimal solution that satisfies the system's economy and provides the lower bound for Equation (26):  
\[\min_{x\in X,y_{s}^{n}\in Y,W}W \tag{30}\]  
\[W\geq\sum_{s=1}^{N_{s}}p_{s}^{m}f(y_{s}),\forall m=1,2,\cdots,n \tag{31}\]  
where, \(m\) is the number of iterations. (2) SP  
The subproblem, given the variable in the first stage \(x^{*}\) of the main problem, seeks the worst-case probability distribution under real-time operation, returns it to the main problem, and provides the upper bound for Equation (26):  
\[f_{\text{sp}}(x^{*})=\max_{p_{s}\in\Omega^{p}}\sum_{s=1}^{N_{s}}p_{s}\min_{y_{ s}\in Y}f(y_{s}) \tag{32}\]  
(3) C&CG Solution Steps  
Since the constraints of the master problem and the subproblem are independent, the master problem can be solved first, and then the subproblem can be solved. The specific solving process is as follows:  
1. Initialization: Set the lower bound \(\text{LB}=0\), the upper bound \(\text{UB}=\infty\). Initialize the iteration counter, say \(m=1\), and utilize the initial probability distribution \(p_{s}^{1}\).
2. Master Problem Solution: Solve the MP to obtain the optimal solution \((x^{*},W^{*})\). Update the lower bound: \(\text{LB}\leftarrow\max\{\text{LB},W^{*}\}\).  
Subproblem Solution: Fix the first-stage variable \(x^{*}\) and solve the corresponding subproblem. Obtain the worst-case probability distribution \(p_{s}^{*}\) and the value of the objective function \(f(x^{*})\). Update the upper bound: \(\text{UB}\leftarrow\min\{\text{UB},f(x^{*})\}\).
4. Convergence Check: If \(\text{UB}-\text{LB}<\varepsilon\), terminate the algorithm and return the optimal solution \(x^{*}\). Otherwise, update the probability distribution for the next iteration \(p_{s}^{m+1}=p_{s}^{*}\), increment the iteration counter \(m\gets m+1\), and return to Step 2.
## 3 Research Methodology
### Features Ranking Using Gini Index  
Supervised MLTs, such as DTs, require appropriate use of approaches to identify the most effective attributes that can impact decision-making processes. Two common approaches are generally considered for this purpose, namely information gains and Gini indices. The former indicates that features with maximum information gains are used as root nodes of DTs, while the latter specifies the features with the lowest GIs (Gini indices) for binary splits (decisions for nodes) [34]. In this work, ranks are added to features before evolving trees. Gini indices are added to the feature ranks to detect imprecision in the features. Gini indices are calculated by subtracting one from the sum of the squaredprobabilities of the classes. According to [35], GIs for characteristics (\(n\)) can be calculated using Equation (2).  
\[GI(n)=1-\sum_{i=1}^{c}(P_{i})^{2} \tag{2}\]  
where \(P_{i}\) denotes the likelihood of tuples in n that belong to separate security classes. This work uses threshold values (\(t=0.02\)) for discovering the most relevant qualities of features in its suggested framework based on tree structures. It is important to realize that this number might change depending on the dataset in question.
## 3 Results  
### TDC Design  
The fine-timestamp maker is only used to achieve the time interpolation covering the whole period of the system clock. A coarse counter is needed to ensure the TDC obtains a more extensive dynamic measurement range. The coarse-timestamp with a resolution of the system clock period generated by the coarse counter is combined with the fine timestamp  
Figure 13: Measured (**a**) DNL and (**b**) INL for the Vernier-measuring module.  
in Section 2 to form the ultimate measurement of the moment when a hit signal arrives (i.e., the timestamp of the hit signal), the complete flow of which is shown in Figure 14.  
Using the ring counter run at the system clock signal, which is automatically cleared to zero and starts counting again, the start and stop signals are synchronized with the system clock for each latch of a coarse timestamp. The difference between these coarse timestamps is the coarse time. This work uses the Eco signal to retrieve the coarse timestamp.  
