esa-sceva/satcom-chunk-collection huggingface dataset:
# Dimension Reduction Using New Bond Graph Algorithm and Deep Learning Pooling on EEG Signals for BCI
## 4 Experiment and Results
### Bond Graph Algorithm (BGA)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
\begin{tabular}{c} **Public** \\ **Function** \\ \end{tabular} & **Parameters** & **PSO** & **GA** & **BGA\_Model1** & **BGA\_Model2** & **BGA\_Model3** & **BGA\_Model4** \\ \hline \multirow{3}{*}{Sphere} & Mean & \(4.72\times 10^{-2}\) & 43.41 & \(4.20\times 10^{+4}\) & \(1.60\times 10^{+4}\) & \(\mathbf{5.40\times 10^{-110}}\) & \(1.56\times 10^{-90}\) \\  & Std & \(5.64\times 10^{-2}\) & 13.74 & \(5.44\times 10^{+3}\) & \(1.11\times 10^{+4}\) & \(\mathbf{2.00\times 10^{-109}}\) & \(7.43\times 10^{-90}\) \\  & Min & \(2.45\times 10^{-3}\) & 22.87 & \(2.43\times 10^{+4}\) & \(4.28\times 10^{+3}\) & \(\mathbf{8.10\times 10^{-120}}\) & \(1.40\times 10^{-102}\) \\  & Max & \(2.24\times 10^{-1}\) & 76.58 & \(4.89\times 10^{+4}\) & \(4.82\times 10^{+4}\) & \(\mathbf{1.00\times 10^{-108}}\) & \(4.14\times 10^{-89}\) \\ \hline \multirow{3}{*}{SumSquares} & Mean & \(6.89\times 10^{-3}\) & 7.15 & \(8.53\times 10^{+3}\) & \(3.04\times 10^{+3}\) & \(\mathbf{3.80\times 10^{-111}}\) & \(3.92\times 10^{-90}\) \\  & Std & \(9.46\times 10^{-3}\) & 2.02 & \(1.40\times 10^{+3}\) & \(1.35\times 10^{+3}\) & \(\mathbf{1.70\times 10^{-110}}\) & \(2.17\times 10^{-89}\) \\  & Min & \(5.11\times 10^{-4}\) & 3.09 & \(5.06\times 10^{+3}\) & \(8.64\times 10^{+2}\) & \(\mathbf{1.10\times 10^{-123}}\) & \(2.50\times 10^{-100}\) \\  & Max & \(4.39\times 10^{-2}\) & 11.20 & \(1.07\times 10^{+4}\) & \(5.86\times 10^{+3}\) & \(\mathbf{9.30\times 10^{-110}}\) & \(1.21\times 10^{-88}\) \\ \hline \multirow{3}{*}{Rosenbrock} & Mean & \(2.48\times 10^{-3}\) & \(1.78\times 10^{+3}\) & \(1.22\times 10^{+8}\) & \(2.02\times 10^{+7}\) & \(\mathbf{33.91}\) & \(33.93\) \\  & Std & \(1.07\times 10^{+4}\) & \(1.07\times 10^{+3}\) & \(3.21\times 10^{+7}\) & \(2.17\times 10^{+7}\) & \(\mathbf{0.05}\) & \(0.06\) \\ \cline{1-1}  & Min & \(58.93\) & \(8.28\times 10^{+2}\) & \(4.82\times 10^{+7}\) & \(1.16\times 10^{+7}\) & \(\mathbf{33.77}\) & \(33.75\) \\ \cline{1-1}  & Max & \(5.89\times 10^{+4}\) & \(5.40\times 10^{+3}\) & \(1.71\times 10^{+8}\) & \(1.11\times 10^{+8}\) & \(\mathbf{33.97}\) & \(33.98\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of 35-dimensional results of GA and PSO and all of the models of the BGA.
# Mobility, Citizens, Innovation and Technology in Digital and Smart Cities
## 3 Cities Transportation Trends
### Electric Based Transportation Systems  
The insertion of electric and hybrid vehicles has been driving advances towards the use of renewable resources, and vice versa. One can say that, in view of the massive integration of Distributed Energy Resources (DER) with microgrids [93], green transportation systems [102; 103] are boosting researchers to re-analyze solutions of classical optimization problems. Although the literature have been addressing some SC troublesome logistics, the integration of renewable resources has received little attention when dealt with in connection to urban planning problems. For example, when considering the relief of a city, vehicles can optimize their routes in the search of maximum energy efficiency [50]. However, several solutions focus only on classical obstacles, disregarding the current social context and the technological evolution that we have gone through. In view of these points, a new class of problems and possibilities set up new paradigms and challenges that demand more innovative solutions.  
