esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Distinguish the Types of Disturbances  
The normalized burn ratio (NBR) [36, 37] in form is a modification of the NDVI, except that it uses near-infrared (NIR) and a shortwave-infrared (SWIR) bands. The NIR band is sensitive to the chlorophyll content of the vegetation, while the SWIR band responds to the soil moisture and vegetation water content. Healthy vegetation has very high NIR reflectance and low reflectance in the SWIR portion of the spectrum. NBR is calculated as follows  
\[\text{NBR}=\frac{\text{B5}-\text{B7}}{\text{B5}+\text{B7}} \tag{5}\]  
A high NBR value generally indicates healthy vegetation, while a low value indicates bare ground and recently burned areas. This ratio spectral index shows a significant decrease following a burn and provides good burned-unburned discrimination using MODIS data.  
Figure 3: Using the season-trend model to simulate GLASS LAI time series compared to the previous two years LAI values in 2003. (**A**) multiplied the coefficient 4/3, (**B**) is 1, (**C**) is 2/3, and (**D**) is 1/3. Where the red line is the curve of the amplitude of the parameter c, the purple line is the amplitude curve of b.  
On 5 May 2003 there was a large forest fire across the Jinhe forestry region, and the NBR was calculated using the surface reflectance data of the fire pixels. The time series curve of the NBR is shown in Figure 4A. It can be seen at the end of May 2003 that the NBR experienced a sharp decline from around 0.4 to \(-\)0.3, and the NBR of the non-fire pixels were always above 0.2 (Figure 4B). This characteristic abrupt decrease in NBR is the primary indicator used within the algorithm to identify burned areas. Combined with the first four steps of the detection results, when the pixel is labeled as disturbance and during this period of time NBR calculation results are less than 0, the candidate pixel was identified and labeled as burned. This algorithm attempts to distinguish the types of forest disturbances.
## 3 GNSS Data and Results
### Corfu-Paxoi Islands
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Latitude** & **Longitude** & **Period** & \(\mathbf{V_{East}}\) & \(\mathbf{V_{North}}\) & \(\mathbf{V_{Up}}\) \\  & **(°)** & **(°)** & & **(mm/Year)** & **(mm/Year)** & **(mm/Year)** \\ \hline  & & & 15 March 2017–17 August & \multirow{4}{*}{17.14 \(\pm\) 0.08} & \multirow{4}{*}{3.32 \(\pm\) 0.07} & \multirow{4}{*}{1.01 \(\pm\) 0.28} \\ FISK & 38.4997 & 20.5771 & & 2020 & & \\  & & & 3.49 years & & & \\ \hline  & & & 15 March 2017–18 May & \multirow{4}{*}{19.81 \(\pm\) 0.07} & \multirow{4}{*}{4.79 \(\pm\) 0.04} & \multirow{4}{*}{\(-\)2.03 \(\pm\) 0.19} \\ ASSO & 38.3778 & 20.5417 & & 4.18 years & & \\ \hline  & & & 1 January 2009–25 January & \multirow{4}{*}{17.10 \(\pm\) 0.04} & \multirow{4}{*}{4.27 \(\pm\) 0.03} & \multirow{4}{*}{\(-\)0.70 \(\pm\) 0.09} \\  & & & 2014 & & & \\  & & & 5.07 years & & & \\  & & & 8 February 2014-4 & \multirow{4}{*}{14.07 \(\pm\) 0.13} & \multirow{4}{*}{\(-\)1.49 \(\pm\) 0.07} & \multirow{4}{*}{0.44 \(\pm\) 0.25} \\ VLSM & 38.1768 & 20.5886 & & September 2016–25 & \multirow{4}{*}{16.10 \(\pm\) 0.03} & \multirow{4}{*}{\(3.15\pm\) 0.03} & \multirow{4}{*}{\(-\)1.20 \(\pm\) 0.09} \\  & & & 2.57 years & & & \\  & & & 5 September 2016–31 & \multirow{4}{*}{16.78 \(\pm\) 0.19} & \multirow{4}{*}{2.17 \(\pm\) 0.18} & \multirow{4}{*}{2.48 \(\pm\) 0.71} \\  & & & October 2021 & & & \\  & & & 5.16 years & & & \\ \hline  & & & 1 May 2016–31 October & \multirow{4}{*}{16.97 \(\pm\) 0.03} & \multirow{4}{*}{1.59 \(\pm\) 0.03} & \multirow{4}{*}{\(-\)3.57 \(\pm\) 0.10} \\ ARGO & 38.1690 & 20.4925 & & 2021 & & \\  & & & 5.50 years & & & \\ \hline  & & & 6 May 2014–16 November & \multirow{4}{*}{14.69 \(\pm\) 0.02} & \multirow{4}{*}{\(-\)1.80 \(\pm\) 0.02} & \multirow{4}{*}{\(-\)1.52 \(\pm\) 0.07} \\ KARA & 38.1308 & 20.5843 & & 2015 & & \\  & & & 1.53 years & & & \\ \hline  & & & 6 February 2016–31 & \multirow{4}{*}{16.78 \(\pm\) 0.19} & \multirow{4}{*}{2.17 \(\pm\) 0.18} & \multirow{4}{*}{2.48 \(\pm\) 0.71} \\ SISS & 38.1009 & 20.6594 & & October 2021 & & \\  & & & 5.73 years & & & \\ \hline  & & & 4 March 2015–13 August & \multirow{4}{*}{16.78 \(\pm\) 0.19} & \multirow{4}{*}{2.17 \(\pm\) 0.18} & \multirow{4}{*}{2.48 \(\pm\) 0.71} \\ SKAL & 38.0746 & 20.7937 & & 2021 & & \\  & & & 4.45 years & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Velocity components for the Cephalonia GNSS stations (ITRF 2014). stations (KASI and KERK), the horizontal velocity components are almost parallel, with the southern site exhibiting a slightly increased eastward motion. The most noticeable aspect is the significant subsidence that occurs at the KERK and PAXO stations.
## 4 Simulations and Results
### Performance Under Different Network Sizes  
The network size is another important factor that affects the performance of the proposed protocol because it determines the duration of the TDMA based handshaking. The performance of SFAMA, ROPA, DSCT and that of the proposed protocol under different network sizes are shown in Figure 9. The packet size is set to 400 bytes and the offered load is set to 5.0 pkt/s in the simulations.  
Figure 9a shows that the throughputs of SFAMA, ROPA and DSCT rise as the network size increases and become saturated at the network size above 10. The throughput of the proposed protocol is much higher than SFAMA, ROPA and DSCT. This means that the pipeline transmission and collision-free parallel handshaking and data transmission help to increase the network throughput. The throughput curve of the proposed protocol also shows a different trend. It decreases slightly as the network size increases. This is because when the network size increase, the duration of handshaking is prolonged. On the other hand, the optimization of the transmission schedules is not so effective as the node number increases, which will causes more vacant time in the transmission and decreases the transmission efficiency.  
Figure 9: Performance of the SFAMA, ROPA, and DSCT protocols and that of the proposed method under different network sizes. (**a**) throughput; (**b**) packet delivery rate; (**c**) average end-to-end delay; and (**d**) average energy consumption.  
In Figure 9b, the delivery rates of SFAMA, ROPA and DSCT decrease as the network size increases because the network load increases with the network size. The delivery rate of the proposed method is nearly 100% for in all the simulations due to the collision-free data transmission.  
Figure 9c shows the average end-to-end delays of the four protocols under different network sizes. The average delays of all protocol rise as the network size increases. DSCT and the proposed protocol give the lowest average delay when the node number is two, while the former maintains the lowest average delay among the four protocols and the latter increases quickly as the node number increases. The proposed protocol gives the highest average delay when the node number is larger than eight. This disadvantage is caused by the TDMA based handshaking.  
Figure 9d shows the average energy consumption of the four protocols under different network sizes. The average energy consumption of SFAMA, ROPA, and DSCT become larger as the network size increases. This is because the useless handshake attempts and data collisions increase as the network size increases under the same offered load. It can also be noticed that the average energy consumption decreases as the transmission parallelism increases for SFAMA, ROPA, and DSCT. The average energy consumption of the proposed protocol is the lowest among the four protocols and maintains the same level for different node numbers. This is because that there is no energy wasted due to collision in the propose protocol.
