esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficient Underlaid D2D Communication for 5G Applications
## 6 Conclusions
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Total Energy Efficiency**} & \multicolumn{2}{c}{**Total Energy Efficiency**} & \multicolumn{2}{c}{**Total Energy Efficiency**} \\
**Cellular** & \multicolumn{2}{c}{**(Kbps/J)**} & \multicolumn{2}{c}{**(Kbps/J)**} & \multicolumn{2}{c}{**(Kbps/J)**} \\
**User** & \multicolumn{2}{c}{**with**} & \multicolumn{2}{c}{**with**} & **with** & **with** \\
**Density** & \multicolumn{2}{c}{**R\({}_{\text{d,ref}}\) = 15 m**} & \multicolumn{2}{c}{**R\({}_{\text{d,ref}}\) = 20 m**} & \multicolumn{2}{c}{**R\({}_{\text{d,ref}}\) = 25 m**} \\
**(user/m\({}^{2}\))** & \multicolumn{2}{c}{**Derivative**} & \multicolumn{2}{c}{**Derivative**} & **Modified** & **Derivative** & **Modified** \\  & **Algorithm** & **Derivative** & **Algorithm** & **Algorithm** & **Algorithm** \\ \hline \(0.5\times 10^{-5}\) & 7.644 & 10.851 & 4.780 & 7.045 & 2.153 & 3.278 \\ \(0.6\times 10^{-5}\) & 5.460 & 7.669 & 3.366 & 4.912 & 1.505 & 2.271 \\ \(0.7\times 10^{-5}\) & 4.138 & 5.760 & 2.509 & 3.632 & 1.113 & 1.666 \\ \(0.8\times 10^{-5}\) & 3.277 & 4.526 & 1.949 & 2.802 & 0.857 & 1.275 \\ \(0.9\times 10^{-5}\) & 2.684 & 3.682 & 1.563 & 2.234 & 0.681 & 1.007 \\ \(1\times 10^{-5}\) & 2.258 & 3.079 & 1.285 & 1.827 & 0.554 & 0.816 \\ \(1.1\times 10^{-5}\) & 1.942 & 2.635 & 1.077 & 1.525 & 0.459 & 0.674 \\ \(1.2\times 10^{-5}\) & 1.702 & 2.298 & 0.917 & 1.294 & 0.387 & 0.566 \\ \(1.3\times 10^{-5}\) & 1.516 & 2.038 & 0.791 & 1.113 & 0.330 & 0.481 \\ \(1.4\times 10^{-5}\) & 1.369 & 1.833 & 0.690 & 0.969 & 0.285 & 0.414 \\ \(1.5\times 10^{-5}\) & 1.251 & 1.670 & 0.607 & 0.851 & 0.248 & 0.360 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of energy efficiency with the cellular density users (user/m\({}^{2}\)). **Author Contributions:** Conceptualization: L.N. ; methodology: A.V.P. ; software validation: S.P. ; formal analysis: B.S. ; investigation: W.S. ; resources: G.S.K. ; data curation: S.P. ; writing--original draft preparation: L.N. ; writing--review and editing: A.V.P. ; visualization: S.P. ; supervision: G.S.K. ; project administration: A.S.A.A. ; funding acquisition: A.S.A.A. All authors have read and agreed to the published version of the manuscript. **Funding:** This study received funding from King Saud University, Saudi Arabia, through researchers supporting project number (RSP-2021/145). Additionally, the APCs will be funded (after acceptance) by King Saud University, Saudi Arabia, through researchers supporting project number (RSP-2021/145). **Acknowledgments:** The authors extend their appreciation to King Saud University, Saudi Arabia, for funding this work through researchers supporting project number (RSP-2021/145). **Conflicts of Interest:** The authors declare no conflict of interest.
