esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Effect of S. convivens Extract on S. frugiperda  
Larval mortality followed a dose-response curve (\(p\) < 0.0001), with a mortality of 55, 70, 80, and 90% at 1000, 2000, 4000, and 5000 ppm, respectively, and LC\({}_{50}\) was 1504.03 ppm. There was a positive correlation (\(p\) < 0.05) between the larval mortality and the increase in the concentration of CHCl\({}_{3}\)Sc. There were differences between treatments in pupal mortality (Table 1). There was a statistically significantly (\(p\) < 0.01) longer larval duration at 7.6, 9.5, 28.2, and 34.7 days at concentrations of 1000, 2000, 4000, and 5000 ppm, respectively, compared with the control (21.8 days). The period of pupal phase was significantly higher at 1.6 and 4.1 days at 1000 and 2000 ppm compared with the control (9.6 days). The results (Table 1) showed a positive correlation between larval (\(p\) < 0.05) and pupal (\(p\) < 0.05) phase duration and increased concentrations of CHCl\({}_{3}\)Sc. Moreover, pupal weight was negatively correlated (\(p\) < 0.05) with the concentration of CHCl\({}_{3}\)Sc. Decreases in pupal weights by 39, 54, and 57.4% were observed at CHCl\({}_{3}\)Sc concentrations of 2000, 4000, and 5000 ppm, respectively, compared with the control (229.8 mg).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Treatment (ppm)**} & \multicolumn{2}{c}{**Mortality \%**} & \multicolumn{2}{c}{**Duration (days)**} & \multirow{2}{*}{**Pupal Weight (mg)**} \\ \cline{2-2} \cline{4-5}  & **Larvae** & **Pupae** & **Larvae** & **Pupae** \\ \hline
5000 & 90 \(\pm\) 6.9 \({}^{\mathrm{a}}\) & 10 \(\pm\) 6.9 \({}^{\mathrm{a}}\) & 56.5 \(\pm\) 4.5 \({}^{\mathrm{a}}\) & ND & 98 \(\pm\) 3 \({}^{\mathrm{a}}\) \\ \hline
4000 & 80 \(\pm\) 9.2 \({}^{\mathrm{a}}\) & 20 \(\pm\) 9.2 \({}^{\mathrm{a}}\) & 50 \(\pm\) 1.2 \({}^{\mathrm{a}}\) & ND & 105.8 \(\pm\) 7.7 \({}^{\mathrm{a}}\) \\ \hline
2000 & 70 \(\pm\) 10.5 \({}^{\mathrm{ab}}\) & 15 \(\pm\) 8.2 \({}^{\mathrm{a}}\) & 31.3 \(\pm\) 2.4 \({}^{\mathrm{b}}\) & 13.7 \(\pm\) 0.3 \({}^{\mathrm{a}}\) & 140.2 \(\pm\) 10.8 \({}^{\mathrm{b}}\) \\ \hline
1000 & 55 \(\pm\) 11.4 \({}^{\mathrm{ab}}\) & 15 \(\pm\) 8.2 \({}^{\mathrm{a}}\) & 29.4 \(\pm\) 0.7 \({}^{\mathrm{b}}\) & 11 \(\pm\)0.4 \({}^{\mathrm{b}}\) & 164.5 \(\pm\) 11.9 \({}^{\mathrm{bc}}\) \\ \hline
500 & 35 \(\pm\) 10.9 \({}^{\mathrm{bc}}\) & 0 \(\pm\) 0 \({}^{\mathrm{a}}\) & 23.7 \(\pm\) 1.1 \({}^{\mathrm{c}}\) & 9.8 \(\pm\) 0.2 \({}^{\mathrm{c}}\) & 207.5 \(\pm\) 11.2 \({}^{\mathrm{bc}}\) \\ \hline
0 & 10 \(\pm\) 6.9 \({}^{\mathrm{c}}\) & 0 \(\pm\) 0 \({}^{\mathrm{a}}\) & 21.8 \(\pm\) 0.7 \({}^{\mathrm{c}}\) & 9.6 \(\pm\) 0.2 \({}^{\mathrm{c}}\) & 229.8 \(\pm\) 4.6 \({}^{\mathrm{c}}\) \\ \hline LC\({}_{50}\) & & & & & \\ \hline \hline \end{tabular}  
* Average values (\(\pm\)SE) followed by the same letter are not significantly different (\(p\) < 0.05, Tukey’s test). ND, no data because all individuals presented mortality of 100%.  