## 5 Materials and Methods  
Both experiments and simulations are important in detecting breast cancer because they serve different purposes and can provide complementary insights into the disease. Experiments involve collecting data from real-world observations or measurements, such as imaging techniques such as thermography. This data can provide indicative evidence of the possibility of the presence or absence of cancerous cells or tumors in the breast tissue, which can then be used to make a diagnosis. Simulations, on the other hand, involve creating computer models of breast tissue and simulating the behavior of cancerous cells or tumors in that model. Simulations can help researchers and doctors to understand the underlying mechanisms of cancer growth and to test the effectiveness of new diagnostic methods in a controlled and repeatable environment. By combining data from experiments with insights from simulations, researchers and doctors can gain a more comprehensive understanding of breast cancer and develop more effective diagnostic strategies. Capitalizing on the above argument, the research presented in this manuscript follows both approaches. First, a simulation model for breast cancer was developed using COMSOL. The developed model was designed such that the thermophysical properties of different breast sizes can be studied. An analysis is then followed to examine how changes in tumor location depth, size, metabolism, blood flow, and heat conductivity affect the different-sized breast skin surface temperature. The details of the breast model used in the COMSOL and the varied thermophysical properties studied are presented below in Sections 5.1 and 5.2. Second, an experiment to emulate the presence and absence of breast cancer was conducted using a silicon breast mounted on a mannequin and fitted with a LED light bulb. Using the assumption that the presence, absence, shape, and size of the tumor, as well as the thickness of the different layers beneath the breast skin, are represented by the amount of heat generated from the LED lying deep underneath the silicon breast. The exact location of the LED, however, is the exact same location assumed in the COMSOL simulation. By carefully varying the intensity of the LED light, through a voltage control circuit, the equivalent heat level is generated and detected using the FLIR One Pro thermal camera mounted in front the silicon breast at a one-meter distance. More details of the experiment are presented in Section 5.3. Finally, the heat detected from the emulation experiment is compared to the one produced by the COMSOL simulation model as a function of tumor size and location. This has facilitated detecting the minute temperature contrast attributed to the different thermophysical properties of the breast, as discussed below in Section 6.
# Exploring the Key Characteristics and Theoretical Framework for Research on the Metaverse
## 1 Introduction
### Major Contributions  
This study makes several major contributions to the understanding and development of the Metaverse:  
1. Comprehensive Review: It presents a comprehensive and in-depth review of the theories and practices of the Metaverse, tracing back its origins, definitions, and main ideas, which provides a strong theoretical background for future research;
2. Theoretical Framework: This study proposes a new theoretical framework that encompasses the \"Rule of Law\", \"Rule of Virtual-Reality Integration\", and \"Rule of Technological Association\", offering a clear and comprehensive perspective to understand and study the Metaverse;
3. Empirical Case Study: Through a case study on the Sandbox Metaverse platform, this study offers practical insights into the operational mechanisms of the Metaverse, demonstrating how key features are realized and what challenges are faced and validating the proposed theoretical framework;
4. Addressing Social, Economic, and Legal Issues: The study explores the often neglected social, economic, and legal aspects of the Metaverse, presenting initial thoughts and suggestions for rational decision making regarding these issues;
5. New Perspective: By considering the Metaverse as an open, decentralized, self-adjusting ecosystem centered around user-generated content and interaction, this study introduces a novel perspective that broadens the understanding of the Metaverse and offers innovative ideas for its development;
6. Identifying Future Research Needs: The study acknowledges the existing challenges and identifies areas where further research is required, including data privacy, platform ecosystem establishment, and balancing real-world and Metaverse relationships.
## 4 Monte Carlo Q-Value Function
### QMC
```
Input: joint action-observation history \(\vec{\theta}^{t}\) at \(t\), joint action \(\vec{a}\) Output: Monte-Carlo Q-value function \(\text{Q}_{\text{MC}}(\vec{\theta}^{t},\vec{a})\)
1:\(K\leftarrow\text{adaptSet}(numA,t)\)//adaptive setting of sample
2:\(immR\leftarrow\text{ComputeLmmR}(\vec{a})\)
3:\(t\leftarrow\vec{\theta}^{t}\)-timestep
4:for\(k<K\)do
5:\(\mathit{f}uthR\gets 0\)
6:for all\(\vec{\sigma}\in O\)do
7:\(\vec{\theta}^{+1}\leftarrow(\vec{\theta}^{t},\vec{a},\vec{\sigma})\)
8:\(\mathit{f}utR+=\ \gamma\cdot P(\vec{\theta}^{+1})\cdot Q^{*}(\vec{\theta}^{+1},t+1)\) //compute future reward
9:return\(immR+\mathit{f}utR\)
```  
**Algorithm 1**\(\text{Q}_{\text{MC}}(\vec{\theta}^{t},\vec{a})\)  
```
