esa-sceva/satcom-chunk-collection huggingface dataset:
# DiffRS-net: A Novel Framework for Classifying Breast Cancer Subtypes on Multi-Omics Data
## 2 Materials and Methods  
In this section, we introduce the DiffRS-net method for breast cancer subtype classification. The overview of DiffRS-net is depicted in Figure 1. As illustrated in Figure 1, DiffRS-net mainly consists of three steps.  
Figure 1: Procedure of DiffRS-net. Step 1 performs differential analysis on mRNA, miRNA, and DNA methylation (DNAm) omics data to detect differentially expressed genes (DE-genes). Step 2 utilizes a robust-aware adaptive SMCCA model to identify multi-way associations among the DE-genes. Step 3 utilizes correlated DE-genes to train the attention learning network model to classify breast cancer subtypes.  
Step 1: Differential analysis. Initially, multi-omics data mRNA, miRNA, and DNA methylation (DNAm) undergo preprocessing to normalize data formats and ranges. Subsequently, differential analysis is performed to detect differentially expressed genes (DE-genes), which may serve as biomarkers for distinguishing between different breast cancer subtypes.  
Step 2: Multi-way association analysis. A robustness-aware adaptive SMCCA algorithm is employed to conduct a multi-way association analysis on the selected DE-genes. The aim here is to discover intrinsic connections among different omics data, detecting highly correlated genes among DE-genes.  
Step 3: Attention model training and classification. Finally, an attention learning model is trained using the correlated genes (Corr-genes) from the multi-way association analysis. This model achieves accurate classification of cases by learning the relationships between different omics.
## 5 Heuristic Scheduling Method
### DMOGWO-Based Global Adjustment Strategy
#### 5.3.1 Chromosome Encoding  
In the context of scheduling problems, an encoding method is used as an effective means of transforming the scheduling problem into a representation within the search space. A well-designed encoding scheme ensures the adequacy of the search space and enhances the efficiency of the problem-solving process. Given the characteristics of the DDFJSP-VPT, a three-layer encoding scheme including factory assignment (FA), operation sequence (OS), and machine selection (MS) is adopted in the proposed DMOGWO, representing each individual as \(S\) = [FA,MS,OS]. It must be noted that the length of the chromosome is variable rather than fixed, because the number of scheduled operations is constantly changing throughout the whole manufacturing process. The details are as follows:  
Factory Assignment (FA): FA represents the assignment of each job to a specific factory for processing. As illustrated in Figure 4, five jobs must be assigned to two factories. The first number, \"1\", in the FA indicates that job \(J_{1}\) is assigned to factory \(U_{1}\). The length of the FA corresponds to the total number of jobs, ensuring that each job is allocated to a specific factory. It should be noted that the factory assignment remains fixed for jobs that have already started processing.  
Operation Sequence (OS): OS determines the processing sequence of operations for each job. It assumes that, at a specific rescheduling point, \(O_{12}\) is the operation whose processing time is currently changing and will not be rescheduled. The operations \(O_{13}\), \(O_{14}\), \(O_{22}\), \(O_{23}\), \(O_{32}\), \(O_{33}\), \(O_{42}\), \(O_{43}\), \(O_{52}\), \(O_{53}\) are left unprocessed from the previous schedule. Therefore, the OS contains the job numbers 1, 2, 3, 4, and 5, and the sequence in which an integer appears indicates the operation sequence of a job. Thus, the operation sequence vector can be interpreted as \(O_{32}\to O_{13}\to O_{33}\to O_{42}\to O_{22}\to O_{23}\to O_{52}\to O_{14} \to O_{43}\to O_{53}\).  
Machine Selection (MS): MS represents the assignment of operations to specific machines for processing. The numbers in the MS indicate the machine selected for each operation within the assigned factory. The length of the MS matches the total number of operations, following the order of jobs and their respective operations. For instance, the first number \"1\" in the MS indicates that operation \(O_{13}\) is assigned to machine \(M_{11}\) in factory \(U_{1}\) for processing.
