esa-sceva/satcom-chunk-collection huggingface dataset:
# IDSMatch: A Novel Deployment Method for IDS Chains in SDNs +
## 6 Conclusions  
It is necessary to have a chain of IDSs on a large and busy network, as a single IDS may be susceptible to packet loss. Further, a centralized controller is responsible for handling all underlying network packets for security services, which results in significant overhead. As an alternative, security services can be deployed as network functions on switches within the data plane. By creating an approximation model, we propose a novel method of detecting attacks in the data plane. We developed a novel method for grouping incoming flows intuitively. Each group consists of a number of flows that are processed into an IDS chain. As a result of assigning flows to IDS chains, grouped flows are diverted on a longer path and processed by the assigned IDS chain. To evaluate our approach, we discussed several factors, including the detection rate, hop count, and delay time. Under different scenarios, the proposed approach meets these measurements. Future work will focus on grouping flows based on the common \(K\) links in their path to the destinations. The flows with the same \(K\) sub-path would be assigned to the same group.  
**Author Contributions:** Conceptualization, N.N. and J.W.; methodology, N.N.; writing--original draft preparation, N.N.; review and editing, J.W.; supervision, J.W.; funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by NSF grants CNS 2214940, CPS 2128378, CNS 2107014, CNS 2150152, CNS 1824440, and CNS 1828363.  
**Data Availability Statement:** Derived data of this study are available from the corresponding author on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flows** & **Shortest Path** & **Balancing** & **Matching** \\ \hline IDS Chain 1 & 13.4 & 10.5 & 3.2 \\ IDS Chain 2 & 9.3 & 7.03 & 4.3 \\ IDS Chain 3 & 11.1 & 6.4 & 4.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Unbalancing factor.
## 4 Discussion
### EPA and DHA
#### 4.8.1 Distribution of Intakes within the Population  
Several studies reported median intakes in addition to the mean intakes. We observed that for intakes of DHA and EPA, means were much higher than medians and differ from medians more than the other PUFA. EPA and DHA intake in populations are generally not normally distributed and skewed to the right due to higher intake of few individuals [36; 106]. This is mainly because not all products rich in n-3 fatty acids were consumed by all participants, while the other PUFA are more evenly distributed in foods. Future studies should aim to include median and mean DHA values to allow for a better comparison across studies and geographical regions.  
8.2 Effect of Methodology (Dietary Assessment, Food Composition Database, Inclusion of Fish Oil Supplement in the Assessment) on the Results for EPA and DHA  
Among the 65 studies included in this review, only six (Table S3) reported n-3 PUFA or fish oil supplement use among their participants. The low proportion of children consuming these supplements (from 0% in Belgium [40] to 3% in Australia [36]) indicates that systematic inclusion of PUFA supplements in the dietary assessment would not greatly alter the global conclusion of our review.  
Several studies from North America and Europe, which had as one objective to assess n-3 PUFA intake, used a methodology more adapted to this parameter [35; 39; 41; 50; 51; 56]: they assessed the diet with FFQ combined with recalls or records; they added to the dietary assessment a FFQ specific for fish intake; they used or developed food composition databases accurate for fatty acids and three of them included fish oil supplements in the assessment [35; 39; 41]. Surprisingly, results of EPA and DHA intakes obtained by these \"best in class\" studies were not higher than intake values found in the other studies. Thus, the low values found for EPA and DHA are not likely due to inappropriate methodologies, but rather because of lower consumption of foods containing n-3 LC-PUFA, i.e., mostly fish and shellfish.
# Automatic Watershed Segmentation of Cancerous Lesions in Unsupervised Breast Histology Images
###### Abstract  
Segmentation of nuclei in histology images is key in analyzing and quantifying morphology changes of nuclei features and tissue structures. Conventional diagnosis, segmenting, and detection methods have relied heavily on the manual-visual inspection of histology images. These methods are only effective on clearly visible cancerous lesions on histology images thus limited in their performance due to the complexity of tissue structures in histology images. Hence, early detection of breast cancer is key for treatment and profits from Computer-Aided-Diagnostic (CAD) systems introduced to efficiently and automatically segment and detect nuclei cells in pathology. This paper proposes, an automatic watershed segmentation method of cancerous lesions in unsupervised human breast histology images. Firstly, this approach pre-processes data through various augmentation methods to increase the size of dataset images, then a stain normalization technique is applied to these augmented images to isolate nuclei features from tissue structures. Secondly, data enhancement techniques namely; erosion, dilation, and distance transform are used to highlight foreground and background pixels while removing unwanted regions from the highlighted nuclei objects on the image. Consequently, the connected components method groups these highlighted pixel components with similar intensity values and, assigns them to their relevant labeled component binary mask. Once all binary masked groups have been determined, a deep-learning recurrent neural network from the Keras architecture uses this information to automatically segment nuclei objects with cancerous lesions and their edges on the image via watershed filling. This segmentation method is evaluated on an unsupervised, augmented human breast cancer histology dataset of 11,151 images. This proposed method produced a significant evaluation result of 98% FI-accuracy score.  
