esa-sceva/satcom-chunk-collection huggingface dataset:
# Early-Age Cracking in Concrete: Causes, Consequences, Remedial Measures, and Recommendations
## 4 Factors Affecting Early-Age Cracking
### Design Process  
Restraints in concrete should be considered during the design process. The restraints due to thermal and autogenous deformation induce stresses in concrete. Cracking is induced when the tensile stress/strength ratio exceeds 60% [34]. The restrained movement depends on the design of the structure and the source of restrained movement, such as concrete cover [35], reinforcement [36; 37], nonuniform concrete depth [37], and the others. The structural design detail which should be considered for the restrained movement includes the dimensions and geometry of the structure [9], location [9; 34], and general structural configuration [9].  
The volume/size of concrete elements is one of the main factors for thermal dilation cracking. The massivity of the structure is defined as the ratio between the volume and the exposure surface [38], while the structure is considered as massive when the structural dimensions of heat diffusion exceed the hydration heat diffusion length [39]. In massive concrete structures, the variation in the degree of hydration happens at different locations due to the low temperature coefficient of concrete [8; 10]. Due to the differential temperature of concrete, cracking occurs in concrete because of thermal dilation. Besides, it was found that the influence of the concrete cover on the tendency of cracking is more significant than the size of reinforcement and slump. Decreasing the depth of concrete cover could increase the tendency of cracking [40; 41].
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 5 Simulations
### Simulation Method  
When we conducted this simulation-based study to evaluate the accuracy of our proposed models, it was necessary to conduct extensive simulations. In particular, PUs are distributed according to HPPP in a plane of area \(200\times 200\) km\({}^{2}\). Figure 4 shows a random topology of a simulation snapshot, where red circles denote PUs, and blue triangles denote SUs (the distance between a pair of SUs is \(r\)). Then, the probability of connectivity \(p_{con}^{s}\) of simulations can be acquired by  
\[p_{con}^{s}=\frac{\text{\# topologies that an SU pair can connect successfully}}{\Omega}, \tag{16}\]  
where \(\#\) denotes \"the number of\". Note that we denote the simulation result by \(p_{con}^{s}\) in order to differentiate it from the analytical one. Once system parameters are chosen, we will repeat the same experiment with different random topology of PUs for \(\Omega\) times. As indicated in [PERSON]'s seminar work [31], to obtain an approximated result to the analytical one, we need to choose a large enough \(\Omega\) (theoretically \(\Omega\rightarrow\infty\)). However, it is extremely time-consuming to obtain such results. In this paper, we choose \(\Omega=20{,}000\) (which was shown to be large enough in a recent work [32]). More specifically, Table 1 shows a comparison between the simulation results with \(\Omega=20{,}000\) and those with \(\Omega=500\). We can observe from Table 1 that the average deviation for simulation results with \(\Omega=20{,}000\) is only \(1.56\%\) (fairly close to the theoretical results), while that with \(\Omega=500\) is \(8.35\%\).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Analytical Value** & **Simulation Value with \(\Omega=500\)** & **Simulation Value with \(\Omega=20{,}000\)** \\ \hline \(r=1\) km & 0.1758 & 0.1520 (13.53\%) & 0.1726 (0.18\%) \\ \(r=2\) km & 0.1624 & 0.1640 (0.99\%) & 0.1612 (0.74\%) \\ \(r=3\) km & 0.1500 & 0.1560 (4.00\%) & 0.1454 (3.07\%) \\ \(r=4\) km & 0.1386 & 0.1180 (14.86\%) & 0.1417 (2.24\%) \\ \hline \multicolumn{3}{c}{Average deviation} & 8.35\% & **1.56\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Deviation of \(p_{con}^{s}\) between simulation results (with different values of \(\Omega\)) and analytical results. System parameters are set as \(f=20\) kHz, \(P_{p}=110\) dB, \(P_{s}=100\) dB, \(\lambda_{p}=0.003\), \(k=1\), \(w=0\) m/s, \(s=1\), \(\delta_{s}=20\) dB, and \(\delta_{d}=20\) dB.  
