esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Comparison Analysis and Research Challenges
### Research Challenges
#### 6.2.9 Forwarder Cooperation  
An efficient cooperation among a group of forwarders can mitigate the aforementioned issues, such as multiple senders, multiple receivers and data redundancy. We can define efficient cooperation as a kind of transmission strategy negotiated by the forwarders; a node actually forwards a packet, while another node gives up the transmission, so as to achieve better energy consumption. However, there will be a tradeoff between the energy saving resulting from the cooperation, and the energy consumption due to the negotiation overhead. For example, ORW [11] used ACK packets for the cooperation between forwarders. Another interesting research topic will be the attempt to find a minimum negotiation overhead due to control packets, in order to have successful cooperation.
## 1 Introduction  
Cast components are produced using an automated process in Industry 4.0, employing molten metal as the primary material. It is poured into a mold to create the desired shape. One of the main advantages of modern casting techniques is their ability to produce complex geometries. Modern casting techniques align the process with Industry 5.0 standards, which allows customized components to be made. The pressure casting process minimizes the probability of producing defective parts. This technique solidifies the molten metal under high pressure, resulting in components without shrinkage and without gas porosity [1, 2]. These modern casting techniques allow manufacturers to adapt the properties of the components to meet the specific requirements of applications [3]. This flexibility in the manufacturing process of the parts further allows adaptation to Industry 5.0 standards, which focuses on creating customized or even unique elements.  
For these reasons, the quality of the parts obtained from these processes must be produced automatically, according to the principles of Industry 5.0. Unlike the previous version, 4.0, which allowed manual analysis by a human operator, the process must be carried out entirely automatically. This has been implemented through Six Sigma methodologies that ensure quality in foundries. These methodologies focus on improving process control [4; 5]. Simulation and modeling techniques help optimize the casting process by minimizing the defects for process parameters [6].  
Another important aspect of Industry 5.0 is sustainable manufacturing practices. Techniques that reduce material waste and energy consumption are prioritized in the context of Industry 5.0. For example, the V-method foundry process minimizes the consumption of molding materials and improves the foundry's surface quality and dimensional accuracy, thus reducing environmental impact [7]. In addition, integrating Computer-Aided Engineering (CAE) and rapid prototyping technologies facilitates the design and production of complex components, reducing delivery time and costs [8].  
This subsection details the various modern casting techniques and their alignment with Industry 5.0 standards. It presents their strengths, such as the ability to produce complex geometries, reduce defects, and improve sustainability, supporting the transition to customized manufacturing in line with Industry 5.0 principles.
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
## 2 Materials and Methods
### Determination of Residual Oil Concentration in Treated Produced Water  
The concentration of residual oil present in the previously treated produced water is determined spectrophotometrically using liquid-liquid extraction. For this purpose, 5 cm\({}^{3}\) of tetrachloroethylene from Labkem (Barcelona, Spain), Tetrachloroethylene GLR > 99.9%, CAS 204-825-9) are added to 5 cm\({}^{3}\) of produced water. Tetrachloroethylene is used because it is superior to other solvents as a reference for measuring the amount of oil in water [36]. Afterwards, the oil concentration in tetrachloroethylene is measured using a UV spectrophotometer (Cary 100 BIO from Varian) based on a standard curve previously prepared at 311 nm:  
\[\mathrm{C}_{p}=105.39\ A \tag{1}\]  
where \(\mathrm{C}_{p}\) is the concentration of residual oil in the produced water, expressed in ppm, and \(A\) is the absorbance.  
The wavelength of 311 nm was chosen because it allows for a perfectly linear standard curve. This curve was established beforehand using concentrations of produced water ranging from 5 to 120 ppm. The fit is excellent since R\({}^{2}\) = 0.9999.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 3 Bitwise Transform
### Ternary Transform  
To suit the input requirements of the QKD system, we allow the inherent fuzzy bits of the stream to introduce the ternary state. We visualize this state through \"expanded key  
Figure 3: Implementation of a ReRAM PUF with QKD using quaternary data.  
When stability is the priority, we cannot let the inconsistencies contaminate the output; thus, we must first expel the inconsistencies before use. Instead of using the raw stream, we exercise the mask operator as a means of processing, and feed the resulting key into the subsequent quaternary transform operation. The following example works under the assumption that the mask is configured to remove 50% of the bits: \(S\otimes M=K\) such that \(|K|=\frac{|S|}{2}\), where \(|K|\) is the cardinality of \(K\).  
