esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Avocado's Composition in Nutrients and Bioactives
### Avocado Bioactive Compounds
#### 3.2.2 Tannins  
Tannins are a chemical family of compounds, mostly detected in some foods and plants. Tannins, especially the condensed type, are reported to have a correlation with possible antioxidant activities and beneficial health effects in humans. They are considered a sub-group of polyphenols because of the phenolic rings in their structure, which make them natural anti-oxidants that reduce CVD's, diabetes, and cancer risks [67; 68]. In the cultivars examined, the quantity of tannins ranged from 0.15 to 0.17%DW in seed and pulp samples, as reported in [31].  
Green tea tannins are naturally occurring polyphenols that have significant radical scavenging properties, efficiently trapping and neutralizing free radicals without harming the skin. These properties make them ideal for skincare applications. Numerous studies that tested in vitro tannins/avocado oils, especially from avocado peel creams, identified UV protection, antifungal and antibacterial activity, and phytochemical characteristics, including spreadability, viscosity, and distribution of particles size. Green tea tannins of avocado such as epicatechin, catechin, epigallocatechin, epicatechin gallate, and epigallocatechin gallate are natural antioxidants and radical scavengers. These tannins stabilize radicals by delocalizing or terminating electrons, which increases their antioxidant activity. Notably, Epigallocatechin gallate (EGCG) has been found to increase the activity of ROS-scavenging  
Figure 2: The structures of the most common phenolic compounds present in avocado (all structures were obtained from Molview).  
enzymes. Green tea tannins, because of their biologically active flavan-3-ol groups, can operate as radical scavenging antioxidants, fighting photoaging and functioning as natural sunscreens against sunburns and wrinkles [69; 70].  
In addition, avocado seed extracts have been proven to reduce triglycerides, blood pressure, and glycemic levels, helping to prevent cardiovascular disease and lower cholesterol. They also possess antioxidant and antibacterial characteristics, which can be utilized in dermatology. Avocado seed oil, on the other hand, which is also high in antioxidants, has pharmacological benefits in preventing diseases, including hypertension, diabetes, hypercholesterolemia, and fatty liver disease, by lowering cardiometabolic risk and exhibiting hepatotoxicity effects. All these advantages of avocado by-products are related to the presence of a variety of polyphenols, which include tannins like procyanidins and catechins, as well as hydroxybenzoic and hydroxycinnamic acid derivates, alkaloids, and flavonoids [70; 71].
## 3 Results
### Effect of Aluminum Sulfate on Performance of Machine-Made Sand Concrete
#### 3.2.2 Effect of Aluminum Sulfate Content on the Compressive Strength of Machine-Made Sand Concrete  
It can be seen from Figure 6 that, when the amount of aluminum sulfate was increased from 0% to 2%, the compressive strength of the concrete firstly increased and then decreased. When the aluminum sulfate content was 1%, the compressive strength of concrete at 7 and 28 days was the highest, respectively at 32.8 MPa and 37.9 MPa, which were increased by 29.6% and 10.5% compared to the mix with 0% of aluminum sulfate. This shows that aluminum sulfate is advantageous for concrete strength when the optimum content is added. However, excess amounts of aluminum sulfate could cause an increase in pore size and microcracks inside the concrete [56], and thus decrease the compressive strength.
