esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Analysis
### Strain Distribution at the Bottom of the Pipeline  
The bottom strain gauge was arranged from the left to the right of the pipeline, and the applied load was located in the center of the pipeline. Therefore, when determining the strain at the bottom of the pipeline and performing data analysis and processing, only the strain generated at the B1-B4 positions could be analyzed. The strain distribution and overall evolution at the B1-B4 positions are shown in Figure 5. Under the double influence of its own gravity and the applied load, the internal bending moment caused the bending deformation of the pipeline and produced additional bending axial stress. The strain at the B1 position of the pipeline increased gradually with the increase in the load in the range of 480-2500 N, decreased gradually with the increase in the load in the range of 2500-6500 N, and reached the maximum at the applied load of 2500 N (Figure 6a); the strain at the B2 position of the pipeline increased gradually with the increase in the load in the range of 480-3550 N, decreased gradually with the increase in the load in the range of 3550-6500 N, and reached the maximum at the applied load of 3550 N (Figure 6b); however, the B1 and B2 positions of the pipeline were in the stage of unapplied load. Therefore, it was considered that the strain distribution at the bottom of the pipeline at the edge of the  
Figure 5: Distribution diagram of strain evolution at the top of the pipeline. (**a**) Point A1; (**b**) point A2; (**c**) point A3; (**d**) point A4; (**e**) overall strain at the top.  
applied load increased first and then decreased. The strain at the B3 position at the end of the uniform bearing section of the pipeline gradually increased with the increase in the load in the range of 480-4470 N, gradually decreased with the increase in the load in the range of 4470-6500 N, and reached the maximum at the applied load of 4470 N (Figure 6c); the strain at B4 located in the center of the uniform bearing section increased gradually with the increase in the load in the range of 480-6500 N (Figure 6d). It was found that with the increase in the load, the maximum strain position gradually shifted to the center of the pipeline from the end position B1 to the center position B4 of the pipeline.  
It can be seen from Figure 6e that the strain at the bottom of the entire pipeline was greater than zero, indicating that the bottom of the natural gas pipeline was subjected to tension under load. Through a careful comparative analysis, it was found that only the strain in B4 in the center of the pipeline increased with the increase in the load under uniform loading, and the strains at the other positions increased first and then decreased with the increase in the load. The overall analysis shows that regardless of the uniform load, the strain at the bottom of the pipeline was roughly symmetrical about the center position B4 of the pipeline after each load was applied, and the maximum strain was in the center of the pipeline.
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 2 Theory
### Non-Collisional Plasmas  
In most low-pressure plasmas the collision frequency \(\
u_{\rm m}\) is much smaller than the angular microwave frequencies \(\omega\) for typical cavities, and therefore the dissipation term--the imaginary part--in Equation (7) can be neglected. Advantageous for the low-pressure case is that it does not require information concerning the losses of the resonant field to obtain the electron density because the electron collision frequency can be assumed zero. Hence, Equation (9) can be reduced to,  
\[\frac{\Delta f}{f_{1}}=\frac{e^{2}}{8\varepsilon_{0}m_{\rm e}\pi^{2}f_{2}^{2}} \frac{\iiint_{V_{\rm cav}}n_{\rm e}(\mathbf{r})|\mathbf{E}_{1}(\mathbf{r})|^{2 }d^{3}\mathbf{r}}{\iiint_{V_{\rm cav}}|\mathbf{E}_{1}(\mathbf{r})|^{2}d^{3} \mathbf{r}}. \tag{11}\]  
In most prior MCRS experiments, the equation has been even further simplified,  
\[\frac{\Delta f}{f_{1}}=\frac{e^{2}\bar{n}_{\rm e}}{8\varepsilon_{0}m_{\rm e} \pi^{2}f_{2}^{2}}, \tag{12}\]  
which can be rewritten into [9, 15, 38, 39],  
\[\bar{n}_{\rm e}=\frac{8\varepsilon_{0}m_{\rm e}\pi^{2}f_{2}^{2}}{e^{2}}\frac{ \Delta f}{f_{1}}. \tag{13}\]  
Here, \(\bar{n}_{\rm e}\) is the cavity averaged and electric-field-squared weighted electron density given by:  
\[\bar{n}_{\rm e}=\frac{\iiint_{V_{\rm cav}}n_{\rm e}(\mathbf{r})|\mathbf{E}_{1} (\mathbf{r})|^{2}d^{3}\mathbf{r}}{\iiint_{V_{\rm cav}}|\mathbf{E}_{1}(\mathbf{ r})|^{2}d^{3}\mathbf{r}}. \tag{14}\]
