esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight and Privacy-Preserving Multi-Keyword Search over Outsourced Data
###### Abstract  
In cloud computing, documents can be outsourced to the cloud server to achieve flexible access control and efficient sharing among multiple users. The outsourced documents can be intelligently searched according to some keywords with the help of cloud server. During the search process, some private information of outsourced documents may be leaked since the keywords may contain sensitive information of users. However, existing privacy-preserving keyword search schemes have high computation complexity, which are not suitable for resource-constrained end devices--that is, the data processing and search trapdoor generation procedures require users to take resource-intensive computations, e.g., high-dimensional matrix operations, exponentiations and bilinear pairings, which are unaffordable by resource-constrained devices. To address the issues of efficiency and privacy for realizing sorted multi-keyword search over outsourced data in clouds, this paper proposes a lightweight privacy-preserving ranked multi-keyword search (PRMS) scheme, which is further extended to allow each outsourced document to be associated with multiple types of keywords. The searched documents can be sorted according to their similarity scores between the search query and the keyword index of documents, so that only when the similarity score exceeds a given threshold, the corresponding searched document will be returned. The security analysis demonstrates that the proposed PRMS schemes can guarantee the privacy of outsourced documents and keywords, and provides unlinkability for search trapdoors. Performance analysis and comparison show the practicality of the proposed PRMS schemes.  
cloud computing; outsourced data; privacy-preserving search; keyword search; privacy protection Article
## 2 Mechanical Analysis and Research Problem
### Mechanical Analysis
#### 2.2.3 Mechanical Influence Analysis of Additional Force  
From Equation (5), to control the horizontal offset \(x_{c}\) and the self-rotation angle \(\beta\) into the given control range, a torque \(M^{\prime}\) must be generated to offset the output torque \(M_{x}\). However, at the same time, an additional force \(F\) in the horizontal direction (or other direction) will be generated as shown in Figure 1 where \(F\) is the resultant force of \(F_{1}\) and \(F_{2}\) which was generated by each air spring vibration isolar. From Equations (3) and (4), the horizontal offset and self-rotation angle caused by the additional force \(F\) are  
\[\begin{split} x_{c}&=\frac{\sum\limits_{i=1}^{n}k_{1 }^{i}\big{(}(a_{x})^{2}-a_{x}a_{y}^{2}\big{)}-\sum\limits_{i=1}^{n}a_{x}^{i}k_{ 13}^{i}(2a_{x}-\xi_{c})+\sum\limits_{i=1}^{n}\big{(}a_{x}^{i}\big{)}^{2}k_{3}^ {i}}{-\left(\sum\limits_{i=1}^{n}a_{x}^{i}k_{13}^{i}\right)+\sum\limits_{i=1}^ {n}\big{(}a_{x}^{i}\big{)}^{2}k_{3}^{i}}F\\ \beta&=\frac{\sum\limits_{i=1}^{n}\frac{a_{x}^{i}k_{ 13}^{i}}{-\sum\limits_{i=1}^{n}k_{13}^{i}}-\sum\limits_{i=1}^{n}k_{1}^{i}a_{x} }{-\left(\sum\limits_{i=1}^{n}a_{x}^{i}k_{13}^{i}\right)+\sum\limits_{i=1}^{n }k_{1}^{i}\sum\limits_{i=1}^{n}\big{(}a_{x}\big{)}^{2}k_{3}^{i}}F\end{split} \tag{9}\]  
From Equations (5) and (9), it can be concluded that the mechanical influence of additional force is different from that of output torque. When eliminating the horizontal offset \(x_{c}\) and the self-rotation angle \(\beta\) caused by the output torque \(M_{x}\), an additional force \(F\) will be generated inevitably to cause the variation of the horizontal offset \(x_{c}\) and the self-rotation angle \(\beta\).  
