esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Frequency Target Detection Techniques for DVB-T Based Passive Radar Sensors
## 5 Experimental Results for Aerial Surveillance Application
### Test Campaign and Data Collection
For the sake of clearness, all the plots within a bistatic range of 6 km have been discarded assuming that they are due to disturbance residuals. As is apparent, even operating with the best performing channel (see Figure 6a), the system is able to continuously detect the targets appearing between 50 and 60 km at bistatic velocities around \(-60\) m/s, which correspond to aircraft landing at the Fiumcino airport. In contrast, just a few isolated plots are obtained for other target track portions provided by the available air-truth. This might  
Figure 6: Detection results over the range-velocity plane for Data Set #1 using single DVB-T channels representing the best and the worst cases, respectively: (**a**) channel at 714 MHz; (**b**) channel at 634 MHz. be caused by the higher velocity of these targets that yields migration effects which might limit the coherent integration gain. Moreover, as these targets usually correspond to higher altitude targets, their detection might be prevented by the vertical-plane radiation patterns of DVB-T transmitters that are tailored to avoid wasting too much power above the horizontal. Many detections appear within the first 16 km (highlighted by the cyan rectangle in Figure 6a) which correspond to the returns from vehicles moving on the stretch of highway near the receiver position (see Figure 5). In fact, the achieved range-velocity values are compatible with the expected range-velocity trajectory of ground moving targets travelling on the highway as shown in [14]. Moreover, other tracks appear between 14 and 35 km (highlighted by the magenta rectangle in Figure 6a). These likely correspond to aircraft, not equipped with a cooperative transponder, that are landing or taking off from the small civilian Urbe airport situated near the Leonardo premises as illustrated in Figure 5.  
The comparison between the Figure 6a,b clearly shows that the target detection capability highly varies with the employed DVB-T channel due to different target backscattering properties as well as to the propagation channels EM conditions. A remarkable advantage with respect to the SF operation can be obtained by exploiting the proposed MF approaches as demonstrated in the next sub-section.
# Departure from Flux-Gradient Relation in the Planetary Boundary Layer
## 2 Methodology
### Observed and Modeled Momentum Fluxes  
Using observations from a profiling lidar, the momentum fluxes can be computed from the difference between the radial velocity variance \(\sigma(v_{r})\) of lidar beams from two  
Figure 2: Map of northern Europe with the location of the offshore (FINO3) and onshore (Hamburg) sites as red dots. The colorbar indicates the height in meters above sea level.  
opposing azimuthal measurements. This was originally proposed by [PERSON] et al. [15] and more recently applied by [PERSON] et al. [4],  
\[\begin{pmatrix}\langle u^{\prime}w^{\prime}\rangle\\ \langle v^{\prime}w^{\prime}\rangle\end{pmatrix}=\frac{1}{2\sin 2\phi}\begin{pmatrix} \cos\delta&-\sin\delta\\ \sin\delta&\cos\delta\end{pmatrix}\begin{pmatrix}\sigma^{2}(v_{r,E})-\sigma^ {2}(v_{r,W})\\ \sigma^{2}(v_{r,N})-\sigma^{2}(v_{r,S})\end{pmatrix}, \tag{5}\]  
where \(\delta\) is the lidar offset mentioned above, defined by the angle between the \(v_{r,N}\) laser beam and the north. For a detailed derivation, see Appendix B.  
Here, we assume horizontal homogeneity of the wind field, as we combine radial wind speeds from a number of points which are separated by up to 261.8 m, at the 500-m height level. Therefore, we expect that the vertical profiles of momentum fluxes up to 500 m are better estimated using Equation (5) at the offshore than at the onshore site given the possible contamination from surface inhomogeneities at the latter site.  
