esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Remarks  
To effectively use the SES method, the forecaster must first choose a proper value for \(\alpha\) and then set the initial value to calculate the smoothed values and make the forecast. The MSE is often used as a criterion for selecting an appropriate value for \(\alpha\). For instance, by assigning the values [0,1], one then selects the value that produces the smallest MSE. Importantly, these values considerably affect the accuracy of the forecast.  
In this study, the Solver in Microsoft Excel and step search methods were used to determine the value of \(\alpha\) that optimally fitted several time series datasets. Since their  
Figure 8: Average accuracy values with MSE for the three initial value setting methods using values of \(\alpha\) ranging from 0 to 1 (0.001, 0.002,..., and 1) for the 15 datasets: small (S1–S5), medium (M1–M5), and large (L1–L5).  
performances were not different, Solver from Microsoft Excel is a powerful alternative method for obtaining the optimal value for \(\alpha\). Moreover, the initial value influenced the forecast using the SES method. The simple initial value setting using the first observed value provided a far worse performance than the other two methods based on small, medium, and large time series datasets. It is worth noting that using values for \(\alpha\) in the range 0.1-0.4 as the initial value with the first observed provided forecasted values far from the actual value, whereas when it exceeded 0.5, all three methods provided similar results. The limitation of SES work is based on the principle that a prediction is a weighted linear sum of past observations. Moreover, the forecasting accuracy is directly affected by the value of \(\alpha\) since it adjusts the weights given to observations and the initial value. Therefore, it is always important to choose a proper value for \(\alpha\).  
Conceptualization, C.W.; Methodology, C.W. and W.J.; Formal analysis, C.W. and W.J.; Writing--original draft, C.W.; Writing--review & editing, W.J.; Visualization, W.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by International SciKU Branding (ISB).  
Not applicable.  
The authors are grateful for funding from the Thailand Institute of Scientific and Technological Research Institute (TISTR). In addition, the authors also would like to thank the Khon Kaen University, Kasetsart University, and International SciKU Branding (ISB) for providing the facilities to conduct the research.  
The authors declare that they have no conflict of interest.
## 5 The Use of AI for the Creation of SRE  
In the previous section we studied the way RIS can be controlled by using AI, even in dynamic environments. However, having a small number of RIS elements to create a small-scale SRE is completely different than creating one that covers a massive area such as a city. In this situation, we have multiple environments with diverse characteristics [45], and they must all be coordinated together. Thus, two questions have to be resolved: (a) the placement of the RISs and (b) the channel estimation.  
The placement of RISs is particularly important because it plays a key role in the final network design. However, it may be considered as a limited intervention parameter since the actual placement positions are basically denoted by the city's architecture and buildings. It is an application-specific parameter, and it must be studied independently for each case.  
The channel estimation over a massive network requires a lot of CSI. The required amount of data is so vast, and the time needed for its transmission is so impractical that a training session becomes rather unfeasible. Thus, a massive ML model able to be reconfigured in order to compensate for every minor change on the network may create a case of \"butterfly effect\". To overcome this, efforts have been made to calculate the parameters of a network having imperfect CSI in the presence of multiple RISs in a dynamic environment [46].  
In ML, we can also employ a learning method that breaks the larger model into a subset of smaller ones, each being calculated locally, keeping data where it is collected and reducing the needs for data exchange between the main hub and the local devices. This learning method is termed as FL [47].
# Diagenesis of Deep Low Permeability Reservoir in Huizhou Sag and Its Influence on Reservoirs
## 4 Results  
### Basic Characteristics of Reservoir  
#### 4.1.1 Reservoir Lithology and Physical Characteristics  
The observation of nine thin sections (H1-H9) in the Huizhou Sag and a statistical analysis conducted on the microscopic characteristics of the rock slices show that reservoir rocks in the Enping Formation have more than a 70% content of quartz in the sandstone of the Enping Formation, while there are smaller contents of feldspar and rock debris.  