\end{table}
Table 1: Insecticide and insecistatic activities from chloroform extract of aerial parts of _Salvia convivens_ against _Spodoptera frugiperda_.
###### Abstract  
Mobile ad hoc networks (MANETs) have gained a lot of interests in research communities for the infrastructure-less self-organizing nature. A MANET with fleet cyclists using smartphones forms a two-tier mobile long-thin network (MLTN) along a common cycling route, where the high-tier network is composed of 3G/LTE interfaces and the low-tier network is composed of IEEE 802.11 interfaces. The low-tier network may consist of several path-like networks. This work investigates cooperative sensing data collection and distribution with packet collision avoidance in a two-tier MLTN. As numbers of cyclists upload their sensing data and download global fleet information frequently, serious bandwidth and latency problems may result if all members rely on their high-tier interfaces. We designed and analyzed a cooperative framework consisting of a distributed grouping mechanism, a group merging and splitting method, and a sensing data aggregation scheme. Through cooperation between the two tiers, the proposed framework outperforms existing works by significantly reducing the 3G/LTE data transmission and the number of 3G/LTE connections.  
ad-hoc network; cycling group; fleet management; mobile long-thin network; sensor network +
Footnote †: journal: Sensors
# DFT-Spread Spectrally Overlapped Hybrid OFDM-Digital Filter Multiple Access IMDD PONs
## 3 Upstream Optimum ONU Operating Conditions
### PAPR Performance of DFT-Spread Hybrid OFDM-DFMA PON
In addition, it also relaxes the constraints on the linear dynamic operating ranges of the transceiver-embedded optical/electrical devices, reduces the optical nonlinearity impairments, and allows reductions in the DSP complexity and overall cost of the transceivers.
## 5 Discussion
The setup of the attenuation images surpassed at least slightly the performance of the method in comparison to the setup of the original data. An interesting finding in this work is that in the classification of A-lines to fibrolipidic or fibrolcific, the accuracy was enhanced from 74.94% to 83.4% by using attenuation coefficients estimation. Concerning deep learning methods, possibly, there is room for improvement since the exhaustive use of original architectures and subsequent hyper-parameter tuning may lead to better classification. However, several tests were carried out with much different convolutional network set-ups that did not achieve significantly different results. Therefore, we believe that we have acquired the maximum of the descriptive features that can be acquired by this dataset. It was chosen to split the dataset into three parts: training, validation, and testing. Each part was derived from a different random group of patients. The validation set was used in training for regularization, and the test set was not used at all in training, which stopped after several epochs with no training accuracy improvement. Therefore, this method has the potential to generalize new data if there is an availability of such data. In conclusion, there are four key steps that are original in the method presented: initial image segmentation (A-lines are not considered whole but part of the arterial wall), the use of OCT-specific transformations, a specific CNN architecture for A-line classification and simple but efficient post-processing. Finally, the annotated dataset developed and used for the purposes of this study is a valuable resource for our scientific community. The results and especially the sensitivity in detecting abnormal A-lines can be found satisfactory since we considered more types of tissues than other studies: normal, lipid, fibrous, calcified, and mixed. Only [PERSON] et al. [15] examined the same tissue types, but they used many heuristic and hand-crafted steps, and therefore, their method cannot easily generalize to new data. The method presented here is very slightly dependent on parameters and has the potential for a more complete semantic analysis of the intravascular OCT images.
###### Abstract
This paper proposes a semantic segmentation method for distorted aerial images which can easily be implemented in software-based radio receivers to provide it as an added functionality to the radio. The image approximating network included in the proposed method can improve the quality of a distorted image in such a way that it is more applicable to the segmentation model, hence the combined model can predict segmentations for distorted aerial images with a higher accuracy than the results produced by using only the pretrained segmentation model. This work skillfully combines several concepts, approaches, techniques, and components such as semantic segmentation, deep  
Figure 1: (**a**) Transmitted image and (**b**) received image. learning, aerial images, and existing segmentation models if needed, such as U-net and Linknet. Further, according to the user's preference or a model's appropriateness in a certain problem domain, different segmentation model architectures can be used as a module in the proposed method. Contributions from the proposed method are listed as follows. * We propose a combined deep learning model of an approximating network to be used with a segmentation network. A programmable interconnection between the approximating block and the segmentation block provides the possibility of changing, training, or adjusting participating deep learning models according to the relevance of the problem to be solved. * A comprehensive loss function is proposed to train the combined model optimally. * The proposed method provides a compatible implementation of the software-based image receiver and aerial image segmentation in a small-scale computer such as a single board computer (SBC). * The developed segmentation model is compared with similar benchmark networks to demonstrate the robustness of the proposed method, verifying that the proposed method obtains relative improvements of up to 80% in terms of mean IoU.
