esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Load Redistribution of Power CPS Based on Comprehensive Index of Coupling Node Pairs
## 3 Power CPS Risk Propagation Considering Load Redistribution
### Initial Load and Capacity of CPS Nodes
#### 3.1.1 Power Side  
On the power side, the power flow consists of active and reactive components and is calculated in terms of apparent power:  
\[S_{l}=\sum_{\begin{array}{c}j\
eq i\\ j=1\end{array}}^{n}S_{ij}=\sum_{\begin{array}{c}j\
eq i\\ j=1\end{array}}^{n}V_{i}y_{ij}^{*}\Big{(}V_{i}^{*}-V_{j}^{*}\Big{)}= V_{i}(YV)_{i}^{*} \tag{8}\]  
In general, due to the existence of line impedance, the voltage of the node at the sending power end and the node at the receiving power end on the same line are different, resulting in different apparent power on both sides of the line. Thus, the power flow of the line can be defined as:  
\[|L_{li}(0)|=\frac{|S_{Fl}|+|S_{Tl}|}{2} \tag{9}\]  
where \(|L_{li}(0)|\) is the initial average power flow on line \(l\), \(|S_{Fl}|\) is the apparent power at the sending-power end of the line, and \(|S_{Tl}|\) is the apparent power at the receiving-power end of the line.  
Expand it to get:  
\[|L_{li}(0)|=\frac{\sqrt{PF_{li}^{2}+QF_{li}^{2}}+\sqrt{PT_{li}^{2}+QT_{li}^{2}} }{2} \tag{10}\]  
Then, the initial load of node \(i\) is the sum of the initial loads of all connected neighboring lines:  
\[L_{pi}(0)=\sum_{li\in\Gamma i}L_{li}(0) \tag{11}\]  
where \(L_{pi}(0)\) is the initial total load of node \(i\).  
Power node capacity is proportional to the initial load:  
\[C_{pi}=(1+\alpha_{p})L_{pi}(0) \tag{12}\]  
where \(\alpha_{p}\) is the power-side capacity margin factor.
# Classification of Valvular Regurgitation Using Echocardiography
## 4 Experimental Results
### Output of Proposed Methodology
#### 4.3.2 Features Extracted  
The different GLCM and Haralick features extracted using Table 1 are stored in a CSV file which look like the image provided in Figure 6. The figure shows obtained features for selected frames. The 19 different features are contrast as \(contr\), dissimilarity as \(dissi\), energy as \(energy\), entropy as \(entro\), correlation as \(corrp\), homogeneity as \(homom\), variance as \(sosvh\), autocorrelation as \(autoc\), sum average as \(saysh\), sum entropy as \(senth\), sum variance as \(svarh\), difference entropy as \(denth\), difference variance as \(dvarth\), information measure of correlation 1 as \(info1\), information measure of correlation 2 as \(info2\), cluster prominence as \(cprom\), cluster shade as \(cshad\), inverse difference normalized as \(maxpr\) and inverse difference moment normalized as \(idmnc\). With the features, class label is also provided for training phase where 0 is for AR, 1 is for MR and 2 is for TR.
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 3 Congestion Detection and Identification
### Congestion Determination
#### 3.2.1 Division of Congestion Degree
This approach helps determine the location and extent of congestion, allowing for faster response and ultimately optimizing the performance of the network.
## 5 Conclusions  
To address the increasing need for classifying mechanical components in pipelines, we propose a state-of-the-art model that effectively identifies mechanical components that are incredibly complicated due to their intriguing details among other visual objects. The model achieves outstanding performance through its use of geometric priori, emphasizing the importance of the local structure of the objects. The geometric variables can describe a mechanical component from scratch in detail. In addition, we scaled the model so that it can capture the global structure of an object by propagating it through a modern graph neural network structure. The model achieves excellent performance. Compared to other state-of-the-art models, the model can achieve better results.  
The model would help to streamline pipelines in mechanical production. Once the point cloud of all mechanical components is available, the new model can be trained with moderate computation within a day, and when classifying objects in the pipeline, no additional task is needed other than inference, which takes seconds. To incorporate the MC-Classifier system in real-world applications, the practitioner should obtain point cloud models for all the mechanical components in production and build a dataset of them; this is the only thing needed to train the framework. During inference, the practitioner should load the components into a point cloud framework for analysis so that the inference works. Other than just classifying, the framework of MC Classifier can also be potentially applied to other areas of the pipeline, such as identifying defects in the components or the efficient maintenance of the pipeline logistics by counting the number of each industrial component.  
