esa-sceva/satcom-chunk-collection huggingface dataset:
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 3 Survey of State-Of-The-Art MAC Protocols
### Time-Slotted Protocols
#### 3.2.4 Cluster-Based MAC Protocol  
[PERSON] et al. [76] proposed a cluster-based MAC protocol for CRAHNs, which aimed to be more robust to the PU activities to utilize the free spectrum more efficiently. Channel access time is distributed into superframes; see Figure 7. The beacon time is issued by the Cluster-Head (CH), which has time synchronization information for cluster control, cluster ID, the CH and the channel hopping sequence. Intra-cluster communication and inter-cluster communication are done through the CH.  
In addition to this, a database that restores spectrum occupancy history is introduced. For each origin-destination pair, CH defines access scheduling of the channel. Nodes not participating in the communication tune to different frequencies making decisions using the experience-based channel hopping sequence to perform neighbor discovery. Using this method, the proposed cluster-based MAC protocol does not need a specific period for neighbor discovery.  
Figure 6: CAD-MAC transmission process.  
Figure 7: CB-MAC superframe structure.  
The proposed MAC protocol forms a cluster based on node geographical position, available channel and experienced statistic. During the first round of neighbor discovery, information from the neighbors is collected, and a value is assigned to links to those neighbors. At the subsequent round of neighbor disclosure, nodes share their link value. Later, according to these values, nodes start forming clusters. After cluster formation, each member in the cluster is controlled by the cluster head.
## 7 Discussions and Clarification of Comparison Results
### Primary Configurations (i.e., LTP LTP PRIM and UDP LTP PRIM)
#### 7.2.2 UDP on the Proximity Segment (i.e., UDP LTP PRIM)  
UDP/IP lacks native rate control or congestion control: whenever the data rate of the underlying link service exceeds the rate at which the receiving UDP/IP implementation can receive and process inbound datagrams, newly received datagrams will overwrite unprocessed reception buffer contents, causing datagram loss due to congestion. The rate of UDP congestion loss in our simulations would have been lower if we were running UDP/IP over a slower link, because datagrams would never arrive at rates in excess of the rate at which the protocol stack could handle them. However, because we were running at relatively high data rates, UDP/IP congestion loss was significant. This high rate of data loss affected not only the nominal UDP/IP (proximate) segments of the end-to-end paths but also the LTP (cislunar space) segments, because the space link protocol underlying LTP was simulated by, again, UDP/IP. In other words, for the configuration of UDP LTP PRIM, congestion loss in the transmission of data from the lunar surface to the orbiter by UDP (without any recovery from data loss, as would be provided by LTP) means that the rate of data arrival at the orbiter is significantly lower than the rate at which the application produces data for transmission. The resulting rate of initiation of traffic on the cislunar segment is in fact so low that congestion loss on the cislunar segment is virtually eliminated: the receiving UDP on the cislunar segment is almost always able to process received datagrams before they can be overwritten by subsequently received datagrams. This accounts for the low but generally flat goodput curves for this configuration: the constant low rate of data arrival (due to uncorrected data loss) at the orbiter dominates, precluding significant congestion loss on the cislunar segment and a resulting decline in goodput.
## 3 Basic Theory
### Fruit Fly Optimization Algorithm  
FOA is a new interactive evolutionary computation method, which was proposed by [PERSON] in 2012. By simulating the process of foraging behavior for fruit fly individuals and populations, global optimum can be obtained through appropriate iteration, as shown in Figure 1. Standard foraging process of FOA can be summarized as follows.  