###### Abstract  
As a case study on sustainable energy use in educational institutions, this study examines the design and integration of a solar-hydrogen storage system within the energy management framework of Kangwon National University's Samcheok Campus. This paper provides an extensive analysis of the architecture and integrated design of such a system, which is necessary given the increasing focus on renewable energy sources and the requirement for effective energy management. This study starts with a survey of the literature on hydrogen storage techniques, solar energy storage technologies, and current university energy management systems. In order to pinpoint areas in need of improvement and chances for progress, it also looks at earlier research on solar-hydrogen storage systems. This study's methodology describes the system architecture, which includes fuel cell integration, electrolysis for hydrogen production, solar energy harvesting, hydrogen storage, and an energy management system customized for the needs of the university. This research explores the energy consumption characteristics of the Samcheok Campus of Kangwon National University and provides recommendations for the scalability and scale of the suggested system by designing three architecture systems of microgrids with EMS Optimization for solar-hydrogen, hybrid solar-hydrogen, and energy storage. To guarantee effective and safe functioning, control strategies and safety considerations are also covered. Prototype creation, testing, and validation are all part of the implementation process, which ends with a thorough case study of the solar-hydrogen storage system's integration into the university's energy grid. The effectiveness of the system, its effect on campus energy consumption patterns, its financial sustainability, and comparisons with conventional energy management systems are all assessed in the findings and discussion section. Problems that arise during implementation are addressed along with suggested fixes, and directions for further research--such as scalability issues and technology developments--are indicated. This study sheds important light on the viability and efficiency of solar-hydrogen storage systems in academic environments, particularly with regard to accomplishing sustainable energy objectives.  
solar-hydrogen storage system; university energy management; renewable energy; energy storage technologies; sustainable energy +
Footnote †: journal: Applied sciences
# Microscope-Based Optical Frequency Comb Generator for 200 GHz Spaced WDM Data Transmission System
## 2 Methods
### Calculation of Microresonator Characteristics
After finding the steady-state temperature field (an increase in temperature by \(\Delta T\)), we estimate a shift of eigenfrequencies (WGM shift). This shift with temperature growth occurs due to both, the dependence of the refractive index on temperature'd\(n\)/d\(T\)' and the increase in a radius by \(\Delta R\) due to the thermal expansion (see Figure 1).
# A Heuristic Approach for a Real-World Electric Vehicle Routing Problem
## 4 Solution Approach
### Choosing a Removal and Insertion Heuristic  
At each iteration, a pair of removal and insertion heuristics is selected from removal and insertion heuristics to generate new solutions. Each heuristic \(i\) has a weight \(w_{i}\) and a score \(\pi_{i}\). The values of \(w_{i}\) and \(\pi_{i}\) are initialized as one and zero, respectively, at the beginning of the ALNS. The score of a heuristic is updated as follows. If a pair of removal and insertion heuristics finds a new global best solution, their scores are increased by \(\sigma_{1}\); if it finds a new solution that has not been accepted before and is better than the current one, their scores are increased by \(\sigma_{2}\); if it finds a new non-improving solution that has not been accepted before but is accepted in current iteration, their scores are increased by \(\sigma_{3}\)The ALNS iterative process consists of a number of segments. Each segment contains 100 iterations. At the start of each segment, the scores of all the heuristics are set to zero. When one segment ends, the weight of heuristic \(i\) to be used in the next segment is updated as \(w_{i}=w_{i}(1-\gamma)+\gamma\pi_{i}/\theta_{i}\) where \(\theta_{i}\) is the number of times heuristic \(i\) is used during the last segment and \(\gamma\in[0,1]\) is a reaction factor that controls how quickly the weight adjustment reacts to changes in the effectiveness of the heuristics. The choice of the removal and insertion heuristics is determined by the well-known roulette-wheel mechanism. Specifically, given \(k\) insertion (or removal) heuristics with weights \(w_{i}\), \(i\in\{1,\,2,\,\ldots,\,k\}\), heuristic \(j\) is selected with a probability of \(w_{j}/\sum_{i=1}^{k}w_{i}\). Note that the insertion heuristic is chosen independently of the removal heuristic (and vice versa).
