esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Tail Entity and Relation Identification  
After deep noise attenuation, there may be a loss of fine-grained information [37]. We connect the noise-weakened \(U^{k}\) and the non-noise-weakened \(S\) to provide fine-grained features. After that, in order to learn the interaction information of the two granularity features, we feed them into _BiLSTM_ to obtain the final representation \(O^{k}\). The specific calculation is as follows:  
\[h^{k}_{i}=u^{k}_{i}\oplus s_{i} \tag{22}\]  
\[o^{k}_{i}=[\overrightarrow{LSTM}(h^{k}_{i});\overleftarrow{LSTM}(h^{k}_{i} )],i\in[1,n] \tag{23}\]  
\[O^{k}=\{o^{k}_{1},o^{k}_{2},\ldots,o^{k}_{n}\} \tag{24}\]  
Finally, we extract the tail entities for each relation based on the head entity \(k\). An example of \(SEO\) is given in Figure 8. For the head entity \"[PERSON]\", we first identify the tail entity \"United States\" based on the relation \"LiveIn\" and then identify the tail entity \"The Shawshank Redemption\" based on the relation \"leadActor\". Through the above steps, two entity-relation triples \"[[PERSON], LiveIn, United States]\" and \"[[PERSON], leadActor, The Shawshank Redemption]\" are extracted from the text.  
The specific calculation process is as follows:  
\[p^{T-start}_{i}=W^{start}_{r}o^{k}_{i}+\ b^{start}_{r} \tag{25}\]  
\[p^{T-end}_{i}=W^{end}_{r}o^{k}_{i}+\ b^{end}_{r} \tag{26}\]  
\(p^{T-start}_{i}\) and \(p^{T-end}_{i}\) represent the probabilities that the \(i_{th}\) word denotes the start and end positions of the tail entity, respectively. \(W^{start}_{r}\) and \(W^{end}_{r}\) denote the weight matrices for relation \(r\). \(b^{start}_{r}\) and \(b^{end}_{r}\) denote the biases for relation \(r\).
# Shock Vibration Control of SDOF Systems with Tubular Linear Eddy Current Dampers
## 4 Transient Response Analysis
### Shock Response Spectrum  
The peak responses, such as structural displacement, damper force, elastic force, and time to reach maximum displacement, are usually of the most concern during the design. The SRS is a graph of the maximum response of the SDOF system as a function of its natural frequency in response to the loaded impulsive excitation [40]. It has been widely used for the prediction of structural responses or the design of related structural parameters to reach the target response.  
Figure 11 shows the SRSs of the SDOF system under half-cycle sine shock, and represents the changes in peak responses with the ratio of duration to the natural period of the system for various mechanical parameters of the ECD. The SRSs of the structure subjected to other forms of shock impulse can be obtained by the same method, and therefore, not all of them are listed here. The maximum elastic force can be calculated by the known maximum displacement and the stiffness of the system \(k\). Based on the SRS, the peak responses of a certain system with certain design parameters of the ECD can be predicted, or the parameters of the system and the ECD can be designed for target peak responses. Because of the dimensionless characteristic, the SRS is applicable to a wide range of systems.  
Figure 10: Time\(-\)histories of structural displacement under the three conditions.  
Figure 9: Time\(-\)histories of ground motion.  
It can be noted that the dimensionless maximum displacement increases with the increase of the ratio of the shock duration to the natural period of the system. In addition, the dimensionless time of the maximum displacement has an opposing trend, but the gap between different dimensionless critical relative velocities and maximum damping forces reduces as the dimensionless shock duration increases. The dimensionless maximum damper force increases with the increase of dimensionless shock duration but remains stable when the ratio of shock excitation duration to the structure's natural period is larger than 0.2.
