esa-sceva/satcom-chunk-collection huggingface dataset:
# Immersive Digital Twin under ISO 23247 Applied to Flexible Manufacturing Processes
## 1 Introduction
The combination of AR and DTs has sparked growing research interest because it has demonstrated its potential to integrate operators into the new generation of the Human Cyber Physical System (HCPS) [16]. It is a technology that combines the real and virtual worlds, interacting in real time to create an immersive and interactive experience in two or three dimensions. This technique allows digital content to be overlaid to the real world, typically through optical displays or videos that can be viewed through glasses or mobile devices such as smartphones and tablets [9]. For the industry, AR promises to change and improve the way operators interact with production processes, enabling access to information through virtual overlays. It can be applied in assembly, maintenance, and monitoring tasks, providing greater flexibility for data access. Industrial augmented reality (IAR) is a crucial technology for Industry 4.0, as it can enhance workers' performance, reduce risks, and improve production processes [17]. IAR refers to integrating digital information with the real world using computer-generated sensory information such as video, sound, graphics, or GPS data. Combining these technologies promises to change how an operator interacts with production processes, enabling access and interaction with real-time information [18; 19]. This proposal aims to implement an architecture for an immersive DT under the ISO 23247 standard in an industrial environment, highlighting the capabilities of including immersive technologies for monitoring, controlling, and simulating industrial processes in real time. It is hypothesized that the implementation of this technology will lead to the integration of immersive technologies in a DT, enhancing the interaction between physical and virtual entities using AR and Senso Glove technologies and improving operational efficiency and decision-making in industrial environments. The contributions of this research are  
1. The implementation of a DT under ISO 23247 supported in augmented reality for monitoring, simulating, and controlling an industrial process using low-cost devices with cloud communication under the Message Queuing Telemetry Transport (MQTT) protocol. 2. The development of an immersive reality method to manipulate variables using Senso Glove and an augmented reality interface for interactive visualization on the immersive DT platform. 3. The analysis of immersive DT results through experimental tests, bidirectional communication, error generation, and measurement of response times.
## 1 Introduction
The high quality of COSMIC-1 RO ionospheric observations was evaluated through direct and statistical comparisons provided by the major benchmark instruments--ground-based ionosondes and incoherent scatter radars--by different research teams [23; 24; 25; 26; 27; 28; 29; 30]. The operational follow-on mission to the COSMIC-1 [31]--the COSMIC-2 mission--was successfully launched in June 2019 [32]. Six identical satellites placed into a low-inclination (24\({}^{\circ}\)) orbit of \(\sim\)550 km altitude offer an unprecedentedly dense observational coverage of the entire equatorial and low-latitude region around the globe--up to 5000 EDPs per day [33]. The advanced dual-frequency onboard GNSS receiver tracks both GPS and GLONASS signals. Due to such an orbit orientation, the COSMIC-2 GNSS RO events occur mainly within \(\pm\)30-40\({}^{\circ}\) geographic latitudes. The high quality of COSMIC-2 GNSS RO ionospheric observations was confirmed using as reference the manually scaled ionograms from the global network of ground-based ionosondes [34]. This study aims to explore ways of combining the two most advanced techniques of ionosphere probing, ionosondes and GNSS RO, to overcome their individual limitations in determining the altitudinal distribution of ionospheric plasma density. Fortunately, the Ebre Observatory and El Arenosillo ionosondes--two of Europe's southernmost vertical HF sounding stations--conducted high-rate campaigns, providing high cadence (1-5 min) ionograms. This enables the investigation of cases with two independent ionospheric measurement colocations to understand the overall performance of combined electron density distribution reconstructions.
