esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Variations in Vegetation Canopy Interception in China Based on a Revised Gash Model
## 3 Results
### Spatial Patterns of Canopy Interception in China  
The mean annual canopy interception in China ranged from 0.98 mm to 1423.47 mm (with an average value of 166.55 \(\pm\) 160.61 mm) from 2000 to 2018 (Figure 3a), showing a significant decreasing pattern from the Southeastern to Northwestern regions. For example, the highest lowest mean annual canopy interception was measured in the tropical rainforest (section VIII) and temperate desert regions, with values of 461.1 \(\pm\) 161.25 mm and 18.07 \(\pm\) 22.34 mm, respectively (Figure 4). The ratio of interception to precipitation displayed similar spatial patterns (Figure 3b), with mean annual values ranging from 0.12% to 60.21% and an average value of 22.30 \(\pm\) 11.45%. In contrast, in the cold temperate coniferous forest region, the Ir was relatively higher, while the Ic was relatively lower.
## 4 Interaction Between the Gut Microbiota and Cardiovascular Diseases
### Bile Acids
In the liver, primary bile acids are synthesised from cholesterol through the action of cholesterol 7\(\alpha\)-hydroxylase (CYP7A1) and are conjugated with glycine or taurine via bile acid-CoA:amino acid N-acyltransferase (BAAT). These conjugated bile acids are secreted into the intestine, where they may be hydrolysed by microbial bile salt hydrolase (BSH), resulting in unconjugated bile acids. The gut microbiota further convert these into secondary bile acids through 7\(\alpha\)-dehydroxylation, dehydrogenation, and epimersion. Conjugated bile acids are actively reabsorbed in the terminal ileum and return to the liver via the portal vein, completing the enterohepatic circulation. A smaller fraction of secondary bile acids is passively reabsorbed in the colon, while the remainder is excreted in faeces.
# Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications
## 7 Conclusions  
In this paper, an uplink NOMA resource allocation algorithm is proposed for a UAV-IoT-based communication network serving a large number of IoT devices of different applications. The optimization problem is formulated under constraints with the objective to maximize the data rate and minimize the delay. The scheduler works in time domain to optimize the delay, and in frequency domain to optimize the data rate. The power allocation is used to ensure fair data rates between users allocated the same RB. The simulation results show that the proposed algorithm significantly enhances the system fairness, delay and reliability, in addition to achieving a spectral efficiency and sum rate that are nearly closer to the system upper bound.  
Conceptualization, R.K., M.A. and F.N.; methodology, R.K. and M.A.; software, R.K.; validation, R.K., M.A. and F.N.; formal analysis, R.K.; writing--original draft preparation, R.K.; writing--review and editing, R.K. and M.A.; visualization, M.A.; supervision, M.A. and F.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 11: Decoding error probability versus the packet size.
# Q-Learning Approach Applied to Network Security
## 4 Results
### Deploying the Policy on the NSL-KDD Dataset  
After training the Q-learning agent, the learned policy was applied to the NSL-KDD dataset. We employed a feature reduction technique, utilizing only the features relevant to the dataset. In our case, we used gain ratio for ranking the features, based on their information gain. For each sample, the agent selected an action of whether to allow or block the traffic. The cumulative reward, accuracy, and confusion matrix were also computed to show how well the policy generalized to the dataset, reflecting the agent's ability to response to intrusions effectively. The following results were obtained.  
Table 6 above displays the result of the trained policy on the NSL-KDD Dataset. The Q-values suggest that the agent assigned zero values in the probe, R2L, and U2R states, indicating it did not encounter enough feedback or samples to update its Q-values. The data set is unbalanced, where attack samples dominate, and the agent did not experience enough normal traffic or other attack types. For the normal state, the agent learned that blocking or  
Figure 8: Comparison of the LP, PI, and VI.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Method** & \(\mathbf{V^{*}(s_{Normal})}\) & \(\mathbf{V^{*}(s_{Do5})}\) & \(\mathbf{V^{*}(s_{Probe})}\) & \(\mathbf{V^{*}(s_{R2L})}\) & \(\mathbf{V^{*}(s_{U2R})}\) & **RMSE** \\ \hline LP & 5.716966 & 10.998375 & 5.592969 & 6.67405 & 8.847963 & 0.127007 \\ \hline PI & 5.716966 & 10.998375 & 5.592969 & 6.67405 & 8.847963 & 0.127007 \\ \hline VI & 5.716965 & 10.998374 & 5.592969 & 6.67405 & 8.847963 & 0.127006 \\ \hline \end{tabular}
\end{table}
Table 5: RMSE for Q-learning evaluation reporting a DoS attack results in positive rewards. Specifically, the Q-value of 11.11111111 for the \"Block\" action indicates that blocking a DoS attack is considered the most rewarding action. Similarly, the agent discovered that allowing traffic (Q-value of 6.10105437) was the best option in the normal state. The agent effectively learned to prioritize blocking malicious activity and allowing normal traffic when it encountered Dos attacks. However, due to the dataset's nature, it had limited experience with other types of attacks.
