esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Urban LULC Prediction
#### 4.3.2 Transition Probability Analysis
As reported by NESDB, the future population in the BMR would reach up to 16,632,220 in 2035. The increase in the rate of the population when compared with the base year 2014 was 0.15 per year. Moreover, the economic growth rate obtained from the GPP change in the same period would approach a value of 0.12 per year. Here, both data were used to derive the transition probability using simple extrapolation and the non-spatial technique of SD. Tables 6 and 7 show the estimated transition values in 2014 and 2035, respectively. Then, the LULC transitions were applied in the CA-MC and MLP-MC environments to generate the transition potential maps in each sub-model for the simulation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Class** & **Agriculture** & **Water** & **Urban** & **Forest** & **Miscellaneous** \\ \hline \multirow{6}{*}{1998} & Agriculture & 0.9486 & 0.0025 & 0.0418 & 0.0032 & 0.0039 \\  & Water & 0.1121 & 0.8490 & 0.0358 & 0.0005 & 0.0026 \\  & Urban & 0.0000 & 0.0041 & 0.9880 & 0.0000 & 0.0079 \\  & Forest & 0.3487 & 0.0073 & 0.0160 & 0.6236 & 0.0044 \\  & Miscellaneous & 0.6223 & 0.0180 & 0.3080 & 0.0000 & 0.0516 \\ \hline \multirow{6}{*}{2003} & Agriculture & 0.9465 & 0.0022 & 0.0451 & 0.0013 & 0.005 \\  & Water & 0.1745 & 0.7905 & 0.0269 & 0.0034 & 0.0047 \\  & Urban & 0.0000 & 0.0032 & 0.9905 & 0.0002 & 0.0061 \\  & Forest & 0.6245 & 0.0016 & 0.0186 & 0.3524 & 0.0029 \\  & Miscellaneous & 0.7160 & 0.0272 & 0.1657 & 0.0067 & 0.0843 \\ \hline \multirow{6}{*}{2008} & Agriculture & 0.9112 & 0.0022 & 0.0682 & 0.0042 & 0.0141 \\  & Water & 0.1277 & 0.8167 & 0.0446 & 0.0051 & 0.0059 \\  & Urban & 0.0000 & 0.0030 & 0.9795 & 0.0011 & 0.0164 \\  & Forest & 0.294 & 0.0010 & 0.0805 & 0.6245 & 0.0000 \\  & Miscellaneous & 0.5427 & 0.0069 & 0.2602 & 0.0024 & 0.1878 \\ \hline \multirow{6}{*}{2011} & Agriculture & 0.9357 & 0.0025 & 0.0458 & 0.0015 & 0.0144 \\  & Water & 0.1349 & 0.8521 & 0.0126 & 0.0005 & 0.0000 \\  & Urban & 0.0000 & 0.0021 & 0.9922 & 0.0002 & 0.0055 \\  & Forest & 0.2310 & 0.0155 & 0.0463 & 0.6426 & 0.0646 \\  & Miscellaneous & 0.3111 & 0.0051 & 0.4806 & 0.0000 & 0.2033 \\ \hline \multirow{6}{*}{2014} & Agriculture & 0.8221 & 0.0097 & 0.1586 & 0.0051 & 0.0045 \\  & Water & 0.1542 & 0.7630 & 0.0650 & 0.0158 & 0.0021 \\ \cline{1-1}  & Urban & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 \\ \cline{1-1}  & Forest & 0.3185 & 0.0159 & 0.0494 & 0.6148 & 0.0015 \\ \cline{1-1}  & Miscellaneous & 0.5736 & 0.0077 & 0.3688 & 0.0142 & 0.0357 \\ \hline \multirow{6}{*}{2035} & Agriculture & 0.1780 & 0.0140 & 0.8023 & 0.0046 & 0.0010 \\ \cline{1-1}  & Water & 0.2400 & 0.0849 & 0.6624 & 0.0111 & 0.0015 \\ \cline{1-1}  & Urban & 0.0000 & 0.0000 & 1.0000 & 0.0000 & 0.0000 \\ \cline{1-1}  & Forest & 0.2504 & 0.0213 & 0.7122 & 0.0147 & 0.0015 \\ \cline{1-1}  & Miscellaneous & 0.1327 & 0.0105 & 0.8525 & 0.0036 & 0.0008 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Transition probabilities of LULC change.
