esa-sceva/satcom-chunk-collection huggingface dataset:
# Structural Damage Detection Based on One-Dimensional Convolutional Neural Network
## 4 Multiple Damage Location Detection
### Detection Result  
The training set and the valid set are inputted into the proposed convolutional neural network. Training accuracy and valid accuracy are shown in Figure 8. When the number of iteration epoch is 100, the training accuracy is 0.9900 and the valid accuracy is 0.9833. The detection accuracy has met the requirements of engineering.  
A detection model can be obtained by training the dataset. Test set is used to test the detection performance of the model, and the test result is shown in Table 4. The number of samples in the test set is 495. The recognition accuracies of \"c0\" and \"c6c11\" are 100%. The recognition accuracy of \"c1c6c11\" is 92.9%, 91 samples can be identified correctly, 4 samples can be misidentified as \"c6c11\", and 3 samples can be misidentified as \"c1c6\". The average recognition accuracy of the five categories is 97.36%. The result showed that this method can identify multiple damage locations accurately.  
Figure 8: Training accuracy and valid accuracy.  
The raw data and the output data of CNN are displayed in two dimensions, as shown in Figure 9. The two-dimensional display of raw data is also terrible. There is no distinction between the categories. However, the two-dimensional display of the output data of CNN is perfect. There is a clear distinction between the categories. It is not as effective as the two-dimensional display of single damage location detection; a small number of samples were misclassified. There are overlaps between different categories in the multiple damage location detection. For instance, \"c1c6c11\" means that the damaged columns are column 1, column 6, and column 11. Other categories overlap with \"c1c6c11\" partially. So, it is more difficult to distinguish. Nevertheless, the recognition accuracy of \"c1c6c11\" is 92.9%. The results showed that the proposed convolutional neural network has a powerful ability to detect multiple damage location.
## Materials and Methods
### Obtaining the Gaze Direction \(\varphi_{g}\)
#### 2.2.3 Determining Gaze Direction in the Wheelchair Coordinate System \(\varphi_{g}\)  
Gaze-point coordinates in the RGB camera image \((x_{g}^{c},y_{g}^{c})\) were converted into the gaze direction \(\varphi_{g}\) in the wheelchair coordinate system. Since both, the LRF and RGB camera are located at the same position in two-dimensional coordinates, the angle between the RGB camera and gaze point is identical to that between the LRF and gaze point. This defines the gaze direction \(\varphi_{g}\) in the wheelchair coordinate system. From the geometrical relationship depicted in Figure 5, the gaze direction \(\varphi_{g}\) is obtained from Equation (4). For the example illustrated in Figure 5, the gaze direction is \(\varphi_{g}=8^{\circ}\)  
\[\varphi_{g}=\arctan\left(\frac{x\tan 35^{\circ}}{960}\right) \tag{4}\]  
In the proposed study, determination of the gaze direction is performed in real time. The total sampling time is approximately 75 ms. Filtering is performed so as to exclude unnatural gaze movement. The filtering operation is performed using a low-pass filter that blocks signals above a specific frequency (1/6 Hz in this case). It is believed that sliding eyeball motion tracking is capable of tracking visual targets moving at speeds of the order of \(30^{\circ}/\)s [19]. Sliding eyeball motion essentially relates to eye movements that occur while tracking a moving visual object. For the purpose of this study, eye movement speeds of the order of \(60^{\circ}/\)s or higher are considered unnatural and are, therefore, discarded. In line with the above consideration, a fourth-order low-pass filter, described by Equation (5), is designed with a cutoff frequency of 1/6 Hz and sampling frequency of 1/0.075 Hz. The order of the low pass filter was determined through preliminary verification.  
\[\hat{\varphi}_{g}=-0.0158\varphi_{g}(t)+0.2502\varphi_{g}(t-1)+0.5319\varphi_{ g}(t-2)+0.2502\varphi_{g}(t-3)-0.0158\varphi_{g}(t-4) \tag{5}\]  
In addition, although the control cycle of eye gaze detection is 75 ms, as described in Sections 2.2.1 and 2.2.2, when the pupil detection accuracy \(S_{pupil}\) or the image matching accuracy \(S_{NCC}\) is less than the threshold value, it is excluded as gaze information with low reliability. In that case, it is necessary to undertake filtering by using the previous value \(\varphi_{g}(t)=\varphi_{g}(t-1)\). The pupil detection accuracy \(S_{pupil}\) is less than the threshold value, for example, when the eyes are not opened properly. The image matching accuracy \(S_{NCC}\) is less than the threshold value when the environment with less features is in front of the wheelchair.  
