esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Particle Dispersion Effect  
The relative standard deviation (RSD) can be introduced to characterize particle dispersion effects, as shown in Figure 9. The RSD of particle numbers in multiple sampling zones should be adopted as an evaluation index. Due to a particle being deposited at the container bottom, the RSD value is large in the initial state, and the particle distribution is uneven. The RSD curve has an upward trend. As the stirring course continues, particles reach impellers and disperse to various regions, and the RSD curves show rapid downward trends. Then it oscillates in a small range to a quasi-steady state. The RSD curve without aeration takes on decreasing trends, and lower RSD values appear. At this time, the particle state is steady, oscillation amplitudes with aeration conditions are large, and dispersion effects are the best. Inflation increases the mixed instability and nonlinear turbulent motion. The above phenomena illustrate that aeration control will improve particle suspension effects and enhance distribution uniformity.
## 3 Results and Discussion
### Dyes' Optical Properties
This depends on the fact that the dye C106, which binds vertically to the TiO\({}_{2}\), with the 2,2\({}^{\prime}\)-bipyridyl (bpy) lying almost perpendicular to the TiO\({}_{2}\) surface, is less sterically hindered, while the Y123 structure, more rigid and planar, binds vertically with a greater steric hindrance due to the presence of the lateral-OC\({}_{6}\)H\({}_{13}\) chains on the triphenylamine substituent. Moreover, since Y123 is prone to aggregation by \(\pi\)-\(\pi\) stacking due to its planar geometry, it is always used in association with disaggregating agents such as chemodeoxycholic acid (CDA): it has been demonstrated that CDA acts as a co-adsorbent on the TiO\({}_{2}\) surface, breaking of the \(\pi\)-\(\pi\) stacking thus avoiding the self-quenching of the dye molecules. [PERSON] et al. demonstrated also that CDA as a co-adsorbent with ruthenium C106 dye is not the best co-adsorber [39]. Moreover, by comparing the absorption spectra of two photoanodes with equal thickness, the organic dye \(\lambda\) max lies (485 nm) in the depression region between the two maxima of the Ru-based one (Figure 2b). The two spectra can be considered complementary, since the two compounds absorb throughout the region between 360 and 700 nm, thus suggesting their possible application in a \"molecular cocktail\" for more efficient panchromatic DSSCs.
## 4 Realization of IFC-Based 4D Construction Information Model of Prefabricated Buildings
### IFC Standard and Its Extension Mechanism
#### 4.2.2 Realizing Resource Information's Extension Based on IFC Standard  
This part is to realize resource information's extension based on IFC standard. Figure 3 illustrates how to extend the schema of the construction resources information model. Firstly, using the entity IfcResource and its subtype IfcConstructionResource to describe resource information. Specifically, IfcConstructionEquipmentResource, IfcConstructionMaterialResource, IfcConstructionProductResource, IfcSubContractEquipmentResource, IfcLaborResource, and IfcCrewResource are used to describe equipment resources, material resources, product resources, subcontracted equipment resources, labor resources, and crew resources, respectively. What is more, IfcConstructionEquipmentResource, the entity representing equipment resources, can be associated with IfcTransportElement, IfcDiscreteAccessory, and IfcProxy to represent equipment information more detailed. IfcConstructionMaterialResource represents the entity of material resources, which can be associated with the entity IfcQuantityWeight to express the required resources' weight information. Following this logic can extend the IFC resource information model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Extension Methods** & **Usability** & **Compatibility** & **Time Consumption** & **Operational Efficiency** \\ \hline Based on increasing entity types. & Difficult & Cannot be guaranteed & Long & High \\ \
\begin{tabular}{c} IfcBuildingElementryProxy, and \\ existing entities. \\ \end{tabular} & Easy & Good & Short & Low \\
\begin{tabular}{c} Based on property sets. \\ \end{tabular} & Easy & Somewhere in between & Somewhere in between & Somewhere in between \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of three extension methods.
# Influence of the Road Model on the Optimal Maneuver of a Racing Motorcycle
## 5 Optimal Maneuver Simulation Results  
The simulation results are compared with experimental data from the Supersport motorcycle used in the MotoAmerica championship. These data are available on the website [46] of one of the riders participating in the series. The shared database includes GPS data, and measurements from the engine control unit and numerous analogue and digital sensors, including suspension potentiometers and wheel speed sensors.  
