esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Handover Decision Using Fuzzy Logic for 5G Ultra-Dense Networks
## 3 Proposed FLDHDT
### Adaptive Handover Decision Using Fuzzy Logic
#### 3.2.1 Data Pre-Processing  
The serving BS can request relevant information from the UE through measurement reports, such as the signal strengths of the UE to all neighboring BSs, geometrical location of the UE, and moving speed of the UE. FLDHDT uses the SINR and horizontal moving speed of the UE as inputs to the FL controller. The signal strength of the UE is always the most important input factor governing handover decision, such as the Event A3 defined in the 4G/5G standards. Moreover, the moving speed of the UE is a crucial input governing handover decision. In general, a lower HOM and shorter TTT are more suitable for relatively high-speed UE because high-speed UE has smaller processing time tolerances for the handover decision. Conversely, a higher HOM and longer TTT are more suitable for low-speed UE to avoid unnecessary handovers.  
The cell coverage of a BS is similar to a circle or an ellipse. Thus, the horizontal scalar speed of the UE velocity vector indicates how fast the UE is moving away from the serving BS. As illustrated in Figure 4, \(\overset{\rightarrow}{u}\) (in light blue) is obtained from the difference between the geometric locations of the UE and the serving BS, and \(\overset{\rightarrow}{v}\) (in dark blue) denotes the velocity of the UE. Thus, we can obtain the angle, denoted by \(\theta\), between the two vectors, as expressed in Equation (1) and the horizontal moving speed of the UE (plotted by a red line segment) as \(\overset{\rightarrow}{v}\) times \(\cos\theta\). Notably, UE1 and UE2 have the same velocity and are at the same distance from the serving BS, but UE1 is moving away from the serving BS faster than UE2 because the horizontal speed of UE1 is higher.  
\[\cos\theta=\frac{\overset{\rightarrow}{u\cdot\overset{\rightarrow}{v}}}{ \left|\overset{\rightarrow}{u}\right|\cdot\left|\overset{\rightarrow}{v} \right|},-90^{\circ}\leqq\theta\leqq 90^{\circ} \tag{1}\]  
Figure 4: Illustration of the horizontal moving speed of UE.
# A Watermarking Technique for Biomedical Images Using SMQT, Otsu, and Fuzzy C-Means
## 3 Proposed Model
### Segmentation
#### 3.2.1 Otsu's Threshold  
Otsu's threshold is used in the proposed model because of its constancy and effectiveness [17]. Every image has foreground and background parts. This algorithm finds the threshold value for those positions of the image where foreground and background expansion is minimally possible [18]. Figure 3 shows the preprocessed host image (depicted in Figure 4) and the image after Otsu threshold segmentation.
## 5 Conclusions  
1. Considering the influence of the fluid vortex at the valve orifice, a three-dimensional prediction model of valve orifice erosion of high-speed on/off valves was established, revealing the causes of valve orifice erosion: valve orifice erosion of high-speed on/off valves is mainly caused by the mainstream beam and centrifugal particle erosion of the valve cavity vortex together.
2. Several factors influencing valve port erosion were simulated to investigate the change rule of valve port erosion under different factors: 1. With the increase in the spool cone angle, the maximum erosion rate of the valve port increased linearly; 2. With the increase in the pressure difference at the valve port, the oil flow rate and the particle mass flow rate at the inlet of the high-speed on/off valve increased rapidly, resulting in a rapid increase in the maximum erosion rate at the valve port; 3. The kinetic energy of large particle contaminants was much larger compared to small particles, resulting in a significant shortening of the service life of the high-speed on/off valve; 4. For each increase in the oil contamination level, the particle mass flow rate at the inlet increased by a factor of one, and the service life of the high-speed on/off valve was also shortened by half; 5. The use of filters with a filtration pore size of 15 um or less effectively filtered the contaminated particles in the oil, and the service life of the high-speed on/off valve was significantly improved.
