esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
## 3 Preliminaries
### Model of Spe-Cks  
In SPE-CKS, there are three roles: e-mail sender, receiver and service provider (server). Let \(pk_{R}\) and \(sk_{R}\) be e-mail receiver's public key and secret key and \(pk_{S}\) and \(sk_{S}\) be e-mail senders' public key and secret key. An e-mail sender can send an encrypted e-mail \(M\) to a server with an encrypted index generated by using the keywords \(w_{1},w_{2},\ldots,w_{n}\) of \(M\) and the keys \(pk_{R}\), \(pk_{S}\), \(sk_{S}\). When an e-mail receiver wants to query e-mails with keywords \(q_{1},q_{2},\ldots,q_{m}\) where \(m\leq n\), the receiver generates a trapdoor by using these keywords and the keys \(pk_{R}\), \(pk_{S}\), \(sk_{R}\) and then sends this trapdoor to the e-mail service provider. When the server receives the trapdoor, the server tests each secure index against the trapdoor and returns the matched e-mails to the receiver. From the above, we give the definition of the SPE-CKS model, which is regared as a variant of PECK model proposed in Reference [4].  
Definition 1: SPE-CKS consists of \(5\) probabilistic polynomial time (PPT) algorithms: KeyGen\({}_{R}\), KeyGen\({}_{S}\), IndexBuild, Trapdoor, Test. There are:  
1. \(KeyGen_{R}(1^{n})\): Given a security parameter \(1^{n}\), the algorithm is executed by the receiver and generates the a pair of key \((pk_{R},sk_{R})\), where \(pk_{R}\) and \(sk_{R}\) are the public and private key for the receiver, respectively.
2. \(KeyGen_{S}(1^{n})\): This is a key generation algorithm for the data sender. The algorithm creates a pair of key \((pk_{S},sk_{S})\), where \(pk_{S}\) and \(sk_{S}\) are the public and private key for the sender, respectively.
3. \(IndexBuild(pk_{S}\), \(sk_{S}\), \(pk_{R}\), \(W)\): The algorithm is executed by the sender to encrypt a keyword set \(W=\{w_{1},w_{2},\ldots,w_{n}\}\) without keyword field. It produces a searchable index \(I_{W}\) of \(W\) by using the keys \(pk_{S}\), \(sk_{S}\) and \(pk_{R}\).
4. \(Trapdoor(pk_{R}\), \(sk_{R}\), \(pk_{S}\), \(Q)\): The algorithm is executed by the receiver to construct a trapdoor. Given the keys \(pk_{R}\), \(sk_{R}\), \(pk_{S}\) and the keyword query \(Q=\{q_{1},q_{2},\ldots,q_{m}\}\) where \(m\leq n\), the algorithm generates a trapdoor \(T_{Q}\).
5. \(Test(pk_{R}\), \(pk_{S}\), \(T_{Q}\), \(I_{W})\): The algorithm is executed by the server to test the trapdoor \(T_{Q}\) whether matches the index \(I_{W}\) or not. It takes a trapdoor \(T_{Q}\), a secure index \(I_{W}\) and the public keys \(pk_{S}\), \(pk_{R}\) as input, then outputs \(1\) if \(Q\subseteq W\) or \(0\) otherwise.  
_Correctness of SPE-CKS._ Let \(W\) and \(Q\) be two keywords sets. \((pk_{R},sk_{R})\), \((pk_{S},sk_{S})\), \(I_{W}\) and \(T_{Q}\) are generated correctly by algorithms \(KeyGen_{R}(1^{n})\), \(KeyGen_{S}(1^{n})\), \(IndexBuild(pk_{S},sk_{S},pk_{R},W)\) and \(Trapdoor(pk_{R},sk_{R},pk_{S},Q)\), respectively. If \(Q\subseteq W\), the \(Test(pk_{R},\)\(pk_{S},\)\(T_{Q},I_{W})\) outputs 1. Otherwise, it outputs 1 with negligible probability.