In this manner, relevant information concerning gaze direction is obtained.  
Figure 5: Determination of gaze direction \(\varphi_{g}\).
## 3 Experimental Setup and Measured Results in the Salt Mine  
The Khewara Salt mine has 19 levels, similar to stories in a building, where all the levels are operational except the ground level, which is reserved for tourists. Seven levels are above ground and twelve are underground, with an average clearance of 50 ft of salt/rock between each level. The mine is 350 m above sea level and extends 730 m deep into the mountain. The 'room and pillar' method is still being used in the Khewara mining operation, with a cumulative length of tunneling inside the mine of more than 40 km, including straight passages and curves. The experiments were conducted on level three, which is an underground level, in straight passages and near the pillars. The actual tunnel cross-sections are usually between rectangles and circles. In our study, the experimental mine tunnel was treated as an equivalent rectangle, with a maximum width of 2.2 m and a maximum height of 2.6 m, for ease of comparison.  
The radio model was validated using link performance experiments carried out on wizzi motes [3; 8; 9; 10]. The hardware of the wizzi mote transceiver is shown in Figure 4a and node deployment in the salt mine is shown in Figure 4b. The nodes were deployed on the roof, as well as the side walls of the mine tunnels. The embedded RF core in the onboard controller can transmit and receive sensor data using the 433 MHz DASH7 protocol. During the experiments, one transmission cycle consisted of 1000 packets, each containing 255 bytes of data, transmitted at a carrier frequency of 433 MHz. The transmission data was modulated via 2 GFSK at a baud rate of 200 K baud [10]. Every transmission took 200 ms of time and was attempted five times before declaring that the connection was lost. It was observed that, for every 1000 packets transmitted using a fixed transmitter power of 12.6 dBm, the measurements consumed 0.1-0.25 mW power. The experiment campaign was carried out for three continuous weeks at different locations of level 3, with an average of 68,000 data packets transmitted every day.
## 5 Experimental Evaluation
In summary, the optimized algorithms obtained a total of 41\(\times\) and 47\(\times\) in speedups, respectively, compared to the unoptimized algorithms. Bandwidth efficiency is another important evaluation standard and is defined as \(bw\_ef=bw\_vl/bw\_th\), where \(bw\_ef\) is the bandwidth efficiency, \(bw\_vl\) is the valid bandwidth, and \(bw\_th\) is the theoretical bandwidth. The total accessed memory of the SpMV is defined as \(dof\_num\times nnz\_row\times(sizeof(double)+sizeof(int))+dof\_num \times sizeof(double)\times 2\), and for the Gauss-Seidel, it is defined as \(dof\_num\times nnz\_row\times(sizeof(double)+sizeof(int))+dof\_num \times sizeof(double)\times 4\). As shown in Figure 11, the bandwidth efficiency increased with the number of DSP cores, which can be explained by the fact that the bandwidth of indirect access to the GSM through the DMA_SG increased with the number of DSP cores. The bandwidth efficiencies of the SpMV on the three grids were all around 72%, and the Gauss-Seidel had an efficiency of 67% on the small-scale grid and about 81% on the other two grids. It is worth mentioning that the bandwidth efficiency of using the DMA to transfer the contiguous memory data was 85%. Figure 10: SpMV and Gauss–Seidel performance test results ((**a,c,e**) describe the SpMV performance on 32 \(\times\) 32 \(\times\) 32, 64 \(\times\) 64 \(\times\) 64, and 128 \(\times\) 128 \(\times\) 128 grid, respectively; (**b,d,f**) describe the Gauss–Seidel performance on 32 \(\times\) 32 \(\times\) 32, 64 \(\times\) 64 \(\times\) 64, and 128 \(\times\) 128 \(\times\) 128 grid, respectively). Figure 12 shows the test results of the mixed-precision algorithms in subsection 4.6. Both the SpMV and Gauss-Seidel achieves speedups of about \(1.2\times\) on the \(32\times 32\times 32\) grid. On the other two scale grids, the FLOPS speedups were about \(1.6\times\) for the SpMV and \(1.45\times\) for the Gauss-Seidel, and all the FLOPSs were around \(8.9\) Gflops/s.
## 3 Results and Discussion
### Hygroscopicity  
Hygroscopicity is linked to the ability to absorb water from an environment and affect the physical, chemical and microbiological stability of the dried materials. The knowledge of the hygroscopic properties of these products is very important mainly when it comes to establishing the drying, packaging and changes in the product during storage [30; 47].  
