esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Interpretation
#### 4.2.5 Floodplain  
Description  
The internal stratification structure of this configuration unit is simple, mainly dark red and thickly stratified mudstone (massive) or grayish white and thinly stratified mudstone (S-M) (Figure 4). The floodplain sediment in the upper part of the section is mainly composed of thick-layered dark red mudstone mixed with a thin layer of grayish white mudstone.  
Figure 7: Outcrop profile photograph (**A**) and interpretation map (**B**) showing the sedimentary characteristics of a typical abandoned channel cross-section (site II, profile 2).  
Scattered massive and dendritic white mud cracks and root traces are found in the thickly stratified dark red mudstone. The top of the section indicates soil development. At the bottom of the section, a thin layer of gray-white mudstone without an internal bedding structure inside is developed (Figure 4), and root traces and biological disturbances are lacking. The element is distributed in the same horizontal layer, although it is eroded and scrubbed locally.  
Interpretation  
Dark red, massive, and grayish white thin layers of mudstone have been interpreted as floodplain deposits, usually formed by the migration of fluvial channels, resulting in the deposition of suspended material during flooding as the riverbed gradually shifts [50]. Modern floodplains in the study area have the same lithological features: red mudstones form during dry seasons (oxidation and soil formation), while gray layers correspond to prolonged submergence [51]. The presence of red soil and plant root traces indicates that well-drained floodplain sediments underwent a relatively long period without deposition, followed by oxidation and soil-forming processes, and paleosols developed accordingly [52]; grayish white thin-layered mudstones represent floodplain deposits in floodplain areas that were submerged for a long time under reduced conditions [53]. Scattered, massive, and dendritic white mudstones are formed by filling the mud crack fractures in dark red mudstone, which is believed to reflect the contraction and expansion of clays related to the periodic infiltration and evaporation of water [51].
## 3 Methods
### Transmitting Messages  
To solve the problem on dropping messages and increase the delivery rate on significant messages, we design two scheduling mechanisms: _Message Management Mechanism_ and _Message-Transfer Mechanism_. _Message Management Mechanism_: In order to make the bandwidth more effective and save buffer space, we set some criteria to coordinate the priority of in-transit messages. The so-called Message Management refers to managing the messages stored in buffers. The core of the management involves  
Figure 2: An example of a selecting transmission path. dropping messages and assigning priority. All nodes in the network are notified by ACK and then remove the delivered messages from their buffers. If the messages have lower delivery predictability, we assign lower priority to them in order to restrain their transmission. If the buffer space is full, messages with the lowest priority will be removed to make more room for forthcoming messages. In this way, the routing protocol avoids generating copies for the messages with low priority and improves the utilization of network resources. _Message-Transfer Mechanism_: In Scheduling-PROPHET, when node \(I\) encounters node \(J\), \(I\) will transmit messages to \(J\). The Messages-Transfer Mechanism is illustrated as follows. Firstly, \(I\) transmits messages to \(J\) which is the destination of those messages. Secondly, \(I\) exchanges the table that holds the delivery predictabilities of all nodes with \(J\), and decides the transmission path. The determinate factors for the path are complete predictability (\(S(i,i+1,\ldots,D)\)), the amount of hops of path (\(h(i,i+1,\ldots,D)\)) and traffic load (\(tl_{i}\)). Here choosing a transmission path with the largest \(S(i,i+1,\ldots,D)\) means choosing the path with the highest delivery predictability. When two or more paths have the same value of delivery predictability, we will check the hop count of path (\(h(i,i+1,\ldots,D)\)). A transmission path with minimum hops means the shortest path. However, there may still exist two or more paths with the same hop count, and then we will compare the traffic load of all possible next hops (\(tl_{i}\)), and choose the node with the least traffic load as the next hop of the transmission path. The Algorithm 1 for Scheduling-PROPHET routing protocol to explain the procedure is given as follows. Nodes will exchange their lists, and transmit messages following the Scheduling-PROPHET routing protocol.
