esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications
## 1 Introduction and Motivation
As both the sender and the receiver start out in the communication process in the dark regarding the source sequence statistics, adaptive Huffman coding results in a longer compression time. The H-RLEAHE starts with the compression using RLE technique based on the statistics of the data sources and compresses the data based on that. Further, the compressed data is given as input to the AHE algorithm. The compressed data is communicated to the BS, where it is decompressed using the decompression algorithm. The outline of the papers is as follows: Section 2 presents the related work for selecting the techniques. Section 3 discusses the basic data compression techniques. Section 4 details the hybrid model for data compression. Section 5 establishes the performance measures and analysis of different data compression algorithms namely RLE, AHE, H-RLEAHE and H-AHERLE. Section 6 discusses the network setup. Section 7 compares the results of different data compression models with RLE, AHE, Hybrid-RLEAHE (H-RLEAHE) and un-compressed data. This paper concludes the entire work with possible avenues of future research.
## 3 Results
### NormFinder Results  
To prevent bias and to gain a more detailed analysis of the \(C_{T}\) values, we used the NormFinder software v0.953. Stability values for each gene are determined via an algorithm. The lower the stability value, the higher the relative stability of the gene. The  
Figure 3: Gene expression of PPIA, 18S, and GAPDH normalized to YWHAZ and the unstimulated control group in Y201 cells under ES regimes of 30 and 60 min per day (m/d) at a seeding density of 10,000 cells/cm\({}^{2}\). N = 2 for controls and n = 3 for both 30 m/d and 60 m/d ES groups. Statistical analysis performed by a two-way ANOVA for each gene, and [PERSON]’s multiple comparisons test.  
Figure 2: Alamar Blue metabolic activity assay results for Y201 cells seeded at 5000, or 10,000 cells/cm\({}^{2}\) on day 5 in either unstimulated (control) conditions, or under ES of 30 or 60 min per day (m/d). * \(p\) < 0.05 versus unstimulated control cells seeded at 10,000 cells/cm\({}^{2}\); \(k\) \(p\) < 0.05 versus all three 10,000 cells/cm\({}^{2}\) groups. N = 3 for controls, and N = 5 for ES groups. Statistical analysis performed by a two-way ANOVA, and [PERSON]’s multiple comparisons test.  
NormFinder results are presented for cells stimulated for 60 m/d at each seeding density in Figure 4. The results show that PPIA is the most stable gene for the 5000 cells/cm\({}^{2}\) group and ACTB is the least stable at this seeding density (Figure 4a). For the cells seeded at 10,000 cells/cm\({}^{2}\), YWHAZ was found to be the most stable, and ACTB was again the least stable (Figure 4b). GAPDH was ranked as the second most stable gene in both seeding density groups (Figure 4a,b).  
Interestingly, the stability of the genes studied increased consistently with increasing seeding density. As would be expected, this would imply that cells seeded at the lower density are more greatly affected by the ES potentially due to their heightened levels of exposure.
## 4 The \"Call Me Maybe\" Framework
### Principle IV: Latency Monitoring  
In order to provide real-time protection to a network and facilitate the dynamic protocol switching described in Principle III, a form of detecting DoS attacks as they occur must also be implemented. In the case of the \"Call Me Maybe\" framework, this function is built into the CC described above, allowing it to monitor the latency of voice traffic within a network, while also dynamically switching the VoIP phones between the two protocols. By detecting the attacks through latency monitoring, a responsive, rather than preventative, approach is adopted. By using a responsive approach, not only are any chances of false negatives eliminated, but the framework is also put in a position to function as an underlying form of defence for any VoIP network and is able to be used in conjunction with any established preventative solutions. This system therefore works to create an adaptable and scalable solution, that will reduce the impact of DoS attacks by reflexively switching the network being used by the VoIP phones to an alternative protocol, in this case TCP.
In summary, the contributions described in this article include:  
* A detailed performance comparison between different cepstral, spectral, and spectro-temporal features for audio classification. * A direct performance comparison of pre-trained models and a detailed study of the effects of network modification on the optimum model. * The development of a modified, compact AlexNet model that maintains the model's accuracy while reducing the network size by over 90%, allowing compatibility with mobile devices and applications. * The development of a multi-channel synthetic domestic acoustic scene and event database to test the overall system robustness. In this work, we focus on the classification and labelling of sound event and scenes, which are relevant for dementia patient monitoring systems. However, applications of the techniques explored in this work are not limited to acoustic scene classification and can be extended to other domains. For example, the compact network and the features examined can be modified to fit any image classification problem, such as emotion detection systems [11] and image-based diagnosis for healthcare applications [12]. Further, features explored in this work, as well as their combination, can also be used for regression problems, such as the estimation of characteristics of seismic waves [13], which is based on STFT features combined with CNN. It is important to note that the compact neural network development is not a step towards an actual deployment in any specific resource-limited system. Rather, we explore and experiment the extent to which the system can be scaled down while maintaining high performance.
