esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Box-Behnken Test Results  
The process for generating the tea browning content was optimized by a Box-Behnken experimental design based on the Plackett-Burman experiment to determine significant influencing factors. To evaluate the mutual interactions of the independent and dependent variables, the \(R^{2}\) value, \(F\)-value, and \(p\)-value were determined from the modeled responses. The data were analyzed by variance (Table 5). \(R^{2}\) = 95.87% and \(R^{2}\)adj = 99.39% for the theabrownins content, indicating significant regression (\(p<0.01\)) and a non-significant lack of fit (\(p=0.44>0.05\)). This shows that the model can be used to optimize the process to produce theabrownins by _Aspergillus niger_ fermentation. For both responses, the results indicate that the degree of precision was high, and the experimental data were reliable. The results show that the model can be used to optimize the process to produce theabrownins by _Aspergillus niger_. The quadratic polynomial equation obtained by regression fitting was \(Y\) = 28.86 + 2.66X2 + 0.67X3 + 0.54X6 + 0.21X2X3 - 0.16X2X6 + 1.13X3X6 - 6.01X2 - 1.44X32 - 1.73X62.
# A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks
## 3 Methodology
### Validation Process  
For the dataset of the 3264 scan images, it was critical to choose the best validation procedure. We used a holdout validation process, keeping 80% of the data for training  
Figure 4: MR images of brain tumors. (**a**) Glioma tumor. (**b**) Pituitary tumor. (**c**) No tumor. (**d**) Meningioma tumor.  
and 20% for testing. The holdout validation technique is the most-commonly used method and produces effective results [46]. The holdout method typically involves splitting the dataset into two parts: a training set and a testing set, which helps the model train faster. The training set was used to train the deep learning model, while the testing set was used to evaluate the model's performance. In the holdout method, 80% of the dataset was randomly selected to be used as the training set, and the remaining 20% was used as the testing set. The model was trained on the training set and then evaluated on the testing set to estimate its performance. The advantage of using 80% of the data for training is that the model has more data to learn from, which can help it generalize better to new, unseen data. However, the testing set is not representative of the overall data, so the performance estimate may be biased.
## 4 Case Study
### Basic Data
As an example, the delay times from 6:00 to 12:00 on 23 February 2023, are shown in Figure 5.  
Route Flow Stability Indicator  
In response to weather changes, the Pareto frontier is used to statistically analyze the number of flight arrivals and departures to estimate the airport's hourly transportation capacity, as shown in Figure 6. In Figure 6, the larger the circle radius, the higher the frequency of flight arrivals and departures at the corresponding point within the statistical period. When the number of arriving flights is zero, the frequency of various departing flight numbers is relatively high, indicating that the airport's dispatch and management efficiency for departing flights is higher in this scenario. When the number of arriving flights is between six and eight, the corresponding number of departing flights is mainly concentrated between four and ten, and the frequency is relatively balanced. This reflects the typical performance of the airport under regular operational dispatch. Through this analytical method, the hourly transportation capacity of each flight can be systematically  
Figure 5: Flight delays on 2023/2/23 from 6:00 to 12:00.  
estimated, providing data support for optimizing flight scheduling and improving airport operational efficiency.