Moreover, the immense range of alternative electric transports that is emerging (skateboards, scooters, bicycles, monowheel systems and so forth) weighs in on considering possible logistics problem, beyond hybrid or pure Plug-in Electric Vehicle have [104; 105]. Nonetheless, emerging technologies should be connected in our transportation system, such as roads with solar panels that might charge and assist this structure [66; 106]. At this point, we highlight the open opportunities for managing vehicles in SC. Which point will the stakeholders balance in order to manage this system? Will it be guided by the amount of CO2 emissions balanced with traffic jam issues? Will the acoustic pollution affect the decision-making process? Undoubtedly, multi-objective optimization problems arise when this class of problems are being dealt with.
# Taguchi Method-Based Synthesis of a Circular Antenna Array for Enhanced IoT Applications
### Design and Validation of a Circular Antenna Array with CST Microwave Studio
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Elements** & **Uniform** & **PSO [22]** & **GA [23]** & **FA [24]** & **Taguchi** \\ \hline
**1** & 1.0000 & 1.0000 & 0.9545 & 0.7081 & **0.1369** \\ \hline
**2** & 1.0000 & 0.7529 & 0.4  
Figure 8a shows the 3D radiation pattern diagram with uniform excitation for 16 antennas. Figure 8b presents the 3D radiation pattern diagram with excitation using the Taguchi method for the same number of antennas. Figure 9a illustrates the 3D radiation pattern of a circular array consisting of 24 antennas, with uniform excitation applied to all elements. In this configuration, the radiation pattern exhibits a symmetric distribution. Figure 9b presents the 3D radiation pattern of the same circular array of 24 antennas, but with excitation optimized using the Taguchi method. The result demonstrates the improved pattern characteristics achieved through the application of this optimization technique. Figure 7: Simulated results for 3D circular antenna array radiation pattern synthesis with 10-elements using PSO and GA algorithms at 2.45 GHz.
# Modulation of Solar Wind Impact on the Earth's Magnetosphere during the Solar Cycle
###### Abstract
Generally, GSs can be classified as moderate, intense, or super storms, according to the value reached by the \(Dst\) index during the geomagnetic event: \(Dst\in(-100,-50]\) nT, \(Dst\in(-250,-100]\) nT, and \(Dst\in(-\infty,-250]\) nT, respectively [14]. The release of solar wind energy to the near-Earth space environment severely perturbs the magnetosphere-ionosphere current system, causing the geomagnetic disturbances outlined above. During GSs, a large amount of solar wind (kinetic and magnetic) energy is transferred to the Earth's magnetosphere, through magnetic reconnection at the dayside magnetopause, allowing the solar wind to be linked to the magnetosphere. In fact, high kinetic energy may compress the magnetosphere on the sunward side. On the other hand, the solar wind magnetic energy might be converted into thermal energy, heating the ionosphere and the thermosphere and leading to their expansion. Therefore, the energy content of the SW appears to be a crucial variable to study the relationship between the SW driver and the magnetospheric response in the field of space weather research. Within this framework, this manuscript represents a follow-up paper to previous reports [15; 16] devoted to the analysis of the correlation between solar-wind energy and the \(Dst\) index. Here, we focus on investigation of the solar activity level and its correlation with the \(Dst\) disturbances during a period spanning 12 years occurring in the interval from January 2005 to December 2016. The analysis was performed using an in situ magnetic field and plasma measurements recorded using a magnetic field investigation fluxgate magnetometer [17] and a solar wind experiment Faraday cup instrument [18] onboard a Wind spacecraft, orbiting around the Lagrangian point L1 upstream of the Earth. The original dataset was split into two separate sub-samples, for periods of maximum (2012-2014) and minimum (2007-2009) solar activity, to compare the geo-effectiveness response to the solar wind energy in the two different conditions.
