esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Material and Methods
### Measurements  
Two different measurement methods were used to calculate the 3-dimensional coordinates of each cylinder's centroid. Physical models (master model and definitive casts) were measured using a computerized coordinate measuring machine (CMM; Micura, Zeiss, Oberkochen, Germany) with a geometric processing software (Calypso, Zeiss) (Figure 4).  
Figure 3: Scanning sequence for digital impression of the master model.  
Figure 2: Definitive cast fabricated by conventional impression.  
For the digital models, geometric inspection software (Geomagic Control X, 3D systems, Rocky Hill, CT, USA) was used to compute the centroid of each cylinder (Figure 5). Once the centroid of each cylinder was calculated using a preset coordinate system (i.e., machine coordinate of the CMM), a new part coordinate system was defined as follows. The centroids of three cylinders (#17C, #11C, and #27C) were used as the reference centroids. The centroid of #11C was defined as the origin (0, 0, 0). A line connecting the centroids of #11C and #17C was designated as the X axis. A plane consisting of three points, centroids of #17C, #11C, and #27C, was designated as the XY plane (Figure 6). The (x, y, z) coordinate of each cylinder centroid was computed based on the part coordinate system defined above. To test the inter-operator reliability, one digital model was chosen from the 25 digital models. Two additional operators ([PERSON] and [PERSON]) measured the digital model using the geometric analysis software to calculate each cylinder's centroid.  
Accuracy consists of trueness and precision [27]. In the current study, trueness was defined as the linear displacement of each cylinder's centroid between the master model and the test groups (Groups CI and IOS). The linear displacement was represented by the difference in the absolute values of the centroid coordinates (\(\Delta\)x, \(\Delta\)y, \(\Delta\)z). In addition to absolute values, raw values were also presented in scatter graphs to visualize the amount of coordinate differences (\(\Delta\)x, \(\Delta\)y, \(\Delta\)z) at each cylinder position. The overall 3-dimensional displacement was represented by \(\Delta\)D, and \(\Delta\)D\(=\sqrt{\left(\Delta\text{x}\right)^{2}+\left(\Delta\text{y}\right)^{2}+\left( \Delta\text{z}\right)^{2}}\). Precision is described as the degree of closeness between repeated measurements. Precision values were calculated based on the difference in coordinates between the 25 specimens in the same group, and the absolute values were used for statistical comparison. Mean and standard deviations of the differences were calculated.  
Figure 4: Measurements of the master model using a computerized coordinate measuring machine.  
Figure 5: Coordinate measurement of the digital model using a digital geometric analysis software.  
Figure 6: The part coordinate system used in the current study. Centroid of #11C was defined as the origin (0, 0, 0). The line connecting the centroids of #11C and #17C was designated as the \(X\)-axis. A plane consisting of three points, the centroids of #17C, #11C, and #27C, was designated as the XY plane.
# A Low Torque Ripple Direct Torque Control Method for Interior Permanent Magnet Motor
## 2 Torque Analysis and Proposed DTC
### Mathematical Model of PMSM and Relationship between Voltage Vector and Torque
In Equations (13) and (14), \(\Delta T_{1k}\) and \(\Delta T_{2k}\) are the torque reduction terms due to impedance, and they are proportional to \(T_{ak}\) and \(T_{bk}\), respectively. 2. \(\Delta T_{3k}\) is the variation of torque involving the voltage vector. The output torque is influenced by the \(\Delta T_{3k}\) term during the period that an active voltage vector is applied to the motor but not during the period with a zero-voltage vector. Based on Equation (15), the relationship between \(\Delta T_{3k}\) and motor voltage vector at \(t_{k}\), \(V_{sk}\) can be obtained as follows: \[\psi_{q(k+1)}=\psi_{qk}+\left(-\frac{R_{s}}{L_{q}}\psi_{qk}+V_{qk}-\omega_{ek} \psi_{dk}\right)\!T_{s} \tag{6}\]3 (17) where \(\circ\) is arithmetic operator representing the Hadamard product, and \(\Gamma\) is a vector expressed as follows: \[\psi_{q(k+1)}=\psi_{qk}+\left(-\frac{R_{s}}{L_{q}}\psi_{qk}+V_{qk}-\omega_{ek} \psi_{dk}\right)\!T_{s} \tag{6}\]4 (18)
3. \(\Delta T_{4k}\) is the variation of torque involving flux linkage. The torque is influenced by this term more significantly when the flux linkage becomes larger with a heavier load applied to the motor [16; 17]. Additionally, when the motor is operated at a high speed, the amplitude of \(\Delta T_{4k}\) would also become large. Based on Equation (16), the relationship between \(\Delta T_{4k}\) and \(\Psi_{qk}\) can be obtained as follows: \[\psi_{q(k+1)}=\psi_{qk}+\left(-\frac{R_{s}}{L_{q}}\psi_{qk}+V_{qk}-\omega_{ek} \psi_{dk}\right)\!T_{s} \tag{6}\]5 (19)  
In what follows, \(\Delta T_{3k}\) and \(\Delta T_{4k}\) are respectively called \"the third torque variation term\" and \"the fourth torque variation term\".
