esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 4 Experimental Results
### Computational Complexity Analysis
#### 4.3.2 Arbitration and AI Inference  
The dynamic round-robin arbiter resolves contention in \(O(k)\) time, where \(k\) is the number of contending nodes (typically \(k\ll N\) due to quadrant-based segmentation). Priority updates require \(O(1)\) operations via round-robin shifts (Figure 6). For fire-risk prediction, the multiclass logistic regression model performs inference in constant time \(O(1)\) using pre-trained coefficients (Equations (9)-(12)). Real-time evaluation involves three fixed linear equations \(f(t+bh+c)\), making it feasible for resource-constrained Arduino nodes.
# Correlations between Anthropometric Measurements and Sports Discipline Aptitude
## 4 Discussion  
The assessment of anthropometric aptitude for a sports discipline is, among other factors, the cornerstone of sports success. Therefore, this report attempted to cross-correlate anthropometric characteristics with a specific sports discipline. To achieve the objective of this study, we built and tested a mathematical model that employed anthropometric ratios  
Figure 4: Confusion matrix for pair differentiation between sports using DTs. (**A**) badminton-wrestling, (**B**) wrestling-tennis, (**C**) wrestling-triple jump, (**D**) badminton-triple jump, (**E**) badminton-tennis, and (**F**) tennis-triple jump.  
Figure 5: Confusion matrix obtained using DT classification of four sport disciplines.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Wrestling** & **Badminton** & **Tennis** \\ \hline badminton & 0.949 & & \\ tennis & 0.951 & 0.756 & \\ triple jump & 0.992 & 0.975 & 0.979 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The percentage of accuracy of differentiation between different sports disciplines using the DT approach.  
of adult sports competitors practicing wrestling, triple jump, badminton, and tennis. We also postulated that the derived model might be applicable to teenage competitors. This hypothesis is supported by the scientific literature that indicates that body part lengths do not change during the transition to adulthood [26; 27; 28].  
A dearth of consistent studies on the relationship between anthropometric proportions and sports discipline makes it difficult to compare the observations derived in this study with others. Nevertheless, the results of this report showed that wrestling is defined by significantly different anthropometric coefficients than other sports disciplines studied. For example, the H/CC ratio is noticeably lower in wrestlers than in other sports disciplines. Wrestlers were also defined by a lower AL/LEL ratio than that observed in badminton, tennis, and triple jumpers. However, badminton and tennis players were defined by an analogous SH/AL ratio, confirming the previous observations [29; 30].  
In contrast to the previous report on comparing anthropometric parameters, this study used ratios and not absolute values such as, for example, height [31; 32], which, in combination with the employed computation scheme, revealed significant differentiation power in pairwise comparison between studied sports disciplines.
# Using Explainable AI (XAI) for the Prediction of Falls in the Older Population
## 4 Discussion
### Simple Falls  
Judging by feature importance scores (\(\geq\)0.1), age was the most important feature in the prediction of future simple falls. However, in the SHAP summary plot, there was also some individual age heterogeneity. The highest SHAP feature importance scores were for age, sex, having a myocardial infarction, and having had a fall in the last year. However, the presence of a myocardial infarction had negative contribution to the model. The SHAP summary plot also showed a similar effect for other features such as weight loss, being unsteady while standing, being unsteady when one is getting up from chair, and having a cognitive impairment, COPD, asthma, and osteoporosis. This may be due to the likely more \"extrinsic\" (e.g., environmental) nature of simple falls, with them having a less likely influence on the \"intrinsic\" morbidity factors. Yet, the presence of other features such as urine incontinence, an abnormal heart rhythm, poor hearing, a poor sense of smell, and psychiatric problems had positive contributions to these. Overall, the findings from the simple falls model are helpful for reminding clinicians that falls in the older population with comorbidities are less likely to be \"accidental\" or even \"mechanical\", and hence, they warrant a timely multifactorial assessment as recommended by the NICE guidelines [4].
###### Abstract  
Methane is the main cause of seven explosion accidents, and the available monitoring methods mainly use portable gas sensors for on-site detection. However, these methods cannot grasp the dynamic changes in methane in a sewer in real time or make good predictions of methane explosion accidents. Here, we present a low-power online monitoring system for sewer methane in order to ensure the safety of urban sewers. A low-power ultrasonic gas sensor was used to monitor the methane content in biogas. Due to the impacts of changes in CO\({}_{2}\), temperature, and humidity in the sewer on the ultrasonic methane measurement results, a corresponding compensation method was proposed. The effectiveness of the compensation method was verified through experiments, which measured the methane concentration under different influences, and the methane values measured before and after compensation were compared. The power consumption was only 1.28 mW when the system worked in intermittent operation mode, and it could operate for about 1.5 years using a lithium battery with a capacity of 5 Ah. The experimental results showed that gas detection accuracy could meet commercial requirements and compensated for the impacts of carbon dioxide, temperature, and humidity.  