# Multi-Layered Interactive Target Guidance with Visual Safety in Convex-Shaped Obstacle Environments
## 2 Problem Formulation
### Interactive Guidance with Visual Safety  
In this paper, when the camera robot approaches, the target object evades the robot due to the term in (7). By utilizing this reaction, the movement of the target object can be interactively guided. We refer to this as interactive guidance.Specifically, in this context, the following two tasks are addressed as applications of interactive guidance.  
**Guidance Task** guiding the target object to the desired location \(p_{task}\) using the camera robot, as shown in Figure 2(a).  
Figure 2: Configuration of the camera robot, target, and obstacle.  
Guarding Task guiding the target object away from the protected location \(p_{task}\) using the camera robot, as shown in Figure 2(b).  
On the other hand, it is assumed that the robot acquires information about the target object using a camera. Therefore, it is important to keep the target object within the camera's field of view even in environments with obstacles. Specifically, it is necessary to continuously achieve field of view maintenance and occlusion avoidance. as defined in Sections 2.2 and 2.3. We call the situation where field of view maintenance and occlusion avoidance are achieved visually safe.  
In this article, the design problem of a control architecture is considered, which enables a camera robot to accomplish an interactive guidance task while ensuring visual safety in a cluttered environment. This problem is formulated as follows.  
Problem 1: _Given pursuit-evasion system (5)-(7) in a cluttered environment with convex-shaped obstacles (13), design a control architecture of the camera robot to achieve interactive guidance of the target object while satisfying the constraint_  
\[x_{pe}(t)\in\mathcal{D}_{fov}\cap\mathcal{D}_{occ},\;\forall t\geq 0. \tag{19}\]  
To achieve Problem 1, this paper proposes a multi-layered control architecture consisting of a planning layer and a safety layer, as shown in Figure 4. In the following sections, Section 3 explains the structure of the safety layer, and Section 4 describes the planning layer.
## 3 Results and Discussion
### Relationship between PAH and Trace Metals Concentrations and Occupant Activities
#### 3.3.1 PAHs  
The analysis demonstrated that smoking inside the house significantly contributes to the increase of the concentrations of Phe (1275 versus 780 ng/g, \(p=0.0730.1\)) and Ant (32.1 versus 18.7, \(p=0.073\)), whereas no statistically significant differences for the other COMPAHs were observed. However, the concentrations of all COMPAHs in smoker's residences were found to be significantly higher than in nonsmoker's dwellings (Figure 2). In particular, Chr indicated 2.5 times higher concentration in smokers' homes (72 vs. 30 ng/g), while Pyrr (153 vs. 63 ng/g), B[b]F (30 vs. 14 ng/g), B[ k] F (16 vs. 7 ng/g) and B [a] \(p\) (10 vs. 5 ng/g) were found twice as high in smokers' houses as in nonsmokers. In a review that was conducted by [PERSON] et al. [36], a moderate but statistically significant relationship between smoking and PAHs concentration has been reported, while [PERSON] et al. (2010) pointed out in their study that smoking was not a strong determinant in the presence of PAHs in house dust. However, other studies indicated no significant difference between smoking and nonsmoking dust [29; 37], with [PERSON] et al. [2] underlying that the effect of smoking on PAH concentration in dust remains uncertain. The use of air conditioning was strongly associated with both COMPAHs (548 vs. 227 ng/g, \(p<0.05\)) and B2 PAHs (226 vs. 66 ng/g, \(p<0.05\)). Finally, an inverse association between the concentration of \(\mathrm{\SIUnitSymbolMicro E}\) PAHs and cleaning activities was found. In particular, houses that were mapped more than once per week indicated significantly lower levels of \(\mathrm{\SIUnitSymbolMicro E}\) PAHs (3.4 versus 2.3 \(\mathrm{\SIUnitSymbolMicro g}\)/g, \(p<0.05\)). This finding suggests that frequent cleaning activities can help reduce the concentration of PAHs in household dust. This observation is in agreement with [PERSON] et al. [18], who reported a weak but statistically significant negative correlation between the frequency of vacuum use and the concentration of PAHs.
# Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter
## 2 Methodology
During the update process of Kalman filter, the estimated state and its covariance error matrix at time \(k+1\) are updated and corrected using the Kalman filter gain \(G_{k+1}\) as follows  
\[G_{k+1}=P_{k+1}C^{T}\Big{(}CP_{k+1}C^{T}+R\Big{)}^{-1} \tag{9}\]  
\[\hat{x}t_{k+1}=\hat{x}_{k+1}+G_{k+1}(z_{k}-C\hat{x}_{k+1}) \tag{10}\]  
\[P_{k+1}=P_{k+1}-C\hat{x}_{k+1}P_{k+1} \tag{11}\]  
where \(C^{T}\) is the transpose matrix of the measurement matrix \(C\), \(\hat{x}t_{k+1}\) is the updated state of the primary user state at \(k+1\), and \(P_{k+1}\) is the updated covariance error matrix \(P_{k+1}\). Once the coordinates of the primary user position \((x_{p},\ y_{p})\) are estimated using the Kalman filter, the distance \(d_{kfi}\) between a fixed position of an anchor node \((x_{i},\ y_{i})\) and the primary user can be obtained using the following equation  
\[d_{kfi}=\sqrt{\big{(}x_{p}-x_{i}\big{)}^{2}+\big{(}y_{p}-y_{i}\big{)}^{2}} \tag{12}\]  
In order to verify if the incoming signal is emitted by a legitimate primary user or by an attacker, a distance \(d_{pi}\) between the transmitter and a node \(i\) is required. This distance can be obtained from the received power signal of the transmitter using the Free Space Path Loss Equation [24]:  
\[P_{r}=\ \frac{P_{t}G_{t}G_{r}\lambda^{2}}{\big{(}4\pi d_{pi}\big{)}^{2}} \tag{13}\]  
where \(P_{r}\) is the received power, \(P_{t}\) is the transmitted power, \(G_{r}\) is the antenna gain of the receiver, \(G_{t}\) is the antenna gain of the transmitter, \(\lambda\) is the wavelength, and \(d_{pi}\) is the distance between an anchor node \(i\) and the transmitter. Equation (13) can be expressed in dB as  
\[P_{r}(dB)=-10\alpha log_{10}\big{(}d_{pi}\big{)}+A \tag{14}\]  
where \(\alpha\) is the propagation path loss exponent and \(A\) is expressed as  
\[A=10 log_{10}\Big{(}P_{t}G_{t}G_{r}\lambda^{2}\ \Big{)}-20 log_{10}(4\pi\ ) \tag{15}\]  
The received signal strength at an anchor node \(i\) can be impacted by the noise, Equation (14) can be written as  
\[P_{r}(dB)=-10\alpha log_{10}\big{(}d_{pi}\big{)}+A+n_{i} \tag{16}\]  
where \(n_{i}\) is a white Gaussian noise that follows the normal distribution \(N\big{(}0,\ \sigma^{2}\big{)}\). Thus, the distance \(d_{pi}\) can be estimated as  
\[P_{k+1}=A_{k}P_{k}A_{k}^{T}+Q_{k} \tag{8}\]0By comparing the difference between the estimated distance \(d_{kfi}\) and \(d_{pi}\) at an anchor node \(i\) to a predefined threshold \(\tau\), the transmitter is considered as a primary user emulation attacker if the \(\left|d_{kfi}-\ d_{pi}\right|\geq\tau\) and as a legitimate primary user when the \(\left|d_{kfi}-\ d_{pi}\right|<\tau\).