# Disaster-Resilient Optical Network Survivability: A Comprehensive Survey
## 4 Disaster-Aware Network Survivability
\begin{table}
\begin{tabular}{c c c c} \hline
**Ref** & **Techniques/Formulation** & **Objectives** & **Assessments/Limitations** \\ \hline
[89] & Polynomial-time algorithm to find the critical region & & Determined the vulnerability of a network to the failure of a certain region embedded in a plane. The methodology was applicable to different geometries of the critical region. \\ Heuristic to find the region disjoint paths & & The critical region may include disconnected pairs of nodes, average shortest path length etc. \\ \hline
[93] & Risk profiling for spatiotemporal characteristics of disasters (link availability values) & Finding risk profile of disasters at different areas of the network at different times. & Preventative measures can be taken if risk profile of spatiotemporal disaster impact is predicted. The most risk-averen path is obtained via the shortest path algorithm using as weights the –log of the link availabilities. \\ \hline
[94] & Polynomial-time algorithms Implementing KD-Tree and R-Tree Data Structures & Detection of spatially close fibers and spatially close risk groups. & Considered the geography of links to detect spatially close fibers. Minimum separation distance on a chosen represent the radius of construction-level and large-scale failures. Identified the spatially close risk groups using the minimum number of DRG. \\ \hline
[77] & ILP Formulation & A robust scheme that minimizes the network resources (wavelength links) needed to handle the requests for communication in data center networks. & Prioritized the minimization of resource utilization and the number of disasters. However, resource utilization increases with the increase in a number of disasters as disasters reduces the capacity. \\ \hline
[99] & Path Geo-Diversification CTGGD & Impact of Path Geo-Diversification on the routing performance & Performance of the proposed mechanism compared with that of when the network was subject to area-based challenge. CTGGD used to distinguish the GeoPath diversity of different network topologies. \\ \hline
[100] & ILP & Path geo-diversification where a demand between two network nodes supported by a pair of geographically separated paths of a minimum distance. & ILP is complex in computation and includes all unnecessary path computations which are time-consuming. Preferably, proposed heuristic algorithms in the literature may provide acceptable solutions of the problem. \\ \hline
[101] & Brute force approach and enhanced K-shortest path algorithm & Geographic aware route selection algorithm to find alternative paths with appropriate geographical separation referred to as the proximity factor. & Work based on the grid-based network and do not consider any real network. Further, PF based on node-to-node separation whereas fiber links should also be considered for alternate/disjoint path computation. \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
# Spinal Cord Segmentation in Ultrasound Medical Imagery
## 4 Experimental Results
### Experiments on Dataset-B
#### 4.3.2 Modifications Applied to U-Net  
To reduce the size and the computations needed for training U-Net, we substituted all the convolutional layers in the model by Depthwise Separable Convolution (DSC). We note that the size of the model was reduced by a factor of 4, and the global cross-validation accuracy was reduced by 0.0055, as shown in Tables 13 and 14.  
Post-Processing and the Set of Training Configurations  
These are the operations and the configuration to be used with U-Net to improve segmentation accuracy:  
* Post-processing: This is an important step to implement especially when dealing with patterns not learned inside the train set. This will have a significant impact on reducing the False Positives and the False Negatives and increasing consequently the True Positives and the True Negatives.
* Data augmentation: we applied vertical flip and horizontal flip transformation, brightness change in range of 20%, and rotation transformation in the range of 20 degree.
* Weighted cross-entropy: We put the value 0.995 for the weight of the class spinal cord.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **U-Net (Before)** & **U-Net (After)** \\ \hline Dataset-B1 & 0.939 & 0.948 \\ Dataset-B2 & 0.874 & 0.888 \\ Dataset-B3 & 0.924 & 0.894 \\ Dataset-B4 & 0.94 & 0.94 \\ Dataset-B5 & 0.925 & 0.93 \\ Dataset-B6 & 0.901 & 0.876 \\ Dataset-B7 & 0.937 & 0.933 \\ Dataset-B8 & 0.961 & 0.946 \\ Dataset-B9 & 0.929 & 0.925 \\ Dataset-B10 & 0.933 & 0.928 \\ \hline Dataset-B (cross validation) & 0.9263 & 0.9208 \\ \hline \hline \end{tabular}
\end{table}
Table 13: IoU measured on Dataset-B before and after applying the DSC on the U-Net network.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **U-Net** & **FC-DenseNet-103** & **BiSeNet** & **PSPNet** & **Deeplab v3+** \\ \hline Dataset-B1 & 0.986 & 0.985 & 0.98 & 0.975 & 0.974 \\ Dataset-B2 & 0.98 & 0.98 & 0.962 & 0.986 & 0.977 \\ Dataset-B3 & 0.987 & 0.966 & 0.985 & 0.977 & 0.977 \\ Dataset-B4 & 0.986 & 0.979 & 0.959 & 0.955 & 0.963 \\ Dataset-B5 & 0.987 & 0.99 & 0.976 & 0.962 & 0.964 \\ Dataset-B6 & 0.981 & 0.957 & 0.984 & 0.96 & 0.972 \\ Dataset-B7 & 0.986 & 0.981 & 0.98 & 0.966 & 0.97 \\ Dataset-B8 & 0.99 & 0.989 & 0.978 & 0.975 & 0.98 \\ Dataset-B9 & 0.983 & 0.981 & 0.965 & 0.956 & 0.943 \\ Dataset-B10 & 0.983 & 0.976 & 0.969 & 0.957 & 0.952 \\ \hline Dataset-B (cross validation) & 0.9849 & 0.9784 & 0.9738 & 0.9669 & 0.9672 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Dice score (F1) measured for every algorithm on Dataset-B (cross-validation).  