# Optimization of Effective Throughput in NOMA-Based Cognitive UAV Short-Packet Communication
## 4 Numerical and Simulation Results
In this case, the UAV requires more sensing time to reduce the probability of a false alarm but at the same time, increases the decoding error probability. Therefore, the optimization of the sensing time in (12) is essential. In Figure 7, we can compare the performance of the different algorithms and schemes. In the NOMA scheme, the joint optimization algorithm can significantly improve the performance of the system compared to the other algorithms, which also reflects the importance of joint optimization. By comparing the performance of the NOMA scheme and the OMA scheme, we find that the NOMA scheme can obtain better results. The proposed algorithm almost matches the exhaustive search algorithm and verifies its global optimality. Figure 4: \(\Phi_{2}\) versus the information bits for different schemes and \(N_{s}\). Figure 5: Decoding error probability versus the SNR for different \(I\) and \(N_{s}\).
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 4 Experimental Evaluations
### Simulation Results and Analysis with NAIE Channel
#### 4.3.1 The NMSE Performance  
The LloydMax scheme has to design multiple codebooks which are optimized for specific channel conditions. The best NMSE performance can be achieved when the \(\mathcal{SNR}_{eval}\) is equal to the \(\mathcal{SNR}_{design}\) in Table 5. The PCQNet has superior NMSE performance than the LloydMax scheme under the same CRs as depicted in Figure 7. The PCQNet is tested with the fixed training SNR value \(\mathcal{SNR}_{design}\) = 0 dB while the LloydMax scheme is evaluated with \(\mathcal{SNR}_{eval}\) = \(\mathcal{SNR}_{design}\).  
Figure 6: The BLER performance comparison over the i.i.d. Rayleigh channel with QPSK and K = 8.  
\begin{table}
\begin{tabular}{c|c c c|c c c} \hline \hline \multicolumn{2}{c|}{**Quantization Factor**} & \multicolumn{3}{c|}{\(\boldsymbol{\beta}\) = 2 (\(\boldsymbol{m}\) = 32)} & \multicolumn{3}{c}{\(\boldsymbol{\beta}\) = 3 (\(\boldsymbol{m}\) = 48)} \\ \hline \multicolumn{2}{c|}{**NMSE (dB)**\(\boldsymbol{SNR}_{design}\) (dB)**} & \multicolumn{1}{c}{\(\boldsymbol{1}\)} & \(\boldsymbol{9}\) & \(\boldsymbol{17}\) & \(\boldsymbol{1}\) & \(\boldsymbol{9}\) & \(\boldsymbol{17}\) \\ \hline \(1\) & \(-22.10\) & \(-21.00\) & \(-21.42\) & \(-26.78\) & \(-26.06\) & \(-26.44\) \\ \(9\) & \(-18.14\) & \(-18.95\) & \(-18.78\) & \(-22.57\) & \(-24.29\) & \(-23.88\) \\ \(17\) & \(-17.34\) & \(-18.44\) & \(-19.19\) & \(-21.71\) & \(-23.84\) & \(-24.32\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The NMSE performance of Lloyd-Max quantization scheme with different quantization factors over the NAIE channel.
## 3 Data and Methodology
### Change Analysis  
To investigate the changes in an urban area, change analysis is an important process to identify the areas that have changed between the two time periods of interest. Change analysis is based on the historical changes that can be assessed between time 1 and time 2 of the LULC maps. The changes identified are transitions from one LULC state to another within the specific time period. It is likely that with many LULC classes the potential combination of transitions can be complex. In this study, a cross-tabulation analysis from the differential times was applied to determine the LULC change over five periods: 1988-1993, 1993-1998, 1998-2003, 2003-2008, and 2008-2011.
