esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Hybrid Wireless Mesh Protocol
#### 2.2.1 HWMP Frame Formats  
There are four frames directly involved in the path discovery process, including the Path Request (PREQ), Path Reply (PREP), Path Error (PERR), and Root Announcement (RANN). The frame formats are shown in Figure 5.  
When considering security for routing frames, it is convenient to separate the fields into MFs and NMFs. In Figure 5, the MFs are the highlighted fields, while the rest of the frames are NMFs. MFs contain information that will be updated whenever the frames are propagated (e.g., the path metric and hop count). NMFs, in contrast, contain information that cannot be modified by an intermediate mesh STA. Note that all frames are encapsulated with the wireless MAC header (HDR) before transmissions. The HDR contains information such as the transmitter address and the receiver address. The HDR is not shown in Figure 5.  
Figure 4: Wireless mesh network.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology
### Enhanced ResNet Architecture  
The proposed model builds upon ResNet-18 [67], a lightweight convolutional neural network known for its residual learning capabilities, which help mitigate gradient degradation in deep networks. To adapt ResNet-18 for the task of carbon stock estimation in complex forest environments, we introduce two key enhancements: multiscale residual blocks and a channel attention mechanism.
## 8 Future Directions
### Interference Mitigation  
Employing spectrum management techniques such as Dynamic Spectrum Access (DSA) can mitigate interference and optimize the use of available frequencies. DSA allows the system to dynamically switch to less congested frequencies. Using cognitive radio technology that senses the spectrum environment and adapts the frequency and transmission parameters can avoid interference with other communication systems.
## 6 Results and Discussion
### Analysis and Prediction of the Results with Artificial Neural Network (ANN) and Fuzzy Logic (FL)  
Compressive strength at 7 and 28 days was assessed using ANN and FL models. After training the ANN model with various algorithms (LM and SCG) and different types of neuron arrangements in the hidden layer, the best neuron arrangement in the hidden layer was determined for each algorithm. The LM algorithm with 10 neurons in the hidden layer showed the highest correlation coefficient. Figure 18 illustrates the compressive strength outcomes at 7 days (Figure 18a) and 28 days (Figure 18b), obtained through actual and predicted results by the ANN and FL algorithms. The prediction error relative to the real results is depicted in Figure 19 for the 7-day (Figure 19a) and 28-day (Figure 19b) compressive strength.  
Figure 17: Compressive strength under the influence of (**a**) changes in the w/b ratio; (**b**) changes in the dosage of SNF.  
The FL model had an average error rate of 4.36% for predicting 7-day compressive strength and 3.18% for predicting 28-day compressive strength. On the other hand, the average error in the ANN model was lower, suggesting that the results derived from the ANN model are more closely aligned with reality. Specifically, the ANN model achieved 0.74% in 7-day compressive strength prediction and 1.03% in 28-day compressive strength prediction (Figure 19). Figure 20 depicts the performance of the ANN model across training, validation, testing, and all datasets. It demonstrates the strong predictive capability of the ANN model in aligning with the experimental results, as indicated by the regression coefficient (>0.95).  
Despite the satisfactory performance of both prediction models utilized in this study, the perceptron neural network model of the LM algorithm with a 2-10-4 architecture demonstrates superior performance compared to the FL model.  
Figure 19: Prediction error by ANN and FL models compared to reality: (**a**) 7 days; (**b**) 28 days.  
Figure 20: Performance of the artificial neural network (ANN) model based on (**a**) training, (**b**) validation, (**c**) test, and (**d**) all datasets.
## 5 Results and Discussion
### Consolidated Trends
#### Conventional Spectral Bands and VIs
Despite these results, most of the conventional VIs associated with Red and NIR regions of the electromagnetic spectrum commonly saturate at moderate to dense canopies [37; 58].
# Apollonian Packing of Circles within Ellipses
## Appendix A Deduction of Hyperbola Parametrization Formulas  
### Parametrizaton in the Canonical Reference Frame  
A simple example of parametrization for the coordinates of a point \(P(x_{P},y_{P})\) belonging to any of the two branches of a hyperbola in the canonical reference frame shown in Figure 10(a) is the following:  
\[x_{P}=\pm a\cosh t,\qquad\qquad y_{P}=b\sinh t \tag{11}\]  
where \(t\) is the real parameter, and \(a\) and \(b\) are two positive real numbers representing the hyperbola real and imaginary semi-axis lengths, respectively. The \(+\) sign is chosen to parametrize the right branch of the hyperbola, made of points with positive abscissas, while the \(-\) sign parametrizes the left branch.
