esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing of Forest Burnt Area, Burn Severity, and Post-Fire Recovery: A Review
## 2 Materials and Methods
### Data Compilation  
Finally, 329 papers were shortlisted according to their suitability to the review and fitting of the search criteria, and organized in the reference management Mendeley platform ([[https://www.mendeley.com/](https://www.mendeley.com/)]([https://www.mendeley.com/](https://www.mendeley.com/)), accessed on 10 May 2022) to conduct further meta-analysis. Details from the selected pool of publications were then extracted and compiled in several different categories related to geographic region (FAO Global Ecological Zone) of the study area, sensor used, spatial extent of the forest fire, temporal scale of the study, type of burnt forest (main  
Figure 1: The schematic diagram of the papers selection order from 2000 to 2020 in the field of remote sensing of forest BA, BS, and post-fire recovery in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework.  
tree species), classification and modelling approaches, vegetation indexes, accuracy method used, year of the publication, and reference (validation) data. The complete list of described categories for each selected paper is provided in the Supplementary Materials (Table S1).
# Virtual Machine Migration Strategy Based on Multi-Agent Deep Reinforcement Learning
## 2 Related Works  
Service placement has been widely studied in the paradigm of cloud computing. In MEC, a key challenge in achieving efficient service placement is to consider device mobility. In the existing research, some work is based on the prediction of future information. For example, in Reference [13], the author solved the tradeoff between execution overhead and delay by using mobility-based prediction schemes, and proposes a data transfer throughput estimation scheme (DTT), MDC handoff time estimation scheme (HTE) and service migration management scheme (SMM). In addition, the author further studies how to place services by predicting the costs of future data transmission, processing and service migration in Reference [14]. In Reference [15], the author proposed a FMeC-based framework in the autopilot scenario, which is based on the analysis of vehicle movement patterns and makes a tradeoff between reducing service migration costs and maintaining end-to-end QoS. The proposed scheme meets the delay requirements of autopilot and minimizes the overall cost.  
However, it is not realistic to accurately predict future information such as user mobility in the real environment. In order to meet the challenge of user mobility that is unpredictable in practice, another recent work applied the Markov Decision Process (MDP). In Reference [16], through Markov chain analysis of whether to migrate services, the author discusses the impact of service migration on mobile users' perceived delay. In References [17,18], the authors determine the optimal threshold of service migration based on MDP. In addition, the author in Reference [19] extended the service migration algorithm to adapt to 2D migration scenarios. In Reference [20], the optimal service migration strategy is designed by defining the service placement problem as a sequential decision problem. The above studies are based on the centralized system model, and the global information of the network needs to be predicted when solving the scheduling strategy. However, MEC is a highly distributed computing environment; obtaining the status of all servers may consume a lot of network resources, and cannot ensure the real-time performance of the migration strategy. In order to solve the above problems, this paper constructs a distributed migration model and proposes a VM migration strategy based on MADRL. The migration process is mainly through live VM migration technology, and live migration is a key function of system virtualization technology [21]. The methods of centralized training and distributed execution are adopted to alleviate the communication bottleneck, speed up the perception of the current environment, and generate the migration strategy more quickly.
# Optimization-Based Approaches to Control of Probabilistic Boolean Networks
## 3 Polynomial Optimization Approach
### Transformation of a PBN into a Polynomial System  
Consider modeling the dynamics of the expected value of the state by a polynomial system. First, the relation between a Boolean function and a polynomial is explained as a preparation. Next, the result for a general PBN is explained. Finally, a simple example is shown.  
As a preparation, the following lemma [48] is introduced.  
**Lemma 1**: _Consider two binary variables \(\delta_{1}\) and \(\delta_{2}\). Then, the following relations hold:_  
1. \(\
eg\delta_{1}\) _is equivalent to_ \(1-\delta_{1}\)_,_
2. \(\delta_{1}\wedge\delta_{2}\) _is equivalent to_ \(\delta_{1}\delta_{2}\)_,_
3. \(\delta_{1}\vee\delta_{2}\) _is equivalent to_ \(\delta_{1}+\delta_{2}-\delta_{1}\delta_{2}\)_,_
4. \(\delta_{1}\oplus\delta_{2}\) _is equivalent to_ \(\delta_{1}+\delta_{2}-2\delta_{1}\delta_{2}\)_._  
By Lemma 1, a given Boolean function can be transformed into a polynomial on the real number field. For example, \(\delta_{1}\vee\
eg\delta_{2}\) is equivalently transformed into \(\delta_{1}+(1-\delta_{2})-\delta_{1}(1-\delta_{2})=1-\delta_{2}+\delta_{1} \delta_{2}\).  
