esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study gives insight into the structural diversity, composition, and plant growth functional genes in sunflower rhizosphere and bulk soil from two different locations. The outcomes from this research suggest that sunflower would thrive in both locations because, compositionally, the microbial diversity was not significant in the two locations, although there was a difference in the microbial structure of both R1 (Palmietfontein) and R2 (Bloemhof) sites. Metagenomes of the agricultural sunflower soil revealed the phyla _Actinobacteria_ and _Proteobacteria_ as the dominant members of the community and these phyla have been established to produce several enzymes with growth-promoting traits in plants. The effect of physicochemical parameters on the microbial structural composition was also a determining factor in driving the sunflower microbiome. These findings suggest that edapmic conditions drive microbial communities. We identified genes that confer fixation of nitrogen, siderophore production, ACC deaminase producing genes, heat and cold shock genes, and phosphate solubilizing producing genes, among others. There was no significant difference in the genes responsible for plant growth in the sunflower rhizosphere microbiome in comparison to the bulk soils; likewise, there was no significant difference between the two locations. However, for most of our identified genes, the location R1 possessed more enriched genes for potential plant growth than the location R2. In conclusion, these genes can be harnessed for biotechnological applications in producing bioproducts such as fertilizers and pesticides to enhance food production. Since some of these microbes are still unculturable, micro-cultivation technology can be potentially harnessed where other cultured microorganisms can be co-cultured with uncultivable microorganisms, as recreating this method in the laboratory will increase throughput and access to unique species for commercial production.  
Conceptualization, T.T.A. and O.O.B.; methodology, T.T.A.; formal analysis, T.T.A.; investigation, T.T.A.; resources, O.O.B.; data curation, T.T.A.; writing--original draft preparation, T.T.A.; writing--review and editing, O.O.B.; visualization, T.T.A.; supervision, O.O.B.; funding acquisition, O.O.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation, South Africa, grant number UID123634.  
Not applicable.  
Sequence data obtained in this work have been deposited in the NCBI Sequence Read Archive with Accession Numbers SRR10426233, SRR10418054, SRR10426310 and SRR10418081.  
T.T.A. would like to thank the North-West University for a postgraduate bursary. O.O.B. thanks the National Research Foundation, South Africa, for a grant (UID123634) that has supported research in our laboratory.  
Conflicts of Interest: The authors declare that they have no conflict of interest, either financial or commercial wise.
# A Critical Examination of the Beam-Squinting Effect in Broadband Mobile Communication: Review Paper
## 2 Broadband Communication
### Millimeter Waves and Massive MIMO
#### 2.1.2 Digital Beamforming  
The radio frequency (RF) signal in a digital array is transformed into a digital signal at the sub-array or element level, and beamforming is accomplished using a digital signal processor [30]. In order to process the control signals for beamforming, a digital signal processor is used, which offers additional flexibility and degrees of freedom for the implementation of effective beamforming algorithms. Due to the method's requirement for a separate RF chain for each antenna element, it consumes a great deal of power and has a complicated architecture [31]. Figure 3 shows the structure of digital beamforming.  
Figure 1: Functional block diagram of massive MIMO system [26].  
Figure 2: An mmwave MIMO system using analog beamforming [29].
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 4 Applications of the LiFi Network
### Industrial Information's Environment  
In industrial areas, data needs to be transmitted properly due to the intensity and complexity of the interconnections of devices and applications. In this context, LiFi can exchange slipper rings, sliding connections, and short cables, such as commercial ethernet. It is also an alternative to furnishing the increasing industrial wireless requirements because of its respective real-time capabilities [23]. Wireless data are transmitted by super-fast modulating light and the process is invisible to the human eye. In addition, the system transfers large volumes of data at high speed offering benefits, such as high protection and reliability, without losing wireless connection This is mainly because of its ability to automate the production process through its excellent connectivity and remarkable data transfer. These features enables LiFi to have considerable effects on business. Industry convergence helps suppliers to handle changes in technological developments. Nevertheless, questions such as how do the performance of systems get affected by technologies such as LiFi and how does LiFi plays an important role in other business roles must be addressed. LiFi, which is recognized as a key player in the Industry 4.0 ecosystem, can affect the significance achieved by the next industrial revolution [119].
