esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Research Gap  
While the existing literature provides valuable insights into information diffusion, several gaps remain (Table 1). First, few studies systematically compare different delay mechanisms across diverse network structures. Second, the relationship between temporal dynamics (such as peak spread time) and final diffusion coverage remains under-explored. Third, the transferability of models between real and synthetic networks needs further investigation.  
Our research addresses these gaps by (1) comparing three distinct delay types across multiple network structures, (2) analyzing the relationship between temporal features and coverage outcomes, and (3) verifying models trained on synthetic data against real-world network data. This comprehensive approach provides a more complete understanding of information diffusion dynamics in social networks with delay mechanisms.
## 3 Proposed Protocol Architecture
### Protocol Architecture
#### 3.1.3 IP-Sensor Node  
The IP-WSN domain consists of numerous sensor nodes based on IPv6 addresses. We consider the domain as a federated IP sensor domain. There are two types of sensor node. One type contains the tiny TCP/IP communication protocol stack with adaptation layer and IEEE 802.15.4 interface. This type can forward information to other node of similar type as well as information sensing from the environment. Actually this type of sensor node acts as a mini sensor router and is considered a fully functional device. The other type of sensor node has the protocol stack and environment sensing capability, but can only forward the sensed information to nearby mini sensor router nodes. These types of sensor nodes are considered reduced functional devices. Nevertheless, both types are able to perform end to end communication.
## 3 Method
### Multi-Variants Correlation Constraint
During the training stage, the MCC module can effectively learn the mapping between the bias matrix and the constraint term through a loss-based gradient propagation mechanism:  
\[\theta_{MCC}\leftarrow\theta_{MCC}-\eta\frac{\partial\mathcal{L}}{\partial \theta_{MCC}} \tag{8}\]  
where \(\eta\) is the learning rate, \(\mathcal{L}\) is the loss function, and \(\theta_{MCC}\) represents the parameters of the MCC module. The gradient calculation follows the chain rule:  
\[\frac{\partial\mathcal{L}}{\partial\theta_{MCC}}=\frac{\partial\mathcal{L}}{ \partial Y}\cdot\frac{\partial Y}{\partial Y_{mc}}\cdot\frac{\partial Y_{mc} }{\partial C_{mc}}\cdot\frac{\partial C_{mc}}{\partial\theta_{MCC}}. \tag{9}\]  
This update forces the MCC module to learn parameters that generate a \(C_{mc}\) term, which minimizes the overall prediction error when applied to the initial prediction and bias matrix. Because meteorological variables exhibit intrinsic physical relationships in the training data, the module naturally learns these relationships through the optimization process without requiring explicit formulation of physics equations.
###### Abstract  
The use of digital elevation models (DEMs) that use airborne-based light detection and the ranging technique (airborne-based LiDAR) to understand large-scale geological structures has become important in geological surveying and mapping. Taking the Dongao Tunnel area in northeastern Taiwan as the study area, this study used the airborne-based LiDAR DEM and related value-added maps to interpret the topographic and geomorphic features of the area and identify locations for geological investigation. The characteristics of the rock mass were observed on-site and revealed by excavation of the highway tunnel in the study area; they were compared with the interpreted topographic and geomorphic features to determine the potential of using 1 m-resolution LiDAR DEM in geological surveys and in the evaluation of engineering characteristics of underground rock masses. The results of this study demonstrated that the DEM accurately captured geomorphic features: the strata composed of slate and schist had distinct appearances in both the clinometric map and the hillshade map; the locations of faults, lineaments, and drainage were consistent with those observed on-site, and the positions of these features were captured more accurately than those on conventional maps. Evident microrelief features, including the distribution of scarps, erosion gullies, and mini-drainage systems provide an effective basis for interpreting a deep-seated gravitational deformation slope and for an on-site inspection for validation. The use of high-resolution LiDAR DEM to interpret geomorphic features along with geological surveys provides a more comprehensive understanding of the survey area, supporting surveys and geological mapping, revealing the locations of potential slope failures, and enabling the assessment of tunnel engineering risks.  
light detection and ranging (LiDAR); digital elevation model (DEM); topographic feature; geomorphic feature; geological survey and mapping; deep-seated gravitational slope deformation +
Footnote †: journal: Applied Sciences  
1
# Advancing the State of the Fog Computing to Enable 5G Network Technologies
## 7 Convergence of 5G and Fog Computing
### The Needs for Fog and 5G Network Technologies
#### 7.1.2 Communication Identification  
It is employed by verifying the identity of the application, devices, gadgets, and network nodes that can deal with communication such as E.164 number, Mac address, IP address, and others [59].
