esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Design of Functional Device for Vehicular Cloud Computing
## 3 System Model
### V2V Communication  
In vehicular networks, both the vehicles and RSUs have short range communications due to their DSRC devices. In addition, a vehicle runs on a road with a steady speed, and the traffic in terms of vehicles remains stable. When one vehicle within the \(RSU_{a}\) coverage range attempts to transmit messages to another vehicle within the \(RSU_{b}\) coverage range, a V2V communication link is required to forward messages through one relay vehicle (such as \(R_{i}\), \(i\in\{1,2,\cdots,N\}\), where \(N\) is the total number of candidate relay nodes). The system model is shown in Figure 1.  
In a Rayleigh-fading work scenario, a moving vehicle \(S\) has critical messages to transmit to a moving vehicle \(D\). Each vehicle is equipped with a GPS device. Hence, the position coordinates are known. The maximum communication range for every vehicle is fixation \(R_{max}\), and the distance between vehicle \(S\) and vehicle \(D\) is denoted as \(d_{SD}\), which is  
\[d_{SD}=\sqrt{(y_{D}-y_{S})^{2}+(x_{D}-x_{S})^{2}}. \tag{1}\]  
where \((x_{S},y_{S})\) and \((x_{D},y_{D})\) are the respective coordinates of vehicle \(S\) and vehicle \(D\).  
When vehicle \(S\) attempts to establish a communication link with vehicle \(D\), there are two cases: direct communication and indirect communication. One case happens at \(d_{SD}\leq R_{max}\) and the other case happens at \(d_{SD}>R_{max}\), where \(R_{max}\) is the maximum communication range of one vehicle. We assume that vehicle \(S\) attempts to transmit a signal \(x(t)\), which has an average power \(P_{s}\), to vehicle \(D\).  
Figure 1: The system model where vehicle \(S\) attempts to transmit a message to vehicle \(D\).
# Review of Advanced Implementation of Doppler Backscattering Method in Globus-M
###### Abstract  
Doppler backscattering (DBS) is a microwave diagnostics method typically used to study the plasma rotation velocity. Apart from conventional techniques, more advanced forms of DBS implementation were suggested on Globus-M. More specifically the study of a variety of oscillating processes was performed using DBS. In this review we present a detailed description of all of the methods and techniques employed in Globus-M alongside results obtained using DBS in all the years up until the shutdown of the tokamak. These include research similar to that done on other devices into the properties of such phenomena like geodesic acoustic modes or limit cycle oscillations, along with innovative works regarding the detection and investigation of Alfven eigenmodes and filaments that were the first of their kind and that provided important and novel results. Apart from that, the specific aspects of DBS application on a spherical tokamak are discussed. An in-depth look into the gradual change and improvement of the DBS diagnostics on Globus-M is also presented in this paper  
Doppler backscattering; spherical tokamak; microwave diagnostics; geodesic acoustic modes; limit cycle oscillations; Alfven eigenmodes; filaments; edge localized modes; quasi coherent fluctuations
## 3 Results
### Performance Comparison  
In this experimental study, we evaluate our proposed model using two distinct data augmentation strategies. The first strategy, known as the \"Baseline\", implements a standard data augmentation approach involving random Time and Frequency Masking with parameters that depend on the dataset. The second strategy utilizes the audio diffusion method, where the original audio serves as the real guidance, and different prompts are employed to generate synthetic audio. As illustrated in Table 3, our findings shows that the proposed data augmentation strategy outperforms the first one in terms of the average pAUC metric. This highlights its superior effectiveness, achieved through the utilization of diverse prompts that generate high-quality samples across various domains. A comparative analysis of our proposed RC_MAE method is presented in Table 4, where it is evaluated against other state-of-the-art techniques, namely GMADE [7], MobileNetV2 [53], AADCL [2], STgram-MFN [39], Glow-Aff [8], and autoencoders. The results demonstrate that our proposed RC_MAE method improves the average performance of anomaly sound detection. Specifically, compared to the best performance achieved by other methods in the literature, our method demonstrates a 2.03% improvement in average AUC and 1.59% improvement in