esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploiting Global Structure Information to Improve Medical Image Segmentation
## 4 Experiments
### Evaluation Metrics  
To evaluate the segmentation performance, multiple performance metrics including overlap and distance measures were adopted.  
* Intersection over union (IOU): the IOU is a measure of the degree of overlap between the region of the ground-truth and region predicted by the segmentation network. In Equation (4), \(S\) is the predicted region, and \(G\) is the ground-truth. The IOU can be defined as the ratio of the intersection and union between \(G\) and \(S\): \[\text{IOU}=\left|\frac{G\ \cap\ S}{G\ \cup\ S}\right|=\frac{TP}{TP+FP+ FN}\] (4)
* Dice similarity coefficient (DSC): the DSC can be used to evaluate the overlap performance, similar to the IOU. This indicator also measures the degree of overlap between \(S\) and \(G\), as indicated in Equation (5). Note that the DSC value is always greater than or equal to the IOU. \[\text{DSC}=\frac{2\mid G\ \cap\ S\mid}{\mid G\mid+\mid S\mid}=\frac{2 TP}{2 TP+FP+FN}\] (5)
* Average contour distance (ACD) and average surface distance (ASD): the ACD and ASD indicate the extent of separation of the ground-truth and predicted region. Notably, the overlap measures such as the IOU and DSC do not consider whether the false positive pixels are near or far from the ground-truth. Let \(s_{i}\), \(i=1,...,n_{S}\) and \(g_{i}\), \(i=1,...,n_{G}\) represent the boundary pixels in \(S\) and \(G\), respectively. \(d(s_{i},G)=\min_{j}\lVert s_{i}-g_{j}\rVert\) indicates the minimum distance from \(s_{i}\) on \(S\) to \(G\). The ACD and ASD can be computed as follows: \[\text{ACD}(S,G)=\frac{1}{2}\ (\frac{\sum_{i}d(s_{i},G)}{n_{S}}+\frac{\sum_{j}d( g_{j},S)}{n_{G}}\ )\] (6)  
Figure 3: Examples of each dataset: (**a**) samples from JSRT (**top**) and MC (**bottom**) for lung segmentation with lesion annotations; (**b**) samples from four sites for spinal cord segmentation.  
\[\text{ASD}(S,G)=\frac{1}{n_{S}+n_{G}}\ (\sum_{i}d(s_{i},G)+\sum_{j}d(g_{j},S)) \tag{7}\]
# Joint Timekeeping of Navigation Satellite Constellation with Inter-Satellite Links
## 3 ISLT Timescale Algorithm
### Basic ISLT Timescale Equation  
Joint timekeeping means the clock ensemble maintains and is synchronized to a uniform time reference, which is essentially the weighted average of the ensemble of timekeeping clocks:  
\[\text{ISLT}(t)=\sum_{i=1}^{n}w_{i}h_{i}(t) \tag{4}\]  
where \(h_{i}(t)\) is the clock reading of satellite \(i\), and \(w_{i}\) is, accordingly, the weight that satisfies the constraint:  
\[\sum_{i=1}^{n}w_{i}(t)=1 \tag{5}\]  
A brief explanation of Equation (4) is that the ISLT is the weighted average of readings of the clock ensemble. Though the clock reading is unmeasurable, the ISLT can be defined by its deviation relative to the GNSS system time, and Equation (4) is reformulated as:  
\[x_{e}(t)=\sum_{i=1}^{n}w_{i}(t)x_{i}(t) \tag{6}\]  
where \(x_{i}(t)\) and \(x_{e}(t)\) are the absolute time deviation of satellite \(i\) and ISLT, respectively.  
