esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
Continuous variables are presented as the mean \(\pm\) standard deviation (SD) if normally distributed and as the median (Interquartile range_IQR) if not normally distributed. Categorical variables are expressed as percentages (%). Patients with AD were assigned to the APOE \(\epsilon 4\) subgroup when carrying at least one \(\epsilon 4\) allele (\(\epsilon 4/\epsilon 4\) or \(\epsilon 3/\epsilon 4\)), while all the remaining patients exclusively carried APOE \(\epsilon 3\) alleles (\(\epsilon 3/\epsilon 3\)).  
Figure 1: An exemplificative representation of the methodological approach used in this study for defining regions of interest (ROIs) using the WFU PickAtlas tool implemented in SPM12. Upper row (**A**): anatomical MRI images displaying ventricular ROIs (highlighted in red). These ventricular ROIs, derived through the WFU PickAtlas, were subsequently applied to PET scans of the patients included in the analysis. Lower row (**B**): corresponding 18F-FDG PET images, onto which the ventricular ROIs were mapped to measure 18F-FDG in CSF. The 18F-FDG values were normalized to pons activity to correct for individual metabolic variability, thus enabling the assessment of cerebrospinal fluid glucose dynamics in relation to Alzheimer’s disease pathophysiology. The ventricles in PET images may appear larger than those in MRI due to the lower 18F-FDG uptake in surrounding white matter. This image is intended purely to clarify the methodological approach and does not represent the exact methodological procedure used in this study (see text for detailed information). R: right side.  
Statistical differences in continuous variables between groups were tested using \(t\) tests or the Mann-Whitney U test in case of a non-normal distribution. [PERSON]'s X2 squared was used for categorical variables. Spearman correlation tests were performed to analyze the relationships between non-parametric variables. To compare the strength of independent correlation coefficients between groups, we used [PERSON]'s r-to-z transformation ([[http://vassarstats.net/rdiff.html](http://vassarstats.net/rdiff.html)]([http://vassarstats.net/rdiff.html](http://vassarstats.net/rdiff.html)), accessed on 23 April 2025). To test if factors including Qalb, p-tau/Ab42, and CSF lactates were linked to changes in the 18F-FDG-PET parameters, we performed different multivariate regression analyses. The analyses were performed in both AD and CTRL groups. We then used separate models, considering only AD patients stratified according to APOE genotype.  
Statistical analysis was performed via JASP(r) (Version 0.18.3-Computer Software-JASP TEAM 2020, [[https://jasp-stats.org/](https://jasp-stats.org/)]([https://jasp-stats.org/](https://jasp-stats.org/)), accessed on 30 April 2025) and GraphPad Prism(r) (Version 9.5.0, GraphPad Software, San Diego, CA, USA, www.graphpad.com, accessed on 8 April 2025).  
All results were computed with two-tailed tests of significance; \(p\)-values < 0.05 were considered statistically significant.
## 3 Results
### Vegetation Sensitivity and Influencing Factors  
We quantified the sensitivity of Central Asian vegetation changes to temperature, precipitation, and the TVDI, as shown in Figures 12 and 13. Due to significant interannual variability in climatic variables, we used the average values from 2011 to 2013 as a reference and calculated the differences between drought years and the average values (2011-2013). The relationships between the changes in the five vegetation indices and the variations in temperature, precipitation, and the TVDI in grids experiencing drought (TVDI \(>\) 0.6) are illustrated in Figure 12. Specifically, the relationship between the changes in the RSVI and TVDI (R\({}^{2}\) = 0.1054, \(p\) \(<\) 0.01) was found to be the strongest among these indices, indicating a greater sensitivity of the RSVI to changes in the TVDI. On the other hand, the changes in the other four indices (EVI, FVC, GPP, and LAI) exhibited stronger correlations with precipitation. These findings suggest that the variations in the EVI, FVC, GPP, and LAI, which represent vegetation changes, are primarily influenced by precipitation and the TVDI. In the grids where no drought occurred (TVDI \(\leq\) 0.6) (Figure 13), the relationships between the changes in the five vegetation indices and the variations in temperature, precipitation, and the TVDI in grids without drought (TVDI \(<\) 0.6) are illustrated in Figure 13. In comparison, the EVI, FVC, GPP, LAI, and RSVI displayed stronger relationships with both temperature and the TVDI. Additionally, the constructed RSVI consistently showed the strongest correlation with the TVDI. These findings further indicated that the variations in the five vegetation indices, which represented vegetation changes in the absence of drought, were primarily influenced by temperature and the TVDI. The analysis of grid cells affected by drought and those unaffected by drought in 2019 indicated that precipitation and the TVDI primarily influenced vegetation (Figures S1 and S2).  