## 2 Materials and Methods
### Implementation of the Proposed Methodology
This is a flexible, manageable solution whose limit is the storage space, which is currently around 10 TB, thus widely supporting the saving of the data analyzed so far. For the storage of these large volumes of data, a geospatial data compression system has been implemented using optimized formats such as GeoTIFF with lossless compression, reducing the volume of stored data and improving efficiency in reading and writing operations. Furthermore, the data were organized in a PostGIS database, which allows for efficient management of spatial data with advanced geospatial query operations, reducing response times and optimizing data access. To process data from the sensors more efficiently, an edge computing process was implemented that allowed the data to be processed locally, close to the acquisition source, instead of sending it directly to the server. For this purpose, advanced data filtering methods were used to eliminate outliers through standard deviation analysis. Furthermore, a data aggregation algorithm was applied on pre-established time intervals, using a moving window, to further reduce the volume of data to be transmitted and to ensure a more stable and precise view of the environmental variables. The aggregation process was implemented on high-efficiency devices, such as ESP32 microcontrollers, which perform real-time filtering and data aggregation operations in a distributed manner. The advantages of this operation include the reduction in communication volume since not all unnecessary data are transmitted. This approach reduces network traffic and allows only relevant information to be transmitted. From an energy efficiency perspective, processing data locally reduces the power consumption of the device. ESP32 microcontrollers used for edge processing require less energy to perform local computation operations, compared to transmitting large volumes of raw data to a central server. In addition, implementing real-time compression algorithms on edge devices further reduces the volume of data sent, improving network efficiency. From a communications network perspective, lightweight communication protocols such as MQTT (Message Queuing Telemetry Transport) have been implemented to optimize bandwidth usage, suitable for IoT applications and capable of efficiently managing messages between devices and servers. MQTT allows optimized network management, reducing the need to constantly transmit large amounts of data, thanks to its pub/sub (publish/subscribe)-based architecture, which sends only the necessary data at the right time.
## 2 Experimental facility
### Sensor Selection  
According to the above analysis, a piezoresistive dynamic water pressure sensor is selected in this paper. The piezoresistive sensor is currently the most widely used sensor, the advantages are listed below.  
1. Wide frequency response range and high natural frequency.
2. Small size. Due to the integrated circuit process, silicon diaphragm sensitive components can be made very small.
3. High precision, good sensitivity, suitable for dynamic measurement.
4. Because the piezoresistive pressure sensor is integrally formed, it has the advantages of reliable operation, shock resistance, impact resistance, corrosion resistance, and strong anti-interference ability.  
The piezoresistive pressure sensor used in this paper has a measurement range of -90 kpa to 700 kpa, an overload accuracy of 0.1%, and a collection frequency of 1000 HZ. The appearance of sensor is a cylinder with a diameter of 2 cm and a height of 6 cm. The upper surface of the cylinder is a hollow cylinder with an outer diameter of 1 cm, an inner diameter of 0.4 cm, and a height of 1 cm. The bottom of the hollow cylinder is a stressed diaphragm with a circle of 0.4 cm and 1 cm from the top surface. As shown in Figure 2. The sensor software smart sensor 4.10 is used for data acquisition and analysis.  
Figure 2: Shape of sensor.
# Orifice versus Converging-Nozzle Grid Turbulence: A Wavelet Perspective
## 4 Results
### Velocity Profile  
The contours of normalised streamwise time-averaged velocity \(\overline{U}/U_{mean}\) at 10D downstream of the perforated plate for the orificed perforated plate (OPP) and the converging-nozzle perforated plate (CNPP) are depicted in Figure 3. The maximum deviation of the local time-averaged velocity observed for the lower \(\rm{Re}_{\rm D}\) with \(U_{mean}\) of 7.4 m/s is about 4% for both the OPP and the CNPP, as can be seen in Table 1 and Figure 3. At the higher with a wind speed of 11 m/s, the downstream flow remained at the same level of homogeneity in the case of the OPP. At this wind speed, the spatial homogeneity downstream of the CNPP deteriorated with a maximum deviation of nearly 10%. This deterioration in homogeneity may be attributed to the effect of nozzle thickness on the nature of flow interactions that lead to turbulence generation by the CNPP. It appears that \"squeezing\" fluid particles through the nozzles at the higher wind speed significantly increased the inhomogeneity of the resulting flow. For the OPP counterpart, the essentially zero thickness has no nozzles for squeezing fluid particles. The orifices simply created intense wake-jet interactions that resulted in homogeneous turbulence.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Plate** & \(\mathbf{U_{mean}}\) **[m/s]** & \
\begin{tabular}{c} **Maximum** \\ **Deviation** \\ \end{tabular} & \(\mathbf{T_{mean}}\) &
\begin{tabular}{c} **Maximum** \\ **Deviation** \\ \end{tabular} \\ \hline OPP & 7.4 & 4.1\% & 0.115 & 6.5\% \\ CNPP & 7.4 & 4.4\% & 0.108 & 9.7\% \\ OPP & 11.1 & 4.2\% & 0.116 & 6.9\% \\ CNPP & 11.2 & 10.2\% & 0.106 & 12.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cross-sectional time-averaged velocity and turbulence intensity at X/D = 10.  
