esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 RACH Success Probability and Power Consumption Model for LEO Satellite-Based NB-IoT Networks
### Interference and Collision Model  
Without a loss of generality, we consider that the number of the data packet received by the LEO satellite in the coverage area is a random variable following the Poisson distribution and the arrival rate is \(\lambda\). Therefore, the probability density function (PDF) is written as follows:  
\[P(X=k)=\frac{e^{-\lambda}}{k!}\lambda^{k} \tag{2}\]  
In order to provide the expressions for the preamble detection success probability in the following parts, we assume that NB-IoT terminals' distribution follows a homogeneous Poisson point process (HPPP) to analyze the SINR. The locations of them are distributed according to a homogeneousHPPP [20]. The PDF of the distance from the LEO satellite to the NB-IoT terminal can be given by the following:  
\[f_{R}(r)=\left\{\begin{array}{ll}\frac{r}{2R_{e}(R_{e}+r_{\min})}&r_{\min} \leq r\leq 2R_{e}+r_{\min}\\ 0&\text{otherwise}\end{array}\right. \tag{3}\]  
where \(R_{e}\) is Earth's radius, \(r_{\min}\) specifies the minimum possible distance from a satellite to a terminal (that is realized when it is directly above the terminal), and \(r_{\max}~{}=~{}\sqrt{2R_{e}\cdot r_{\min}+r_{\min}^{2}}\) is the maximum possible distance (that is realized when the LEO satellite is at the horizon).  
Figure 4a shows the interference model of NB-IoT via LEO satellite-based networks. NB-IoT terminals initiate the RA procedure with the same transmit power \(p_{5}\). We consider that the LEO additive noise of the LEO satellite is subject to additive white Gaussian noise (AWGN) with constant power \(\sigma^{2}\). Moreover, the LEO satellite antenna receiving gain \(G_{0}\) satisfies the following equation [21]:  
\[G_{0}=\eta\left(\frac{\pi D}{\lambda}\right)^{2}=\eta\left(\frac{f\pi D}{c} \right)^{2} \tag{4}\]  
where \(\eta\) is aperture efficiency, \(D\) is reflective surface aperture, \(f\) is the frequency band, and \(c\) is the speed of light.  
As shown in Figure 4b, when NB-IoT terminals are in the overlapping region of the beam coverage of LEO satellite A and LEO satellite B, the downlink RSRP received by NB-IoT terminals in LEO satellite B will be similar to the RSRP broadcast from LEO satellite A. Therefore, this situation will result in NB-IoT terminals which should connect to LEO satellite B causing interference to LEO satellite A. Due to the presence of satellite constellation, the interference area \(A_{I}\) can be approximated as a circle. Meanwhile, collisions  
Figure 4: Interference model: (**a**) interference model architecture diagram; (**b**) top view of interference area.  
may occur when NB-IoT terminals are in the same LEO satellite coverage area \(A_{s}\). Given the above, we will analyze the successful probability of preamble detection and the probability of preamble collision in the following subsection.
## 2 Materials and Methods
### Data  
#### 2.2.1 Flux Data  
FLUXNET 2015 synthesizes eddy covariance measurements from globally distributed regional networks [42; 43]. This open-access dataset ([[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)), accessed on 16 December 2024) provides multi-temporal observations (half-hourly to yearly) of carbon, water, and energy fluxes from 212 globally distributed sites [43]. All records have undergone rigorous quality assurance procedures with standardized processing protocols to ensure cross-site consistency [43]. Sites with observations representing different vegetation types were used to directly compare the LUEmax's patterns of change. Moreover, sites are strategically distributed across different climate zones, thereby facilitating the study of variations in LUEmax values under diverse environmental conditions during the same period. The variables used in this study include net ecosystem exchange ('NEE_VUT_REF'), photosynthetic photon flux density incident ('PPFD_IN'), vapor pressure saturation deficit ('VPD_F'), air temperature ('TA_F'), soil temperature ('TS_F_MDS'), precipitation ('P_F'), CO\({}_{2}\) mole fraction ('CO\({}_{2}\)F_MDS'), and net radiation ('NETRAD'). We refined high-quality data to eliminate abnormal data exhibiting PPFD_IN_QC values of \(-9999\) or 0.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site\_ID** & **Latitude** & **Longitude** &
\begin{tabular}{c} **K\(\ddot{\text{oppen}}\)-Geiger** \\ **Classification** \\ \end{tabular} & **Biome Type** & **Data Period** & **Reference** \\ \hline NL-Loo & 52.1666 & 5.7436 & temperate & ENF & 2007–2009 & [44] \\ US-Me2 & 44.4526 & \(-121.5589\) & temperate & ENF & 2013–2014 & [45] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of flux sites.  