## 4 LoRaWAN-Based Outdoor Positioning in Application-Specific IoT Domains
### Smart Healthcare
In [107], the authors designed and implemented a sea rescue system by using LoRaWAN in such an application and shows how it can be used to transfer data from a device attached to a Personal Flotation Devices (PFD), such as life jackets, to a receiver placed in a sea rescue helicopter. User in the proposed method equipped with a pulse sensor, to determine the health status of the user. During a natural disaster, setting up a wireless communication infrastructure for enabling emergency message exchange among individuals and rescue teams for people localization and situations determination is necessary. Several works proposed a LoRaWAN base communications system for victim message exchange and localization [108, 109, 110]. Author in [111] proposed a phone-based Emergency Communication Systems (ECS) which allows multihop dissemination of emergency messages over LoRaWAN links. They investigated how to implement ECS able to leverage the pervasiveness of smartphones, and on the excellent propagation characteristics of the LoRaWAN technology to support data exchange even in highly critical scenarios. In [112], they extend their work and proposed a novel multihop dissemination algorithm that maximizes the probability to deliver an emergency request to the destination. The authors in [113, 114] proposed a LoRaWAN-based system for mountain search and rescue (SaR) operations. In the proposed method, the localization of the persons is obtained through an algorithm based on path-loss measurements. For doing this, they characterized the LoRaWAN Path Loss in three relevant mountain scenarios. In smart healthcare systems, the accuracy and battery life of the proposed method is so important. There are different proposed methods on network architecture based on multi-modal and multihop nodes for collecting and transmitting environment sensor data with more effective latency and battery life. In all published studies until now on multihop network topologies, radio duty cycling is not considered. We think that a content-aware system for radio duty cycle determination is a requirement.
## 2 SLAM Based on Multi-Sensor Fusion  
In the field of robot navigation, the problem of SLAM can be simply described as follows: A robot moving in an unknown environment can estimate its own pose according to the pose and map information and can build an incremental map in the process of moving to achieve autonomous obstacle avoidance and navigation [22]. In order to describe the SLAM problem, the motion of the vehicle in a continuous period of time is decomposed into discrete moments \(t=1,2,\)\(\cdots,k\), and the corresponding moment positions \(x_{1},x_{2},\)\(\cdots,x_{k}\) constitute the motion trajectory of the vehicle. For the nonlinear system in the actual motion process, the motion model and observation model of the system can be expressed as follows:  
\[\left\{\begin{array}{l}x_{k}=f(x_{k-1},\ u_{k},w_{k})\\ z_{k}=h(x_{k},v_{k})\end{array}\right. \tag{1}\]  
where \(x_{k}\) represents robot pose, \(z_{k}\) represents the observed value of the system, \(f\) (\(\cdot\)) is the system motion equation function, \(h\) (\(\cdot\)) is the system observation equation function, \(u_{k}\) represents the control value of the system, \(w_{k}\) represents system process noise, and \(v_{k}\) stands for system observation noise.
## 2 Optimization Methodology
### Contour Tracking Control
#### 2.1.2 PDC-PID Control  
A PDC-PID controller is considered as the time domain representation of the PID controller which is given by:  
\[\tau_{si}(q_{m})=k_{pi}e_{si}(q_{m})+k_{Di}e^{\prime}_{si}(q_{m})+k_{li}\int_{ 0}^{qm}e_{si}(s)ds \tag{4}\]  
where \(\tau_{si}\) the track of slave motions and \(k_{li}\), \(k_{Di}\), \(k_{pi}\) the control gains of slave motions.  
The slave motion tracking errors are given by:  
\[tracking\ error=\left\{\begin{array}{c}e_{s}(qm)=q_{sd}(qm)-q_{s}(qm)\\ e^{\prime}_{s}(qm)=q^{\prime}_{sd}(qm)-q^{\prime}_{s}(qm)\end{array}\right. \tag{5}\]Note that master motions are still being controlled by the traditional PID controller in the time domain, so the DE algorithm is adopted in contour tracking to get an optimum gain control of the PDC-PID controller.