## 2 Backhaul Design as a Graph Optimization Problem  
In this paper, we argue that the problem of designing backhaul topologies using graph theory can be stated as determining means of linking a given number \(n\) of nodes, where both wireless objectives and backhauling constraints are expressed in terms of graph invariants. In this way, the network topology design can be seen as a graph optimization problem. The mathematical formulation of this problem is fully described in [32]. The definitions of all the graph invariants used in this work can be found in Appendix A.  
In the design of any backhaul network, the most basic constraint is that each element of the system has to be connected to the network; in graph theoretic terms, at least one path must connect each pair of nodes, resulting in the network being a connected graph. This requirement outlines a search space for our optimization. The size of this space grows rapidly with the number of nodes considered within a network: there are 853 possible connected graphs with seven nodes and if we simply double the number of nodes, this number grows to 29,003,487,462,848,061 [33]. Such a rapid growth of search space prevents an exhaustive search for telecommunication topologies. The problem when dealing with C-RAN is even more acute, given the dynamicity that sets C-RAN apart, mainly due to the fact that the graph often changes, where RRHs can be dynamically associated to a different BBU pool when the backhaul becomes overloaded, preventing pre-processing techniques to be applied.  
Adding constraints related to interconnection of backhauling elements further bounds our search space. For example, the practical constraint of the number of communication interfaces that may be deployed maps into a limitation of the size of a topology. Alternatively, the number of communication interface edges supported by any single node may be constrained through limiting the degree of nodes. Applied in this manner, additional invariants provide the means to restrict the search on the basis of the interconnection constraints of a backhaul network to meet specific network requirements.  
Even during dynamic changes occurring in C-RAN, the graph invariants remains as a rule to be followed preventing the aforementioned problem.  
Section 2.1 discusses how the resilience requirements of C-RAN backhaul could be mapped to a set of graph invariants. Although this paper focuses on invariants that can capture network metrics related to resilience in C-RAN backhaul, the method proposed here can be applied to any network requirement that can be mapped to a graph invariant. Thus, Section 2.2 outlines a generic method for backhaul design as a graph optimization problem.
## 3 Experimental Results and Discussions
### Surface Pressure Distribution
However, the local flow separation was improved in the lower external surface of the controlled cases that caused a more considerable fluctuation of the pressure at X/B = 0.31, as given in Figure 4d. The fluctuation of the surface pressure close to the trailing edge was mitigated on both the upper and bottom surfaces of the SBG model with the trailing edge jets. That is because the jet flow can interrupt the alternating vortex shedding and lead to the wake flow being more steady. According to the pressure measurement results, the case \(J_{sj}=0.0094\) exhibited the best control ability for alleviating the mean value and fluctuation of the surface pressure. Obtaining better control effectiveness does not need larger non-dimensional jet momentum coefficients. There is an optimal non-dimensional jet momentum coefficient to realize the control effect, which is consistent with the study by [PERSON] et al. (2018) and [PERSON] et al. (2018). Figure 4: The external surface pressure distributing on the test model with and without the trailing edge jets. The mean of the upper surface (**a**) and lower surface (**b**) pressure distributions, RMS of the upper surface (**c**), and lower surface (**d**) pressure distributions.
