esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobile User Connectivity in Relay-Assisted Visible Light Communications
## 1 Introduction
However, none of the existing works reporting on the relay-assisted VLC systems have investigated the use of a mobile phone (MP) as a relay. In this paper, for the first time, to the best of authors' knowledge, we provide results for performance evaluation of a relay-based VLC system employing MP as an RN for miscellaneous configurations. We give distinctive statistics of AF and DF-based relays for ceiling mounted light sources via MP, taking into account MP node orientation and a range of channel parameters. It is very important in such cases to estimate the area where such a node can be searched for, which is fully dependent on the elevation and the azimuth of MP and the required BER or the allocated optical power level. All these aspects are studied in following sections. The rest of the paper is organized as follows. Section 2 discusses the indoor VLC channel model and the specific functionality of the MP for utilization in a relay-assisted system. Section 3 outlines a channel model for the VLC relay system and describes the cooperation techniques for the relay-assisted systems and provides analytical model for BER of AF and DF VLC. In Section 4 numerical results for the BER performance of the relay-assisted network with AF and DF modes are summarized. Finally, the summary and conclusions are given in Section 5.
# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 4 Simulation Results  
This section presents details of simulations carried out to demonstrate the effectiveness of the proposed methodology in comparison with the existing approaches. The proposed ensemble models were compared with two boosting classifiers: classical gradient boosting and extreme gradient boosting, which are further described in Section 4.3. Moreover, the performance of the ensemble classifiers was compared with the standalone LDA, SVM, \(k\)NN, and CNN models.
# LSTM Network for the Oxygen Concentration Modeling of a Wastewater Treatment Plant
## 4 Results
### LSTM Results  
The LSTM network proposed in this work presents an architecture composed by a single-layer LSTM network with 160 neurons, trained for 1000 epochs, using the Adam optimizer with learning rate \(\alpha=0.002\) and Mean Squared Error (MSE) loss function defined as:  
\[MSE=\frac{1}{S}\sum_{k=1}^{S}(y_{k}-y_{k})^{2} \tag{11}\]  
The values of the hyperparameters were obtained through an optimization procedure with KerasTuner [36], employing the RandomSearch algorithm, with the number of neurons spanning the range [32; 512] and the learning rate in the range [0.001; 0.01].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset Name** & \(\delta O\) & \(\delta q_{\textit{oss}}\) & \(\delta q_{\textit{in}}\) & \(\delta q_{\textit{ol}}\) \\ \hline Dataset1 & \(-0.0054\) (\(0.2724\)) & \(-0.4061\) (\(3355\)) & \(-0.5910\) (\(25.58\)) & \(0.1471\) (\(2.9851\)) \\ Dataset2 & \(-0.3214\) (\(0.1863\)) & \(24.0992\) (\(1608\)) & \(-0.7720\) (\(47.79\)) & \(-0.3733\) (\(4.5006\)) \\ Dataset3 & \(0.0618\) (\(0.2406\)) & \(-14.1781\) (\(3378\)) & \(-1.0119\) (\(49.85\)) & \(-1.1114\) (\(5.4282\)) \\ Dataset4 & \(0.2023\) (\(0.2633\)) & \(-25.0323\) (\(3423\)) & \(-1.1772\) (\(46.95\)) & \(-1.4870\) (\(4.7857\)) \\ Dataset5 & \(0.1414\) (\(0.3130\)) & \(-4.2068\) (\(4826\)) & \(0.8875\) (\(48.54\)) & \(0.8547\) (\(5.9122\)) \\ Dataset6 & \(-0.0841\) (\(0.2038\)) & \(19.3180\) (\(3739\)) & \(2.0735\) (\(85.26\)) & \(2.1169\) (\(6.7172\)) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of signals: mean (variance).  
The LSTM architecture proposed in this work has the final goal of being used for control purposes, so the minimization of complexity is one of the required features, and only a single-layer LSTM network is considered. The selected LSTM network has 160 neurons and a learning rate of 0.002.  