We choose to evaluate the NEWA-WRF outputs in this work, which were described in detail by [PERSON] et al. [10]. These outputs offer a time series of a number of meteorological variables covering the period of both lidar campaigns, with a temporal and horizontal resolution of 30 min and 3 km, respectively, and seven vertical levels at 50, 75, 100, 150, 200, 250, and 500 m. A linear interpolation over the spatial domain was performed using the nearest neighbor grid cells to extract the time series at FINO3 and Hamburg locations. [PERSON] et al. [10] compared the mean wind speed and wind direction profiles using several PBL schemes in the WRF model against observations from several meteorological masts, including FINO3. A further comparison of the NEWA-WRF outputs using more meteorological masts was performed by [PERSON] et al. [16]. The results of these comparisons demonstrated that a modified version of the Mellor-Yamada Nakanishi-Nino (MYNN) scheme [17] resulted in the lowest wind speed bias at most sites.  
The MYNN level 2.5 (MYNN2) is a local scheme, i.e., \(K_{m}\) is derived from local quantities. We can thus estimate the local momentum fluxes using Equations (1) and (2) from the NEWA-WRF output by computing first \(K_{m}\) as  
\[K_{m}=lqS, \tag{6}\]  
where \(l\) is a master length scale, \(q=\sqrt{2e}\) with \(e\) being the turbulent kinetic energy (TKE), which in MYNN2 is based on the prognostic TKE from Mellor-Yamada [18], and \(S\) is a stability function. Since \(l\) is not stored in the NEWA-WRF output, we derive it following the work of [PERSON] and [PERSON] [17].
## 2 Theoretical Background
### Machine Learning-Based Feature Reduction  
Most of the proposed condition monitoring approaches include the estimation of high-dimensional sets of features as a part of the diagnosis procedure. Precisely, the feature calculation is carried out aiming to obtain the characteristic patterns associated to malfunction operations. Thus, the sets of features are obtained from signals that describe the behavior of a system, i.e., an electromechanical system. Subsequently, unhelpful and correlated information may be estimated if a large number of features is estimated. In this sense, the application of machine learning-based feature reduction techniques represents a practical solution to retain the most significant and representative information.  
Thus, the Principal Component Analysis (PCA) and the Linear Discriminant Analysis (LDA) are within the best well-known techniques used to reduce the dimensionality of a high-dimensional set of features. The PCA is an unsupervised linear method that allows reducing the dimension of a correlated data set to a linear space of unrelated indicators while preserving the greatest possible variance. The application of the PCA results in the extraction of a new set of features that are sorted according to the cumulative variance that preserves and are known as principal components [5; 11; 42]. On the other hand, the LDA is a supervised linear method that allows a dimensionality reduction by extracting a new set of features, in which the maximization of the data separability is achieved for a \(C\) number of considered classes. Although both methods have been widely used in several condition monitoring strategies, the implementation of the LDA is appropriated to be applied in multi-class problems leading to high effectiveness during the condition assessment [19; 37].
## 4 Discussion  
### The Influence of Data Quality Uncertainty on Simulation Results  
In previous studies, [PERSON] et al. [9] and [PERSON] et al. [54] analyzed the factors that affect the accuracy of snow cover simulation by land surface process model, and found that the uncertainty of forcing data has a greater impact on the simulation results than the structure and parameterization scheme of the model itself. In this study, meteorological station observation data were used to evaluate the accuracy of CMFD and ERA-Interim data. Since the data of Habahe, Altay, and Fuyun stations are used in the production of CMFD data, only the observation data of Kuwei station were selected. Meteorological data from CMFD and ERA-Interim were extracted based on the longitude and latitude of the Kuwei site. Scatter plots were drawn based on the CMFD, ERA-Interim, and the observed  
Figure 5: Spatial distribution of annual average precipitation based on CMFD (**a**) and ERA-Interim (**b**) data in the Irtysh River Basin.  