Figure 4: Random topology of a simulation snapshot of underwater cognitive acoustic sensor networks (UCANs), where red circles denote PUs and blue triangles denote SUs.  
In summary, we observe that frequency \(f\) and ambient environment factors such as spreading factor \(k\) and wind speed \(w\) have a significant influence on the link connectivity of SUs in UCANs.
## 2 Materials and Methods
### Modifications of Model Parameters  
To calculate the microdosimetric parameter \(y_{D}\) using the above models (X94, Xcom, XO), we must first establish the needed material parameters, namely the mean excitation energy of stopping-power theory (I) (entering Equations (8), (10) and (11)), the constants A and B (of Equations (12) and (14)) and the cut-off energy \(\Delta\). The choice of the cut offenergy \(\Delta\) is defined by the electron energy that corresponds to a penetration depth equal to the mean chord length \(\overline{l}=\frac{2d}{3}\) of the target size. For reasons of availability, the original implementation of X94 and X96 models used data for condensed water vapor. Vapor water refers to the so-called gas phase approximation, whereby interaction cross sections for water vapor are extrapolated to unit density medium. Therefore, vapor and liquid water share the same density (and also both temperature and pressure values) but differ upon the underlying physics of their interaction cross sections. Specifically, vapor water cross sections do not account for intermolecular effects (screening, etc.) which are considered (according to the theoretical model adopted) in the case of liquid water. To examine the degree of which the results are sensitive to the parameters of the water medium, we here deduced the corresponding parameters for liquid water. Table 1 summarizes the values of the model parameters used in the present work. The cut-off energy \(\Delta\) for the different sphere diameters was estimated from electron penetration-depth values for liquid water obtained from Geant4-DNA MC simulations using the latest electron physics list (Option 4) for liquid water [57]. The corresponding values for condensed water vapour were determined from the analytic fitting formulas for the electron penetration-depth obtained by the MC4 code [58]. For the calculation of the material constants, \(A\) and \(B\), for electrons and protons, we followed the method described in [PERSON] et al. (1994), whereby Equations (11) and (12) and their derivatives, with respect to \(Min[\Delta,T_{el,max}]=\Delta\), are continuous at a \(\Delta\) value that equals twice the K-shell binding energy of water (2 \(\times\) 540 eV = 1.08 keV).
# Experimental Investigation of Controlled and Uncontrolled Rectifiers for Low-Power Wind Turbines
## 8 Conclusions  
Low-power wind energy systems are proliferating rapidly, so improving and optimizing their mechanical and electrical parts is pivotal. First, the paper provided a valuable review of low-power wind energy systems and their rectifier circuits. Next, various AC to DC rectifier topologies suited for low-power wind energy systems were discussed. Next, the single-phase half-wave rectifier was studied regarding output voltage ripple performance. The rectifier was mathematically analyzed, followed by simulations and experiments. It was found that the filtering capacitor must be large enough to supply DC power to the load during the negative half-cycle. The filtering capacitor improves both the output voltage regulation and voltage ripple performance. The relationship between the charging time constant and the output voltage was also validated for single-phase half-wave rectifiers. Next, the single-phase bridge rectifier was studied in terms of voltage ripples and voltage regulation. The output voltage remains regulated and ripple-free for a reasonable range of the load change (25-250 \(\Omega\)). Finally, the fully controlled three-phase rectifiers were implemented. The output voltage was controlled through a firing angle delay. The simulations and the experiment showed that the output DC voltage can be regulated through SCR firing angle control at the desired value during wind speed variation and load change.  
This work was funded by the Research Groups Funding program grant code (NU/RG/ SERC/11/2).  
Not applicable.  
The author is thankful to the Deanship of Scientific Research at Najran University for funding this work under the Research Groups Funding program grant code (NU/RG/ SERC/11/2).  
The author declares no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} SCR & Silicon Controlled Rectifier \\ PIV & Peak inverse voltage \\ WE & Wind Energy \\ RF & Ripple Factor \\ RMS & Root Mean Square \\ SWE & Small Wind Energy \\ \end{tabular}  
Figure 15: Experimental results for three-phase full\(-\)wave rectification used in small wind turbine application.