3. Gray relational analysis was used to establish the pressure difference at the valve orifice, the particle size, and the filter aperture as the three most important factors affecting the erosion of the valve orifice of the high-speed on/off valve. In other words, reducing the pressure difference at the valve port, reducing the proportion of large particle pollutants in the oil, and selecting a filter with an appropriate pore size can significantly improve the service life of the high-speed on/off valve.  
Validation, Y.L. ([PERSON]), H.D. and H.H.; Writing--original draft, J.D.; Writing--review & editing, Y.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the subproject of the National Key R&D Program, Key Technology of Design and Manufacture of a Micro High-Speed On/Off Valve, grant number 2019 YFB2005103.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Influencing Factors** & **Correlation Values** & **Rank** \\ \hline Pressure difference & 1.000 & 1 \\ Particle size & 0.741 & 2 \\ Filter pore size & 0.694 & 3 \\ Oil contamination level & 0.620 & 4 \\ Spool taper angle & 0.576 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculation results of correlation degrees.
# Channel Estimation Algorithm Based on Parrot Optimizer in 5G Communication Systems
###### Abstract  
Accurate and efficient channel estimation (CE) is critical in the context of autonomous driving. This paper addresses the issue of orthogonal frequency-division multiplexing (OFDM) channel estimation in 5G communication systems by proposing a channel estimation model based on the Parrot Optimizer (PO). The model optimizes for the minimum bit error rate (BER) and the minimum mean square error (MMSE) using the Parrot Optimizer to estimate the optimal channel characteristics. Simulation experiments compared the performance of PO-CE with the Least Squares (LS) method and the MMSE method under various signal-to-noise ratios (SNR) and modulation schemes. The results demonstrate that PO-CE's performance approximates that of MMSE under high SNR conditions and significantly outperforms LS in the absence of prior information. The experiments specifically included scenarios with different modulation schemes (QPSK, 16 QAM, 64 QAM, and 256 QAM) and pilot densities (1/3, 1/6, 1/9, and 1/12). The findings indicate that PO-CE has substantial potential for application in 5G channel estimation, offering an effective method for optimizing wireless communication systems.  
Parrot Optimizer; 5G; least squares; mean square error; channel estimation Article
## 3 Results
### Machine Learning and Deep Learning Applications
#### 3.3.1 Deep Learning Methods in Data Extraction
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference** & **Year Published** & **Brief Summary** & **Technology Used** & **Evaluation Scores** \\ \hline
[59] & 2017 & Introduced TTPDrill, a tool that automates threat action knowledge extraction from unstructured reports. & NLP, IR & Precision = 84\% Recall = 82\% \\ \hline
[27] & 2020 & Created a knowledge extraction pipeline from AARs to fill a cybersecurity KG. & CRF, Gibbs’ sampling, RelExt & Precision  Recall  F1-score \\ \hline
[90] & 2020 & Proposed a model for enhancing NER. & BiLSTM, CRF & F1-score = 85.27\% \\ \hline
[91] & 2021 & Proposed SECURITYKG, a system for automating the processing of open-source CTL & NLP & - \\ \hline
[92] & 2021 & Created Open-CyKG for presenting APT reports as a queryable KG. & BiGRU, CRF, XLM-RoBERTa & Dataset Microsoft (CTI) Recall = 98.7\% (80.8\%) Precision = 99.2\% (78.9\%) F1-score = 98.9\% (79.8\%) \\ \hline
[93] & 2021 & Used a DL model for knowledge extraction and constructed a cyber threat ontology. & BERT, BiLSTM, CRF & Recall = 97\% Accuracy = 96\% \\ \hline
[29] & 2022 & Proposed a CTI extraction system using DL models and constructed a KG. & BERT, BiLSTM, CNN & Precision = 65.58\% Recall = 72.25\% F1-score = 68.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Works relevant to DL methods for knowledge extraction from text data combined with ontology/KG construction or usage. Records are arranged in ascending order based on their publication year and alphabetically by author name when the publication years of two or more papers coincide.
