esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction Method of Unsteady Flow Load of Compressor
## 4 Results and Discussion  
### Influence about Training Sizes  
Seven groups of data with different training set proportions were selected to train the aforementioned SCNN network model. When the proportion of training set data changes, the prediction error of test set will also change. Figure 6 shows this trend. The flow field is predicted to be the unsteady pressure of a section of blade height on the stator blade surface. The airflow force prediction parameter is the airflow load on the blade, where the FT is the average value of the tangential airflow load, and the FX is the average value of axial load. In addition, \"1 order FT\" represents the first order tangential airflow force amplitude. Similarly, \"1 order FX\" represents the force amplitude of the first order axial flow. To further explain the figure below, it is important to point out the definition of the relative error. It represents the average error of all of the predicted values and the real values for the field prediction model. In terms of the load prediction model, the relative error represents the average error between the model prediction parameters of all of the load variables and the real values which are calculated by the CFD.  
Figure 6: The trend of model prediction error. (**a**) Mean prediction error of flow field. (**b**) Relative error of airflow force prediction.  
It can be clearly observed from the figure that the values of the relative error decrease rapidly as the proportion of training sets increases. Additionally, the error tends to be stable when the training set size reaches 0.3. The reason for this phenomenon occurring is that the proportion of data is too low for the small training set, and the constructed neural network system cannot be fully trained. Therefore, the prediction error of lower training set size is large. However, the training effect will not always increase with the increase in the training set size. Because too much training data can easily lead to over fitting of the model. At this time, the network training effect is no longer significantly improved. Therefore, the large training set size prediction error tends to be stable.  
For the research in this paper, satisfactory prediction results can be obtained when the training set ratio is 0.3. At this time, the efficiency and accuracy of the neural network model reach a relatively balanced state. Its training effect and resource consumption are considerable. Therefore, only 300 groups of samples are required to participate in the network training for the data that were used in our research. It is very necessary to reduce the consumption of the computing resources in the data preparation stage, which is also conducive to the popularization and application of the model.
# A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding
###### Abstract  
The widespread popularity of smart meters enables the collection of an immense amount of fine-grained data, thereby realizing a two-way information flow between the grid and the customer, along with personalized interaction services, such as precise demand response. These services basically rely on the accurate estimation of electricity demand, and the key challenge lies in the high volatility and uncertainty of load profiles and the tremendous communication pressure on the data link or computing center. This study proposed a novel two-stage approach for estimating household electricity demand based on edge deep sparse coding. In the first sparse coding stage, the status of electrical devices was introduced into the deep non-negative k-means-singular value decomposition (K-SVD) sparse algorithm to estimate the behavior of customers. The patterns extracted in the first stage were used to train the long short-term memory (LSTM) network and forecast household electricity demand in the subsequent 30 min. The developed method was implemented on the Python platform and tested on AMPds dataset. The proposed method outperformed the multi-layer perception (MLP) by 51.26%, the autoregressive integrated moving average model (ARIMA) by 36.62%, and LSTM with shallow K-SVD by 16.4% in terms of mean absolute percent error (MAPE). In the field of mean absolute error and root mean squared error, the improvement was 53.95% and 36.73% compared with MLP, 28.47% and 23.36% compared with ARIMA, 11.38% and 18.16% compared with LSTM with shallow K-SVD. The results of the experiments demonstrated that the proposed method can provide considerable and stable improvement in household electricity demand estimation.  
deep K-SVD; household electricity demand estimation; patterns extraction; smart meters data analytics +
Footnote †: journal: Information
# Hybrid Machine Learning-Statistical Method for Anomaly Detection in Flight Data
## 3 Local Outlier Factor  
### Basic Concept  
The local outlier factor (LOF) models outliers as the points in the data space that are isolated from the remaining dataset based on the density of data points. The dense regions in the dataset are found, and outliers are defined as those points that do not lie in these dense regions or are far away from the dense regions [18]. Conceptually LOF is very similar to clustering and distance-based methods for outlier detection but combines the strength of all three-proximity based outlier detection methods, namely clustering, distance-based and density-based. LOF segments the data space instead of segmenting data points, as in the case of clustering. Instead of finding sparse data points, LOF finds sparse regions in the data space. With LOF, sparse regions in the data space can be easily identified with the combination of many original features (flight parameters). Thus, LOF is suitable for high-dimensional data such as flight data. LOF divides the data space into small local regions (defined as distance-based regions). The number of other data points within this small local region is used to define local density. In LOF, these local density values are converted into outlier or anomaly scores. The runtime and the memory requirements of the LOF algorithm scale by n \(\times\) 2 [15]. Thus, a dataset of thrice the size requires six times as much memory and calculation time.
