esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Laser source emits spatially coherent light and therefore can be used for creating fiducial patterns with specific topological properties. The created pattern could then be delivered by clipping the laser projection component to an endoscope [9; 15; 16; 17; 18; 19], or by employing a surgical endoscope [20]. It is noteworthy that optical coherence tomography is another imaging modality that could provide calibrated measurement capabilities [21; 22; 23]. Two main approaches of parallel laser markers and multiple laser points have been used for creating the laser-fiducial markers in the field of voice [24]. The projection of the parallel laser markers is the simplest approach. Two-point laser projection [15; 16; 25], two-line laser projection [26], and multiple line laser projection [9] are some examples of this category. The multiple-laser-points projection is more sophisticated and involves the projection of many laser points on the FOV [8; 20; 27; 28]. Each method has its own merits. The parallel laser projection category benefits from the simplicity of its optical design and subsequent measurement methodology. Detection of the laser markers on the image is the only required step for measurement in those systems. After that, the distance between the laser markers may be used similarly to a scale on a map and calibrated horizontal measurements may be achieved using a simple caliper. The main assumption of this method is that all pixels in the image have the same mm lengths which could be violated if different objects of the image have different distances from the camera, or if different locations of the image have different pixel size representations [14]. Violation of this assumption will lead to measurement errors. Conversely, multiple-laser projection systems benefit from the presence of laser points in all parts of the image. Not only this information helps with vertical measurements [24] and 3D reconstruction of the envelope of the FOV [19], but it also means that with a high probability some laser points would be near to, or on the target surface. Therefore, the above-discussed problems would be resolved. However, systems from this category require more sophisticated optical hardware and processing software design. Horizontal measurements from these systems depend on a calibration step, where the confounding factors of pixel-to-mm conversion scale are determined and accounted for. The main purpose of this paper is to present the method for horizontal calibration and subsequent horizontal measurements using a recently-developed multiple-laser point transnasal fiberoptic endoscope [20]. The rest of this paper is organized as follows. The Materials and Methods section presents details of the algorithm and the recordings used for development and evaluation of the proposed method. The Experiments and Results section consists of four experiments that evaluate the performance of the algorithm in different conditions and scenarios, followed by relevant discussions of the results. The implications of the results are put into research and clinical contexts in the Discussion section.
## 2 Coding Metamaterials for RCS Reduction
### Cuboid Coding Metamaterial Design  
Cuboid coding metamaterial designs were proposed in this research; an analysis was conducted by performing comparisons between the numbers of lattices from 4 to 12. For each lattice, the best-performing coding metamaterial designs among three were selected for this analysis. Examples of the cuboid design structures for the one-bit coding metamaterials are illustrated in Figure 13a-d. The monostatic RCS values of the adopted lattices are demonstrated in Figure 14; they indicate uniform RCS-reduction values when the size of the cuboid increases based on the number of lattices. Moreover, uniform reductions in the bistatic RCS values are demonstrated in Figure 15a-d. The flow of the values was as follows: \(-17.1\) dBm\({}^{2}\), \(-12.7\) dBm\({}^{2}\), \(-7.6\) dBm\({}^{2}\), and \(-4.36\) dBm\({}^{2}\), respectively. Therefore, it can be clearly seen that RCS value was inversely proportional to the size of the proposed coding metamaterials. In a nutshell, a comparison of a cuboid design with a lattice form indicated that almost 65% of reduction value was successfully at 8 lattice coding design.  
Figure 12: Monostatic RCS values and bistatic RCS values at 8 GHz for one-bit coding metamaterials with (**a**) double layers and (**b**) triple layers.  
Figure 14: Monostatic RCS data of one-bit cuboid design.  
Figure 13: Cuboid coding metamaterial structures of (**a**) Coding Sequence 3 with 4 lattices; (**b**) Coding Sequence 2 with 6 lattices; (**c**) Coding Sequence 3 with 8 lattices; and (**d**) Coding Sequence 3 with 12 lattices.
## 2 Methods
### Governing Equations
#### 2.2.6 Isotherms and Kinetics Models  
The equilibrium uptake can be determined from isotherms equations defined for the investigated composite adsorbent/ethanol pair [26]:  
\[X_{eq}=X_{max}\ \text{exp}\ \left[-(\frac{RT}{E}ln\big{(}\frac{P_{s}}{p} \big{)})\right.^{n}\right] \tag{16}\]  
where \(X_{max}\) is the maximum adsorption capacity, \(E\) is the adsorption characteristics parameter, and \(n\) is the heterogeneity parameter.The isotherms of the composite adsorbent/ethanol pair are plotted in Figure 3.  