# Different Formulations to Solve the Giesekus Model for Flow between Two Parallel Plates
## 3 Different Formulations to Obtain the Fully Developed Laminar Flow with the Giesekus Model
### [PERSON] and [PERSON] Formulation
The Equation (9) means that:  
\[\left(T_{yy}+\frac{(1-\beta)}{2\alpha_{G}ReWi}\right)^{2}\leq\frac{(1-\beta) ^{2}}{4\alpha_{G}^{2}Re^{2}Wi^{2}}, \tag{18}\]  
and also  
\[T_{xy}^{2}\leq\frac{(1-\beta)^{2}}{4\alpha_{G}^{2}Re^{2}Wi^{2}}. \tag{19}\]  
The restriction given by Equation (19) leads to \((1-\beta)^{2}-4\alpha_{G}^{2}Re^{2}Wi^{2}\geq 0\), i.e., the square root term presented in Equation (17) is always non-negative since Equation (8) has to be taken into account. Therefore, this restriction must be respected in this paper. Integrating Equation (4) with respect to \(y\) and using that \(T_{xy}=u^{{}^{\prime}}=0\) at the centerline \(y=0\), one can arrive at:  
\[T_{xy}=\frac{-\beta}{Re}u^{{}^{\prime}}+p_{x}y,\ \ -1\leq y\leq 1, \tag{20}\]  
where \(p_{x}\) is a negative constant. Substituting Equation (20) into Equation (17), one can obtain an implicit expression for \(u^{{}^{\prime}}\):  
\[u^{{}^{\prime}}=\frac{2\alpha_{G}Re(\frac{-\beta}{Re^{2}}u^{{}^{\prime}}+p_{ x}y)[(1-\beta)+b\sqrt{(1-\beta)^{2}-4\alpha_{G}^{2}Re^{2}Wi^{2}(\frac{-\beta}{Re }u^{{}^{\prime}}+p_{x}y)^{2}]}}{[b(1-\beta)+\sqrt{(1-\beta)^{2}-4\alpha_{G}^{2} Re^{2}Wi^{2}(\frac{-\beta}{Re}u^{{}^{\prime}}+p_{x}y)^{2}]^{2}}}. \tag{21}\]In order to obtain the analytical solution, it is necessary to follow the next steps:  
1. Solve Equation (21) to obtain \(u^{{}^{\prime}}\) for a given \(p_{x}\);
2. Solve Equation (20) to obtain \(T_{xy}(y)\);
3. Solve Equation (11) to obtain \(T_{yy}(y)\);
4. Solve Equation (15) to obtain \(T_{xx}(y)\). Again, we should note that Equations (21), (20), (11) and (15) are similar to Equations (5.2) to (5.5), given by [PERSON] and [PERSON] (2010), respectively, with some differences in the dimensional system. Since sequence 2, 3, and 4 is followed, it is possible to see that all of the components of the extra-stress tensor can be obtained explicitly, just using some algebraic calculations. However, in step 1, it is not easy to solve \(u^{{}^{\prime}}\) analytically. Therefore, in the next section, we will discuss the assumptions and the numerical strategies adopted to choose \(p_{x}\) properly, to calculate \(u^{{}^{\prime}}\), and to calculate the component of the velocity \(u\) for complementing the present section, and for providing applicability of the mathematical work by [PERSON] and [PERSON] (2010) in the engineering field.
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 2 Materials and Method
### HY-2B Scatterometer Data  
The HY-2B satellite is equipped with a Ku-band scatterometer (HSCATB), which is mainly used for global sea surface wind field measurements. The HY-2B satellite ground data processing system is constructed and managed by the NSOAS. The 25 km along-orbit grid wind field products (L2B-level data) of the HY-2B satellite scatterometer (SCA) are distributed to users around the world. According to the product introduction of HY-2B, its wind speed measurement accuracy is 2 m/s, and the effective wind speed measurement range is 2 m/s to 24 m/s. A total of 213 scenarios of GF3-02 SAR data are matched in this paper, and space-time matching with SCA wind products is carried out, and the time interval is within 1 h.