Figure 1: The distribution K\(\ddot{\text{o}}\)ppen–Geiger climate zones and the 23 flux sites.
## 3 Results
### Determination of Optimal FOD Order
Although CARS requires multiple iterations to identify the optimal band combination, the incorporation of EDF and ARS algorithms allows for the elimination of low-importance bands during the early stages of iteration, shortening the modeling time from 13.58 s (CC-PLSR) to 5.27 s (CARS-PLSR). Compared to the CC+PLSR method, the CARS+PLSR approach proved to be a superior band selection algorithm, capable of identifying the optimal combination of bands more efficiently and enhancing the inversion capability of the model.
## 3 Experiments and Analysis  
### Datasets  
To verify the effectiveness of the proposed method, two open fundus retinal datasets are used in the experiments, the details of the datasets are shown as follows.  
DRIVE [26]: The Digital Retinal Images for Vessel Extraction (DRIVE) dataset was published in 2013 from a diabetic retinopathy screening program in the Netherlands. The screening population was 400 diabetic subjects aged 25-90 years. Forty images were randomly selected, 33 photos had no signs of diabetic retinopathy and 7 photos showed signs of mild early diabetic retinopathy, the size of each image is 584 \(\times\) 565.  
CHASE_DB1 [27]: The Child Health and Heart Studies in England (CHASE_DB1) contains images of different diseases in which retinal images along with ground truth vessel can be found in the first database entitled CHASE_DB1. It includes 28 retinal images taken from the eyes of 14 school children. Typical 20 images are used for training and 8 for testing, the size of each image is 999 \(\times\) 960.
# Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery
## 3 Change Detection with the Proposed Dual-Dense Convolutional Neural Network  
In general, generic change detection algorithms consist of two phases: pre-processing and change detection. Figure 3a depicts a general procedure of the conventional change detection system. The pre-processing stage performs radiometric correction, geometric rectification, and image registration. The registered images are then fed into a change detection algorithm in order to identify changed areas with feature vectors, for example, a difference image. In the general change detection systems, the radiometric correction and image registration stages are important and indispensable for better performance. The radiometric correction is performed in order to alleviate distortion for radiometric consistency. Then, the geometric correction is performed by aligning the global earth coordinates with the corresponding image points. Even though two images are compensated using multiple steps, they are still not perfectly registered, as they are independently processed with many error factors. Thus, an additional registration between two images is frequently required in order to reduce mis-alignment.  
Figure 3: Change detection system schema. (**a**) Conventional change detection system and (**b**) the change detection system with the proposed dual-DCN.  
Figure 2: Conventional approaches. (**a**) Difference Image (DI) + CNN and (**b**) Feature Fusion (FF) + CNN.  
In urban and mountainous areas in particular, most automatic image registration methods remain ineffective. It would degrade performance of change detection by direct pixel-wise comparison using the difference image. In order to resolve imperfect registration impacting on the change detection, a dual-DCN, as shown in Figure 3b is proposed by employing a dissimilarity distance in order to overcome the mis-alignment problem for better performance of change detection even without a perfect image registration. The proposed algorithm employs two deep convolution networks to keep all the information of the original data. The generic characteristics of the CNN handle some local distortion and alignment, thus, the proposed algorithm absorbs the misalignment problem. In addition, the dense connectivity in the convolution layer is introduced by reusing all preceding feature-maps to enhance the feature-map representation.