## 3 The Scheme for Simulating Fully Mechanized Mining with a Large Mining Height
### Hydraulic Support Simulation Method
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Layer** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{**Rock Type**} & \multirow{2}{*}{**Particle Size**} & \multirow{2}{*}{**Thickness**} & \multirow{2}{*}{**Density**} & \multicolumn{2}{c}{**Elastic Modulus**} & \multirow{2}{*}{**IF Angle**} & \multirow{2}{*}{**Pb\_coh**} & \multirow{2}{*}{**Pb\_ten**} \\  & & & **m** & **kg/m\({}^{3}\)** & **GPa** & \({}^{\circ}\) & **MPa** & **MPa** \\ \hline
14 & Sandy mudstone & 0.0625–0.1 & 15.39 & 2660 & 12.1 & 32.5 & 106.0 & 22.4 \\
13 & Coal & 0.0625–0.1 & 0.63 & 1440 & 3.9 & 29.8 & 58.8 & 9.2 \\
12 & Mudstone & 0.0625–0.1 & 0.38 & 2620 & 11.7 & 27.4 & 73.0 & 11.8 \\
11 & Limestone & 0.0625–0.1 & 0.95 & 2715 & 15.0 & 33.4 & 101.0 & 20.0 \\
10 & Medium sandstone & 0.0625–0.1 & 4.35 & 2696 & 11.9 & 27.8 & 94.8 & 10.6 \\
9 & Mudstone & 0.0625–0.1 & 12.25 & 2620 & 11.8 & 27.6 & 74.0 & 12.0 \\
8 & Limestone & 0.0625–0.1 & 7.25 & 2715 & 15.8 & 36.6 & 102.0 & 22.3 \\
7 & Mudstone & 0.0625–0.1 & 2.80 & 2620 & 11.1 & 27.2 & 106.0 & 12.2 \\
6 & Limestone & 0.0625–0.1 & 12.30 & 2715 & 15.0 & 36.8 & 123.0 & 15.0 \\
5 & Mudstone & 0.0625–0.1 & 0.35 & 2620 & 11.4 & 34.7 & 75.0 & 12.1 \\
4 & 9\# Coal & 0.0625–0.1 & 1.13 & 1440 & 4.0 & 29.5 & 58.0 & 9.2 \\
3 & Mudstone & 0.0625–0.1 & 0.50 & 2630 & 11.9 & 38.8 & 76.0 & 12.5 \\
2 & 10\# Coal & 0.0625–0.1 & 6.71 & 1440 & 4.0 & 29.4 & 59.1 & 9.2 \\
1 & Mudstone & 0.0625–0.1 & 4.41 & 2630 & 12.1 & 38.7 & 84.8 & 10.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Micro parameters of rock formation in DEM model. The support force of the hydraulic support underwent three stages of changes: the initial support force formed by tightening; the increase in the working resistance before the opening of the safety valve; and the constant resistance after the opening of the safety valve. According to the parameters of the hydraulic support of the mine, the roof width of the hydraulic support is set to be 2 m; the support length is 6 m; the working resistance is 12,000 kN; and the average support strength is 10 MPa. In order to meet the requirements of large mining height and the strong mine pressure, the support strength is matched according to the sinking volume of the roof to simulate the increased resistance and constant resistance of the hydraulic support at different stages so that the hydraulic support is in a working state with low initial support and high working resistance to provide effective support to failures. The curve of the simulated support strength with the sinking volume is shown in Figure 6.
# An Overview of Variants and Advancements of PSO Algorithm
## 3 Parameters of PSO
### Constriction Coefficient  
To ensure and enhance the convergence speed of the PSO algorithm and to ensure a balance between exploration and exploitation, [PERSON] (2017) suggested the parameter constriction coefficient \(\chi\)([PERSON] and [PERSON]) (2018). He implemented it on the basic velocity update Equation (1) of the original PSO algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Values of Acceleration Coefficients** & **Impact of Acceleration Coefficients** & **Corresponding Change in Velocity Update Equation (I)** \\ \hline \hline \multicolumn{3}{c}{In this case, the particle moves on with the same current speed till it reaches the boundary of the search space as its velocity is independent from the impact of personal best and global best position. Here, the social component of the velocity does not influence the particle’s velocity and particle will move in the global search space according to its own best position. Here, the cognitive component of the velocity does not influence the particle’s velocity and particle will move in the global search space according to its neighbor’s best position. Here, the social component of the velocity does not influence the particle’s velocity and particle will move in the global search space according to its neighbor’s best position. \(\mathrm{V_{k}(i+1)=V_{k}(i)+c_{1}=c_{2}}\). The particle is attracted towards the average of both best and best position. Here, the personal best position will generally effect the particle’s velocity more, that leads to excessive wandering in the search space. Here, the best position of other members of the swarm has more impact on particle’s velocity that leads to pre mature convergence. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Impact of different values of Acceleration Coefficients.  
