esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Routing Issues and Challenges in WBSNs
### Path Loss  
Path loss or path attenuation is a measure of the decline in power density of an electromagnetic wave as it propagates through the wireless medium. It is the ratio of the power of transmitted to received signals [30]. The wireless communication between the implanted sensor nodes is through the human body, where the path loss exponent varies from four to seven [31], which is very high as compared to the free space, where it is two. The researcher must consider the path loss while designing routing protocols for wireless body sensor networks.
# Pharmacokinetics of Pullulan-Dexamethasone Conjugates in Retinal Drug Delivery
## 2 Materials and Methods
### Size and Zeta Potential  
Aqueous dispersions of pullulan-DEX, Cy3-pullulan-DEX and BDP-pullulan-DEX were analyzed by dynamic light scattering (DLS) using the Zetasizer Nano ZS (Malvern Instrument Ltd., Malvern, Worcestershire, UK). Zeta potentials were analyzed from 1 mg/mL polymer dispersions in 1 mM phosphate buffer (pH 7.4) at room temperature. All analyses were performed in triplicate.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 9 Ultrasonic Method  
### Introduction and Principle of the Application  
The ultrasonic waves are defined as sound waves with a frequency in excess of 20 kHz that exceeds the human hearing limit [105; 106; 107]. The ultrasonic method can be used to detect defects that are embedded in sound conducting materials. Ultrasonic testing (UT) is recognised in many industries, such as composite material production [108], and electrical [109] and electronic components [110] manufacturing, as the most effective and efficient inspection method [111; 112].  
A principle of UT is that induced stress waves generated from an actuator are used to detect and analyze damage conditions, defects and variability of mechanical properties of elements (ASTM E1316-16, 2016). During testing, signal analysis is used to determine material properties such as modulus of elasticity because some wave characteristics differ from the properties and geometry of the medium of propagation [113]. Ultrasonic pulses are produced when an electric charge is applied to a piezoelectric crystal, causing it to vibrate for a while at frequencies depending on the thickness of the material or crystal. Figure 13 shows the basic description of the structure regarding the ultrasonic measurement system [114].  
The ultrasonic technique uses various methods depending on how mechanical vibrations or waves are generated and detected within the specimens being tested [115]. Amongst the most used methods are the contact method, which uses piezoelectric transducers, and the non-contact method, which employs capacitive transducers [116]. In the  
Figure 13: Ultrasonic measurement system [114].  
case of the contact method, the transducers are made to contact and press onto the surface of the element with the aid of constant springs or weight. A couplant, such as gel or electromagnetic material, is employed to intensify sound transmission. The non-contact type is termed the air-coupled method (because it uses air as a couplant) have been applied in industries. In this method, the transducers do not make contact with the element or material surface for transmission and detection of stress waves. It has been demonstrated that the non-contact method is more efficient in the detection of structural defects than the corresponding contact ones [116]. A more advanced non-contact type that uses computerised scanning technology has also been developed and applied in industries [117]. In addition, there is a Phased Array ultrasonic technique (PAUT) where phased array ultrasonic probes are used [118].  
The ultrasonic wave non-destructive method is based on the theory of acoustoelastic laws. The theory states that the elastic wave that is propagated through an element depends on the mechanical stresses as well as wave propagation, in addition to initial stress [119; 120]. The following are some of the applications of the ultrasonic waves as non-destructive tool in civil engineering:
## 5 Approaches of Optimal State Estimation Utilizing Filtering
### Gaussian Mixture Model (GMM)  
The Gaussian Mixture Model (GMM) could be a parametric likelihood probability density function that is represented as a weighted sum of Gaussian element densities. It is for the most part utilized as a parametric model of the likelihood distribution of persistent estimations such as sounding-tract, which related spectral features in a speaker acknowledgment framework [121].  
We have also developed robust non-linear filters for navigation problems which we could extend easily to geolocation and target tracking problems. From Figure 25, it can be observed that both the speed and attitude estimation have improved by the derivation of GMM EKF, GMM-UKF, GMM-CKF and GMM-CDKF with CRLB and approximate CRLB bound from the authors' own elaboration.  
Figure 25: Performance comparison of GMM non-linear filters for robust navigation and information fusion algorithms. Authors’ own elaboration.  
In [122], the comparison of three non-linear filters for estimating the position and velocity of a moving emitter and their result analysis was carried out collected by two UAVs flying over the area of monitoring using TDOA measurements. A multiple-model filter (MMF) with UKF Bank and an MMF with EKF Bank and a Gaussian mixture measurement integrated track splitting filter (GMM-ITSF) based Gaussian mixture (GM) model-based posterior PDF is utilized in the three proposed algorithms. The non-linear filters that are considered in the comparison are a multiple-model filter (MMF) with UKF Bank and an MMF with EKF Bank and a Gaussian mixture measurement integrated track splitting filter (GMM-ITSF). The performance of estimation errors was verified and analyzed using the derived CRLB. The performance analysis clarified that the UKFB had a better performance. Furthermore, MM-ITSF had a smaller number of diverged tracks.
