esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Coverage Control Using Memetic Algorithm
### The CoCMA Proposed for Cluster-based WSNs
#### 3.2.4 Local Search Scheme  
In this study, a simple local search strategy is developed to feature the CoCMA with fast convergence and thus further improves the goodness of the population computed by GA operations. For one generation, local search modifies every chromosome (individual) by the proposed local search strategy. This strategy changes the value of every allele from one to zero and determine whether to keep the modification by comparing the fitness of the original and the new chromosomes. The strategy is applied to allele \(\ell_{i,j}\) of chromosome \(i\), where \(j=1\),..., \(N\). This means that we can get a better schedule for nodes than the original one through the process of local search. The pseudo code of local search scheme used in the CoCMA is described as follows. In this search scheme, let \(\mathit{Pop}_{k}=\left\{u_{1},\ u_{2},\...,\ u_{\rho}\right\}_{k}\) be the population \(k\), which is a set of \(\rho\) chromosomes.  
_Pseudo code of the proposed local search scheme_  
**Step 1**: Input a population \(\mathit{Pop}_{k}=\left\{u_{1},\ u_{2},\...,\ u_{\rho}\right\}_{k}\) to the local search unit.  
**Step 2**: Let \(u_{i}\) be the \(i\)-th chromosome in \(\mathit{Pop}_{k}\), and \(N\) be the length of \(u_{i}\). According to the definition of allele \(\ell_{i,j}\), we know that \(u_{i}\) is a binary string composed of \(\ell_{i,j}\), where \(\forall i,\ 1\leq j\leq N\).  
**For \(i=1\)** to \(\rho\)**do**  
**For \(j=1\)** to \(N\)**do**  
**If the value of \(\ell_{i,j}\) is equal to one then**  
**Record the allele \(\ell_{i,j}\) in the array \(S_{i}\).**  
**End**  
**End**  
**Step 3**: **For \(y=1\) to \(\rho\)**do**  
Let \(h\) be the length of \(S_{y}\).  
**For \(l=1\)** to \(h\)**do**  
\(\mathit{fit}nessl=\mathit{fit}(u_{\
u})\); // \(\mathit{fit}()\) is the fitness function.  
In \(u_{\
u}\), let the allele \(c_{\mathit{imp}}\), corresponding to the \(l_{\mathit{in}}\) element in \(S_{y}\), be zero.  
\(\mathit{fit}ness2=\mathit{fit}(u_{\
u})\); // Re-evaluate the fitness  
**If \(\mathit{fit}nessl>\mathit{fit}ness2\) then**  
Let \(c_{\mathit{imp}}\) equal to one.  
**End**  
**End**  
**Step 4**: Output the improved \(\mathit{Pop}_{k}\).  
The chromosome could be refined to a better one if unnecessary nodes are found. Unlike the conventional GAs, the proposed CoCMA can get better results by performing the local search process. Using the developed local search scheme, the CoCMA converges quickly. It is worthy to note that selecting an appropriate number of chromosomes in each generation is important, since the large number of chromosomes will slow down the process of convergence.
# Protocol-Specific and Sensor Network-Inherited Attack Detection in IoT Using Machine Learning
## 4 Results and Discussion
### Performance Parameters, Evaluation and Comparison
#### 4.1.5 Cohen's Kappa  
CK is a statistical evaluation metric which is used to measure the inter-rater agreement and reliability. It considers TP, TN as agreement values, and FP, FN as agreement by chance or disagreement values for evaluating a model within the range of -1 and 1. The closer the value is to 1, it indicates that the better the model has performed. CK is represented by \(\kappa\). The equation to calculate CK is presented below (Equation (6)) where \(p_{0}\) and \(p_{e}\) can be calculated using the confusion matrix parameters through Equations (4) and (5), respectively.  
\[p_{0}=\frac{TP+TN}{N} \tag{4}\]  
where \(N\) is equal to the sum of all the observations.  
\[p_{e}=\frac{z_{1}\times y_{1}}{N^{2}}+\frac{z_{2}\times y_{2}}{N^{2}}+\frac{z_ {3}\times y_{3}}{N^{2}} \tag{5}\]  
where \(z_{1}\), \(y_{1}\), \(z_{2}\), \(y_{2}\), \(z_{3}\), and \(y_{3}\) are derived from the confusion matrix presented in Figure 14.  