The training of the network is performed on a computer with an Intel i9-10920X CPU with 3.50 GHz, with a GPU NVIDIA GeForce RTX 2080 Ti graphics processing unit; it was written in Python 3.9, using TensorFlow [37], Keras API [38] and KerasTuner [36]. The results are quite satisfying, with \(FIT=60.56\%\), \(\rho=0.921\) and \(RMSE=0.206\) mg/L. An example of the prediction obtained with the LSTM network is reported in Figure 2, with a global overview in Figure 2a and a closer detail of the first 12 h in Figure 2b, to better emphasize the accuracy of the result. It can be observed that both the global dynamic of the signal and the oscillation frequencies are correctly reproduced, with an overall satisfactory outcome.  
Figure 2: Results of the LSTM model.  
Between hours 30 and 37, the performances are degradated because the LSTM model underestimates the real data. Additional information on the plant could explain the sources of uncertainties or highlight additional inputs that affect the system behavior.
# Structural Modal Time Domain Identification Method Based on the Bayesian Uncertain Quantification
## 3 Bayesian Modal Identification Based on Modal State Space Prediction Models
### Uncertainty Analysis of Modal Parameters
In addition, time domain signals with a high signal-to-noise ratio can be obtained by optimizing excitation methods or using high-sensitivity sensors to improve the reliability of the identification results.
# Short-Run Contexts and Imperfect Testing for Continuous Sampling Plans
## 4 Navy Applications
### Imperfect Testing  
This next example uses the same input values as the Perfect Testing example; however, the testing is imperfect with \(\theta\) = 0.8, meaning that defective items are correctly identified 80% of the time.  
For Plan 1, the AOQ after CSP-1 is 1.07% and the APS is equal to 58.15%. In Plan 2, the AOQ is 1.52% and the APS is 29.69%. In comparison to the perfect testing example, the AOQ is higher and the APS is lower. AOQ increases because defective items can escape the test procedure and end up in the batch that is delivered to the customers. APS decreases because some defective items are counted as non-defective, making it easier to switch to reduced inspection and therefore inspect less items.  
Figure 4 below illustrates the AOQ versus Initial Defective Rate and the APS versus Initial Defective Rate for both plans while varying the number of failures \(F\) from 0 to 320. In this case, there is no mound shape and that is a consequence of imperfect testing. If \(\theta\geq 0.82\), the mound shape returns. With imperfect testing, AOQ starts at 0 and increases to \(1-\theta\), with no guarantee that there is going to be a mound shape. The upper limit on AOQ of \(1-\theta\) can be explained by considering what happens when the initial defect rate is large. In that case, it becomes increasingly difficult to move from 100% Inspection to reduced inspection, and under 100% Inspection the fraction of failed items that go undetected will be equal to the probability that the test fails to detect them, namely \(1-\theta\).  
Figure 4: AOQ and APS for Navy sampling plans with imperfect testing (top row is Plan 1 and bottom row is Plan 2). The top-left shows AOQ versus Initial Defective Rate for Plan 1 with imperfect testing; the top-right shows APS versus Initial Defective Rate for Plan 1 with imperfect testing; the bottom-left shows AOQ versus Initial Defective Rate for Plan 2 with imperfect testing; the bottom-right shows APS versus Initial Defective Rate for Plan 2 with imperfect testing.
# Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses
## 2 Data and Methods
### Methodology
#### 2.3.3 Analysis of Spatial and Temporal Trends in Deciduous Forest Phenology  
In this study, the Theil--Sen median (TSM, equivalent to Sen's slope) method was applied to reveal the dynamics of phenological change in mid- to high-latitude deciduous forests in the Northern Hemisphere. The TSM method is suitable for this type of analysis due to its robustness, making it an effective nonparametric tool for trend assessment of long-term series data with a high tolerance for measurement error and outliers [57; 58]. To assess the statistical significance of these trends, we coupled TSM with the Mann-Kendall (MK) test (\(p<0.05\)). The MK test evaluates monotonic trend significance without assuming linearity and is resilient to missing data [59]. The TSM estimation method of SOS/EOS is shown in Equation (6). Here, SOS and EOS denote the start and end of the growing season, respectively. Both are estimated independently using the following equation:  
\[\mathrm{S_{SOS/EOS}}=\mathrm{Median}\bigg{(}\frac{\mathrm{SOS/EOS_{i}-SOS/EOS_{ j}}}{\mathrm{j-i}}\bigg{)},2008\leq\mathrm{i<j\leq 2017} \tag{6}\]  
To verify whether the observed trends in phenology were statistically significant, we further employed the -Mann-Kendall (MK) test in our analysis.  