# Removal of Cu(II) from Aqueous Solutions Using Amine-Doped Polyacrylonitrile Fibers
## 2 Materials and Methods
### Adsorption Model Analyses  
The kinetic data were fit using pseudo-first- and pseudo-second-order kinetic models (Equations (3) and (4), respectively), and the best fit was determined using the Sigma Plot 10 software.  
\[q_{t}=q_{e}\big{(}1-e^{-k_{1}t}\big{)} \tag{3}\]  
and  
\[q_{t}=\frac{k_{2}q_{e}{}^{2}t}{1+k_{2}q_{e}t} \tag{4}\]  
where \(q_{t}\) and \(q_{e}\) (mg Cu/g) are the amounts of Cu removed at time \(t\) and at equilibrium, respectively, and \(k_{1}\) (1/h) and \(k_{2}\) (g/(mg Cu h)) are the pseudo-first- and pseudo-second-order rate constants, respectively.  
The most common mathematical models used to analyze isotherm data are the Langmuir and Freundlich models. The Langmuir model was derived to describe adsorption on homogeneous adsorbent surfaces, where each adsorptive site could be occupied only once. Mathematically, the Langmuir model can be described as follows [16]:  
\[q_{e}=\frac{q_{m}K_{L}C_{e}}{1+K_{L}C_{e}} \tag{5}\]  
where \(q_{m}\) (mg/g) is the maximum sorption capacity of the metal ions on the adsorbent, and \(K_{L}\) (L/mg) is the Langmuir adsorption equilibrium constant.  
The Freundlich model is used to describe the adsorption of an absorbate on heterogeneous adsorbent surfaces, and its mathematical expression could be written as follows [16]:  
\[\mathrm{q_{e}=K_{F}C_{e}^{\frac{1}{n}}} \tag{6}\]  
where \(\mathrm{K_{F}}\) and n are the Freundlich constants.
# Shifting of Meteorological to Hydrological Drought Risk at Regional Scale
###### Abstract  
The drought along with climate variation has become a serious issue for human society and the ecosystem in the arid region like the Soan basin (the main source of water resources for the capital of Pakistan and the Pothohar arid region). The increasing concerns about drought in the study area have brought about the necessity of spatiotemporal analysis and assessment of the linkage between different drought types for an early warning system. Hence, the streamflow drought index (SDI) and standard precipitation index (SPI) were used for the analysis of the spatiotemporal variations in hydrological and meteorological drought, respectively. Furthermore, statistical approaches, including regression analysis, trend analysis using [PERSON], and moving average, have been used for investigation of the linkage between these drought types, the significance of the variations, and lag time identification, respectively. The overall analysis indicated an increase in the frequency of both hydrological and meteorological droughs during the last three decades. Moreover, a strong linkage between hydrological and meteorological droughs was found; and this relationship varied on the spatiotemporal scale. Significant variations between hydrological and meteorological droughs also resulted during the past three (3) decades. These discrepancies would be because of different onset and termination times and specific anthropogenic activities in the selected basin for the minimization of hydrological drought. Conclusively, the present study contributes to comprehending the linkage between hydrological and meteorological droughs and, thus, could have a practical use for local water resource management practices at the basin scale.  
d 2022
## 3 Proposed STPAP Beamforming Algorithm
### Adaptive Algorithm for Calculating the Weight Vector  
The direct inverse matrix (DMI) method or constraint least mean square (CLMS) method can be usually adopted as adaptive algorithms to implement the proposed MVDR-CMS criterion for the STPAP. The DMI method is sample in form but it is very computationally intensive due to the inversion of matrix. In comparison, the CLMS method has a lower computational complexity, which is feasible to calculate the weight vector \(\mathbf{W}_{\text{all}}\) iteratively in this paper. Therefore, the iterative expression to acquire the weight vector for the proposed STPAP beamforming algorithm will be described in the following work. With (15), the iterative function based on the steepest descent method can be presented as  