Step 1.1: The key initial parameters of the FOA are the population amount (_PA_), the fruit fly group location range (_LR_), the maximum iteration number (_IN_max) and the random fly distance range (_FR_). The initial location of the fruit fly group can be presented as follows:  
\[\left\{\begin{array}{l}X_{\_}axis=\text{rand}\left(LR\right)\\ Y_{\_}axis=\text{rand}\left(LR\right)\end{array}\right. \tag{6}\]  
Step 1.2: The random direction and distance for the search of food using osphresis by an individual fruit fly is given as follows:  
\[\left\{\begin{array}{l}X_{\_}i=X_{\_}axis+\text{rand}\left(FR\right)\\ Y_{\_}i=Y_{\_}axis+\text{rand}\left(FR\right)\end{array}\right. \tag{7}\]  
Step 1.3: Since the food location cannot be known, the distance to the origin of coordinates (_Dist\({}_{i}\)_) and the smell concentration judgment value (_S\({}_{i}\)_) are calculated as follows:  
\[Dist_{\_}{i}=\sqrt{X_{\_}{i}^{2}+Y_{\_}{i}^{2}},\ \ \ S_{\_}{i}=1/Dist_{\_}{i} \tag{8}\]  
The fruit fly with maximal smell concentration among the fruit fly group can be searched according to the smell concentration judgment function (or called Fitness function), which can be presented as follows:  
\[smell_{\_}{i}=\text{function}(S_{\_}{i}),\ \ \ [bestsmell\ bestindex]=\max(smell) \tag{9}\]  
where _bestsmell_ donates the maximal smell concentration, _bestindex_ is the corresponding fruit fly number and smell is the smell concentration set of the group.  
Figure 1: Process of foraging behavior for fruit fly group.  
Step 1.4: The smell concentration is compared with that of the former iteration. If it is inferior to the last generation, Steps 1.2 to 1.3 are repeated; else the best location and smell concentration can be presented as follows:  
\[\begin{array}{c}{\small{best}={bestsmell}}\\ \\ {\left\{\begin{array}{ll}{X_{\_}{axis}=X(bestindex)}\\ {Y_{\_}{axis}=Y(bestindex)}\\ \end{array}\right.}}\end{array} \tag{10}\]  
Step 1.5: When the smell concentration reaches the preset precision value or the iteration number reaches the maximal _IN_, the circulation stops. Otherwise, Steps 1.2 to 1.4 are repeated.
###### Abstract  
The regulatory mechanism of _PCSK7_ gene is still unknown, although its encoded protein PC7 is the most ancient and highly conserved of alloprotein converses and exhibits enzymatic and non-enzymatic functions in liver triglyceride regulation. Bioinformatics algorithms were used to predict regulatory microRNAs (miRNAs) of _PCSK7_ expression. This led to the identification of four miRNAs, namely miR-125a-5p, miR-143-3p, miR-409-3p, and miR-320a-3p, with potential binding sites on the 3'-untranslated region (3'-UTR) of human _PCSK7_ mRNA. The expression patterns of these miRNAs and _PCSK7_ mRNA were assessed in three different cell lines with quantitative polymerase chain reaction (qPCR), which revealed reciprocal expression patterns between the expression levels of the four selected miRNAs and _PCSK7_. Next, the interactions and effects of these miRNAs on _PCSK7_ expression levels were investigated via cell-based expression analysis, dual-luciferase assay, and Western blot analysis. The data revealed that _PCSK7_ mRNA levels decreased in cells transfected with vectors overexpressing miR-125a-5p, miR-143-3p, and miR-409-3p, but not miR-320a-3p. The dual-luciferase assay demonstrated that the above three miRNAs could directly interact with putative target sites in _PCSK7_3'-UTR and regulate its expression, whereas miR-320-3p exhibited no interaction. Western blot analysis further revealed that the overexpression of miR-125a-5p in Huh7 cells inhibits the expression and ability of PC7 to cleave human transferrin receptor \(1\). Our results support a regulatory role of these miRNAs on _PCSK7_ expression and function and open the way to assess their roles in the regulation of PC7 activity in vivo in the development of hepatic steatosis.  
PCSK7; miR-125a-5p; miR-143-3p; miR-409-3p; RNA regulator;oprotein converses 2022
## 5 Experimental Results
### Experiment 2: Classification Problem  
A popular benchmark classification problem--the Iris classification problem--was also used to test the performance of the proposed hybrid algorithm in training FNNs. The Iris dataset consists of 150 samples that can be divided into three classes, including Setosa, Versicolor, and Virginica. Each class accounts for 50 samples. All samples have four features: sepal length, sepal width, petal length, and petal width. Therefore, we used FNNs with the structure 4-S\\({}_{2}\\)-3 to solve this classification problem, where \\(S_{2}\\) is the number of hidden nodes with \\(S_{2}=4\\), 5, 6,..., 16. In this study, 100 samples were used for training and the rest were used for testing. The search space was [(50; 50)]. Every procedure was run five times successively, and then the mean values were calculated for these five results and are shown in Table 2. The results show that PSOCS-FNN outperforms PSO-FNN and CS-PSO.  
