esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Works  
[PERSON] _et al._[7] investigated the problem of constructing a tree consistent with a set of rooted triplets for the first time. They designed a simple recursive algorithm, which runs in \(O(mn)\) time and returns a tree consistent with all of the given triplets, if at least one tree exists. Otherwise, it returns null. Later, [PERSON] _et al._[8] improved [PERSON] _et al._'s algorithm to run in \(min\{O(n+mn^{1/2}),O(m+n^{2}logn)\}\) time. The time complexity of this algorithm was further improved to \(min\{O(n+mlog^{2}n),O(m+n^{2}logn)\}\) by [PERSON] _et al._[9] using more recent data structures introduced by [PERSON] _et al._[10]. MaxRTC is proven to be NP-hard [6, 11, 12]. [PERSON] _et al._[13] reported that this proof is an L-reduction from an  
Figure 1: Example of a phylogenetic tree and a consistent triplet. (**a**) A phylogenetic tree; (**b**) the triplet \(xy|z\).  
APX-hard problem, meaning that the problem is APX-hard, in general (non-dense case). Later, [PERSON] _et al._[14] proved that MaxRTC is NP-hard, even if the input triplet set is dense.  
Several heuristics and approximation algorithms have been presented for the so-called MaxRTC problem, each performing better in practice on different input triplet sets. [PERSON] _et al._[15] proposed two algorithms by modifying [PERSON] _et al._'s algorithm. Their first algorithm, which is referred to as One-Leaf-Split[5] runs in \(O((m+n)logn)\) time, and the second one, which is referred to as Min-Cut-Split[5], runs in \(min\{O(mn^{2}+n^{3}logn),O(n^{4})\}\) time. The tree generated by the first algorithm is guaranteed to be consistent with at least one third of the input triplet set. This gives a lower bound for the problem. In another study, [PERSON] [11] introduced a bottom-up heuristic approach called BPMF (Best Pair Merge First), which runs in \(O(mn^{3})\) time. In the same study, he proposed an exact exponential algorithm for the problem, which runs in \(O((m+n^{2})3^{n})\) time and \(O(2^{n})\) space. According to the results of [PERSON] [11], BPMF seems to perform well on average on randomly generated data. Later, [PERSON] _et al._[16] presented a modified version of BPMF, called BPMR (Best Pair Merge with Reconstruction), which employs the same approach, but with a little bit different of a reconstruction routine. BPMR runs in \(O(mn^{3})\) time and, according to [PERSON] _et al._'s experiments, outperforms BPMF. [PERSON] _et al._[13] designed a modified version of BPMF to achieve an approximation ratio of three. They also investigated how MinRTI (Minimum Rooted Triplet Inconsistency) can be used to approximate MaxRTC and proved that MaxRTC admits a polynomial-time, \((3-\frac{2}{n-2})-\), approximation.
## 2 Data and Methods
### Data
#### 2.1.2 Observational Data  
In this study, we used Geostationary Ocean Color Imager (GOCI) and Aerosol Robotic Network (AERONET) data to validate the horizontal distribution of AOD. The GOCI provides hourly daylight spectral images eight times a day from 00:30 to 07:30 UTC over East Asia; these data cover a 2500 \(\times\) 2500 km region centered on 36\({}^{\circ}\) N and 130\({}^{\circ}\) E in eight spectral channels (412, 443, 490, 555, 660, 680, 745, and 865 nm, [7]). AERONET is a ground-based sun photometer network distributed worldwide that provides the optical properties of aerosols at several wavelengths between 340 and 1640 nm [33]. In this study, we use level-2 set of AERONET data from ([[http://aeronet.gsfc.nasa.gov](http://aeronet.gsfc.nasa.gov)]([http://aeronet.gsfc.nasa.gov](http://aeronet.gsfc.nasa.gov)), accessed on 17 February 2022). The estimated AOD from D01 was examined using 15 AERONET datasets over East Asia, as shown in Figure 1a. D02 was examined using six AERONET datasets over the Korean Peninsula (Korea) region, as shown in Figure 1a. The Yonsei-YSU station, located in the Seoul Urban Area of South Korea, was selected to compare the temporal variation of the estimated AOD, because it had the smallest number of missing values during our research period.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & **Description** \\ \hline Model version & WRF v3.3 \\ Microphysics & WRF Single-Moment 3-class scheme \\ Cumulus convection & Kain-Fritsch \\ Longwave radiation & RRTM scheme \\ Shortwave radiation & Goddard shortwave \\ Land surface & NOAH [31] \\ PBL scheme & YSU [32] \\ \hline Model version & CMAQ v4.7 \\ Chemical mechanism & SAPRC99 \\ Aerosol module & AERO5 \\ Chemical solver & EBI \\ Advection scheme & YAMO \\ Horizontal diffusion & Multiscale \\ Vertical diffusion & Eddy \\ Cloud scheme & ACM \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of the National Institute of Environmental Research (NIER) operational air quality forecast system.