We begin by cleaning up the stream:  
\[\begin{split} S&:01010101\ 01010101\\ \circled{M}&:00110011\ 10011001\\ K&:01011010\end{split} \tag{11}\]  
In order to trim the mask for re-use with the key in the next and final stage, we can do just that, or add more entropy to the system by splitting and XORing the mask to make it the right size:  
\[\begin{split} M&:00110011\ 10011001\\ M&:101010101001\\ M&:10101010\end{split}\quad\longmapsto \quad\begin{split} M_{1}&:00110011\\ \circled{M}&:10011001\\ M&:10101010\end{split} \tag{12}\]  
Using this new mask, we perform quaternary transform:  
\[\begin{split} K&:01011010\\ \circled{\mathbb{O}}_{4}M&:10101010\\ K_{4}&:12123030\end{split} \tag{13}\]  
\[\begin{split} S&\ M&\ S\oplus_{4}M\\ \hline 0&\ 0&\ 0\\ \hline 0&\ 1&\ 1\\ \hline 1&\ 0&\ 2\\ \hline 1&\ 1&\ 3\end{split} \tag{14}\]  
The output conveniently takes the form of the binary interpretation, where order of the inputs matter. This form lends itself nicely to programming.  
Thus, you can see how we had a means to achieve the fourth state all along, but simply did not use it. Using this concept of reduction and expansion, we will explore the octal base and the lesser states we get for free in between.
However, previous drinking water studies used unrealistic levels that exceeded the maximum contaminant limits by at least 700\(\times\)[39; 40]. Regardless of the animal model or exposure route, Cr(VI) neurotoxicity studies repeatedly report impaired locomotor function, decreased activity, learning and memory deficits, widespread neurodegeneration, and widespread oxidative damage [39; 40; 41; 42; 43]. However, behavior assessments have been limited in scope, and there is little consideration for age or sex as biological variables. To address these knowledge gaps, we considered Cr(VI) neurotoxicity in Sprague-Dawley rats across three distinct ages (3, 7, and 18 months old) exposed to low concentrations of Cr(VI) (0.05 and 0.1 mg/L) (Figure 1). Critically, these drinking water levels reflect the maximum contaminant levels from the World Health Organization (WHO) and the U.S. Environmental Protection Agency (US EPA). Here, we present data from female rats and report differences compared to previously published results in male rats [44]. During exposure, rats performed a battery of behavioral assays to assess Cr(VI) effects on grip strength, locomotor function, anxiety, spatial memory, activity, and social memory (Table 1). \begin{table}
\begin{tabular}{c c c} \hline _Week \#_ & _Behavior Assay_ & _Behavior(s) Assessed_ \\ \hline \(0\) & & _(Cr[VI] Exposures Started)_ \\ \hline \(1\) & Grip Strength Test & Neuromuscular function \\ \hline \(2\) & Open-Field Assay & Exploration, rearing, locomotion \\ \hline \(3\) & Elevated Plus Maze & Anxiety \\ \hline \(4\) & Y-Maze & Spatial memory, activity \\ \hline \(5\) & \(3\)-Chamber Assay & Sociability, social novelty preference \\ \hline \(6\) & & _(No behaviors assessed)_ \\ \hline \(7\) & Grip Strength Test & Neuromuscular function \\ \hline \(8\) & Open-Field Assay & Exploration, rearing, locomotion \\ \hline \(9\) & Elevated Plus Maze & Anxiety \\ \hline \(10\) & Y-Maze & Spatial memory, activity \\ \hline \(11\) & \(3\)-Chamber Assay & Sociability, social novelty preference \\ \hline \(12\) & & _(No behaviors assessed)_ \\ \hline \(13\) & & Rats Sacrificed \\ \hline \end{tabular}
\end{table} 
Table 1: Schedule of Behavior Assays Over the 90-Day (13 Weeks) Exposure Period. Figure 1: **Rat Model to Assess Cr(VI) Neurotoxicity Using the Toxic Aging Coin Approach. ** We exposed 81 female rats to 0, 0.05, or 0.1 mg Cr(VI)/L for 90 days. We considered three age groups to compare across young (3 months), middle-aged (7 months), and geriatric (18 months) rats. During the 90-day exposure period, behavioral effects were assessed using a battery of assays to assess grip strength, locomotor function, anxiety, spatial memory, activity, and social memory.
# Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review
## 4 Discussion
### Preprocessing  
Real clinical ECG signals often contain diverse noise and interference. However, many existing works do not consider ECG signal denoising, which could raise risks when they are implemented for real clinical scenarios. Thorough studies about the impact of ECG denoising on the DL-based ECG classification methods in clinical applications are in demand. Furthermore, the existing denoising methods assume that the characteristics of noise and interference differ from that of the useful signals in a predetermined domain, such as the frequency domain and the wavelet domain [96]. In general, the DWT-based denoising methods could better retain the details of higher frequency signal components compared to traditional filtering-based denoising methods, which rely on discrete Fourier transform [36]. However, the DWT-based denoising methods are offline algorithms that cannot apply to real-time ECG signal denoising [125]. Design for advanced denoising algorithms specifically considering ECG characteristics is needed to be explored.  
As ECG datasets are often imbalanced, data augmentation could be a necessary step to further level up the performance of DL models. It can be concluded that synthetic-based methods have gained higher popularity in recent years. In particular, the GAN-style model actually learns to generate synthetic ECG signals based on the characteristics of ECG signals [126]. However, this type of method is still a data-driven process that relies heavily on the quality of ECG data. Research [46] proposes a DL model which jointly considers synthetic ECG signals generated by a mathematical model and real clinical ECG data. It could be a better solution for imbalanced data as the generated data set as the generated new data samples are not simply the linear combinations of other data samples but contain the theoretical a priori knowledge from cardiologists.
## 3 Building Blocks of Vehicular Networks
### Clouds  
Remote computing in the form of cloud computing is gaining importance in vehicular networks. This paradigm distinguishes newer networks based on the IoT from earlier systems such as VANETs. Leveraging the capabilities of remote cloud data centers such as on-demand service, resource pooling, and rapid elasticity facilitates the usage of big data and artificial intelligence with the aim of providing improved performance for both networks and applications [39]. Even though cloud computing aids in the performance of those systems, it has been shown to be vital to employ edge and fog computing concepts due to the time sensitivity of some applications, which may be observed in using base stations as edge devices [40]. Even though these paradigms helped vehicular networks to gain traction by providing virtually unlimited means for computation and storage, several new problems arose, such as which processes should be moved to the cloud or edge and when.
## 3 Results and Discussion
### Feasibility of Applying Circular Economy Strategies to Different Building Types
#### 3.2.2 Industrial Buildings  
Industrial buildings were ranked second in the feasibility of implementing CE principles, with a total score of 24. Out of the ten participants, seven rated industrial buildings as having high feasibility. The majority who rated industrial buildings highly emphasized the simplicity and flexibility in their design. Industrial buildings are often constructed with standardized components, making them ideal for disassembly and reuse. C3 highlighted their structural advantages by stating that \"_Industrial buildings are usually constructed with  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Building Type**} & \multicolumn{8}{c}{**Participant Responses**} & \multirow{2}{*}{**Total**} & \multirow{2}{*}{**Rank**} \\ \cline{2-2} \cline{4-11}  & & & & & & & & & & & & & \\ \hline Storage & 1 & 1 & 6 & 1 & 1 & 1 & 1 & 1 & 1 & 28 & 1 \\ \hline Industrial & 6 & 2 & 7 & 2 & 3 & 2 & 2 & 2 & 2 & 24 & 2 \\ \hline Business (offices) & 2 & 5 & 3 & 6 & 2 & 3 & 3 & 3 & 4 & 3 & 20 & 3 \\ \hline Educational & 4 & 3 & 4 & 3 & 4 & 5 & 4 & 4 & 5 & 4 & 18 & 4 \\ \hline Commercial & 3 & 6 & 1 & 5 & 5 & 4 & 5 & 5 & 3 & 5 & 15 & 5 \\ \hline Healthcare & 7 & 4 & 5 & 4 & 6 & 6 & 6 & 6 & 7 & 6 & 10 & 6 \\ \hline Residential & 5 & 7 & 2 & 7 & 7 & 7 & 7 & 7 & 6 & 7 & 5 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ranking of the feasibility of applying CE strategies to different building types.  
portal frames and large open spans, which makes them easy to adapt for different uses and deconstructed easily\"_. Moreover, D1 highlighted that industrial buildings usually have simpler design requirements focused on functionality rather than appearance. This makes it easier to integrate reused/recycled materials, which might not satisfy the esthetic standards required in commercial or residential buildings. The potential for integrating sustainable energy solutions was another advantage mentioned by CN1. They commented \"_Industrial buildings often have large, flat roofs that are perfect for installing solar panels. This makes it easier to integrate on-site renewable energy solutions which enhance the generation of clean energy\"_. In contrast, C1, who rated the potential as low, believed that in some industrial sectors, there may be a perception that CE principles are not essential for operations, especially if the focus is on immediate profitability and efficiency.
# Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network
## 4 Example Analysis
### Calculation of Interval of Fault Probability of Leaf Nodes  
According to the fault state of root nodes, the fault state of leaf nodes is speculated. In accordance with bucket-elimination method and Formula (7), \(\overline{P}(T=1)>\overline{P}(T=0)>\overline{P}(T=0.5)\). Moreover, it is known that the fault probability of leaf nodes is the largest and the probability in the normal state is slightly larger than that under soft fault.  
\[\begin{array}{ll}\overline{P}(T=0)&=\overline{P}(x_{3})\sum\limits_{y_{1},x _{3}}\overline{P}(T=0|y_{1},x_{3})\sum\limits_{x_{1},x_{2}}\overline{P}(y_{1} |x_{1},x_{2})\ \overline{P}(x_{1})\overline{P}(x_{2})\\ &=\{0.00072,\ 0.09612,\ 0.13765\}\\ \overline{P}(T=0.5)&=\overline{P}(x_{3})\sum\limits_{y_{1},x_{3}}\overline{P}( T=0.5|y_{1},x_{3})\sum\limits_{x_{1},x_{2}}\overline{P}(y_{1}|x_{1},x_{2}) \overline{P}(x_{1})\overline{P}(x_{2})\\ &=\{0.00039,\ 0.07712,\ 0.08677\}\\ \overline{P}(T=1)&=\overline{P}(x_{3})\sum\limits_{y_{1},x_{3}}\overline{P}( T=1|y_{1},x_{3})\ \sum\limits_{x_{1},x_{2}}\overline{P}(y_{1}|x_{1},x_{2})\ \overline{P}(x_{1})\overline{P}(x_{2})\\ &=\{0.20221,\ 0.62680,\ 1\}\\ \end{array} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Fault State of Node**} & \multicolumn{3}{c}{**Value of Fault Probability**} \\ \cline{2-4}  & **0** & **0.5** & **1** \\ \hline \(x_{1}\) & [0.3, 0.4, 0.5] & [0.3, 0.5, 0.7] & [0, 0.1, 0.15] \\ \(x_{2}\) & [0, 0.1, 0.2] & [0.1, 0.2, 0.3] & [0.5, 0.7, 0.8] \\ \(y_{1}\) & [0, 0.1, 0.2] & [0, 0.1, 0.2] & [0.6, 0.8, 0.9] \\ \(x_{3}\) & [0, 0.1, 0.2] & [0.1, 0.2, 0.3] & [0.5, 0.7, 0.8] \\ \hline \end{tabular}
\end{table}
Table 4: Fuzzy numbers of fault states of root node T.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(y_{1}\) & \(x_{3}\) & \(P(T\)=0|y_{1},x_{3})\) & \(P(T\)=0.5|y_{1},x_{3})\) & \(P(T\)=1|y_{1},x_{3})\) \\ \hline \(0\) & 0 & 1 & 0 & 0 \\ \hline \(0\) & 0.5 & \(\overline{P}(T=0|y_{1}=0,x_{3}=0.5)\) & \(\overline{P}(T=0.5|y_{1}=0,x_{3}=0.5)\) & 0.2 \\ \hline \(0\) & 1 & 0 & 0 & 1 \\ \hline \(0.5\) & 0 & \(\overline{P}(T=0|y_{1}=0.5,x_{3}=0)\) & \(\overline{P}(T=0.5|y_{1}=0.5,x_{3}=0)\) & \(\overline{P}(T=1|y_{1}=0.5,x_{3}=0)\) \\ \(\{0.5,0.6,0.7]\}\) & [0.2, 0.3, 0.4] & [0.0, 0.2] \\ \hline \(0.5\) & 0.5 & 0.3 & \(\overline{P}(T=0.5|y_{1}=0.5,x_{3}=0.5)\) & \(\overline{P}(T=1|y_{1}=0.5,x_{3}=0.5)\) \\ \(\{0.4,0.5,0.6]\}\) & [0.1, 0.2, 0.3] \\ \hline \(0.5\) & 1 & 0 & 0 & 1 \\ \hline \(1\) & 0 & 0 & 0 & 1 \\ \hline \(1\) & 0.5 & 0 & 0 & 1 \\ \hline \(1\) & 1 & 0 & 0 & 1 \\ \hline \end{tabular}
\end{table}
Table 3: CPT of leaf node \(T\).
