esa-sceva/satcom-chunk-collection huggingface dataset:
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 4 Protocol Design
### Neighbor Discovery  
When position-based routing protocols are considered, the general way for a node to announce its presence to the neighborhood is by periodical beacon broadcasting. Each node locally transmits in broadcast a short hello message (beacon) in order to announce its presence and position. Each node, upon receiving a beacon, stores the information of the neighbor node in a neighbor table. If a node does not receive any beacon from its neighbors within a certain time interval, the corresponding node is considered to have left the transmission range, or to be unreachable for some reason, and is deleted from the neighbor table. In Ref. [21], the authors analyze the impact of diverse beacon settings (beacon timing, node density, node speed, etc.) on the routing performance.  
In our solution, in addition to the node's position, the future path of the node should be communicated. Typically, this information is expressed under the form of a list of coordinates, which represent the waypoints. Hence, at every connection between two UAVs, they exchange their future path in order to process a possible bundle forwarding.  
Differently from classic position-based routing protocols, our DTN routing protocol (and more generally, almost all the DTN routing protocols) is just partially affected by the beaconing, because of their intrinsic characteristic. Classic position-based routing protocols need the correct neighborhood information almost every time, since they totally rely on the current position of the nodes to make the forwarding decision; in this case, inaccurate and outdated neighbor information could significantly affect the correct next-hop decision process. On the other hand, our DTN routing protocol mostly relies on the future path of nodes, which does not change over time; once the first beacon is arrived, subsequent beacons are not relevant for the routing process. Hence, we should pay attention to the beacon timing regarding only the first beacon.  
In this work, we do not analyze this aspect, but we intend to explore the beaconing in the future, when moving from The One simulator to _Network Simulator 3_, which reproduces more realistically the communication and protocol aspects of the network.
# Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China
## 2 Data and Methods  
Eastern China (EC) mainland, a large carbon storage area in the mid-latitudes [24], was selected for this case study evaluation of carbon uptake trends because it shows persistent vegetation greening (Section 3.1) (outlined in red in Figure 1). Land cover consists of forest, irrigated and rain-fed cropland, and grassland in south (\(<\)34\({}^{\circ}\)N) and north (\(>\)=34\({}^{\circ}\)N) parts of EC.  
Datasets used in this research are summarized in Table 1. They include four categories: (1) Vegetation parameters, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), and gross primary production (GPP); (2) XCO\({}_{2}\) time series based on observations from multiple greenhouse gas satellites, including Scanning Imaging Absorption spectrometer for Atmospheric Chatrography (SCIAMACHY) onboard the Environmental Satellite (ENVISAT), Greenhouse Gases Observing Satellite (GOSAT), and Orbiting Carbon Observatory 2 (OCO-2); (3) CO\({}_{2}\) fluxes and corresponding modeled XCO\({}_{2}\) concentrations from two inversion models, CarbonTracker (CT) 2017 and Monitoring Atmospheric Composition and Climate (MACC-III), and one forward model GEOS-Chem v11.4; (4) supporting independent datasets, including tree canopy cover percentage (TCCP) and climate factors like land surface temperature (LST) and precipitation, are described in more detail in Section 2.4.  
Figure 1: Global land-cover data in 2015 from 0 to 60\({}^{\circ}\)N obtained from the European Space Agency. Global greening and XCO\({}_{2}\) trends are evaluated in Eastern China, shown with red edge for this case study. The names of Total Carbon Column Observing Network (TCCON) measurement sites are labeled (Section 2.4.2).
## 3 Preliminaries
### Threat Model  
The widely used Dolev-Yao (DY) threat model [54] is used in the proposed scheme. According to the DY model, an insecure public channel (open channel) is used for communication between any two parties and the end-point entities have an untrustworthy nature. Therefore, the system is prone to eavesdropping of exchanged messages and deletion/modification attempts by the attacker. Moreover, as the UAVs may roam around in unattended hostile areas, there exists the probability of them getting physically captured. This may lead to leakage of precious data from a UAV's memory. The KGC, on the other hand, is a fully trusted entity.
# Optimization Design of RV Reducer Crankshaft Bearing
## 3 Mathematical Model of Optimization
### Definition of Design Variables  
The mechanical behavior of the crankshaft bearing is significantly affected by the macro-geometric structure of the bearing. The crowned profile gives an axially uniform pressure distribution. These factors significantly affect the fatigue life of the bearing. Therefore, the macro geometry and roller profile are considered in the optimization design. The design parameters of the macro geometry mainly include the roller diameter \(D_{we}\), number of rollers \(Z\), bearing pitch diameter \(D_{m}\), and radial clearance \(P_{d}\). In addition, the radius of the crankshaft distribution circle \((r_{o})\) determines the installation position of the crankshaft in the cycloidal gear and the force of the crankshaft bearing.  
