esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Data Extraction and Synthesis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Outcome** & **Author, Year** & **Type of Materials Used** & **Main Result(s)** \\ \hline Al-shibani, 2019 [62] & LLLT & \begin{tabular}{c} The analysis of the follow-up data demonstrated \\ a statistically significant decrease in wound \\ healing for all study groups. \\ \end{tabular} \\ \hline [PERSON], 2021 [64] & PRF & \begin{tabular}{c} In six studies, PRF promoted faster wound \\ healing compared to control groups during the \\ first 2 weeks (\(p<0.005\)). However, no significant \\ difference was observed in the third week after \\ surgery when PRF was compared to collagen \\ dressing and oxidized regenerated cellulose \\ (2 studies). \\ \end{tabular} \\ \hline [PERSON], 2021 [65] & PRF, PRF + butyl-cyanoacrylate, \begin{tabular}{c} All studies examining palatal wound \\ epithelialization, regardless of the analytical \\ method, showed that PRF led to complete \\ healing in a shorter period compared to the \\ respective control group. \\ \end{tabular} \\ \hline Wound healing & [PERSON], 2021 [66] & Cyanoacrylate, cyanoacrylate + hemostatic sponge & \begin{tabular}{c} In most studies, the cyanoacrylate group showed \\ either less graft retraction (\(p<0.05\)) or improved \\ epithelialization, indicating better tissue healing \\ compared to other groups. In contrast, two \\ studies showed similar results between the \\ tested groups (conventional suture \\ and cyanoacrylate). \\ \end{tabular} \\ \hline [PERSON], 2021 [23] & LLLT & \
\begin{tabular}{c} Four studies reporting the results at donor sites \\ after FGG showed a significantly higher \\ percentage of complete epithelial healing at day \\ 14 in the test groups, while at day 21, no \\ difference was found between the test and \\ control groups. \\ \end{tabular} \\ \hline [PERSON], 2021 [30] & Cyanoacrylate tissue adhesive &
\begin{tabular}{c} The use of cyanoacrylate tissue adhesive with a \\ hemostatic agent, like platelet-rich fibrin, \\ resulted in more complete epithelialization at \\ weeks two and three compared to cyanoacrylate \\ and wet gauze. Additionally, cyanoacrylate \\ tissue adhesive combined with a hemostatic \\ sponge and suture (mean score: 9.0 \(\pm\) 0.7) \\ achieved better healing outcomes than a \\ hemostatic sponge with suture alone (mean score: 6.8 \(\pm\) 1) (\(p\)-value \(<\) 0.001). \\ \end{tabular} \\ \hline Abbreviations: RCT—randomized controlled trial; LLLT—low-level laser therapy; HA—hyaluronic acid. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The characteristics of the studies included in the present systematic review of wound healing. Figure 2: This figure illustrates the adjunctive therapeutic interventions for wound healing and pain control.
# Spreading-Based Voice Encryption by Means of OVSF Codes
## 3 Proposed Method  
The proposed encryption scheme for voice signals is based on the spreading of the input signal using OVSF codes, obtaining a signal that will sound like noise. Next, the operation of the proposed method will be explained, including the encryption and recovery stages.
## 3 Fresh and Hardened Properties of GPC
### Shrinkage  
Drying shrinkage has a significant role in the development of the cracks of hardened GPC [149]. Several researchers have found that GPC exhibits higher shrinkage than OPC concrete. [PERSON] et al. [150] studied the shrinkage characteristics of alkali-activated FA-slag (AFS) GPC and showed that AFS paste shows higher drying shrinkage than OPC due to presence of large mesopore volume in AFS paste as compared to OPC paste. Moreover, the alkali-activated binder has two times more shrinkage than OPC (Figure 19); however, this shrinkage can be reduced to OPC level with the usage of additives [149]. In addition, cracks in both types of pastes are visually presented in Figure 20 [149].  
Figure 19: Shrinkage with respect to time of both OPC and alkali activator binder [149].  
Figure 18: Effect of NaOH content on elastic modulus, adopted from [147]; Elsevier, 2016.  
[PERSON] et al. [151] determined the effect of FA and GGBFS proportions on strength and shrinkage characteristics of geopolymer pastes. It was found that the inclusion of slag decreases the setting time and enhances the compressive strength and bulk modulus, but also results in higher autogenous shrinkage. Furthermore, [PERSON] et al. [152] studied the effect of different content of MK/GGBFS ratios on shrinkage of GPC. They appraised that GPC after 28 days exhibits lowest drying shrinkage with 25-30% MK content and 70-75% GGBFS content. Hence, it is concluded that the shrinkage of GPC can be reduced by addition of optimum content of aluminosilicate and admixture.  