# A Sketch-Based Fine-Grained Proportional Integral Queue Management Method
## 3 System Model
### Traffic Statistics
#### 3.1.2 Basic Operations  
The execution process of the traffic statistics module used by Sketch as the S-PIE algorithm is divided into three steps: item mapping, hash table update, and result query.  
1. Item mapping: when a packet arrives, it is mapped to store the count in the form of (_key_, \(value\)). where _key_ uniquely represents the data stream from which the packet came, and \(value\) is used to count the packet frequency value of that stream in a two-dimensional array.
2. Hash table update: during the execution of the algorithm, the arrival of new packets (_key_, \(value\)), \(d\) a separate hash function that does the same for _key_ is calculated separately and the count operation is updated for the counter in the corresponding hash table. For example, the hash function \(h\) with index value \(i\) updates the counter, as shown in Equation (1). When the data stream to be calculated is passed and updated, statistics on the frequency of occurrence of the inserted data can be obtained. \[count[i,h_{i}(key_{i})]+=\text{ value }\quad i\in 1,2,3\ldots d\] (1)
3. Result query: the Sketch query for packet occurrence frequency estimation. However, because collisions are predicted, the number was potentially incremented by occurrences of other items that were mapped to the same position. Given a collection of counters having the desired count plus noise, the best estimate of the true count of the desired item is to choose the smallest count of Mincount of these counters [30], as shown in Equation (2). \[\text{query}(key_{i})=\text{Mincount}[i,h_{i}(key_{i})]\] (2)
# Vision-Based Structural FE Model Updating Using Genetic Algorithm
## 4 Validation Test
### FE Model  
The FE model for the validation test is as shown in Figure 6. A simple frame model was used [33; 34; 35; 36] to validate the proposed vision-based FE model updating method. The element used in the FE model is the beam type, and the material properties are assumed to be elastic, and the basement and slab were applied steel (Unite Weight: 7.85 t/m3, E: 210 GPa, Poisson's Ratio: 0.3), and the wall was applied stainless steel (Unite Weight: 7.90 t/m3, E: 201 GPa, Poisson's Ratio: 0.3). The boundary condition of the FE model was assumed to be fixed at the lower part of the structure, and Cholesky factorization was used to calculate the natural frequency of the structure.  
In finite element analysis, the size of the element is an important factor affecting the results. [PERSON] et al. [37] analyzed the sensitivity of the element size applied to the finite element analysis to the analysis in a study proposing a methodology for automatically determining the most appropriate Ci constants for modeling the behavior of  
Figure 5: Setup for shaking table test.  
Figure 6: FE model used for model updating.  
a group of elastomers. Therefore, this study also confirmed the effect of element size on the calculation of the natural frequency of the structure. The size of the element was calculated by applying the sizes of 1/2, 1/4, 1/8, and 1/16 based on the FE model shown in Figure 6 and the results are as shown in Table 1.  
As a result of the sensitivity analysis, it was found that the FE model applied in this study did not significantly affect the natural frequency of the structure even if the structural analysis was performed while reducing the size of the element. Therefore, it was confirmed that the FE model shown in Figure 6 has a sufficient element size to perform model updating.
## 5 Discussion
### Future Work  
The selection of YOLOv8 for the robotic system was primarily driven by its ability to balance accuracy, speed, and computational efficiency, which are critical factors for real-time robotic applications. In environments where rapid decision making is essential, YOLO-based models offer the advantage of high accuracy in object detection while maintaining the low processing latency required for real-time performance. In the proposed system, the gesture recognition latency is approximately 500 ms, ensuring timely and responsive interaction for the robotic application. This is particularly important in robotics, where systems must react quickly to dynamic environmental changes. Additionally, YOLO-based models are well-established in the field of computer vision and have proven effective in embedded systems, which makes them highly suitable for deployment in robotic workplaces.  
Despite the advantages offered by YOLOv8, we also recognize the growing potential of transformer-based detectors, such as DETR [59], which have shown promise in recent research for handling complex object relationships and improving generalization across various tasks. Transformer models, due to their self-attention mechanism, can provide a more nuanced understanding of the spatial and contextual relationships between objects, which is particularly useful in scenarios where interactions between multiple objects or a deeper understanding of the scene is required. These models have been shown to outperform traditional CNN-based detectors in certain contexts, particularly in tasks that require more complex reasoning about object relationships and scene dynamics. Thus, while YOLOv8 is currently optimal for our application, the exploration of transformer-based detectors or newer YOLO models represents a promising approach for future research, where these models could be assessed for their applicability and potential performance benefits.