## 3 Privacy-Preserving Aggregation with Authentication
### Efficiency and Scalability  
As mentioned in [64], smart meters can collect metering information at varying intervals (as an example, for the Hydro One project, the reading intervals can be 5 to 60 min). With technology evolution, measurement even can be done at interval of less than every minute [65]. In Europe, as a result of the Powerfox project, customers can directly access consumption information via an Android app in 2017 [66]. The provided information is displayed by hourly with 15 min aggregations for a week based on the collected data every minute. Across the U.S., the advanced metering information (AMI) infrastructure records 15 min consumption data [67]. World Energy Council also mentioned that smart meters send metering information at pre-defined intervals from hourly to every 15 min [68], while Japanese smart meters are supposed to provide metering information in 30 minute intervals [69].  
As metering information increases, the cost of aggregation based on accumulated data also increases. In addition, an aggregation technique should take into account the computing power of a smart meter with limited computing resources. According to the comparison of computation time in [70], the schemes using existing (or modified versions of) public key encryption algorithms supporting additive homomorphic properties such as Paillier, ElGamal, and RSA require a considerably larger amount of computing resources than the additive homomorphic encryption performing only simple modular additions presented by [PERSON] et al. [47], which was designed with smart grid system efficiency as a priority.  
However, this kind of somewhat homomorphic encryption supports only limited arithmetic operations due to its limited plaintext space. When the plaintext space increases, so does the corresponding computation time. A more serious limitation is that these studies provide only simple aggregation manipulations, such as addition, and do not support more comprehensive operations. To derive more meaningful metering information, it is necessary to consider the use of fully homomorphic encryption (FHE), which gives a high degree of freedom in operations.  
In another aspect, scalability depends on the intervals between gathering metering information. As the acquisition interval is shortened, the amount of information obtainable from the smart meter increases. This might enable more accurate predictions for efficient resource management. However, when the data communication cost and computation capability are overloaded, the expected output cannot be collected. Therefore, it is desirable to establish a systematic deployment plan and an appropriate collection interval in consideration of the performance that smart meters and their central controller can afford.
## 7 Conclusions  
This work has proposed a novel LVRT control strategy for the two-stage three-phase SPV system, to ensure a satisfactory operation during grid fault conditions. A DCL voltage control scheme using MPPT and a \"control without MPPT\" controller has been proposed and analyzed using simulations, under symmetrical and asymmetrical fault conditions. The simulation results show that the proposed scheme can effectively protect the system against DCL over-voltage occurances under any fault conditions. For the benefit of numerical quantification of the system performance, some indices have been calculated and indeed support the graphical representation of the simulation results. The proposed algorithms support seamless operation mode transitions, besides they support reactive power injection according to the grid codes (GCs). The advantages of the proposed scheme are that it does not interfere with normal operation conditions and, as a result, it is always active. Furthermore, the method does not lead to instability during the most critical time, when the fault occurs and is cleared, as may occur when other methods are used. Moreover, in the proposed control scheme, there is no component to calculate the amount of excess power to be removed, and it uses only the DCL voltage to determine the mode change from the MPPT control scheme to the proposed control  
Figure 18: Simulation results of the DC-breaking chopper control scheme. (**a**) DC-link voltage (DCL), (**b**) active power (P).  
scheme, which makes it simple and easy to implement with the existing controllers. Compared with the previous LVRT schemes, the proposed scheme requires only a PI based DCL voltage regulator and mode-changing relay. Regarding the outer loop in the VSC control which carries out the function of the DCL voltage regulator, only a mode changing circuit is required for the proposed control, which reduces the practical implementation difficulties. Therefore, the proposed LVRT control strategy can be extensively applied to grid connected two-stage solar PV systems.  
Investigation of the usefulness of the proposed control scheme in a large distribution network will be undertaken in future research.  
Conceptualization, S.R.M., P.A.J., and D.D.; methodology, S.R.M., P.A.J., and D.D.; software, S.R.M.; validation, D.D. and P.A.J.; formal analysis, S.R.M. and P.A.J.; Investigation, S.R.M., and D.D.; resources, S.R.M. and A.A.; writing--original draft preparation; S.R.M., P.A.J., and D.D.; review and editing; S.R.M., D.D., P.A.J.,and M.H.S.; supervision, P.A.J. and D.D.; funding acquisition, S.R.M. and A.A.  
