esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Environment Features for Labeling and Feature Classifications  
Consideration of terrain features includes pavement, gravel, dirt, trees, grass, vehicles, and pedestrians. After our initial assessment of the real dataset, semantic classes were selected to provide adequate discretization and coverage of the data. The initial set of semantic classes included grass, soil, water, trees, paved and unpaved roads, fences, shrubs, and a class for miscellaneous manmade objects. An example image labeled with these classes is shown in Figure 8.  
Figure 8: Example of semantic labeling of real-world data.  
Figure 7: Sample Raw Images on the left (**a**,**c**,**e**) and Denoised Images on the right (**b**,**d**,**f**). Images (**a**,**b**) 1 rays per pixel, (**c**,**d**) 100 rays per pixel, and (**e**,**f**) 10,000 rays per pixel.  
However, we discovered that there were limitations with our selection of semantic labels. First, because of the high-resolution and detail of the real images, each image took about 30 min to semantically label. Second, the boundaries between many of the classifications such as soil, grass, and unpaved road were unclear, making consistent labeling difficult.  
Therefore, because our primary goal in this work was to develop the automation process, rather than develop new techniques in machine learning or investigate optimal methods for hand-labeling sensor data, we chose to simplify the labeling process by focusing only on buildings. This allowed us to use box-labels, rather than pixel labels. In addition, since there only a few buildings per image, the average labeling time was reduced to only a few minutes per image. An example box-labeled image is shown in Figure 9.  
Of the 119 real images, 37 had at least one building, while the remainder did not have buildings. Of those images with buildings, 16 had exactly one building, while the other 21 had 2-8 buildings.  
For this project, we used the MATLAB Image Labeler [37] to perform the labeling tasks. Although we considered other solutions for performing the labeling, MATLAB was chosen because we could perform the labeling on our local machines without uploading the data to a 3 rd-party server.
## 3 Secure FSO Link in GNU Radio Simulation
### Simulation Results  
The communication link in the simulation has been designed to replicate a realistic lab environment. The simulation parameters of the link are link length = 0.5 m, receiver diameter = 4 mm, transmitter diameter = 3 mm and transmitter laser power = 10 mW. The laser's half-angle divergence is assumed to be very small (very close to 0) due to the short link length and use of lenses that collimates the beam. System performance is measured by packet delivery rate (PDR) which is the ratio of the received packet number to the transmitted packet number.  
Figure 7 shows the FSO link output of a secure conversation between [PERSON] and [PERSON]. The output shows a different format of the encrypted and packetized message at [PERSON] and [PERSON]. For example, the difference between an encrypted message and an encrypted packet is that the nonce and message have been combined into one and at the start of the message the four-byte preamble is added (85 85 85 85), four-byte sync is added (225 90 232 147), and four header bytes are added.  
In an eavesdropping attack, [PERSON] can receive the data by using the same demodulation as [PERSON]. [PERSON] can also generate their keypair using the same method as [PERSON] and [PERSON] to decrypt the incoming messages using their private key and the sender's ([PERSON]) public key. However, since [PERSON] does not have the correct private encryption key, they cannot decrypt the gathered data.  
Figure 8 shows that [PERSON] has successfully received the message and can decipher the packet format. [PERSON] can also identify the nonce and message begin but they cannot decrypt the message. The crypto_box_open_easy function from libsodium returns \(-1\) \"Failed to decrypt message.\" meaning the message cannot be decrypted and remains a secret.  
To study the performance of the proposed system under realistic satellite to ground-station beam geometry and channel conditions, a simulation-based FSO link is implemented with realistic parameters shown in Table 1 for low earth orbit satellite mission that uses miniature optical communication transceiver (MOCT) developed at the University of Florida [16]. Figure 9a shows that this FSO link achieves a PDR of 88% using transmitted laser power of 10 W. When the transmitted laser power is reduced to 0.1 W (i.e., a fraction of leakage power \(10^{-2}\)[5]), the PDR decreases to 0%, unless [PERSON] is 100 times more sensitive or can boost the received signal using an amplifier with gain of 20 dB. In this case, the PDR increases to 96% as illustrated in Figure 9b. The results also shows that [PERSON] failed to decrypt any encrypted messages.  