## 2 The Proposed Timing Mismatch Calibration Technique
### Proposed Calibration Architecture  
The flowchart of the proposed algorithm is shown in Figure 5. Figure 6 depicts the proposed timing mismatch calibration architecture for a M-channel TIADC system, which comprises a compensation module and an estimation module. The estimation module encompasses several modules, including the correlation computation module (CCM), error function generation module (EFGM), matrix calculation module (MCM) and down-sampling module (DM). The EFGM comprises multiple subtractors that calculate timing error functions \(B_{k}\). CCM computes the product's average of adjacent channels \(R_{k,k+1}\), which serves as a moving average filter and is composed of various multipliers and adders [22]. The output of \(R_{k,k+1}\) can be expressed as:  
\[R_{k,k+1}[n] = \frac{N-1}{N}R_{k,k+1}[n-1]+\frac{1}{N}p_{k}[n] \tag{39}\] \[= \frac{1}{N}(p_{k}[n]-R_{k,k+1}[n-1])+R_{k,k+1}[n-1]\]  
where \(p_{k}[n]\) represents the product of adjacent channels and \(N\) denotes the number of samples used in the average calculation. The structure of CCM is described in Figure 7. One of the benefits of utilizing the CCM is that it does not necessitate dedicated memory. Furthermore, \(N\) can be set as an integer power of two, enabling the replacement of division with a straightforward shift operation. This approach circumvents the need for a divider. The MCM can perform a matrix multiplication operation to estimate the time skew errors of sub-ADCs. The M-P inverse matrix \(C_{T}^{+}\) can be computed in advance and be stored in memory buffers for a given M.  
Figure 5: The flowchart of the proposed algorithm.  
Figure 6: The structure of the proposed calibration architecture.
## 6 Recent Studies on CRSN Routing  
Recent relevant studies have consistently chosen the proactive approach to the design of routing protocols for CRSNs because this strategy provides an easy way to manage the dynamic nature of the topology by securing a path to the sink before beginning the data transfer stage. However, this strategy falls short in online opportunistic utilization of the dynamic topology, due to the varying status of the channels. Thus, the final route decision cannot be justified as the optimal route at any time. Hence, proactive strategies manage the situation rather than taking advantage of it. Additionally, because of this property, the proactive strategy can experience increased delays during the data transmission stage, except when alternative techniques are incorporated at the cost of complexity and energy overhead. The protocols in this respect are described below.  
Figure 6: Success rate of all protocols _versus_ primary user activity.  
Figure 7: Energy efficiency of all protocols _versus_ primary user activity.  
It is important to note that routing in CR-based networks must generally be considered with the spectrum sensing decision [30, 31]. Thus, protocols relevant to routing in CRSNs can generally be categorized as shown below.
# A Novel Energy-Efficient Contention-Based MAC Protocol Used for OA-UWSN
## 3 OA-UWSN MAC Protocol Design  
### Data Link Layer Channel Access  
In this paper, combined with the postponed access mechanism in the CSMA/CA data link layer access method, a spatial multiplexing-based spatial division multiple access is introduced, making full use of the directional transmission characteristics of light to increase the data transmission distance. Figure 2 shows a superframe structure designed for the OA-UWSN in this paper. The protocol divides a superframe into different periods: An acoustic handshake period, an optical handshake period, a data transmission period, and a postponed access period.  
Figure 1: Network topology.
# Combined Fusion Rules in Cognitive Radio Networks Using Different Threshold Strategies
## 2 Theoretical Background of Cognitive Radio Networks
### Detection Performance of Cooperative Sensing  
Owning to severe radio conditions, such as multipath fading, shadowing and noise uncertainty, collaboration between CRs provides a solution to alleviate the impact of those conditions on the detection performance of individual CRs [3,4]. There are three collaboration schemes, they as centralized, non-centralized or relay-assisted. In a centralized scheme, every individual CR detects the existence of the PU and then forwards its local observation to a central entity which gathers observations and fuses them to extract a final decision about the existence of the PU. In non-centralized scheme, individual CRs detect the existence of the PU and then share their local observations between each other and come up with a final decision about the existence of the PU through a consensus. Finally, in relay-assisted scheme, after detection, a CR amplifies or decodes its observation and forwards it to another CR [23].  
