esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations
\begin{table}
\begin{tabular}{c c c c} \hline
**USV** & **Sea State** & **Communication** & **Situational Awareness** \\ \hline Otter & 2 & MIMO radio, Wi-Fi, 4G & Camera, AIS Class B \\ \hline Mariner & 4 for survey, 6 for transit, 7 for survival \textless{}E (4G) & Camera, AIS class B, radar \\ \hline Mariner X & 4 for survey, 6 for transit, 7 for survival \textless{}E (4G) & Camera, AIS class B, radar \\ \hline Armada 8 & & \\ (A8) & & \\ (A78) & & \\ \hline Armada 78 & & \\ (A78) & & \\ \hline SEA-KIT X & 7 & \\ \hline Tupan & 5 & Wi-Fi, 5G, COFDM and satellite \\ \hline Shadow Fox & & IP mesh radio, UHF,VHF, satellite, 4G, \\  & & Wi-Fi \\ \hline C-Cat 3 & 2 & 100 mW OFDM IP mesh radio; tuneable RF channel bandwidths of 1.25 MHz to 10 MHz; 4G; Wi-Fi & 360-degree camera \\ \hline C-Worker 4 & 4 for operations, 5 for survival & 5 W COFDM IP mesh radio, tuneable RF channel bandwidths of 1.25 MHz to 10 MHz, 4G, Wi-Fi & 360-degree camera and one forward-facing thermal (IR) \\ \hline C-Worker 5 & 3 & 5 W COFDM IP mesh radio, tuneable RF channel bandwidths of 1.25 MHz to 10 MHz, 4G, Wi-Fi & 360-degree camera and one forward-facing thermal (IR) \\ \hline C-Worker 7 & 4 for operations, 5 for survival & 5 W COFDM IP mesh radio, tuneable RF channel bandwidths of 1.25 MHz to 10 MHz, 4G, Wi-Fi & 360-degree camera and six thermal (IR) cameras \\ \hline \end{tabular}
\end{table} 
Table 4: USVs’ Sea state, Communication, and Situational awareness technologies.
examined 21 players twice pre-season and four times in-season. Although both studies revealed significant differences in F\({}_{0}\) and P\({}_{\text{max}}\), the variations are not consistent across the period of the season [9; 15]. Additionally, the time elapsed between testing points--2 months for [PERSON] et al. (2020) [9] and 1-6 months (unspecified) for [PERSON] (2018) [15]--was excessive, which might inherently predispose to changes in the sprint mechanical properties. In that sense, frequent FV profile assessment may increase stress levels in elite teams with limited training time [16], so that new approaches are needed to monitor sprint performance in a more efficient way.  
Interestingly, [PERSON] et al. (2021) [16] proposed the acceleration-speed (AS) profile, obtained from global positioning system (GPS) devices that players wear during training and matches. Note that GPS data has proven valid and reliable for time-motion analysis [17; 18; 19; 20]. To highlight, the AS profile allows coaches to monitor individual acceleration capacity daily without requiring players' implications. This new method is summarized through the A\({}_{0}\): theoretical maximal acceleration (y-intercept of the AS linear relationship) and S\({}_{0}\): theoretical maximum speed (x-intercept of the AS linear relationship). In addition to the results presented by [PERSON] et al. (2021) [16] gathered only from training sessions (6.55; 8.43 m/s\({}^{2}\) and 8.41; 10.3 m/s range values for A\({}_{0}\) and S\({}_{0}\), respectively), recently, the data for different match days of a microcycle has also been described [21]. However, since these previous studies only gathered session data for the first period of season without showing individual player data, it is still unknown whether the A\({}_{0}\) and S\({}_{0}\) vary throughout the season and if there are differences between players.  
Therefore, the objectives of this study were (i) to describe the average AS profile of elite soccer players during match season, (ii) to analyse team and individual seasonal changes, and (iii) explore intra-player differences in AS profile values over the five analysed periods (pre-season 1, in-season 1, in-season 2, pre-season 2 and in-season 3).