The model of the Supersport motorcycle was created based on measurements taken on a motorcycle similar to the one used by the American rider. The vehicle parameters used in the simulations are shown in the Table 1. The longitudinal friction coefficient \(\mu_{x}\) and the lateral friction coefficient \(\mu_{y}\) of the tires were determined by trial and error, based on a comparative analysis between simulation results and experimental data.  
Figure 11(a) shows the torque and power curves of the engine obtained on a chassis dynamometer in a transient condition. The driving force for gears 2-6 is shown in Figure 11(b). Gear number 1 was omitted because it was not used during the actual vehicle motion. The stock gear ratios were assumed, while the unknown final drive ratio was estimated based on the relationship between engine speed and vehicle speed, as measured by GPS.  
The coefficients \(\beta^{r}_{0}\) and \(\beta^{r}_{1}\) included in the hyperbolic constraints (26) and (27) on the allowable values of \(j_{x}\) and \(j_{y}\) were determined by the method described in [24] and are presented in Table 2. They have been calculated using the entire shared database, which includes measurements from several different racetracks. Furthermore, a semi-empirical g-g diagram envelope described in [24] was used, where the maximum negative longitudinal acceleration \(\hat{a}_{x}\) is obtained when driving straight forward.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Value** \\ \hline \(\beta^{\frac{j_{x}}{\mu_{y}}}_{0}\) & \(15.440\) \\ \(\beta^{\frac{j_{x}}{\mu_{y}}}_{1}\) & \(-0.148\) \\ \(\beta^{\frac{j_{x}}{\mu_{y}}}_{1}\) & \(-28.440\) \\ \(\beta^{\frac{j_{y}}{\mu_{y}}}_{0}\) & \(0.148\) \\ \(\beta^{\frac{j_{y}}{\mu_{y}}}_{1}\) & \(32.559\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of the coefficients \(\beta^{r}_{0}\) and \(\beta^{r}_{1}\) used in simulations.  
Figure 12: Plots related to the vehicle powertrain: (a) engine torque and power measured at the rear wheel, (b) driving force for gears 2–6 and total resistance force.  
Three simulations were performed for the 3D-local, 3D-global, and 2D road models, then compared with the best lap from the provided database (later referred to as AQ).  
In the graphs presented later, the elapsed distance \(s_{dist}\) was used as the independent variable instead of the curvilinear abscissa \(s\), to match the experimental data domain. The experimental longitudinal and lateral accelerations presented on the following graphs were determined using GPS data post-processing.
# Assessing Crisis Management Tools for Sustainability of Industrial Safety
## 2 Problem Statement  
In industrial settings that involve hazardous chemicals, such as ammonia, effective risk assessment and crisis management are critical for safeguarding public safety and mitigating environmental hazards. Tools such as ALOHA have proven valuable in predicting the dispersion of hazardous substances and informing emergency response efforts. However, despite its widespread use, ALOHA demonstrates limitations when applied to complex, real-world scenarios. Specifically, the software's current version lacks the ability to incorporate real-time atmospheric data, dynamically adjust to fluctuating meteorological conditions, and accurately model leak behaviour in diverse geographic terrains. Moreover, the predictive models within ALOHA often fail to account for long-term health risks and broader environmental impacts, which introduces a gap in comprehensive risk assessment and management.  
This research underscores the importance of addressing these limitations by critically evaluating ALOHA's performance across multiple ammonia leakage scenarios. In doing so, the study highlights the need for integrating advanced technologies, such as Artificial Intelligence (AI), Geographic Information Systems (GIS), and drones, to enhance the software's real-time data processing and predictive accuracy. Although these technologies are not yet integrated within the current framework of ALOHA, their potential for improving the emergency response and risk management is substantial. Incorporating these advancements would allow for more timely, accurate predictions and improved decision-making in crisis situations.  
Furthermore, this research emphasises the necessity of validating theoretical simulations through real-world case studies. Such validation would bridge the gap between theoretical models and practical applications, ensuring that the proposed enhancements to ALOHA are both functional and relevant in addressing the complex challenges of industrial hazard management.