Figure 8: FSO link output of [PERSON] intercepting a message sent from [PERSON] under ideal channel conditions.  
Figure 7: FSO link output of a secure conversation between [PERSON] and [PERSON] under ideal channel conditions.  
Figure 9: Simulation results of realistic satellite to ground-station FSO link parameters when the transmitter laser power values are: (**a**) 10 W and (**b**) 0.1 W and the receiver uses 20 dB amplifier.
## 3 Results and Discussion
### Oxide Layer Adhesion Results  
Representative VDI 3198 oxide layer adhesion quality test results are provided in Figure 9. Both oxides exhibited good oxide adhesion quality to the titanium substrate without any evidence of coating delamination. Mild microcracking was shown around the indents in both oxides. Comparing these images to the commonly used VDI 3198 oxide-layer adhesion reference images proved that these oxides showed acceptable adhesion quality. This indentation methodology has been commonly used in other recent coating studies [5,28,30,43].  
Conventional hydroxyapatite coating techniques, such as plasma spraying, have often resulted in less-than-ideal implant-substrate adhesion [14,15,44]. Commonly identified limitations of the hydroxyapatite plasma spray coating techniques include the high processing temperatures, poor control over coating crystallinity, expensive equipment requirements, and [14,15,44]. In contrast, previous anodization studies that have formed hydroxyapatite have reported higher coating adhesion strengths resulting from the formation of an intervening oxide layer between the titanium substrate and hydroxyapatite layer [5,16]. The indentation-derived adhesion results shown in Figure 9 in this study agree well with the reported higher substrate adhesion strengths shown by the bi-layered hydroxyapatite coatings in previous studies [16,45]. The higher adhesion strengths for the bi-layered coatings have been ascribed to stronger interface interlocking between the titanium dioxide-rich inner layer and the carbonated apatite-rich outer layer [16,45].  
Figure 8: **(A)** EDS-estimated Ca-, P-, and Mg-dopant uptake levels from the outermost 2 microns of all the oxides and **(B)** Corresponding EDS Ca/P ratio for the NOJ CP oxides was significantly higher with a value of approximately 1.5 compared to the NOJ MP oxides with a value of approximately 1.2 (\(p<0.0001\)). These ratios fall within the Ca/P ratio ranges for biphasic calcium compounds such as \(\alpha\)-tricalcium phosphate and hydroxyapatite. **** indicates (\(p<0.0001\)).  
Figure 9: VDI indentation oxide layer adhesion quality results for each oxide. Both revealed some microcracking around the indents but showed overall good adhesion quality with no coating delamination observed.  
One limitation of the present study is the lack of in vitro or in vivo response assessments of these juiced fruit-based anodized oxides. We performed a mouse MC3T3-E1 pre-osteoblast cell viability study in a similar anodization study, producing calcium titanate oxide coatings from electrolytes based on commercially available calcium-fortified orange juice [23]. The calcium titanate-containing oxides exhibited >90% cell viability after a 7-day culture period, as assessed by MTT assay, and showed confluent live cells with very few dead cells in the 7-day fluorescent live/dead images [23]. Future studies are planned to evaluate the in vitro responses to these \(\alpha\)-tricalcium phosphate and carbonated hydroxyapatite-containing anodized oxides produced from juiced navel orange-based electrolytes in the present study.