Figure 3: The isotherms of the given consolidated composite adsorbent/ethanol pair.  
The linear-driving-force equation is typically applied for simulating the kinetics of the given adsorbent material which can be written as [22; 26]:  
\[\frac{\partial X}{\partial t}=K_{LDF}\left(X_{eq}-X\right) \tag{17}\]  
where \(K_{LDF}\) is the mass transfer coefficient and it can be expressed as follows:  
\[K_{LDF}=\frac{F_{o}D_{s}}{r_{p}^{2}} \tag{18}\]  
where \(F_{o}\) is 15 for spherical shape, and the \(\frac{D_{s}}{r_{p}^{2}}\) parameter is described by Arrhenius equation as a function of the local temperature as follows:  
\[\frac{D_{s}}{r_{p}^{2}}=\frac{D_{so}}{r_{p}^{2}}exp\left(\frac{-E_{a}}{\mathcal{ R}T}\right) \tag{19}\]
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 1 Introduction
This proposal is based on a set of distinct approaches, and its main contributions can be summarized as follows: (1) we present a new handover mechanism compatible with the IEEE 802.15.4 standard where; (1.1) the handover decision is anticipated; (1.2) the orphan scan step is suppressed; (1.3) the number of channels used in the scan operation is minimized; (1.4) the message exchange sequence defined in the IEEE 802.15.4e optional FastA association mechanism is used; (1.5) the proposed handover mechanism has been assessed by simulation and the achieved results highlight significant improvements in the whole handover procedure; (2) a suitable setup of the IEEE 802.15.4 MAC layer parameters is proposed for the association procedure; and (3) a review of state-of-the-art approaches addressing improvements in the handover process of the IEEE 802.15.4 standard. This paper is organized as follows. Section 2 discusses related works, presenting a systematic literature review about handover mechanisms in wireless sensor networks. Section 3 describes the assumed network model, the problem statement and the proposed handover scheme. Section 4 gives an assessment of the proposal via simulation. Finally, Section 5 presents conclusions and future works.
# Shape Optimization of an Integrated Mast for RCS Reduction of a Stealth Naval Vessel
## 2 Analysis Procedure
The geometric mean and the arithmetic mean are given as follows, respectively. \[\text{Geometric mean}=\frac{1}{N}\big{(}10\log_{10}\sigma_{1}+10\log_{10} \sigma_{2}+\dots+10\log_{10}\sigma_{N}\big{)}\text{ dBsm.} \tag{1}\]  
\[\text{Arithmetic mean}=10\log_{10}\Bigg{(}\frac{1}{N}\sum_{k=1}^{N}\sigma_{ k}\Bigg{)}\text{ dBsm.} \tag{2}\]  
The representative RCS value is one of the most important stealth parameters because it is used to determine the probability of survivability. As evaluation results may differ depending on the type of the representative RCS value, at least two representative values should be used and the performance should be evaluated comprehensively. Whether the goal is achieved or not should then be checked and an analysis on the major causes of the RCS increase based on the ray path and the RCS hotspot should be performed. The parts of the integrated mast that should be optimized are defined and they are redesigned using the shaping technique. The integrated mast is redesigned with low RCS propertiesin the primary threat sectors except for the cardinal points. The RCS reduction is basically achieved by adjusting the side slope of the integrated mast. Corner reflectors such as dihedrals, trihedrals and cavities should be absolutely avoided in a low RCS design as they extremely increase the RCS because of the influence of multiple reflections. In particular, for a naval ship equipped with an integrated mast, unnecessary RCS spikes can be generated by the integrated mast and the RCS can be increased due to the multiple reflections caused by the interaction of the mast and the ship. The side slope of the integrated mast is designed with the same slope of the superstructure of the naval ship to avoid unnecessary RCS spikes. In order to minimize the multiple reflections caused by the corner reflectors formed by the integrated mast and the superstructure or deck, the intersecting surfaces of the corner reflectors should be designed with acute or obtuse angles. It is also necessary to reduce the RCS by removing or minimizing the round shape of the integrated mast such as cylindrical and spherical structures and truncated cones. Finally, the integrated mast is iteratively redesigned to obtain an even better shape. The optimized mast should be designed to focus the reflected EM fields on the cardinal points and it should be in harmony with the naval vessel itself so that the RCS pattern of the naval vessel is not changed by the integrated mast. Figure 1: Optimization procedure. Figure 2: Cardinal points of the naval vessel.