## 4 Experiments and Results
### The Verification of Communication Distance
Another point is that a too-large coil radius could make the node a specific target for submarine detection. This would defeat the purpose of magnetic induction, which is designed for secure and confidential communications. We also compared the signal path loss at different magnetic induction communication frequencies, as shown in Figure 1. The path loss increased as the communication distance increased. The lower the magnetic induction communication frequency, the higher the path loss. Magnetic induction communication with a relatively high frequency at the same distance resulted in lower communication losses. The strength of the communication magnetic field is highly dependent on the amount of current passing through the coil, the number of turns of the coil, and the conductive magnetic material in the center. Due to power limitations of underwater nodes, underwater nodes based on disposable battery power cannot use high energy for communication, which leads to the rapid depletion of the node's power. In addition, the use of high-performance magnetically conductive materials in the development of magnetically induced MIMO nodes also significantly impacts the communication distance of the nodes. High-performance soft magnetic materials are expensive, and some metal-based magnetically conductive materials can be subject to severe seawater corrosion and are unsuitable. The design of related active or passive relay technologies based on waveguide communication to achieve long-range underwater MI-MIMO will be an essential research task in the future. Unfortunately, these technologies are currently beyond our reach in practical scenarios. On the one hand, simulation-based techniques need to give an entirely realistic picture of underwater applications. On the other hand, deploying relayed long-range magnetically induced underwater MI-MIMO technology in deep water is still a significant challenge.
## 1 Introduction  
A highly accurate virtual model is essential for the success of the final outcome of the implant-based restorations obtained using CAD-CAM (computer-aided design and computer-aided manufacturing) technology [1]. The performance of the intraoral digital impression technique is similar to that of the classic impression for implant restorations [2]; this approach has been proposed as a clinical alternative, especially for single implant-supported restorations and short-span fixed dentures (FDRs) [3; 4].  
The virtual impression technique has some challenges, which may affect the final result [5; 6]. In favorable clinical situations or to limit the fees, implant fixed restorations typically use standard (prefabricated) abutments. When the entire workflow is digital, occlusal or cervical refinements are made by the clinician extraorally, usually using an implant analog to sustain standard abutment during the process; however, the grip is challenging to hold.  
Another potential issue in the digital implant impression procedure is the subgingival margin exposure for the impression, which implies the use of a lateral gingival displacement technique or the registration of subgingival contour based on previous interim restorations [7; 8], custom impression copings [9], or healing caps [10].  
Another challenge of the digital implant impression technique is the need for intermediate impression accessories (i.e., scan bodies), which are sometimes available in one standard dimension for a particular prosthetic connection. They cannot be customized, unlike conventional impression copings [8]. The geometry of these devices has different characteristics that are specific to each manufacturer and restoration [11]. The accuracy of digital intraoral impressions seems to be affected by both the geometry of the scan body and the scan strategy [12]. In the everyday clinical practice, there are conditions when the complete insertion of a scan body is not possible (e.g., narrow spaces, angled neighboring teeth) or the gingival exposure of a scan body is limited. In some cases, the implants are inserted deep subrestally, and, thus, the length of the scanable portion of the scan body that is screwed into the implants is reduced. Basically, the scan body does not reach an optimal supragingival level so that it can be scanned accurately. In this study, we propose an accessory for intra/extraoral impression transfer, which could solve all the above mentioned problems, i.e., a slight modification of the Wolfcraft Keyless Chuck Metal 2625000 (Wolfcraft, Kempenich, Germany).
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 4 Application of UAV-Aided Non-Terrestrial Networks in UAIS
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Unmanned Autonomous** & **Application** & **Communication** \\
**Intelligent System** & **Application** & **Requirements** \\ \hline \multirow{3}{*}{UGV} & Command and Control & Ultra-low latency \\  & Perception & High data rate \\  & Data Offloading & High data rate \\ \hline \multirow{3}{*}{UAV} & Flight Control & Ultra-low latency \\  & UAV Traffic Management & Low latency \\  & Object Delivery & Low latency \\  & Mobile Edge Computing & High data rate \\ \hline \multirow{3}{*}{others} & Monitor and Survey & High data rate \\  & Reconnaissance & High data rate \\ \cline{1-1}  & Swarm Coordination & Low latency \\ \cline{1-1}  & Formation Control & Low latency \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Typical communication requirement of UAIS in NTN. The various applications of UAIS in NTN communication scenarios will be discussed in detail in the following part of the paper. From the perspective of 6G communication, network coverage will extend beyond current boundaries and cover multi-dimensions: on the ground, in the air, space, underwater, and underground. Thus, an integrated communication coverage method utilizing both terrestrial and non-terrestrial infrastructure will be beneficial for UAIS in terms of quality of service (QoS), resilience, and sustainability of the networks. Unmanned aerial vehicles (UAVs) and satellites will play vital roles in enabling UAIS applications.