Figure 11: Recovery periods for different vegetation types: (**a–c**) display the proportions of drought recovery periods for the five vegetation indices in forests, grasslands, and croplands, respectively, for 2012; (**d–f**) display the proportions of drought recovery periods for the five vegetation indices in forests, grasslands, and croplands, respectively, for 2019.
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 3 Channel Estimation Schemes
### STA Estimation Scheme  
To track rapidly changing channel variations, the STA scheme continuously updates the channel estimates. First, the m-th frequency domain received symbol \(Y(m,k)\) is divided using the previous channel estimate \(H(m-1,k)\) as follows:  
\[\hat{T}(m,k)=\frac{Y(m,k)}{H(m-1,k)},m=1,\cdots,M. \tag{3}\]  
where \(H(0,k)\) is equal to (1)  
Then, \(\hat{T}(m,k)\) is demapped to \(\hat{X}(m,k)\) and the demapped symbol is again used to obtain initial channel estimates \(\hat{H}(m,k)\) such that,  
\[\hat{H}(m,k)=\frac{Y(m,k)}{\hat{X}(m,k)}. \tag{4}\]  
Next, to mitigate noise effects and demapping errors in \(\hat{H}(m,k)\), the moving average of the data subcarriers in the frequency-domain received symbols is implemented in the frequency and time domains as  
\[\hat{H}(m,k)=\sum_{\lambda=-\beta}^{\lambda=\beta}\omega_{\lambda}\hat{H}(m,k+\lambda) \tag{5}\]  
\[H(m,k)=\left(1-\frac{1}{\alpha}\right)H(m-1,k)+\frac{1}{\alpha}\hat{H}(m,k), \tag{6}\]  
where \(\omega_{\lambda}\) is a set of weighting coefficients defined by \(1/(2\beta+1)\), and parameter \(\beta\) and \(\alpha\) are the moving average parameters in frequency and time domains, respectively. These parameters should  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Distance** & **Velocity** & **Doppler Shift** & **Maximum Excess** \\
**TX \& RX (m)** & **(km/h)** & **(Hz)** & **Delay (\(\mu\)s)** \\ \hline V2V Expressway Oncoming & 300–400 & 104 & 1000–1200 & 0.3 \\ \hline V2I Urban Canyon & 100 & 32–48 & 300 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Channel model for simulation.  
be adaptively varied depending on the channel conditions, but it is very difficult task. Therefore, STA uses fixed values that show optimal performance, i.e., \(\beta=\alpha=2\)[25].  
Using the moving average, STA can effectively reduce the demapping errors induced by noise. However, in high-SNR regions, demapping errors mainly occur due to fading; thus, channel estimation error is generated by the moving average, thereby, causing an error floor.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Universal Feature Extraction from Dual- and Compact Polarimetric SAR Data
#### 2.4.7 Correlation Coefficient and Coherence Coefficient  
Following the same rationale as in Section 2.2.8, the correlation coefficient in CP SAR mode can be defined as [6]:  
\[Corr=\frac{\text{Re}\big{\{}-i\big{\langle}E_{H}E_{V}^{*}\big{\rangle}\big{\}}}{ \sqrt{\Big{\langle}\big{|}E_{H}\big{|}^{2}\Big{\rangle}\Big{\langle}\big{|}E_{ V}\big{|}^{2}\Big{\rangle}}} \tag{29}\]  
Additionally, for CP SAR modes, the coherency coefficient can be derived by:  
\[Coh=\frac{|D_{12}|}{\sqrt{D_{11}D_{22}}} \tag{30}\]  
where the coherency matrix \(D\) for dual- and compact polarimetric SAR modes can be defined as:  
\[D=\left(\begin{array}{cc}\langle E_{H}+iE_{V}\rangle^{2}&\langle E_{H}+iE_{ V}\rangle\langle E_{H}-iE_{V}\rangle^{*}\\ \langle E_{H}+iE_{V}\rangle^{*}\langle E_{H}-iE_{V}\rangle&\langle E_{H}-iE_{ V}\rangle^{2}\end{array}\right) \tag{31}\]
# An Experimental and Numerical Study on Reduction of Generated Axial Force
## 2 GAF Model
### Friction Coefficients  
As mentioned in Section 2.1, the GAF is produced by pure sliding and rolling-sliding between the track and the spherical roller. Hence, both motions should be considered to establish an accurate GAF model. Especially for the rolling-sliding motion, rolling-sliding ratio is required along with friction coefficient whereas only friction coefficient is required for the pure sliding.  
