esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ranging Error Source Analysis and Modeling
### Multipath and Non-Line-of-Sight Error Analysis and Modeling  
When a satellite signal has a multipath phenomenon, regardless of NLOS reception, the receiving antenna receives the direct signal and several multipath signals, so the radio frequency received signal processed by the receiver is the superposition of these direct signals and multipath signals [30]. However, in the actual environment, especially in harsh and challenging environments, the LOS path of radio propagation is blocked by obstacles, such as mountains, bushes and buildings, and radio waves can only be refracted or reflected; other NLOS dissemination methods can be used to spread the signal.  
Generally, the receiver cannot distinguish between direct signal and multipath signal and the receiver loop will directly track and lock the composite signal; therefore, we propose the following compound measurement error model:  
\[\varepsilon_{all}=\varepsilon_{\text{LOS}/mult}+\varepsilon_{\text{NLOS}} \tag{6}\]  
where \(\varepsilon_{\text{LOS}/mult}\) is the error caused by multipaths in the LOS environment and \(\varepsilon_{\text{NLOS}}\) is the error caused by the NLOS. In the NLOS environment, its value is a positive number.  
We define the ratio of the sum of the error caused by multipath influence in the LOS environment and the error caused by NLOS to the amplitude of the direct signal (MLNSR) to describe the influence of multipath and NLOS ranging errors of the signal, which is a dimensionless relative value. The definition is as follows:  
\[\text{MLNSR}=\frac{A_{all}}{A_{S}}=\frac{A_{\text{LOS}/multi}+A_{\text{NLOS}}}{ A_{S}} \tag{7}\]  
where \(A_{all}\) is the combined amplitude of the amplitude \(A_{\text{LOS}/multi}\) of the error signal caused by multipath effects in the LOS environment and the amplitude \(A_{\text{NLOS}}\) of the error signal caused by NLOS and \(A_{S}\) is the amplitude of the direct signal.  
In this way, we can analyze the MLNSR without having to discuss the number of complex multipath paths and how to model them. In particular, when MLNSR = 0, we believe that there is only a direct signal and no multipath or NLSO signal interference or that the error signal caused by multipaths in the LOS environment cancels with the error signal caused by NLOS, which is an ideal navigation situation. Generally, MLNSR > 0 and its size are related to external environmental factors.
## 2 Methods
### Dynamic Time Shifting  
From Equations (3) and (4), we obtain the time shifting for different geophones:  
\[\Delta t_{i}=t_{i}-t_{1}=(i-1)\Delta hp_{n}=(i-1)\Delta h\frac{\cos\varphi_{n} }{v_{n}}. \tag{9}\]  
From Equation (9), the variation of According to Equation (9), the time shift for a geophone is affected by the velocity of the seismic wave and the direction of wave arrival. The upgoing or downgoing waves contain reflected waves from different interfaces, even if they are of the same type (such as an upgoing P-wave), and their propagation directions are not completely consistent. As a result, the static time shift (traditional median filtering method) cannot accurately align the events. Thus, the method of dynamic time shifting is necessary.  
the slowness of the target wavefield with the intercept time is obtained. Therefore, in the space-time domain, the time shift of different geophones at different intercept times is satisfied:  
\[\Delta t_{n}(i,\tau)=(i-1)\Delta hp_{n}=(i-1)\Delta h(a_{n}+b_{n}\tau)=t_{i}-\tau, \tag{10}\]  
Equation (10) can be deduced by eliminating \(\tau\) as follows:  
\[\Delta t_{n}(t_{i})=\frac{(i-1)\Delta h(a_{n}+b_{n}t_{i})}{(i-1)\Delta hb_{n}+ 1}, \tag{11}\]  
which indicates that the time shift varies with different geophones and time. According to the variation of the slowness of the target wavefield, the seismic data are dynamically aligned by Equation (11), and the target wavefield is separated by the median filter with Equation (1).
## 4 Discussion  
### BRDF Model Correction  
The heterogeneity of the forest canopy structure resulted in directional spectroscopic observations of the vegetation canopy reflectivity. Compared to the traditional vertical observation method, using multi-angle observations makes it easier to capture the spatial structure characteristics of the vegetation canopy. Using the BRDF model correction, the spatial structure characteristics of vegetation can be more accurately observed through spectral observations [45], can eliminate the uncertainty brought by the sun and observation angles, and increase the comparability of different observations. Both the observation angle and the sun angle were changed before the model was corrected. The changing angle will cause the variance of the observation data to increase. After the model is corrected, the angle is fixed, the uncertainty caused by the angle factor will no longer exist, and the estimation accuracy of the LUE and PRI will be improved [11]. This result is also consistent with the results obtained by [PERSON] et al. [11]. Compared with the results obtained by [PERSON] et al., the R\({}^{2}\) of exponential adjustment in our study is lower. This may be due to the different types of forests. However, spectroscopic observations are also affected by environmental factors such as radiation and temperature, which are also factors that affect the accuracy of PRI in estimating LUE [10; 24; 45; 46; 47].