The velocity update Equation (1) changes to Equation (5):  
\[\mathrm{V_{k}(i\ +\ 1)\ =\ \chi\ [V_{k}(i\ )\ +\ c_{1}\ r_{1}\Big{(}p_{best,i}^{k} \ -\ X_{k}(i)\Big{)}\ +\ c_{2}\ r_{2}\Big{(}g_{best,i}\ -\ X_{k}(i)\Big{)}] \tag{5}\]  
where the constriction coefficient \(\chi\) is given by  
\[\chi\ =\ \frac{2\kappa}{\Big{|}2\ -\ \varphi\ -\ \sqrt{\varphi\ (\varphi\ -\ 4)} \Big{|}} \tag{6}\]  
where \(\varphi\ =\ \varphi_{1}\ +\ \varphi_{2}\) and \(\varphi_{1}\ =\ c_{1}\ r_{1},\varphi_{2}\ =\ c_{2}\ r_{2}\), \(\kappa\) is a function of \(c_{1}\ \text{ and }\ c_{2}\).  
Equation (6) is used under the condition that \(\varphi\geq 4\) and \(\kappa\ \in\ [0,1]\).  
Parameter \(\kappa\) controls the exploitation and exploration abilities of the swarm. If \(\kappa\approx 0\) then the convergence is faster with local exploitation, and if \(\kappa\approx 1\) then the convergence is slow with a higher degree of exploration. Usually \(\kappa\) is set to a constant value. However, a higher degree of exploration with local exploitation can be achieved by taking the initial value of \(\kappa\) near to 1, then decreasing it to 0. When [PERSON]'s constriction factor is used with the PSO algorithm, then \(\varphi\ =\ 4.1\) and thus constant multiplier \(\kappa\ \approx 0.729\).
## 4 Problem Definition  
A set of users (u1, u2,..., un) and their tasks (t1, t2,..., tn) are considered in a cloud system. The primary objective of this research is to improve the following metrics for each user task:  
1. Response time;
2. Makespan time;
3. Resource utilization;
4. Service reliability.  
In [1], the temporal task scheduling algorithm (TTSA), which dispatches an entire task into a private CDC and public clouds, was used to schedule tasks. A hybrid simulated annealing-PSO is used to solve the cost minimization problem in each iteration of TTSA. The throughput can be increased, the cost of a private CDC can be decreased, and the delay bounds of all tasks can be met by TTSA. However, the convergence speed of simulated annealing is slow, and computation time is considerable. In [2], the multi-rumen anti-grazing approach was used to balance loads. This approach comprises three phases. In phase one, VM capacity and task size are used to calculate the task completion time (TCT) with a communication delay matrix. In phase two, tasks are assigned to respective VMs, and makespan and TCT values are updated for every iteration. In phase 3, tasks are rescheduled into new VMs in accordance with the new TCT and makespan values. Task rescheduling increases computational time and complexity. In [4], a hybrid of SJF and RR schedulers that considers only dynamic variable quantum tasks is adopted to complete task scheduling. The ready queue is divided into Q1 and Q2, where Q1 is for storing short tasks, whereas Q2 queue is for storing long tasks. The burst time for each task is acquired, which determines short and long tasks, and used to identify the median value that separates tasks. Waiting and response times are shortened in this process, but computation time for large tasks is increased due to the use of dynamic quantum computation to calculate the median value. In [9], a hybrid algorithm that combines BFO and PSO algorithms was used to balance loads. Jobs are collected as batches, and specific resources are allocated for each job. Makespan, operational cost, and utilization are calculated after the resource allocation process. The obtained values are used to schedule jobs to appropriate VMs. Slow convergence time and poor local search capability caused by the PSO algorithm are determined. In the current research, we use efficient task scheduling and load balancing algorithms to solve the problems highlighted in [1; 2; 4; 9].
## 6 Conclusions  
We introduced the exponentiated moment-based exponential distribution in the study of multicomponent stress-strength reliability, when stress and strength variables concern the same population. We used Monte Carlo simulations to compute large-sample confidence intervals. The larger the sample size, the smaller the bias and the mean squared error. The average bias is negative when \(\alpha_{1}\leq\alpha_{2}\); otherwise, the bias is positive for \((s,k)=(1,3)\) and \((s,k)=(2,4)\). The mean squared error decreases as \(\alpha_{1}\) increases for a fixed \(\alpha_{2}\) and increases as \(\alpha_{2}\) increases for a fixed \(\alpha_{1}\) for \((s,k)=(1,3)\) and \((s,k)=(2,4)\). The confidence intervals shrink with larger sample sizes and the performance of the coverage probability is satisfactory.  