## 3 Results and Discussion
### Recognition of Flow Patterns
Herein, the data distribution separated by the boundaries was much clear to improve the recognition accuracy significantly in Experiment 2. Figure 4 illustrates that the difference in prediction accuracy between Experiments 1 and 2 can be attributed to the clearance of data distribution.
## 3 Numerical Simulation Approach and Settings
Figure 2: Schematic illustration of modeling the TILAS system operating with a target moving with speed \(\sigma_{T}\) in presence of distributed turbulence represented by five phase screens positioned at relative distances \(\{z_{j}/L\}\), \(j=1,\ldots,5\). Here, the target’s position is fixed, while the phase screens are shifted with the speeds \(\{v_{j}\ \}\) in the direction opposite to the target velocity. The phase screen shift distances \(\{\Delta s_{j}\}\) acquired during the time interval \(\Delta t\ =\ t^{\prime\prime}-t^{\prime}\) are indicated by red bars. As the TILAS outgoing beam, a collimated Gaussian beam with fixed wavelength \(\lambda=1.064\)\(\upmu\)m and radius \(W_{0}=1.5\) cm, truncated by the circular aperture of diameters \(d_{\mathrm{TILAS}}=3.3\) cm and 7 cm is considered. The propagation distance to the target is fixed at \(L=7\) km. The target's glint is modeled by the circular retro-reflector of diameter \(d_{T}=1\) cm. Note that for the set of the parameters selected, \(d_{T}\) is smaller than the diffraction-limited target-plane beam footprint radius \(W~{}=~{}W_{0}[1+(2L/kW_{0}^{2})^{2}]^{1/2}\simeq 16\) cm, i.e., the retro-reflector is unresolved. The target speed relative to the TILAS system platform with the TILAS sensor is set to \(v_{T}~{}=~{}200\) m/s. All computations are performed using numerical grids with resolution of \(2048\times 2048\) pixels; the simulation area width and sampling size are fixed at 4.096 m and 2 mm, respectively, to meet sampling requirements [21]. The sampling time, i.e., the time between the consecutive phase screen shifts, is fixed at 0.1 ms. The integration interval \(T\) is chosen to be equal to 5 s. During this time, the target moves over a distance of 1 km; this results in about 70-m propagation distance change, which is insignificant compared to \(L=7\) km and can be neglected. At the same time, as mentioned earlier, this interval should exceed the characteristic correlation time \(\tau_{\mathrm{at}}\), which can be estimated for the \(j\)th screen as \(\tau_{\mathrm{at}}^{(j)}~{}=~{}\rho_{0}^{(j)}/v_{j}\), where \(\rho_{0}^{(j)}\) is the \(j\)th screen's phase fluctuations correlation length, also known as the Tatarskii parameter for spherical wave [22]. Assuming the turbulence layer of width \(\Delta z=1.4\) km is located at \(z=0.1\)\(L=0.7\) km (closest to the TILAS transceiver aperture) and moving with the speed \(v~{}=~{}0.1v_{T}~{}=~{}20\) m/s, \(\tau_{\mathrm{at}}\approx 28\) ms for \(C_{n}^{2}~{}=~{}1\times 10^{-16}\) m\({}^{-2/3}\) and 1.7 ms for \(C_{n}^{2}~{}=~{}1\times 10^{-14}\) m\({}^{-2/3}\), which means that the condition \(T\gg\tau_{\mathrm{at}}\) is satisfied. The results of our numerical simulations are presented and discussed in the following section.
