esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultra-Wideband Analog Radio-over-Fiber Communication System Employing Pulse-Position Modulation
## 3 Experimental Setup
### ARoF Transmitter  
The optical output (\(\sim\)1550 nm wavelength) from the Cobrite DX-1 (ID Photonics, Neubiberg, Germany) continuous wave (CW) laser, with a linewidth of 25 kHz and an output power of +16 dBm, was first directly connected to a Mach-Zehnder modulator (MZM)--a 40 GHz MX-LN-40 intensity modulator (Exail Technologies, Paris, France) that has a 3.5 dB insertion loss and a 20 dB extinction ratio. The bias point was adjusted near its zero level. A sinusoidal signal generator MG3690C (Anritsu, Vienna, Austria), producing  
Figure 3: Captured experimental illustrations of ARoF-based UWB communication system employing PPM, (a) ARoF transmitter, (b) RAU transmitter, (c) RAU receiver.  
a 14 GHz sinusoidal electrical signal (half of the proposed intermediate frequency), was directly connected to the electrical signal input of the first MZM. As a result, the desired RF of 28 GHz was achieved between the generated tones in the optical domain at the output of the first MZM. Polarization controllers were positioned before and after the first MZM to precisely adjust the polarization states of the optical signal during the experiment. The output of the first MZM, comprising two equal optical tones (carriers) spaced 28 GHz apart, was subsequently amplified using an erbium-doped fiber amplifier (EDFA) to compensate for optical power loss. The optical signal was then modulated with the TR-PPM waveform using a second MZM (Covega 10G, Jessup, MD, USA). The TR-PPM waveform was generated using the M8195A AWG (Keysight, Colorado Springs, CO, USA) with a 25 GHz analog bandwidth and a 65 GSa/s sampling rate. Due to the limited output power (maximum 1 V-pp) of the AWG, an RF broadband amplifier (SHF 100 BP, 17 dB gain, up to 25 GHz) was used to amplify the TR-PPM waveform before launching it into the RT input of the second MZM.  
An optical signal pre-amplification by a second EDFA with an optimized gain was performed before launching the optical signal into a 20 km long ITU-T G.652 rec. compliant SMF span, which has 0.02 dB/km attenuation and a 17 ps/nm/km dispersion coefficient at 1550 nm.
# Dual-Modality Cross-Interaction-Based Hybrid Full-Frame Video Stabilization
## 2 Background
### Limitations  
Conventional methods have several limitations. Firstly, shooting with cameras that utilize sensors with a rolling-shutter mechanism, where the shutter closes sequentially, results in stretched and shaken photographs. Secondly, cropping occurs, a phenomenon where the edges of the video are cut off, reducing the resolution, due to the warping process based on the pixel values between adjacent frames. Lastly, distortion exists, a condition where pixel values are distorted due to visual artifacts including shakiness, blur, and wobble caused by the camera's movement or vibration, making the space appear twisted, elongated, and wavering.
# A Trust-Influenced Smart Grid: A Survey and a Proposal
## 2 NIST Priority Areas On Smart Grid
### Network Communications  
Communication within the Smart Grid is important to ensure real-time monitoring, operations, and maintenance within the Smart Grid. Therefore, various technologies such as fibre-optics, wireless, and cellular (currently trending is 5G) are required in strategic areas or locations to aid in Smart Grid operations. Different routing algorithms are also required to ensure fast communication for the time-sensitive operations of some devices within the Smart Grid. Access to public and private communication networks will be required with various restrictions in place. Furthermore, critically important is ensuring that there is no collision or loss of messages during their transmission. Power network interfaces are required for long-distance transmission, and cost-effective solutions are always required. The efficient translation of protocols is also required as well as global standards to ensure that vendors can comply, thereby making communication seamless.
## 2 Methods
### Assessment of Accuracy Optimization  
Accuracy optimization implies a balancing of effort with return. The primary optimization parameter in our proposed technique is the choice of change probability threshold. The combination of the commission and omission assessments provides information about error rates across the full spectrum of change probabilities and can provide a measure of the utility of assessing different change probability thresholds. To assess these we assigned all of the reviewed polygons, those from the commission and omission assessments, into 5% probability bins. The polygons in the bins with probability \(\geq\)25% constituted a complete census as we reviewed every polygon of that sort. The polygons from the bins with \(<\)25% probability were samples from the much larger population of lower change probability polygons. For each bin we plotted the observed percentage of reviewed polygonsthat were actually change polygons against the median modeled probability for that bin. This provided a graphical representation of changing commission and omission rates by change probability. To assess an actual utility of reviewing different bins, a value must be assigned for finding additional change that can be weighed against analyst effort [84]. We will not assign a value at this time, but the framework to quantitatively assess a threshold is discussed.
