esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigation of Gravity Segregation by Foam to Enhance Sweep Efficiency
## 5 Summary and Discussion  
The assessment of foam-based enhanced oil recovery (EOR)relies significantly on the gravity-segregation length (\(L_{g}\)) as predicted by [PERSON]'s model, underscoring its importance as a key parameter. It highly depends on permeability distribution. In turn, the heterogeneity affects foam strength. In horizontal reservoirs with uniform and isotropic permeability, foam can effectively mitigate the gravity-segregation process and improve sweep efficiency. In practice, the distance between wells is limited, and the injection rate is much higher. We can infer that the foam strength we used in the field test can be weaker than that obtained from the laboratory, then the injectivity is improved by weakening foam strength.  
For layered reservoirs, [PERSON]'s model predicts the distance to complete segregation accurately only in cases where the average permeability is close to that of an individual layer. This suggests that the harmonic average permeability for \(K_{v}\) may not adequately represent the effect of heterogeneity on effective vertical permeability. For more severe heterogeneous reservoirs, the analytical model may not be a good tool to predict the segregation length due to lacking criteria to evaluate the vertical permeability.  
The impact of heterogeneity on rock-wetting behavior is not considered in our study. This assumption is not rigorously correct because the connate water saturation is usually different in different rocks. In the context of heterogeneous reservoirs, wherein the connate water remains constant, the foam-model parameter, \(fmdry\), may exhibit inaccuracies, despite successful experimental data fitting in the low-quality regime.  
In addition, the surfactant may not fully fill the reservoir in practice, so the transition zone between the override and underride zones should be close to that in the case without surfactant. This assumption can affect the final sweep efficiency. In the process of foam injection, the extent to which the gas and surfactant propagate can differ based on various factors such as injected quality and adsorption. Additionally, geological heterogeneity is expected to be more intricate than the simplified model utilized in this study, thereby potentially influencing the gravity-segregation process. While these factors have not been accounted for in this research, they should be considered in future investigations.
# Rapid and Accurate PPA Prediction for the Template-Based Processor Design Methods
###### Abstract  
The template-based chip design method aims to build rapidly. However, it still need synthesis and simulation flows to get the performance, power, and area (PPA) reports and find the proper parameters set in the design space exploration, which takes a long time. Therefore, a rapid and accurate PPA prediction method is proposed. At first, the PPA Prediction Model based on Multivariate Linear regression (ML-PM) is proposed to fit the multiple parameters' influence on the PPA via the single parameter affection. Moreover, a Multivariate NonLinear regression Prediction Model (MNL-PM) based on [PERSON]'s law is introduced to improve the accuracy of the PPA estimation. The empirical evaluation of the method shows that the PPA prediction for the template-based chip design methods can reach 98.60%, 99.19%, and 98.53% accuracy on performance, power, and, area separately, when compared with the PPA generated via the synthesis and simulation flows.  
PPA prediction; template-based methods; multivariate regression; agile chip design Article
## 2 Materials and Methods
### Data Analysis
The average temperature values were calculated by taking the mean of temperature readings within 30 s epochs for each participant. Subsequently, these mean values, obtained from the 30 s epochs, were further averaged over 2 min intervals for each participant. These averaged values over 2 min were then subjected to statistical analysis, and the resulting average temperature values were plotted against time. To evaluate the variability in temperature tracking among the 10 participants at specific time points, the figures included bars along the average temperature trendline. These bars represent the standard deviation, providing a visual representation of the spread of temperature values around the average. The present study also examined the influence of various individual characteristics on CT measurements. Specifically, factors such as participants' BMI, past experience exercising in hot conditions, and the intensity of their workouts were gathered and considered in the analysis. BMI categories were established according to the CDC classification as underweight (BMI < 18.5), healthy weight (18.5 < BMI < 24.9), overweight (25 < BMI < 29.9), and obese (BMI > 29.9) [33]. To assess the impact of these individual factors on the mean and maximum CT recorded by the Zephyr and SS devices, a Mann-Whitney U test and Kruskal-Wallis Test were employed. A significance level of \(\alpha\) = 0.05 was used to determine the statistical significance of the results. The results are presented in the following section.
