esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Responses of GPP and SIF to the Environment
#### 3.1.2 The Responses of GPP and SIF to EF and Ta under Different Radiation Bins  
Section 3.1.1 found that the radiation drove the increase in GPP and SIF simultaneously. However, the increasing Ta and EF also could drive the increase in GPP and SIF. To understand the impact of Ta and EF on GPP and SIF, we investigated the half-hourly responses of GPP and SIF to the Ta and EF under different PAR bins, respectively. Figures 4 and 5 showed the responses of GPP and SIF to EF and Ta in different PAR bins, respectively. Obviously, the impacts of EF and Ta on GPP and SIF were smaller than that of PAR though GPP and SIF both increased with increasing EF and Ta. The responses of GPP and SIF to EF and Ta also had difference. For the GPP and SIF EF responses, GPP increased faster than SIF in each PAR bin which indicated that EF had more influence on GPP than on SIF. Similarly, for the GPP and SIF Ta responses, GPP and SIF both increased first then decreased with increasing Ta bins, while GPP increased and decreased faster than SIF which indicated that GPP was more sensitive to Ta. In general, PAR was the main factor driving the increase in GPP and SIF, and EF and Ta had less influence on GPP and SIF than PAR especially on SIF.  
Figure 4: The responses of half-hourly SIF **(a)** and GPP **(b)** to EF under different PAR bins.  
Figure 3: The responses of half-hourly SIF **(a)** and GPP **(b)** to PAR in different Ta bins.  
Figure 2: The responses of half-hourly SIF **(a)** and GPP **(b)** to PAR under different EF bins.  
3.1.3. The Light Responses of GPP and TROPOSIF  
Figure 6 showed the responses of TROPOSIF and GPP to SW in different EF bins. GPP and SIF exhibited similar responses to SW which both increased with increasing SW. While in the lower EF bins, SIF increased faster than GPP, and the increase speeds of GPP were gradually higher than that of SIF with increasing EF which indicated that SIF was more sensitive to SW than GPP when the water status was not well. Moreover, both SIF and GPP showed positive correlation with EF, and the GPP increased larger than SIF and EF had more influence on GPP than on SIF. Figure 7 showed the light responses of GPP and TROPOSIF under different Ta bins. In general, GPP and SIF had similar responses to the SW which both increased with increasing SW bins. However, the difference between the responses of GPP and SIF to SW in different Ta bins was not as significant as that in different EF bins.
# An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF
## 4 Experimental Results
### Segmentation Results of Images with Intensity Inhomogeneity  
To demonstrate the satisfactory performance and effectiveness of the SPFLIF-IS model, a series of experimental results are presented. We compare our model with the following five existing models: (1) the C-V (The code is available at [47]) model [34], (2) the LBF (The code is available at [48]) model [24], (3) the LIF (The code is available at [47]) model [31], (4) the SBGFRLS (The code is available at [47]) model [23], and (5) the LSACM model [28]. The five representative ACM algorithms are the state-of-the-art level set methods published recently for image segmentation. The algorithms show improvements over the classical ACM and are specially selected based on the level set method for comparison experiments. The chosen parameters for these models can be found in [23; 24; 28; 31; 34]. The segmentation results obtained for images with intensity inhomogeneity using the six models are illustrated in Figure 2, where the original images shown in Figure 2a can be found in [2].  
Figure 2: The segmentation results of images with intensity inhomogeneity for the six models. (**a**) Original image, (**b**) C–V model, (**c**) LBF model, (**d**) SBGFRLS model, (**e**) LIF model, (**f**) LSACM model, and (**g**) SPFLIF-IS model.  
Figure 2b,d,f shows that the C-V model, the SBGFRLS model, and the LSACM model fail to analyze the first image with intensity inhomogeneity. As shown in Figure 2d,f, the SBGFRLS model and the LSACM cannot yield the ideal segmentation results for the second image. The object boundaries of the third image are not identified by the LIF model, and the results are shown in Figure 2e. Figure 2e,f shows that the true boundaries of the fourth image are not accurately extracted by the LIF model or the LSACM model. The SPFLIF-IS model detects the true boundary, and the results are illustrated in Figure 2g. Meanwhile, Figure 2c,g shows that the LBF model perform as well as the SPLIF-IS model.  