## 5 Experiment Simulation
### Performance on the ISP-like Topologies
#### 5.3.1 Comparison in Zipf Distribution Workload  
Figure 11 shows the performance of different caching strategies in ISP-like topologies under Zipf distribution workload when the cache size ratio changes. PB-NCC keeps its advantages in EBONE, TELSTRA, and ABOVENET, while in SPRINTLINK, PROB has a  
Figure 10: Comparison in ISP-like topologies when the Zipf parameter \(\alpha\) changes. (**a**) performance in EBONE topology under a different Zipf parameter \(\alpha\); (**b**) performance in TELSTRA topology under a different Zipf parameter \(\alpha\); (**c**) performance in ABOVENET topology under a different Zipf parameter \(\alpha\); (**d**) performance in SPRINTLINK topology under a different Zipf parameter \(\alpha\).  
Figure 9: Comparison in the tree topology under YouTube traffic workload. (**a**) the cache hit ratio under a different cache size ratio; (**b**) the average latency under a different cache size ratio; (**c**) the server load under a different cache size ratio.  
slight advantage when the cache size ratio is small. Due to the large scale of SPRINTLINK, each caching node is allocated less cache space, PROB makes better use of the limited space. However, PROB performs poorly when the cache size ratio increases, and the performance of PB-NCC is improved significantly with a larger cache size ratio.
# Research and Application of Slag-Nanosilica Stabilizer for Silt Subgrade
## 5 Conclusions  
Through laboratory and field tests, the optimal ratio of the new stabilizer and its stabilizing influence on silt were studied, and the following conclusions can be made:  
1. Taking slag, quicklime, and nanosilica as the main components of the stabilizer, through orthogonal test and grey correlation analysis, the optimal mix ratio of SNS was finally determined as quicklime: nanosilica: slag = 32:3:65.
2. The new stabilizer had gelation and filling effects on silt, and the gelation increased first and then stabilized with the increase in time.
3. Compared with traditional cement stabilizers, the new stabilizer could effectively improve the strength, water stability, and antifreeze properties of silt. The optimum content of the new stabilizer was 10%.  
To sum up, SNS-S has higher strength property and durability, which can meet the requirements of highway construction. Applying it to silt subgrade can solve the problem of accumulation, which provides reference for future engineering.  
Conceptualization, X.W. and S.Q.; methodology, S.Q.; formal analysis, B.D. and K.C.; investigation, B.D. and S.Q.; writing--original manuscript preparation, S.Q. and K.C.; writing--review and editing, X.W., M.W., J.X., and S.Q. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Details on all data supporting results can be obtained in Tables 1-12 and Figures 1-6, this original manuscript.  
The authors declare no conflict of interest.
# Lightweight Neural Network Optimization for Rubber Ring Defect Detection
###### Abstract  
Surface defect detection based on machine vision and convolutional neural networks (CNNs) is an important and necessary process that enables rubber ring manufacturers to improve production quality and efficiency. However, such automatic detection always consumes substantial computer resources to guarantee detection accuracy. To solve this problem, in this paper, a CNN optimization algorithm based on the Ghost module is presented. First, the convolutional layer is replaced with the Ghost module in CNNs so that feature maps can be generated using cheaper linear operations. Second, an optimization method is used to obtain the best replacement of the Ghost module to balance computer resource consumption and detection accuracy. Finally, an image preprocessing method that includes inverting colors is applied. This algorithm is integrated into YOLOv5, trained on a dataset of rubber ring surface defects. Compared to the original network, the network size decreases by 30.5% and the computational cost decreases by 23.1%, whereas the average precision only decreases by 1.8%. Additionally, the network's training time decreases by 16.1% as a result of preprocessing. These results show that the proposed approach greatly helps practical rubber ring surface defect detection.  