## 4 The Dynamic Effects  
[PERSON] et al. [78] summarized a great deal of the recent researches regarding the impact of aerosols on convective clouds and precipitation and pointed out that aerosols impact clouds and precipitation by two dynamic mechanisms. The first mechanism is stronger updrafts/downdrafts resulting from enhanced latent heat release. The second mechanism is stronger convergence due to stronger evaporative cooling. These results were mostly derived from researching strong convective systems. Is there a difference between the physical mechanisms pointed out by [PERSON] et al. [78] and those at work in the Mei-Yu frontal system? How do the various physical processes or mechanisms work together? Those questions motivate us to do more research.
## 3 Memory Management in MIROS
### Dynamic Memory Allocators in MIROS
#### 3.2.1 MIROS Heap-Extendable Segregated Free List Allocation  
To improve the flexibility and ease the pre-reservation problem, the heap extending mechanism is applied in the MIROS SFL allocator. With this mechanism, no partition needs to be reserved to the maximum size, but to a moderate size which will be enough for most run-time contexts. In case that a partition overflows, the allocation will not fail but continues in the extended heap area, as seen in Figure 7. In so doing, the MIROS SFL allocator becomes flexible. This flexibility feature is important for the WSN as the WSN contexts are diverse and it is impossible to find an ideal pre-reserved solution which will be suitable for all the diverse WSN contexts, e.g., if a WSN node is configured to be a router, the size required by the _packet buffering_ partition will be greatly different from the case when this node is configured to be an end-device. However, after the MIROS heap-extendable strategy is applied, this problem can be eased.
# Lacase in Biorefinery of Lignocellulosic Biomass
## 8 Conclusions  
After cellulose and hemicellulose, lignin has attracted the attention of researchers Worldwide as the renewable feedstock for various green processes. Laccase is a lignin-modifying and lignin-degrading enzyme, and is known for its versatile and nonspecific nature. Though laccase action on lignin is quite slow, pretreatment of biomasses via fungi producing laccase is the most sustainable approach, as none of the chemicals are required and it is an extremely natural process.  
Although the action of laccase on lignin is slow, pretreatment of biomasses, using fungi that produce laccase, has been identified as the most sustainable approach, as it does not require any chemical treatments, making it an extremely natural process. This approach has been shown to significantly enhance lignin degradation and improve the efficiency of subsequent processes. In the \"lignin first approach\", laccase can be used to selectively degrade lignin, which can then be utilized as a feedstock for various applications. The products obtained from lignin degradation using laccase can be used to produce a variety of value-added products, including biofuels, chemicals, and materials. These products can potentially replace fossil-based materials and contribute to the development of a more sustainable economy. However, research into laccase and lignin degradation is still in its early stages. This direction has the potential to pave the way for green solutions to environmental challenges. The development of efficient and sustainable lignin biorefinery processes could contribute to reducing greenhouse gas emissions, mitigating climate change, and promoting a circular bioeconomy.  
Conceptualization, R.R.S. and A.T.; software, A.T. and D.H.; resources, C.-D.D.; original draft preparation, R.R.S. and A.T.; writing--review and editing, A.K.P., D.H. and R.R.S.; visualization, A.T.; supervision, C.-W.C., C.-D.D. and R.R.S.; project administration, C.-W.C.; funding acquisition, R.R.S. and C.-D.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NSTC No. 111-2222-E-992-006.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Performance Tests of the Proposed PRNG
### Entropy Analysis  
In order to measure the uncertainty degree in pseudorandom sequences generated by the proposed PRNG, we use the Shannon's entropy (\(H\)). For this test, we generate 1000 pseudorandom sequences of 1,000,000 with 16-bits numbers. In this case, we use the 8-bit entropy function. Thus, each pseudorandom sequence \(S^{t}\) was observed in 8-bit numbers and \(H_{8}(S^{t})\) can be calculated using Equation (17).  
\[H(S^{t})=\sum_{i=0}^{2^{bits}-1}p\big{(}S^{t}_{i}\big{)}log\frac{1}{p\big{(}S^{ t}_{i}\big{)}}, \tag{17}\]  
where \(p(S^{t}_{i})\) represents the probability estimated for each \(S^{t}_{i}\), \(t=1,2,...,1000\) and \(bits=8\).  
