esa-sceva/satcom-chunk-collection huggingface dataset:
# \(H_{2}/H_{\infty}\) Output Tracking Control with a Performance Compensator for Aeroengines
## 6 Conclusions  
In order to improve the tracking performance of the traditional robust tracker, a novel \(H_{2}/H_{\infty}\) output tracking control approach is proposed by combining a nominal controller and a compensator. Firstly, a nominal output tracking controller was developed for a step reference. Subsequently, a compensator is calculated online according to the nominal controller and the current reference. The sovability of the compensator is ensured for both stable and bounded unstable references.  
The proposed approach is compared with the traditional robust tracking approach through numerical simulations. Compared to the traditional robust tracker, the proposed controller can enable the output to track the reference better for both stable and unbounded unstable references. Meanwhile, from the simulation results, the proposed controller can track the reference well under three kinds of disturbances, which indicates that it has strong robustness.  
Due to the positive effect of the verification, further researche is encouraged: (a) The performance of the proposed approach will be studied in the presence of more uncertainties, such as model mismatch and the saturation of the actuator. (b) The algorithm will be verified on a hardware in the loop platform.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Ramp**} & \multicolumn{3}{c}{**Exponential**} & \multicolumn{3}{c}{**Sine**} \\ \cline{2-10}  & **CS1** & **CS2** & **CS3** & **CS1** & **CS2** & **CS3** & **CS1** & **CS2** & **CS3** \\ \hline \(RMSE(10^{-3})-n_{H}\) & **0.75** & 3.12 & 2.05 & **1.78** & 3.29 & 2.17 & **7.33** & 15.45 & 10.41 \\ \(RMSE(10^{-3})-EPR\) & **0.32** & 0.49 & 0.72 & **0.25** & 0.52 & 0.71 & **2.31** & 3.33 & 4.48 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE of responses of different references.  
Figure 7: The elements of \(K_{3}(t)\).  
**Author Contributions:** [PERSON] conceived the main idea, [PERSON] and [PERSON] designed the model structure, [PERSON] carried out the numerical experiments and analyzed the data, and [PERSON], [PERSON], and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was founded by the National Science and Technology Major Project (2017-V-0004-0054) and the Funding of the Jiangsu Innovation Program for Graduate Education (No. KYLX15_0255).  
**Conflicts of Interest:** The authors declare that there is no conflict of interest.
# Integrated Photodetectors Based on Group IV and Colloidal Semiconductors: Current State of Affairs
## 3 Group IV Semiconductors
### Carbon
#### 3.3.2 Graphene
Simulation of magnitude of x and y components of the electric field was performed, and results are shown in Figure 5c, where is clearly visible a strong plasmonic field enhancement along the edges and in the gap of the bowtie-shaped structures. The single-layer graphene device exhibits a high external responsivity of 0.5 A/W under a bias of \(-0.4\) V as measured with an input power of 80 uW, while if double-layer graphene device is considered, a higher input power can be reached obtaining 0.4 A/W at a bias of \(-0.6\) V for an input signal of 1.3 mW (see Figure 5d,e). Moreover, the device demonstrates a fast photoresponse up to at least 110 GHz [112]. The results obtained so far, together with the growing study of high performance self-powered silicon-graphene heterostructures detectors paves the way for the technological implementation of Si-based monolithic optoelectronic devices for highest speed communication applications. Figure 5: Plasmonically enhanced waveguide-integrated graphene PD. (**a**) 3D perspective view of the PD. (**b**) Cross sectional view of the graphene PD. (**c**) In-plane view of the magnitude of Ex and Ey electric fields, obtained by 3D full-wave finite-element method (FEM) simulations. (**d**) Responsivity of the PD as a function of bias voltage for a single-layer (blue scatters) and a double-layer graphene device (red scatters). (**e**) Responsivity of the PD as a function of light input power. (Adapted from [112]).
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 4 Experimental Simulation and Result Analysis
### Experimental Environment  
A workstation with an Intel Core i7-10700 processor, NVIDIA Quadro P400 4 GB graphics card, and 512 GB RAM was used for the computations. The software environment was a Tensorflow 2.4 deep learning framework, which is based on Windows 10. Since the greyscale pixel values are linearly related to grid point data, the loss function during the training process was directly calculated based on the pixel values using mean square error (MSE). The batch size was set to 16, the Adam optimiser was used, the learning rate was set to \(1\times 10^{-4}\), and the number of training epochs was set to 60 during the training.