optimization; neural network; lightweight; rubber ring Article
# A Novel Hybrid Unsupervised Learning Approach for Enhanced Cybersecurity in the IoT
## 4 Proposed Method for Intrusion Detection and Prevention
### Detection Stage 2: OCSVM + Deep Autoencoder (dAE)
#### 4.3.5 Establishing the Threshold  
After completing the training process, the distribution of reconstruction errors pertaining to the normal data is analyzed. Typically, this distribution reveals a clustering of low reconstruction errors, demonstrating the model's effectiveness in reconstructing normal traffic. To determine the anomaly detection threshold, the standard deviation method is used on the reconstruction errors.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using SPSS(r) version 28 (SPSS(r) Inc., Chicago, IL, USA) software. The static balance data were analysed using a t-test and non-parametric test, depending on whether the variables were normally distributed. For the dynamic balance trials, muscle electrical activity and muscular strength, a general linear model for repeated measures, were used. This model allows for multi-way comparison, including gender and BMI as interactive factors. Thus, gender was used as the between-subject factor and body mass index was used as the covariate, while the main factor to be compared was the within-subject variable, i.e., before and after CLS. This method allowed for analysis of the within-subject, between-subject, and interactive factors together. This method not only tested the difference between before and after CLS situations, but also highlighted the differences among the selected factors. Results were reported by using the estimated means and standard error of means (SEM) with the significance level from SPSS to Excel, which produced the resultant graphs. The adjustment option was Bonferroni. The significance level was \(p\) < 0.05 (\(p\)-value) between the two groups of data, which was reasoned as a significant difference if \(p\)\(\leq\) 0.05, symbolised as *; as a high confidence in the difference if \(p\)\(\leq\) 0.01, symbolised as **; and as an extremely high confidence in the difference if \(p\)\(\leq\) 0.001, symbolised as ***. However, if the \(p\) > 0.05, there was no significant difference.
## 4 Discussion  
### Test of Antagonism  
The antagonism test was conducted to assess the ability of three bacterial species of the genus _Bacillus_ (_B. amyloliquefaciens_, _B. subtilis and B. thuringiensis_) to inhibit the growth of the fungus _F. oxysporum_. Among them _B. amyloliquefaciens_ exhibited the strongest inhibitory effect. Initially, the antagonism assay was performed using a two-compartment Petri dish, as described by [PERSON] et al. [31]. However, in the control setup, _Fusarium_ colonies rapidly overgrew the partition of the Petri dish, making it impossible to accurately measure mycelial growth. Consequently, we adopted the sealed-plate method [25], which involves combining two Petri dishes of the same diameter. In this setup, the test fungus and bacterium are not in direct contact but are separated by approximately 2 cm, with the fungus placed on the lower plate and the bacterium on the upper one. It is worth noting that this method--also referred to in the literature as the double-plate method [25; 30] or sandwich assay [29]--enables the evaluation of volatile organic compounds (VOCs) produced by antagonistic _Bacillus_ strains and their inhibitory effects on _F. oxysporum_.  
The maximum inhibition of _Fusarium oxysporum_ growth (70%) by _Bacillus amyloliquefaciens_ VOCs was observed after 24 h but dropped to 45% after 96 h. We hypothesize that this reduction may be attributed to one or more of the following factors:  
Depletion or transformation of VOCs over time: VOCs are typically low-molecular-weight compounds with high volatility. As the antagonism test progresses, their concentration in the sealed environment may decline due to evaporation, adsorption to surfaces or microbial metabolism.  
Adaptive fungal response: Prolonged exposure to sublethal concentrations of VOCs may induce stress response mechanisms in _F. oxysporum_, such as increased expression of detoxification enzymes or efflux transporters, leading to partial resistance [32; 33].  
Changes in _Bacillus_ metabolic activity: As _F. oxysporum_ continues to grow, it may compete for nutrients or create unfavourable conditions that suppress the bacterial production of VOCs.
## 5 Performance Evaluation
### Experiment Setup  
The environment on which the experiments are conducted are as follows. Terminal Nodes run on a desktop PC with Intel(R) Core(TM) i5-4670 CPU @ 3.40 GHz and 8 GB RAM, with Windows 10 Pro x64 multi-threading as the operating system. Service Nodes and Blockchain Nodes are established on four different enterprise level servers. Service layer is composed with Docker containers to run a large number of SNs. Blockchain layer is built as a permissioned blockchain based on Hyperledger.  