## 1 Introduction
### Contribution and Organization  
The following is a summary of the principal contributions of this study:  
* MECNs are currently exploring a problem related to energy optimization using DRL, which involves selecting the appropriate mode and allocating radio resources. This optimization problem considers various constraints, such as limited computing and radio resources, and quality-of-service requirements for individual user equipment (UE) in a constantly changing wireless environment;
* In order to minimize energy consumption in MECNs, a joint optimization method for mode selection, radio resource allocation, and distributed computing resource allocation is proposed. We modeled the mode selection and resource allocation problem under the Markov decision process (MDP) problem. Due to a large amount of system states and activities in our problem, we used the DRL approach, which employs a DNN as a function approximator to predict value functions. We implemented the fixed target network and experience replay technique to ensure a stable training process. In order to allocate the optimal number of computing resources, we utilized the greedy algorithm;
* Extensive simulation results demonstrate that the proposed DRL-based offloading and radio resource allocation mechanism can achieve a stable state very quickly and performs well in terms of energy efficiency in MECNs, while computing resource allocation is handled separately with a greedy algorithm. The proposed approach demonstrates better performance when compared to Q-learning, fixed and random approaches, and partial resource allocation schemes.  
The article is organized as follows. The system model is described in Section 2, while Section 3 presents the analytical formulation of the energy optimization problem. Section 4 offers the DRL-based computing offloading policy. The simulation results are presented in Section 5, which validates the performance improvement of the proposed DRL-based computing offloading strategy. Finally, the paper is concluded in Section 6.
## 2 Materials and Methods
### The Significance of Matrix Expandability to Higher Orders  
Expanding the dimension of the matrix to a higher order significantly enhances its ability to process the information carried by a large number of cardiac intervals. One of the hypotheses raised in this paper claims that the sensitivity and the variability of the analyzed data typically increases with the matrix size. In other words, the algorithm becomes more responsive and capable of capturing subtle changes between cardiac intervals as more information is added to it. Variability, on the other hand, is defined as the qualitative measure that reflects the extent of the data spread under distribution curves, which is crucial for the classification of individuals [20]. For instance, as demonstrated in [20], the PMLD approach shows an increase in both sensitivity and variability when the matrix order is increased from the second to the third.  
While the MA1 matrix can be expanded to higher orders, the multiplication of the zero-order derivative elements by a scalar greater than one could affect its ability to detect subtle variations in the analyzed signals. However, the MA2 architecture is not expandable to higher orders because it fails to meet the necessary conditions for matrix expandability.  
Therefore, it is natural to focus on the expansion of the PMLD matrices for further analysis in this paper. As mentioned previously, five cardiac parameters (JT wave, QRS complex, RR cardiac interval, AP wave, and DP wave) are recorded for each participant. The main objective of this paper is to explore whether increasing the matrix size from the second to the fifth order would improve the classification of individuals for the detection of AF episodes or not. Consequently, this paper methodically expands the PMLD architecture from the second order (JT, QRS and JT, RR cardiac parameters) to the third order (JT, QRS, RR cardiac parameters), to the fourth order (JT, QRS, RR, AP cardiac parameters), and finally to the fifth order (JT, QRS, RR, AP, DP cardiac parameters), respectively.