When the rolling elements are only lightly profiled or subjected to excessive loads, edge stresses can arise. Cylindrical rollers (or raceways) are usually crowned to solve this problem [19]. [PERSON] [21,22] proposed a modified logarithmic shape that allowed for a flat region on the roller, as shown in Figure 5. The crown drop in the axial direction at position \(x\) is  
\[z(x)=A_{c}\ln\frac{1}{1-\left(1-e^{(-\frac{2\pi i}{\lambda c})}\right)\left( \frac{x-L_{we}/2}{K_{2}L_{we}/2}+1\right)^{2}} \tag{28}\]  
\[A_{c}=\frac{2K_{1}Q_{\max}}{\pi L_{we}E^{\prime}} \tag{29}\]  
where \(Q_{max}\) is the maximum load acting on the rollers, \(K_{1}\) is the load coefficient, \(K_{2}\) is the coefficient of the crown length, and \(Z_{m}\) is the crown drop at the edge of the roller end.  
Figure 5: Logarithmic profile parameters of roller.  
Therefore, there are nine mutually independent design variables. \(r_{o}\), \(D_{ue}\), \(D_{m}\), \(L_{ue}\), \(Z\), and \(P_{d}\) define the internal geometric dimensions, and \(K_{1}\), \(K_{2}\), and \(Z_{m}\) define the profile of the roller.  
\[X=[r_{o},D_{ue},D_{m},L_{ue},Z,P_{d},K_{1},K_{2},Z_{m}] \tag{30}\]
## 4 Discussion
### Mechanistic Considerations  
The significant improvements seen in cardiometabolic biomarkers are likely reflective of an interplay of physiological adaptations following the combination of nutritional and exercise interventions. Improvements in glucose metabolism, as reflected by the reduction in glucose levels in one study [41], represent a potential metabolic adaptation, though insulin sensitivity markers showed no significant between-group improvements across studies. Physical activity enhances glucose uptake by skeletal muscle via insulin-stimulated and insulin-independent mechanisms, which include GLUT4 translocation, while appropriate dietary changes can reduce glycemic load and improve postprandial glucose responses [21; 23].  
Changes in lipid profiles reported in various studies demonstrate the indirect and direct impact of changes in lifestyle. Exercise training increases the activity of lipoprotein lipase, promotes reverse cholesterol transport, and favorably alters the particle size distribution, while dietary changes may result in decreased intake of saturated fats with an increase in fiber intake [27; 53; 57]. The improvements in lipid profiles observed in four studies [41; 42; 43; 46] suggest enhanced lipid metabolism, with total cholesterol reductions being the most consistent finding.  
The reductions in blood pressure reported in six studies [40; 43; 44; 47; 48; 50] can most likely be explained by improvements in endothelial function, reduced activity of the sympathetic nervous system, and enhanced vascular reactivity--changes that can quickly arise from lifestyle modification and that can particularly be pronounced in children because of their increased vascular plasticity [22]. Increased physical activity promotes production of nitric oxide and reduction of arterial stiffness, while dietary modification can impact vascular inflammation and sodium homeostasis. The anti-inflammatory effects of combination intervention can contribute to improvement in various categories of biomarkers in that chronic low-grade inflammation links to metabolic impairment and development of cardiovascular diseases.
## 3 Results  
### Environmental Conditions  
The two-year data series of air temperature, rainfall, soil moisture, and groundwater level are presented in Figure 2. The growing season is marked in grey.  