augmentation; stain normalization; watershed segmentation; deep learning Article
## 3 Results
### Economic Profits  
Analysis of the economic profits of various treatments showed that the WG treatment with micro-spray irrigation was the least expensive (9575.33 yuan hm\({}^{-2}\); Figure 8) because this treatment had no associated cost of surface mulching material. However, the WG treatment showed no significant difference compared with the JG and NF treatments because of the higher labor cost associated with no mulching in the WG treatment. Net profit was the highest in the JG treatment, which was significantly higher by 18.26% and 17.71% than those in the NF and WG treatments, respectively (\(p<\)0.05). The profitcost ratio was 1.78 in the JG treatment, which was 22.14% and 13.93% higher than that in the NF and WG treatments, respectively. According to the regression fitting analysis, the net profit of maize showed a significantly negative linear correlation with P\({}_{y}\) and S\({}_{\text{P}}\), with the coefficient of determination (R\({}^{2}\)) of 0.898 and 0.989, respectively (Figure 9). The higher the water saving potential, the smaller the net economic profit of maize; this explained why the net economic profit of maize in the JG treatment was higher than that in the NF and WG treatments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Index** & **Treatment** & **Y (kg hm\({}^{-2}\))** & **Index** & **Y\({}_{\text{c}}\) (kg m\({}^{-2}\))** & **Y (kg hm\({}^{-2}\))** & **ET\({}_{\text{c}}\) (mm)** \\ \hline \multirow{3}{*}{P\({}_{y}\) (m\({}^{3}\) kg\({}^{-1}\))} & JG & 0.951 * & \multirow{3}{*}{S\({}_{\text{P}}\) (m\({}^{3}\)} & \(-\)0.982 ** & 0.905 * & 0.887 * \\  & NF & \(-\)0.999 ** & & \(-\)0.982 ** & \(-\)0.982 ** & \(-\)0.982 ** & \(-\)0.997 ** \\ \cline{1-1}  & WG & 0.982 ** & \(\text{hm}^{-2}\)) & \(-\)0.817 * & 0.982 ** & 1.000 ** \\ \hline \hline \end{tabular} Notes: * represents the significant difference at the level of 0.05 (bilateral); ** represents the significant difference at the level of 0.01 (bilateral).  
\end{table}
Table 3: Correlations between indexes affecting water saving potential.  
Figure 7: Light and temperature production potential, yield, water use efficiency and water consumption of maize under various mulching patterns. Note: JG represents straw mulching; NF represents organic fertilizer mulching; WG represents no mulching. Y\({}_{\text{c}}\) represents light and temperature production potential; ET\({}_{\text{c}}\) represents water consumption of maize; WUE represents water use efficiency. Bars labeled with different letters (lowercase) differed significantly among the treatments (\(p<\)0.05).
# Vulnerability Analysis of Column-Supported Reinforced Concrete Silo Structures
## 2 Numerical Model of the Column-Supported Reinforced Concrete Silo
### Modeling and Simulation Methods  
A 3D finite element model of the column-supported reinforced concrete silo structure was established by applying a separate modeling method as well as the ABAQUS software. In the test, the S4R element (3D shell element) was used to simulate the silo wall, the C3D8R element to simulate the ring beam, funnel, and supporting column, and the T3D2 element to simulate the steel bar, which was defined using the rebar keyword. The concrete damaged plasticity (CDP) model provided by ABAQUS was chosen as the constitutive model, where the damage of the silo under tension and compression were represented  
Figure 1: Model dimensions and typical section reinforcing bar: (**a**) Model plane, (**b**) Reinforcing bar of the silo wall section, and (**c**) Reinforcing bar of the column section.  