The pure sliding occurs with respect to the trunnion axis whereas the rolling-sliding occurs with respect to the radial direction of the spherical roller. Equations (20)-(22) represents the friction forces due to the friction coefficients of pure sliding and rolling-sliding.  
\[F_{s} =\mu_{s}N_{st} \tag{20}\] \[F_{rs} =\mu_{rs}N_{st}\] (21) \[\mu_{rs} =\frac{l_{s}}{l_{t}}\mu_{s} \tag{22}\]  
where \(F_{s}\) = friction force of pure sliding, \(\mu_{s}\) = friction coefficient of sliding, \(N_{st}\) = normal force between track and spherical roller, \(F_{rs}\) = friction force of rolling-sliding, \(\mu_{rs}\) = friction coefficient of rolling-sliding, \(\gamma\) = rolling-sliding ratio, \(l_{t}\) = total travelling distance of spherical roller, \(l_{s}\) = sliding distance of spherical roller.  
As in Equation (22), the friction coefficient of rolling-sliding is the product of sliding friction coefficient and rolling-sliding ratio [13; 17]. From a theoretical point of view, the rolling-sliding ratio is very difficult to estimate because it varies with the driving conditions. Accordingly, in the previous study, it was measured by a measurement system [10]. In addition, the friction coefficients between the track and the spherical roller were also measured using a tribometer as they are determined by sliding velocity, contact pressure and lubricant [18; 19].
# A Linear Strong Constraint Joint Solution Method Based on Angle Information Enhancement
## 3 A Synchronization and Fine Ranging Method Based on the 5G Signal
### A Three-Stage Time-Frequency Synchronization Method Based on Group Peak Time Sequence Tracing
#### 3.1.3 The Fine Timing Synchronization Method Based on PSS Sliding Cross-Correlation  
After completing coarse timing synchronization, the clock difference between the receiver and transmitter can be regarded as symbol timing offset (STO). When its time-domain length is equal to \(\delta\) samples, we can obtain:  
\[y[n]=x[n+\delta] \tag{12}\]  
\[Y[k]=e^{2\pi\delta\frac{k}{N_{FFT}}}X[k] \tag{13}\]  
When the STO length is less than one sampling interval, the phase of the received signal will be affected, but it can be restored through channel estimation and channelequalization. When the STO length is greater than one sampling interval, it not only affects the phase, but also causes errors in the starting point position of FFT, resulting in inter symbol interference and inter subcarrier interference. Therefore, it is necessary to estimate the STO to obtain accurate timing information.  
After completing the CFO compensation, this chapter conducts fine timing synchronization through the PSS sliding cross-correlation method. After obtaining the coarse timing result \(\hat{\delta}\), the received signal is truncated:  
\[\hat{y}=y[n+\hat{\delta}]\epsilon[n] \tag{14}\]  
Among them, \(\epsilon[n]\) is a step function. The PSS sequence has good correlation in the time domain and good noise resistance in weak signal reception. The following method is used for fine timing estimation:  
\[\left\{\hat{i},\hat{\Delta\delta}\right\}=\arg_{i,\Delta\delta}\left\{\frac{ \left\lfloor\sum_{n=0}^{N_{IFT}+N_{D}-1}\hat{y}(\Delta\hat{\delta}+n)s_{i}^{ \prime}(n)\right\rfloor^{2}}{\sum_{n=0}^{N_{FFT}+N_{D}-1}\hat{y}(\Delta\hat{ \delta}+n)\hat{y}^{*}(\Delta\hat{\delta}+n)}\right\} \tag{15}\]  
Among them, \(\hat{\Delta\delta}\) represents the estimated optimal fine time synchronization error, and \(s_{i}(n)\) represents the PSS local sequence corresponding to sector \(i\), \(i\in\{0,1,2\}\). After obtaining fine time synchronization errors, compensation is made for timing information based on coarse timing synchronization to obtain accurate time-frequency information. Afterwards, by performing correlation operations between the received signal and the local PSS and SSS, \(N_{ID}^{(2)}\) and \(N_{ID}^{(1)}\) are obtained, thereby identifying the cell ID information [23]:  
\[N_{ID}^{cell}=3N_{ID}^{(1)}+N_{ID}^{(2)} \tag{16}\]
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
## 3 Modulation Techniques for VLC in E-Healthcare Systems
### On-Off Keying (OOK)
The proof-of-concept OOK-based VLC system demonstrates that even at longer distances (up to 15 m), transmission quality remains acceptable, as evidenced by the low EOP values reported. Despite its advantages, OOK has limitations. It is particularly vulnerable to noise and interference, especially in hospital environments where ambient light sources and reflections may affect signal clarity. This can increase BER, particularly in non-line-of-sight (NLoS) conditions or at longer distances. Furthermore, OOK's spectral efficiency is lower compared to more advanced modulation schemes, such as OFDM, which can handle higher data rates and mitigate intersymbol interference (ISI). Therefore, in situations where high data throughput and noise resilience are required, alternative modulation techniques may be better suited.
# Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association
## 3 Multiple Target Tracking
### Mode State Estimate and Covariance Update  
The state and covariance of target \(t\) for mode \(j\) are updated as:  
\[\hat{\mathbf{x}}^{t}_{j}(k|k)=\hat{\mathbf{x}}^{t}_{j}(k|k-1)+W^{t}_{j}(k)\mathsf{v}^{ t}_{\hat{n}^{t}_{j}(k)j}(k), \tag{19}\]  
\[P^{t}_{j}(k\Big{|}k)=P^{t}_{j}(k\Big{|}k-1)-W^{t}_{j}(k)S^{t}_{j}(k)W^{t}_{j} (k)^{T}. \tag{20}\]  
If no measurement is associated, they merely become the predictions of the state and the covariance as:  
\[\hat{\mathbf{x}}^{t}_{j}(k|k)=\hat{\mathbf{x}}^{t}_{j}(k|k-1), \tag{21}\]  
\[P^{t}_{j}(k\Big{|}k)=P^{t}_{j}(k\Big{|}k-1). \tag{22}\]  
The mode probability is updated as:  
\[\mu^{t}_{j}(k)=\frac{\Lambda^{t}_{j}(k)\sum^{M}_{i=1}p_{ij}\mu^{t}_{i}(k-1)}{ \sum^{M}_{j=1}\Lambda^{t}_{j}(k)\sum^{M}_{i=1}p_{ij}\mu^{t}_{i}(k-1)}, \tag{23}\]  
\[\Lambda^{t}_{j}(k)=N\bigg{(}\mathbf{0};\gamma^{t}_{\hat{n}^{t}_{j}(k)j}(k),S^{t}_ {j}(k)\bigg{)}, \tag{24}\]  
where \(N\) denotes Gaussian probability density function. If no measurement is associated, the mode probability becomes:  
\[\mu^{t}_{j}(k)=\sum^{M}_{i=1}p_{ij}\mu^{t}_{i}(k-1). \tag{25}\]  
Finally, the state vector and covariance matrix are updated as:  
\[\hat{\mathbf{x}}^{t}(k|k)=\sum^{M}_{j=1}\hat{\mathbf{x}}^{t}_{j}(k|k)\mu^{t}_{j}(k), \tag{26}\]\[P^{t}(k|k)=\sum_{j=1}^{M}\mu_{j}^{t}(k)\bigg{\{}P_{j}^{t}(k|k)+\left[\hat{\mathbf{x}}_ {j}^{t}(k|k)-\hat{\mathbf{x}}^{t}(k|k)\right]\left[\hat{\mathbf{x}}_{j}^{t}(k|k)-\hat{ \mathbf{x}}^{t}(k|k)\right]^{T}\bigg{\}}. \tag{27}\]  
The procedures from Equations (7)-(27) repeat until the track is terminated.