This work is funded by Deanship of Scientific Research at King Faisal University, grant number 186092.  
The authors express appreciation to the Deanship of Scientific Research, King Faisal University administration for continued research support.  
The authors declare no conflict of interest.
# Fingerprinting Indoor Positioning Based on Improved Sequential Deep Learning
## 2 Preliminaries
### Conventional DNN-Based Positioning  
The traditional DNNR positioning algorithm is trained to map RSSI vectors to Cartesian coordinates. It operates with \(R\) input nodes representing the RSSI samples and two output nodes corresponding to the \(\ell_{o}\) and \(\ell_{a}\) coordinates. As the model aims to predict locations using signals from APs, the number of input nodes corresponds to the number of APs. The DNN architecture includes an activation function \(f^{l}\), weight matrix \(W_{l}\), and input vector \(v_{l}\) for the \(l\)th layer. During training, the model optimizes the layer weights to minimize the mean squared error (MSE) between the actual and predicted coordinates, applying the following objective function [30]:  
\[\mathcal{L}_{MSE}=\frac{1}{M}\sum_{M}^{i=1}\left(\left(\ell_{oi}-f_{\ell_{o}}^ {l}(i)\right)^{2}+\left(\ell_{ai}-f_{\ell_{a}}^{l}(i)\right)^{2}\right) \tag{4}\]  
where \(\ell_{oi}\) and \(\ell_{ai}\) are the actual coordinates of the \(r\)th RP, and \(f_{\ell_{o}}^{l}(i)\) and \(f_{\ell_{a}}^{l}(i)\) are the predicted coordinates for the RP. The training process involves minimizing this error through backpropagation [34], which consists of two phases: forward and backward propagation. Once optimized, the trained DNN model can directly map test RSSI samples to Cartesian coordinates.  
However, this traditional approach overlooks the statistical characteristics of RSSI samples at each RP. In the next section, we present our proposed method, which integrates all RSSI samples during the training phase to improve positioning accuracy.
# Bit Error Rate of Multi-Gaussian Correlated Asymmetric Bessel Beam Through Turbulent Ocean
## 4 Conclusions  
Taking source coherence into account, we construct a theoretical model to investigate the propagation of the MGCAB beam under QAM. With OTOPS to characterize oceanic turbulence, the reception probability of OAM mode is derived by use of spiral harmonic decomposition and then the expression of BER is given. Numerical simulations show that oceanic channels with lower temperature and salinity both induce less turbulence perturbations, but the former is more significant. In contrast with OOK modulation, low-order QAM has better turbulence resistance and BER performance. In terms of the MGCAB beam, lower coherence and lower asymmetry are preferable. Moreover, beam field optimizations towards long wavelength, small topological charge, and moderate waist radius are also beneficial to the reduction in BER. The research provides references for the underwater propagation of the vortex beam combined with signal modulation.  
Conceptualization, Z.Z. and L.Y.; methodology, Z.Z. and L.Y.; software, Z.Z. and X.P.; formal analysis, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, L.Y. and Y.Z.; funding acquisition, L.Y. and Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under Grant 11904136 and in part by the Natural Science Foundation of Jiangsu Province under Grant BK20231039.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflicts of interest.
