esa-sceva/satcom-chunk-collection huggingface dataset:
Comparative Analysis of Cell-Free miR-205-5p, let-7f-5p, and miR-483-5p Expression in Ovarian Cell Cultures and Plasma Samples of Patients with Ovarian Cancer  
[PERSON]  
1 Department of Human Genetics, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary; [EMAIL_ADDRESS] (E.M.); [EMAIL_ADDRESS] (A.V.); [EMAIL_ADDRESS] (B.S.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (B.N.)  
[PERSON]  
1 Department of Human Genetics, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary; [EMAIL_ADDRESS] (E.M.); [EMAIL_ADDRESS] (A.V.); [EMAIL_ADDRESS] (B.S.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (B.N.)  
[PERSON]  
1 Department of Human Genetics, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary; [EMAIL_ADDRESS] (E.M.); [EMAIL_ADDRESS] (A.V.); [EMAIL_ADDRESS] (B.S.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (B.N.)  
[PERSON]  
1 Department of Human Genetics, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary; [EMAIL_ADDRESS] (E.M.); [EMAIL_ADDRESS] (A.V.); [EMAIL_ADDRESS] (B.S.); [EMAIL_ADDRESS] (A.P.); [EMAIL_ADDRESS] (B.N.)  
[PERSON]  
3 Department of Obstetrics and Gynecology, Faculty of Medicine, University of Debrecen, H-4032 Debrecen, Hungary; [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (R.P.)  
[PERSON]  
4 Correspondence: [EMAIL_ADDRESS]; Tel.: +36-524165314553145  
of pregnancies, mutations (e.g., BRCA1/2) or the application of estrogen-based hormone replacement therapy that is often applied among post-menopausal women in order to prevent age-related diseases [6]. The 5-year survival rate highly depends on the time of diagnosis and proved to be 29% in the early and 93% in the advanced stages [5; 6]. Two biomarkers are used routinely in ovarian cancer diagnosis: Human Epididymis Protein 4 (HE4) and Carbohydrate Antigen-125 (CA125) [7]. However, the application of these biomarkers has several limitations and they have not improved ovarian cancer diagnostics successfully [7; 8]. The lack of specific diagnostic tools rises the need for additional biomarkers.  
In our previous studies, we identified several cell-free miRNAs (e.g., miR-200 family members and miR-203a) as candidate biomarkers with good diagnostic parameters in ovarian cancer [9; 10]. We also confirmed high cell-free miR-200s expression in estrogen sensitive epithelial ovarian cell cultures [11]. Here, we aimed to identify additional new miRNAs that might be used as future biomarkers in liquid biopsy. We present that miR-205-5p and let-7f-5p has high intracellular and cell-free expression in estrogen sensitive ovarian cell cultures in contrast to miR-483-5p. Furthermore, miR-205-5p was overrepresented in the plasma samples of ovarian cancer patients, suggesting that this miRNA might be a promising biomarker candidate in non-invasive ovarian cancer diagnostics in the future.
## 6 Conclusions
### Implications  
It is possible to identify certain implications based on the findings. From an academic point of view, this study further develops knowledge regarding the use and adoption of OSS. Hence, the feasibility of combining the TAM and SDT in order to explain ICT adoption has been demonstrated. Moreover, from a managerial perspective, understanding the behavioral intention to use OSS is meaningful for the OSS movement. In fact, some practical implications can be extracted.  
First, a better understanding of the motivations of OSS users could be advantageous for OSS developers in determining which software functionalities encourage OSS expansion. Second, with these findings, the OSS movement might achieve segmentation of users in order to identify behaviors that favor OSS use, and thus can anticipate the success of this type of ICT.