# NC-OFDM Satellite Communication Based on Compressed Spectrum Sensing
## 4 Subcarrier Power Allocation Algorithm  
NC-OFDM is an unlicensed access system based on cognitive radio. The system needs reasonable resource allocation to maximize the spectrum utilization. Because the bottom noise temperature of each satellite spectrum hole monitored by the spectrum and the sub-carrier channel gain varies, the resource allocation strategy, particularly the power allocation strategy, is a key technology for NC-OFDM cognitive users to achieve high-speed communication.
## 1 Introduction
This research investigates the integration of advanced camera technology on spacecraft to enhance the eigenfrequency optimization of structures automatically modeled via computer vision. Utilizing topology optimization with minimal human intervention, this study aims to revolutionize the design and construction of habitable structures in extraterrestrial environments. By leveraging cutting-edge 3D printing methodologies, the research seeks to automate and optimize structural design processes, thereby advancing the capabilities and efficiency of space habitation construction [52; 53; 54; 55]. As a design methodology, this study employs topology optimization to maximize eigenfrequency, a critical parameter in structural dynamics [56; 57; 58; 59; 60]. Several researchers investigated topology optimization for design structures with a desirable vibration characteristic. [PERSON] et al. and [PERSON] et al. performed topology optimization in 2D and 3D models to maximize the natural frequencies [61; 62]. [PERSON] et al. established both multi-material and multi-scale models for concurrent vibroacoustic topology optimization [63], and [PERSON] et al. used shape interpolation to generate multiple microstructure samples for concurrent optimization of the macrostructure and multiple microstructures and their distributions to maximize natural frequencies [64]. Dynamic problems often involve eigenfrequency problems because when a structure has undesirable natural frequencies relative to its surrounding environment, resonance phenomena can lead to structural destruction and noise. Therefore, it is important to design the natural frequencies of the vibration modes so that they do not match the excitation frequency in order to avoid resonance phenomena. By utilizing camera vision, the computational system identifies design domain parameters and applies topology optimization techniques for efficient design execution. The research is motivated by several factors. Firstly, the weight constraints of space missions necessitate minimizing additional measurement devices, such as laser scanners, to reduce costs. Secondly, considering energy efficiency, computationally intensive methods, such as machine learning, while effective for emulating expert systems, impose significant computational loads compared to more resource-efficient optimization approaches like the method of moving asymptotes or optimality criteria [65]. Thirdly, the logistical challenges of vast distances in space emphasize the importance of locally executable design processes for swift communication between Earth and extraterrestrial habitats. Lastly, topology optimization is well suited for 3D printing applications, supporting its viability for space construction projects. The paper is structured as follows: Section 2.1 discusses computer vision, Section 2.2 covers topology optimization, Section 3 presents numerical investigations, and Section 4 provides conclusions.
## 2 Multiphysics-Coupled Analysis-Based Induction Hardening Process
### Phase Transformation Analysis  
The primary principle of high-frequency heat treatment involves strengthening materials by inducing a martensitic phase transformation during rapid cooling following high-temperature heating. If heating is not conducted appropriately or cooling methods are inadequate, an undesired austenite phase transformation may occur, resulting in reduced strength and deformation. To address this issue, the analysis incorporates the effect of phase transformations on material hardness.  
In this study, the simulation software LS-Dyna4.11 employs the MAT-244 material card to account for phase transformations [15]. This material card calculates phase ratios based on temperature values using a Continuous Cooling Transformation (CCT) diagram. It also integrates flow curves, as illustrated in Figure 3, to compute strain values according to the stress characteristics of each phase for structural analysis. By considering phase transformations, the simulation accurately captures changes in phase distribution during the heating and cooling processes. This approach enables precise calculations of deformation based on variations in material hardness across different regions of the component. The strain induced by phase transformations is critical for predicting residual stresses and can be calculated using Equation (16).  
\[\epsilon_{t}=\frac{\Delta V}{V_{0}} \tag{16}\]  
Figure 3: Hardness variation of boron steel according to phases.  
Accurate modeling of this strain ensures better predictions of distortion and residual stress during quenching.
# A Method to Optimize Routing Paths for City-Pair Airlines on Three-Layer Air Transport Networks
###### Abstract  
Air transportation is a huge, complex system with emergence and self-organization, which makes it important for it to be modelled. In this paper, to model the air transportation system with more accuracy, from physical facilities to traffic applications, three-layer networks, including the air route network, the city-pair airline network, and flight operation network, are built, where the air route network is regarded as the physical layer, and city-pair airline network and flight operation network are the application layers. Furthermore, a powerful tool, complex network theory, is applied to discuss the topology characteristics of the three-layer networks. Moreover, considering the path diversity of city-pair airlines, a simulated annealing-based framework is proposed to optimize the routing paths on an air route network for each city-pair airline, such that the traffic congestion of the air route network can be alleviated, where an elaborated method for perturbing solutions, named Selection after Remove (SAR), is adopted. Experimental results show that, compared with the default routing paths, the shortest routing paths, and the random routing paths, the proposed routing-path optimization strategy can reduce the maximum traffic flows of the air route network by 2.4%, 4.6%, and 4.8%, respectively, which shows that the proposed optimization method performs well in alleviating the traffic congestion of the air route network.  