# Anti-Screenshot Watermarking Algorithm for Archival Image Based on Deep Learning Model
## 3 Proposed Algorithm
### Model Training  
DLM needs to select the appropriate loss function to guide network training. The loss function carries on the back propagation through the error between the predicted result and the ground truth. It also guides the learning of network parameters. In this paper, we choose different loss functions for different sub-networks. The mean square error (MSE) loss function and Learned Perceptual Image Patch Similarity (LPIPS) sensing loss function [19; 20] are applied in the encoder, which are recorded as \(L_{R1}\) and \(L_{P}\). MSE loss function and LPIPS perceived loss function are shown in Equations (1) and (2), respectively.  
\[L_{R1}\big{(}I_{\text{Cover}},I_{\text{Stego}}\big{)}=\frac{1}{C\times H \times W}\big{\|}I_{\text{Cover}}-I_{\text{Stego}}\big{\|}_{2}^{2} \tag{1}\]  
\[L_{P}\big{(}I_{\text{Cover}},I_{\text{Stego}}\big{)}=\sum_{l}\frac{1}{H_{l}W_{ l}}\sum_{h,w}\left\|w_{l}\times\left(\hat{y}^{l}-\hat{y}^{l}_{0}\right)\right\|_{2}^{2} \tag{2}\]  
where \(I_{\text{Cover}}\) is the cover image, and \(I_{\text{Stego}}\) is the watermarked image. \(\left\|\cdot\right\|_{2}\) denotes summing the squares of each element and then calculating the square root of the sum. The \(l\) feature maps are extracted from the convolutional layer and normalized in channel dimension \(\hat{y}^{l}\in R^{H_{l}\times W_{l}\times C_{l}}\) and \(\hat{y}^{l}_{0}\in R^{H_{l}\times W_{l}\times C_{l}}\), respectively. We compute distance \(L_{2}\) by scaling activation in the channel dimension by \(W_{l}\in R^{C_{l}}\). Finally, the average distance in space and the sum in channel is calculated.  
The loss function \(L_{R2}\) of moire network also uses MSE as shown in Equation (3)  
\[L_{R2}(I_{\text{Cover}},I_{\text{Screen}})=\frac{1}{C\times H\times W}\|I_{ \text{Cover}}-I_{\text{Screen}}\|_{2}^{2} \tag{3}\]  
where \(I_{\text{Screen}}\) is the watermarked image after the moire attack.  
The decoder uses the cross entropy loss function \(L_{M}\)[21], which is widely applied in classification tasks. The formula for \(L_{M}\) is shown in Equation (4).  
\[L_{M}\big{(}M,M^{\prime}\big{)}=-\frac{1}{N}\sum_{i=1}^{N}\big{[}M_{i}\log \big{(}M^{\prime}{}_{i}\big{)}+(1-M_{i})\log\big{(}1-M^{\prime}{}_{i}\big{)} \big{]} \tag{4}\]  
where \(M\) is the original watermark, \(M^{\prime}\) is the extracted watermark, and \(N\) is the length of image watermark. The total loss function \(L\) of the model is shown in Equation (5)  
\[L=\lambda_{R1}L_{R1}+\lambda_{P}L_{P}+\lambda_{R2}L_{R2}+\lambda_{M}L_{M} \tag{5}\]  
where \(\lambda_{R1}\), \(\lambda_{P}\), \(\lambda_{R2}\) and \(\lambda_{M}\) are the parameters of corresponding loss function. Finally, the encoder, the moire network and the decoder are trained against each other, which make the model iterates continuously until convergence [22].  
Figure 8: Decoder network structure.
## 3 Data
### Solar Activity Index  
Two solar activity indexes are mainly considered for the abovementioned ionospheric TEC: the Sunspot Number (\(R\)) and the 10.7 cm Solar Radio Flux (\(F_{10.7}\)).  