# Predicting the Impact of Data Poisoning Attacks in Blockchain-Enabled Supply Chain Networks
## 4 Experiment
### Experiment Design  
The aim of this experiment is to evaluate the resistance of each model to the attacks and suggest some mitigation strategies by applying various machine learning models to the dataset, such as logistic regression, random forest, support vector classifier, XGBoost and an artificial neural network with eight layers. These models are trained and tested with and without data poisoning attacks, which are random label flipping and distance-based label flipping, as shown in Figure 5.  
Figure 4: Feature correlation with the labels in the dataset.  
One of these models will use logistic regression, which was selected as a baseline linear classifier widely used for binary classification problems [23]. The support vector classifier is also used as an example of nonlinear classifiers that can capture complex patterns in the data [24]. Furthermore, random forest and XGBoost were also employed. By comparing these models, the main aim is to investigate how different learning algorithms react to data poisoning attacks.  
Random label flipping is a simple and naive way of poisoning the training data by randomly selecting some instances and changing their labels to any other class [5]. This can introduce noise and confusion to the machine learning algorithm, especially if the noise level is high or the classes are imbalanced [25]  
Distance-based label flipping is a more sophisticated and targeted way of poisoning the training data by selecting some instances that are close to the decision boundary of the machine learning algorithm and changing their labels to the opposite class. This can create more damage and misclassification than random label flipping, as it exploits the vulnerability of the machine learning algorithm to instances that are hard to classify [26].
## 2 Statistical Analysis of Tunnel Floor Heave
### Statistical Analysis of Influencing Factors of Tunnel Floor Heave  
The deformation of the bottom structure in 18 tunnels across 10 railway lines was investigated, followed by a statistical analysis of the factors influencing this deformation. These tunnels represent a wide range of current engineering conditions, including surrounding rock grade, physical and mechanical properties of the rock, and burial depth. In the fourth section of this paper, typical conditions are selected for a numerical simulation and analysis. This research indicates that the primary factors contributing to deformation can be classified into five categories: groundwater, expansive rock, ground stress, structural alignment, and construction quality. These factors may act independently or in combination, leading to tunnel floor heave. The distribution of these five deformation factors in the investigated tunnels is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Influencing Factors** & **Proportion (\%)** & **Including Projects** & **Pie Chart** \\ \hline \hline \multirow{6}{*}{Groundwater} & \multirow{6}{*}{42} & A: High-pressure water & \\  & & B: Surrounding rock deterioration in water & \\  & & C: Expansive rock expands with water & \\  & & A: Tuff and shale & \\  & & B: Argillaceous sandstone & \\ Expansive rock & 20 & & C: Silty clay \\  & & A: High ground stress & \\ Ground stress & 5 & B: Tectonic stress & \\  & & A: Insufficient understanding of bias voltage & \\ Structural matching & 10 & B: Insufficient curvature of inverted arch & \\  & & A: Insufficient thickness of inverted arch & \\  & & B: The thickness of the filling layer is not uniform & \\  & & C: The filling layer of the second lining is too early & \\ Construction quality & 23 & D: The filling layer is not dense & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical table of influencing factors of tunnel floor heave.
## 4 Experimental Design and Results
### Results  
This study utilized the CR, BPP, and SS metrics to conduct a comprehensive evaluation and in-depth analysis of the developed algorithm. To ensure a thorough and reliable comparison, the encoding and compression algorithms detailed under the Comparison Algorithms Section were used.  
Furthermore, the developed DEA analyzed the characters that followed each character in an image or segment and assigned appropriate bit lengths accordingly. For the R, G, and B values, frequently used characters were computed individually using the mathematical model described in Equation (1).  
The palette counts calculated separately for the R, G, and B values of the Baboon image, as shown in Figure 4a, are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **R** & **G** & **B** \\ \hline Baboon & 139 & 138 & 140 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Palette numbers for R, G, and B values of the Baboon image.  
The palette counts for the R, G, and B values of the segments of the Baboon image, as shown in Figure 4b, were also calculated and presented in Table 3.  
The frequently occurring characters that followed a specific character in each image segment were dynamically evaluated to determine their inclusion in the palette. This approach focused on reducing the bit representation of the segment, leading to better compression performance.