Note that because the visual evaluations in Figure 2 are partial to subjective measures, to strengthen the objective results of our experiments, the corresponding tables should be added to defend the arguments for all the tested images in the following visual evaluations, in which each failure is clearly labeled to avoid ambiguity. To more clearly illustrate this state, the following symbols are adopted in the tables: F1: fail to detect boundaries, F2: nonideal boundaries detected, F3: fail to detect internal boundaries, and T: true boundaries detected. Table 1 objectively describes the segmentation results of Figure 2 in detail. It can be clearly concluded from Table 1 that the LBF performs as well as the SPLIF-IS, the C-V exhibits slightly bad results, and the LSACM produces the worst results. Therefore, the experimental results shown in Figure 2 and Table 1 indicate that the SPFLIF-IS model can analyze the images with intensity inhomogeneity well.
# P4-KBR: A Key-Based Routing System for P4-Programmable Networks
## 4 P4-Kbr
### Translation of Ranges Solution  
The prefix extension algorithm was proposed as a useful tool that allows programmers to implement this key-based routing protocol in OpenFlow networks [4]. However, as described before (see the example in Section 4.1), this prefix extension algorithm involves the insertion of a relatively high number of flow table entries in the nodes.  
A way to address this drawback, which we called the \"translation of ranges solution\", was theoretically defined in [4]. However, its implementation was left pending awaiting new versions of the OpenFlow standard that would allow it.  
Although the problem of non-bitmasked ranges is solved in P4 networks thanks to the definition of the RANGE matching format, it is interesting to point out that data plane programmability and P4 language do also offer us an adequate framework for implementing the \"translation of ranges solution\", as described below.  
\"Translation of ranges solution\" was deduced after analyzing the structure of the Chord finger table (see Figure 2) and how it affects the definition of the key value ranges. It can be concluded that the drawback of the bitmasked representation of the ranges does not exist if n = 0. In this case, each one of the last (L \(-\) 1) ranges could always be represented by one bitmasked value. Making use of this feature, the problem of ranges that have to be expanded would not exist if, after obtaining the ranges associated to any node n, they were moved or translated by subtracting the vid of the node to the limits of each range (see green box in Figure 3).  
However, the use of these translated ranges instead of the original ranges makes two additional actions necessary during the routing mechanism (marked in red in Figure 3): one that must be applied when a packet is received, and other that must be applied before a packet is forwarded. In other words, because the value used in the matching field (that is, the destination address) has been shifted, when receiving a packet, and before looking up the matching entry, the destination address of the packet has to be modified by subtracting the vid of the node. Likewise, once the adequate sending action has been obtained from the matching entry, and before forwarding, the destination address of the packet must be restored by adding the vid value.  
Unfortunately, the action set defined by OpenFlow standards does not include these specific actions, preventing the implementation of the \"translation of ranges solution\". However, as is described below in Section 5.3, the P4 language provides an enhanced programmability of data planes offering new tools that do make its implementation possible.
# Robust Quadrotor Control through Reinforcement Learning with Disturbance Compensation
## 3 Disturbance Observation and Control Strategy
### Disturbance Compensator  
When an external disturbance \(\mathbf{F}_{ext}\) is acting on the quadrotor (Figure 3), this disturbance generates a translational acceleration vector \(\mathbf{a}_{ext}\). For disturbance compensation, a new compensation thrust vector \(\mathbf{g}_{fc}\) is defined. This vector combines \(\mathbf{a}_{ext}\) and the gravitational acceleration vector \(g\mathbf{i_{z}}\) and can be expressed as follows:  
\[\mathbf{i_{zc}}=\frac{\mathbf{a}_{ext}+g\mathbf{i_{z}}}{|\mathbf{a}_{ext}+g \mathbf{i_{z}}|}, \tag{15}\]  
which only considers the hovering situation without an acceleration command from the trajectory tracking reference. The normalized vector \(\mathbf{g}_{fc}\) is then used to formulate a new coordinate frame (force compensation frame) with a rotation matrix \(\mathbf{R}_{ci}\) relative to the inertial frame.  
Figure 3: Force compensation frame.  
In three-dimensional space, any rotation coordinate system about a fixed point is equivalent to a single rotation by an angle \(\theta\) about a fixed axis (called the Euler axis) that passes through the fixed point.  