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 3 Dynamic Characteristics of Positioning Stage
### Fundamental Vibration Modes of Stage  
Theoretically, stage rigidity is closely related to the stiffness of the linear guide, which has an important influence on the dynamic behavior of the structure. The dynamic characteristics of the stage were assessed by conducting a vibration test, as shown in Figure 5. In the tests, the stage was excited using an impact hammer applied at a properly selected position. The vibration signals in the time domain were assessed using accelerometers and converted into frequency response functions by a fast Fourier transformation (FFT analyzer). The modal parameters, such as vibration frequency, dynamic compliance, and rigidity of the positioning stage structure were also obtained.  
Moreover, the vibration characteristics of the stage could be predicted using the finite element analysis approach. As shown in Figure 6, a finite element model of the stage was created and used to determine the vibration frequency associated with the vibration mode. Details of the creation and modeling of the feeding components, such as ball screws and linear guides, are available in [5,6]. In the modeling of the linear guide, the four ball grooves or raceways were simplified into rolling interfaces, with neglect of the balls. The contact stiffness at each rolling interface can be determined by the contact geometry and preload of the linear guides, based on the Hertz contact theory [39,40]. For the linear guides used in this study, the contact stiffness was calculated as 194, 280, and 332 N/um, when they were preloaded at small, medium, and high amounts, respectively.  
According to the modal analysis, the fundamental vibration modes of the stage are illustrated in Figure 7, including the yawing, rolling, and pitching motions. These vibration modes were directly determined by the vibration of the sliding blocks on the rails. The mode shapes also indicate the points for mounting the accelerometer and the excitation point in the vibration tests. For example, when measuring the rolling mode, accelerometers were attached at the middle and side positions of the stage, and the other side was impacted by a hammer in the vertical direction to trigger stage vibration in the rolling mode.  
Figure 5: Schematics of vibration test of stage: (**a**) rolling mode and (**b**) yawing mode.  
Figure 6: Finite element model of the stage with simplified linear guide.
# Polar Region Integrated Navigation Method Based on Covariance Transformation
###### Abstract  
Aircraft flying the trans-arctic routes usually apply inertial navigation mechanization in two different navigation frames, e.g., the local geographic frame and the grid frame. However, this change of navigation frame will cause filter overshoot and error discontinuity. To solve this problem, taking the inertial navigation system/global navigation satellite system (INS/GNSS) integrated navigation system as an example, an integrated navigation method based on covariance transformation is proposed. The relationship of the system error state between different navigation frames is deduced as a means to accurately convert the Kalman filter's covariance matrix. The experiment and semi-physical simulation results show that the presented covariance transformation algorithm can effectively solve the filter overshoot and error discontinuity caused by the change of navigation frame. Compared with non-covariance transformation, the system state error is thereby reduced significantly.  