* ADAM optimizer: the best learning rate we tested in this task is 0.0001.  
Table 15 illustrates the effect of using these predefined configurations on the improvement of IoU on Dataset-B using U-Net.  
We conclude that the post-processing step and the set of training configurations have a significant impact on improving the segmentation accuracy of U-Net.
## 3 The Proposed Method
### Preprocessing with ICEEMDAN and Feature Extraction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Steps** & **PerEn** & **SampEn** \\ \hline
1 &
\begin{tabular}{c} Reconstruct each IMF d into state vector \(d_{i}=(d_{i},d_{i+\tau},\ldots,d_{i+(m-1)\tau})\) (\(i=1,2,\ldots,L-(m-1)\tau\)) with embedding dimension \(m\), time delay \(\tau\), and \(\tau=1\) for SampEn. \\ \end{tabular} \\
2 & \
\begin{tabular}{c} Sort \(d_{i}\) in ascending order \(d_{i}=(d_{i1},d_{i2},\ldots,d_{im})\) and the \\ ordinal \(i_{i}i_{2}\ldots i_{m}\) is mapped as a permutation pattern \\ \(\pi_{00\downarrow\ldots\ldots\ldots\ldots\downarrow 1}\). \\ \end{tabular} &
\begin{tabular}{c} Calculate distance \(D_{ij}=D(d_{i},d)=\max_{i=0,1,m+1}[d_{i+\tau}\) \\ \(-d_{i+\tau}]\) and \(\bigtriangleup(\tau_{i})=\frac{\sum_{i}(I-D_{ij})}{L-\sum_{i=1}(I+i)}(j\
eq i)\), where \(\tau\) is the tolerance for favorable function \(H(\cdot)\). \\
3 & \
\begin{tabular}{c} Calculate frequency of patterns: \\ \(p(\pi_{00\downarrow\ldots\ldots\ldots\downarrow 1})=\pi(\frac{\pi(i_{0}i_{0} \ldots\ldots\ldots\downarrow 1}{L-(m-1)\tau})\) \\ PerEn(d,m,\tau)=\(-\sum p(\pi_{00\downarrow\ldots\ldots\ldots\downarrow 1})\)\(\mathrm{ImP}(\pi_{00\downarrow\ldots\ldots\downarrow 1})\) \\ \end{tabular} &
\begin{tabular}{c} Summarize across \(i\)\(C^{m}(\tau)=\frac{\sum_{i}C^{m}(\tau)}{L-m}\) and \(m\leftarrow\)\(m+1\) to repeat Steps 1–2 to further obtain \(C^{m+1}(\tau)\). \\ \(\mathrm{SampleEn}(\mathbf{d},m,\tau)=\mathrm{ln}C^{m}(\tau)-\mathrm{ln}C^{m+1}( \tau)\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Procedures for calculation of PerEn and SampEn. Figure 1: Framework of the proposed multi-fault diagnosis method for rolling bearings. large, small variations may be unable to be observed. Based on the abovementioned facts, embedding dimension \(m\) is usually set as 2. According to previous studies [23; 27], \(\tau\) has a very small effect on the estimation of DisEn and aliasing may occur when \(\tau>1\). Hence, \(\tau\) is usually selected as 1 to guarantee computational efficiency and to provide a reliable analysis. From Table 1, two parameters of embedding dimension \(m\) and time delay \(\tau\) should be chosen in PerEn analysis. For the embedding dimension, it is appropriate to choose \(m\) from 3-7 for bearing vibration analysis based on previous literature [20]. To make full use of the original vibrational signal, \(\tau\) is often set to be 1. Also, there are two parameters in the calculation of SampEn: reconstruction embedding dimension \(m\) and similarity comparison tolerance \(r\). Practically, the tolerance \(r\) is a value from 0.1-0.25 std(\(d\)) and the parameter \(m\) is set to be 2 in SampEn analysis.