Compositionally, the sandstones dominantly include feldspar quartz sandstone, lithtic quartz sandstone, lithtic feldspar sandstone, and feldspar lithtic sandstone, as determined using an analysis of the X-ray diffraction data of 31 rock samples from the Enping Formation in the Huizhou Sag (Figure 3).  
The measurement results of rock porosity show a distribution range of 1.1-19.8%, and the permeability is about 0.0017\(\sim\)612.02 \(\times\) 10\({}^{-3}\)\(\upmu\)m\({}^{2}\). Through careful observation, it is evident that the porosity and permeability increase with depth in response to various compaction processes. At shallow burial depths, the compaction trend shows good agreement with the experimental compaction of loose Enping sand, indicating that effective stress is the controlling factor of the physical properties at shallow depths. However, in areas where the stratum depth exceeds 3500 m, porosity and permeability still have a good correlation. This suggests that there are areas with relatively high quality throughout the low-porosity and low-permeability reservoirs that have great exploration potential. In conclusion, the reservoir is generally a porous reservoir with low porosity and permeability (Figure 4).  
Figure 4: Physical characteristics of sandstone of Enping Formation in Huizhou Sag.  
Figure 3: Rock component projection map and sandstone types of Enping Formation in Huizhou Sag. Q—Quartz; F—Feldspar; L—Lithic.
## 1 Introduction
Consequently, lipases have specific kinetic properties, which are related to their catalytic site conformation conferred by a flexible subdomain lid or flap located over the active site. In the presence of insoluble substrates, this structure undergoes a structural change-called interfacial activation [32]-conferring an open conformation to the catalytic site and increasing the lipase activity. In a more aqueous condition, the lid assumes a closed structure, making the lipase inactive [33]. Lipases, in general, act in a wide range of pH conditions. Typically, bacterial lipolytic enzymes tend to act in alkaline pH conditions, while fungal lipases prefer acidic conditions [34]. Apart from those aspects, lipases can be investigated based on their substrate selectivity, including regio- and enantioselectivity, which makes these enzymes excellent choices for the production of enantiopure compounds with high added value [35]. Although lipases have tremendous potential industrial use, they often need to have their properties improved due to the biocatalytic bioprocess requirements. Therefore, using techniques, such as site directed mutagenesis based on rational design, different mutants can be constructed showing improved thermal stability, tolerance against organic solvents and optimized selectivity [36; 37]. This is due to the necessity to achieve optimal performance in different industrial fields, and to effectively catalyze synthetic reactions in micro-aqueous conditions where organic solvents are required. In addition, directed evolution techniques can be applied to improve lipase properties [38; 39]. In this review, we will focus on different aspects of recombinant lipases, addressing bottlenecks in enzyme production, engineering, immobilization and application in the pharmaceutical industry. There is major focus on the limitations and strategies used to overcome drawbacks found in lipase use reported in recent investigations.
