esa-sceva/satcom-chunk-collection huggingface dataset:
# An Emerging Role of Long Noncoding RNAs as Novel Biomarkers for Breast Cancer Metastasis
## 4 The Role of lncRNAs beyond Metastasis
### Tumor Environment  
LncRNAs profoundly influence the tumor microenvironment (TME) by modulating interactions between cancer cells and surrounding stromal and immune cells. UCA1 activates the Wnt/\(\beta\)-catenin signaling pathway, crucial for creating a supportive milieu for cancer cells [17]. Linc-ROR regulates hypoxia-inducible factors (HIFs), aiding cancer cells in adapting to low-oxygen conditions within the TME, thereby promoting survival and metastasis [18]. HOTAIR enhances the activation of cancer-associated fibroblasts (CAFs), which secrete cytokines and growth factors that drive tumor progression [19]. MALAT1 and BANCR influences the extracellular matrix (ECM) by regulating matrix metalloproteinases (MMPs), enzymes that degrade ECM components, facilitating tumor invasion and metastasis establishment [2].
## 5 Results and Discussion
### On Comparison with Other Algorithms  
Table 8 shows the benchmarking results with another 10 algorithms, in terms of number of classes (some of the algorithms did not classify all 16 classes), methods, number of prediction stages, and accuracy. In terms of accuracy, refs. [46] and [47] have the closest but still a lower accuracy than our proposed algorithm, i.e., 99.61% and 99.80%, respectively. Note that, ref. [46] only performs classification for eight classes, while [47] only performs classification for four classes, while we perform classification for sixteen classes. Hence, it is evident that our proposed algorithm with the [256 256] ensemble of CNNs outperforms the other algorithms. Particularly, we managed to classify all 16 classes from the MIT-BIH database, while reducing the number of stages to two (lower computational cost), and achieving highest accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & & \\ \cline{2-13}  & **N** & **L** & **R** & **V** & **/** & **A** & **f** & **F** & **1** & **j** & **x** & **a** & **E** & **J** & **e** & **Q** \\ \hline
**Ground Truth** & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** \\ \hline N & 0 & 63,521 & 4 & 13 & 70 & 2 & 79 & 11 & 36 & 3 & 44 & 6 & 2 & 0 & 1 & 0 & 3 \\ L & 1 & 22 & 5211 & 0 & 13 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 1 \\ R & 2 & 11 & 0 & 4699 & 0 & 0 & 9 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ V & 3 & 46 & 10 & 0 & 4528 & 1 & 11 & 0 & 22 & 10 & 0 & 0 & 7 & 0 & 0 & 0 & 0 \\ / & 4 & 3 & 0 & 0 & 3 & 4553 & 0 & 9 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 \\ A & 5 & 143 & 0 & 5 & 16 & 0 & 1484 & 0 & 2 & 0 & 3 & 0 & 0 & 0 & 2 & 0 & 0 \\ f & 6 & 13 & 0 & 0 & 1 & 6 & 0 & 470 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 \\ F & 7 & 52 & 0 & 0 & 36 & 0 & 0 & 0 & 313 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 \\! & 8 & 2 & 0 & 0 & 8 & 0 & 0 & 0 & 0 & 225 & 0 & 1 & 0 & 0 & 0 & 0 \\ j & 9 & 15 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 97 & 0 & 0 & 0 & 2 & 0 & 0 \\ x & 10 & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 5 & 0 & 89 & 1 & 0 & 0 & 0 \\ a & 11 & 6 & 0 & 0 & 15 & 0 & 9 & 0 & 0 & 1 & 0 & 0 & 44 & 0 & 0 & 0 & 0 \\ E & 12 & 2 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 50 & 0 & 0 & 0 \\ J & 13 & 7 & 0 & 0 & 0 & 0 & 3 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 32 & 0 \\ e & 14 & 3 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 3 & 0 \\ Q & 15 & 11 & 0 & 2 & 1 & 0 & 0 & 3 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix for [256 256 256] ensemble of CNNs.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**ECG** & **Total** & **Train** & **Test** & **TN** & **FN** & **TP** & **FP** & **Sn** & **Sp** & **_Pp_ & _Acc_ \\
**Class** & **Beats** & **Beats** & **Beats** & & & & & & & & & & & \\ \hline N & 75,052 & 11,257 & 63,795 & 22,026 & 274 & 63,521 & 337 & 99.57 & 98.49 & 99.47 & 99.29 \\ L & 8075 & 2826 & 5249 & 80,895 & 38 & 5211 & 14 & 99.28 & 99.98 & 99.73 & 99.94 \\ R & 7259 & 2540 & 4719 & 81,418 & 20 &
# Loop-Back Quantum Key Distribution (QKD) for Secure and Scalable Multi-Node Quantum Networks
###### Abstract  
Quantum key distribution (QKD) is a cornerstone of secure communication in the quantum era, yet most existing protocols are designed