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## 5 Experimental Study
### Impact of Factors  
From the overall performance analysis, we could see that TPEG has great improvement compared with other schemes on the overhead of message transmissions. The advantage lies in the two modes of sensing, as well as message suppression through thresholds. In this subsection, we vary the parameter settings and study their impacts on the performances.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **NAIVE** & **PROPHET** & **ESSMD** & **TPEG** \\ \hline True Positive (\#/ratio) & 42.2/0.2497 & 147.62/0.8242 & 150.63/0.8389 & 147.47/0.8317 \\ True Negative (\#/ratio) & 7.51/0.0444 & 17.32/0.0967 & 16.68/0.0929 & 14.66/0.0827 \\ False Positive (\#/ratio) & 2.11/0.0125 & 3.47/0.0194 & 2.4/0.0134 & 2.81/0.0158 \\ False Negative (\#/ratio) & 117.19/0.6934 & 10.69/0.0597 & 9.84/0.0548 & 12.38/0.0698 \\ Recall Rate (\(p1\)) & 0.2648 & 0.9325 & 0.9387 & 0.9226 \\ Precision (\(p2\)) & 0.9524 & 0.977 & 0.9843 & 0.9813 \\ Transmissions (\(\times 10^{4}\)) & 2.34 & 3421.98 & 2583.36 & 403.26 \\ Average Time (minute) & 36.29 & 28.83 & 30.6 & 33.69 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of algorithms under default parameters.  
Figure 4 shows the impact of the number of nodes on the recall rate, which goes up with the amount of nodes within the network. When there are fewer nodes, e.g., five, many events are not detected, and the recall rate is less than 0.27. This is because there are not many vehicular nodes sensing the events, and consequentially, only a small number of samples is generated and many events missed. As mentioned previously, in the NAIVE scheme, data are uploaded only when a node encounters an RSU, so its recall rate is the lowest, and the increase in the number of nodes has less impact on the rate. When the number of nodes increases, the recall of all the schemes except NAIVE goes up quickly. The recall of TPEG is a little bit smaller than those of PROPHET and ESSMD. When there are more than 60 nodes within the network, its recall rate goes up to about 0.92, which is almost the same as those of PROPHET and ESSMD.
## 3 Results, Analysis, and Simulations
### Simulation Planning Results Based on Link Budget, Coverage, and Capacity Calculations
#### 3.3.1 Scenario 1-2: O2O Downlink--SS-RSRP  
In Figure 4, the average SS-RSRP obtained from the prediction using eight sites of gnodeB is \(-\)85 dBm. This means the signal strength is allocated into the mean values; meanwhile, the minimum is \(-\)110 dBm and the maximum value is \(-\)70 dBm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Key Parameter** & \multicolumn{3}{c}{**Spectrum Details**} \\ \hline Technology/Duplex & \multicolumn{3}{c}{5G NR/TDD} \\ \hline Carrier Frequency/Bandwidth & 3.5 GHz & 100 MHz & 28 GHz & 200 MHz \\ \hline Start Frequency–End Frequency & 3300 MHz & 4200 MHz & 26,500 MHz & 29,500 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 6: The main system parameter [12].
## 3 Data and Methods
### Field Survey  
We divided the study area into two domains: (i) urban (UR) and (ii) wildland (WLD) (Figure 1b). The two domains differed in terms of total area (40 ha vs. 85.3 ha for UR and WLD, respectively), as well as in vegetation composition. The dominant tree species in WLD are mostly native species, like Aleppo pines (_Pinus halepensis_ [PERSON]) and common oaks (_Quercus calliprinos Webb_), while the UR domain contains a greater variety of ornamental species and a much lower percentage of native species (Table A1).  