# Engineering Properties and Environmental Impact of Soil Mixing with Steel Slag Applied in Subgrade
###### Abstract
The strength and stiffness of cement-stabilized aggregates were optimal with an SL of 50%. [PERSON] et al. [18] also obtained the best mechanical properties and durability of water-stabilized subgrade material when SL was mixed at 50%. [PERSON] et al. [19] applied SL in the construction of highway roadbeds, and the compaction, surface flatness and compressive strength of water-stabilized SL aggregate subgrade met road construction requirements. Therefore, another major application for SL is road base construction, with the prospects even more promising. The high strength, wear resistance and granular properties of the friction strength and mechanical stability of SL can effectively improve the load-bearing capacity and stability of road foundations [20]. The main structure of SL is similar to the composition of cement, so it is also used as a cementitious material instead of cement and lime, for reinforcement and improvement of poor soil properties [21; 22; 23]. The application of SL for road bases in China began in the 1960s, and the flat-furnace SL combined with soil was used as a road-base material, which proved that SL can be used for subgrade filling [24; 25; 26]. Compared to its application in pavement, the application of SL in the subgrade will be much larger, and the requirements for the homogeneity and index of raw materials are relatively lenient. However, the potential swelling hazards caused by active substances in SL cannot be ignored. The application of steel slag in road-base engineering in China is still used in small-scale blending, mostly  
Figure 1: The main uses of SL. together with other by-products. There are few cases of large-scale SL use, and the research on the mechanical characteristics and stability control of SL as a subgrade filler is not yet perfect. In addition, the potential environmental pollution of SL is of great importance because it generates several by-products due to the use of a number of agents during its production process. When SL is used as subgrade fill, the metal ions, trace elements and acidity within the infiltration fluid produced from surface water or groundwater can potentially contaminate surrounding land and water resources. Based on the above background, this paper presents a study on the application of subgrade with large doses of SL as typical fills in Guangxi, China, with the aim to obtain a subgrade fill that is reliable and environmentally friendly. Through compaction, liquid-plastic limit and CBR tests, the optimum proportion of soil mixing with SL (SSL) was determined, and its dynamic characteristics were investigated through static and dynamic tests. Finally, the environmental impact of SSL in the subgrade was evaluated. The test results, mechanical properties, and environmental analyses of the SSL subgrade in this study aim to promote the application of waste materials to support sustainable development, while achieving improved performance of subgrade materials.
## 4 Experiments and Discussion
### Evaluation Metrics  
In this experiment, we used multiple evaluation metrics to measure the model's segmentation performance. Common evaluation metrics for remote sensing image segmentation include intersection over union (IoU) and F1 score.  
* IoU (Intersection over Union): IoU is a standard metric for evaluating image segmentation model performance, calculated as the intersection area of the predicted and ground truth regions divided by their union area. A higher IoU indicates better segmentation accuracy. In the experiments, we computed the IoU for each class and averaged them to obtain the overall IoU. A high IoU value indicates that the model performs well in handling complex boundaries and small objects.
* F1 Score: F1 score is the harmonic mean of precision and recall and is typically used to evaluate models with class imbalance. In remote sensing image segmentation tasks, some classes may have significantly fewer samples than others. The F1 score provides a more comprehensive evaluation of the model's performance across different object types. A higher F1 score indicates better balance between precision and recall.  
To evaluate the segmentation results from multimodal remote sensing data, we used the mean F1 score (mF1) and mean IoU (mIoU) as the key statistical indicators. These metrics were used to compare the performance of the proposed PLGTransformer with other state-of-the-art methods. Specifically, we computed mF1 and mIoU for the four foreground classes and averaged them to get the final results.
## 5 Numerical Example
### Example 2--A Channel Beam  
Figure 6 shows a U-shaped structure, fixed at both ends, with an initial thickness of 0.5 m. The elasticity module of the material is E = 210 GPa, and [PERSON]'s ratio \(\
u\) = 0.30 is assumed. It is considered a combination of gravitational and external loads, with a density of \(\rho\) = 2700 kg/m\({}^{3}\) and a distributed load of intensity q = 0.094 MPa. The domain was discretized with a 90 \(\times\) 18 \(\times\) 18 mesh, and the constraint volume equals V = 13.5 m\({}^{3}\). Figure 7 shows the optimal setting in the form of an arched bridge with hangers. The optimal topologies are shown in Figure 7. It is evident that an arch profile with fairly uniform thickness is generated above the deck. The four topologies are similar because the combined load requires more materials in the arc to support heavier loads. If a fine mesh was used, the arc profile could be smoother. However, there is a great similarity between the optimal topologies and the real arch bridges. This means that the optimization process is valid. It should be noted that the structural design using the SERA method added material at the top, bracing the two arches. The SESO, SERA, ESO, and SIMP methods effectively find optimal solutions to problems that include the combined use of gravity loading and external forces. Additionally, SERA reached the lowest value for the objective function, approximately \(C=1186.87\) Nm, 3.4% in relation to the SESO and ESO methods and 41% in relation to the SIMP.  
