esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Liquefaction Susceptibility  
For the non-plastic materials shown in Figure 7, the state parameter was calculated and graphed against \(\mathrm{G_{0}}/\mathrm{q_{t}}\) (estimation without correction in Figure 11). Notably, around 94% of the tailings exhibited \(\mathrm{\Psi}\) values greater than \(-0.05\), indicating a contractive behavior during shear. This observation suggests a potential susceptibility to flow liquefaction [36]. A similar analysis was conducted following the correction of \(\mathrm{q_{t}}\) for partially drained conditions. Given the notable disparities between the \(\mathrm{B_{q}}\), \(\mathrm{Q_{m}}\) and \(\mathrm{V}\) methods, the assessment of liquefaction susceptibility accounted for the \(\mathrm{q_{t}}\) correction specific to each test identified as partially drained using any of the three methods. Upon correcting \(\mathrm{q_{t}}\), the SCPTu estimations with corrections showed a slight upward adjustment, aligning more effectively within the stress limits, defined by [PERSON] and [PERSON] [37] as in situ stresses generally experienced by soils (estimation with correction in Figure 11). However, in the context of the analysis of state parameters and susceptibility to liquefaction, the material still exhibited a significant risk of experiencing flow liquefaction. In fact, even when accounting for the adjustment of partially drained conditions, the analysis based on \(\mathrm{B_{q}}\) revealed that 96% of the tests still indicated susceptibility to flow liquefaction (\(\mathrm{\Psi}>-0.05\)) [36], while the \(\mathrm{Q_{m}}\) analysis identified susceptibility in 98%, and the \(\mathrm{V}\) analysis identified susceptibility in 99% of the cases.  
Figure 10: Comparison of \(\mathrm{I_{c}}\) with \(\mathrm{q_{t}}\) on standard velocity and subsequent application of drainage correction based on (**a**) \(\mathrm{B_{q}}\), (**b**) \(\mathrm{Q_{m}}\) and (**c**) \(\mathrm{V}\).
# Facility Layout Problem with Alternative Facility Variants
## 4 Proposed Method
### Cooling Schedule  
Simulated annealing (SA) is a metaheuristic optimization technique. It searches for the optimal solution in an iterative process, where in each iteration, it generates a random neighbor of the current solution and either accepts the neighbor as the new current solution or rejects it. The neighbor is always accepted if it is not worse than the current solution. It may also be accepted with probability \(p^{c}\), even if it is worse than the current solution. The probability \(p^{c}\) is high at the beginning of the run and gradually decreases through time, approaching zero towards the end of the run. This way, SA gradually changes its search mode from a global exploration to a local one. The probability \(p^{c}\) changes according to the _cooling schedule_, which is either defined by the user or automatically adapted during the course of the run [34].  
Here, we adopt a similar approach to control the search mode along the optimization process. The idea behind it is based on the relation between the priority list and the mapping process. The mapping process starts at the upper-left corner of the empty hall. Then, the workstations are added to the floor plan one by one, filling up the available hall space in a left-right and top-down manner. The workstations are served in the order they come in the priority list; see Figure 2. This means that changes made to the head part of the priority list have a greater impact on the resulting floor plan than changes made within the tail part of the priority list. This is reflected by the cooling schedule proposed in this work, as it gradually restricts variation operators' scope to the tail section of the priority list as the algorithm converges. It is realized by preventing the variation operators (i.e., crossover and mutations) from perturbing elements of the priority list whose position is less than or equal to the left-most variation point. In this way, the search mode slowly turns from a global one to a local one.  
There are many possible ways to define the cooling schedule. The one used in this work is shown in Figure 3. It sets the left-most variation point to 1 for the first half of the run. Then, it jumps to \(|T|/4\), making the leading quarter of the priority list fixed. As the run progresses towards the end, the fixed portion of the priority list linearly increases to \(3*|T|/4\). Thus, in the first half of the run, the algorithm works in a standard global exploration mode. Then, the operation mode changes to the local one. However, even in the final phase of the run, the exploration capabilities stay at a significant level, as the tail quarter of the priority list can still be modified by variation operators.
## 3 Implementation
### Algorithm for Computing Dispersion Curves by SBFEM  
Figure 3 shows a schematic representation of the proposed algorithm for estimating the dispersion curves by SBFEM. The first step is to set the model parameters by defining the plate thickness \(t\), the material, and the density \(\rho\), as well as the elasticity matrix \(E\), which includes the shear modulus \(G\) and Poisson's ratio \(\
u\).  