Biofilms are more resilient to various environmental stressors, including pH changes, chromium, earthworm predation, UV rays, osmotic shock, desiccation, and pathogen defense [7; 21; 25]. Despite the abundance of Actinobacteria bacteria, very few reports are available on biofilm formation regarding this phylum [25]. A few studies have shown that a biofilmed biofertilizer used as monoculture or as a consortium significantly increases N\({}_{2}\) fixation in legumes (by ca. 30%). In addition, plant growth promotion (by approximately 25%) increased rice plant dry weight in its early growth stage compared to conventional bioinoculants; for example, _P. fluorescens_ biofilm, which increased endophytic colonization of tomato, showed a biocontrol activity of over 1000% compared to inoculation with a planktonic culture [7; 21; 26]. Compared with those examples, successful fertilization has been reported in rice, wheat, soybean, corn, tea, sorghum, rubber crops, and some other cereals and vegetables under greenhouse conditions, offering multiple benefits but highlighting the 50% reduction in chemical fertilization in field crop conditions [6; 27; 28; 29; 30; 31; 32]. Though not all microorganisms can form a biofilm, a few actinobacteria can. It is believed that actinobacteria are not capable of forming biofilms by themselves, that is, without the help of another microorganism or consortium [12; 22]. A key issue with conventional inoculant technology (planktonic cells) is the low survival of introduced microorganisms in the soil as a result of numerous environmental stress factors; thus, the need to use carriers has been increasing [7]. A carrier is the main component that transports a substantial quantity of PGP into the soil in agriculture and whose function, in addition to transport, is to maintain the viability of the microorganism for a prolonged period [33; 34]. Although many options are commercially available depending on the application method or selected microorganisms, peat is the most popular carrier. However, other carriers such as perlite, vermiculite, bentonite, sepiolite, attapulge, compost, calcium alginate, biochar, or polymers have been used with different results and are not always positive [35; 36; 37]. A carrier is considered ideal if it meets specific characteristics, such as high water retention capacity, high surface area, inertness, ease of handling and sterilization, environmental friendliness, neutral or adjustable pH, chemical and physical stability, and low cost [33; 34; 35]. The present study investigated the role of PGP actinobacteria strains in a novel biofilined inoculant regarding the growth-promoting efficiency of _T. aestivum_ through greenhouse experiments. Furthermore, its introduction using perlite was assessed to determine microbial viability and survival owing to its ideal characteristics as a carrier.
## 9 Results and Discussion
### Predicted Maximum Effective Temperature Formula
The comparison is presented in Figure 12 and shows that the FE maximum effective temperatures are higher than the CHBDC values for the representative cities. The maximum difference was 6 \({}^{\circ}\)C in Victoria (region 4), and the minimum difference was 1.5 \({}^{\circ}\)C in Winnipeg (region 2). On average, the FE values were 2.5 \({}^{\circ}\)C higher than the CHBDC values. The CHBDC underestimated the maximum effective temperature compared to FE values, which might lead to a reduction in the structure's durability as the thermal expansion calculations are underestimated [1]. Therefore, it is essential to consider the superstructure's depth and flange width when  
Figure 11: Predicted maximum effective temperature vs. FE maximum effective temperature. calculating the maximum effective temperatures to ensure the optimal safety and durability of the structure. The CHBDC recommended a stationary thermal gradient design guideline for steel beams [1]. In the code, the thermal gradient profile showing the temperature distribution along the beam's depth was not provided. Instead, temperature differentials based on the beam's depth were recommended. However, based on the parametric studies conducted, it can be said that one stationary thermal gradient profile cannot be considered applicable for all climate regions of Canada. The climate regions were divided into two main zones for thermal gradient calculations. The pattern of the thermal gradient profiles was observed to be stationary for the climate regions. The thermal gradient variations at the standard steel beams' top surfaces were developed for the climate zones. The thermal gradient variations at the steel beams' bottom surfaces were observed to be slightly less than the thermal gradient variations measured at the top surfaces, which was not considered in the CHBDC [1]. In addition, the proposed thermal gradient variations depend not only on the beam's depth but also on the flange width and thickness.
## 5 Limitations and Discussion  
Several numerical simulations were carried out to highlight the advantages and disadvantages of the proposed procedure in the following contexts: (1) the ability to achieve situational awareness regarding the position and speed of each agent in the formation; (2) the capability to localize a static target in a distributed manner using the transceivers on each UAV; (3) resilience to communication delays and interruptions; and (4) integration with a comprehensive guidance, navigation and control procedure, simulating a realistic post-avalanche search-and-rescue mission.  