At the end of Section 3, Figures 21 and 22 are proposed to highlight the several parameters which significantly alter the fresh and hardened properties of GPC. Molarity of activators was found to significantly contribute towards both fresh and hardened properties. Figure 21 shows the factors influencing the compressive strength, tensile strength, workability, and setting time of GPC, while Figure 22 represents factors contributing to elastic modulus and drying shrinkage of GPC properties from top to bottom on different parameters. The highly desirable factor is represented by red color to the least desirable factor with a light orange color. Moreover, the fresh and hardened properties of GPC in term of its different influential parameters are summarized in Table 7. This table critically analyzes and concludes the crucial considerations taken for development of both fresh and hardened properties.  
Figure 20: SEM image of (**a**) OPC paste and (**b**) alkali activated binder paste [149].  
Figure 21: Contributing factors to strength and workability of GPC.  
Figure 22: Contributing factors to elastic modulus and shrinkage.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
## 2 Data and Methods
### Image Normalization  
Due to the fact that SDO/AIA and Hi-C are two different instruments, the images they capture have different image attributes. In order to ensure that the dataset has the same scale, accelerate the training speed, prevent model gradient explosion, reduce the chance of the model falling into local optima, facilitate subsequent processing, and maintain numerical stability, we normalized the images. This standardization process adjusts the pixel values to a common scale, allowing the model to train more efficiently and effectively. We employ Z-score normalization to rescale the pixel values of the dataset images, resulting in images with a mean of 0 and a standard deviation of 1. For each pixel value \(I\) in the image, the normalized value \(I^{\prime}\) is calculated as follows:  
\[I^{\prime}=255*\frac{I-\mu}{\sigma} \tag{1}\]  
where \(\mu\) is the mean of all pixel values calculated from the images, and \(\sigma\) is the standard deviation.
# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 4 Database Optimization Methods
### Index-Based Optimization Methods  
The optimization that most significantly reduces query response times is the addition of indexes to the fields used in the query's conditions: simple indexes if only one field from the table is used, or compound indexes if multiple columns in the query are used.  
In relational MySQL, adding indexes can be conducted in two ways; in document-based MySQL, adding indexes is carried out by using the predefined command _createdIndex_ which has two parameters: the name of the index, and its definition, as can be seen in Table 1:Using the second possibility in relational MySQL, indexes are visible in entities and can be easily identified. For document-based MySQL, in the definition of the index, the path to the field on which we want to add the index must be specified, with its type, text, or integer in the case of numbers, whether it is required or not. If an index is of the text type, its length must also be specified. In the case of the relational MySQL application, the indexes were added directly to the entities so that they can be easily observed and modified; in the case of the document-based MySQL application, they were added from the application when it was run. In order to obtain the best possible results for all operations, we analyzed each operation separately and noted which columns it used to obtain the data, centralizing these columns and adding an index for each combination obtained at the end. Indexes added to the two databases are described in Table 2.  
In document-based MySQL, because each entry is a separate document, the _deleted_ field and _created_at_ are applied per document, meaning only one index is added. In relational MySQL, each table automatically creates an index on the primary key; therefore, to maintain the consistency in both structures and to have the same indexes in both databases, we needed to add two more indexes in the relational MySQL database, one for the _business_id_ field, and one for the _user.id_ field. By adding these two indexes, the two databases are identical from this point of view. We added several indexes, simple and compound, to cover all the column combinations used in the case of the operations performed. Thus, each database will use the required index or a combination of several indexes, when performing the query, to obtain the highest possible performance, thus highlighting their effect. In the case of all operations, the database chooses the indexes it uses, without specifying certain indexes to be used.
# Effects of Supplementation of Different Proteins on the Rheological Properties of Liquid Whole Eggs
## 2 Materials and Methods
### Examination of Rheological Properties  
On the day of production, samples were used to examine the rheological behavior of liquid whole egg. The analysis was conducted using a MCR 92 rheometer ([PERSON], Les Ulis, France) in rotational mode, equipped with a concentric cylinder (cup diameter 28.920 mm, bob diameter 26.651 mm, bob length 40.003 mm, active length 120.2 mm, positioning length 72.5 mm). To control the equipment, [PERSON] RheoCompass software version 1.30.1164 was used. A constant temperature of 15 \({}^{\circ}\)C was kept throughout the rheological measurements. Shear stress was measured by logarithmically increasing and decreasing shear rate between 1 and 1000 1/s for 32 measurement points, with each sample tested in triplicate [22].  