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 4 Application to GPS and Galileo Signals
### Comparison of Galileo E1 and E5 Signals Acquisition
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sensitivity**} & \multicolumn{4}{c}{**Parallel without Assistance**} & \multicolumn{4}{c}{**Serial with Assistance**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Non-Coherent**} & \multicolumn{2}{c}{**Coherent Only**} & \multicolumn{2}{c}{**Non-Coherent**} & \multicolumn{2}{c}{**Coherent Only**} \\ \cline{2-9}  & **Average** & **Worst** & **Average** & **Worst** & **Average** & **Worst** & **Average** & **Worst** \\ \hline \(-140\) dBm & 41.9 & 41.9 & 41.9 & 41.9 & 10.5 & 10.5 & 5.4 & 6.2 \\ \(-145\) dBm & 41.9 & 42.0 & 41.9 & 42.6 & 10.5 & 10.8 & 6.1 & 6.3 \\ \(-150\) dBm & 43.3 & 43.5 & 32.3 & 27.6 & 10.9 & 10.9 & 6.7 & 6.8 \\ \(-155\) dBm & 41.6 & 44.2 & 30.2 & 28.7 & 10.9 & 11.0 & 6.5 & 6.5 \\ \(-160\) dBm & 44.3 & 44.0 & 29.1 & 27.6 & 10.9 & 11.2 & 6.5 & 6.5 \\ \hline \(-140\) dBm & 41.8 & 41.8 & 41.8 & 41.8 & 18.4 & 18.4 & 6.3 & 9.1 \\ \(-145\) dBm & 41.8 & 41.2 & 41.2 & 45.5 & 18.4 & 18.4 & 8.8 & 12.7 \\ \(-150\) dBm & 41.8 & 42.3 & 31.5 & 28.6 & 16.0 & 28.0 & 11.7 & 19.0 \\ \(-155\) dBm & 35.4 & 42.5 & 28.4 & 30.3 & 17.4 & 40.7 & 13.9 & 23.5 \\ \(-160\) dBm & 23.4 & 46.9 & 25.1 & 29.5 & 16.4 & 45.8 & 15.0 & 26.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Ratio of complexity (product of processing time ratio and memory ratio) between the E5 signal and the E1 signal with a sampling frequency of 4.096 MHz. Top rows do not consider the input memory, while bottom rows consider the input memory. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sensitivity**} & \multicolumn{4}{c}{**Parallel without Assistance**} & \multicolumn{4}{c}{**Serial with Assistance**} \\ \cline{2-9}  & **Non-Coherent** & **Coherent Only** & **Non-Coherent** & **Coherent Only** \\ \cline{2-9}  & **Average** & **Worst** & **Average** & **Worst** & **Average** & **Worst** \\ \hline \(-140\) dBm & 3.0 & 3.0 & 3.0 & 3.0 & 3.0 & 3.0 & 3.0 \\ \(-145\) dBm & 3.0 & 3.0 & 3.0 & 3.0 & 3.0 & 3.0 & 3.0 \\ \(-155\) dBm & 2.8 & 3.0 & 3.0 & 3.0 & 3.0 & 3.0 & 3.0 \\ \(-160\) dBm & 3.0 & 3.0 & 3.1 & 3.0 & 2.8 & 3.0 & 3.0 \\ \hline \(-140\) dBm & 3.0 & 3.0 & 3.0 & 3.0 & 3.0 & 3.0 & 3.4 \\ \(-145\) dBm & 3.0 & 2.9 & 3.0 & 3.2 & 3.0 & 3.8 & 3.0 & 3.8 \\ \(-150\) dBm & 3.0 & 3.1 & 3.0 & 3.2 & 3.0 & 4.8 & 3.1 & 4.5 \\ \(-155\) dBm & 2.9 & 4.3 & 3.0 & 3.5 & 3.0 & 7.2 & 3.1 & 5.0 \\ \(-160\) dBm & 3.0 & 7.5 & 3.0 & 4.0 & 3.0 & 8.7 & 3.1 & 5.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 19: Ratio of complexity (product of processing time ratio and memory ratio) between the E1 signal with a sampling frequency of 6.144 MHz and the E1 signal with a sampling frequency of 4.096 MHz. Top rows do not consider the input memory, while bottom rows consider the input memory.