## 4 Simulation
### Simulation on ISSA Performance
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Functions** & **Algorithm** & **Best Value** & **Worst Value** & **Average Value** \\ \hline \multirow{8}{*}{F5} & SSA & \(0.0000\times 10^{0}\) & \(3.0119\times 10^{-124}\) & \(3.0119\times 10^{-125}\) \\  & ISSA & \(0.0000\times 10^{0}\) & \(0.0000\times 10^{0}\) & \(0.0000\times 10^{0}\) \\  & PSO & \(6.2717\times 10^{1}\) & \(1.7519\times 10^{2}\) & \(1.2573\times 10^{2}\) \\  & Chimp & \(1.7803\times 10^{0}\) & \(2.2152\times 10^{1}\) & \(9.1301\times 10^{0}\) \\  & GWO & \(1.4177\times 10^{-11}\) & \(4.5254\times 10^{-9}\) & \(1.0949\times 10^{-9}\) \\  & WOA & \(5.0206\times 10^{2}\) & \(2.9749\times 10^{2}\) & \(4.3256\times 10^{2}\) \\ \hline \multirow{8}{*}{F6} & SSA & \(8.8818\times 10^{-16}\) & \(8.8818\times 10^{-16}\) & \(8.8818\times 10^{-16}\) \\  & ISSA & \(8.8818\times 10^{-16}\) & \(8.8818\times 10^{-16}\) & \(8.8818\times 10^{-16}\) \\  & PSO & \(1.0527\times 10^{0}\) & \(3.0465\times 10^{0}\) & \(2.2359\times 10^{0}\) \\  & Chimp & \(1.9961\times 10^{1}\) & \(1.9964\times 10^{1}\) & \(1.9963\times 10^{1}\) \\  & GWO & \(5.0626\times 10^{-14}\) & \(7.5495\times 10^{-14}\) & \(6.1995\times 10^{-14}\) \\  & WOA & \(8.8818\times 10^{-16}\) & \(7.9936\times 10^{-15}\) & \(4.0856\times 10^{-15}\) \\ \hline \multirow{8}{*}{F7} & SSA & \(1.4742\times 10^{-6}\) & \(1.4673\times 10^{-2}\) & \(5.0658\times 10^{-3}\) \\  & ISSA & \(5.7401\times 10^{-5}\) & \(2.4232\times 10^{-2}\) & \(3.3781\times 10^{-3}\) \\  & PSO & \(2.1128\times 10^{-1}\) & \(9.0880\times 10^{-1}\) & \(4.6949\times 10^{-1}\) \\  & Chimp & \(2.6782\times 10^{0}\) & \(2.9905\times 10^{0}\) & \(2.8295\times 10^{0}\) \\  & GWO & \(3.0732\times 10^{-1}\) & \(7.2904\times 10^{-1}\) & \(5.0930\times 10^{-1}\) \\  & WOA & \(1.2416\times 10^{-1}\) & \(9.4357\times 10^{-1}\) & \(3.1349\times 10^{-1}\) \\ \hline \multirow{8}{*}{F8} & SSA & \(3.1419\times 10^{-4}\) & \(1.2254\times 10^{-3}\) & \(5.2091\times 10^{-4}\) \\  & ISSA & \(3.0750\times 10^{-4}\) & \(3.6394\times 10^{-4}\) & \(3.1646\times 10^{-4}\) \\ \cline{1-1}  & PSO & \(7.9768\times 10^{-4}\) & \(9.9879\times 10^{-4}\) & \(9.1620\times 10^{-4}\) \\ \cline{1-1}  & Chimp & \(1.2354\times 10^{-3}\) & \(1.3394\times 10^{-3}\) & \(1.2804\times 10^{-3}\) \\ \cline{1-1}  & GWO & \(3.0755\times 10^{-4}\) & \(2.0363\times 10^{-2}\) & \(4.4465\times 10^{-3}\) \\ \cline{1-1}  & WOA & \(3.1812\times 10^{-4}\) & \(6.9223\times 10^{-4}\) & \(4.9395\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results for six different optimization algorithms.
# Towards a Multi-Perspective Time of Flight Laser Ranging Device Based on Mirrors and Prisms
## 5 Conclusions  
In this article, we explored the influence of first surfaces and regular mirrors, as well as AR-coated prisms, on the performance of a light ToF ranging device with TOT-based walk error compensation. This was carried out for eleven distances to mirrors or prisms, while the target was moved in 1 cm increments. It has been determined that there is no significant difference between direct measurements and the ones where the FOV was with clean mirrors. Furthermore, there was no significant difference between a first surface or a regular mirror. When the mirror is dusted, the measurements are essentially the same as with direct measurements, so long as the reflecting surface is within the partially dead zone or closer. Beyond that, light reflecting off dust introduces a negative measurement error that is more pronounced when the target is further away. This effect is more noticeable when the mirror is closer to the ranging device. Beyond a certain distance, between the redirection optics and ranging device, dust's influence on the measurements becomes undetectable. A method for detecting and compensating for the reflection off lint on a mirror might be explored in the future to determine whether redirecting FOVs with mirrors shows potential for use in industrial environments.  
