esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks
## 4 Applications of RL in FANET
### Routing Protocol  
We discuss the basics of the routing protocol, the RL-based approaches for solving routing protocol problems such as energy consumption, end-to-end delay, and path stability and we present a comparative analysis among them. The routing protocol specifies how one node communicates with other nodes in a wireless ad-hoc network [12]. Figure 10 illustrates two possible routing paths from source to destination in multi-UAV FANET. The main goal of the routing protocol is to direct the traffic toward the destination regardless of the node mobility [41]. There are no dedicated routing protocols currently available for FANETs [41]. FANET still uses conventional routing protocols used in mobile ad-hoc network (MANET) an VANET. There are different types of conventional routing protocols [42], given as follows:  
* **Proactive routing:** Like wired network routing, all nodes in an ad-hoc network maintain a route table consisting of routes to other nodes. Whenever a node transmits data, a route table is used to determine the route to the destination. The route table continues to be updated to maintain the change in topology. This type of routing  
Figure 9: A taxonomy of the applications of RL in FANET.  
protocol is unsuitable for FANETs owing to the frequent high-speed mobility of the nodes [43].
* **Reactive routing:** Whenever a node initiates communication, this type of routing protocol starts discovering routes to the destination. Predefined routing tables were not maintained in this protocol. These types of routing protocols are known as on-demand routing protocols. The main drawbacks of this protocol in terms of FANETs are poor stability, high delay, high energy consumption, and low security [44].
* **Hybrid routing:** This is a combination of and a trade-off between proactive and reactive routing protocols. In this protocol, nodes maintain a route table consisting of routers to their neighbors and start route discovery whenever the nodes try to communicate the nodes beyond their neighbors. [45].
* **Others:** In addition to the conventional routing protocols, different types of routing protocols, such as energy-based routing, heterogeneous-based routing, swarm-based routing, and hierarchical routing, etc., have been established by different researchers in the past [27; 46].  
Owing to the complex flying environment and high mobility, UAV nodes are unpredictable [47]. Hence, conventional protocols of VANETs and MANETs cannot cope with changes in the network in real time. Therefore, many researchers have attempted to develop a self-learning, highly reliable, adaptive, and autonomous routing protocol using reinforcement learning (RL) [48]. The main purpose of using RL in FANET routing is to ensure fast and stable routing with minimum energy consumption.
# Design of Tunable Broadband Graphene-Based Metasurface with Amplitude-Phase Modulation
## 5 Performance Comparison  
Table 4 shows the comparison between the metasurface proposed in this manuscript and other metasurfaces. The findings indicate that phase coding constitutes the bulk of the metasurface coding methods. Furthermore, the majority of metasurfaces that utilize graphene to accomplish beam refactoring operate in terahertz (THz). In this work, graphene was applied in millimeter-wave frequency bands to achieve the design of a broadband reflection amplitude-phase encoding metasurface. Additionally, the proposed metasurface has the capacity to execute beam steering and multi-beam switching concurrently.
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 4 Design of Pido
### Performance Evaluation of Pido
#### 4.4.3 Performance Metrics and Simulation Parameters  
To evaluate the performance of Pido, we define the performance metrics as follows:  
**Delay:** The goal of Pido is to select a path with minimal end-to-end delay. During the evaluation part, we calculate the delay in cycles, which is measured from the time of packet generation until it is received by the sink. In the simulation, the system cycle is 1 min.  
**Energy Consumption:** The energy consumption of nodes includes the following: (a) energy consumption during the sleep mode; (b) energy consumption on activating the MCU, sensing the environment during the active mode; and (c) transmitting packets with neighbors during the active mode. Since nodes in IT-WSN work in duty cycle synchronously, we assume that the first two parts of energy consumption are the same for CTP, ORW and Pido. To compare the energy consumption, we focus on measuring the energy for data transmission. We take the average transmission times of a packet during the simulation period as an alternative for energy consumption.
# Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link
## 5 Distributed Orbit Determination
### Balanced Extended Kalman Filter
The BEKF method modifies the denominator of the gain matrix \(\mathbf{K}_{k}^{ij}\) to \(\mathbf{H}_{k}^{i}\,\overline{\mathbf{P}}_{k}^{i}\mathbf{H}_{k}^{\,\mathrm{T}}+\mathbf{H}_{k }^{i}\overline{\mathbf{P}}_{k}^{i}\mathbf{H}_{k}^{\,\mathrm{T}}+\mathbf{R}_{k}^{i}\), which is similar to the method of IMEKF. Furthermore, it modifies the gain matrix by a factor of \((I-M_{C})\). Therefore, the BEKF algorithm is expected to yield results with higher precision. 2. It seems that BEKF requires more ISL processes than the other EKFs. In fact, the state vectors and their covariance matrices on both ends are improved at the same time. It is unnecessary to repeat the ISL process for the same two satellites. The computation load of BEKF is similar to that of IMEKF. 3. The iteration process that is implemented in the ICEFK algorithm is not required in the BEKF method to achieve high accuracy. 4. The improving state vectors are balanced in such a way that the satellite with lower-state precision will undergo more increments in accuracy while the satellite with higher-state precision will have fewer adjustments. 5. Compared to the other EKFs, in Equation (64), \(M_{C}\biggl{[} \begin{array}{cc}\overline{\mathbf{P}}_{k}^{i}&0\\ 0&\overline{\mathbf{P}}_{k}^{j}\end{array} \biggr{]}\) is subtracted from the state vectors' covariance matrices of the two satellites. Therefore, the values in the matrices are reduced and the accuracy of the state vectors is improved. 6. The two satellites are correlated by \(\mathbf{P}_{k}^{ij}\) and \(\mathbf{P}_{k}^{ji}\) in Equation (65); however, these two matrices have to be ignored in this distributed filter. As a result, the current method should be categorized as a reduced-order sub-optimal orbit determination method.
## 2 Materials and Methods
### Application of the Attended Encoder-Decoder to the Short-Term Load Forecasting  
#### 2.4.1 Training Data  
As mentioned in Section 2.1, the training data consists not of \(n\) recent days like it is established through the sliding window approach. It contains \(n\) most similar days to the day under forecast. The similarity of two days \(day_{i}\) and \(day_{j}\) is expressed by the Equation (13) and it considers the weighted Euclidean distance \(d(day_{i},day_{j})\) between the features measured at that days. Most similar days have the smallest distance between the respective features.  
\[d(day_{i},day_{j})=\sqrt{\sum_{k=0}^{N}w_{k}\left(f_{i,k}-f_{j,k}\right)^{2}} \tag{13}\]  
The weight allows for larger differences in features that are wider spread and for smaller differences in those which are contained within a narrow interval.  
\[w_{k}=\frac{1}{|f_{k}^{max}-f_{k}^{min}|} \tag{14}\]  
The feature set used for filtering of the most similar days consists of:  
* daily minimum ambient temperature and wet bulb
* daily maximum ambient temperature and wet bulb
* daily minimum next day ambient temperature and wet bulb
* load power one hour before the intended forecast start before forecast start
* the type of the day (working day, weekend or holiday)
* the length of the day
* the type of the day concerning the ambient temperature (hot, cold, regular day)  
To predict the hourly load curve for the next day, data of 96 most similar training days is used.
# Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques
###### Abstract  
The Internet of Things (IoT) has gained an incredible importance in the communication and networking industry due to its innovative solutions and advantages in diverse domains. The IoT network is a network of smart physical objects: devices, vehicles, buildings, etc. The IoT has a number of applications ranging from smart home, smart surveillance to smart healthcare systems. Since IoT consists of various heterogeneous devices that exhibit different traffic patterns and expect different quality of service (QoS) in terms of data rate, bit error rate and the stability index of the channel, therefore, in this paper, we formulated an optimization problem to assign channels to heterogeneous IoT devices within a smart building for the provisioning of their desired QoS. To solve this problem, a novel particle swarm optimization-based algorithm is proposed. Then, exhaustive simulations are carried out to evaluate the performance of the proposed algorithm. Simulation results demonstrate the supremacy of our proposed algorithm over the existing ones in terms of throughput, bit error rate and the stability index of the channel.  
