esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Currently, manufacturing processes in industry are becoming more and more complex because of global competition and the need for more advanced products. The answer to the growing demand of the global market is the Fourth Industrial Revolution (Industry 4.0, I4.0) [1, 2, 3, 4, 5, 6].  
The I4.0 concept is under development mainly in the European Union, as it was originally designed as a national initiative for the development of the German economy in 2011 [7]. Differently, in the United States, the terms Smart Manufacturing or Digital Factory are commonly used, and the National Network for Manufacturing Innovation was established [7]. In China, there is an initiative \"Made in China 2025\" under development, which sets to modernize China's industry, focusing heavily on Intelligent Manufacturing. Similarly, in Japan, a \"Society 5.0\" plan was proposed for the development of science and technology in a future society based on systems that highly integrate cyberspace and physical space [7].  
Large international companies from the manufacturing sector were the ones most involved in the Industrial Revolution, including, at the forefront, the mechanical, automotive, and electrotechnical industries [8]. There is also an increasing number of publications related to Industry 4.0 and Logistic 4.0 in Small and Medium Enterprises (SME), which constitute a great part of the economy of the European Union [9].  
The main goal of this work is to develop a framework for developing a simulation model as close to real production processes as possible, including the changes of the WIP and building a framework of a Digital Twin. We have analyzed some manufacturing lines for assembling and welding automotive components. Typically, the workspace consists of some robotic welding stations and some manually operated workstations, therefore, human and robot factors should both be taken into consideration. In addition, there is a significant equipment failure rate and variable quality rate, which are related to irregular changes in the WIP [10; 11]. Human labor especially is very stochastic, and therefore the results of simulation experiments often deviate from the production plans, and the actual production volume may also differ. At a later stage, the model may be developed further, along with the subsequent phases of the production system life cycle, and refined to obtain a Digital Twin. Integration, including the exchange architecture, simulation creation, performance optimization, and predictive analysis of the production process conditions combine in order to obtain high flexibility of the production system [12].  
This article considers the framework model of a Digital Twin for an automotive production line. The next chapters include a literature review and description of the simulation methods used to model Digital Twins and a description of the problem under consideration with an example case study. Subsequent chapters contain the results of simulation experiments for various scenarios and a discussion of the WIP results as well as final conclusions.
# Integrated DVB-X2 Receiver Architecture with Common Acceleration Engine
## 2 System Description
### Multi-Mode Broadcasting Receiver  
There are several issues in design and implementation of the multi-mode broadcasting receiver. First, DVB-T2, DVB-C2, and DVB-S2 transmission systems operate with a number of different transmission modes and parameters, which makes the receiver architecture more complex. A software (S/W)-based implementation enables one to implement complex functions easily and more rapidly than a H/W-based implementation [5]. Moreover, addition and modification of functions are easily extensible through the S/W upgrade. However, the S/W-based implementation is slower and consumes more power than the H/W-based implementation. For these reasons, it is efficient to implement complex and numerous functions in the S/W-based implementation and only implement simple functions that require heavy computation in the H/W-based implementation. Therefore, an efficient receiver architecture that can reduce the complexity of the receiver through co-design of the H/W and S/W, is required to support various transmission modes and parameters. Second, DVB-T2, DVB-C2 and DVB-S2 systems consist of similar or practically the same functional blocks and system-specific blocks of each system. When similar or the same functional blocks are implemented with a common accelerator, an integrated receiver that supports multi-mode can be efficiently implemented. Furthermore, a large volume of data can be transferred between the common accelerator and specific blocks of each system. Therefore, an access method for the common accelerator affects the system performance such as latency and processing time. In this regard, an effective interface scheme is required to access the common accelerator. Lastly, DVB-C2 uses the same OFDM sub-carrier spacing as a 4K FFT mode used in DVB-T2, and it uses a part of the guard interval (GI) lengths used in DVB-T2. It also uses the same scattered pilot patterns, which allow implementation of the same channel estimation block for both systems [4]. Consequently, a low complexity demodulator architecture that combines the DVB-T2 demodulator and the DVB-C2 demodulator without severe overhead is required.