To obtain the rotation matrix \(\mathbf{R}_{ci}\) from \(\mathbf{i}_{\mathbf{z}}\) to \(\mathbf{g}_{fc}\) represent in quaternion with the following equations can be used:  
\[\begin{split}\mathbf{v}&=[0,\mathbf{g}_{fc}]\\ \mathbf{q}&=[\cos(\frac{\theta}{2}),\sin(\frac{ \theta}{2})\mathbf{u}]\\ \mathbf{v}^{\prime}&=\mathbf{qvq}^{*}=\mathbf{qvq}^{ -1}=\mathbf{i}_{\mathbf{z}},\end{split} \tag{16}\]  
where \(\mathbf{v}\) is the original quaternion vector, \(\mathbf{u}\) is the unit vector of the rotation axis, \(\mathbf{v}^{\prime}\) is the rotated quaternion vector, \(\mathbf{q}\) is the rotation vector between \(\mathbf{v}\) and \(\mathbf{v}^{\prime}\), and \(\theta\) is the rotation angle.  
By substituting \(\mathbf{q}_{ci}\) into (3), \(\mathbf{R}_{ci}\) can be determined as follows:  
\[\begin{split}\mathbf{v}^{\prime}=\mathbf{q}_{ci}\mathbf{vq}_{ci }^{*}&=\mathbf{q}_{ci}\mathbf{vq}_{ci}^{-1}\\ &=\mathbf{R}_{ci}\mathbf{v}.\end{split} \tag{17}\]  
The aforementioned equation is equivalent to Rodrigues' rotation formula. After obtaining the rotation matrix \(\mathbf{R}_{ci}\), the quadrotor attitude in the force compensation frame \(\mathbf{R}_{cb}\) can be calculated using the following equation:  
\[\mathbf{R}_{cb}=\mathbf{R}_{ci}\mathbf{R}_{ib}. \tag{18}\]  
After obtained the corrected coordinate for compensation, the magnitude of thrust of motors with compensation is  
\[T_{ci}=\frac{\mathbf{\hat{F}}_{ext}\cdot\mathbf{b}_{\mathbf{z}}}{4}+\alpha_{i} (s), \tag{19}\]  
where \(\alpha_{i}(s)\) is the \(i\)-th motor action output of neural network which would be specified in following section. By modifying the quadrotor attitude in the compensation frame \(\mathbf{R}_{cb}\) and using this attitude as the input state of the RL controller, the controller can generate corresponding motor thrust to maintain the target attitude and therefore eliminate the disturbance acting on the quadrotor.
## 3 Approach
### Collision Avoidance Algorithm
#### 3.2.1 Elliptic VO Algorithm  
The basic principle of VO algorithm was introduced in detail in [22], and the key difference between the elliptic VO algorithm and the VO algorithm is the process of solving the tangent. In the VO algorithm, if you want to judge whether a USV will collide with an obstacle, you must require two tangent lines of the obstacle relative to the USV.  
As shown in Figure 6, in order to find the tangent lines of elliptical obstacle conveniently, a body-fixed coordinate system and an obstacle coordinate system was established. The \(xOy\) is the body-fixed coordinate system of the USV and \(x^{\prime}O^{\prime}y^{\prime}\) is the obstacle coordinate system. The red ellipse is an obstacle and the green ellipse is an USV, which was simplified as a particle. Point \(O\) is the center of the USV. \(O^{\prime}\) is the center of the obstacle. If the coordinates of the USV in the body-fixed coordinate system are \(USV_{boat}=[0,\ 0]^{T}\), the coordinates of the USV in the obstacle coordinate system are:  
\[USV_{obs}=R^{-1}(USV_{boat}-C)\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Throttle Value** & **300** & **400** & **500** & **600** & **700** & **800** \\ \hline
**Time delay constant T\({}_{1}\)** & 6 & 5.5 & 5 & 5 & 3.5 & 2 \\ \hline \end{tabular}
\end{table}
Table 1: Selection of \(T_{1}\) for each throttle value.  
where \(C\) is the coordinate of the center point of the obstacle in the body-fixed coordinate system and \(R\) is the rotation matrix.  