for point-to-point transmission, limiting their scalability in networked environments. In this work, we introduce Loop-Back QKD, a novel QKD protocol that supports both two-party linear configurations and scalable multiuser ring topologies. By leveraging a structured turn-based mechanism and bidirectional pulse propagation, the protocol enables efficient key distribution while reducing the quantum bit error rate (QBER) through a multi-pulse approach. Unlike trusted-node QKD networks, Loop-Back QKD eliminates intermediate-node vulnerabilities, as secret keys are never processed by intermediate nodes. Furthermore, unlike Measurement-Device-Independent (MDI-QKD) and Twin-Field QKD (TF-QKD), which require complex entanglement-based setups, Loop-Back QKD relies solely on direct polarization transformations, reducing vulnerability to side-channel attacks and practical implementation challenges. Additionally, our analysis indicates that multi-pulse Loop-Back QKD can tolerate higher QBER thresholds. However, this increased robustness comes at the cost of a lower key rate efficiency compared to standard QKD schemes. This design choice enhances its robustness against real-world adversarial threats, making it a strong candidate for secure multiuser communication in local and metropolitan-scale quantum networks.  
Keywords:quantum key distribution; BB84; polarization without measurement; ring topology; multiuser quantum key distribution +
Footnote †: journal:
## 3 Results and Discussion  
### Analysis and Results  
#### 3.1.1 Identification of Metabolites (GC-MS Data Analysis)  
Based on the nontargeted metabonomic method of GC-MS, the samples of secondary and tertiary fermentation of _P. ostreatus_ from the industrial tunnel of Lianzhong Cooperative of Shanghai Province and Yuguan Company of Jiangsu, respectively, were collected after each stage of fermentation, and the metabolites were analysed. A total of 1002 compounds (Table S1) were identified during data preprocessing (removal of eigenvalues with missing values > 100%, filling method with minimum missing values, and data normalization method with summation).  
All the metabolites were obtained after identification by MS and compared with the KEGG Database and Human Metabolome Database (HMDB). Then, the annotation information in the database was obtained, and the number of annotated metabolites in the database was counted. The metabolites identified were compared to the KEGG compound database to obtain the classification of the metabolites (Figure 2). All the compounds were mainly concentrated in compounds with biological roles (mainly amino acids and vitamins, etc.), phytochemical compounds (mainly alkaloids and terpenoids) and lipids (mainly PR01 isoprenoids).  
From Figure 2, the main metabolites obtained were terpenoids, alkaloids, fatty acid-related compounds, amino acid-related compounds, and polyketides. Of these, there were 10 and 7 terpenoids and alkaloids, respectively.  
According to the information of metabolic pathways involved in metabolites (Figure 3), metabolites were mainly concentrated in such metabolism pathways, including amino acid metabolism, metabolism of cofactors and vitamins, carbohydrate metabolism, and metabolism of terpenoids and polyketides. The important pathways included ABC transporters (ATP-binding cassette transporter), aminoacyl-tRNA biosynthesis, and sesquiterpenoid and triterpenoid biosynthesis.  
Figure 3: _Cont._  
Figure 2: Classification of metabolites based on Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation: (**a**) compounds with biological roles; (**b**) phytochemical compounds; (**c**) lipids; (**d**) pesticides; (**e**) endocrine disrupting compounds.
## 2 Mathematical Formulation
### Energy Losses Minimization (FO\({}_{2}\))  
The objective function \(FO_{2}\) aims to minimize the total energy losses associated with power transmission within the MG. This is expressed in Equation (2).  
\[FO_{2}=\min E_{loss}=\min\left(\sum\limits_{k\in\Omega_{R_{l}}}\sum\limits_{i \in\Omega_{L}}R_{l}|I_{l}|^{2}\Delta h\right) \tag{2}\]  
In this equation, \(\Omega_{L}\) denotes the set of lines in the system, while \(R_{l}\) and \(I_{l}^{2}\) represent the resistance of line \(l\) and the squared current flowing through it, respectively, as a result of power management within the MG.
# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 6 Open Issues
### Resource Allocations  
Each vehicle is equipped with computing resources, such as computing power, storage, and so on [152]. The SDVN with inter-vehicular communication is an emerging technology for effective safety and entertainment information distribution. However, due to fast mobility and variable vehicle density, highly dynamic VANET topology might produce unstable wireless connections, resulting in more significant packet loss and increased transmission latency, posing problems in ensuring the QoS [153]. DVZI and V2V communications can both be enabled by Dedicated Short-Range Communications (DSRC).  
There have been a slew of new services fueled by mobile devices and the exponential growth of mobile internet traffic, including computation-intensive, content-centric, and delay-sensitive services [154]. To maximize VANET speed, it is vital to allocate networking, caching, and computing resources. Spectrum/bandwidth, power, and time slots are some of the radio resources that can be managed in vehicular communication [155]. As a result, the allocation and management of resources are challenging to resolve and differ significantly from one another.
## 7 Visible Light Communication and LiFi
### IEEE 802.15.7 and IEEE 802.15.13  
IEEE 802.15.7 was the first visible light communication standard proposed by IEEE, and has laid the foundation for the most recent light communication standards [67]. This particular standard introduced both MAC and physical layer specifications for VLC devices. The MAC layer supports multiuser communication using CSMA/CA-based MAC protocols, and includes a variety of modulation schemes, such as OOK, variable pulse position modulation (VPPM) and colour shift keying (CSK) in the physical layer. Due to the diversity of modulation schemes used, the data rates can also range from 11.67 kbps to 96 Mbps. This standard has now been superseded by IEEE 802.15.13, which offers significantly better features [68].  
IEEE 802.15.13 introduced new modulation schemes as well as improvements in the MAC layer. As a result, data rates up to 10 Gbps and an operating distance of up to 200 m were achieved in IEEE 802.15.13. The newly introduced physical layers feature OFDM and pulsed modulation (PM) for high-data-rate operations. There are several physical layer modes available in this standard. The data rates, modulation techniques, and coding schemes supported in each physical layer mode are listed in Table 4. As shown in Table 4, PHY I, PHY II, and PHY III are based on OOK, PPM, and CSK. However, the latest additions are based on pulsed modulation and OFDM (LB-PHY and HB-PHY), which are capable of reaching multi-Gbps data rates [69; 70]. The diverse range of modulation schemes allows VLC to support different applications in the 6G environment. For instance, energy-constrained IoT devices might choose pulse position modulation (PPM) over OFDM, whereas a VR headset can be operated using OFDM. Hence, IEEE 802.15.13 offers a great deal of versatility in modulation schemes.  
IEEE 802.15.13 supports all common topologies, such as peer-to-peer, star, and broadcast, as well as a new coordinated topology, as shown in Figure 7. As illustrated in the figure, the coordinated topology allows for a primary coordinator to coordinate multiple VLC networks via backhaul. Supporting different topologies can motivate novel networking arrangements in 6G indoor applications and will lead to better use of network resources.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Physical Layer Mode** & **Data Rate** & **Modulation** & **Coding Scheme** \\ \hline PHY I & 11.67–266 kbps & OOK/PPM & Convolutional codes \\  & & Reed–Solomon codes \\ \hline PHY II & 1.25–96 Mbps & VPPM/OOK & Run-length limit codes \\ \hline PHY III & 12–96 Mbps & CSK & \\ \hline Pulsed Modulation PHY & 100s of Mbps & 2-PAM & 8B10B line codes \\ \hline Low-Bandwidth PHY (LB-PHY) & 10s of Mbps & OFDM & Bit-interleaved codes \\ \hline High-Bandwidth PHY (HB-PHY) & Multiple Gbps & OFDM & Adaptive bit loading \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical layer modes in IEEE 802.15.13.
## 4 Methodology
### Research Framework and Proposed Methods  
This project comprises three phases, and the output from each phase will come as input to the next, as shown in the following Figure 7.  
