esa-sceva/satcom-chunk-collection huggingface dataset:
# A Capillary-Based Micro Gas Flow Measurement Method Utilizing Laminar Flow Regime
## 2 Materials and Methods
### Structural Design of the Novel Capillary Laminar Flow Component  
For the purpose of simplifying the structure of the capillary laminar flow element, reducing the complex production processes caused by multiple capillaries, and facilitating maintenance while reducing measurement errors arising from the gap area between capillaries, making it more applicable to micro gas flow measurement, this study proposes a novel micro gas flow measurement device that replaces conventional capillary tube bundles with a single capillary element. The laminar flow element comprises four key structural components: external connection pipes, sealing chambers, pressure tap conduits, and a capillary element. The capillary element features a 0.15 mm inner diameter, with its ends interfacing with external pipes through specially designed sealing chambers. Hermetic sealing is achieved using compressible gaskets installed between the sealing chambers and capillary ends. Two pressure taps are precisely positioned 116 mm apart along the external pipes flanking the capillary element. Figure 3 illustrates the complete assembly configuration, while Table 1 provides detailed dimensional specifications of the entire laminar flow measurement device.  
Figure 2: Structural diagram of capillary laminar flow element.
## 3 Mechanical Design
### Brazing Solution  
For the 1.3 GHz deflecting cavity with geometric beta of 1, the length of a single cell is slightly longer than the width of the standard rectangular waveguide, so the waveguide can be brazed to the cavity directly. For the deflecting cavity with geometric beta of 0.634, its single cell length is shorter than the width of the standard rectangular waveguide, the rectangular waveguide occupies the top of the total middle cell and partial end cells, the conventional brazing method cannot be used here. In order to solve the brazing problem, a special transition plate is designed as shown in Figure 8. Its one side equipped arc surfaces and location steps which can be brazed with the middle cell equipping the corresponding arc surfaces and steps, and the other side is flat which can be brazed with the waveguide. By using the transition plate, the waveguide brazing has ignored influence on the end cells.
## 4 Results and Discussion
### Advantages
#### 4.2.2 Reducing Overlapping Phenomena through Higher-Dimensional Embedding  
When model (24) is set up with the parameter set \(\text{Para3}=(\mathbf{a}_{1},\mathbf{a}_{2},\mathbf{a}_{3},\mathbf{\alpha},\mathbf{\beta},\mathbf{\lambda},\mathbf{\omega},\mathbf{f_{1}})=(-\mathbf{5},\mathbf{5},\mathbf{0},\mathbf{1},\mathbf{0}-\mathbf{1},\mathbf{1}, \mathbf{2},\mathbf{0},\mathbf{1},\mathbf{1}\mathbf{9})\). At this point \(\mathbf{f_{1}}\) is slightly increased compared to the value of \(\mathbf{f_{1}}\) taken in Para3. We observe a weak bursting oscillation phenomenon in the local zoomed-in plot of the time-course curve (Figure 7), which partly verifies the judgment in Section 4.2.1 about the appearance of bursting oscillation.  
Figure 7: (**a**–**e**) Show experimental plots with Parameter Set Para3, \(r=4\), \(m=200\). (**a**) The original two-dimensional phase diagram, with a box indicating the approximate region where the ‘overlap  
phenomenon' occurs; **(b)** The original time history curve; **(c)** The three-dimensional-embedded flow field, with a box indicating the approximate region of the 'overlap phenomenon' in **(a)**; **(d)** The two-dimensional-embedded flow field, where the 'overlap phenomenon' in the boxed area of **(a)** disappears; **(f,g)** are the two-dimensional-embedded flow field and its reconstructed flow field plotted with Parameter Set Para3, \(r=4,\ m=20\), both of which show the same 'overlap phenomenon' as in **(a)**.  
In addition, the \"overlapping phenomenon\" in the 2D phase diagram (Figure 7a), where the trajectories overlap in the phase diagram, may lead to the inability of the researcher to correctly distinguish the different states of the system. Embedding the flow field in a higher-dimensional embedded flow field may help to understand the reason for the overlapping phenomenon in the 2D phase diagram. Similarly, based on the need of plotting the 3D-embedded flow, we continue to choose \(m=200,\ r=4\). At this time, MSE = 3.5956 \(\times\) 10\({}^{-10}\). The reconstruction can be roughly judged to perform well by Figure 7e.  
Figure 7c,d and Figure 7f,g show the two-dimensional embedded flow field and its reconstructed flow field at \(m=200\) and \(m=20\), respectively. By comparing them, we can find that elevating the embedding dimension can make the trajectories more stretched in the embedding space, which may reduce or avoid the reasons for the occurrence of overlapping. This is also one of the reasons why we choose a higher embedding dimension, in the case that the test accuracy meets the experimenter's needs, choosing a slightly higher embedding dimension is both more able to enhance the smoothness of the mapping in the phase space, and may reduce the overlapping of the mapping in the phase space, which is convenient for analyzing and understanding the dynamical behavior of the system.
