esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Preliminary Knowledge
### Paillier Homomorphic Encryption  
[PERSON] et al. [31] made the initial proposal for homomorphic encryption in 1978. Semi-homomorphic encryption and fully homomorphic encryption are the two categories into which it can be separated. Complete homomorphic encryption is extremely expensive and unable to handle high transaction throughput demands. The classical probabilistic homomorphic encryption technique employed in this study, the Paillier algorithm, is moresophisticated than other methods and has been extensively used in the Internet of Things, cloud computing, and other applications. The following is a description of the Paillier homomorphic encryption algorithm:  
* Key generation: arbitrarily select large prime numbers \(p\), \(q\), and calculate \(n=p\times q\) and \(\lambda=lcm(p-1,q-1)\); If an integer is arbitrarily selected and \(gcd(L(g^{\lambda}modn^{2}),n)=1\) is satisfied, \(pk_{P}=(n,g)\) and \(sk_{P}=(p,q)\) are the public key and private key, respectively.
* Encryption: arbitrarily select \(r<n\), and for plaintext \(m<n\), the encrypted ciphertext \(c=g^{m}r^{m}modn^{2}\).
* Decrypt: assuming the ciphertext is \(c\), the decrypted ciphertext \[m=\frac{L(c^{\lambda}modn^{2})}{L(g^{\lambda}modn^{2})}modn,\] (7)
* Additive homomorphism: for two plaintext \(Msg_{1}\) and \(Msg_{2}\), Paillier homomorphic encryption operation is expressed as: \(Enc(\cdot)\). Following Paillier homomorphic encryption, the ciphertext is, accordingly, \(Enc(Msg_{1})\) and \(Enc(Msg_{2})\), then \(Enc(Msg_{1})\) and \(Enc(Msg_{1})\) satisfy the following requirements: \[Enc(Msg_{1})\times Enc(Msg_{2})=Enc(Msg_{1}+Msg_{2}),\] (8) During the above calculation, \(lcm(\cdot,\cdot)\) is defined as the least common multiple of 2 parameters \(L(u)=\frac{(u-1)}{n}\).
## 3 Results
### Effect of Temperature and pH on HoFucl Activity  
When the temperature effect on the activity of _Ho_Fucl on L-fuculose was investigated in the presence of 0.5 M NaCl (Figure 4a), the maximum activity was observed at 50 \({}^{\circ}\)C and 60 \({}^{\circ}\)C with no significant difference (\(p>0.05\)), and the enzyme activity sharply decreased at temperatures higher than 60 \({}^{\circ}\)C, showing around 60% of relative activity (RA) at 70 \({}^{\circ}\)C, but only around 10% activity at 80-100 \({}^{\circ}\)C. At 30 and 40 \({}^{\circ}\)C, 65% of RA was maintained. This result indicates that _Ho_Fucl is a thermophilic enzyme that correlates well with the optimal growth conditions of _H.__oreui_ at 60 \({}^{\circ}\)C, with a range of 42-70 \({}^{\circ}\)C [18,19].  
When the influence of pH on the enzyme activity of L-fuculose was examined (Figure 4b), the optimal pH was found to be pH 7, and more than 60% of RA was retained at a pH of 8 and 9. At pH values lower than 7 and higher than 9, the activity decreased sharply, showing only approximately 20% of RA at a pH of 6 and 10, and less than 10% of RA at a pH of 4, 5, and 11.  
Figure 3: Effect of NaCl concentration on the L-fuculose-isomerizing activity of _Ho_Fucl. The enzyme reactions were performed with 7 \(\mathrm{\SIUnitSymbolMicro g}\) of _Ho_Fucl and 10 mM L-fuculose at 60 \({}^{\circ}\)C for 5 min in 20 mM sodium phosphate (pH 7.0) buffer in the presence of 1 mM Mn\({}^{2+}\). The reaction solution contained various NaCl concentrations ranging from 0 to 5 M. The experimental data represent the means \(\pm\) standard deviations of three replicates. Relative activity (%) represents the enzyme activity relative to that obtained without supplementation of NaCl, set as 100%.