\[\mathbf{W}_{\text{all}}(i+1)=\mathbf{W}_{\text{all}}(i)-\mu\
abla_{\mathbf{W}_{\text{all}}} \text{F}(\mathbf{W}_{\text{all}}), \tag{18}\]where \(i\) represents the iteration number, \(\mu\) denotes a fixed step factor, and \(\
abla_{\text{W}_{\text{all}}}\text{F}(\text{W}_{\text{all}})\) represents the gradient function. Moreover, function \(\text{F}(\text{W}_{\text{all}})\) is the combination of the cost function and constraint function in (15) and it can be given by  
\[\text{F}=\frac{1}{2}\text{W}_{\text{all}}^{\text{H}}\widetilde{\text{R}}\text {W}_{\text{all}}+\text{B}_{\lambda}(\text{W}_{\text{all}}^{\text{H}}\overline{ \text{C}}-\overset{\text{\tiny{H}}}{1}_{J})^{\text{H}}, \tag{19}\]  
where \(\text{B}_{\lambda}\) is the Lagrange multiplier and the coefficient \(1/2\) is adjoined to simplify calculation. Note that the Lagrange multiplier \(\text{B}_{\lambda}\) for the proposed MVDR-CMS criterion is not a constant but a \(1\times J\) vector, which varies with iteration. Taking the gradient of (19) with respective to \(\text{W}_{\text{all}}\), the gradient function \(\
abla_{\text{W}_{\text{all}}}\text{F}(\text{W}_{\text{all}})\) can be presented as  
\[\
abla_{\text{W}_{\text{all}}}\text{F}(\text{W}_{\text{all}})=\widetilde{\text {R}}\text{W}_{\text{all}}+\overline{\text{C}}\text{B}_{\lambda}^{\text{H}}. \tag{20}\]  
Substituting (19) into (18), the iterative function can be rewritten as  
\[\text{W}_{\text{all}}(i+1)=\text{W}_{\text{all}}(i)-\mu\widetilde{\text{R}}(i )\text{W}_{\text{all}}(i)-\mu\overline{\text{C}}\text{B}_{\lambda}^{\text{H} }(i), \tag{21}\]  
where \(\text{W}_{\text{all}}(i+1)\) must satisfy the constraint function in (15) and thus  
\[\text{W}_{\text{all}}^{\text{H}}(i+1)\overline{\text{C}}=\overset{\text{ \tiny{H}}}{1}_{J}. \tag{22}\]  
Then, substituting (21) into (22), we can obtain the Lagrange multiplier \(\text{B}_{\lambda}(i)\) as  
\[\text{B}_{\lambda}(i)=\frac{1}{\mu}[\text{W}_{\text{all}}^{\text{H}}(i) \overline{\text{C}}(\overline{\text{C}}^{\text{H}}\overline{\text{C}})^{-1}- \mu\text{W}_{\text{all}}^{\text{H}}(i)\widetilde{\text{R}}(i)\overline{\text{ C}}(\overline{\text{C}}^{\text{H}}\overline{\text{C}})^{-1}-\overset{\text{ \tiny{H}}}{1}_{J}(\overline{\text{C}}^{\text{H}}\overline{\text{C}})^{-1}]. \tag{23}\]  
Using (23), (21) can be expressed as  
\[
# Large-Area Coverage Path Planning Method Based on Vehicle-UAV Collaboration
## 3 Coverage Path Planning Method Based on Vehicle-UAV Collaboration  
### Overview of the Methodologies  
Firstly, the vehicle-UAV collaboration system for coverage scanning path planning over large areas needs to be modeled. Based on the coverage area, the boundary contour needs be obtained, which will be used as a virtual coverage 2D map, and all the coverage path planning will be performed on this 2D map. The UAV energy model is also simply modeled. Secondly, inspired by the divide-and-conquer algorithm, a coverage area segmentation method based on dynamic adjustment is presented for dividing the large area into sub-areas with approximately equal sizes. All supply points are adjusted multiple times to ensure the points are located in the most reasonable position, and all sub-areas are adjusted again to ensure that the points within a sub-area have a shorter distance to the corresponding supply point. Thirdly, generation of vehicle-UAV cooperative coverage paths based on the sub-area contours and the supply points takes place. The vehicle path should traverse all supply points with the shortest possible length, and the UAV path should cover all sub-areas with path simplicity and operability. A flowchart of the proposed CCP method based on vehicle-UAV collaboration is shown in Figure 2.