# How Can We Achieve Query Keyword Frequency Analysis in Privacy-Preserving Situations?
## 1 Introduction
### Our Contribution  
In this work, we propose a PEKS scheme with keyword frequency analysis (KFA-PEKS), which takes into account privacy protection and frequency analysis of keywords. Furthermore, KFA-PEKS supports multi-user participation and multi-keyword search; therefore, it is suitable for distributed IoT scenarios such as cloud manufacturing [7]. Overall, our contributions in this paper are as follows:  
* We propose a novel KFA-PEKS scheme that combines searchable encryption with keyword frequency analysis, while maintaining high levels of security and scalability. Our solution allows the server to periodically analyze the frequency of queried keywords without linking this information to the user's identity, thereby preserving user privacy.
* We conduct a comprehensive functional comparison of our KFA-PEKS scheme with existing SE schemes. The comparison results demonstrate that our scheme is unique in achieving multi-keyword search, resistance to KGA, and frequency analysis of query keywords within a multi-writer/multi-reader model. Furthermore, the complexity analysis of our scheme indicates that it is highly practical and applicable.
* We implement our KFA-PEKS scheme using Python, and perform extensive security evaluations, including computational and communication overhead analysis, to demonstrate its feasibility and effectiveness. Our experimental results highlight the advantages of our KFA-PEKS scheme compared to other PEKS schemes, showcasing its superior performance and suitability for real-world applications.
## 2 Materials and Methods
### Description of Reference Buildings  
To select robust and reliable building references, the correlations between local climate and building inventories were previously analysed [27]. The results highlighted the necessity of using different reference buildings to represent the typical buildings in different climate zones. In this study, two different reference buildings that represent the \"Portici house\" in Climate zone I and II are used. \"Portici houses\" are the most important urban typologies of residential buildings in the cultural, social, and economic centres of towns in South Tyrol, and their history dates back to the Middle Ages.  
Portici houses have a compact form and buildings are adjacent to each other forming a dense settlement. These buildings have a uniform building structure, ordered ridge heights, and a controlled alignment line. Under the continuous facade, arcades cover the walkway on the ground floor forming an extension of the trade spaces traditionally found on the ground floor. As a trading-residential building model, shops occupy the ground floor and apartments are located on the upper floors (Figure 2).  
In the reference building of Climate zone I, the shop and apartments extend towards the back, with an inner courtyard (Portici-I, Figure 3). In the reference building of Climate zone II (Portici-II, Figure 4), on the other hand, a small yard is located behind the shop, originally leading to stables for livestock, with access from the back for staff and animals. Nowadays, stables have often been transformed into offices and the storage above converted into living areas. The buildings' geometry, layout and photographs are presented in Figures 3 and 4. Portici house I has a higher window-to-wall ratio compared with Portici house II in the south. Other key architectural details are presented in Table 3.  
Figure 2: A scheme of a typical Portici house (© [PERSON], [[http://www.antoniomonteverdi.com/sito/?page_id=1228](http://www.antoniomonteverdi.com/sito/?page_id=1228)]([http://www.antoniomonteverdi.com/sito/?page_id=1228](http://www.antoniomonteverdi.com/sito/?page_id=1228)), accessed on 10 July 2022).  
Local retrofit practices that have achieved notable energy-saving and are sufficiently documented were collected and analysed serving as input for the energy retrofit measures proposed in this study. The prevailing retrofit measures are defined instead of near-zero solutions, since they could better reflect local preferences, and the corresponding investigation may explore the potential obstacles in energy efficiency or thermal comfort. Construction, retrofit solutions, and thermal characteristics of the buildings are summarized in Table 4.  
Figure 4: Architectural layout (**a**) and photograph (**b**) of the Portici house in Climate zone II.  
Figure 3: Architectural layout (**a**) and photograph (**b**) of the Portici house in Climate zone I.
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 3 System Model
### Training Process
#### 3.1.1 Client Selection and Model Distribution  
During the training process, the clients are mobile and their movement is unpredictable, making it challenging for the cloud server to determine the participating clients. Also, the uncertainty in client association may lead to a failure of model parameter uploading, especially when the number of clients is huge but the available bandwidth is limited. To tackle this challenge, it is crucial to determine which clients should participate in the training process and how to associate the clients with different edge servers.  
In this paper, we propose an approach that considers the available bandwidth of each edge server and traffic flow to determine the optimal number of clients. Our approach involves distributing the latest model parameters and enabling clients to locally train the model using their own datasets. The goal is to enhance the performance of the models shared among the clients. Given the powerful computation and communication capabilities of the cloud servers, the calculation time required for selected clients and the distribution time can be neglected.