## 3 Results
### \"Real-Life\" Data Sample
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline  & **Total** & **TP** & **TN** & **FP** & **FN** & **NPV** & **PPV** & **Sensitivity** & **Specificity** & **Accuracy (C95\%)** & **F1 Score** & **LR+** & **LR\(-\)** \\ \hline All test sets & 744 & 403 & 143 & 41 & 403 & 0.26 & 0.91 & 0.50 & 0.78 & 0.73 \(\pm\) 0.032 & 0.64 & 2.24 & 0.64 \\ \hline Unambiguous PET studies & 177 & 99 & 67 & 11 & 99 & 0.40 & 0.90 & 0.50 & 0.86 & 0.94 \(\pm\) 0.036 & 0.64 & 3.54 & 0.58 \\ \hline Ambiguous PET studies & 567 & 304 & 76 & 30 & 304 & 0.20 & 0.91 & 0.50 & 0.72 & 0.67 \(\pm\) 0.039 & 0.65 & 1.77 & 0.70 \\ \hline \end{tabular}
\end{table} 
Table 5: Results of the “real-life” based trained CNN. For a better understanding of the model, the algorithm's performance was evaluated by separating the \"unambiguous\" from the \"ambiguous\" PET studies (Table 5). A better performance was obtained on the \"unambiguous\" sub-test set with a PPV and NPV of 90% and 40%, with a slight decline in the NPV (20%) on the \"ambiguous\" sub-test set.
## 3 Results
### Real-World Applications and the Impact of JAVIS in Hospital Workflows  
JAVIS has played a critical role in enhancing physician efficiency, facilitating large-scale clinical data labeling, and enabling AI-assisted research within secure hospital environments. The following subsections outline key applications where JAVIS has been effectively utilized.
## 3 Elastographic Methods
### Two-Dimensional Shear Wave Elastography (2D-SWE)
#### 3.3.3 Liver Stiffness Estimates in Healthy Controls  
Liver stiffness estimates in healthy subjects have been assessed in several studies, and most information comes from the control group of case-control studies. The mean 2D-SWE value was 5.5 \(\pm\) 1.3 kPa in free-breathing status and 5.5 \(\pm\) 1.1 kPa, with a range of 3.7-7.7 kPa [73]. The breathing method does not seem to have an impact on 2D-SWE values and their variability [72,73]. The gender seems not to significantly affect 2D-SWE values [73], with average values of 5.4 \(\pm\) 1.1 kPa in males versus 5.6 \(\pm\) 1.1 kPa in females (\(p\) = 0.637) [73]. LSMs were found to correlate with children's age (\(r\) = 0.429, \(p\) = 0.006), and to be significantly higher (6.1 \(\pm\) 1.1 kPa) in babies older than 60 days (\(n\) = 10) than in babies of 60 days or below (5.3 \(\pm\) 1.0 kPa) (\(n\) = 30) (\(p\) = 0.026) [73]. However, another study didn't find any significant difference between different age groups (\(p\) = 0.11) [25] and only a trend to an increase of LSMs with age was found when using the linear transducer (\(p\) = 0.05). Technical factors may also affect LSMs, including the transducer used: a significant difference was found for mean elasticity between the linear and convex transducers: 5.96 kPa \(\pm\) 1.31 and 6.94 kPa \(\pm\) 1.42, respectively (\(p\) = 0.006) [25].
## 5 Conclusions and Future Work  
In this paper, we proposed a SOT-based MOT method in order to increase the accuracy of tracking results focusing on ID preservation. We introduce a binary-channel verification model using a separate single object tracker and detection provided by an external detector equally. Moreover, a delay processing model is proposed to handle the drift problem that easily emerges when applying a single object tracker under a MOT scenario. Our method outperforms other state-of-the-art methods on the MOT17 benchmark according to the _IDF1_ and _MT_ metrics, which proves our method is capable of preserving identity consistency when dealing with occlusion. Finally, we have shown some qualitative results under different hard-to-solve circumstances.  
