esa-sceva/satcom-chunk-collection huggingface dataset:
# An Energy Efficient Routing Approach for Cloud-Assisted Green Industrial IoT Networks
## 6 Experimental Results and Evaluation
### Performance Metrics  
To compare and analyze the performance of the routing techniques with other OFs available, we have defined the following qualitative performance metrics.  
**Packet delivery ratio (PDR):** This is the ratio of the number of the data packet received at the root node and the total number of packets sent by the source nodes, which is as follows:  
\[\text{PDR}\ =\ \frac{\text{\# of packets successfully received at root node}}{\text{\# of packets sent}} \tag{13}\]  
\[\text{\# of packets sent}\ =\ \text{\# of packets sent by each node}\times\sum_{v_{i}=1}^{n}\text{\# of nodes} \tag{14}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Operating system & Contiki 3.0 \\ Radio model & Unit Disk Graph Medium (UDGM) \\ Traffic rate & CBR flow \\ Energy consumption & CC2420 based \\ MAC protocol & Contiki MAC \\ Transport & UDT/pv6 \\ Max queue size & 30 packets \\ Transmission power & \(-17\) dBm \\ Initial energy of each node & 10 Joule \\ Channel check rate & 16 Hz \\ Routing protocol & RPL \\ Traffic rate & 15, 30, 45, 60, (ppm) \\ Max retransmission & 3 \\ \(\gamma\) & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters for our experiment.  
**Network lifetime:** We considered it as the time to network partition. Network partition occurs when the average number of nodes dead in the network. Similar to [54], in our evaluation, we use it as a metric that will use energy variance, which can be defined as follows.  
\[\begin{array}{rcl}\text{{Network}}&\text{{lifetime}}&=&I_{e}-(\overline{U}_{e }+\varsigma),\\ \overline{U}_{e}&=&\frac{\varsigma_{e}U_{e}}{N}\end{array} \tag{15}\]  
where \(I_{e}\) is the initial energy of a node, \(U_{e}\) is the average used energy, and \(N\) is the total number of nodes in the network. The energy is calculated based on the power consumed by the node in each of the states such as Tx, Rx, idle, and CPU (radio is off, but the system is active while processing) in which a node can function. Now, \(\varsigma\) can be expressed as follows:  
\[\varsigma^{2}\ =\ \frac{\left(U_{e}-\overline{U}_{e}\right)}{N} \tag{16}\]  
**Number of nodes alive:** This is known as the number of active nodes in the network that can operate. In our analysis, we have considered the node to be alive, whose remaining energy is more than 2% of its initial energy.  
**Energy cost per data received:** This is defined as the average energy consumed for a successful data packet delivered to the destination node.
# Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method
## 3 Methodologies
### Forecasting Method  
To forecast typhoon intensity for a longer time, it is necessary to consider the rolling forecasting method [23; 24; 25]. Based on this method, the LSTM models used the typhoon intensity data, typhoon position data, and typhoon moving speed data at the current time and in the previous 6 h for typhoon intensity forecasting of the next time point, so that the data could be obtained with a lead time of 6 h, and then, the output data were used to predict the typhoon intensity at the next time point, so that the data could be known in the future 12 h. Through the above process, the typhoon intensity data could be obtained based on the LSTM models with lead times of 6, 12, 24, 48, 72, 96, and 120 h. In order to better reflect the forecasting effect of the LSTM models, the typhoon intensity forecast experiments were repeated 20 times (also called 20 ensemble members in numerical model methods) for the next 6, 12, 24, 48, 72, 96, and 120 h.  
To study the performance of the forecasting models accurately, the ensemble forecasting method based on the LSTM models was used to forecast typhoon intensity. For example, in the experiment, it was able to obtain the prediction value Z(1) based on the model at the first time point and then obtained the prediction value Z(2) at the second time point based on the models, so the prediction value based on the ensemble forecasting method is (Z(1)+Z(2))/2. The processes of the rolling forecasting and ensemble forecasting methods can be seen in Figure 2.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Hyperparameters** & **LSTM Models** & **FNN Model** \\ \hline Number of hidden layers & 1 & 1 \\ Number of prediction factors & n & n \\ Number of hidden layer neurons & 4*n & 4*n \\ Activation function of the hidden layer & Tanh & Sigmoid \\ Loss function & MSE & MSE \\ Maximum iteration step & 300 & 5000 \\ Optimizer & Adam & Default \\ Learning rate & 0.01 & 0.001 \\ Goal learning rate & Default & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The hyperparameters of the typhoon intensity forecasting models. MSE, mean squared error; LSTM, long short-term memory; FNN, feed-forward neural network.  