## 3 AI in Dental Image Technologies
### Dental Radiographs Dataset  
Building any AI model requires the existence of a training dataset, which is, in dentistry, dental radiographs. A dental radiograph is a common technique used by dentists in their daily practices, and it has a big future in treatment planning for dentures, braces, extractions, and implants. In other words, it is an important diagnostic tool. In this phase, the images should be cropped to remove any information about the patient. In addition, it is preferred to include radiographs with a variety of cases to cover most of the situations.  
There are four types of radiographs: periapical, bitewing, panoramic X-ray, and cone-beam CT.  
1. Periapical radiograph: aims to capture the root's tip on the film. Because it allows a dentist to see the tooth and the surrounding bone in their entirety, this can be useful in determining the cause of pain in a specific tooth.
2. The bitewing radiograph is the most common dental X-ray taken during a routine dental checkup. It shows the upper and lower back teeth in a single view and is taken to see how the upper and lower teeth line up, to check for caries and identify bone loss due to infection and serious gum disease.
3. The panoramic X-ray gives the denti a \"snapshot\" or overview of the entire mouth and jaws, including the teeth, the TMJ, and the sinus cavity. Panoramic images are not suitable for detecting tiny cavities, but they are good at finding more significant concerns like infections, impacted teeth, tumors, degenerative joint disease, and cysts.
4. A dental cone-beam computed tomography scan (CBCT) is a particular type of X-ray machine used in situations where regular dental or facial X-rays are insufficient.
# Negation and Speculation in NLP: A Survey, Corpora, Methods, and Applications
## 3 Methods and Techniques
### Rule-Based Methods
It expresses the syntactic structure as directed dependencies between the tokens. The authors built their syntactic rules for negation using 41 sentences from the I2B2/VA corpus [81] based on the dependency paths between the named entity and the negation terms. If a named entity is on the same dependency path as the negation clue, this means that it is negated. DepNeg was compared with NegEx by adding to the Clinical Text Analysis and Knowledge Extraction System (cTAKES) ([[http://www.ohnlp.org](http://www.ohnlp.org)]([http://www.ohnlp.org](http://www.ohnlp.org)) (accessed on 10 March 2022)), an information extraction tool for medical records, and the results showed that DepNeg reduced the number of false positives. DEpEndency ParsEr Negation (DEEPEN) ([[https://code.google.com/p/deepen](https://code.google.com/p/deepen)]([https://code.google.com/p/deepen](https://code.google.com/p/deepen)) (accessed on 12 March 2022)) is an open-source algorithm for negation detection in clinical texts [10]. It uses dependency patterns after NegEx as a post-processing step to remove false positives of negative findings. DEEPEN uses the transition-based dependency parser to find the dependency relationship between the negation words and medical terms, unlike DepNeg, which used the graph-based dependency parser. The algorithm rules were built using the Stanford Dependency Parser (SDP) [82] based on 1929 clinical reports from the Indiana University dataset, where only medical terms negated by the NegEx algorithm are considered. After that, a production chain of three levels of tokens is generated for each sentence, marking the medical term as negated if found in the production chain. Unfortunately, the basic rule failed in sentences with complex structures; further rules were added to address these structures. Moreover, two domain experts reviewed the output after adding these rules, and the IAA between them was 0.95. NegBio is an open-source tool for detecting negation and uncertainty in radiology reports [37]. This algorithm uses the universal dependency graph to find the grammatical relationships in a sentence, in addition to subgraph matching for graph traversal search. Consequently, the scope of negation and uncertainty is not limited to a specific window size. As a result, NegBio should outperform other rule-based algorithms by identifying the scope of triggers that include explicit negation cases as complex sentences. The input is a list of findings with corresponding UMLS, which are processed to detect negated and uncertain cases.