# Geolocation and Tracking by TDOA Measurements Based on Space-Air-Ground Integrated Network
###### Abstract  
Due to the development of manufacturing and launch technologies for satellites, there are now more and more satellite networks. Hence, cooperative reconnaissance is possible to implement among satellite networks, aerial vehicles and ground stations. In this paper, we study the method of geolocation and tracking by time difference of arrival (TDOA) measurements based on space-air-ground integrated (SAGI) network. We first analyze the Cramer Rao lower bound (CRLB) for the source localization accuracy in different coordinate systems. Then, we compare the effects of different system errors, such as clock synchronization error, position bias of the observers, elevation bias of the target and non-horizontal velocity of the target. Further, we also develop a maximum likelihood (ML) estimator for target position and velocity. Finally, the theoretical performance of the proposed estimator is validated via computer simulations.  
time difference of arrival; space-air-ground integrated; maximum likelihood; system error 20231544. [[https://doi.org/10.3390/rs15010044](https://doi.org/10.3390/rs15010044)]([https://doi.org/10.3390/rs15010044](https://doi.org/10.3390/rs15010044))
## 3 Discussion
It was shown that cells exposed to hypoxia in a primary tumor have four-fold greater probability of becoming viable circulating tumor cell. Moreover, post-hypoxic cells have ability to form lung metastases, suggesting that they have an enhanced metastasis-initiating capability [14]. If FAM13A overexpression plays an adaptative role to hypoxia in cancer cells, silencing of this gene should have more severe effect on features of primary tumor cells. Our results confirmed this hypothesis, because FAM13A silencing caused milder inhibition of the growth and proliferation in cells derived from metastatic site. Moreover, FAM13A silencing had no influence on the invasive properties of CORL-105 cells. In the current study, we focused on downregulation of functional isoform 1 of FAM13A protein (117 kDa), which contains Rho GTPase activating protein (GAP) domain. It was reported that the reduced expression of FAM13A resulted in increased RhoA activation in A549 cells and primary human bronchial epithelial cells from cystic fibrosis patients [13]. RhoA protein function during cell migration and invasion was confirmed although this contribution depended on the environment and cell type [21]. In our study, we demonstrated that FAM13A knockdown inhibits the invasive activities and migration of NSCLC cells, which may be connected with the Rho GTPase pathways and also with hypoxia induced pathways. The effect of FAM13A inhibition on cell migration/invasion can be related to great plasticity of cancer cells in their movement mechanisms [22] and to the activation of other signaling pathways involved in cell migration, e.g., Rac GTPase-mediated signaling [23]. However, the detailed mechanism of this process needs to be verified. Numerous studies demonstrated that the Rho/Rho-kinase signaling pathway may be involved in the apoptosis of lung cancer cells [24]. Thus, we decided to investigate if silencing of FAM13A gene expression affects the apoptosis in NSCLC cells. We found that FAM13A knockdown did not induce apoptosis in A549 or CORL-105 cells. Previously, it was shown that TGF\(\beta\) induced apoptosis in A549 cells and it was associated with downregulation of two main FAM13A isoforms [14]. Further studies are necessary to better understand the role of FAM13A in programmed tumor cell death. In summary, we confirmed that FAM13A silencing has a negative effect on proliferation, migration via altering the actin cytoskeleton, and invasion activity in NSCLC cells in normal and hypoxic conditions. Our study showed that oxygen deprivation further enhanced phenotypic changes in FAM13A depleted cells. We demonstrated that FAM13A depleted post-hypoxic cells have a decreased metastatic potential, which indicates FAM13A as a potential therapeutic target in lung cancer.
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 5 Experimental Results and Discussion
### Image Registration Contrast Experiment  
To verify the improvement of overlapping area extraction, Azimuth1_subswath2 and Azimuth2_subswath2 and their overlapping areas were used for the keypoint detection and matching experiments. The overlapping rates estimated from the geolocation of the two images were 7.46% and 7.50%, respectively. Ideally, the matched keypoints should have the same coordinates when projection finishes. Therefore, after stitching, the matched keypoints whose Euclidean distances between the coordinates were not greater than one were regarded as correctly matched keypoints. The ratio of correctly matched keypoints to matched keypoints is defined as the correct matching rate:  
\[E_{m}=\frac{P^{\prime}{}_{m}}{P_{m}} \tag{17}\]  
where \(P_{m}\) is the number of matched keypoints and \(P^{\prime}{}_{m}\) is the number of correctly matched keypoints.  