A value less than 0 indicates that there is no agreement between the actual classes and the predicted classes, while a range of 0-20 shows imperceptible agreement. The ranges between 21-40, 41-60, 61-80, 81-99 indicate fair, moderate, substantial, and excellent agreement, while a value of 1 is designated for perfect performance of the model.  
\[\kappa=\frac{p_{0}-p_{e}}{1-p_{e}} \tag{6}\]  
Figure 15 shows the performance of ProSenAD in terms of kappa coefficient. The comparison indicates that the proposed model performs better than the benchmarks. GB shows a value of 0.827 in the graphical representation of results which indicates it gaining the poorest results among all methods used for comparison, while LGBM performs better than XGBoost. However, ProSenAD outperforms LGBM with a value of 0.95.
## 3 Data Analysis and Processing Method
### Data Extraction using the CSP Filtering Method
#### 3.1.1 Analyze the Extracted Data  
After extraction and processing of the original EEG data and CSP-filtered data, the data are regularized by channel to increase readability; the data will have a value between 0 and 1. Before processing the data, similarities and differences between the two data are distinguished (Figure 9). The two signals within the same interval appear to be the same, but differences before and after CSP filter processing can be distinguished.  
Left- and right-handed data for each normalized channel are added by the global summing method, which is divided left-to-right to extract the intensity of the frequency. The results of the FFT (fast Fourier transform) conversion of raw data produced by this process and the energy distribution and channel-specific contrast results can be shown in Figure 10.
# Improving Water Efficiency in a Municipal Indoor Swimming-Pool Complex: A Case Study
## 3 Results
### Proposed Measures and Their Viability to Promote Water Use Efficiency  
As mentioned in Table 3, these measures were organized in scenarios. The estimated water consumption reduction after implementing the water-saving measures proposed for each is presented in Table 4.  
In order to reduce water consumption, scenarios 2 and 3 are the most effective, since both include shower replacement measures. Indeed, the showers were the devices that presented the highest water consumption. The predicted costs for the implementation of each scenario are \(\epsilon\)2290.51 for scenario 1, \(\epsilon\)858.97 for scenario 2, and \(\epsilon\)3149.48 for scenario 3. It should be stressed that the regulation of flushing cisternes imposes no costs on the municipality. The annual water bill reduction would be \(\epsilon\)7115.44 (scenario 1), \(\epsilon\)11,518.11 (scenario 2), and \(\epsilon\)17,655.92 (scenario 3). The turnover of the investment for scenarios 1, 2, and 3 would be four, one, and three months, respectively. Indeed, the turnover periods obtained were considerably low--less than one year for all the scenarios. Despite presenting the largest initial investment, scenario 3 is the most effective, promoting higher water savings. The predicted turnover for scenario 3 can be justified by including measures from both scenarios 1 and 2. Indeed, the replacement of taps (scenario 1) combined with  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multicolumn{2}{c}{**Consumption without Measures**} & \multicolumn{2}{c}{**Predicted Consumption with Measures**} & \multicolumn{2}{c}{**Predicted Reduction**} \\ \cline{2-5}  & **(m\({}^{3}\))** & **(m\({}^{3}\))** & **(m\({}^{3}\))** & **(\%)** \\ \hline
1 & 2363.9 & 1034.2 & 1329.7 & 8.03 \\
2 & 6165.1 & 3980.6 & 2184.6 & 13.19 \\
3 & 8115.4 & 4739.0 & 3376.4 & 20.39 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Water consumption without and with water-saving measures.  
shower replacement (scenario 2) represents the highest initial investment. Nevertheless, these costs can be offset by the reduction in water consumption. Therefore, one year after implementing the water-saving measures, the predicted turnover for scenarios 1, 2, and 3 is \(\epsilon\)4824.93, \(\epsilon\)10,659.14, and \(\epsilon\)14,506.44, respectively. Moreover, the predicted water consumption reduction can reach 20.39% (Table 4).