The changes in the hygroscopicity of kiwiberry foam mats are shown in Figure 2. The hygroscopic kinetics present a different pattern, depending on the shelf temperature. The foam mats dried at 10 \({}^{\circ}\)C and 20 \({}^{\circ}\)C presented higher hygroscopicity in comparison to the sample dried at 40 \({}^{\circ}\)C during the measurement. After 72 h, the samples were characterized by hygroscopicity of 4.07 g water/100 g d.m. (10 \({}^{\circ}\)C), 4.41 g water/100 g d.m. (25 \({}^{\circ}\)C) and 1.41 g water/100 g d.m. (40 \({}^{\circ}\)C). The opposite trend with increasing drying temperature was observed for the foam-mat-dried banana pulp [21] and foam-mat-dried cantaloupe pulp [43].  
Hygroscopic properties are mainly related to chemical composition, structure, enzymatic and chemical reactions [8; 10] as well as the structure of the dried materials [48]. For example, [PERSON] et al. [35] observed that the spray-dried kiwiberry powders with a lower moisture content were more hygroscopic. The explanation is linked to an increasing water gradient between the analyzed material and surrounding environment in a desiccator. The current study contradicts this explanation. That is why the higher hygroscopicity of kiwiberry foam mats dried at lower temperatures (10 and 25 \({}^{\circ}\)C) is related to better structure preservation and higher porosity (see Section 3.6) as well as evenly distributed thin-walled pores, which affect the better ability to absorb water vapour [21; 48]. The hygroscopic properties of the tested foam mats were affected by the shelf temperature during freeze drying. For practical reasons, a low hygroscopicity is preferable, reducing the adverse changes during storage of the dried material. These expectations were met by the foam mat dried at 40 \({}^{\circ}\)C.  
The results of the hygroscopic kinetics modelling showed that the solution of [PERSON]'s second law is suitable and satisfying for adjusting the experimental data of freeze-dried foam-mat kiwiberry pulp based on the high correlation coefficient (\(R^{2}\)) values ranging from 0.993 to 0.997 and the root mean square (RMS) values ranging from 4.20 to 13.11% (Table 2). The increasing shelf temperature during freeze drying caused a significant lowering of the equilibrium moisture content of the foam-mat kiwiberry pulp. It seems that the higher shelf temperatures provided the dry foam mats with a more collapsed and less porous structure, which resulted in a worse adsorption capacity. On the other hand, a low adsorption capacity is favourable for storage purposes and better rehydration properties [46].
# Wideband Spectrum Sensing Based on Reconfigurable Filter Bank in Cognitive Radio
###### Abstract  
In order to ease the conflict between the bandwidth demand of high-rate wireless communication and the shortage of spectrum resources, a wideband spectrum sensing method based on reconfigurable filter bank (RFB) with adjustable resolution is presented. The wideband signals are uniformly divided into multi-narrowband signals by RFB, which is designed by polyphase uniform Discrete Fourier Transform (DFT) modulation, and each sub-band is sensed by energy detection. According to the idle proportion of detected sub-bands, the number of RFB sub-bands is reset in next spectrum-sensing time. By simulating with collected wideband dataset, the influence of filter bank sub-bands number and idle state proportion on the sensing results is analyzed, and then on the basis of the trade-off between spectrum-sensing resolution and computational complexity, the optimal sub-bands number of filter bank is selected, so as to improve the detection performance and save resources.  
reconfigurable filter bank; adjustable resolution; energy detection; wideband spectrum-sensing 2019 11 244 10.3390/f11110244
# Volumetric Modulated Arc Therapy for Radiosurgery of Brain Metastases: A Single-Center Study
## 2 Materials and Methods
### Treatment Technique  
The planning and treatment protocols were similar to those carried out previously [9; 11]. First, after obtaining the magnetic resonance image (gadolinium-enhanced 3D T1-weighted imaging, Gd T1 WI), GTV is contoured. Subsequently, the PTV is generated with a margin of 2 mm (GTV-PTV) to consider inaccuracies in both GTV delimitation and patient positioning. At the time of radiosurgery, patients are immobilized using a thermoplastic mask and a frameless fixation system. All patients are treated using a VMAT technique consisting of 5 non-coplanar arcs and 6 MV X-rays, using optimized rotational delivery in order to avoid collision between gantry and patient while simultaneously minimizing treatment time. Our study included patients treated with single fractions as well as those undergoing fractionated treatment schedules (fSRS). Thus, while the planning aimed for an isodose at 100% of the prescribed dose to cover at least 99% of the PTV and 100% of the GTV for treatments through single-fraction SRS, patients treated with fSRS received a dose of 98% of the PTV and 98% of the GTV. The treatment planning optimization process was carried out using the Eclipse 10.0 software (Varian Medical System, Palo Alto, CA, USA) [21], and doses were calculated via the analytical anisotropic algorithm with a 1.2 mm grid. The treatment was administered using a Varian iX lineal accelerator equipped with a multi-leaf collimator (MLC) with a 5 mm central leaf width at the isocenter and a 10 mm leaf width in the outer field. Patient positioning was performed with Cone Beam CT (CBCT) assistance prior to the first arc and following the third arc delivery, with the couch situated in the neutral position. Regarding the treatment doses prescribed for each BM, in general, for patients who had previously received whole brain radiotherapy, the prescribed dose was 12 to 15 Gy, while for the rest, the prescribed dose ranged from 16 to 20 Gy. Figure 1 shows an example of treatment planning for a patient with three BMs treated with a single isocenter.