Since the bin widths of both the fine time and ultra-fine time are not equal everywhere, an offline bin-by-bin calibration is required to improve the measurement accuracy of the TDC. The timestamp (\(t\)) is calculated as shown in Equation (3):  
\[t=\left\{\begin{array}{l}(c-u)T_{\text{clk}}+\sum\limits_{j=1}^{m-1}W_{j}+ \left(\sum\limits_{i=1}^{u-1}W_{i}+\frac{W_{u}}{2}\right),\ T_{\text{osc}}<T_{ \text{clk}}\\ (c-u)T_{\text{clk}}+\sum\limits_{j=1}^{m}W_{j}-\left(\sum\limits_{i=1}^{u-1} W_{i}+\frac{W_{u}}{2}\right),\ T_{\text{osc}}>T_{\text{clk}}\end{array}\right. \tag{3}\]  
where \(c\) is the coarse counter count result, \(u\) is the ultra-fine counter count result, \(m\) is the fine time result, and \(T_{\text{osc}}\) is the period of oscillation.  
Two identical TDC channels are necessary to measure a time interval, one measuring the timestamp of the start signal and the other measuring the stop signal. The difference between the two timestamps is the measured time interval.
# A Wireless Monitoring System for Cracks on the Surface of Reactor Containment Buildings
## 4 Software Implementation
### Monitoring Stage  
The main task for sensor nodes in the monitoring stage is to measure changes of crack width and send them to cluster heads. As the widths of concerned cracks change slowly during pressure tests, a 30 min sampling period is enough for capturing the variation of cracks. In addition, duty cycling schemes are employed by sensor nodes. Every 30 min, sensor nodes wake up to get data from LVDT crackmeters and PT100 temperature detectors, store it and send it to cluster heads. Then, they go into idle mode and turn off their sensors and radios. In this way, sensor nodes can save a lot of energy, as they sleep most of the time. Although cluster heads do not need to monitor the cracks, they may need to always keep their radio on to receive packets from sensor nodes. Then cluster heads may consume much more power than sensor nodes, and they need to be powered by batteries with more capacity or use alternating current (AC) power when possible. Thanks to the time synchronization scheme, TDMA is used by sensor nodes for media access control to prevent transmission interference. Sensor nodes turn off their radio after sending messages out in order to save energy, which means usually they do not receive messages from cluster heads during the monitoring stage. Then, an unreliable data transfer protocol is used to adapt this feature. Sensor nodes just send measured data once to cluster heads, and there is no feedback from cluster heads about packet loss and thus no retransmission occurs. Data loss is inevitable in wireless transmission, and tests in Section 5 about data loss rate of our system under different sampling periods show that data loss rate under typical 30 min sampling period is tolerable. In addition, this means that the unreliable data transfer protocol is appropriate for our system. Unlike other high sampling rate applications, the amount of data in our system is low because of low sampling periods, and sensor nodes just send what they measure without any compression. The GUI that runs on the laptop receives all the monitoring data of the network with the help of the sink node, and it also provides functions for storing, inquiring and drawing data. The GUI is shown in Figure 3, which is implemented with C# language.
