esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The age distribution in our study aligns with these findings, reinforcing the need for heightened clinical vigilance in older DM patients. Studies suggest a positive correlation between glycemic levels and _Candida_ colonization [18]. In our study, 47 out of 63 patients with oral candidal lesions had fasting blood glucose levels exceeding 110 mg/dL. However, other research indicates that glucose concentration alone does not affect _Candida albicans_' invasive behavior [30], suggesting that additional risk factors contribute to lesion development. A statistically significant association between smoking and _Candida_ lesions was observed. Studies in Iran reported a dose-dependent relationship, with increased cigarette consumption correlating with higher oral candidiasis risk [31]. A Sri Lankan study also found a significant association between smoking and oral _Candida_ carriage in DM patients [5]. Smoking-related alterations, such as reduced salivary flow, lowered salivary pH, and decreased immunoglobulin A levels, contribute to increased _Candida_ colonization [31]. A notable association was found between betel quid chewing and _Candida_-associated lesions. A Thai study reported elevated _Candida_ levels in betel quid chewers [32]. Further research in Bangladesh linked betel chewing to hyperglycemia in DM patients, potentially influencing _Candida_ carriage and lesion development [33]. However, a contrasting study found no significant effect of betel quid chewing on _Candida_ carriage [34].
## 2 Background
### Classifier Combination  
Classifier combination is an effective method to improve the performance of pattern recognition [15]. It has been applied in various fields, including economic decision-making, forecasts of natural phenomena, and military decisions in national defense. The combinations can be categorized into mathematical and behavioral approaches. Mathematical approaches construct models and derive combination rules using logic and statistics to combine different classification models and outputs. Behavioral approaches assume discussions between experts in the applicable fields to combine multiple decisions into a single one.  
Classifier combination has garnered great interest in the field of optical character recognition and biometric applications in the last decade. It has been widely applied in image classification for text recognition, voice recognition, facial image recognition, fingerprint recognition, and biometric applications. The combination function accepts N-dimensional vectors from M classifiers and outputs N final classification scores. Results for the problem can be finally redefined through combination rules or model definition. The combination allows for the determination of the optimal model, thus minimizing the misclassification cost. Several different types of classifier combinations exist, e.g., score combination function, ensembles, operating level of classifiers, classifier combination based on output types, and classifier combination based on complex types. A study proposed a feature combination network model [16] (Figure 2) in the field of DL to improve the model prediction accuracy.  
Figure 1: Super-resolution convolution neural network (SRCNN) model structure.
## 3 Results
### Genome Feature of EPS-Producing B. velezensis VTX20  
The complete genome of strain VTX20 was obtained and submitted to GenBank under the accession number CP075054. Briefly, the complete genome comprised a single and circular chromosome of 3,891,321 bp with a G + C content of 46.1% (Table S2). Moreover, the strain comprised a total of 118 RNAs including 86 tRNA genes, 27 rRNAs, and 5 ncRNAs. The VTX20 genome did not contain a plasmid sequence and harbored one CRISPR sequence. Based on the genome annotation, the primary virulence factors in _Bacillus_ species, including hemolysis BL, nonhemolytic, emetic toxin, and one-component cytotoxin K, were not found in the genome (Figure 3A), confirming the safety of this strain.  
In support of 16s rRNA analysis, OrthoANI software was utilized to determine the ANI values between isolate VTX20 and six closely related _Bacillus_ species. The values indicated that isolate VTX20 had 76.94-99.48% genome sequence identities with other strains (Figure 3B). VTX20 showed the highest OrthoANI value of 99.48% with _B. velezensis_ L-S60 and 99.47% with _B. velezensis_ LPL061, which was statistically significant enough for species demarcation. This result further confirmed the classification of VTX20 as belonging to _B. velezensis_ species. This strain was then deposited at VAST-Culture Collection of Microorganisms (VCCM) under accession number 14,175.  
