esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Processing Time and Latency for Closed-Loop Control  
To test the system's performance, the average processing time of 1000 images in memory was measured for two subsets per image. To get the processing time for different steps, these steps were commented out measuring the difference in processing time. Figure 4 shows the result of the performance test for different correlation sizes \(\mathbf{u}\) measured for each processing step as described above. As soon as an image is acquired, CPU and GPU have to be synchronized by transferring the image subsets. In the second step, each subset is normalized on the GPU according to Equation (3). The third step covers Equation (4): the multiplication of the FFT transform of each search subset \(\overset{\wedge}{\mathbf{S}}_{\mathbf{k}}\) with the transformed image of the corresponding template subset \(\overset{\wedge}{\mathbf{T}}_{\mathbf{k}}\) from the first image and inverse FFT to obtain the cross-correlation result \(C_{k}\) shown in Figure 3c. The final step comprises copying the \(3\times 3\) pixel neighborhood of \(\mathbf{C}_{k,max}(\mathbf{u_{0}},\mathbf{v_{0}})\) back to the CPU and doing the subpixel-evaluation of the peak position according to Equations (5) and (6). The elongation \(\mathbf{\Delta}\mathbf{l}\) between the two subsets is transformedinto an analogue 0-10 V signal for the PID controller. All processing times given in Figure 4 are average processing times measured for 1000 images.  
The processing times shown in Figure 4 are well below 800 microseconds, so they can be executed synchronously in a single thread running parallel to a second thread acquiring images from the camera with 1.2 kHz. Therefore, the latency of the system is the sum of the acquisition time for the camera image, the processing time given in Figure 4 and some latency for the DMA transfer to the analogue output. This assumption was confirmed by measuring the latency between the camera trigger signal and the analogue output with an oscilloscope. The result were 1.4 milliseconds for externally triggered image acquisition and 0.6 milliseconds for image processing. The resulting average dead time of the strain measurement is below 2 milliseconds.  
Another important observation in Figure 4 is that the processing times rise only slowly with correlation size \(n\). For example, the total processing time for two subsets rises from 276 to 391 microseconds when the correlation size increases from 32 \(\times\) 32 to 256 \(\times\) 256 pixel because the synchronization of CPU and GPU as well as the organization on the GPU causes some significant overhead whereas the processing time is rather low. This makes it efficient on GPU-based systems to increase the search subset size \(n_{S}\) instead of introducing additional algorithms for tracking. As discussed above, this increases the measurement robust against occasional mismeasurements.
## 2 Methodology
### Radio Interferometer Phase Detection
Therefore, in order to use the phase as the orbit observable, it is required to express it in unwrapped form: including the integer number of cumulative cycles lost along the path between the satellite and the receiving antenna. \[x(t)=s(t)e^{i(2\pi f_{c}t+\phi_{0})} \tag{2}\]2  
where \(M\) indicates the unknown integer number of the phase cycles, since the orbit determination procedure retrieves the satellite trajectory from the observation of the path differences, which are related to the unwrapped phases. The integer number of cycles of the phase (\(M\)) must be estimated, avoiding discontinuities in the interferometric phase acquisition. Otherwise, the observed phases would be satisfied by multiple possible satellite orbits. Since the Hydroterra mission operates at the C-band and the central frequency of the signal of opportunity used to perform the experimental proof of concept is at the Ku-band, ionospheric perturbations have not been considered [29]. Should this technique be applied to missions working at lower frequencies, the ionospheric perturbations must be further studied.
# Trusted Data Access Control Based on Logistics Business Collaboration Semantics
## 4 Construction of Access Control Model
### Semantic Access Policy Set
#### 4.3.3 Adaptive Rule Formulation for Logistics Scenarios  
These SWRL rules represent a framework for defining access permissions that can be tailored to the specific needs of logistics operations. The inclusion of a \"privilege\" attribute after a deny decision introduces a nuanced approach to access control, acknowledging the potential need for flexibility in urgent situations. To effectively implement this model, logistics organizations must consider their unique requirements and contexts, adjusting and expanding upon these foundational rules to suit their operational needs.  