## 4 Discussion
### Impact of SIF-GPP Footprint Matching on Their Relationship  
Matching the two downscaled SIF products to the EC flux footprints resulted in a noteworthy improvement in the relationship between SIF and GPP. This aspect, which has received limited attention in previous studies, has hindered comprehensive research on the SIF-GPP relationship. The linear correlation between the SIF\({}_{\text{ECW}}\) and GPP shows improvement compared to the SIF\({}_{\text{ECA}}\), indicating a high correlation between the spatial patterns of SIF within the EC flux footprint and the contribution distribution of GPP. However, this pattern does not hold true for the VIs (Figure 8); even though the VIs showed similar patterns in SHB1, they disappeared in SHB2. The coupling of the SIF distribution in the flux footprint and the EC flux footprint supports the idea that SIF is a good proxy for GPP at a seasonal scale. The superior ability of SIF to spatially capture GPP may be attributed to its richer signal compared to the VIs. SIF is an inherent byproduct of photosynthesis, possessing both the physiological and structural characteristics of vegetation [21; 22]. SIF can be decomposed into (NIRvP \(\times\)\(\Phi_{\text{F}}\)), where \(\Phi_{\text{F}}\) represents the fluorescence emission efficiency of vegetation and physiological information, while NIRvP (NIRv \(\times\) PAR) represents structural information [22]. Compared to NIRv, SIF incorporates two additional pieces of information: PAR and \(\Phi_{\text{F}}\). PAR reflects the source of photosynthetic energy, and its significance in relation to the SIF-GPP relationship is explained in Section 4.1. The physiological component \(\Phi_{\text{F}}\) of SIF is its unique characteristic, making it the \"essence\" of SIF, as it is more directly associated with the photosynthetic process [22; 27]. Due to the poor PAR information in downscaled TROPOSIF and VIs, the main differences between TROPOSIF and VIs are primarily reflected in \(\Phi_{\text{F}}\). However, at the SHB2 site, the linear relationship and seasonal patterns between downscaled TROPOSIF and GPP are notably superior to those of vegetation indices (Figures 6-9), indicating the crucial importance of the spatial-temporal distribution of \(\Phi_{\text{F}}\) in modulating the relationship between SIF and GPP. This can also be observed in Figure 10d, where the spatial coverage of VIs at the SHB2 site does not adjust its relationship with GPP, whereas the spatial coverage of TROPOSIF is closely related to GPP, further highlighting the essential role of \(\Phi_{\rm F}\) in regulating SIF. Furthermore, recent studies have verified the richer spatial information of SIF compared to that of the VIs. For example, [PERSON] et al. [51] estimated plant functional diversity using the NDVI, hyperspectral coefficient of variation (CV), and SIF, finding that SIF was more accurate than the NDVI and hyperspectral CV. [PERSON] et al. [52] discovered that SIF was closely related to the functional diversity of peatland vegetation and was more successful than VIs in capturing different vegetation signals from extremely heterogeneous peatland biomes.
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 7 UOC Near-Future Network Scenarios and Research Challenges
### UOC--Today's Research Challenges
#### 7.2.10 UOC between UAVs  
A distributed sensing system set on mobile platforms is described in [504], where the main challenges are localization, synchronization, and processing capabilities. A new approach was examined to meet the needs of a dependable and effective distributed system. UOC links can connect multiple nodes to aid in localization and synchronization, as well as provide a high-speed communication link between sensor units for optimum processing flexibility. To show the practicality of multi-Gbps OWC, the effect of atmospheric turbulence on the UOC link between UAVs was demonstrated. UOC enables high-data-rate sensor information exchanges as well as secure communications, which are essential for a variety of tactical applications. Interest in UAVs is rapidly increasing due to their critical civil and military applications, such as the surveillance of specific regions, with a variety of sensors using multiple UAVs in cooperative swarm mode to collect a massive amount of data in a short period of time, increase robustness by sharing information between UAVs, and review a larger area. Finally, the information gathered by UAVs is transmitted to a command center on the ground, where appropriate action is taken. If UAVs are equipped with UOC using the most advanced technological devices and optical networks, UOC technology can meet these challenges [505].  