Out of 3834 genes predicted, a total of 3124 proteins were classified into 19 cluster of orthologous groups (COG) categories. Of note, the cluster for \"function unknown\" was found to be the largest functional group (Figure 4). Among the other functional groups, the clusters for \"transcription\" (278 genes) and \"amino acid transport and metabolism\" (253 genes) were the most represented functional categories. EPS genes were reported to be contained within \"cell wall/membrane/envelop biogenesis\" (185 genes) and \"secondary metabolites biosynthesis, transport and catabolism\" (61 genes). This indicated that EPS biosynthetic pathways might be present in the strain VTX20.  
Figure 3: Circular map (**A**) and heat map (**B**) based on OrthoANI values determined for _B. velezensis_ VTX20 and related strains.
## 2 Materials and Methods
### Structural Construction
#### 2.1.1 Three-Dimensional Modeling Design
Figure 3 shows the changes in average pressure of the bone tissue repair scaffold model at different heights for the four grid groups. As depicted in the data graphs, the disparity in the average pressure at varying heights across the four groups of models is minimal. When comparing the average pressure at different heights between two groups of models with adjacent grid cell counts, the most pronounced data divergence occurs between the grid with 990,000 cells and the one with 2.22 million cells, reaching a maximum divergence of 12.46%. The maximum divergence between the group with 2.22 million cells and the group with 3.72 million cells is 1.43%, and that between the group with 3.72 million cells and the group with 7.69 million cells is 1.95%. Consequently, it can be inferred that the simulation results start to stabilize once the number of grid cells surpasses 2.22 million. Taking into account the restricted computational resources, the numerical simulation results corresponding to a grid cell count of 3.72 million are employed for the analysis in this instance. * Model Validity Test Simulations were carried out at different flow velocities (1 mm/s, 0.5 mm/s, 0.3 mm/s, and 0.1 mm/s) for the square-pore unit cell-type scaffold model to obtain the pressure drop of the fluid flow through the bone tissue repair scaffolds, and the permeability of the scaffolds of this structure at different flow velocities was calculated according to Equation (3). The results are shown in Figure 4.  
As shown, for the same scaffold structure, the permeability of the scaffold is independent of the velocity when the fluid physical properties are kept constant, which is in accordance with the definition of permeability, and the slight fluctuation of the permeability with the velocity (with a maximum difference of 0.11%) may be due to the entrance and exit effects or the accuracy of the simulation experiments. The permeability of this bone tissue repair scaffold was in the range of the permeability of bone trabeculae from 0.02 to 20 \(\times\) 10\({}^{-9}\) m\({}^{2}\), which is in the same order of magnitude as data in the literature for scaffolds of similar structure, proving the validity of the model. Figure 4: Permeability of scaffold under different flow velocities. Figure 3: Mesh irrelevance study.
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 1 Introduction  
Recovering 3D structure from 2D images captured from different views is an important and basic task of computer vision. Benefited from point feature detectors and descriptors, the point-based reconstruction received major attention. However, line segments are common in urban scenes and provide more intuitive structural information. Though more complicated than point reconstruction, line construction is meaningful for urban and poorly-textured scenes. Recently, line segments are widely applied to structure from motion [1; 2; 3; 4], SLAM systems [5; 6; 7; 8; 9; 10], visual odometry algorithms [11; 12], mapping [13; 14; 15; 16] and modelling [17]. Compared with point reconstruction, line reconstruction suffers from much more severe degeneracy [18]. Lines coplanar with two camera centers cannot be determined by triangulation these two observations. Lines that are close to the epipolar plane can be poorly localized even with a large baseline and a small reprojection error. In 3D reconstruction, reprojection error has always been a golden rule to evaluate the accuracy of reconstructed points of lines. In general cases, the setting threshold on reprojection can reject poorly reconstructed lines from wrong matches. However, small reprojection error does not guarantee a well-reconstructed line in nearly-degenerate cases and fails to provide a numerical evaluation of direction or location accuracy of the line in 3D. When cameras are mounted on a car and move along the road, many lines on the buildings along the road are approximately coplanar with camera motion. It is necessary to analyze the uncertainty of the reconstructed lines to distinguish those lines that are badly reconstructed with ahigh probability. In this work, we focus on uncertainty analysis of 3D line reconstruction and intend to provide a numerical evaluation of the reconstruction accuracy, with which a more reliable line map can be obtained.