Next, a recursive representation of a general PBN is explained. Let \(\hat{f}^{(i)}\) denote the polynomial that can be obtained from the Boolean function \(f^{(i)}\). Let \(\hat{f}^{(i)}_{j}\) denote the polynomial that can be obtained from the Boolean function \(f^{(i)}_{j}\). Hereafter, for simplicity of notation, the condition in \(E[x_{i}(k)|*]\) is omitted.  
In [24], the following theorem was obtained.  
**Theorem 1**: _Suppose that, for the PBN (1), the initial state \(x(0)=x_{0}\) is given. Then, the expected value of the state, \(E[x_{i}(k)]\), is expressed as the following polynomial system:_  
\[\Sigma_{i}:\ E[x_{i}(k+1)]=\sum_{j=1}^{l(i)}c^{(i)}_{j}  
_In a similar way, \(E[x_{2}(1)]\) and \(E[x_{3}(1)]\) can be obtained as_  
\[E[x_{2}(1)] = x_{1}(0)(1-x_{3}(0))\] \[= 0,\] \[E[x_{3}(1)] = 0.7\{x_{1}(0)(1-x_{2}(0))\}+0.3(x_{2}(0)u(0))\] \[= 0,\]  
_respectively._  
_Next, suppose that \(u(1)\) is given by \(u(1)=0\), and consider deriving \(E[x(2)]\). Then, noting that a Boolean function is independently switched for each state, we can obtain_  
\[E[x_{1}(2)] = E[f^{(1)}(x(1),u(1))]\] \[= 0.8E[f^{(1)}_{1}(x(1),u(1))]+0.2E[f^{(1)}_{2}(x(1),u(1))]\] \[= 0.8E[x_{3}(1)u(1)]+0.2E[1-x_{3}(1)]\] \[= 0.8E[x_{3}(1)]u(1)+0.2-0.2E[x_{3}(1)]\] \[= 0.2,\] \[E[x_{2}(2)] = E[f^{(2)}(x(1),u(1))]\] \[= 1.0E[x_{1}(1)(1-x_{3}(1))]\] \[= E[x_{1}(1)]-E[x_{1}(1)]E[x_{3}(1)]\] \[= 0.2,\] \[E[x_{3}(2)] = E[f^{(3)}(x(1),u(1))]\] \[= 0.7E[x_{1}(1)-x_{1}(1)x_{2}(1)]+0.3E[x_{2}(1)u(1)]\] \[= 0.14.\]  
_By recursively repeating, we can obtain \(E[x(k)]\), \(k\geq 3\)._  
From this example, we see that the expected value of the state can be expressed as a polynomial system using Theorem 1.
## 3 Classification
### Type of Applications
#### 3.4.1 Safety Applications  
Safety applications are the V2P crash prevention systems. There may be multiple V2P safety applications that may each address different types of VRU, pre-crash scenarios, and vehicles. Table 1 lists various V2P safety efforts that target different types of VRUs. There have also been efforts to deploy applications for specific groups of pedestrians and vehicles [46; 47]. _Mobile Accessible Pedestrian Signal System_ is an application deployed for visually impaired pedestrians for signalized street crossing scenario [46]. _Pedestrian in Signalized Crosswalk Warning_ is a V2P safety application that warns public transport bus operators about the pedestrians that are in the path of the bus at signalized intersection [47].
# Distributed Learning for Dynamic Channel Access in Underwater Sensor Networks
## 6 Performance Evaluations
### Performance Evaluations  
Figure 4 presents the performance comparison of the proposed algorithm and the baseline schemes described above. Figure 4 illustrates that the proposed algorithm performs much better than the random algorithm. On the other hand, the performance of the proposed algorithm is lower than those of the exact CEP algorithm and optimal algorithm. Nevertheless, we emphasize that, to implement the exact CEP algorithm at each sensor, the exact information on the CEPs for all available channels should be notified, which might be impractical in the case of UASNs. In contrast, even though the proposed algorithm only exploits two-bit feedback information on the previous transmission result to estimate their CEPs, its performance is almost similar to that of the exact CEP algorithm. Moreover, the proposed algorithm shows at least 80 % performance, when compared to the performance of the optimal algorithm, which requires a fully centralized entity for assigning the channels to each sensor. These results illustrate the practical validity of the proposed algorithm.  