Following the literature, this study chose the Herschel-Bulkley model to describe the rheological behavior of liquid whole egg. The Herschel-Bulkley model is often chosen for liquid egg products because it accounts for both yield stress and shear thinning behavior,which are the characteristics of liquid whole eggs. The model effectively incorporates these factors, making it suitable for analyzing the rheological properties for liquid whole eggs [13; 15; 18].  
Equation (1) was used to analyze the flow curves (shear rate-shear stress diagrams).  
\[\tau=\tau_{0}+\text{K}\gamma\text{n} \tag{1}\]  
where the following hold:  
\(\tau=\text{shear stress (Pa)}\)  
\(\tau_{0}=\text{yield stress (Pa)}\)  
\(\gamma\text{'}=\text{the shear rate (1/s)}\)  
\(\text{K}=\text{the consistency coefficient (Pas}^{\text{n}})\)  
\(\text{n}=\text{the flow behavior index}\).
## 2 Experimental Method  
### Material Description and Sample Preparation  
Basalt, an igneous rock rich in iron and magnesium, is widely distributed across the globe. Its primary mineral components, pyroxene and plagioclase, exhibit dipolar properties that are particularly significant under microwave radiation. When exposed to microwave radiation, the electric field interacts with these dipolar minerals, causing rapid vibrational movements that generate heat. As a result, basalt exhibits a highly responsive behavior to microwave irradiation. For this study, fine-grained basalt was sourced from Wutai County, Shanxi, China. The specimens were extracted from the same fresh rock mass and shaped into cylinders with a diameter of 50 mm and a height of 100 mm, following the standards recommended by the International Society for Rock Mechanics (ISRM). The ends of each specimen were polished to ensure parallelism with an accuracy of \(\pm\)0.05 mm and a surface roughness of less than 0.02 mm. The physical properties of these specimens are presented in Table 1. X-ray diffraction (XRD) analysis of the mineralogical composition revealed that the basalt used in the experiments consisted primarily of mesquite feldspar (59.33%), pyroxene (26.84%), olivine (11.95%), and ilmenite (1.88%), as illustrated in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \begin{tabular}{c} **Longitudinal** \\ **Wave Velocity,** \\ _V/(m\(\cdot\)s\({}^{-1}\))_ \\ \end{tabular} & \begin{tabular}{c} **Density,** \(\rho\)**/(g\(\cdot\)cm\({}^{-3}\))** \\ **Strength,** \(\sigma\)**/**Mpa** \\ \end{tabular} & \
\begin{tabular}{c} **Compressive** \\ **Blasticity,** \(E\) **(Gpa)** \\ \end{tabular} &
\begin{tabular}{c} **Modulus of** \\ **Poisson’s Ratio,** \(\mu\) \\ \end{tabular} \\ \hline Specim 1 & 5850.7 & 2.91 & 103.99 & 30.48 & 0.21 \\ Specim 2 & 5716.5 & 2.89 & 102.25 & 30.31 & 0.23 \\ Average & 5783.6 & 2.90 & 103.12 & 30.39 & 0.22 \\ Standard deviation & 94.894 & 0.014 & 1.230 & 0.120 & 0.007 \\ \hline \end{tabular}
\end{table}
Table 1: Rock mechanical parameters of basalt specimens at room temperature (25 \({}^{\circ}\)C).  
Figure 1: Schematic diagram of damage mechanism of basalt under microwave radiation (
# Evaluation of Different Work Zone Road-Occupation Schemes for Monorail Construction
## 1 Introduction
The second category focuses on urban work zones, including studies related to urban travel origin-destination (OD) matrices [19], alternate route selection [20], and traffic organization management [21]. Existing studies have notably overlooked research into the layout and location selection of work zones, and there has been limited investigation into the corresponding rationality of work zone layout and road geometry design. Furthermore, the establishment of work zones at intersections has demonstrated substantial adverse effects on sustainable traffic flow. This study selects the monorail work zone at an intersection in Xi'an as the research subject. Two monorail work zone layouts and location schemes were proposed, and corresponding intersection road layout schemes were proposed based on the actual data collection location of setting up the work zone at intersections. The entropy weighted method (EWM) is an objective, comprehensive evaluation method. Based on on-site survey data, we used VISSIM simulation [22; 23; 24] and the EWM [25; 26; 27] to calculate and evaluate the three schemes and selected the best scheme under different traffic volume combinations; we utilized VISSIM software to simulate various schemes. Ultimately, using the EWM, we conducted a comprehensive scoring of different schemes to determine the optimal construction organization method under varying traffic volumes. The remaining sections of this article are outlined as follows. Section 2 provides a literature review of relevant studies. Section 3 presents the problem statement and describes the collection of case data. In Section 4, we modeled, calibrated, and analyzed intersections with work zones occupying different lane positions. In Section 5, we conducted a performance improvement analysis of the three proposed schemes for different traffic volumes. In Section 6, we utilized the entropy weighted method (EWM) to comprehensively evaluate the three schemes, calculate and describe the final selected scheme, and draw conclusions. Section 7 provides a summary of the paper.
