esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Trend Analysis of Crop Identification Overall Accuracy and Kappa Coefficient  
In this study, the Cart decision tree, GBDT, RF and SVM classifiers were applied to remote sensing recognition of crops in each period. This included 1 recognition result on 8 May, 7 recognition results on 13 May, 63 recognition results on 28 May, 1023 recognition results on 12 June, and 32,767 recognition results on 7 July. The OA and kappa coefficients were used to evaluate the overall crop recognition performance of each classifier in each period. The overall accuracy and kappa coefficient trends in different periods were analyzed as follows.  
Figure 5 shows the maximum overall recognition accuracy of the four classifiers in different periods. It can be seen that the most significant increase occurred from 12 June to 7 July, when the recognition accuracy increased from 75% to 97%, followed by 8 May to 13 May (60% to 74%). From 13 May to 28 May and 28 May to 12 June, the recognition accuracy changes were relatively low, with ranges of 67-77% and 71-81%, respectively. The OA of the four classifiers tended to increase as more feature information was added over time. The GBDT and RF performed better than the SVM and Cart classifiers throughout the period, with the maximum identification accuracies achieved on 12 June (about 81%) and 7 July (about 97%).  
Figure 6 shows the maximum kappa coefficients of the four classifiers in different periods. The upward trend is similar to that of OA during the early stage. The most significant increase occurred from 12 June to 7 July, with a kappa coefficient range of 62-95%, followed by 8 May to 13 May (37-68%). From 13 May to 28 May and 28 May to 12 June, the changes were relatively low (63-68% and 62-71%, respectively). The GBDT  
Figure 5: Maximum OA changes of the classifiers in 2020 different periods.  
and RF performed better than the SVM and Cart classifiers throughout the period, with the kappa coefficients peaking at around 71% on 12 June and about 95% on 7 July.  
Overall, the identification performance of GBDT and RF was better than those of other classifiers in the early stage, and Figure 7 shows the best recognition results for each period. The performance of SVM was comparable to that of Cart in the early stage, while the recognition ability gradually increased in the later stage, with Cart being worse than the other classifiers.  
Figure 6: Maximum kappa coefficient changes of the classifiers in 2020 different periods.  
Figure 7: Optimal identification results in 2020 different periods. (**a**) Identification results on 8 May; (**b**) Identification results on 13 May; (**c**) Identification results on 28 May; (**d**) Identification results on 12 June; (**e**) Identification results on 7 July.
## 4 Artificial Hummingbird Algorithm (AHA) and XGBoost  
### Artificial Hummingbird Algorithm (AHA)  
The artificial hummingbird algorithm (AHA) represents an emerging heuristic approach [10], distinguished by its unique biological inspiration. AHA meticulously models three characteristic hummingbird flight maneuvers--axial, diagonal, and omnidirectional--alongside their intelligent foraging strategies, encompassing guided, regional, and migratory patterns. This formulation yields an artificial intelligence foraging model adept at tackling high-dimensional optimization problems, offering benefits like computational simplicity and high solution accuracy. Nevertheless, similar to many heuristic methods, AHA is not without limitations, exhibiting a tendency to converge prematurely on local optima and experiencing slower convergence speeds.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data
#### 3.2.16 Global Forest AGB Map at the 0.01\({}^{\circ}\) Spatial Resolution  
[PERSON] et al. [77] integrated reference biomass datasets compiled from extensive field plot datasets and high-resolution biomass maps, with multiple satellite high-level products including leaf area index, forest height, forest cover, topographic and climatic variables using the random forest ensemble tree regression algorithm, and estimated global forest AGB at 0.01\({}^{\circ}\) spatial resolution for the 2000s. Results of cross-validation and inter-comparison with nine other published datasets showed that the generated global forest AGB was more accurate than the published studies, with a coefficient of determination of 0.70 and RMSE of 46.91 Mg/ha, respectively.