# Model Based Robust Predictive Control of Ship Roll/Yaw Motions with Input Constraints
## 1 Introduction
Although there is no need to model in theory, the online calculation workload is huge, and it is difficult to apply in practice [2]. [PERSON]'s MPC control method [4] can effectively solve the problem of actuator constraints, but it is still not enough to deal with the aforementioned problems in fin roll reduction or rudder roll reduction. [PERSON] proposed a distributed MPC [27] for multiship collision avoidance decision support and path following, which offers a complete set of solutions for multiship collision avoidance in intelligent navigation. It is also worth mentioning that classic MPC approaches usually optimize a control sequence with an open-loop form, which inevitably deteriorates the robustness of the control system. In this paper, we consider employing MPC to deal with the ship roll/yaw motion control problem, in order to guarantee the ship performance and the actuator action reliability. To be more specific, the \(H_{\infty}\)-type robust MPC is employed in ship control. As far as we know, the relevant work has not been reported in the literature. The main advantage of the method is that it can actively address the disturbance, especially when the upper bound of disturbance can be determined in advance. Furthermore, the control input within the prediction horizon adapts a state feedback control law, which renders more robust control performance compared with the open-loop scenarios. In the rudder-fin joint MIMO control system, the receding-horizon optimization strategy is implemented in real time, and the input constraints of rudder angle and fin angle are considered. In such a way, ship roll/yaw motion control is handled with the minimized disturbance attenuation level. The main contributions of this paper lie in the following facts:  
* To the best of the authors' knowledge, the achievement in this paper is the first attempt to apply \(H_{\infty}\)-type model predict control strategy to ship roll/yaw motion control. * Compared with the existing processing methods to input constrains, the delay characteristic of practical fin stabilizer system and saturation limitation of the rudder/fin stabilizers are simultaneously considered and addressed. The subsequent sections are arranged as follows. In Section 2, the three degrees of freedom (3-DOF) coupling ship motion model is constructed to be consistent with the predictive model. Section 3 focuses on presenting the \(H_{\infty}\)-type state feedback model predictive control (\(H_{\infty}\)-SFMPC) strategy, which is the main part of this paper. In Section 4, simulations are demonstrated to show the effectiveness using the approach. Conclusions and the future work are given in Section 5.
# Benchmarking Change Detector Algorithms from Different Concept Drift Perspectives
## 4 Material and Methods  
Selecting an appropriate change detection algorithm is crucial to analyze the characteristics of the data stream in order to detect unexpected and various drift types. Our perspective is that change detectors that rely on monitoring the distribution of the data stream have the potential to detect a wider range of drifts compared to algorithms that utilize mean and variance (such as ADWIN and PH). The latter is a very good choice when it comes to a specific concept drift, such as abrupt ones.  
To evaluate the performance of data stream drift detectors over several scenarios, we conducted a benchmarking study using four popular detection methods: DDM, EDDM, HDDMA, and HDDMW. We randomly generated the data streams with three concept drift types: abrupt or sudden, gradual, and incremental. The detectors were evaluated using some performance measures, including F1 score, detection time, and detection delay. To ensure fairness, the same evaluation criteria were adopted for all detectors, which had their performance compared in terms of both accuracy and speed.  
Our experiments involved generating streams of three datasets of different types (i.e., abrupt, gradual, and incremental) through an automatic process, as detailed in Section 4.1. We made the tool used for this process publicly available at the following link: [[https://github.com/gysakurai/datastream-synthetic](https://github.com/gysakurai/datastream-synthetic)]([https://github.com/gysakurai/datastream-synthetic](https://github.com/gysakurai/datastream-synthetic)) (accessed on 24 April 2023). Using these datasets, we evaluated four change detection algorithms from four different perspectives, as described in Section 4.2. Specifically, we assessed the algorithms using three evaluation metrics: F1 score, detection time, and detection delay, which are presented in detail in Section 4.3.
## 2 Preliminaries
### 3-Stage ASA Switch Architecture
Consequently, a 3-stage ASA switch can send a signal from each input to any output when the wavelength is correctly chosen (Equation (3)). An example is given in Figure 2b, where input [0, 0] can reach outputs [0, 0], [1, 1], and [0, 2] by using wavelengths \(w=0\), 2, and 1, respectively. It is worth noting that each port can transmit \(N\) wavelengths simultaneously without blocking each other if signals from inputs with the same member field, (i.e., \(m_{\mathrm{s}}\)), are destined to distinct outputs. This is because signals in a 3-stage ASA switch can only collide at the same link in stage 2 [24]. The collision occurs if signals are from inputs with the same member field and to the same output (Equation (3)). Note that signals from inputs with the same member field and to the same output pass through the same link in stage 2 and use the same wavelength (Equation (3)). An example is given in Figure 2d, where inputs [0, 0] and [1, 0], which use the same member field, cannot reach output [0, 0] simultaneously because they collide at the same link in stage 2. Therefore, the 3-stage ASA switch with an odd \(N\) is nonblocking if signals from inputs with the same member field are destined to distinct outputs. Since each port of an \(N\times N\) AWG can transmit up to \(N\) wavelengths simultaneously, the total capacity of a 3-stage \(N^{2}\times N^{2}\) ASA switch is close to \(N^{3}\times\) the bandwidth of a wavelength channel.