average PAUC. These findings highlight the effectiveness of our method in enhancing abnormal sound detection performance. These results confirm that, in general, the combination of Contrastive and Generative self-supervised learning proves to be a powerful tool for anomaly detection. Another contributing factor to achieving this improvement is the consideration of various generated real-world noises in audio, allowing the model to better distinguish between noises and anomalies. To illustrate the effect of the learned audio feature representation, Figures 3 and 4 display the t-distributed stochastic neighbour embedding (t-SNE) visualization of the latent features from two distinct phases: pre-training and fine-tuning. Figure 3 illustrates how the pre-training stage can proficiently distinguish various machine sounds in the absence of supervision, solely aided by diffusion augmentation. The Figure 4 clearly demonstrates that incorporating additional metadata during the fine-tuning process enhances the ability to distinguish between different audio features of different machines. Furthermore, our proposed method effectively minimizes the overlap between the latent features associated with normal and anomalous machine IDs, as depicted in Figure 4. This figure confirmed that anomalies and normal sounds were mapped to distinct clusters and were distinguishable in the latent space. This result further demonstrates the effectiveness of the proposed method.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 5 Performance Evaluation
### Comparison with Swadloon and Borealis  
To examine the optimality of our system, we compare our system with Swadloon [3] and Borealis [5] in Table 4. First, the direction finding accuracy of our system and Swadloon is acceptable in indoor environments. However, Borealis's accuracy of direction finding is more suitable for outdoor scenarios. The received signal strength (RSS) measurements are not precise enough for indoor direction finding; Second, the Swadloon system and the VULA system have the Blind Zone problem. When the user turns his back on the acoustic source, the accuracies of these systems will be significantly affected. However, the VUCA and Borealis provide 360\({}^{\circ}\) azimuth coverage. Third, due to the MUSIC algorithm we leverage, the VUCA system and VULA system have the features of anti-multipath. This feature will ensure the stability of our system in a heavy multi-path environment. Finally, the user's motion patterns of using these systems all fall within the range of acceptable behaviors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System** & **Direction Finding Accuracy** & **Blind Zone** & **Anti-Multipath** & **User’s Motion Pattern** \\ \hline VULA & 4.01\({}^{\circ}\)(Mean Error) & Yes & Yes & Shake or move the smartphone \\ VUCA & 3.4\({}^{\circ}\)(Mean Error) & No & Yes & 360\({}^{\circ}\) body rotation \\ Swadloon & 2.1\({}^{\circ}\)(Mean Error) & Yes & No & Shake or move the smartphone \\ Borealis & 80\% less than 30\({}^{\circ}\) & No & No & 360\({}^{\circ}\) body rotation \\ \hline \hline \end{tabular}
\end{table}
Table 4: System comparison.  
Figure 14: (**a**) the CDF of the standard deviation of the angular velocity; and (**b**) the angular errors of different sampling times.
# Terahertz Rotational Spectroscopy of Greenhouse Gases Using Long Interaction Path-Lengths
## 2 Materials and Methods
### Cu-THz Spectroscopy by Photomixing  
Alternatively, higher resolution measurements can be made using monochromatic THz sources. The generation of THz radiation by photomixing is a frequency down-conversion technique which was used for the measurements of individual rotational lines of CH\({}_{4}\) up to 2.6 THz [13]. Two extended cavity laser diodes operating around 780 nm are mixed together in a LTG-GaAs device. The lasers are detuned to the desired THz frequency inducing a corresponding current in the photomixer device which is coupled to an integrated log spiral antenna [14]. The radiation is pre-collimated by a silicon hyper-hemispherical lens and propagated in free space. This type of source is monochromatic with excellent spectral purity and can be used in the range of 100 GHz to 3.3 THz. The available power is limited to 0.1 nW from 2.5 THz. By locking onto a frequency comb, the generated THz frequency is determined with a kHz accuracy [15]. Both amplitude and frequency modulation schemes can be easily implemented with this source, it does however suffer from available low power levels at the highest frequencies.