## 3 Results and Discussion
### Mixed Time Partitioning Methods vs. Sub-Cycling and Partitioning Methods
#### 3.1.1 Computational Efficiency of Sub-Cycling Methods  
We will discuss the case with the same time integration schemes but with different time steps used in the casting and mold. In this approach, we use a fixed time step \(\Delta t\) in the cast domain and much larger time steps \(m\Delta t\) in the mold domains. The maximal multiplication factor we study in this paper is \(m=15\). It is the largest acceptable value of \(m\) chosen based on the numerical stability criterion. This strategy allows accounting for the essential physical features occurring in the casting which are not important in the mold. From Table 4 we can see that, like in the case of a traditional schemes combination, \(E-E^{m}\) methods are faster than the \(I-I^{m}\) ones (long-lasting iterations). A dynamically changing physical phenomena in the casting require very small time step. It is favorable for explicit steppers. Implicit schemes fulfill many iterations per one time step. Therefore, the run time is extended. Despite this fact, the sub-cycling methods are faster than the standard methods (for \(m=1\)).
# MAC Layer Protocols for Internet of Things: A Survey
## 2 Short Range MAC Layer Protocols  
Short range coverage medium access control (MAC) protocols are defined by the Institute of Electrical and Electronics Engineers (IEEE) as Wireless Personal Area Networks (WPAN), which is the network established between elements that surround the human body. WPAN communication technologies differ from other conventional wireless network technologies. These networks call for easy connectivity in order to reach personal wearable or hand-held devices. Moreover, WPAN requires power efficiency, small size, low cost and maybe most importantly easy to use devices [2, 3].  
Short-distance technologies such as near field communication (NFC) and radio frequency identification (RFID) are technologies that fit into this study context due to their usage with differentiated mechanisms for the physical and linking layers. Thus, their characteristics are less critical when compared to the IEEE 802.15.6 standard [4], which is dedicated to wireless body area networks (WBAN). Such networks have different scenarios and prerequisites that are very different from those that are supported by the networks of things. Body sensor networks have very critical requirements when compared to the networks of things such as WBANs. These network characteristics should achieve a maximum latency of 125 ms to attend medical applications, they cannot surpass 250 ms to be applied to non-medical applications, and their jitter must be lower than 50 ms. Low power consumption, automatic connection and disconnection of new elements in the network, mixed typologies, low overhead and other characteristics are examples of the high critical parameters in WBANs [5; 6; 7].  
There are technologies that use differentiated methods to treat their PHY and MAC layers as Long Term Evolution (LTE) mobile networks. These technologies offer differentiated techniques of connection establishment methods, communication controls, and physical access controls, among others. Thus, it becomes difficult to compare some protocols with the standard offered by the open systems interconnection (OSI) reference model. The physical medium approach and connection establishment methods, communication controls, and other mechanisms are dedicated to systems that cannot be directly compared to the OSI. Another difficulty of establishing a fair comparison with other protocols that follow the OSI reference model is that these protocols are, in the majority of applications, dedicated to the point-to-point communication or large data volumes. These characteristics release them from the need for more elaborate connection establishment methods and data transfer control systems [8].
# Optical Fibres for Condition Monitoring of Railway Infrastructure--Encouraging Data Source or
## 2 Optical Fibres--A Brief Overview  
The rise of distributed fibre optic sensing (DFOS) is closely linked to the invention and development of lasers in 1960 [12]. It was also at that time when the possibility to transfer information through an optical fibre carried by a light wave (laser pulse) was detected. Due to high fibre losses, however, only limited transport distances could be realized. A major breakthrough was achieved by [PERSON] and [PERSON] [13] who demonstrated that high losses in optical fibres occur due to fibre impurities resulting from the production process. This led to intensive activities aimed at removing these impurities, resulting in optical fibres with losses of 20 dB/km in 1970 [14]. Since then, fibre optic sensing has become a major technology, especially in a discipline known as structural health monitoring (SHM), which aims to monitor and determine the structure of large infrastructures (e.g., bridges and tunnels), oil and gas pipelines, vehicles and biomedical devices. Fibre optic sensors can be classified, based on the number of sensors or rather their spatial resolution, into three groups: (1) point sensors; (2) quasi-distributed sensors and (3) distributed sensors [15].  