In essence, the Semantic Access Policy Set, through the use of SWRL, offers a sophisticated means to encode access control policies that are dynamic, context-aware, and capable of handling the complexities of logistics business collaboration. This approach not only enhances the security and efficiency of data access within logistics but also ensures that access control policies are adaptable to the evolving needs and scenarios within the industry.
# Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces
## 5 Experimental Evaluation
### Pre-Processing and Descriptive Statistics
An example of such normalization for subject 3 is shown in Figure 8.  
The values of this coefficient can additionally be used to filter out some of the optimal frequencies selected by the values of the \(K_{fa}\) coefficients, although it should be noted that they are highly empirical in nature, and the same value of this coefficient can mean a completely different level of discomfort for different subjects. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Frequency** \\ **(Hz)** \\ \end{tabular}  & **Subj 1** & **Subj 2** & **Subj 3** & **Subj 4** & **Subj 5** & **Subj 6** & **Subj 7** & **Subj 8** & **Mean** & **SD** \\ \hline Closed & & & & & & & & & & \\ eyes & 11.22 & 5.90 & 4.08 & 2.62 & 3.26 & 3.93 & 9.09 & 3.58 & 5.46 & 3.10 \\
# Analysis on Response of a Single Pile Subjected to Tension Load Considering Excavation Effects
## 5 Validation of the Proposed Approach
### Case Four  
Another centrifuge test was conducted to validate the proposed approach [38]. The centrifuge test was conducted with an acceleration of 100 g. A sketch of the centrifuge tests is shown in Figure 10. A circular excavation was modeled. The model box was a circular stainless box with a diameter of 800 mm and a height of 1010 mm, respectively. Four piles were used in the test. The aluminum piles, P1 and P2, were uplifted before excavation. The shaft resistance within the excavation depth was eliminated by a sleeve. Another two aluminum piles, P3 and P4, were uplifted after excavation of 334 mm. The diameter of the pile was 8 mm, and the effective length of the pile was 486 mm. The effective length was defined as the pile length below the excavation surface.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Water Content** & **Density** & **Void Ratio** & **Constrained Modulus** & **Cohesion** & **Effective Friction Angle** \\
**(\%)** & **(kg\(\cdot\)m\({}^{-3}\))** & **(MPa)** & **(MPa)** & **(kPa)** & **(\({}^{\circ}\))** \\ \hline
9.85 & 1540 & 0.95 & 3.92 & 1.08 & 34.0 \\ \hline \end{tabular}
\end{table}
Table 2: Physical and mechanical properties of sandy silt.  
Figure 9: Comparison between the calculated and centrifuge test results of an uplift pile before and after excavation.  
The soil used in the model test is sandy silt with a dry density of 1.53 g/cm\({}^{3}\). The soil mechanical and physical properties are summarized in Table 3. After saturation of the soil, the piles P1 and P2 were uplifted with a loading speed of 1.2 mm/min. Then, the soil was excavated up to a depth of 334 mm, and the piles P3 and P4 were uplifted with the same loading speed.  
Figure 11 shows a comparison of the load-displacement curves obtained by the centrifuge test and the proposed approach. Only the test results of piles P2 and P3 are presented, due to the symmetry. The numerical simulations conducted by [PERSON] et al. [38] are also presented in this figure. It can be seen in the figure that the proposed approach performed well in predicting the load-displacement curves before and after excavation. The pile uplift capacity before and after excavation was 17,000 kN and 12,000 kN, respectively, showing a 29% capacity loss caused by the excavation.  
Figure 11: Comparison between the results from finite element method, centrifuge test and the proposed approach before and after excavation.  
Figure 10: A sketch of the centrifuge tests: (**a**) plane view, (**b**) front view.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Pile-Soil Friction** & **Unit Weight** & **Coefficient of Earth** & **Constrained** & **Cohesion** & **Soil Friction** \\
**Angle (’)** & **(kN-m\({}^{-3}\))** & **Pressure** & **Modulus (MPa)** & **(kPa)** & **Angle (’)** \\ \hline \(18\) & 18.6 & 0.49 & 48 & 1.7 & 30.6 \\ \hline \end{tabular}
\end{table}
Table 3: Mechanical properties of sandy silt and parameters of pile–soil interface.