The essential purpose of forming ISLT is to improve frequency stability. To avoid the time or frequency steps because of the variation of the number of clock ensemble, the time deviation of each timekeeping clock should be predicted, and the time deviation measurement value can be detrended with the predicted deviation:  
\[x_{e}(t)=\sum_{i=1}^{n}w_{i}(t)[x_{i}(t)-\hat{x}_{ie}(t)] \tag{7}\]  
where \(\hat{x}_{ie}(t)\) is the predicted time deviation of satellite \(i\) relative to the ISLT. Equation (7) is the so-called basic timescale equation of the ISLT. When operating autonomously, the navigation satellite cannot establish a time comparison link with the ground station. Therefore, the absolute time deviation \(x_{i}\) is also unmeasurable. Fortunately, the time deviations of slave satellites relative to the master satellite can be estimated by two-way inter-satellite ranging. Another form of the basic ISLT timescale equation is:  
\[x_{i1}(t)=\sum_{i=1}^{n}w_{i}(t)[x_{i1}(t)-\hat{x}_{ie}(t)] \tag{8}\]where \(x_{e1}(t)=x_{e}(t)-x_{1}(t)\) is the time deviation of the ISLT relative to the reference clock and \(x_{i1}(t)\) is the time deviation of satellite \(i\) relative to the reference clock, as obtained from an inter-satellite time comparison. The time deviation of the ISLT relative to any satellite \(i\) is calculated by the relationship:  
\[x_{ei}(t)=x_{e1}(t)-x_{i1}(t) \tag{9}\]  
According to the basic timescale Equation (8), three elements--inter-satellite time comparison, the short-term prediction of \(x_{ie}(t)\) in the calculation cycle, the weight adjustment--are crucial for the ISLT.
# Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model
## 3 Knothe-n Time Function Model Improvement  
### Knothe-n Time Function  
In 1952, [PERSON], in Poland, mathematically described the complex spatial and temporal process of surface subsidence caused by underground mining and investigated time-dependent surface movement and deformation. The surface subsidence rate, \(dw(t)/dt\), which is proportional to the difference between the final surface subsidence value, \(w_{m}\), and the dynamic subsidence value at a certain moment, \(t\), \(w(t)\), was also obtained. Finally, the time function was obtained as [12]:  
\[w(t)=w_{m}(1-e^{-\eta t}) \tag{1}\]  
where \(\eta\) is the Lithologic time factor.  
Although the Knothe time function can predict dynamic subsidence, tilt, curvature, horizontal movement, and horizontal deformation, it cannot reflect the change rule for surface subsidence velocity and acceleration. Therefore, according to the principle of balancing accuracy and practicality, [PERSON] [22] corrected the Knothe time function with a pair of time functions by introducing a power index \(n\) in time \(t\). The change rule for the velocity and acceleration curves corresponding to the new Knothe-n model is more in line with the practical situation. The model also contains the time parameters \(\eta\) and \(n\) that jointly determine the curve characteristics and solve the Knothe time function. This function can better fit and predict the physical process of subsidence over time at the surface monitoring points in the mining subsidence area and has a wider application range than the Knothe model. Its functional equation is as follows:  
\[w(t)=w_{m}(1-e^{-\eta t^{n}}) \tag{2}\]  
where \(\eta\) and \(n\) are lithological time coefficients. \(\eta\) determines the duration of the subsidence at the surface monitoring point; and \(n\) determines the movement path of the surface monitoring point on the time axis during subsidence, which can be represented by the change rule for subsidence velocity and acceleration.  
The corresponding time function is:  
\[(t)=1-e^{-\eta t^{n}} \tag{3}\]  
Figure 5: Surface subsidence nephograms at different stages.  