Figure 2: The processes of the rolling forecasting and ensemble forecasting methods.
# A Novel Hybrid Approach to Deal with DVL Malfunctions for Underwater Integrated Navigation Systems
## 3 Proposed PLSR-SVR Hybrid Predictor  
It is obvious that there is a connection between the SINS outputs and the DVL outputs. Moreover, the SINS is more reliable, so we aim to establish the relationship between the SINS calculating results and the DVL measurements. Thus, the measurements of the DVL can be predicted when it malfunctions. It is worth mentioning that the employed SINS calculating results are revised by the closed-loop KF. In order to improve the prediction accuracy and reliability, both the current and the past calculating velocities of the SINS are taken as the inputs of the predictor. In this case, strong relevance exists between the independent variables, which could possibly compromise the robustness of the predictor. To solve the problem, partial least squares regression (PLSR) which is good at dealing with multi-collinearity, is employed to build the predictor. Since PLSR is a kind of linear regression, a nonlinear regression model is necessary to further improve the prediction precision. Here, support vector regression (SVR) is chosen to overcome the shortcomings of PLSR. The hybrid approach of linear and nonlinear regression models has proven to be effective [27; 28]. The proposed hybrid approach combines the advantages of both PLSR and SVR, thus effectively handling the complex data relationship (including linear and nonlinear relationships). The specific implementation of the hybrid predictor is presented below.
###### Abstract  
In this paper, we analyze the performance of a dual-hop cooperative decode-and-forward (DF) relaying system with beamforming under different adaptive transmission techniques over \(\kappa-\mu\) shadowed fading channels. We consider multiple antennas at the source and destination, and communication takes place via a single antenna relay. The published work in the literature emphasized the performance analysis of dual-hop DF relaying systems, in conjunction with different adaptive transmission techniques for classical fading channels. However, in a real scenario, shadowing of the line-of-sight (LoS) signal is caused by complete or partially blockage of the LoS by environmental factors such as trees, buildings, mountains, etc., therefore, transmission links may suffer from fading as well as shadowing, either concurrently or separately. Hence, the \(\kappa-\mu\) shadowed fading model was introduced to emulate such general channel conditions. The \(\kappa-\mu\) shadowed fading model is a general fading model that can perfectly model the fading and shadowing effects of the wireless channel in a LoS propagation environment, and it includes some classical fading models as special cases, such as \(\kappa-\mu\), Rician, Rician-shadowed, Nakagami-\(\hat{\mu}\), One-sided Gaussian, and Rayleigh fading. In this work, we derive the outage probability and average capacity expressions in an analytical form for different adaptive transmission techniques: (1) optimal power and rate adaptation (OPRA); (2) optimal rate adaptation and constant transmit power (ORA); (3) channel inversion with a fixed rate (CIFR); and (4) truncated channel inversion with a fixed rate (TIFR). We evaluate the system performance for different arrangements of antennas and for different fading and shadowing parameters. The obtained analytical expressions are verified through extensive Monte Carlo simulations.  
relaying; beamforming; adaptive transmission techniques; outage probability; average capacity; \(\kappa-\mu\) shadowed fading channels +
Footnote †: journal: Electronics Letters
## 4 Test Protocol for Empirical Studies
### Test Environment and Apparatus  
As a public vehicle that serves multiple target user communities [62; 63], the implementation and planning of SAVs require the support and collaboration of various entities such as government agencies, city road planners, and transportation systems [64]. Therefore, the primary obstacle in assessing user acceptance of SAVs is the need for a tangible prototype [65]. Due to manufacturing costs and lead times, user research should be conducted on autonomous shuttles before their formal market deployment to ensure user acceptance. Thus, technologies other than physical prototypes are more suitable for testing in such research.  