## 2 System and Channel Model
### The Atmospheric Turbulence Effect  
By assuming weak to strong turbulence conditions, the G-G distribution is used. Thus, the corresponding probability density function (PDF), as a function of the irradiance, \(I_{a,\{i,j\}}\), is given as [3; 20]:  
\[\begin{array}{ll}f_{\text{GG},I_{a,\{i,j\}}}\left(I_{a,\{i,j\}}\right)&= \frac{2(\alpha\beta)^{\frac{\alpha+\beta}{2}}}{\Gamma(\alpha)\Gamma(\beta)}I_{a,\{i,j\}}^{\frac{\alpha+\beta}{2}-1}\\ &\times K_{\alpha-\beta}\left(2\sqrt{\alpha\beta\ I_{a,\{i,j\}}}\right),&I_{a,\{i,j\}}>0\end{array} \tag{3}\]  
Figure 1: Mixed decode-and-forward (DF) relayed FSO communication system.  
where \(K_{\
u}(.)\) denotes the modified Bessel function of the second kind of order \(\
u\) (Equation (8.432.2) in [25]), \(\Gamma(.)\) represents the gamma function (Equation (8.310.1) in [25]), and the parameters \(\alpha\), \(\beta\) are estimated from each link's parameters through the following expressions [22; 26]:  
\[\begin{split}\alpha&=\left[\exp\biggl{(}\frac{0.49 \delta^{2}}{\left(1+0.186^{2}+0.56\delta^{2/5}\right)^{7/6}}\biggr{)}-1\right] ^{-1}\\ \beta&=\left[\exp\biggl{(}\frac{0.51\delta^{2}\left(1 +0.69\delta^{2}\left(1^{-}\right)^{-5/6}}{\left(1+0.9\delta^{2}+0.62\delta^{ 2}\delta^{2/5}\right)^{5/6}}\biggr{)}-1\right]^{-1}\right.\end{split} \tag{4}\]  
where \(d=0.5D\sqrt{2\pi\lambda^{-1}L^{-1}}\), \(\delta^{2}=0.5C_{n}^{2}\kappa^{2/7}\ell^{11/6}\), \(\kappa=2\pi/\lambda\), with \(D\) representing the receiver's aperture diameter, \(\lambda\) the operational wavelength, \(L\) the link's length and \(C_{n}{}^{2}\) the refractive index structure parameter which is proportional to the atmospheric turbulence strength, and varies between \(10^{-17}\) m\({}^{-2/3}\) and \(10^{-13}\) m\({}^{-2/3}\) for weak to strong turbulence, respectively [27].  
Additionally, by assuming saturated turbulence conditions, the negative exponential is the most suitable model and its PDF, as a function of \(I_{a,\{i,j\}}\), is given as [21; 22; 28]:  
\[f_{NE,I_{a,\{i,j\}}}\left(I_{a,\{i,j\}}\right)=1-\exp\Bigl{(}-I_{a,\{i,j\}} \Bigr{)},\ \ I_{a,\{i,j\}}>0 \tag{5}\]
## 3 Results and Discussion
### Elution  
After the adsorption of the metal onto the adsorbents, the next step should be the recovery of the metal from the adsorbent for a final recovery or dumping step. This desorption step, also called the elution step, is of equal importance as the adsorption step [20], though it is sometimes neglected by authors in their publications, i.e., References [2; 7; 12; 21; 22].  
In the present investigation and in the case of gold(I), besides the methods described in the literature for gold desorption or elution from gold-loaded activated carbon [17], the elution of this element from the loaded carbon nanotubes can be accomplished using acidic thiourea solutions (1 g L\({}^{-1}\) thiourea in 0.1 M HCl), reaching yields of near 65% of gold recovery (batch experiments at 20 \({}^{\circ}\)C and a volume of eluant/weight of gold-loaded carbon nanotubes relationship of 200) after 15 min of contact of the gold-loaded nanotubes and the eluant.  
In the case of gold(III) and due to the presence of metallic gold in the nanotubes, the elution step may be considered as a dissolution problem rather than a true elution step. Thus in this case, the removal of gold from the nanotubes may be accomplished by the use of aqua regia, which dissolves the gold particles and renders a concentrated and pure gold solution from which gold can be recovered as gold nanoparticles (Figure 6), accordingly with the procedure described in the literature [23].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metal Pair** & **a** **Chloride Complexes** & **[M], mg g\({}^{-1}\)** & **[M], mg L\({}^{-1}\)** & **D, L g\({}^{-1}\)** & \(\beta_{\text{Au(III)/M}}\) \\ \hline Au(III) & AuCl\({}_{4}\)\({}^{-}\) & 28.8 & 2.8 & 10.3 & \\ Ni(II) & NiCl\({}^{+}\) & 0.23 & 2.9 & 0.1 & 103 \\ \hline Au(III) & AuCl\({}_{4}\)\({}^{-}\) & 24.8 & 3.8 & 6.5 & \\ Fe(III) & FeCl\({}_{2}\)\({}^{+}\), FeCl\({}^{2+}\) & 0.22 & 2.7 & 0.08 & 81 \\ \hline Au(III) & AuCl\({}_{4}\)\({}^{-}\) & 27.6 & 2.9 & 9.5 & \\ Cu(II) & CuCl\({}^{+}\) & 0.26 & 3.1 & 0.08 & 119 \\ \hline \hline \end{tabular}  
* At pH 1 and HCl 0.1 M. The values of D and \(\beta\) calculated as in Equations (4) and (5), respectively.  