\[Z=\left\{\begin{aligned} &\frac{\mathrm{S-1}}{\sqrt{\mathrm{s(S)}}},\\ & 0\\ &\frac{\mathrm{S+1}}{\sqrt{\mathrm{s(S)}}}\end{aligned},\mathrm{S}>0, \mathrm{S}=\sum_{\mathrm{j=1}}^{n-1}\sum_{\mathrm{i=j+1}}^{n}\mathrm{sgn} \big{(}\mathrm{SOS/EOS_{j}-SOS/EOS_{i}}\big{)} \tag{7}\]  
\[\mathrm{Sgn}\bigg{(}\frac{\mathrm{SOS}}{\mathrm{EOS_{j}}}-\frac{\mathrm{SOS}}{ \mathrm{EOS_{i}}}\bigg{)}=\left\{\begin{aligned} & 1,\frac{\mathrm{SOS}}{ \mathrm{EOS_{j}}}-\frac{\mathrm{SOS}}{\mathrm{EOS_{i}}}>0\\ & 0,\frac{\mathrm{SOS}}{\mathrm{EOS_{j}}}-\frac{\mathrm{SOS}}{ \mathrm{EOS_{i}}}=0\\ &-1,\frac{\mathrm{SOS}}{\mathrm{EOS_{j}}}-\frac{\mathrm{SOS}}{ \mathrm{EOS_{i}}}<0\end{aligned}\right.,\mathrm{s(S)}=\frac{n(n-1)(2n+5)} {18} \tag{8}\]  
In Equations (7) and (8), SOS/EOS\({}_{i}\) and SOS/EOS\({}_{j}\) denote SOS/EOS for year i and year j, respectively; the function \"sgn\" refers to the sign function; while n refers to the span of the time series. The statistic \(Z\) can take values from \((-\infty,+\infty)\). For a specified significance level \(\alpha\), a trend change is considered statistically significant when \(|Z|>\mathrm{u_{1-\frac{\alpha}{2}}}\). The statistical significance of the trend variation in the SOS/EOS time series was evaluated using a confidence level \(\alpha=0.05\). This study investigates the trends in phenological metrics derived from various SIF products for deciduous forests in the mid- and high-latitudes of the Northern Hemisphere, covering the period from 2008 to 2017. Additionally, we conducted a comparison and assessment of the variations in the trends between the two phenological metrics.
## 4 Results
### EASY Concept Design Details: System Functionality for Different Users
#### 4.4.2.6 Temporal Viewing and Analysis Tools  
Time series remotely sensed information is increasingly being used in agricultural planning and management. Monitoring and analysis of changes in resource conditions through the visualisation and reporting on a time series of remotely sensed images would be a key base package requirement for EASY. More sophisticated temporal analysis could be provided through the development of topic packages. Though this was implemented as a prototype topic package for demonstration purposes in the EASY project (see Figure 4), it is anticipated that some capacity for temporal analysis and viewing would be included as part of the EASY base package functionality.
## 2 Materials and Methods
### US-LA3 Wetland  
NDVI-based classification and ELM simulations were implemented for a mesohaline marsh in the Barataria Basin of Louisiana. The site is a permanent wetland primarily covered by _Juncus roemerianus_ and _Spartina alterniflora_, and with coordinates of (29.4936, \(-\)89.9153). The site's terrain is flat with wind predominantly from the southeast. The site is a microtidal marsh that is flooded 16.88% of the time; it has an annual average salinity of 9.93 ppt, and 20-40% of its organic matter content is based on the Coastwide Reference Monitoring System database, station CRMS 0224 [19]. In addition, it is an Ameri-flux site (Site ID US-LA3, [20]), where Eddy Covariance flux measurements are taken in addition to other ground-based measurements of aerenchyma conductance, chamber-based measurements of fluxes from specific patch-types. The site's mean annual temperature is 20.9 degree Celsius and annual precipitation is 1623 mm [20]. The Eddy Covariance data include carbon fluxes and meteorological data (air temperature, relative humidity, pressure, wind speed and direction, net radiation, photosynthetic active radiation, soil temperature, and precipitation). The Gross Primary Production (GPP) and ecosystem respiration (ER) were modeled from the observed net ecosystem exchange of CO\({}_{2}\) (NEE) and additional meteorological variables using an artificial neural network [21] and following standard Ameriflux data processing [22]. The GPP and ER were used for model parameterization. Water depth, vegetation cover fraction, and surface water depth data were retrieved from the Coastwide Reference Monitoring System database, station CRMS 0224 [19], and used as input for the ELM.