\[R=\left[\begin{array}{cc}cos\theta&sin\theta\\ -sin\theta&cos\theta\end{array}\right] \tag{6}\]  
When the coordinates of the USV in the obstacle coordinate system are obtained, the tangent coordinate \((x_{T},~{}y_{T})\) in the obstacle coordinate system can be obtained by Equation (7) (\(T1_{obs}\) and \(T2_{obs}\) in Figure 6):  
\[\left\{\begin{array}{c}\frac{a_{T}^{2}}{a^{2}}+\frac{y_{T}^{2}}{b^{2}}=1\\ \frac{x_{T}m}{a^{2}}+\frac{y_{T}n}{b^{2}}=1,\end{array}\right. \tag{7}\]  
Then, \(T1_{obs},~{}T2_{obs}\) is converted into the body-fixed coordinate system by using Equation (8):  
\[T_{bat}=RT_{obs}+C \tag{8}\]
## 3 Applications of the Proposed Model
### Direct Method
This discrepancy arises because the central layer in the EGMS model is far from being constant, unlike in the HCMS model. It is important to note that a simulation of the sounding with the chosen objective function should closely match the sounding data obtained with the measurement device, provided that the parameters of the objective function have been well determined and that the soil resistivity aligns well with the chosen model. In the described case, the objective function is the apparent resistivity, for which the parameters of the EGMS model shown in Table 2 are optimal. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Params.** & \(\mathbf{C_{11}}\) & \(\mathbf{C_{12}}\) & \(\mathbf{\beta_{1}}\) & \(\mathbf{C_{21}}\) & \(\mathbf{C_{22}}\) & \(\mathbf{\beta_{2}}\) & \(\mathbf{C_{31}}\) & \(\mathbf{C_{32}}\) & \(\mathbf{\beta_{3}}\) & \(\mathbf{h_{1}}\) & \(\mathbf{h_{2}}\) \\ \hline
**Values** & 0.0358 & 0.0349 & 0.0515 & 0.0861 & \(-\)0.0976 & 0.1590 & 0.0995 & 0.0269 & 0.0119 & 2.16 & 1.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Non-continuous EGMS model parameters for the soil depicted in the graph shown in the right panel of Figure 2.  
Figure 3: BRL simulations on three-layered soil models, HCMS (**left panel**), and EGMS (**right panel**). The colored lines in the subfigure of the right panel correspond to the variable resistivities in the considered layers.
###### Abstract  
City-wide near-miss data can be beneficial for traffic safety estimation. In this study, we evaluate urban traffic safety and examine spatial patterns by incorporating city-wide near-miss data (59,277 near-misses). Our methodology employs a grid-based method, the Empirical Bayes (EB) approach, and spatial analysis tools including global Moran's I and local Moran's I. The study findings reveal that near-misses have the strongest correlation with observed crash frequency among all the variables studied. Interestingly, the ratio of near-misses to crashes is roughly estimated to be 1957:1, providing a potentially useful benchmark for urban areas. For other variables, an increased number of intersections and bus stops, along with a greater road length, contribute to a higher crash frequency. Conversely, residential and open-space land use rates show a negative correlation with crash frequency. Through spatial analysis, potential risk hotspots including roads linking bridges and tunnels, and avenues bustling with pedestrian activity, are highlighted. The study also identified negative local spatial correlations in crash frequencies, suggesting significant safety risk variations within relatively short distances. By mapping the differences between observed and predicted crash frequencies, we identified specific grid areas with unexpectedly high or low crash frequencies. These findings highlight the crucial role of near-miss data in urban traffic safety policy and planning, particularly relevant with the imminent rise of autonomous and connected vehicles. By integrating near-miss data into safety estimations, we can develop a more comprehensive understanding of traffic safety and, thus, more effectively address urban traffic risks.  
near-misses; empirical Bayes; vulnerable road users; urban area; safety estimation Article
# Seamless Indoor-Outdoor Localization Through Transition Detection
## 5 Experimental Results
The transition engine is fully affected by the direction error since it only relies on the integration of the AI-IMU measurement to estimate the position and direction. The outdoor engine is also affected since the Kalman Filter requires multiple updates from the GNSS measurement to reduce the direction error. Furthermore, the GNSS measured the position input with significant position error, which can be observed in the example in Figure 7, which also affected the estimation errors. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Device** & \multicolumn{2}{c}{**S10**} & \multicolumn{2}{c}{**S20**} & \multicolumn{2}{c}{**S21**} \\ \hline
**Trial** & **Estimated** & **FLP** & **Estimated** & **FLP** & **Estimated** & **FLP** \\ \hline
1 & 0.9376 & 0.8174 & 0.9498 & 0.7157 & 0.9246 & 0.7810 \\
2 & 0.9316 & 0.8119 & 0.9065 & 0.6011 & 0.8657 & 0.7800 \\
3 & 0.9374 & 0.8057 & 0.9457 & 0.7828 & 0.8819 & 0.8549 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The state estimation accuracy of each trial at Namsan Green. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Device** & \multicolumn{2}{c}{**S10**} & \multicolumn{2}{c}{**S20**} & \multicolumn{2}{c}{**S21**} \\ \hline
**Trial** & **Estimated** & **FLP** & **Estimated** & **FLP** & **Estimated** & **FLP** \\ \hline
1 & 0.9083 & 0.4156 & 0.9148 & 0.4139 & 0.8918 & 0.4125 \\
2 &  
For the Namsan Green building, the proposed method shows a better result compared to the FLP and GPS for S10 and S20, while S21 shows a slightly larger mean error compared to the GPS. The trials of this building were also affected by the GNSS measurement inputs with large position errors, which can be observed in the example in Figure 8.  