This study has also examined the usability of prisms for redirecting FOV of a direct ToF ranging device. As long as the entire prism is well within the ranging device's partially dead zone or closer, a strong correlation between the set and measured distance can be observed, even though parasitic reflections have some influence on measurements. When the prism is placed further, parasitic reflections quickly exceed and overpower the strength of reflections off the target when the target moves away, at which point the measurements plateau. This was observed in the best-case scenario for the prisms--a clean prism with AR-coated legs. The results of this study show that the prisms in general are not a feasible option for achieving the set goals, unless transmitted and received light paths could be separated entirely.  
Conceptualization, M.M. and L.P.; methodology, M.M. and L.P.; software, L.P.; validation, M.M. and L.P.; formal analysis, M.M. and L.P.; investigation, L.P.; resources, M.M. and L.P.; data curation, L.P.; writing--original draft preparation, L.P.; writing--review and editing, M.M.; visualization, L.P.; supervision, M.M.; project administration, M.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the financial support from the Slovenian Research Agency (research core founding No. P2-0228).  
Not applicable.  
All data are an integral part of the corresponding author's PhD thesis.  
The authors declare no conflict of interest.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 4 Simulation
### Simulation Parameter Setting  
Since the PI scheme and DuRT scheme require two trajectories, we designed a triangular prism sensing area. The top of the triangular prism is an equilateral triangle, the left side of the top triangle is the trajectory 1 of the mobile anchor node, and the right side of the top triangle is the trajectory 2. The length of the trajectory is \(L=600\) m, and the depth of the triangular prism is \(H=100\) m.  
Figure 10: Judging the location of the point C.  
\(N=100\) nodes are randomly deployed in a triangular prism. The interval between adjacent broadcast points \(\Delta L\) was set to 1 m, 5 m, 10 m, 15 m, and 20 m. The interval between adjacent extended broadcast points is \(\Delta L_{ext}=0.5\) m. The spacing between adjacent reference trajectories \(\Delta d\) is 1 m. The acoustic parameters are: \(SL=100\) db, \(f=24\) KHz, and \(k=2\). The speed of the ship is \(v=5\) m/s. In the localization process of this paper, the underwater sensor node does not need to transmit messages, thus the coverage distance of the underwater sensor node is short and is set to \(cd_{s}=100\) m. The mobile anchor node needs a large enough coverage distance so that the transmitted message can be received by all sensor nodes in the area, and is set to \(cd_{a}=600\) m. For the convenience of viewing, the parameter settings of the simulation are as shown in Table 2.
## 3 Design of the Bifocal DRAA Configuration
The amplitude distributions of the incident field on the main-RA produced by the feeds F\({}_{1}\), F\({}_{3}\) and F\({}_{5}\) (the two foci of the bifocal antenna and the central feed) are shown in Figure 3:0 Note that the elliptical illuminations obtained on the main-RA are a consequence of applying the bifocal technique for a high degree of beam spacing reduction in the \(xz\)-plane, which spreads the illumination along the vertical axis of the main-RA, while the illumination along its horizontal axis is determined by the monofocal design (adjusted for a 1.8-m width of the main-RA). The elliptical shape of the main-RA allows obtaining a proper illumination and ensures an acceptable spillover. The designed bifocal DRAA has been used to produce multiple spots considering the cluster of horns shown in Figure 9a. This cluster includes the five initial feeds, from F\({}_{1}\) to F\({}_{5}\), and ten additional feeds placed adjacent to the previous ones. The pattern contours of 40 dBi and 47.5 dBi (which is around \(-3\) dB below the maximum gain) for the beams generated by bifocal DRAA at 20 GHz are shown in Figure 9b. The bifocal antenna is able to produce adjacent beams with 0.56\({}^{\circ}\) separation in planes \"\(v=\) constant\". Due to the use of an elliptical main RA, the antenna generates elliptical spots with a different beamwidth in each of the principal planes (e.g., the beamwidth at 46 dBi gain level for the central beam is 0.39\({}^{\circ}\times 0.67^{\circ}\)). The interleaved beams required for providing the complete multi-spot coverage will be generated in the orthogonal polarization. Figure 3:1 Simulated radiation patterns at 20 GHz in the azimuth plane of the beams generated by the bifocal DRAA (solid colored lines) and the beams produced by the equivalent monofocal DRAA (dashed lines), showing the maximum side-lobe levels (SLL, in black dotted lines) that produce the interference among beams with equal polarization.