When we calculated the statistical distribution of \(H_{8}(S)\) for the pseudorandom sequences considering 8-bit numbers, the mean of \(H_{8}(S^{t})\approx\)7.99991 is very close to 8 as we expected; i.e., the proposed PRNG generates pseudorandom numbers of 8 bits approximately with equal distribution, which corresponds to a uniform statistical distribution.
# Development of a Cone Penetration Testing Apparatus with a Textured Shaft
## 4 Conclusions  
In this study, a laboratory model chamber and small-scale cone system are developed to evaluate the cone resistance with a textured shaft. The interface shear behavior between the cone surface and the sand according to two loading directions (penetration \(\longrightarrow\) pull-out) are quantitatively analyzed using cones with various surface asperities under constant vertical stress conditions, and the salient conclusions are as follows:  
* Preliminary experiments are conducted using cones with smooth surfaces to quantitatively analyze the boundary effect related to model chamber size and the distance between the cones. Similar penetration resistance and pull-out resistance values are measured for cones penetrated at four different positions, confirming that the distance between the model chamber size and the cone developed in this study is sufficiently secured.
* A cone with surface asperity exhibits larger maximum penetration resistance than a smooth cone shaft, regardless of the load direction. During cone penetration, the shearing A direction condition produces a lower maximum penetration load compared to the shearing B direction condition. After the cone is penetrated, the pull-out resistance is dramatically changed with pull-out displacement in the shearing B direction condition, whereas the change in pull-out resistance is minimal in the shearing A direction condition.
* Given scale geometry, a higher scale height or shorter scale length mobilizes greater penetration and pull-out resistance. In particular, the change in shear resistance according to the geometry scale is significantly affected by scale length rather than scale height. A higher height of asperity or shorter length of asperity indicates a rough surface that individual soil deformation wedges. Meanwhile, shorter height or longer length of asperity produces more uniform shear bands along the asperities.  
Future studies will focus on identifying cone resistance behavior characteristics using the developed cone penetration test apparatus under various conditions (relative density, grain size distribution, overburden pressure) and conducting numerical analyses to analyze the mechanism of how the local shear failure propagated along the interface between the cone shaft and the sand affects the measured cone resistance.  
Conceptualization, S.-H.C. and T.-Y.K.; experimental test, K.-H.J. and T.-Y.K.; investigation, T.-Y.K., K.-H.J. and S.-H.C.; writing--original draft preparation, T.-Y.K. and K.-H.J.; writing--review and editing, S.-H.C.; supervision, S.-H.C.; project administration, S.-H.C.; funding acquisition, S.-H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1C1C1006003).  
Figure 7: Measured force against displacement during pull–out tests after the cone is penetrated: (a) L = 20 mm, H = 0.3 mm; (b) L = 20 mm; H = 0.5 mm; (c) L = 6 mm, H = 0.3 mm.
## 3 Pendulum Search Algorithm
### The Algorithm  
PSA is a population-based metaheuristic, where the search for the optimal solution is driven by a group of agents. In PSA, each of the agents acts like a pendulum with independent parameters. They move around the search space looking for the optimal solution, driven by the pendulum movement that centres around their own current position.  
The agent's position updated equation is shown in Equation (1):  
\[P_{i}^{d}(t)=P_{i}^{d}(t-1)+\mathit{pend}_{i}^{d}(t)\Big{(}\mathit{Best}^{d}-P _{i}^{d}(t-1)\Big{)} \tag{1}\]  
Figure 1: Pendulum Harmonic Motion.  
where \(P_{i}^{d}(t)\) represents position of agent \(i\)th at dimension \(d\)th for time \(t\)th, while \(Best^{d}\) is the best solution found so far since the start of the search by the entire population. The agent moves towards or away from the best solution based on \(pend_{i}^{d}(t)\), which is a random number with maximum amplitude oscillates obeying the pendulum harmonic equation as illustrated in Figure 2. Different agents have a different \(pend_{i}^{d}(t)\) value. The \(pend_{i}^{d}(t)\) is calculated according to Equation (2).  
