esa-sceva/satcom-chunk-collection huggingface dataset:
# Crossover Effect in Cement-Based Materials: A Review
## 7 Materials Controlling COE Phenomenon  
The existing literature does not contain sufficient information about COE phenomenon in CBMs. Therefore, few materials have been mentioned to decrease or control the loss in compressive strengths at later ages at high curing temperatures. [PERSON] et al. [15] found that COE in compressive strength was not observed for BFS80 (by replacing Portland cement with 80% GGBS by mass) in temperature range of 20-60 \({}^{\circ}\)C up to 365 days.  
[PERSON] [67] stated that the compressive strength will increase by replacement of slag powder after 28 days. Moreover, slag powder will significantly increase the later-age compressive strength. [PERSON] et al. [33] suggested that 25% replacement of slag is optimum for the strength development with steam and normal curing. Autoclaving has no effect on the strength developed by replacing 50% slag with Portland cement, whereas 75% slag replacement with Portland cement reduces compressive strength in all types of curing methods. Normal curing methods provide higher compressive strength than the accelerated curing methods up to 50% replacement of slag with OPC.  
[PERSON] et al. [8] stated that Portland type V cement concretes do not undergo COE compared with Portland type I cement concretes up to curing temperature of 50 \({}^{\circ}\)C for 28 day because the rate of hydration of Portland type V cement concrete is lower than that of Portland type I cement concrete. Thus, the COE of Portland type V cement concrete is postponed. [PERSON] et al. [14] used blended cement mortars with different percentages of natural pozzolan replacement with OPC. They studied their behaviour under water curing temperatures of 20 \({}^{\circ}\)C, 40 \({}^{\circ}\)C and 60 \({}^{\circ}\)C for up to 90 days. They detected  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Paste Name** & **Age at Which COE Occurred** & **COE (\%)** \\ \hline  & 28 & 10.9 \\ OPC & 60 & 16.3 \\  & 90 & 15.3 \\ \hline  & 28 & 8.2 \\ OPC-NP10 & 60 & 8.3 \\  & 90 & 15.7 \\ \hline  & 28 & 14.7 \\ OPC-NP20 & 60 & 13.9 \\  & 90 & 17.7 \\ \hline  & 28 & 13 \\ OPC-NP30 & 60 & 11.4 \\  & 90 & 2.1 \\ \hline  & 28 & 1.5 \\ OPC-NP40 & 60 & 3.2 \\  & 90 & 17.6 \\ \hline  & 28 & 6 \\ OPC-L5 & 60 & 13 \\  & 90 & 17.1 \\ \hline  & 28 & 17.3 \\ OPC-L15 & 60 & 17.5 \\  & 90 & 17.2 \\ \hline OPC-L25 & 90 & 7.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: COE records with 20–60 \({}^{\circ}\)C increase in water curing temperature at ages up to 90 days for OPC, natural pozzolan and limestone powder blended cement mortars.  
no COE in compressive strength of blended cement mortars at curing temperatures of 20 \({}^{\circ}\)C, 40 \({}^{\circ}\)C and 60 \({}^{\circ}\)C due to pozzolan in mixtures. Its presence decreases negative influence of rise in temperature. The optimal replacement rate of OPC by natural pozzolan is approximately 15% for normal room temperature curing and 20% for accelerated temperature curing of 40 \({}^{\circ}\)C and above.
# Development of Korea Airport Pavement Condition Index for Panel Rating
## 2 Materials and Methods
### Pavement Condition Index Rating Scale  
An evaluation criterion classified into separate subjective categories, as listed in Table 1, is required to determine the definitions of the distress and DV. This criterion provides a PCI range and M&R standard using pavement condition rating, which are technical indices required for reasonable and subjective evaluation of the effect of a given distress. The PCI range becomes a standard for determining the pavement condition, and the M&R type is determined accordingly [23]. One of the limitations of the existing PCI is the lack of practical connection between the evaluation results and the need for repair. Therefore, a standard for the pavement condition evaluation was prepared in advance to ensure a practical connection between the PCI and the need for repair. This paper prepared the M&R standard in terms of the PCI range by incorporating the circumstances in Korea and results of expert group meetings based on the PCI rating scales suggested in ASTM D5340, as listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**PCI Range**} & \multirow{2}{*}{**Pavement Condition**} & \multicolumn{2}{c}{**Maintenance/Rehabilitation (M&R) Type**} \\ \cline{3-4}  & & & **Asphalt Pavement** & **Concrete Pavement** \\ \hline \multirow{2}{*}{90–100} & \multirow{2}{*}{Good} & Routine maintenance & Routine maintenance \\  & & - Crack sealing & - Crack sealing \\  & & & Joint replacement \\ \hline \multirow{2}{*}{80–89} & \multirow{2}{*}{Fair} & Routine maintenance & Routine maintenance \\  & & - Crack sealing & - Crack sealing \\  & & - Patching & - Joint replacement \\ \hline \multirow{2}{*}{60–79} & \multirow{2}{*}{Caution} & Preventive maintenance & Preventive maintenance \\  & & - Crack sealing & - Crack sealing \\  & & - Patching & - Joint replacement \\  & & Partial Grind/Overlay & Partial section fix \\ \hline \multirow{2}{*}{40–59} & \multirow{2}{*}{Poor} & Overlay and Grind/Overlay & Asphalt overlay or \\  & & (7.5–10 cm) & Grind/Overlay (over 10 cm) \\ \cline{1-1}  & & Construction & Construction \\ \hline
0–39 & Very Poor & Construction & Construction \\ \hline \hline \end{tabular}
\end{table}
Table 1: PCI Score Rating and M&R Type.  