To sum up, the alignment controllability of HPDME-ASVIS mainly focuses on offsetting the influence of output torque \(M_{x}\) and additional force \(F\), ensuring that the horizontal offset, self-rotation angle, and vertical deformation of the air springs move into the given control precision range. It needs to solve three problems. The first is how to establish the alignment control response method of HPDME-ASVIS. The second is how to establish the alignment controllable judgment model and analyze the alignment reconfigurability. The third is how to establish an autonomous control for HPDME-ASVIS through the soft constraints adjustment strategy.
# Optical Helicity and Chirality: Conservation and Sources
## 2 Review and Motivation
### Helicity and Chirality
This field does have a clear intuitive sense of rotation: if there is no frequency difference between the two circular plane waves, the resulting superposition is simply a linearly polarised plane wave, but for small frequency differences, the result is approximately a linearly polarised wave with a frequency equal to the average frequency and a plane of polarisation that slowly rotates at \((\omega_{1}-\omega_{2})/2\). This rotation of the polarisation plane is directly analogous to the amplitude modulation in the \"beats\" observed in the addition of two linearly polarised waves of different frequency. Our simple example may also be of some practical interest, as these fields form the basis of an \"optical centrifuge\", a procedure that can be used to excite very high rotational states in molecules [35]. This is accomplished by introducing a linear chirp into each of the two waves, one chirped up and the other down, so that the speed of rotation increases with time, driving the molecule up a ladder of rotational transitions. The rotation of the polarisation plane is in the same sense as that of the higher-frequency circularly polarised wave. Therefore, insofar as the sign of these quantities should be a guide to the sense of \"rotation\" in the field, the chirality density might appear to assign the correct sign (the chirality of the combined field has the same sign as the chirality of the higher frequency wave), while the helicity has the opposite (the helicity of the combined field has the opposite sign to that of the higher frequency wave). However, the angular momentum content of the combined field is actually dominated by the lower frequency plane wave. This can be shown by a simple photon-counting argument: if the two plane waves have the same field strength (and therefore the same energy), then there are more photons in the lower frequency mode than in the higher, as the photons in the lower frequency mode have less energy each. As each photon in the higher frequency mode carries an angular momentum of \(\hbar\) and each in the lower frequency mode an angular momentum of \(-\hbar\), it is clear that the sign of total angular momentum is indeed reflected by the helicity, rather than the chirality.
## 4 Discussion
### Uncertainties of HSI Image Inversion
In addition, the PLSR models were constructed with the spectra of air-dried soils, which were different from the soil spectra in situ. Lab spectra were collected under a careful geologically orientated sampling in this study, but HSI imagery collected a far more complex spectral response in situ than the lab spectra. The water, large debris, stones and stubbles in soils can bring noises into the image reflectance and cause deviation in the model inversion from the images. Another important consideration in the deviation was the residual noises in the HSI images after atmospheric correction. In addition, the HSI images have limited bandwidths and spatial resolution. These results are also attributed to the lower quality of the HSI spectra than the laboratory-measured spectra. All these noises inevitably affected the accuracies of model inversion with HSI images. Nevertheless, this study demonstrated a good potential to quantitatively estimate soil alkalinity and salinity with satellite hyperspectral data in the VIS-NIR regions. When better-quality hyperspectral satellites become available, the method explored in this study may be further applied to map soil properties in a large area. The optimal spectral bands identified in this study may also provide useful information for further satellite-assisted soil studies.
# Improved Shear Strength Equation for Concrete Wide Beams
## 2 Shear Strength Equation
### Modified ACI Building Code ([PERSON] et al. [12])  
[PERSON] et al. [12] proposed a shear strength equation for concrete beams with shear reinforced FRP plates by modifying the ACI 318-11 shear strength equation [11]. Plate type shear reinforcement consists of horizontal and vertical strips, as shown in Figure 1. [PERSON] et al. [12] reported that the horizontal components of the plate did not contribute significantly enough to the shear strength. Based on this investigation, the shear strength equation by [PERSON] et al. [12] only considered vertical components and used Equation (4) to calculate the cross-sectional area of the FRP reinforcement.  
\[A_{s}=nw_{f}t_{f} \tag{4}\]  
where \(n\) is number of the GFRP and steel plate vertical components, \(w_{f}\) is the width of the GFRP and steel plate and \(t_{f}\) is the thickness of the GFRP and steel plate.  