## 7 Simulation Results and Comparison
### Comparison of the Simulation Results  
Figure 13a shows the comparison results of the torque ripple between the CCC, the linear, the sinusoidal, and the proposed TSF1 (\(r=4\)) when the torque reference is set to 1.5 Nm. The CCC is the worst choice in all methods. For the conventional TSFs, the torque ripples are almost two to three times higher at 3000 rpm than at 1000 rpm. And at 3000 rpm, the torque ripple of the proposed TSF1 (\(r=4\)) is nearly one half of the other TSF methods. That means the proposed TSF1 (\(r=4\)) have a better performance than the other control method. Actually, at a lower rotor speed, the torque ripple of the proposed TSF is obviously affected by the hysteresis band of the current controller. Therefore, the torque ripple can be decreased further more by reducing the inherent current ripple of the hysteresis controller.  
Figure 13b shows the comparison results of the rms current for all control methods and the CCC is also the worst choice. At the rotor speed less than 500 rpm, the proposed and the conventional TSFs exhibits almost a similar rms current. As the rotor speeds up, due to the poorer current tracking performance, the rms currents of the linear and sinusoidal TSFs increase significantly. However, the rms current for the proposed TSF remains relatively as the speed increases.  
The peak current is much smaller for the TSF1 (\(r=4\)) than those of the other methods, which have smaller hardware pressure (Figure 13c). At a higher speed, the peak currents of the conventional TSFs decrease significantly due to the poorer current tracking performance by the inductance effect. Therefore, the TSF1 (\(r=4\)) is a better choice for reducing torque ripple and lower copper loss relatively.  
Figure 13: Comparison of CCC and different TSFs: (**a**) torque ripple; (**b**) rms phase current; (**c**) peak current.  
Figure 14a,b show the values of the evaluation function \(J\) for different weight factors \(w_{f}\) at speeds of 1000 rpm and 3000 rpm. It is evident that the linear TSF is the worst choice because its \(J\) values are always much larger than those of the other TSFs at any weighting factor. The sinusoidal and cubic TSFs have almost the same \(J\) values at a speed of 1000 rpm. However, as the weight factor \(w_{f}\) value increases, the cubic TSF exhibits a better performance. So, the choice of weight factor wf is depended on the emphasis of reducing the copper loss or increasing the maximum speed range. At any weight factor \(w_{f}\) or any speed, the proposed TSF1 (\(r=4\)) has the best minimum \(J\) values when compared to the sinusoidal or cubic TSFs.
## 5 Numerical Validation of the Model
### Modal Analysis  
To validate the elastic properties of the DFM and the accuracy of its mass matrix definition, a modal analysis was conducted on two structures assumed to be perfectly embedded in their foundations. The objective of this analysis was to compare the results obtained from a reference 3D numerical analysis with those from the DFM for two simplified, hypothetical structures composed of URM walls. These structures, which lack roofs and reinforcing elements, exhibit deformations that are intentionally simplified and somewhat unrealistic. The selected structures are as follows.  
* A square wall: This structure represents a simple yet fundamental form in masonry architecture. It enables the evaluation of the DFM's ability to capture basic vibrational modes and ensures that the model accurately reflects the stiffness and mass distribution of this straightforward structure.
* A five-wall structure with openings: This configuration is designed to test the DFM's effectiveness in simulating the dynamic interactions between adjoining walls, resulting in complex modal behaviors. This structure presents a challenge to the model's ability to handle the increased complexity and interactions that are characteristic of real-world masonry buildings.  
The DFM is a simplified model based on macro-elements consisting of beams and struts. For both structures, the primary vibration modes and associated natural frequencies identified by the DFM are compared with results from the numerical software Cast3M [32], which utilizesa refined mesh of cubic finite elements. This comparison highlights the DFM's effectiveness in capturing the primary vibration modes and assesses the impact of varying mesh sizes within the DFM's computational framework. Note that a convergence study has been performed to make sure the results given by the 3D FE model are relevant as reference [33].  