The corresponding velocity and acceleration functions are:  
\[v(t)=w_{m}\eta nt^{n-1}e^{-\eta t^{n}} \tag{4}\]  
\[a(t)=w_{m}\eta ne^{-\eta t^{n}}\Big{[}(n-1)t^{n-2}-\eta nt^{2(n-1)}\Big{]} \tag{5}\]  
Let \(a(t)\) be equal to 0  
\[t_{0}=\left(\frac{n-1}{\eta n}\right)^{\frac{1}{n}} \tag{6}\]  
According to the principle of differential extrema, the sinking velocity \(v(t)\) reaches its maximum when \(t=\tau\), the maximum sinking velocity \(v_{m}\):  
\[v_{m}=w_{m}(n-1)^{\frac{1-n}{n}}(\eta n)^{\frac{1}{n}}e^{\frac{1-n}{n}} \tag{7}\]  
Substituting (Equation (6)) into the subsidence time function (Equation (7)) yields the amount of subsidence at the surface observation point when the subsidence rate is maximized:  
\[(t)=w_{m}(1-e^{\frac{1-n}{n}}) \tag{8}\]
###### Abstract  
Fine particulate matter (PM\({}_{2.5}\)) has attracted extensive attention due to its harmful effects on humans and the environment. The sparse ground-based air monitoring stations limit their application for scientific research, while aerosol optical depth (AOD) by remote sensing satellite technology retrieval can reflect air quality on a large scale and thus compensate for the shortcomings of ground-based measurements. In this study, the elaborate vertical-humidity method was used to estimate PM\({}_{2.5}\) with the spatial resolution 1 km and the temporal resolution 1 hour. For vertical correction, the scale height of aerosols (H\({}_{a}\)) was introduced based on the relationship between the visibility data and extinction coefficient of meteorological observations to correct the AOD of the Advance Himawari-Imager (AHI) onboard the Himawari-8 satellite. The hygroscopic growth factor (\(\ell\)(RH)) was fitted site-by-site and month by month (1-12 months). Meanwhile, the spatial distribution of the fitted coefficients can be obtained by interpolation assuming that the aerosol properties vary smoothly on a regional scale. The inverse distance weighted (IDW) method was performed to construct the hygroscopic correction factor grid for humidity correction so as to estimate the PM\({}_{2.5}\) concentrations in Sichuan and Chongqing from 09:00 to 16:00 in 2017-2018. The results indicate that the correlation between \"dry\" extinction coefficient and PM\({}_{2.5}\) is slightly improved compared to the correlation between AOD and PM\({}_{2.5}\), with r coefficient values increasing from 0.12-0.45 to 0.32-0.69. The r of hour-by-hour verification is between 0.69 and 0.85, and the accuracy of the afternoon is higher than that of the morning. Due to the missing rate of AOD in the southwest is very high, this study utilized inverse variance weighting (IVW) gap-filling method combine satellite estimation PM\({}_{2.5}\) and the nested air-quality prediction modeling system (NAQPMS) simulation data to obtain the full-coverage hourly PM\({}_{2.5}\) concentration and analyze a pollution process in the fall and winter.  
Keywords:Himawari-8 AOD; vertical correction; humidity correction; NAQPMS; IVW +
Footnote †: journal:
## 3 Materials and Method
### The Workflow
#### 3.2.5 Data Augmentation  
The model was trained using a batch size of 32, processing 32 samples before updating the internal parameters. The training was conducted over 10 epochs, in which each epoch represents a complete pass through the entire training dataset. The learning rate, implicitly set by the Adam optimizer, was 0.001, which determines the step size at each iteration while moving towards the minimum of the loss function. The Adam optimizer was selected for updating the model parameters due to its efficiency and adaptive learning rate capabilities. The categorical cross entropy loss function was used to measure the discrepancy between the predicted labels and the true labels. To enhance the model's robustness and prevent overfitting, data augmentation techniques were employed, including rescaling pixel values by 1./255, applying random shearing transformations with a range of 0.2, random zooming within a range of 0.2, and horizontal flipping of images.