ACI 440.1R [14] provides a shear strength equation for concrete with FRP flexural reinforcement. However, [PERSON] et al. [15] verified that in the case of a specimen reinforced by a plate type, substituting the tensile strength for the shear strength in the equation in ACI 318-14 is more reasonable than using the equation described in ACI 440.1R. In this paper, when considering plate type and stirrups as shear reinforcement nominal shear strength is calculated using the equation in ACI 318-14 and [PERSON] et al., as shown in Equations (1)-(4).
## 2 Materials and Methods
### Acoustic Signal Parameters
#### 2.3.1 Autocorrelation  
The Autocorrelation provides a measure of the similarity of successive phonatory periods repeated throughout the signal. The higher the Autocorrelation value, the greater the periodicity of the signal.  
The studies undertaken by [PERSON] were considered to determine this parameter [27].  
The \(x(t)\) signal, already with the mean value removed, will be multiplied by a window function \(w(t)\) to obtain a signal windowed \(x_{w}(t)\) (Equation (1)).  
\[x_{w}(t)=x(t)\cdot w(t) \tag{1}\]  
The window function, \(w(t)\), is symmetric, and is zero outside the time interval [0, T], as shown in Figure 2 (upper right side). The Hamming, Hanning, and Blackman windows have been used in this work.  
The normalized Autocorrelation \(a(\tau)\) of the selected segment of the signal is calculated according to Equation (2). This signal is in the time domain (\(\tau\)) and is symmetric to the delay \(\tau\) (Equation (2)) with \(-T<\tau<T\).  
\[a(\tau)=a(-\tau)=\frac{\int_{0}^{T}x_{w}(t)x_{w}(t+\tau)\ dt}{\int_{0}^{T}x_{w }^{2}(t)\ dt} \tag{2}\]  
Finally, it is necessary to calculate the window function's normalized Autocorrelation \(a_{w}(\tau)\).  
To estimate the Autocorrelation \(a_{x}(\tau)\) of the original signal segment, the normalized Autocorrelation of the windowed signal segment \(a(\tau)\) is divided by the normalized Autocorrelation \(a_{w}(\tau)\) of the window used (Equation (3)).  
\[a_{x}(\tau)=\frac{a(\tau)}{a_{w}(\tau)} \tag{3}\]The Autocorrelation value (\(A\)) will be assumed as the magnitude of the first peak with delay \(\tau>0.003\) s. This delay must correspond to the fundamental period \(T_{0}\). The value of the Autocorrelation will be between 0 and 1. For perfectly periodic signals, Autocorrelation will be 1. For voiced segments of normophonic voices, it tends to be very close to 1, and for dysphonic voices, it tends to be slightly lower, as shown in [PERSON] et al. in 2018 [PERSON] et al. (2018). This is because the Autocorrelation is directly related to the voiced sound's harmonic component produced by the glottis.
## 7 Experimental Verification
### Packet Length Effect Verification  
Figure 23 shows the mutual coherence and recovery error comparison with the packet length being a four-byte payload and a 32-byte payload, respectively. Just as the simulation results presented, when \(M/N\) is the same, a larger packet length yields larger mutual coherence, and as the \(M/N\) increases, the coherence difference caused by the packet length decreases. Furthermore, when \(M/N\) is the same, a shorter packet length generally can achieve a lower recovery error, and once its measurement number exceeds the threshold required for accurate recovery (Equation (3)), its recovery error diminishes drastically. Note that the value of the error in this part may be different from the recovery error in the simulation results, which is because the signal used in the experiment has a much larger sparsity. Another reason is that the signal has been corrupted with noise in the data sampling process. However, the trend is the same, which is consistent with the simulation results.
## 4 Experiments and Analysis
### Model Performance Evaluation Metrics  
In this paper, we used precision (\(P\)), recall (\(R\)), mean average precision (\(mAP\)), model parameter, Giga Floating-point Operations Per Second (GFLOPs), and model size to evaluate the model. The GFLOPs and model size (model size) were used to assess the model; \(P\), \(R\), and \(mAP\) were calculated as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
Figure 5: GSConv convolution operation.  