**Author Contributions:** Conceptualization, G.S.R., F.A.B., M.A. ([PERSON]) and M.A. ([PERSON]); Methodology, G.S.R., F.A.B., M.A. ([PERSON]); Software, G.S.R.; Validation, M.A. ([PERSON]); Formal analysis, G.S.R., F.A.B., M.A. ([PERSON]); Investigation, G.S.R., F.A.B., M.A. ([PERSON]); Writing--review and editing, G.S.R., F.A.B., M.A. ([PERSON]).  
**Funding:** This article was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia. The authors, therefore, acknowledge with thanks DSR technical and financial support.  
**Acknowledgments:** The authors are deeply thankful to the editor and reviewers for their valuable suggestions to improve the quality of this manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**(s,k)**} & \multirow{2}{*}{**(u,m)**} & \multicolumn{4}{c}{**EMED**} & \multicolumn{4}{c}{**Burr XII Distribution**} \\ \cline{3-8}  & & **MSE** & **ACL** & **ACP** & **MSE** & **ACL** & **ACP** \\ \hline (1,3) & (15,15) & 0.0059 & 0.2651 & 0.9264 & 0.0105 & 0.3722 & 0.9403 \\  & (20,20) & 0.0042 & 0.2307 & 0.9331 & 0.0078 & 0.3239 & 0.9430 \\  & (25,25) & 0.0033 & 0.2070 & 0.9305 & 0.0059 & 0.2922 & 0.9470 \\  & (30,30) & 0.0027 & 0.1887 & 0.9382 & 0.0051 & 0.2684 & 0.9380 \\ (2,4) & (15,15) & 0.0114 & 0.3732 & 0.9226 & 0.0136 & 0.4247 & 0.9347 \\  & (20,20) & 0.0083 & 0.3266 & 0.9305 & 0.0099 & 0.3730 & 0.9397 \\  & (25,25) & 0.0066 & 0.2939 & 0.9298 & 0.0078 & 0.3367 & 0.9440 \\  & (30,30) & 0.0054 & 0.2688 & 0.9380 & 0.0067 & 0.3086 & 0.9330 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of MSE, the average confidence length (ACL), and 95% average coverage probability (ACP) at \((\alpha_{1},\alpha_{2})\) = (1.5, 1.5) for two distributions. EMED, exponentiated moment-based exponential distribution.
## 1 Introduction
Therefore, substrate properties, such as whiteness, gloss, and smoothness, may affect digital print quality [36]. Consequently, it is not surprising that certain paper attributes, including colour and structure, can determine the legibility and quality of a printed text [37]. While paper attributes significantly contribute to the clarity and legibility of a print, the influence of typographic characteristics on enhancing text legibility also plays an important role [38]. Notably, in smaller type sizes, differences in stroke weight and typographic tonal density significantly affect text legibility [39]. Typographic tonal density (TTD) denotes the darkness or grayscale of text on a page and can be quantified by ink concentration per unit area, be it in square centimetres, picas, or inches [40]. Thicker stroke widths imply more ink coverage per area [34; 41]. Enhancing text legibility involves considering specific typeface characteristics, including distinct character features, x-height, ascenders, descenders, stroke weight contrast, letter width, typeface size, leading (line spacing), and more [42]. _X-height_ represents the baseline-to-midline distance of lowercase letters, which impacts reading speed based on its visual angle [43]. _Ascenters_ mark the distance between the midline and ascender line, while _descenders_ indicate the space between the baseline and descender line. _Contrast_ arises from the variance in thick and thin strokes within a letter [41]. Typically, for a continuous text at a standard reading distance of 30-35 cm, the recommended type size ranges from 8 or 9 to 11 or 12 points (1 point = 4.233 mm) [35]. However, the ideal _type size_ depends on the typeface's x-height--moderately larger x-heights generally enhance legibility, particularly at smaller sizes [43]. The aim of the present research was to establish the usability, i.e., legibility, of prints on different papers with cellulose fibres that are extracted from the above-mentioned IAPS. The most relevant characteristics, such as the paper, typographic, and colorimetric properties, of inkjet prints on papers made from Japanese knotowed, giant goldenrod, and black locus were measured and compared to the prints on commercial office paper. To evaluate legibility, a group of observers read texts in the sans-serif typeface Arial, a typeface which was [35] found to be more legible compared to the transitional typeface Times New Roman in previous research. Texts in three different type sizes were printed on all four paper grades for this assessment.