Figure 5: Detailed query steps of _SendCoin_ function.  
On this setup Figure 6, four most frequently used functions (among a larger set) were chosen to evaluate DDNB as follows: _FindHost_, _GetNonce_, _GetBalance_, and _SendCoin_. In order to analyze the correlation between execution time and the number of steps a function has, each function was chosen according to the number of query steps they require.  
Each function is executed total of 1000 times by multiple TNs (end-users) on various topologies having different sets of TNs and SNs. All functions are executed asynchronously, that is, all requests are submitted without waiting for response from BN. The number of TNs are set to 5, 10, 20, 50, 100, 250, 500, and 1000 (emulating the number of end users), and SNs 1, 5, 10, and 20 (emulating service provider servers). The interactions between TN and SN are accomplished with HTTP requests on C++ application, and all queries between SN and BN are transmitted upon the RESTful APIs.  
We assess the performance of DDNB in term of the _average execution time_ and _transaction per second (TPS)_. For this purpose, following data are collected for each transaction.  
* **Transaction deployment time** (\(T_{deploy}\)) is the time when a transaction is deployed to TN (beginning of execution).
* **Transaction completion time** (\(T_{complete}\)) is the time when a transaction completed the whole process.
* **Step (\(p\)) start time** (\(T_{p\_start}\)) and **Step (\(p\)) end time** (\(T_{p\_end}\)) are the start and end times of every step (\(p\)) in a transaction (when the request was responded from other node). Although detailed records have been made to determine the cause of outliers, no step-by-step analysis is conducted for the performance evaluation of the overall architecture.  
Then for each topology, execution time is the total amount of time which the system took to execute and confirm all transactions in the data set (\(T_{execute}=\sum_{i=1}^{1000}T_{deploy}-T_{complete}\)). Since we are more interested in the average _execution time_ for each TN, we divide it by the number of TNs performed in the experiment.
# Toward Improving the Reliability of Discrete Movement Recognition of sEMG Signals
## 5 Results  
### Classifiers with a Reject Option  
The LDA, kNN, and GMM classifiers can be extended to classifiers with a reject option. The kNN and GMM classifiers with reject options have been presented before. The LDA classifier with a rejection option (LDAR) proposed in [24] gives a predicted probability for each observation. The probability was compared with a predefined threshold. If it is below the threshold, the prediction will be rejected. Otherwise, it will be accepted. The optimal threshold reported in [24] is 0.97, and the same value was used in [27]. Unlike the study in [24], the test sample of each movement applied in this study consists of multiple observations, so [24] cannot be used directly. To use LDA as a baseline classifier, we extend it by thresholding the percentage of the majority voting results. Additionally, the final prediction decision for the test sample is based on the thresholding result.  
The classifier performance of LDA-R, kNN-R, and GMM-R on recognizing 12 trained hand movements with features extracted by using RMS and WL is shown in Figure 3. It can be observed in Figure 3 that the tAcc of kNN-R is the highest of all. The tAcc and aAcc of kNN-R and GMM-R are much higher than the LDA-R at the same rejection rate level. The performance of the classifiers with different features is different. GMM-R and LDA-R have a higher aAcc with WL feature than the RMS feature. However, for kNN-R, the RMS feature produces a higher accuracy than the WL feature at a low rejection rate, and then the aAccs of the two features tend to overlap at a high rejection rate. Therefore, for different classifiers, the more appropriate features are varied. For practical application, it is desired that the system has a low rejection rate and high accuracy. Considering the performance of kNN-R and GMM-R with the two features, the feature adopted by kNN is RMS, and the features used by GMM is WL in this study.