On the other hand, the model presented in this paper should inspire more geometry-based computer vision models. For tasks that involve dealing with objects with detailed local structures, other than stacking neural network structures, one could approach them by considering the incorporation of geometric variables into the whole neural network model. As presented in this paper, such considerations regarding the objects' geometric properties could result in models with better performance than models without such considerations. For now, factories could use the proposed model in the industry to automate tasks when producing such components. On the other hand, the tremendous potential that this paper offers is that similar geometry methods could be applied to different datasets involving delicate objects such as plants, components for automobiles, and so on, and using geometry before boosting a neural network's performance would be worth studying further.  
Figure 6: Accuracy of classifying each category of mechanical components for our model and the DGCNN. The X axis shows the types of mechanical components, and the Y axis displays the accuracy of classifying each category.
# Data-Driven Voltage Control in Isolated AC Microgrids Subject to Sensor Saturation
## 5 Conclusions  
This paper introduces a model-free distributed data-driven adaptive iterative learning secondary voltage control method. Initially, utilizing input-output data from DREGUs, a time-variant linear data model that does not depend on specific microgrid system information is established. Subsequently, in response to measurement disturbances and sensor saturation, the DAILC algorithm is designed to achieve data-based adaptive restoration of voltage to reference values. Finally, we experimentally verify the convergence of this algorithm under conditions of sensor saturation and disturbances, confirming its effectiveness in restoring the secondary voltage of AC microgrids.  
Although the test system is a standard benchmark that reflects realistic conditions, physical experimental validation is necessary to fully confirm the algorithm's performance and robustness in real-world applications. Future research will focus on two main directions. First, we plan to implement and test the proposed DAILC algorithm on a hardware-in-the-loop (HIL) platform or a laboratory-scale microgrid to validate its practical feasibility. Second, this paper is limited to the study of voltage restoration to reference values. Therefore, future research will consider integrating reactive power sharing with secondary voltage regulation, addressing this issue comprehensively through a data-driven approach.  
Formal analysis, X.Y.; Funding acquisition, Z.W.; Investigation, S.W.; Project administration, Z.W.; Supervision, Z.W.; Validation, X.Y.; Visualization, S.W.; Writing--original draft, X.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under No. 62373089.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Lightweight Design of Multi-Objective Topology for a Large-Aperture Space Mirror
## 5 Conclusions  
Using a topology optimization with multi-objective function combined with parametric optimization method, we designed a new configuration lightweight mirror considering axial gravity, radial gravity, and polishing pressure simultaneously. In the application example, the RMS value under each loading case was replaced by the corresponding structural compliance, and the minimum structural weighted compliance was selected as the objective function. Under the constraints of the volume fraction, nodal displacement, draft direction, and cyclic symmetry condition, a new mirror configuration was proposed. Then sensitivity analysis and parametric optimization methods were used to determine the size of the mirror structure. Compared with two classic lightweight mirrors, we found that the optimized mirror combines the high lightweight ratio of the hexagonal configuration lightweight mirror and the excellent optical performance of the triangular configuration lightweight mirror. In addition, the surface error of the mirror mounted by given BF under 1 g radial gravity was analyzed. We investigated the effects of non-coplanar distance \(L\) on the surface figure RMS and coefficient of Pri Astig-A. When \(L\) is 3.5 mm, the surface error of the mirror is minimized only with a RMS value of 3.58 nm. It shows an extraordinary merit that the surface figure of the PMA is far better than the optical requirement (\(\lambda/60\) RMS, \(\lambda=632.8\) nm). The results show superiority of the new configuration mirror and illustrate the effectiveness of the proposed optimization design method.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], and [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
This work was supported by the National Natural Science Foundation of China (Grant No. 51402351).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Tribological Tests  
A ring-block friction/wear tester (MRH-3G, Jinan Hengxu Tribological Testing Technology Co., Ltd., Jinan, China) was used to characterize the friction and wear properties of water-lubricated blocks. Ring rotation caused friction on the block (a normal load was applied from the top), which was recorded by the integrated force sensors. The wear scar of block surface was characterized by a laser microscope (Olympus LEXT OLS5100, Olympus Co., Ltd., Tokyo, Japan). Figure 3 shows a photo of the ring-block friction testing machine used for the friction and wear test. The ring was made of ZCuSn10 Zn2, and the size (\(\varphi\)) was 49.22 \(\times\) 13.05 mm. Each block (water-lubricated material) had a size of 19.05 \(\times\) 12.32 \(\times\) 12.32 mm.  
Figure 3: Picture of ring-block friction testing machine used for friction and wear testing. (F\({}_{\text{n}}\) is normal load.)  
Figure 2: **(a)** The forming mold consists of two parts: a top plate with four convex terraces that were manufactured with a negative microstripe texture or finished as a pristine surface and a bottom substrate with four containers to place water-lubricated blocks. **(b)** The 3D topography images of the microstripe-textured and pristine surfaces. Periodic microstripe texture with intervals of 5 \(\upmu\)m or pristine surface topography can be observed.  