### Methodology  
The methodology used in this study is shown in Figure 3. First, raw bearing vibration data were collected from the CWRU bearing database, originally stored as MATLAB files in a one-dimensional format. They were then pre-processed for 1-D and 2-D CNN inputs;  
Figure 2: Vibration data collection process for CWRU ball bearing system.  
for the 1-D CNN input, each sample was created from 1600 data points; for the 2-D CNN input, a two-dimensional image representation of 40 \(\times\) 40 pixels was created. We then used each model to perform feature extraction and classification. Finally, we performed a performance analysis and drew conclusions.
# Multi-Decision Dynamic Intelligent Routing Protocol for Delay-Tolerant Networks
## 6 Conclusions  
In this paper, we propose a Multi-Decision Dynamic Intelligent (MDDI) routing algorithm for delay-tolerant networks. The algorithm fully considers the characteristics of nodes interacting with information and messages and the variability of network topology. Firstly, we introduce the intelligent double Q-learning algorithm, enabling nodes to learn throughout the entire network. Each node maintains two Q-tables and decides whether to forward a message based on the Q-values in the tables. If the connected node does not have the maximum Q-value in the Q-table, decisions will be made based on the relationship between the nodes, at which point we first determine the relationship between the nodes, i.e., whether they are friends, colleagues, or strangers, and combine the message attributes to make a final decision. Through multiple decisions and intelligent algorithms, we can effectively utilize various resources of the network and sense the states of nodes and messages in real-time, thus improving the network performance.  
The simulation results show that our proposed protocol consistently maintains the highest delivery rate as well as the lowest latency and network overhead for different cache sizes, as well as different message survival times and generation intervals. Moreover, analyzing the result graphs, it can be seen that the network overhead of MDDI remains low in all network states, while the message delivery rate can be high even in extreme cases, such as node congestion and short message survival time.  
In the next step of our research, we plan to enhance the cache management component in our subsequent research to minimize message loss. Additionally, we will design a node authentication mechanism to enhance data transmission security. Moreover, we intend to implement a confirmation mechanism to promptly discard duplicates of messages that have already been successfully transmitted in the network.  
**Author Contributions:** Supervision, S.J.; writing--original draft: Y.X.; writing--review and ending, Y.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was Supported funded by the Innovation Program of Shanghai Municipal Education Commission of China under Grant No. 2021-01-07-00-10-E00121.  
**Data Availability Statement:** The program code used in the research can be obtained from the corresponding author upon request.  
**Conflicts of Interest:** The author declares no conflict of interest
# A Systematic Analysis of Narrowband IoT Quality of Service
## 2 Narrowband IoT Fundamentals
### Data Rate and Latency Boundaries
[1] performed system-level simulations to evaluate the latency under different signal conditions and deployment modes, as shown in \[NPUSCH\;single\;tone:\;TBS_{max}=1000\;\text{b},MCS_{max}=10\to N_{RU,min}=6 \tag{5}\]0 The simulation found that at 144 dB coupling loss, NB-IoT provided a low latency of about 300 ms, which was mostly determined by the time needed to access the network and acquire the necessary configuration information. At the MCL of 164 dB, the latency was dominated by the signal repetitions used for coverage extension, but still met the limit of 10 s for delivering exception reports, as defined by 3 GPP [4]. The differences between the individual deployment modes could be attributed to the output power restrictions that apply for in-band and guard-band installations. Overall, the latency estimation can serve as a useful benchmark for real-live evaluations.
## 2 Materials and Methods
### Electrode Importance Scoring  
Once the mean saliency maps were computed for each class, the importance of each EEG electrode was quantified. This step was crucial for understanding which brain regions were most influential in the classification process. To achieve this, the saliency values were summed across the time dimension for each electrode, resulting in a single saliency value per electrode for each class:  
\[E_{c,k}=\sum_{l}\tilde{\textbf{W}}_{c,k,l}, \tag{13}\]  
where \(E_{c,k}\) is the summed saliency value for class \(c\) at electrode \(k\), and \(\tilde{\textbf{W}}_{c,k,l}\) is the mean saliency value at time \(t\) for electrode \(k\). The next step involved normalizing these values, to account for the different scales of saliency values across the electrodes:  
\[E^{\prime}_{c,k}=\frac{E_{c,k}}{\sum_{j}E_{c,j}}, \tag{14}\]  
where \(E^{\prime}_{c,k}\) is the normalized importance score for electrode \(k\) in class \(c\). This normalization allowed us to compare the relative importance of each electrode within the class.  
