esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modelling and Parameterisation
### Research Framework  
The research framework of this paper is divided into five main phases. (1) Extraction of data on rail traffic flows and the built environment independent variables associated with them, which involved transforming the coordinate system and analysing behaviour  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Learning Rate (n)** & **Maximum Depth** & **R\({}^{2}\) (Total)** & **R\({}^{2}\) (Inbound)** & **R\({}^{2}\) (Outbound)** \\ \hline
0.01 & 1 & 0.86 & 0.84 & 0.87 \\
0.01 & 2 & 0.87 & 0.85 & 0.88 \\
0.01 & 3 & 0.85 & 0.83 & 0.86 \\
0.05 & 1 & 0.89 & 0.87 & 0.9 \\
0.05 & 2 & 0.9 & 0.88 & 0.91 \\
0.05 & 3 & 0.87 & 0.85 & 0.88 \\
0.1 & 1 & 0.83 & 0.81 & 0.84 \\
0.1 & 2 & 0.85 & 0.83 & 0.86 \\
0.1 & 3 & 0.82 & 0.8 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sensitivity analysis at M = 300.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Learning Rate (n)** & **Maximum Depth** & **R\({}^{2}\) (Total)** & **R\({}^{2}\) (Inbound)** & **R\({}^{2}\) (Outbound)** \\ \hline
0.01 & 1 & 0.85 & 0.83 & 0.86 \\
0.01 & 2 & 0.86 & 0.84 & 0.87 \\
0.01 & 3 & 0.84 & 0.82 & 0.85 \\
0.05 & 1 & 0.88 & 0.86 & 0.89 \\
0.05 & 2 & 0.9 & 0.88 & 0.91 \\
0.05 & 3 & 0.88 & 0.86 & 0.89 \\
0.1 & 1 & 0.84 & 0.82 & 0.85 \\
0.1 & 2 & 0.86 & 0.84 & 0.87 \\
0.1 & 3 & 0.83 & 0.81 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sensitivity analysis at M = 200.  
based on encrypted data. (2) An 800-m buffer zone with the metro station as centre of mass was created and the number of respective variables within the buffer zone was calculated. (3) Quantifying the characteristics of the built environment by collecting and analysing data from route networks, POIs, and urban street landscapes using tools such as ArcGIS and PSPNET. (4) Linear regression and MGWR models were used to analyse the spatial heterogeneity between built environment factors and riderships. (5) Biased dependency graphs analysed using the GBDT model were used to investigate the nonlinear relationship between built environment elements affecting patronage in order to understand the dynamics and factors contributing to spatial heterogeneity. The research framework is shown in Figure 1.  
In this paper, the model analysis and results will be discussed based on this research framework using Shenzhen Railway ridership data.
## 4 Methodology
### Extract Cross-Sectional Sketches and Reconstruct Features
#### 4.2.1 Segmental Fitting of Curves on Sketch  
Intersecting a feature section \(S\) in the basic feature with an edge or vertex of the triangular mesh feature area forms a continuous ordered set of ring data points \(\{p_{1},p_{2},\cdots,p_{N}\}\), which are sequentially connected to create a polyline \(\sum\). The analysis of various basic line shapes within the polyline \(\sum\) follows the method outlined in Section 4.1.3. This method categorizes the basic line shapes into three types: straight lines, arcs, and B-splines. The judgment considers both the curvature of the data points and the normal vectors. As the curvature value of the set of ring data points intercepted by the feature section is notably higher than that of the center of mass points of the feature path \(\Gamma\) presented in Section 4.1, with a potential abrupt change in curvature, a modification to the tolerance condition is necessary:  
\[\left\{\begin{array}{l}0\leq K_{i}\leq 0.5\\ 0\leq\varphi_{i-1,i}\leq 5^{\circ}\end{array}\right. \tag{8}\]  
As the line pattern judgment method was outlined in Section 4.1.3, this section will not reiterate the precise procedure. Each categorized line pattern requires a sequential fitting operation to fully convert it into the respective line pattern type.  