## 4 Stability Analysis of the High Fill Slope
### Numerical Simulation Analysis of High Fill Slope Stability with Different Slope Rates  
#### 4.4.1 Establishment of Slope Model and Setting of Boundary Conditions  
In this numerical simulation, the research focuses on the typical cross-section of the high fill slope in section A2-A3 of the relied-on project. A numerical analysis model was established using Geo-Studio software to study the evolution law of slope stability under different slope gradient conditions. The base width of the slope is 80 m, with a height of 36 m, and it was graded at level 1. Three different slope gradient conditions (30\({}^{\circ}\), 40\({}^{\circ}\), and 50\({}^{\circ}\)) are investigated, with an example model established for a 30\({}^{\circ}\) slope gradient, as shown in Figure 17.  
The model was discretized using quadrilateral and triangular meshes, as well as plane, four-node, strain-controlled pressure elements. A total of 2155 nodes and 2061 elements were incorporated into the model. Based on on-site survey data and in-house geotechnical tests, the slope soil from top to bottom comprises compacted fill, clayy soil, clay, and medium-weathered rock.  
When analyzing slope stability under rainfall infiltration conditions, rainfall is applied to the slope top and all the slope's surfaces without considering surface runoff. The downslope surface is free to drain, while the rainfall boundary function is represented by appropriate rainfall intensity for different conditions. In the numerical simulation analysis, a fixed boundary condition of x = 0 and y = 0 was defined at the bottom of the model; similarly, a fixed boundary condition of x = 0 was defined for the side.  
The upper surface of the slope was left undefined. The soil constitutive model utilized in this analysis is based on Elasto-Plastic theory using the Mohr-Coulomb constitutive model.
# Comparing Link Budget Requirements for Future Space-Based Interferometers
## 2 Tracking Error in Phasemeters
Loss of lock occurs when the tracking error is so large that the signal of interest completely leaves the tracking bandwidth of the phasemeter. This work will use this value, an RMS of 0.03 cycles, as the maximum tolerable tracking error for stable phasemeter performance. We use analytical modeling and numerical simulations to explore how phasemeter tracking error varies as a function of phasemeter bandwidth, received optical power, and optical mission configuration.
### Main Contributions  
This paper first addresses the general applications of UAV groups or UAV swarms. Then, communication routing protocols for UAVs are considered, as they support the applications considered. Furthermore, control algorithms are mentioned to assist UAVs to operate at the right location for specific purposes. AI-based methods are considered to address core challenges in UAV networks. Traditional methods have limitations such as real-time multi-sensor data processing, adaptive routing in dynamic environments, and multi-task swarm control for coordinated operations among UAVs. We provide the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Survey Scope** & **Features of the UAV Covered** & **AI-Based** \\ \hline
[18] & UAV network, multiple aircraft coordinated & Connectivity, adaptability, safety, privacy, security, and scalability & No \\ \hline
[16] & AI orchestrates UAV-enabled IoT networks & Advanced AI architectures, models, and methods applied to different aspects of UAV-enabled IoT networks & Yes \\ \hline
[19] & Impact of Edge AI on Key technical aspects of UAVs and applications & Autonomous navigation, fleet control, energy management, security and privacy, computer vision, communications, applications & Yes \\ \hline
[20] & The main application of GAI in improving UAV communication and network performance & New GAI framework for advanced UAV networks, UAV-assisted spectrum map estimation, transmission rate optimization between UAV and GAI & Yes \\ \hline
[21] & Quadcopter control algorithm & Control algorithm, performance of UAV & No \\ \hline
[22] & ML solutions for UAVs & Role, collaboration, partnership, and changing network landscape of UAVs & No \\ \hline
[23] & ISAC supports UAVs, optimizes S\&C, UAV motion control, wireless resource allocation, interference management & No \\ \hline
[24] & ML engineering and UAV communications & Combining UAV and ML, ML techniques, and UAV applications & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of relevant surveys.  
latest work or review the existing results that can suggest suitable solutions for specific applications in practice. This work can be considered a comprehensive survey of general or specific problems with UAVs operating in the field of surveillance and sensing. Finally, we discuss the open issues and challenges for future research and development.