## 2 Related Works
### Transformer-Based Remote Sensing Semantic Segmentation  
Originally successful in natural language processing [29], transformers have since been adopted in computer vision. The vision transformer (ViT) [30] processes images as sequences of fixed-size patches to model global context but suffers from quadratic computational complexity relative to input image size, limiting its applicability to high-resolution remote sensing images. The Swin transformer alleviates this limitation by introducing a shifted window mechanism. Its hierarchical structure and localized self-attention computation allow for global dependency modeling while maintaining linear computational complexity, making it especially suitable for large-scale remote sensing images. The Swin transformer's ability to represent multi-scale features enables outstanding performance in remote sensing segmentation tasks, particularly in modeling extensive regions and intricate textures. [PERSON] et al. [31] proposed Swin-UNet, which integrates the Swin transformer into a U-Net framework, combining transformer-based global modeling with U-Net's detailed feature recovery. [PERSON] et al. [32] developed a hierarchical feature extraction network based on the Swin transformer to address multi-scale semantic segmentation in remote sensing images. [PERSON] et al. [33] designed the RS-ST model specifically for high-resolution remote sensing imagery, incorporating improved window partitioning and feature fusion strategies to enhance sensitivity to complex boundaries. [PERSON] et al. [34] further incorporated a spatial attention module into the Swin transformer, improving the model's perception of spatial distribution patterns in remote sensing data. Despite their strength in modeling long-range dependencies, transformers still face challenges in capturing fine-grained local details, motivating research into hybrid architectures that combine CNNs and transformers to leverage their complementary strengths.
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 3 Methodology
### Fitting into a DTMC Model
#### 3.4.2 Markov Packages-R and MATLAB  
The R programming language contributes significantly to scientific research and analysis. This study handles Markov Chain Modelling using the R package \"markovchain\" [17]. The package contains versatile methods for creating a DTMC object based on the transition matrix, as well as robust algorithms for generating exhaustive results. Assuming that we already have the transition matrix \(A\) and a list of states \(L\) (state space in the Markov Chain) in R studio, Table 1 below lists the R package functions that we can utilize for our study. In addition, the package can aid in the visualisation of the DTMC model using the R _plot()_ function.  
In addition, to create discrete-time Markov Models, MATLAB also has an inbuilt function 'dtmc' (discrete-time Markov Chain), where, given a transition matrix \(A\) and we let the state names be [1,2,3,4], the Markov Chain can be constructed using the command: [63] \"_mc = dtmc(A, 'StateNames', [\"1,\" \"2,\" \"3,\" \"4\"])_\".  
This chain can then be observed as a directed graph to clearly show which states are communicating, if any states are absorbing or if there exist multiple state systems in the chain by executing the \"graphplot\" function. The MATLAB command is as follows: \"_graphplot(mc, 'ColorEdges', true)_\". The 'ColorEdges' parameter allows the transition probabilities to be viewed as different colours on a scale of 0-1.  
Furthermore, MATLAB can calculate the steady-state vector (xFix) of the chain and the mixing time (tMix), which is defined as the time until the Markov chain is close to reaching its steady-state distribution by the following command [64]: \"_[xFix, tMix] = asymptotics(mc)_\". Besides, MATLAB also provides solutions to calculate the expected time to reach every state (mean first passage time).