As summarized by previous studies, Wizard-of-Oz experiments [66], structured questionnaires [67], and VR experiments [53] are three standard methods for testing user acceptance [8]. Wizard-of-Oz experiments pose safety risks as they require simulation in actual road conditions. At the same time, structured questionnaires are unsuitable for this study as they cannot verify whether participants' responses align with their actual behavior. In contrast, VR technology is highly applicable in investigating user acceptance of emerging technologies. The advantages of this technology in experimental testing, as highlighted by [PERSON] et al. (2012), include safety, availability, and data provision [68]. Safety is unquestionably crucial, as VR technology can prevent injuries compared to experimental procedures in real-world settings. Availability refers to the ability to repeatedly simulate specific tasks without the time and cost required to set up actual scenes. Data provision entails collecting participants' test data through software programs to aid researchers in updating and iterating designs. VR technology also has its limitations. A potential risk involves the potential for \"motion sickness\", which is a temporary feeling of nausea that may occur after prolonged use of a VR headset.  
We recommend utilizing widely employed game development engines to construct virtual testing environments within experimental devices. Specifically, an automated shuttle is used as a prototype to maneuver in the virtual setting following a predetermined driving route, aided by a head-mounted display (HMD) to facilitate user immersion in the virtual environment for real-time interaction with SAV and non-motorized interaction partners inside and outside the vehicle.
# Calibrating the SAR SSH of Sentinel-3A and CryoSat-2 over the Corsica Facilities
## 3 Results  
### Solving the Vertical Offset in the Ajaccio Tide-Gauge-Derived SSH  
In [3] we showed that there was an anomalous SSH bias of \(-30.5\pm 4.5\) mm between the Ajaccio tide gauge and our GPS-based sea level measurements. We thus retrieved the GPS data from recent surveys performed in 2013, 2014 and 2015 and computed new height differences between the antenna reference point (ARP) of the permanent receiver (AJAC) and the fundamental marker close to the tide gauge (G). This processing was done in baseline mode with the GAMIT software [15] and the results are shown in Table 1. The standard errors of the individual determinations range from 0.2 to 1.1 mm mainly due to the number of days of observation. It provides a much more coherent weighted mean value of 50.0371 m, rather than the 2005 reference, with a standard error of the weighted mean of 0.4 mm (Table 1), thus changing our historical reference by 18.3 mm.  
Additionally, during the deployment of the Ajaccio tide gauge in 2012, the Service Hydrographique et Oceanographique de la Marine (SHOM) performed SSH observations with a contact gauge and determined a 13.0 mm bias (standard deviation of 3.6 mm) which was introduced as a \"calibration offset\" into the tide gauge system. We decided to remove this quantity from the instrument and by adding it to the new correction (of 18.3 mm); the total systematic error of the tide gauge SHH has been established to be 31.4 mm. This is in very good agreement with the SSH bias observed between the Ajaccio tide gauge and our GPS-based sea level measurements (of \(-\)30.5 \(\pm\) 4.5 mm [3]). We then changed our datum to a rounded offset of \(-\)30 mm in the tide gauge SSH. The results presented in this paper take this correction into account.  
Another geodetic survey is planned for next year to confirm the height difference between the ARP of the permanent receiver (AJAC) and the fundamental marker close to the tide gauge (G), but we are already very confident in the current result given the 0.4 mm standard error.
# Research on a Simplified Estimation Method for Wheel Rolling Resistance on Unpaved Runways
## 2 Theory and Methods
### Uncertainty Analysis of Rolling Resistance Estimation  
To evaluate the prediction accuracy of the simplified resistance model \(F=Am^{N}\), an error propagation analysis was conducted based on experimental data. The model parameters are defined as:  
* \(F\): Wheel rolling resistance (N);
* \(A=181.2\): Dimensionless composite coefficient (determined by experimental fitting);
* \(N=1.39\): Dimensionless soil deformation index (determined by experimental fitting);
* \(m=152\): Aircraft mass (t).  