The second step is the meshing of the plate cross-section. So, the number of isoparametric elements and the order of the shape functions in the case of spectral elements are determined.  
The third step is the calculation of the coefficient matrices \(E_{0}\), \(E_{1}\), \(E_{2}\), and \(M_{0}\) using integration and the Gaussian quadrature method of approximation for the isoparametric and spectral methods, respectively. The coefficient or elementary matrices are assembled into a global matrix with size as a function of the number of elements used.  
The fourth step is to solve the eigenvalue problem to find the eigenvalues \(\omega\) and their corresponding eigenvectors \(\lambda\) for each value of the wavenumber \(k\). as a solution, we obtain the (\(\omega\),\(k\)) couples that are the basis for generating the dispersion curves, according to the following equations:  
\[C_{p}=\frac{\omega}{k},\textit{where }\omega=2\pi f, \tag{31}\]  
\[C_{g}=\frac{d\omega}{dk} \tag{32}\]  
The numerical results of the two discretization schemes are compared with the analytical solution to verify the accuracy of the method. The maximum frequency of the dispersion curves is approximated to obtain a wavelength \(\lambda\) equal to or greater than the structural thickness.
# RCA-PixelCNN: Residual Causal Attention PixelCNN for Pulsar Candidate Image Lossless Compression
## 4 Experiments
### Generated Positive Pulsar Candidate Samples  
Pulsar observation data suffer from severe class imbalance in classification tasks. As of now, there are over 3000 confirmed pulsars, while the number of daily pulsar candidate observations reaches hundreds of thousands. Candidate selection is a crucial aspect of pulsar search efforts. Taking the HTRU1 dataset as an example, the positive-negative sample ratio is 1194:58,806, with positive samples accounting for only 2% of the total. Machine learning is employed to sift through pulsar candidates, and addressing class imbalance is a primary concern [28].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **NLL (bpp)** \\ \hline PixelCNN & 3.11 \\ RCA-PixelCNN-1 & 2.82 \\ RCA-PixelCNN-2 & 2.78 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of model with different numbers of Res-Cause-Attention blocks.  
Using a class-label conditioned PixelCNN model can generate the required positive samples. We encode the class information using one-hot encoding and linearly map it to features of the same shape as the images. The label features are input to each layer. The three sub-images of each synthetic HTRU1 sample describe the same candidate's information, and the three-channel sub-images are interdependent. As shown in the rightmost four columns of Figure 7, the positive samples generated by the class-conditioned PixelCNN model are displayed. The three rows of sub-image correspond to period-dispersion, time-phase, and frequency-phase representations. It is evident that the generated positive samples exhibit distinct pulsar characteristics.
## 3 Results
### Concurrent Validity  
For the concurrent validity of the neck region, DIMS-D subscale showed moderate correlation (\(r_{s}\) = 0.41 95% CI,0.00-0.95, \(p\) = 0.003) while DIMS-CA subscale showed weak correlation (\(r_{s}\) = 0.33 95% CI 0.00-0.94, \(p\) = 0.018) with the DIS scores of the second requested voluntary activity (i.e., neck rotation). No correlation was found with the first DIS requested voluntary activity (i.e., neck lateroflexion) or with DIS neck rest postures.  
For the arm RP region, DIMS-D subscale showed strong correlations (\(r_{s}\) = 0.63 95% CI 0.00-0.97, \(p\) = 0.000) and DIMS-CA showed moderate correlations (\(r_{s}\) = 0.46 (95% CI 0.00-0.95, \(p\) = 0.001) with arm RP rest postures of the DIS. No correlations were found with the DIS overflow movements.  