## 3 Results and Discussion  
### RUL Estimation Using Paris-Paris Model  
The prediction results for the delamination growth area using the particle filtering approach are presented here. All three delamination datasets shown in Figure 1 were analyzed, but for the sake of brevity, we have discussed the prediction results of delamination dataset 1 here.  
The prediction algorithm starts when there is a minimum of two measurement data available. For simplicity and coverage of wider range of scenarios, the prediction results at impact energies of 5 J, 25 J, and 50 J for Paris model is shown in Figure 3. The number of measurement data available at these impact energies are 2, 6, and 9, respectively. Similarly, the results for Paris-Paris model are shown in Figure 4.  
Figure 2: The proposed online prognostic framework using particle filter algorithm.  
Figure 3: The prediction results for delamination 1 of sample S1 using the **Paris** model with failure threshold assumed to be at 1200 mm\({}^{2}\). The grey lines represent the particle trajectories for 1000 particles in the particle filter framework.  
For both the models, the prediction results at impact energy of 5 J when only two measurement data are available is poor and the damage propagation model completely predicts the damage propagation model.  
Figure 4: The prediction results for delamination 1 of sample S1 using the **Paris–Paris** model with failure threshold assumed to be at 1200 mm\({}^{2}\). The grey lines represent the particle trajectories for 1000 particles in the particle filter framework.  
fails to follow the actual degradation trend. At 25 J, the prediction algorithm detects the jump energy based on the slope difference between current and the previous prediction instants. The results in Figure 3b clearly show that the prediction results for the Paris model keep exponentially increasing and start to diverge away from the actual data. Even when a greater number of measurement data are available for prediction at 50 J, the Paris model fails to learn the degradation behavior. On the other hand, the results shown in Figure 4b clearly show that the Paris-Paris model starts to follow the actual degradation trend using the Paris law parameters \(\{m_{2},C_{2}\}\) for Region 2 at 25 J and 50 J.  
The accuracy of the predictions was evaluated by computing the root mean square error (RMSE) values for both the models for each prediction instants as per the following Equation (14):  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{(a_{i}-\hat{a}_{i})^{2}}} \tag{14}\]  
where \(n\) is the total number of observations. The RMSE value for the predictions for several delamination instants (including 5 J, 25 J, and 50 J) are plotted in Figure 5 (for three different delamination data sets). The results clearly show that in the case of the Paris-Paris model, the RMSE values drop drastically after the algorithm detects the jump energy at 25 J. These results clearly justify the need for using an optimized damage propagation model over the conventional Paris model.  
Figure 5: _Cont._
## 6 Discussion  
The installation of the new equipment was completed at the end of November 2017. The subsequent commissioning and optimization phase lasted about a year. During this phase, the main task was to adjust the response velocity of the hydraulic equipment, in particular the relief valves. The dynamics of the process phenomena leading to overpressure in the upper part of the BF are very rapid. As a result:  
* A too slow valve response would have led to valve sets too close to normal operating pressure, resulting in an unbearably high number of activations;
* A too fast valve response would have led to premature wear of the mechanical and hydraulic components of the system.  
The compromise found allowed to minimize the intervention of the BF top bleeders, as can be seen in Figure 21. After the installation of the new equipment, the total opening time of the BF top bleeders per year has been reduced by more than 50%, and after the tuning of the equipment in the field, by more than 96%. The result is even more striking if one compares it with the production trend, which in the same period has instead been growing. This testifies to the fact that the good functioning of the system has given the operators the necessary confidence to increase the production level, ensuring nonetheless a minimization of the bleeder valves openings and the associated emissions.  
Even in the final part of 2019, the total opening time of the dirty bleeder proved negligible despite the low productivity, considering the need to empty the fossil and mineral parks because of the expected shutdown of the hot area plants, with a consequent deterioration in the quality of the BF charging materials.  
Figure 20: Evolution over time of pressure in the circuit with by-pass opening at 850 mmWC without Clean Relief Valve and C3 compared with the no bleeders case in a high severity event. (**A**) BF top pressure; (**B**) cyclones outlet pressure; (**C**) network pressure.