Using extremely small, low-weight, low-power (SWP), and fast optical devices, UOC can be perfectly mounted on existing UAVs. The authors also described a developing AO beam divergence scheme to keep communication links operational over atmospheric turbulence. The following is an example scenario from their paper: The distributed sensors are in a UAV array of size \(N\), the UAVs are spaced 175 m apart, and the UAVs are at the height of 5 km above the ground. The turbulence strength values considered are \({C_{n}}^{2}\) in the range of \(10^{-17}\) to \(10^{-13}\) m\({}^{-2/3}\). A UOC transmitting wavelength of 1550 nm; laser transmitting power = 50 mW, with a transmitting beam size, \(w_{0}\) = 2 cm; and receiver diameter, \(D\) = 10 cm, were assumed. The BER is very low, from \(10^{-13}\) to \(3.10^{-5}\) within the \({C_{n}}^{2}\) range. The BER increases significantly for a longer communication range of 700 m, from \(10^{-12}\) for weak turbulence to roughly \(10^{-2}\) for moderate turbulence strength. To mitigate the loss induced by a combined geometric and pointing loss, an adaptive beam divergence technique, or sometimes boosting of the laser transmission power, can be used. Future study could focus on employing retro-reflectors implanted in two UAVs to automatically align them, removing the requirement for a mechanical tracking and pointing device.
# Wireless May Benefit Blockchain +
## 2 System Model
### Communication Among Wireless Nodes  
The nodes are connected to each other via wireless communications. Each node holds 100 m of communication range. This paper assumes a general ad hoc network [37] in which no central mediator exists, but instead, the nodes are connected to their own neighbors, e.g., device-to-device communications [38], C-V2X mode 4 [39], etc. The rationale is that a centralized network (e.g., cellular, Wi-Fi, etc.) will likely employ a permissioned blockchain (or a private blockchain), whose performance is not exactly what we are interested in measuring. Importantly, the aforementioned assumptions are not to represent a certain setup or an arbitrary value. Instead, the objective is to establish a generalized analytical framework in which such parameters are treated as variables. This enhances the robustness of the proposed framework by explicitly demonstrating how variations in these parameters influence the consensus performance of a blockchain network formed among wireless nodes.  
As a means to accomplish the finality of data among nodes, distributed systems often adopt a gossip protocol for propagation of information [40]. In a network of size \(N\)  
Figure 1: Example scenario of gossip protocol among 400 nodes with \(\text{p}_{\text{fail}}=0.5\) (green dots—nodes having successfully received the block; red dots—nodes having not received the block; black dots—nodes having not even been reached due to disconnection; magenta box—cluster of node failure).  
these protocols consist of \(N\) local, pairwise, and periodical gossiping operations between neighboring nodes.  
Figure 1 shows an example of the gossip protocol operated on a wireless ad hoc network. The green dot in Figure 1a indicates the node commencing the flooding, while the black dots are all the other nodes that will potentially receive the block that will be propagated. As shown in Figure 1b, each node propagates the block to all the neighboring nodes within its communication range. Each connection is supposed to cause a Byzantine fault at the rate of \(\mathsf{p_{fail}}\), which is shown as a red line. Such a failed connection yields a failed reception at the receiver node, which is drawn as a red dot.
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 3 Channel Access
### FBE Variants
#### 3.2.6 Greedy-Enhanced FBE  
[PERSON] et al. [36] propose another approach for LTE-U which is similar to E-FBE. We refer to it as greedy-enhanced FBE (GE FBE). Contrary to E-FBE, ICCA is repeated until the node detects an idle channel, and if a transmission occurs during ECCA, ICCA is performed again, without a new backoff value selection. Figure 7 shows an example of the GE FBE procedure. Initially, the node chooses \(N=3\). ICCA is followed by a successful ECCA, but during the second ECCA, a transmission occurs. Then, ICCA is repeated twice until idle channel detection. Finally, the node performs the remaining ECCA successfully and begins transmission.