Internet of Things; cognitive radio networks; channel scheduling; quality of service; smart building +
Footnote †: journal: Sensors
# Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles
###### Abstract  
Reliable aerodynamic performance estimation is essential for both preliminary design and optimization in various aeronautical applications. In this study, a hybrid deep learning model is proposed, combining convolutional neural networks (CNNs) and operating directly on raw airfoil geometry, with parallel branches of fully connected deep neural networks (DNNs) that process operational parameters and engineered features. The model is trained on an extensive database of NACA four-digit airfoils, covering angles of attack ranging from \(-5^{\circ}\) to \(14^{\circ}\) and ten Reynolds numbers increasing in steps of 500,000 from 500,000 up to 5,000,000. As a novel contribution, this work investigates the impact of random seed initialization on model accuracy and reproducibility and introduces a seed-based ensemble strategy to enhance generalization. The best-performing single-seed model tested (seed 0) achieves a mean absolute percentage error (MAPE) of 1.1% with an R\({}^{2}\) of 0.9998 for the lift coefficient prediction and 0.57% with an R\({}^{2}\) of 0.9954 for the drag coefficient prediction. In comparison, the best ensemble model tested (seeds 610, 987, and 75025) achieves a lift coefficient MAPE of 1.43%, corresponding to R\({}^{2}\) 0.9999, and a drag coefficient MAPE of 1.19%, corresponding to R\({}^{2}\) = 0.9968. All the tested seed dependencies in this paper (ten single seeds and five ensembles) demonstrate an overall R\({}^{2}\) greater than 0.97, which reflects the model architecture's strong foundation. The novelty of this study lies in the demonstration that the same machine learning model, trained on identical data and architecture, can exhibit up to 250% variation in prediction error solely due to differences in random seed selection. This finding highlights the often-overlooked impact of seed initialization on model performance and highlights the necessity of treating seed choice as an active design parameter in ML aerodynamic predictions.  
aerodynamic prediction; NACA airfoils; hybrid deep learning; ensemble learning; random seed sensitivity; convolutional neural network; deep neural network
# QPSK MMW Wireless Communication System Based On p-i-n InGaAs Photomixer
## 4 Experimental Results and Discussion
Figure 6: Real-time received QPSK signal reconstruction using VSA code for transmitted 3.5 Vpp PRBS signal. (**a**) IQ constellation and (**b**) eye pattern. The generation of the carrier wave and detection methods for the realization of MMW wireless communications of the reported works in Table 2 is done using different approaches. In the W-band, at 87.5 GHz and 100 GHz, experimental demonstration of wireless communication links was reported using photonic upconversion of a polarization multiplexed 16-QAM optical baseband signal and an optical wireless integration system delivering two-channel Polarization Division Multiplexing Quadrature Phase Shift Keying (PDM-QPSK), respectively [4,10]. Both of the experiments achieved close BER performance for 100 Gb/s data rate transmission. In the THz coherent receiver, where a phase-locking between the transmitter and the carrier wave is required, the homodyne configuration can be used. This configuration uses the same reference signal for the generation or detection of the THz modulated waves. Using this approach, a transceiver operating at 196 GHz carrier frequency based on split-block waveguide integrated microwave assemblies, with MMICs realized using advanced InP HEMTs with sub-50 nm gate lengths [17]. In this case, an EVM of below 10% rms achieved for a transmission distance of 0.5 m. The reported works in [18,19] used a photonic 1.55 um approach based on UTC-PD, driven by standard laser lines, and dedicated dual-mode lasers. The 1.55 um photomixer composed of a UTC-PD monolithically integrated with a wide bandwidth antenna was used for the generation of 200 GHz carrier frequency with a receiver based on a heterodyne mixing and amplitude modulation detector achieved 1 Gb/s data rate for 2.6 m free-space transmissions [20]. Due to the lack of fast real-time demodulators, as the data rate increased, most reported works used off-line demodulation processes. Compared to the reported methods in Table 2, using an InGaAs photomixer module for the generation of the carrier wave and heterodyne mixing at the receiver with a real-time DSP gives a better BER value for 100 Mb/s data rate transmission at a distance of 0.5 m. In contrast to other works, where dedicated laser sources, demodulation methods, and carrier wave generators are required, this work achieved better performance based on standard telecom and customer available components.
## 4 Numerical Examples
In the case of the elliptical anomaly \(w\) and the semifocal anomaly \(\Psi\), delta takes the values \(0.5\) and \(1\), respectively. It appears that the precision in position and speed improves with respect to the eccentric anomaly \(g\), which corresponds to a greater concentration of points in the apsidal regions, with the values being better in the case of \(w\). It can be understood that for large values of \(\delta\), too many points have been removed over the region corresponding to the ellipse intersection with its semiminor axis, causing a certain loss of precision.