air transportation system; flight operation network; city-pair airline network; air route network; routing path; simulated annealing 2023 by the authors, Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Materials and Methods
### Stage III: Experiment Execution  
#### 2.3.1 Data Collection  
Data collection involves the use of tools capable of collecting, during the experiment execution, critical information for future analysis and showing the variation in critical parameters in real time. This activity allows us to obtain comprehensive and reliable insights into the performance, usability, and impact of the occupational exoskeletons being evaluated during the experimental study.  
The primary aim of data collection is to gather both quantitative and qualitative data that support the investigation of specific research questions. In the context of an experimental study on occupational exoskeletons, these data form the basis for analyzing and understanding the effects of these devices on various aspects, such as biomechanical loading, metabolic cost, task performance, user experience, and overall effectiveness in an industrial setting. For example, biomechanical data may include joint kinematics such as angles and ranges of motion, joint kinetics, such as forces and moments, muscle activity, and postural analysis. Physiological data, such as oxygen consumption, heart rate, and skin temperature, support the investigation of metabolic costs, fatigue levels, and thermal comfort. To measure task performance, various data points can be collected and analyzed, such as task completion time, accuracy or quality of task execution, productivity and other output measures, and ergonomic risk factors, such as repetitive movements and awkward postures adopted during the task execution. Finally, the collection of user experience data completes the set of information to be collected to achieve a holistic perspective. These data include perceived exertion, measured through Borg scale ratings, comfort and usability ratings, subjective feedback, and user acceptance. In addition, environmental data complete the study framework, providing more information about the context in which the study is developed. These data include ambient temperature, humidity, lighting conditions, terrain or surface conditions, workspace dimensions, and operational layout. These data are collected across distinct phases of the study, including baseline measurements without the exoskeleton, during exoskeleton-assisted tasks, and potentially during recovery periods. Through this comprehensive approach, it is possible to compare and analyze the effects of the exoskeleton on different parameters.  
Finally, the aim and scope of data collection during an experimental study on occupational exoskeletons must be aligned with the research objectives and hypotheses, guaranteeing complete comprehension of the exoskeleton's performance, usability, and potential impact on the user's well-being, productivity, and safety in the workplace.
# Photonics-Based Microwave Image-Reject Mixer
## 3 Photonic IRM Based on Phase-Cancellation Techniques
### IRMs Based on Microwave Photonic Phase Shifters  
To avoid using the high-frequency electrical 90\({}^{\circ}\) hybrid, photonic IRMs based on microwave photonic phase shifters were proposed [21; 22; 23; 24], since the microwave photonic phase shifters can realize a 90-degree phase difference across a wide frequency range at high frequencies. The key component is an advanced modulator with two sub-modulators, which can be implemented by a dual-polarization dual-drive Mach-Zehnder modulator (DPol-MZM) [21], a DPMZM [22; 23], or a polarization-division multiplexing Mach-Zehnder modulator [24]. Each sub-modulator (i.e., a sub-dual-drive Mach-Zehnder modulator (sub-DMZM) [21], and a sub-Mach-Zehnder modulator (sub-MZM) [22; 23; 24]) performs single-ended mixing. Meanwhile, together with optical filters, photonic microwave phase shift was achieved with a phase shift controlled by either bias voltages of the sub-modulators [21; 22; 23], or the polarization state at the output of the modulator [24]. Thus, a precise 90-degree phase shift over a wide frequency range can be introduced between two parallel single-ended mixers (i.e., two sub-modulators). After photodetection, quadrature IFs are combined by low-frequency electrical quadrature hybrid to realize image rejection. An image-rejection ratio as high as 60 dB is realized when RF/LO frequency is tuned from 10 to 40 GHz [21]. However, system stability is affected by bias drift or polarization fluctuation, because phase shift is controlled by the bias voltages [21; 22; 23] or polarization state [24]. Additionally, although optical filters can remove most undesirable sidebands, leading to large suppression of the mixing spurs [21], their limited edge roll-offs could restrict IRM performance at a low frequency regime.