The keypoint detection and matching performances in the whole images and overlapping areas are compared in Table 2. It can be seen that the number of keypoints detected from the overlapping areas was much less than that detected from whole images, but the numbers of matched keypoints in the two situations were almost the same. Therefore, acquiring keypoints from overlapping areas can provide enough matched keypoints  
Figure 8: Image preprocessing experiment: (**a**) result without preprocessing; (**b**) result with the classic Wallis filter (the existing method of traditional process); (**c**) result with the improved Wallis filter (the method we proposed).  
for subsequent operations. In addition, keypoint detection and matching will be more efficient. More importantly, compared to the situation without an overlapping area extraction, the correct matching rate was improved by nearly four percentage points to 98.89%, which means there were some mismatched keypoints obtained from the whole images. In that case, the interference of mismatched keypoints from nonoverlapping areas was avoided, which improves the accuracy of matching and provides a strong guarantee for the calculation of the homography matrix.
# Review of Magnetic Properties and Texture Evolution in Non-Oriented Electrical Steels
###### Abstract  
Electrical steels can be classified into two groups: grain-oriented (GO) and non-oriented (NGO) electrical steel. NGO electrical steels are mainly considered as core materials for different devices, such as electric motors, generators, and rotating machines. The magnetic properties and texture evolution of NGO electrical steels depend on multiple factors (such as chemical content, heat-treatment, and rolling process) making the development of new products a complex task. In this review, studies on the magnetic properties of NGO electrical steels and the corresponding texture evolution are summarized. The results indicate that further research is required for NGO electrical steels to ensure high permeability and low core loss properties.  
electrical steels; twin-roll strip casting; magnetic properties; core loss; texture +
Footnote †: journal: Applied Sciences
# Potential of Room Acoustic Solver with Plane-Wave Enriched Finite Element Method
## 3 Numerical Experiments  
We examine the PUFEM performance using plane-wave enriched quadrilateral elements through two numerical examples on the calculation of sound fields in a single room and in a coupled room, respectively, including local reaction impedance boundaries of a resonant absorber and a porous absorber. The two room models were created from reference to an existing office plan of the authors in Kobe University. The accuracy of PUFEM is shown in comparison with the standard FEM using linear quadrilateral elements, which has second-order accuracy with respect to the dispersion error. In addition, the PUFEM efficiency is measured by the reduction effect of DOF for achieving the equivalent level of accuracy. In both rooms, sound fields generated by acoustic emission from a loudspeaker placing in the room were calculated at 20 Hz to 2.5 kHz with a 1 Hz interval. Because the two numerical examples have no analytical solutions, we used reference solutions calculated using a fourth-order accurate FEM with a dispersion reduction technique called modified integration rules [47]. The performance of fourth-order accurate FEM for 3D room acoustic simulations was described in earlier reports of the literature [16; 18; 28; 48]. For this paper, a 2D version is used in the frequency domain [49]. The speed of sound and the air density were set, respectively, as 340 m/s and 1.205 kg/m\({}^{3}\).