## 3 Results
### Spatial Patterns and Elevational Gradients of Climate-Driving Effects on Corpland Ecosystem NPP  
Figure 6 shows the spatial patterns and elevational gradients of linear regression results between cropland ecosystem NPP and climate factors from 1981 to 2014. SRCs of precipitation and solar radiation were positive in most parts of the investigated area, while SRCs of temperature were negative in south and northeast parts of investigated area but were positive in Xuefeng Mountain, Dalou Mountain, Hengduan Mountains, west Sichuan Plateau, and Songpan Plateau. SRCs of solar radiation were much higher than those of temperature and precipitation, which indicated solar radiation was the main factor driving NPP change positively. Elevational gradient of linear regression result showed that SRCs of temperature changed from negative between 1000-2100 m ASL to positive between 2200-4500 m ASL, while those of solar radiation were significantly positive at all elevation bins. Along increasing elevations, SRCs of temperature showed an elevation-dependent increasing pattern, but SRCs of solar radiation showed an elevation-dependent, decreasing pattern from 1000 m ASL to 3200 m ASL, and then, both of them tended to be stable above 3300 m ASL.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**NPP** & **Climate Factor** &
\begin{tabular}{c} **Standardized Regression** \\ **Coefficient** \\ \end{tabular} & \(p\)**-Value** \\ \hline Averaged annual NPP of the entire study area from & Temperature & \(-\)0.13 & 0.07 \\
1981 to 2014 &
\begin{tabular}{c} Precipitation \\ Solar radiation \\ \end{tabular} & 0.13 & 0.03 \\ \hline NPP trends of all elevation & Temperature trends & 0.82 & \(<\)0.001 \\ bins from 1000 m ASL to & Precipitation trends & \(-\)0.53 & \(<\)0.001 \\
4500 m ASL & Solar radiation trends & 0.34 & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linear regression results between cropland ecosystem NPP and climate factors.  
Figure 6: Spatial patterns and elevational gradients of standardized regression coefficient betweencropland ecosystem NPP and climate factors, respectively. Subfigure (**a-c**) show spatial patterns of SRCs of temperature, precipitation, and solar radiation respectively, while subfigure (**d-f**) show elevational gradients of SRCs of temperature, precipitation, and solar radiation respectively. One asterisk next to column bar means the correlation reached a statistically significant level (_p_ < 0.05). Two asterisks next to column bar refer to a _p_-value of the correlation of less than 0.01. Three asterisks next to column bar refer to a _p_-value of the correlation of less than 0.001.
## 2 Materials and Methods
### Unsupervised Homography Estimation Model
#### 2.1.2 Tensor Direct Linear Transformation Layer  
This layer essentially applies the Direct Linear Transformation (DLT) algorithm to the tensors in the deep-learning network. Its function is to convert the 4-point parameterized matrix, \(H_{4 points}\), obtained in the previous stage into a \(3\times 3\) homography matrix, \(H\), using  
Figure 2: HomographyNet network model.  