Addressing the challenge of enhancing the adaptability of sea surface glare suppression, this paper proposes a method based on a geographic polarization suppression model. By constructing a model to calculate real-time positioning and orientation information, dynamically adjusting the polarization suppression based on these calculations, and utilizing pixel normalization techniques, this method enhances the potential for polarization suppression of sea surface glare. Additionally, it differentiates the impacts of sunlight and moonlight based on geographic and astronomical data, enabling precise all-time suppression of sea surface glare. Precise and efficient methods of sea surface glare suppression play a crucial role in advancing marine surface optical imaging technology. In the field of marine environmental remote sensing, effective glare suppression methods can significantly enhance the accuracy of marine remote sensing data, thus making the monitoring and analysis of marine biological community distribution, marine pollution, and the impact of climate change more precise. In marine scientific expeditions, glare suppression can optimize the quality of image collection, providing researchers with high-resolution image information. In drone-based marine environmental monitoring, effective suppression of sea surface glare further improves the quality of the acquired images, offering more efficient support for marine exploration, maritime target tracking, and maritime environmental regulation. Therefore, methods of sea surface glare suppression not only directly enhance the effectiveness of marine surface imaging, but also contribute significantly to the development of marine science and technology. The remainder of this paper is organized as follows: Section 2 introduces the basic principles of the proposed algorithm, including an analysis of the polarization characteristics of sea surface glare, the geographic polarization suppression model, and the pixel normalization method. Section 3 validates and analyzes the proposed method through experiments. Section 4 concludes the paper by summarizing the content.
## 4 Experimental Results and Discussion of IHSSAO Performance Verification
### Experiment I: Classical Benchmark Function  
In this study, there are three various categories including unimodal (UM), multimodal (MM), and fix-dimension multimodal (FM) in the 23 benchmark functions. Among them, the UM functions (\(F_{1}\)-\(F_{7}\)) including only one global minimum are often selected to test the local exploitation and convergence rate ability of the algorithm. \(F_{8}\)-\(F_{13}\) are MM functions containing multiple locally optimal solutions in the search space, and their number grows exponentially as the number of dimensions increases. It is well suited to evaluate the ability of the algorithm to explore  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Function** & **Name** & \(Dim\) & **Range** & \(F_{\text{min}}\) \\ \hline \(F_{8}(x)=\sum\limits_{i=1}^{D}-x_{i}\sin\Bigl{(}\sqrt{|x_{i}|}\Bigr{)}\) & \
\begin{tabular}{c} Schwefel 2.26 \\ \end{tabular} & 30 & \([-500,500]\) & \(-418.9829\times Dim\) \\ \(F_{9}(x)=\sum\limits_{i=1}^{D}[x_{i}^{2}-10\cos(2\pi x_{i})+10]\) & Rastrigin & 30 & \([-5.12,5.12]\) & 0 \\ \(F_{10}(x)=-20\exp(-0.2\sqrt{\frac{1}{n}\sum\limits_{i=1}^{D}x_{i}^{2}})-\) & Ackley & 30 & \([-32,32]\) & 0 \\ \(\exp(\frac{1}{n}\sum\limits_{i=1}^{D}\cos(2\pi x_{i}))+20+e\) & & & \\ \(F_{11}(x)=\frac{1}{4000}\sum\limits_{i=1}^{D}x_{i}^{2}-\prod\limits_{i=1}^{D} \cos(\frac{x_{i}}{\sqrt{i}})+1\) &
\begin{tabular}{c} Griewank 30 \\ \end{tabular} & \([-600,600]\) & 0 \\ \(F_{12}(x)=\frac{\pi}{D}\{10\sin(\pi y_{1})+\sum\limits_{i=1}^{D-1}(y_{i}-1)^{ 2}[1+10\sin^{2}(\pi y_{i+1})]+\) & & \\ \((y_{n}-1)^{2}\}+\sum\limits_{i=1}^{D}u(x_{i},10,100,4)\) & Penalized & 30 & \([-50,50]\) & 0 \\ \(y_{i}=1+\frac{x_{i}+1}{4},u(x_{i},a,k,m)=\left\{\begin{array}{c}k(x_{i}-a)^{m} x_{i}>a\\ 0-a<x_{i}<a\\ k(-x_{i}-a)^{m}x_{i}<-a\end{array}\right.\) & & & \\ \(F_{13}(x)=0.1\{\sin^{2}(3\pi x_{i})+\sum\limits_{i=1}^{D}(x_{i}-1)Figure 5: Three-dimensional view of the search space for 23 benchmark functions. (**a**) Unimodal benchmark function, benchmark functions. (**b**) Multimodal benchmark function, benchmark functions. (**c**) Fix-dimension multimodal benchmark function, benchmark functions.