Figure 6: Average \(\text{SD}_{\text{max}}\) (**a**), \(\text{SWE}_{\text{max}}\) (**b**), snow days (**c**), STSA (**d**), and ETSM (**e**) in the Irtysh River Basin from 2001 to 2017.  
data, and the accuracy of the two meteorological forcing data was evaluated using MD and RMSE statistical parameters (Figure 7). It can be seen from Figure 7 that there are some deviations between CMFD, ERA-Interim and the observed temperature, wind speed, relative humidity, precipitation, and downward shortwave and longwave radiation. On the one hand, the reason for this phenomenon lies in the difference in spatial range between grid points and stations; on the other hand, the error of meteorological forcing data itself is also an important reason. From the calculated MD and RMSE values, the accuracy of CMFD temperature, wind speed, and downward shortwave and longwave radiation data is higher than ERA-Interim data. Although the accuracy of ERA-Interim relative humidity and precipitation is slightly better than that of CMFD at the Kuwei site, considering that the CMFD precipitation and relative humidity data were generated through fusion of remote sensing products, reanalysis datasets, and in situ station data [42], it is considered that CMFD also has high accuracy at the watershed scale. This is also the reason why the modeled snow depths by using the CMFD data are more consistent with GDSD data.  
Figure 7: Scatter plot based on the hourly CMFD, ERA-Interim, and the observed temperature (**a**,**g**), relative humidity (**b**,**h**), wind speed (**c**,**i**), precipitation (**d**,**j**), downward shortwave radiation (**e**,**k**), downward longwave radiation (**f**,**l**) data during the study period.
# Evaluation of Horizontal Shear Performance of Composite Girders Using Separable Shear Connectors
## 4 Verification of Flexural Tests on Composite Girders  
### Fabrication of Composite Girder Specimens  
Flexural tests were conducted to assess the applicability and performance of composite girders on a larger scale. The specifications for the girder (longitudinal length \(\times\) width \(\times\) height) were 9.6 m \(\times\) 0.45 m \(\times\) 0.65 m, and those for the precast deck (longitudinal length \(\times\) width \(\times\) thickness) were 9 m \(\times\) 1.2 m \(\times\) 240 mm. To eliminate the effect of the longitudinal joints between decks, the entire deck was constructed from a single structure without longitudinal joints, focusing solely on the shear connection between the girder and the deck. Figure 12 depicts detailed drawings of the girder and composite girder specimens. As listed in Table 5, the specimens were fabricated in three sets of each type: one with conventional U-shaped rebar shear connectors (Rebar_F) and two with the separable shear connectors developed in this study (DY_F) and (DY_H). The number of shear connectors for each specimen type, Rebar_F, DY_F, and DY_H, were 74, 48, and 20, respectively, tailored to the design conditions. Models Rebar_F and DY_F were designed to induce flexural failure by applying 100% of the required quantity of rebar and shear connectors, whereas specimen DY_H was equipped with approximately 42% of the required shear connectors to induce horizontal shear failure at the interface.  
Figure 11: Comparison of horizontal shear strengths.  
Figure 12: Bending test specimen drawing for PSC composite girder.
# An ARCore-Based Augmented Reality Campus Navigation System
###### Abstract  
Currently, the route planning functions in 2D/3D campus navigation systems in the market are unable to process indoor and outdoor localization information simultaneously, and the UI experiences are not optimal because they are limited by the service platforms. An ARCore-based augmented reality campus navigation system is designed in this paper in order to solve the relevant problems. Firstly, the proposed campus navigation system uses ARCore to enhance reality by presenting 3D information in real scenes. Secondly, a visual inertial ranging algorithm is proposed for real-time locating and map generating in mobile devices. Finally, rich Unity3D scripts are designed in order to enhance users' autonomy and enjoyment during navigation experience. In this paper, indoor navigation and outdoor navigation experiments are carried out at the Lingang campus of Shanghai University of Electric Power. Compared with the AR outdoor navigation system of Gaode, the proposed AR system can achieve increased precise outdoor localization by deploying the visual inertia odometer on the mobile phone and realizes the augmented reality function of 3D information and real scene, thus enriching the user's interactive experience. Furthermore, four groups of students have been selected for system testing and evaluation. Compared with traditional systems, such as Gaode map or Internet media, experimental results show that our system could facilitate the effectiveness and usability of learning on campus.  