The analysis focuses on the five predominant modes, which are selected based on their associated modal masses, as proposed by [PERSON] et al. [34]. These modes correspond to Modes 1, 5, 10, 23, and 25, which are ordered by increasing natural frequencies. To evaluate the DFM's accuracy in estimating natural frequencies, the error is defined as the absolute relative difference between the natural frequencies predicted by the DFM \(f_{mod}\) and those obtained from the FE reference model, \(f_{ref}\). Mathematically, this is expressed as Error=\(|(f_{ref}-f_{mod})/f_{ref}|\). This metric provides a clear quantitative assessment of the DFM's precision in determining natural frequencies.  
The mechanical properties of the masonry in the modal analysis were derived from the quasi-static tests conducted by [PERSON] [35]. These properties are detailed in Table 2.
## 2 Conventional Control Strategy of a Three-Phase Grid-Connected Inverter  
Figure 1 shows the typical topology of a grid-connected inverter with an L filter.  
The conventional control strategy of a three-phase grid-connected inverter can be roughly divided into four steps, i.e., (a), (b), (c) and (d), as shown in Figure 2.  
1. Sample the three-phase line voltage \(V_{line}\) at the PCC point. Then, convert \(V_{line}\) into the phase voltage \(V_{phase}\); and then lock the phase of Vphase to obtain \(\sin\varphi/\cos\varphi\), as the phase-lock result.
2. Sample the three-phase grid current \(I\). Based on the phase-lock result, the three-phase grid current \(I\) is decomposed by dq coordinate transformation to obtain the dq axis components \(I_{\text{d}}/I_{\text{q}}\).
3. Take the errors between \(I_{\text{d}}/I_{\text{q}}\) and target current \(I_{\text{dref}}/I_{\text{qref}}\), and enter the errors into PI controllers, respectively, and then perform dq/abc transformation, adopting the phase-lock result \(\sin\varphi/\cos\varphi\) to obtain three-phase modulated waves.
4. Compare the modulated waves with the carrier wave to generate the six-channel PWM switching signals.  
The conventional grid-connected inverter control strategy is designed based on the three-phase voltage balance condition, which can ensure equal amplitude among the three-phase grid currents. In other words, the grid-connected power of each phase is fixed to be the same. The conventional strategy cannot realize individual control of grid-connected power or current of each phase.
# An Undersampling Method Approaching the Ideal Classification Boundary for Imbalance Problems
## 3 Proposed Method
### Undersampling That Enables Close-Fitting Data Distribution at the Ideal Classification Boundary
### Effect of Proper Separation of the Hyperplane on Research Results

We suggest that the separating hyperplane chosen will affect the final result of the algorithm. Therefore, our FDUS method selects an optimal \(H_{opt}\) from among the separated hyperplanes \(H\) obtained by the SVM, linear classification, and logistic classification before proceeding with the other steps. To verify this idea and prove that the optimization of \(H\) can assist the algorithm, we apply the \(H\) that was obtained using the above three methods directly to the FDUS method in order to perform experiments. Each experiment was performed 20 times, and then their average results were entered in Table 4. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data Set**} & \multicolumn{3}{c}{**SVM**} & \multicolumn{3}{c}{**Linear Classification**} & \multicolumn{3}{c}{**Logistic Regression**} \\ \cline{2-11}  & **AUC** & **F** & **G** & **AUC** & **F** & **G** & **AUC** & **F** & **G** \\ \hline abalone-17\_vs\_7-8-9-10 & 0.8823 & 0.7782 & 0.7463 & 0.8691 & 0.7757 & 0.7643 & 0.8445 & 0.8391 & 0.8381 \\ car-vgood & 0.9739 & 0.7935 & 0.7877 & 0.9813 & 0.7827 & 0.8001 & 0.9520 & 0.8669 & 0.8803 \\ dermatology-6 & 1 & 1 & 0.9817 & 0.9659 & 0.8961 & 0.9056 & 0.9750 & 0.9333 & 0.9333 \\ ceo11 & 0.9308 & 0.8801 & 0.8526 & 0.8631 & 0.8288 & 0.7903 & 0.9531 & 0.8387 & 0.7983 \\ glass-0-1-6\_vs\_4-5-6 & 0.9958 & 0.8923 & 0.8595 & 0.9300 & 0.8789 & 0.8437 & 0.9482 & 0.8729 & 0.8144 \\ glass-0-1-6\_vs\_5 & 1 & 1 & 1 & 0.8667 & 0.8667 & 0.8309 & 0.8500 & 0.8333 & 0.8309 \\ iris0 & 0.9961 & 0.9895 & 0.9897 & 0.9895 & 0.9897 & 0.9900 & 0.9895 & 0.9897 & 0.9897 \\ k-vs\_k-three\_vs\_4 & 0.8484 & 0.7385 & 0.7172 & 0.8429 & 0.7331 & 0.7138 & 0.8490 & 0.7392 & 0.7181 \\ yeast5 & 0.9862 & 0.9581 & 0.9534 & 0.9836 & 0.9569 & 0.9514 & 0.9878 & 0.9518 & 0.9515 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Different ratios of AUC, F-measure (’F’ for short), and G-mean (’G’ for short) with different separated hyperplanes.