# Current Research on the Effects of Non-Digestible Carbohydrates on Metabolic Disease
## 3 NDC Characteristics Related to Health Benefits  
The main characteristics of NDCs related to health benefits in the human body are water retention, fecal bulking, viscosity, and fermentation, and these characteristics mainly differ according to their water solubility, as mentioned above. Soluble NDCs are generally viscous and can ferment quickly in the intestine, whereas insoluble NDCs are non-viscous and slowly fermentable. A high intake of soluble NDCs with high viscosity-forming properties reduces postprandial blood glucose and blood cholesterol levels because high viscosity can interfere with the absorption of cholesterol and monosaccharides in the intestine [32]. Moreover, some in vitro studies have suggested that soluble NDCs could decrease gastric and pancreatic lipase activities because of the reduction of lipid emulsion caused by the high viscosity of these soluble NDCs, resulting in a decrease in lipid absorption, small bowel motility, and intestinal miscibility and an increase in the thickness of the unsettled water layer, which might delay the final stage of lipid assimilation [32]. In addition to soluble NDCs, a high intake of insoluble NDCs provides a fecal bulking effect linked to various intestinal functions, including promoting regular bowel movement and increasing fecal volume [33]. Although differences in the effects of soluble and insoluble NDCs on gut microbiota are not clear, these properties are strongly related to changes in the gut microbiota population [34]. The many functions of gut microbiota include contributing to changing the bile acid pool in the gut, especially secondary bile acids, such as deoxycholic acid and lithocholic acid, which are associated with a number of physiological functions, including inflammation, CVDs, the immune system, and colon cancer [35]. Moreover, during fermentation, the population of healthy gut microbiota increases, and by-products such as SCFAs, including acetate, butyrate, and propionate, are produced [36]. SCFAs play important physiological roles associated with various health benefits [37] throughout the body as well as in the large intestine, including reducing the risk of coronary heart disease, diabetes, CVD, and some cancers, and improving the immune system [35; 38]. Accumulating evidence suggests that the population and diversity of gut microbiota associated with the production of secondary bile acids and/or SCFAs significantly change according to the type, source, dose, and duration of NDC intake [37; 39]. A piglet model study showed that insoluble fibers such as cellulose and soluble fibers such as inulin increased the relative abundance of _Bacteroidetes_, _Phascalcrobacterium_, and _Coproccus_, and _Actinobacteria_, _Proteobacteria_, and _Blautia_, respectively, which are the main bacteria that produce SCFAs [40].
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 3 PIDL for Deterministic Tse  
### PIDL for Traffic State Estimation (PIDL-TSE)  
**Definition 3**.: _PIDL for traffic state estimation (PIDL-TSE) aims to infer the spatiotemporal fields of traffic states by integrating physics-based and deep learning methods._  
_Define the (labeled) observation set as \(\mathcal{O}_{d},\mathcal{O}_{p}\), the boundary and initial observation sets as \(\mathcal{B},\mathcal{I}\), and the (unlabeled) collocation point set as \(\mathcal{C}\) below:_  
\[\left\{\begin{aligned} &\mathcal{O}_{s}=\{(\mathbf{x}^{(i_{s})},t^{(i_{s})}); (\vartheta^{(i_{s})},a^{(i_{s})})\}_{i=1}^{N_{w}}:\text{ stationary sensors,}\\ &\mathcal{O}_{m}=\{\{\mathbf{x}(n,t^{(i_{m})})\}_{i_{m}=1}^{N_{w }}\}_{n=1}^{N_{w}}:\text{ mobile trajectories,}\\ &\mathcal{B}=\{t^{(i_{s})};(\vartheta^{(i_{s})},a^{(i_{s})})\}_{ i=1}^{N_{w}}:\text{boundary observation,}\\ &\mathcal{I}=\{\mathbf{x}^{(i_{s})};(\vartheta^{(i_{s})},a^{(i_{s })})\}_{i=1}^{N_{w}}:\text{initial observation,}\\ &\mathcal{C}=\{(\mathbf{x}^{(j)},t^{(j)})\}_{j=1}^{N_{c}}:\text{ collocation points.}\end{aligned}\right. \tag{8}\]  
_where \(i_{s},i_{m}\) are the indices of data collected from stationary and mobile sensors, respectively; \(i_{b},i_{0}\) are the indices of data collected from the boundary and initial conditions, respectively; and \(j\) is still the index of the collocation points. The values of the stationary sensor data, mobile data, boundary and initial conditions, and collocation points are denoted as \(N_{o_{s}},N_{o_{m}},N_{b},N_{0},N_{c}\), respectively. The number of mobile trajectories is denoted as \(N_{n}\). \(\mathbf{X}(n,t^{(i_{m})})\) is the \(n^{th}\) vehicle's position at time \(t^{(i_{m})}\). Observation data in \(\mathcal{O}\) are limited to the time and locations where traffic sensors are placed. In contrast, collocation points \(\mathcal{C}\) have neither measurement requirements nor location limitations, and are thus controllable._  
In the next two sections, we will elaborate the PIDL-TSE framework on the architecture of HCG and training methods.