Figure 5 shows the performance of each underwater sensor under the application of the proposed algorithm under \(|K|=2\). From Figure 5, it can be observed that, despite performing learning in a distributed manner, each sensor occupies the channel separately to avoid collisions, which results in the improvement of the total network throughput.  
Figure 3: Illustration of the performance of the proposed algorithm.  
Figure 6 shows the performance comparison between the proposed and slotted ALOHA in considered UASNs with \(|K|=1\). Under the slotted ALOHA, the number of backlogged queues in a data sink cannot exceed the number of associated sensors in a data sink, such that we set the channel access probability as \(1/\left|U\right|\). As such, to implement the slotted ALOHA, the information on the number of underwater sensors associated with a same data sink is needed at each sensor. Figure 6 illustrates that the proposed algorithm outperforms better than the slotted ALOHA, not long after starting the learning of the proposed algorithm. This illustrates the validity of the proposed algorithm.  
Figure 4: Illustration of performance comparison of the proposed and baseline algorithms.
## 4 Conclusions  
HWWC was successfully prepared by a simple magnetic separation method. The XRD and SEM-EDS results revealed that the HWWC consisted of a mixture of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) and \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\). The magnetic saturation of HWWC was sufficient to be separated by conventional magnets, which can facilitate their application for water treatment. The control experiment showed that OTC was removed by the generated radical species when both HWWC and PS were present. SO\({}_{4}\)\({}^{\bullet-}\) was the dominant oxidizing species in the OTC degradation by persulfate activation using HWWC catalyst. Influencing parameters such as HWWC dose, PS concentration, and solution pH were evaluated, and the degradation efficiency of OTC increased with increasing HWWC dose and PS concentration, and the optimal pH values for OTC degradation were 3 and 4. In addition, the HWWC degraded OTC after eight repeat experiments with great stability. Degradation efficiency was significantly affected by NOM present in the secondary effluent, while the effect of chloride ions was negligible. Overall, these results suggest that PS activation using magnetic Fe\({}_{2}\)O\({}_{3}\) catalysts derived from hand-warmer waste could be an effective alternative for removing OTC and other recalcitrant organic compounds in water.  
Figure 8: (**a**) Sequential oxytetracycline (OTC) degradation test ([PS]\({}_{0}\) = 1 mM, [OTC]\({}_{0}\) = 20 \(\mu\)M, [HWWC]\({}_{0}\) = 0.2 g/L, pH = 6); (**b**) OTC degradation in the presence of Cl\({}^{-}\) ([Cl\({}^{-}\)]\({}_{0}\) = 10 mg/L); (**c**) Degradation kinetics of OTC by persulfate activation using HWWC in the secondary effluent (pH = 7.2, [DOC]\({}_{0}\) = 4.71 mg/L, UV\({}_{254}\) = 0.100, SUVA = 2.12). PS: persulfate; HWWC: hand-warmer waste catalyst.  
**Author Contributions:** Conceptualization, Y.-J.L. and C.-G.L.; methodology, Y.-J.L.; validation, Y.-J.L.; formal analysis, Y.-J.L.; writing--original draft preparation, Y.-J.L.; writing--review and editing, C.-G.L., S.-J.P. and E.H.J.; visualization, Y.-J.L.; supervision, C.-G.L., S.-J.P. and E.H.J.; funding acquisition, E.H.J. All authors have read and agreed to the published version of the manuscript.  