# Digital-Twin-Based System for Foam Cleaning Robots in Spent Fuel Pools
## 5 Obstacle Detection and Localization
### Obstacle Coordinate Localization  
Upon obtaining obstacle pixels from the target detection process, denoted as \(P\), the point \(P\) is translated into the image coordinate system. Subsequently, the localization point \(P\) is mapped to three-dimensional space using a projection transformation. The final transformation relationship between the pixel coordinate system and the camera coordinate system is as follows:  
\[Zc\begin{bmatrix}u\\ v\\ 1\end{bmatrix}=\begin{bmatrix}f_{x}&0&u_{0}\\ 0&f_{y}&v_{0}\\ 0&0&1\end{bmatrix}\begin{bmatrix}Xc\\ Yc\\ Zc\end{bmatrix} \tag{18}\]  
where \((u,v)\) represents the two-dimensional location point of the obstacle in the pixel coordinate system, \(Zc\) is obtained from the depth camera, and \(f_{x},f_{y},u_{0},v_{0}\) represents the intrinsic parameters of the depth camera, which are factory-calibrated by default. The three-dimensional coordinates of the obstacle localization point in the camera coordinate system can be solved using Equation (18) (assuming \(Zc=0\) by default).  
\[\begin{cases}Xc=\dfrac{Zc(u-u_{0})}{f_{x}}\\ Yc=\dfrac{Zc(v-v_{0})}{f_{y}}\\ Zc=0\end{cases} \tag{19}\]  
As the aquatic robot operates in the water surface coordinate system along the planned route, local adjustments to the global path are necessary when obstacles are detected. To facilitate obstacle avoidance path planning, the obstacle information in the camera coordinate system needed to be transformed into a water surface coordinate system. This ensures that the position information of obstacles and the localization information of the aquatic robot are described in the same coordinate system.  
\[\begin{bmatrix}Xw\\ Yw\\ Zw\\ 1\end{bmatrix}=\begin{bmatrix}R&T\\ 0&1\end{bmatrix}^{-1}\begin{bmatrix}Xc\\ Yc\\ Zc\\ 1\end{bmatrix} \tag{20}\]  
Figure 14: YOLOv5 Network Architecture Diagram.  
where \((Xw,Yw,Zw)\) represents the coordinates in the water surface robot coordinate system, \(R\) is the product of the rotation matrices in the \(X,Y,Z\) directions, and \(T\) is the translation matrix.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 8 Challenges and Opportunities
### Interoperability and Standards
#### 8.3.3 Unified Standards  
Unified standards are an asset of agreed-upon protocols, formats, and guidelines that ensure compatibility and interoperability across different systems and platforms within the metaverse [96]. These standards aim to eliminate the barriers created by fragmentation, allowing for seamless integration and interaction between diverse virtual environments, digital assets, avatars, and user experiences [267]. By adopting unified standards, developers and platforms can ensure that users can move fluidly between different parts of the metaverse without losing functionality or having to recreate their digital presence.  
Implementing unified standards is crucial for the metaverse to achieve its vision of a cohesive and interconnected digital universe [90; 268]. These standards would covervarious aspects such as data formats, communication protocols, security measures, and user interface guidelines. For example, a standardized avatar format would allow users to maintain the same digital identity across different virtual worlds. In contrast, standardized asset formats would ensure that digital goods purchased or created in one environment can be used in another. Unified standards thus foster a more inclusive and expansive metaverse, enhancing user experience, encouraging innovation, and promoting collaboration among developers and platforms.
# Safe Three-Dimensional Assembly Line Design for Robots Based on Combined Multiobjective Approach
## 5 Conclusions and Outlook  
This paper proposes a systematic optimization approach in order to generate a safe layout plan for assembly lines with robots. The modeling rules for robots and irregular equipment are presented, and a combined SE-NSGA2 method that is based on NSGA-II and the DE strategy is used for generating the initial optimal plan set. Subsequently, we define the verification process, propose a quantitative safety indicator, and assess the cooperative assembly statuses of robots. Finally, 3D safety collision detection is performed in order to verify the safety and effectiveness of the layout scheme.  
When compared to state-of-the-art algorithms, our approach has superior performance based on the convergence and distribution results. The case study involves an industrial assembly line with ten pieces of equipment, including three robots, and the established model and improved methods are verified to be safe and useful. Our experimental and statistical results of optimal layout plan for assembly lines increase by 14.5%, and our approach can generate a safety layout to optimize economics and safety. The safe layouts increase by 20.63% when compared with the original NSGA-II method.  