Figure 2: Development Process of KPCI.
# A DFTB-Based Molecular Dynamics Investigation of an Explicitly Solvated Anatase Nanoparticle
## 3 Results
### Electronic Density of States  
Finally, a key requirement for any electronic structure method is the accurate computation of the band structure and electronic density of states (EDOS) of the system. As the nanoparticle is a finite, zero-dimensional system with artificial periodicity, computing the band structure makes little sense. However, the shell-resolved EDOS is still calculable, and is shown in Figure 9.  
Figure 8: VDOS of hydrogen atoms in the nanoparticle system. The y-axis here is intensity, in arbitrary units.  
Figure 9: Shell-resolved partial EDOS of the nanoparticle system, with the corresponding EDOS for bulk anatase inset. The \(y\)-axis is the number of states occurring at that energy level.  
Curiously, the Fermi energy (E\({}_{\text{F}}\)) of this system is placed in the middle of a band, implying that the system is metallic. This is likely due to the inclusion of water molecule states in the calculation, raising the average energy of the occupied states. From the calculations of the bulk EDOS of anatase (Figure 9 inset), it can be deduced that the band in which the Fermi energy resides in the solvated nanoparticle system is the conduction band of bulk anatase, as it is primarily composed of Ti d-orbitals. This leads to another interesting observation, as follows: in the total EDOS (dashed black line), a minor peak is present at approximately \(-4.0\) eV, which does not contain any contributions from nanoparticle atoms. If the aforementioned deduction is correct, this peak would represent mid-band states, likely formed by adsorbed oxygen atoms on the TiO\({}_{2}\) surface. These states were not reported previously in DFT calculations of hydrated anatase surfaces [26]. These mid-band states can play an important role in the water-splitting reaction [27], and further investigation is necessary to fully understand the nature and origin of this peak.
# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 4 Proposed Study
### Proposed Feature Extraction Models
#### 4.2.1 Proposed Edge Detection Method
Since it is a complicated task, this study proposes an innovative genetic algorithm, which minimizes the time and the steps involved in finding the best template and which uses the image pixel values to maximize Equation (2). The pixel value that represents a template that maximizes Equation (2) is considered an edge. However, all the templates do not maximize the threshold function; rather, a suitable template that maximizes the threshold function has to be selected. The proposed genetic algorithm selects the best template based on the crossover function represented in Equation (3). This research proposes an innovative cross-mutation function, which helps the genetic algorithm to switch efficiently between different templates. Any template that has a value greater than the crossover function will be the next template used by the image pixels to identify the presence of an edge. \[CF=0.9,\frac{f_{max}-f}{f_{max}-\widehat{f}} \tag{3}\]  
where \(f_{max}\) denotes the maximum fitness function, \(f\) represents the mean fitness function, and \(\widehat{f}\) depicts the fitness function of an individual pixel. Figure 6 illustrates the flow of the proposed genetic-algorithm-based edge detection model, and Algorithm 1 elaborates the overall functioning of the genetic algorithm. Equation (3) displays the proposed crossover function, whose value determines whether the genetic algorithm must switch between different templates to determine the maximum fitness function.