Figure 3: Normalised streamwise time-averaged velocity \(\overline{U}/U_{mean}\) at X/D = 10 downstream for OPP at (a) \(U_{mean}\) = 7.4 m/s and (b) 11.1 m/s, and for CNPP at (c) \(U_{mean}\) = 7.4 m/s and (d) 11.2 m/s.
# An Experimental Study on Repairing of Reinforced Concrete Beams Having Damaged Longitudinal Bars
## 3 Results and Discussion
### Load-Displacement Curves
#### 3.2.4 Ductility of the Beams  
The ductility values of all repaired beams except for RB8-EBR-G gave between 29% and 286% higher values than the damaged beam (RefB-D). The lowest ductility was obtained in the RB8-EBR-G specimen. This value was even lower than that of the damaged reference beam (RefB-D). The load of the specimen decreased suddenly because of the debonding damage in the RB8-EBR-G specimen during loading. Therefore, while the ductility of all beams increases with respect to the damaged specimen (RefB-D), the ductility of the RB8-EBR-G specimen decreases. The greatest increase in ductility occurred in the RB4-NSM-G-U specimen for both comparisons with RefB and RefB-D. An increase in ductility was observed in all U-wrapped specimens. No significant difference was observed in the ductility values obtained from beams repaired with the NSM and SNSM methods using GFRP rebar.
## 4 Effects of a Road Noise Barrier with Different Flow States
### Effect of Vehicle Type  
Traffic noise caused by heavy vehicles is quite different in frequencies compared to other vehicles based on the results of Section 2.2. Hence, the effects of the proportion of heavy vehicles on insertion loss of barrier were analyzed. The speed of the vehicles in this part is set as 50 km/h, and the flow of 1000 vehicles per hour consists of light and heavy vehicles with 11 different proportions. To ensure that the results are not affected by the different vehicles' sound source strong, the emission intensity of each vehicle source is set as 85 dB. Traffic noise for different heavy vehicle proportions (0, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100%) on each point was calculated.  
With different proportions of heavy vehicles, the insertion losses at three points are shown in Figure 7. \(L_{D}\) presents a distinct pattern regarding points R\({}_{1}\), R\({}_{2}\), and R\({}_{3}\), and the insertion loss at all of the receivers have relationships with the proportion of heavy vehicles. For point 1, \(L_{D}\) and heavy vehicles \(a\) (%) follow the linear relationship of \(L_{D}=-0.0293a+24.661\), i.e., 10 percent of heavy vehicles can cause a 0.29 dB decline on insertion loss in this scene. The results show that the SPL behind a barrier of a heavy vehicle flow is approximately 2.3 dB higher than that a light vehicle flow with the same source emission intensity. The same rule is also applicable to the overall shadow area. The heavy vehicles have more acoustical constituents at low frequencies compared to light vehicles, which are more significantly shaded by barriers. The insertion loss of heavy vehicle flow is the highest, followed by the insertion loss of mixed traffic flow, and the insertion loss of light vehicle flow is the lowest; this result indicates that the lower-frequency sound can bypass the barrier more easily. Thus, flow control of heavy vehicles whose sound is concentrated in low frequencies is an effective measure to improve the acoustic environment near roadways.
## 3 Data and Methodology
### Modeling Methods
#### 3.4.1 Support Vector Machine  
The support vector machine (SVM) is a binary classifier based on the structural risk minimization principle [66], which takes advantage of the quadratic optimization technique to calculate the optimum separating hyperplane between datasets with a maximum margin on either side. In this way, two different classes of points can be separated as far as possible. The classification function of the SVM can be expressed as a set of constrains as below:  
\[\text{minimize}\ \frac{1}{2}\ ||\omega||^{2} \tag{3}\]  
\[\text{subject to}:\ y_{i}(\omega\!:\!x_{i}-b)\geq 1\ \ \ \ \forall x_{i} \tag{4}\]  
where \(\omega\) is an n-dimensional vector normal to the optimum hyperplane, \(b\) is bias, and \(y_{i}\) is the classified variable (landslide and non-landslide) which belongs to set \([1,-1]\).  