covariance transformation; integrated navigation; polar region +
Footnote †: journal: Applied Sciences  
12 October 2021  
119572[[https://doi.org/10.3390/app11209572](https://doi.org/10.3390/app11209572)]([https://doi.org/10.3390/app11209572](https://doi.org/10.3390/app11209572))
# Person Re-Identification by Low-Dimensional Features and Metric Learning
## 2 Related Works  
### Person Re-Identification  
Person Re-ID judges whether pedestrians in different images captured in a non-overlapping field of view belong to the same person. Therefore, the low resolution of pedestrian images, the diversity of portrait shooting angles and pedestrian postures are the main problems faced by this task [13]. Early research has mainly been focused on hand-craft feature extraction [3; 14; 15] and metric learning methods [16], which still have much room for improvement. [PERSON] et al. [4] combined spatial information with low-level features and proposed a feature representation method called local maximal occurrence (LOMO). [PERSON] et al. [5] also used pedestrian attribute characteristics to re-identify pedestrians, and they designed and manually labeled 15 pedestrian attributes based on low-level characteristics for clothing style, hairstyle, personal belongings, and gender. Recently, deep learning has become unique in the field of pedestrian dynamics, and many research advances have been made [9; 11; 17; 18]. In [17], the authors proposed using a generative adversarial network (GAN) to generate unlabeled samples to improve the baseline. [PERSON] et al. [9] regarded the different local features of pedestrians as different nodes of the graph to learn relational information and then used graph matching algorithms to enhance the robustness of the model. [PERSON] et al. [16] proposed a multiscale deep representation learning model to capture distinguishing features at different scales.  
Although the ability of convolutional neural networks to extract visual features is widely recognized, the biggest difference between the person re-identification task and other computer vision tasks is the resolution of images, as shown in Table 1. A low-resolution image contains much less information about a person, which may make the neural network learn many useless features, and at the same time makes the computational resources required to train the model larger.
# Hypergraph-Clustering Method Based on an Improved Apriori Algorithm
## 3 Method Design of HDHPA
### The Stage of Constructing a Hypergraph  
During the construction of the hypergraph stage, the goal is to find sub-hypergraphs that meet clustering conditions and are mutually connected. Based on the generated frequent sub-hypergraphs, the relationships (or hyperedges) between the entities (or vertices) of the encapsulated objects are established. To ensure the objectivity of selecting frequent sub-hypergraphs, we decided to follow the structure of the Apriori algorithm to find frequent itemsets. To avoid the additional computational burden, we improved it based on discovering frequent itemsets with the Apriori algorithm, which can maximize the avoidance of excessive iterations and achieve effective pruning with minimal time complexity, compared with other improved association rule algorithms (such as FP-stream [30] and EDFS [31]). Based on this idea, in the hypergraph construction stage of the HDHPA method, the vertices correspond to the itemset containing transactions; a set of hyperedges containing multiple hyperedges in a node is called an itemset. At first, this stage of the method enumerates all frequent single-hyperedge and double-hyperedge sub-hypergraphs based on Equation (2). Then, parallel retrieval is performed based on these two sets. In each iteration round, candidate sub-hypergraphs are first generated, with a size one edge larger than the previous frequent sub-hypergraph (line 7 of the algorithm). Next, the support count of each candidate item is calculated, measuring the correlation between hyperedges by Equation (3), and hyperedges that meet the threshold are merged into sub-hypergraphs. Finally, sub-hypergraphs that do not satisfy the prior principle are pruned to generate hypergraphs by Equation (1) (lines 13-16). The discovered frequent sub-hypergraphs satisfy the downward closure property of the support condition, making the constructed hypergraph robust to noisy data. The method for constructing the hypergraph stage is shown in Algorithm 2 below. Given a minimum support threshold count of \(\sigma\) to construct the hypergraph, \(C^{k}\) is a candidate set with \(k\) hyperedges, then \(H_{C}{}^{k}=\left(L,C^{k}\right)\) is a sub-hypergraph with \(k\) candidate sets. A hyperedge \(e_{i}\) that meets the minimum support is called a frequent itemset \(F(e_{i})\), then \(H_{F}{}^{k}=(L^{\prime},F)\) is a sub-hypergraph with \(k\) frequent sets, where \(L^{\prime}\) is a set of frequent transactions and \(F\) is a set of \(F(e_{i})\). Sparse graphs are used to store input transactions, intermediate candidates, and frequent sub-hypergraphs. This representation saves memoryand speeds up computation. The topology, hyperedges, and vertex labels of each frequent sub-hypergraph are randomly selected, resulting in a complete set of valid candidate items with no duplication. The weights adhere to a unit-mean exponential distribution, and the total weight of all frequent sub-hypergraphs is normalized to 1. Based on the previous analysis, the time complexity of Algorithm 2 is \(\text{O}\Big{(}NC^{k}\log(k)\Big{)}\).