# Low-Cost, Distributed Environmental Monitors for Factory Worker Health
## 3 Results and Discussion  
### Particulate Sensors  
A scatterplot of the average Sharp DN outputs relative to the pDR sensor, as observed during the salt experiment, is shown in Figure 5. Each point in the scatterplot represents the average value reported by the 30 sensors. The \(y\)-axis error bars represent the standard deviation between sensors.  
The R\({}^{2}\) of the PM sensors ranged between 0.98 and 0.99. The fit slopes varied between 1.0 and 2.4 mV/ug/m\({}^{3}\). The fit intercepts varied between 662 and 1778 mV.  
Wide standard deviations between the monitors was insufficient to discriminate between practically important differences in particle mass concentration. However, by individually calibrating each sensor to the reference instrument, it was possible to adequately discriminate among substantially different levels of particulate mass density [37].
# Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection
## 3 Materials and Methods
### Data Processing
#### 3.2.2 Wavelet Transform Modulus Maxima Image  
After pre-processing, the image is enhanced. The next step will be edge detection using the wavelet transform modulo maxima method. In the process, a convolution kernel, i.e., a wavelet function, is firstly generated from the image and the constructed two-dimensional Gaussian function. Then, the generated convolution kernel is used to convolve with the image to obtain the detail information, during which the continuous wavelet transform (CWT) is carried out, as shown in Figure 6. Based on the wavelet transform results, the modulus and amplitude angles of the image are calculated. The resulting magnitude angle is divided into four directions: horizontal, vertical, positive diagonal and negative diagonal. Each pixel point is examined in turn to determine whether it is an extreme value in the direction to which it belongs, and if so, the gradient value is recorded, and if not, the gradient value is set to zero. The maximum value of the gradient is found and the value is normalized. Compare the normalized gradient value for each pixel and consider it a true edge when the gradient value is greater than the threshold, otherwise it is considered a pseudo-edge.  
The results of the transformation of the image in both horizontal and vertical directions after the continuous wavelet transform have been shown in Figure 6. By taking the pixel values of the corresponding pixels of the two images, finding the sum of the squares of the two and performing a square root calculation, the overall modulus image can be obtained, as shown in Figure 7. The modulus image is further processed, i.e., the extreme value of the pixel in the current image is found and retained, and the result is shown in Figure 8.  
Figure 6: (**a**) Horizontal. (**b**) Vertical.  
The advantages of OTSU in calculating global thresholds are simple, fast and unaffected by brightness or contrast. The disadvantage is that it is sensitive to noise. It is not effective when the difference between the target and background areas is too large. With equal proportion of the target and background, modulus maxima images are suitable for the OTSU method to calculate thresholds. The result is 0.06.
# Development of a Device and Algorithm Research for Akhal-Teke Activity Level Analysis
## 4 Discussion  
### Device Positions and Frequencies  
Wearing the device on the horse's neck ensures it is easy to put on and can collect the horse's activity data. Despite potential shifts in the device's position due to neck movements, this study addresses these inaccuracies using combined acceleration data (Figure 5). This approach guarantees both high accuracy and reliability in the data gathered.  
In the experiment, a frequency of 2 Hz was adopted for the MPU6050. This is due to the algorithm being deployed directly onto wearable devices, with the core microcontroller STM32 tasked with data collection and executing the analysis algorithm to ascertain activity levels. Utilizing too high a frequency would exceed the microcontroller's processing capabilities, thus impacting the efficiency and accuracy of the analysis. To mitigate the risk of collecting and processing an excess of unnecessary data, a balanced frequency of 2 Hz was chosen, striking a compromise between data quality and quantity. This decision aims to minimize data overload, optimize processing efficiency, and reduce computation.  
Figure 12: Comparison results between actual and predicted activity durations.