# Possibility of Replacing Sugar with Apple Puree in Muffins
## 3 Results
### Volume of Muffins  
As in the case of height, the volume of the final product depends on the amount of air incorporated in the dough and the ability of the dough to retain that air during baking [66], with sucrose favoring this process. As to the volume of muffins obtained by replacing sucrose with apple puree, it decreased with the increase in the percentage of substituted sugar. The volume of muffins prepared with different levels of apple puree as a substitute for sucrose together with that of the control muffin is shown in Figure 8. Samples in which sugar was substituted in a percentage higher than 50% showed significantly lower volume values (\(p<0.05\)) versus the control sample. The incorporation of fewer air bubbles, and the reduced capacity of the dough to retain air during baking lead to decreased expansion of sugar-free muffins [77]. Decreases in muffin volume by replacing sucrose with stevia, erythritol, or a combination of fiber (apple fiber/wheat fiber/pea fiber), water, and steviol glycosides were also obtained by [PERSON] et al. (2019), [PERSON] et al. (2012), and [PERSON] et al. (2016), respectively [56; 77; 78].  
Figure 7: Graphic representation of muffin height. Values followed by different superscript letters (a, b, c) are statistically different at 95% confidence level.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 5 Results and Testing
### Image Encryption Result
#### 5.2.4 Image Fidelity  
A common prerequisite for image encryption techniques is the ability to compare the encrypted and original images using two metrics, MSE and PSNR [47]:  
\[PSNR=1-\text{Log}_{10}\frac{R2}{MSE}\]  
Tables 5 and 6 list the _MSE_ and _PSNR_ values of our proposed approach compared with those from the literature.  
Figure 8: RGB histogram analysis for multiple test images (House, Peppers, and Baboon) before and after encryption. (**a**) Histogram analysis of the House image showing the red, green, and blue channel distributions before and after encryption using the proposed method. (**b**) Histogram analysis of the Baboon image showing the red, green, and blue channel distributions before and after encryption using the proposed method. (**c**) Histogram analysis of the Pepper image showing the red, green, and blue channel distributions before and after encryption using the proposed method.
## 1 Introduction
### Our Contribution  
Several researchers have presented a clustering-based routing scheme. However, all the existing approaches have different issues when considering 5G-based smart healthcare applications. The optimal cluster head selection and best routing path for data transmission are challenging tasks due to the high density and high heterogeneous network with different parameters such as different energy levels, mobility speeds, and low transmission ranges. The main contributions of this research are:  
1. Clustering-based routing protocol using game theory and reinforcement learning to reduce energy consumption and increase network lifetime for smart healthcare scenarios.
2. An algorithm to select the best optimal cluster head (CH) from the available cluster heads (CHs) to avoid the situation of more cluster head (CH) occurrence.
3. Reinforcement learning-based route selection algorithm for data transmission.
4. Comparison of existing approaches with the proposed method.  
The rest of the paper is organized as follows. In Section 2 the brief overview of existing protocols is presented. In Section 3 network model, energy model, clustering game and Theoretical analysis are presented. In Section 4 the detail of our clustering algorithm is given. In Section 5 the detail of our routing algorithm is presented. In Section 6 the time complexity of our proposed algorithms is presented. In Section 7 results analysis are discussed. Finally, the conclusion is presented in Section 8.
# Dynamical Networks Modelling Applied to Low Voltage Lines with Nonlinear Filters
## 1 Introduction  
### Motivation and Contribution  
The core motivation of this research comes from a reflection about network sustainability and robustness. We consider network robustness as a composition of two factors: the network physical structure (static) and the processes running (dynamical) on that structure. Currently, networks are analysed, mostly, from a static perspective. Vertices and edges build static networks from which several measurements and results are extracted. This approach works very well for many theoretical and practical problems. What happens, however, when those vertices and edges are changing, arising, disappearing and creating new relationships anytime? What if these changes are nonlinear? Are these networks still sustainable and robust during and after the nonlinear occurrences?  
In a real-world system, we could get acceptable outcomes for a process even when its physical structure is severely damaged. For example, the transport and delivery of goods can happen despite roads being in bad condition. In other cases, the physical structure is fine but the processes are out of control and there are no outcomes, like when deliveries do not occur because of traffic jams, even if the road infrastructure is good.  
In this work, we consider that the complementary relations of these two factors, (1) the physical structure and (2) the processes running on top of it, are responsible for the general system robustness level, i.e., its level of robustness (or sustainability) depends on how simultaneously robust its structure and its processes are. That level of robustness should be measured every and anytime, especially if nonlinear occurrences come to affect the system as a whole.  