## 4 Performance Evaluation Results and Discussion
### Performance Evaluation for User Activity
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Rank** & **User ID** & **Activity** & **Influence** & \begin{tabular}{c} **Num** \\ **Elite** \\ \end{tabular} & \begin{tabular}{c} **Num** \\ **Fans** \\ \end{tabular} & \begin{tabular}{c} **Friends** \\ \end{tabular} & \
\begin{tabular}{c} **Num** \\ **Reviews** \\ \end{tabular} &
\begin{tabular}{c} **Neent Date** \\ \end{tabular} \\ \hline Top 1 & kGgAARL2 UnvCCTRisqug & 10,832.10917 & 85.14843 & 1 & 143 & 632 & 301 & 11 December 2013 \\ \hline Top 2 & DrWLhrK8 WMZ7B-Oq7 ww & 10,337.21580 & 7.18467 & 0 & 11 & 20 & 323 & 27 January 2014 \\ \hline Top 3 & 6 bNXPqquQeByYZu9 tGgQ & 7282.89702 & 74.09587 & 7 & 114 & 344 & 417 & 27 November 2013 \\ \hline Top 4 & CfoMaxYdLIT5 Wf4 fZYLiuA & 4794.69461 & 36.28776 & 7 & 49 & 709 & 339 & 5 January 2014 \\ \hline Top 5 & pEViSGksh9 tHUL5 pizz6 LA & 4733.53982 & 34.25725 & 5 & 49 & 46 & 287 & 1 December 2013 \\ \hline Top 6 & qPxgQyNN8 Sqq2z6_SO3-OQ & 4429.29002 & 36.26023 & 7 & 49 & 403 & 269 & 22 January 2014 \\ \hline Top 7 & HOleJ3r1 MLNU6 cc1x0 Pw & 4227.67528 & 22.42461 & 6 & 27 & 252 & 147 & 21 November 2013 \\ \hline Top 8 & ecefpk8G1 etUtQtaTq5 XLg & 3954.32938 & 4.79183 & 0 & 7 & 13 & 158 & 24 January 2014 \\ \hline Top 9 & wHgTyXcZdZq9 WbJ0 TBgqHtQ & 3522.54059 & 30.31668 & 7 & 39 & 168 & 212 & 8 January 2014 \\ \hline Top 10 & klyk4G1p1 PccNjEY9-YMoQ & 3288.75876 & 2.16633 & 0 & 2 & 14 & 168 & 21 January 2014 \\ \hline Low 5 & vCbrHCnLG5 TECwpMgeFiRQ & 5.03457 \(\times\) 10\({}^{-45}\) & 0.5 & 0 & 0 & 1 & 1 & 1 July 2008 \\ \hline Low 4 & wqPaSr7t6 czs-w3N1C07g & 2.38432 \(\times\) 10\({}^{-46}\) & 0.4375 & 0 & 0 & 0 & 1 & 1 May 2008 \\ \hline Low 3 & p5 FcpR2d8u58 rbTCDQ1 nw & 7.95725 \(\times\) 10\({}^{-48}\) & 0.16666 & 0 & 0 & 0 & 2 & 23 February 2008 \\ \hline Low 2 & M1 JCxPUk[K8]9 AHLcfg & 6.70276 \(\times\) 10\({}^{-59}\) & 1.58316 & 0 & 1 & 1 & 1 & 1 October 2006 \\ \hline Low 1 & t5 Xb5 GY1Q1J7 jVyugOdbg & 1.50334 \(\times\) 10\({}^{-41}\) & 0.0 & 0 & 0 & 0 & 1 & 1 June 2006 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Top 10 and lowest 5 active user data. Figure 4: Evaluation difference between top 10 influential users and the average of all users: (**a**) the difference is shown for each restaurant; (**b**) the average evaluation difference is shown for each of the top 10 influential users. Figure 5: Comparison of the evaluations by the top 10 active users to the average evaluations by all users.
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 2 Homomorphic Encryption
### Big Integer-Based Mixing Method
#### 2.2.3 Homomorphic Encryption and Mixing  
* The additive homomorphism property of the Paillier homomorphic encryption algorithm allows numerical addition to be performed on encrypted data.
* For instance, consider two plaintext numbers, 100 and 200. After applying homomorphic encryption, they become E(100) and E(200). When these encrypted values are combined using additive homomorphism, the result is the ciphertext E(300). Decrypting this ciphertext yields the sum of the original plaintexts, which is 300.
* Utilizing this property enables the mixing of audio data while these remain encrypted, eliminating the need to decrypt before mixing.  
Figure 1 illustrates the process of obtaining mixed audio information after the user's voice data undergo the aforementioned steps. The operation of the homomorphic encryption mixing algorithm is revealed too.  
As shown in Figure 1, the voice data of [PERSON], [PERSON], and [PERSON] are captured by their microphones and converted into digital signals composed of three eight-bit samples. For example, [PERSON]'s digital signal is [11110110, 00000110, 00011110]. Using [PERSON], the process is presented as follows.  
Figure 1: Homomorphic encryption process.