## 1 Introduction
These are the reasons of its increasing popularity in the general rehabilitation field, together with its potential to overcome barriers present in other more traditional training methods such as high costs and lack of time [24]. VR can also be highly realistic through stimulating the users' senses to enhance the learning experience by targeting various feedback mechanisms such as auditory, visual, vestibular, and force feedback [31]. The immersion and involvement in a VR environment elicits the Sense of Presence (SoP), defined as \"experiencing the computer-generated environment rather than the actual physical locale\", which positively correlates with the effectiveness of VR-based training [31]. Two reviews have been published focusing on VR for wheelchair skills training [24; 31]. [PERSON] et al. [31] specifically looked at the correlation between Sense of Presence (SoP) and the effectiveness of wheelchair training in VR. [PERSON] et al. [24], on the other hand, provided an overview of the current VR technologies for wheelchair skills training. With the exponential growth of technology, VR methods are likely to increase in popularity although there are aspects of real-life training methods that might not be replaceable with technology. Thus, it is important to develop VR training systems which can best complement real-life ones. To do so, it is beneficial to identify how VR could support real-life training by reviewing the strengths and limitations of current VR training systems. Specifically, our review aims to answer the following question: How can VR be used to complement real-life training for wheelchair users? While, it has the following two main objectives: (1) to identify gaps of real-life training methods that can be addressed by VR; (2) to study the main features of VR training systems in order to identify their strengthsand weaknesses. In order to answer our research question and address our objectives, our review investigates recent papers published in peer-reviewed journals from 2017 onwards about VR for wheelchair skills training (more details about the selection criteria can be found in Section 2) by extracting key information regarding their simulation hardware, their software features, their data analysis methods, their conclusions and limitations. This qualitative analysis of the included papers allows for a comprehensive overview of their developed systems, thus proving us with the information necessary to meet the aim of our paper. However, limitations of this analysis method may be a lack of comparison analyses between the papers and a quantitative evaluation of the effectives of VR systems.
## 3 Prototype Description
### SLSa Stability  
Another method for analyzing possible solutions with respect to the number of clusters is the SLSa, which evaluates the cluster-level stability over several solutions and allows changes in cluster structures such as joins and splits to be identified, providing information about the history of a cluster regarding its composition. This method applies the concept of relabeling and uses the entropy measure formulated by [PERSON] [59].  
For the dataset used in this work, although the focus is on candidate solutions with \(k=4\), \(k=5\), and \(k=6\), it was chosen to apply relabeling on solutions with \(k=2\) through \(k=9\) for a better understanding of the cluster formation process. The entropy represents the uncertainty in a probability distribution \((p_{1},p_{2},p_{3},\ldots,p_{k})\)[60]. It is described by:  
\[H=\ -\sum_{i=1}^{n}p_{j}\log p_{j}, \tag{8}\]  
where \(p_{j}\) is the probability distribution in question.  
The maximum entropy value consists of a probability distribution where all values are equal, resulting in an entropy value \(H=1\). The minimum entropy value consists of a probability distribution where only one of the values is \(1,[0,0,1,0,0]\), for example, resulting in an entropy value \(H=0\), and in the context of the algorithm, signaling that all the data in one cluster in a solution are the same as all the data in another cluster in a previous solution.  
To apply the SLSa, it is necessary to calculate the entropy measure \(H\) of each cluster \(S_{j}^{i}\) (cluster 1 belonging to the solution i) with respect to all clusters of the previous solution \(S_{j}^{i-1}\) (clusters \(j=1,\ldots,k_{i-1}\) belonging to the previous solution \(i-1\)). Therefore, the SLSa value of a segment \(l\) belonging to a solution with \(k_{i}\) segments is defined by:  
\[SLSa(S_{l}^{i})=\ -1\frac{H}{\log{(k_{i-1})}}, \tag{9}\]  
where a minimum value of 0 represents the worst possible stability, while 1 indicates the best possible stability. In short, a cluster with \(SLSa=1\) is equivalent to a cluster that was not formed from other clusters, while a cluster with \(SLSa=0\) was created from two or more clusters in the previous \(k-1\) solution.