## 2 Results and Discussion
### Total Phenolic Amounts and Targeted Phenolic Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**RT**} & **LOD** & **LOQ** & \multicolumn{3}{c}{_Ohica (mg/kg)_} & \multicolumn{3}{c}{_Lastovka (mg/kg)_} & \multicolumn{3}{c}{_Leventinaka (mg/kg)_} \\  & & & **(mg/kg)** & **(mg/kg)** & **Min** & **Max** & **Mean \(\pm\) SD** & **Min** & **Max** & **Mean \(\pm\) SD** & **Min** & **Max** & **Mean \(\pm\) SD** \\ \hline
1. & Hydroxytrosol1 * & 10.9 & 0.34 & 1.02 & 4.3 & 6.0 & 5.26 \(\pm\) 0.61a & 5.5 & 9.9 & 7.80 \(\pm\) 1.25b & 5.6 & 7.1 & 6.23 \(\pm\) 0.56a \\
2. & Tyrosol1 * & 13.4 & 0.40 & 1.21 & 3.0 & 4.8 & 4.00 \(\pm\) 0.62a & 3.2 & 5.9 & 4.60 \(\pm\) 0.83a & 5.1 & 6.6 & 5.94 \(\pm\) 0.44b \\
3. & 3,4-DHPEA-EDA \({}^{\gamma}\) & 22.4 & 0.94 & 2.86 & 76.4 & 93.5 & 85.00 \(\pm\) 7.19a & 26.2 & 68.1 & 45.40 \(\pm\) 15.00b & 31.5 & 36.1 & 34.4 \(\pm\) 1.75c \\
4. & Pinoresinol x & 25.2 & 0.16 & 0.47 & nd & nd & nda & 2.9 & 3.0 & 2.96 \(\pm\) 0.05b & 3.6 & 3.8 & 3.72 \(\pm\) 0.08c \\
5. & Lutcolin x & 25.7 & 0.46 & 1.40 & 6.0 & 7.4 & 6.95 \(\pm\) 0.25a & 7.4 & 7.6 & 7.49 \(\pm\) 0.09b & 4.0 & 4.4 & 4.22 \(\pm\) 0.15c \\
6. & \(p\)-HPEA-EDA y & 26.3 & 0.94 & 2.86 & 134.8 & 158.5 & 146.30 \(\pm\) 8.40a & 66.1 & 98.1 & 82.6 \(\pm\) 11.01b & 83.1 & 91.5 & 88.10 \(\pm\) 2.96b \\
7. & Abiginia x & 28.2 & 0.36 & 1.10 & 1.7 & 1.9 & 1.81 \(\pm\) 0.09a & 2.5 & 2.6 & 2.55 \(\pm\) 0.05b & 1.5 & 1.7 & 1.61 \(\pm\) 0.09e \\
8. & 3,4-HPEA-EA \({}^{\gamma}\) & 28.5 & 0.94 & 2.86 & 82.1 & 96.4 & 89.50 \(\pm\) 5.36a & 130.3 & 136.4 & 133.34 \(\pm\) 0.24b & 67.7 & 82.8 & 7.15 \(\pm\) 0.59e \\ \hline \hline \end{tabular} RT = retention time; Min = minimal value; Max = maximal value; Mean = average value; LOD = limit of detection; LOQ = limit of quantification; SD = standard deviation; nd = not detected (<LOD); * Identification based on RT and UV-Vis spectra of pure compounds; \({}^{\gamma}\) Tentative identification by UV-Vis spectra and comparison of retention times with the literature data, and compounds dosed using the eleurobein calibration curve; 3,4-DHPEA-EDA, dialdehyde form of decarboxymethyl elenolic acid linked to hydroxytrosol (decarboxymethyl) elaeropein aglyvon); \(p\)-HPEA-EDA, dialdehyde form of decarboxymethyl elenolic acid linked to tyrosol (decarboxymethyl) ligestoside aglyvon); 3,4-DHPEA-EA, oleuteropein aglyvon; The mean values within each row labeled with different letters are significantly different (Tukey’s test, \(p<0.05\)).  