### Procedure 1: Color Scheme Group, Number of Colors
#### 4.1.2 Number of Colors  
The second post hoc test is performed for the number of colors and the results are presented in Table 8. [PERSON]'s test shows a significant difference between the accuracy of user readings in maps with three and nine colors (\(p=0.009\)) as well as in maps with six and nine colors (\(p=0.013\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Test Statistic** & **df1** & **df2** & \(p\) \\ \hline Color scheme groups & 56.370 * & 1.826 & 488.704 & 0.000 \\ Number of colors & 42.461 * & 1.970 & 488.704 & 0.000 \\ Interaction (Color scheme groups: & 45.533 * & 3.315 & 488.704 & 0.000 \\ number of colors) & & & & \\ \hline \hline \end{tabular}
*Statistically significant values with \(p<0.05\).  
\end{table}
Table 6: The results of the ANOVA-type statistics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Comparison** & **Z** & \(p\) & **Cohen’s d** \\ \hline Diverging—Qualitative & 4.879 * & 0.000 & 0.534 \\ Diverging—Sequential & \(-2.256\)* & 0.024 & 0.201 \\ Qualitative—Sequential & \(-7.711\)* & 0.000 & 0.758 \\ \hline \hline \end{tabular}
*Statistically significant values with \(p<0.05\).  
\end{table}
Table 7: The results of the post hoc test for color scheme group, with additional information—effect size (Cohen’s d).  
According to the descriptive statistics (Table 5) and spread of error (Figure 10), maps with three and six colors obtain lower error in user readings than maps with nine colors. Thus, the result of the post hoc test means that maps with nine colors obtained the significantly worst score in user readings. The effect size for this difference is small (Table 8). Since there is no significant difference between maps with three and six colors, there is no indication of which number of colors is best to choose. The findings in this analysis confirm the H2 hypothesis that accuracy of user readings is related to the number of colors in the color scheme.
###### Abstract  
The hardware in the loop (HIL) technique allows you to reproduce the behavior of a dynamic system or part of it in real time. This quality makes HIL a useful tool in the controller validation process and is widely used in multiple areas including photovoltaic systems (PVSs). This study presents the development of an HIL system to emulate the behavior of a PVS that includes a photovoltaic panel (PVP) and a DC-DC boost converter connected in series. The emulator was embedded into an NI-myRIO development board that operates with an integration time of 10 us and reproduces the behavior of the real system with a mean percent error of 2.0478%, compared to simulation results. The implemented emulator is proposed as a platform for the validation of control systems. With it, the experimental stage is carried out on two controllers connected to the PVS without having the real system and allowing to emulate different operating conditions. The first controller is based on the Hill Climbing algorithm for the maximum power point tracking (MPPT), the second is a proportional integral (PI) controller for voltage control. Both controllers generate settling times of less than 3 s; the MPPT controller generates variations in the output in steady state inherent to the algorithm used. For both cases, the comparison of the experimental results with those obtained through software simulation show that the platform fulfills its usefulness when evaluating control systems.  
FPGA; hardware in the loop; maximum power point tracking; PI controller; photovoltaic system 2020 10 8690 30 November 2020; Published: 4 December 2020
## 3 Proposed System Model
### RSU Overview  
RSUs are the static nodes, which are used to gather and store all the information provided by the vehicles. In the proposed model, distributed connections are established between RSUs and edge nodes to provide services like road information, traffic rate, road accident details and nearly located charging stations. In real-time vehicular environments, data generated by smart vehicles is large in quantity. Therefore, most of the data are stored in RSUs because storing such huge amount of data at edge nodes is a time consuming task. To ensure uninterruptible information provisioning to all vehicles and edge nodes, RSUs remain in active state at all times.
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 5 Experiments and Results
### V-PDR Testing Result
Our method makes some corrections at the stairwell exit and around the corner on the first and third floors. The results show that, after walking upstairs, the trajectory of PDR is still on the first floor, while the trajectory of V-PDR is on the correct (third) floor. The end point of PDR is 42.64 m away from the actual end point, while the distance in V-PDR system is merely 0.79 m. These comparisons demonstrate that our method outperforms PDR in a three-dimensional space. More detailed error information is shown in Table 3.  