## 4 Proposed mmWave Assisted CR-IoV
### Results Discussion  
In this subsection, we summarize the overall results obtained for proposed and existing works. The numerical comparison is provided in Table 5.  
The numerical results show that the proposed work achieves a better mean value in each performance metric which is a notable improvement in this area. Furthermore, the comparison shows that the mmWave-CR-IoV environment needs to focus on three aspects: Security, Beamforming and Routing. When these three aspects are optimized, the result will be more suitable for real-time applications.  
In Table 6, the impact of the proposed approaches on obtained results is highlighted. Each proposed approach assists in improving the performance of mmWave-assisted CR-IoV. Finally, we achieved the expected performance with the combination of security, beamforming and routing aspects.  
Figure 16: Detection accuracy (PUE attack).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Existing Work** & \multicolumn{2}{c}{**Throughput (Mbps)**} & \multicolumn{2}{c}{**PDR (\%)**} & \multicolumn{2}{c}{**Delay (ms)**} \\  & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline SVM [46] & 82 & \(\pm\)2 & 70.6 & \(\pm\)2.7 & 33.4 & \(\pm\)2.6 \\ Machine Learning-NN [47] & 77 & \(\pm\)2.6 & 72.6 & \(\pm\)2.3 & 33.4 & \(\pm\)1.14 \\
2D-Beamforming [48] & 83 & \(\pm\)1.6 & 74 & \(\pm\)2.6 & 29 & \(\pm\)0.74 \\ OLSR [49] & 76 & \(\pm\)1.8 & 72.5 & \(\pm\)2 & 31.7 & \(\pm\)0.8 \\
5G-VANET [50] & 84 & \(\pm\)2.9 & 79 & \(\pm\)2.9 & 26.8 & \(\pm\)1.9 \\
**Proposed mmWave assisted CR- IoV** & **98** & \(\pm\)**1.12** & **96.2** & \(\pm\)**1.6** & **15.9** & \(\pm\)**0.89** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Numerical Analysis on Results.
# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 4 Experiment and Results
### Results and Discussion
#### 4.2.4 Prediction Based on ACDM Calculation Time  
To investigate the impact of different traffic indicator calculation nodes on prediction results, in this section, we use the calculation time points CTOT, COBT, CLDT, and CIBT from the ACDM system as nodes for calculating traffic indicators. These values are typically determined within 30 min to 1 h before flight departure. The results of ATT prediction using these indicators are shown in Table 8.  
From Table 8, it can be observed that the NOA-XGBoost algorithm proposed in this paper performs the best, with a \(\pm\)3 min accuracy of 51% and a (+5, \(-\)10) min accuracy of 85%. This prediction accuracy is lower compared to the results in Section 4.2.1, with a reduction of 33.77% for \(\pm\)3 min accuracy and 12.37% for (+5, \(-\)10) min accuracy. This discrepancy is due to the deviation between the calculation time nodes in ACDM and the actual time nodes. Although the accuracy has decreased, the (+5, \(-\)10) min accuracy still reaches 85%, which is within the acceptable error range for domestic flight operation management.
## 3 Positioning Based on DVB-T  
It is well known that the position of an object capable of measuring the distances between itself and some reference points can be performed by using the trilateration method; this is the technique typically employed in GNSS [13], and the same method can be utilized when the references points are the emitters of DVB-T signals. To implement the method, the PD has to:  
1. identify a reference frame,
2. identify the emitter locations, \(\mathbf{p}_{i}=(x_{i},y_{i},z_{i})\), where \(i=1,\cdots,N_{e}\) and \(N_{e}\) are the number of visible DVB-T emitters,
3. measure the ranges, \(r_{i}\), between each emitter and the PD
4. write and solve the navigation equations.  
The implementation of the first and fourth tasks does not present any difference with respect to a classical GNSS receiver; so, it is not described in this paper. The second task is the most challenging, since the signals transmitted by the DVB-T emitters do not contain the station identifier; so, the receiver is not able to associate each received signal to a specific station. This is not an issue for TV reception, but it is a problem for positioning. Notice that the emitter identifier is foreseen by the DVB-T standard, but its transmission is optional; so, it is ignored in this study. The method proposed in this paper to solve this problem is described in Section 3.1. The third task requires the processing of an OFDM signal to estimate the range; so this has to be re-designed with respect to a GNSS receiver.  