# Mutual Information, the Linear Prediction Model, and CELP Voice Codes
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AR & Autoregressive \\ ADPCM & Adaptive differential pulse code modulation \\ AMR & Adaptive multirate \\ AbS & Analysis-by-synthesis \\ CELP & Code-excited linear prediction \\ CNG & Comfort noise generation \\ DPCM & Differential pulse code modulation \\ EVS & Enhanced voice services \\ MOS & Mean opinion score \\ MSPE & Mean squared prediction error \\ MMSPE & Minimum mean squared prediction error \\ MMSE & Minimum mean squared error \\ SNR & Signal to quantization noise ratio \\ SPER & Signal to prediction error ratio \\ VoIP & Voice over Internet protocol \\ \end{tabular}
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 3 System Model
### Routing Decisions
By effectively utilizing the energy factor, it ensures that data can be forwarded to the next hop by selecting nodes with sufficient energy and higher reliability, thereby optimizing the energy utilization efficiency of the network and prolonging the overall lifespan of the network. The energy factor is defined as follows:  
\[E_{(i)N}=\frac{E_{(i)R}}{E_{(i)I}}-kd_{i,j}{}^{2} \tag{17}\]  
where \(E_{(i)R}\) represents the remaining energy of node \(i\), with higher values indicating greater stability between the two nodes. \(E_{(i)N}\) is the cost function for the remaining energy, and \(E_{(i)I}\) denotes the initial energy of node \(i\). Since all nodes have the same initial energy level, a larger \(E_{(i)N}\) leads to relatively lower energy consumption for the node. Calculate the distance factor. To accurately assess the distance between neighbor nodes and the target node, this paper introduces the concept of a distance factor. This factor comprehensively considers the relative position of neighbor nodes to the target node and the distance between this node and the target node. The calculation of the distance factor is as follows:  
\[DF_{i}(j,d)= \begin{cases}\cos\theta\times e^{\frac{D(i,d)-D(j,d)}{R}}-1,&d_{ ij}<R\\ 0,&otherwise\end{cases}  \tag{18}\]  
where \(i\), \(j\), and \(d\) represent the predicted positions of the current node, neighbor node, and destination node, respectively. \(R\) denotes the transmission range of the current node, while \(\theta\) represents the angle between the current node, destination node, and neighbor node. \(d_{ij}\) represents the Euclidean distance between the predicted positions of the two nodes. The consideration of distance factor ensures that the routing transmission quickly approaches the target node. Hello packet. Nodes use the extended Kalman filter algorithm to predict their movement trajectories, and dynamically adjust the sending interval of Hello packets based on the predicted results. In addition, nodes can also predict the moment when a neighboring node exceeds the communication range. Additionally, the algorithm dynamically adjusts the interval for sending Hello packets according to Equation (13), enabling timely discovery of new neighbors and real-time update of the neighbor table. The algorithm utilizes Q-learning for routing decisions according to the neighbor list is shown in Algorithm 2. Considering the link stability, energy consumption, and distance to the destination, a reward function is constructed based on these three metrics to determine candidate forwarding nodes. Adaptive learning parameters are built based on node stability and mobility, dynamically adjusting the learning rate according to link stability and adjusting the discount factor based on node mobility. Through Equation (22), the Q-value function is updated, and the node with the highest Q-value is selected as the forwarding node, ensuring efficient and reliable data transmission.
# NN-Based 8 FSK Demodulator for the Covert Channel
## 4 NN-Based 8 FSK Demodulator and Its Evaluation
### NN-Based 8 FSK Demodulator  
The block diagram of the receiver with the NN-based FSK demodulator is presented in Figure 12, with the NN architecture in Figure 13. The NN-based covert channel demodulator is a result of searching for the optimal solution. Originally, it was assumed that NN can perform SIC operation and demodulation simultaneously. However, NN was not able to learn, due to the fact that the covert information contained in far decimal places was lost by it. Finally, the input vector is composed of signals \(\hat{s}_{1}\) (signal after remodulation process, i.e., demapping) and \(\hat{s}_{2}\).  