## 2 System Modeling  
### AUV Modeling in Horizontal Plane  
A mathematical model for the horizontal motion of underactuated AUVs can be examined in two separate reference frames: the inertial frame and the body-fixed frame, as shown in Figure 1. The inertial frame is defined relative to the Earth's reference ellipsoid, and the body-fixed frame is a moving coordinate frame that is fixed to the craft.  
In this paper, we consider the horizontal motion of under-actuated AUVs, which could neglect the dynamics of heave, roll, and pitch. Additionally, the kinematic equation could be expressed as follows:  
\[\left\{\begin{array}{l}\dot{x}=u\cos(\psi)-v\sin(\psi)\\ \dot{y}=u\sin(\psi)+v\cos(\psi)\\ \dot{\psi}=r\end{array}\right.. \tag{1}\]  
Additionally, the dynamic equation is provided as follows:  
\[\left\{\begin{array}{l}\dot{u}=M_{1}(X_{u}u+a_{23}vr+T+d_{1})\\ \dot{v}=M_{2}(Y_{v}v+a_{13}ur)\\ \dot{r}=M_{3}(N_{r}r+a_{12}uv+\tau_{r}+d_{2})\end{array}\right.. \tag{2}\]  
The main model parameters are shown in Table 1, and some parameters are calculated as \(M_{1}=1/(m-X_{u})\), \(M_{2}=1/(m-Y_{v})\), \(M_{3}=1/(I_{zz}-N_{r})\), \(a_{12}=Y_{v}-X_{u}\), and \(a_{13}=X_{u}-m\), \(a_{23}=m-Y_{v}\).  
Figure 1: Vehicle coordinate systems.
## 1 Introduction
In the latter case, the catalytic properties of Eosin Y rely on its redox potential: the oxidation potential range is between \(-1.06\) V and \(1.10\) V, whereas the reduction potential is between \(+0.78\) V and \(+0.83\) V (Scheme 2a). The maximum of absorption sits at 539 nm, with a molar extinction coefficient \(\varepsilon=60\) 803 M\({}^{-1}\) cm\({}^{-1}\) (Scheme 2b) [17; 18; 19], and for this reason, green LEDs are usually employed as convenient light sources for Eosin Y activation (maximum emission around 530 nm). Upon excitation by light, Eosin Y undergoes a rapid intersystem crossing from the ground state to the lowest energy triplet state. Direct singlet-triplet transition is not allowed, but light excites Eosin Y electrons to a higher excited singlet state from where they relax quickly to the lowest excited singlet state; only at this point do excited electrons undergo intersystem crossing to the high reactive triplet state, which has a lifetime of 24 ms [18; 19]. When in solution, Eosin Y equilibrates into four different forms due to the presence of two relatively acidic protons (pKa 2.0 and 3.8 in water, Scheme 2c). All these forms are pH dependent: at pH \(<2\), the protonated spirocyclic form **EY1** is in equilibrium with the neutral form **EY2**, whereas at \(2<\) pH \(>3.8\), the monoanionic form **EY3** is in equilibrium with the dianionic form **EY4**, which becomes predominant at pH \(>3.8\). Only **EY3** and **EY4** are catalytically active, but this behavior has generated a lack of clarity in many publications regarding the nature of the dye involved in the transformation [20]. In order to ensure the presence of the dianionic form **EY4**, Eosin Y sodium salt is employed as a photocatalyst, although the experimental reaction conditions of chemical transformation should be taken into account.
# Machine Learning and Deep Learning Based Model for the Detection of Rootkits Using Memory Analysis
## 4 Methodology
### Data Acquisition
#### 4.1.1 Collection of Rootkit Samples  
The rootkit binary files were collected from VirusShare and VirusTotal. A total of 400 binary files from various categories of rootkits (e.g., kernel rootkits, memory rootkits, etc.) were collected.