by damage variables Dt and Dc, respectively, with 1 representing complete damage, and 0 representing no damage. The C3D8 element was adopted for the bulk wheat in the silo, and Niemunis and Herle's modified hypoplastic constitutive model was used as its constitutive model [25]. The modified hypoplastic constitutive model considers the intergranular strain, and its effectiveness has been verified in the application of numerical models under dynamic loading conditions [26]. The findings obtained by Guo et al. [14] and Tuska [16] have been incorporated into ABAQUS as the material constitutive of the bulk wheat through a user-defined material subroutine (UMAT). Table 1 presents the specific material parameters.  
The face-to-face contact in ABAQUS was used to simulate the interaction between the silo wall and the stored material. To prevent the intrusion of the secondary surface from the main surface, the penalty function friction model was chosen to study the friction between the two surfaces, and the friction coefficient was taken as 0.4 according to the \"Standard for design of reinforced concrete silos\" [27]. In the structural analysis, only a one-way horizontal seismic action was considered, and the second-order \(\mathrm{P}-\Delta\) effect of structural gravity was studied under the assumption that the structure rigidly contacted the ground and that the soil-structure dynamic interaction was not considered. Moreover, in the time-history analysis, Rayleigh damping was used with a damping ratio of 5%.
# Automated MRI Video Analysis for Pediatric Neuro-Oncology: An Experimental Approach
## 3 Results
#### 3.3.2 Differences  
1. Direct Video Analysis
2. In the frontal plane, the model indicates an inability to directly play or analyze the video, suggesting the need for additional information or specific queries. In the sagittal plane, the model undertakes direct analysis by extracting frames and providing key information about the video;
3. Frame Extraction
4. In the frontal plane, only the first frame is extracted without details about its content. In the sagittal plane, 10 frames are extracted at equal intervals, with a description of each frame's content;
5. Frame Content Description
6. The frontal plane lacks a detailed description of the first frame, while the sagittal plane provides detailed descriptions of each of the 10 extracted frames, including scene context, perspective, motion, and interactions.  
Summary and Tabulation of Differences and Similarities in AI Models' Responses to the Third Set of Questions  
The third set of questions focuses on ChatGPT 4o's ability to identify potential tumor regions in video data from the frontal and sagittal planes. Table 3 presents a comparison of video content recognition, frame extraction, analysis methods, and identified elements, with an emphasis on tumor detection. The table highlights differences in frame extraction and the specific contours used to mark potential tumor areas, providing insights into the distinct approaches applied in each plane for detecting pathological changes.  
\begin{table}
\begin{tabular}{|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|} \hline
**Criterion** & \multicolumn{2}{c|}{**Frontal Plane**} & \multicolumn{2}{c|}{**Sagittal Plane**} \\ \hline
**Video Contention Recognition** & I cannot directly play or analyze the video. Please provide more details or specific queries. & I can analyze video content starting with frame extraction and key information. \\ \hline
**Detailed Video Data** & 200 frames, 20 FPS, 10 s & 180 frames, 20 FPS, 9 s \\ \hline
**Frame Extraction** & Extracted first frame, no detailed content provided. & Extraction of 10 frames at equal intervals, with detailed description of each frame. \\ \hline
**Frame Content Description** & No detailed description of the first frame. & Detailed description of each of the 10 extracted frames, focusing on scene context, perspective, movement, and interactions. \\ \hline
**Next Steps** & Request for further information or specific queries. & Suggested further analysis or detailed video content information based on frames. \\ \hline \end{tabular}
\end{table}
Table 2: Table comparing the differences in video content recognition, frame extraction, and analysis between the frontal and sagittal planes. It highlights the distinct approaches used for processing video data and conducting frame-by-frame analysis, emphasizing how each method impacts the recognition and interpretation of content in the respective anatomical views.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
###### Abstract  
Remote monitoring applications in urban vehicular ad-hoc networks (VANETs) enable authorities to monitor data related to various activities of a moving vehicle from a static infrastructure. However, urban environment constraints along with various characteristics of remote monitoring applications give rise to significant hurdles while developing routing solutions in urban VANETs. Since the urban environment comprises several road intersections, using their geographic information can greatly assist in achieving efficient and reliable routing. With an aim to leverage this information, this article presents a receiver-based data forwarding protocol, termed Intersection-based Link-adaptive Beaconless Forwarding for City scenarios (ILBFC). ILBFC uses the position information of road intersections to effectively limit the duration for which a relay vehicle can stay as a default forwarder. In addition, a winner relay management scheme is employed to consider the drastic speed decay in vehicles. Furthermore, ILBFC is simulated in realistic urban traffic conditions, and its performance is compared with other existing state-of-the-art routing protocols in terms of packet delivery ratio, average end-to-end delay and packet redundancy coefficient. In particular, the results highlight the superior performance of ILBFC, thereby offering an efficient and reliable routing solution for remote monitoring applications.  