Although our approach works well in the experiments, improvement is still needed in future work. First, we plan to expand the types of robots in order to increase the complexity of the modeling. Second, the modeling of operators employs the anthropometric tool RAMSIS [39]. In addition, we will apply mixed-reality visualization for training and to display and verify the layout changes during operations. This approach could increase the intuitiveness of decision makers and aid in effectively producing optimal layouts.  
Methodology, S.W. and H.W.; software, S.W; validation, S.W.; visualization, S.W.; supervision, R.G. and B.V.-H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation, under grant No. 2019 ZX04014001-004; the Industrial Major Project of Liaoning Province, under grant No. 2019 JH1/10100007; the Shenyang High-level Talents Innovation and Entrepreneurship, under grant No. 2019-SYRCCX-C-08; and the China Scholarship Council Scholarship under grant No. 201904910641.  
The authors declare no conflict of interest.  
Figure 10: Top view of safe and optimal layout by SE-NSGA2.
# Protection of Personal Data in the Context of E-Commerce
## 3 Protection of Personal Data on the Internet
### Gdpr  
A European Union regulation known as the General Data Protection Regulation (GDPR) has been established to ensure the standardization of personal data processing principles, define the rights of data subjects, and impose obligations on data controllers and processors within member states. It governs the organization of data protection systems, superseding national laws, including Croatia's former Personal Data Protection Act. Since May 2018, the GDPR has been in full effect to safeguard EU citizens' privacy and personal data. It mandates that all organizations, regardless of location, must comply if they interact with EU residents.  
The \"right to be forgotten\" (RTBF) is a provision introduced by the GDPR that allows individuals to request the removal of their personal data from third-party records. The primary objective of the regulation is to grant individuals enhanced control over their data and guarantee its safeguarding. Applying this law to any organization that collects data, does business, or offers services to EU residents ensures uniform data protection across the EU.  
Figure 1: An online webshop data workflow example.  
The impact of stricter regulations, such as the GDPR, on customers' perceptions of data security and their subsequent purchasing behavior is examined in this study. The respondents were asked questions regarding their knowledge of the GDPR, the significance of Internet security, and their inclination to share personal data online. A comparison was made between customer habits and awareness before and after the implementation of the GDPR to ascertain if the regulation has resulted in a rise in online purchases.
# Channel Measurement and Feasibility Test for Wireless Avionics Intra-Communications
## 3 Experimental Setup  
In this section, we introduce the details of the experimental setup including software, hardware, and measurement environment. An overview of major components (hardware and software) in our experimental setup is described in Figure 2.
# Multiple-Parallel Morphological Anti-Aliasing Algorithm Implemented in FPGA
## 3 Implementation of the Algorithm in FPGA
### Implementation of the Acquisition of the Blend Weight Coefficients  
The module Get_Blend_Weights in Figure 11 is used to acquire the weight coefficient of each pixel blended by its adjacent pixels. It can be divided into horizontal and vertical directions, and we take the horizontal direction as an example to introduce this module.  
After the module starts to work, we first read the aliasing pattern from BRAM6, and then we determine whether the length of the aliasing pattern is greater than 0. If it is greater than 0, we need to read the weight coefficient of each pixel in the current aliasing pattern blended by its adjacent pixels from BRAM8; otherwise, we can determine that the current aliasing pattern is invalid. At this time, we can directly start the determination of the next aliasing pattern.  
After reading the weight coefficients of each pixel in the aliasing pattern blended by its adjacent pixels, we store these coefficients in BRAM10, and then we determine whether the current aliasing pattern is the last aliasing pattern of the image. If the current aliasing pattern is the last aliasing pattern of the image, it means that we have completed the acquisition of the blend weight coefficients; otherwise, we need to continue the determination of the next aliasing pattern.
## 3 Approaches
### Sentiment-Based  
Sentiment analysis (SA) can be defined as a natural language processing method employed in most cases for the sake of knowing and understanding the content of text emotion, including negative, positive, and neutral. SA is usually used as feedback for business products in order to improve the quality, defects, and price of a product as well as understand customer needs.  
A set of recent works applied SA as a mechanism to detect spam reviews. For example, [PERSON]. [160] designed an opinion spam detection framework based on SA. The framework adopted the Persian language to identify the spam reviews and generate novel features related to that language characteristics. Using AdaBoost and Decision Tree the classification accuracy reach 98% and 98.6%, respectively. Besides, the newly created features were utilized and compared with other features set from literature. [PERSON] et al. [161] stated that opinion mining is generally performed for SA recognition. However, not all data can be sure of its trustworthiness. As such, it's important to detect spam reviews. Therefore, this work presented a product-based SA and a spam identification model for online reviews (ALOSI). Consequently, emphasize the dissimilarity of opinion sentiment with and without considering spam reviews.  