For the arm RD region, DIMS-D subscale showed a strong correlation (\(r_{s}\) = 0.63 95% CI 0.00-0.97, \(p\) = 0.000) with DIS rest postures and no correlation with DIS overflow movements. The DIMS-CA  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{2}{c}{**Duration**} & \multicolumn{2}{c}{**Amplitude**} & \multicolumn{2}{c}{\(\Sigma\)**(D + A)**} \\  & **ICC** & **95\% CI; \(p\)-value** & **ICC** & **95\% CI; \(p\)-value** & **ICC** & **95\% CI; \(p\)-value** \\ \hline \multicolumn{2}{c}{DIMS} & & & & & & & \\
**Total score** & 0.76 & 0.00–0.98; 0.112 & 0.84 & 0.00–0.98; 0.058 & 0.80 & 0.00–0.98; 0.079 \\ \multicolumn{2}{c}{DIMS-D _SUBSCALE_} & & & & & & \\
**1** & **Neck** & 0.82 & 0.00–198; 0.072 & 0.97 & 0.78–0.99; 0.001 & 0.92 & 0.37–0.99; 0.018 \\
**2** & **Arm RP** & 0.94 & 0.43–0.99; 0.005 & 0.97 & 0.57–0.99; 0.001 & 0.96 & 0.45–0.99; 0.001 \\
**3** & **Arm LP** & 0.64 & 0.00–0.96; 0.070 & 0.75 & 0.00–0.97; 0.007 & 0.69 & 0.00–0.96; 0.023 \\
**4** & **Arm RD** & 0.87 & 0.00–0.99; 0.051 & 0.91 & 0.18–0.90; 0.026 & 0.89 & 0.00–0.99; 0.036 \\
**5** & **Arm LD** & 0.88 & 0.09–0.99; 0.016 & 0.83 & 0.00–0.98; 0.066 & 0.88 & 0.14–0.99; 0.030 \\
**Total score** & 0.90 & 0.26–0.99; 0.022 & 0.94 & 0.35–0.99; 0.004 & 0.93 & 0.42–0.99; 0.006 \\ \multicolumn{2}{c}{DIMS-CA _SUBSCALE_} & & & & & & \\
**1** & **Neck** & 0.96 & 0.66–0.99; 0.006 & 0.96 & 0.54–0.99; 0.008 & 0.96 & 0.63–0.99; 0.006 \\
**2** & **Arm RP** & 0.79 & 0.00–0.98; 0.087 & 0.86 & 0.04–0.98; 0.032 & 0.78 & 0.00–0.98; 0.084 \\
**3** & **Arm LP** & 0.00\({}^{\star}\) & 0.00–0.86; 0.631 & 0.00\({}^{\star}\) & 0.00–0.90; 0.518 & 0.00\({}^{\star}\) & 0.00–0.88; 0.611 \\
**4** & **Arm RD**subscale showed weak correlation (\(r_{s}\) = 0.29 95% CI 0.00-0.93, \(p\) = 0.049) with DIS overflow movements and no correlation with DIS rest postures.  
For the arm LP region, both the DIMS-D and the DIMS-CA subscale showed moderate correlations of \(r_{s}\) = 0.45 (95% CI 0.00-0.95, \(p\) = 0.001) and \(r_{s}\)=0.41 (95% CI 0.00-0.95, \(p\) = 0.003) only with DIS rest postures.  
For the arm LD region, both the DIMS-D (\(r_{s}\) = 0.29 95% CI 0.00-0.93, \(p\) = 0.057) and the DIMS-CA (\(r_{s}\) = 0.37 95% CI 0.00-0.94, \(p\) = 0.011) subscale showed weak correlations only with the DIS rest postures.
###### Abstract  
Heat island cooling strategies (HICSs) are used to mitigate urban heat island phenomena and adapt to climate change as proposed by the U.S. Environmental Protection Agency (EPA), the Intergovernmental Panel on Climate Change (IPCC), and the World Health Organization (WHO). This study investigated urban heat island reduction and assessed the cooling effect of HICSs under various temporal and spatial conditions in urban areas. The study area was the Mayge-dong urban area in South Korea. To identify the effectiveness of heat island cooling strategies (HICSs), unmanned aerial vehicle (UAV)-based remote sensing and microclimate sensors were used to generate land cover, sky view factor (SVF) distribution, and land surface temperature (LST) maps of the study area. Differences in cooling effect according to spatial density (SD) were identified by dividing the SVF into five intervals of 0.2. Temporal changes were investigated throughout the day and under cloudiness-based meteorological conditions affected by solar radiation or less affected by solar radiation. Lower SD was associated with a greater cooling effect; meteorological conditions affected by solar radiation had a stronger cooling effect. The variation of the daytime cooling effect increased with decreasing SD. The difference in cooling effect between morning and afternoon was \(<\)1 \({}^{\circ}\)C under conditions less affected by solar radiation. Under conditions affected by solar radiation, the maximum temperatures were \(-\)6.716 \({}^{\circ}\)C in urban green spaces and \(-\)4.292 \({}^{\circ}\)C in shadow zones, whereas the maximum temperature was \(-\)6.814 \({}^{\circ}\)C in ground-based albedo modification zones; thus, differences were greater under conditions affected by solar radiation than under conditions less affected by solar radiation. As a result, it was found that HICS show a high cooling effect, high diurnal variation, and high morning-afternoon deviation under weather conditions with low SD and under conditions affected by solar radiation. This study quantitatively calculated the cooling effect of HICSs applied in urban areas under various spatiotemporal conditions and compared differences by technology. Accordingly, it is believed that it will serve as a basis for supporting the practical effects of the concepts presented by international organizations for climate change adaptation.  