link duration; remote monitoring; receiver-based; road intersections; vehicular ad-hoc networks; winner relay management +
Footnote †: journal: Sensors
###### Abstract
Therefore, researchers consider random variables, such as traffic volume, pavement strength, pavement modulus, and pavement depth, to establish the reliability concepts in the pavement design procedure [7; 8]. Different reliability methods for pavement analysis, such as Monte-Carlo simulation (MCS) [9], first-order reliability methodology (FORM) [10], Rosenblueth [11; 12], and response surface methodology (RSM) [13; 14] have been used in reliability-based pavement design. Although reliability concepts have been applied in previous studies (the variability in traffic such as traffic volume and even traffic wandering were considered in reliability-based pavement design), it is worth mentioning that the random factors (especially the randomness of realistic traffic loads, and the viscoelastic properties of the asphalt layer) were not sufficiently considered in previous studies. The random variables of realistic traffic loads, including tire inflation pressure, wandering, and speed--where tire inflation pressure has an effect on the amplitude of the mechanical responses of the pavement, wandering changes the distribution of the mechanical responses, and the variability in speed causes the variability in the moduli of asphalt layers due to the viscoelastic properties of the asphalt mixture--cause variability in the mechanical responses of the pavement. These random variables of realistic traffic loads, which cause the variability in mechanical responses, hence affect the variability in pavement performance. Moreover, the temperature inside the asphalt layers, which affects the moduli of the asphalt layers, also causes variability in the mechanical responses and damage to the pavement. Therefore, obtaining the accurate mechanical responses inside the pavement under realistic moving loads and pavement temperatures can be beneficial to predict pavement performance. The non-deterministic problem--that is, the probability distributions of asphalt pavement responses and performance under random moving loads and pavement temperature--was investigated in this study. It could be considered complementary to reliability-based pavement design.
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 3 Methodology
### Model Training  
Apache Spark MLlib's operators, including K-Means, Random Forest, and Word2 Vec, were configured with varied hyperparameters:  
* K-Means: Number of clusters (\(c\)) varied from 2 to 20.
* Random Forest: Tree depths ranged from 5 to 50.
* Word2 Vec: Sentence lengths varied between 15 and 50 words.  
Training was conducted on a distributed multi-node Spark cluster to ensure scalability.
# Revealing Development Trends and Key 5G Photonic Technologies Using Patent Analysis
## 4 Empirical Study
### Postanalysis: Changes in the Key 5G Photonic Technologies Over Time  
Changes in the key technologies related to CPC codes H04B, H04W, H01Q, H04L, G02F, G02B, and H01S in each country over the past decade were further analyzed to understand the trends related to 5G photonic technologies. The analysis results are shown in Figure 2.  
According to Figure 2, most patents in 2015 were related to wireless communication networks (H04W). However, the technologies developed in the past two years have mostly been related to optical elements, systems, or apparatuses (G02B), indicating the gradual maturation of wireless communication network-related technologies and the year-on-year growth of optic-related technologies. Additionally, the development of data transmission-related technologies (H04B) has been rela  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**CPC**} & \begin{tabular}{c} **Closeness** \\ **Centrality** \\ \end{tabular} & **CPC** & \
\begin{tabular}{c} **Betweenness** \\ **Centrality** \\ \end{tabular} & **CPC** &
\begin{tabular}{c} **Reinforced** \\ **Structural Holes** \\ \end{tabular} \\ \hline H04B & 18.000 & H04B & 112 & H04B & 0.707 \\ H04W & 16.167 & G02F & 36 & H04W & 0.612 \\ H01Q & 12.667 & H04W & 33.833 & H01S & 0.437 \\ H04L & 12.167 & G02B & 8.833 & G02B & 0.435 \\ \hline \hline \end{tabular}
\end{table}
Table 3: 5G Photonic technology for the top 4 Cooperative Patent Classification (CPC codes).  