While point sensors are only rarely used, the latter two have found valuable applications in railways. Quasi-distributed sensors are mainly represented by fibre Bragg grating (FBG), where an interrogator analyses an array of optical sensors inscribed into a fibre. Distributed fibre optic sensors (DFOS) utilize the optical fibre as a continuous sensor where the fibre itself is the sensor.
# Advances in Chronic Kidney Disease in Africa
## 8 Newer Findings into the Pathogenesis of PET  
Advances in research of genetic and inflammatory system involvement in PET in the African setting has increased in recent years. The inflammatory system's role in the pathogenesis of PET has been studied. Tumour necrosis factor alpha (TNF-\(\alpha\)) polymorphisms, altering the regulatory function and leading to increased transcription and production, has been associated with PET. Recent research suggests polymorphisms, particularly 1031C/A and the haplotype CA, contribute to an increased risk of PET in Tunisian women [82]. Other TNF-\(\alpha\) SNPs, mostly the \(-\)308 G/A, have been studied in different ethnic cohorts (European and Asian) with varying results [83; 84]. Research into genetic predispositions for pre-eclampsia is being studied. APOL1 G1 risk allele in pregnant women of African origin may contribute to the development of PET and early-onset PET [85]. International data have shown that maternal-foetal APOL1 genotype discordance and foetal high-risk APOL1 genotypes independently contribute to PET in African American mothers [86]. In a study reviewing the association of the infant APOL1 genotype and PET, it was found that the infant APOL1 genotype was significantly associated with preeclampsia in a dominant inheritance pattern with an odds ratio of 1.41 (\(p\) = 0.029, 95% CI 1.037, 1.926) [87]. In patients of African origin, the R536Q gene mutation is found in a higher proportion of women with pre-eclampsia versus the controls [88]. This mutation alters the beta subunit of the epithelial sodium channel and is a genetic cause for hypertension in Africans. By understanding the association of these genetic variants and inflammatory components with PET, the development of future useful screening tests may assist in predicting at-risk pregnancies and targeted interventions to improve pregnancy outcomes.
# An Admission Control Mechanism for 5G LWA
## 3 System Model
### LWA Activation and Deactivation Procedures
#### For LWA Activation  
1. The RRM entity of the eNB gets the packet data convergence protocol (PDCP) layer to configure the bearer of the UEs to the WLAN. Configurations of bearers can be of two types: a switch or a split bearer. * Switch bearer: Since only one data path is possible, the RRM suggests PDCP for switching the bearer configuration from LTE to WLAN only. * Split bearer: The LB entity commands execution of split scheduling at the PDCP layer that strips the incoming packets (PDCP protocol data units [PDUs]) of the LWA UE bearer into two links. However, the data transmission ratio between the links can be variable based on the traffic condition and load of the WLAN network, which is outside the scope of this article.
2. Before a PDU is transmitted by the WLAN interfaces, the LWA adaptation protocol (LWAAP) entity processes the data and generates an LWAAP PDU containing a DRB identity (DRBID) (a one-byte header to each PDCP PDU). Furthermore, Ether Type 0X9E65 is added by the WLAN AP for forwarding LWA data packets to UEs over the WLAN. Later on, this information is used by UEs to identify the LWA bearer of the receiving PDU.
3. After reception of data by UEs, the UEs differentiate the LWA packets from normal packets by processing them through the LWAAP entity, where the LWAAP header is removed, and then forwards the packets to the PDCP instance based on the DRBID. In return, an LWA status report is sent via LTE to the eNB to acknowledge correct reception of the LWA packets. * The LWA status report constitutes the first missing sequence number (FMS), the highest received sequence number on the WLAN (HRW), and the number of missing PDUs (NMP).