The coefficient of friction (COF) was determined as the ratio of the friction and normal forces. The specific wear rate was calculated according to [22]  
\[\mathrm{W_{s}=V_{s}/F*2\pi R*nt} \tag{1}\]  
where F, n, and t denote the normal load, ring rotary speed, and test time, respectively. R represents the radius of the corresponding copper ring. Vs is the wear volume of each specimen calculated according to the following equation [22]:  
\[\mathrm{V_{s}=\left[R^{2}\frac{\pi}{180}\mathrm{arcsin}\frac{b}{2R}-\frac{b}{ 2}\sqrt{R^{2}-\frac{b^{2}}{4}}\right]B} \tag{2}\]  
where b and B represent the widths of the abrasion marks and sample block. A friction/wear test for each material was repeated five times, and the average values together with the corresponding standard deviation were plotted for comparison.  
To compare the friction and wear performance of the pristine surface and microstrip-textured specimens, a normal load of 50 N (the corresponding pressure is 0.2 MPa.) and rotary speed of 100 rpm (the corresponding linear velocity is 515.17 mm/s) were implemented. For comparison, tap water was used as the lubricant in the tribological tests under the same conditions. All the tests were conducted with water lubrication at a room temperature of 25 \(\pm\) 2 \({}^{\circ}\)C and 35 \(\pm\) 5% relative humidity for 120 min.
## 3 Research Method
### Development of the Research Hypotheses
#### 3.1.5 Relationship between Customer Satisfaction and Continuous Usage Intention  
CS has emerged as a pivotal determinant of various behavioral outcomes, including loyalty, intention to recommend, and CUI [49]. Satisfied customers are inclined to cultivate stronger bonds with specific service providers, whereas dissatisfaction often triggers a re-evaluation of existing relationships and an exploration of alternatives [50]. This relationship is evident across diverse e-service domains. [PERSON] et al. [51] and [PERSON] et al. [52] found that CS significantly influences CUI in food delivery and social applications, respectively. [PERSON] et al. [53] presented further evidence of this relationship using online shopping. Consequently, the current study hypothesizes the following relationship between CS and CUI in e-commerce marketplace platforms:  
**Hypothesis 5 (H5).**_CS is positively related to CUI._
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 4 Solutions and Algorithms
### Solution Development
#### 4.1.2 Lyapunov Drift  
To maintain the stability of RSN, we define a Lyapunov drift function \(\Delta(t)\) to describe the difference of the Lyapunov function between two adjacent time slots, with the following expression  
\[\Delta(t)=\mathbb{E}[L(t+1)-L(t)|\Pi(t)], \tag{13}\]  
where \(\Pi(t)\) is system state at slot \(t\) with the expression of \(\Pi(t)\triangleq(\mathcal{D}(t),E(t))\). By minimizing \(\Delta(t)\), the queues \(\mathcal{D}_{i}\) and \(E_{i}(t)\) can approach a stable state over the operation process.
# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 2 Signal Model and Potential Error Sources
### Carrier-to-Noise Ratio (CNR)  
CNR is one of the key performance indicators of GNSS signal quality measurements. It denotes the relationship between signal strength and noise at a particular frequency. It is noted that CNR is highly influenced by MP/NLOS in a challenged environment and fluctuates inconsistently from its standard values due to combination of direct and indirect (MP) signals. CNR is the function of elevation angle.  
\[CNR=f(\theta_{u}^{m}) \tag{4}\]  
where  
* \(f\) is the function of third-order polynomial.
* \(\theta_{u}^{m}\) indicates the elevation angle between receiver \(u\) at the ground and satellite \(m\) in the space. According to Equation (3), the relationship is expressed as \[CNR=A_{m}^{2}=A_{d}^{2}+A_{\xi}^{2}+2A_{d}^{2}A_{\xi}^{2}cos\Delta\phi\] (5)  
In a severe MP environment, there are significant deviations in CNR, and it varies from its nominal value with increasing and decreasing trends [39; 40] due to the constructive or destructive multipaths. This trend depends on the surroundings and can lead to worsening of the signal measurements and breaks the locking of satellites with the ground receiver (loss of lock).