# Research on a Decoupling Algorithm for the Dual-Deformable-Mirrors Correction System
## 2 Principle of Wavefront Correction with Dual Deformable Mirrors
### Principle of Separation Coefficient Algorithm
Next, the initial voltage is calculated based on its conversion matrix. The voltage at the previous moment is used to solve the voltage of FSM and DM at the previous moment. The PI controller is employed to determine the final control voltage for both FSM and DM. These voltages are then used to drive the FSM and DM, obtaining the corrected residual wavefront correction. The [PERSON] wavefront sensor can then obtain the slope data of the corrected residual wavefront, forming a closed-loop correction. The Peak-to-Valley (PV) value represents the difference between the maximum and minimum values of the wavefront phase, indicating the maximum level of wavefront phase defects. However, it ignores the proportion of this part in the overall wavefront. Therefore, evaluating wavefront quality based solely on PV value is not rigorous enough. On the other hand, the Root-Mean-Square (RMS) value focuses more on the overall information of the wavefront phase. It reflects the degree of deviation of all points on the wavefront from their ideal positions. Therefore, combining PV and RMS values can more accurately represent the wavefront condition. This study mainly uses PV and RMS values to evaluate the level of distortion in the wavefront [16]. Based on the phase compensation of two deformable mirrors, the coupling coefficient is used to quantitatively describe the coupling effect of the adaptive optics dual-deformable-mirrors correction system [4]. A coupling coefficient value closer to 0 indicates a smaller coupling effect between Woofer and Tweeter, while a coupling coefficient value closer to 1 indicates a larger coupling effect.
## 3 UVSQ-SAT Data Method and Map Reconstruction of the Variables (Observations and Model)
### UVSQ-SAT Data Processing to Obtain Observation Time Series
#### 3.1.2 Instrumental Equations of the Optical Sensors Based on Photodiodes  
The UVSQ-SAT scientific payload has also six photodiodes to measure the total solar irradiance and the outgoing shortwave radiation, and four additional photodiodes for observing the Sun (three measure the total solar irradiance and one measures the UV solar spectral irradiance in the Herzberg continuum). Each face of the satellite has at least one photodiode who observes external fluxes. A photodiode is a semiconductor p-n junction device that converts light into an electrical current. The resulting current is converted into ADU and stored by the UVSQ-SAT onboard computer. Once data are retrieved, the ADU signals \(Sp_{ADU}\) are converted into physical units to express the measurements \(\Phi_{pho}\) using the transfer function (Equation (4)):  
\[\Phi_{pho}=\left(\frac{Sp_{ADU}(T_{pho},T_{b})-V_{ADU}^{ref}(T_{b})}{Np_{samp} }+Cp_{1}(T_{pho},T_{b})\right)\times\frac{1}{G_{pho}(T_{b})}\times\frac{1}{S_{ pho}(\text{T}_{pho})} \tag{4}\]  
where \(Np_{samp}\) is the number of samples for a measurement, \(G_{pho}\) is the gain of the electronic unit, \(S_{pho}(T_{pho})\) is the responsivity of the photodiode (calibration done by the manufacturer), \(Cp_{1}\) is a corrective offset, which depends on the temperature of the photodiode. \(T_{pho}\) is the satellite structure temperature close to the photodiode.  
For the six photodiodes that measure the total solar irradiance and the outgoing shortwave radiation, the albedo of the Earth can be obtained using an equation similar to that proposed in Equation (3).