## 3 Results and Discussion
### Analysis of the Oenological Composition of the Base Wines
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Code**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Control (C)**} & \multirow{2}{*}{**Lees (L)**} & \multicolumn{3}{c}{**Ultrasound-Treated Lees**} \\ \cline{5-6}  & & & & **30\%** & **60\%** & **90\%** \\ \hline TA & Total acidity (g/L) & 5.3 \(\pm\) 0.0 & 5.3 \(\pm\) 0.1 & 5.3 \(\pm\) 0.2 & 5.3 \(\pm\) 0.1 & 5.5 \(\pm\) 0.1 \\ pH & pH & 3.39 \(\pm\) 0.04 & 3.50 \(\pm\) 0.08 & 3.37 \(\pm\) 0.26 & 3.35 \(\pm\) 0.37 & 3.37 \(\pm\) 0.03 \\ AD & Alcohol degree (\%, \(v/v\)) & 11.4 \(\pm\) 0.3 & 11.4 \(\pm\) 0.0 & 11.5 \(\pm\) 0.0 & 11.4 \(\pm\) 0.0 & 11.4 \(\pm\) 0.2 \\ VA & Volatile acidity (g/L) & 0.44 \(\pm\) 0.06 & 0.42 \(\pm\) 0.04 & 0.43 \(\pm\) 0.05 & 0.47 \(\pm\) 0.05 & 0.50 \(\pm\) 0.03 \\ ANT & Total anthocyanins (mg/L) & 190 \(\pm\) 44 & 195 \(\pm\) 52 & 176 \(\pm\) 69 & 138 \(\pm\) 19 & 197 \(\pm\) 61 \\ NP & Neutral polysaccharides (g/L) & 2.04 \(\pm\) 0.33 a  & 2.11 \(\pm\) 0.51 a  & 2.18 \(\pm\) 0.39 a  & 2.03 \(\pm\) 0.06 a  & 2.46 \(\pm\) 0.23 b  \\ SP & Soluble proteins (g/L) & 0.40 \(\pm\) 0.04 & 0.39 \(\pm\) 0.02 & 0.35 \(\pm\) 0.02 & 0.35 \(\pm\) 0.02 & 0.38 \(\pm\) 0.02 \\ FAN & Free amino nitrogen (mg/L) & 92 \(\pm\) 5 & 96 \(\pm\) 4 & 93 \(\pm\) 4 & 96 \(\pm\) 10 & 100 \(\pm\) 7 \\ TAN & Tannins (g/L) & 2.00 \(\pm\) 0.26 & 2.23 \(\pm\) 0.56 & 2.29 \(\pm\) 0.58 & 1.70 \(\pm\) 0.15 & 1.89 \(\pm\) 0.27 \\ \%Y & \% yellow & 30.06 \(\pm\) 0.33 & 30.05 \(\pm\) 0.35 & 30.04 \(\pm\) 0.21 & 30.29 \(\pm\) 0.08 & 30.05 \(\pm\) 0.34 \\ \%R & \% red & 54.31 \(\pm\) 0.65 & 54.42 \(\pm\) 0.60 & 54.60 \(\pm\) 0.84 & 53.92 \(\pm\) 0.20 & 54.77 \(\pm\) 1.22 \\ \%B & \% blue & 15.63 \(\pm\) 0.32 & 15.53 \(\pm\) 0.26 & 15.36 \(\pm\) 0.63 & 15.79 \(\pm\) 0.12 & 15.18 \(\pm\) 0.88 \\ TO & Tonality & 0.55 \(\pm\) 1.43 & 0.55 \(\pm\) 1.39 & 0.55 \(\pm\) 2.58 & 0.56 \(\pm\) 0.17 & 0.55 \(\pm\) 2.78 \\ CI & Colour intensity & 12.30 \(\pm\) 0.01 & 12.64 \(\pm\) 0.01 & 12.33 \(\pm\) 0.01 & 10.96 \(\pm\) 0.00 & 12.60 \(\pm\) 0.02 \\ TPI & Total polyphenol index (A\({}_{\rm 280\ nm}\)) & 7 \(\pm\) 0 & 7 \(\pm\) 1 & 7 \(\pm\) 1 & 6 \(\pm\) 0 & 7 \(\pm\) 1 \\ HA & Hydroxycinnamic acids (A\({}_{\rm 320\ nm}\)) & 20.55 \(\pm\) 1.41 & 21.5 \(\pm\) 1.9 & 20.8 \(\pm\) 1.1 & 22.1 \(\pm\) 2.6 & 21.1 \(\pm\) 2.2 \\ FLA & Flavonols (A\({}_{\rm 365\ nm}\)) & 6.88 \(\pm\) 0.53 & 7.13 \(\pm\) 0.74 & 6.93 \(\pm\) 0.53 & 7.40 \(\pm\) 1.20 & 7.05 \(\pm\) 1.06 \\ ASTR & Astringency (g/L) & 0.44 \(\pm\) 0.05 b  & 0.41 \(\pm\) 0.06 b  & 0.37 \(\pm\) 0.04 ab  & 0.34 \(\pm\) 0.05 a  & 0.33 \(\pm\) 0.06 a  \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Oenological parameters of the wines aged on ultrasound-treated and untreated lees (different letters indicate significant differences between samples (\(p<0.05\))).