# Bio-Inspired Traffic Pattern Generation for Multi-AMR Systems
## 2 Related Work
The classification shows that, while several approaches address specific aspects of roadmap generation and optimization, few methods address all the desired features simultaneously. In particular, most approaches either focus on AGVs rather than AMRs, neglect weighted graph representations that are critical for traffic balancing, or lack the computational efficiency needed for practical deployment. Furthermore, many methods do not consider the entire system holistically, focusing only on the graph structure without accounting for the task distribution, fleet size, and actual movement patterns. Our proposed approach aims to address these limitations by combining the strengths of bio-inspired optimization with a holistic consideration of the complete AMR-based intralogistic system. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Category** & **Subcategory** & **References** & & & & **Features** & & & \\ \hline \multirow{5}{*}{**Category**} & \multirow{5}{*}{Graph-metric} & [7,8] & AGV & ✓ & ✓ & & & & \\ \cline{2-10}  & & [6,10] & AGV & ✓ & & & & & \\ \cline{2-10}  & & [14] & AGV & ✓ & ✓ & ✓ & ✓ & ✓ \\ \cline{2-10}  & & [15] & AMR & ✓ & ✓ & & ✓ & & \\ \cline{2-10}  & & [9] & AGV & ✓ & ✓ & ✓ & ✓ & & \\ \hline \multirow{5}{*}{**Category**} & \multirow{5}{*}{Multi-agent} & [16] & & ✓ & & & & & \\ \cline{2-10}  & & [17] & & ✓ & & ✓ & & & \\ \cline{2-10}  & & [18] & AGV & ✓ & & & & & \\ \cline{2-10}  & & [20,21] & AGV & ✓ & & ✓ & & & \\ \hline \multirow{5}{*}{**Category**} & \multirow{5}{*}{Category**} & PSO & [23, 24, 25, 26] & & & & & & ✓ \\ \cline{2-10}  & & GA & [27, 28] & & & & & & ✓ \\ \cline{2-10}  & & [19] & AGV & ✓ & & ✓ & & & \\ \cline{2-10}  & & [30, 32, 33, 34] & & & & ✓ & & ✓ \\ \cline{2-10}  & & [31, 35] & AGV & & & ✓ & & ✓ \\ \cline{2-10}  & & [36] & & & ✓ & & & ✓ \\ \cline{2-10}  & & [37] & AGV & ✓ & ✓ & ✓ & & ✓ \\ \cline{2-10}  & & Our approach & AMR & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \multicolumn{10}{c}{\(\checkmark\): Feature is present.} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of approaches for costmap/roadmap optimization in multi-robot systems. In our previous work [37], we developed an ACO-inspired algorithm that autonomously generates roadmaps for AGVs by considering the full set of problem characteristics. Combined with Safe Interval Path Planning (SIPP), the method proved to be highly effective: the generated roadmaps achieved up to 15% higher throughput compared to the solutions presented in the literature. This paper significantly upgrades the previously proposed ACO algorithm and extends these concepts to multi-AMR systems, focusing on spatial movement constraints that proactively reduce conflicts without requiring temporal coordination.
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 3 Phishing Methods and Techniques
### Phishing Technical Approaches
#### 3.3.13 Typo Squatting  
Typo squatting is a type of URL hijacking that targets users who make a typographical error when entering a website address (e.g., a user types Facebook.com instead of Facebook.com). This is a form of cyber squatting in which a malicious user creates websites that use someone else's brand and copyright. When potential victims make a typographical error, they are led to an alternative website that is owned by the hacker and looks like the original website. Using this website, the hacker can then proceed to perform various malicious activities, such as stealing sensitive information (e.g., login or payment details) using fake login forms or selling products or services. These sites are also capable of downloading malicious software to the victim's system, requiring only that the victim visits the website. These types of downloads are called drive-by downloads and are employed by most typo squatters to spread malicious software capable of stealing sensitive data. Typo squatting is not limited only to a misspelled domain name but can also be used if the user enters a wrong domain extension, e.g., com instead of.org.  
A few preventive measures that a user can employ are to bookmark or pin websites that are visited frequently; use speech recognition software; use web searches to ensure the intended website is reached; never click suspicious links received from chats, email, or other media; and always have anti-viral software updated to the latest version and anti-virus databases updated to the latest available. In a phishing attack, these malicious hackers register or buy domain names which are similar to the original company with the intention of tricking victims. Such an instance occurred when the website AnnualCreditReport.com was launched; multiple domain names with similar names were purchased by malicious users to trick visitors into sharing their sensitive and personal information without their knowledge. Attackers also sent the users phishing emails pretending to be the legitimate website to induce targets to follow a link to the malicious website.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
###### Abstract  
New wireless network paradigms will demand higher spectrum use and availability to cope with emerging data-hungry devices. Traditional static spectrum allocation policies cause spectrum scarcity, and new paradigms such as Cognitive Radio (CR) and new protocols and techniques need to be developed in order to have efficient spectrum usage. Medium Access Control (MAC) protocols are accountable for recognizing free spectrum, scheduling available resources and coordinating the coexistence of heterogeneous systems and users. This paper provides an ample review of the state-of-the-art MAC protocols, which mainly focuses on Cognitive Radio Ad Hoc Networks (CRAHN). First, a description of the cognitive radio fundamental functions is presented. Next, MAC protocols are divided into three groups, which are based on their channel access mechanism, namely time-slotted protocol, random access protocol and hybrid protocol. In each group, a detailed and comprehensive explanation of the latest MAC protocols is presented, as well as the pros and cons of each protocol. A discussion on future challenges for CRAHN MAC protocols is included with a comparison of the protocols from a functional perspective.  