# Hybrid Beamforming for Reduction of Inter-Beam Interference in Millimeter-Wave Cellular Systems
## 2 Hybrid Beamforming Techniques for a Multicell Multiuser Environment
### Type-3 Precoder
\begin{split}&\|\mathbf{F}_{RF}^{b_{n}}\mathbf{\epsilon}_{BB,u_{n,m}}^{b_{n }}\|^{2}=\Big{(}\mathbf{F}_{RF}^{b_{n}}\mathbf{\epsilon}_{BB,u_{n,m}}^{b_{n}}\Big{)} ^{*}\Big{(}\mathbf{F}_{RF}^{b_{n}}\mathbf{\epsilon}_{BB,u_{n,m}}^{b_{n}}\Big{)}\\ &=[\mathbf{\Lambda}]_{:u_{n,m}:}\Big{(}\Big{(}\mathbf{D}^{b_{n}} \Big{)}^{*}\Big{)}^{-1}\bigg{(}\Big{(}\mathbf{A}_{BS}^{b_{n}}\Big{)}^{*}\mathbf{ A}_{BS}^{b_{n}}\Big{)}^{-1}\Big{(}\mathbf{A}_{BS}^{b_{n}}\Big{)}^{*}\mathbf{A}_{BS}^{b_{n}} \bigg{(}\Big{(}\mathbf{A}_{BS}^{b_{n}}\Big{)}^{*}\mathbf{A}_{BS}^{b_{n}}\bigg{)} ^{-1}\Big{(}\mathbf{D}^{b_{n}}\Big{)}^{-1}[\mathbf{\Lambda}]_{:,u_{n,m}}\\ &=[\mathbf{\Lambda}]_{u_{n,m},u_{n,m}}^{2}[\mathbf{\mathcal{D}}]_{u_{n,m},u_{m}}^{-2}\Big{(}\Big{(}\mathbf{A}_{BS}^{b_{n}}\Big{)}^{*}\mathbf{A}_{BS}^{b_ {n}}\Big{)}_{u_{n,m},u_{n,m}}^{-1}=1\end{split}  \tag{18}\]  
Using (15) and (18), \([\mathbf{\Lambda}]_{u_{n,m},u_{n,m}}\) can be expressed as  
\[[\mathbf{\Lambda}]_{u_{n,m},u_{n,m}}=\sqrt{\frac{N_{BS}N_{MS}}{\bigg{(}\Big{(} \mathbf{A}_{BS}^{b_{n}}\Big{)}^{*}\mathbf{A}_{BS}^{b_{n}}\Big{)}^{-1}}}\,| \mathbf{x}_{u_{n,m}}| \tag{19}\]  
when the interference signals are completely removed by the Type-3 precoder, the achievable rate in (8) is given by  
\[R_{u_{n,m}}^{b_{n}}=\text{log}_{2}\Bigg{(}1+\frac{\beta}{U\sigma^{2}}\frac{N _{BS}N_{MS}|\mathbf{\epsilon}_{u_{n,m}}|^{2}}{\bigg{(}\Big{(}\mathbf{A}_{BS}^{b_{n }}\Big{)}^{*}\mathbf{A}_{BS}^{b_{n}}\Big{)}^{-1}_{u_{n,m},u_{n,m}}}\Bigg{)} \tag{20}\]
# Research on Innovative Apple Grading Technology Driven by Intelligent Vision and Machine Learning
## 2 Materials and Methods
### Apple Feature Extraction
#### 2.4.5 Apple Shape Feature Extraction  
In current apple grading technologies, the research on shape features is limited. However, high-quality apples also possess a good shape. Therefore, this study introduces the concept of shape features to further enhance apple grading quality. Apples are generally spherical, and their grading is determined by their similarity to a standard circular shape, known as the shape index. The Canny [27] algorithm is a widely used method for edge detection tasks. It begins by applying a Gaussian filter to the image to suppress noise, followed by the computation of horizontal and vertical gradients using the Sobel operator. The algorithm then examines pixels along the gradient direction, suppressing non-maximum values to retain only local maxima. This process helps distinguish between strong and weak edges, ultimately connecting them to form continuous edges. This paper utilizes the Canny algorithm in conjunction with the minimum enclosing rectangle to calculate the  
Figure 8: DPC-AWKNN algorithm fruit segmentation results.  
apple shape index. The segmented RGB image is converted to a grayscale image, and the Canny algorithm is employed to detect the edges of the fruit body. The edge image of the apple is used to calculate the vertical and horizontal diameters via the minimum enclosing rectangle, leading to the determination of the apple shape index. The specific calculation process is detailed in Equation (10).  
\[E=\frac{h}{w} \tag{10}\]  
In this context, \(h\) represents the vertical diameter, while \(w\) represents the horizontal diameter.  
