esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This paper has proposed ACTOR, an extension to RPL that adapts the transmission power using reinforcement learning. The routing and transmission power control mutually improve each other. The information derived using the routing protocol reduces the number of transmission power settings that must be evaluated. Transmission power control also helps RPL keep the topology stable by mitigating the interference among the nodes. We have provided compelling evidence, including simulation and experimental results indicating that the proposed method improves the performance of multi-hop networks in different scenarios, such as dense networks and a high traffic load.  
The results are indicative of a positive correlation between the success of the exploration strategy to optimize the transmission power and networking performance. The results vary, depending on the scenarios, but generally for dense networks, the experiments showed a 20-40% increase compared to the benchmark in PDR, as well as the achievement of up to 10 dBm lower transmission power. This enhancement is accompanied by a more stable topology and lower routing overhead, which are attributable to the better management of channel contention. The main catalyst behind the results is the intelligent UCB and discounted UCB algorithm, which explores the transmission power settings with an intelligent mechanism.  
As future work, we plan to integrate ACTOR with solutions for the mobility of the nodes under which RPL exhibits poor performance. The support for a dynamic environment can be extended to mobility, but it requires the tailoring of the exploration strategy and integration with the existing mobility management solutions.  
Conceptualization, I.R. and H.F.; methodology, I.R., M.A. and H.F.; software, I.R.; analytical evaluation, I.R.; writing--original draft preparation, I.R.; writing--review and editing, H.F., M.A., M.V. and M.B.; visualization, I.R.; project administration, H.F.; funding acquisition, H.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Swedish Research Council (Vetenskapsradet) through the MobiFog starting grant, and by Vinnova through GREENER project.  
Not applicable.  
Data Availability Statement:Data are contained within the article.  
Conflicts of Interest:The authors declare no conflict of interest. The funding agencies had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & **RPL** & **TPP** & **ACTOR** & **ACTOR-D** \\ \hline PDR & 59.00\% & 73.4 \% & 82.5\% & 79.84\% \\ Transmission power (dBm) & 0 & \(-\)6.5 & \(-\)7.39 & \(-\)7.95 \\ Routing overhead (pk/node) & 12 & 7 & 6 & 7 \\ No. of retransmissions & 17.3 & 14.8 & 12.77 & 13.58 \\ No. of parent switch & 1.8 & 1.45 & 1.18 & 1.63 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Measurements for the 12-node scenario.
## 3 Void Avoidance Algorithm (VAA)  
We propose VAA to design a void-free topology for position-based WSNs. The basic idea in our algorithm is to remove all stuck nodes by transforming the routing graph and make a void-free topology. Our algorithm constructs a sink tree from the sensors to BS. Initially a BS broadcasts a hello message, and the reverse broadcast tree is used by the sensors to route packets to the BS. Periodic broadcast of hello message is necessary to maintain the sink tree, because wireless links are dynamic in nature and appear/disappear all the time.  
Considering that a packet has to detour around a void from a sensor to a BS, the effective distance between the sensor and the BS is greater than the Euclidian distance. By appropriately upgrading the distances of some sensors, we can direct the packets along the efficient routes towards the BS. We design this algorithm considering real world scenarios, such that if there is no route available to progress to the destination, the algorithm looks behind for a better path and detours intelligently. We handle it creating some virtually ordered distance factors as a cost.
## 4 Hardware Architecture
### Hardware Architecture of CBHF Module  
At the pre-lookup phase, the IP address is input into the CBHF module, and the subtrie matching queries of variable length are performed in parallel. The CBHF module checks if there are existent subtries of different levels for the input IP address. As mentioned previously, the binary trie in the PR-Trie architecture is divided into four levels for IPv4 lookups, which are also adopted in the proposed satellite IP route lookup. Figure 6 shows the parallel architecture of the CBHF module. Each level has an individual Bitmap Address Generator and an individual Subtrie Membership Query. Bits [31:24], bits [31:16], bits [31:8], and bits [31:0] of the input IP address are used as the input for the Level 1 submodule, Level 2 submodule, Level 3 submodule, and Level 4 submodule, respectively. Note that these submodules are basically the same in structure and operation, except that cuckoo filters are used in Level 3 and Level 4 submodules and the rest submodules use Bloom filters. Therefore, we take the Level 3 submodule as an example, which is shown in Figure 7.  
In the Level 3 submodule, the most significant 17 bits of the input (i.e., IPv4_L3 [23:7]) are used to calculate the P-Trie bitmap address in the Bitmap Address Generator and perform the membership query in the Subtrie Membership Query. The least significant 7 bits of the input (i.e., IPv4_L3 [6:0]) used as the R-Trie address indexing the R-Trie bitmap are stored in a FIFO buffer until the P-Trie address hashing procedure is completed, then concatenate the above two addresses to generate a candidate PR-Trie address.  