A field-based burn severity map with polygons containing three levels (low, moderate, and high) was created using orthophoto (taken in December 2016) and complementary information from an extensive field survey [35]. Burn severity was characterized in the field following conventional classification [54; 55]. It was found that nearly 64% of the inspected area was within the low-moderate burn severity class, while 36% was in the high severity class. A second field survey was conducted approximately one month after the fire, based on the orthophoto-based burn severity map. Tree diameter at breast height (DBH in cm tree\({}^{-1}\)) was measured for trees in 212 randomly selected 10 m \(\times\) 10 m plots within the burnt area (Figure 3). Trees within each plot were characterized by species type and counted to derive stand density (trees ha\({}^{-1}\)) in the plot (Figure 3 and Table A1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Satellite**} & \multicolumn{2}{c}{**Before**} & \multicolumn{2}{c}{**After**} \\ \cline{2-6}  & **Date 1** & **Date 2** & **Date 3** & **Date 4** & **Date 5** \\ \hline Sentinel-2A & 13 September 2016 & 13 October 2016 & 12 November 2016 & 22 November 2016 & 11 January 2017 \\ Planet & 23 August 2016 & 13 October 2016 & 12 November 2016 & 21 November 2016 & 5 December 2016 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dates of images used prior to and following the fire of 24–25 November 2016.  
Figure 2: Time series of NDVI from 250-m MODIS, 10-m Sentinel-2A and the 3-m Planet Scope Ortho Scene (a constant value of 0.16 was added to the Planet-derived NDVI time series—see main text).  
Tree stand density was found to be significantly higher (\(p<0.001\); Student's \(t\)-test) in plots within the UR domain (5.4 trees plot\({}^{-1}\); N = 135) than in the WLD domain (2.2 trees plot\({}^{-1}\); N = 77). In general, the WLD was much shrubbier than the UR domain.
# Adaptive, Observer-Based Synchronization of Different Chaotic Systems
## 7 Example
### Example 1--Observer Performance  
First, we demonstrate features of the observer designed in Section 2. The tuning of the observer starts with placing the eigenvalues of \(\mathbf{A_{0}}\) by an appropriate selection of gains \(\mathbf{k}\). In this way, the dynamics of the linear part of the observer is decided. The speed of adaptation, which depends on \(\mathbf{\Gamma}\), must correspond with the observer \"time constant\" defined by the maximal eigenvalue of \(\mathbf{A_{0}}\)--i.e., faster observer requires faster adaptation (higher \(\mathbf{\Gamma}\)). Matrices \(\mathbf{Q}\) and \(\mathbf{R}\), although used in the Lyapunov function (12) and its derivative (13), do not influence the observer directly.  
The plots below are obtained for initial conditions of the [PERSON] system \(\mathbf{x}^{T}(0)=[1\ 0\ 0]\); hence, for the transformed system \(\mathbf{z}(0)=\mathbf{\Phi}\mathbf{x}(0)=[1\ 13.8]^{T}\), and initial conditions for the observer were selected as \(\hat{\mathbf{z}}(0)=0.8\mathbf{z}(0)=[0.8\ 0.8\ 3.04]^{T}\). The initial conditions for adaptive parameters were selected \(20\%\) lower than the real values.  
Results of experiments shown in Figures 5 and 6 are typical for the obtained observer performance. All errors considered (\(\epsilon\), \(\mathbf{e}\), \(\mathbf{\tilde{\theta}}\)) converge to zero. By calculation of \(\hat{\mathbf{x}}=\mathbf{\Phi}^{-1}(\hat{\mathbf{\theta}})\hat{\mathbf{z}}\), it is verified that \(\mathbf{x}-\hat{\mathbf{x}}\to 0\), so the proposed observer provides estimates of original state variables \(\mathbf{x}\) as well. This is demonstrated in Figure 5, in which the influence of design parameters is also illustrated. The highest estimation errors are observed just after the start of the system. Errors caused by shifting the attractor (change in \(r\)) are far smaller. A rapid change in the unknown parameter \(a\), although it is not expected according to initialassumptions, is well tolerated. The observer recovers after such disturbance and returns to a perfect estimation of state variables.  