## 2 Materials and Methods
### Strategy of the Analysis
We utilized various proportions of the \"original\" dataset, ranging as low as 0.5% and 1.0%, corresponding to 50 and 100 subjects in the \"sample\", respectively. 6. The WGANs were applied in the \"sample\", which led to the synthesized values, which form the \"generated\" dataset (Figure 2). The size of the synthesized data initially was set equal to the population size, namely, 10,000 subjects. Later in this analysis, smaller sizes of the \"generated\" dataset were used, such as 1000, 500, and 100. Figure 2: Proposed approach for the implementation of WGANs in clinical studies. * To simplify the process and avoid complexities, a simple statistical criterion (\(t\)-test) was applied to compare the T and R populations. Pairwise comparisons were then made among the \"original\", \"sample\", and \"generated\" datasets to assess their performance. * Each success (or failure) of the \(t\)-test was recorded for all datasets. * Since the \"original\" data represents the entire population, reflecting the true results, an additional comparison metric was introduced to assess the analysis outcomes. This metric, termed \"similarity\", gauges the concordance of statistical results between the \"sample\" and \"generated\" datasets in comparison to the \"original\" dataset. During each repetition of the trials, we recorded whether the statistical outcomes derived from analyzing the \"sample\" and \"generated\" datasets agreed with those obtained from the \"original\" population. This comparison aimed to evaluate the fidelity of the generated datasets in capturing the underlying characteristics of the original population. The concept of \"similarity\" encompasses the simultaneous success or failure of the statistical evaluation, translating into the degree of resemblance between the distributions of the \"sample\" and \"generated\" datasets with the \"original\" population. A high \"similarity\" rate indicates that the statistical properties of the generated datasets closely mirror those of the original population, which is desirable for the validity and reliability of the generated data. * The above steps (i-vii) were repeated many times (5000) to obtain robust estimates. Statistical power for each case was calculated as the number of successes after all repetitions divided by the total number of repetitions. The ideal architecture of the WGANs and the fine-tuning of hyperparameters were exhaustively examined following the preceding step 'vi'. Additionally, seeds were set in all stochastic processes to ensure reproducible results across different program executions. Once the final hyperparameters were determined, a variety of scenarios were executed, which remained consistent throughout the entire experimental procedure. These measures were implemented to ensure the reproducibility of the entire framework.
# Anisotropic Effective Elastic Properties for Multi-Dimensional Fractured Models
###### Abstract  
The size, distribution, and orientation of fractures are generally multiscale and multi-dimensional in nature, leading to complex anisotropic characteristics. Theoretical or semi-analytical methods to determine the effective elastic properties depend on several assumptions, including the absence of the stress interaction and idealized fractures. On the basis of finite-element models, we conduct numerical oscillatory relaxation tests for determining the effective elastic properties of fractured rocks. The numerical approach for calculating equivalent stiffness tensors in two-dimensions is compared to the theoretical models for different fracture densities. Due to fracture interactions at high fracture densities, the suggested model makes a physical prediction. The effective elastic properties obtained from the application to a real fractured model, established from an outcrop, obviously disperse at different frequencies, which can be used to investigate fracture interactions and dynamic stress disturbances. The algorithm is extended to three-dimensional cases and also validated by using conventional effective medium theories. It is found that the fracture density obviously impacts the effective anisotropy properties, and the proposed method gives a reasonable prediction for high-fracture density. This work is significant because it enables the calculation of effective elastic properties of multi-dimensional fractured models and the fracture interaction mechanism.  