## 3 Results
### Systematic Evaluation of the Identified Literature
#### 3.4.2 Areas of Operation and State of Development  
A total of 10.71% of the identified papers were assigned to the cluster \"areas of operation and state of development\". The results of the literature review on implementation of DTs show discussions in various application areas within the product lifecycle. A total of 50 papers were included, with 38% assigned to prognostics and health management (PHM), 35% to production, 18% to design, and 9% to other areas. In contrast, applications for dispatching optimization and operational control are currently missing from the literature [12]. [PERSON] et al. conducted another literature review, focusing only on production and including and categorizing 43 papers to analyze the production category in more detail: 55% of the publications were concept papers, 26% case studies, 14% reviews, and 5% definitions of DTs. A majority of 49% dealt with production planning and control, 14% with maintenance, 12% with the product lifecycle, 9% with manufacturing in general, 9% with layout planning, and 7% with process design. However, the analysis of the degree of implementation showed that only 2.38% of the case studies met the definition of a DT [11]. [PERSON] et al. analyzed 14 papers to DES and DT in the context of warehouse logistics. They further stated that most available DES software solutions are only capable of offline simulation, while a trend to real-time simulation can be seen. Thus, current implementation only includes some of the DT dimensions. A lack of applications of DTs in scientific publications was identified, emphasizing the very early stage of DT development in warehousing. At the same time, a strong potential to solve several problems in warehousing is highlighted when DTs are applied, for example, to routing, scheduling, resource allocation, waste and energy reduction, storage reduction, transport reduction, estimating warehouse employee intentions, optimizing space utilization, KPI presentation, decision support, forecasting, and prediction. Nevertheless, implementation or integration of DTs in this area are missing and the potential remains theoretical [35].
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 3 Uncertainty Analysis of Renewable Energy
### The Implementation  
Here are the procedures of the uncertainty analysis of renewable energy.  
1. Collect monthly solar radiation and latitude information.
2. Use HOMER to synthesize time series of solar radiation intensity for 8760 h in a year.
3. Calculate the Beta distribution for two parameters \(\alpha\) and \(\beta\).
4. Generate 1000 scenarios using LHS on Beta distribution.
5. Reduce scenarios to 10 representative scenarios using backward reduction.
6. Obtain PV output value and corresponding probability at each time and each scenario.
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
## 4 Experimental Results and Comparison Analysis
### Performance of Tamper Detection and Self-Recovery
#### 4.2.4 Image Deletion Attack  
To test the performance of the proposed method under deletion attack, Figure 9 exhibits the comparison results of these three methods in tamper detection and recovery. The ship in watermarked Venice image is directly deleted from the image. From the detection results given in Figure 9d-f, we can see that all these three methods can find the tampered regions precisely, and the restored images presented in Figure 9g-i have almost the same subjective image quality. However, due to the different recovery strategies, there is still a difference for the restoration of image details. In order to show the restoration effect more intuitively, Figure 10 gives the partial enlarged details of the restored images in Figure 9g-i. It can be observed that all the restored images have become blurred because of the block-wise recovery method. However, compared to the restored images in [13; 25], the restored image by the proposed scheme is relatively smooth, and it is much closer to the original ship image in Figure 10a, especially in the places that have been circled in red. From the objective data listed in Table 6, we can draw the same conclusion that these three methods achieve similar tamper detection performance. Though the FPR of the proposed scheme are slightly higher than the other methods, the PSNR value of the restored image by the proposed scheme has been improved by 2 dB. This conclusion further proves that the recovery strategy designed in the proposed scheme has an advantage over references [13; 25].  
Figure 9: Tamper detection and recovery performance for image deletion attack: (**a**) Original Venice image; (**b**) watermarked Venice image; (**c**) attacked image; (**d**) tamper detection result using the method in [13]; (**e**) tamper detection result using the method in [25]; (**f**) tamper detection result of the proposed method; (**g**) image recovery result using the method in [13]; (**h**) image recovery result using the method in [25]; (**i**) image recovery result of the proposed method.
###### Abstract  
Daily use of mouthwash is generally recommended to control dental plaque development and limit further adhesion of oral bacteria. To this purpose, Chlorhydeidine di-gluconate (CHG)-containing products are still the most used antiseptic in oral health. The aim of this study was to compare the antimicrobial and antibiofilm activity of three different mouthwash commercial formulations containing CHG at the same concentration (0.2%) on the main etiological agent of dental plaque, _Streptococcus mutans_ (_S. mutans_). Liquid Chromatography coupled with tandem mass spectrometry (LC-MS/MS) was used to confirm the CHG concentration in the commercial formulations. The Minimal Inhibitory Concentration (MIC) and Minimal bactericidal concentration (MBC) were measured to evaluate the antimicrobial activity of mouthwashes on planktonic cells. The biofilm prevention concentration (BPC) and CHG formulation activity towards mature biofilm were investigated. Both total cell titer and viability in _S. mutans_ pregrown biofilms were evaluated after treatment with formulations at different time points and concentrations, using Crystal Violet (CV) and tetrazolium dye assay (MTT). The Shapiro-Wilk and Levene tests were used to evaluate the normality and overall homogeneity of the datasets, respectively. The chi-square test, Fischer's exact test and Student's _t_-test were used to evaluate the inhibitory capacity of the commercial formulations in CV and MTT assays. Results showed that even though only slight differences in MIC and MBC values were found on planktonic bacteria, the antibiofilm effect of the different formulations exhibited wide variation when mature biofilms were treated. In addition, results were discussed based on the different compositions of the three formulations, suggesting that herbal extracts, when present in mouthwash formulations, may not always have a beneficial effect, especially when mature biofilms are concerned.  