The radial basis function (RBF) kernel trick is usually recommended for the case where the classification task is non linearly separable [67; 68]. In this work, the SVM with the RBF kernel was utilized.
## 1 Introduction
Due to the influence of Gaussian white noise in the channel, there is an error in the channel estimation, resulting in an error in the SCO\(-\)AN design. The channel estimation error affects the accuracy of the power allocation optimization. This paper considers the imperfect channel state information for power allocation. The power allocation objective function of SCO\(-\)AN and the information-bearing signal containing channel estimation errors is constructed. The expression for the channel estimation errors is derived for the first time. This expression can then be applied to future physical layer security research examining imperfect channel estimation.The power allocation function is then converted to a function with only one variable-the SCO\(-\)AN-simplifying the function's overall computational complexity. This paper is structured as follows:  
* In Section 2, the system model and the framework are introduced. * In Section 3, the objective function for the power allocation between SCO\(-\)AN and the information-bearing signal, with and without considering imperfect channel estimation, is proposed. ISQP is then applied to optimize the power allocation. The algorithm flow of ISQP algorithm is constructed. * In Section 4, simulation results are shown and discussed. * In Section 5, the conclusion is drawn, and the suggestions for future work are presented. In this paper, the following notations are used: Boldface upper case denotes matrices, boldface lower case denotes vectors, italics case denotes numbers; \([\cdot]^{\mathsf{T}}\) denotes the matrix transpose operation; \([\cdot]^{\mathsf{*}}\) denotes the complex conjugate operation; \([\cdot]^{\mathsf{+}}\) denotes the conjugate transpose operation (conjugate complex number) for the matrix (number) \"...\"; \(E\{\cdot\}\) denotes the mathematical expectation; \(\|\cdot\|\) denotes the norm of a vector; and \(|\cdot|\) denotes the determinant of a matrix.
# Analytic Combinatorics for Computing Seeding Probabilities
## 5 Discussion  
In this article, we exposed the analytic combinatorics approach to compute seeding probabilities in the read mapping problem. The general strategy of analytic combinatorics is to define combinatorial \"atoms\" with simple weighted generating functions (e.g., nucleotide symbols), combine these atoms into objects of increasing complexity (e.g., error-free intervals or reads without seed), construct their weighted generating functions from simple rules (e.g., through Theorem 1), and finally analyze the singularities of the weighted generating functions to approximate the quantities of interest (e.g., through Theorem 2). We can also use the generating function to set up an exact recurrence to find those quantities.  
The seeding probabilities derived here are robust and relatively straightforward, as they only entail solving a polynomial equation in real space. For short reads, where the precision of the approximations may be an issue, the solution is better computed by recurrence. Mapping high throughput sequencing reads generates a sufficient amount of data to estimate the parameters of the error model. One can thus envision auto-tuning the seeding heuristic of read mapping during the run. This can give tight and automatic control over the seeding probability. Alternatively, the theory developed above could be used to help users choose the parameter values of the mapping algorithm.  
Seeding is not only used in mapping, but also in other alignment problems. In this regard, the work presented above can be applied to different contexts. That said, mapping high throughput sequencing reads is a \"sweet spot\" for analytic combinatorics because the sequences are usually long enough for the approximations to be accurate.  
In summary, analytic combinatorics is a powerful strategy that comes with a rich toolbox that has many applications in modern bioinformatics. More applications will see the light when this theory is more widely known in the bioinformatics community.  
I would like to thank the anonymous reviewers for their important contributions to this work. I would also like to thank [PERSON], [PERSON] and [PERSON] for their comments on this work. I acknowledge the financial support of the Spanish Ministry of Economy and Competitiveness ([PERSON] Excelencia Severo Ochoa 2013-2017, Plan Nacional BFU2012-37168), of the CERCA (Centres de Recerca de Catalunya) Programme / Generalitat de Catalunya, and of the European Research Council (Synergy Grant 609989).  
Figure 10: _Accuracy of the approximations in the uniform substitution model._ The estimates of Proposition 2 were computed for \(\gamma=17\) and different values of the substitution rate \(p\) ranging from \(0.01\) to \(0.50\) and for different read sizes \(k\) ranging from \(15\) to \(10,000\). The reference target value was computed by recurrence through expression (12) and the relative error between the two terms was computed. All calculations were done in double precision arithmetic using R [27].