# Predictive Models for Correlation of Compaction Characteristics of Weakly Cohesive Soils
## 5 Results and Discussions
### The Law of the Dependence of the Specific Pressure in the Soil and Settlement
This technological index of the machine plays a crucial role in determining the influence of the equipment being compacted on the overall performance of the compaction process. This directly impacts the compaction technology by guiding the selection of appropriate machines to achieve high-efficiency compaction. For example, the technological index of the VV 170 compactor is \(\mathrm{i}_{\mathrm{m}}=0.27\ \mathrm{kg}/\mathrm{cm}^{2}\). Additionally, for Equation (16) to hold, the initial moisture content of the soil must not exceed the optimal moisture level determined by the Proctor test. Figure 14 presents a comparative analysis of the evolution of compaction levels in the first three static passes, focusing on the measurement points (F, G, H) that satisfy the conditions for applying the proposed relationship. It is noted that the proposed relationship (16) provides a more accurate prediction of the degree of compaction achieved after the first three static passes, confirming the linear correlation between the key parameters describing the soil state in the initial phase of the compaction process. Figure 15 offers an overview of the compaction performance for the four analyzed types of soil under operating conditions with the VV 170 compactor (without vibrations). It shows a significant increase in the degree of compaction up to the fifth pass, after which the rate of increase begins to slow down for subsequent passes. Figure 14: Comparison between approximation laws for the degree of compaction, for the first three static passes, corresponding to measurements from point F (**a**), point G (**b**), and point H (**c**). In the analysis of layer thicknesses 28 cm and 29.5 cm, it is observed that after the fifth to the tenth pass, the increase in compaction degree becomes negligible, followed by a slight rise until reaching the maximum compaction value. Conversely, for layer thicknesses 24.5 cm and 26.5 cm, a distinct three-phase pattern of compaction increase, each phase with varying intensity, can be identified for each individual plot. These observations highlight the differing responses of the four soil types to the same compaction load applied by the roller, underscoring the complexity and specificity of the compaction process as a whole. This variability complicates the accurate prediction of achieving the desired compaction degree, even when a comprehensive range of parameters characterizing the initial soil condition is known. In all four cases analyzed, the compaction process exhibited performance improvement, reflected in an increase in compaction degree ranging from 18.3% to 22.2%. This variation is attributed to the different initial soil conditions, influenced by parameters such as moisture content, dry density, static longitudinal modulus, and initial compaction degree, despite using the same compaction technology in each instance.
## 2 Materials and Methods
### Material Properties  
The ECC was prepared by mixing with an on-site mixer and naturally maintained for 28 days. The main materials included P-O42.5 grade ordinary silica cement, 70-120 mesh extra fine quartz sand, 600 kg/m\({}^{3}\) silica fume density, coal gangue powder, polyvinyl alcohol (PVA) fiber, and polycarbonate acid water reducing agent, which did not contain coarse aggregates. The fiber volume admixture is 2.5%, and the water-cement ratio is 0.3. The mass ratio of cement, silica fume, coal gangue powder, and quartz sand is 1:0.14:0.6:0.87. The material properties of ECC concrete were tested by 500 T universal mechanical testing machine, and the compressive strength and bending strength of concrete were tested by displacement loading, in which the displacement loading rate was 1 mm/min for testing  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Symbol** & **The Thickness of ECC/mm** & **Bolts Spacing/mm** \\ \hline \multirow{3}{*}{1} & SE-50-150-1 & 50 & 150 \\  & SE-50-150-2 & 50 & 150 \\  & SE-50-200-1 & 50 & 200 \\  & SE-50-200-2 & 50 & 200 \\  & SE-75-150-1 & 75 & 150 \\  & SE-75-150-2 & 75 & 150 \\  & SE-75-200-1 & 75 & 200 \\  & SE-75-200-2 & 75 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The grouping of the test specimens.  
Figure 1: Steel-ECC composite bridge deck construction diagram (Unit: mm).