# A Trust-Influenced Smart Grid: A Survey and a Proposal
## 11 Models and Scenario
### Modbus TCP  
Due to its documentation being readily available and it being used by modern and legacy substations (which form a significant percentage of substations worldwide [99]), Modbus TCP [100]--which is the TCP variant of Modbus [101]--is used. Furthermore, reinforcing our selection is the fact that there is current literature that is centred around its security [102], vulnerabilities [103], attack mitigation [104, 105], and utilization in testbeds [106, 107]. Utilizing TCP port 502, its implementation requires a client-server architecture. Modbus does not support unsolicited responses from servers. The Modbus TCP frame/packet consists of the Modbus Application Header (MBAP) header and the Protocol Data Unit (PDU) with their sizes and those of their components specified in Figure 7.  
The function code determines the request type that is sent to the server and the server responds using the same function code. The address(es) and/or the value written to/read being accessed from the server are specified in the data section of the PDU. The minimum Modbus request size is 12 bytes and that for response is 10 bytes and a maximum of 260 bytes for both. Table 10 shows a selection of the function codes selected for this work based on multiple datasets that were reviewed.
# Bacterial Aerosol in Ambient Air--A Review Study
## 3 Discussion
### Interactions with Meteorological Factors
#### 3.2.4 Wind Speed  
Wind speed has been positively associated with both the concentration and diversity of bacterial populations in various studies. It plays a crucial role in facilitating the generation of bioaerosols in soil and water environments [90]. The data presented in Table 1 illustrate the complex relationship between wind speed and airborne bacterial concentrations. Wind can influence bacterial levels by resuspending particles from surfaces such as soil and vegetation. [PERSON] and [PERSON] [91], along with [PERSON] and [PERSON] [34], have documented how wind enhances atmospheric dilution, which can lead to a reduction in local bacterial concentrations. Strong winds, in particular, are known to contribute to this dilution effect, especially during pollution events [8].  
Recent advances in backward air trajectory modeling have demonstrated that typical wind patterns significantly contribute to the long-distance dispersal of airborne bacterial communities. Research on dispersion patterns over the past two decades has shown that changes in air mass circulation directly influence microorganism dispersal, often occurring within relatively short time frames [97]. Despite these findings, the relationship between airborne bacterial community composition and seasonal variations driven by atmospheric processes remains poorly understood and underexplored in many global environments.  
Studies underscore the variability in wind's impact on bacterial concentrations. For instance, a correlation of 0.659 (\(p<0.05\)) suggests that wind may sometimes contribute to increased bacterial levels through resuspension [17]. However, the effect of wind speed on bacterial concentrations is complex, often reflecting local environmental conditions and seasonal variations.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 4 Discussion
### Case 1: CWRU Bearing Dataset
#### 4.2.5 Comparative Experiments and Analysis with Small Sample Sizes  
Due to the difficulty of obtaining large amounts of fault data in practical engineering applications, this experiment evaluated the diagnostic performance of HFDF-EffNetV2 with small sample sets by dividing the samples based on the total length of the sampled vibration signals and creating three datasets (A, B, and C) of different sizes. The number of training samples in datasets A, B, and C was 100, 200, and 300, respectively, while the number of test samples was fixed at 700.  
The experimental results are shown in Table 6. ResNet-18 performed well, with an average accuracy of 98.55%, which is only 0.14% lower than that of the proposed model. EfficientNetV2-S, RepViT, and MPNet, which exhibited relatively good performance in the noise-interference experiments, showed instability when the training data were scarce, with average accuracies lower than that of the proposed model by 2.68%, 6.06%, and 2.72%, respectively. In contrast, the accuracies of SqueezeNet and MobileNetV3 were significantly lower than that of the proposed model, at 10.89% and 7.74%, respectively. On dataset A, the accuracies of all six comparison models decreased significantly, demonstrating that the models failed to extract effective fault features. Although the accuracy of the proposed model decreased compared to those of the other two training datasets on dataset A, it still remained above 98%, which was higher than those of the other six models by 6.35%, 0.09%, 12.69%, 16.12%, 6.09%, and 14.49%, respectively. The results indicate that HFDF-EffNetV2 is less influenced than the other models by the number of training samples and that its performance remains stable across varying sample sizes.