urban heat island; unmanned aerial vehicle; heatwaves; heat island cooling strategies; climate change adaptation 2023  
1
# Self-Assembled MoS2/ssDNA Nanostructures for the Capacitive Aptasensing of Acetamiprid Insecticide
## 4 Conclusions  
We report a platform of aptasensing based on MoS\({}_{2}\) nanomaterials and the capacitance signal readout for the detection of acetamiprid as a pesticide widely used in agriculture. The device was built on SPCE in two steps to obtain self-assembled MoS\({}_{2}\)/ssDNA nanostructures. We demonstrated that the high affinity of MoS\({}_{2}\) nanosheets for ssDNA and their pseudo redox properties enable the biosensor to achieve rapid detection and high sensitivity. The detection system takes advantage of the adsorption/desorption process provided by the ssDNA and the aptamer complex with the MoS\({}_{2}\) surface. In addition, this biosensor has demonstrated high analytical performance with a signal \"ON\" in the presence of acetamiprid and a detection limit in the fM range, lower than that set by the UPA and EFSA. The study of the detection of spiked acetamiprid in tomatoes showed a measurement comparable with those obtained in a buffer and underlines the ability of this biosensor to measure low levels of pesticides in fresh foodstuffs. These results demonstrated that the methodology developed with these biosensors can be easily adapted to detect other targets of interest. This work paves the way for the development of different highly sensitive and cost-effective aptasensors for food control applications by simply changing the aptamer specific to other pesticides.  
Conceptualization M.H. and N.R.; methodology M.H., N.R., and M.H., validation, N.R. and H.K.-Y.; formal analysis, M.H.; N.R., and H.K.-Y.; writing--original draft preparation, M.H. and N.R.; writing--review and editing, M.H. and H.K.-Y. Supervision, N.R. and H.K.-Y.; project administration, H.K.-Y.; funding acquisition, H.K.-Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by MEAE and MESRI PHC grand number 39382 RE.  
Not Applicable.  
Data Availability StatementData is contained within the article.  
The authors acknowledge technical support from Instrumental Platform of ICMMO University Paris-Saclay.  
The authors declare no conflict of interest.
# A Simple Technique for Studying Chaos Using Jerk Equation with Discrete Time Sine Map
## 3 Proposed Methodology  
The simple sine map given in Equation (5) has been a part of literature for quite some time [37].  
\[x_{n+1}=R\times[sin\pi x_{n}] \tag{5}\]  
here, 'R' is called the control parameter  
Using the Taylor series expansion, it can be approximated as follows  
\[x_{n+1}=R\times\left[\pi x_{n}-\frac{(\pi x_{n})^{3}}{6}+H.O.T\right] \tag{6}\]  
By ignoring the higher-order terms (H.O.T.), the approximated sine map can be easily realized using Op-Amps which produces comparable results. The effect of the control parameter (R) variations for this approximated discrete sine map is further characterized by finding the bifurcation diagram, maximum Lyapunov exponent, cobweb plot and time series plot as shown in Figure 1.  
This approximated map is added as a nonlinear function of the state \(x_{1}\) i.e., \(f(x_{1})\) in the Equation (4) to get the modified jerk based chaotic system  
\[\dot{x}_{1}=x_{2} \tag{7}\]  
\[\dot{x}_{2}=x_{3} \tag{8}\]  
\[\dot{x}_{3}=-\alpha x_{3}-\beta x_{2}-R\times\left[\pi x_{1}-\frac{(\pi x_{1} )^{3}}{6}\right] \tag{9}\]  
Defining \(R\pi=\sigma\) and \(R(\pi)^{3}=\gamma\), Equation (9) becomes  
\[\dot{x}_{3}=-\alpha x_{3}-\beta x_{2}-\sigma x_{1}+\frac{\gamma x_{1}^{3}}{6} \tag{10}\]  
The control parameters (\(\alpha\), \(\beta\), \(\sigma\), and \(\gamma\)) can be varied to get different strange attractors. The proposed discrete-time map based jerk system is the simplified autonomous chaotic system capable of depicting different periodic and chaotic attractors.