the DLT algorithm. Additionally, it is differentiable to ensure that backpropagation can occur during training. The input of this layer is the coordinates of the four corner points before and after the transformation, and the output is the 3 \(\times\) 3 homography matrix, \(H\). Suppose \(\overset{\sim}{H}\) is the homography matrix of the corresponding corner points \(x_{i}\to x_{i}^{\prime}\). According to the homography relationship, \(x_{i}^{\prime}\sim\overset{\sim}{H}x_{i}\), which is equivalent to \(x_{i}^{\prime}\times\overset{\sim}{H}x_{i}=0\). Let \(h^{\prime\text{T}}\) represent the \(j\)-th row of \(\overset{\sim}{H}\), and let \(h^{j}\) represent the column vector of \(h^{\prime\text{T}}\). Then, \(\overset{\sim}{H}x_{i}\) can be expressed as:  
\[\overset{\sim}{H}x_{i}=\begin{bmatrix}h^{1T}x_{i}\\ h^{2T}x_{i}\\ h^{3T}x_{i}\end{bmatrix}=\begin{bmatrix}x_{i}^{T}h^{1}\\ x_{i}^{T}h^{2}\\ x_{i}^{T}h^{3}\end{bmatrix} \tag{4}\]  
Substituting \(x_{i}^{\prime}=\left(u_{i}^{\prime},v_{i}^{\prime},1\right)^{T}\) into \(x_{i}^{\prime}\times\overset{\sim}{H}x_{i}=0\), we have:  
\[\begin{bmatrix}u_{i}^{\prime}\\ v_{i}^{\prime}\\ 1\end{bmatrix}\begin{bmatrix}x_{i}^{T}h^{1}\\ x_{i}^{T}h^{2}\\ x_{i}^{T}h^{3}\end{bmatrix}=\begin{bmatrix}v_{i}^{\prime}x_{i}^{T}h^{3}-x_{i}^ {T}h^{2}\\ x_{i}^{T}h^{1}-u_{i}^{\prime}x_{i}^{T}h^{3}\\ u_{i}^{\prime}x_{i}^{T}h^{2}-v_{i}^{\prime}x_{i}^{T}h^{2}\end{bmatrix}=0 \tag{5}\]  
We can arrange this as follows:  
\[\begin{bmatrix}0_{3\times 1}^{T}&-x_{i}^{T}&v_{i}^{\prime}x_{i}^{T}\\ x_{i}^{T}&0_{3\times 1}^{T}&-u_{i}^{\prime}x_{i}^{T}\\ -v_{i}^{\prime}x_{i}^{T}&u_{i}^{\prime}x_{i}^{T}&0_{3\times 1}^{T}\end{bmatrix} \begin{bmatrix}h^{1}\\ h^{2}\\ h^{3}\end{bmatrix}=0 \tag{6}\]  
Therefore, given the position changes of the four corner points, substituting them into the above equation allows us to directly solve the system of equations for \(h^{j}\)\((j=1,\ 2,\ 3)\), thereby obtaining the 3 \(\times\) 3 homography matrix.
## 2 Autonomous Driving in Adverse Weather Conditions
### Impact of Adverse Weather on Sensor Performance
#### 2.1.3 Other Sensor Types and Their Weather-Related Limitations  
Global Navigation Satellite System (GNSS): While not directly affected by weather conditions at the vehicle level, GNSS accuracy can be impaired during severe weather events due to atmospheric disturbances. [PERSON] et al. [41] discussed the challenges of using GNSS in various conditions, including their integration into multi-sensor systems for detect-and-avoid applications.  
Figure 2 illustrates the comprehensive sensor array deployed in modern vehicles with Advanced Driver Assistance Systems (ADASs) [36]. It showcases how various sensor  
Figure 1: Perception capabilities of autonomous driving systems in adverse weather conditions [36].  
types--including radar systems, LiDAR systems, cameras, and ultrasonic sensors--work in concert to provide a wide range of safety and assistance features. From adaptive cruise control and emergency braking to parking assistance and blind spot detection, the image demonstrates how these technologies create a 360-degree sensory field around the vehicle, enhancing the safety and driving experience through multi-layered environmental awareness and responsiveness. Adverse weather conditions present significant challenges to the sensor systems relied upon by autonomous vehicles. While some sensors, like radars, show greater resilience, others, particularly optical sensors, can experience severe performance degradation. This variability underscores the importance of sensor fusion and adaptive algorithms in developing robust autonomous driving systems capable of operating safely in diverse weather conditions.