# A Study on Combustion Characteristics of Insensitive Triple-Base Propellant
## 1 Introduction
To know the flame structure of energetic materials, the methods of probe mass spectrometry, micro thermocouple technology, and spectroscopy are always utilized. Spectroscopic methods include Planar Laser Induction Fluorescence (PLIF), Spontaneous Raman Scattering (SRS), and Coherent Anti-Stokes Raman Scattering (CARS). Among them, PLIF technology has the advantages of high sensitivity, as well as high spatial and temporal resolution. It is considered to be well suited for diagnosing flame structure and understanding combustion processes. [PERSON] et al. [18] studied the deflagration process of HNF, RDX, HMX, and XM39 by PLIF, ultraviolet/visible absorption, and thermocouple methods. [PERSON] et al. [19] used PLIF technology to study the distributions of CN and OH of cocrystals of CL-20 and a polycrystalline composite crystal of HMX/AP. However, as innovation of energetic materials continues, propellants are becoming more and more diverse in their composition and more complex in their combustion reactions, particularly with triple-base propellant. Until now, triple-base propellant has not been well understood by investigating the chemical reaction process from the micro-perspective. Therefore, in this study, self-luminescence, NO, and NH chemiluminescence, as well as OH-PLIF, were adopted to carry out an in-depth analysis on the combustion of insensitive tri-base propellant. The distribution patterns of NO, NH, and OH free radicals were obtained, which can characterize the microscale flame structure to a certain extent, help to identify the dominant factors in combustion, and also help modelers to create dynamic and flame structure models of triple-base propellants. Additionally, evaluating the combustion behaviors of some insensitive propellant from a microscopic perspective was expected to guide the design and use of insensitive propellant.
## 5 Conclusions  
We constructed a high-performance microwave frequency synthesizer using a dual-loop PLL architecture in the context of a cesium-beam atomic clock application. This architecture provides excellent phase noise performance and a suitable settling time. PLL1 is driven by an external 10 MHz reference source, generating a precise and low-jitter 200 MHz frequency signal that acts as the reference driver clock for PLL2. We achieved ultra-low phase noise by allowing the OCXO to dominate the phase noise at low offsets, while the phase noise of the DRO remained dominant at high offsets. The absolute phase noise levels at 9.192 631 770 GHz were measured at \(-63.7\), \(-75.7\), \(-107.1\), and \(-122.5\) dBc/Hz at 1 Hz, 10 Hz, and 10 kHz offset frequencies, respectively. Furthermore, a mathematical model was established and analyzed. The phase-settling time was theoretically analyzed and verified through simulations, showing agreement with the actual measurements. Our work will be useful for the design of a compact, high-performance frequency synthesizer for different atomic clock applications.  
Conceptualization, D.H. and F.S.; methodology, D.H.; software, G.G. and D.H.; validation, G.G., C.L. and K.L.; formal analysis, G.G.; investigation, D.H. and G.G.; resources, F.S. and C.L.; data curation, G.G. and C.L.; writing--original draft preparation, G.G.; writing--review and editing, F.S. and H.D.; visualization, G.G. and D.H.; supervision, D.H., F.S. and S.Z.; project administration, F.S.; funding acquisition, F.S. and S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 62271109, the Natural Science Foundation of Sichuan Province, grant number 2023 NSFSC0445, the Equipment Advance Research Field Foundation, grant numbers 315067207 and 315067206, the Fundamental Research Funds for the Central Universities of China, grant number ZYGX2021J019, and State Key Laboratory of Advanced Optical Communication Systems Networks, China, grant number 2023 GZKF09.  
Not applicable.  
The authors declare no conflict of interest.