This work was conducted with the support of the Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01571602), Rural Development Administration, Korea.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Application of Machine Vision Technology in Citrus Production
## 2 Pest and Disease Detection
### Pest and Disease Identification Based on RGB Image
#### 2.1.2 Deep Learning for Pest and Disease Recognition
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Ref.** & **Year** & **Purpose** & **Methods** & **Category** & **Metis** & **Demerits** & **Accuracy** \\ \hline
[19] & 2022 & Identification of citrus & Improved VGG19-INC model & Huanglongbing, leaf miner, nematode & The weight space & The dataset is & \\  & & Improve the & F-ResNet is a new model obtained & Guangdong, & and the model training & & \\
[20] & 2021 & identification accuracy of & by the fusion of S-ResNet and & Huanglongbing, & & There are few images of & \\  & & citrus diseases & M-ResNet & such & black spot, canker, & black spot, canker, & \\  & & & The improved VGG4 model & & & \\  & & & was used for leaf detection, and & & & \\
[21] & 2021 & intelligent identification & the DenseNet and Xception & Red spider, canker, & High classification & & \\  & & and diseases & models were fused for & leaf miner & accuracy & & \\  & & & pest classification & & & \\
[22] & 2018 & Six diseases were & Detection of disease spots and & Anthracnose, black spot, & Feature fusion improves & & \\  & & & disease classification & & the segmentation & & \\
[23] & 2021 & Identify three & & Leaf miner, sooty mold, & & & \\  & & common pests & Ensemble classifier & Delhiuria & High accuracy & Fewer pest species & 99.04\% \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 7 Discussion
### Aerobot Speech Interaction  
Figure 18 shows the comparison of the speech word (SW) and speech command (SC) trendline characteristic curve. The _red solid line_ represents the SC curve while the _black dash-dot line_ represents the SW curve. Observe that the two curve trends are very similar with the SW curve being below the SC curve, although the SW curve seems to be a replica of the SC curve. The result of the characteristic curve comparison suggests that the speech command phrase slightly outperforms the speech command word, thereby supporting the argument in favour of multi-word commands over single-word commands.  
Based on this analysis and the observations in this research, it is recommended that the upper limit of 75 dB noise application level should be set for practical aerobot speech interaction. This was because, at 75 dB, the speech recognition accuracy/success was about 65%, falling below average at 80 dB, and below 5% at 85 dB. In order words, speech is not an effective means of control interaction with an aerobot beyond 75 dB noise level, as the speech control interaction becomes very unreliable due to poor recognition. However, based on the typical UAV noise level data presented in Table 1, it is clear that this limit is below the application range of most small multirotor UAVs. Therefore, there is a need to push the upper limit beyond 75 dB to atleast 80 dB or more. Therefore, the following suggestions are given in order to improve the aerobot speech application:1. Alternative ASR engine: Consider the use of alternative speech ASR technology with a different learning model from the CMU Sphinx's hidden Markov model, such as artificial neural network or deep learning, for improved noise performance,
2. Low noise design: Improve small multirotor UAV propulsion system design to be low noise,
3. Application environment: Deploy application only in sub-75 dB noise conditions,
4. Speech capture hardware: Consider the use of directional, noise canceling, or array microphones,
5. Speech command selection: Optimise the selection of multi-word and single-word speech command in favour of more resilient command variant, as observed in this particular study.  
In [3], a previous work, it was observed that ambient noise level above 80 dB significantly affected speech capture. While this conclusion still holds true for the current research work, the current research work had more experiment data to analyse in order to more precisely determine the practical application limits, as presented in this section.
## 3 Results
### Test Measurements at Cuiab/Real Scenario
As we can see, the proper operation of a low/medium complexity MODCOD mitigates the harmful effects by maintaining a higher margin than specified during satellite visibility, except for low elevation angles. The throughput for this configuration is about 1.4 times the minimum required of 36.922 Gbits. In this case, no processing failures would be expected for the images. We can recalculate the MODCOD by considering another approach to achieve the maximum throughput on the WFI channel. In return, we will have a high-complexity technology modulator. This selection can be reassessed regularly, considering the reception systems of the ground stations. A low-cost station, for example, might need to use our case solution. On the other hand, when the reception system is composed of a high-performance receiver with high G/T, we can transmit a higher data rate, including other recorded data, as we have a greater throughput capacity. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Elev. (\({}^{\circ}\))** & _I (dBm)_ & _C (dBm)_ & _C/I (dB)_ & **INR (dB) (\({}^{\textbf{(1)}}\))** & **MODCOD (ID)** & \(R_{b}\) (Mbps)** & **Time (\({}^{\textbf{(2)}}\) \%** \\ \hline \(10\) & \(-21\) & \(-20.2\) & \(0.8\) & \(18.97\) & \(9\Rightarrow\)M = \(-1.05\) dB & \(0\) & \(5.88\) \\ \(20\) & \(-32.5\)
## 1 Introduction
The surface free energy test results showed that the addition of carbon nanofibers and carbon nanotubes increased the adhesion free energy of the aggregate and asphalt binder system. The asphalt-aggregate bonding properties and moisture resistance also improved. Therefore, it is feasible to use carbon nanotubes to modify recycled asphalt binder. However, there are few studies on the application of carbon nanotubes in a waste engine oil, recycled asphalt binder, and the influence on the performance of a waste engine oil, recycled asphalt binder is still unknown. On the basis of the related regeneration research carried out by the above researchers, this paper studied the improvement in the performance of a waste engine oil, recycled asphalt mixture modified by carbon nanotubes, which provide a new method for the recycling of old asphalt. This paper prepared a heat-recycled asphalt mixture without RAP, a heat-recycled asphalt mixture with 40% RAP, a waste engine oil, recycled asphalt mixture with 40% RAP, and a carbon-nanotube-modified waste engine oil, recycled asphalt mixture with 40% RAP. The road performance of the RAP heat-recycled asphalt mixture was tested using the high-temperature rutting test, low-temperature bending test and Marshall stability test, and the influence of waste engine oil and different contents of carbon nanotubes on the road performance of the RAP heat-recycled asphalt mixture was analyzed. The experimental flow chart is shown in Figure 1.