\end{table} 
Table 5: Targeted phenolics from _Ohica_, _Lastovka_ and _Leventinaka_ VOOs hydrophilic fraction determined by HPLC-DAD.
# Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network
## 4 Experimental Verifications
### Data Preprocessing  
#### 4.2.1 Signal Normalization  
In order to increase the reliability of the model, the input signal needs to be normalized. As shown in the following equation.  
\[\lambda=\frac{x-\overline{x}}{\max(x)-\min(x)} \tag{6}\]
# ZSM Framework for Autonomous Security Service Level Agreement Life-Cycle Management in B5G Networks
###### Abstract  
In the rapidly evolving landscape of telecommunications, the integration of commercial 5G solutions and the rise of edge computing have reshaped service delivery, emphasizing the customization of requirements through network slices. However, the heterogeneity of devices and technologies in 5G and beyond networks poses significant challenges, particularly in terms of security management. Addressing this complexity, our work adopts the Zero-touch network and Service Management (ZSM) reference architecture to enable end-to-end automation of security and service management in Beyond 5G networks. This paper introduces the ZSM-based framework, which harnesses software-defined networking, network function virtualization, end-to-end slicing, and orchestration paradigms to autonomously enforce and preserve security service level agreements (SSLAs) across multiple domains that make up a 5G network. The framework autonomously manages end-to-end security slices through intent-driven closed loops at various logical levels, ensuring compliance with ETSI end-to-end network slice management standards for 5G communication services. The paper elaborates with an SSLA-triggered use case comprising two phases: proactive, wherein the framework deploys and configures an end-to-end security slice tailored to the security service level agreement specifications, and reactive, where machine learning-trained security mechanisms autonomously detect and mitigate novel beyond 5G attacks exploiting open-sourced 5G core threat vectors. Finally, the results of the implementation and validation are presented, demonstrating the practical application of this research. Interestingly, these research results have been integrated into the ETSI ZSM Proof of Concept #6: \"Security SLA Assurance in 5G Network Slices', highlighting the relevance and impact of the study in the real world.  
ZSM; B5G; security; automation; E2E network slicing; multi-domain; MANO
# Post-FEC Performance of Pilot-Aided Carrier Phase Estimation over Cycle Slip
## 1 Introduction
Experimental results show that, compared with CPE, CPE + PAPU can release the required PRE-FEC optical signal noise ratio (OSNR) about 3.1 dB, 1.3 dB, and 0.6 dB at BER of \(1.8\times 10^{-2}\) under the CPE filter length of 8, 16, and 20. Meanwhile, the CPE + PAPU scheme achieves respectively about 3 dB, 1 dB, and 0.5 dB POST-FEC OSNR gain compared with the CPE scheme under the CPE filter length of 8, 16, and 20. The results also show that the CPE + PAPU scheme respectively improve the FEC limit from \(2.5\times 10^{-3}\), \(8.9\times 10^{-3}\), \(1.6\times 10^{-2}\) to \(1.4\times 10^{-2}\), \(1.8\times 10^{-2}\), \(1.9\times 10^{-2}\) compared with CPE, with the CPE filter length of 8, 16, and 20.
# Bridging the Appearance Domain Gap in Elderly Posture Recognition with YOLOv9
## 3 Materials and Methods
### Performance Evaluation  
The performance metrics included precision, accuracy, recall, and F1-score for each posture category (_lying_, _sitting_, and _standing_), along with their respective averages. These metrics are defined as follows:  
* Precision: The proportion of true positive detections out of the total number of detections for each posture.
* Accuracy: The overall correctness of the model's predictions, calculated as the ratio of correctly predicted instances to the total number of instances.
* Recall: The proportion of true positive detections among the actual instances for each posture.
* F1-score: The harmonic mean of precision and recall, providing a balance between the two metrics.  