# DDS: A Delay-Constrained Duty-Cycle Scheduling Algorithm in Wireless Sensor Networks
## 3 Network Model and Problem Definition
### Problem Definition  
Satisfying delay requirements is one of the most critical concerns in delay-constrained applications in WSNs. For such applications, data packets must reach the sink within a given time. Otherwise, the information in the packets might be useless. Therefore, E2E delay, which is the latency of packets from the source node to the sink, should be bounded according to specific application demands.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Studies** & **Objective** & **Time Synchronization** & **Node Deployment Requirement** \\ \hline
[7; 8; 9; 10; 11; 12] & to minimize expected E2E delay & yes & no \\
[13; 14; 15; 16; 17] & to minimize expected E2E delay & no & no \\
[18; 19] & to minimize expected E2E delay & no & uniformly random distribution \\
[20; 21; 22] & to satisfy the delay constraint & yes & no \\
[25] & to satisfy the delay constraint & no & uniformly random distribution \\ DDS & to satisfy the delay constraint & no & no \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison table between our proposed duty-cycle scheduling (DDS) and existing studies.  
This work considers both the delay bound and the required DSR, e.g., an application requires 95% of the transmitted packets (the required DSR) to arrive at the sink within 30 s (the given delay bound) [21; 23].  
Let \(\theta\) and \(\gamma\) denote a given delay bound and a required deadline success ratio of an application, respectively. Random variable d is defined as the packets' E2E delay, which is the sum of one-hop delays. In duty-cycled WSNs, as the duty cycle interval increases, energy consumption can be reduced. However, a large duty cycle interval leads to an increase in E2E delay. Therefore, the objective of the problem is to find the maximal duty cycle interval, \(T\), subject to the constraint that the probability of d being smaller than \(\theta\) should be greater than \(\gamma\). The detailed algorithm for finding the maximal value of \(T\) is presented in Section 4.
## 6 COTS Hardware Components
### Antennas  
For this implementation, we used simple printed circuit board omni-directional antennas, depicted in Figure 10. In a previous iteration, we also demonstrated that rudimentary dipole antennas are also sufficient for close-range operations [12]. These antennas cover both the US 915 MHz ISM band and the EU 783 MHz licensed experimental bands used for this prototype. These antennas connect to the FMCOMMS5 board using basic SMA cables, which are fed through carbon fiber tubes for stability and mounted to the aluminum frame using plastic brackets (orange).  
Figure 8: Analog Devices FMCOMMS5 RF evaluation board [87].  
Figure 9: Customized transmit-receive switching and amplifier board.
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Results of Analysis of the Selected Optimization Methods
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & & & & & & & & \\ \hline Metric & CT [s] & CFEs & COST & LEN [m] & SMOO & NITS & EEE [kWh] & NCOL \\ Mean & 239.818 & 11,804 & 5332.872 & 178,701 & 0.750 & 1791 & 2.517 & 0 \\ StDev & 95.159 & 5229 & 3404.326 & 67,139 & 0.025 & 672 & 1.792 & 0 \\ Min & 84.167 & 3700 & 797.913 & 98,243 & 0.705 & 986 & 0.607 & 0 \\ Max & 417.574 & 20,100 & 13921.534 & 343,875 & 0.800 & 3443 & 7.031 & 0 \\
1 st quartile & 177.420 & 8900 & 3083.056 & 126,182 & 0.728 & 1265 & 1.138 & 0 \\ Median & 232.707 & 11,100 & 4695.423 & 159,921 & 0.750 & 1604 & 1.771 & 0 \\
3 rd quartile & 307.695 & 13,900 & 7036.044 & 218,643 & 0.762 & 2190 & 3.763 & 0 \\ \hline \multicolumn{8}{c}{**ACO\({}_{\mathbb{R}}\)**} \\ \hline Metric & CT [s] & CFEs & COST & LEN [m] & SMOO & NITS & EEE [kWh] & NCOL \\ Mean & 171.881 & 33,932 & 6271.053 & 120,938 & 0.792 & 1213 & 1.121 & 0 \\ StDev & 139.482 & 23,494 & 17800.854 & 52,168 & 0.035 & 522 & 1.205 & 0 \\ Min & 47.071 & 11,150 & 374.211 & 75,409 & 0.717 & 757 & 0.286 & 0 \\ Max & 719.909 & 90,050 & 111,199.651 & 317,829 & 0.861 & 3184 & 5.364 & 0 \\