Input: joint action-observation history \(\vec{\theta},\text{ time step }t\) Output: the optimal reward at \(\vec{\theta}\)
1:if\(\vec{\theta}\) has unexpanded actions then
2: return Rollout(\(\vec{\theta},t\)) //calling Rollout function
3:else
4:\(bestA\leftarrow\underset{\vec{a}\in A}{\arg\max}(immR+\mathit{f}utR+c\sqrt{ \frac{2\ln N(\vec{\theta})}{N(\vec{\theta},\vec{a})}})\)
5:\(immR\leftarrow\text{ComputeLmmR}(bestA)\)
6:if (\(\
eg\;laststep\)) then
7:for all\(\vec{\sigma}\in\ O\)do
8:\(\vec{\theta}^{t}\leftarrow(\vec{\theta},bestA,\vec{\sigma})\)
9:\(\mathit{f}utR+=\ \gamma\cdot P(\vec{\theta}^{t})\cdot Q^{*}(\vec{\theta}^{t},t+1)\)
10:\(N(\vec{\theta})\gets N(\vec{\theta})+1,N(\vec{\theta},bestA)\gets N( \vec{\theta},bestA)+1\ \ //\text{backpropagation}\)
11:\(Q(\vec{\theta},bestA)+=\ \frac{1}{N(\vec{\theta},bestA)}(immR+\mathit{f}utR-Q( \vec{\theta},bestA))\)
12:return\(Q(\vec{\theta},bestA)\)
```  
**Algorithm 2**\(\text{Q}^{*}(\vec{\theta},t)\)
## 2 Background and History of MIPs
When considering the nature of the imprinted template, the process for small molecules is somewhat robust and reliable. For macromolecules, however, due to their conformational flexibility, suboptimal selectivity and affinity of the resulting imprinted sites are often reported. Moreover, template-assisted imprinting of exotic biomacromolecules may be excessively costly, whereas, removing from or rebinding by the imprinted polymer large, bulky macromolecules is also accompanied by severe limitations. To address these shortcomings, alternative methods, such as epitope and surface imprinting, have been proposed and developed over the last two decades in MIP-based purification and sensing [17; 18; 19]. Evidently, sustained efforts are also being made towards rational approaches in selecting the most suitable and representative short sequence of oligopeptide (epitope) as a template equivalent with the ability to efficiently and reproducibly generate selective binding sites with homogenous affinity towards the native protein target [20]. The first designed MIPs for drug delivery applications were based on imprinting theophylline with methacrylic acid (MAA) as the functional monomer and EGDMA as the cross-linker [21]. In addition to the good recognition properties for theophylline, compared to its structural analogue caffeine, these MIPs could prolong the release of theophylline [5]. The first MIP to be administered and studied in vivo conditions was designed by [PERSON] et al. in 2010. Their research group developed a MIP-based platform for the recognition, neutralization, and removal of peptides [22]. Additionally, the first MIP that has been specifically used for drug delivery and studied in vivo on mice was developed by [PERSON] et al. [23]. Their work, published in 2015, focused on the therapy of the _Helicobacter pylori_ infection treated by amoxicillin-loaded MIPs [23]. In recent years, an increasing trend in the administration and study of MIPs in living organisms is observed. However, the issue of their biocompatibility is still a subject of controversy, and their long-term safety is yet to be evaluated and discussed.
## 5 MTTF Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Redundant-Switch Configuration**} & \multicolumn{3}{c}{**Operation Mode**} \\ \cline{2-4}  & **Three-Phase** & **Two-Phase** & **Single-Phase** \\ \hline Parallel & 0.248 & 0.2847 & 0.0826 \\ Standby & 0.2709 & 0.1602 & 0.0893 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Defuzzified MTTF analysis (\(10^{6}\) h). Figure 8: Fuzzy curves related to three-phase converter (**a**) failure rate of a switch in parallel-switch configuration, (**b**) failure rate of a switch in standby-switch configuration, (**c**) failure rate of parallel-switch configuration, (**d**) failure rate of standby-switch configuration, and (**e**) MTTF of three-phase converter with parallel-switch and standby-switch modules. According to Table 2, it can be concluded that:  
* Standby-switch-based converter is suitable for three-phase and single-phase operation mode. * The MTTF of the three-phase converter is lower than that of the parallel-switch-based two-phase converter. Hence, the parallel-switch-based two-phase converter offers the highest reliability level. For instance, the overall MTTF of the standby-switch-based converter in three-phase operation mode is 0.279 (10\({}^{6}\) h). This means that, regarding all the uncertainties, the mean life span of the converter in this operation mode is approximately 32 years ((0.297 \(\times\) 10\({}^{6}\))/(365 \(\times\) 2)). It is worthwhile to show the reliability of the redundant-switch converters as a function of time. To do so, the reliability curves of the converters, with respect to the obtained fuzzy MTTF memberships, against the life span are plotted in Figure 9. In these figures, the reliability functions with their fuzzy weights are shown. The reliability curves of the parallel-switch-based converters (blue curves) are in a wider range than that of the standby-switch-based ones (red curves). Figure 9: Life span and reliability of the redundant switch configurations. (**a**) Parallel-switch-based single-phase converter, (**b**) standby-switch-based single-phase converter, (**c**) parallel-switch-based two-phase converter, (**d**) standby-switch-based two-phase converter, (**e**) parallel-switch-based three-phase converter, (**f**) standby-switch-based three-phase converter.