## 3 Numerical Validation and Parametric Analysis  
In this section, we initially validate the accuracy of the previous section's analytical results by comparing them with numerical integration outcomes. Subsequently, we examine how different parameters, especially nonlinear stiffness, influence the vertical displacement of the pavement. Taking into account that the pavement is infinitely long and the vehicle velocity is constant, placing the coordinate system on the vehicle and setting \(t=0\) can demonstrate the maximum amplitude of the pavement. The vehicle's position serves as the reference origin for our comparative analysis.
## 1 Introduction
and Zhuang [57]. Within the NTA framework, an entropy \(S\) has been introduced [20; 54]; see Section 2.3 for its detailed properties. Most importantly, \(S\) has found useful applications in seismicity as well as in a variety of other physical systems, see, e.g., Chapters 7, 8, 9, and 13 of [38]. Earthquake nowcasting [58; 59; 60; 61; 62; 63; 64; 65; 66], which enables seismic hazard estimation, is also based on natural time; see Section 2.6. As [PERSON] et al. [58] state, natural time when applied to EQ seismicity has the advantage that it is not necessary to decluster the aftershocks. It is important to note, however, that the study of the time-occurrence series of EQs may provide additional information to that obtained by NTA. The fractal and multifractal character of such time-series has been studied by [PERSON] et al. [67] and [PERSON] [68], revealing new properties of seismicity. Recently, the fractal characteristics of seismicity identified by the interevent times have found useful applications to cases of induced seismicity [69], see also [70], as well as in the case of the Anatolian region with implications for seismic hazards before and during the 2023 Kahramramaras seismic sequence [71]. Additionally, in forecasting fractal studies, see, e.g., [72; 73], declustered catalogs are used. To date, NTA has not been applied to such catalogs. Such an application, however, may lead to interesting results and is within the future plans of our group. So far, the new procedure introduced by [PERSON] et al. [5] has been applied to the case of the super-giant 2011 \(M9\) Tohoku EQ in Japan as well as in three strong EQs in the western part of northern America [74], which included the 2017 \(M8.2\) Chiapas Mexico EQ, the deadly 19 September 2017 \(M7.1\) Mexican flatslab EQ, and the 2019 Ridgecrest \(M7.1\) EQ in California. It is the purpose of the present paper to examine whether this procedure could have been applied before the Kahramanmaras-Gaziante \(M_{w}7.8\) Pazarcik EQ in order to provide an improved estimate of the EQ occurrence compared to that previously found solely by means of NTA [20; 51; 75; 76; 77]. The paper is organized as follows. In Section 2, the EQ catalog and the methods used are presented, the results are shown and discussed in Section 3, and our conclusions are summarized in Section 4.
# A Study on Design Requirement Development and Satisfaction for Future Virtual World Systems
## 4 Virtual World Architecture Inspection
### Peer-To-Peer
#### 4.2.2 Limitations  
Sufficiency and reliability are the two weaknesses of P2P architecture, and they are becoming outstanding with the increase of mobile clients. In pervasive games or augmented reality, user clients could be wearable devices, IoT devices, and custom-built devices. They have nonstandard interfaces and heterogeneous capabilities [74; 117], which exhibit several limitations in the P2P design. First, resource-limited wearable devices may not have enough computing resources or standard interfaces toplay the role of a server for running full game logic. Yet, the gaming experience on mobile devices can be unsatisfying because of limited processing capacity. For example, Final Fantasy XV Pocket Edition provided lower graphics [133] and AI experience [134] than the desktop and console edition.  
Moreover, P2P virtual worlds may also suffer from heterogeneous peer resources. Mobile devices and wearable devices can run fewer services than desktop devices. The departure of users with major resources provision can unduly stress the other peers [135].  