Spatial straight lines are fitted using the least squares method [38]. Consider the set of points to be fitted as \(\sum(x_{i},y_{i},z_{i})\), the parametric equation for a spatial straight line is presented as follows:  
\[\frac{(x-m)}{p}=\frac{(y-n)}{q}=\frac{z}{1} \tag{9}\]  
where parameters to be determined are \(m\), \(n\), \(p\), and \(q\).  
To fit spatial circular arcs, the Lagrange multiplier method [39] is employed. Consider the set of points to be fitted as \(\sum(x_{i},y_{i},z_{i})\) and designate the coordinates of the center of the circular arc as \(C_{O}(x_{O},y_{O},z_{O})\). The spatial circular parametric equation is then constructed as follows:  
\[\left\{\begin{array}{l}Ax+By+Cz+D=0\\ \left(x-x_{o}\right)^{2}+\left(y-y_{o}\right)^{2}+\left(z-z_{o}\right)^{2}=r^{ 2}\end{array}\right. \tag{10}\]  
where parameters to be determined are \(A\), \(B\), \(C\), \(D\), \(x_{o}\), \(y_{o}\), \(z_{o}\), and \(r\).  
Similarly, for points in polyline \(\sum\) that cannot be classified as either straight lines or arcs, a B-spline curve fitting process [37] is employed. The points to be fit as B-spline curves are located on the flow mesh surface, reflecting the contour characteristics of the model. To optimize the alignment between the fitted B-spline curve and the original mesh surface contour, these points should be used as the data points for the reverse calculation of the B-spline curve's control points [40].
## 3 Results
### Comparison of Optimized Model Results with Other Products
#### 3.3.2 Comparison of Spatial Consistency in GPP Estimates  
Using the XGB model, forest GPP for Jilin, Guangdong, Jiangxi, and Yunnan provinces were estimated and compared with RS-based GPP products from GLASS, GOSIF, and FLUXCOM. The results are shown in Figure 10. ML-based GPP and the RS-based GPP showed good consistency in spatial distribution, with the most significant agreement existing in GLASS products (Figure 10).  
The CC was calculated to analyze the spatial trend consistency between ML-based GPP and the three RS-based GPP products. The results indicated that the spatial trends of ML-based GPP are consistent with those of GLASS, GOSIF, and FLUXCOM. Additionally, the spatial distribution difference level between GPP and the three products was measured using \(D\). The results demonstrate that the differences between GPP and the three products exhibit reasonable consistency, with \(D\) ranging from 0.31 to 58.01 g Cm\({}^{-2}\) d\({}^{-1}\) (Figure 10). Overall, GPP shows the best consistency in comparison with GLASS, which also implied that the ML-based GPP products adopted in this study is reliable for estimating the GPP of forest ecosystems.  
Figure 9: Comparison of GPP estimation models against eddy covariance (EC) measurements. (**a**) Temporal GPP dynamics (g Cm\({}^{-2}\) d\({}^{-1}\)) at four flux sites (DHS, QYZ, CBS, XSBN) from 2009 to 2010, showing FLUXCOM, GOSIF, GLASS, and XGBoost model estimates versus EC observations (dotted line). (**b**) Taylor diagram summarizing statistical performance of the four models relative to EC measurements through correlation coefficient (\(R\)), standard deviation, and \(RMSE\) metrics.
## 4 Discussion  
### Summary of Findings  
In our analysis, after a meticulous research and selection process, focusing on a singular imaging assessment method (transvaginal ultrasound) and a specific form of artificial intelligence (DL), we identified only four studies that aligned with our research objectives.  
We did find that DL models do not perform better than expert evaluation for discriminating benign from malignant adnexal masses. However significant heterogeneity across studies was observed. This heterogeneity was explained by a different prevalence of malignancy in the studies included, ranging from 19% to 48%. This different prevalence could be explained by the different inclusion and exclusion criteria used in the studies.  
The quality of studies was considered as good, related to the index and reference tests, and the flow and timing domain. However, we noticed that only retrospective studies have been published. This means that the methods used as diagnostic tests (DL architectures and criteria for examiner impression diagnosis) as well as the reference test (histopathology) were deemed as having low risk of bias. However, we did consider that the retrospective design in all studies poses a high risk of bias, since this kind of study design may have an inherent risk of bias as the authors cannot control for all potential confounding variables regarding patient selection.