cognitive radio ad hoc network; medium access control; cognitive radio; spectrum mobility; spectrum sensing; spectrum sharing 1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] 2017 172136 doi:10.3390/s17092136
# Multi-Scale Audio Spectrogram Transformer for Classroom Teaching Interaction Recognition
## 4 Experimental
### Experimental Results and Analysis
#### 4.3.3 Experimental Results of Event Classification on CSID  
Finally, we transferred the proposed audio classification model in this paper to the classroom interaction recognition domain for experimental validation in a self-built CSID dataset. To avoid chance in the experiments, each set of experiments was conducted three times and the experiments were repeated three times using random seeds to obtain the average accuracy. For the experimental data processing, the experimental parameters such as sampling rate were consistent with the ESC-50 and AudioSet datasets, except for the frameshift parameter, which was set to 640.  
Since some codes of the above methods are not publicly available, we selected representative methods from the public methods for experimental comparison. Table 6 shows the Accuracy and Average accuracy of the different models for the three categories in CSID, as well as the model-related parameters. It can be seen that the MAST model, when applied to speech interaction recognition in the classroom, has higher accuracy than other  
Figure 10: Confusion matrix based on the ESC-50 dataset.  
algorithms. It is worth noting that even without pre-training on the AudioSet dataset, MAST still outperforms the PANNN and AST methods by 2.4% and 2.8%, respectively.  
When comparing different categories of recognition, our model still achieved the highest accuracy in each category. However, all types of methods tend to have lower accuracy in the recognition of the Teaching and Interaction categories, which is greatly alleviated by the pre-trained MAST method. The confusion matrix generated by MAST on the CSID dataset is shown in Figure 11 for this case.  
As can be seen from Figure 11, the classification accuracy of the Silent scenes tends to be higher, especially as the pre-trained MAST obtained a recognition rate of 95%. However, there are still confusion-prone Teaching scenes and Interaction scenes in Figure 11b, where 7% of the samples of the Teaching scene are identified as Interaction scenes, and 7% of the samples of Interaction scenes are identified as Teaching scenes. The analysis of the samples shows that some transient Interaction scenes are easily misidentified as Teaching scenes, while audio interference from outside the classroom can easily lead to misidentification in Teaching scenes. Therefore, by eliminating audio interference from outside the classroom, exploring classroom audio noise reduction methods, and combining the MAST model with the advantages of high accuracy and fewer parameters in classroom tasks, the intelligent classroom recognition method can be more practical.
## 4 Results
### Comparison of LDS U-Net with Other Contemporary Models  
The LDS U-Net was compared with three architectures, namely U-Net, Attention U-Net, and MultiResUNet. Each of these models has its unique advantages. However, when it comes to the segmentation of a noisy ultrasound image, these architectures might not support deep-level data extraction and high image variability.  
2.1 LDS U-Net Outperforms U-Net, Attention U-Net, and MultiResUNet in the Segmentation of Ultrasound Spine Image Dataset  
In Table 2, the segmentation output of the proposed LDS U-Net is compared with other models using four segmentation evaluation indices: Jaccard index, Dice score, F1 score, and pixel accuracy. Dice score is the most direct evaluation index. The average Dice score of LDS U-Net was 0.8964, which was 2.79%, 4.57% and 6.89%, higher than MultiResUNet, Attention U-Net, and U-Net, respectively. In terms of the Jaccard index, F1-score, and pixel accuracy, the results of LDS U-Net were much better than the other mentioned networks. The standard deviations of the results of the proposed LDS U-Net also showed a smaller spread. The proposed LDS U-Net is more capable of learning almost all the features from the training dataset.