It was assumed that every symbol of the 8 FSK consists of 16 complex samples (16 real and 16 imaginary). Accordingly, for the input of the neural network (Figure 13), a vector of length 64 is supplied. There are three hidden layers with a sigmoid activation function. The output layer consists of three softmax activation functions, one for each bit. In the simplest case, such a neural network, it operates as a classifier that classifies the received signal into the corresponding transmitting bit combination. To train the NN, we use hard bit information as labels. The cross-entropy was used as the loss function during the training. The best performance of the model was achieved for the batch size, in each training round set to 256 and epochs to 1000. The input set consisted of 100,000 samples, where 80% of samples were used to train NN, with 20% for validation.  
As mentioned before, the presented NN can work as both hard- and soft-decision demodulators. The hard-decision is based on the selection of the softmax output with a higher value. The decision is made for every 3 bits (data symbol). In the case of soft demodulation, we utilize the property, and that sigmoid output represents the probability  
Figure 12: Block diagram of the receiver structure with the NN-based 8 FSK hard and soft demodulator.  
Figure 13: Deep Neural Network (DNN) architecture for covert channel demodulation.  
that the bit has a certain value, 0 or 1. For every two outputs, for every softmax function (out\({}_{1,N}\) and out\({}_{2,N}\), \(N=1,\ 2,\ 3\)), the relation can be expressed as: out\({}_{1,N}=1-\text{out}_{2,N}\). Thus, the soft-decision metrics are given as [9], as shown in Equation (12):  
\[\xi_{j}=log\frac{out_{1,N}}{out_{2,N}}=logit(out_{1,N}),\ \ N=1,2,3 \tag{12}\]
## 2 Methodology
### Evaluation Indicators  
This paper employed the mean absolute error (MAE) and R-squared score (R\({}^{2}\)) to assess the performance of porosity and permeability model based on XGBoost algorithm. The objective was to provide guidance on the selection of the combination of feature variables and the hyper-parameter. Additionally, the impact of the K-means method on the XGBoost porosity and permeability model was evaluated.  
The mean absolute error (MAE): The average value of the absolute difference between the predicted value and the true value is calculated as shown in Equation (4). This metric measures the average difference between the predicted value and the true value, with smaller values indicating a more accurate prediction.  
\[MAE=\frac{\sum_{i=1}^{n}\lvert\hat{y}_{i}-y_{i}\rvert}{n} \tag{4}\]  
Figure 4: Plot of [PERSON]’s correlation coefficient of feature variables.  
Figure 3: Normalized distribution of feature variables and target labels.  
The R-squared score (R\({}^{2}\)) is also known as the coefficient of determination and is used to measure the extent to which a model explains the variability of the dependent variable, as shown in Equation (5). R\({}^{2}\) take values between 0 and 1. The closer R\({}^{2}\) is to 1, the better the fit between the predicted and true values, and the closer R\({}^{2}\) is to 0, the worse the fit.  
\[R^{2}=1-\frac{\sum(\hat{y}_{i}-\overline{y}_{i})^{2}}{\sum(y_{i}-\overline{y} _{i})^{2}} \tag{5}\]  
In the above equation, the variables are defined as follows:  
\(y_{i}\) is actual data value.  
\(\overline{y}_{i}\) is actual data average value.  
\(\hat{y}_{i}\) is regression prediction value.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 4 Theoretical Framework  
Generally, SPs are excited by the photons in SPR-based optical sensors. SPs are optically excited when the wave vector of the electromagnetic wave matches that of SPs, which is known as the phase-matching condition or resonance condition. Next, we discuss the different excitation methods of SPs with the prism, grating, and waveguide.
## 3 Results and Discussion
### Numerical Simulations
The maximum displacement is 6.42 mm, defined by the SMA spring moving upward with the rod and pintle to regain its initial position after compressing the bias spring. There is a difference of 0.06 mm between the closing and opening displacements from the residual energy of the SMA spring after mechanical force application. The coils' axial pitches of the bias spring were changed, which caused the bias spring to be recorded as the highly displaced part of the actuator. The moving parts, like the rod and pintle in this position, move back to their initial positions. During the design, the desired actuator's stroke was 6 mm. The decimal values outside of the desired stroke are the operation tolerance. The nature of each analysis method causes apparent differences between numerical and analytical results [56; 57]. In the analytical analysis, the solutions were exact based on the inputs we inserted into the equations for forming and material properties. However, numerical analysis provides approximate solutions based on the inputs, material properties assigned, and others already in COMSOL Multiphysics, as well as a nonlinear solver with high computing capability.