## 2 Materials and Methods  
### Patient Selection  
This retrospective study recruited consecutive patients who received orthodontic treatment and orthognathic surgery in the senior authors' Crani Center from January 2017 through December 2020. Inclusion criteria were patients with complete unilateral cleft lip and palate, having cone-beam computed tomography (CBCT) with 3D surgical simulation, and surgery performed by the senior surgeon (LJL). Exclusion criteria were the presence of traumatic or syndromic craniofacial deformities, reoperated orthognathic surgery, segmental or subapical osteotomy, and incomplete data. In our practice, we did not perform maxillary expansion before the orthognathic surgery. We preferred to do minimal or minor presurgical orthodontic treatment, i.e., modified surgery (first approach). Maximillary expansion, if required, will be performed after the orthognathic surgery by orthodontic methods. Many times, the expansion was required in the cleft-side canine area, rather than the molar area. Fifty-two patients were recruited in the study. All were two-jaw OGS procedures. It must be mentioned that two-jaw surgery was routinely performed for patients with cleft in our center. Eleven patients were excluded because of incomplete data or poor quality of images (6 patients), combining segmental or subapical osteotomy (4 patients) and redo surgery (1 patient), leaving 41 patients eligible for final evaluation. All patients received preoperative and postoperative care in the same multidisciplinary team. The study was approved by the hospital ethical committee (IRB 202100523B0).  
In this study, the original two-jaw surgical design was converted into a one-jaw procedure using the virtual surgical planning. LeFort I osteotomy with advancement was achieved keeping the same final occlusion. Comparisons of the maxillary and mandibular positions between the two-jaw and one-jaw surgical designs were performed.
# A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment
## 2 Problem Formulation  
The UC problem is occupying an outstanding position in the electric power system operation area due to its complexity and importance. The efficient optimization of UC problem can effectively reduce economic cost given that even 1% decrease would save millions of dollars a year. Generally, the economic cost of units during a 24 h day horizon is modeled as the objective function for UC problem. Meanwhile, it also needs to satisfy several equality and inequality constraints, for example, power demand limit, spinning reserve limit, generation limit, etc. In this section, the formulation of objective function and constraints conditions will be addressed.
# A High-Resolution MIR Echelle Grating Spectrometer with a Three-Mirror Anastigmatic System
## 3 Optical Design Method
\right. \tag{27}\]  
where \(f^{\prime}\) is the system focal length. Parameters \(r_{1}\), \(r_{2}\), and \(r_{3}\) are defined as the radius of curvature values for the primary, secondary, and tertiary mirrors, respectively; Accordingly, we obtain the following expression:  
\[\left\{ \begin{array}{l}r_{1}=\frac{2f^{\prime}}{\beta_{1}\beta_{2}}\\ r_{2}=\frac{2\alpha_{1}f^{\prime}}{(1+\beta_{1}\beta_{2}}\\ r_{3}=\frac{2\alpha_{1}\alpha_{2}f^{\prime}}{1+\beta_{2}}\end{array} \right. \tag{28}\]  
From the given parameters \(\alpha_{1},\ \alpha_{2},\beta_{1}\), and \(\beta_{2}\) and the system focal length \(f^{\prime}\), the curvature radius and distance of the three mirrors can be obtained from the formulas above. According to the constraint relationship between the echelle grating and each element of the spectrometer, a parameter calculation was performed, with the relationships between the parameters obtained as Table 2[18; 19]. Figure 8 shows the spectrometer design process and optimization cycle. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Spectral range & \(\sim\)3.7–4.8 μm \\ Spectral resolution & \(\sim\)30,000 \\ Main disperser & Immersion Grating \\ Cross disperser & Concave Grating \\ Pixel size & 30 μm/pixel \\ Detector & 320 pixel \(\times\) 256 pixel \\ Configuration & Quasi-Littrow \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specification requirements of the spectrometer.