# Equivalent Electrical Circuit Modeling of CNT-Based Transparent Electrodes
## 2 Modeling of a Carbon Nanotube  
The lowest-level modeling of carbon nanotubes is aimed at predicting the properties of the tube from both the mechanical and the electrical point of view. The properties of a CNT are determined by several factors, such as the chirality vector, the number of walls, the aspect ratio (involving diameter and length). The topic has been widely covered by several reviews, which agree in classifying the modeling of CNTs in atomistic modeling, continuum modeling and hybrid atomistic-continuum mechanics modeling [13; 15; 16]. with respect to the equivalent circuit modeling, CNTs have often been described as transmission lines [17]. Choosing between the atomistic, continuum and equivalent circuit level modeling depends on the kind of properties that need to be assessed according to the length scale involved (see Figure 1). Table 1 synthetically reports the description, the applications, the strengths, and limitations of the modeling of CNTs at different levels.  
In this review, we will describe the modeling at CNT-level briefly, focusing on the modeling of CNT networks for the circuital simulation of electronic devices.
## 3 Security Implications and Fundamental Approaches of Blockchain-Based Data Storage Methods
### The Role of Cryptographic Methods in Blockchain-Based Data Storage Systems
#### 3.2.7 Multi-Party Computation (MPC)  
Multi-Party Computation (MPC) is a privacy-preserving cryptographic method that allows multiple parties to jointly compute a function without revealing individual private inputs. This approach is particularly effective in industries such as healthcare and finance, where sensitive data must be analyzed across multiple entities while preserving confidentiality. MPC is often integrated with off-chain computation, allowing data from different institutions to be securely processed, with only verification results stored on-chain [137; 139]. Its advantages include ensuring data privacy and establishing trust among participants, whereas high computational overhead and scalability challenges remain key limitations [138; 140].
## 2 Acoustic Propagation Models
### Bellhop (BH)  
Bellhop Ray Tracing requires the solution of the ray equations to determine the ray coordinates of the acoustic signal propagation. Amplitude and acoustic pressure requires the solution of the dynamic ray equations which are described in detail in [14]. This tool is integrated with empirical data updated from world databases that measure the Sound Speed Profile (SSP), bathymetry and floor sediment such as the General Bathymetric Chart of the Oceans (GEBCO) and National Oceanic and Atmospheric Administration (NOAA) [15,16]. The ocean wave's motion is also included to calculate the rays' trajectories; so taking into account the type of sediments and the sound speed profile (SSP) this propagation model shows a behavior that it is very close to experimental studies for acoustic propagation in underwater environments (more details can be found in [14,17]).  
For a system with cylindrical symmetry, the ray equations can be written as:  
\[\frac{dr}{ds}=c\xi(s)\ \ \ \ \,\ \ \ \ \ \ \frac{d\xi}{ds}=\ -\frac{1}{c^{2}} \frac{\partial c}{\partial r} \tag{7}\]\[\frac{dz}{ds}=c\zeta(s)\quad,\quad\quad\frac{d\zeta}{ds}=\ -\frac{1}{c^{2}}\frac{ \partial c}{\partial z}\]  
where _r(s)_ and _z(s)_ represent the ray cylindrical coordinates and \(s\) is the arclength along the ray; the pair _c(s) [\(\zeta\)(s), \(\zeta\)(s)]_ represents the tangent versor along the ray. Initial conditions for _r(s)_, _z(s)_, \(\zeta\)(s)_ and _\(\zeta\)(s)_ are  
\[r(0)=\ r_{s}\quad,\quad\quad z(0)=\ z_{s}\quad,\quad\quad\xi(0)=\ \frac{cos\,\theta_{s}}{c_{s}}\quad,\quad\quad\zeta(0)=\ \frac{sin\ \theta_{s}}{c_{s}} \tag{8}\]  
where \(\theta_{s}\) represents the launching angle, _(r\({}_{\rm s}\), z\({}_{\rm s}\))_ is the source position, and \(c_{s}\) is the sound speed at the source position. The coordinates are sufficient to obtain the ray travel time:  
\[\tau=\int\limits_{\Gamma}\frac{\mathrm{ds}}{\mathrm{c(s)}} \tag{9}\]  
which is calculated along the curve _[r(s), z(s)]_.  