# Efficient Attention Mechanism for Dynamic Convolution in Lightweight Neural Network
## 2 Related Works
### Dynamic Neural Networks (DyNet)  
To increase the complexity of the model and improve the performance of neural networks, researchers have tried to make deep neural networks dynamic [28; 29; 30; 31; 32; 33; 34]. Among them, D2 NN [29] combines backpropagation and reinforcement learning [35] to achieve dynamic effects. SkipNet [30] uses reinforcement learning methods to decide whether to implement a certain layer of the network to achieve dynamic goals. BlockDrop [31] uses the method of reinforcement learning to determine whether to use residual blocks. MSDnet [32] will determine whether the classifier outputs the result in advance, according to the confidence of the prediction. SlimmableNets [33] is to jointly train sub-networks of different widths in the same network model and share parameters. Once-For-All [34] uses the progressive shrinking approach to train the network to make the accuracy of the sub-network consistent with the effect of individual training. The above DyNet all have a static convolution kernel, but a dynamic network structure, while the recently proposed CondConv [11] and dynamic convolution [13] have a dynamic convolution kernel but a static network structure. CondConv [11] cleverly combines conditional calculation, integration technology, and attention mechanism, so that the network achieves high efficiency while having fewer parameters. Its method is similar to that in [8; 36; 37; 38]. Dynamic convolution [13] adds dynamic attention weights in the convolution so that the static weight of the perceptron in the convolution is replaced by the dynamic weight affected by feature maps to achieve the effect of a dynamic network. However, these DyNet have a lot of redundancy while inputting feature maps, which increases the number of network parameters and affects the speed of network operations.
## 3 Paradigm Shift to U-Learning
### E-Learning and M-Learning  
In the 21 st century, with advances in technology and learner needs, the educational system has been redesigned and modernized. Thus, a paradigm shift in education takes place leading to a transition from a traditional education system where teaching is tutor-centered to digitized formats of education where learning is learner-centered. The emergence of the internet prepared a platform for distance learning programs where books and lectures have been turned into eBooks, PDFs (portable document files) webinars, presentations, Google Meet, Microsoft Teams, Perculus, Zoom, etc. [25]. This model of the education system, known as an E-learning system, has enabled us to acquire information in a more efficient manner.  
E-learning platforms enhance the level of understanding since it is equipped with web-based learning, virtual classrooms, web conferences, smart classes, virtual laboratories, etc. They offer the opportunity to learn anytime and anywhere. Therefore, they provide learning flexibility. Therefore, universities can become sustainable if they positively integrate E-learning into their teaching system and consolidate their quality standards from an E-learning perspective [26; 27]. This system can be categorized as [28; 29]:  
1. **Synchronous**--learning through real-time demonstrations like a live telecast of any technique or experiment on satellite, TV, webinars, web conferences, etc.
2. **Asynchronous**--learning through recorded videos, PDFs, e-mail, LMS, etc.  
Mobile learning (M-Learning) is learning that can take place anywhere and anytime. It is supported by a mobile device that the learners use to access the information. Students may watch lectures on-demand and take tests directly from their smartphones while using M-Learning to study for assignments. Mobile devices provided students with the ease of retrieving saved files and relearning through reading or repeating exercises. The presence of mobile devices containing social communication applications, various types of digital data, connectivity, and Internet access increased the opportunities for students to be reminded of educational content, aiding in frequent retrieval and retention of learning [30; 31]. Students can develop stronger social relationships through mobile learning, which can increase the likelihood of interaction and learning from each other outside of traditional educational settings. Additionally, mobile learning applications such as MAVL, LGC, WhatsApp, and Telegram allow students to share knowledge with their peers through group chat rooms or private messages.  
The primary distinction between E-learning and M-learning is that E-learning supports knowledge acquisition, whereas M-learning does not. In addition, M-learning acquires knowledge from everywhere all the time, whereas E-learning supports the collection of information from all over the world in one site [32; 33]. These learning paradigms have been grouped differently, notably because they operate in a separate environment and require a unique protocol.
## 4 Experimental Results and Analysis
### Analysis of Experimental Results  
#### 4.3.1 Balance Data Set  
According to the steps of optimized BT-MTS, fitness function values can be obtained, as shown in Table 2, from which it can be seen that the fitness function value of class 2 is the smallest, which implies that, if the MTS constructs the MS with this class as the positive class and treats the remaining two classes as the negative class, it will have a smaller misclassification cost and better class separability, while the MS has better robustness. After removing this class, the fitness values of the remaining categories are calculated, and it can be found that class 3 has a smaller fitness function value than class 1. Therefore, class 3 is used as the benchmark at the second node to construct the MS. Finally, the classification order of the balanced dataset is 2, 3, and 1. The feature subsets of base classifiers MTS1 and MTS2 obtained according to the proposed method are shown in Table 3, with threshold values for the base classifier of 1.6316 and 0.9470, respectively.  