Regarding the ability to achieve situational awareness, a sensitivity analysis of the main parameters showed that the algorithm is nearly insensitive to the number of communication steps, at least when dealing with a limited number of UAVs. Instead, performance is primarily influenced by the communication scheme. As expected, the topology of the communication graph is crucial, representing how the agents share information among themselves. The communication scheme \(\mathcal{A}=\mathcal{A}_{2}\) is considered the worst-case scenario and is thus employed in the other tests.  
From the perspective of target localization capability, a comparison with a centralized EKF showed that the proposed algorithm achieves nearly identical performance in terms of target localization, as presented in Table 7, for both the \(MAE\) and standard deviation \(\sigma\). The distributed procedure is only \(5\) s slower than the centralized one, as depicted in Figures 9-11.  
The robustness to communication delays and interruptions was confirmed through numerical simulations. These assessments included evaluating the algorithm capability to resume UAV position estimation following disruptions in communication links at specified time intervals (Figure 7) and showing the convergence capabilities in the presence of three different constant communication delays among UAVs (Figure 14). As observed, the position estimation is temporarily interrupted during communication loss but promptly resumes once the connection among agents is re-established. The procedure remains resilient to communication delays, albeit affecting the convergence time for target localization, as expected.  
Finally, the last set of simulation results proves the algorithm capability to be employed within a comprehensive guidance navigation and control procedure. It has been demonstrated to be useful in estimating UAV positions, which are essential for trajectory tracking, collision avoidance and target localization. The results show a localization error of less than \(1\) m, with no faults during the mission.  
Figure 27: Simulation #5, Scenario #2—graphical representation of the estimation error. The true position of the skier is depicted by means of the blue marker. (**a**) 3D view. (**b**) Top view.
## 7 Conclusions and Future Work
Random-access, very-low-power, and wide-area networks (RA-vLPWANs), as low-power satellite communication protocols, provide uncoordinated multiple access in scenarios characterized by poor signal-to-noise ratios and very low signal power [111]. Furthermore, the CEEA algorithm, as suggested by [PERSON] [102], serves the purpose of establishing a post-disaster sensor network by effectively connecting operational sensors with remaining energy. Notably, this algorithm bears a resemblance to the Bee algorithm [112]. Future research endeavors may be directed toward examining the potential applicability of the Bee algorithm in optimizing sensor networks during the post-disaster phase. Thirdly, future studies also can combine sensors with unmanned aerial vehicles. We can install sensors on unmanned aerial vehicles. Future studies may investigate what types of sensors are suitable to install on unmanned aerial vehicles. If we use cameras on unmanned aerial vehicles, it is useful to investigate how to transmit the image data from unmanned aerial vehicles to the operation centers. Since we may control unmanned aerial vehicles remotely, how do we ensure the connects between controllers and unmanned aerial vehicles? Unmanned aerial vehicles can also serve as communication tools, especially in the post-disaster stage. It is interesting to optimize unmanned aerial vehicles' cruising trajectory to balance the communication coverage and cost. Future studies may also investigate how to use unmanned aerial vehicles to locate the sensors or trapped people. Fourthly, some studies mentioned user participation in sensing in the pre-disaster stage. However, people may upload false information to the system, affecting the credibility of the system. Future studies may investigate how to ensure the authenticity of data with Blockchain. In addition, because of the wide application of cameras, sensor communication must be able to transmit a larger amount of image data. Fifthly, future studies can use sensors to calculate the time from departure to the successful completion of the rescuers in the post-disaster stage, which can allocate rescue personnel more efficiently. For example, personal devices can record the rescuers' routes and calculate the time. The rescuers may record the completion of the rescue when they save the people successfully. Writing--original draft preparation, F.Z. ; writing--review and editing, C.P. and H.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding.
# A Family of ACO Routing Protocols for Mobile Ad Hoc Networks
## 3 AntOR-Based Protocol
### Data Structures
#### 3.1.1 Routing Table  
Like all routing ACO algorithms for mobile ad hoc networks, the information related to routing is organized into the so-called routing tables. This data structure is present also in ACO routing protocols for wired networks as AntNet or routing classic protocols for mobile ad hoc networks as AODV.  
These tables contain the utilized information by the algorithm in its forwarding of local decisions. The kind of information contained, as well as the way in which it is used and updated depend solely on the characteristics of the algorithm. The routing table is in turn a local database and a local model of the global state of the network.  