###### Abstract  
The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) and other EIFs, such as fragments of MYHF and large, newly formed hummock fields. The main objectives for the paper included demonstration of various satellite capabilities over specific regions in the Canadian Arctic to assess their utility to detect and characterize EIFs. Stereo pairs of very-high-resolution (VHR) imagery provided detailed measurements of sea ice topography and were used as validation information for evaluation of the applied techniques. Single-pass interferometric SAR (InSAR) data were used to extract ice topography including hummocks and ice islands. Shape from shading and height from shadow techniques enable us to extract ice topography relying on a single image. A new method for identification of EIFs in sea ice based on the thermal infrared band of Landsat 8 was introduced. The performance of the methods for ice feature height estimation was evaluated by comparing with a stereo or InSAR digital elevation models (DEMs). Full polarimetric RADARSAT-2 data were demonstrated to be useful for identification of ice islands.  
SAR; InSAR; VHR; electro-optical and infrared data; icebergs; ice islands; hummock fields
## Time Reversal Acoustic Communication Using Filtered  
**Multitone Modulation**  
**[PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2,*, [PERSON] 1,2 and [PERSON] 1,2**  
1 College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China; E-Mails: [EMAIL_ADDRESS] (L.S.); [EMAIL_ADDRESS] (B.C.); [EMAIL_ADDRESS] (T.Z.); [EMAIL_ADDRESS] (R.L.)  
2 Science and Technology on Underwater Acoustic Laboratory, Harbin Engineering University, Harbin 150001, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-451-8258-9147 (ext. 8001); Fax: +86-451-8258-9147 (ext. 8002).  
Academic Editor: [PERSON]  
_Received: 1 July 2015 / Accepted: 8 September 2015 / Published: 17 September 2015_  
**Abstract:** The multipath spread in underwater acoustic channels is severe and, therefore, when the symbol rate of the time reversal (TR) acoustic communication using single-carrier (SC) modulation is high, the large intersymbol interference (ISI) span caused by multipath reduces the performance of the TR process and needs to be removed using the long adaptive equalizer as the post-processor. In this paper, a TR acoustic communication method using filtered multitone (FMT) modulation is proposed in order to reduce the residual ISI in the processed signal using TR. In the proposed method, FMT modulation is exploited to modulate information symbols onto separate subcarriers with high spectral containment and TR technique, as well as adaptive equalization is adopted at the receiver to suppress ISI and noise. The performance of the proposed method is assessed through simulation and real data from a trial in an experimental pool. The proposed method was compared with the TR acoustic communication using SC modulation with the same spectral efficiency. Results demonstrate that the proposed method can improve the performance of the TR process and reduce the computational complexity of adaptive equalization for post-process.  
**Keywords:** underwater acoustic communication; filtered multitone modulation; time reversal; adaptive equalization
# Obtaining Human Experience for Intelligent Dredger Control: A Reinforcement Learning Approach
## 2 Cutting Process Analysis
### General Layout of a CSD  
Figure 1 shows the main components of a CSD and depicts the cutting process that will be studied in this work. The cutter head is mounted at the forefront of the cutter ladder, connecting to the dredge pump(s) through a suction pipe. The cutter head can cut hard soil or rock into fragments by rotating along the axis of the suction pipe, and it is an efficient tool for excavating nearly all kinds of soils. When the soil is cut or loosened, it is then sucked up by centrifugal dredge pumps. A suction inlet located beneath the cutter head is connected by the suction pipe directly to one or more centrifugal dredge pumps. The vacuum force at the suction inlet sucks up the loosened soil. Afterwards, the pumped slurry will be transported to a discharge zone through a pipeline.  