# A Review of Mixture Theory for Deformable Porous Media and Applications
## 1 Introduction  
The deformation of the porous material due to fluid flow brings changes in physical properties such as permeability and porosity of the material. This ultimately alters the fluid flow through the porous material and develops a complex coupling between the matrix of porous material and fluid flow. This type of phenomena occurs in many industrial as well as biological settings and mathematical modeling of these processes is performed using the mixture theory. In mixture theory, individual components of the mixture, that is, solid matrix and a fluid can be modeled as superimposing continua so that each point in the solid-liquid mixture is occupied simultaneously by a material point of each constituent (see for example, [PERSON] and Drumheller [1]). The principals of modern mixture theory by using the continuum mechanics and proposing balance equations appropriate to mixtures irrespective of their constitution are formulated by [PERSON] [2], [PERSON] [3,4] and [PERSON] [5]. The detailed review of historical development of continuum theories of mixture is presented by [PERSON] and [PERSON] [6] in 1976. The main focus of the present paper to present a review of the work done after 1976, while special emphases is given on the applications of mixture theory in deformable materials.  
The first study of fluid flow through deformable porous material was presented by [PERSON] et al. [7]. Later on, [PERSON] [8] studied the problem of soil consolidation where [PERSON]'s law was used to describe the fluid flow coupled with linear elasticity model for solid deformation. This paved the path for further work of [PERSON] [9,10], used extensively in soil mechanics. [PERSON]'s theory for deformableporous media was then combined with the mixture theory to investigate the compressive behavior of articular cartilage by [PERSON], [PERSON] and other co-authors [11; 12; 13] in 1980s. The general framework of the mixture theory is presented in the next section. In Section 3, application and literature survey of the theory in fluid flow through porous biological tissues, compression moulding process in connection with composite manufacturing process and capillary rise phenomena are presented. The detailed literature survey on applications of mixture theory in allied areas of sciences and engineering are given in Section 4. Finally, concluding remarks are furnished in Section 5.
## 4 Experimental Results and Discussion
### Relationship between Intrinsic Permeability and Volume Gas Content  
A direct factor, volumetric gas content, is employed to investigate gas permeability by many previous studies [63; 64; 65; 66; 67]. Most existing models for estimating gas permeability are also built up based on the volumetric gas content [68; 69; 70]. Among these relationships, the power-law model is the most common:  
\[K=\alpha\eta^{\beta} \tag{13}\]  
where \(\eta\) is volumetric gas content, \(\alpha\) is constantly related to the pore connectivity, and \(\beta\) is a power-law exponent labeled as a water blockage factor [71]. Figure 10 shows the relationship between intrinsic permeability and volumetric gas content at different temperatures. Additionally, the correlation curves and fitted equations obtained by [PERSON] are shown in Figure 10[72]. As expected, the intrinsic permeability increases as the volumetric gas content increases. Generally, the measured intrinsic permeability and [PERSON]'s curves agree satisfyingly with the power-law model. However, since [PERSON]'s study does not discuss the effect of temperature differences, the subsequent analysis does not involve [PERSON]'s curve. Based on Equation (13), \(\alpha\) for 25, 45, and 60 \({}^{\circ}\)C are 2.35 \(\times\) 10\({}^{-8}\), 2.05 \(\times\) 10\({}^{-8}\), and 1.27 \(\times\) 10\({}^{-8}\), respectively. It indicates that a higher temperature leads to small pore connectivity. It is noted that in the small region of volumetric gas content (0-0.1), a relatively large difference occurs between the measured and estimated results. It indicates that it is necessary to use the advanced apparatus to measure the gas permeability of the soil specimen, especially in small regions of volumetric gas content.  
Figure 9: Relationship between intrinsic permeability and negative pore water pressure (curves obtained from [PERSON] et al. [61] and [PERSON] et al. [62]).