The next sections will be devoted to the methods used to implement tasks 2 and 3, while the position computation can be implemented by solving the navigation equations with a classical extended Kalman filter (EKF) technique [14, 15]. The experimental results shown in this paper have been obtained by using this approach.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Possible Values** \\ \hline \(N_{FFT}\) & 2,048 (Mode 2 K) \\  & 4,096 (Mode 4 K) \\  & 8,192(Mode 8 K) \\ \(\rho_{CP}\) & 1/32, 1/16, 1/8, 1/4 \\ \(T_{samp}\) & 7/64 us (8 MHz)) \\  & 1/8 us (7 MHz) \\  & 7/48 us (6 MHz) \\  & 7/40 us (5 MHz) \\ \hline \hline \end{tabular}
\end{table}
Table 1: DVB-T parameters.
###### Abstract  
In this paper, AE signals collected during fatigue crack-growth of aluminum and titanium alloys (AI7075-T6 and Ti-6 Al-4V) were analyzed and compared. Both the aluminum and titanium alloys used in this study are prevalent materials in aerospace structures, which prompted this current investigation. The effect of different loading conditions and loading frequencies on a proposed AE-based crack-growth model were studied. The results suggest that the linear model used to relate AE and crack growth is independent of the loading condition and loading frequency. Also, the model initially developed for the aluminum alloy proves to hold true for the titanium alloy while, as expected, the model parameters are material dependent. The model parameters and their distributions were estimated using a Bayesian regression technique. The proposed model was developed and validated based on post processing and Bayesian analysis of experimental data.  
reliability; acoustic emission; structural integrity; crack growth; fatigue life prediction; uncertainty analysis; nondestructive testing Article
# Visual Analysis of Spatiotemporal Data Predictions with Deep Learning Models
## 4 Spatiotemporal Prediction Models
### Feature Selection with Correlations  
Feature selection is the process of constructing a subset of correlated variables and is an essential technique that is directly related to training performance. In general, feature selection generates a data subset according to the data relationships, such as mutual information and the Pearson correlation coefficient. However, the feature selection of spatiotemporal data makes it challenging to choose subsets based only on simple correlation coefficients or scores because we must examine both temporal and spatial relationships. In this work, we employ the Pearson correlation coefficient, temporal autocorrelation, spatial autocorrelation, and the LISA algorithm to support the feature selection of the spatiotemporal data.  
The linearity of correlation between variables is meaningful in determining feature association. We employ the Pearson correlation coefficient, visualize the correlations, and use it as an indicator of feature selection, depending on the data features. We also visualize temporal and spatial autocorrelation of features. We visualize LISA (local indicators of spatial association) values as indicators of spatial association. In addition to the feature selection, feature extraction techniques, such as PCA, t-SNE, and LDA, can also be applied. However, this paper does not cover features from feature extraction techniques.
## 4 Calculation of Various Reliability Indices
### Reliability of the WSN
#### 4.1.2 WSN Reliability with Fluctuation in Transceiver Failure  
The reliability of the WSN with respect to the transceiver is obtained by varying the failure rate of the transceiver between 0.11, 0.12, 0.13, and 0.14 and keeping other failure rates fixed in the reliability expression obtained in Equation (20). The obtained results are reflected in Figure 5b.
## 4 Interference-Aware Cooperative Anti-Jamming Distributed Channel Selection Algorithm  
In this section, the interference-aware cooperative anti-jamming distributed channel selection algorithm is designed, and then, the complexity analysis is described.
## 3 Results  
### XPS Spectrum of GOs  
The obtained elemental ratio of GO is shown in Table 1. The ratio of C and O was roughly C:O = 7:3, suggesting GO was successfully obtained, and a slight change in the ratio was observed with a change in the oxidation time.  