## 4 SARA: Main Characteristics and Architecture Components
### The Communication Service (CS)  
This service is the most critical of the architecture since it is in charge of managing the communication among all the others; i.e., in case this service is not running, the rest of the system will not work. Figure 5 illustrates an example in which three users participate in the collaboration.  
Inside the _CS_ there is the _Network Manager_ component, which is the main entry point to this service. When a _SARA Client_ or some other services require to communicate with the _CS_ they must establish a connection by using one of the possible technologies this service offers. In this case the possibilities are MQTT, TCP, UDP and WebSockets, which have support in the vast majority of development environments and programming languages. Once that connection has been made, the _CS_ knows which kind of connection was used and the same method will be used for the communication. All the messages/events received are abstracted from the communication layer in the _Communication Proxy_. From there, all events can be understood by the _Event Manager_, which will parse them and react to their content.  
There is also a component labeled as _Clients Manager_. It is in charge of storing the information of the connection with the clients and to storing their information. User credentials are not stored in this component, but only connection information.  
The _Session Status Manager_ stores the information of the sessions handled by the system. It reacts to update events of session status and apply them to the proper _Session_. Since different development platforms use different coordinate systems to arrange the digital 3D AR content, it is mandatory  
Figure 5: Sequence diagram that illustrates the communication between users and the _Communication Service_.  
to have a component in charge of establishing a common framework. Hence, the _Session Status Manager_ can cooperate with the _Transformation Interpreter_, with the aim of maintaining a consistent status of the _Session_ understandable by all. The most common operation to be performed will be the \(Z\)-axis inversion of the coordinate systems, because some systems use a right-handed coordinate system and others use a left-handed one.  
Finally, the _Interaction Interpreter_ reacts to interaction events by translating them to a common representation. Once that is done, those events can be applied to the required _Session_. For example, it is possible for a user to interact with an AR element by touching a smartphone screen while another user does so by gestures while she is wearing an HMD.
## 3 Results and Discussion
### OPA as Adder  
As given in Equations (2)-(4), an adder circuit is required for signal regrouping. The proposed OPA is configured as an adder circuit as shown in Figure 7a. Figure 7b shows the transient simulation result of the circuit. The output voltage of the circuit is the sum of the applied voltages V1, V2, and V3. The gain of the circuit is set to unity by choosing all the resistors of the same value. The amplitude of the applied input voltages is 1 mV, 2 mV, and 3 mV, respectively, and the output voltage is 6 mV as can be seen from Figure 7b.
## 2 Related Work  
### Polyp Segmentation  
Medical image segmentation continues to be a prominent area of interest in the realm of medical image analysis and processing. The ongoing advancements in deep learning have consistently spurred innovation, resulting in a proliferation of segmentation methodologies underpinned by neural networks. The fully convolutional neural network (FCN) [14] is the first end-to-end pixel-level classification method, which has opened a new paradigm in the segmentation field. UNet [15], a prominent variant of fully convolutional neural network (FCN), is renowned for its symmetric U-shaped encoder-decoder architecture, integrating skip connections to fuse deep and shallow features across different scales, making it a standard benchmark architecture for polyp segmentation methods. Successive adaptations, exemplified by UNet++ [16], ResUNet [17] and U2-Net [18] have aimed to enhance feature extraction, receptive field, and multi-scale information integration. Despite the progress in polyp segmentation, there remains a need for comprehensive environmental context around polyps. Various strategies have been explored to enhance polyp segmentation and boundary recognition, including dilated convolutions [19], integration of ASPP modules [20], and utilization of the RFB module [21]. Approaches like SFA [22] introduced a shared encoder branch and two decoder branches, along with an innovative edge-sensitive loss to enhance the precision of polyp segmentation and the clarity of boundary delineation. ACSNet [23] leveraged both local and global contextual features to guide encoder modules, with a particular emphasis on the edge region. CCBANet [24] obtained more comprehensive context awareness through cascaded context and balanced attention to improve segmentation performance.  
Although these methods have made significant progress, they often require a large amount of training data to produce desirable results. This requirement is challenging for specific tasks, such as colonoscopy polyp segmentation, where the dataset is limited. To confront this challenge, this work fine-tunes the SAM model pre-trained on large-scale datasets so as to exploit its general knowledge for the specific task.
## 2 The Overall Structure of the Control System
This control system is responsible for (i) controlling the \(DC\) bus voltage, (ii) detecting the power grid phase, and (iii) adopting the \(VPWM\) algorithm to perform grid-connected power generation functions. A programmable logic controller (\(PLC\)) is an industrial computer used to implement the control system.