esa-sceva/satcom-chunk-collection huggingface dataset:
# New Hydraulic High-Pressure Impulse Generator for Long-Duration Impulse Tests
## 6 Conclusions  
In this paper, a new hydraulic high-pressure impulse generator is proposed for providing the standard water hammer wave for long-term usage. The proposed generator has a simple and open-looped structure. The architecture and the working principle are introduced together with the established mathematical models.  
In the study, the simulation results based on a commercial software guided the optimization of critical parameters and also provided potential factors that influence the formation of the standard water hammer wave. Subsequently, a test rig was manufactured. The experimental results show the feasibility of the new impulse generator. The specific advantages and limitations of the novel setup are as follows:  
1. By simply combining a sleeve with three pairs of windows connected to different pressure sources and a rotary spool with two symmetric slots, the standard water hammer wave can be generated easily. The booster piston here can drastically reduce the input pressure. Thus, the system comprises hydraulic components that are easily available and durable for long-duration tests.  
Figure 11: Repeatability results for 10 periods.  
Figure 10: Experimental results for one period.  
2. To obtain the standard water hammer wave accurately, a throttle valve inside the rotary valve is used to control the rate of pressure rise. A withdraw chamber on the booster piston is designed to ensure that the wave decays as soon as possible. The pressure gradients and heights of the three windows are carefully designed and optimized to maintain the main body of the wave.
3. The primary limitation of the new impulse generator is that the parameters are generally designed for one tested hydraulic component at a time. The simulation results indicate that capacity for modification for different tested hydraulic components is not good. The rotary valve part requires redesigning when the working condition is changed.  
In conclusion, this new impulse generator has a simple structure and is built using easily available hydraulic components. Hence, the manufacturing cost is low, and the generator is suitable for repeatability tests or durability tests on hydraulic components.  
Under supervision by [PERSON], [PERSON] developed mathematical modeling and wrote the manuscript; [PERSON] performed calculations; [PERSON] performed data analysis; [PERSON] performed design; [PERSON] performed manufacturing of the prototype. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China under Grant 2019 YFB2005202, in part by the Natural Science Foundation of China, Grant 51805480 and in part by the Zhejiang Provincial Natural Science Foundation, grant number LY21E050013.  
The authors declare no conflict of interest.
## 3 Shift Strategy Modification Based on Vehicle Load and Road Slope
### Modification of Shift Strategy Based on Road Slope  
The slope of the mine road changes frequently, and the maximum road slope is generally greater than the urban road. The maximum slope of the mine road referred to this paper is \(\alpha_{\max}=7\%\). On actual roads, the slope is a continuously changing random variable, so it is impossible to design the shift schedule for every road slope. After using the aforementioned method to obtain the shift schedule of the maximum road slope, the vehicle speed at any other road slope can also be obtained by using the interpolation method.  
\[f(\alpha)=\frac{\alpha-\alpha_{\max}}{\alpha_{0}-\alpha_{\max}}f(\alpha_{0}) +\frac{\alpha-\alpha_{0}}{\alpha_{\max}-\alpha_{0}}f(\alpha_{\max}). \tag{17}\]  
Let \(\sigma_{\alpha}=\frac{\alpha-\alpha_{\max}}{\alpha_{0}-\alpha_{\max}}\), then Equation (17) can be transformed into  
\[v_{V,A}=\sigma_{\alpha}v_{V,A0}+(1-\sigma_{\alpha})v_{V,\max}. \tag{18}\]  
Still taking the 3-4 upshift as an example, the shift strategy based on road slope modification is shown in Figure 6b. It can be seen that the shift point would be delayed under the same throttle opening in climbing working conditions.
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
## 3 Technical Overview
### MAC Sublayer
#### 3.2.1 Flicker Mitigation  
Flicker is a temporal modulation of light, which is visible by the human eye. The maximum period over which the light intensity is changing is defined as Maximum Flickering Time Period (MFTP). Flicker within the MFTP is not visible by the human eye. Any Flicker beyond the MFTP must be mitigated to assure a stable communication. If the flicker occurs within the data frame of communication, it is called intra-frame flicker. If the flicker occurs between adjacent data frames it is called inter-frame flicker.  
Intra-frame flicker can be mitigated using various modulations OOK mode dimming and RLL coding. By using these modulations, the average brightness can be maintained at 50%, which in turn reduces the flicker immensely. On the other hand, inter-frame mitigation can be handled by the transmission of an idle pattern between the data frames, as shown in Figure 1. The brightness of these idle frames will be equivalent to the data frames in order to avoid flickering.  
Figure 1: Mitigation of interframe flickering.