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 2 ZoomFFT Algorithm
### Energy Efficiency  
When the number of points in the ZoomFFT is \(N\), the total number of sampling points is \(ND\), the order of the low-pass filter is \(R\), and the FFT requires \(\frac{N}{2}\)\(\log_{2}N\) complex multiplications, the total number of complex multiplications (NCM) for the ZoomFFT algorithm, including frequency shifting, low-pass filtering, and FFT, can be expressed as [21; 24]  
\[\begin{split} T_{ZoomFFT}&=T_{shift}(n)+T_{LP}(n)+ T_{FFT}(n)\\ &=\Theta((R+1)ND+\frac{N}{2}log_{2}N)\end{split} \tag{3}\]  
If the traditional FFT algorithm is required to achieve the same frequency resolution as the ZoomFFT, the number of points needs to be increased by \(D\). Therefore, the NCM for the traditional FFT algorithm is  
\[T_{FFT}=\Theta(\frac{ND}{2}log_{2}ND) \tag{4}\]  
It is shown from Equation (3) and (4) that the spectrum resolution with ZoomFFT is the same as the spectrum of the original signal with FFT, but the computational complexity has been reduced. When the value of \(D\) is relatively small (e.g., \(D=2,3\)), the computational complexity of the ZoomFFT is not significantly different from that of the FFT due to the  
Figure 1: Block diagram of ZoomFFT.  
frequency shift and filtering steps involved in the ZoomFFT. However, as \(D\) increases, in order to achieve the same frequency resolution, the FFT requires a much larger size, specifically \(D\) times that of the ZoomFFT. Given that the growth rate of \(N\log_{2}N\) is greater than that of \(N\), this results in a substantial increase in the complexity of the FFT, far exceeding that of the ZoomFFT. Therefore, the larger the value of \(D\), the more pronounced the advantage of the ZoomFFT algorithm becomes.  
Using a benchmark signal with a center frequency of 31 kHz, a bandwidth of approximately 2 kHz, and a sampling rate of 144 kHz, both FFT and ZoomFFT are implemented on the same hardware platform [36]. Under the condition of the refinement factor, \(D\) is 3, and the execution time of ZoomFFT is 9.528 ms, while that of FFT is 9.722 ms, showing little difference between the two. When the refinement factor \(D\) is increased to 12, the execution time of ZoomFFT is 8.578 ms, while that of FFT is 32.604 ms. To achieve the same resolution, the FFT algorithm requires an increase in the number of points, resulting in a much longer execution time compared to ZoomFFT. It is evident that for a specific frequency band, ZoomFFT exhibits a shorter execution time, thereby reducing overall energy consumption.