multi-dimensional fractured models; effective elastic properties; numerical oscillatory relaxation tests; anisotropy characteristics
# Deep Learning-Based Automatic Segmentation of Mandible and Maxilla in Multi-Center CT Images
## 2 Materials and Methods
### Data
Examples of all datasets are illustrated in Figure 2. CenterA dataset is different from the PDDCA and TCIA datasets in terms of pixel spacing, slice thickness, and the scan range of the CT images. Comparatively, the PDDCA and TCIA datasets include a wider range of bodies that target OARs. CenterB uses cone beam CT (CBCT), which is fundamentally different from multidetector CT (MDCT) datasets, meaning the performance of a model trained with MDCT may be hindered when inferencing CBCT. Generally, seg mention of CBCT is more laborious and time-consuming than MDCT as the edge of the image is more blurred and noisy. Additionally, CenterB dataset includes many cases with orthognathic surgery or orthodontics, which makes segmentation more difficult owing to the noise caused by surgery plates or orthodontic appliances (Figure 3). By externally testing using datasets, including PDDCA, TCIA, and CenterB datasets, with various characteristics, it was possible to evaluate the compatibility of the models. All datasets were preprocessed using the same procedure. A threshold of \(-1000\) and \(2500\) HU was employed for each scan and normalized between zero and one. Both CT scans and segmentation slices were cropped to fit the skull. All CT and segmentation volumes were resampled to be isotropic (\(512\times 512\times 512\)). For a fair evaluation, the predicted segmentations were conversely uncropped and resampled into the original spacing and thickness before the evaluation metrics were calculated. For the PDDCA and TCIA datasets, we only used the range of the mandible for the training dataset, while using the entire range of slices for the validation and testing. Figure 2: Example cases of the datasets. Datasets were input to the models as 2.5D [46], in which the input was a volume of images consisting of the target slice and its adjacent slices, and the segmentation map corresponding to the center target slice was produced as an output of the model. This method enables the use of adjacent context information in 3D, whilst lowering the computational power required relative to 3D inputs. The proposed approach is applicable to mandible and maxilla segmentation as the adjacent upper and lower spatial information is important for distinguishing the mandible and maxilla in a slice that appears similar. In this study, the 11 slices, composed of one target slice and five upper and lower slices, were input for one slice of the segmentation map.
# A Fast Acquisition Algorithm for Hybrid Signals of 5G and BeiDou B1
## 4 Conclusions  
We analyze the Beidou and 5G systems' signals starting from the signal part of the 5G out-of-band signal falling into the Beidou B1 signal band, summarize the 5G frequency band close to the Beidou signal, discuss the NR spectrum characteristics and radiation characteristics of 5G, and analyze the current ITU protection standards for navigation signals. We provide a detailed analysis of the mixed-signal and frequency planning and design a hybrid receiver architecture compatible with both movements. We provide a detailed analysis of the hybrid signal regime and frequency planning and create a hybrid receiver architecture compatible with both signals, including the hardware design of the RF front-end circuit, clock circuit, baseband circuit, and interface circuit. We propose a strong-correlation-based SC-PMF-FFT fast capture algorithm, which takes advantage of the strong correlation of signals broadcast on the B1 frequency point of BeiDou, performs fast decoding phase ambiguity processing for strongly correlated signals from B1I to B1C, and reuses the structure of the CDMA system signal capture algorithm to complete fast capture of 5G signals using the OFDM system. Experiments show that the method can achieve a capture sensitivity of \(-154\) dBm for BeiDou B1 signals with a whole constellation capture time of no more than 40 ms when the inlet power of 5G signals does not exceed \(-45\) dBw. Conceptualization, X.Y. ([PERSON] 1) and C.Z. ; methodology, W.F. ; software, X.Y. ([PERSON] 1) and S.H. ; validation, X.Y. ([PERSON] 1), Z.Y. and Q.W. ; formal analysis, C.Z. ; investigation, X.Y. ([PERSON] 2); resources, X.Y. ([PERSON] 2) and S.H. ; data curation, X.Y. ([PERSON] 2); writing--original draft preparation, X.Y. ([PERSON] 1) and S.H. ; writing--review and editing, X.Y. ([PERSON] 1) and C.Z. ; visualization, X.Y. ([PERSON] 2); supervision, C.Z. ; project administration, W.F. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, under grant number 61901015. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** The authors acknowledge graduate student [PERSON] for his contribution to literature search and collation. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
The following abbreviations are used in this manuscript:
## 2 Multipath Modelling and Mathematic Tools for Analysis
### Correlation  
The Pearson correlation coefficient (\(\rho\)) is employed to analyze the correlation between residual sequences of any two frequencies. The formula for \(\rho\) is given by [32]  
\[\rho_{XY}=\frac{cov(X,Y)}{\sigma_{X}\sigma_{Y}} \tag{13}\]  
where \(X\) and \(Y\) represent two sets of residual sequences; and \(cov\) and \(\sigma\) denote the covariance and standard deviation operators, respectively. Typically, a \(\rho\) value between 0.8 and 1.0 indicates a very strong correlation, 0.6 to 0.8 represents strong correlation, 0.4 to 0.6 is moderate, 0.2 to 0.4 is weak, and 0.0 to 0.2 reflects very weak or no correlation.  
Calculating the correlation between residuals for frequencies within the same system is straightforward, as the satellite positions remain consistent across all observations. However, for different systems, this process becomes more complex due to the non-overlapping satellite trajectories.  