## 2 Results and Discussion
### Extraction Temperature and Time Effect  
A study to optimize extraction conditions, including the sample temperature and extraction time of volatile compounds present in samples, was performed using SPME fiber (50/30 \(\upmu\)m DVB/Carboxen/PDMS). The sample temperature and extraction time are important parameters in the SPME sampling process and can increase the extraction efficiency when optimized. The sample temperature and extraction time are discussed for the 1:1 proportional combination of _L. delbrueckii_ subsp. _bulgaricus_ IMAU20401 and _S. thermophilus_ ND03. Extraction was compared at temperatures of 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 70 \({}^{\circ}\)C. The effective peak numbers were enhanced at temperatures up to 50 \({}^{\circ}\)C and then began to decline (Figure 1a). Therefore, a temperature of 50 \({}^{\circ}\)C was used to study extraction time. Using extraction times ranging from 40 to 70 min, the effective peak numbers were enhanced as time increased up to 60 min and then began to decline (Figure 1b). Therefore, the optimum extraction conditions were 50 \({}^{\circ}\)C for 60 min.
## 2 Network Models
### Deep Autoencoder Network Model  
As an artificial neural network used in semi-supervised learning and unsupervised learning, the autoencoder (AE) [13] has an excellent feature extraction ability by encoding the input sample \(x\) into a highly abstract feature representation \(h\) and then decoding it back to \(y\). After the feature extraction of fault samples by the AE, the fault diagnosis effect will significantly improve. The conventional AE uses a shallow network. The encoding and decoding processes can be described as follows:  
\[\left\{\begin{array}{l}h=f_{e}(\omega_{e}x+b_{e})\\ y=f_{d}(\omega_{d}h+b_{d})\end{array}\right. \tag{15}\]  
where \(\omega_{e}\) and \(b_{e}\) are the weights and biases of encoders, \(\omega_{d}\) and \(b_{d}\) are the weights and biases of decoders, and \(f_{e}\) and \(f_{d}\) are the activation functions of encoders and decoders. The error between the input \(x\) and the output \(y\) can be expressed as follows:  
\[J(W,b)=\sum_{i=1}^{n}{(x_{i}-y_{i})^{2}} \tag{16}\]  
\[J(W,b)=-\sum_{i=1}^{n}{(x_{i}\log(y_{i})+(1-x_{i})\log(1-y_{i}))} \tag{17}\]  
Equations (16) and (17) are two commonly used loss functions, respectively, which are derived from the encoder network by minimizing the loss function \(\underset{W,b}{\operatorname{argmin}}J(W,b)\)[11].  
The original AE network has fewer layers and a simple structure. For an ITF in a PMSM, the ability to extract sample features is limited. A deep autoencoder (DAE) combined with a Softmax classifier is built in this paper. Its deep network structure can extract sample features well, with excellent classification results. The DAE structure is shown in Figure 4.  
Figure 4: Structure diagram of deep autoencoder.
# BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning
## 2 Data and Methods
### Processing Strategies  
GBM products provided by GeoForschungsZentrum (GFZ), including the precise orbit and clock of BDS-3, were used in precise positioning. The igs14.atx file provided by IGS was employed to correct the BDS-3 satellite phase center offset (PCO) and phase center variation (PCV). The BSX file of CAS provided the DCB parameters. The \(TGD_{1}\) was obtained from the broadcast ephemeris provided by the IGS, and the \(TGD_{B1 cp}\) and \(TGD_{B2 ap}\) were obtained from the broadcast ephemeris provided by the iGMAS.  
The BDS-3 GEO satellites have quasistatic characteristics, and the quality of the precise orbit and clock is worse than those of other types of satellites [30]. Additionally, the new signals, B1C and B2a, are used by the BeiDou satellite based augmentation service (BDSBAS) and the B2b signal is employed for satellite based PPP [3; 31; 32]. Therefore, only C19-C46 were used for the experiments. It should be noted that some outliers are excluded in the calculation of the root mean square errors (RMS) of the positioning deviations [22; 23].  