Figure 1 shows how the Bellhop tool draws the ray trajectories to calculate the travel of the acoustic signals and thus obtain the attenuation at different points of the scenario.
# Modeling and Fault Tolerance Analysis of ZigBee Protocol in IoT Networks
## 3 The Reference Scenario
Sensor nodes may communicate with each other using radio signals. A node is equipped with sensors and computing devices, radio transmitters and receivers, and power modules. Individual nodes in the network have limited resources: data processing speed, memory capacity, or communication bandwidth. Once sensor nodes are deployed, they are responsible for the organization of the network infrastructure, often multi-hop. Sensors collect information and can also respond to requests sent from the control side to execute specific instructions. Sensor nodes can operate continuously or can be controlled remotely. Local positioning algorithms and Global Positioning System (GPS) can be used to obtain location and positioning information. Sensors can also be equipped with servos that activate when certain conditions are met [38; 39]. Figure 1 is a general scenario showing the service-oriented architecture of the IoT for which the research is being carried out in the WSN subsystem, which is a common component of the network and sensing layers. A schematic of a typical WSN structure along with a sensor node diagram is shown in Figure 2. In our research, the IEEE 802.15.4/Zigbee protocol with routing based on AODV (Ad-hoc On-demand Distance Vector) algorithm is analyzed.
## 3 Results and Analyses
### Localization of AE Event Sources: Two-Stage AIC Result  
We applied the two-stage AIC picker to improve the accuracy in source localization. Figure 9 compares the source localization results of the two-stage AIC method against those of the single-stage AIC picker for the cases of Zones 2 and 14 as representatives of the corner and central zones. Table 1 also  
Figure 8: The accuracy of source localization estimated from the single-stage AIC method: (**a**) average Euclidean distance error, (**b**) zoning accuracy. The corner region includes Piles #1, 2, 3, 4, 5, 10, 15, 20, and 25. The central region includes Piles #7, 8, 9, 13, 14, and 19.  
Figure 7: Localized sources estimated using the single-stage AIC method when the hit sources were on a border of the sensor network (or corner region): when a hit was applied (**a**) on the raft top, (**b**) under the raft bottom, and (**c**) at the middle of Pile #2. In (**c**), the middle of the pile at a distance 10 cm down from the top of the raft was struck.  
tabulate all the results of zonation accuracy and the average EDE. The results revealed that the average EDE was reduced and the zonation accuracy was significantly improved in most of the cases. Both in the corner and central zones of RB cases and RT central case, there was a clear but small improvement after the application of two-stage AIC due to the sufficient accuracy with the single-stage AIC.  
Figure 9: Localized sources estimated using the two-stage AIC method when the hit sources were on a central and a corner region of the sensor network. Hits were applied on the raft top of Zone #14 (**a**) and #2 (**d**), under the raft bottom of Zone #14 (**b**) and #2 (**e**), and in the middle of Zone #14 (**c**) and #2 (**f**). The blue filled dot shows the result of two-stage AIC and the hollow gray dot shows the result of single-stage AIC. (**g**) The average Euclidian distance errors estimated from the single-stage and two-stage AIC methods.  
By contrast, the greatest improvement in accuracy was achieved in the RT corner zones, in which the zonation accuracy was improved from 25% to 100% by using the two-stage AIC picker. Additionally, the average EDE was reduced from 8.24 cm to 0.87 cm. This significant improvement in accuracy at the RT corner zones can be explained by the large signal amplitude levels, which enables a two-stage AIC picker to distinguish between noise and signal and to increase the sensitivity of the AIC function value. The application of two-stage AIC reduced the average EDE from 6.72 cm to 4.81 cm in the PM corner zones, which is still considerably large with the lowest accuracy. This low accuracy is attributable to the low SNR of the signals in PM cases.