The classification order of BT-MTS is 2, 3, and 1. The threshold values for MTS1 and MTS2 are 1.5816 and 1.5813, respectively, and their feature selection results are shown in Table 4. Figure 4 compares the DRR of the two MTS methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) \\ \hline MTS1 & 1 & 1 & 1 & 1 \\ MTS2 & 1 & 0 & 1 & 1 \\ \hline \hline \end{tabular} Note. “1”means using the variable, “0” means not using the variable.  
\end{table}
Table 4: Feature variable selection results of BT-MTS of of the balance data set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(c_{1}\) & \(c_{2}\) & \(c_{3}\) \\ \hline Stage 1 & 418.0586 & \(-\)11.4987 & \(-\)3.8107 \\ Stage 2 & 37.4477 & / & 16.1342 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of fitness function at different stages of balance data set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) \\ \hline MTS1 & 1 & 1 & 1 & 1 \\ MTS2 & 0 & 1 & 1 & 0 \\ \hline \hline \end{tabular} Note. “1”means using the variable, “0” means not using the variable.  
\end{table}
Table 3: Feature variable selection results of the Optimized BT-MTS of the balance data set.  
The balance data set comparison results of the six methods are presented in Table 5.  
The feature variable selection result of FCBF-SVM is \(x_{1},x_{3},x_{4}\). As shown in Table 5, from the comprehensive view of accuracy, macro-F1, kappa coefficient, CBA, and cost, the classification performance of optimized BT-MTS is significantly better than that of BT-MTS. The dimensionality reduction of the proposed method is slightly higher than that of BT-MTS, and the results indicate that it effectively improves the multi-classification performance of BT-MTS.  
Furthermore, comparison of the proposed method with four machine learning algorithms shows that the algorithm in this paper and BPNN performed better on all five evaluation metrics than the ensemble learning algorithms RF, FCBF-SVM, and SMOTE-KNN.
# An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization
## 5 Expanded Experiments, Results, and Discussion
### Evaluation of Random Forest Model Performance  
For the Random Forest classifier, we performed hyperparameter tuning using GridSearchCV with 5-fold cross-validation to optimize model performance. The parameter grid included n_estimators = [50, 100, 200], max_depth = [10, 20, None], min_samples_split = [2, 5, 10], min_samples_leaf = [1, 2, 4], and max_features = ['sqrt', 'log2']. The F1-score was used as the evaluation metric to account for the class imbalance in our dataset. The optimal parameters found were max_depth = None, max_features ='sqrt', min_samples_leaf = 1, min_samples_split = 10, and n_estimators = 100, achieving a cross-validation F1-score of 0.9275.  
Our Random Forest classifier performed well in identifying retracted and non-retracted papers, with an overall accuracy of 90% on the test set of 16,052 articles (for more details for the test dataset, see the end of Section 3). From our experiment results (Table 2), we were able to determine that the model is good at detecting problematic papers. For retracted papers, the model showed a high precision (0.90) and recall (0.97), giving an F1-score of 0.93. For non-retracted papers, the model had good precision (0.89) but lower recall (0.71), with an F1-score of 0.79. The Area Under the ROC Curve (AUC) was 0.93, showing a strong ability to separate the two classes. We will give more details of the AUC in Section 5.2.  
Figure 5: Patterns of cosine similarity decay for query results of three different topics using TF-IDF and BERT.  
For the feature importance (Figure 6B), we found that the citation count is the most important factor (53.26%), followed by the Altmetric score (30.19%) and impact factor (16.55%). This suggests that the level to which an article is cited and discussed matters more for predicting retraction than the reputation of the journal where the article is published.
