esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of RINEX-2 Files With Mixed GPS L2P(Y)/L2C Carrier Phase Observations
## 3 Detection Algorithms
### Algorithm 1: Melbourne-Wubbena Linear Combination in PPP Mode  
In this approach, widelane ambiguities are estimated based on the Melbourne-Wubbena (MW) linear combination in single station processing (PPP). \(\mathrm{FC}_{\mathrm{MW}}\) corrections must be applied to obtain unbiased ambiguities.  
All differences between the ambiguities of one satellite are treated as cycle-slips and can usually be fixed to their true integer values and removed. Thus, we obtain one MW ambiguity estimate \(\mathrm{A}_{\mathrm{MW}}\) per satellite.  
The ambiguities are separated into two groups: non-L2C and L2C satellites. Within each group, the estimated ambiguities are averaged using the algorithm suggested by [PERSON] and [PERSON] (2002) [14]. In a first step, the ambiguity angles are transformed to vectors applying sine and cosine functions. These functions also remove the integer portions of the ambiguities. Summing these vectors creates the two components of a resulting vector:  
\[\begin{array}{l}\Sigma_{\mathrm{sin}}=\sum sin\ (2\pi\cdot A_{MW})\\ \Sigma_{\mathrm{cos}}=\sum cos\ (2\pi\cdot A_{MW})\end{array}. \tag{8}\]  
The average fractional ambiguity \(AFA_{MW}\) is then recovered by:  
\[AFA_{MW}=\frac{1}{2\pi}arctan2\ (\Sigma_{\mathrm{sin}},\Sigma_{\mathrm{cos}}), \tag{9}\]  
Figure 3: Distribution of fractional ambiguities after the alignment of L2P(Y)/L2C carrier phase observations. The same observation stations and identical figure structure as Figure 2 are used.  
where the \(arctan2\) function ensures the correct quadrant. This function is available with many programming languages.  
Since we are also interested in quality measures of these average values, residuals are computed for all ambiguities by:  
\[R_{MW}=A_{MW}-AFA_{MW} \tag{10}\]  
and fractional residuals \(FR_{MW}\) are obtained by removing any integer portions by:  
\[FR_{MW}=\frac{1}{2\pi}arctan2\ (\sin\ [2\pi\ R_{MW}]\,\cos\ [2\pi\ R_{MW}]). \tag{11}\]  
The L2 bias between non-L2C and L2C satellites is the difference:  
\[b_{MW}=AFA_{MW}^{L2C}-AFA_{MW}^{non-L2C}. \tag{12}\]  
Its standard deviation is computed from the fractional residuals of the two groups of satellites:  
\[s_{b}=\sqrt{\frac{\Sigma\Big{(}FR_{MW}^{non-L2C}\Big{)}^{2}}{n\cdot(n-1)}+ \frac{\Sigma\big{(}FR_{MW}^{L2C}\big{)}^{2}}{m\cdot(m-1)}}, \tag{13}\]  
where \(n\) is the number of ambiguities of non-L2C satellites and m the number of ambiguities of L2C satellites.  
In a few cases, this detection algorithm produced dubious results with large standard deviations. The detailed analysis revealed that FCB corrections, which work well for the majority of stations, fail with some data sets. In these cases, alternative detection algorithms are needed.