Figure 2: Changes in the key 5G photonic technologies from 2014 to 2019. Note: The 2019 statistics only contain data until 30 April 2019.  
that improving the data transmission rate has always been crucial to the development of optical communication technology. In the future, data transmission-related technologies and patents will assume a pivotal role as the amount of data continues to grow.
## 2 Measurement of Complete Geometric Errors
### Geometric Error Transfer Modeling based on HTM
\right], \tag{32}\]  
where the coordinates of the workpiece (x\({}_{\rm w}\), y\({}_{\rm w}\), z\({}_{\rm w}\)) relative to the C-axis and the tool (x\({}_{\rm t}\), y\({}_{\rm t}\), z\({}_{\rm t}\)) relative to the B-axis are determined directly by the machine structure. Moreover, (x\({}_{\rm w}\), y\({}_{\rm w}\), z\({}_{\rm w}\)) is (0, 0, 400) and (x\({}_{\rm t}\), y\({}_{\rm t}\), z\({}_{\rm t}\)) is (200, 0, 0). With respect to the translation and rotation motion of each axis, the coordinates of the machining point are derived as below (Equation (33)) from Equations (17), (31), and (32). Since (x\({}_{\rm w}\), y\({}_{\rm w}\), z\({}_{\rm w}\)) and (x\({}_{\rm t}\), y\({}_{\rm t}\), z\({}_{\rm t}\)) are known, the forward kinematics solution p\({}_{\rm m}\) (x\({}_{\rm m}\), y\({}_{\rm m}\), z\({}_{\rm m}\)) is obtained as Equation (33):  
\[\left\{ \begin{array}{c}200\cos\beta_{i}+x_{i}-200\cos\gamma_{i}=x_{m}\\ y_{i}=y_{m}\\ -200\sin\beta_{i}+z_{i}-400=z_{m}\end{array} \right.. \tag{33}\]  
(2) The inverse kinematics solution [36]  
The inverse kinematics solution aims to determine the translation and rotation motion of each axis according to the coordinates (x\({}_{0}\), y\({}_{0}\), z\({}_{0}\)) of the point p\({}_{0}\) to be machined in the ideal relative motion trajectory of the tool and workpiece, which is more significant than the forward kinematics solution in the machine design stage. Similarly, its inverse kinematics equation is obtained as follows in Equation (34):  
\[\left\{ \begin{array}{c}x_{t}\cos\beta_{i}+y_{t}\sin\alpha_{i}\sin\beta_{i}+z _{t}\cos\alpha_{i}\sin\beta_{i}+x_{i}-x_{w}\cos\gamma_{i}+y_{w}\sin\gamma_{i}=x _{0}\\ y_{t}\cos\alpha_{i}-z_{t}\sin\alpha_{i}+y_{i}-x_{w}\sin\gamma_{i}-y_{w}\cos \gamma_{i}=y_{0}\\ -x_{t}\sin\beta_{i}+y_{t}\sin\alpha_{i}\cos\beta_{i}+z_{t}\cos\alpha_{i}\cos \beta_{i}+z_{i}-z_{w}=z_{0}\end{array} \right.. \tag{34}\]  
Obviously, there are six unknown numbers: x\({}_{\rm i}\), y\({}_{\rm i}\), z\({}_{\rm t}\), \(\alpha_{\rm i}\), \(\beta_{\rm i}\), \(\gamma_{\rm i}\), which cannot be solved from the 3 equations above. Actually, for the multi-axis grinding machine tool investigated in this paper, the A-axis only participates in the positioning process, not in the machining process. Thus \(\alpha_{\rm i}\) is set as constant \(\alpha\) according to the thickness d and helix angle \(\beta\) of the gear to be machined. Because the B-axis and the C-axis keep rotating in the whole machining process, their mean values are selected as the calculation factor. Finally, the inverse kinematics solution is solved as:  
\[\left\{ \begin{array}{c}200\cos\beta+x_{i}-200\cos\gamma=x_{m}\\ y_{i}=y_{m}\\ -200\sin\beta+z_{i}-400=z_{m}\end{array} \right.. \tag{35}\]