For subtrie membership queries in each level, if a subtrie corresponding to the input IP address is found, or to be more precise, the filter of this level returns a positive, the candidate PR-Trie address will be input into the priority checker module. The priority checker module inspects the candidate PR-Trie address set based on the longest prefix matching (LPM) principle, and these candidate addresses will be stored in a FIFO buffer in  
Figure 5: Overview of hardware block diagram.  
priority order. Once the correct PR-Trie address is accessed next (in Bitmap Lookup), the FIFO buffer is cleared at the same time.
## 2 Experiments
### Surface Preparation  
The flat copper samples, with a thickness and diameter of 2 mm and 16 mm, respectively, were used as the test surfaces in the boiling experiments. The procedure for fabricating smooth copper surfaces is illustrated in Figure 4a. To remove the oxidation layer, the copper samples were polished using 2000-grit sandpaper. After polishing, the samples were rinsed with deionized water and acetone. Subsequently, the copper samples were immersed in acetone and subjected to sonication for 10 min to completely remove the residue on the surface. Finally, the copper samples were dried in an oven for 10 min.  
The plain copper samples were textured with microgrooves, using a femtosecond laser (Tongtai, TLFS-500). The parameters of the femtosecond laser are listed in Table 2. The laser system had an average power of 5.4 W, a scanning speed of 100 mm/s, and a pulse repetition rate of 1 MHz. A schematic of the laser-scanning path is shown in Figure 5. The horizontal line interval for each scanning path was 10 \(\upmu\)m, and the diameter of the laser spot size was 15 \(\upmu\)m. A total laser fluence of \(\sim\)3 J/cm\({}^{2}\) was used to fabricate microgrooves on the plain surfaces. All the test surface conditions are listed in Figure 6. S1 represents the plain copper surface; S2, S3, and S4 represent the microgrooved surfaces under the condition of different groove spacings. The various groove spacings were produced by changing the number of laser-scanning paths.  
\begin{table}
\begin{tabular}{c c c} \hline
**Property** & **DI Water** & **Novec-7100** \\ \hline Bolling point (\({}^{\circ}\)C) & 100 & 61 \\ \hline Density (kg/m\({}^{3}\)) & 957.9 & 1418 \\ \hline Thermal conductivity & & \\ (W/m\(\cdot\)K) & 0.679 & 0.062 \\ \hline Heat of vaporization (kJ/kg) & 2257 & 112 \\ \hline Specific heat (J/kg\(\cdot\)K) & 4217 & 1254 \\ \hline Surface tension (mN/m) & 60.8 & 9.3 \\ \hline GWP & – & 297 \\ \hline CA on plain copper surface & \(\sim\)73\({}^{\circ}\) & \(\sim\)15\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 1: Properties of Novec-7100 and DI water at their boiling points.  
Figure 4: Surface treatment before the boiling experiments: (**a**) sandpaper polishing process; (**b**) laser texturing process.
## 2 Related Work and Contributions
### Analytical Models for 802.11 Mechanisms  
The Distributed Coordination Function (DCF) is the fundamental channel access mechanism defined by the 802.11 Standard. Extensive work has been undertaken to evaluate the performance of this mechanism. More specifically, a two dimensional Markov chain model to compute the throughput of DCF under network saturation was proposed in [21], being the most widely-used analytical model to evaluate the performance of DCF. Refinements of this basic model were then introduced in various papers to account for details of specific operation of DCF, such as finite retransmission attempts [22; 23], backoff freezing [24], and other operation details. In addition, some papers analyze the throughput and delay of other DCF extensions, such as DCF with frame bursting [25] and the 802.11e Enhanced Distributed Channel Access (EDCA) with its Transmission Opportunity (TXOP) mechanism [26; 27].  
While the aforementioned papers mainly concentrate on the throughput and delay performance, other papers, such as [28; 29], analyze the energy consumption of DCF. These papers show that DCF causes high energy consumption in an STA during idle and overhearing periods. In [30], the focus is on PSM as an extension of DCF to save energy, as defined by the 802.11 Standard. More specifically, such paper provides an analytical model to compute the throughput, delay, and power consumption of PSM. In addition, an energy efficiency analysis of a TXOP PSM-based mechanism using burst transmissions is presented in [20]. Results obtained from such analysis show that only when microsleep operation is feasible can the TXOP PSM-based mechanism achieve higher energy efficiency as compared to DCF and PSM under high traffic loads. Such results also show that the feasibility of microsleep operation strongly depends on the system parameters used, such as the transmission data length, the PHY data transmission rate, and the transition time between awake and sleep states.