season in 2021 lasted 244 days and began on 24 March and ended on 22 November. In 2022, the growing season was three days longer (247 days) and lasted from 14 March until 15 November. The average daily temperature in 2021 was 9.1 \({}^{\circ}\)C, whereas 2022 was 1.0 \({}^{\circ}\)C warmer. The minimum mean daily temperatures were \(-\)10.8 \({}^{\circ}\)C and \(-\)7.4 \({}^{\circ}\)C in 2021 and 2022, respectively. By contrast, the maximum mean daily temperature in 2021 was 26.6 \({}^{\circ}\)C, whereas in 2022, it was 1\({}^{\circ}\)C higher. The years under study also differed in the amount of precipitation. The yearly sums of precipitation were equal to 539.5 mm and 604.7 mm in 2021 and 2022, respectively. The highest monthly rainfall was recorded in August in both years. In 2021 it reached 84 mm, and in 2022, 135 mm. The driest month in both years was March, with 20 mm of rain in 2021 and no rainfall in 2022. During the entire study period, the WTD levels directly correlated with precipitation episodes. This is most evident in August 2022, when just after an extreme rain event of 83 mm recorded on 20 August, a sharp rise in WTD levels was noted at both HWL and LWL sites (Figure 2b). Within the analyzed years, WTD varied between 0.29 and 1.09 m below ground level (mbgl) as well as between 0.09 and 0.92 mbgl, at LWL and HWL sites, respectively. The highest WTD levels at both sites occurred during winter, whereas the lowest levels were recorded during the summer months (June-August). The same pattern is reflected in soil moisture. The maximum soil moisture value at the HWL site was 66% and occurred on 18 July 2021, immediately after the rainfall. At the LWL site, the highest value of up to 57% was also recorded on the same day. The lowest values of 13% for HWL and 12% for LWL were observed in July 2022. When comparing both years, it can be observed that during the spring-summer period of 2022, soil moisture values were lower than in 2021. The values of the measured parameters during the individual cut periods and collectively during the 2021-2022 growing season are summarized in Table 1.  
Figure 2: _Cont._
# Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding
## 2 Related Work
### Retinex Model  
The physics-based Retinex model [16] solves the problem by transforming the Retinex theory into a more physical form through a series of equations or optimization problems. These algorithms have been widely studied in recent years because of their ability to remove the entire illumination from an image. As mentioned above, the algorithm in this category expresses the image value \(S\) as a product of the illumination function \(L\) and the reflectance function \(R\) as seen in Equation (1). Here, we further assume that \(0<R<1\) (reflectivity) and \(0<L<\infty\) (illumination effect). Based on these assumptions, Equation (1) implies that \(L>S>0\). In order to handle the product form, we first convert it into the logarithmic domain, that is, \(s=log(S)\), \(l=log(L)\), and \(r^{\prime}=log(R)\). Then, we obtain \(s=l+r^{\prime}\). Note that \(0<R<1\). We set \(r=-r^{\prime}>0\); then the above model takes the following form:  
\[l=s+r \tag{2}\]  
Thus, the illumination component \(l\) and the reflectance component \(r\) can be easily decomposed by converting them to a logarithmic addition.
# A Survey of Vehicular VLC Methodologies
## 8 Conclusions  
Vehicular VLC plays a crucial role in ITSs by facilitating information exchange between vehicles and infrastructure to achieve key objectives, such as improving road safety, passenger comfort, and traffic flow. This study encompassed a comprehensive literature review covering the general architecture of VLC and its applications in vehicular communication, including platooning, intersection assistance, and lane-change applications. The study also highlighted the main challenges faced by vehicular VLC systems, such as the influence of road reflections, noise from sunlight and artificial lighting, and varying weather conditions. Furthermore, critical parameters for vehicular VLC channel modeling were discussed, including the asymmetrical pattern of vehicular light sources, vehicle mobility, and the atmospheric propagation medium. Moreover, the existing vehicular VLC channel models for V2V communication utilizing headlights and taillights, as well as I2V communication involving streetlights or traffic lights, were examined. Furthermore, some key directions were highlighted, including the utilization of digital twin technology and the applications of AI and ML algorithms in vehicular VLC systems, showcasing their potential to optimize resource allocation, enhance security, and enable intelligent decision making. Finally, we provided insights into the integration of vehicular VLC with mm Wave networks as an open research topic, highlighting its potential for achieving continuous connectivity, low latency, and advanced vehicular services. This survey aims to serve as a valuable reference and guide for both experts and newcomers in the field of vehicular VLC.  
Conceptualization, R.A.H., I.M.; methodology, R.A.H. and I.M; investigation R.A.H.; Resources R.A.H.; writing--original draft preparation R.A.H.; supervision I.M.; writing--review and editing R.A.H. and I.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to acknowledge the editor and the anonymous reviewers for their comments and suggestions that have improved the presentation of this work.  
The authors declare no conflict of interest.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 7 Challenges
### Transmission and Reception Antennas Require a Large Lens Group  
This problem could be solved via the combination of a Fresnel lens and an optical antenna that is capable of radiating directionally and switching to wide-angle rapidly. In 2018, [PERSON] et al. proposed a SiN-based optical phase antenna (OPA); the performance of such a design is estimated to have a \(\pm 12.7^{\circ}\) beam steering in Azimuth angle, \(15.6^{\circ}\) in polar angle using wavelength tuning in a range of 60 nm [139]. OPA is a promising candidate due to the advantages of its small size, light-weight, rapid switching speed, and multi-beam steering [140].