(1) \(R\): \(R\) is caused by vortex-like air currents due to strong magnetic fields on the sun, located in the solar photosphere. Since \(R\) is commonly used to observe solar activity levels, and the ionosphere's variations are governed by solar activity, \(R\) can also describe changes in ionospheric parameters and is used in models predicting these parameters [26]. When describing long-term changes in ionospheric parameters or establishing prediction models, the 12-month smoothed average of \(R\) is used to observe solar activity and incorporated into research on long-term predictive modeling of ionospheric parameters [27]. Corre  
Figure 1: Location diagram of the Gakona station.  
Figure 2: Monthly average TEC data for the Gakona station.  
sponding data can be obtained from [[https://www.sidc.be/silso/datafiles](https://www.sidc.be/silso/datafiles)]([https://www.sidc.be/silso/datafiles](https://www.sidc.be/silso/datafiles)) (accessed on 18 October 2022).  
(2) \(F_{10.7}\): The \(F_{10.7}\) is emitted from the outer chromosphere and a portion of the inner corona of the solar atmosphere. It is observed in solar flux units (sfu), and 1 \(\rm{sfu}=10^{-22}\)\(\rm{Wm^{-2}Hz^{-1}}\)[28]. Since \(F_{10.7}\) is primarily determined by the number of sunspot groups on the solar surface, it is also suitable as a surrogate parameter for EUV, representing the intensity of solar activity [29; 30]. Similar to \(R\), when describing long-term changes in ionospheric parameters or establishing prediction models for these parameters, the 12-month smoothed average of \(F_{10.7}\) is used to observe solar activity and incorporated into research on long-term predictive modeling of ionospheric parameters [31]. Corresponding data can be accessed from [[https://www.ngdc.noaa.gov/stp/space-weather/solar-data/](https://www.ngdc.noaa.gov/stp/space-weather/solar-data/)]([https://www.ngdc.noaa.gov/stp/space-weather/solar-data/](https://www.ngdc.noaa.gov/stp/space-weather/solar-data/)) (accessed on 18 October 2022).  
The formula for calculating the 12-month smoothed average is:  
\[\rm{SA}_{12}=\frac{1}{12}\left[\sum_{i=n-5}^{n+5}\overline{\rm{SA}}_{i}+\frac {1}{2}(\overline{\rm{SA}}_{n-6}+\overline{\rm{SA}}_{n+6})\right], \tag{6}\]  
where SA represents the solar activity index for which the 12-month smoothed average is to be calculated, \(\rm{SA}_{12}\) denotes the monthly average of the solar activity index, \(n\) represents the month for which the 12-monthly smoothed average is calculated, and \(i\) is the index corresponding to \(n\). The 12-monthly smoothed averages of \(R\) and \(F_{10.7}\) are denoted as \(R_{12}\) and \(F_{12}\), respectively.  
Figure 3 shows the \(R_{12}\) and \(F_{12}\). The overall trends of \(R_{12}\) and \(F_{12}\) are generally similar, but there are differences in the details.
# Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
###### Abstract  
This study aims to assess the potential of the LDAS-Monde platform, a land data assimilation system developed by Meteo-France, to monitor the impact on vegetation state of the 2018 summer heatwave over Western Europe. The LDAS-Monde is driven by ECMWF's (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite-derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). The study of long time series of satellite derived CGLS LAI (2000-2018) and SSM (2008-2018) highlights marked negative anomalies for July 2018 affecting large areas of northwestern Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the domain of interest have never been observed in the LAI product over this 19-year period. LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) (January 2008 to October 2018) and 0.10\({}^{\circ}\)\(\times\) 0.10\({}^{\circ}\) (April 2016 to December 2018). Both configurations of LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5- and IFS HRES-driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g., associated to a better representation of the land cover, topography) and using HRES forcing still enhances the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedback in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model-only initial conditions.  