* Phase 1: Data preprocessing and cleaning are performed in this phase, including missing, duplicate, infinity, and NaN values. Furthermore, it includes processes such as dropping unnecessary features, feature importance, feature selection, data encoding, and data standardization.
* Phase 2: In this phase, we will use four scenarios for the training based on DNNs. We will build two models, the first using MLP-BP and the other using MLP-PSO for both binary and multi-class classification.  
Figure 6: Distribution of attack classes in CSE-CIC-IDS2018.  
Figure 7: The framework of research.  
Phase 3: We will use the confusion matrix and equations of performance metrics to evaluate the outcomes of our models.  
The flowchart of the proposed methodology is illustrated in Figure 8, and Figure 9 describes the frameworks of our models.
###### Abstract  
Nanoparticles have found widespread application in a variety of fields, despite growing worry about their possible hazardous effects on both the environment and human health. In recent years, research efforts have focused on plants and vegetable oils, which have been identified as abundant sources of many bioactive compounds. Many of these substances are known to participate in antioxidant processes. As a result, the current study was designed to investigate the antioxidant and protective properties of oleaster oil against cytotoxicity and oxidative stress induced by silica nanoparticles (SiNPs) in albino Wistar rats. Forty male rats were randomly assigned to four equally sized cohorts: a control group, SiNP-treated group (at a dose of 50 mg/kg), SiNP-treated group supplemented with oleaster oil (at a dose of 2 mL/kg), and those receiving only 2 mL/kg of oleaster oil. The findings demonstrated that SiNPs initiated an oxidative stress environment, as evidenced by higher lipid peroxidation levels and changes in antioxidant defense mechanisms. Antioxidant enzymes were significantly reduced, including glutathione levels between the control and SiNP-exposure treatments (36.01%, 36.59%, 60%), glutathione-S-transferase (29.74%, 29.90%, 13.49%), catalase (24.14%, 28.19%, 30.85%), and tissue superoxide dismutase (11.90%, 37.78%, 37.79%) in the liver, kidney, and heart, respectively. Furthermore, histological investigations revealed significant liver, kidney, and heart damage, as indicated by pathological alterations such as vascular dilatation and congestion, inflammatory cellular infiltration, and hepatocellular dysfunction. Encouragingly, the administration of oleaster oil significantly ameliorated a majority of these detrimental effects.  
These data suggest a potential protective effect of oleaster oil against the adverse histological effects induced by SiNP injection.  
Keywords:oleaster oil; _Oleacae_; silica nanoparticles; hepatotoxicityoxicity; cytotoxicity; oxidative stress
## 1 Introduction  
In 2019, road traffic accidents were among the top ten leading causes of death in different countries worldwide [1]. The 2018 \"Global Status Report on Road Safety\" released by the World Health Organization pointed out that in 2016, the number of deaths due to traffic accidents globally reached 1.35 million, and vulnerable road users (pedestrians, cyclists and motorcycle cyclists) accounted for more than half of the total deaths [2]. Research based on the Pedestrian Crash Data Study (PCDS) found that the main collision direction of pedestrians (approximately 70%) is side impact, and pelvis and lower limb injuries accounted for the highest proportion of traffic injuries [3]. Even after excluding minor injuries (such as bruises), the lower limb and pelvis remain the most vulnerable parts [4]. Pelvic anatomy is complex, and numerous complications exist after pelvic fractures often leading to internal organ injuries and hemorrhage [5], and the mortality is extremely high, reaching 19-31% [6]. The injury mechanism of pelvic fractures in traffic accidents is complicated, primarily caused by impact and run-over, which represents one of the challenges in forensic identification. Classic identification relies on expert judgment and is relatively subjective. Therefore, more objective and quantitative methods are needed to evaluate the mechanism of pelvic fractures [7].  
Research on the mechanism of pelvic injury generally focuses on epidemiological investigations [3; 8; 9; 10], postmortem cadaver tests [11; 12] and digital dummy simulations [4; 13; 14; 15; 16]. The current FE human models with detailed geometries and biofelic material will accurately reconstruct the injury biomechanical responses [17], which can obtain the displacement, stress and strain data of all nodes in the model and will help identify the injury mechanisms and patterns of different loading conditions.  
Distinguishing the injury manners and force directions is important in forensic identification. Presently, the clinical classification of pelvic fractures is dependent on the Tile, Tile/AO, Young and Burgess classification [18; 19; 20], which cannot cover most of the forensic problems and is difficult to apply directly to practice. Additionally, a lack of systematic studies exists on the effects of boundary and loading conditions on pelvic fracture based on biomechanical approaches [21].  