# Feature Deep Continuous Aggregation for 3D Vehicle Detection
## 3 Approach
### Feature Deep Continuous Aggregation Network
#### 3.2.3 Refinement Network  
The features of each proposal were further fused to output the categories, locations, sizes, and orientations of the vehicles. At present, there are three fusion modes [6]: (1) early fusion, where features are merged at the beginning of processing; (2) late fusion, where features of each branch are processed separately and merged in the last stage; and (3) deep fusion, where features of each branch are mixed in different layers, resulting in a more general fusion scheme.  
All three modes utilize fully connected layers that are connected step-by-step to process the fused feature and perform the 3D box regression, orientation estimation, and classification using the last layer only. Therefore, the cross-layer link structure in the feature extractor was also applied here to improve the efficiency of the fully connected parameters without increasing their numbers, as well as making full use of the characteristics of each layer. The cross-layer link structures under the three fusion modes are shown in Figures 8-10.  
Figure 6: The anchors in the BEV map, where the green box represents the valid area.  
Figure 7: 3D proposal generator network. BEV: bird’s-eye view. F: fusion. FCN: fully convolutional network. NMS: non maximum suppression.  
Assuming that the fusion network had L layers, all fully connected layers were further fused to ensure that the final detection result depended on the parameters of each layer:  
\[f_{L}=\omega_{1}f^{\prime}_{1}\oplus\cdots\oplus\omega_{L-1}f^{\prime}_{L-1} \oplus\omega_{L}f^{\prime}_{L}, \tag{3}\]  
where \(f^{\prime}_{i}\) represents the output of the features from \(i\)th fully connected layer, \(\omega_{i}\) is the weight of the \(i\)th fully connected layer, and \(\oplus\) is the fusion operation method used (e.g., concatenation, mean and maximum).
## 3 Results  
In the optimization study carried out using the BESA on a fuel cell electric vehicle, the optimal minimum and maximum SoC values that would minimize the total hydrogen consumption and battery cycle were determined. Two driving conditions were considered in the study: constant drive and hybrid drive. Since the optimization study also included the battery cycle status, the effect of the number of battery packs was also included. The results obtained can be considered as two groups: constant drive with two and three battery packs, and hybrid drive. In each result group, the hydrogen consumption amount and battery cycle values before and after optimization were shared and the saving rates were stated. Additionally, the SoC behavior graphs obtained from the digital vehicle model operated according to optimal SoC values and the power conditions of the fuel cell, battery, and motor were also shared.  
The BESA in the combination of constant drive with two battery packs revealed the optimization result in 33,186 s. According to this combination, the optimal minimum SoC was determined as 20% and the maximum SoC was determined as 60%. With optimal SoC values, hydrogen consumption was 7.729 kg and battery cycle was two. Before optimization, hydrogen consumption was 9.638 kg, and the battery cycle was three. According to this result, a total improvement of 23% was achieved. Optimal minimum and maximum SoC values for three battery packs were found as 20% and 62%, respectively. The optimization process resulted in 32,325 s. Hydrogen consumption was 6.144 kg and the battery cycle was one with the optimal SoC, while  
cles have been reduced after optimization and a decrease in the number of cycles can be achieved by increasing the number of battery packs.  
Figure 16: Power behavior of the vehicle in constant drive (**a**) before optimization with two battery packs, (**b**) after optimization with two battery packs, (**c**) before optimization with three battery packs, and (**d**) after optimization with three battery packs.  
Figure 15: SoC behaviors in constant drive (**a**) before optimization with two battery packs, (**b**) after optimization with two battery packs, (**c**) before optimization with three battery packs, and (**d**) after optimization with three battery packs.  
In hybrid drive, the defined optimal minimum SoC was 25% and the maximum SoC was 51% within 36,124 s. While hydrogen consumption was 3.641 kg and the battery cycle was two before optimization, hydrogen consumption decreased to 1.378 kg and the battery cycle to one after optimization. According to these results, a total improvement of 57.8% was achieved. The SoC graphs before and after optimization are shared in Figure 17.  
The power behaviors in the hybrid drive are shown in Figure 18. Figure 18 shows the power behavior before optimization, while Figure 18 shows the after optimization. Since this driving condition had frequent stop-and-go conditions, the motor power demand and battery power response made the graph noisy. However, the switching on and off interval of the fuel cell can be seen in blue.