## 2 Fundamental Principles
### Extracting Spatial Depth from Spectral Interferogram  
From Equations (8) and (10), the spatial distance measurement which is of interest is a sinusoidal frequency dependent on \(cos(2n(z_{R}-z_{S}))\); hence, the period of the wave is \(\pi/n(z_{R}-z_{S})\).  
The Fourier transformed result is composed from a series of discrete steps dependent on the sampling resolution \(\delta k\) of the spectrometer with an array of \(N_{s}\) pixels. The spectral sampling resolution can be determined by \(\delta\lambda=\Delta\lambda/N_{s}\) or \(\delta k=\delta\lambda/\lambda_{0}^{2}\), hence the sampling interval in the \(z\)-domain is given by \(\delta\hat{z}=2\pi/2\delta_{s}k\), where \(\hat{z}=2z\) to account for the apparent depth-doubling factor shown in the modulation frequency term \(cos(2n(z_{R}-z_{S}))\). Hence, a peak located at pixel \(N_{i}\) on the spectrometer reading will correspond to a depth shown in Equation (11):  
\[z_{i}=\left(\frac{\pi}{\delta k}\right)\left(\frac{N_{i}}{N_{s}}\right). \tag{11}\]  
However, systems are not perfectly representative of their theoretical counterparts, errors in various parts of the system such as pixel spacing and diffraction grating ruling errors will bleed through into the measurement data if such a linear fit approach is taken. As such, further calibration of the system can be undertaken to improve the accuracy of this transfer function. Previous work discovered for one setup that, during measurements in air, this would lead to errors of up to approximately 50 nm and whilst operating in water approximately 100 nm [12]. Hence, depending on the measurement accuracy requirements, further action may be required to compensate for nonlinearities in the system.  
To calibrate the LCI system, sensor readings were obtained from a mirror sample at a range of offsets from the fibre tip. Simultaneous reference measurements were taken using a reference interferometer (Renishaw XL-80, United Kingdom), allowing accurate determination of \(\delta k\) and \(\Delta k\) across the useable range of operation.
# A Survey on Depth Ambiguity of 3D Human Pose Estimation
## 4 Future Potential Development  
According to the current works and shortcomings, we propose several next future directions worthy of attention for disambiguating research as follows.  
* Weakly supervised and unsupervised methods. The traditional deep convolutional neural network requires adequate manual annotations.The weakly supervised method does not need a large number of 3D annotations but 2D annotated data does, which reduces algorithm costs. Unsupervised methods follow this trend even more so.
* Interaction and reconstruction between scene object and human. The contact between human and object is the essential visual cue for inferring 3D movement. The current works based on a single RGB image generally first identify a human target using a bounding box and then estimate the cropped body, rarely paying attention to the scenes and objects that contain rich clues. The 3D HPE can utilize interpenetration constraints to limit the intersection between the body and the surrounding 3D scene.
* The 3D HPE from multi-view images. Multi-view images can significantly mitigate ambiguity, and their typical methods include fusing multi-view 2D heatmaps, enforcing consistency constraints between multiple views, and triangulation measurement, etc.
* Sensor technology. Some works use sensors, such as RGB-D depth cameras, inertial measurements units (IMUs) and radio frequency (RF) to add the collected depth, joint direction and other information [36; 37; 38]. Compared with depth images, using sensors to capture point clouds can provide more information.
## 4 Improved Adaptive Backoff Algorithm (I-ABA)
### The Downside of ABA and Introducing I-ABA  
To better understand the downside of ABA, we conducted several experiments to find the optimal size of the contention window that will result in the best performance (in terms of the metrics that will be explained later in this paper) under different network sizes. The results of our experiments are illustrated in Figure 2. In this figure, we show the optimal value of the contention window (\(W_{opt}\)) compared to the contention window computed using Equation (1) with \(W_{max}\) set to 256, 2048, and 4096.  