4. Based on LWA status feedback through UEs, the eNB maintains traffic through the WLAN for LWA UEs.
## 1 Introduction
[25] presented a novel approach to solving the ambiguous problems by considering the local visibility structure of the repeated features and then presented a network theory-based method to score the repeated features. Recently, [PERSON] et al. [26] designed an optimization framework for extracting repeated features in images of urban facades, while simultaneously calibrating the input images and estimating the 3D point-cloud model using a graph-based global analysis. Although some novel approaches have been proposed for the problem of ambiguous structures, they only work in the symmetric scenes. To defend the ambiguous problem, we have paid much attention to investigate deeply the existing works [9; 27; 28], the following reasons may cause to produce ambiguous point-cloud model, that is repeated feature, untextured region where few keypoints can be found. As a result, we propose a superpixel segmentation-based feature tracking method for repeated and untextured scenes. Considering the simplicity, the superpixel-based feature tracking is abbreviated as \"SPFT\". The SPFT consists of feature detection, superpixel segmentation, and Markov Random Field (MRF)-based superpixel matching. Owing to the used superpixel segmentation, the SPFT can find sufficient keypoints in untextured scenes. Moreover, the SPFT can be considered as a general framework for feature tracking, which can be integrated with various local feature approaches such as SIFT, SURF, KAZE [29], and MSD [30]. Several challenging experiments made in Section 5 can efficiently prove the effectiveness and efficiency of the SPFT. The main contributions of this work are summarized as follows:  
* A Superpixel-based feature tracking method is proposed to locate keypoints and produce feature correspondences. The SPFT method has the fast speed and high matching confidence. Thus, SPFT can largely improve the quality of point-cloud model produced by SFM system. * A combined descriptor extractor is proposed for producing robust descriptions for the detected keypoints. The proposed descriptor is robust to image rotation, lighting changes, and even can distinguish repeated features. * We conduct a comprehensive experiment on several challenging datasets to assess the SPFT method, and comparison with the state-of-the-art methods. According to the evaluation, some valuable remarks are presented, which can be as a guide for developers and researchers. The rest of this paper is organized as follows: related work is presented in Section 2. The proposed method is described in Section 3. In Section 4, a prototype 3D reconstruction system based on SFM is presented. Experimental results are given in Section 5. The conclusions and final remarks are given in Section 6.
# A Simplified Algorithm for Dealing with Inconsistencies Using the Analytic Hierarchy Process
## 3 Results  
### A Simplified Three Attribute Example  
Consider an initial preference matrix for a hypothetical example with three attributes, given by \(A=\begin{bmatrix}1&4&6\\ 1/4&1&5\\ 1/6&1/5&1\end{bmatrix}\). From this, we can estimate the attribute weights.  
In this example, we find the value of the GCI is 0.483, greater than the critical value (given three attributes) of 0.315 [43]. From step 1, we can derive the expected value of each bivariate comparison given the values for the other bivariate comparisons, assuming transitivity and reciprocity hold: \(\hat{A}=\begin{bmatrix}1&1.2&9\\ 1/1.2&1&1.5\\ 1/9&1/1.5&1\end{bmatrix}\). From this, we can estimate the deviations from the original scores. Taking the values for the upper diagonal only, we find that the normalized deviations of \(\left(\hat{a}_{i,j}-a_{i,j}\right)^{2}/a_{i,j}\) are 1.96, 1.5 and 2.45 for \(a_{1,2}\), \(a_{1,3}\) and \(a_{2,3}\) respectively. Given this, the value of \(a_{2,3}\) is replaced with \(\hat{a}_{2,3}\) and the weights and GCI re-estimated.  
In this example, the revised GCI now falls to zero, so no further iterations are required. The impact of the approach on the initial and modified relative weights is given in Table 1. In some case, however, the estimated value may be constrained by an upper limit of 9 (e.g., \(\hat{a}_{1,3}\) in the above example), or lower limit of 1/9. Thus, perfect consistency is not assured.
# Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
## 5 Conclusions  
In order to solve the problem of tracking accuracy decrease or even tracking failure due to target in occlusion, a target tracking method based on intensity image and point cloud data fusion is proposed in this paper. Firstly, the PSLR and ISS are used to effectively judge the occlusion state of the target. Then, an adaptive factor is proposed to fuse the tracking results of KCF and Kalman filter based on multi-feature fusion according to the different occlusion states of the target. Finally, accurate target location information is obtained. Based on KITTI data set, the proposed algorithm is verified by simulations. The average CLE of the proposed algorithm is 0.1182m, which is 21.67% better than that of Kalman filter, 7.94% better than that of EKF, and the average processing time per frame is 51 ms. Based on GM-APD LiDAR data, the proposed algorithm is verified by experiments. The average CLE of the proposed algorithm is 0.1262m, which is 23.1% better than that of Kalman filter algorithm and 8.68% better than that of EKF algorithm. The average processing time per frame is 36 ms. Both simulation and experimental results show that the proposed algorithm can greatly improve the tracking accuracy of targets in different occlusion states. In the future, we will consider validating the performance of the proposed methodology across a broader spectrum of scenarios, accounting for uncertainties arising from diverse environments including varying backgrounds, targets, lighting conditions, and obstacles. Simultaneously, we will conduct a more comprehensive categorization of occlusion situations and undertake an in-depth investigation into traffic congestion and high-density crowd scenarios.  
Figure 13: CLE curve of different algorithms in the second outdoor detection scenario.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Kalman Filter** & **EKF** & **Proposed** \\ \hline Average CLE/m & 0.2125 & 0.1797 & 0.1542 \\ Average algorithm speed per frame/ms & 18 & 27 & 39 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average CLE of different algorithms.  
**Author Contributions:** Conceptualization, T.H., Y.W. and B.X.; methodology, Y.W. and B.X.; software, Y.W., X.Z. and B.X.; validation, Y.W., B.X. and T.H.; formal analysis, Y.W.; investigation, T.H.; resources, T.H.; data curation, T.H.; writing--original draft preparation, Y.W. and X.Z.; writing--review and editing, X.L. and D.X.; visualization, X.Z. and Y.W.; supervision, X.L. and Z.L.; project administration, Z.L. and C.W.; funding acquisition, X.L. and C.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Key R&D Program of China, grant number 2022 YFC3803702.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
###### Abstract  
Fifth-generation (5G) and beyond networks are envisioned to serve multiple emerging applications having diverse and strict quality of service (QoS) requirements. To meet ultra-reliable and low latency communication, real-time data processing and massive device connectivity demands of the new services, network slicing and edge computing, are envisioned as key enabling technologies. Network slicing will prioritize virtualized and dedicated logical networks over common physical infrastructure and encourage flexible and scalable networks. On the other hand, edge computing offers storage and computational resources at the edge of networks, hence providing real-time, high-bandwidth, low-latency access to radio network resources. As the integration of two technologies delivers network capabilities more efficiently and effectively, this paper provides a comprehensive study on edge-enabled network slicing frameworks and potential solutions with example use cases. In addition, this article further elaborated on the application of machine learning in edge-sliced networks and discussed some recent works as well as example deployment scenarios. Furthermore, to reveal the benefits of these systems further, a novel framework based on reinforcement learning for controller synchronization in distributed edge sliced networks is proposed.  
network slicing; edge computing; machine learning; SDN controller synchronization +
Footnote †: journal: 2022, 12, 6617. [[https://doi.org/10.3390/app12136617](https://doi.org/10.3390/app12136617)]([https://doi.org/10.3390/app12136617](https://doi.org/10.3390/app12136617))
## 5 Simulation Results and Discussion
### Comparative Study  
#### 5.2.1 Solution Quality  
Tables 1, 3, 5, 7 and 9 present the cheapest generation cost determined by the RLGWO algorithm for five different test systems. It can be clearly seen that the proposed approach usually provides a better solution compared to the results obtained by various most technique. Additionally, the maximum, minimum, and average values acquired using different methods are illustrated in Tables 2, 4, 6, 8 and 10. These results emphasize the ability of RLGWO to achieve better solutions than most existing techniques.