land surface modeling; data assimilation; leaf area index; surface soil moisture; summer 2018 heatwave
# Evaluation of IEEE802.15.4g for Environmental Observations
## 2 Materials and Methods
### Regional Regulations
#### 2.2.1 Europe
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequency** & **Maximum Effective** & **Channel Access and** & **Maximum Occupied** & **Other Usage** \\
**Band** & **Radiated Power (e.r.p.)** & **Occupation Rules** & **Bandwidth** & **Restrictions** \\ \hline \hline
863–865 MHz & 25 mW e.r.p. & \(\leq\)0.1\% duty cycle or polite spectrum access & the entire band except audio/video apps. limited to 300 kHz & \\ \hline \hline
865–868 MHz & 25 mW e.r.p. & \(\leq\)1\% duty cycle or polite spectrum access & the entire band except audio/video apps. limited to 300 kHz & DHSS and any techniques other than FHSS \\ \hline \hline
868.0–868.6 MHz & 25 mW e.r.p. & \(\leq\)1\% duty cycle or polite spectrum access & the entire band except audio/video apps. limited to 300 kHz & \\ \hline \hline
868.7–869.2 MHz & 25 mW e.r.p. & \(\leq\)0.1\% duty cycle or polite spectrum access & the entire band except audio/video apps. limited to 300 kHz & \\ \hline \hline
869.40–869.65 MHz & 25 mW e.r.p. & \(\leq\)0.1\% duty cycle or polite spectrum access & the entire band exceed audio/video apps. limited to 300 kHz & \\ \hline \hline
869.40–869.65 MHz & 500 mW e.r.p. & \(\leq\)0.1\% duty cycle or polite spectrum access & the entire band & Analogue audio apps. excluded. Analogue video apps excluded. \\ \hline \hline
869.7–870 MHz & 5 mW e.r.p. & No requirement & the entire band & Audio and video apps. are excluded. \\ \hline \hline
869.7–870 MHz & 25 mW e.r.p. & \(\leq\)1\% duty cycle or polite spectrum access & the entire band &
\begin{tabular}{} \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: EU Harmonized NRI for the 863–870 MHz band. A device following these rules can be used across Europe. This band is divided into sub-bands, each with a specific regulation. Channel access and occupation rules refers to the duty cycle that devices must respect. Each sub-band also defines the maximum bandwidth a channel can have. The other usage restrictions column indicates the applications that can take place and the transmission techniques than can be used. DSSS is short for Direct Sequence Spread Spectrum, FHSS is short Frequency Hopping Spread Spectrum. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Frequency** & **Maximum Effective** & **Channel Access and** & **Maximum Occupied** & **Other Usage** \\
**Band** & **Radiated Power (e.r.p.)** & **Occupation Rules** & **Bandwidth** & **Restrictions** \\ \hline \hline
863–870 MHz & 25 mW e.r.p & \(\leq\) 0.1\% duty cycle or polite spectrum access & the entire band and 869.25–869.4 MHz and limited to 300 kHz & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Non-EU-wide harmonized National Radio Interfaces. Some European countries accept the use of the 863–870 MHz band with these characteristics. Channel access and occupation rules refers to the duty cycle that devices must respect. The other usage restrictions column indicates the applications that can take place and the transmission techniques than can be used.
## 2 Materials and Methods
### Corrosion Tests  
Corrosion tests were carried out using a CEC/TH three-electrode cell setup from Radiometer Analytical SAS (Villeurbanne, France), using VoltaMaster 4 software for data acquisition and control. A saturated calomel electrode served as the reference electrode, a platinum electrode as the counter electrode, and the coated samples were used as the working electrode.  
The coating surface roughness was approximately Ra 0.2 for each sample. The specimens were then embedded in resin, leaving only a 1 cm\({}^{2}\) surface area exposed to the electrolyte.  
The following two electrolytic environments were used:  
* A 3.5% sodium chloride solution to simulate seawater-like conditions (M1);
* A 3.5% sodium chloride solution acidified with HCl to a pH of 3.5 to simulate corrosive acidic media (M2).  
For the potentiodynamic polarization measurements, a scan rate of 16 mV/min was used within a potential range of -800 mV to +1000 mV. A total of 5 samples were measured for each coating and medium.
## 3 Results and Discussion  
### RVE-Based Prediction of Orthotropic Material Properties for PA66/GF50 (wt%)  
The fiber volume fraction was set to 32%, and periodic boundary conditions were applied to ensure accurate homogenization. FEA was performed to extract the orthotropic stiffness matrix, from which the elastic moduli, Poisson's ratios, and shear moduli were calculated.  