## 3 Results
### Odor Activity Values of Volatile Compounds in Marastina Wine  
A total of 16 volatile compounds out of 96 identified in the Marastina wines were present at concentrations above their thresholds with an OAV > 1, and contributed to the aroma of the wines. Fourteen compounds had OAV > 1 in all wines, regardless of treatment: \(\beta\)-damascenone, isoamyl acetate, ethyl butanoate, ethyl 2-methylbutanoate, ethyl 3-methylbutanoate, ethyl hexanoate, ethyl octanoate, 1-octen-3-ol, 1-heptanol, 2-octenal, benzeneacetaldehyde, hexanoic acid, \(\gamma\)-dectalactone, and 6-methyl-5-hepten-2-one. Compounds with the greatest OAV across all treatments were ethyl octanoate (273.61), carrier of pineapple, pear, and floral notes; and 2-octenal (105.08), carrier of herbal and nutty notes. PF wines differed from the others in having higher OAV values for \(\beta\)-damascenone than ethyl hexanoate. Linalool had an OAV > 1 in wines from PF, CT, and untreated C, while 4-vinylguaiacol had an OAV > 1 in CT and C.  
The data for 16 volatile variables with OAV > 1 were processed using principal component analysis (PCA) to differentiate among wines from the five different canopy-management treatments according to their volatile compounds. In a projection of 16 volatile variables that defined the principal components F1 and F2, the first two principal components with eigenvalues > 1 explained 73.7% of the variance. The contribution of F1 was 42.93%, and of F2, 30.75% (Figure 2A). According the PCA, wines of different treatments were clearly separated (Figure 2B). Wine sample replicates grouped together for all five canopy-management treatments. V and CT wines were located on the positive side of PC1 and were characterized by 11 OAV values: linalool, isoamyl acetate, four ethyl esters (ethyl hexanoate, ethyl octanoate, ethyl butanoate, and ethyl-3-methylbutanoate), 2-octenal, hexanoic acid, 4-vinylguaiacol, and \(\gamma\)-declactone. Nine compounds belonged to floral, tropical fruity, sweet, spicy, or nutty aroma odor classes [17], with only two from the green and mineral class, 1-octen-3-ol and hexanoic acid (Flavornet, [[http://www.flavornet.org/flavornet.html](http://www.flavornet.org/flavornet.html)]([http://www.flavornet.org/flavornet.html](http://www.flavornet.org/flavornet.html)), accessed on 28 February 2022). Veraison samples were located in different quadrants: V1, V2, and V3 in the first; and CT1, CT2, CT3 in the second. Early defoliation treatments (BS and PF) had little or no influence on wine OAV values compared to control wines, all of which were located on the negative side of PC1. Those wines correlated with five OAVs: 6-methyl-5-hepten-2-one, ethyl-2-methylbutanoate, \(\beta\)-damascenone, 1-heptanol, and benzeneacetaldehyde, compounds associated with floral, vegetable, pome, and citrus fruit characteristics. Control and preflowering wines (C1, C2, C3, PF1, PF2, PF3) were located in the third quadrant of the plot, while berry set wines (BS1, BS2, BS3) were in the fourth quadrant. PCA confirmed that the canopy-management treatment affected the volatile profiles of the wine samples.
# Theoretical Approach to Predicting the Diffusion Radius of Fracture Grouting in Soil-Rock Mixtures
## 4 Results and Discussion
### The Fracture Diffusion Formula of Bingham Fluid in the Soil-Rock Mixtures
Despite individual cases of large errors, the test values for each group are still relatively close to the calculated values, indicating that the solution formula for the fracture diffusion radius of the Bingham fluid is applied to the fracture grouting diffusion of the soil-rock mixtures in general. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Slurry Flow Pattern** & **Forms of Diffusion** & **Groups** \\ \hline Newtonian fluid & Permeation diffusion & \#6, 7 \\ Newtonian fluid & Fracture diffusion & \#3, 6, 13 \\ Bingham fluid & Permeation diffusion & \#4, 5, 8, 9 \\ Bingham fluid & Fracture diffusion & \#1, 2, 4, 5, 11, 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Classification of diffusion forms. Figure 18: Comparison of calculated values of diffusion radius and measured values. Since the object of the theoretical derivation and test in this paper is the soil-rock mixture in the low water level area, the influence of groundwater is not considered, and the calculation of grouting diffusion radius for the soil-rock mixture in the saturated state is not applicable.