sewer gas detection; ultrasonic method; low power Article An Ultra-Low-Power-Consumption Urban Sewer Methane Concentration Monitoring System
## 3 The Test Results and Discussion
### EnergyDissipation Behavior  
Referring to the consideration of energy dissipation in the [PERSON] damage cumulative model [58], the cumulative energy dissipation is dealt with in the dimensionless method, and the energy dissipation capacity is reflected by the cumulative energy dissipation coefficient \(\xi_{a}\). The expression is shown in Equation (1) [58]. \(E_{u}\) is the ultimate hysteretic energy under monotonic loading, which can be calculated from the area enclosed by the stress-strain curves of monotonic tensile test and the coordinate axis. \(E_{i}\) is the actual dissipated energy of the No. \(i\) semi-cycle, which can be calculated  
Figure 8: Stress amplitude-dimensionless (\(\sigma_{\text{m}}\)-\(\eta\)) half cycle of welding material and steel. (**a**) WA1; (**b**) WA2; (**c**) WA3; (**d**) WB1; (**e**) WB2; (**f**) WB3; (**g**) WA; (**h**) WB; (**i**) SA1; (**j**) SB2.  
from the area enclosed by the cyclic stress-strain hysteretic curves of cyclic loading test and the coordinate axis. Then, \(\xi_{a}\)-\(\eta\) curve of welded material is depicted in Figure 9, it can be seen that the slope of the curves decreases with the increase of cyclic cycles, which means that the rate of cumulative energy dissipation decreases gradually, in other words, the enclosing area of the hysteretic loops decreases with the increase of cyclic cycles, and the energy dissipation capacity decreases gradually. It is due to the continuous damage accumulation of the welding materials under cyclic loading. In a word, the cumulative damage rule of the welding material can be discussed through cumulative energy dissipation behavior is consistent with the law of stress amplitude degradation. That is, with the continuous development of damage accumulation under cyclic loading, the amplitude of cyclic stress, unloading stiffness, and energy dissipation capacity of the welded metal, these degenerate gradually until failure occurs, and compared with the base metal, the welded metal is more prone to damage accumulation.  
\[\xi_{a}=\frac{\sum\limits_{i=1}^{n}E_{i}}{E_{u}} \tag{1}\]
# Real-Time Interaction for 3D Pixel Human in Virtual Environment
## 3 Pixel-Style Avatar Generation Method
### Pixel Human Visualization
#### 3.3.1 2D Head Visualization  
Head visualization directly uses the high-resolution head images obtained in Section 3.2. To demonstrate the necessity of using a high-resolution head image, we generate and compare a pixel avatar with a high-resolution head and a pixel avatar with a low-resolution head matching the resolution of the body. As shown in Figure 4a, when both the head and body of the avatar are downsampled to the same low resolution to form a point cloud, the styles of the two parts are more consistent. However, based on low resolution of the head, it is difficult for the pixel avatar to reflect the user's real-time facial expressions and actions, which may affect communication efficiency during online video conferences. In contrast, the proposed avatar with a high-resolution head in Figure 4b has the advantage of enhancing the communication experience, similar to a 2D video conference. Therefore, we adopt a high-resolution head video stream for the head of a pixel avatar.  
Figure 3: The head image is a square image with the nose as the center and twice the length from the nose to the neck as the side length.  
The obtained high-resolution head images are displayed in a virtual space in the form of a 2D plane that forms the head of a pixel avatar. To control the avatar head movement, we consider the 3D coordinates of the nose joint in the virtual space as the center point of the plane and move the head plane according to the nose joint position in the video stream. To ensure that the ratio of the 2D head plane matches the ratio of the point cloud body in the pixel avatar, we scale the head plane according to the ratio in the original frame.