This paper focuses on the centralized CSS scheme using using several fusion rules. After detection, CRs forward their observation as either hard decision, i.e., one-bit as '0' or '1', or soft decision, i.e., energy form as a stream of bits. However, the latter requires a large bandwidth and incurs extra computational complexity for decision fusion in the central entity. Therefore, to reduce the bandwidth and computational complexity, a hard decision is the most widely used. However, the employment of hard decision reduces the accuracy of detection. To improve the performance of hard decision, two-bit scheme, i.e., softened hard decision scheme, has been developed such that detection performance improves with a little increase in the required bandwidth and complexity [18; 24; 25]. More details are provided below.
## 3 Numerical Simulation
### Horizontal Wind Speed Changes at Different Heights  
The changes of horizontal velocity of airflow on both sides of the embankment at the height of 0.3 m, 1.3 m, 3.3 m, and 3.3 m under different terrain conditions are shown in Figures 10 and 11 where the horizontal wind velocity at the height of 0.3 m and 1.3 m is within the influence range of dune and embankment height, thus the velocity curve is discontinuous. The horizontal wind velocity at the height of 3.3 m and 4 m presents an \"M\" shape distribution and if there is a dune, the maximum wind speed will concentrate on the top of the dune. When the wind speed is 18 m/s, the greatest reduction in wind speed is observed at the junction between the embankment and the dune where the wind speed at the foot of the leeward slope of the dune is 1.81 m/s at the height of 1.3 m, which is 87.9% lower than the original wind speed, followed by the leeward slope foot of embankment where the wind speed is 5.69 m/s and 4.78 m/s at the height of 0.3 m on flat terrain and dune, respectively, which are 55.7% and 62.8% lower than the original wind speed. The least reduction in wind speed is found at the windward side of the embankment and the dune where the wind speed at the height of 0.3 m is 8.52 m/s and 8.46 m/s, respectively, which are 33.6% and 34.1% lower than the original wind speed.  
By comparing the horizontal wind speeds at different heights of the two kinds of flow fields--there are dunes or no dunes--around the embankment in Figures 9 and 10, it can be found that the structure of sandy air current around the road is closely related to the cross-section form of the embankment and the surrounding environment. Under different wind speeds of the same highway structure, the variation laws of sandy air velocity around the embankment are basically the same, while the variation range of wind speed gradient is more severe when sand-carrying wind with high speed passes the embankment. For each point on the embankment section, the areas with large decreasing range of wind speed are respectively the junction of dune and embankment, the leeward slope foot of embankment, and the leeward slope foot of embankment.  
Figure 9: Distribution of wind-sand flow field around subgrade in flat terrain (**a**) and windward dune (**b**).
# Molecular Subtyping and Outlier Detection in Human Disease Using the Paralique Algorithm
## 3 Experimental Results
### Alignment with Previously Known Subtypes
#### 3.3.1 Gastric Cancer  
GEO series GSE35809 data, from the Affymetrix Human Genome U133 Plus 2.0 Array, were derived from 70 primary gastric tumors intended for use as a validation set for subtype classifier testing [62; 63; 64]. The data contain values for 54,675 probes. Arrays are subdivided into a collection of 29 identified as coming from proliferative tumors, 26 from invasive, and 15 from metabolic.  
Filtering was irrelevant, because no healthy tissues were studied that could be used as controls. At a threshold of 0.955, paraclique produced subsets of size 29 and 16 and performed admirably. All but one of the invasive samples it classified were placed in the first paraclique, while all but one of the proliferative samples it classified were placedin the second. Metabolic samples proved only slightly more challenging, with 75% of those classified placed in the first paraclique. See Table 4.