# A Method for Enterprise Architecture Model Slicing
## 1 Introduction  
Enterprise Architecture (EA) is defined as \"fundamental concepts or properties of an enterprise in its environment and governing principles for the realization and evolution of this entity and its related life cycle processes\" [1]. EA has been applied widely to address communication issues and align business and IT. However, traditional EA practices follow formal processes with pre-defined frameworks and pursue extensive and rigid upfront planning [2], which might bring a heavy workload and limit EA's application in many organizations [3].  
Theories and agile EA practices were proposed to reduce the workload and improve the cost-effectiveness of EA implementation [4; 5]. Compared with traditional EA practices, agile EA is more lightweight, suggesting following an informal or no specific process and developing and using EA for particular purposes when needed (e.g., to catch rapid business changes) [2]. Studies, e.g., [6], following this direction, indicate that reusing existing artifacts could reduce the cost and alleviate the effort of maintaining an EA Repository (EAR). However, specific methods/techniques to find relevant EA models in a repository for reuse are still missing.  
In this research, we propose to \"borrow\" (adapt) the methods and techniques from Program Slicing (PS) [7] to support the model reuse in the EA field. PS was originally introduced as a method to extract a minimal form of interest information from a software system [7] and has been widely used in various software engineering activities, such as understanding, debugging, reusing, and maintaining programs. To the best of our knowledge, no study has addressed how to leverage slicing methods in an EAR for reusing EA models and save costs of developing new EA artifacts.  
We pursue to answer the Research Question (RQ): _How to find the relevant set of models in an EAR to develop new EA artifacts?_ We propose a method of EA Model Slicing or EA Slicing(EAS) for short by adapting existing Program Slicing (PS). We validate the EAS method with a case study. The result shows that the proposed method helped us find the expected set of EA components and relations in an extensive repository effectively and flexibly.  
The remains of this article are organized as follows. Section 2 briefly introduces background information about EA and PS. Section 3 introduces the research method. In Section 4, we formalize our EAS method. We validate the method with a case study in Section 5. Then we discuss the method application in Section 6 and conclude the paper in Section 7.
# Analyzing TCP Performance in High Bit Error Rate Using Simulation and Modeling
## 3 Results
### FTP Client--Traffic Received  
Figure 7 shows the traffic receiving rate characteristics of _FTP_ clients over 2700 s (simulation time). We observe the effect of five selected packet drop probabilities (\(p_{drop}=0\%\), \(p_{drop}=0.01\%\), \(p_{drop}=0.1\%\), and \(p_{drop}=1\%\), and \(p_{drop}=10\%\)) on system performance. It is interesting to note that FTP traffics are dropped out significantly at high packet drop probability (\(p_{drop}\geq 1\%\)). For instance, there were no traffics received at all (100% dropped out) at a packet drop probability of 10%. This low TCP performance is clear especially for a simulation time over 2000 s.  
Figure 6: Average FTP Throughput for various probabilities of packet dropping.  
Figure 7: Traffic receiving rate of FTP Clients over 2700 s simulation.  
In Figure 8, we have redrawn Figure 7 for the first 80 s of simulation time. We observe that the FTP traffics started to drop out at \(p_{drop}\geq 1\%\). A faster fall of traffic delivery indicates that the FTP client traffic receiving rate is diminishing over time. The main conclusion is that TCP does not perform well (in terms of traffic delivery) at high BERs.
## 2 Relate Work
### Pseudo-Label-Based Methods  
The core idea of pseudo-label-based methods is to train a base model using a small amount of labeled data, predict labels for unlabeled data with the trained model, select high-confidence predictions as pseudo-labels, and finally retrain the model by combining pseudo-labeled data with labeled data [20]. The pseudo-label estimates are updated after several training epochs, with the expectation that their quality improves through iterative training. However, directly incorporating initial pseudo-labels into the segmentation loss function may degrade performance if the estimates are incorrect. To address this issue, [PERSON] et al. [21] selected high-confidence pseudo-labels for training, reducing noise interference by dynamically updating these pseudo-labels during training to iteratively correct errors. [PERSON] et al. [22] proposed a Mean Teacher framework that quantifies prediction uncertainty using Monte Carlo dropout, selecting low-uncertainty pseudo-labels for subsequent training to minimize misinformation. Another approach, Noisy Student [23], trains a teacher model on labeled data to estimate pseudo-labels and then trains a student model on both real and pseudo-labels while injecting noise via data augmentation and dropout to improve robustness. In this paradigm, the student model typically outperforms the teacher and can be iteratively updated as a new teacher to estimate refined pseudo-labels.  
[PERSON] et al. [24] introduced a mutual learning framework leveraging the synergy between multiple segmentation networks to improve semi-supervised performance by exchanging \"reliable\" pseudo-labels. FixMatch [16] and its variant Unimatch [25] enhance performance using high-confidence pseudo-labels and consistency regularization.