## 4 Conclusions  
The study's goal was to develop a unique empirical formula using algorithms to simulate the simultaneous prediction of fragmentation and ground vibration using the same set of blast design parameters, including joint angle. There was no fruitful blast design parameters database accessible that included the proper joint angle to direct input to the algorithm, thus achieving the purpose. One hundred and fifty-two blasts were conducted in various opencast mines in the Singareni coal mines of Telangana, measuring the Se/Be ratio, firing pattern, and total explosive, and recording the joint angle. The data  
Figure 27: PPV Pair Grid Function.  
was used to train three models: XG Boost, Random Forest, and KNN, which were then evaluated using three metrics: MAPE, RMSE, and R\({}^{2}\). XG Boost model was selected.
* Use of O-PITBLAST SOFTWARE aided in the design of blasting and provided preliminary warnings for iterations.
* Available technical tools such as STRAYOS SOFTWARE are helpful in identifying the joint angle for rock mass characterization as well as fragmentation analysis
* A Correlation matrix was used to understand relationships between dependent and independent variables, and it proved to be quite useful.
* The XG Boost Regression Algorithm was found to be useful for creating an empirical formula to forecast simultaneous fragmentation and peak particle velocity utilizing joint angle and other blast design parameters such as Se/Be ratio, Total Explosive, and Firing Pattern while keeping the charge per delay constant.
* Empirical formulas of fragmentation and ground vibration are substantial in predicting results.  
\[\text{Fragmentation}=0.77+0.1(\text{Tree 1})+0.1(\text{Tree 2})\]  
\[\text{Peak Particle Velocity}=3.60+0.1(\text{Tree 1})+0.1(\text{Tree 2})\]  
Conceptualization, methodology, investigation, software and writing--original draft preparation has been conducted by N.S.C.; supervision and formal analysis have been conducted by B.S.C., writing--review and editing done by M.V.T., M.S.V. and N.S.R.K.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Availability Statement: Available in [[https://github.com/[PERSON]/XGBoost-Public/blob/6c14d44a4](https://github.com/[PERSON]/XGBoost-Public/blob/6c14d44a4) ae26 fc68f3 ce276394235c5145 ee2/XGBoOST_FRAG.ipynb]([https://github.com/[PERSON]/XGBoost-Public/blob/6c14d44a4](https://github.com/[PERSON]/XGBoost-Public/blob/6c14d44a4) ae26 fc68f3 ce276394235c5145 ee2/XGBoOST_FRAG.ipynb) (accessed on 20 April 2022).  
I would like to express my heartfeelt gratitude to my mentor, [PERSON], Associate Professor, IIT (ISM) Dhanbad, for meticulously tracking and adding his research inputs to this paper. I am always grateful to my college IIT (ISM) Dhanbad. I would like to express my gratitude to the Principal, Director, and management of Malla Reddy Engineering College, Hyderabad for allocating adequate time to carry out research.  
The authors declare no conflict of interest.
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 3 The Proposed Mechanism
### Preliminary Formation for the Proposed Work
#### 3.1.2. Neighbors  
In multi-hop communication, the presence of neighboring nodes plays a vital role. A higher number of neighbors leads to a higher availability of routes towards the sink. Each node in the network keeps a list of all possible nodes that are in the transmission range. The neighbors can be classified as upward, downward, or sibling nodes. Upward nodes are neighbor nodes one hop level up. Downward nodes are the neighbors with a lower hop level, while the siblings are the nodes with the same hop level. A node with \(HL\) equal to \(HL^{max}\) indicates that this node is at the very bottom of the network. Such nodes do not have downward nodes so they only transmit data through forwarding or sibling nodes.
## 6 Conclusions
### Limitations of the Research  
(1) Model complexity: Our method CAKGC has two versions, BERT-base and BERT-tiny. CAKGC (BERT-base) uses NVIDIA RTX 3090, which has high video memory requirements. This reliance on high-end hardware may limit the deployment of the model in environments with limited computing resources and may increase the cost of research and practice. (2) Dependence on the quality of text descriptions: The performance of the model may rely heavily on the quality of the text descriptions of entities and relations. If these descriptions are uninformative or biased, it may affect the quality of the embedding and the predictive power of the model. (3) Limitations of the attention mechanism: Although multiple layers of residual attention networks are used, the attention mechanism may still have difficulty capturing all key contextual information, especially in large-scale graphs with complex relations or a large number of entities. (4) Completeness and accuracy of entity category information: The performance of the model depends in part on the completeness and accuracy of the entity classification information. If the category information is incomplete or wrong, it may affect the performance of the model.  
Conceptualization, Q.R. and Y.Y.; Methodology, Q.R.; Validation, Q.R. and K.W.; Writing--original draft, Q.R.; Writing--review & editing, T.W. and X.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the 2023 Basic Research Operating Expenses of Central Universities (No. 31920230175), National Natural Science Foundation of China (No. 62166035), Natural Science Foundation of Gansu Province (No. 21 JR7 RA163).  
Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.