## 4 Optimization of Channel MIMO Capacity by Combining OSTBC and Polarization-Diversity with XPI
### Diversity Transmission Using OSTBC-MRRC MIMO Technique
Dealing with more stringent and challenging assumptions, for example, a selective channel in the time dimension produces a much more significant challenge. The combining MRRC rules are based on the assumption that the channel response components are consistent in a period of two-time symbol slots (e.g., for example, \(h_{xx}(t)=h_{xx}(t+T_{s})\). Two essential constraints are added to this challenge of dealing with selective channel response. The first constraint is based on the fact that there is no physical possibility to create overhead information feedback or opportunity that is not limited in time so that feedback can share the Channel-State-Information-at-the-Transmitter (CSIT). (For example, in the scenario of selective channels in the spatial dimension. In this situation, the share of overhead information with the feedback is very limited). The second constraint is based on restricting the reevaluation time of the channel- response-matrix in each change of symbol time. That is, if the channel reevaluation time is higher than the symbol time, the decoding process in the receiver will ultimately collapse. In this section, we offer a process of optimization that leads to solutions of efficient and fast feedback that deals with the phenomena of changes in multi-lane channels that change every symbol time. In the first step, we assume the same communication model described in Section 2 (apart from the assumptions of \(H_{\mathcal{TR}}\), that we will describe in details below), with the same assumptions of rain distortion and architecture system that are described in Section 4. The XPI optimization is based on case 1 in Section 4.2. The second step, the optimization process is described as follows:  
for \(t_{0}=t\) the channel matrix response, \(H_{\mathcal{TR}}\), is:  
\[H_{\mathcal{TR}}(t_{0})=\bigg{[} \begin{array}{cc}h_{x,x}(t_{0})&h_{x,y}(t_{ 0})\\ h_{y,x}(t_{0})&h_{y,y}(t_{0})\end{array} \bigg{]}, \tag{18}\]  
and for \(t_{1}=t+T_{S}\), the channel matrix response, \(H_{\mathcal{TR}}\), is:  
\[H_{\mathcal{TR}}(t_{1})=\bigg{[} \begin{array}{cc}h_{t,x}(t_{1})&h_{x,y}(t_{ 1})\\ h_{y,x}(t_{1})&h_{y,y}(t_{1})\end{array} \bigg{]}. \tag{19}\]  
Applying the pre and post polarization operations and gathering the \(t_{0}\) and \(t_{1}\) channel matrices, we get:  
\[H_{\mathcal{TR}}(t_{0},t_{1})= \tag{20}\] \[=\left[ \begin{array}{cc}Ah_{xy}(t_{0})+Bh_{xx}(t_{0})+Ch_{yx}(t _{0})+Dh_{yy}(t_{0})&Ah_{xx}(t_{0})+Bh_{xy}(t_{0})+Ch_{yy}(t_{0})+Dh_{yx}(t_{0 })\\ Ah_{yy}(t_{0})+Bh_{yx}(t_{0})+Ch_{xx}(t_{0})+Dh_{xy}(t_{0})&Ah_{yx}(t_{0})+Bh_{ yy}(t_{0})+Ch_{xy}(t_{0})+Dh_{xx}(t_{0})\\ Ah_{xy}(t_{1})+Bh_{xx}(t_{1})+Ch_{yx}(t_{1})+Dh_{yy}(t_{1})&Ah_{xx}(t_{1})+Bh_{ xy}(t_{1})+Ch_{yy}(t_{1})+Dh_{yx}(t_{1})\\ Ah_{yy}(t_{1})+Bh_{yx}(t_{1})+Ch_{xx}(t_{1})+Dh_{xy}(t_{1})&Ah_{yx}(t_{1})+Bh_{ yy}(t_{1})+Ch_{xy}(t_{1})+Dh_{xx}(t_{1})\end{array} \right].\]  
Now, the columns of \(H_{\mathcal{TR}}(t_{0},t_{1})\) are orthogonal if and only if  
\[
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