However, creating a general measurement for the system robustness level is not an easy task and it is not accomplished in this work. Instead of a general system robustness measurement, we contribute here with insights and themes to explore. We do this by studying a simple system and conducting experiments with it. We include nonlinear disturbances in that system, exploring the ideas about its physical structure and its processes. To tackle the problem, we use a simple one-dimensional electrical network as the basic case study, where we analyse (1) the physical structure (a communication line in this study) and (2) the processes running on top of it (the signals traveling to communicate, and unexpected surges). Further, two- and three-dimensional examples are shown to illustrate more generalized cases.
###### Abstract  
Many models have been created and attempted to describe the temperature-dependent viscosity of glass-forming liquids, which is the foundational feature to lay out the mechanism of obtaining desired glass properties. Most viscosity models were generated along with several impact factors. The complex compositions of commercial glasses raise challenges to settle these parameters. Usually, this issue will lead to unsatisfactory predicted results when fitted to a real viscosity profile. In fact, the introduction of the reliable viscosity-temperature data to viscosity equations is an effective approach to obtain the accurate parameters. In this paper, the Eyring viscosity equation, which is widely adopted for molecular and polymer liquids, was applied in this case to calculate the viscosity of glass materials. On the basis of the linear variation of molar volume with temperature during glass cooling, a modified temperature-dependent Eyring viscosity equation was derived with a distinguished mathematical expression. By means of combining high-temperature viscosity data and the glass transition temperature (\(T_{g}\)), nonlinear regression analysis was employed to obtain the accurate parameters of the equation. In addition, we have demonstrated that the different regression methods exert a great effect on the final prediction results. The viscosity of a series of glasses across a wide temperature range was accurately predicted via the optimal regression method, which was further used to verify the reliability of the modified Eyring equation.  
**Keywords:** viscosity model; glass; Eyring equation; glass transition temperature +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
### Objective of the Study  
This study evaluates the capability of two numerical methods, two-phase volume-of-fluid and Euler-Euler, in replicating the behavior of a high-velocity free-water jet impacting a plunge pool. Specifically, it aims to assess their performance in predicting mean dynamic pressures and air concentration, as well as their accuracy in simulating pressure fluctuations observed experimentally. By comparing the VoF approach of interFoam with the Euler-Euler method of twoPhaseEulerFoam, the study highlights the strengths and limitations of each model in modeling complex jet diffusion and air entrainment processes.  
The experimental data used for validation were obtained at the Laboratorio Nacional de Engenharia Civil (LNEC), where a vertical circular jet with a velocity of 7.4 m/s was directed into a plunge pool of 0.8 m depth. Pressures on the pool floor were measured to characterize the hydraulic conditions. While the experimental setup has been detailed in a previously published paper [14], this study focuses on validating the numerical models under the same jet conditions. A section summarizing the experimental results is included to ensure continuity and context.  
The results from each method of mean dynamic pressure, velocity, and air concentration inside the plunge pool were compared with experimental data, considering mesh sensitivity analyses. With these results, a mesh and a model were selected. Based on that, the influence of the mesh boundary layer and the effects of initial turbulence properties on the pressure field were analyzed. Finally, the pressure distribution at the plunge pool bottom, the turbulent components of the pressure, and the velocity profile along the jet axis were obtained.
## 4 Results and Discussion
### Sensitivity Analysis of Basic Parameters
#### 4.1.2 Distribution Pattern of Formation Temperatures  
High-temperature drilling fluids intrude into the formation under differential pressure, and the in situ temperature gradually increases after heat exchange with the drilling fluids. Once the temperature and pressure conditions of the hydrate phase equilibrium state are broken, the solid hydrate begins to decompose. Since heat transfer occurs throughout the drilling fluid intrusion into the hydrate reservoir, it is crucial to find out the pattern of reservoir temperature change after drilling fluid intrusion. Figure 8 shows the distribution cloud of the reservoir temperature and near-well zone temperature at different times. Figure 9 shows the distribution pattern of the temperature in the near-well zone at different times. From the graph, it can be seen that, with the continuous invasion of drilling fluid, the temperature inside the formation gradually increases. However, the propagation of temperature has a certain lag compared to pressure. The rate of temperature increase in the near wellbore zone is relatively slow, and the range of temperature changes in the formation is small. After 30 min, the pressure has already propagated to a range of 4 m around the wellbore. The temperature disturbance caused by the invasion of drilling fluid within 20 h only has a significant impact on the range of about 0.5 m around the wellbore. Therefore, the risk of wellbore instability is highest within a range of 0.5 m around the wellbore.  
Figure 8: Variations of formation temperature and near-wellbore zone temperature over time. (**a**) reservoir temperature (**b**) near-well zone temperature.  
Figure 7: Curves of pressure around the wellbore.