Though a re-initialization part is implemented in the delay processing model to handle geometry changes of certain objects, tracking objects with significant deformation after occlusion still remains unsolved since appearance features are mostly dissimilar for a tracker to reclaim the object. In our future work, we plan to train a generative model for the tracker to better recognize deformations of the same object without misalignment with other distraction information including similar objects.  
Conceptualization, M.L.; Data curation, M.L.; Formal analysis, M.L.; Funding acquisition, Z.M.; Methodology, M.L.; Resources, A.K.; Software, M.L.; Supervision, X.H., Z.W. and A.K.; Validation, M.L.; Writing--original draft, M.L.; Writing--review and editing, X.H., Z.W., J.W., Z.M. and A.K.  
This research was funded by Jilin Scientific and Technological Development Program, grant number: 20180201013 GX.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
## 6 Conclusions  
In order to solve the problem of system instability when the centralized cooperative strategy is adopted, a distributed cooperative guidance law is designed based on the consistency theory. The structure of the guidance law consists of two parts: the bottom non-singular sliding mode guidance law and the upper distributed coordination strategy. First, a new time-to-go estimation method is proposed, which is more accurate than the existing methods when the leading angle is large. Second, a non-singular sliding mode guidance law (NSMG) of impact time control with equivalent control term and switching control term is designed, which still appears to have excellent performance even if the initial leading angle is zero. Then, based on the predicted crack point strategy, the NSMG law is extended to attack maneuvering targets. Finally, adopting hierarchical cooperative guidance architecture, a terminal distributed cooperative guidance law based on consistency theory is designed. The simulation results show that:  
1. The time-to-go estimation method proposed in this paper is more accurate than [27] under large leading angles.
2. The non-singular sliding mode guidance law with impact time constraint at the bottom layer can be applied to strike missions under different initial conditions and designated impact time. For maneuvering targets, the predictive collision point strategy is used to extend the form of the guidance law, which can still achieve precise strike.
3. In this paper, the upper-layer distributed coordination strategy and the lower-layer non-singular sliding mode guidance law are combined to make the formation members exchange information with each other, so as to realize the time cooperative online closed-loop guidance under the condition of fixed or switching network topology of the UAV formation.  
Figure 11: Simulation results of switching the network topology.  
**Author Contributions:** Conceptualization, Z.J. and J.G.; Formal analysis, Q.X. and Z.J.; Methodology, Z.J., T.Y. and J.G.; Software, Z.J.; Validation, Z.J. and J.G.; Writing--original draft, Z.J.; Writing-review and editing, T.Y. and Q.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
_Acronyms_  
UAVUnmanned Aerial Vehicle  
NSMGIn nonsingular sliding mode guidance  
PNGProportional Navigation Guidance  
NTSMCIn nonsingular terminal sliding mode control theory  
LOSline of sight  
SMCSliding mode control
## 2 System Architecture
### Software
#### 2.2.3 Image and Video Compression  
Image compression is a critical process that reduces the amount of data required to represent a digital image. This technique is essential for efficient storage and transmission in networks where the bandwidth is limited. There are two different types of compression: lossless and lossy compression. The former preserves all the original data, allowing for the reconstruction of the image without losing quality when reconstructed. The latter sacrifices some data to achieve higher compression ratios, resulting in quality reduction.  
There are different compression methods, such as Portable Network Graphics (PNG), Graphics Interchange Format (GIF), WebP, Joint Photographic Experts Group (JPEG), and JPEG2000. JPEG compression is one of the most popular and widely used techniques. One of the main advantages of the JPEG method is that it allows the user to adjust the compression level, enabling users to select a balance between image quality and file size. The JPEG format is supported by different platforms, including ROS, and using this format, images can be reduced by 70% to 90% of their original size, making it ideal for image transmission in robotic networks where multiple robots transfer large amounts of data. For example, Table 2 shows the sizes and ideal FPS values for different quality compression parameters of an image.  