# MCIDN: Deblurring Network for Metal Corrosion Images
## 4 Experiment
### Ablation Studies
#### 4.4.1 Influence of Each Module  
Figure 10 shows the PSNR and SSIM curves for the baseline model and the models with each added component, where the curve labeled MCIDN represents the baseline network with both SCAM and FCAM added. The results indicate that the baseline model has limited effectiveness. When SCAM and FCAM are added separately, the PSNR curve shows improvements, and the SSIM curve also improves. This is due to the effectiveness of the spatial and frequency-domain attention mechanisms in SCAM and FCAM, respectively. When both components are added to the baseline network, the curve achieves the best results, indicating the significant improvement provided by these components.  
Table 5 shows that our baseline network achieves a PSNR of 31.5345 dB on the blurry metal corrosion dataset. Adding SCAM to the baseline increases the PSNR by 0.7761 dB. Adding FCAM, which integrates frequency signals, improves the PSNR by 1.1415 dB. The complete model, incorporating both SCAM and FCAM, achieves the highest PSNR and SSIM scores. The experimental results demonstrate the effectiveness of our component design. Compared to the baseline model, our method produces smoother edges and retains finer details in the corrosion images.  
Figure 9: A visual comparison of edge and high-frequency maps of the blurry image, sharp image, and deblurred image using the proposed method. It is evident that the blurry images of metal corrosion exhibit significant loss of edge and high-frequency information. The proposed method effectively restores the edge details and high-frequency components of metal corrosion images.
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
## The Proposed Scheme
### Watermark Generation and Embedding  
The watermark generation and embedding processes are performed in the sending side. Figure 2 shows the block diagram of watermark generation and embedding processes. The proposed watermarking scheme is a block-based algorithm. In other words, the watermark generation and embedding processes are completed based on the image blocks. In addition, the watermarking bits are finally embedded into the LSBs of each block. Therefore, before watermark generation, the original image needs to be preprocessed. The original host image with size of \(M\times M\) is firstly split into 2 \(\times\) 2 image blocks. For each block, the three LSBs of each pixel are then removed. After preprocessing,  
Figure 1: The residual images formed by sub-images: (**a**) Original Lena image; (**b**) residual image formed by \(\mathbf{u}_{1}\mathbf{v}_{1}^{\mathrm{T}}\); (**c**) residual image formed by \(\mathbf{u}_{2}\mathbf{v}_{2}^{\mathrm{T}}\); (**d**) residual image formed by \(\mathbf{u}_{3}\mathbf{v}_{3}^{\mathrm{T}}\); (**e**) residual image formed by \(\mathbf{u}_{4}\mathbf{v}_{4}^{\mathrm{T}}\).  
the range of each pixel in the block goes from 0 to 31. For fragile watermarking with self-recovery ability, the watermark information of each block includes two parts: authentication watermark and recovery watermark. The generation processes of these two watermarks are described in the following subsections.
# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 3 DS-CNN: Sound Recognition Model Based on CNN and DS Evidence Theory
### DS Evidence Theory-Based Prediction Fusion  
The most basic concept of DS evidence theory is to establish a frame of discernment, \(\Theta\), and a finite set of elements \(\Theta\ =\ \{A_{1},A_{2},...,\ A_{n}\}\), where n is the number of elements. Each element is incompatible and independent. The power set of \(\Theta\) is \(2^{\Theta}\). In the DS theory of evidence, if the elements in the frame of discernment satisfy the exclusiveness condition, when element \(A\) is assigned \(m(A)\) by the basic probability assignment function \(m\), the mapping of the power set \(2^{\Theta}\) to the \([0,1]\), \(m:2^{\Theta}\subset[0,1]\) satisfies:  
(1) \(m(\varnothing)\ =\ 0\), indicating that the probability of an impossible event is 0.  
2. \(\sum\limits_{A\subseteq\Theta}m(A)\ =\ 1\), indicating that the total probability of the event is 1. \(m(A)\) is referred to as the basic probability assignment (BPA) of the element \(A\), reflecting the degree of trust in the element itself.  