The maximum absolute error of \(F\) was computed using the total differential method:  
\[\Delta F=\left|\frac{\partial F}{\partial A}\right|\Delta A+\left|\frac{ \partial F}{\partial N}\right|\Delta N+\left|\frac{\partial F}{\partial m} \right|\Delta m \tag{23}\]  
where the partial derivatives are:  
\[\frac{\partial F}{\partial A}=m^{N} \tag{24}\]  
\[\frac{\partial F}{\partial N}=A\cdot\,\mathrm{m}^{N}\cdot\ln m \tag{25}\]  
Figure 7: Takeoff roll distances calculated using six simplified equations.  
\[\frac{\partial F}{\partial m}=N\cdot\ A\cdot\ m^{N-1} \tag{26}\]  
According to relevant studies on determining the maximum takeoff mass of aircraft, the relative error of m is set at 1% (\(\Delta m=1.52\)) [34]. Then, the impacts of \(\Delta A\) and \(\Delta N\) on the error are analyzed. Due to the limited sample data used to fit Figure 6a, it is impossible to conduct a quantitative analysis of \(\Delta A\) and \(\Delta N\) solely based on the test data in Table 3. Drawing on relevant experience [33], the relative error of A is assumed to be 5% (\(\Delta A=9.06\)), and the relative error of \(N\) is assumed to be 1% (\(\Delta N=0.0139\)). Finally, the calculated \(\Delta F\) is 22.1 kN, indicating a relative error of 11.4%. Additionally, considering the errors in the empirical formula used to calculate the aircraft takeoff roll distance, it can be preliminarily concluded that a takeoff roll distance error of 13.3% is acceptable for the preliminary evaluation of unpaved runway construction on soil runways.
# Electromagnetic Radiation Shielding Using Carbon Nanotube and Nanoparticle Composites
## 8 Conclusions
A solid metal sheet may provide greater shielding than a nanocomposite fabric on a per volume basis, but the solid metal sheet might provide less shielding than a nanocomposite fabric on a per weight basis. Because of the large number of parameters involved in developing radiation-shielding materials for specific energy levels of radiation, a comparison of results between different researchers is difficult. A standardized approach, such as based on a comparison of experimental mass attenuation coefficients with XCOM results, would simplify the evaluation of radiation-shielding performance. Further research should also systematically study the effect of the size, thickness, layering, shape, and configuration of nanoparticles on radiation-shielding performance and develop scalable methods for the continuous manufacturing of nanoparticle-based materials. The overall outlook is that as research and development in this field continue to advance, it is expected that new and innovative applications will emerge, further expanding the use of CNT-based fabrics, thin films, and powder additives for radiation shielding and protection. B.C., B.F., A.H. ([PERSON]), H.H., F.I.M., O.S.O., A.D., A.H. ([PERSON]), L.P. and G.P.--Literature surveys performed by ten people to cover the field. P.G.--Original draft preparation, review and editing, figure drawing. H.S.--Radiation analysis, review and editing. M.S.--Original draft preparation, writing, review and editing, figure and tables. All authors have read and agreed to the published version of the manuscript. This study was partially supported by the Ohio Workplace Safety Innovation Center Grant WSIC24-230331-033, National Institute for Occupational Safety and Health through the Pilot Research Project Training Program of the University of Cincinnati Education and Research Center Grant T42 OH008432, and the University of Cincinnati Armstrong Institute for Space, Technology, and Research. Not applicable. The data used in the paper are available in the paper or by entering parameters into XCOM, which is open access. The authors acknowledge the assistance of [PERSON] of the University of Cincinnati Advanced Materials Characterization Center in suggesting methods for characterizing nanoparticles. The authors declare no conflicts of interest.
# Polarization Sensitive Optical Coherence Tomography: A Review of Technology and Applications
## 2 Principles of Light Polarization and PS-OCT
### Brief Basics of OCT
For each interface, the acquired spectral interference signal  
\[S(k,\Delta z)=S_{R}(k)+S_{S}(k)+2\sqrt{S_{R}(k)S_{S}(k)}\cos[2\Delta z\ k]. \tag{5}\]  
contains three terms, similar to Equation (4). The first two terms contain the spectral densities returning from the reference arm (\(R\)) and the sample arm (\(S\)). Via \(\sqrt{S_{R}(k)S_{S}(k)}\), the last term again is proportional to the spectral densities of the reference beam and the sample beam. Note that the last term is subject to a modulation \(\cos[2\Delta z\ k]\) across wavenumber \(k\), whose modulation frequency is proportional to the path length difference between the light path to sample interface and the light path to the reference mirror, \(\Delta z=z_{S}-z_{R}\). The factor 2 accounts for the double pass through the interferometer arms. Since every path length difference \(\Delta z\) is encoded by a different spectral modulation frequency, the interference signals from multiple depth locations can be recorded simultaneously. A frequency analysis using the Fourier transform then provides the axial depth scan similar to Equation (4).