The localization errors listed in Table 3 were calculated by projecting the trajectories into the horizontal plane. Comparing the average localization errors, V-PDR gains an 89.2% improvement in localization accuracy under the test containing stairs. For the cost of the setup period, due to the accuracy of the VPR, it can be successfully located within three-dimension VPR. In most special cases, the local identification cannot be accurately performed, and the location may need to be changed to successfully locate. In this case, it may take a long time for the setup. However, the cost is acceptable for accurate positioning. A simple PDR algorithm can only obtain the relative position between two positions but not the exact position. With the assistance of VPR, the exact position can be obtained, which improves the positioning of the PDR. In the future, more improvements can be added to the setup, such as the use of panoramic images for positioning. This can obtain richer visual information and more accurate positioning. Although our system uses a mobile phone to take pictures, which is complicated for personal use, this algorithm is to explore the fusion of PDR and vision. After wearable cameras and other devices are put into use, or used on a humanoid robot, users will no longer need to take pictures, which makes a lot of sense in actual use. It can be indicated from above experiments that our indoor localization method successfully improves the localization accuracy by reducing accumulative errors of PDR and makes good performance in the cross-floor test. There are many advantages of the proposed V-PDR compared with the standalone VPR system. Since VPDR can obtain a continuous walking path, VPR can only obtain discrete positioning points. The two method cannot be directly compared by experiments. However, we can analyze it from the experimental results of V-PDR. First, V-PDR can obtain continuous paths, and the amount of information obtained is greater than VPR. In addition, PDR has a correction effect on the inaccurate value of VPR, and V-PDR positioning is more accurate. Finally, V-PDR does not need to call the VPR network in real time, where it takes less calculation and time than VPR.
## 2 Beidou-Binocular Cooperative Navigation Model
### Model Framework and Implementation
#### 2.4.1 Model Framework Design  
The system framework of the BBCN model consists of three main layers: a sensor data input layer, a data processing and fusion layer, and a path planning and control layer. The tasks undertaken by each layer in the system are as follows:  
1. Sensor Data Input Layer: This includes the Beidou navigation module and the binocular vision module. It is responsible for collecting global positioning data from the Beidou receiver and real-time images, depth maps, and environmental information captured by the binocular cameras.
2. Data Processing and Fusion Layer: The Beidou and binocular vision data are fused using the Unscented Kalman Filter (UKF) to generate a unified state estimate. Based on the fused data, the robot's global position and local environmental information are updated.
3. Path Planning and Control Layer: At the global level, a global path is generated based on the target position provided by Beidou navigation. At the local level, the path is dynamically adjusted using obstacle information from vision recognition, and dynamic obstacle avoidance and path optimization are performed through Riemannian Motion Policies (RMP). The output includes low-level robot control commands, such as linear velocity and angular velocity.
## 4 Nonlinear Dynamic Response Calculation Method  
The harmonic balance method has some advantages, including clear physical meaning, strong applicability, and high computational efficiency. It is very suitable for the steady-state response calculation of strong nonlinear structures [26; 27; 28; 29; 30].  
First, under the basic assumptions, the steady-state response displacement solution is obtained by a Fourier series:  
\[{\bf u}(t)={\bf U}^{0}+\sum_{k=1}^{N_{\rm h}}{\bf U}^{\rm ck}\cos(k\omega t )+{\bf U}^{\rm sk}\sin(k\omega t), \tag{11}\]  
Figure 4: Simulated fractal surface profiles under different three-dimensional fractal dimensions; (a) \(D=2.2\), \(\rm G=1\times 10^{-8}\); (b) \(D=2.3\), \(\rm G=1\times 10^{-8}\); (c) \(D=2.49\), \(\rm G=1\times 10^{-8}\).  