All three buildings were surrounded by near buildings, which thus heavily limited the sky view. This led to multi-path errors that significantly affected the accuracy of GNSS position measurements. The effect of multi-path errors in the FLP measurements was not distinguishable from the measurement data, as the accuracy value provided by the LocationManager did not show a significant difference from the location measurements, which was suspected to be heavily influenced by the multi-path error compared to more accurate GNSS measurements. These measurements with large errors updated the Kalman Filter with a smaller measurement error covariance matrix than it should have had, increasing the errors in the direction and position. The direction and position errors caused by those measurements required a certain time until it was reduced, increasing the errors in the estimations during that period. The estimated state shows significantly better results compared to the state estimation based on the FLP location on every device and trial, accurately estimating the state for 82.63% of the trial duration in the worst case.
## 4 Image Fusion Applications in NDT Inspection
### Wavelet-Based Approaches
Moreover, a number of other studies in the literature attempted to interpret the effectiveness of the decomposition level and selected a filter using multiple options over a specific application [20; 77]. For example, a hybrid image fusion structure of wavelet-based multi-scale transforms (W-MST) and saliency region analysis (SRA) was proposed to combine the advantages of the terahertz and X-ray CT techniques [20]. Here, the study compared and contrasted 36 fusion combinations, including various fusion rules, decomposition levels and basis functions. Experimental samples were formed using glass fibre (GFRB) with different-shaped inclusions and defects with various thicknesses and depths. First, saliency maps, which show the indication of the human eyes' first focus in an image, were obtained for both methods. Then, the main outline in the low-frequency component and edge contour information of the high-frequency sub-bands were transmitted to the last fused image by W-MST. Assessment of these combinations was performed by calculating the standard deviation (SD), which shows the image's grey level nature, and spatial frequency (SF), which provides the imaging resolution for the defect details. Among these combinations, the energy, average gradient and constant fusion strategies showed the  
Figure 5: Two-level wavelet decomposition structure (recursive filtering and subsampling). highest increment between the source and fused images in terms of the two indicators. For completeness and useful information to the reader, Table 5 lists important current studies on wavelet-based fusion strategies for different materials and defects. These include the ones discussed in more detail in this section.
## 3 Methods and Data  
### Methods  
Due to its capability to obtain global high-resolution and ultra-high-resolution three-dimensional satellite images, and its visual interpretation method, the Google Earth platform has been extensively utilized for landslide identification [12; 13; 14]. In this study, satellite images from 2012 to 2020 on the Google Earth platform were used to further validate the locations of landslides provided by the Zhaotong City Natural Resources and Planning Bureau. According to the interpretation signs such as topographic features and vegetation cover in the region, clear differences in color and texture in images before and after landslides were observed, which facilitated the identification of landslide boundaries (Figure 2). Field surveys were conducted in Ludian, Qiaojia, Yiliang, and Yanjin counties--areas with concentrated landslide activities--to further validate the accuracy of the interpreted data on landslides and potential landslides in the Zhaotong area. Ultimately, 161 landslides and 3646 potential geological disasters have been determined since 2012 in the Zhaotong area.  
Landside abundance (or landslide density) is commonly used as an index to measure the patterns and characteristics of landslides [15; 16; 17]. In this study, landslide number density (LND) was employed to analyze the relationship between influencing factors and landslide occurrence [18; 16]. LND is defined as the number of landslides per square kilometer (number/km\({}^{2}\)) and is calculated by Formula 1. A low LND value indicates a low likelihood of landslides occurring in the area, while a higher value suggests a greater propensity for landslides.  
\[\text{LND}=\frac{\text{The number of landslides in a category}}{\text{Total area in a category}} \tag{1}\]
# Integrated GNSS/IMU-Gyrocompass with Rotating IMU. Development and Test Results
## 3 Materials  
The developed GNSS/IMU gyrocompass is a tightly coupled integrated GNSS/INS system using satellite signals with both code and frequency division (GPS/GLONASS). Below we describe the gyrocompass structure, its specific features and the prototype used in the tests.