## 3 System Model
### Clustered PD-NOMA
Received signals at \(u_{1}\), \(u_{2}\),..., \(u_{n}\) presented within the \(c\)th sub channel can be given by  
\[y_{c,1}=h_{c,1}\ \ \sqrt{\alpha_{c,1}p_{c,1}}+h_{c,1}\ \ \sum_{t=1}^{n-1}\sqrt{\alpha_{c,1}p_{c,t}}+N_{o} \tag{3}\]  
\[y_{c,2}=h_{c,2}\ \ \sqrt{\alpha_{c,2}p_{c,2}}+h_{c,2}\ \ \sum_{t=1}^{n-1}\sqrt{\alpha_{c,2}p_{c,t}}+N_{o} \tag{4}\]  
\[y_{c,n}=h_{c,n}\ \ \sqrt{\alpha_{c,n}p_{c,n}}+h_{c,n}\ \ \sum_{t=1}^{n-1}\sqrt{ \alpha_{c,n}p_{c,t}}+N_{o} \tag{5}\]  
where '\(\alpha\)' is the power co-efficient for the user, '\(h\)' is the corresponding channel gain, and '\(p\)' is the received power at the receiver end. Thus, after implementing SIC at the receiver's end, the \(SNR\) can be expressed as:  
\[SNR_{c,1}=\frac{\alpha_{c,1}\ \ p|h_{c,1}|^{2}}{N_{o}} \tag{6}\]  
\[SNR_{c,2}=\frac{\alpha_{c,2}\ \ p|h_{c,2}|^{2}}{N_{o}+\alpha_{c,1}\ \ p|h_{c,1}|^{2}} \tag{7}\]  
\[SNR_{c,n}=\frac{\alpha_{c,n}\ \ p|h_{c,n}|^{2}}{N_{o}+\sum_{t=1,\ t\
eq n}^{n-1} \left(\alpha_{c,t}\ \ p|h_{c,n}|^{2}\right)} \tag{8}\]  
\(SNR_{c,1}\) is received at \(u_{c,1}\), and it is considered to be closest to \(s\) as it applies SIC to eliminate interference occurring from other users \(i\); therefore, the \(SNR\) in Equation (5) consists of average noise without any interference, whereas \(u_{c,2}\) is relatively far from \(s\), and \(u_{c,n}\) is the farthest. User \(u_{c,n}\) cannot implement SIC to cancel the weak signals of other users, as its received power '\(p\)' is not strong; therefore, the \(SNR\) in Equation (7) consists of average noise along with the interfering signals. Moreover, as NOMA is implemented within the dense \(s\)-comprised clusters, the maximum number of users will be close to some '\(s\)' and receive adequate '\(p\)' to improve the data rate. However, users at the edges of the cluster may experience strong interference from neighboring users. In addition, it is assumed that there will be a limited number of users within the defined clusters. The achievable data rate over a sub channel '\(c\)' from SBS '\(s\)' transmitted to user '\(u\)' will be given as:  
\[R_{usc}=wlog_{2}(1+SNR_{usc}) \tag{9}\]
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 3 Scalable TCP for 5G MmWave Networks
### MmWave Scalable TCP  
To impose additional stability on S-TCP that can ensure sufficient throughput in mmWave networks, cwnd increase and decrease mechanisms were both modified. The redesign of the increase/decrease mechanisms improves the stability and fairness of the S-TCP in the mmWave network while maintaining the throughput of S-TCP.
## 4 Results and Discussion
### Effect of Gabion on Wind Pressure and Air Infiltration  
The wind pressures on the exterior surfaces of buildings with/without the gabion in three simulation conditions are shown in Figure 8. It can be seen that the wind pressure distribution on exterior surfaces is significantly altered by setting the gabion under different weather, which is more pronounced in cold wave weather. While the wind pressure distribution of the building without gabion has both positive and negative pressure zones, that of the building with gabion only has the negative pressure zone; the wind pressures of the No. 1 and 3 exterior surfaces change from positive to negative. The higher variation in wind pressure shows that the No. 1 and 3 exterior surfaces are more sensitive to the gabion. However, the wind pressures of the others that are not windward are almost unaffected after setting the gabion. This might be attributed to the fact that the gabion weakens this effect on wind pressure when the No. 1 and 3 exterior surfaces are considerably affected by the incoming airflow as windward sides.  
The gaps in windows and doors are the main pathway for cold air to enter the building [28]. For multi-story buildings, infiltration heat loss is mainly influenced by the amount of infiltration through external windows due to wind pressure, which is related to the position of the external windows [42]. The critical factor of the wind-driven infiltration is the indoor-outdoor wind pressure difference, increasing with the difference between the wind pressure on the windward side and that of the leeward side [43]. Under all simulation conditions, the maximum pressure difference is obtained between the No. 1 and 8 exterior surfaces for the building without the gabion, while between the No. 1 and 5 exterior surfaces after using the gabion. And the maximum pressure difference is, respectively, reduced by 4.283 Pa (43.5%), 9.186 Pa (33.9%), and 31.41 Pa (30.6%). Therefore, based on simplified theoretical equations [44], the wind-driven air infiltration rates of the building with gabions, respectively, are all reduced considerably in conditions I, II, and III.  
Figure 8: Wind pressure on exterior building surface under three simulation conditions: (**a**) condition I; (**b**) condition II; (**c**) condition III.  
Due to its impact on the wind pressure distribution of the exterior building surface, the gabion can reduce the air infiltration rate to decrease the building heat load caused by cold air infiltration. Apart from taking proactive measures to increase the airtightness of the envelope, installing the gabion is also an effective way to mitigate the effect of the environment and air infiltration on building energy consumption in a harsh outdoor environment.