# Energy Efficient Constellation for Wireless Connectivity of IoT Devices
## Appendix A Maximum A-Posteriori Criterion  
We first recall the base equations for maximum a-posteriori (MAP) criterion. Let us denote with \(P(\cdot)\) the discrete probability function and with \(p(\cdot)\) the probability density function (pdf). Given the a-posteriori observation of received vector \(\bar{r}\), we define the a-posteriori probability of signal \(\bar{s}_{m}\) as [32]  
\[P(\bar{s}_{m}|\bar{r})\qquad m=0\ldots M-1 \tag{1}\]  
where \(M\) is the number of symbols in the constellation. By using the [PERSON]'s rule [32]:  
\[P(\bar{s}_{m}|\bar{r})=\frac{p(\bar{r}|\bar{s}_{m})P(\bar{s}_{m})}{p(\bar{r})} \tag{2}\]  
we obtain that the a-posteriori probability is function of a-priori symbol probability and conditional pdf of the received vector observed. At denominator of Equation (2) we find the term \(p(\bar{r})\) which can be written as  
\[p(\bar{r})=\sum_{m=0}^{M-1}P(\bar{s}_{m})p(\bar{r}|\bar{s}_{m}) \tag{3}\]  
For an information source which maps also the zero symbol we can write  
\[P(\bar{s}_{m})\begin{cases}P_{0}&m=0\\ P_{m}&m=1\ldots M-1\end{cases} \tag{4}\]  
where \(P_{0}\) is the probability of emission of a zero-energy symbol and \(P_{m}\) is the probability of emission of the \(m\)-th non-zero symbol. When the non-zero energy information symbols are equally probable, Equation (4) can be rewritten as  
\[P(\bar{s}_{m})\begin{cases}P_{0}&m=0\\ \frac{1-P_{0}}{M-1}&\text{otherwise}\end{cases} \tag{5}\]  
The MAP criterion is adopted when the decoded symbol which maximizes Equation (2) is selected. Thus, the detected index \(\hat{m}\) is  
\[\hat{m}=\arg\max_{m}P(\bar{s}_{m}|\bar{r}) \tag{6}\]  
Since denominator of Equation (2) is independent of \(m\), MAP criterion can be written as  
\[\hat{m}=\arg\max_{m}p(\bar{r}|\bar{s}_{m})P(\bar{s}_{m}) \tag{7}\]  
**Author Contributions:** Conceptualization, L.M. and L.S.R.; methodology, L.M. and L.S.R.; formal analysis, L.M. and L.S.R.; writing--original draft preparation, L.M. and S.J.; writing--review and editing, S.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the European Telecommunications Standards Institute (ETSI) SmartBAN, and in part by the European Union's Horizon 2020 Research and Innovation Programme Grant 872752.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Applications in Multi-Channel Speech Coding and Enhancement
### Application in Multi-Channel Speech Coding
The new bit rate becomes (2560 bits/frame \(\times\) 1 channel + 5 bits/frame) \(\times\) 50 frames/s = 128.25 kbps. In this work, in order to show the best effect of the proposed codec, the 16 kHz sampling rate and 128 kbps bit rates are used to set the parameters of the EVS codec, while other parameters remain unchanged by default. In order to better demonstrate the superiority of the proposed method, the Multi-EVS method is considered as a reference method. In the Multi-EVS method, the collected speech signal of each microphone is encoded separately by the EVS and transmitted in the form of bitstream integration for realizing multi-channel codec. Thus, the bit rate of 10 channels is calculated as 2560 bits/frame \(\times\) 50 frames/s \(\times\) 10 channels = 1280 kbps. The PESQ, STOI, segment SNR (SSNR) [28], and logarithm spectral distortion (LSD) [29] are used to evaluate the performance of the decoded speech. The average test results of Target 1 to Target 20 are shown in Figure 12.  
Figure 11: Block diagram of the up-sampling (US)-Multi-EVS-TDOA. Figure 12: Performance comparison of multi-channel speech coding: (**a**) Perceptual evaluation of speech quality (PESQ) results; (**b**) short-time objective intelligibility (STOI) results; (**c**) segment SNR (SSNR) results; (**d**) logarithm spectral distortion (LSD) results. From Figure 12, we can see that the Multi-EVS obtains the highest PESQ, STOI, SSNR, and the lowest LSD because it encodes each channel separately under the maximum bit rates. Although Multi-EVS-TDOA only encodes one channel and time delays, its PESQ and STOI scores are larger at 4.4 and 0.999. respectively, and its LSD is also less than 1.0. However, due to precision problems of time delay estimation, the SSNR of the Multi-EVS-TDOA shows a downward trend, or even drops to 0 dB or below. By contrast, the up-sampling used in the US-Multi-EVS-TDOA greatly increases the accuracy of delay estimation. This leads to a significant improvement of the SSNR. At the same time, compared with the Multi-EVS-TDOA, the US-Multi-EVS-TDOA has a certain degree of increase in LSD due to the introduction of up-sampling and down-sampling. Fortunately, the increase of the LSD does not exceed the scope of transparent coding; that is, the LSD should be less than 1.0 for transparent. So, the US-Multi-EVS-TDOA greatly improved the coding efficiency while maintaining the PESQ, STOI, SSNR, and LSD within an acceptable range. Therefore, we can draw a conclusion that the speech simulated by the proposed MASS method can be used in the research of multi-channel speech coding.