To solve this, we first generate uniformly distributed positions close to the trajectories of both systems, ensuring a maximum distance of 0.02 rad from each trajectory. Next, the multipath values at these positions are interpolated for each system's residuals using a spatial autocorrelation-based method. Once the multipath values are obtained for both systems at the uniformly distributed positions, the correlation coefficients are calculated.  
The positions are illustrated in Figure 1, where the blue and green solid lines represent the satellite trajectories from different systems, and the red-dotted triangles indicate the uniformly distributed sample points located within 0.02 rad of both trajectories.  
The spatial correlation characteristics of multipath are analyzed to enhance its mitigation in multi-GNSS, multi-frequency observations. This involves calculating the correlation coefficient between different GNSS frequencies, examining the spatial autocorrelation of residuals, and identifying consistent modeling parameters for the covariance function. Based on these findings, a spatial autocorrelation-based method utilizing a unified covariance function is proposed. Additionally, the impact of orbit shifts on long-term multipath error mitigation is evaluated, and strategies to reduce this impact are assessed. The flowchart of the procedure is plotted in Figure 2.  
Figure 1: Schematic diagram of the data locations used to calculate the correlation between residuals from different frequencies. The blue and green solid lines represent satellite tracks from two distinct systems, while the blue and green dashed lines indicate the 0.02 radians boundaries to the satellite tracks. All red points, including the red circles and red triangles, represent uniformly distributed samples generated for the correlation analysis. The red triangles are the points that fall within the 0.02 radians overlapping region around the satellite tracks of both systems, i.e., the region where the dashed lines overlap. The red circles represent points that fall outside the overlapping region and are therefore excluded from the analysis.
# An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies
## 3 The Proposed Method
### The Embedding Procedures  
In this section, the embedding procedures of the proposed method are described in the following algorithm. Let \(C=\{a_{i},\ b_{i},\ B_{i}\}_{i=0}^{N-1}\) be the AMBTC codes used to embed the authentication codes. The embedding procedures are listed below.  
* Input: AMBTC compressed codes \(C=\{a_{i},\ b_{i},\ B_{i}\}_{i=0}^{N-1}\), key \(\mathcal{K}\), and parameters \(n\) and \(T\).
* Output: Marked AMBTC codes \(\{\hat{a}_{i},\ \hat{b}_{i},\ \hat{b}_{i}\}_{i=0}^{N-1}\).
* Step 1: Scan each code \((a_{i},\ b_{i},\ B_{i})\) in \(\{a_{i},\ b_{i},\ B_{i}\}_{i=0}^{N-1}\) and calculate the difference of \(a_{i}\) and \(b_{i}\).
* Step 2: If \(|b_{i}-a_{i}|<T\), use Equation (3) to hash \(\{B_{i,j}\}_{j=0}^{1}\), \(a_{i}\), \(b_{i}\) and \(i\) to generate the 6-bit \(ac_{i}\). Embed \(ac_{i}\) into \(\{B_{i,j}\}_{j=2}^{15}\) using the matrix encoding described in Section 2.3 to obtain \(\{\hat{B}_{i,j}\}_{j=2}^{15}\). Concatenate \(\{B_{i,j}\}_{j=0}^{1}\) and \(\{\hat{B}_{i,j}\}_{j=2}^{15}\), and the marked bitmap \(\hat{B}_{i}\) is obtained. Then, we have the marked code \((\hat{a}_{i},\ \hat{b}_{i},\ \hat{B}_{i})\), where \((\hat{a}_{i},\ \hat{b}_{i})=(a_{i},\ b_{i})\).
* Step 3: If \(|b_{i}-a_{i}|\geq T\), use the key \(\mathcal{K}\) to generate \(\|_{i}\), and construct the reference table \(R_{64}^{\|}\) using Equation (4). Then, use the APPM to embed \(ac_{i}\) into \((a_{i},\ b_{i})\), and we have \((\hat{a}_{i},\ \hat{b}_{i})\). If \(\left|\hat{b}_{i}-\hat{a}_{i}\right|\geq T\), the marked code \((\hat{a}_{i},\ \hat{b}_{i},\ \hat{B}_{i})\) is outputted and \(\hat{B}_{i}=B_{i}\). Otherwise, \((a_{i},\ b_{i},\ B_{i})\) is embedded using the technique described in Section 3.3 to obtain \((\hat{a}_{i},\ \hat{b}_{i},\ \hat{B}_{i})\).