For quantification of the degree of oxidation, C1s peak fitting profiles are shown in Figure 1 and the ratio of five peak-fitting areas to the area of the C-C peak is shown in Table 2. From this result, no obvious change was seen in the ratio of functional groups owing to the different oxidation times of GO except hydroxyl (OH). GO oxidized for 0.5 h has twice the amount of hydroxyl groups that 30 h oxidized GO has. This may be due to the hydroxyl groups being oxidized to carboxyl groups (O-C=O) or carbonyl groups (C=O) with increasing oxidation time. The proportions of both carbonyl groups and carboxyl groups slightly increased with the oxidation time, as shown in Table 2. GO (0.5 h) contained more hydroxyl groups and is thus likely to have better mechanical properties as it is expected to exhibit stronger interactions with PVA owing to the hydrogen bonds.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Oxidation Time (h)** & **0.5** & **30** \\ \hline C:O & 71:28 & 69:29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Atomic ratio of carbon and oxygen in GO (graphene oxide) as determined by XPS.
# Precision Calibration and Linearity Assessment of Thin Film Force-Sensing Resistors
## 1 Introduction
Specifically, we compare the performance of two widely used FSR sensors, i.e., Flexiforce A201-1 and Interlink FSR-402, to assess their linearity and repeatability [1,2]. Flexiforce A201-1 and Interlink FSR-402 have their own strengths and are suitable for different applications. Flexiforce A201-1, with its high sensitivity, precision, and larger active area, is ideal for applications requiring high-precision force measurement. On the other hand, Interlink FSR-402, with its compact size and lower cost, is suitable for force measurement in limited spaces or applications where cost efficiency is crucial. Selecting the appropriate sensor depends on the intended use and budget considerations. Manufacturers of FSR sensors typically do not provide detailed calibration data, instead recommending that users perform their own calibration tailored to their specific application conditions [22,23,24,25,26,27]. This gap highlights the need for a thorough investigation into the output characteristics and calibration of FSR sensors under various loading conditions. By conducting precise calibration and analysis, we seek to derive an equation that relates the output of the FSR sensor to the applied load, thereby enhancing the sensor's reliability for various practical applications. This study contributes to the body of knowledge by providing a detailed examination of FSR sensor performance and offering insights into their potential improvements for future use in fields such as medical devices, robotics, and consumer electronics.
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 3 Dummy Packets Techniques
### Optimal Filtering Scheme (OFS)  
The _OFS_[45] technique is based on _TFS_ and _PFS_ techniques. Each node in _OFS_ has the possibility to become a proxy. This method can provide an optimal routing and filtering that eventually leads to optimal lifetime for the network. Once a sensor node is selected to be a proxy, it can use its filtration rules for further optimization to maximize the network lifetime. Moreover, a proxy has the ability to filter packets coming from other proxies, unlike _TFS_, where the proxy transmits all incoming packets from other proxies without any filtration. In this protocol, proxies have two options: First, proxies can work individually, which increases the delay and provides a high level of anonymity. Second, they work together to decrease the delay with a low level of anonymity. This technique is considered as a trade-off between delay and anonymity. The challenge in _OFS_ is to select the best locations for the proxies.
## 2 Methodology
### Control Strategy for DVR with Multi-Frequency Injection  
Figure 1 presents the structure employed in the DVR for multi-frequency voltage injection at the PCC [36]. The primary objective of the voltage loop is to maintain the load-side voltage at a predefined reference composed of the fundamental, 5 th, and 7 th harmonic components. The instantaneous voltage error is formulated as follows:  
\[e_{v}(t)=V_{ref,\beta}-V_{pcc,\alpha\beta} \tag{12}\]  
where \(V_{ref,\beta}\) is the desired voltage reference, including the 50 Hz fundamental and selected harmonic contents, and \(V_{pcc,\alpha\beta}\) is the measured PCC voltage.  
This error is regulated by a multi-resonant proportional-resonant (PR) controller, which provides high gain at the targeted frequencies while maintaining stability across the operating range. The voltage controller transfer function is defined as follows:  
\[G_{PR,v}(s)=k_{pv}+\sum_{n\in\{1,5,7\}}\frac{2k_{rc,n}s}{s^{2}+\omega_{n}^{2}} \tag{13}\]  
where  
* \(k_{pv}\) is the proportional gain for the voltage loop;
* \(k_{rc,n}\) are the resonant gains at the respective harmonic frequencies \(\omega_{n}\) (fundamental \(\omega_{1}\), 5 th \(\omega_{5}\), and 7 th \(\omega_{7}\)). The controller output generates the reference current signal for the current regulation stage: \[i_{cc,n\beta}^{ref}=G_{PR,v}(s)\cdot e_{v,n\beta}\] (14)  
This ensures that the injected DVR voltage compensates for disturbances at the targeted frequencies, maintaining voltage quality at the PCC. The current control loop ensures accurate tracking of the DVR output current according to the reference generated by the voltage controller. The current error is defined as follows:  
\[e_{i,\beta}=i_{cc,n\beta}^{ref}-i_{cc,n\beta} \tag{15}\]  
where \(i_{cc,n\beta}\) is the measured converter output current.  