## 3 Results and Discussion
### Extraction of Schottky Parameters
#### 3.3.2 Method 2: d\(V\)/d(\(ln\)) vs. \(J\) and H(\(J\)) vs. \(J\)
When applying a voltage to the barrier, the generation rate of the electron-hole pair starts to increase in the depletion region producing current transport between the GNW and the silicon. Generation current is a common cause of the unsaturated current in reverse bias, which clearly can be seen in the I-V curve of the GNW-TMBS diode (Figure 5a). Looking at Table 1, we can see that the barrier height and ideality factor of the TiSi-TMBS diode did not have significant change until the device was tested at 373 K. We believe since both parameters did not change significantly until 373 K, the major current transport mechanism at that temperature range is thermionic emission. This is supported by the fact that the ideality factor for TiSi\({}_{2}\)-TMBS is near unity at that temperature range. However, both parameters start to change above 373 K possibly due to the multiplication of a large number of free carriers transported between the metal-semiconductor causing high leakage current. The GNW film deposited on the silicon interface looks rough and different film height is observed in Figure 8a,b. This is due to the morphology of the GNW layer, as can be seen from Figure 2a,b. The difference in height of the GNW layer and no surface interaction between the GNW and the silicon could cause deviation in the ideality factor and a high barrier height. The TiSi\({}_{2}\) surface and interface with silicon in Figure 8c,d is more uniform and smoother. The TiSi\({}_{2}\) layer covers the entire silicon interface giving it a near unity in ideality factor. The TiSi\({}_{2}\)-Si interface is better than the GNW-Si interface because of the interaction of Ti and Si forming the TiSi\({}_{2}\) layer. Figure 8: Cross-sectional view of TMBS diode at low and high magnification for (**a,b**) GNW-TMBS diode (**c,d**) TiSi\({}_{2}\)-TMBS diode.
## 1 Introduction
Additionally, the hydraulic parameters of the lake, such as high water potential energy, coupled with poor geotechnical properties of the moraine (e.g., weak internal matrix composition), further elevate the risk of failure [23]. One of the major issues related to assessing the stability of the slope in the mountainous region is associated with \"jointed rock mass\". [PERSON] et al. [24] stated that the stability of the rock mass slopes is affected by the presence of discrete fractures, and slope stability analysis using continuum mechanics is not reliable. A discontinuous approach is then suggested, in which the effect of discrete fractures is taken into account by considering independent grids between the rock matrix and fractures, and stability evaluation is suggested through the integration of a strength reduction technique. The numerical analysis procedure is verified through case studies, and it was noted that the developed method has the capability of analysing non-circular slip surfaces. [PERSON] et al. [25] performed a kinematic analysis for the stability analysis of the jointed rock mass slope and suggested an approach for the slope mass rating (SMR) to assess the stability of planar, wedge, and topple-type failure modes in terms of indices. To ease the computation work, taking into account a large number of joints, a computer code, EasySMR, was developed and validated through 35 rock slopes in the Himalayan region of India. It was claimed that the numerical code provided a rigorous and accurate analysis of the jointed rock mass slope stability. This research aims to assess the impact of lake water level increase and permafrost thaw on the stability of the moraine dams at Gurudongmar Lake and to evaluate the potential increase in GLOF hazards as a result. At first, the study area and its geological setting are presented, followed by the methodology used for geotechnical and climate trend analysis. The results from the laboratory testing are then discussed, leading to the modelling of lake level increases and an evaluation of the climate profile and its implications for permafrost thaw. The subsequent section analyses the impact of permafrost thaw on moraine dam stability, considering scenarios of melting and steady seepage conditions.