Figure 6a illustrates the nature of the \"composite\" error \(\epsilon\). The chaotic motion is eliminated from this signal, and it converges exponentially according to Equation (11).  
From Figure 6b, we infer that the observer provides a good estimation of unknown parameters as well. After the initial transient, the estimates remain accurate and robust against the input signal changes.  
Although the observer working alone behaves properly, the main aim is cooperation with the feedback controller, according to the structure presented in Figure 1.  
Figure 4: (a) A 2D projection of the state–space trajectories presented in Figure 3a on \((x_{1},x_{3})\) plane; (b) a 2D projection of the state–space trajectories presented in Figure 3a on \((x_{2},x_{3})\) plane.  
Figure 3: (a) State–space trajectories for initial condition \(\mathbf{x}=[1\:0\:0]^{T}\); (b) a 2D projection of the state–space trajectories with Figure 3a on \((x_{1},x_{2})\) plane.
# 3D Path Planning Algorithms in UAV-Enabled Communications Systems: A Mapping Study
## 6 Open Issues and Research Challenges
### Parameters Involved in System Formulation  
The parameters commonly employed in system formulation encompass the number of UAVs, obstacles, and the environment. However, to enable communication capabilities, it becomes crucial to include additional parameters like Channel Models, which incorporate path loss and fading models within the communication system is vital. Understanding these models is essential for determining the required transmission power to transmit information effectively. Additionally, The access technique utilized in UAV networks to communicate with users is another critical parameter. It aids resource allocation and becomes especially significant in scenarios involving multiple UAVs, where interference analysis between UAVs is necessary. The application of this UAV network communication type comes into play when UAVs gather data from IoT devices.
###### Abstract  
Laser interferometers can achieve a nanometer-order uncertainty of measurements when their frequencies are locked to the reference frequencies of the atom or molecule transitions. There are three types of displacement-measuring interferometers: homodyne, heterodyne, and frequency modulation (FM) interferometers. Among these types of interferometer, the FM interferometer has many advantageous features. The interference signal is a series of time-dependent harmonics of modulation frequency, so the phase shift can be detected accurately using the synchronous detection method. Moreover, the FM interferometer is the most suitable for combination with a frequency-locked laser because both require frequency modulation. In previous research, low modulation frequencies at some tens of kHz have been used to lock the frequency of laser diodes (LDs). The low modulation frequency for the laser source means that the maximum measurement speed of the FM interferometers is limited. This paper proposes a novel contribution regarding the application of a high-frequency modulation for an LD to improve both the frequency stability of the laser source and the measurement speed of the FM interferometer. The frequency of the LD was locked to an I\({}_{2}\) hyperfine component at 1 MHz modulation frequency. A high bandwidth lock-in amplifier was utilized to detect the saturated absorption signals of the I\({}_{2}\) hyperfine structure and induce the signal to lock the frequency of the LD. The locked LD was then used for an FM displacement measuring interferometer. Moreover, a suitable modulation amplitude that affected the signal-to-noise ratio of both the I\({}_{2}\) absorption signal and the harmonic intensity of the interference signal was determined. In order to verify the measurement resolution of the proposed interferometer, the displacement induced by a piezo electric actuator was concurrently measured by the interferometer and a capacitive sensor. The difference of the displacement results was less than 20 nm. To evaluate the measurement speed, the interferometer was used to measure the axial error of a high-speed spindle at 500 rpm. The main conclusion of this study is that a stable displacement interferometer with high accuracy and a high measurement speed can be achieved using an LD frequency locked to an I\({}_{2}\) hyperfine transition at a high modulation frequency.  