The compliances of solutions SESO, SERA, and ESO are very close. However, with \(p=3\), the SIMP method converges to a great location with higher compliance. Figure 8 shows the evolution history of the objective function using the four topology optimization methods. Compliance for the SESO, SERA, and ESO methods increases with small jumps (due to the formation of hangers on the structure) as the total volume gradually decreases. After reaching the prescribed volume, in subsequent iterations, while the volume remains unchanged, compliance gradually converges to a constant value. Unlike SESO, SERA, and ESO, the SIMP method has the volume restriction met throughout the iterative procedure. Thus, the volume remains constant, and compliance gradually decreases until the convergence criterion is reached.  
Figure 5: Optimal topology—**(a)** SESO, **(b)** ESO, **(c)** SERA and **(d)** SIMP.  
Figure 8: Compliance by Iteration—**(a)** SESO, (**b**) ESO, (**c**) SERA and (**d**) SIMP.  
Figure 6: Design domain of a channel beam Adapted from [20], measures in meters.
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
## 5 Numerical Analysis
### Throughput Analysis  
As PHY I and PHY VI support 11-100 Kbps and 5-256 Kbps, we take a burst of 30 Kb and vary the arrival rates to learn its respective impact. From Figure 7, it is visible that the throughput is a proportionate to the arrival rate. The throughput is considerably larger for superframe orders till four. When the superframe order increases further than four, the throughput is lowered and stabilizes. The saturation and reduction in throughput for the larger superframes occurs as the size of the superframe becomes large and is thus able to accommodate the data frame, but the following data frames wait for longer optical frames to get accommodated.  
For the next set of experiments, we set the arrival rate to be constant at 120 Kbps and vary the burst sizes to learn its respective impact. From Figure 8 it is clear that despite the size of the burst, as the superframe order goes higher, throughput reduces steeply. However, the initial superframe orders (0-4) show a larger throughput that is highly dependent on the burst size.  
Figure 7: Maximum throughput of a Guaranteed Timeslots (GTS) allocation as a function of arrival rate.  
We now repeat the same set of experiments with higher data rates supported by PHY II and PHY III, respectively. For this experiment we fix a data rate of 100 Mbps and vary the size of the burst (10-90 Mb) to learn its impact.  
From Figure 9, we can learn that for these PHY that support higher data rates, they also exhibit a similar behavior to their lower data rate counterparts. As expected, larger bursts originate higher throughput; however, when the superframe order increases, the throughput reduces to almost the same level as of a lower burst size.  
An important conclusion we draw from these experiments is that high superframe orders are not guaranteed to ensure efficient usage of GTS allocation in terms of throughput. This is because larger superframe orders will increase the size of the superframe and eventually result in wasted bandwidth, resulting in deterioration of the throughput.
# Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
## 3 Countermeasures against Web Tracking
### Tracking Prevention
#### 3.2.5 JavaScript Dynamic Taint Analysis  
Dynamic taint analysis (DTA) is a technology that tracks when a program designates a particular input as taint and then transfers the taint to another value [65]. If the script initiates a Web request that delivers contamination (fingerprint), DTA can block this request. [PERSON] et al. developed FPFlow [19], which monitors taint propagation in JavaScript objects during webpage visits and intercepts related requests. Research using FPFlow confirmed that 66.6% of the top 10,000 websites on Tranco used fingerprinting. In the future, taint analysis should expand its capabilities to also track implicit data flows and detect fingerprinting that does not rely on API returns, such as from WebRTC or JavaScript font probing.