# Effectiveness of High-Intensity Therapy in Rehabilitation of Parkinson's Disease Patients
## 2 Methods
### Outcomes
#### 2.2.1 Functional Performance Outcomes  
Functional mobility assessments included the Timed Up and Go (TUG) test, the 10-Meter Walk Test (10 MWT), and the Six-Minute Walk Test (6 MWT), which helped quantify agility and endurance improvements. Furthermore, the Schwab and England Functional Independence Measure (FIM) was included to assess the degree of autonomy in daily life.
## Materials and Methods
### UPLC-QTOF/MS Conditions for Ginsenoside Profiling  
UPLC was performed using a Waters ACQUITY H-Class UPLC (Waters Corp., Milford, MA, USA) with an ACQUITY BEH C18 column (2.1 mm \(\times\) 100 mm; 1.7 \(\upmu\)m). The column oven was maintained at 40 \({}^{\circ}\)C, and the mobile phases included solvent A [water and 0.1% formic acid (\(v/v\))] and solvent B [acetonitrile and 0.1% formic acid (\(v/v\))]. The elution gradient was as follows: 0-0.5 min, B 15%; 0.5-1 min, B 15-20%; 1-6 min, B 20%; 6-13 min, B 20-30%; 13-23 min, B 30-35%; 23-24 min, B 35-38%; 24-27 min, B 38-60%; 27-31 min, B 60-90%; 31-32 min, B 90-15%; and 32-35 min, B 15%. The flow rate was 450 \(\upmu\)L/min, and the injection volume was 2 \(\upmu\)L for each run.  
Next, MS analysis was performed using a Waters Xevo G2-S QTOF MS (Waters Corp., Milford, MA, USA) operating in negative ion mode. The mass spectrometers performed alternating high- and low-energy scans, known as the MSE acquisition mode. The operating parameters were set as follows: cone voltage 40 V; capillary 3.0 kV; source temperature 120 \({}^{\circ}\)C; desolvation temperature 550 \({}^{\circ}\)C; cone gas flow 30 L/h; and desolvation gas flow at 800 L/h. Accurate mass measurements were obtained by means of an automated calibration delivery system that contained an internal reference [Leucine-enkephalin, \(m/z\) 554.262 (ESI-)]. Data were collected between 100 and 2000 \(m/z\).
# A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems
## 4 A Cache Policy Based on Request Association Analysis for Reliable NAND-Based Storage Systems
### Request Association Analyzer
#### 4.2.3 Request Association Pairs Management  
Request association pairs management is to manage association pairs and select the hot association pairs from them. Request association pair management uses a two-level LRU to maintain the association pairs, where the first LRU list stores association pairs that appear once, and the second LRU list stores association pairs that appear more than two times and are considered as the hot association pairs, as shown in Figure 6. As the hot association pairs appear more than two times in the second LRU list, they usually conform to the temporal locality and can be useful in the future.  
After the hot association pairs are selected, we can extract each request's starting LPN from the hot association pairs and count the number of occurrences of each starting LPN in all association pairs. For example, Figure 7 shows the number of occurrences of each starting LPN in all association pairs such as (100,101), (100,200), (100,201), (101,200), (101,300), (200,201) and (200,400). Then, we can set a threshold to separate the strong requests according to the number of occurrences of each request's starting LPN in all association pairs. As shown in Figure 7, assume that the threshold is 50%, the first 50% of all requests' starting LPNs (sorted by the number of occurrences) are considered as strong requests (e.g., 200, 100 and 101) and are maintained in a strong request list. Other requests are considered as weak requests (e.g., 201, 300 and 400) and are maintained in a weak request list. Later, we will propose how to dynamically adjust the threshold (e.g., 50%) according to the current workload to improve the accuracy and performance in Section 4.3.2.  
Figure 6: A two-level LRU list to maintain the hot association pairs.