\end{table}
Table 8: Adsorption of gold(III) and base metals from acidic medium and separation factors.
## 4 Discussion
### Error Results for THMA (Error 1)
* When the roof error is examined (except for variable \(u_{1,\text{error},\text{root}}\)), results present a larger dispersion, and in general are more un-conservative than those of the average or first story. The minimum errors are more extreme for this story, and in fact \(x^{10}\) for most cases generally falls below \(-0.15\); large maximum errors are also found, in some variables (\(a_{1,\text{error},\text{root}}\), \(V_{\text{D1},\text{error},\text{root}}\)) even above the corresponding maxima of the first story error. * Regarding the influence of inherent damping, comparison of results between B0-1, B3-1 and B5-1 in Figures A3-A5, are virtually identical for all variables and stories, showing that this parameter does not influence the error; because inherent damping has been modeled as modal damping this result was expected. * Regarding the influence of ground type, comparison of error results between B3-1 vs. C3-1, and B3-\(\infty\) vs. C3-\(\infty\) show that this parameter exerts a very mild influence on the error. The influence is only apparent in the extreme errors, particularly in the maximum error, but \(x^{10}\), \(x^{50}\) and \(x^{90}\) are virtually unaffected. This influence is due to the mean spectrum peculiarities of each set. * Shear-type and frame-type systems render similar median errors, but with a larger dispersion in the former. As is clear in Figures A3-A5, the median value \(x^{50}\) is quite stable across cases, but the min and \(x^{10}\) decrease for shear-type systems, whereas the max and \(x^{90}\) increase. These differences are also clear in Figure 3. Therefore, the maximum and minimum errors expected are those corresponding to shear-type systems, except for \(V_{\text{SI},\text{error},\text{first}}\), where frame-type systems present more extreme max and min errors. It is worth noticing that \(\Delta_{1,\text{error}}\) and \(V_{\text{S1},\text{error}}\) are equal in shear-type systems, whereas they differ in frame-type systems. * It is concluded that although average errors across the system height tend to be centered around 0, individual stories (particularly first story and roof) will present large errors; the tendency of average errors to be small is due to the negative correlation between errors at the first story and roof; this trend is clear in Figure 6, where a scatter plot of first story THMA error (error 1) versus THMA roof error for all variables is presented; the plot shows that positive errors at roof are generally accompanied by negative errors at first story, and conversely; due to the different sign of top and bottom error, the average across stories is small. This stands for all variables except displacement \(u_{1}\) (due to the small error at roof) and acceleration \(a_{1}\).
## 2 Materials and Methods
### Methods  
#### 2.3.1 Image Data Preprocessing  
The SAR data consisted of 37 ENVISAT ASAR WSM (Wide-Swath Mode) images acquired between December 2004 and March 2012 (Table 1). All the SAR images were HH-polarized and from descending pass orbits. The specific product type of the images is ENVISAT ASAR WSM 1P. NEST (Next ESA SAR Toolbox) and ERDAS IMAGINE were used to perform the required pre-processing steps. Standard SAR processing methods were used for the ENVISAT ASAR WSM data. The SAR image processing included applying DORIS (The Doppler Orbitography and Radio-positioning Integrated by Satellite) precise orbit state vectors generated by the Centre de Traitement Doris Poseidon (CTDP) and radiometric correction using latest the ENVISAT auxiliary files. Thereafter, orthorectification by Range-Doppler terrain correction was carried out using the SRTM DEM (3 arc-second grid) and bilinear interpolation as the image and DEM resampling method. In addition, to enhance the image quality, Frost speckle suppression [36] with a window size of 3 \(\times\) 3 pixels was applied. The Frost filter is a standard tool for SAR speckle suppression and the window size was selected to avoid degradation of spatial resolution [37]. Finally, the ENVISAT images were stacked to three multichannel images (multitemporal multiband image).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Land-Use Class** & **Number of Ground Truth Points** \\ \hline Water & 4 \\
2 \(\times\) Irrigated rice crop & 2 \\ Aquaculture & 20 \\
2 \(\times\) Rainfed rice crop & 1 \\ Rainy season rice and shrimp culture & 4 \\ Settled areas, coconut, sugar cane and orchard & 19 \\
3 \(\times\) Irrigated rice crop & 21 \\ Urban & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the ground truth points.  