Figure 4: Convergence of PSO-FNN, CS-FNN, and PSOCS-FNN in the function approximation with \\(S_{1}=3\\), 4, 5, 6, and 7.  
CS-FNN were 0.856 and 0.868, respectively; while the best accuracy rate for PSOCS-FNN was 0.948. These results prove that PSOCS-FNN is capable of solving the Iris classification problem more reliably and accurately than PSO-FNN and CS-FNN.  
Based on the obtained results, it can be concluded that PSOCS-FNN outperforms PSO-FNN and CS-FNN due to the capability of the proposed hybrid PSOCS algorithm.  
Figure 5: Convergence of PSO-FNN, CS-FNN, and PSOCS-FNN in the Iris classification problem with \\(S_{2}=10\\), \\(11\\), \\(12\\), \\(13\\), \\(14\\), and \\(15\\).
# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 2 Materials  
### Test Area  
The test area, located at Adams Park, Canada, is about 574,600 m\({}^{2}\) (1690 \(\times\) 340 m) at a geometric elevation ranging from 28 m to 93 m. The park is a suburban area along the shoreline of Lake Ontario that contains both land and water regions (Figure 1a), and the land cover includes buildings, roads, trees, grass, and bare soil.
# Vectorized Numerical Algorithms to Solve Internal Problems of Computational Fluid Dynamics
## 4 Addressing Mesh Cells  
A structured mesh covering the computational domain has a structure similar to a two-dimensional array. This allows to address the cells (or nodes) with two indices, \(i\) and \(j\). A mesh node with indices \((i,j)\) has neighbors that are addressed, giving them increments or decrements.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 4 Applications of Blockchain in Agricultural and Animal Husbandry IoT
### Data Management
#### 4.1.2 Data Sharing and Security
The most promising advancements stem from using hybrid storage models, efficient consensus protocols, and clear collaborative frameworks to optimize both performance and trust in decentralized environments.
# Fatigue Life Prediction of High Modulus Asphalt Concrete Based on the Local Stress-Strain Method
## 8 Case Study  
### Structure and Parameters of Pavement  
Based on the proposed fatigue life prediction method, a typical HMAC overlay pavement situated in a long steep road in Shandong province of China was selected and its fatigue life was predicted below.  
The pavement structure and material parameters are listed in Table 11. Five axle loads (80 kN, 100 kN, 120 kN, 150 kN, and 180 kN) and four varied temperature range (5, 10, 15, 20 \({}^{\circ}\)C) were considered in the analysis of nominal stress and strain. For each grade, fatigue life was calculated.
# SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
## 2 Problem Definition  
In this paper, the chip component consists of two solder regions and a package region, including a chip capacitor, chip diode, chip register, and tantalum capacitor. Figure 1(a) shows an image of these components. Chip capacitors, diodes, chip resistance, and tantalum capacitors are in order from the left. Components vary in size from 0.6 mm \(\times\) 0.3 mm to 3.2 mm \(\times\) 1.6 mm. Figure 1(b) shows the structure of the chip component, consisting of two solders and one package.  
Each pixel vector \(i_{mn}\in R^{1\times 3}(m=1,\cdots,M,n=1,\cdots,N)\) of the \(M\times N\) image input from the illumination system includes red, green, and blue pixel values.  
\[i_{mn}=[r_{mn},\ g_{mn},\ b_{mn}] \tag{1}\]  
Figure 2: Chip components: (a) various chip components mounted on the printed circuit board (PCB), (b) top view (left) and side view (right) of chip component.  
The chip component image \(I\in\mathbb{R}^{M\times 3N}\) is defined as:  
\[I=\left[\begin{array}{cccc}i_{11}&i_{12}&\dots&i_{1N}\\ i_{21}&i_{22}&&i_{2N}\\ \vdots&&\ddots&\vdots\\ i_{M1}&i_{M2}&\dots&i_{MN}\end{array}\right] \tag{2}\]  
The channel image for each color channel of chip component image \({}^{I}R\in\mathbb{R}^{M\times N}\), \({}^{I}G\in\mathbb{R}^{M\times N}\), \({}^{I}B\in\mathbb{R}^{M\times N}\) is expressed as a matrix.  