# Intra-Reliability of a Wearable Near-Infrared Sensor for Monitoring the Intensity of Exercise
## 4 Discussion
### Strength and Limitations  
The lower absolute reliability reported in the present study compared to research in previous studies is likely due to the recent improvements in NIRS technology and processing and filtering data specific for use in sports science. It should be noted that this is the first study that evaluated the Q-LAC sensor; thus, we could not directly compare results from other studies about the assessment of this device. Furthermore, Q-LAC is inexpensive in relation to other wearable NIRS devices. The Q-LAC device is small enough to be worn without encumbering most types of exercise and possesses data-capture capabilities; thus, it will have some utility in field-based conditions. Despite interesting findings and data shown by the current research, some limitations should be described. First, the SmO\({}_{2}\) response was only assessed in the dominant lower limb. Another limitation was the absence of measurements for adipose tissue at the locations where the portable NIRS devices were placed. It is worth noting that the existing literature suggests that increased adipose tissue and hypertrophy muscle can impede the penetration of light emitted by the source, consequently influencing the data recorded by NIRS-based devices [34]. The penetration of Q-LAC is maintained at approximately 3 cm and is higher compared to Moxy, at approximately 2 cm [13]. We did not detect significant artifacts in the SmO\({}_{2}\) measurements. This can be attributed to the absence of obesity-related characteristics among our study participants. It can be inferred that their physical characteristics align with those of individuals engaged in regular sports training, as indicated by the recorded BMI of 24.2 \(\pm\) 2.4 and a body fat percentage of 13.4 \(\pm\) 3.9%. In future research, it would be pertinent to explore the thickness of adipose tissue using techniques such as caliper measurements (e.g., \(>\)7.5 mm ATT cut-off point [15]), ultrasonography, or alternative methods. This approach would enable a comprehensive evaluation of the muscles involved and provide more comprehensive data. Additionally, we did not examine SmO\({}_{2}\) during higher intensities, e.g., above the anaerobic threshold/critical power, which could be useful to determine local tissue hemoglobin oxygenation during time-trial performance [35].
## 6 Performance Evaluation
### Evaluation for the Analytical Model
#### 6.2.5 One-Hop Transmission Distance for the Inter-Lane Scenario  
Figure 9 gives the comparison between the one-hop transmission distance's distribution from simulations and analysis in the inter-lane scenario. Again, the analytical model can illustrate the realistic distribution properties of the one-hop transmission distance in the inter-lane scenario. Different from the distribution in Figure 9, we can see that the one-hop distance shows a declining tendency.
# A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging
## 2 Materials and Methods
### In Vivo Experiment Setups  
#### 2.4.1 In Vivo Ultrafast Imaging  
The proposed method was evaluated using a New Zealand white rabbit model. Normal vasculature was imaged in the kidney of a healthy rabbit using the pulse inversion (PI) technique. The rabbits weighed 3.5-4.5 kg. Before the experiment, we carried out depilation on the rabbit's abdomen. The in vivo scans were performed using the Verasonics ultrasound system (Verasonics, Kirkland, WA, USA) with channel-domain data acquisition capabilities. A 6.3 MHz, 128-element linear array transducer (L11-4v) (Verasonics, Kirkland, WA, USA) with a pitch of 0.30 mm was used and the sampling frequency was set to 36 MHz. One and a half cycle Gaussian envelope-modulated pulses with a carrier frequency of 4.5 MHz were used in order to capture the harmonic components. Entire images were acquired using plane-wave acquisitions at a frame rate of 750 Hz (pulse repetition frequency of 1500 Hz) and low mechanical index of 0.05. Short cine loop of 400 frames were acquired for the rabbit model. Sonovue (Bracco, Milan, Italy), a clinically approved commercial ultrasound contrast agent [29], was used in our experiment. The microbubbles were injected via the ear vein of the rabbit in bolus injections of 1 mL with concentration of 10 \(\mu\)L/Kg and flushed with an additional 1 mL of saline. All experimental protocols were under the approval of the ethics and academic committee with the number (FD-ZS2016-082).
# Three-Dimensional Broadband and Isotropic Double-Mesh Twin-Wire Media for Meta-Lenses
## 3 Design of Luneburg Lens
The results in Figure 6 are obtained with absorbing boundary conditions around the wire structure. Furthermore, the lens is surrounded by unit cells of the same dimensions as the one on the edge of the lens. This is done to avoid reflections at the border of the lens. In order for the Luneburg lens to be used as an antenna, a structure that matches the mode of the wire mesh to the one in free space must be added around the lens. The design of the matching structure is a future work and is out of the scope of this paper.