The eight optical satellite images from Landsat and Spot programs were used for the land-use classification of Ben Tre and Tra Vinh provinces for years 1979, 1987, 1995, 2002, and 2005 (Table 2). The ERDAS IMAGINE 2013 software package was used for the optical image processing. In the orthorectification process (using the topographic maps and SRTM DEM), clearly visible point-type features with known map coordinates, such as road junctions and bridges, were used. The estimated accuracy of the orthorectified products were in order of 0.6-0.8 pixels. The DEM resolution was not a problem since the area is relatively flat. Moreover, to enhance the radiometric image quality for the classification purposes, a standard atmospheric correction and color adjustment were carried out (de-haze [38] and color stretch) using the ERDAS IMAGINE 2013 software. In addition, the SPOT images were mosaicked since each SPOT scene consists of two consecutive frames.
## 3 Results
### Influence of Registration  
The mean TRE was \(0.84\pm 0.36\) mm (mean \(\pm\) SD) with a median of 0.79 mm, in three cases the TRE could not be quantified. The TRE did neither correlate with the Dice coefficient (r = 0.14, \(p\) = 0.4369, Spearman correlation) nor the Hausdorff distance (r = \(-0.04\), \(p\) = 0.8242, Spearman correlation). TRE and the Euclidean distance of the geometric center of gravity correlated negatively (r = \(-0.3616\), \(p\) = 0.0420, Spearman correlation), demonstration only a week association of the registration accuracy and brain shift.
# From Nearly Zero Energy to Carbon-Neutral: Case Study of a Hospitality Building
## 5 Results
### Energy Simulation Results
#### 5.1.5 Overall Energy Balance  
All of the calculations previously discussed led to the overall electric energy use for both scenarios. In Figure 9, each electric energy and natural gas use considered is depicted, along with the local energy production (represented with negative values), and the overall nonrenewable primary energy need.  
The annual electric energy use for all the services of the building is 386.4 \(\mathrm{MWh_{EL}}\)/year and 198.6 \(\mathrm{MWh_{EL}}\)/year for the nZEB and the CNB, respectively, while the natural gas consumption for the operation of the CHPs in the CNB scenario is equal to 210.7 \(\mathrm{MWh}\)/year. The local energy production is 18.5 \(\mathrm{MWh_{EL}}\)/year and 140.6 \(\mathrm{MWh_{EL}}\)/year, corresponding to 4.8% and 70.8% of the total use. It is important to notice that, in both scenarios, the electrical energy uses related to lights and appliances are comparable to the local production through PV, due to the limited roof area available and the projected shadows typical of a congested urban context. This demonstrates that it is important to take this element of the energy balance into consideration when dealing with the operational phase of a building in urban environment. Moreover, in terms of primary energy, considering that the national average conversion factors are equal to 2.42 and 1.05 for electricity and natural gas, respectively [49], along with a conversion rate of 10.6 \(\mathrm{kWh}\)/m\({}^{3}\) for the latter, the overall nonrenewable primary energy for the nZEB is 890.4 \(\mathrm{MWh_{PR}}\)/year, while for the CNB, it is 361.7 \(\mathrm{MWh_{PR}}\)/year (140.5 \(\mathrm{MWh_{PR}}\)/year from electricity use and 221.2 \(\mathrm{MWh_{PR}}\)/year from gas). This means that the design process that led to the CNB scenario allowed for a reduction in the primary energy of about 59%, compared to the nZEB scenario (Table 4).  
Figure 9: Overall consumption and production.