A dedicated multi-resonant PR controller is employed in the current loop to enforce accurate current tracking across the fundamental, 5 th, and 7 th harmonic frequencies [36]. The current controller transfer function is expressed as follows:  
\[G_{PR,i}(s)=k_{pc}+\sum_{n\in\{1,5,7\}}\frac{2k_{rc,n}s}{s^{2}+\omega_{n}^{2}} \tag{16}\]  
where  
* \(k_{pc}\) is the proportional gain for the current loop;
* \(k_{rc,n}\) are the resonant gains for the selected harmonic frequencies. The output of the current controller defines the inverter voltage reference: \[v_{cc,n\beta}^{ref}=G_{PR,i}(s)\cdot e_{i,n\beta}\] (17)  
which is then used for PWM generation to control the inverter switching states.  
The control strategy provides precise voltage tracking and selective harmonic injection at the point of common coupling to synthesize the desired multi-frequency voltage profile while maintaining system stability and improving power transfer flexibility across harmonic channels.
# Design, Implementation and Power Analysis of
## 2 Materials and Methods
### Perceptive Light Automation Algorithm for Light Control Mode  
PLA algorithm functions with the light bulbs in three states--ON, DIM and OFF--as tabulated in Table 4 by checking two conditions:  
1. [label=(0)]
2. PIR sensor detection of intervention
3. Daylight detector sensing the intensity of daylight in the deployment site.  
On true conditions of the first case with illuminance value less than 300 lx, light bulbs will be turned on while for factual condition of the first instance with illuminance value greater than 300 lx but less than 500 lx the lights will turn to a dim state. False of the first condition will not invoke the PLA algorithm. Thus, a reduction in unwanted energy consumption is possible when there is an essential daylight distribution and when no intrusion occurs around the adjacent zone of the bulb.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Perceptive Light Automation Algorithm in Light Control Mode of Operation**} \\ \hline \hline
**Light State** & **Condition and Logic to be Satisfied** \\ \hline ON & Intervention detection _AND_ Value of Illuminance \textless{} 300 lx \\ DIM & Intervention detection _AND_ Value of Illuminance \textgreater{} 300 lx \& \textless{} 500 lx \\ OFF & No intervention _OR_ Value of Illuminance \textgreater{} 500 lx \\ \hline \hline \end{tabular}
\end{table}
Table 4: Conditions and logic to be satisfied for PLA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Functionality** & **Light Control (LC)** & **Alert (AL)** \\ \hline Use of mode & The main purpose is energy saving. & Intrusion monitoring with alarm. \\ \hline Function of mode & Controls light only when the PiR senses & Controls lights and buzzers when the PiR \\  & motion. & senses motion. \\ \hline \multirow{3}{*}{Indoor application} & In indoor applications, when the & In indoor application, when the people are \\  & inhabitants are present. & in closed offices during holidays and a \\  & In outdoor places with frequent human & In outdoor places with rare human \\  & intervention areas like highways, malls, & intervention like high risk places, twengasing \\  & beaches, gardens and parks. & into restricted environments, lonely places. \\ \hline \multirow{3}{*}{Future Scope of mode} & Much more extensive research can & Can be paired with GSM or a camera facility \\  & enhance the mode to control all appliances & to send messages or capture images on \\  & (fans, computers, air conditioners). & detection of intervention. This area has wide \\ \hline \multirow{3}{*}{Future method of mode  selection} & Currently, in this proposed the mode is selected with a push button. In future, neural \\  & Currently, in this proposed the mode is expected with a push button. In future, neural \\ \cline{1-1}  & networks can be incorporated for switching between two modes which can be time-based \\ \cline{1-1}  & or event-based, making the system fully automatic. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of two modes.