Here, each class of sounds in the dataset can be regarded as one element in \(\{A_{1},A_{2},...,\)\(A_{n}\}\) under the frame of discernment (\(n\ =\ 10\) for ESC-10 and Urbansound8k; \(n\ =\ 50\) for ESC-50). All elements are exclusive and independent. Meanwhile, the output values of the activation functions (softmax) of the MelNet and the RawNet models are used as the basic probability assignments \(m_{1}\) and \(m_{2}\), respectively, under the same frame of discernment, \(\Theta\). \(m_{1}\) and \(m_{2}\) meet the following conditions:  
\[0\leq m(A)\leq 1,\forall A\subset\Theta \tag{1}\]  
\[\sum\limits_{A\subseteq\Theta}m(A)\ =\ 1 \tag{2}\]  
Then, we use an orthogonal operation to effectively synthesize the basic probability assignments of \(m_{1}\) and \(m_{2}\) generated by these two models. For any \(A\in\Theta\), the fusing formula is as follows:  
\[(m_{1}\oplus m_{2})(A)\ =\ \frac{1}{K}\sum\limits_{A_{i}\cap A_{j}=\varnothing}m _{1}(A_{i})\cdot m_{2}(A_{j}) \tag{3}\]  
\[K\ =\ 1-\sum\limits_{A_{i}\cap A_{j}\
eq\varnothing}m_{1}(A_{i})\cdot m_{2}(A_ {j})\ =\ \sum\limits_{A_{i}\cap A_{j}=\varnothing}m_{1}(A_{i})\cdot m_{2}(A_{j}) \tag{4}\]  
\(1-K\) is a coefficient used to measure the degree of conflict between the various evidences of fusion. The output value \((m_{1}\oplus m_{2})(A)\) is also a basic probability assignment and satisfies \(\sum\limits_{A\subset\Theta}(m_{1}\oplus m_{2})(A)\ =\ 1\), which is called the comprehensive probability assignment of \(m_{1}\) and \(m_{2}\). It is a normalization constant. We use \((m_{1}\oplus m_{2})(A)\) as the final prediction result of the fusion.
# Bio-Sourced and Biodegradable Membranes
## 5 Green Membranes Challenges and Limitations  
Despite their wide range of potential applications, biodegradable membranes pose a number of challenges and limitations. The biocompatibility, mechanical properties, and degradability of the membranes, in terms of degradation behavior and degradation rate, must be thoroughly evaluated to determine their suitability for the desired applications [276]. Degradation rate/time is directly related to the type of the materials, the concentration of enzymes and microorganisms, pH, humidity, oxygen, and light levels in the surrounding environment. In other words, the degradability of different materials might vary depending on their surrounding environment [277]. The long-term biodegradation of some membranes would cause serious environmental problems owing to the increased waste disposal in nature. On the other hand, in biomedical applications where drugs should be slowly released into the environment or membranes should not degrade prior to tissue regeneration, rapid membrane degradation would have negative impacts. Thus, a thorough understanding of the membrane degradation mechanisms is necessary to determine the membrane's performance in a particular application.  
Another challenge is incorporating non-biodegradable or toxic materials into the biodegradable membranes (especially bio-based materials) through the blending, crosslinking, and copolymerization processes to improve their mechanical stability, thermal resistance, or to gain structural diversity and specific properties. The composition of green membranes with non-environment friendly materials results in secondary pollution since the biodegradable part degrades and the non-biodegradable material converts into small molecules that would persist in the environment for a very long time [2; 10; 231]. In addition, some biodegradable membranes are water-soluble. Therefore, degraded materials dissolve in water (ground/surface water) due to membrane breakdown and negatively affect the water quality and plant growth [2].  
The economic performance of bio-based biodegradable membrane production has been evaluated and compared with conventional membrane production in several studies. Although it is essential to develop viable biodegradable alternatives in order to overcome the challenges associated with conventional membranes, bio-based biodegradable polymers have not been accounted for more than 1% of the total plastics production (335 million tons) in 2019. This is mainly due to their uneconomical production processes and expensive raw materials compared to low-cost fossil-fuel-based plastics [278; 279; 280; 281].