This table consists of the following fields:  
* Regular pheromone (\(\mathbf{\tau^{d}_{ij}}\)): It indicates the path through which the data travel. It is a heuristic value that contains an estimate of goodness to relay data packets along the route that goes from \(i\) to the destination \(d\) with next hop \(j\). This value is expressed as the inverse of a time estimate or cost, as will be explained when Equation (7) is introduced. This cost is based on the metric used for the algorithm evaluation.
* Virtual pheromone (\(\mathbf{\omega^{d}_{ij}}\)): It indicates a path that can possibly be good. This virtual heuristic value has the mission of the auxiliary value and is utilized as an alternative. It is created or updated in the pheromone diffusion process.
* Average hop number (\(\mathbf{h^{d}_{ij}}\)): It is utilized in the local route repair process to indicate correctly how long the process needs to run.  
The regular pheromone and average number of hops values are related in the following way: when a route has a value from one, it also has that from the other. This is due to the fact that these two values are involved in the use of the backward ants in the reactive process, the proactive (exploration of new alternative routes) and the local route repair. However, the virtual pheromone value is created or updated independently, because it is utilized in the pheromone diffusion process.  
Table 2 shows the structure of the routing table in AntOR. This structure stores the following information for each entry: reachable destination of the data session, next hop to which the data are routed, the value of the regular pheromone and virtual pheromone and the average number of hops.  
This table grows dynamically according to the reachable routes that are calculated.
# Visualization of Emergency Evacuation Physical Behavior under Multi-Agent Decision-Making
## 4 Behavioral Evacuation Simulation Model Design and Analysis
### Simulation Model Design of Partnering Behavior Evacuation
#### 4.1.2 Analysis of Simulation Results  
Using the group creation function in the PedSource property, the expression uniform_discr (2,3) is used to reflect the evacuation crowd of the partnering model; setting that each group is mainly composed of 2-3 people, the group formation is carried out in a group manner, setting the interval time as exponential (3)s. Figure 3a shows the evacuation process under the normal mode (non-partnering behavior). Figure 3b shows the evacuation process in the partnering mode. On the contrary, in the evacuation under companionship behavior, due to the companionship-seeking behavior of the personnel in the evacuation progress, the bottleneck is mainly at the main exit of the museum, and the congestion at other locations is low due to the bottleneck effect. Comparing the HCM2000 level of service criteria table [40], the level of service for people at the exit location at this point is level E and level D at the central area. On the other hand, the evacuation under companionship behavior, due to the companionship-seeking behavior of personnel in the evacuation flow, leads to a large orange area with severe congestion already at the exit of each room as well as at the central location, which, however, is more severely congested at the exit location, with a level of service E. Obviously, to a certain extent, the resulting congestion is in a more forward position with the congested area expanding.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Service** & **Space** & **Flow Rate** & \multirow{2}{*}{**Velocity (m/s)**} & **Density** \\
**Levels** & **(m\({}^{2}\)/Person)** & **(Person/min/m)** & & **(Person/m\({}^{2}\))** \\ \hline A & \textgreater{}5.6 & \textless{}16 & \textgreater{}1.30 & \textless{}0.18 \\ B & 3.7–5.6 & 16–23 & 1.27–1.30 & 0.18–0.27 \\ C & 2.2–3.7 & 23–33 & 1.22–1.27 & 0.27–0.45 \\ D & 1.4–2.2 & 33–49 & 1.14–1.22 & 0.45–0.71 \\ E & 0.75–1.4 & 49–75 & 0.75–1.44 & 0.71–1.33 \\ F & \textless{}0.75 & Unlimited & \textless{}0.75 & \textgreater{}1.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: HCM2000 Service Level Standards.  
Figure 2: Group model diagram (Companionship is represented by the green markings indicated by the arrows).  
The whole evacuation process time is shown in Table 4. It can be seen that the evacuation time in the companion mode is longer than that in the normal operation due to the activities of seeking and waiting in the evacuation process, the crowding at the exit, and the long waiting time due to the wide range of the bottleneck effect.