Figure 6: VoV-GSCSP structure.  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
\[AP=\int_{0}^{1}P(R)dR \tag{6}\]  
\[mAP=\frac{\sum_{i=1}^{n}AP_{i}}{\text{n}} \tag{7}\]  
where \(TP\) denotes the number of positive categories predicted to be positive, \(FP\) denotes the number of negative categories predicted to be positive, and \(FN\) denotes the number of positive categories predicted to be negative; \(AP\) reflects the accuracy of the target detection for a single category and \(mAP\) denotes the averaging of the \(AP\) values over multiple categories.
## 4 Results and Discussion
### Determination of Orifice Diameter  
To select a proper orifice diameter for a hydrostatic bearing, the bearing stiffness and the flow rate according to the recess pressure ratio were calculated. As shown in Figure 1, because the pump-drive turbine to be developed was installed and operated in the vertical direction, the shaft center operated at the concentric location of the bearing. Therefore, analysis was conducted for a case with an eccentricity of 0, and the rotating speed was the rated speed of 21,000 rpm.  
Figure 5 shows the bearing stiffness and flow rate according to the recess pressure ratio. In this study, two translational degrees of freedom were considered for the rotor, and the bearing stiffness described in Figure 5a indicates the value of \(k_{xx}\) as defined in the coordinate system shown in Figure 2. Figure 5a also shows the orifice diameter corresponding to the recess pressure ratio. The analytical  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Symbol** & **Unit** & **Value** \\ \hline Diameter & \(D\) & mm & 20 \\ Length & \(L\) & mm & 14 \\ Radial clearance & C & mm & 0.039 \\ Rotating speed & \(\omega\) & rpm & 50,000 \\ Lubricant & - & - & Water \\ Density & \(\rho\) & kg/m\({}^{3}\) & 998.6 \\ Viscosity & \(\mu\) & μPas & 1005 \\ Discharge coefficient & \(C_{d}\) & - & 0.875 \\ Supply pressure & \(p_{s}\) & bar & 17 \\ Recess size & - & mm & \(10\times 8\) \\ Recess number & \(n\) & EA & 4 \\ Orifice hole diameter & \(d\) & mm & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design parameters of hydrostatic bearing [22].  
Figure 4: Stiffness and damping for hydrostatic radial bearing in [22]. (**a**) Stiffness; (**b**) Damping.  
results show that there is a recess pressure ratio that results in the highest stiffness. In this study, the highest stiffness was present at the recess pressure ratio of 0.6, at which the orifice diameter was 1.8 mm. In addition, as shown in Figure 5b, the flow rate increased linearly with the pressure ratio. The orifice diameter was set at 1.8 mm according to the analytical results above and the bearing flow rate was predicted to be 0.9 kg/s.
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 4 Multi-User Equalizer Design  
In this section, we design a hybrid analog-digital sub-connected equalizer for multi-user mmW mMIMO to be employed at the receiver side. A decoupled transmitter-receiver optimization problem is assumed in this paper, since a joint optimization problem is a very complex task. The overall analog matrix \(\mathbf{W}_{a}\) defined in (7) and the digital matrices \(\left\{\mathbf{W}_{d,k}\right\}_{k=0}^{N_{c}-1}\) are optimized by minimizing the BER, which is equivalent to minimize the MSE.