## 8 Conclusions  
This work collects the results of the preliminary research activity on the dynamic maneuverability analysis (DMA) application on the innovative AURV designed by the Department of Industrial Engineering of the University of Florence [8,9]. As a matter of fact, autonomous underwater reconfigurable vehicles [3] do arise as a promising tool for incorporating several reconfigurable modules and accomplishing several complex tasks as intervention, maintenance, and repair (IMR) operations. The activity focused on the study of the maneuverability methodology for underwater vehicles, by starting from the analysis of state-of-the-art solutions in the field of robotics arm [10], and leading to the specific case-study of the UNIFI DIEF AURV. While the concept of maneuverability has already been studied in underwater robotics, as, for instance, for the thruster configuration optimization [12,13], we introduce a novel analysis which relates the vehicle body-fixed accelerations with the rotational speed of each thruster, taking into account also the AURV dynamic model for the several configurations, and the propulsion system assumed in the designing phase. It is worth noticing that, despite the specific application of this work, the proposed methodology can be applied to any unmanned underwater vehicle (UUV), if its dynamic and propulsion system parameters are correctly identified. In this context, the application of artificial intelligence (AI) algorithms has been arisen as innovative solutions for the identification of the UUV dynamic parameters, as suggested in [22]. As a result, further investigations and comparisons will be performed among the UNIFI DIEF AURV and other AUVs. Moreover, [14] inspires the possibility for further studies on multibody modeling for reconfigurable vehicles. The last, but not least, key topic which is worth being explored in the future is the introduction of all the nonlinear properties which have not been considered in the AURV dynamic model, as the estimate of the added mass terms as well as the dead-zone boundary values of the thrusters. All these open problems and research areas, whose resolution and deepening constitute a natural continuation of the cutting-edge research activity carried out thus far, will be subjected to further investigations in the near future.
## 3 Basic Principle of Calculating Tropospheric Zenith Delay
### VMF Calculation of Tropospheric Delay
#### 3.1.2 Tropospheric Delay Grid Plane Interpolation Scheme  
In order to analyze the effect of the horizontal interpolation strategy on the results, BIL and IDW are used respectively. The mathematical model of the BIL method is as follows:  
\[p=(\lambda-\lambda_{00})/\Delta\lambda,q=(\varphi-\varphi_{00})/\Delta\varphi \tag{3}\]  
where \(0\leqslant p<1,0\leqslant q<1\), \(\lambda\) and \(\varphi\) are the longitude and latitude of the interpolation point; \(\lambda_{00}\) and \(\varphi_{00}\) are the longitude and latitude of the lower left corner of the grid cell, where the interpolation point is located. At first, points A, B or C, D are linearly interpolated. Then, point \(Z\) is linearly interpolated, and the \(Z(\lambda,\varphi)\) value of point \(Z\) is calculated as:  
\[Z(\lambda,\varphi)=Q_{00}\times Z_{0,0}+Q_{10}\times Z_{1,0}+Q_{01}\times Z_{0, 1}+Q_{11}\times Z_{1,1} \tag{4}\]  
\[\begin{cases}Q_{00}=(1-p)\times(1-q)\\ Q_{01}=(1-p)\times q\\ Q_{10}=p\times(1-q)\\ Q_{11}=p\times q\end{cases} \tag{5}\]  
where \(Z_{0,0},Z_{1,0},Z_{0,1},Z_{1,1}\) are ZTD values of the four grid points around the point P, respectively shown in Figure 1, which can be obtained jointly by Equations (1) and (2); \(Q_{00},Q_{01},Q_{10},Q_{11}\) are the corresponding factors. The mathematical model of inverse distance weighted interpolation (IDW) reads as follows:  
\[\lambda_{i}=\frac{1/d_{i}^{x}}{\sum_{i=1}^{n}1/d_{i}^{x}} \tag{6}\]\[S_{site}=\sum_{i=1}^{n}\lambda_{i}S_{grid,i} \tag{7}\]  
where \(n\) is the total number of grid points around the station; \(d_{i}\) is the distance between the \(i\) grid point and the station; \(\lambda_{i}\) is the weight of the corresponding mesh point; \(x\) is the power value of inverse distance weighting, generally 1.3; \(S_{site}\) and \(S_{grid,i}\) are tropospheric delay quantities at the station and the ith grid point, respectively.