Figure 10 illustrates the RVE model generation results for different fiber orientation ratios, visually representing the microstructural variations. Table 8 presents the corresponding orthotropic material properties derived from each RVE model, which are used to assign accurate anisotropic behavior in the FEM analysis. The RVE cubes are therefore not arbitrary but are directly linked to the fiber orientation tensor values obtained from injection molding simulation. Each RVE cube corresponds to a specific fiber orientation ratio in the primary flow direction (X-axis), and the table provides the resulting orthotropic elastic constants (E\({}_{1}\), E\({}_{2}\), E\({}_{3}\)), Poisson's ratios (v\({}_{12}\), v\({}_{13}\), v\({}_{23}\), etc.), and shear moduli (G\({}_{12}\), G\({}_{13}\), G\({}_{23}\)). As the fiber orientation ratio increases from 40% to 90%, a clear trend is observed: the stiffness in the flow direction (E\({}_{1}\)) increases significantly, from 4.97 GPa at 40% to 9.88 GPa at 90%. This demonstrates the strong anisotropic behavior of the composite and highlights the importance of accurately capturing fiber orientation in multi-scale modeling.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**X-Axis** & **\(\begin{array}{c}\textbf{{E}}_{1}\end{array}\)** & \(\begin{array}{c}\textbf{{E}}_{2}\end{array}\)** & \(\begin{array}{c}\textbf{{E}}_{3}\end{array}\)** & \(\begin{array}{c}\textbf{{v}}_{12}\end{array}\)** & \(\begin{array}{c}\textbf{{v}}_{13}\end{array}\)** & \(\begin{array}{c}\textbf{{v}}_{23}\end{array}\)** & \(\begin{array}{c}\textbf{{G}}_{12}\end{array}\)** & \(\begin{array}{c}\textbf{{G}}_{13}\end{array}\)** & \(\begin{array}{c}\textbf{{G}}_{23}\end{array}\)** \\ \hline
90\% & 9.881 & 3.497 & 3.507 & 0.387 & 0.386 & 0.499 & 0.137 & 0.137 & 0.499 & 1.358 & 1.371 & 1.132 \\ \hline
80\% & 8.564 & 3.736 & 3.677 & 0.384 & 0.398 & 0.499 & 0.167 & 0.171 & 0.499 & 1.526 & 1.537 & 1.229 \\ \hline
70\% & 7.461 & 3.929 & 3.982 & 0.391 & 0.384 & 0.469 & 0.206 & 0.205 & 0.476 & 1.615 & 1.629 & 1.327 \\ \hline
60\% & 6.424 & 4.053 & 4.113 & 0.388 & 0.384 & 0.437 & 0.245 & 0.246 & 0.443 & 1.676 & 1.719 & 1.427 \\ \hline
50\% & 5.632 & 4.290 & 4.225 & 0.373 & 0.383 & 0.412 & 0.284 & 0.287 & 0.406 & 1.722 & 1.727 & 1.535 \\ \hline
40\% & 4.967 & 4.462 & 4.484 & 0.366 & 0.368 & 0.375 & 0.329 & 0.332 & 0.379 & 1.699 & 1.719 & 1.620 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Orthotropic material properties for PA66/GF50 (wt%).  
Figure 9: Simulation process from injection molding to structural analysis.
###### Abstract  
This paper presents a novel optoelectronic transimpedance amplifier (OTA) for short-range LiDAR sensors used in 180 nm CMOS technology, which consists of a main transimpedance amplifier (m-TIA) with an on-chip P\({}^{+}\)/N-well/Deep N-well avalanche photodiode (P\({}^{+}\)/NW/DNW APD) and a replica TIA with another on-chip APD, not only to acquire circuit symmetry but to also obtain concurrent automatic gain control (AGC) function within a narrow single pulse-width duration. In particular, for concurrent AGC operations, 3-bit PMOS switches with series resistors are added in parallel with the passive feedback resistor in the m-TIA. Then, the PMOS switches can be turned on or off in accordance with the DC output voltage amplitudes of the replica TIA. The post-layout simulations reveal that the OTA extends the dynamic range up to 74.8 dB (i.e., 1 \(\upmu\)Ap\({}_{\text{PP}}\)-5.5 mA\({}_{\text{PP}}\)) and achieves a 67 dB\(\Omega\) transimpedance gain, an 830 MHz bandwidth, a 16 pA/ \(\upmu\)Hz noise current spectral density, a \(-\)31 dBm optical sensitivity for a \(10^{-12}\) bit error rate, and a 6 mW power dissipation from a single 1.8 V supply. The chip occupies a core area of 200 \(\times\) 120 \(\upmu\)m\({}^{2}\).  
AGC; APD; CMOS; LiDAR; optoelectronic; sensors +
Footnote †: journal: _Photonics_  
_et al._ Article_