* Step 4: Repeat Steps 1-3 until all codes are embedded and output the marked codes \(\{\hat{a}_{i},\ \hat{b}_{i},\ \hat{B}_{i}\}_{i=0}^{N-1}\), key \(\mathcal{K}\), and parameters \(n\) and \(T\).
## 4 Experimental Setup  
The experiments developed to test the proposed methodology were carried out with a 0.75 kW IM from Siemens. To vary the load conditions, a 1.1 kW electromagnetic break was used. Additionally, the motor was not directly connected to the power supply, and it was fed through a VFD CFW 11 from WEG instead.  
To perform the data acquisition and collection, a proprietary FPGA-based DAS was used. This device was set to acquire the data from the three current signals of the IM simultaneously. The data acquisition process was made considering a sampling frequency of 8 kHz with a 16-bit resolution. All the data were stored using 128 GB standard micro SD card. Finally, to measure the currents in the IM, the YHDC SCT-013-010 current clamps were used. These current clamps can measure currents up to 10 A with a 100 mV per ampere resolution.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 2 Routing on Wireless Sensor Networks
### Data Centric Based  
In several sensor networks, it is not obvious to assign an identifier to each node because of the large number of nodes deployed. Besides the problem of identification of nodes, the random deployment of nodes makes it difficult to select a specific node during the routing of data through the network. Nevertheless, since data are usually transmitted from each node within the deployment region, the redundancy of these data can be significant then waste a lot of energy.  
The resolution of redundant data during routing has led to the data centric approach, which is different from the traditional address-based routing where routes are created between addressable nodes [12; 13; 14]. In data centric based routing, before data have to be sent by nodes in a selected region, the sink node should send queries to a selected region and wait for the incoming data [12]. The first and the most popular data centric protocol is the Sensor Protocols for Information via Negotiation (SPIN), in which negotiation between nodes is considered in order to eliminate redundant data and reduce the energy consumption. There are several kinds of data centric based routing like Directed Diffusion [15], Energy-aware routing, Rumor routing, or Gradient-Based Routing [13; 15].
## 4 SMART DShot: ML-Enhanced Architecture
### ML Algorithm Framework
#### 4.2.2 Algorithm Selection Strategy  
The selection strategy employs real-time error characterization to optimize performance through statistical analysis of error variance, linear trend detection, and non-linearity assessment. HATC achieves optimal 31.01% success rate by dynamically selecting the most appropriate algorithm based on current error characteristics, significantly outperforming individual algorithms, which achieve 24.51% (KFTC), 26.94% (RLSTC), and 29.34% (FLTC) success rates, respectively.  
Table 3 summarizes the performance characteristics of each algorithm in key metrics. The results demonstrate that while HATC incurs higher computational overhead (180 ns latency, 7.1 KB memory), this investment yields superior performance across all error scenarios, achieving excellent (\(\star\star\star\star\)) ratings in all test conditions.
## 2 Materials and Methods
### Extraction and Quantification of Enzyme Activity by Spectrophotometry
#### 2.7.1 Protease Activity  
The protease activity was assayed following the method described by [PERSON] et al. [21], with some modifications to the incubation time of the first reaction. The assay was conducted as follows. The supernatants of the amazake extract (1 mL) and a 0.65% \(w/v\) casein solution (5 mL) were mixed and incubated at 37 \({}^{\circ}\)C for 20 min. The reaction was then stopped with 0.4 M trichloroacetic acid (5 mL), and the samples were incubated at 37 \({}^{\circ}\)C for 30 min. Next, the samples were centrifuged at 1100 RPM for 5 min and then filtered using 0.2 \(\upmu\)m Acrodisc\({}^{\circledR}\) syringe filters. The filtrate (2 mL), [PERSON]'s solution (1 mL), and 0.4 M of sodium carbonate (5 mL) were mixed and incubated at 30 \({}^{\circ}\)C for 30 min. After the incubation period, the absorbance of the reaction mixture was observed at 660 nm using a Pharmacia Biotech Ultrospec 2000 UV-visible spectrophotometer. One unit of protease activity was defined as the amount of protease required to release 1 \(\upmu\)g of tyrosine per minute from 0.65% \(w/v\) of casein.