## 7 Summary and Perspective
### AM Framework Development  
Data-driven models have been used in several stages of the L-PBF process lifecycle. That is because the process involves a vast amount of manufacturing data, including design data, materials data, process data, online monitoring data, and postprocessing data. Using a data-driven framework to handle the data transfer during the lifecycle of the metallic part would enable the smart transformation of metal AM processes [166]. Data-driven models to test manufacturability and test design for AM rules have been suggested by [PERSON] et al. [167], while data-driven models to predict surface roughness of printed parts were suggested by [PERSON] et al. [168]. [PERSON] et al. [166] proposed a data-driven framework to handle real-time and non-real-time data for the product lifecycle of AM. The proposed framework consists of four phases: data acquisition, storage, processing integration, and management of this data. Then, data mining and decision-making takes place. Finally, application services take place based on the decisions that were made in the previous phase. To date, most of the research focuses on developing and proposing framework structure in a single stage of AM lifecycle; very limited research discusses the realization of such conceptual ideas [140]. To date, there is no data-driven framework capable of handling the data collected during the lifecycle of the L-PBF process. Therefore, it is suggested that more studies integrate real-time data with non-real-time data and construct a data-driven framework for L-PBF processes. Future research in these areas might help with the smart transformation of the L-PBF processes to take its role as one of the pillars of Industry 4.0.  
**Author Contributions:** Writing-original draft preparation, D.M.; writing-review and editing, M.M. writing-review and editing, J.B.; supervision, project manager and revised the paper, M.A.E.; writing-review and editing, M.G.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interes.
# CCAIB: Congestion Control Based on Adaptive Integral Backstepping for Wireless Multi-Router Network
## 4 Performance Evaluation
### Results and Analysis
#### 4.2.1 Comparison between CCAIB and Hoo Algorithm  
Figure 2 demonstrates the performance of CCAIB and Hoo in queue length stability, window size and packet loss ratio in the queue for case 1. Results for the other three cases are not shown to save space because they have the same trends as case 1. It is observed that the queue length of CCAIB converges in 10 s but that of Hoo is misconvergence throughout the time. CCAIB's queue packet loss ratio is about 0.1% of Hoo algorithm. This is because for CCAIB, packet loss ratios of the wireless links are estimated and are taken into account when determining the packet loss ratio in queue, which improves the networks performance obviously. The window size of CCAIB is about 100 times that of the Hoo algorithm. This is easy to understand because the queue packet loss ratio of CCAIB is much smaller than the Hoo algorithm. Besides, the Hoo algorithm causes larger and more jitters in queue packet loss ratio and window size.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values** & **Remark** \\ \hline \(K_{i,0}\) & 10 & \(i=1,2,3,4,5\) \\ \hline \(K_{i,1}\) & 10 & \(i=1,2,3,4,5\) \\ \hline \(K_{i,2}\) & 10 & \(i=1,2,3,4,5\) \\ \hline \(\hat{\Gamma}_{i,0}\) & \(10^{7}\) & \(i=1,2,3,4,5\) \\ \hline \(\hat{\Gamma}_{i,1}\) & \(10^{7}\) & \(i=1,2,3,4,5\) \\ \hline \(\hat{\Gamma}_{i,2}\) & \(10^{7}\) & \(i=1,2,3,4,5\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Controller parameters of CCAIB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Link Capacity** & **Delay** & **Packet Loss Ratio** \\ \hline
1 & 1000 packets/s & 100 ms & 0.005 \\ \hline
2 & 3000 packets/s & 100 ms & 0.005 \\ \hline
3 & 1000 packets/s & 100 ms & 0.05 \\ \hline
4 & 3000 packets/s & 100 ms & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Network parameters for different cases.  
\begin{table}
\begin{tabular}{c c} \hline \hline Router location & Router 1 \\ \hline Link capacity & 4000 packets/s \\ \hline Propagation delay & 100 ms \\ \hline Wireless packet loss ratio & 0.02 \\ \hline Desired queue length & 100 packets \\ \hline \hline \end{tabular}
\end{table}
Table 3: Default network parameters.
## 2 Materials and Methods
### The Concept and Infrastructure of the Created System
The beacons chosen by the authors (Estimote), equipped with sensors, would send the following data:  
* The beacon's identifier. * Temperature in degrees centigrade. * Acceleration divided into three components in the direction of axis X, Y and Z. * Binary information determining the state of the device (motion or lack of motion). * Specifically aggregated duration of a given beacon's state, including motion or lack of motion. The aforementioned information encrypted in the radio signals was received by the base stations equipped with receivers Bluetooth 4.0. Having in mind economical issues, a decision was made to use Motorola smartphones, model Moto E (2 nd generation) Android system 5.0. The above devices met the basic technical requirements, which allowed rengecivi signals in the standard Bluetooth 4.0, while at the same time ensuring the possibility of programming them with proper libraries. The aggregated data in the form of csv files were successively subject to synchronization on Dropbox platform. The reason for this operation were, on the one hand, the limited capacity of SD cards in the smartphones, and on the other hand, that it was dictated by data protection. They were one of the data sources powering data warehouse. The creation of this information existence was carried out based on two scenarios using different technologies. The created data warehouses became the key tool used to make analysis and evaluation of usefulness of the proposed solution.