## 3 Multi-Objective Optimization
### Multi-Objective Optimization Based RSM
These results both achieve the design targets and have an acceptable margin for deviation. Because the electromagnetic force \(\mathrm{F_{m}}\) is freely varied, there is no response for it from Minitab software. But after re-checking by FEA, it is expected to be around 2638.3 N, which is higher than the limitation (\(\leq\)2000 N). Secondly, output power and power density are maximized, and electromagnetic force is minimized without any constraints. The responses from the Minitab software for tooth width \(\mathrm{w_{t}}\) and axial PM width \(\mathrm{w_{am}}\) are 7.4 and 7.2 mm, respectively. In this case, the maximum \(\mathrm{P_{m}}\) and average power \(\mathrm{P_{av}}\) are expected to be 349.2 and 125.2 W, while force \(\mathrm{F_{m}}\) is around 2121.4 N. Although force decreased by nearly 24.4%, it is still slightly higher than the limitation. Thirdly, power and power density are maximized without any constraints, and maximum value of the electromagnetic force is limited to \(\leq\)2000 N, and the responses from the Minitab software for tooth width \(\mathrm{w_{t}}\) and axial PM width \(\mathrm{w_{am}}\) are 8.6 and 7.2 mm, respectively. In this case, the average power \(\mathrm{P_{av}}\) and power density \(\mathrm{P_{d\_av}}\) are expected to be 121.6 W and 0.114 W/cm\({}^{3}\). Although these results satisfy the original design targets, the margin for deviation is still relatively small. However, the maximum electromagnetic force \(\mathrm{F_{m}}\) was successfully reduced to approximately 1755.1 N.  
The results summarized in Table 4 suggest that all of the design targets, as well as the constraints for the machine's performance, can be achieved using multi-objective optimization. Additionally, it can be intuitively observed that when electromagnetic force is reduced with a different setup, output power, and power density are decreased as well. It should be noted that the detent force is not mentioned during optimization because of its significantly smaller value than the electromagnetic force.
## 3 Analysis of Experimental Results
### Analysis of Ground Parameters  
According to the above orthogonal test results, the influence of grinding parameters on the grinding effect of welds was analyzed. The results are shown in Figure 11.  
As shown in Figure 11, the influence of grinding parameters such as grinding pressure, feed speed, and grinding line speed on Ra of the face and ridge surfaces of the weld is shown. Under the conditions of grinding pressure of 15 N and 25 N, the face Ra of the weld was 1.72 and 0.9 and the ridge Ra was 1.7 and 1.03, respectively. Under the feed rate of 0.01 m/s and 0.015 m/s, the Ra of the face of the weld was 1.48 and 1.22, respectively. When the grinding speed was 10 m/s and 15 m/s, the Ra of the face of the weld was 1.4 and 1.3 and Ra of the ridge of the weld was 1.5 and 1.3, respectively. The range indicates the difference between different grinding parameters under the same grinding factor.  
From the above experimental results, it can be seen that the surface roughness of titanium alloy EBW can be reduced to less than 0.1 after grinding. Ra will decrease with the increase of belt speed, the decrease of feed speed, and the improvement of grinding positive pressure. At the same time, it can be seen that the range of grinding positive force is more significant than the other two factors.  
Figure 10: Ground surface of type II.  
We can thus say that the effect of \(F_{n}\) on Ra is followed by \(v_{p}\) then \(v_{s}\). This conforms to the fundamental technological rules of cutting. In addition, the surface roughness on the weld face is generally lower than weld ridge. This is mainly because the width of the weld face is more comprehensive, and the grinding smoothness is better.
# The Clean Label Concept: Novel Approaches in Gluten-Free Breadmaking
## 7 Use of Fiber
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hydrocolloid** & **Dosage \%** & **Volume** & **Porosity** & **Crumb Elasticity** \\ \hline Pectin & 1 & No impact & Positive impact & Negative impact \\ Pectin & 2 & Positive impact & Positive impact & Positive impact \\ CMC & 1 & Positive impact & Positive impact & Negative impact \\ CMC & 2 & Negative impact & No impact & Positive impact \\ Agarose & 1 & Positive impact & Negative impact & Negative impact \\ Agarose & 2 & Negative impact & Negative impact & Positive impact \\ Xantham gum & 1 & No impact & Positive impact & Negative impact \\ Xantham gum & 2 & Negative impact & Negative impact & Positive impact \\ Beta glucan & 1 & Positive impact & Positive impact & Negative impact \\ Beta glucan & 2 & Positive impact & Positive impact & Positive impact \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Impact of thickeners on volume, porosity and crumb elasticity depending on the dosage [45]. [PERSON] et al. [52] investigated the impact of fortifying gluten-free breads with fiber preparations of various origins. Fiber formulations (in paste form) were used alone or in combination. The rheological properties of the gluten-free batters were examined, as well as the volume and porosity of the bread crumb. It was found that lupin fiber alone or in combination with inulin had the most beneficial effect on the rheological properties of the batter. Lupin fiber also had the most beneficial effect on the volume and porosity of the bread. This research confirmed previous observations regarding the significant difference between the rheological properties of doughs fortified with only one type of hydrocolloid and those fortified with a blend of two or more. By using mixtures of polysaccharides, it is possible to obtain compounds with much higher viscosity than with a single hydrocolloid. The influence of the high absorption capacity of fibers on the rheological properties of dough was also highlighted.