Two more metrics were introduced to ensure that the YOLOv9 model could accurately identify and localise objects within an image:  
* mAP50: The mean average precision when evaluated at a single intersection over union (IoU) threshold of 0.50. This means that a predicted bounding box is considered a successful detection if it overlaps with the ground truth by at least 50%. This metric provides an indication of the model's ability to detect objects with a reasonable degree of accuracy.
* mAP50-95: This provides a more comprehensive evaluation by averaging precision across multiple IoU thresholds, ranging from 0.50 to 0.95 in increments of 0.05. This metric is more stringent, as it requires the model to perform well at varying levels of overlap, giving a better assessment of its overall detection performance.
## 2 Materials and Methods
### The Index Establishment of DBS Slip Anchoring Performance Assessment Method
#### 2.2.1 Structural Safety Evaluation Index: Strength Criteria  
During horizontal-well-staged fracturing operations, the casing experiences significant indentation due to the seating force, subjecting both the slip teeth and casing to substantial stress concentrations. Furthermore, when a flanged shackle is employed, the tooth grooves on the slip base may also endure high stress levels, necessitating the rigorous strength verification of the slip base structure.  
To prevent damage to both the DBS slip and casing, the maximum equivalent stress is adopted as a critical safety evaluation metric for the DBS anchoring system. The assessment criteria stipulate that (1) the maximum equivalent stress on the slip teeth, slip base, and casing must remain below the yield strength of their respective materials, and (2) the maximum equivalent stress should be minimized to ensure reliable DBS anchoring performance under downhole conditions.
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 5 Evaluation
### Evaluation of Single-Receiver Topology  
#### 5.1.1 Experiment Settings  
The comparison test was performed under the network topology of Figure 1, and the experiment was divided into 3 groups of streams, with each sender belonging to the same group of flows. The path of each group of flows is shown below.  
* Sender Group 3 \(\rightarrow\) Switch 2 \(\rightarrow\) Switch 3 \(\rightarrow\) Receiver  
We compared BFCC with existing end-to-end congestion control schemes (e.g., P4 QCN, TCN, DCQCN). The comparison primarily focused on four performance metrics: (1) FCT of single flow; (2) throughput; (3) RTT per packet; (4) queue length of the buffer.  
**BFCC.** BFCC implements switch-to-switch congestion control by detecting congestion based on buffer queue depth and queue delay and identifying congested flows by analyzing queue occupancy. The parameter for detecting congestion threshold was set to 15, with T\({}_{\text{min}}\) = 80,000 and T\({}_{\text{max}}\) = 150,000.  
**DCQCN.** DCQCN [18] uses ECN marking and end-to-end congestion control to manage buffers on switches. To unify the test environment, we implemented DCQCN on a P4-based Mininet, matching all the details in the original paper. DCQCN was used as a comparison because it is still commonly used in today's data centers. The specific parameter settings were K\({}_{\text{min}}\) = K\({}_{\text{max}}\) = 11, g = 1/256, N = 50\(\upmu\)s, and K = 55 \(\upmu\)s.  
**P4 QCN.** P4 QCN [37] is a flow-level rate-based congestion control protocol using ECN marking policy to mark congestion and extends the QCN protocol to make it compatible with IP routing networks based on the P4 framework. We implemented P4 QCN and set the parameters as suggested in the paper: Q\({}_{\text{P4 QCN}}\) = Q\({}_{\text{min}}\) = Q\({}_{\text{max}}\) = 11, N = 5, \(\beta\) = 1, and R\({}_{\text{AI}}\) = 5 Mbps.  
**TCN.** TCN [38] is a time-based display congestion feedback mechanism that uses the waiting time of the packet rather than queue length, as a congestion signal; ECN marking thresholds are computed based on the current packet's delay. TCN is stateless as it does not modify the state in the data plane by performing stateless transient ECN marking.