# On IRS-Assisted Covert Communication with a Friendly UAV
## 6 Conclusions  
In this paper, we have proposed an IRS-assisted covert communication system including a friendly UAV deployed in complex environments with communication barriers. The closed form of the DEP of covert communication for [PERSON] is derived by considering [PERSON]'s uncertainty about the transmitting power. To maximize the covert rate, we formulate the joint optimization problem by adjusting the jamming power of the UAV and the transmitting power of the transmitter, where the optimal DEP for [PERSON], the transmitting power of the transmitter, and the transmitting power of the AN are established as the constraints. An iterative algorithm based on Dinkelbach is designed to solve the established optimization problem. Our simulation results demonstrate that the covertness and covert  
Figure 5: The covert rate achieved by the system as \(\varepsilon\) varies, where \(\sigma_{w}^{2}\) and \(\sigma_{B}^{2}\) denote the noise variance at [PERSON] and [PERSON], respectively.  
Figure 6: The covert rate achieved by the system as the transmission power \(P_{max}\) increases, where \(\hat{P}_{u,\max}\) and \(T\) denote the maximum AN power and the flying time of UAV, respectively.  
rate of the system can be improved by increasing the number of IRS, the UAV flying time, and the interference power.  
This work serves as a first foray into the design of a covert communication system assisted by IRS and UAVs. Many interesting directions follow from this work and are deserving of further investigation. First, covert wireless communication aided by UAVs can serve multiple users. How to extend the traditional covert wireless communication model to one with more receivers of covert messages and more monitors remains an open issue. Second, because the base station is equipped with multiple antennas, different ways of selecting more than one antenna to transmit covert messages from among all antennas is a matter that requires further investigation. Third, in order to be deployed in complex environments, the proposed covert communication system with IRS and UAVs could be extended to include scenarios in which, in addition to interfering with [PERSON]'s attempts to detect covert communication, the UAV is able to forward covert messages from [PERSON] to [PERSON] as well.  
Conceptualization, Y.Q. and S.Y.; methodology, X.X.; software, L.H.; validation, X.X., L.H. and S.W.; formal analysis, X.X.; investigation, Y.Q.; resources, F.S.; data curation, L.H.; writing--original draft preparation, L.H.; writing--review and editing, X.X.; visualization, S.W.; supervision, F.S.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2020 YFB2104200).  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Multiple-Mode Orthogonal Time Frequency Space with Index Modulation
## 2 System Model
### The Receiver
Finally, the modulation bits and index bits of the subblock are recovered by the modulation information matrix \(\mathbf{B}\) and index mapping information \(\hat{\mathbf{S}}_{w^{\beta}}\). ```
1:\(\hat{X}^{DD}_{\beta}\), \(m\), and \(n\)
2:\(\hat{p}_{1}\) and \(\hat{p}_{2}\)
3:\(X_{\beta}=vec\{\hat{X}^{DD}_{\beta}\}=\{X_{\beta,1},...,X_{\beta,mn}\}\)
4:for\(i=1:mn\)do
5:for\(j=1:mn\)do
6:\(B_{i,j}=\text{demod}\{S_{j},X_{\beta,i}\}\)
7:\(D_{i,j}=\text{abs}\big{(}\text{mod}\{S_{j},B_{i,j}\}-X_{\beta,i}\big{)}\)
8:endfor
9:endfor
10:\(\hat{\mathbf{S}}_{w^{\beta}}=\mathbf{Hungarian}(\mathbf{D})=\left\{\hat{S}_ {w^{\beta}_{1}},...,\hat{S}_{w^{\beta}_{mn}}\right\}\)
11:\(\hat{p}_{2}=\left\{B_{1,k_{1}},\cdots,B_{1,k_{mn}}\right\}\)
12:\(\hat{p}_{1}=\text{Indexdemod}\big{(}\hat{\mathbf{S}}_{w^{\beta}}\big{)}\)
```  
**Algorithm 1** Detecting algorithm.