Mobility also greatly increases the chance of client failure due to connection loss [136] or battery depletion [137]. Client failure may cause unsaved state loss. Additionally, reliability is important to the gaming experience. Pervasive games require high connectivity to game masters (i.e., specially selected players) for content distribution, diegetic communication, and game progress track [74]. Although Pithos [89] adds reliability through a two-tier game state replication, the design does not address the replica synchronization issue, which may lead to inconsistent game state.
# Narrowband Internet-of-Things to Enhance the Vehicular Communications Performance
###### Abstract
These modifications were introduced to enhance vehicle-to-everything (V2X) communications in addition to autonomous vehicles. Therefore, two different modes, namely Mode 3 and Mode 4, were discussed extensively. They showed the performances for these two modes either in supporting the V2V communication or in representing alternatives to the IEEE802.11p or the DSRC. Accordingly, a room of investigation is opened. In this work, to enhance the VANET QoS from the reachability point of view, a Multi-Input Multi-Output (MIMO)-OFDM-based benchmark has been proposed to be compatible with LTE infrastructure. Although this combination is preferable to maximize the system's throughput and robustness against the channel effects, it also burdens an extra load on the system's QoS from the delay point of view. Accordingly, the relationship between the mobility factor and the QoS must be studied and investigated [20]. Therefore, this benchmark is proposed to include a new reclustering technology and the multiparallel processing technique with a special type of antenna deployment. Based on a such proposition, the physical layer is modified to include the needed modifications. The contribution of this work falls in enhancing the QoS of vehicular communications from both the latency and the complexity point of view. Therefore, and according to fulfill such contribution, the following studies are needed:  
* The latency issues have been investigated based on the causes of different types of collisions [20; 21]. * The effect of the harmonics retrieval and the frequency leakage has been thoroughly studied [22]. Accordingly, this work proposes a V2V benchmark based on MIMO-OFDM that has a specific antenna deployment to overcome the self-interference. The transmission through such deployment will use the full-duplex concept. Furthermore, a modification has been made to the used OFDM stage to contain the windowed Discrete Fourier-Transform (W-DFT) to combat the leakage frequency main drawback; i.e., the system complexity. This paper is organized as follows: the modified benchmark is detailed in Section 2, the analysis and the simulation results are given in Section 3, and Section 4 details the conclusion.
## 4 Thermodynamic Performance Comparison and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Item** & \
\begin{tabular}{c} **Hydrogen Production Based on** \\ **Chemical Regenerative Gasification** \\ \end{tabular} &
\begin{tabular}{c} **Hydrogen Production Based** \\ **on GE Gasification** \\ \end{tabular} \\ \hline \hline \multicolumn{5}{c}{**Total System Input**} \\ \hline \multicolumn{5}{c}{**Energy Input, kJ**} \\ \hline Coal input for gasification (including to gasifier and combustion unit) & 25,842.9 & 96.3\% & 24,071.7 & 89.7\% \\ \hline Coal input for auxiliary power generation & 992.9 & 3.7\% & 2764.1 & 10.3\% \\ \hline subtotal & 26,835.8 & 100.0\% & 26,835.8 & 100.0\% \\ \hline \multicolumn{5}{c}{**Gasification Island**} \\ \hline \multicolumn{5}{c}{**Energy Input, kJ**} \\ Coal input for gasification & 25,842.9 & - & 24,071.7 & - \\ LHV of purge gas recovered from PSA & 4588.9 & - & - & - \\ Subtotal & 30,431.8 & & 24,071.7 & \\ Gas output from gasifier and COG, kJ & 25,333.0 & & 18,677.7 & - \\ Cold gas efficiency of gasification & & 83.2\% & & 77.6\% \\ \hline \multicolumn{5}{c}{**WGS and PSA Unit**} \\ \hline \multicolumn{5}{c}{**Energy Output, kJ**} \\ \multicolumn{5}{c}{Hydrogen} & 19,214.4 & 15,806.3 \\ LHV of purge gas & 4588.9 & - & \\ \hline \multicolumn{5}{c}{**Coal to H\({}_{2}\) Efficiency**} \\ \hline Coal to H\({}_{2}\) efficiency & & 71.6\% & 58.9\% \\ \hline Energy consumption for unit & & 151.2 & 183.8 \\ hydrogen production, MJ/kg & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Thermodynamic performance of the hydrogen production plant.