# Effects of Tube Radius and Surface Tension on Capillary Rise Dynamics of Water/Butanol Mixtures
## 3 Results and Discussion
### Effect of Surface Tension of the Fluids on Capillary Rise Dynamics
To clarify these relations for parameters such as capillary tube radius and liquid surface tension on the capillary rise characteristics, we analyzed a consolidated quantity between \(r\) and \(\sigma\) on characteristic heights. The quantity, which is a formulation analogous to pressure (\(\sigma L/2\pi r^{2}\)), was a consolidated expression of liquid surface tension (\(\sigma\)), capillary tube length (\(L\)), and tube surface area (\(2\pi r^{2}\)). Figure 9 shows the characteristic heights \(h_{1}\), \(h_{2}\), and he as a function of \(r\), \(\sigma\), and \(\sigma L/2\pi r^{2}\). Straight lines represent linear fits between characteristic heights and each of the comparative parameters. In Figure 9a, characteristic heights showed a linear decrease with capillary tube radius. The characteristic heights are also linearly dependent on \(\sigma\), as shown in Figure 9b. Each of characteristic heights becomes a linear function of \(\sigma L/2\pi r^{2}\) as shown in Figure 9c. In Figure 9c, the linearity (R-square) of \(h_{1}\), \(h_{2}\), and \(h_{\rm e}\) with \(r\) and \(\sigma\) was 0.78, 0.93, and 0.95. Hence, all the characteristic heights increase with an increase of the said function. This linear increment was more noticeable in the sequence of \(h_{1}<h_{2}<h_{\rm e}\). As such, the slope value of \(h_{1}\), \(h_{2}\), and he with the quantity was 0.00139, 0.00506, and 0.00796. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Mixture (Butanol wt.\%)** & **Stage I (mm)** & **Stage I (mm)** & **Stage I (mm)** & \(h_{1}\) (mm)** & \(h_{2}\) (mm) & \(h_{\rm e}\) (mm) & \(\sigma\) **(mN/m)** \\ \hline B0\% (DW) & 11.2 (31.2\%) & 16.4 (45.7\%) & 8.3 (23.1\%) & 11.2 & 27.6 & 35.9 & 72 \\ B0.3\% & 9.3 (29\%) & 14.3 (44.5\%) & 8.5 (26.5\%) & 9.3 & 23.6 & 32.1 & 64 \\ B0.5\% & 8.9 (31\%) & 12.5 (43.6\%) & 7.3 (25.4\%) & 8.9 & 21.4 & 28.7 & 60 \\ B0.7\% & 8.6 (32.3\%) & 11.2 (42.1\%) & 6.8 (25.6\%) & 8.6 & 19.8 & 26.6 & 56 \\ \hline \end{tabular}
\end{table} 
Table 2: Lengths of characteristic stages for butanol/water mixtures at the test case of \(r=0.4\) mm. Figure 8: Effect of surface tension of liquids (\(\sigma\) = 56, 60, 64, and 72 mN/m) on capillary rise dynamics at \(r=0.4\) mm. (**a**) height and (**b**) velocity.
## 3 The Proposed Algorithm and Its Applications
### Applications Of FRSVssMP in Vehicle Health Monitoring  
FRSVssMP is applicable for scenarios requiring high reconstruction accuracy and efficiency, especially for real-time health monitoring of vehicle status signals.  
Reliable operations of vehicle systems are relevant to all aspects of vehicle applications. With the development of technology, high-power devices, nonlinear electrical components and large numerous of new electronics are continuously integrated to vehicles, which brings significant challenges for vehicle health monitoring [29]. Hence, real-time monitoring of vehicle status signals plays an increasingly important role.  
Vehicle status signals reflect the reliable operations of vehicle systems. Due to numerous vehicle status signals and long-term signal sampling, large amounts of data are required to be sensed, transformed, and stored [30]. CS can be well exploited for efficient sampling and data compression. With the concepts of real-time health monitoring and analysis for vehicle systems proposed, the compressed status data are transmitted to monitoring center for reconstruction and information exchange, which requires extremely high reconstruction accuracy and efficiency. Therefore, it is extremely significant to apply FRSVssMP to the process of reconstruction, which can decrease reconstruction time with high accuracy obviously.
# River Water Quality Monitoring Using LoRa-Based IoT
## 5 Results
### LoRa Radio Coverage  
LoRa radio coverage tests were carried out and a maximum distance of 2.53 km was obtained, in an urban and Non-Line of Sight (NLOS) environment. For practical reasons, to facilitate this test, the IoT Node was fixed, and the Gateway was on the move; see Figure 31.