# Comparison of EPIC-Simulated and MODIS-Derived Leaf Area Index (LAI) across Multiple Spatial Scales
## 3 Results
### Comparison 1: LAI-RM to MODIS LAI (Mc5; Mc6) at 1.0 km
#### 3.1.2 Comparison of MODIS Mc6 0.5 km and MODIS Mc5 1.0 km to LAI-RM 1.0 km  
Comparing LAI-RM (1.0 km) with the corresponding 1.0 km Mc5 across five of the eight dates showed Mc5 LAI values 1.0 to 2.4 times larger than that of the in situ upscaled LAI values (Table 6 and Figures 5 and 6). Mean Mc6 LAI exhibited better fit with LAI-RM estimates across these same seven dates with Mc6 exceeding 1.0 LAI units on only one date. Across all eight dates, the mean difference LAI-RM and MODIS LAI was 2.22 (SD = 1.50) for Mc5 and 0.97 (SD = 0.78) for Mc6.
# A Privacy-Preserving Scheme for a Traffic Accident Risk Level Prediction System
## 3 Preliminaries
### Security Notions
#### 3.2.1 Indistinguishability  
Indistinguishability is a security notion that establishes whether a Public-Key Encryption (PKE) is secure. If a cryptosystem has this notion, the adversary cannot distinguish cyphertexts from plaintexts. In cryptography, there are some security notions in terms of indistinguishability [33].  
* Indistinguishability under Chosen-Plaintext Attack (IND-CPA): This is the most basic security notion. In this case, a passive (computationally limited) adversary, given a \(pk\), cannot distinguish the cyphertexts of a pair of messages.
* Indistinguishability under Chosen-Ciphertext Attack (IND-CCA): This is the standard security notion for PKE. In this case, an active adversary, given a \(pk\) and even a decryption oracle, cannot distinguish the cyphertexts of a pair of messages.
# Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City
## 3 A Large-Region Sensor-Based Source Tracking Model Based on a Fully Connected Network
### Evaluation Indicators  
The evaluation indicators used for classification in this paper are _Accuracy_, _Precision_, _Recall_, and _F1-score_. Not only the whole dataset but every single potential leaking source is evaluated. The definitions of the indicators are as follows:  
\[Accuracy=\frac{(TP+TN)}{(TP+TN+FP+FN)} \tag{11}\]  
\[Precision=\frac{TP}{(TP+FP)} \tag{12}\]  
\[Recall=\frac{TP}{(TP+FN)} \tag{13}\]  
\[F1\text{-}score=\frac{2\cdot Precision\cdot Recall}{(Precision+Recall)} \tag{14}\]  
where \(TP\) represents true positive, \(TN\) represents true negative, \(FP\) represents false positive, and \(FN\) represents false negative. In summary, accuracy is the ratio of the correctly predicted sample size to the total sample size, precision refers to the ratio of the number of correctly predicted positive samples to the number of all predicted positive samples, recall means the ratio of the correctly predicted number of positive samples to the total number of positive samples, and the \(F1\)-_score_ is equivalent to the harmonic average of precision and recall.
###### Abstract
The rest of the paper is organized as follows: Section 2 presents the related work. The RF-WC technology background and technical details are presented in Section 3. Section 4 presents the experimental setup and the obtained results. Section 5 discusses the experiments' practical challenges. Section 6 presents a use case for WC in the context of WSN. Finally, Section 7 concludes the paper and proposes future work.