# Secure Cooperative Routing in Wireless Sensor Networks
## 2 Materials and Methods  
### System Design and Problem Formulation  
This research work innovatively merges AOMDV routing, which has the features of multipath routing, and LEACH protocol, capitalizing on their respective strengths to address routing attacks, such as sinkhole and wormhole attacks in WSNs. This integration offers a comprehensive solution that improves network resilience and adaptability, making a significant contribution to WSN performance and security. The security of the routing process is achieved through several key features such as cluster head (CH) selection and intrusion detection, multipath routing and round-trip time (RTT) monitoring, and non-cryptographic resource-efficient approach. By combining these techniques, the MIDS algorithm ensures secure, efficient, and reliable data routing in WSNs, effectively handling common routing attacks while maintaining high detection accuracy and resource efficiency.  
In this research work, a wireless sensor network is deployed with \"N = 200\" sensor nodes. This network incorporates the criteria of first node die (FND), with the values of \"0\" and \"1\". In this process of FND, if the value is 1, it means the algorithm will stop whenever there exists a dead node with an energy value less than the predefined threshold which is \"0.01 J\". If the value is \"0\", it will count the dead nodes in the further processing and the algorithm will not stop based on any dead node. The network is deployed on a simulation area of 100 \(\times\) 100 m\({}^{2}\).Anchor-based distance calculation is done by considering \"4\" anchor nodes.  
The AODV protocol is used as an underlying protocol. The proposed version of the IDS mechanism is known as a multipath intrusion detection system (MIDS). As the name depicts, this MIDS also facilitates multipath routing in the case of main path failure using the AOMDV protocol along with minimizing the adverse effects of both attacks. The proposed multipath intrusion detection system (MIDS) assumes the following:  
* The base station has the highest energy resource.
* The malicious nodes show energy levels higher than the normal sensor nodes.
* The network possesses the initial energy of 1 J.
* This algorithm does not require any clock synchronization.
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
## 6 Data Analysis  
### Reducing the First Sidelobe Level (SLL) Nearest to the Main Lobe of a Uniform LAA  
In the first example, the NCGWO algorithm was utilized to synthesize the pattern of a uniform LAA. The performance of the NCGWO algorithm was compared with that of the IGWO [23], GWO, PSO, and MA [35] algorithms. Assuming zero-phase excitation, the amplitudes of the excitation current were optimized for LAA with \(2N=10\) to achieve the desired peak sidelobe level (PSLL). The main lobe width was set to \(28^{\circ}\), while the sidelobe regions were \(\theta=\left[0^{\circ},76^{\circ}\right]\cup\left[104^{\circ},180^{\circ}\right]\). Meanwhile, the first sidelobe, which is closest to the main lobe, was minimized within the region defined by \(\theta=\left[69^{\circ},76^{\circ}\right]\cup\left[104^{\circ},111^{\circ}\right]\). The NCGWO algorithm was executed with a population size of 20 for 1000 iterations, which is consistent with previous studies [34].  
Figure 9a depicts the array pattern obtained using the NCGWO algorithm, which was compared with the results of the IGWO, GWO, PSO, and MA algorithms. Figure 9b illustrates the iteration curves that correspond to the GWO, IGWO, and NCGWO algorithms. Evidently, the NCGWO algorithm converges to the optimal value within approximately 100 generations, whereas the GWO and IGWO algorithms require approximately 400 generations to achieve the same outcome. This outcome underscores the superior performance of the NCGWO algorithm compared to the GWO and IGWO algorithms. Figure 10 shows the 3D radiation patterns of reducing the 10-element LAA's first SLL before and after the NCGWO optimization, which clearly demonstrates the effectiveness of the NCGWO algorithm in significantly reducing the first SLL.  
Figure 8: Planar array antennas geometry.  
Table 4 shows the optimization results in the example. The NCGWO algorithm achieved a minimized nearest SLL of \(-36.4027\) dB and a running time of 0.8290 s. This minimized nearest SLL was lower than those of GWO, IGWO, PSO, and MA by 4.5627, 4.4898, 3.9827, and 3.9527 dB, respectively. The PSLL achieved by the NCGWO algorithm was \(-22.4420\) dB, which was 5.585, 2.1659, 3.0379, and 2.4053 dB lower than those of GWO, IGWO, PSO, and MA, respectively.