## 2 Materials and Methods
### Image Quality Assessment  
Image quality can be assessed by subjective and objective methods. Subjective assessment presents images to users, and their evaluation relies on subjective perceptions. This method is less reliable than the objective assessment method because it depends on user experience. Objective assessment methods are further classified into full-reference image quality assessment methods, which evaluate images with reference images, and no-reference image quality assessment (NR-IQA) methods, which evaluate images without reference images. In this context, the lack of images to be referred to (also known as the ground truth) arises because the radiation from the arc plasma prevents conventional vision sensors from capturing images. Consequently, the NR-IQA was selected. For NR-IQA, the BRISQUE technique introduced by [PERSON] et al. [27] was employed to assess image quality. BRISQUE analyzes the statistical characteristics of images to evaluate the image quality based on the observation that the image pixel statistics are distorted when the acquired  
Figure 5: Image of GMAW without imaging-band design.  
image is distorted. This technique was trained to yield low scores for high quality and high scores for low quality. After selecting the conditions with the highest image quality assessment scores using the BRISQUE technique, the most suitable image acquisition conditions were determined using an ND filter.
# Regulated Power Supply with High Power Factor for Hyperspectral Imaging Applications
###### Abstract
Firstly, the light source is limited in power (max 150 W), which makes it difficult to capture images in scenes where the illumination is poor due to the intermediate optic elements, such as fiber optic light guides for obtaining cold light illumination. As a result, due to the low power of the lighting system, it is sometimes necessary to add more lighting systems to the current one. Secondly, the optimal light intensity used to illuminate the scene to be captured depends on the ambient light conditions. Furthermore, the operator must manually adjust the light power, which results in images being captured under non-optimal conditions. In order to deal with the above limitations, this work presents the development of a stable, programmable, and low-cost regulated power supply for use in HSI instrumentation. This research contributes to the understanding of how power sources influence the quality and performance of HSI acquisition systems. The primary objective was to systematically explore this relationship, culminating in the design and development of a custom-regulated power supply. The custom power supply was integrated into a HSI acquisition system, and a series of experiments were conducted to assess the stability of the light source. To provide a robust comparative framework, stability measurements were also conducted using two commercially available power supplies. This comparative approach not only emphasizes the performance of the custom solution but also generates valuable benchmarking data. Additionally, image analysis techniques were employed to extract various stability parameters of the light emitted by both the custom and commercial systems. A statistical analysis of the findings revealed the influence of the power supply for enhanced stability in HSI applications. The evaluation of light stability across different wavelengths shows the significant influence of the power supply on the performance of the acquisition system. To the best of our knowledge, this research represents the first comprehensive study of the effects of power supply characteristics on hyperspectral imaging systems, paving the way for improved design and optimization of power sources in such applications.