# Thermal Failure Propagation in Lithium-Ion Battery Modules with Various Shapes
## 3 Results and Discussion
### Hexagon Module  
The surface temperature variations of batteries in the hexagon modules during tests are drawn in Figure 17. The domino effect in the failure propagation is displayed in Figure 18, which consisted of two phases of failure. The three batteries near the heater composed the first phase and the others were the second. It was an arcuated propagation centered on the heater. For the module with discharging treatment, the thermal failures took place at 412 s and 504 s, respectively, compared to that of the module without discharging treatment, at 470 s and 587 s, respectively. In the hexagon module, the propagation distance was two times the battery diameter, 36 mm. In other words, the propagation speed of hexagon modules was 0.071 mm/s and 0.061 mm/s, corresponding to the two modules, respectively. Additionally, Figure 19 exhibits the typical curves of mass loss for hexagon modules in tests and the specifics are further listed in Table 6.  
Figure 17: The typical surface temperature curves of batteries in hexagon modules: (**a**) with discharging treatment; (**b**) without discharging treatment.
## 1 Introduction
This approach leverages Nash bargaining and Lagrangian multiplier methods to ensure efficient and fair resource distribution, effectively optimizing network performance in dynamic and competitive environments. 2. We introduce an adaptive dynamic resource allocation method that combines network coding and multi-path transmission strategies. Supported by Software-Defined Networking (SDN) for domain partitioning, this method enhances the system's adaptability and performance in highly dynamic and heterogeneous satellite network environments by efficiently balancing loads and minimizing latency. 3. We propose a fair resource allocation strategy based on the Shapley value, integrating Pareto optimality and Nash equilibrium principles. This approach ensures global resource allocation optimality, achieving fairness and efficiency across the network, even in scenarios with fluctuating resource demands and varying network conditions. * To validate the effectiveness of the proposed CMPT method, we conducted experiments on multiple satellite network simulation datasets using the Common-computing Simulation Tool Kit (CSTK) simulation platform. Our experiments, comparing CMPT with four baseline algorithms across four different satellite network datasets, demonstrated that CMPT outperformed existing methods in terms of resource allocation, computational complexity, and task scheduling efficiency.
## 2 Related Work
The pH level sensor, DO sensor, and GPS focus specifically on groundwater resources as part of a cutting-edge system for measuring and tracking water quality [32]. The temperature, phosphate concentration, oxygen dissolvability, resistance, pH, opacity, and water depth are just a few parameters that may be monitored using Intelligent Coast Multi-Sensor Technology [33]. A framework is presented for evaluating freshwater using the Internet of Things (IoT) in a real-world environment. To assess the marine systems in real-world systems, a pH sensor coupled with a Wi-Fi mode is used [34]. In addition, long-range communication is presently used in various applications regularly because it offers low-power, long-range, and dependable communication throughout sending data from edge devices to the server. The recommended system can carry out the long-range IoT water quality evaluation technique without causing any data loss; the platform focuses on using various sensors, including those that measure pH, humidity, sedimentation, and conductivity. Researchers came across this information in a literature review when looking into the different techniques for evaluating the quantities of water quality criteria. The researchers from [35] used web scraping to measure the many factors that make up the water's quality. They used a YSI 600 module and Raspberry Pi to measure these parameters. The researchers only found instances of individuals utilizing Internet of Things equipment to run a Raspberry Pi with a few sensors [36]. The researchers devised an innovative approach for assessing water quality and created a packaging system with a microprocessor and other sensors in this investigation. They used innovative, one-of-a-kind sensor components that monitor and broadcast real-time information. The sensors form a system that, combined with a Zigbee module, may offer both a visual and an audible depiction of the experimental measurements in the water [37]. Examining water quality also incorporates computer vision and neural network techniques. In building models for water quality classification, the Recurrent Neural Network (RNN) and the Long Short-Term Memory (LSTM) artificial neural techniques are used. The remaining sections are categorized as follows. In Section 2, we briefly cover some pertinent literature. Section 3 describes the proposed methodology, Chlorine level Assessment and Prediction in Water Monitoring System, using a fuzzy set specifically using a decision tree algorithm. In Section 4, the data processing workflow of the proposed model is detailed. In Section 5, the results and discussion are illustrated. Section 6 concludes with the conclusion.
# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 7 Future Research Directions  
Looking ahead, several avenues for future research hold promise for improving the current state of MFR, FMR, and FU technologies. Key areas of focus should include the following:  
* Real-World Dataset Availability: A major challenge for both MFR and FU is the limited availability of real-world masked face data. Future research should focus on developing methods for collecting and organizing diverse, high-quality masked face datasets that represent a wide range of mask types, facial features, and environmental conditions. These efforts will be essential to enhance the generalization capabilities of models beyond controlled, synthetic datasets.
* Synthetic Dataset Generation: Addressing the challenge of dataset scarcity, generating high-quality synthetic masked face datasets can provide a significant solution. Advanced techniques like GAN-based augmentation, domain adaptation, and multi-task learning can be employed to produce more realistic masked face images, closely simulating real-world conditions, and occlusions.
* Lightweight Models for Real-Time Applications: To reconcile computational demands with real-time performance needs, future research should prioritize the development of efficient models for MFR and FU tasks. This involves exploring lightweight architectures and hardware-aware optimizations capable of running effectively on edge devices, while still achieving high accuracy.
* Face Unmasking as a Preprocessing Step: Combining face unmasking with masked face recognition could enhance the robustness of recognition systems. By treating FU as a preprocessing step, models can achieve better accuracy in scenarios with varying degrees of facial occlusion. This approach would also help mitigate the challenges posed by inconsistent facial coverings.
* Cross-Disciplinary Integration: Finally, the integration of MFR, FMR, and FU with other biometric systems (such as voice recognition or gait analysis) could offer multimodal solutions that enhance the reliability and accuracy of identity verification systems, especially in security-critical applications.  
By addressing these areas, future research can contribute significantly to advancing the state of MFR and FU systems, making them more adaptable, efficient, and reliable for real-world deployment.
## 3 Results and Discussion
### Far-Field Distance
#### 3.2.2 Results of 16 m Far-Field Distance  
The distributions of \(\rm P/P_{t}\) and \(\rm T/T_{t}\) are presented in Figure 25, and the mean error ratios are contained in Table 19. The total mean error ratios under different configurations are calculated and the optimal results are highlighted in red in Figure 26. In terms of the NACA0012 model, the related finest performances are 2.74%, 3.8%, 2.64%, 2.05%, 2.76%, and 2.46%. In terms of the turbulence model, the optimal result of SST is 2.46%, and that of SA is 2.05%. In terms of the flux type, the optimal result of ROE is 2.05%, and that of AUSM is 3.26%. In general, we could conclude that SA + 200 points, combined with ROE, is the optimal configuration. The minimum total error ration is 2.05% and the relative accuracy gains are 25.25%, 37.82%, 48.09%, 46.10%, 22.46%, 57.41%, 37.3%, 25.79%, 62.07%, 16.78%, and 41.22%.  
Figure 22: Comparison of the simulation performances of the sharp trailing edge based on 400 points formula under different simulation configurations.  
Figure 23: (a) Distributions of \(\mathrm{P}/\mathrm{P}_{\mathrm{t}}\) of 12 m far-field distance; (b) Distributions of \(\mathrm{T}/\mathrm{T}_{\mathrm{t}}\) of 12 m far-field distance.