augmented reality; campus navigation; ARCore; high-precision localization; Unity3D; VIO (visual inertial odometry) +
Footnote †: journal: Applied Sciences
###### Abstract  
Lime stabilization is one of the main methods to achieve efficient treatment and resource utilization of waste slurry. This study investigated the compressibility and permeability of lime-stabilized slurry with different granular gradations based on the ultra-low stress consolidation/permeability test and identified the stabilization mechanism of lime-stabilized slurry with high water content by mercury intrusion porosimetry (MIP) and scanning electron microscopy (SEM) tests. The test results indicated that (i) lime-stabilized slurry with high water content showed obvious evidence of remolded yield stress and (ii) the remolded yield stress increased with the increasing lime dosage. Lime was found to induce the flocculation of clay to form aggregates through ion exchange, further stabilizing them through the volcanic ash reaction, thus increasing the remolded yield strength of the stabilized slurry. The remolded yield stress of the Hangzhou stabilized slurry with a 1% lime dosage was shown to increase from 0 kPa to 5.71 kPa, while the compression index \(C_{\text{S1}}\) decreased by 68.8%. In addition, the pore volumes and diameters of the soil increased once the flocculation was completed, leading to increased permeability of the stabilized slurry. It was, however, observed that the stabilized slurry permeability did not increase infinitely with the increasing lime dosage, but on the contrary decreased once the lime dosage exceeded a certain threshold value. The permeability of the Hangzhou stabilized slurry was found to be one order of magnitude higher than that of the remodold slurry at the optimal dosage. Whereas for slurry with high clay content, the recommended lime dosage was established to be 2% to reduce its compressibility or enhance its permeability; for slurry with high silt content, the recommended lime dosage was ascertained to be 3%.  
lime stabilization; slurry with high water content; granular gradation; compressibility; permeability; microstructure 2023
# Noise Suppressed Image Reconstruction for Quanta Image Sensors Based on Transformer Neural Networks
## 6 Conclusions  
This paper first introduces the shortcomings of current QIS noise reconstruction methods, then introduces the non-ideal imaging model of QIS imaging and proposes a noise suppression reconstruction method based on neural networks. The proposed neural network integrates convolutional neural networks and Transformer. Its architecture combines the Anscombe transformation with serial and parallel modules to enhance denoising performance and adaptability across various scenarios. Finally, the experiment demonstrates the rationality of the neural network architecture, showing that the reconstruction method proposed in this paper effectively suppresses photon shot noise in the QIS imaging process at a feasible and acceptable oversampling rate. This makes it an ideal solution for practical QIS imaging applications under extreme noise conditions. It achieves superior noise suppression compared to MLE [15], TD-BM3D [17], QISNet [18] and DPIR [31].  
**Author Contributions:** Conceptualization, G.W.; methodology, G.W.; software, G.W.; validation, G.W.; formal analysis, G.W.; investigation, G.W.; resources, G.W.; data curation, G.W.; writing--original draft preparation, G.W.; writing--review and editing, Z.G.; supervision, Z.G.; project administration, Z.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be made available upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Trellis Tone Modulation Multiple-Access for Peer Discovery in D2D Networks
## 4 Multi-User Detection of TTMMA via Message-Passing
### Successive Interference Cancellation and Threshold Adjustment  
The performance can be improved by performing successive interference cancellations (SICs) in the demodulation process of the TTMMA signal. If only part of the tone-path of all \(n\) UEs is recovered, removing the signal from the received signal reduces the number of signals remaining in the corresponding DRU, so that additional signal detection can be expected when the demodulation is performed again through the message-passing demodulation.  