\[{}^{I}R=\left[\begin{array}{cccc}r_{11}&\cdots&r_{1N}\\ \vdots&\ddots&\vdots\\ r_{M1}&\cdots&r_{MN}\end{array}\right] \tag{3}\] \[{}^{I}G=\left[\begin{array}{cccc}g_{11}&\cdots&g_{1N}\\ \vdots&\ddots&\vdots\\ g_{M1}&\cdots&g_{MN}\end{array}\right]\] \[{}^{I}B=\left[\begin{array}{cccc}b_{11}&\cdots&b_{1N}\\ \vdots&\ddots&\vdots\\ b_{M1}&\cdots&b_{MN}\end{array}\right]\]  
For the \(k\) defect types, the \(i^{th}\) defect type number is defined as \(d_{i}(i=1,\cdots,k)\). Then, the set of defect types \(D\) is as follows.  
\[D=d_{1},\cdots,d_{k} \tag{4}\]
# Co-Production of Hydrogen and Methanol Using Fuel Mix Systems: Technical and Economic Assessment
## 3 Results and Discussion
### Process Performance Analysis  
#### 3.2.1 Methanol and Hydrogen Production Rates  
Two case studies are developed in this study for the dual production of methanol and H\({}_{2}\). In this study, the fuel production represents the simultaneous production of H\({}_{2}\) and methanol. The comparative analysis is performed between two cases to determine the fuel (H\({}_{2}\) and methanol) production rates, overall process performance, CO\({}_{2}\) emissions and carbon conversion efficiencies. The methanol production rate is calculated as 29.97 kg/s and 37.91 kg/s from the case 1 and case 2 designs, respectively. On the other hand, the hydrogen generation capacity is estimated as 4.22 kg/s and 3.87 kg/s for case 1 and case 2, respectively. The comparative analysis for both cases in terms of methanol and hydrogen flow rates are also represented in Figure 4, where, the case 2 design takes a lead in the methanol production rate between the two cases.  
However, case 1 takes a lead in terms of H\({}_{2}\) production rate compared to the case 2 design. The comparative analysis between the two cases showed that the case 2 design offers 22.2% higher overall fuel production rates compared to the case 1 design. The results showed that case 2 offers higher production of the fuel (methanol and H\({}_{2}\)) and, therefore, requires higher heating and cooling duties compared to case 1. The energy required in the syngas processing is also higher in case 2 due to larger volumes of the syngas. Table 2 provides a summary for both cases in terms of fuel production rates and other process performance indicators for comparative analysis. CO\({}_{2}\)-specific emission is used for estimating the environmentally friendly nature of the process. The processes showing higher carbon emissions may increase the overall carbon footprint and is therefore less preferred. The comparative analysis between the two cases for the CO\({}_{2}\)-specific emissions reveals that case 2 has 38% fewer emissions than the case 1 design.  
Figure 4: Hydrogen and methanol production rates.
## 2 Methods
### Time Varying Filtering Based Empirical Mode Decomposition  
EMD is an adaptive signal processing method that can decompose the signal into a series of IMFs and a non-zero mean residual [28], the expression is shown in Equation (1):  
\[x(t)\ =\ \sum_{i\ =\ 1}^{N}\mathit{imf}_{i}(t)+r(t) \tag{1}\]  
where \(\mathit{imf}_{i}(t)\) is the \(i\)th \(\mathit{imf},\ i\ =\ 1,2,\ldots,N\). The EMD screening process can be divided into five steps, as shown in Appendix B.  
As an IMF, the following conditions should be satisfied: (i) the number of zeros and poles must either be equal or differ at most by one; (ii) the local mean value of the upper and lower envelopes is zero. However, the above requirements have two limitations: (i) in the actual screening process, it is too rigid for stopping criterion; (ii) the second requirement of IMF may not be valid at a low sampling rate [27]. Thus, the model mixing occurs during decomposition. Aiming to overcome the weakness of EMD, [PERSON] et al. [27] proposed a TVF-EMD screening method to solve the above problems by developing local narrow-band signal. The local narrowband signal is not only similar to the IMF but also provides a Hilbert spectrum with physical significance. The filtering process of this method is completed by time-varying filtering, which is divided into three steps: (i) estimation of the local cut-off frequency; (ii) calculation of the local mean function; (iii) judgement of the residual signal.