# MEASegNet: 3D U-Net with Multiple Efficient Attention for Segmentation of Brain Tumor Images
###### Abstract  
Brain tumors are a type of disease that affects people's health and have received extensive attention. Accurate segmentation of Magnetic Resonance Imaging (MRI) images for brain tumors is essential for effective treatment strategies. However, there is scope for enhancing the segmentation accuracy of established deep learning approaches, such as 3D U-Net. In pursuit of improved segmentation precision for brain tumor MRI images, we propose the MEASegNet, which incorporates multiple efficient attention mechanisms into the 3D U-Net architecture. The encoder employs Parallel Channel and Spatial Attention Block (PCSAB), the bottleneck layer leverages Channel Reduce Residual Atrous Spatial Pyramid Pooling (CRASPP) attention, and the decoder layer incorporates Selective Large Receptive Field Block (SLRFB). Through the integration of various attention mechanisms, we enhance the capacity for detailed feature extraction, facilitate the interplay among distinct features, and ensure the retention of more comprehensive feature information. Consequently, this leads to an enhancement in the segmentation precision of 3D U-Net for brain tumor MRI images. In conclusion, our extensive experimentation on the BraTS2021 dataset yields Dice scores of 92.50%, 87.49%, and 84.16% for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET), respectively. These results indicate a marked improvement in segmentation accuracy over the conventional 3D U-Net.  
3D U-Net; brain tumor segmentation; attention mechanism Article
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## Appendix B  
To obtain the outage probability for a combined effect of signal power and interference power variations, the integral \(\mathrm{P}_{\mathrm{out}}=\int_{0}^{\infty}F_{X}(\gamma_{t}(y+N_{0}))f_{Y}(y)dy\) needs to be evaluated. Here, \(F_{X}(x)\) is the CDF of the desired signal power and \(f_{Y}(y)\) denotes the PDF of interference power. The threshold SINR value is \(\gamma_{t}\). Then, the integral would be  
\[\mathrm{P}_{\mathrm{out}}=\int_{0}^{\infty}\left\{1-Q_{1}\left(\sqrt{\delta}, \sqrt{\frac{\gamma_{t}(y+N_{0})}{\alpha}}\right)\right\}\sum_{n=0}^{\infty}a_ {n}\beta^{-1}\chi^{2}\left(\frac{y}{\beta};2(J+n),\Delta\right)dy.\]  
Here, \(\int_{0}^{\infty}\sum_{n=0}^{\infty}a_{n}\beta^{-1}\chi^{2}\left(\frac{y}{ \beta};2(J+n);\Delta\right)dy=1\) as it represents the PDF of the total interference. Using the series representations of the Marcum-Q function (The series representation of Marcum-Q function is \(Q_{M}(a,b)=e^{-\frac{\rho^{2}+\bar{\rho}^{2}}{2}}\sum_{k=1-M}^{\infty}\left( \frac{\rho}{b}\right)^{k}I_{k}(ab)\)) and the modified Bessel function (The infinite series representation of a modified Bessel function is \(I_{k}(x)=\sum_{m=0}^{\infty}\frac{1}{m!\Gamma\left(m+k+1\right)}\left(\frac{ x}{2}\right)^{2m+k}\)) the integral can be transformed to multiple sums,  
\[\mathrm{P}_{\mathrm{out}} =1-\sum_{n=0}^{\infty}a_{n}\beta^{-1}\int_{0}^{\infty}Q_{1} \left(\sqrt{\delta},\sqrt{\frac{\gamma_{t}(y+N_{0})}{\alpha}}\right)\chi^{2} \left(\frac{x}{\beta};2(J+n),\Delta\right)dy\] \[=1-\sum_{n=0}^{\infty}\frac{a_{n}\beta^{-1}}{2}\int_{0}^{\infty} e^{-\frac{a\ell+\gamma_{t}(y+N_{0})}{2\alpha}}\sum_{m=0}^{\infty}\left(\frac{ \alpha\delta}{\gamma_{t}(y+N_{0})}\right)^{\frac{m}{2}}I_{m}\left(\sqrt{\frac {\delta\gamma_{t}(y+N_{0})}{\alpha}}\right)\] \[\times e^{-\frac{y+\delta\Delta}{2\beta}}\left(\frac{y}{\beta \Delta}\right)^{\frac{1+n}{2}-\frac{1}{2}}I_{J+n-1}\left(\sqrt{\frac{\Delta y} {\beta}}\right)dy\] \[=1-\sum_{n=0}^{\infty}\sum_{m=0}^{\infty}\sum_{p=0}^{\infty}\sum_ {q=0}^{\infty}\frac{a_{n}}{2^{2(p+q)+m+j+n}}e^{-\left(\frac{\delta+\Delta}{2 }+\frac{\gamma_{t}N_{0}}{2\alpha}\right)}\frac{\delta p^{+m}\gamma_{t}^{p}}{ \delta^{q+j+n}}\] \[\frac{1}{p!q!(p+m)!(q+J+n-1)!}\int_{0}^{\infty}e^{-\left(\frac{ \gamma_{t}}{2\alpha}+\frac{1}{2\beta}\right)y}(y+N_{0})^{p}y^{q+j+n-1}.\]  