## 5 Discussion
### Cybersecurity Challenges in Environmental Subsystem
#### 5.3.1 Internal-Environment-Related Cybersecurity Challenges  
The internal-environment-related cybersecurity challenges subtheme explains all cybersecurity incidents that happened due to a lack of strategic policies designed or prepared by the management and leadership beforehand to be prepared for any external threats, including the crisis in the HERS in Australia.  
**Insufficient Monitoring and Detection Capabilities**Insufficient monitoring and detection capabilities are defined as a security event that is not correctly detected, logged, and monitored to ensure adequate and timely response to the incident or breach [115]. Participants have reported increased malicious and DoS attacks due to insufficient monitoring and detection capabilities in the current organizational internal systems during the recent crisis in the HERS. The literature also highlighted that an increase in cybersecurity attacks has been observed during the COVID-19 pandemic due to the lack of surveillance systems and suitable protocols in the current organizational systems [8,116]. Although these studies were conducted during the recent crisis, both research studies are literature reviews and do not focus on a particular sector. The current study extends the literature by empirically exploring and reporting insufficient monitoring and detection capabilities as a significant internal environment cybersecurity challenge in the HERS amidst the major crisis. R017 reported, _\"I believe most of the cybersecurity issues arose because our internal systems were not prepared for such massive pandemic conditions\"_. Similarly, R015 mentioned, _\"inadequacy of internal monitoring systems led to several cybersecurity issues. All these unnoticed disruptions, malicious attacks, and others could be controlled if we had developed strong cybersecurity monitoring assessment tools before the pandemic\"_.
## 2 Simulation Method and Model
### Substrate Model  
The substrate was composed of three layers: the free motion layer, the thermal control layer, and the fixed layer, as shown in Figure 2. The width of the substrate trench was about 10 nm, the wing (pitch) of the substrate was 5 nm, the height was 30 nm, and the depth was 2.5 nm, with a slight difference between different materials. The free motion layer was marked as green. Atoms in the free motion layer could interact with thermal control layer atoms and the incident atoms freely for copper deposition. The thermal control layer was marked as blue. The temperature of the atoms in thermal control layer was modified by the [PERSON] thermostat [20] as follows:  
\[\dot{P}_{i}=m_{i}\dot{v}_{i}=-\
abla U-m_{i}\gamma_{i}v_{i}+\sqrt{2\gamma k_{B} T}R(t) \tag{7}\]  
where \(P_{i}\) is the momentum of atom \(i\); \(m_{i}\) is the mass of the atom \(i\); \(v_{i}\) is the velocity of the atom \(i\); \(U\) is the particle interaction potential; \(\
abla\) is the gradient operator such that \(-\
abla U\) is the force calculated from interatomic potentials; \(\gamma\) is the viscosity; \(T\) is the temperature; \(k_{B}\) is Boltzmann's constant; and \(R(t)\) is delta-correlated stationary Gaussian distributed random force. The fixed layer was marked as yellow. Forces acting on the atoms in the fixed layer were set as 0 to prevent the substrate from moving, which presented the stability of the substrate in reality.  
Figure 1: Flow chart of molecular dynamics simulation.  
The temperatures of the substrate discussed in this study were 350 K, 500 K, 650 K, 800 K, and 950 K, respectively, since the typical process temperature for physical vapor deposition is between 520 K and 720 K, and in some cases, ionized physical vapor deposition can be deposited at temperatures below 340 K or up to 870 K, depending on the expected behavior in the application.  
The materials of the substrate are discussed in the literature review section, which are Ta/TaN, Ti/TiN, Ti/TiN/Ti, Co/TaN, Mo/MoN, and W/WN. Since the cut-off radius is about 5 A to 10 A, only the liner of the substrate is considered. Thus, the material of the substrate is the liner material, which in this study were tantalum (Ta), titanium (Ti), molybdenum (Mo), cobalt (Co), and tungsten (W). The crystal structure and lattice parameters of each material are shown in Table 1. The orientation of the substrate was (1 0 0) for bcc and (0 0 0 1) for hcp. For tantalum, crystal planes of (1 0 0), (1 1 0), (1 1 1) were discussed in this study.  
30 atoms/ps, 10 atoms/ps, 7 atoms/ps, 5 atoms/ps, 3 atoms/ps, and 1 atom/ps, which is larger than the actual process. The results of the copper seed layer with respect to the deposition rate are discussed in the following section.