## 2 Materials and Methods
### Additional Machine Learning Models  
In addition to the MLP model, three other ML models: Random Forest (RF), XGBoost, and Support Vector Machine (SVM), were developed and evaluated for comparative predictive performance. These models are well-established machine learning techniques and have been extensively described in statistical learning literature [37]. These models followed consistent data handling procedures, with identical training and test data splits and the same K-fold cross-validation approach. RFE was applied across all models to select the most relevant radiomic features. Uncertainty quantification was implemented using dropout during inference for the MLP and RF models, while a 95% prediction interval (PI) was calculated for XGBoost. All models were evaluated using mean absolute error (MAE) as the primary metric. Among the tested models, the MLP model demonstrated the highest predictive accuracy, making it the primary focus of this study. Key parameters and results for RF, XGBoost, and SVM are provided in Supplementary Table S5.
## 5 Results and Discussion
### SVM-Based Alpha-Beta Filter Results  
Figure 10 shows the sample data used for the SVM-based alpha-beta filter algorithm. The SVM took two inputs, i.e., temperature and humidity, with ten support vectors, and these support vectors were used to map functions and apply the kernel transformation. The final values were input into the \(\alpha\)-\(\beta\) filter to produce the output results. However, this method is complex and did not produce a good output performance. Sample data used for SVM-based alpha-beta filter (200 data instances) is shown in Figure 11.  
Figure 10: SVM-based alpha-beta filter output.  
Figure 9: The deep-extreme-learning-machine-based alpha-beta filter results.  
For the performance assessment of the SVM-based \(\alpha\)-\(\beta\) filter, the mean absolute error and root mean square error of the typical alpha-beta filter and the newly proposed SVM-based \(\alpha\)-\(\beta\) filter were estimated. When we applied the SVM, the result was overwhelmingly positive. The three-fold cross-validation method was used to comprehensively calculate the performance. The best results were an RMSE of 4.015 and an MAE of 3.218. The SVM-based alpha-beta filter results are shown in Figure 10.  
Table 3 shows comparisons of the different algorithms' performances; we reached the conclusion that the DBN algorithm prevails compared to the DELM and SVM when applied to the alpha-beta filter. Figures 12 and 13 show the comparison of the typical alpha-beta filter and the proposed multi-method in terms of RMSE and MAE.
## 2 Related Work
### Deep Learning-Based Approaches  
Deep learning-based solutions allow for the creation of fully automated segmentation methodologies by exploiting previous examples in the form of manually segmented data. The limitations of the approaches consist in the training process, for which large quantities of data and computational resources are needed. The techniques have been applied with great success to an increasing amount of research problems in recent years [21].  
The pioneering solution is attributable to [PERSON] et al. [22], in which the authors propose automated breast and fibroglandular tissue (FGT) segmentation using a UNet approach [23]. They obtained considerable improvements in accuracy over the state of the art at that time in terms of Dice Similarity Coefficient (DSC), achieving a DSC of 0.944 against 0.863 from the previous reported state of the art [12; 15]. The current state of the art is attributable to [PERSON] et al. [8], who implemented a multi-planar UNet approach, obtaining some improvements over [PERSON] et al. [23]. The methodology uses three discrete UNets on the transverse, sagittal and coronal planes instead of a three-dimensional (3D) UNet, to increase the computational efficiency for the solution. The 3D aspect of the network, however, effectively triples the computational cost. Moreover, the current state of the art [8] was trained on a dataset composed of fully labeled data from 117 patients, well above the data available to many developers. As the labeling process is the most time-consuming aspect of the development, solutions should aim at maximizing performance with a minimal amount of data. To this end, novel techniques need to be employed, both architecturally and from a data pre-processing point of view.  
Given the limitations of the approaches presented and their advantages, the convolutional neural network (CNN) methodologies are the most applicable for problems in computer-aided diagnostics (CAD). However, all DL approaches available in the literature fail to address the most limiting aspect of the issue, which is data availability. The proposed solution aims to improve the current state of the art by focusing on data efficiency, by employing architectures that are less prone to overfitting and behave better in transfer learning scenarios. Moreover, recent advancements in DL research, such as self-attention [24], have the potential to further improve upon the state of the art. In addition, computational efforts need to be compliant with the hardware that is potentially available in a hospital; hence, the algorithms should process patient data in a two-dimensional fashion, as 3D data can be generated if needed.  
The current paper reports on improvements to the current state of the art by employing several techniques that have been successful in parallel fields. To this end, a total of 18 configurations were evaluated, corresponding to the combination of three different architectures and three different techniques to be applied.