# MLGen: Generative Design Framework Based on Machine Learning and Topology Optimization
###### Abstract  
Design and manufacturing processes are entering into a new era as novel methods and techniques are constantly introduced. Currently, 3D printing is already established in the production processes of several industries while more are continuously being added. At the same time, topology optimization has become part of the design procedure of various industries, such as automotive and aeronautical. Parametric design has been gaining ground in the architectural design literature in the past years. Generative design is introduced as the contemporary design process that relies on the utilization of algorithms for creating several forms that respect structural and architectural constraints imposed, among others, by the design codes and/or as defined by the designer. In this study, a novel generative design framework labeled as MLGen is presented. MLGen integrates machine learning into the generative design practice. MLGen is able to generate multiple optimized solutions which vary in shape but are equivalent in terms of performance criteria. The output of the proposed framework is exported in a format that can be handled by 3D printers. The ability of MLGen to efficiently handle different problems is validated via testing on several benchmark topology optimization problems frequently employed in the literature.  
os +
Footnote †: 10.000  
development of new methods, able to handle large amounts of data and extremely complex problem definitions. The scope of this study is to examine and combine modern machine learning methods with topology optimization procedures aiming to generate multiple forms in the concept of generative design. Recently, there is growing interest for applying deep learning techniques in topology optimization, mainly for accelerating its procedure [6]. MLGen, a generative design procedure based on machine learning integrated with topology optimization, is presented herein. More specifically, MLGen is formulated by combining the topology optimization solution approach of solid isotropic material with penalization [7; 8; 9], long short-term memory networks [10], image filtering techniques and derivative-free search algorithms [11].  
The layout of this study begins with a short introduction on structural topology optimization together with some notes on the solid isotropic material with penalization (SIMP) approach in Section 2, followed by a description of long short-term memory (LSTM) networks in Section 3. Subsequently, in Section 4 the proposed MLGen (machine learning-based generative design) framework is described in detail. Section 5 presents the results of the numerical tests performed in order to assess its efficiency to generate multiple optimized solutions.
# Numerical Design and Laboratory Testing of Encapsulated PCM Panels for PCM-Air Heat Exchangers
## 2 Methods  
### Design Procedure  
The design method used CFD simulations (ANSYS Fluent) assuming a steady state condition, incompressible and constant air properties (at 300 K), and fully developed flow. Figure 2 shows the steps of the design from concept to fabrication. The steps were the following:  
* Initially, a set of twelve 3D designs were simulated based on literature findings on the shape of ridges, their dimensions and separation; more details are included in Section 2.1.1. Correlations and a laboratory test were used to validate the simulation; this is presented in Section 2.1.2. One design was selected as the most promising which is uniform along its width.
* 2D CFD simulations were carried out for 21 different geometries using a two-step approach. First, one panel was simulated in order to find the optimum panel dimensions (based on the five KPIs). Secondly, a refinement was carried out to select the optimum spacing between panels and the thickness of the panel. This is described in Section 2.1.3.
* Finally, the most promising panel was fabricated and further tested in the laboratory.
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 3 Star Background Simulation
### Star Catalog Selection  
In practical applications, the background for space targets primarily consists of deep-space and various objects present in the universe. Given the limited FOV of sensors, we selected deep-space and stars as the background. Since the distance between stars and the Earth is vast, they can be considered as point targets in theory. The selection of a star catalog depends on the target's equilibrium temperature and the sensor's waveband  
Figure 4: Star background simulation process.  
range. In practice, the equilibrium temperature of the target is generally around 300 K, and the waveband range of the real sensor is generally medium wave or long wave. The MSX catalog provides spatial data, including the orientations and proper motions of 177,860 stars, as well as irradiance data ranging between 4 and 22 \(\upmu\)m, making it a more suitable source of simulation data than other catalogs.
# Deep Learning Stranded Neural Network Model for the Detection of Sensory Triggered Events
## 4 Experimental Scenarios
### Scenario I: Training and Evaluating Compressor Temperatures  
Given a big dataset with millions of paired (temperature, alert) values produced by a set of sensors (Figure 2), it is mandatory to annotate each temperature \(T_{i}\) to one of the five characteristic classes according to Table 2. Once the dataset was annotated, then it is split into a training set (80%) and a testing set (20%).  