The latter, non-corr, is without TGD/DCB correction, tgd-corr is with TGD correction and dcb-corr is with DCB correction. Since TGD values for B2b were not available, only TGD corrections for B1I/B2a/B3I and B1C/B2a/B3I combinations were performed. The detailed processing strategies are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Description** \\ \hline Observations & Code and phase observations on B1I, B1C, B2a, B2b and B3I from BDS-3 \\ Model & IF123, IF1213 and UC123 \\ Combinations & B1I/B2a/B3I, B1C/B2a/B3I, B1I/B2b/B3I and B1C/B2b/B3I \\ Sampling interval & 30 s \\ Satellite elevation mask & 7\({}^{\circ}\) \\ Observation weighting & Elevation – dependent : \(a^{2}+b^{2}/sin^{2}E\) \\ Satellite precise orbit & GBM products from GFZ analysis center \\ \hline \end{tabular}
\end{table}
Table 2: BDS-3 triple-frequency data processing strategy.  
Figure 3: Distribution of 25 MGEX stations.
## 3 Results and Analysis
### Causes of Schedule Deficiencies Related to Planning Stages  
Table 4 presents the relationship between 22 causes of schedule planning deficiency obtained from the study conducted by [PERSON] et al. [41] and the five stages of schedule management planning proposed for this study: (1) Road Schedule Management Planning, (2) Definition of Construction Activities, (3) Sequencing Construction Activities, (4) Estimating Duration of Construction Activities, (5) Road Construction Schedule Development. Each intersection marked with a 'tick' () indicates that the cause is highly associated with that stage, according to the interviews conducted with the ten industry professionals. This analysis is adopted to identify and prioritize the stages that present deficiencies and require improvement, which could facilitate the implementation of specific strategies to enhance the planning of road project schedules. \begin{table}
\begin{tabular}{c c c c c} \hline C\({}_{1}\) & Poor estimation of workforce performance & & & \\ \hline C\({}_{2}\) & Poor estimation of workforce quantity & & & \\ \hline C\({}_{3}\) & Deficient coordination with public utilities (water, sanitary, electrical, and other surface or subway networks) & & & \\ \hline C\({}_{4}\) & Inadequate estimation of permit acquisition durations & & & \\ \hline C\({}_{5}\) & Inadequate weather forecasting & & & \\ \hline C\({}_{6}\) & Deficient estimation of material quantities & & & \\ \hline C\({}_{7}\) & Inadequate estimation of construction document approval times & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Relationship between stages and causes of deficiency in the planning of road project schedules. The relationship between causes of deficiency and planning stages shows that each stage can be affected by one or more specific causes. These results reinforce the need for project planners to adopt methodologies and strategies according to the particularities of each stage to address the problems inherent in road intersection planning. Knowing the causes associated with each stage makes it possible to plan actions and implement tools to mitigate these causes, improving the planning process's accuracy. In addition, it addresses the fundamental importance of managing clear and precise information in aspects such as objectives, roles and responsibilities, tasks, dependencies between activities, resource estimation, and integration of activities, among others. An incorrect development in any of these areas can result in uncoordinated planning, unrealistic schedules, and inefficient use of resources, which inevitably leads to delays and cost overruns. Thus, schedule planning requires professionals who correctly execute each process and can integrate and coordinate all activities coherently aligned with the project objectives.
## 4 Results and Discussion of Explainable AI (XAI) for VGG19 Model
### Comparison of LIME and Grad-CAM Explanations for VGG19
#### 4.1.6 Conclusions  
LIME and Grad-CAM both contribute significantly to understanding how the VGG19 model makes decisions. The selection of either technique hinges on the analysis's particular requirements. If in-depth explanations at the feature level are necessary, LIME is the ideal choice. On the other hand, Grad-CAM offers a wider perspective on the areas that impact the model's predictions. Utilizing both techniques together can provide a well-rounded analysis of the model's actions, thereby improving the AI system's interpretability and reliability in identifying drilled holes in melamine-faced chipboard.
## 2 Methods  
### Noise Classification  
The laser stripe in a structured-light image is the foreground, while othersignals interfering with the stripe extraction are noise. By analyzing snow sculpture measurement in actual scene, we found the main noise is from sunlight, shadows, surface color, surface reflections, and occasionally colored lights. As multiple types of noise are superimposed on images, it is hard to analyze such multi-source noise by constructing an optical path model.  
In space distribution, light and surface color are global noise, whereas shadow, surface texture, and reflections are local. Snow sculpture is generally located in an open field, surrounded by snow. Since there are generally no colored buildings, flowers or other objects around, the possibilityof color pollution can be virtually eliminated.During measurement, the structured-light scanner is calibrated with sunlight. According to color distribution, sunlight and shadow usually have similar R, G, and B components, whereas the color values of colored light and surface colors are usually higher in one color dimension. Reflective noise is related to illumination and surface characteristics, and shows localized high intensity. The laser stripe isoverlaid by a large amount of environmental noise under sunlight, and the intensity distribution in R, G and B space is very close, thus presenting single peak in the histogram, as shown in Figure 3. Conversely, the similarity between the three histograms is greatly reduced when there is interference from colored light or surface color. In the case of surface reflection, the similarity of intensity in space distribution decreases.  