Channel estimation is required for the successive interference cancellation operation. The TTMMA does not use a separate pilot for channel estimation because the channel gain can be directly detected from the single-tones. When performing message-passing with tone-space expansion, the demodulator can determine whether or not the detected tone-path is superposed. If a tone-path is determined to be a single path or a separate path in the \(k\)-th symbol, the signal value at the corresponding position in the \(k\)-th symbol can be used as the channel estimation value. If the channel gain varies over time over \(N\) symbols, the channel estimation for the superposed symbol may be performed using an average value of the signals in adjacent symbols determined as single-tones, or a weighted-sum can be used.  
In addition, the receiving UE can further demodulate by adjusting the threshold even if it has not detected any tone-path in the demodulation process. Since the background noise is a random variable, the noise added to a particular symbol may have an unusually large value compared to the average value. Therefore, if the receiving UE does not identify any discovery signal, it can attempt to demodulate again by adjusting the default threshold value \(\delta\) used in the message-passing process. For example, if the threshold value \(\delta=2\sigma^{2}\) has been set for the first message-passing process, it is increased to \(\delta=3\sigma^{2}\), \(\delta=4\sigma^{2}\), etc. in the next message-passing process. In this way, it is possible to expect additional tone-path discrimination by alleviating the condition of the tone-transition test through upward adjustment of the threshold value.  
Algorithm 2 summarizes the procedure of multi-user detection of TTMMA signals.
```
Receive \(\mathbf{Y}\) Perform Pre-Prediction for\(k=1\) to \(N-1\)do Find H\({}_{k}\) using message-passing end Concatenate H\({}_{k}\)'s Extract valid tone-paths (or codeword) with codeword verification using CRC code. Tone-space expansion is used for discriminating SIC or Threshold Adjustment (if needed) and Return to Perform Pre-Prediction step.
```  
**Algorithm 2** Procedure of Multi-User Detection of TTMMA Signals
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
###### Abstract  
Machine learning models for spatial prediction have been applied in various types of research. However, spatial relation has not been fully considered in modeling, since the Cartesian coordinates of the observed points are directly employed as the location information for machine learning features. This study presents a machine learning modeling process which incorporates spatial autocorrelation for geotechnical subsurface modeling. A new set of features called the Euclidean distance field (EDF) was generated based on the distance between the query points and the observed boreholes in order to incorporate spatial autocorrelation into the machine learning model. Principal component analysis (PCA) was performed to reduce the increasing dimensionality of the dataset caused by the EDF features. Optimized machine learning models based on several popular algorithms (Support Vector Machine, Gaussian Process Regression, Artificial Neural Network, and \(k\)-Nearest Neighbor) were employed for predicting several geotechnical information as the targets. The results showed that the optimized machine learning models constructed with the EDF modeling approach generate a slightly lower Root Mean Square Error (RMSE) score compared to the model with the direct XY coordinate approach by 0.041, 0.046, 1.302, and 1.561 for ground surface elevation, groundwater level, SPT-N value, and percent finer than 0.075 mm sieve, respectively. Both modeling approaches performed well for USCS-based soil classification with the EDF model having slightly improved classification accuracy by 0.72%. Furthermore, the model can perform balance multiclass classification as indicated by the >95% precision, recall, f1-score, and balanced accuracy score. These results indicate that spatial autocorrelation has a noticeable effect. Hence, it needs to be considered to improve the overall performance of spatial machine learning modeling. Comparison of geotechnical subsurface predictions generated based on different machine learning algorithms showed that the selection of the best-performing model based only on the lowest prediction error is not appropriate for spatial prediction modeling. Therefore, thorough analysis of the predicted data by visualization is necessary in the selection process for spatial prediction modeling.  
machine learning; geotechnical subsurface modeling; spatial prediction modeling; Euclidean distance field; spatial autocorrelation; geotechnical properties prediction; soil classification +
Footnote †: journal: Applied Sciences