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 5 Tabu-Search-Based Enhanced Rosenbrock Algorithm
### Boundary Problems
#### 5.3.3 Computational Complexity and Limitation  
The computational complexity and limitation of the proposed algorithm is investigated in this section. Compared with the classical RA, extra tabu search is required only when rounding to the integer brings the result to a solution searched previously. Moreover, some storage resources are also required to memory the tabu table. In the lower bound case, the increased search times are max_iter\(\times\)1, and in the upper bound case, the increased search times are max_iter\(\times\)R. The main factor which has much effect on the complexity is the calculation of the object, i.e., the \(surt(p,\ q)\). The calculation includes \(M\) times multiplication of the signal by the transmission weight vector \(w\) at the transmitter, and \(N\) times multiplication of the signal by the receive weight vector \(c\) at the receiver. In total, the computation complexity is max_iter\(\times\)\((M+N)\) in terms of complex multiplication in the lower bound case, and max_iter\(\times\)\((M+N)\times\)R in the upper bound case. Clearly, the computation complexity scale linearly with the number of antenna elements at both the transmitter and receiver. However, the cost incurred by the increased computation complexity can make up for the cost saved by the decreased computation complexity due to the forbidden repeated search.  
Although the proposed methods are promising for BF in mm Wave, considering that it is essentially a kind of direct search optimization algorithm, the proposed algorithm is more beneficial when the objective function exhibits some kind of smoothness. In other words, the optimum parameters of the method may be influenced by the mm Wave environment, and should be carefully designed, we leave this as our future research work.
## 2 Related Works
### AVE Localization  
Nowadays, with the development of machine learning and deep learning, there are more and more cases of neural network techniques being used in practical applications [44]. As single-modal deep learning is becoming increasingly mature in applications [45; 46; 47; 48], the machine learning community has turned to multimodal cases. For video content understanding based on visual and audio modalities, [PERSON] et al. [6] first defined the AVE localization task, which is to detect whether an event that is both visible and audible happened in a video segment. Other than task definition, they also propose a complete set of solutions and initially study several basic fusion strategies of features. As the encoded feature of a video is sequential, [PERSON] et al. [9] employ a bidirectional long short-term memory (Bi-LSTM) [49] network to better fuse the concatenated audio-visual feature. Although the localization task is built on the segment level, [PERSON] et al. [7] consider the potential intra-modal misalignment within the segment so they propose a dual attention-matching module to model high-level global information over a longer range. Note that in their method, to compute global features, _background_ segments need to be additionally supervised during training. Furthermore, in [10; 11], [PERSON] introduced several attention-based modules to interact between audio and visual both locally and globally. [PERSON] et al. [12] presented a different network with multiple attention modules to deal with the task under temporal inconsistency. [PERSON] et al. [13] took a further step on multimodal attention and utilized joint co-attention for modeling both inter-modal and intra-modal relations, aiming to learn robust fused features by recursively stacking attention blocks. [PERSON] et al. [50] complete the encoding of temporal features and information fusion simultaneously through their newly proposed audio-visual transformer. The above works emphasize a variety of attention-based designs, resulting in a large increase in computational cost when improving performance. [PERSON] et al. [19] revisit the localization task from a more concise perspective and propose a positive sample propagation module to only aggregate information from high-relevance audio and visual features. This cross-modal attention-based module selects positive audio-visual pairs that are of high attention scores through threshold operation. However, it cannot be ignored that most of the features that carry event-related information are inconsistent in time. Since this inconsistency reflects the fact that features from different modalities describe different states of the event, these asynchronous features should be distinguished from those feature pairs that are both synchronous and associated. Therefore, we apply weights on the attention map based on the time difference to integrate information more effectively by weakening the less relevant connections caused by inter-modal asynchrony.
## 4 Results and Discussion
### Analysis of Metrics for the Implementation of V2G with Power Smoothing Methods  
This study evaluated various metrics (see Figure 15) to analyze the performance of different smoothing methods applied to power generation curves in the context of V2G. The considered metrics included standard deviation, crest factor, form factor, ripple index, and coefficient of variation. Each metric was analyzed in different scenarios using various smoothing methods on the original curve within the same time horizon.  