## 6 Conclusions  
Based on two SWIPT protocols, namely TSR and PSR, we have presented various two-way multi-antenna relaying schemes, where an energy-constrained relay node harvests energy from two source nodes and uses that harvested energy to forward their information. We have compared the BER performance of these SWIPT-based relaying schemes, and have studied the effect of several network parameters, including the number of relay antennas, power splitting ratio, and energy harvesting time. The results have revealed that the benefit of multiple-antenna deployment at the relay depends primarily on the number of antennas (e.g., two antennas versus four antennas), the signal-processing technique (e.g., AF versus DF), and the power allocation strategy (e.g., equal power allocation versus optimal power allocation). In addition, despite the fact that the optimal power allocation is preferable to the equal power allocation, the latter seems an attractive choice for the PS-AF and TS-AF schemes. For the other relaying schemes (i.e., PS-DF, PS-DF-STC, TS-DF, and TS-DF-STC), algorithmic determination of the optimal power splitting ratio and optimal energy harvesting time (see, e.g., [32; 33]) could be an interesting direction for future work.  
**Acknowledgments:** This work was supported by Graduate School, Rangsit University.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 2 Spectrum-Sensing Methods
### Cooperative Spectrum Sensing
#### 2.3.1 Centralized Cooperative Spectrum Detection  
Under the centralized cooperative sensing framework, there exists a cognitive base station, the fusion center, which is responsible for controlling and coordinating the whole sensing process. The basic idea is that multiple terminal devices work together to jointly sense the surrounding spectrum environment. These terminal devices feed spectrum-sensing information to a central node responsible for spectrum processing and allocation. Figure 6 shows the basic model for centralized cooperative spectrum detection.  
Centralized cooperative spectrum sensing can effectively manage the cognitive nodes involved in the collaboration and reduce the requirements on the individual cognitive nodes in terms of detection accuracy, energy consumption, and computational power. At the same time, the accuracy of spectrum sensing can be improved by fusing the heavy sensing information collected by the central node. Therefore, a large portion of the current research is based on the system model of centralized cooperative spectrum sensing. [PERSON], in [39], proposed a centralized cooperative directional sensing technique for cognitive radio networks using directional antennas for spectrum sensing. The sensing information is collected from the auxiliary nodes by FC, and the gradient descent algorithm is refined to solve the nonlinear optimization problem. Simulations show that the proposed method is applicable to existing CR environments. Since CSS methods may suffer from, for example, network attacks like sensed data forgery (SDF), [PERSON], in [40], proposed a scalable, partial CSS algorithm that is able to improve the sensing accuracy while reducing the sharing overhead, and the results show that the performance of the proposed algorithm is proportional to the number of devices and that it is suitable for multi-device connectivity. As for the underwater cognitive network environment, [PERSON], in [41], proposed an underwater cooperative spectrum-sharing (UCSS) protocol for centralized underwater cognitive acoustic networks, which can effectively utilize non-proprietary hydroacoustic frequencies by periodically detecting the random appearance of interference, and two  
Figure 6: Centralized cooperative spectrum detection model.  
heuristic resource allocation algorithms of multi-round RA (MRRA) and mono-regular RA (SRRA), and the results show that MRRA outperforms SRRA in a variety of scenarios and is superior to SRRA.