frequency-locked laser diode; frequency modulation interferometer; I\({}_{2}\) hyperfine structure; lock-in amplifier +
Footnote †: journal: Applied Physics Letters
# Cryptanalysis of Permutation-Diffusion-Based Lightweight Chaotic Image Encryption Scheme Using CPA
## 2 Review of the Original Scheme  
A general flowchart of the original scheme is presented in Figure 1. As shown in Figure 1, the encryption design of the original scheme is composed of two processes: the permutation phase and diffusion phase. The permutation phase scrambles the original pixels by swapping the pixel values in the original image with the element values in the key matrices **X** and **Y**, which can be carried out due to the superiority of the 2D Baker's map, i.e., it can perform one-to-one the unit square onto itself. The diffusion phase further diffuses the permuted pixels by XORing a random matrix **r** that depends on a random number \(R\) generated by the initial setting, constant value matrix \(T(i,\,j)=((M/i)+(N/j))\) of the permuted plaintext **P**.  
In the following, we discuss the permutation and diffusion phases of the original scheme in detail. Let \(\mathbf{I}\) denote a grayscale plaintext image with the size of \(M\times N\) and let \(\zeta\) and \(\vartheta\) denote the 2D Baker's map function and the swap function, respectively. The details of the pixel scramble and diffusion of the original plaintext are as follows.  
Step 1: Input the plaintext image \(\mathbf{I}\) and compute the key matrices \(\mathbf{X}\) and \(\mathbf{Y}\) using the map function \(\zeta\).  
\[\mathbf{X},\ \mathbf{Y}=\zeta(\alpha,\ x_{0},\ y_{0}). \tag{1}\]  
Step 2: Scramble the original plaintext \(I(i,\ j)\) by swapping it with \(I(x,\ y)\) to generate the permuted image \(\mathbf{P}\), as shown in (2).  
\[\mathbf{P}=\vartheta(I(i,\ j),\ I(x,\ y)), \tag{2}\]  
where \(x\) and \(y\) meet the conditions \(x\in X(i,\ j)\) and \(y\in Y(i,\ j)\), respectively; \(i\) and \(j\) satisfy the conditions \(i=1,\ 2,\ \cdots,\ M\) and \(j=1,\ 2,\ \cdots,\ N\).  
Step 3: Compute the random number \(R\) by using the map function \(\zeta\) again, and compute the matrix \(\mathbf{r}\) used to diffuse the permuted plaintext \(\mathbf{P}\).  
\[R=\zeta(\alpha^{\prime},\ x_{0}^{\prime},\ y_{0}^{\prime}), \tag{3}\]  
\[\mathbf{r}=\mathrm{mod}((P(i,\ j)+R),\ 256). \tag{4}\]  
Step 4: Diffuse the permuted image \(\mathbf{P}\) by performing XOR operation on the permuted image \(\mathbf{P}\), the matrix \(\mathbf{r}\), and the constant value matrix \(T(i,\ j)=((M/i)+(N/j))\) controlled by the image sizes and the position of each pixel directly.  
\[\mathbf{C}=\mathrm{mod}((\mathbf{r}\oplus P(i,\ j)\oplus T(i,\ j)),\ 256). \tag{5}\]  
Until now, we have obtained the permuted and diffused image, i.e., the final ciphertext image \(\mathbf{C}\).
# Comparison of Radio Frequency Path Loss Models in Soil for Wireless Underground Sensor Networks
###### Abstract  
A wireless Underground Sensor Network (WUSN) is a group of sensors that collectively communicate through the underground medium. Radio Frequency (RF) signal transmission of the sensors through the ground is the most challenging aspects of a WUSN due to the high attenuation of the electromagnetic (EM) signal in the soil. Signals are often required to travel through soils with a high density or water content and generally through a non-isotropic and non-homogenous soil mixture with different boundaries, both of which can attenuate the signal sharply. The variability of the these conditions and complexity of the behaviour of signal attenuation with respect to these parameters makes accurate estimation of EM signal attenuation in soil challenging. Two main EM signal attenuation models exist to estimate attenuation (modified-Friis and Complex Refractive Index Model-Fresnel (CRIM-Fresnel). These were reviewed and a methodology was developed in order to measure the attenuation of the EM signals in the laboratory. Results from the laboratory measurements were compared with the estimation values calculated from the attenuation models. These comparisons showed a large difference between the estimated values by the models. In addition, analysis of the comparison tests showed that the CRIM-Fresnel model provides a better estimation of attenuation in samples with lower permittivity values while the modified-Friis model had a higher accuracy in samples with higher clay/water content which have higher permittivity values.  