## 2 Biological Signal Extraction and Processing
### EMG and EEG Signal Abstraction
#### 2.1.1 Measurement and Extraction of EMG  
For extraction of surface muscle information due to muscle activation, an EMG sensor and an Ag/Cl electrode are used. Three electrodes are used to measure muscle activity information due to muscle contractions and relaxation. Muscle information is measured through the active electrode (measurement electrode) and reference electrode to generate zero potential through the ground electrode. To extract electrical signals from a specific area through both sensor signals, it is recommended that measurements be made as close as possible. The chemical properties of the electrodes are activated three to five minutes after attachment [9]. The electrical signal can be obtained by attaching the electrodes that can measure the circuit using a snap electrode for muscle signals. The signals with frequency activity below 10-500 Hz are typically extracted and used as a valid signal range [10]. In some cases, up to 2 kHz is used, and some studies use signals less than 300 Hz as a valid signal [11]. The sampling frequency of the signal can be applied at 500 Hz to a size of 2 kHz. If small differences in precise signals can affect the output of the system, the signal can be measured with a high sample rate. The output of the muscle signal has a small amplitude, from several \(\upmu\)V to several mV, or usually less than 10 mV with a voltage level. Thus, the measuring circuit uses the differential amplification circuits with the filter circuit for filtering the effective signal to increase the output. Generally, circuits amplified up to 300-800 times can be applied independently, depending on the type of measurement sensor or area of measurement. Figure 3 shows the process and method of EMG sensing for measurements. Muscles activated through electrodes are amplified at a constant amplification rate via radio frequency interference (RFI) filter circuits [12] for high-frequency noise and interference removal, and are output through frequency filters. This output is digitized via the analogue to digital converting (ADC) circuit for conversion to data for analysis, and the resulting data is defined as \"raw EMG\". The muscle output of the data is measured with the minimum amount of muscle contractions by the subjects. Experimentation minimizes the EMG of subjects because brain waves can increase the noise impacted on the output if muscle flexion increases during muscle operation.  
EMG outputs also vary depending on the subject's condition or muscle activity, so it is necessary to make them as general as possible using information about the subject's characteristics. In this study, the EMG data first served as an analysis of changes in brain waves through muscle signals, which were then measured to determine whether the movement of the arm could be distinguished from brain waves in the event of muscle contractions.
## 5 Feasibility Study Through System Simulations
### Generation of Datasets of Fingerprints
#### 5.2.1 Validation Datasets  
Two validation datasets are used. The FTV dataset is used to select the set of training weights that produce the minimum error among the training epochs. The MTV dataset is used to select the set of training weights that produce the minimum error among different initial random weights.  
The rationale behind the use of two validation sets is as follows. The training phase is carried out with a dataset of fixed tiles (FTT); however, the trained NN model is expected to generalize for a testing dataset of moving tiles (MTT). Then, for the selection of the best training weights among epochs and aiming to reduce the generalization error, we rely on a validation dataset of fixed tiles (FTV), which is consistent with the dataset type used for the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Base Station** & \(x\)**Coordinates** & \(y\)**Coordinates** \\
**Number** & **in Meters** & **in Meters** \\ \hline \(1\) & \(0\) & \(0\) \\ \(2\) & \(15\) & \(0\) \\ \(3\) & \(0\) & \(7\) \\ \(4\) & \(15\) & \(7\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Base station positions in the scenario.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Acronym** & **Description** \\ \hline FTT & Fixed Tiles for Training \\ FTV & Fixed Tiles for Validation \\ MTV & Moving Tiles for Validation \\ MTT & Moving Tiles for Testing \\ \hline \hline \end{tabular}
\end{table}
Table 8: Acronyms for datasets.  
training. However, as the target testing dataset consists of moving tiles (MTT), we use a second validation dataset, this time with moving tiles (MTV). The use of the MTV dataset aims to reduce the generalization error using moving tiles by selecting the weights that produce the minimum error metric in the performance of the model among different initial random weights. Since we are trying to optimize a non-convex problem, the training of the model is repeated a number of times with different initial random weights in an attempt to obtain the most favorable solution (as will be explained in step 5 of the execution flow of the model training presented in Section 5.6.3).
# Enhancing Border Gateway Protocol Security Using Public Blockchain
## 3 Design  
The whole architecture is shown in Figure 2. Our design connects the two modules. The one describes how to distribute configuration file securely into the router over the blockchain network. The second architecture proposes a reliable way for authentication assigned BGP sources against IP prefix hijacking. Although, if the border router receives BGP update message from a neighbour, it verifies Route Origin Authorisation (ROA) information in blockchain for every IP prefix. This solution protects us against accepting unauthorized IP prefixes, which might be used in IP prefix hijacking. However, it does not any inhibit the spread of these IP prefixes. Therefore, we introduce the architecture focused on adverting only authorized prefixes in BGP update messages by routers. We propose the following solution: the router downloads IP prefixes from the blockchain network which it inserts into an update message. Before this step, the administrator must upload a list of IP prefixes for the router. Nodes in blockchain subsequently verify it. Also, it checks if there is the valid entry stored in blockchain for every prefix of the list. This architecture includes only prefixes that are verified to be assigned to the AS of the router. Thus, routers add to their configuration only valid IP prefixes from the blockchain network and will advertise through BGP only legitimate information. This approach protects against administrator accidental misconfiguration or malicious appropriation of IP prefix of other AS. Nevertheless, it does not affect the dissemination of prefixes that have been learned from neighbouring ASes.  