## 4 Results
### Jilin-1 GP01/02 PMS Angle Effect Correction Validation
In contrast, the Fixed-Jilin-1 method demonstrated superior correction accuracy, with R\({}^{2}\) values exceeding 0.85 for all three bands and a reduction in the MRE of over 2%. For smaller viewing and solar angles (\(\theta_{s}<30^{\circ}\) and \(\theta_{v}<20^{\circ}\)), the impact of the angular effect on reflectance was minimal, and the correction outcomes from the four methods were largely comparable, with the exception of the Fixed-Landsat method's performance in the red band. However, as angles increased (\(\theta_{s}>30^{\circ}\) and \(\theta_{v}>20^{\circ}\)), the reflectance values corrected by the four methods converged, with the angular effect becoming more pronounced and leading to varying correction outcomes among the methods. Overall, the comparative effectiveness of the five methods for angular correction on Jilin-1 GP01/02 PMS data can be ranked as follows: Fixed-Jilin-1, AFX, MODIS-based, Fixed-Landsat, and VJB.
# Sampling Based on Kalman Filter for Shape from Focus in the Presence of Noise
## 7 Conclusions  
For SFF, an object is translated at a constant step size along the optical axis. When an image of the object is captured in each step, mechanical vibrations occur, which are referred as jitter noise.  
Figure 16: 3D shape recovery of letter-I, before and after filtering, using SML, GLV, and TEN in the presence of speckle noise. (**a**) Before filtering for SML; (**b**) Before filtering for GLV; (**c**) Before filtering for TEN; (**d**) After particle filtering for SML; (**e**) After particle filtering for GLV; (**f**) After particle filtering for TEN; (**g**) After Bayesian filtering for SML; (**h**) After Bayesian filtering for GLV; (**i**) After Bayesian filtering for TEN; (**j**) After Kalman filtering for SML; (**k**) After Kalman filtering for GLV; (**l**) After Kalman filtering for TEN.  
In this manuscript, jitter noise is modeled as Gaussian function with mean \(z_{n}\) and standard deviation \(\sigma_{n}\) for simplicity. Then, the focus curves obtained by one of the focus measure operators are also modeled as Gaussian function, with mean \(z_{f}\) and standard deviation \(\sigma_{f}\), for the application of the proposed method. Finally, a new filter is proposed to provide optimal estimation results in a linear SFF system with the jitter noise, utilizing Kalman filter to eliminate jitter noise in the modeled focus curves. Through experimental results, it was found that the Kalman filter provided significantly improved 3D reconstruction of the experimented objects compared with before filtering, and that the 3D shapes of the experimented objects were recovered with more accurate and faster performance than with other existing filters, such as the Bayes filter and the particle filter.  
Conceptualization, H.-S.J. and M.S.M.; Methodology, H.-S.J. and M.S.M.; Software, H.-S.J. and G.Y.; Validation, H.-S.J.; Writing--original draft preparation, H.-S.J.; Writing--review and editing, M.S.M.; Supervision, D.H.K.; Funding acquisition, D.H.K. This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2018-0-00677, Development of Robot Hand Manipulation Intelligence to Learn Methods and Procedures for Handling Various Objects with Tactile Robot Hands).  
We thank [PERSON] for his assistance with useful discussion.  
The authors declare no conflict of interest.
## 3 Methods
### Signal Preprocessing Method
To extract relevant features from the original signal, this module employs SSA to perform data denoising, which includes steps such as embedding, singular value decomposition (SVD), grouping, and reconstruction, particularly for vibration signals. Since two-dimensional images encapsulate rich fault-related information, apply the STFT to convert bearing vibration signals into time-frequency representations, which will subsequently be input into the RGCNN model. Similarly, after performing SSA on acoustic or current signals, the resulting one-dimensional signals will be fed into 1 DCNN.