wireless underground sensor network (WUSNs); radio frequency (RF); Electromagnetic (EM); free space path loss (FSPL); dielectric permittivity (\(\epsilon\))
# Bacteriotherapy in Breast Cancer
## 3 Anticancer Substances Released by Bacteria
### Bacterial Toxins in Cancer Therapy
#### 3.3.5. Hyaluronidase (HylP)  
The hyaluronidase (Hyals) is an enzyme from _Streptococcus pyogenes_ which is able to degrade predominantly hyaluron (HA), and it exhibits an anticancer activity on different cancers, as well as breast cancer. One of the features observed in many solid tumors is an increase in the extracellular matrix (ECM) deposition. Hyaluronan (HA) is one of the ECM components that increased in many solid tumors, leading to a decrease in the elasticity of tumor tissue and an increase of interstitial fluid pressure [90; 91; 92]. Recent studies have demonstrated that the addition of hyaluronidase to chemotherapeutic regimens could significantly improve efficacy [93; 94]. In one study, Hs578T, MDA-MB-231, and MCF-7 cell lines were exposed to the bacteriophage H4489A hyaluronidase (HyIP) of _Streptococcus pyogenes_. The result suggested that the inhibitory and invasive effect of HyIP is due to a reduction of the Hyaluronan (HA) that is a nonsulfated glycosaminoglycan present in the extracellular matrix of tissue homeostasis and structural integrity [95].
## 2 Enhancing Food Production through RS Applications in Agriculture
### Monitoring Agricultural Crop Status
[PERSON] and [PERSON] [93] stated that crop biomass condition information at the pre-harvest stage can be used to indicate potential food shortages and surpluses and support related policy-making decisions. A recent systematic review of extreme weather events (EWE) in agriculture found that monitoring and detecting the impacts of EWE using RS techniques is still underdeveloped [94]. It was also highlighted that gaps still exist concerning the impacts of EWE on major cash crops and the agronomic dynamic of EWE in developing countries [94]. In addition, other researchers found that RS data can be used to monitor and assess the impact of EWE on crop yield [95; 96]. Therefore, accurate and timely detection, monitoring and mapping of crop health, growth and seasonal progress due to crop diseases and pests and extreme weather events is critical for forecasting and assuring food security. The occurrence of diseases and pests on crops and the canopy surface can cause changes in chemical concentration, pigments, nutrients, gas exchanges and water uptake [97; 98]. In response, the colour and temperature of the canopy may change and result in variation in canopy reflectance characteristics, which can be measured by RS [98; 99]. Existing RS approaches can discriminate various diseases and pests, evaluate their infection severities and map their spatial distribution at various scales [47; 48; 49; 50]. For example, using Landsat images, it was possible to map the severe infestation of the take-all disease in wheat [100]. [PERSON] et al. [50] developed a multi-temporal satellite data-based early detection method for regional mapping of powdery mildew disease. In addition, remote sensing estimation of soil moisture was used to plan desert locust surveys for preventive management [101]. Furthermore, it was demonstrated that fine spatial and temporal resolution remotely sensed data can be used to monitor crop condition, assess crop damage in flood areas [52], assess crop condition in relation to soil moisture [51], and monitor crop condition in extreme events such as droughts [102]. Furthermore, RS can be integrated into early warning systems to alert local and international authorities about any negative events that could impact overall production. For example, Anomaly hot Spots of Agricultural Production (ASAP) is an early warning system focused on anomalies in agricultural production that aims to prevent food security crises and anticipate resource needs [103].