# Microalgal Biomass Generation via Electroflotation: A Cost-Effective Dewaring Technology
## 2 Overview of Microalgal Biosystems
### Development of Microalgal Culture System
#### 2.1.2 Closed Photobioreactor  
Photobioreactors have been extensively investigated to better control cultivation conditions, such as pH, temperature, CO\({}_{2}\), light intensity, and nutrient concentration to achieve optimal biological and physiological characteristics of microalgae cultures [26]. Table 3 shows a comparison between an open pond and photobioreactor, and each presents a unique set of benefits and disadvantages. A photobioreactor is a closed system that allows monoculture of microalgae for prolonged durations with high biomass productivity, short harvesting time, and low risk of contamination [38; 44]. This system is normally used to cultivate microalgae for the production of protein, fatty acids, pigments, animal feed, and antioxidants in large-scale. Example of microalgal species that have been tested to grow in photobioreactors are _Chlorella vulgaris_, _Chlorella sarokiniaana_, _Arthrospira platensis_, _Nannochloropsis_ sp., _Phorphyridium curetum_, _Selenstrum capricornutum_, _Haematococcus pluvialis_, and _Phaeodactylum tricornotum_, among others [40; 45]. Specifically, photobioreactors allow higher volumetric productivity for the enhanced capture of radiant energy, depending on microalgal strains and scales used. Hence, it is expected that photobioreactor can attain higher oil yield per hectare (136.9 m\({}^{-3}\) ha\({}^{-1}\) based on 70 wt.% oil in biomass) when compared to raceway pond (99.4 m\({}^{-3}\) ha\({}^{-1}\)) [46].  
Photobioreactors can be placed either indoors with artificial light or outdoor using direct sunlight and they can be operated in batch or continuous mode [44]. Further, photobioreactors can be designed with distinct structures, such as tubular and flat plate. Tubular photobioreactors are preferred for outdoor cultivation of microalgae as this system can be constructed in various configurations, either in a vertical, horizontal, or inclined plane, depending on the specifications of the system. Vertical design allows better gas-liquid mass transfer and less energy consumption, while the horizontal design is scalable, but requires a large land area [47]. On the other hand, a flat-plate photobioreactor is cuboidal and equipped with a minimal light pathway. These photobioreactors can be made via transparent materials, such as polycarbonate, glass, and plexiglass [44], and have a high surface to volume ratio [47]. Although less study on flat-plate photobioreactors is available in the literature, it has been reported that bulk cultivation of microalgae is plausible in this system due to low accumulation of dissolved oxygen (O\({}_{2}\)) and high illumination area [47]. However, flat-plate photobioreactor usage has resulted in a lower yield of biomass per area as compared to tubular photobioreactors due to shorter light penetration depths and lack of light dilution, which led to photo-inhibition of microalgal growth [47]. Table 4 shows the comparison between tubular and flat-plate photobioreactor systems.
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
###### Abstract  
Learning object shapes from a single image is challenging due to variations in scene content, geometric structures, and environmental factors, which create significant disparities between 2D image features and their corresponding 3D representations, hindering the effective training of deep learning models. Existing learning-based approaches can be divided into two-stage and single-stage methods, each with limitations. Two-stage methods often rely on generating intermediate proposals by searching for similar structures across the entire dataset, a process that is computationally expensive due to the large search space and high-dimensional feature-matching requirements, further limiting flexibility to predefined object categories. In contrast, single-stage methods directly reconstruct 3D shapes from images without intermediate steps, but they struggle to capture complex object geometries due to high feature loss between image features and 3D shapes and limit their ability to represent intricate details. To address these challenges, this paper introduces SS3 DNet-AF, a single-stage, single-view 3D reconstruction network with an attention-based fusion (AF) mechanism to enhance focus on relevant image features, effectively capturing geometric details and generalizing across diverse object categories. The proposed method is quantitatively evaluated using the ShapeNet dataset, demonstrating its effectiveness in achieving accurate 3D reconstructions while overcoming the computational challenges associated with traditional approaches.  