Figure 14: Real-time adjustment of the smoothing factor (%R-R).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**(20\%)** SoC\({}_{SC}\)** & **Variance without** & **Variance with** & **Renewable Energy** & **Renewable Energy** & **Energy** & **Utilized Life** \\
**Initial** & **Compensation** & **Compensation** & **Reduction (\%)** & **Compensation** & **Compensation** & **(kWh)** & **Cycles** \\ \hline V2 GSmooth & 8.54 & 7.35 & 14 & 9.54 & 9.32 & 0.23 & 1.170 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Generated power results with and without applying various proposed power smoothing methodologies.
# The Role of PET and SPECT Imaging in Prostate Cancer Targeted Alpha Therapy: When and How?
## 5 \({}^{225}\)Ac/\({}^{213}\)Bi-PSMA TAT
### Nuclear Medicine Imaging for Patients' Selection and Follow-Up after PSMA TAT
#### 5.1.3 \({}^{99m}\)Tc-PSMA  
There are a host of \({}^{99}\)Tc labeled PSMA ligands (Tc-MIP-1404, Tc I and S PSMA, Tc-HYNIC-PSMA) that have been evaluated for imaging of prostate cancer. \({}^{99m}\)Tc-PSMA-I and S have been tested at various clinical stages with detection rates that are clearly inferior to data reported for PET-imaging. However, in the case of unavailable PET imaging, \({}^{99m}\)Tc-PSMA-I and S may provide useful information, showing at low PSA levels inferior detection rates, whereas at higher PSA levels, it provides higher detection rates [98]. Similarly, further papers describe a significant relationship between quantitative \({}^{99m}\)Tc-MIP-1404 uptake, PSA level, and Gleason score [99, 100], but the correlation with PSA has not been confirmed in a further paper. Comparable to \({}^{99m}\)Tc-MDP in terms of detecting bone metastases, \({}^{99m}\)Tc-PSMA also showed the advantage of giving details on visceral illness. In our resource-constrained setting, \({}^{99m}\)Tc-PSMA may be a superior option to \({}^{99m}\)Tc-MDP for the staging, restaging, and evaluation of patients with biochemical progression following radical PCa treatment. It may also help identify patients who are candidates for PSMA-labeled radioligand therapy [101]. A comparative study defined that Tc-99m HYNIC PSMA may be useful in the imaging of prostate cancer, although with a lower sensitivity for lesion detection compared to \({}^{68}\)Ga PSMA PET/CT [102]. Even if the tracers used for PSMA-PET/CT have been shown to have greater imaging properties and results, there are some advantages in the usage of \({}^{99m}\)Tc-PSMA ligands, which are mainly linked to costs and logistic aspects. Moreover, the \({}^{99m}\)Tc-PSMA ligand may have a potential role in radioguided surgery with intraoperative probe localization [103, 104, 105].
## 3 Results and Discussion
### In Vitro Experiments
#### 3.2.2 Reactive Oxygen Species (ROS) Generation
On the other hand, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [22] observed that 15-50 \(\upmu\)M of curcumin dissolved in ethanol were effective in reducing the colony-forming ability of _A. flavus_. A reduction of up to 3 log units could be observed. However, a further increase of the curcumin concentration to 100 \(\upmu\)M did not cause any additional inactivation of the spores. DPBF is the most used quencher to estimate the concentration of ROS in media. The decrease of DPBF intensity is due to its oxidation with ROS generated upon illumination of the photosensitizer solution. On the other hand, incubation in the dark showed no reduction in the DPBF absorption intensity since the generation of ROS to interact with the probe does not occur when the photosensitizer is incubated in the absence of light [37; 38]. The results show that ROS are efficiently generated upon the irradiation of curcumin with 430-nm LED, and the higher the concentration of curcumin, the more DPBF is consumed which suggests a high generation of ROS. Differences in ROS yielded by curcumin upon illumination has also been reported in previous studies [39]. A hypothesis explaining the differences in ROS yield of a photosensitizer based on the solvent used was proposed by [PERSON] and [PERSON] [28] who studied the ROS generation by Methylene Blue (MB) dissolved in different solvents such as 70% polyethylene glycol (PEG), 70% glycerol, and a mixture of glycerol: ethanol: water (30:20:50, \(v/v/v\)). The authors observed that the photobleaching of DPBF was lower in PEG and glycerol than in the formulation containing ethanol. It was suggested that the difference could be due to the type of interaction between the solvent molecules and the excited state of the photosensitizer. Nevertheless, understanding the effect of the solvent on the ROS generation yield by the photosensitizer is a subject that merits a thorough evaluation in future studies. Figure 3: _Cont._
# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 4 Results and Discussion
The NF-ToN-IoT-v2 dataset, characterized by its vast size and imbalanced data distribution, benefits significantly from the wrapper method's iterative feature selection, resulting in high recall values for all classifiers. Notably, Decision Tree achieves perfect recall (1.000) and F1-score (0.998) on this dataset, showcasing its ability to identify all XSS attack instances. Similarly, the Edge-IIoTset dataset, despite its smaller scale, demonstrates the adaptability of the model, with Random Forest achieving an F1-score of 0.998 under the wrapper method, reaffirming the robustness of the proposed solution. Figure 7 highlights the performance of three classification algorithms Naive Bayes, Random Forest, and Decision Tree using filter and wrapper feature selection methods for detecting XSS attacks. The wrapper method consistently outperforms the filter method across all metrics, demonstrating its effectiveness in optimizing feature subsets for specific classifiers. Random Forest achieves the best results with the wrapper method, attaining an accuracy of 99.8%, precision of 0.997, recall of 0.999, and F1-score of 0.998, showcasing its robustness in handling complex feature dependencies. Decision Tree also performs exceptionally well with the wrapper method, matching Random Forest in key metrics, including perfect recall (1.000) and F1-score (0.998), reflecting its reliability in minimizing false negatives. Naive Bayes shows the most improvement with the wrapper method, with its F1-score increasing from 0.941 (filter) to 0.969 (wrapper), though it still trails behind the other classifiers due to its simplistic probabilistic assumptions. The filter method, while computationally efficient, demonstrates slightly lower performance, particularly in precision and recall, underscoring its limitations in capturing intricate feature interactions. Figure 7: Performance Comparison of Classification Algorithms with Filter and Wrapper Feature Selection Methods. This study's findings emphasize the proposed model's robustness and scalability in addressing key challenges in IoT security, including data imbalance, feature redundancy, and evolving attack patterns. By integrating advanced feature selection techniques with robust classifiers, the model achieves unparalleled detection accuracy, precision, and recall. These results underscore the practical applicability of the proposed approach in real-world 5G IoT environments, offering a reliable, adaptive, and scalable solution for mitigating XSS attacks and other cybersecurity threats.
###### Abstract  
Computed tomography (CT) quality control (QC) is regularly performed with standard phantoms, to bar faulty equipment from medical use. Its accuracy may be improved by replacing qualitative methods based on good visual distinction with pixel value-based quantitative methods. We hypothesized that statistical texture analysis (TA) that covers the entire phantom image would be a more appropriate tool. Therefore, our study devised a novel QC method based on the TA for contrast resolution (CR) and spatial resolution (SR) and proposed new, quantitative CT QC criteria. TA of CR and SR images on an American Association of Physicists in Medicine (AAPM) CT Performance Phantom were performed with nine CT scanner models. Six texture descriptors derived from first-order statistics of grayscale image histograms were analyzed. Principal component analysis was used to reveal descriptors with high utility. For CR evaluation, contrast and softness were the most accurate descriptors. For SR evaluation, contrast, softness, and skewness were the most useful descriptors. We propose the following ranges: contrast for CR, \(29.5\pm 15\%\), for SR, \(29\pm 10\%\); softness for CR, \(<\)0.015, for SR, \(<\)0.014; and skewness for SR, \(>\)\(-1.85\). Our novel TA method may improve the assessment of CR and SR of AAPM phantom images.  
caputed tomography; quality control; contrast resolution; spatial resolution; texture analysis +
Footnote †: journal: Medical Image Evaluation.  
0000-0001-999-2270-3800][PERSON]  
1 Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center,  
88 Olympic-ro 43-gjl, Songpa-gu, Seoul 05505, Korea; [EMAIL_ADDRESS]