Based on the above analysis of color histogram, a qualitative description of the ambient noise in R, G and B space is made, which representsan approximate Gaussian distribution with different mean values and variances, and it will guide the color value space transformation.
## 2 The Proposed VA-RAIM
### Vision Model with Calibration
#### 2.2.2 Calibration Method
According to Equations (8)-(10), the vision equation at the initial time \(k=0\) and time \(k>0\) is described as:  
\[\mathbf{z}_{V(0)}=\mathbf{H}_{V(0)}\mathbf{x}_{(0)}+\mathbf{A}_{(0)}^{-1} \delta\mathbf{c}_{(0)}+\mathbf{A}_{(0)}^{-1}\mathbf{B}_{(0)}(\delta\mathbf{d }+\Delta\mathbf{d})+\mathbf{A}_{(0)}^{-1}\Delta\mathbf{c}_{(0)} \tag{12}\]  
and  
\[\mathbf{z}_{V(k)}=\mathbf{H}_{V(k)}\mathbf{x}_{(k)}+\mathbf{A}_{(k)}^{-1} \delta\mathbf{c}_{(k)}+\mathbf{A}_{(k)}^{-1}\mathbf{B}_{(k)}(\delta\mathbf{d }+\Delta\mathbf{d})+\mathbf{A}_{(k)}^{-1}\Delta\mathbf{c}_{(k)} \tag{13}\]  
Multiply Equation (12) by \((\mathbf{A}_{(0)}^{-1}\mathbf{B}_{(k)}\mathbf{B}_{(0)}^{-1}\mathbf{A}_{(0)})\) and subtract the result from Equation (13) to eliminate the error and fault of landmark locations, and then we have  
\[\mathbf{z}_{V(k)}=\mathbf{H}_{V(k)}\mathbf{x}_{(k)}+\mathbf{A}_{(k)}^{-1}( \delta\mathbf{c}_{(k)}+\Delta\mathbf{c}_{(k)})+\mathbf{A}_{(k)}^{-1}\mathbf{B }_{(k)}\mathbf{B}_{(0)}^{-1}\left(\mathbf{A}_{(0)}\mathbf{z}_{V(0)}-\delta \mathbf{c}_{(0)}-\Delta\mathbf{c}_{(0)}\right)-\mathbf{A}_{(k)}^{-1}\mathbf{B }_{(k)}\mathbf{B}_{(0)}^{-1}\mathbf{A}_{(0)}\mathbf{H}_{V(0)}\mathbf{x}_{(0)} \tag{14}\]  
Substituting \(\mathbf{x}_{(0)}=\tilde{\mathbf{x}}_{(0)}-(\tilde{\mathbf{x}}_{(0)}-\mathbf{ x}_{(0)})\) into Equation (14) yields:  
\[\mathbf{z}_{V(k)}+\mathbf{A}_{(k)}^{-1}\mathbf{B}_{(k)}\mathbf{B}_{(0)}^{-1} \mathbf{A}_{(0)}(\mathbf{H}_{V(0)}\tilde{\mathbf{x}}_{(0)}-\mathbf{z}_{V(0)} )=\mathbf{H}_{V(k)}\mathbf{x}_{(k)}+\mathbf{A}_{(k)}^{-1}(\delta\mathbf{c}_{( k)}+\Delta\mathbf{c}_{(k)})+\mathbf{A}_{(k)}^{-1}\mathbf{B}_{(k)}\mathbf{B}_{(0)} \mathbf{B}_{(0)}^{-1}\mathbf{A}_{(0)}\mathbf{H}_{V(0)}(\tilde{\mathbf{x}}_{(0)} -\mathbf{x}_{(0)})-\delta\mathbf{c}_{(0)}-\Delta\mathbf{c}_{(0)} \tag{15}\]  
Denote  
\[\mathbf{\hat{z}}_{V(k)}=\mathbf{z}_{V(k)}+\mathbf{A}_{(k)}^{-1}\mathbf{B}_{( k)}\mathbf{B}_{(0)}^{-1}\mathbf{A}_{(0)}(\mathbf{H}_{V(0)}\tilde{\mathbf{x}}_{(0)}- \mathbf{z}_{V(0)}) \tag{16}\]  
The vision model with calibration is obtained as:  
\[\mathbf{\hat{z}}_{V(k)}=\mathbf{H}_{V(k)}\mathbf{x}_{(k)}+\mathbf{\hat{z}}_{V (k)}+\mathbf{\hat{\textit{V}}_{(k)}} \tag{17}\]  
where \(\mathbf{\hat{\textit{{k}}}}_{V(k)}=\mathbf{A}_{(k)}^{-1}(\delta\mathbf{c}_{(k)} -\mathbf{B}_{(k)}\mathbf{B}_{(0)}^{-1}\delta\mathbf{c}_{(0)})+\mathbf{A}_{(k)} ^{-1}\mathbf{B}_{(k)}\mathbf{B}_{(0)}^{-1}\mathbf{A}_{(0)}\mathbf{H}_{V(0)}( \tilde{\mathbf{x}}_{(0)}-\mathbf{x}_{(0)})\), \(\mathbf{\hat{\textit{{k}}}}_{V(k)}=\mathbf{A}_{(k)}^{-1}(\Delta\mathbf{c}_{(k)} -\mathbf{B}_{(k)}\mathbf{B}_{(0)}^{-1}\mathbf{A}\mathbf{c}_{(0)})\).