# Enhancing the Quality of Sampled Phase-Only Hologram (SPOH) Based on Time-Division Comb Filtering
## 5 Conclusions  
Past research has demonstrated that a sampled phase-only hologram (SPOH) can be used to represent an image with acceptable visual quality. However, the reconstructed image is masked with the down-sampling pattern, resulting in a sparse appearance with numerous empty voids. In this paper, we have proposed a method to enhance the quality of the reconstructed image of SPOH. Time-division comb filtering (TDCF) is applied to merge, through the persistence of vision of human perception, four shifted versions of a single SPOH that are sequentially displayed at sufficiently high frame rate with an SLM. The reconstructed image is more solid and smooth as compared with the one obtained with the SPOH alone. As the shifted versions of the SPOH can be obtained with only negligible amount of computation, the complexity of our proposed method is practically identical to the generation of the SPOH itself. This is an obvious advantage over existing methods, such as the generation of complementary sampled phase-only hologram, that require computation of multiple holograms for reducing the sparsity in the reconstructed image.  
[PERSON] developed and implemented the method, conducted the experiments and simulation, drafted the manuscript, and served as the corresponding author; [PERSON]-P.L. conducted the experiments and simulation, screened the manuscript, provided advice and discussion on the development; [PERSON] screened the manuscript, provided advice and discussion on the development; [PERSON]-C.P. screened the manuscript, provided advice and discussion on the development. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the General Research Fund (GRF) of Hong Kong SAR, China (Grant No: 9042822). This research is also supported by the National Natural Science Foundation of China (NSFC) (61865007), the Key Program of Science and Technology of Yunnan Province [2019 FA025], and Ministry of Science and Technology of Taiwan (106-2628-E-035 -002 -MY3).  
The authors declare no conflict of interest.
# Lightweight Human Ear Recognition Based on Attention Mechanism and Feature Fusion
## 4 Experimental Results and Analysis
### Ear Recognition Experiments of the Improved YOLOv5s-MG-CBAM on Three Datasets
From Table 1 and Figure 6, on the USTB human ear dataset, YOLOv5s-MG-CBAM has the highest ear recognition rate and the [EMAIL_ADDRESS] value is 1. CCU-DE follows; [EMAIL_ADDRESS] is above 0.98 and the [EMAIL_ADDRESS] value of YOLOv5s-MG-CBAM was within \(\pm\)0.2% compared with other methods. Compared with the CCU-DE and USTB ear datasets, the EarVN1.0 dataset has the lowest ear recognition rate. However, on the EarVN1.0 dataset, when epoch = 1000, the [EMAIL_ADDRESS] value of YOLOv5s-MG-CBAM is 0.883. The human ear recognition rate is the highest among the six methods, which is 0.1% higher than that of YOLOv5s, 2.8% higher than that of YOLOv5s-MG, 2.4% higher than that of YOLOv5s-MG-SE, 5.0% higher than that of YOLOv5s-MG-ECA and 2.8% higher than that of YOLOv5s-MG-CA. It can be seen from Table 1 and Figure 6 that the params, GFLOPS and model size of the YOLOv5s-MG-CBAM method are slightly higher than those of YOLOv5s-MG-SE, YOLOv5s-MG-ECA, YOLOv5s-MG-CA and YOLOv5s-MG. This is mainly because CBAM takes into account both channel attention and spatial attention. Theoretically, its parameter quantity and calculation amount are larger than those of SE, ECA and CA, and the experiment also verifies this. From Table 1 and Figure 6, it can be seen that the params, GFLOPS and model size of the improved YOLOv5s-MG-CBAM model are much smaller than those of YOLOv5s models. On the CCU-DE human ear dataset, the params, GFLOPS and model size of the improved YOLOv5s-MG-CBAM model are 48.6%, 37.2% and 50.1% of YOLOv5s, respectively. On the USTB human ear dataset, the params, GFLOPS and model size of the improved YOLOv5s-MG-CBAM model are 49.9%, 39.1% and 53.6% of YOLOv5s, respectively. On the EarVN1.0 ear dataset, the params, GFLOPS and model size of the improved YOLOv5s-MG-CBAM model are 58.4%, 40.9% and 59.1% of YOLOv5s, respectively. From Table 1 and Figure 6, it can be also seen that the params, GFLOPS and model size of the improved YOLOv5s-MG-CBAM model are much larger than those of YOLOv5s-MG models. Comparing those of the YOLOv5s-MG method, the params, GFLOPS and model size of the improved YOLOv5s-MG-CBAM model are increased by 60%, 64.9% and 59.5%, respectively, on the CCU-DE human ear dataset, 56.4%, 57.1% and 63.0%, respectively, on the USTB human ear dataset and 91.7%, 81.1% and 86.6%, respectively, on the EarVN1.0 human ear dataset.