# Soil Spectral Behavior Related to Its Load-Bearing Capacity Based on Moisture Content
## 3 Results  
### Soil Density at Different Moisture Contents  
The properties and the measurement parameters of the five tested soil samples are shown in Table 1. Each sample had a different moisture content (MC%). The dimensions of the soil container were height H1 = 12 cm and inner diameter D1 = 22 cm. The diameter size of the Bevameter pressure plate, D2, was 20 cm, and the height of the soil before and after the measurement was H2 = 10 cm and H3, respectively. The soil was initially filled to the height of H2. The soil mass m was measured to calculate the density of the soil at different moisture contents.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample No.** & \(\rm H_{2}/D_{2}\) & **MC (\%)** & **Mass (kg)** & **Height (cm)** & **Density (g/cm\({}^{3}\))** & **Density Ratio (\(-\))** \\ \hline
1 & 0.5 & 1.05 & 4.62 & \(H_{2}\) = 10, \(H_{3}\) = 8.4 & \(\rho_{1}\) = 1.47, \(\rho_{2}\) = 1.75 & 0.19 \\
2 & 0.5 & 3.34 & 4.52 & \(H_{2}\) = 10, \(H_{3}\) = 6.9 & \(\rho_{1}\) = 1.44, \(\rho_{2}\) = 2.09 & 0.45 \\
3 & 0.5 & 4.82 & 4.23 & \(H_{2}\) = 10, \(H_{3}\) = 5.9 & \(\rho_{1}\) = 1.35, \(\rho_{2}\) = 2.28 & 0.69 \\
4 & 0.5 & 6.05 & 4.21 & \(H_{2}\) = 10, \(H_{3}\) = 5.8 & \(\rho_{1}\) = 1.34, \(\rho_{2}\) = 2.31 & 0.72 \\
5 & 0.5 & 7.63 & 4.12 & \(H_{2}\) = 10, \(H_{3}\) = 5.4 & \(\rho_{1}\) = 1.31, \(\rho_{2}\) = 2.43 & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Five samples of sandy loam soil measured at different moisture contents.  
Figure 3: Shows (**a**) the spectrophotometer (CM-700d) and the 8 mm head (where the electromagnetic waves pass through), and (**b**) the spectrophotometer (CM-700d) while measuring and recording the data.  
The initial density of the soil \(\rho_{1}\) and the density of the soil \(\rho_{2}\) after being compressed by the Bevameter were calculated using Equation (1). Having the soil filled in the bin with H\({}_{2}\)/D\({}_{2}\)\(\leq\) 0.5 showed that the soil when tested in a bin behaved in a similar way to when tested in infinite space [30]. Keeping the H\({}_{2}\)/D\({}_{2}\) value fixed (0.5) at different moisture contents, the additional amounts of the moistured soil (remaining after filling the bin to the fixed H\({}_{2}\)/D\({}_{2}\)) were removed (after stirring the water and the soil since the water occupies space in the soil). The mass of the soil at different moisture contents used for filling the bin to the required height (keeping the H\({}_{2}\)/D\({}_{2}\) value equal to 0.5) was recorded.  
The density was calculated using the mass and the height.  
\[\rho_{1,2}=\frac{m}{\pi.r^{2}.H_{2,3}}\, \tag{1}\]  
Table 1 shows that the density before pressing the soil decreases with the increase in the moisture content, showing that the water is occupying a place in the soil by filling the space and the voids between the particles; thus, upon removing an amount of the moistured soil from the total amount in order to keep the H\({}_{2}\)/D\({}_{2}\) value fixed, the remaining soil mass decreases.
###### Abstract  
Wireless sensor networks have recently been widely used in several applications and scenarios, especially because they have the ability and flexibility for establishing a scalable and reliable wireless network. Cooperative multi-robotic systems (CMRS) are one example of these applications where establishing a wireless network between robots is essential and paramount to their operation. Further, these robots can utilize their mobility to provide sensing functionality for areas that are not covered by the static sensor. This can be achieved by equipping the robots with specific sensors to sense the area of interest (AoI) and report the sensed data to a remote monitoring center for further processing and decision-making. However, the nodes that form the sensor network have limited energy, and, as such, efficient algorithms in clusters' formation, packets' routing, and energy and mobility management are paramount. In this paper, a literature survey is presented containing the most related works that have been proposed to solve these challenges utilizing fuzzy logic. Most of the literature work attempted to utilize a de-centralized approach, where certain input parameters such as the residual energy, communication link quality, network congestion status, the nodes' distance to the sink node and its location with respect to the other nodes, and the data and their sampling rate are all used as inputs to the fuzzy logic controller. These input parameters are used to determine several performance vital factors such as the cluster formation and its cluster head, best route to the sink node, optimal power management policies in terms of sleep/awake times needed to maximize the network lifetime, nodes' mobility management policies to maintain network connectivity, and best route in terms of packet loss and delay.  
fuzzy logic; wireless sensor networks; clustering; routing; power management +
Footnote †: journal: Electronics Electronics