Based on the aforementioned image preprocessing strategies and feature extraction methods, the MB Filter was initially employed to suppress the noise in apple images. Adaptive histogram equalization was then applied in the L color space to correct areas with uneven surface brightness. The DPC-AWKNN method was utilized to accurately segment the apple body. Subsequently, specific feature extraction techniques were used to derive the color, diameter, and shape features of 126 apples. These apples were then classified into corresponding grades according to apple grading standards. The dataset was split into training and testing sets in a 7:3 ratio. The distribution of apples across the four grades is depicted in Figure 9.
# Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation
## 3 Methods
### LEO-Enhanced GNSSs PPP
However, it cannot be separated directly with single-station positioning. Usually, the two are combined for estimation, and the float solution is used. Comprehensively, considering the error source of the LEO simulation data, the observation equation of LEO-enhanced GNSSs PPP is as follows:  
\[\left\{ \begin{array}{l}P_{r,\mathrm{IF}}^{s,K}=\rho_{r}^{s,K}+c\overline{d}t _{r}^{K}+T_{r}^{s,K}+\varepsilon_{r,\mathrm{IF}}^{s,K}\\ L_{r,\mathrm{IF}}^{s,K}=\rho_{r}^{s,K}+c\overline{d}t_{r}^{K}+T_{r}^{s,K}+ \lambda_{\mathrm{IF}}^{s,K}\overline{N}_{r,\mathrm{IF}}^{s,K}+\varepsilon_{L _{r,\mathrm{IF}}^{s,K}}\\ P_{r,\mathrm{IF}}^{s,L}=\rho_{r}^{s,L}+c\overline{d}t_{r}^{L}+\varepsilon_{P_ {r,\mathrm{IF}}^{s,L}}\\ L_{r,\mathrm{IF}}^{s,L}=\rho_{r}^{s,L}+c\overline{d}t_{r}^{L}+\lambda_{ \mathrm{IF}}^{s,L}\overline{N}_{r,\mathrm{IF}}^{s,L}+\varepsilon_{L_{r,\mathrm{ IF}}^{s,L}}\end{array} \right., \tag{16}\]  
In the formula, the superscript \(L\) represents the LEO system. The state vector of unknowns is \(\overrightarrow{X}=\left[x\ y\ z\ \overline{d}t_{r}^{K}\ \overline{d}t_{r}^{L}\ ZWD\ \overline{N}_{r,IF}^{s,K}\ \overline{N}_{r,IF}^{s,L}\right]^{T}\), which will be estimated using a Kalman filter approach. Because the tropospheric dry and wet delays of the LEO simulation data are calculated using the Saastamoinen model (see Section 2), the LEO system does not need to estimate the tropospheric delay parameters, but instead directly uses the model to correct them. A stochastic model based on elevation angle [32] has been adopted, and the cut-off elevation angle was 7 degrees.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 1 Introduction
### Search with Swarms
Each zone must be re-visited frequently enough to ensure up-to-date information. * Patrolling: Similar to surveillance, the area of interest is restricted to singular points or lines (for instance, corridors inside a building). * Probabilistic maps and obstacles: Another extension of the search task in its simplest form is the inclusion of probabilistic maps, which consider prior knowledge about where it is more likely that a target is located in the area. Moreover, obstacles may be included, it being impossible for the agents to go through them. Authors frequently define area coverage as deploying the agents until they reach certain fixed positions so that the covered area (by either the sensor or the communication footprint) is maximized. However, some other authors use the word coverage to define a task similar to search. For instance, in [18], the search area is covered by subdividing it into \(k\) regions, which are individually assigned to \(k\) robots. Each robot searches then in its assigned area following regular patterns. Another example can be found in [19], where the coverage task is addressed using the spanning tree coverage method. In any case, the strategies proposed in these works do not differ from the strategies listed above.