## 4 Results and Discussion  
As described in the previous sections, OCS is one of the modern modes of telecommunication networks. In order to communicate over a long distance with optical fiber for high capacity data transmission, the NLEs such as XPM, SPM, and FWM effects dominate. This work developed the theoretical modeling studied in Section 3 to quantify the balancing of FWM. In addition, the FWM is analyzed for various values of input power, length of fiber, nonlinear effective area, nonlinear refractive index, number of channels, and channels spacing. For the model proposed in Figure 1, the performance of the model is evaluated for different lengths of fiber and BER against 2, 4, and 6 dBm transmitted power as is shown in Figure 5.  
The optimum improvement is shown in Figure 6 with managed FWM as compared to without managed FWM. AT 100 km transmission range, \(10^{-1}\) BER is enhanced for managed FWM than without managed FWM. The system performance is analyzed for power penalty and transmission span in terms of 12.5, 25 and 50 GHz channel spacing as described in Figure 6.  
The power penalty of 0.5 dBm is observed between operating with and without managed FWM as shown in Figure 6. Furthermore, narrow channel spacing decreases the system performance due to FWM. Figure 7 demonstrates the FWM for 12.5, 25, and 50 GHz channel spacing, 100 Gbps data rate and 32 channels using nonlinear effective area and BER parameters.  
The system outcomes in the presence of FWM effect are attained below margin line at 80 \(\upmu\)m\({}^{2}\) nonlinear effective area. Moreover, it can be observed from Figure 7 that transmission channels with 50 GHz channel spacing accommodate huge capacity and large propagation cover path due to less FWM as compared to 12.5 and 25 GHz channel spacing. The results between symbol rate and BER are shown in Figure 8 for 12.5, 25, and 50 GHz channel spacing. It also compares the results for 16 and 32 channels at 60 and 100 Gbps data rate. High capacity with low channel spacing (12.5 GHz) transmission produces FWM greater than 25 and 50 GHz channel spacing in LHOCS. Figure 9 includes the proposed model efficiency against FWM in terms of input power and BER for 8, 16, and 32 channel in LHOCS at 25 and 50 GHz channel spacing. This clarifies that 100 Gbps capacity at 25 GHz channel spacing attains BER margin line early as compare to 50 GHz channel spacing based LHOCS.  
Figure 8: Symbol rate against BER for 12.5, 25, and 50 GHz channel spacing.  
Figure 6: Comparison of power penalty as a function of fiber length for with and without FWM using 12.5, 25, and 50 GHz channel spacing.  
Figure 7: 12.5, 25, and 50 GHz channel spacing comparison applying nonlinear effective and BER calculating parameters for 100 Gbps speed and channel =32.  
Figure 11: RF spectrum analyzer. (**a**) Without addressed FWM at 500 km fiber range and 25 GHz channel spacing, (**b**) With addressed FWM at 500 km fiber range and 25 GHz channel spacing.  
Figure 10: Optical received power against BER for analyzing FWM using 400 and 500 km fiber length and 8, 16 and 32 channels.  
Figure 9: Optical input power vs BER for 8, 16 and 32 channels.