Therefore, based on the common pelvic impact and run-over injuries in traffic accidents, we employed the Total Human Model for Safety (THUMS) model system to simulate impacts and run-over manners in different directions and at different velocities and analyzed the effects of boundary and loading conditions on pelvic fractures. The study findings will improve our understanding of the biomechanical of pelvic injuries and provide more objective evidence to the forensic community.
## 1 Introduction
### Contribution  
This work introduces an intelligent signal quality estimation and power adaptation framework for FSO communication systems using machine learning models applied to eye diagram analysis. The primary objective is to enhance signal integrity and link reliability under adverse weather conditions, particularly in harsh snow-prone environments. The key contributions of this work are as follows:  
* Intelligent signal quality prediction from eye diagrams: We develop and train lightweight CNN architectures, including LeNet and Wide ResNet, to accurately predict the Q-factor or the BER directly from eye diagram images. This regression-based prediction enables the fine-grained assessment of optical signal quality under varying channel impairments.
* Classification-based reliability decision using CNNs: In parallel, we design a binary classifier CNN that determines whether the BER exceeds a critical threshold of \(10^{-4}\), providing a rapid decision-making mechanism for triggering signal retransmission with adaptive power control.
* Dual-model power adaptation strategy: Based on the outputs of the prediction or classification model, a dual-amplifier configuration is selectively activated. When the predicted BER exceeds the threshold, the system engages a pre-channel booster amplifier (maximum gain 25 dB) and a post-channel preamplifier (maximum gain 10 dB) to maintain robust communication over a 0.6 km FSO link in harsh environments.
* Validation under harsh weather conditions: The proposed system is evaluated under simulated dry snow conditions to emulate the signal degradation typically encountered in cold regions such as Northern Europe, Canada, and Northern Japan. The experimental results demonstrate the effectiveness of the proposed models in maintaining acceptable signal quality.
* Comprehensive performance evaluation: The LeNet and Wide ResNet regression models are evaluated using training and validation loss curves along with the mean square error (MSE) and R\({}^{2}\) score to ensure convergence and generalization. The CNN classifier is assessed using confusion matrices, precision, recall, and the F1-score, providing a thorough evaluation of classification performance.  
Generalizability across datasets: All developed models are further tested using eye diagram samples from an external dataset, confirming their generalization capability and robustness to dataset variability.  
The rest of the paper is organized as follows. Section 2 presents the design of the CNN classifier, LeNet-based regression model, and Wide ResNet-based regression model. Section 3 describes the proposed intelligent power adaptation framework employing DL models within the FSO communication system. Section 4 provides the simulation results along with a detailed analysis and discussion. Finally, Section 5 concludes the paper with a summary of the key findings.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 4 Test Results and Analysis
### Temperature Change Test  
The system needs to meet the requirement of real-time measurement without interruption for a long time; therefore, it is bound to be affected by the change of ambient temperature. However, the long distance laser reference propagation has a magnifying effect on the laser's own angle change.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Accuracy (mm)** \\ \hline
1. Calculation of the extremum after Gauss fitting & 0.28 \\
2. Calculation of the expectation from original data & 0.42 \\
3. Calculation of the expectation after low-pass filtering & 0.21 \\
4. Acquisition of the maximum probability data & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results comparison.  
Figure 19: Schematic diagram of electromagnetic wave emission principle.  
Figure 20: Receiving antenna power map. (**a**) Parallel attitude; (**b**) vertical attitude.  
Since the maximum temperature difference in the tunnel during day and night is only 4 \({}^{\circ}\)C, the linear thermal deformation of the laser system can be ignored, but the angle change will have a large error. As shown in Figure 21, for a temperature difference 10 \({}^{\circ}\)C during 14 h, the \(x\)-axis coordinate difference of the spot position caused by thermal deformation can reach 38 pixels on a 50 m propagation distance. For the 4 \({}^{\circ}\)C temperature difference in the tunnel, the thermal deformation will cause less \(x\)-axis coordinate difference.  
To reduce the effect of thermal deformation on the precision of the system, the mechanical structure was designed to be symmetrical to minimize the angle torsion caused by thermal deformation.