\[pend_{i}^{d}(t)=2\exp(-t/tmax)(\cos(2\pi*rand)) \tag{2}\]  
This equation mimics the harmonic equation, \(tmax\) is the maximum number of iterations, and \(rand\) is a random number between 0 to 1.0 drawn from uniform distribution. The maximum displacement amplitude 2 is chosen so that exploration is favoured during the early iterations. Once the \(pend_{i}^{d}(t)\) is below 1, the agents switch to favour exploitation. Additionally, \(pend_{i}^{d}(t)\) oscillates between the positive and negative range. A positive value \(pend_{i}^{d}(t)\) encourages an agent to move towards the direction of the best solution, while a negative \(pend_{i}^{d}(t)\) drives the agents towards the opposite direction from the best solution.  
There are only two simple mathematical equations employed in PSA with two parameters to be selected, namely the number of agents and maximum number of iterations. This makes PSA a very simple optimization algorithm with low computational cost. The pseudocode of PSA is shown in Algorithm 1.  
```
Initialize the agents' parameters and positions randomly.  For i = 1: maximum iteration  For each agent  Update agents using Equation (1) & (2)  Evaluate agent's fitness  End  Identify the best agent  End  Solution: best agent
```  
**Algorithm 1** Pseudocode of PSA
# A Distributed Approach to the Evasion Problem
## 2 Materials and Methods
### Enumerating Evasion Paths
#### 2.4.3. Examples  
We will use two simple examples of the evasion path algorithm in action to illustrate the evasion path enumeration algorithm variations.  
The evasion path log that we will use for the forward propagation is depicted in Figure 3: a single enclosed region \(X\) gets split into two regions \(A\) and \(B\), each of which is split again into \(A\) and \(A^{\prime}\), \(B\) and \(B^{\prime}\). Finally, two of the resulting four regions are covered by sensors, killing the corresponding region.  
For the forward propagation, the algorithm goes through the following steps:  
\begin{tabular}{c|c} Time step & Evasion path list \\ \hline
1 & \([X]\) \\
2 & \([X,A]\), \([X,B]\) \\
3 & \([X,A,A]\), \([X,A,A^{\prime}]\), \([X,B,B]\), \([X,B,B^{\prime}]\) \\
4 & \([X,A,A^{\prime},A^{\prime}]\), \([X,B,B,B]\) \\ \end{tabular}  
The graph grows, tracking more and more possible paths through the graph, until the regions \(A\) and \(B^{\prime}\) die, at which point it is pruned back, discarding the evasion path classes that no longer are possible because they lead into dead ends.  
In the backward propagation case, we use the example depicted in Figure 4: a single enclosed region \(X\) gets split into two regions \(A\) and \(B\), each of which is split again into \(A\) and \(A^{\prime}\), \(B\) and \(B^{\prime}\). Finally, the regions are pairwise merged again into \(Y\) from \(A\) and \(B\) and \(Y^{\prime}\) from \(A^{\prime}\) and \(B^{\prime}\).  
The information tracked through the algorithm looks as follows:  
\begin{tabular}{c|c} Time step & Evasion path list \\ \hline
4 & \([Y]\), \([Y^{\prime}]\) \\
3 & \([A,Y]\), \([B,Y]\), \([A^{\prime},Y^{\prime}]\), \([B^{\prime},Y^{\prime}]\) \\
2 & \([A,A,Y]\), \([B,B,Y]\), \([A,A^{\prime},Y^{\prime}]\), \([B,B^{\prime},Y^{\prime}]\) \\
1 & \([X,A,A,Y]\), \([X,B,B,Y]\), \([X,A,A^{\prime},Y^{\prime}]\), \([X,B,B^{\prime},Y^{\prime}]\) \\ \end{tabular}
## 5 Conclusions  
We proposed a relation vector modification method to apply to the ATDL. Our approach was based on a constrained pairwise repulsive force. Experimental results indicated that, by reusing all layers, our method was effective for a small \(N^{t}\). We also showed that the distance between the relation vectors was related to the classification performance. These results indicate that the task-specific layer can be reused by appropriately estimating the relation vectors.  