## 4 Hardware Implementation and Result Discussion
### Analysis of Throughput  
According to the results of average iteration number and \(T_{norm}\) in Sections 4.2 and 4.3, the throughput ratio between the priority-based decoder and hybrid decoder [15] is exhibited in Table 2. In Table 2, \(AIN\) represents the average iteration number and \(T\) represents the throughput of decoders. \(TR\) represents the throughput ratio between the priority-based layered decoder with double update queues and the hybrid layered decoder. To meet all the practical applications, \(AIN\) s are rounded up, slightly different with the results shown in Figure 6. From Table 2, it can be seen that \(TR\) ranges from 158% to 285%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**This Work**} & \multicolumn{2}{c}{[15]} & \\ \hline \multirow{2}{*}{**SNR [dB]**} & \multirow{2}{*}{\(T_{norm}\) [Gbps]} & \multirow{2}{*}{\(AIN\)} & \(T\) & \multirow{2}{*}{\(T_{norm}\) [Gbps]} & \multirow{2}{*}{\(AIN\)} & \(T\) & \multirow{2}{*}{\(TR\)} \\  & & & [Gbps] & & & & [Gbps] & [\%] \\ \hline
5.9 & & 11 & 2.85 & & 30 & 1.0 & 285 \\
6.0 & & 9 & 3.5 & & 21 & 1.5 & 233 \\
6.1 & & 8 & 3.93 & & 16 & 2.0 & 197 \\
6.2 & & 7 & 4.5 & & 14 & 2.3 & 196 \\
6.3 & & 6 & 5.2 & & 12 & 2.6 & 200 \\
6.4 & & 6 & 5.2 & & 11 & 2.9 & 179 \\
6.5 & & 6 & 5.2 & & 10 & 3.2 & 163 \\
6.6 & & 5 & 6.3 & & 9 & 3.5 & 180 \\
6.7 & & 5 & 6.3 & & 8 & 4.0 & 158 \\
6.8 & & 5 & 6.3 & & 8 & 4.0 & 158 \\
6.9 & & 4 & 7.9 & & 8 & 4.0 & 198 \\
7.0 & & 4 & 7.9 & & 7 & 4.5 & 176 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Throughput ratio between priority-based layered decoder with double update queues and hybrid layered decoder [15].  
Figure 9: The resource usage of every module in the hardware implementation of priority-based decoder with double update queues.
## 4 Experiments
### Application in Low Textured Scenes
#### 4.4.3 Results of Low-Texture Scene Experiments  
As can be seen from Table 6, in the absence of a prior spatial distribution of camera poses, both translational and rotational errors are considerably large compared to the real poses. After aligning the estimated poses with the real poses, as observed in Figure 10, although there is a significant error in the poses of all cameras, the relative positions of all poses are correctly arranged. This one-to-one correspondence becomes more evident when the aligned results are projected onto the xy-plane in Figure 11. This indicates that the pose of each camera has been optimized. Furthermore, by observing the level of detail in the rendered images in Figure 12 and the state of the ceramics in the depth images, we can also infer that the structure of the object has been initially reconstructed. This validates the effectiveness of our algorithm in performing 3D reconstruction in scenes without pose, low textures and reflectiveness.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scene** & **Rotation Error (*)** & **Translation Error** \\ \hline Ceramics & 25.848 & 1.416 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pose error in the ceramic scene.  
Figure 10: Visualization of aligned poses.