# Boosting the Performance of CDCL-Based SAT Solvers by Exploiting Backbones and Backdoors
###### Abstract  
Boolean structural measures were introduced to explain the high performance of conflict-driven clause-learning (CDCL) SAT solvers on industrial SAT instances. Those considered in this study include measures related to backbones and backdoors: backbone size, backbone frequency, and backdoor size. A key area of research is to improve the performance of CDCL SAT solvers by exploiting these measures. For the purpose of guiding the CDCL SAT solver for branching on backbone and backdoor variables, this study proposes low-overhead heuristics for computing these variables. Through these heuristics, a set of modifications to the Variable State Independent Decaying Sum (VSIDS) decision heuristic is suggested to exploit backbones and backdoors and potentially improve the performance of CDCL SAT solvers. In total, fifteen variants of two competitive base solvers, MapleLCMDistChronoBT-DL-v3 and LSTech, were developed. Empirical evaluation was conducted on 32 industrial families from 2002-2021 SAT competitions. According to the results, modifying the VSIDS heuristic in the base solvers to exploit backbones and backdoors improves its performance. In particular, our new CDCL SAT solver, LSTech_B86 cr_v1, solved more industrial SAT instances than the winning CDCL SAT solvers in 2020 and 2021 SAT competitions.  
SAT; CDCL; VSIDS; Boolean structural measures; backbone; backdoor Article Boosting the Performance of CDCL-Based SAT  
Exploiting Backbones and Backdoors  
[PERSON], [PERSON],  
[PERSON]. [PERSON] Boosting the Performance of CDCL-Based SAT  
Solvers by Exploiting Backbones and Backdoors. _Algorithms_**2022,**_15_, 302. [[https://doi.org/10.3390/a15090302](https://doi.org/10.3390/a15090302)]([https://doi.org/10.3390/a15090302](https://doi.org/10.3390/a15090302))
## 3 Numerical Results  
### Scattering Properties  
Figure 4 shows the scattering properties (\(\mu_{s}\), \(\mu_{s}^{\prime}\), \(g\)) calculated from the DST for the aqueous alumina suspensions at different volume fractions. As shown in Figure 4a, at a lower volume fraction less than approximately 5%, the scattering and reduced scattering coefficients depend on the volume fraction linearly. Meanwhile, at a higher volume fraction, the curvilinear dependence of the coefficients is observed. As shown in Figure 4b, the anisotropy factor is almost zero, although the value decreases as the volume fraction increases. The \(g\)-result indicates the light scattering is almost isotropic for the suspensions. This is because the particle diameter (mean diameter \(d_{mean}\) of 55 nm) is much smaller than the optical wavelength \(\lambda\) of 600 nm; i.e., the size parameter \(x_{s}=2\pi n_{\mathrm{c}s}d_{mean}/\lambda\) is much smaller than unity. This fact suggests that the Rayleigh theory holds. Because of the \(g\)-result, the reduced scattering coefficient values are almost the same as those of the scattering coefficient.  
In Figure 5, we plot the normalized phase functions \(p(\theta)\) calculated from the DST with the scattering angle \(\theta\). The results using the DST almost agree with those using the Rayleigh theory (\(3[1+\cos^{2}\theta]/(16\pi)\)) because of the small \(x_{s}\)-value. We also plot the HG phase functions [50] with the same \(g\)-values as those using the DST because the function has been widely used. The HG phase functions deviate from the results using the DST, although the deviations are small in the current conditions.  
Figure 4: Scattering properties calculated from the DST for aqueous alumina suspensions at different volume fractions. (**a**) Scattering and reduced scattering coefficients and (**b**) anisotropy factor.  
Figure 3: Fraction of the alumina particle size based on the reference [49].
# Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities
## 4 Proposed Methodology
### Networks Creation Using EPANET  
We used EPANET to simulate hydraulic systems, including nodes, pipes, tanks, and reservoirs. Nodes represent connection points with properties like ID, coordinates, elevation, and base demand. Pipes connect nodes and their key properties are length, diameter, and roughness. Tanks store water with modifiable properties such as elevation and capacity. Reservoirs are primary water supply sources.  
City selection was based on diverse criteria, including climate, population density, and infrastructure complexity. We selected cities with distinct climate zones (oceanic, Mediterranean, and semi-arid in the case of Lugo, Moratalla, and Balerma, respectively) to ensure our approach's applicability to various environmental conditions. Additionally, factors such as altitude, average annual rainfall, and urban infrastructure were considered to observe how these variables impact water distribution optimization. This diversity enables our models to address a range of urban water challenges, enhancing generalizability.  
Figure 4: Flowchart Methodology.