In the future, we plan to investigate the generation of effective first DNNs, e.g., using ImageNet. It is known that the classification performance decreases when there is no relationship between the source and target domain [3]. Therefore, ImageNet, while providing better performance [28], is not always valid for all target domain tasks. For successful transfer learning, these open problems need to be solved. In addition, we will improve the classification performance by combining other methods, such as [29; 30]. Furthermore, collecting 2-DE images, performing weight analysis from a biological perspective, and applying the method to other diseases are important for clinical environments and will be included in future studies.  
Writing-original draft preparation, methodology, and analysis, Y.S ([PERSON]); Analysis, Y.S ([PERSON]); data curation, K.U. and S.Y.; project administration, T.N.; supervision, N.H.; writing-review and editing, all of authors.  
This research received no external funding.  
We acknowledge [PERSON] from Juntendo University School of Medicine, for his help in collecting samples to generate 2-DE images.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{l c c c c c} \hline  & **PPV** & **NPV** & **MCC** & **F1** & **ACC** \\ \hline Caltech-101 & 0.923 & 0.917 & 0.804 & 0.857 & 0.918 \\ CIFAR-10 & **0.936** & **0.985** & **0.929** & **0.951** & **0.969** \\ MNIST & **0.936** & **0.985** & **0.929** & **0.951** & **0.969** \\
2-DE images & **1** & **0.971** & **0.952** & **0.966** & **0.980** \\ \hline \end{tabular}
\end{table}
Table 5: Performance of our method in different source tasks. The red bold is the best performance of each evaluation. The black bold is the second best performance.
## 6 Prediction of Remaining Useful Life
### Prediction Model and Model Verification  
The RUL distribution is sensitive to many aspects [204]: the initial condition supplied by damage diagnosis having a significant impact; the accuracy of predictive models for future loadings and physics of failure models as another major uncertainty source; the sensitivity of system measurements to the damage; the noise in the training and measurement data; and the sampling frequency of measurements. Therefore, besides the RUL distribution that accounts for the uncertainty coming from many sources, a prognosis methodology should have several other important components, namely, the prediction model for future loadings, the transition between situations when measurements are available and unavailable, and the prognosis verification and validation.  
The prediction of structure deterioration when subjected to future loadings is another important component of a successful damage prognosis. Future loadings are always forecasted based on the previous loading histories using various data-driven prediction modeling techniques. For example, meta-models such as the state-space representation and multivariate ARMA models can be employed to track previous loadings and to predict future loadings [196]. [PERSON] et al. [209] used the wavelet neural network method to predict the future system loading based on data from the operational and environmental sensors.  
Since the damage prognosis is affected by many sources of uncertainty, the validation of prognosis models is very essential to establish the confidence in the RUL estimate. The process of establishing this confidence in the predictive capabilities is termed as verification and validation (V&V) [196]. The performance can be measured by evaluating the errors between the predicted and the actual RULs, and computing metrics based on accuracy and precision such as standard deviation, mean absolute deviation, mean squared error (MSE), mean absolute percentage error (MAPE), etc. These metrics provide statistical information about the variations in RUL distributions. [PERSON] et al. [210] introduced several evaluation metrics (e.g., prediction horizon, \(\alpha\)-\(\lambda\) performance, relative accuracy, and convergence) tailored for prognosis and presented a detailed discussion on how to interpret and use these metrics. They proposed a standard offline metric test to compare and evaluate prognosis algorithms, while [PERSON] and [PERSON] [211] discussed the online prognostics performance evaluation. In the area of prognosis validation, a significant challenge is to validate nonlinear models, although most of the current research is still at the stage of validating linear dynamic and stress models [212].