###### Abstract  
The worst forest fire in South Korea occurred in April 2000 on the eastern coast. Forest recovery works were conducted until 2005, and the forest has been monitored since the fire. Remote sensing techniques have been used to detect the burned areas and to evaluate the recovery-time point of the post-fire processes during the past 18 years. We used three indices, Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), and Gross Primary Production (GPP), to temporally monitor a burned area in terms of its moisture condition, vegetation biomass, and photosynthetic activity, respectively. The change of those three indices by forest recovery processes was relatively analyzed using an unburned reference area. The selected unburned area had similar characteristics to the burned area prior to the forest fire. The temporal patterns of NBR and NDVI, not only showed the forest recovery process as a result of forest management, but also statistically distinguished the recovery periods at the regions of low, moderate, and high fire severity. The NBR\({}_{21}\) for all areas, calculated using 2.1 \(\upmu\)m wavelengths, reached the unburned state in 2008. The NDVI for areas with low and moderate fire severity levels became significantly equal to the unburned state in 2009 (\(p\) > 0.05), but areas with high severity levels did not reach the unburned state until 2017. This indicated that the surface and vegetation moisture conditions recovered to the unburned state about 8 years after the fire event, while vegetation biomass and health required a longer time to recover, particularly for high severity regions. In the case of GPP, it rapidly recovered after about 3 years. Then, the steady increase in GPP surpassed the GPP of the reference area in 2015 because of the rapid growth and high photosynthetic activity of young forests. Therefore, the concluding scientific message is that, because the recovery-time point for each component of the forest ecosystem is different, using only one satellite-based indicator will not be suitable to understand the post-fire recovery process. NBR, NDVI, and GPP can be combined. Further studies will require more approaches using various terms of indices.  
Keywords:forest fire; forest recovery; satellite remote sensing; vegetation index; burn index; gross primary production; South Korea 2018 10 918 10.3390/rs10060918 10.
# Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care
## 2 Background
Our proposed multi-objective optimization framework for IoT device allocation therefore builds upon a comprehensive understanding of global research trends. Unlike prior work, it explicitly considers both financial constraints and the delivery of high-quality care through appropriate device assignment, addressing the identified gaps in current approaches.
## 3 Results and Discussion
### Correlation among Sensory and Phytochemical Values  
The correlations among the analysed attributions in case of the oils and the flour samples are demonstrated in Tables S1 and S2, respectively. In these results, we highlight, especially, the connection between the experienced sensory features and the analytical composition of the samples.  
The sensory features of the oil samples are influenced both by fatty acid and volatile compositions, to some extent. There was a strong negative connection detected between saturated fatty acid ratio (SFA) and all the flavour and odour characteristics. Correlation of the individual SFAs with these features is also strongly negative, except stearic acid, where the correlation was positive. The throat-catching feeling seems to be in connection with the total polyunsaturated fatty acid (PUFA) content and with that of linoleic acid. As the latter one is the main component of each poppy oil also having beneficial effects for human health, the throat-catching property may only be decreased but not eliminated. The throat-catching effect was in moderate connection also with other chemical constituents, either positively (e.g., arachidonic acid) or negatively (e.g., elaidinic and oleic acids).  
From the headspace volatile compounds, 2-pentylfuran demonstrated a strong negative effect on each of the sensory characteristics except throat-catching feeling. As it was found that its presence varies among genotypes (Section 3.3), this feature presumably might be improved by breeding. Similarly, a negative but moderate connection was detected between the sensory odour and flavour traits, and the concentration of hexanal. In parallel, the presence of 2-pentanone and hexanol contribute positively to these features (good and moderate correlations). In general, straight-chain alcohols and aldehydes like hexanal and hexanol are known as \"green leafy odour\" volatiles, which do not seem very advantageous for poppy aroma [35]. Besides, among the volatiles, \(\gamma\)-n-caprolactone and pentane seem to be in connection with the throat-catching property of the oil.  
Somewhat surprisingly, oil density was shown negatively correlating with colour, odour and flavour values. Nevertheless, differences in density of the samples are not significant.  
The oil content of poppy flour has a positive effect on several sensory properties, like colour, appearance, tactility and poppy flavour. On the other hand, the ratio of total \(\omega\)-3 fatty acid and especially that of \(\alpha\)-linoleic acid show strong negative correlations with poppy odour and moderately negative ones with poppy flavour. However, both contribute positively to the overall flavour intensity. Odour intensity is in good positive correlation with both margaric acid and eicosenoic acid ratios, although they are present in the poppy oils only in low concentrations.