## 1 Introduction
Therefore, this article describes the design and implementation of a proactive, reliable data transmission mechanism with fault-tolerant capacity based on the principle of random linear network coding. This fault-tolerant scheme includes two aspects: the grouping of greedy nodes and the construction of coding trees. As long as the gateway receives a sufficient number of linear independent encoding combinations, original packets can be recovered quickly, even when some errors occur. Numerical results have shown that this resilience transmission mechanism works better than previous methods in increasing the ratio of successful deliveries and the effective throughput rate, and in reducing the degree of resource redundancy and end-to-end delay. The remainder of this article is organised as follows. Research progress in fault-tolerance is reviewed in Section 2. The network model is then formulated in Section 3. In Section 4, details are presented of how we can generate a spanning tree based on network coding theory. The source-grouping strategy is described in Section 5. Simulation results are discussed in Section 6. Finally, we conclude the paper in Section 7.
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 4 System of Detections
### Eigenvalue Based Detection  
Eigenvalue methods can be used for detection [44; 45]. Eigenvalue decomposition separates a matrix to a set of orthogonal Eigenvectors and Eigenvalues. The magnitude of each Eigenvalue describes the contribution of the associated Eigenvector to the original matrix. The Eigenvalues are the characteristic roots of the system [46], whereby the distribution of the Eigenvalues describes the coherency of the elements of the linear system they represent. It directly links to the rank of the matrix. As such, these can be effectively used to detect the monotonicity of the matrix. Eigenvalue decomposition is described as:  
\[\mathbf{A}=\mathbf{Q}\mathbf{\Lambda}\mathbf{Q}^{-1}\,, \tag{30}\]  
where \(\mathbf{A}\) is the matrix to be decomposed, \(\mathbf{Q}\) is a matrix where each column is a unique Eigenvector, and \(\mathbf{\Lambda}\) is a diagonal matrix containing the Eigenvalues. For the remainder of the analysis, it is assumed that the Eigenvalues are placed in descending order. The maximum Eigenvalue to the mean of the Eigenvalues is used as a detection method [47]:  
\[\lambda_{\text{eig}}\overset{H_{0}}{\gtrless}\frac{\|\lambda_{1}\|}{\frac{1} {N}\sum_{i}\|\lambda_{i}\|}=\frac{\|\Lambda(1,1)\|}{\frac{1}{N}\|\mathbf{ \Lambda}\|}\,, \tag{31}\]  
where \(\lambda_{\text{eig}}\) is the detection threshold, \(\lambda_{1}\) is the largest Eigenvalue, \(\lambda_{i}\) is the \(i\)-th Eigenvalue from the matrix, and \(N\) is the number of Eigenvalues. If the largest Eigenvalue is similar in size to the mean of the Eigenvalues and consequently below the threshold, the null-hypothesis \(H_{0}\) of no spoofing is accepted. Otherwise, the alternative hypothesis \(H_{1}\) is accepted.
## 4 Proposed Method
### RevAuc Procedure Using D2D Communication
In the proposed method, a REP communicates with the server through a cellular network on behalf of its one-hop neighbors. Thus, a REP needs to send the locations of its one-hop neighbors in addition to its own location. However, if a REP sends the locations of all its one-hop neighbors, cellular traffic increases as the number of one-hop neighbors increases. To reduce cellular traffic, it is useful to put the mobile host location data into a small fixed-size packet by abstracting the location of each mobile host in some way. Additionally, the method with which the server selects \(R_{q}\) needs to be able to accommodate abstracted locations of the REP and its one-hop neighbors. Figure 6: Example of a case where \(S_{p}(q)\supseteq S_{e}(q)\). (ii) Mitigating the effects of misbehaving REPs that fail to fulfill the role of REP  
In the presence of misbehaving REPs, RevAuc does not work correctly. Specifically, a misbehaving REP may fail to forward bid invitations to its one-hop neighbors or it may fail to forward bids from its one-hop neighbors and itself to the server.