chiorhexidine; _S. mutans_; mouthwash; antibacterial activity; antibiofilm activity; oral microbiome; dental plaque; drug formulation
# Polychrony as Chinampas
## 2 Nonlinear Signal Flow Graphs  
In this article, we shall study a kind of _nonlinear signal flow graph_ called a directed graph. We think that a signal propagates throughout an edge by following its orientation. When several signals reach a vertex simultaneously, a built-in condition called the _threshold_ determines whether it will react by firing signals. The concept of the signal flow graphs was developed by [PERSON] and [PERSON] [2; 3]. If the condition is linear in the intensity of the input signals, then the graphs are called _linear signal flow graphs_.  
We study the following _nonlinear_ condition of a signal flow graph:  
* Every signal has an intensity of one.
* Every vertex has a threshold intensity of two.
* If a vertex coincidentally receives signals of an intensity higher or equal to the threshold, then the vertex fires a signal through each of its outgoing edges.  
Nonlinear signal flow graphs are used to study circulatory regulation [4], to design automatization of nonlinear data converters [5], to compare system-level and spice-level static nonlinear circuits [6], to build models for DC-DC buck-boost converters [7], and to analyze the problem of inverting a system consisting of nonlinear and time-varying components [8]. The nonlinear condition of our signal flow graphs is an abstraction of neural spikes. Neural spikes are used in cognitive computing to develop hardware that emulates the human nervous system [9; 10; 11; 12; 13], to implement robust chaotic communication [14], and to power efficient channel coding [15]. The link between neural spikes and pulse position modulation is explained in [16].  
In particular, we are interested in polychrony groups defined as a group of _primary neurons_ (vertices) that fire at specific times, leading to _secondary neurons_ firing. As a result, a _cascade_ occurs when the number of primary neurons is below the number of secondary neurons (see [1] for more details).  
We study the general phenomena of polychronization of nonlinear signal flow graphs of the form  
\[1\to 2\to 3\rightarrow\cdots\to n.\]Our initial objective is to characterize the polychrony groups that lead to cascades. A second goal is to count the families of the cascades. We also explain the algebraic structure of cascades using cellular automata theory. Our final goal is to develop an algorithm that can answer certain types of queries without the need to compute all possible interactions. An example of such a query might be establishing which neuron (vertex) will be activated at some future time.
## 5 Discussion  
Based on the presented results, it can be concluded that the experimental results correspond with those obtained through simulations. It should be noted that the estimation errors of resistances were greater for experiments than for simulations, however, they did not exceed 10%. These discrepancies resulted from the simplifications of the simulation model.  
Nevertheless, the main contributions of the presented research as regards MRAS estimators are:  
* simultaneous estimation of stator and rotor resistance;
* estimator requires only measurable input signals (independence of the control structure).  
Additional attributes of the presented estimator include:
* stable estimation during speed reversals (including zero-speed range) and load torque variations;
* estimation of parameters during steady-states and transients;
* variations of adaptation mechanism coefficients impact only subsystem dynamics; the estimator works stably.  
Further research directions will focus on employing an analytical method to adaptation mechanism coefficients setting. The impact of other parameters, such as inductances, on the estimator performance will be also taken into the consideration.  
Conceptualization, S.A.B., M.D.; methodology, S.A.B., M.D.; software, S.A.B.; validation, M.D., S.A.B.; formal analysis, M.D.; investigation, S.A.B., M.D.; resources, S.A.B., M.D.; data curation, S.A.B.; writing--original draft preparation, S.A.B.; writing--review and editing, M.D.; visualization, S.A.B.; supervision, M.D.; project administration, M.D.; funding acquisition, M.D.  
This research was funded by statutory funds of the Faculty of Electrical Engineering of the Wroclaw University of Science and Technology under grant number 049M/0004/19.  
The authors declare no conflict of interest.
# Unsupervised Learning in Precision Medicine: Unlocking Personalized Healthcare through AI
## 2 Artificial Intelligence
### Unsupervised Learning: Clustering
#### 2.2.2 Hierarchical Clustering  
Hierarchical clustering [20] builds a hierarchy of clusters through two primary approaches: agglomerative (bottom-up) and divisive (top-down). In the agglomerative approach, the process begins with each data point as its cluster. These clusters are then iteratively merged based on a chosen distance metric. The process must be repeated until the desired number of clusters is achieved. This method can be described mathematically as follows:  
Figure 2: Key objectives and visualization models of cluster analysis are presented. At the top, data structures investigation, classification, and compression are illustrated. Below, hierarchical and K-means clustering techniques are displayed.  