## 2 Data and Methods
### MODIS Land Products Subsets
#### 2.1.2 MOD15 LAI and FPAR  
MOD15 is the 1 km, 8-day composite LAI and FPAR product [35]. The main algorithm for retrieving LAI and FPAR is based on a lookup table (LUT) inversion approach. The biome-specific LUTs, generated using a three-dimensional radiative transfer model [30, 35], consists of spectral surface reflectances as a function of LAI, soil reflectances and view geometry. The inversion results in minimizing the difference between the observed surface reflectances and the simulated LUT entries to generate an average LAI value from a solution set [30]. If the main algorithm fails, a back-up algorithm based on the relationship between NDVI and LAI/FPAR is used in its place (in this study, the main algorithm had been applied to 72.8% of the LAI/FPAR data). Summarizing LAI validation studies, [PERSON] _et al._[35] showed that the MOD15 LAI product had an \(R^{2}\) of 0.87 when compared to measured LAI from 29 field campaigns, with an average overestimation of 12% (RMSE = 0.66). In a separate validation effort over the tropics, [PERSON] _et al._[36] reported an overestimation by a factor of 1.18 at Tapajos, Brazil. The new Collection 5 (C5) MOD15 data, used in this study, was an improvement over the earlier collections, especially for the forest biomes [35, 37, 38]. A recent study by [PERSON] _et al._[37] shows that the C5 MOD15 data has higher spatial consistency (\(R^{2}\) = 0.42) than Collection 4 (\(R^{2}\) = 0.28), when compared to upscaled _in situ_ measurements of mixed coniferous forests. We screened the C5 MOD15 product by assigning the LAI and FPAR of each 8-day composite to zero when the quality assurance fields indicated that snow was present over a pixel. We calculated annual mean LAI and FPAR as for NDVI and EVI.
## 2 Materials and Methods
### Teleoperation System Overview
#### 2.1.2 MR Subspace  
The MR subspace serves as an intermediary between the user command loop and robot control loop. A digital twin system is generated in the virtual environment and features a synchronous representation of the physical UR5 robot (Universal Robotics, Odense, Denmark) employed. The remote human operator can inspect the sensory information, robot control command input, and robot pose in its work environment through a 3D MR subspace interface. The virtual replica allows the user to inspect how the physical robot is situated in the remote environment without deploying an array of static cameras. The digital twin subscribes to the joint state data of the physical UR5 robot and updates its configuration accordingly in MR using rosbridge software provided by the Robot Operating System (ROS). In the MR scene, the user and the remote robotic platform share the same space virtually. The digital twin-enhanced MR subspace interface is shown in Figure 3.  
In typical telerobotic systems with a 2D camera feed, the user is provided with a monocular RGB stream of the remote scene. In the virtual subspace, a human operator monitors the spatial manipulation process using the real-time 3D point clouds for remote environmental visualization. The digital twin can either be situated in front of the user or superimposed on the user, depending on their requirements.  
The operator uses the handheld HTC Vive controller as a motion input device to grant human-level dynamic performance to the robotic arm-hand for remote telerobotic spatial manipulation tasks. The Vive tracking system tracks the controller position to a sub-millimeter level accuracy. User hand movements are tracked by two base stations tracking the handheld HTC Vive controllers at a refresh rate of 90 Hz. The HTC Vive base stations use alternating horizontal and vertical lasers to scan across the HTC Vive headset and handheld controllers, which are equipped with sensors to detect the lasers as they pass. The HTC Vive system integratesthe position and rotation data of the components being tracked in the 3D MR subspace. The acquired position and orientation data are calculated and transmitted to the ROS-based controller of the UR5 robot, where the input velocity values are converted into robot joint velocities.