To determine the overall importance of each electrode across all classes, the normalized importance scores for each electrode were aggregated:  
\[E^{\prime\prime}_{k}=\sum_{c}E^{\prime}_{c,k}, \tag{15}\]  
where \(E^{\prime\prime}_{k}\) is the aggregated importance score for electrode \(k\) across all classes \(c\). This aggregation provided a holistic view of each electrode's contribution to the classification task, irrespective of class.  
The final step was to rank the electrodes based on their aggregated importance scores. Electrodes with higher scores were deemed more influential in the model's decision-making process:  
\[R=\text{argsort}(-E^{\prime\prime}), \tag{16}\]  
where \(R\) is the ranked list of electrodes, and the argsort function sorts the electrodes in descending order of importance.  
The ranked list of electrodes \(R\) provided a clear indication of which EEG electrodes (and, thus, which corresponding brain regions) had the most significant influence on classifying different classes. This information is valuable for understanding the neural mechanisms underlying the effects of TEAS, and it could guide the placement of electrodes in future studies or clinical applications.
# Sentiment Analysis Based Requirement Evolution Prediction
## 4 Evaluation and Experimental Results
### Evaluation Metrics  
We seek to assess whether sentence-level sentiment analysis based on the joint deep neural network and hierarchical topic model could provide support for hierarchical product feature requirement evolutionary prediction. To evaluate the presented approach, we applied it on the review dataset and chose to compare different review text content analysis models for requirements evolution prediction to analyze of our approach experimentally. The evaluation comprises two parts. First, we evaluated the sentiment analysis technique using standard measurements, and second, we evaluated the relevance and the effect of feature-dependent sentiment of the automatically identified hierarchical product feature requirements from the reviews.  
The accuracy of the sentiment classification results from our model framework was used to evaluate the quality of the results. The Area Under the Curve (AUC) is a common evaluation metric for binary classification problems. The AUC value is equal to the probability that a randomly-chosen positive example is ranked higher than a randomly-chosen negative example. We used AUC to show the change in accuracy of sentence-level sentiment analysis under our joint model framework.  
We utilized a further metric to determine the quality of the multi-granularity product feature generated by the HLDA. Product feature requirements relevance assesses how well the extracted text topics represent real product features, which show that the topics contain information that helps understand, define, and evolve the feature requirements of the product. Feature-dependent sentiment classification accuracy evaluates how well the extracted hierarchical structure represents a product feature, and the children nodes represent the sentiment polarities associated with it.  
We evaluated both the requirements relevance and feature-dependent sentiment classification using precision, recall, and the F-measure. A feature was defined as a true positive if it was extracted from an online user's review by HLDA and was also manually identified in that review. A feature was defined as a false positive if it was automatically associated with a review in a topic, but was not identified manually in that review. False negatives were features that were manually identified in a review, but were not present in any of the extracted topics associated with the review.
## 3 Results and Discussion  
### Structural Characterization of the Magnetic Nanocomposites  
#### 3.1.1 X-ray Diffraction (XRD)  
X-ray diffraction analysis was applied to determine the crystalline structure of the synthesized magnetic nanocomposites. Figure 1 illustrates the XRD pattern of the PM1, PM2, PM3 and PM4 samples compared to activated carbon.  
Figure 1 indicates that AC exhibits a very broad diffraction peak characteristic for an amorphous structure, which is in accordance with the previous studies that documented the diffraction pattern of AC [45]. The synthesized PM presented several diffraction peaks that were assigned to magnetite (Fe\({}_{3}\)O\({}_{4}\)), according to JCPDS sheet no. 190629. The well-evidenced diffraction peaks were located at 30.10, 35.42, 43.05, 53.39, 56.94 and 62.52 degrees on the 2 theta axis, corresponding to magnetite diffraction planes (220),  
Figure 1: X-ray diffraction pattern of activated carbon and magnetic nanocomposites.  