# Evaluation of Thorax Diameter Changes through Trunk Morphology and Different Running Intensities
## 3 Results
### Anteriorogter Thorax Cross-Section  
The effect of the exercise intensity in the anteroposterior cross-section IP was significant (F2,32 = 52.340, \(p\) < 0.001, \(\eta^{2}\) = 0.469). The runners' thoracic expansion increased significantly when the HRR increased from 45% to 85% HRR (2.963%, \(p\) < 0.001) and from 70% to 85% HRR (2.268%, \(p\) < 0.001), reaching a large effect size. The morphology factor did not show a significant effect on the changes of the anteroposterior cross-section (F2,16 = 0.737, \(p\) = 0.494, \(\eta^{2}\) = 0.063) as there were no differences between morphology groups when comparing them in the same intensity (Table 4 and Figure S2).  
Interestingly, when pair comparisons were analysed group by group, some significant differences were found: subjects with a deeper thorax (group 3) had similar behaviour to the total group; however, the subjects of group 1 (flatter thorax) and group 2 experienced only a significant increase from 45% to 85% HRR (3.147%, \(p\) < 0.001 and 2.986%, \(p\) = 0.006, respectively).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{6}{c}{**Estimated Marginal Means—CTC * Morphology Group**} \\ \hline \hline \multirow{2}{*}{**Morphology Group**} & \multirow{2}{*}{**CTC**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**SE**} & \multicolumn{2}{c}{**95\% Confidence Interval**} \\ \cline{5-6}  & & & & **Lower** & **Upper** \\ \hline \multirow{3}{*}{1} & CTC\_45\% & 2.45 & 0.536 & 1.0703 & 3.83 \\  & CTC\_70\% & 2.22 & 0.9 & \(-\)0.0922 & 4.53 \\  & CTC\_85\% & 3.06 & 0.997 & 0.4994 & 5.62 \\ \hline \multirow{3}{*}{2} & CTC\_45\% & 2.27 & 0.438 & 1.1452 & 3.4 \\  & CTC\_70\% & 2.91 & 0.735 & 1.0172 & 4.79 \\  & CTC\_85\% & 3.24 & 0.814 & 1.1442 & 5.33 \\ \hline \multirow{3}{*}{3} & CTC\_45\% & 1.39 & 0.438 & 0.2649 & 2.52 \\  & CTC\_70\% & 1.91 & 0.735 & 0.0183 & 3.8 \\ \cline{1-1}  & CTC\_85\% & 2.52 & 0.814 & 0.4298 & 4.61 \\ \hline \multirow{3}{*}{Total} & CTC\_45\% & 1.98 & 0.287 & 1.31 & 2.66 \\ \cline{1-1}  & CTC\_70\% & 2.36 & 0.415 & 1.38 & 3.34 \\ \cline{1-1}  & CTC\_85\% & 2.92 & 0.438 & 1.89 & 3.96 \\ \hline \end{tabular} SE: standard error; CTC\_45\%: IP of craniocaudal thorax cross-section at a running intensity of 45% HRR; CTC _70\%: IP of craniocaudal thorax cross-section at a running intensity of 70% HRR; CTC _85\%: IP of craniocaudal thorax cross-section at a running intensity of 85% HRR. * Statistical difference between IP at 45% HRR and 70% HRR. # Statistical difference between IP at 45% HRR and 85% HRR. # Statistical difference between IP at 45% HRR and 85% HRR.  
\end{table}
Table 3: IP results of the craniocaudal thorax cross-section (CTC) among different exercise intensities and divided by morphology groups.
###### Abstract  
Hybrid satellite-terrestrial network access is gaining significant attention in the 5G ecosystem and is predicted to be an essential part of future 6G standards. Multipath support is required to utilize hybrid access features that are summarized as the ATSSS (Access Traffic Steering, Switching and Splitting) paradigm. Simulation frameworks can be very helpful to economically evaluate the performance of hybrid network access. In this paper, we propose a distributed AI (artificial intelligence)-driven simulation framework that can evaluate the performance of hybrid satellite-terrestrial network access. The AI part of the framework enables optimal session switching between satellite and terrestrial access networks based on SNR (Signal-to-Noise) predictions. The successful operation of the AI-based prediction model as well as the complete simulation framework is demonstrated in the UDP (User Datagram Protocol) streaming session scenario. The proposed framework is made to be flexible so it can be adjusted to various multipath scenarios of hybrid satellite-terrestrial network access.  
hybrid network access; LEO satellite; 5G; terrestrial communication; satellite communications; distributed simulation; embedded system; neural networks; SNR prediction; spectral efficiency