Figure 1: General layout and the cutting process of a CSD. (**a**) The CSD usually contains a cutter head, a cutter ladder, a suction pipe, a discharge pipe, dredger pump(s), a working spud pole and an auxiliary spud pole. (**b**) In the cutting process, the CSD moves around the working spud pole, achieved by pulling and slacking on the fore sideline wires of two side anchors.  
As shown in Figure 1, the CSD is not driven by a propeller to carry out the cutting process. Instead, the dredging operations take place in a stationary position, even a self-propelled CSD will be moored with spuds and anchors while at work. The CSD usually has two spud poles. The working spud pole, mounted on a movable spud carriage, can be moved lengthwise along the vessel, while the auxiliary spud pole is set out of the centerline, usually on the starboard side of the stern of the pontoon. The auxiliary spud pole is used to keep the CSD in position when the working spud pole is raised, and the spud carrier is move back to its initial position. When cutting the soil, the CSD rotates around the working spud pole, producing an arc trajectory. The control action of such a swing movement is the swing speed. When a swing movement is completed, the CSD will be pushed forward a small step to start a new swing.
## 2 Electron Cyclotron Resonance, Cyclotron Autoresonance, and Brillouin Diagrams  
### Dispersion Equation of a Hollow Smooth-Wall Metallic Waveguide  
The dispersion equation of a waveguide, which relates the longitudinal (axial) wavenumber \(k_{z}\) (along the propagation axis \(z\)) to the frequency \(\omega\) of the mode with a cut-off frequency \(\omega_{c}\), is as follows:  
Figure 1: Structure and main parts of a gyrotron (modified from [[https://archive.org/details/gyrotron](https://archive.org/details/gyrotron)]([https://archive.org/details/gyrotron](https://archive.org/details/gyrotron)), accessed on 15 April 2024).  
\[\omega=\sqrt{\omega_{c}^{2}+c^{2}k_{z}^{2}}, \tag{1}\]  
where \(c\) is the speed of light in vacuum. This relation is plotted in Figure 2 in normalized coordinates \(\frac{\omega}{\omega_{c}}\) and \(\frac{k_{z}}{k_{\perp}}\), where \(k_{\perp}=\frac{\omega_{c}}{c}\) is the transverse wavenumber. The cut-off frequency of a transverse (TE\({}_{\rm mn}\) or TM\({}_{\rm mn}\) mode) in a waveguide of radius \(R\) is \(\omega_{c}=c\frac{\lambda_{m,n}}{R}\), where \(\chi_{m,n}\) is the eigenvalue of the mode (n-th root of the equation \(\frac{dI_{m}(x)}{dx}=0\) for the derivative of the Bessel function of first kind and order \(m\) in the case of a TE\({}_{\rm mn}\) mode, and of the equation \(J_{m}(x)=0\) for a TM\({}_{\rm mn}\) mode, respectively). At an arbitrary point (\(k_{z0}\), \(\omega_{0}\)) of the dispersion curve (see Figure 2), the phase and group velocity of the wave are defined as follows:  
\[v_{\rm ph}=\frac{\omega_{0}}{k_{z0}}\ {\rm and}\ v_{\rm gr}=\frac{d\omega}{dk_{z 0}}(k_{z0},\omega_{0}), \tag{2}\]  
respectively, and are connected by the relation \(v_{\rm ph}v_{\rm gr}=c^{2}\). Since the total wavenumber \(k\) is as follows:  
\[k^{2}=\frac{\omega^{2}}{c^{2}}={k_{\perp}}^{2}+{k_{z}}^{2}, \tag{3}\]  
the phase velocity normalized to the speed of light can be written in the following form:  
\[\beta_{\rm ph}=\frac{v_{\rm ph}}{c}=\sqrt{1+\frac{k_{\perp}^{2}}{k_{z}^{2}}}. \tag{4}\]