## 4 Results
### Validation of Phenological Estimates
Figure 8: Comparison between field-referenced phenology and satellite-predicted phenological phases for (**a**) winter wheat (**b**) corn, and (**c**) sugar beet. The grey dashed lines represent the 1:1 lines, and the two black dashed lines are the deviation of \(\pm 10\) days from a perfect prediction. Figure 9: Predicted parcel-scale crop phenophases for three crop-specific stages (Harvest for winter wheat, Closed Stand for sugar beet, and Emergence for corn) aggregated into hex cells in the left panel. The middle and right panels display the locations of three example parcels (Each parcel is limited by a red outline) along with their _NDVI_ temporal profiles. The vertical line marks the reference DOY, while the dashed line shows the satellite DOY estimates. Red indicates harvest, green represents closed stand, and magenta denotes emergence. In the various produced phenology maps either at the field level or state level, we can observe the differences in colour, indicating that the DOY to reach each phenological stage varies. There was less spatial variability observed for winter wheat and sugar beet with a certain continuity on the map, suggesting that most areas and their surrounding regions experience similar phenological stages. However, more spatial variability has been observed for corn, where this variation in space may be attributed to the difference in transplantation dates and management practices and also to soil and drainage properties. Exemplary temporal profiles for selected sample fields that have a BBCH observation, along with estimated phenophases, are also presented in Figure 9. This further illustrates the temporal effectiveness of our optimisation process in accurately identifying the correct phenophases.
## 4 Conclusions  
The temporal decoherence of the echo induced by turbulence causes phase fluctuations and introduces an extra random phase, which directly affects the phase stability of the echo, resulting in imperfect performance in the ISAL imaging process. In this paper, we studied systematically the adverse effects of atmospheric turbulence on the ISAL imaging quality. We used the ILPS method to simulate dynamic evolution of atmospheric turbulence and defined the atmospheric coherent time to describe the variation speed of the phase fluctuations induced by atmospheric turbulence.  
The main results can be summarized as follows. The TMC algorithm can effectively compensate for the phase fluctuation induced by atmospheric turbulence when \(\tau_{c}\gg\tau_{T}\). The TMC algorithm can compensate for the turbulence-induced phase fluctuations in the cross-range dimension but cannot compensate for those in the range dimension. This means that with TMC the relationship between the pulse duration time and atmospheric coherent time, rather than between the atmospheric coherent time and ISAL imaging time, ultimately determines the ISAL imaging quality in turbulent conditions. For a certain range resolution, reducing the pulse duration time could overcome the adverse effect of atmospheric turbulence. In addition, decreasing the ISAL imaging time \(\tau_{T}\) reduced the cross-range resolution, and it could not overcome the adverse effect of turbulence. Therefore, these adverse effects of atmospheric turbulence can be mitigated by increasing the atmospheric coherent time or decreasing the pulse duration time, and the ISAL imaging quality can be enhanced.  
Figure 8: ISAL imaging with PSLR values. (**a**) The single-point ISAL imaging with TMC + RMC compensation and (**b**–**d**) The PSLR values at different atmospheric coherent times, pulse duration times, and ISAL imaging times, respectively.  
**Author Contributions:** [PERSON] and [PERSON] conceptualized the study and contributed to the article's organization; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the discussion of the simulation results; [PERSON] drafted the manuscript, which was revised by all authors. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (grant number 11904369) and HFIPS Director's Foundation (grant number YZJ)2023 QN05).  
**Data Availability Statement:** The next steps are also based on this research, therefore it is not appropriate to share procedures or data at this time.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Future Directions
### Sustainable Networks  
Many resources are limited in nature, such as the RF spectrum. It is vital that all resources are used efficiently and are sustainable globally. The sustainable use of resources in the digital world is also very important. In a world experiencing a global energy crisis, the use of energy-efficient networks is, of course, one of the inevitable priorities. Resource management, power consumption, and the optimization of all processes in telecommunication networks will help to make the sustainability goals realistic. 6G aims to increase energy efficiency by 100 times (bits per Joule) and keep the total energy consumption lower than the previous mobile generation. In addition, improvements should also be made in technologies on both the hardware and software sides in order to make energy efficiency goals a reality. Finding innovative solutions for reduced power amplifier (PA) optimization becomes a challenge, as spectra such as THz using higher frequencies will be used [37]. There are various studies to increase sustainability in the digital world [131; 132; 133]. The studies conducted show that AI has a positive effect on 134 of the sustainability goals, that is, 79% [134]. Thanks to the use of AI technologies, the use of energy resources at a more optimal level acts as a catalyst for the realization of sustainable development goals such as lower carbon cities. However, the increase in data providing resources in AI is increasing logarithmically every two-three months. This increasing data potential also brings new challenges to be overcome, such as compressing, storing, and processing. In order to develop more environmentally friendly and human-friendly technologies in the future, there is a need for deep studies on resources, energy, traffic, routing approaches, data compression, processing, and the development of new hardware in 6G and satellite networks [78].
## 3 Approach
### Rail Track Topology Reconstruction
#### 3.2.1 Coordinate System Setup
(**b**) Depicts the transformation relationships among these coordinate systems, where the information from images and point clouds is projected onto the railway coordinate system through BEV projection and extrinsic parameter transformations. The subsequent detection algorithm and the corresponding output results are based on the rail coordinate system as the reference coordinate system.