# Towards a Hybrid Approach to Context Reasoning for Underwater Robots
## 5 The Proof of Concept: A Case Study on Oil Spill Detection
### Context Reasoning in the Case Study
#### 5.3.2 Rule-Based Reasoning  
Rules can be defined to provide a more accurate specification for some ontology elements. For instance, to provide a clear boundary and explanation for different Thickness degrees, rules can be set as follows:  
_Thickness(?x), hasDegree(?x,?y), swrlb:greaterThan(?y,?1200) \(\rightarrow\) Thick(?x)_  
_Thickness(?x), hasDegree(?x,?y), swrlb:lessThan(?y,?1200), swrlb:greaterThan(?y,?20) \(\rightarrow\) Thin(?x)_  
Based on the ontological representation enriched with the rules listed above, a specific spill with a thickness measurement (e.g., 2500 micron) can be classified as Thick after invoking rule-based reasoning. Likewise, SWRL rules can be defined to encode experts' understanding of other concepts, such as Currents and EstimatedSize. In this way, the raw data stored in the ontology model can be inferred to get higher-level contexts with more meanings.
# Numerical Study on Redbed Slope Stability Under Multi-Factor Conditions
## 5 Study on the Deformation and Instability Modes of the Slope Under Fluid-Structure Coupling
### Slope Saturation Analysis  
Figure 18 shows the variation in slope saturation at different rainfall times. As the duration of rainfall increases, the saturation range at the slope surface continuously expands and deepens. The infiltrating rainwater gradually collects towards the bottom of the slope, causing the groundwater level to rise and resulting in complete saturation of the underlying rock mass.  
The soil and rock mass at the slope surface and along the stratification are the first to reach saturation. The argillaceous siltstone layer at the slope crest, situated within the heavily weathered zone, exhibits relatively high porosity and permeability owing to its advanced weathering state [40]. As a result, the infiltration capacity of the rainwater exceeds the rainfall intensity, leading to a slower increase in saturation compared to other rock layers.  
Figure 18: Slope saturation conditions at different times: (**a**) 0.5 days; (**b**) 1 day; (**c**) 3 days.
# Connectivity Maintenance Based on Multiple Relay UAVs Selection Scheme in Cooperative Surveillance
## 6 Conclusions and Future Works  
Multi UAVs cooperative surveillance comprised of ground station and multiple homogeneous UAVs for remote command and situation awareness is represented in this paper. Considering history detection information and surveillance payoff estimation, a multi UAVs cooperative surveillance framework is implemented. For achieving tradeoff between maximizing surveillance payoff and maintaining network connectivity, the membership degrees of UAV alternatives are evaluated by fuzzy optimum selection according to four proposed attributes, and then a multiple relay UAVs selection scheme, which consists of two successive stages, namely multiple relay UAVs selection and role conversion, is designed to dynamically adjust UAV roles and communication links with surveillance scope change. Besides, according to different UAV roles and local neighbor information, the UAV control command sequence is optimized by PSO with elite mechanism based on decentralized receding horizon control.  
In this work, multiple relay UAVs selection scheme is performed in ground station, namely a centralized method, which depends on the entire necessary information of surveillance group. Obviously, a decentralized method based on local neighbor information is essential for improving the reliability and performance of cooperative surveillance. Moreover, complex sensing model and connectivity disruption caused by UAV failure will be considered in the future.  
The authors would like to express their acknowledgement for the support from the National Natural Science Foundation of China (No. 61273349 and No. 61573042).  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] wrote the paper; and [PERSON] and [PERSON] provided some suggestions and revised the paper.  
The authors declare no conflict of interest.
## 5 Experiments
### Comparison with Existing Methods  
We select two rail track instruction detection techniques--the 'LiDAR-based method [43]' and the 'fusion method [7]'--to compare with our approach. Inspired by [44; 45; 46], the  
Figure 14: Statistics of intruding objects and their respective distances in the dataset. Figure 15: Hardware devices for engineering applications. 'LiDAR-based method' uses prior information to segment the rail track areas from the point cloud and calculates the intrusion. In Tables 2-4 of the updated manuscript, we selected the best results from [43; 45; 46] as the outcomes of the \"LiDAR-based\" technique and compared them with other methods. However, we found that different methods led to some variations in results but there was no significant improvement or deterioration. This is because the performance of this type of technique depends on the characteristics of the LiDAR equipment. As shown in Figure 1, when the input point cloud data contain few track points and are close in distance, even with ground truth used for prediction, the algorithm cannot achieve a long effective working distance. We reproduce the method proposed by [7] as a 'fusion method'; where the track is segmented, the point cloud is reprojected onto the image, and then a rail track area surface is fitted using the point cloud. If there is a point cloud in the track area that appears above the fitted surface, then the area is considered to be intruded. In the diagrams of this paper, we refer to the 'fusion method' as 'Wang et.al' to distinguish it from our work. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Distance** & **Method** & **Rec.** & **F1** & **Pre.** & **Acc.** \\ \hline \multirow{4}{*}{Switchback} & LiDAR only [43; 45; 46] & 0.372 & 0.381 & 0.077 & 0.990 \\  & [PERSON] et al. [7] & 0.770 & 0.249 & 0.141 & 0.936 \\  & Ours & **1.000** & **0.956** & **0.938** & **1.000** \\  & LiDAR only [43; 45; 46] & 0.524 & 0.352 & 0.108 & 0.992 \\ \multirow{4}{*}{Tunnel} & [PERSON] et al. [7] & 0.732 & 0.660 & 0.892 & 0.997 \\  & Ours & **0.955** & **0.947** & **0.963** & **0.999** \\ \cline{1-1}  & LiDAR only [43; 45; 46] & **0.934** & 0.514 & **0.938** & 1.000 \\ \cline{1-1} [PERSON] & [PERSON] et al. [7] & 0.443 & 0.667 & 0.443 & 0.981 \\ \cline{1-1}  & Ours & 0.635 & **1.000** & 0.675 & **0.996** \\ \cline{1-1}  & LiDAR only [43; 45; 46] & \textbackslash{} & \textbackslash{} & \textbackslash{} \\ [PERSON] & [PERSON] et al. [7] & 0.667 & 0.276 & 0.206 & 0.948 \\ \cline{1-1}  & Ours & **0.975** & **0.975** & **0.987** & **1.000** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of the rail-obstacle approach in different scenarios. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Rec.** & **F1** & **Pre.** & **Acc.** & **Time** \\ \hline LiDAR Only [43; 45; 46] & 0.401 & 0.354 & 0.452 & 0.990 & 0.147 s \\ [PERSON] et al. [7] & 0.892 & 0.418 & 0.374 & 0.971 & 3.162 s \\ Ours & **0.982** & **0.909** & **0.900** & **0.999** & 0.270 s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overall results of different methods on the proposed dataset.
## 4 Results and Analysis
### Optical Image Analysis  
From the GF-2 images in Figure 7, South Lhonak Lake exhibits a spindle-shaped form, gradually tapering from west to east. Positioned to the west of the lake is its parent glacier, the South Lhonak Glacier. The dashed red line delineates the landslide area, while the dashed blue line outlines the lake boundary of South Lhonak Lake before the GLOF. Significant changes occurred in the glacial lake area during this period due to the substantial time gap between the two GF-2 images. Consequently, Landsat 8 images from 25 June 2023 were utilized to delineate the lake outline before the disaster. By comparing the lake extent in Figure 7a with the boundary marked by the dashed blue line, we can visually discern the changes in South Lhonak Lake's area from the end of 2020 to the middle of 2023. In Figure 7b, post-GLOF observations indicate a decrease in lake area and a significant drop in water level. The yellow circle highlights the breach in the glacial moraine dam, with evident erosional marks visible after the breach. A comparison of optical images before and after the GLOF reveals conspicuous signs of moraine landslide at the junction of the South Lhonak glacier's terminus and the glacial lake. Measurements indicate that the axial length of South Lhonak Lake is approximately 2800 m, with a maximum width of about 700 m. We further enlarged the GF-2 images of the landslide area to observe more subtle surface  
Figure 6: Time-series deformation of selected points (P1–P4) before the disaster. (**a**) Ascending (Track 12); (**b**) descending (Track 48). There is no deformation point near P4 in the deformation results obtained from the descending images.  
changes, as depicted in Figure 8. According to measurements, the landslide area spans approximately 1000 m in length and 360 m in width, covering an area of approximately 0.29 km\({}^{2}\). In Figure 8b, a clear and nearly parallel tensile crack is visible at the trailing edge of the landslide. To the east of the landslide area, there is a gully formed by melting water from alpine ice and snow.  
Figure 7: GF-2 images of South Lhonak Lake and its surroundings. (**a**) Prior to the GLOF; (**b**) after the GLOF.
## 5 Experiment Results
### Tunnel Exit Positioning Result  
The GNSS signal was completely lost in the tunnel. After exiting the tunnel, the receiver needed to capture and track the GNSS signal again. We set reference points near the tunnel exit using real-time kinematic (RTK) positioning technology. The GNSS module still could not achieve positioning at the reference points. Based on the sampled data in the real tunnel environment, the navigation results of four algorithms were compared: MIMU/ODO/MC integrated navigation; Considers the Installation Angles of MIMU for Motion Constraints aided Inertial Navigation System (CIAMC-INS); Traditional Motion Constraints without installation angles aided Inertial Navigation System (TMC-INS); Pure Inertial Navigation System (P-INS). The results were projected to the same layer of Google Earth, as shown in Figure 9. The trajectories are shown in blue, green, black, and red, respectively. Statistical results of the horizontal position errors at the reference points are shown in Table 2.  