The infinite sum over \(m\) is straight forward, and the remaining integral can be expressed in terms of a confluent hypergeometric function \(U(a,b,\bar{z})\) with \(a=q+J+n\), \(b=p+a+1\) and \(z=\left(\frac{\gamma_{t}}{2\alpha}+\frac{1}{2\beta}\right)N_{0}\). Algebraic manipulations lead to  
\[\mathrm{P}_{\mathrm{out}}= 1-\frac{e^{-\left(\frac{\beta}{2}+\frac{\gamma_{t}N_{0}}{2 \alpha}\right)}}{2^{p+2q+m+j+n}}\sum_{n=0}^{\infty}\sum_{p=0}^{\infty}\sum_{q= 0}^{\infty}\frac{a_{n}\Delta^{q}}{\alpha^{p}\beta^{q+j+n}}\frac{N_{0}^{p+q+j+n }}{(p!)^{2}q!}\left(p!-p\Gamma\left(p,\frac{\delta}{2}\right)\right)U(a,b,\bar{ z})\]  
where \(\Gamma(a,b)\) is the upper incomplete gamma function.
# A Semantic Information-Based Optimized vSLAM in Indoor Dynamic Environments
## 5 Conclusions  
To address indoor dynamic environments, we design a semantic information based VSLAM scheme, which is improved in several aspects, such as dynamic region detection,  
Figure 14: Details of map construction by our algorithm.  
Figure 13: Details of map construction by ORB-SLAM2.  
3D semantic annotation, and geometric segmentation of depth images. Experimental results on the TUM RGB-D dataset show that our algorithm is better than ORB-SLAM2 and DS-SLAM in terms of bit pose estimation in dynamic scenes. This shows that the VSLAM scheme with the addition of a dynamic region detection module can well improve the ability of the camera to localize and build images in dynamic environments. In addition, the experimental results of real dynamic scenes show that our algorithm can build point cloud in unknown environments that contains semantic information rich enough to ensure the downstream work. Our scheme improves on the ORB-SLAM2 algorithm with the goal of removing dynamic information from the environment. However, due to the limitation of manual feature description algorithms such as ORB, feature matches usually still exist when there is no co-vision between neighbouring frames when the camera is moving intensely, which is disastrous for position estimation and mapping. Therefore, filtering the feature matches or using a data-driven approach to build a more robust feature descriptor is worthy of in-depth research.  
Conceptualization, H.L.; Methodology, S.W. ([PERSON]), H.L. and G.L.; Validation, H.L. and G.L.; Investigation, S.W. ([PERSON]), S.W. ([PERSON]) and H.L.; Data curation, T.Y. and C.L.; Writing--original draft preparation, G.L.; Writing--review and editing, S.W. ([PERSON]); Visualization, S.W. ([PERSON]); Project administration, S.W. ([PERSON]); Funding acquisition, S.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Project of China (2021 YFB2600101), the National Natural Science Foundation of China (41601409), the Nanchang Major Science and Technology Research Project (Hongkezi [2022] No. 104), the Project Funding of Science and Technology Plan of Beijing Municipal Education Commission (KM202010016010), the Jiangxxi Geological Bureau Young Science and Technology Leaders Training Program Project (2022 JXDZKJRC06) and the Postgraduate Innovation Project of Beijing University of Civil Engineering and Architecture (PG2021082).  
Not applicable.  
The data used in the current study can be accessed from the corresponding authors depending on the purpose of use.  
We are grateful to Jiangxi Nuclear industry Surveying and Mapping Institute Group Co., Ltd. for the funding support, especially [PERSON] and Junlin Fan for helping us to create a good experimental environment and giving us hardware support such as GPU and industrial camera.  
The authors declare no conflict of interest.