On the other side, there are also video compression algorithms like H.264 and H.265 that can achieve high efficiency by significantly reducing the sizes of video file. These algorithms use advanced techniques such as motion prediction and block-based compression. Due to their versatility and quality, these methods are widely used in video streaming applications such as video conferencing and media storage. The main disadvantage is that these methods suffer from latency issues that can be problematic in real-time applications such as the teleoperation of intervention robots.
## 3 Results and Discussion  
### 10-Fold CV Performance Evaluation  
During the optimization process, the performance of RF and AD models was slightly improved, but the ET model showed no improvement. This could be due to models' complexity and capability, as well as the data characteristics under consideration. However, the performance of the CB model achieved a notable enhancement, as illustrated in Figure 6. The final results obtained from 10-fold CV on unseen data for the optimized models were visualized using boxplots, as shown in Figure 7. This visualization helps to understand not only the average performance but also the consistency and variability of performance across different folds of unseen data. In general, the size of boxes (the interquartile range) represents the variability in the scores. A smaller box indicates less variability, suggesting more consistent performance. A larger box indicates greater variability, suggesting instability or potential issues with performance. The red line in the middle of the boxes represents the median/mean value of scores, suggesting a high or poor performance. The whiskers at the ends of boxes represent the range of scores. A longer whisker suggests a wider range of performance, and vice versa. Accordingly, the CB model achieved the highest median value (91.7%), the smallest box, and shortest whiskers, exhibiting strong and consistent generalization performance over the other models. ET also achieved consistent performance (small box) but with a lower median value (88.8%) and a longer whisker. On the other hand, RF and AB showed unstable performance (large boxes, long whiskers, and low median/mean values). A more detailed evaluation can be found in the following subsection.
# Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
## 3 Preliminaries  
### Classification Techniques Used in the Present Study  
#### 3.1.1 K-Nearest Neighbor (KNN)  
The K-nearest neighbor algorithm, proposed by [PERSON] and [PERSON] [32], assigns class labels to unseen data by adopting the most common class label among the unseen data's K-nearest neighbors' classes. The 'K' in K-nearest neighbors represents the number of neighborhoods. A small value of K could lead to over-learning, while a large value might result in generalization. Several criteria can be used to measure distance.  
When there are \"p\" attributes, the distance between points \"i\" and \"j\" can be calculated using the Minkowski function, as represented by Equation (1). Notably, this equation calculates the Manhattan distance when \"q = 1\", the Euclidean distance when \"q = 2\", and the Chebyshev distance when \"q = 0\". In this study, \"q\" was considered as \"2\" in order to calculate the Euclidean distance.  
\[d(i,j)=\sqrt[]{\left(\left|x_{i1}-x_{j1}\right|^{q}+\left|x_{i2}-x_{j2}\right|^{ q}+\ldots+\left|x_{ip}-x_{jp}\right|^{q}\right)} \tag{1}\]
## 4 Discussion
The commercial U-Blox receiver can be assured to exploit some advanced techniques inside the GNSS receiver design to confirm the pseudo-range measurement to maintain a relatively high quality. Still, the way to improve the performance of the pseudo-range observation is not the primary contribution in this work. Therefore, it is acceptable that the positioning results based on the DGPS algorithms can be addressed better using the U-Blox receiver than the SDR with the A-VTTCES-KF in the north and vertical directions. Regarding the comparisons in terms of the GNSS SDR using 10-Hz-VT-LR and 5-Hz-VT-LR algorithms, some conclusions can be subsequently drawn. Sixthly, it can be found that the positioning accuracies in the north and vertical directions for both RTK and DGPS algorithms decline when using 10-Hz-VT-LR GNSS SDR. Accordingly, we can infer that when the VT LR increases, it will be harder to get rid of the time-correlated error in the VT loop through the proposed algorithm. Finally, the eastern positioning accuracy are generally enhanced when the VT LR increases. For example, when compared with the O-KF-VT, the improved percentage of the RTK and DGPS with the 5-Hz VT LR is 0.83% and \(-\)0.10%, while the one with the 10-Hz VT LR is 5.59% and 4.32%, respectively.