The constructed annotated dataset contains 7,439,584 temperature samples, and the number of samples per class is described in Table 3. To this point, it should be mentioned  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **State** & **Range of Temperature \(T_{i}\)** \\ \hline
0 & _Normal_ & \(0\leq T_{i}<0.25*alert\) \\
1 & _Low-Risk_ & \(0.25*alert\leq T_{i}<0.5*alert\) \\
2 & _Caution_ & \(0.5*alert\leq T_{i}<0.75*alert\) \\
3 & _Critical_ & \(0.75*alert\leq T_{i}<alert\) \\
4 & _Danger_ & \(alert\leq T_{i}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Annotation process of a given set of temperature values \(T_{i}\), correlated to a given \(alert\) threshold (in the temperature case \(alert=60\)\({}^{\circ}\)C).  
that, unluckily for the majority of classes \(\{0,1,2,3\}\), data are collected, and class 4, related to measurements above the alert threshold, lacks real data. The experimental results follow.
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 5 Discussion  
According to the description of the blockchain system architecture for special equipment safety supervision, it can be seen that this proposed system architecture can well meet the requirements of improving the traditional system, and the current supervision problems are expected to be solved after the system is actually applied. For the analysis of effectiveness and efficiency of the proposed architecture, it is necessary to take the ability to complete the task objectives, the feasibility of system application and promotion, the expansibility of the system function, and the shortcomings of system into consideration.
## 4 Discussion
### Future Improvements in the Approach  
Although the model inversion and optimization technique used by [PERSON] _et al._[24] provides considerable improvements in the quantitative assessment of soil properties at large spatial scales in comparison with conventional mapping approaches, independent measurements of LAI\({}_{\text{max}}\), ASWC and FR would be desired. To this end, a number of remote sensing techniques show promise, although some are not yet available from orbiting satellites. Three-dimensional laser scanning (Light Detection and Ranging, LiDAR) could provide useful validation over the full range in LAI\({}_{\text{max}}\)[59]. LiDAR is similar to radar, but uses pulses of light rather than microwaves to distinguish properties of the Earth's surface.  
There are many ways that the modeling approach could be improved. Recent refinements in the 3-PG model have been made by [PERSON] _et al._[60]. At decadal intervals, however, such refinements yield values of LAI\({}_{\text{max}}\) within 5% of those reported here [61]. In plantations, where climatic and soils data are gathered on site, 3-PG in its current configuration generally predicts interannual growth and canopy dynamics within the error of field measurements [62; 63; 64; 65].
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 2 Background
### Genetic Algorithms  
In this section, we take a brief look at GAs, which are search-based, gradient-free, optimization algorithms inspired by the process of _natural selection_ and _survival of the fittest_[12]. GAs start with a _population_ of potential solutions, called _members_ or _individuals_. Much like biological life, each individual is characterized by a _chromosome_, composed of smaller units called _genes_. The genes encode specific characteristics of the overall solution represented by the chromosome. The encoding scheme is therefore essential for the success of the GA and can determine the size of the search space, time to convergence, and overall quality of solutions. A _fitness function_ is used to evaluate solutions and rank them based on their quality. Fitness functions can be maximizing or minimizing and they can be designed to track single or multiple objectives.  
The population undergoes a _reproduction_ step to produce a new _generation_ of individuals from the first. There are two processes that are generally used to achieve this--_crossover_ and _mutation_. In the process of crossover, individuals, called _parents_, are combined to produce new individuals, called _offspring_. Parents are usually chosen with consideration to their relative fitness within the population. For example, individuals with greater fitness can be chosen to try to increase the fitness of the population. However, maintaining population diversity is crucial to prevent premature convergence. To balance these factors, several selection processes exist with various trade-offs. In addition to crossover, a mutation operation is also applied randomly to a subset of the population. This operation causes random modifications to chromosomes and increases the genetic diversity of the population. Here, diversity refers to the chromosomal variation between individuals within a population. This is necessary for the GA to search wide areas within the problem space and escape local minima/maxima [13; 24].  
Once the reproduction step is complete, the survival step determines which individuals in the population will survive and carry on to the next generation. An ideal survival step aims to preserve the most desirable traits and while maintaining diversity. To guarantee that the population will eventually converge to an optimal solution, it is important to implement _elitism_--a process where the best individuals survive from generation to generation. Survival strategies differentiate GAs from unstructured random search, the latter not utilizing already discovered solutions to search future ones. This step ensures that each generation is progressively more desirable than the last. The iterations (or generations) of evaluation, crossover, mutation, and survival, halt when a specified end criteria is achieved.