# Ultra-Thin Metamaterial Beam Splitters
## 1 Introduction
The fishnet metamaterial structures can be fabricated by standard electron-beam lithography and lift-off processes [21] or focused ion-beam milling [22]. The major advantage of the fishnet metamaterial is that none of the above mentioned beam splitters can offer designs with deep subwavelength thicknesses. The dielectric metasurfaces typically require much larger thicknesses to achieve the necessary phase gradients. Although an ultra-thin PBS based on embedded metal nanowires considering self-assembling deposition techniques [23] was proposed [24] at larger infrared wavelengths using Maxwell-Garnett theory, it has not been addressed if the performance and ultra-thin properties of the PBS are maintained close to our operating wavelength of designed fishnet structures at 1.55 \(\upmu\)m. Ultra-thin beam splitters can be utilized in the miniaturization of many bulky classical and quantum photonic devices and systems. For example, the miniaturization can improve the scalability and efficiency of photonic quantum computing and communication systems [25]. The reflectance and transmittance for the TE-polarized and TM-polarized light can be controlled in anisotropic fishnet metamaterial structures by changing primarily the wire widths and the periods along different directions as well as metal layer thickness. Away from the resonance, with the increasing wire widths, the reflectance can be increased, while the transmittance decreases. This is because the impedance mismatch increases with the increased metal filling ratio. However, due to the anisotropy, reflectance and transmittance for different polarizations do not change at the same rate. This makes it possible to have different values of reflectance and transmittance for different polarizations. The filling ratio, hence the impedance mismatch and resultant reflectance and transmittance for different polarizations, can also be controlled similarly by the periods and metal layer thickness in the fishnet structure. On the other hand, around the resonance frequency, a rapid change occurs in the reflectance and transmittance spectra at the expense of increased absorbance. By tuning the resonance frequency, this spectral region can be shifted and thus allow for different values of reflectance and transmittance at a fixed wavelength. The resonance frequency can be shifted by adjusting the effective circuit inductance and capacitance through geometric parameters as prescribed in [26]. In the regions close to the resonance frequency, the bulk skin depth technique in [27,28] can be useful in improving the efficiency.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 6 Scalability Analysis  
Our proposed 3D-SM and the existing AUV-PN and DDRP are simulated by varying the number of nodes in the network ranging from 200 to 1000.  
In Figure 14, the behavior of the throughput of the three routing protocols with different numbers of nodes is shown. By increasing the nodes in the network with the same dimensions for SM-3D and AUV-PN, throughput has an increasing behavior; while in DDRP, there is a slight change in the throughput. DDRP supports multi-hopping; with the increase in the number of nodes, the maximum nodes send their data through multi-hopping, which increases the transmission losses (Figure 15), path losses (Figure 16) and end-to-end delay (Figure 17). While AUV-PN and SM-3D are receiving data directly from nodes, end-to-end delay may increase relatively.  
Figure 14: Throughput comparison with different numbers of nodes.  
Figure 13: End-to-end delay. (**a**) when all nodes die; (**b**) when first node dies.  
Figure 16: Path loss.  
Figure 17: End-to-end delay.  
Figure 15: Transmission loss.  
Figure 15 shows the transmission losses, with the change in the number of nodes. In SM-3D, transmission losses linearly increase. In AUV-PN and DDRP, transmission losses exponentially increase with increased node density.  
Path loss with increased node number is shown in Figure 16. There is a slight variation in the path loss value in 3D-SM and AUV-PN, while in DDRP, by increasing the number of nodes in the network, the path loss also increases exponentially.  
As the node density in the network increases, the end-to-end delay of 3D-SM increases as compared to AUV-PN. In AUV-PN, more clusters are formed, and also, the number of PNs increases, as well as the load on the PNs is managed in terms of end-to-end delay. While in 3D-SM, the number of the MS and CNs is fixed, by increasing the number of nodes, the MS and CNs get burdened, and the delay increases.  
In DDRP, end-to-end delay exponentially increases because of multi-hopping.  
Figure 18 shows that 3D-SM achieves high throughput at the cost of increased packet drop.  
We concluded from this analysis that 3D-SM is more scalable as compared to AUV-PN and DDRP.