(311), (400), (422), (511) and (440). Based on the XRD analysis, one can conclude that magnetic nanocomposites prepared by combustion synthesis consist of nanocrystalline magnetite incorporated/mixed in an amorphous matrix of activated carbon, which is in total agreement with previous studies carried out on similar themes [45; 46].  
The crystallite size of magnetite was determined according to the Debye-Scherrer equation (Equation (4)), using the most intense diffraction peak [47]. The obtained crystallite sizes were 6.9 nm for the PM1 sample, 7.1 nm for the PM2 sample, 6.4 nm for the PM3 sample, and 6.2 nm for the PM4 sample, which indicate that in this case, the surface area of the activated carbon and the thermal treatment time span had no influence on the magnetite crystallite size.  
\[\text{D}=(0.9\cdot\lambda)/(\beta\cdot\cos\ \theta) \tag{4}\]  
where D is the crystallite size, \(\lambda\) is the X-ray wavelength, \(\beta\) is the full width at half maximum (FWHM) and \(\theta\) is the Bragg angle for the diffraction peak.
# Dog Identification Method Based on Muzzle Pattern Image
## Materials and Methods
### Dataset
#### 3.1.2 Data Screening  
Many improper images for the dog individual identification were included in the acquired images because of indoor light and dog's movement as shown in Figure 2. As a first step for the individual identification, it is necessary to select the proper images with no light reflection, and the high sharpness.  
Figure 1: (**a**) Device for acquiring dog muzzle image; (**b**) Region of interest.  
First, a histogram is used in order to discard images with light reflection. The dog nose area is typically dark, and, thus, the brightness values are low. On the other hand, the area where light was reflected has high brightness values. The histograms of images with light refection and without light reflection were manually inspected. After the manual inspection, it was determined to discard the images having more than 200 pixels between 150 and 255 in the histogram. The histograms of the images with light reflection and without light reflection are shown in Figure 3.  
Then, the blurry image screening was done. A histogram equalization was applied to ROI images to enhance the sharpness and then blur detection were performed with the algorithm using Haar wavelet transform [19]. An image that did not pass the appropriate threshold of the algorithm was determined as a blurry image and then discarded. In the study, the threshold was 50. After all the processes, a dataset was composed of 55 ROI images by obtaining 5 images from each dog out of 11 dogs. The size of ROI images ranged from a minimum of \(290\times 280\) pixels to a maximum of \(841\times 825\) pixels, with an average of \(549\times 515\) pixels. In this study, this dataset is called the original test dataset.
## 4 Experimental Results and Discussion
### Statistical Analysis of Variance on Dice Score, Precision and Recall Metrics  
We use the ANOVA tool to analyze whether there are any statistically significant differences between the means of two or more methods at each evalutation metrics. We use one-way ANOVA to compare two methods. The standard hypothesis for one-way ANOVA, used to compare two methods, are null and alternative. The null hypothesis is that the two means are equal. The alternative hypothesis is that the two means are unequal. If the \(p\)-value from output of ANOVA tool is less than the significant level, we can reject the null hypothesis. In other words, our sample data provide strong enough evidence to conclude that the means are not equal. We show the statistical analysis of Dice score, Precision, and Recall metrics on TCGA, CVC-ClinicDB, and VIP-CUP18 datasets in Table 9.  
Table 9 presents the conduction of \(p\)-value analysis on Dice score, Precision, and Recall metrics for the three dataset CVC-ClinicDC, TCGA, and VIP-CUP18. The term \"Deep UNet-scAG: UNet\" represents the comparison between the proposed Deep UNet-scAG model and the traditional UNet model, \"Deep UNet-scAG: Attention UNet\" represents the comparison between the proposed Deep UNet-scAG model and the attention UNet model, and \"Deep UNet-scAG: UNet++\" represents the comparison between the proposed Deep UNet-scAG model and the UNet++ model. In the table, all of the comparisons have \(p\)-value < 0.05 for TCGA dataset related to Precision metric, indicating that the proposed model and conventional models have statistical significance; it means that the proposed model performs better than other three models on TCGA dataset in terms of Precision. Moreover, the \"Deep UNet-scAG: Attention UNet\" has \(p\)-value < 0.05 for VIP-CUP18 dataset related to Precision metric, indicating that the proposed Deep UNet-scAG model performs better on VIP-CUP18 dataset in term of Precision.