# Comparison of Basic Notch Filters for Semiconductor Optical Amplifier Pattern Effect Mitigation
## 4 ODI vs. MRR Comparison Rationale
Then, using the electric field responses [40; 46]:  
\[H_{ODI}(\lambda) =\frac{1}{2}\Bigg{\{}1+\exp\Bigg{[}-j\Bigg{(}2\pi c\frac{\lambda- \lambda_{amp}}{\lambda_{amp}^{2}}\Delta\tau+\Phi_{b,ODI}\Bigg{)}\Bigg{]}\Bigg{\}} \tag{4a}\] \[H_{MRR}(\lambda) =\frac{r-l\exp\Bigg{\{}-j\Bigg{[}2\pi c\frac{\lambda-\lambda_{amp}} {\lambda_{amp}^{2}}T_{R}+(\pi+\Phi_{b,MRR})\Bigg{]}\Bigg{\}}}{1-rl\exp\Bigg{\{}- j\Bigg{[}2\pi c\frac{\lambda-\lambda_{amp}}{\lambda_{amp}^{2}}T_{R}+(\pi+ \Phi_{b,MRR})\Bigg{]}\Bigg{\}}} \tag{4b}\]  
for the ODI and MRR, respectively, and substituting in (3) yields, after appropriate algebraic manipulations and numerical calculations for \(T_{car}=75\) ps and \(\alpha_{LEF}=8\), \(\Phi_{b,ODI}\simeq 0.7\pi\) and \(\Phi_{b,MRR}\simeq 0.98\pi\). Note that the MRR phase bias has mathematically been shifted by '\(\pi\)', so that the transfer functions of both considered filters become null when the wavelength position of the input data carrier coincides with that of the nearest notch, which happens when \(\Phi_{b,ODI}=\Phi_{b,MRR}=\pi\). These phase bias values correspond to \(\Delta\lambda_{max,ODI}=-0.27\) nm and \(\Delta\lambda_{max,MRR}=-0.48\) nm. The difference in the detuning values is attributed to the fact that, as shown in Figure 5, the ODI and MRR have different FSR, i.e., \(FSR_{MRR}\simeq 10\cdot FSR_{ODI}\). Furthermore, they imply that \(\lambda_{natch,ODI}\) is closer to \(\lambda_{amp}\) than \(\lambda_{natch,MRR}\), since \(\lambda_{natch,ODI}-\lambda_{amp}=0.25\) nm while \(\lambda_{natch,MRR}-\lambda_{amp}=0.27\) nm. The suitability of the critical parameters values specified above is further verified by observing that both filters extended the modulation response bandwidth of the SOA, which is inherently limited by the SOA finite carrier lifetime responsible for the pattern effects. This is shown in Figure 6, which has been obtained by plotting first the modulation response of the stand-alone SOA and then combining it in a multiplicative manner with that of the ODI or MRR. These responses are mathematically described by closed-form expressions obtained through appropriate small-signal analysis, which allows one to find for each module the transfer function between small signal changes of the output power incurred by small-signal perturbations of the input power, as reported in [47] for the SOA and ODI and in [48] for the MRR.
# Detection of Quasi-Static Trapped Human Being Using Mono-Static UWB Life-Detection Radar
## 3 Experimental Results
### Experimental Results for Scanning Operating Mode
#### 3.2.2 The Type-II Experiment
According to our proposed \"scanning operating mode\", the cross-correlated range \(\times\) range 2D image is shown in Figure 14c, and the VSF is remarkable. The comparison results of the positioning result calculated by the radar system and actual position of the person are shown in Figure 14d, and the positioning error is 25 cm. In the measurement C, the tested person stands 22 m behind the wall and moves horizontally to the left by 5 m, also in a static state and breathing normally, and the angle between people and radars is 13.14\({}^{\circ}\). Figure 15a,b shows the output range-frequency images of the normal FFT-based respiration detection methods using the echoes from a single radar. Similarly, they are too noisy to distinguish the VSF. According to our proposed \"scanning operating mode\", the cross-correlated range \(\times\) range 2D image is shown in Figure 15c, and the VSF is remarkable. The comparison results of the positioning result calculated by the radar system and actual position of the person are shown in Figure 15d, and the positioning error is 21 cm. Figure 14: The experiment B result. (**a,b**) The output range-frequency images using the normal FFT-based respiration detection methods using the echoes from a single radar. (**c**) The output two-dimensional (2D) slice (range \(\times\) range) image for the specific respiration frequency. (**d**) The positioning result calculated by the radar system is compared with the actual position of the person.