1 st quartile & 75.746 & 15,950 & 546.130 & 90,619 & 0.763 & 910 & 0.429 & 0 \\ Median & 118.626 & 24,200 & 727.699 & 101,784 & 0.792 & 1023 & 0.619 & 0 \\
3 rd quartile & 220.260 & 45,350 & 1530.986 & 129,861 & 0.817 & 1301 & 1.313 & 0 \\ \hline \multicolumn{8}{c}{**PSO**} \\ \hline Metric & CT [s] & CFEs & COST & LEN [m] & SMOO & NITS & EEE [kWh] & NCOL \\ Mean & 136.278 & 21,688 & 2481.475 & 119,923 & 0.783 & 1203 & 1.073 & 0 \\ StDev & 35.723 & 4570 & 6914.403 & 40,466 & 0.030 & 404 & 1.012 & 0 \\ Min & 59.430 & 10,800 & 382.175 & 79,731 & 0.714 & 801 & 0.273 & 0 \\ Max & 224.549 & 32,000 & 36,092.829 & 281,648 & 0.849 & 2820 & 4.994 & 0 \\
1 st quartile & 107.395 & 18,800 & 546.170 & 95,966 & 0.761 & 963 & 0.477 & 0 \\ Median & 138.544 & 21,800 & 733.795 & 108,130 & 0.788 & 1085 & 0.629 & 0 \\
3 rd quartile & 157.436 & 24,800 & 1220.731 & 123,539 & 0.803 & 1239 & 1.313 & 0 \\ \hline \multicolumn{8}{c}{**GA**} \\ \hline Metric & CT [s] & CFEs & COST & LEN [m] & SMOO & NITS & EEE [kWh] & NCOL \\ Mean & 201.779 & 32,669 & 2095.801 & 164,967 & 0.748 & 1653 & 2.187 & 0 \\ StDev & 30.020 & 1989 & 864.810 & 43,910 & 0.023 & 439 & 1.077 & 0 \\ Min & 145.928 & 31,500 & 493.135 & 76,622 & 0.719 & 770 & 0.363 & 0 \\ Max & 273.989 & 38,625 & 4374.112 & 272,821 & 0.828 & 2733 & 4.825 & 0 \\
1 st quartile & 181.028 & 31,500 & 1459.975 & 132,111 & 0.727 & 1324 & 1.353 & 0 \\ Median & 199.026 & 31,500 & 1991.016 & 164,131 & 0.744 & 1645 & 2.201 & 0 \\
3 rd quartile & 217.77 & 32,925 & 2607.089 & 190,135 & 0.766 & 1903 & 2.991 & 0 \\ \hline
# Real-Time Seismic Data from the Bottom Sea
## 2 Standalone Broadband Ocean Bottom System  
In this paper we describe a moored OBS system, with continuous telemetry through the mooring line and buoy to the shore station, capable of providing good azimuth observations of regional seismic events. The real-time observations are useful to determine the magnitude and the hypocentre of the regional seismic events. Therefore, the moored OBS system should detect seismic events of low seismicity (1.5 \(<\) M \(<\) 5), located at depths up to 100 km, and the nominal natural period of 1 second. This requires a bandwidth of 0.5 to 30 Hz with 18 bits of resolution, with a signal to noise ratio above 60 dB, and a communication data loss rate less than 10%. Hence, the degraded seismic data transfer is assumed, as short-term packet loss may occur in moored OBS system [8].  
An anchored OBS has been built and tested to acquire regional and distant seismicity in near real time. The overall latency of the system is low (a few tens of seconds), and is dominated by the time to gather a group of data to telemeter, the time to send the data through the inductive link, and the time to send the data through the GPRS or satellite network. The first deployment was done in June of 2017, at a depth of 20 m, 2 nautical miles off coast of Vilanova i la Geltru (Barcelona), near the OBSEA underwater observatory [9]. The design consists of a seafloor OBS with a broadband sensor unit and a surface buoy connected to a cable with an intermediate buoy (Figure 1). The validation of this prototype seismic system was done, comparing the data with the existing OBSEA seismometer [4]. The seismic system continuously transmits data to a controller in the surface buoy, at a rate of 1000 bps through an inductive link provided by two inductive modems [10] attached to the mooring line as illustrated in Figure 1. Next, the controller in the buoy forwards the data to the land station through a GPRS link, if the system is placed offshore (tens of kilometres), or through satellite communications, if the system is placed in a far offshore ocean environment. Seismic data from the moored OBSs can be integrated into the existing seismic data network thanks to the GPS receiver on the surface buoy, which is configured to timestamp all the data coming from the seafloor seismometer. The seismic data is continuously recorded in the vertical channel with a triggering algorithm to switch to 3 components for special events. On demand, the users can also request the data packet of all three channels for short time periods.