It is apparent from Figure 2 that the case of \(W_{max}=256\), which was used with ABA, is highly diverted away from the curve corresponding to \(W_{opt}\). The diversion is evident for both small-scale and large-scale networks. It is also noticeable that with \(W_{max}\) set to 2048 or 4096, we can achieve a boost in the performance (as we show later in this paper, with \(W_{max}=2048\), the channel utilization increases from 30% to 65% in a network of 200 nodes). Our introduction of I-ABA relies on the observations evident in Figure 2.  
In light of our understanding of what Figure 2 reflects, we generate a new curve that captures the optimal values of the contention window, where the performance is optimal, under different values of the probability of collision. The curve is shown in Figure 3 in blue. Since we are plotting contention window sizes against the probability of collision, we can apply curve fitting techniques to formulate an analytical expression of the probability of collision. By using quadratic regression, we can write the following formula:  
\[h(P_{c})\ =\ \left(5.18\times{P_{c}}^{2}\right)-\left(0.65\times P_{c}\right)+0.05. \tag{2}\]  
Figure 3: The use of quadratic regression to curve-fit the new expression of the contention window (W).  
Figure 2: Contention window sizes under different network sizes.  
Based on Equation (2), we now introduce an improvement to Equation (1) and use \(h(P_{c})\) to control the size of the contention window:  
\[W\ =\ h(P_{C})W_{max}. \tag{3}\]  
Equation (3) is plotted in Figure 3 in orange. It is apparent that the quadratic regression helped in achieving an excellent approximation to \(W_{opt}\). The importance of Equation (3) lies in its use of the main strength of ABA, that is, its use of \(P_{c}\) as a parameter to control the contention window (Equation (1)), while achieving a performance that is close to optimal. Similar to ABA, \(P_{c}\) can be calculated locally by the node based on the ratio of the failed frame transmissions to the total frames sent.  
The flowchart that describes the I-ABA algorithm is shown in Figure 4. This is an updated version of the flowchart in Figure 1. In the following sections, we develop a mathematical model based on the Markov chain to evaluate the performance of I-ABA; then, we conduct extensive simulations to investigate the impact of using \(h(P_{c})\) on the network's performance. We use MATLAB 7.6 to verify the accuracy of our mathematical model while we use user-defined tools in our simulations.
# A Programmable SRv6 Processor for SFC
## 3 Protocol-Independent Pipeline Design
### Programmable SR Forwarding for Network Slice
#### 3.4.3 Support for uSID  
The protocol overhead introduced by SRv6 is much higher than that of SR-MPLS. Therefore, SRv6 imposes high requirements on network devices. Although SRv6 has huge advantages in network programmability and load balancing, in order to take advantage of these advantages, there is an urgent need to address SRv6's protocol overhead, maximum transmission unit (MTU), and hardware requirements.  
These questions are essentially the same question, as follows: how can we improve the efficiency of the SRv6 segment? To solve this problem, uSID (micro SID) and G-SID (general SID) header compression schemes are proposed [29; 30]. Cisco extended the existing SRv6 framework and defined a new segment type, uSID.  
In a normal SRv6 header (SRH), each SID would need to hold the same common prefix, but these identical parts are redundant. The uSID extracts the same public prefix (network segment) from the segment list as the prefix block of the compressed packet header, and uses node ID and function ID as the compressed SID (uSID) together with the public prefix to form the compressed segment list.  
The programmable SRv6 processor supports uSID header compression, which is a stateful process. When SRv6 packets enter the pipeline, only the packet type and flow ID are written into the metadata. The result parser analyzes the metadata and searches the action table to obtain the compress instructions to be executed. The content of the instruction contains the compressed opcode, SID prefix, SID length, packet length field offset, etc., to configure the PDT module. Figure 4 shows a program for compressing the uSID header in PDT. The PDT module sends the compress instruction and SID prefix to the action module for SRH compression. The action module places the uSID prefix in the first 16 bits of the 128 bit SID, and then sequentially compresses the last 16 bits of SIDs with the same prefix after the first uSID prefix. After seven SIDs are compressed, a 128 bit SID is created for further compression until a different prefix or compression is completed. Finally, the difference between the compressed header length and the packet length before compression is calculated, and the difference is used as the offset to guide the re-fragmentation and forwarding of packets.