# Simultaneous Coded Plane-Wave Imaging Using an Advanced Ultrasound Forward Model
## 2 Materials and Methods
### On the Gain in Acquisition Time  
For a conventional emission/reception of three PWs, the total acquisition time can be approximated by:  
\[t_{3\text{PW}}=3(t_{s}+t_{\Omega}+t_{\text{SW}})+\sum_{k=1}^{3}t^{(k)}, \tag{32}\]  
where \(t_{s}\) is the duration of the excitation signal, and \(t_{\Omega}\) is the total time needed for the ultrasound wave to propagate forward and backward inside the medium \(\Omega\). \(t_{\text{SW}}<<t_{s}<<t_{\Omega}\) is a buffer time that most ultrasound scanners require to switch between an emission event and a reception event, and \(t^{(k)}\) is the maximal emission delay for each PW \(k\). Indeed, for the proposed method, the excitation signals are longer than for a conventional acquisition scheme (\(t_{a}>t_{s}\)); however, as all three PWs are emitted simultaneously, the total acquisition time can be approximated by:  
\[t_{\text{SPW}}=t_{a}+t_{\Omega}+t_{\text{SW}}+\max_{k\in[1.3]}t^{(k)}. \tag{33}\]  
Thus, using Equations (32) and (33), we can deduce the gain in acquisition time as:  
\[\eta_{3\text{PW}}=\frac{t_{3\text{PW}}-t_{\text{SPW}}}{t_{3\text{PW}}}\approx \frac{2t_{\Omega}+3t_{s}-t_{a}}{3t_{\Omega}+3t_{s}}. \tag{34}\]  
In Equation (34), it can be seen that if \(t_{\Omega}>>t_{a}\) then \(\eta_{3\text{PW}}\approx 2/3\); in other words, if the imaged medium is very large, the time gain provided by the proposed method is 66%. In this study, the value of \(t_{\Omega}=53.6\)\(\upmu\)s is not much higher than that of \(t_{a}=18\)\(\upmu\)s. Thus, the time gain provided for this configuration is \(\eta_{3\text{PW}}=55.57\%\).
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 3 Composite Islanding Detection Solution
### Explanation of the Design  
In general, the power shall be balanced on all three phases whether in a grid-connected or islanding condition. So, if islanding condition happens due to the opening of the interconnection CB,the three phase voltage vectors should shift in the same direction with generally balanced phase angles. However, unbalanced system faults usually cause the three phase voltage vectors to shift in different directions with unbalanced phase angle changes. So, the balanced three phase voltage vectors shifts can be applied to differentiate the islanding condition from the unbalanced system faults.  
For three phase system faults, it is possible that the three phase voltage vector shifts are balanced and the calculation of the accumulated phase angle drift is initiated. Normally three phase faults shall be tripped off within 100 ms. Hence, the islanding detection solution should remain stable for at least 100 ms. If there are specific requirements on fault ride through capability of the DG unit, the islanding detection solution shall also remain stable during the required duration. The tripping time of the PAD solution for islanding detection depends on the frequency change caused by the three-phase fault and the accumulated phase angle drift setting.  
Some other system events, such as load or generation switching operation, could also cause the balanced three phase voltage vectors shifts and the transient frequency fluctuation. Such events would usually cause a transient but they will not cause the continuous frequency deviation. So, a proper threshold for the accumulated angle drift setting \(\sum Ang\_set\) can be applied to differentiate the islanding condition from the normal system operation events.  
The calculation of the accumulated phase angle drift is terminated when the max rate of change of frequency in five power cycles is less than the setting \((df/dt)\_set\). To reset the value of phase angle drift before the system frequency completely settled down is helpful to the stability when the frequency is under a long-time fluctuation with small ROCOF value.
# Sentiment Classification Using Convolutional Neural Networks
## 2 Background
### Convolutional Neural Network for Text Classification  
Among the existing studies using deep learning to classify texts, the CNN takes advantage of the so-called convolutional filters that automatically learn features suitable for the given task. For example, if we use the CNN for the sentiment classification, the convolutional filters may capture inherent syntactic and semantic features of sentimental expressions, as shown in [47]. It has been shown that a single convolutional layer, a combination of convolutional filters, might achieve comparable performance even without any special hyperparameter adjustment [1]. Furthermore, the CNN does not require expert knowledge about the linguistic structure of a target language [48]. Thanks to these advantages, the CNN has been successfully applied to various text analyses: semantic parsing [49], search by query [50], sentence modeling [2].  
One may argue that the Recurrent Neural Network (RNN) [51] might be better for the text classification than for the CNN, as it preserves the order of the word sequence. However, the CNN is also capable of capturing sequential patterns, as concerns the local patterns by the convolutional filters; for example, the convolutional filters along with the attention technique have been successfully applied to machine translation [52]. Moreover, compared to the RNN, the CNN mostly has a smaller number of parameters, so that the CNN is trainable with a small amount of data [43]. The CNN is also known to explore the richness of pretrained word embeddings [53].  
In this paper, we design a CNN model for the sentiment classification and show that our network is better than other deep learning models through experimental results.