## 4 Discussion
### The Change Pattern of SIF in Growing Season  
In previous studies, the SIF data based on satellites and UAV platforms only had the instantaneous data when the sensor crossed and not consequent on a daily scale and beyond. There is a lack of understanding of SIF variations on a smaller timescale. This research is based on the continuous ground observation data during the growing season and has a more detailed understanding of the change pattern in SIF of the cork oak canopy under different weather conditions. During the study period, the canopy SIF showed different diurnal patterns but showed an accordant trend for incident radiation during sunny and cloudy days. PAR was the main driver of the change of SIF under different sky conditions [53]. This result is consistent with research conducted on a rice paddy, which found that when compared with GPP, SIF has a stronger correlation with absorbed light [19]. Our results show that SIF and GPP fluctuated greatly seasonally and were significantly affected by environmental conditions. Therefore, fluorescence measurements performed only on sunny days will overestimate the seasonal mean SIF, especially in seasons with abundant cloudy and rainy days. The change of SIF on the seasonal scale is consistent with the change of PAR, we concluded that SIF is not only driven by incident radiation on the daily scale, but also affected by this factor on the seasonal scale, as found in previous studies [54; 55]. The key reason contributing to the variation of SIF related to absorbed light lies in the distribution ratio of light energy, which is affected by the leaf biochemical components and the intensity of incident radiation in the environment. SIF is an important dissipation pathway for absorbed light and also a protective pathway for the photoreaction center when absorbed light is excessive [5]. Therefore, SIF continues to increase with the increase of PAR during the growing season and no midday depression or saturation in SIF were found in this study.  
Despite PAR being the prominent factor in SIF variations, there is a slight discrepancy in the response of SIF to PAR in the morning and afternoon. The regression fit between SIF and PAR had different slopes in the morning and afternoon, the apparent SIF\({}_{\text{yield}}\) (SIF/PAR) in the morning is higher than in the afternoon under the same light levels, which was consistent with previous studies on maize [21] and wheat [56]. This may be due to the fact that the canopy escape coefficient is higher in the afternoon when PAR is at the same level, which is influenced by canopy structure, tree species, and moisture status [49].
## 4 Performance Evaluation
### Confusion Matrix for Mobility Pattern Classification  
In this section, we evaluate the accuracy of the classification scheme based on the decision tree using a confusion matrix. The Figure 12 illustrates the confusion matrix obtained when 10 samples each of linear, non-linear, and random trajectories are tested on the trained classifier. This result serves as an initial validation to demonstrate the classifier's behavior under controlled conditions and is not intended to represent a comprehensive evaluation. The horizontal axis represents the predicted labels, while the vertical axis indicates the actual labels. Test results show that the decision tree correctly classified all linear and non-linear trajectories. For random trajectories, nine out of ten were correctly identified, with one misclassified as non-linear. Despite this minor error in classifying random trajectories, the scheme demonstrates strong overall classification performance.  
Figure 12: The examples of confusion matrix with initial validation results with 10 samples per class.
## 2 Reliable, Low-Latency Fronthaul Model
In this experimental, function blocks are sequentially processed on a general purpose processor;
* Include switch queueing delay: the amount of time spent queuing at the switch aggregator to demonstrate the feasibility of aggregating traffic in the fronthaul network, resulting in more realistic latency measurements. The worst case scenario happens when all the \(n\) encoded blocks arrive at the switch aggregator at the same time. Such a scenario is very likely to occur when the \(n\) encoded blocks are serialised at the same time on \(n\) separate fronthaul paths with the same capacity;
* Include network driver delay: the amount of time it takes to send a packet onto a fronthaul network, which is subject to a queueing delay;
* Emulate traffic congestion on the fronthaul to account for scenarios with background traffic. For the analysis, we continuously generate data in the (CU + DU) entity to be transmitted to the user equipment via the fronthaul. The experiment is repeated several times, then the average latency and the probability of being able to transmit a packet over the fronthaul within a latency deadline are measured.