where \(N_{\rm h}\) represents the truncated harmonic order, \(\omega\) represents the external excitation frequency, \(\mathbf{U}_{0}\) represents the Fourier series corresponding to the '0' order harmonic, and \(\mathbf{U}_{\rm ck}\) and \(\mathbf{U}_{\rm sk}\) represent the Fourier series corresponding to the \(k\)-order harmonic. In addition, \(\mathbf{N}_{\rm d}\) denotes the degree of freedom of the whole system, and the Fourier series vectors of dimension \(N_{\rm d}\cdot(2N_{\rm h}+1)\) are arranged as follows during the calculation:  
\[\mathbf{U}=\left[\mathbf{U}^{0},\mathbf{U}^{c1},\mathbf{U}^{s1},\cdots, \mathbf{U}^{cN_{\rm h}},\mathbf{U}^{sN_{\rm h}}\right]^{T}, \tag{12}\]  
Given that the nonlinear force and the external excitation force can also be expressed as the product of the transformation matrix and the corresponding Fourier coefficients,  
\[\begin{split}&\mathbf{f}(t)=\mathbf{H}(\omega t)\times\left[ \mathbf{F}^{0},\mathbf{F}^{c1},\mathbf{F}^{s1},\cdots,\mathbf{F}^{ck},\mathbf{ F}^{sk},\cdots\right]^{T}=\mathbf{H}(\omega t)\mathbf{F}\\ &\mathbf{f}_{\rm nl}(\mathbf{u},\dot{\mathbf{u}},t)=\mathbf{H}( \omega t)\times\left[\mathbf{F}^{0}_{nl},\mathbf{F}^{c1}_{nl},\mathbf{F}^{s1} _{nl},\cdots,\mathbf{F}^{ck}_{nl},\mathbf{F}^{sk}_{nl},\cdots\right]^{T}= \mathbf{H}(\omega t)\mathbf{F}_{\rm nl}(\mathbf{U})\end{split}, \tag{13}\]  
Finally, an equilibrium equation of the multi-harmonic balance method with the Fourier coefficients as unknowns is obtained:  
\[\mathbf{G}(\omega,\mathbf{U})=\mathbf{P}(\omega)\mathbf{U}+\mathbf{F}_{nl}( \mathbf{U})-\mathbf{F}=\mathbf{0}, \tag{14}\]  
where \(\mathbf{P}(\omega)=\omega^{2}\mathbf{N}_{\rm M}\
abla^{2}+\omega\mathbf{N} \mathbf{c}\
abla+\mathbf{N}_{\rm K}\) represents the dynamic stiffness matrix, \(\mathbf{N}_{\rm M}\) represents a block diagonal matrix consisting of a mass matrix \(\mathbf{M}\), \(\mathbf{N}_{\rm M}=diag(\mathbf{M},\mathbf{M},\cdots)\), and \(\mathbf{N}_{\rm C}\) and \(\mathbf{N}_{\rm K}\) have the same structure as \(\mathbf{N}_{\rm M}\). The algebraic equations can be solved using arc-length continuation [31] and the Newton-Raphson iterative method [32].
## 2 Positioning in the Forest
### Post-Processing of GNSS Data  
If raw satellite observations of the reference and the rover are stored, the position of the rover can be computed in post-processing using DGNSS. Another method is to use only the data logged at the rover and precise point processing (PPP). The PPP approach utilizes precise clocks and orbits, two of the most significant error sources of GNSS positioning. GPS satellite ephemerides, satellite and ground station clocks and GLONASS satellite ephemerides can be downloaded from International GNSS Service (IGS). These data are typically available after a couple of days (GPS) or two weeks (GLONASS) after the observation session [24]. Post-processing often results in better positioning accuracy, but, as a real-time method, it is highly dependent on the satellite visibility, instrumentation, _etc._ Data logged with additional sensors, such as an IMU and an odometer, can be used to improve the accuracy and reliability of the post-processed position solution.