# Image Storage in DNA by an Extensible Quaternary Codec System
## 1 Introduction
Such DNA origami frameworks not only enhance error resilience through spatially constrained redundancy but also expand application horizons by enabling in situ computational operations within biomolecular matrices. These above studies leverage various source coding methods from the fields of informatics and communication theory to encode the information to be stored, resulting in binary data streams. A series of intricate mapping rules are then designed to map the binary data streams, composed of zeros and ones, into DNA sequences consisting of the four bases A, T, C, and G. The most basic mapping rule is a quaternary encoding scheme where one base corresponds to two binary digits, for example 00 \(\rightarrow\) A, 01 \(\rightarrow\) T, 10 \(\rightarrow\) C, 11 \(\rightarrow\) G. This completes the so-called quaternary encoding. However, the encoding process of \"data \(\rightarrow\) binary data stream \(\rightarrow\) ATCG base sequence\" increases the steps in the DNA  
Figure 1: The process of DNA-based information storage, which involves two key stages—information writing and information reading. In the writing process, information is encoded into DNA via DNA synthesis, conducted both in vivo and in vitro. In the reading process, the DNA is sequenced, allowing for the retrieval of the stored information. storage pipeline and fails to fully utilize the inherent advantages of DNA sequences formed by the arrangement of the A, T, C, and G bases. Under this framework, the theoretical maximum information storage density is 2 bits/nt (bits per nucleotide). Otherwise, after accounting for sequence indexing, error correction redundancy, characteristic primers, and other overheads, the actual storage density is typically about half of the theoretical value. In this study, we developed a True Quadratic Codec System, termed ETQ (ETQ consists of the initials of extensible, true, and quadratic), which uses A, T, C, and G (i.e., 0, 1, 2, 3) as symbols for direct information encoding. This system bypasses the conversion process from binary data streams to DNA sequences, directly encoding data into ATCG nucleotide sequences. Our proposed approach consists of three main components:  
* Dividing an image into B, G, and R color channels for separate encoding and storage. * Applying quaternary Huffman encoding to each color channel, directly encoding image information into nucleotide sequences composed of ATCG, eliminating the traditional \"data \(\rightarrow\) binary data stream \(\rightarrow\) ATCG sequence\" process, and overcoming the storage density limit of 2 bits/nt. * Incorporating Reed-Solomon error correction codes into each DNA sequence to enhance the extensibility of the proposed method.
# Study on a Quality Evaluation Method for College English Classroom Teaching
## 4 The Evaluation Index System of College English Classroom Teaching Quality
### Quality Evaluation Index System of College English Classroom Teaching  
College English is regarded as a tool subject, which has obvious practical and applied characteristics. The overall goal of college English education is to train a comprehensive language ability in students, and the students' learning and mastering conditions are the basic starting point and the ultimate goal of evaluating classroom teaching. Moreover, the key of college English classroom teaching evaluation is whether the teachers pay attention to the development of subjectivity, warm up the enthusiasm of language learning, and inspire thinking and learning interest for students in the classroom. Therefore, a college English classroom teaching evaluation should fully reflect its disciplinary characteristics. Based on the existing references for quality evaluation indexes of college English classroom teaching [34; 35; 36], according to the actual teaching purposes, training objectives, and the opinions of students in Dalian Jiaotong University, teachers and experts are integrated in order to propose a quality evaluation index system of college English classroom teaching in Table 1.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Content** & **First-Level Index** & **Second-Level Index** \\ \hline \hline \multirow{7}{*}{Teaching attitudes (\(u_{1}\))} & \multirow{7}{*}{Teaching attitudes (\(u_{1}\))} & Clear course teaching plan, fully prepared lessons (\(u_{11}\)) \\  & & Strict teaching and requirements (\(u_{12}\)) \\  & & Finish class on time and never adjust (stop) class (\(u_{13}\)) \\  & & Earnestly and promptly correct homework (\(u_{14}\)) \\  & & Often tutor and communicate with students (\(u_{15}\)) \\ \hline \multirow{7}{*}{The evaluation index system of college English classroom teaching quality (\(l\))} & \multirow{7}{*}{Teaching contents (\(u_{2}\))} & Rich teaching content and the right viewpoint (\(u_{21}\)) \\  & & Mastering books, exact concepts, and clear \\ \cline{1-1}  & & principles (\(u_{22}\)) \\ \cline{1-1}  & & Highlight key points, in appropriate detail, and at the \\ \cline{1-1}  & & proper difficulty (\(u_{23}\)) \\ \cline{1-1}  & & Absorb new ideas, new achievements, and new \\ \cline{1-1}  & & techniques to update teaching contents (\(u_{24}\)) \\ \cline{1-1}  & & Integrating theory with practice, communication \\ \cline{1-1}  & & learning, and research methods (\(u_{25}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The quality evaluation index system of college English classroom teaching.