The architecture consists of a set of smart contracts that represent participants and their operations in the RPKI model. Additionally, there is a smart contract for storing ROA entries and list of prefixes  
Figure 1: Blockchain architecture for management network devices. Reproduced from [8] and permission obtained from IEEE.  
Figure 2: Design architecture.  
(hereinafter referred to as'subconfig') for the router. The process flow is outlined as follows (see Figure 3):  
1. RIR \(R_{x}\) allocates prefixes.
2. \(R_{x}\) registers to BGPMC, it is a main smart contract where are stored ROA entries and subconfiguration of the router.
3. \(L_{x}\) requests to create LIR contract.
4. \(R_{x}\) allocates prefixes for LIR which LIR will assign ISPes.
5. \(R_{x}\) adds LIR to ACL in BGPMC smart contract.
6. \(ISP_{x}\) requests LIR to assign an IP prefix to it.
7. The prefix is verified to see if ROA record already exists with this IP prefix.
8. Verified IP prefix is recorded in a new block together with mask and max length as ROA entry in the blockchain.
9. \(ISP_{x}\) adds a new subconfiguration for border router.
10. For every prefix of the subconfiguration is checked ROA entry in the blockchain.
11. Verified subconfiguration is uploaded to the blockchain.
12. Affected router downloads the block and loads the new subconfiguration.
# An Experimental Study on D2D Route Selection Mechanism in 5G Scenarios
## 7 Conclusions and Future Work
### Future Work  
Nevertheless, our 5G-based testbed can still be further improved to implement a more realistic 5G network by: (a) increasing the number of nodes for achieving ultra-densification among small cells; and (b) increasing the number of available routes so that the source node has more options of routes with different levels of PUs' activities and hop counts. In addition, investigation using simulation can be performed as results are useful for estimating network performance in different scenarios, such as the random reappearance of PUs' activities in each route based on their respective ON/ OFF time.  
Figure 9: Cumulative end-to-end delay of D2D and backbone routes in progression of data transmission in the presence and absence of PUs’ activities.  
**Author Contributions:** Conceptualization, M.K.C. and K.-L.A.Y.; Methodology, M.K.C.; Software, M.K.C; Validation, K.-L.A.Y., R.M.N. and C.W.; Formal Analysis, M.K.C.; Investigation, M.K.C.; Resources, K.-L.A.Y., R.M.N. and C.W.; Data Curation, M.K.C.; Writing--Original Draft Preparation, M.K.C.; Writing--Review and Editing, K.-L.A.Y., R.M.N. and C.W.; Visualization, M.K.C.; Supervision, K.-L.A.Y., R.M.N. and C.W.; Project Administration, K.-L.A.Y., R.M.N. and C.W.; Funding Acquisition, K.-L.A.Y., R.M.N. and C.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was part of the project entitled \"A Novel Clustering Algorithm based on Reinforcement Learning for the Optimization of Global and Local Network Performance in Mobile Networks\" funded by the Malaysian Ministry of Education under Fundamental Research Grant Scheme FRGS/1/2019/ICT03/SYUC/01/1, and the Partnership Grant CR-UMSTDCIS-2018-01 and RK004-2017 between Sunway University and University of Malaya.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Digital Signal Processing of the Inharmonic Complex Tone
## 5 Discussion  
In Section 3, three different recordings were used to present different algorithms included in the current form of the application. In Table 4, the major characteristics of the analyzed recorded signals important for the present experiment are summarized.  
The characteristics of the signals are different in aspects of the number of prominent partials and values of the inharmonicity coefficient \(B\). The harp tone is characterized by a low number of prominent partials as well as the presence of other sinusoidal components, near the expected positions of the partials. Those components can influence the estimation of the frequency array that is used as an input to the inharmonicity coefficient \(B\) PDF estimation algorithm. By simultaneous inspection of the signal in time and frequency, as described in example 2, the problem can be detected and even corrected by replacing the \"corrupted\" partial with the expected one.  
The piano tone is usually the one easily recognized. Because of that, the example based on the piano tone is used for the illustration of the tool for the simple recombination of subchannels that can be used for the modification of the signal in a controlled manner. Realized applications can be used as a signal preparation tool for subjective tests.  
The harpsichord recording is characterized by a large number of prominent partials, requiring a large number of channels in the filter bank design. It is shown that the proposed approach can provide the required bank.  
All presented examples illustrate the scope of the proposed approach based on the decomposition of the signal into channels corresponding to signal partials.