# Real-Time Eye Diagram Monitoring for Optical Signals Based on Optical Sampling
## 3 Results and Discussion
To further verify the measurement capacity of the proposed eye diagram monitoring system, the eye diagrams and time-domain parameters of the optical signal generated from a commercial optical transmitter (HUAWEI-2.5 GHz-80 km) are measured in Figure 7. The eye diagrams and the corresponding time-domain parameters are measured in Figures 6a, 6b and 6c when the optical signals under test have different powers of \(-\)10, \(-\)17, and \(-\)22 dBm, respectively. It is seen that the periods of the optical signals with different powers are measured the same as 401.8 ps. The RMS jitters are estimated to be 4.3, 6.4, and 49.8 ps when the optical signals have different powers of \(-\)10, \(-\)17, and \(-\)22 dBm. When the optical signals have different powers of \(-\)10, \(-\)17, and \(-\)22 dBm, the \(Q\) values are measured to be 20.4, 11.5, and 4.3, corresponding to different BERs of 2.3 \(\times\) 10\({}^{-92}\), 6.5 \(\times\) 10\({}^{-31}\), and 8.5 \(\times\) 10\({}^{-6}\), respectively. In addition, the measured results of the proposed eye diagram monitoring systems are verified by the measured eye diagram in Figure 6d from a commercial real-time oscilloscope. The performance comparison of the proposed eye diagram monitoring method with the state-of-the-art works is given in Table 1, where the key figures of merit for eye diagram monitoring methods are listed. Among these works, the nonlinear-optical-sampling-based eye diagram monitoring systems can measure the eye diagram of a high-speed optical signal. Still, the linear-optical-sampling-based eye diagram measurement system has a higher sensitivity than the nonlinear-optical-sampling-based eye diagram measurement system. The low sampling rate limits the eye diagram update time of these previous works. In addition, these previous works did not obtain time-domain parameters such as period, time jitter, \(Q\) value, and bit error rate (BER) for the optical signals under test. Thanks to the use of broadband MZM, the high-dynamic ADC, and the highly repetition-rate optical sampling pulse in this work, the proposed eye diagram monitoring method has the advantages of higher sensitivity and faster eye diagram update time. In addition, time-domain parameters such as period, time jitter, \(Q\) value, and bit error rate (BER) for the optical signals under test are also obtained according to the achieved eye diagram in this work. Figure 6: The measured eye diagrams and time jitter histograms when the optical signals under test from an optical transmitter (HUAWEI-2.5 GHz–40 km) have different powers of (**a**) \(-\)8 dBm, (**b**) \(-\)14 dBm, and (**c**) \(-\)20 dBm. (**d**) The measured eye diagram by the commercial real-time oscilloscope.
# Dynamic Stability Assessment of High-Speed Railway Bridges Using Numerical Model Updating
## 4 Dynamic Stability Assessment for High-Speed Railway Bridges
### Numerical Model and Sensitivity Analysis  
In this study, numerical model updating was performed by using ABAQUS V6.14, a commercial finite element analysis program, based on the structural design documents of the target bridges. Figure 7 shows the numerical models of the target bridges, which used the shell element, along with the vertical and torsional modes, which are the target values required for numerical model updating.  
The natural frequency, effective length, and damping ratio of the main mode are the dynamic properties estimated from the ambient vibration test of the target bridges. Based on the response ratio at each measurement point, the effective length of the mode shape was defined as the distance between two points with zero vertical displacement when the mode shape was assumed to be a sine wave. Consequently, variables associated with these dynamic properties must be set. Variables were limited to the elastic modulus and the distance between the bridge supports because the cross section of each bridge presented in the structural design documents cannot be changed. The distance between the supports can be replaced with the rotational stiffness of the supports. However, the position of the supports must be arbitrarily assumed in order to use the rotational stiffness as a variable. Therefore, supports were assumed to be hinges and the distance between the supports was used as a variable in this study. Table 4 shows the sensitivity analysis results of the dynamic properties according to the change in variables for the numerical model of the Imgi 2 nd Bridge.  
Figure 7: Numerical models of the target bridges using a commercial finite element analysis program: (a) Imgi 2 nd Bridge; (b) Maeaji Bridge; (c) Hwalchun Bridge.  
In Table 4, the natural frequency of the Imgi 2 nd Bridge is sensitive to both the support length and the elastic modulus of concrete. The effective length of the mode shape is highly sensitive to the support length; however, it exhibits low sensitivity to the elastic modulus of concrete. Therefore, for numerical model updating, the univariate search method was applied by using the support length as a variable for the effective length of the mode shape and the elastic modulus as a variable for the natural frequency.  
For precise numerical model updating, many variables need to be set. In the case of numerical model updating based on the univariate search method, the number of variables that can be set is the same as the number of target values that can be determined. As a result, expected changes in variables that cannot be set may appear as changes in variables that are set for numerical model updating. This indicates that the set variables can be considered as representative engineering values rather than physical values.