The horizontal position error of MIMU/ODO/MC was 98.8%, 95.0%, and 91.6% lower than that of P-INS, TMC-INS, and CIAMC-INS, respectively. The horizontal errors and percentage errors of P-INS, TMC-INS, and CIAMC-INS algorithms all accumulated over time in the absence of a GNSS signal. The CIAMC-INS had a more obvious suppression effect on the position error of P-INS than TMC-INS, and the error increased more slowly. The MIMU/ODO/MC integrated navigation added a forward speed constraint of the train body on the basis of the CIAMC-INS algorithm and realized seamless positioning inside and outside the tunnel.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Point**} & \multirow{2}{*}{**Distance(m)**} & \multicolumn{2}{c}{**P-INS**} & \multicolumn{2}{c}{**TMC-INS**} & \multicolumn{2}{c}{**CIAMC-INS**} & \multicolumn{2}{c}{**MIMU/ODO/MC**} \\ \cline{3-10}  & & **Error (m)** & **Percentage** & **Error (m)** & **Percentage** & **Error (m)** & **Percentage** & **Error (m)** & **Percentage** \\ \hline
21 & 9163.1 & 291.4 & 3.18\% & 73.2 & 0.80\% & 43.3 & 0.47\% & 3.7 & 0.04\% \\
22 & 9228.2 & 294.2 & 3.19\% & 73.8 & 0.80\% & 43.7 & 0.47\% & 3.7 & 0.04\% \\
23 & 9295.7 & 297.9 & 3.21\% & 74.8 & 0.80\% & 44.2 & 0.48\% & 3.7 & 0.04\% \\
24 & 9349.5 & 301.6 & 3.23\% & 76.1 & 0.81\% & 44.7 & 0.48\% & 3.8 & 0.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Horizontal position errors of different algorithms at the reference points near the 9063 m tunnel exit.  
Figure 9: Trajectory located at the exit of 9063 m tunnel.
## 3 Seismic Input, Load Scenarios and Analyses  
The current Italian regulations (NTC 2018) [41] infer that masses associated with verified traffic loads should not be considered in the seismic load combination. As such, the considered model accounts only for combined vertical loads due to self-weights associated with structural and non-structural elements: G1 + G2 (hereinafter called \"scenario A\"). In any case, the purpose of this research is to investigate the structural behaviour of the viaduct subjected to a more critical scenario given the strategic nature of the bridge under consideration. Subsequently, an alternative vertical load scenario (termed \"scenario B\") was defined, in which the traffic and seismic loads were considered in the load combination rule for the ultimate limit state (ULS or collapse prevention). In other words, the assumed traffic scenario is of stationary vehicles (i.e., in a queue or tailback), considered as seismic load in the design with regard to a life-safety ultimate limit state (or SLV [41]). Therefore, in the next section, a finite element model was developed in which the vertical traffic loads will also be present (\"scenario B\") according to the provisions at point 5.1.3 in [41], and briefly reported in Table 1 (values were reported per unit length corresponding to a 1 m strip of deck). Traffic loads were modelled as uniformly distributed loads by summing the contribution of concentrated and distributed loads as per the Italian legislation (Table 1). These uniform loads were applied to the case study bridge along the barycentric axis of the beam elements that will make up the modelling of the deck as outlined in Section 4.  
As highlighted in Table 1, vertical loads of the slab yield a total weight of 539.8 kN/m applied on the deck, which are assumed to be reduced by the combination coefficient \(\Psi_{2,\mathrm{k}}=0.2\) and resulting in a weight of 107.96 kN/m, to be applied on the northern and southern spans of the viaduct (Figure 1). In addition, four seismic inputs were defined for the two different analyses carried out: two for nonlinear static analysis (NLSA) and two for nonlinear dynamic analysis (NLDA).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Slab Width [m]** & **Conventional IT Code Lanes** & **Length L [m]** & **L Remaining [m]** \\ \cline{2-4}  & 8.12 & 2 & 3 & 2.12 \\ \hline  & **Conventional IT Code Lane** & **Concentrated Loads [kN/m]** &
\begin{tabular}{c} **Distributed Loads** \\ **[kN/m\({}^{2}\)\(\times\) m]** \\ \end{tabular} \\ \cline{2-4}  & 1 & 300 & 27 \\ \cline{2-4}  & 2 & 200 & 7.5 \\ \cline{2-4}  & remaining & 0 & 5.3 \\ \hline \multicolumn{4}{c}{**Total loads**} & 500 & 39.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vertical load analysis of the viaduct. The table shows the conventional calculation of traffic loads that will be applied to the viaduct model in load scenario B.