**Step 1.** Calculate pairwise distances between all clusters: Let \(X=\{x_{1},x_{2},\cdots x_{n}\}\) represent the dataset with \(n\) data points, where each \(x_{i}\) is in its cluster \(C_{i}\). The distance between two clusters \(\text{d}(C_{i},C_{j})\) can be defined using different linkage methods.
**Step 2.** Merge the Closest Clusters: find the two clusters \(C_{i}\), \(C_{j}\) with the smallest distance \(\text{d}(C_{i},C_{j})\) and merge them into a new cluster: \(C_{ij}=C_{i}\cup C_{j}\).
**Step 3.** Update the Distance Matrix: after merging, update the distance matrix to reflect the new distances between the merged cluster \(C_{ij}\) and all remaining clusters.  
The divisive approach, in contrast, starts with all data points grouped into a single cluster. This cluster is then split into progressively smaller clusters until the desired number of clusters is obtained. Compared to K-means, hierarchical clustering does not require specifying the number of clusters in advance, which provides flexibility in determining the appropriate number of clusters. However, the computational complexity of hierarchical clustering can be substantial, especially with large datasets, making it less scalable than K-means.
## 2 The Current Approach
It could also be mentioned that there is a research focused on modelling buildings and their subsequent servicing by firefighting vehicles. Research from the National Central University of Taiwan [16] focuses on simulating fires on individual floors of high-rise buildings and their subsequent firefighting strategies. The proposed method involved the exploration of a 3D geometric network model (GNM) based on a building information model (BIM) to simulate firefighting. The operation of firefighting ladders during a response was also simulated in a 3D virtual environment. Finding the ideal location for the fire trucks to stop was supported by a methodology that can be used to determine the ideal location of the truck placement before the actual arrival of the engine in the fire area. At the same time, future research considers the use of real-time traffic sensors data to predict traffic conditions and adapt the optimal routing of the intervention line. Similarly to the abovementioned strategy of deploying fire trucks around burning buildings, the research published by Belgian, Dutch, and Chinese academics [17] focuses primarily on water refilling of fire trucks in the process of fighting oil fires. The procedure uses the eM-plant simulation tool to model the firefighting process. In the context of the so-called chemical accident and to prevent its escalation, strategies are also considered to allocate fire trucks according to the distance from the hydrants and to divide fire trucks according to their number. Improving the strategy of serving a given area is also discussed in a publication from the Netherlands [18]; it aims towards the optimisation of the allocation of firefighting vehicles to the location of an accident from the perspective of the emergency control room, based on historical dispatching problems where multiple vehicles from different locations are dispatched to the location of an incident and a decision has to be made as to which fire engine will be on site first. In this paper, a method to dispatch fire trucks is described in a way that strikes the right balance between rapid response to the actual fire and also good coverage of any simulated incidents. After reviewing the currently available publications, it can be concluded that areas in close proximity to the actual intervention site are not being addressed in greater detail. However, these areas may have the highest loss times when the IRS vehicle is not able to access the actual location of the intervention. Currently, the intervention of the IRS forces is guided by the fastest possible route to the place of the critical accident, but without any knowledge of the actual situation in the target area. The innovation of the presented concept lies in the application and combination of knowledge from the Road Safety Audit (RSA), new methods of detail analysis of the area, especially by using aerial photogrammetry, and subsequent simulation in a virtual environment.
# A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems
## 3 A GAN Based Channel Estimation Scheme  
In this section, a novel channel estimation method based on GAN is provided. The pipeline of our proposed channel estimation scheme is illustrated in Figure 2. As shown in Figure 2, the procedure contains two stages, which are the model training and model usage stages, respectively. During the model training stage, a designed GAN model is trained to be capable of producing synthetic OFDM channel samples that are similar to the real OFDM channel samples. In particular, the model training stage can be conducted offline or online. For the offline training case, the model can be trained with pre-collected channel samples so that it can be applied directly during communications. In the case of online training, the model is trained with samples collected in the current channel environment, which makes the learned characteristic distribution of channels more in line with the current scenario. Hence, there is a trade-off about deployment latency and synthetic channel samples similarity between offline and online modes. The selection of an appropriate training mode is customized according to different requirements of services. In addition, the joint utilization of both modes is feasible, which can be done by training a preset model offline and fine-tuning it online. During the model usage stage, the already trained GAN model combined with the estimated pilot channel coefficients \(\hat{\mathbf{h}}_{p}\) is imported into a designed interpolation function \(\mathcal{H}\) to obtain the data channel coefficients \(\hat{\mathbf{h}}_{d}\). The details of these two stages are presented in the following two subsections.