Furthermore, the authors of [162] developed an oriented sentiment mining approach able to recognize spam reviews based on designated topics. The findings show that their method exceeds different state-of-the-art techniques based on two aspects burstiness of time and content duplication. While, the study introduced by [163] examined several facets, which are language, rating sentiment, and content. Also, an extra investigation has been provided to explore the deception and behavior for detecting spam reviews. Above 20 features have been extracted to characterize reviews and multiple machine learning models evaluated on spam reviews detection. The outcome of this study suggests that there are discrepancies in reviews and have plosive impacts on the spam detection performance. Another recent framework devolved for the sake of detecting spam reviews using the opinion mining method [164]. Two machine learning models have been proposed, one for spam reviews detection and the other for knowing the rates of these reviews. The models were designed using random forest and Naive Bayes techniques. Yelp dataset was considered in order to evaluate both models for the purpose of spam reviews detection and opinion mining.  
In certain scenarios of sentiment analysis applications, knowing reviews' reliability is considered crucial for different parities. Various methods have been employed to combine the two methods as seen above. Such studies achieved another level or additional aspect of just knowing the sentiment of the reviews but also comprehending if ts spam or not. As a result, more understanding of the type of reviews and their properties in various cases. Nevertheless, more study needs the detection of spam reviews using SA techniques.
## 4 Results and Discussion
### Influence Factors in the Mixing Process
#### 4.1.2 The Effects of Cross-Flow Flux and Mixing Ratio on Mixing Effect  
Figure 9 shows the effects of cross-flow flux and mixing ratio on \(L_{EML}\). It can be observed that the \(L_{EML}\) augments with the increase of cross-flow flux when other conditions remain the same, and increases when the mixing ratio is decreased. However, the effects of cross-flow flux and mixing ratio are minimal. Similar to the law of \(L_{EML}\) with the pipe diameter, the salt solution velocity increases when raising the mixing ratio, which results in the augment of the diffusion rate of salt solution and the enhanced salt solution mixing effect to decrease the \(L_{EML}\).  
Figure 8: Variation tendency of \(L_{EML}\) under three kinds of main pipe diameters: \(\varphi\) = 51.4 mm; \(\varphi\) = 61.4 mm; \(\varphi\) = 73.6 mm.  
Figure 9: Variation tendency of \(L_{EML}\) under different cross-flow fluxes and mixing ratios.  
A longitudinal section was selected for the position of 8 mm behind the T-junction to extract the region where the VOF of salt solution was more than 5%. As shown in Figure 10, the average velocities of mixed solution, water, and salt solution in the region are derived, and it can be observed that the flow rate and direction of salt solution into the main pipe are obviously affected by the water. Other things being equal, the raised water velocity by the cross-flow flux leads to the increase of the horizontal velocity of the salt solution, so that the horizontal distance of the salt solution is increased. Although the increase of velocity led to more violent mixing and accelerated the mixing of the solution, the effect was relatively small so that \(L_{EML}\) still increased.
## 5 Results
Additionally, some authors have reported using dynamic switching gains to mitigate chattering ([PERSON] et al., 2019; [PERSON] et al., 2020), while others such as [PERSON] (2014); [PERSON] et al. (2019) highlighted the use of observers. In addition, the relevance of using observers in DDSMC should be mentioned. For instance, around 46% of the articles incorporated an observer within their control system design, with 3.5% falling under the sliding mode type. In some cases, observers handled unmeasured variables, but others were the primary component of the model-free control strategy. Observers can be utilized to estimate lumped uncertainty and time-varying parameters by compensating for the poorly understood dynamics of the system, besides reducing chattering. Observers have been used for various applications and can be improved using adaptive techniques and intelligent algorithms, such as fuzzy or neural networks. In addition, high-gain, extended-state, finite-time, and nonlinear disturbance observers were employed. In terms of the latest advancements in SMC design, studies like that of [PERSON] et al. (2019) propose a novel framework combining ISMC with MPC to address the consensus problem in continuous interconnected linear heterogeneous systems. This approach enhances the system's disturbance rejection performance by employing a distributed iterative optimization algorithm to determine the steady-state solutions of interconnected systems, representing a step forward in resolving the consensus issue in networked systems. Another contribution comes from [PERSON] et al. (2019), who utilized a virtual controlled object (VCO) to simplify the SMC design. This approach uses a Tsukamoto-type fuzzy inference system to mitigate the chattering. Finally, [PERSON] et al. (2019) introduce a multistate fractional-order terminal sliding surface that improves the anti-swing capabilities of cargo transfer systems, reducing the chattering and providing robust control in complex dynamic environments. Deep learning models were employed to forecast the swings of the cargo, and at the same time, they helped define constrained workspaces. To summarize, there is a clear trend toward combining SMC structures with intelligent algorithms to provide a model-free framework and reduce chattering.