## 2 Materials and Methods
### Structure Prediction, Refinement, and Validation  
#### 2.5.1 Secondary Structure Prediction  
The secondary structures of VLP were predicted using the SOPMA tool ([[https://npsa.lyon.inserm.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html](https://npsa.lyon.inserm.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html)]([https://npsa.lyon.inserm.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html](https://npsa.lyon.inserm.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html)) (accessed on 28 July 2024)). The analysis employed default settings, which included four conformational states: alpha helix, beta sheet, turn, and coil. A window width of 17 and a similarity threshold of 8 were used for the prediction [41]. The PSI-blast-based secondary structure prediction (PSIPRED) ([[http://bioinf.cs.ucl.ac.uk/psipred/](http://bioinf.cs.ucl.ac.uk/psipred/)]([http://bioinf.cs.ucl.ac.uk/psipred/](http://bioinf.cs.ucl.ac.uk/psipred/)) (accessed on 28 July 2024)) [42] predicted the secondary structure of VLP in a graphical cartoon format using an artificial neural network and machine learning technologies in its algorithm.
## 3 Methodology
### Meta-Learners
#### 3.2.2 Rotation Forest  
The rotation forest (RoF) meta-learner uses feature extraction to construct classifier models. By randomly partitioning the feature set into \(N\) subsets, RF generates training data for a baseline learner, and principal component analysis (PCA) is used for each of the produced subsets [52; 53]. All primary components are preserved to maintain the variability in the data. As a result, N axis rotations occur to provide new features for the baseline learner (in this case, FPA). The rotation's essence is to allow for contemporaneous independent accuracy and variety within the ensemble. FPA's diversity is achieved by feature extraction. In Algorithm 2, the proposed RoF_F_FQA algorithm is provided on the premise that \(X\) is the training dataset, \(Y\) is the class label, and \(F\) is the feature sets.  
```
Input: The training set, \(X=\{x_{i},y_{i}\},\ i=1\ldots m,\ y_{i}\in Y,\ Y=\{c_{1},\ c_{2},\ \ldots,c_{k}\},\ c_{k}\), is the class label;  Baseline Learner: \(FPA\)
1. Choose a value for \(K\) that is a factor of \(m\); let \(F\) be randomly divided into \(K\) parts of the distinct subsets;  each subset must contain \(N=n/k\) number of features.  Select the corresponding columns of attributes in the subset, \(T_{ij,i}\) from the training dataset, \(X\); then, form a new matrix, \(X_{ij,i}\). Extract a bootstrap subset of objects \(\
icefrac{{3}}{{4}}\) of \(X\) to make a new training dataset, \(X^{\prime}_{ij}\)
3. Use Matrix \(X^{\prime}_{ij,i}\) as feature transform to produce the coefficient in the matrix, \(P_{ij,i}\), in which the \(j\)th column coefficient is the characteristic component, \(j\)th.  Construct a sparse rotation matrix, \(S_{i}\) using the coefficient obtained in the matrix, \(P_{ij,i}\). Output: classifier \(T_{i}\) of \(d_{ij}(X^{\prime}_{ij})\) to determine \(x\) belongs to the class \(y_{i}\). Then, calculate class confidence: \(\varepsilon_{j}(x)=\frac{1}{L}\sum\limits_{i=1}^{L}d_{ij}\big{(}X^{\prime}_{ ij}\big{)}\) Assign the category with the largest \(\varepsilon_{j}(x)\) value to \(x\).
```  
**Algorithm 2**Rotation Forest-based Forest Penalizing Attribute (RoF_FPA) Algorithm.
## 1 Introduction
For soil erosion mapping, landslide inventory (dependent variable) and elevation, slope, aspect, curvature, drainage, road, precipitation, LULC, and soil (independent variables) are considered influential characteristics [31]. This study is unique in that it investigates the current study area for soil erosion using GIS-based statistical models such as FR, WOE, and IV that have previously been overlooked by researchers. In this study, we also look at the relationship between soil erosion and elevation, slope, aspect, curvature, drainage, roads, precipitation, LULC, and soil. The study adds significantly to the insufficient literature on soil erosion-related risks in Pakistan's Murree area. The primary goal of this research was to identify and analyse the effects of soil erosion-causing factors and probable soil erosion zones in the Murree region of the Sub-Himalayas.