## 2 Preliminary  
### Offset Min Sum Decoder and Scale Min Sum Decoder  
We consider (N,K) LDPC, BCH and Hamming code where N denotes the length of codeword, K denotes the number of information bits in the message. We use binary phase shift keying (BPSK) modulation, where the check matrix is \(H\). The message \(X\) is encoded by generator matrix \(G\). The message obtained is \(Y=X+Z\) over the additive white gaussian noise (AWGN) channel, where \(Z\in\mathbb{R}^{n}\) is the channel noise. The log-likelihood ratio (LLR) of the \(v\)-th bit can be computed as  
\[I_{v}=log(\frac{P(Y_{v}=y_{v}|X_{v}=-1)}{P(Y_{v}=y_{v}|X_{v}=+1)})=\frac{2y_{v} }{\sigma^{2}} \tag{1}\]where \(\sigma^{2}=N_{0}/2\) is the variance of the channel noise, \(N_{0}\) is noise spectral density, and we use \(E_{b}/N_{0}=1/(2(K/N)\sigma^{2})\) as the signal-to-noise ratio, where \(E_{b}\) is the energy per information bit, \(y_{v}\) is the \(v\)-th value of the received message.  
The decoding graph is a bipartite graph, which consists of variable nodes (VNs) and check nodes (CNs). LLRs \(l_{v}\) are transmitted from the VNs \(u_{v,c}^{t}\) to the CNs \(u_{c,v}^{t}\), and then return back. Each iteration requires the following steps:  
Step 1: From the VNs to CNs the message \(u_{v,c}^{t}\) is passed in the graph as.  
\[u_{v,c}^{t}=l_{v}+\sum_{c^{\prime}\in N(v)\setminus c}u_{c^{\prime},v}^{t-1}. \tag{2}\]  
where the neighboring node set \(v\) consisting of CNs is denoted as \(N(v)\), the symbol \(N(v)\setminus c\) denotes the set \(N(v)\) except the check node \(c\), \(u_{v,c}^{0}\) is set to 0, add \(t\) denotes the number of iteration.  
Step 2: From the CNs to VNs the message \(u_{v,c}^{t}\) is passed in the graph as.  
\[u_{c,v}^{t}=max(\min_{v^{\prime}\in M(c)\setminus v}|u_{v^{\prime},c}^{t}|- \beta,0)\bullet\prod_{v^{\prime}\in M(c)\setminus v}sign(u_{v^{\prime},c}^{t}). \tag{3}\]  
where the neighboring node set \(c\) consisting of VNs is denoted as \(M(c)\), the symbol \(M(c)\setminus v\) denotes the set \(M(c)\) except the variable node \(v\).  
Step 3: The soft estimation message \(s_{v}^{t}\) after \(t\) iterations is written as  
\[s_{v}^{t}=l_{v}+\sum_{c^{\prime}\in N(v)}u_{c^{\prime},v}^{t}. \tag{4}\]  
Repeat Step 1, 2, 3 until conditions of iterations are reached. The recovered binary vector \(\hat{x}_{v}^{t}\) is computed by \(s_{v}^{t}\) as  
\[\hat{x}_{v}^{t}=\left\{\begin{array}{ll}1,&\mbox{if}\quad s_{v}^{t}>0\\ 0,&\mbox{otherwise},\end{array}\right. \tag{5}\]  
In the iterative process, we use the Equation (6) below to replace the Equation (3) in step 2 of CNs layers, and the decoding process is collectively called scale min-sim (SMS) decoder.  
\[u_{c,v}^{t}=(\alpha\times\min_{v^{\prime}\in M(c)\setminus v}|u_{v^{\prime},c }^{t}|-\beta)\bullet\prod_{v^{\prime}\in M(c)\setminus v}sign(u_{v^{\prime},c }^{t}). \tag{6}\]
# Applying Vision-Based Pose Estimation in a Telerehabilitation Application
## 2 Materials and Methods
### ExerWeb Module  
The ExerWeb module provides a dynamic web interface (see Figure 6) from where the therapist can manage patients, prescribe tasks or games and consult or analyze the results in a ubiquitous and totally remote way with the patient. Once the therapist is authenticated on the website, ExerWeb has a top menu accessible from any page of the website. From this menu, the therapist can access the Therapist section and the enabled management mechanisms.