SS3 DNet-AF; 3D reconstruction; attention-based fusion; point clouds +
Footnote †: journal: applied sciences
## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(I\)} & \multicolumn{3}{c}{_Tight Inventory_} & \multirow{2}{*}{\(I\)} & \multicolumn{3}{c}{_Relaxed Inventory_} & \multirow{2}{*}{\(I\)} & \multicolumn{3}{c}{_Abundant Inventory_} \\ \cline{2-2} \cline{5-11}  & \(LB\) & \(Sim_{\text{BRLH}}\) & & \(UB\) & & \(LB\) & \(Sim_{\text{BRLH}}\) & \(UB\) & & \(LB\) & \(Sim_{\text{BRLH}}\) & \(UB\) \\ \hline
**b1** & 1110.9 & 2164.5 & 2167.0 & **b21** & 1030.0 & 2096.3 & 2154.6 & **b41** & 760.5 & 1731.0 & 1858.7 \\
**b2** & 1378.2 & 2084.7 & 2084.7 & **b22** & 1275.7 & 1996.6 & 1996.6 & **b42** & 870.4 & 1574.1 & 1636.8 \\
**b3** & 2437.2 & 3465.3 & 3465.3 & **b23** & 2021.9 & 3043.9 & 3043.9 & **b43** & 1259.6 & 2239.4 & 2239.4 \\
**b4** & 1930.3 & 3184.3 & 3184.3 & **b24** & 1632.2 & 2891.1 & 2989.5 & **b44** & 1284.9 & 2553.4 & 2553.4 \\
**b5** & 2395.3 & 3790.2 & 3790.2 & **b25** & 1980.4 & 3363.6 & 3363.6 & **b45** & 1364.3 & 2753.9 & 2753.9 \\
**b6** & 1389.4 & 2628.3 & 2775.6 & **b26** & 1268.0 & 2487.9 & **b46** & 1024.3 & 2269.2 & 2269.2 \\
**b7** & 1769.3 & 2544.9 & 2544.9 & **b27** & 1652.1 & 2446.5 & 2446.5 & **b47** & 1135.1 & 1919.3 & 1944.4 \\
**b8** & 2620.5 & 3715.6 & 3715.6 & **b28** & 2101.7 & 3178.6 & 3178.6 & **b48** & 1355.2 & 2420.6 & 2442.4 \\
**b9** & 2836.9 & 4209.9 & 4209.9 & **b29** & 2395.4 & 3725.6 & 3725.6 & **b49** & 1777.8 & 3138.7 & 3146.9 \\
**b10** & 3787.2 & 5258.7 & 5258.7 & **b30** & 2819.0 & 4269.7 & 4269.7 & **b50** & 1869.2 & 3308.8 & 3312.6 \\
**b11** & 1817.1 & 3261.5 & 3401.5 & **b31** & 1679.1 & 3099.3 & 3099.3 & **b51** & 1414.7 & 2814.0 & 3095.4 \\
**b12** & 2109.1 & 2978.8 & 2983.5 & **b32** & 1960.7 & 2819.4 & 2832.9 & **b52** & 1366.8 & 2233.4 & 2250.7 \\
**b13** & 2765.1 & 39
## 3 Experimental Results  
### Experimental Data  
The experimental data obtained from the five participants are pooled together and SNR values computed from error rates are presented in Table 2. Note that, \(SNR^{in}\) and \(SNR^{out}\) are calculated using \(E_{r}^{in}\) and \(E_{r}^{out}\) values.  
The results in Table 2 also emphasize the differences between inward (\(SNR^{in}\)) and outward (\(SNR^{out}\)) SNR values. It can be seen from the table that the SNR values of the inward cases are consistently larger than the outward ones except for the \(SNR_{LO}^{out}\) at \(0^{\circ}\) azimuth. For the TO, LO, and TL conditions, the SNR difference (\(SNR^{in}\)-\(SNR^{out}\)) increases with the increasing azimuth. This can be considered as a bias between inward and outward decisions, meaning that the participants mostly  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(SNR_{TO}^{in}\) & \(SNR_{TO}^{out}\) & \(SNR_{LO}^{in}\) & \(SNR_{LO}^{out}\) & \(SNR_{TL}^{in}\) & \(SNR_{TL}^{out}\) \\ \hline \(0^{\circ}\) & 0.154 & 0.073 & 0.490 & 0.497 & 0.941 & 0.866 \\ \(10^{\circ}\) & 0.249 & 0.074 & 0.474 & 0.293 & 0.915 & 0.634 \\ \(20^{\circ}\) & 0.496 & 0.023 & 0.528 & 0.064 & 0.907 & 0.298 \\ \(30^{\circ}\) & 0.618 & 0.0004 & 0.494 & 0.054 & 0.924 & 0.055 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SNR values calculated from the experimental error rates.  
chose to make a \"toward center\" decision for sound's azimuth changes when they find it difficult to make a decision. Note that this bias occurs regardless of the type and azimuth of the imposed cue and gets smaller as the azimuth moved toward the horizontal mid-line.
## 2 Methods
### Detection of Wall Segments
#### 2.1.4 Post-Processing  
The main task of the post-processing step is to refine the segmentation results, including the removal of spurious lines, connecting adjacent collinear ones, and the overall adjustment of the line to building domain orientation. The orientation can be estimated by the median value of all lines' direction angles, similar to the estimation of grid orientation in Section 2.1.2. We then consider the consistency between each line and the estimated orientation and adjust them to the target direction if the angle difference is lower than \(Th_{thz}\). After the adjustment, adjacent parallel lines with a distance lower than \(Th_{dis}\) are combined. Moreover, spurious lines that have anomalous orientation and low point density are removed directly.