_Appl. Sci._**2014**, \(4\), 79-98; doi:10.3390/app4010079  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
**Undersampling in Orthogonal Frequency Division Multiplexing Telecommunication Systems**  
**[PERSON]**  
Computer Engineering Department, Technological Educational Institute of Thessaly, Larissa, 41110, Greece; E-Mail: [EMAIL_ADDRESS]; Tel.: +30-241-068-4542; Fax: +30-241-068-4573  
_Received: 12 September 2013; in revised form: 24 February 2014 / Accepted: 28 February 2014 / Published: 17 March 2014_  
**Abstract:** Several techniques have been proposed that attempt to reconstruct a sparse signal from fewer samples than the ones required by the Nyquist theorem. In this paper, an undersampling technique is presented that allows the reconstruction of the sparse information that is transmitted through Orthogonal Frequency Division Multiplexing (OFDM) modulation. The properties of the Discrete Fourier Transform (DFT) that is employed by the OFDM modulation, allow the estimation of several samples from others that have already been obtained on the side of the receiver, provided that special relations are valid between the original data values. The inherent sparseness of the original data, as well as the Forward Error Correction (FEC) techniques employed, can assist the information recovery from fewer samples. It will be shown that up to 1/4 of the samples can be omitted from the sampling process and substituted by others on the side of the receiver for the successful reconstruction of the original data. In this way, the size of the buffer memory used for sample storage, as well as the storage requirements of the Fast Fourier Transform (FFT) implementation at the receiver, may be reduced by up to 25%. The power consumption of the Analog Digital Converter on the side of the receiver is also reduced when a lower sampling rate is used.  
**Keywords:** undersampling; OFDM; DFT; FFT; compressed sensing; analog digital conversion
## 2 Materials and Methods
### Functional Properties Evaluation
#### 2.4.2 Piezoresistive Behavior  
For the piezoresistive behavior evaluation, we simultaneously used a Hounsfield tensile test equipment with a 250 N load cell, with a 0.04 mm _Z_-axis deformation, and a compression speed of 0.08 mm/min over the sample in a circular surface, with a diameter of 10 mm, for 10 cycles. Simultaneously, to measure the variation of electrical resistance, 2 electrodes were placed on the clamps, as shown in the scheme of Figure 2. Through these electrodes connected to a digital multimeter (Agilent 34461A), it was possible to measure and record the variation in electrical resistance over the 10 compression cycles.  
The piezoresistive effect of the membranes can be determined by calculating their mechanical deformation (\(\varepsilon\)) and their corresponding electrical resistance variation (\(\Delta R\)) [31]. The mechanical deformation was determined as the ratio between the transverse deformation (mm) and the initial thickness of the sample (mm).  
For this test, overlapping a known number of layers of the analyzed membrane was necessary. Then, \(\Delta R\) was determined according to Equation (5):  
\[\Delta R=(R-R_{0}) \tag{5}\]  
where \(R\) represents the electrical resistance in a time t and \(R_{0}\) the initial electrical resistance (at time 0). Then, the behavior curves were analyzed in the same graph to study the relationship between the mechanical deformation and the electrical resistance variation of the material over time. Finally, the \(GF\), which determines the sensitivity of a piezo resistor (the fractional change in the resistance per unit strain), was calculated as follows:  
\[GF=\frac{\frac{\Delta R}{R_{0}}}{\varepsilon} \tag{6}\]