# Vision-Based System for Black Rubber Roller Surface Inspection
## 1 Introduction
Experimental tests have demonstrated impressive results, including reduced defect detection time, high accuracy rates, and improved object detection evaluation indices. [PERSON] et al. [11] have introduced a multi-task convolutional neural network that was created to find flaws in order to control the surface quality of bearing rollers. By using a shared convolutional neural network, the defects' characteristics were extracted, and the classification and positioning of the faults were determined simultaneously. [PERSON] et al. [12] have presented a general method for automated surface inspection that only requires a small amount of training data. This method creates a classifier based on the attributes of picture patches that have been taken from a deep learning network that has already been trained. [PERSON] et al. [13] have proposed an intricately crafted deep convolutional neural network (CNN) that can automatically extract potent features for defect detection with less prior knowledge about the images, while at the same time being noise-resistant. [PERSON] et al. [14] have presented an optical inspection system for inspecting the bubbles in silicone rubber with high accuracy in the millimeter range. [PERSON] et al. [15] have proposed a method to recognize and classify multiple rolling bearing faults. In this work, ensemble empirical mode decomposition (EEMD) is used to split the signal into various intrinsic mode functions (IMFs) in order to evaluate the fault characteristics. A continuous wavelet transform is then applied, transforming the 1D reconstructed vibration signal into a 2D image. The signal is moved into the time-frequency domain, which lessens the vibration signal's nonstationary effects. Additionally, [PERSON] et al. [16] have suggested an automatic detection system based on a machine vision technique in response to the high demand for productivity and bearing quality as well as the dearth of conventional detection methods. In summary, these studies highlight the potential of vision-based systems in detecting and evaluating defects on rubber roller surfaces. Various approaches have been explored, ranging from traditional image processing techniques to deep learning-based methods. Each approach offers unique advantages and contributes to the advancement of defect detection in the field of rubber roller surfaces. The paper is organized as follows: Section 2 details the design of the visual inspection system, encompassing both hardware and software systems. Sections 3 and 4 present the experimental results and discuss the developed machine vision system. Lastly, Section 5 summarizes the entire paper.
## 5 Conclusions and Prospects
### Prospects  
Despite the results of this study, there is still room for expansion in the theoretical research and practical application:1. Applicable scenario expansion can be expended in the future. The three-dimensional cloud model can be applied to more complex assembly building types, such as ultra-high-rise buildings, modular buildings, or intelligent buildings, so as to validate the model's versatility and applicability in diversified scenarios.
2. Enriching the dimensions of risk evaluation. Although this paper introduces a three-dimensional cloud model (probability, consequence, and hiddenness), future research can explore more dimensions affecting risk to improve its scientific and comprehensive nature. For example, economy, as the direct or indirect impact of risk occurrence on cost. The time factor, as the dynamic monitoring of risk trends over time. Socio-environmental impacts, such as assessing the long-term impacts and social satisfaction of assembly building quality risks on the community and the environment.
3. Optimization of indicator selection. The selection of indicators in the current study mainly relies on the literature research, accident case analysis, and expert opinions, which is somewhat subjective. In the future, data-driven indicator mining can be combined to mine high-frequency risk points in the whole life cycle of the building through the introduction of big data analysis technology, which can automatically screen key markers and reduce the impact of human intervention on the indicator system, as well as dynamically updating the indicator system according to the characteristics of different types of projects (e.g., building scale, regional conditions, construction environment, etc.) to make it more adaptable.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15073417/s1](https://www.mdpi.com/article/10.3390/app15073417/s1)]([https://www.mdpi.com/article/10.3390/app15073417/s1](https://www.mdpi.com/article/10.3390/app15073417/s1)).  
Conceptualization, Q.S.; methodology and software, Z.Y.; formal analysis, X.W.; investigation, Z.W.; writing--original draft preparation, Z.Y.; supervision, Q.S. and D.L.; funding acquisition, D.L. 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, No. 42061011 and Dalian University Interdisciplinary Key Projects, DLUXK-2024-ZD-003.  
Not applicable.  
The data presented in this study are available in article and Supplementary Materials.  
The authors would like to thank the researchers, [PERSON], [PERSON], [PERSON], and [PERSON] for their discussions during the development process of this study. The authors are very thankful to [PERSON] for the financial support.  
The authors declare no conflicts of interest.