esa-sceva/satcom-chunk-collection huggingface dataset:
# How Can We Achieve Query Keyword Frequency Analysis in Privacy-Preserving Situations?
###### Abstract  
Recently, significant progress has been made in the field of public key encryption with keyword search (PEKS), with a focus on optimizing search methods and improving the security and efficiency of schemes. Keyword frequency analysis is a powerful tool for enhancing retrieval services in explicit databases. However, designing a PEKS scheme that integrates keyword frequency analysis while preserving privacy and security has remained challenging, as it may conflict with some of the security principles of PEKS. In this paper, we propose an innovative scheme that introduces a security deadline to query trapdoors through the use of timestamps. This means that the keywords in the query trapdoor can only be recovered after the security deadline has passed. This approach allows for keyword frequency analysis of query keywords without compromising data privacy and user privacy, while also providing protection against keyword-guessing attacks through the dual-server architecture of our scheme. Moreover, our scheme supports multi-keyword queries in multi-user scenarios and is highly scalable. Finally, we evaluate the computational and communication efficiency of our scheme, demonstrating its feasibility in practical applications.  
searchable encryption; keyword frequency analysis; multi-keyword search; keyword guessing attacks; multi-user access +
Footnote †: journal:  
Article
###### Abstract
Theoretical mean-square analysis is conducted for the RIS, where the result corroborates that the RIS asymptotically achieves the CRLB if the noise and error are mild. Numerical simulations validate the analysis and illustrate the advantages compared to existing solutions. Moreover, the complexity of the RIS is manageable, similar to GN in terms of processing time. The main contributions of this paper include the following:  
1. A new closed-form solution using nullspace projection to generate the initial guess. 2. A robust iteration solution based on factor graphs and message passing, resilient to noise and AN position error, ensuring global convergence. 3. Theoretical analysis corroborating the optimality in the RMSE of the proposed RIS. 4. Extensive simulations validating the analytical results, demonstrating the best bias performance, and confirming the manageable computational complexity. The remaining sections of this paper are structured as follows. Section 2 elucidates the measurement model and formulates the JLAS problem based on sequential TOA for mobile UNs. Section 3 presents the proposed method that firstly solves the JLAS problem using two-stage weighted least squares, followed by a novel robust iteration. The CRLB and theoretical analysis are performed in Section 4. Section 5 presents the simulation experiments conducted to assess the performance of the proposed method. The summary and future research of this paper are concluded in Section 6. To enhance readability, this paper uses bold uppercase letters and symbols to represent matrices and bold lowercase for vectors. \(\|\cdot\|\), \({}^{\top}\), and \({}^{-1}\) are the Euclidean norm, transpose, and inverse operators. \(x(i)\) is the \(i\)-th element of \(\mathbf{x}\). \(\mathbf{x}(i:j)\) denotes a subvector constructed by the \(i\)-th to \(j\)-th elements of \(\mathbf{x}\). \(\mathbf{X}(:,i)\) means the \(i\)-th column of matrix \(\mathbf{X}\). Notations with a superscript \({}^{o}\) denote true values. Letters and symbols with a hat \(\hat{\cdot}\) represent the estimate values, while those without any superscript signify measured values. The notations used in this paper are listed at the end of this paper.
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 2 Background
### Anonymization  
Anonymization is a method of transforming information that can be uniquely identified (PII) into an unidentifiable form so that it cannot be to re-linked to an individual without additional information [6]. The goal is to secure the personal identification of a person while enabling the public sharing of data. The data collector removes the particular uniquely identified information, such as name, phone number, or location. However, there are still challenges in data anonymization, even if specific identifiers are removed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Technique** & **Strengths** & **Weaknesses** & **Attribute Preservation** & **Damage to Data Utility** & **Complexity** & **Accuracy of Data Analytics** \\ \hline \multirow{4}{*}{Anonymization} & Simple, easy to implement, and widely used & Vulnerable to reidentification attacks if not done properly & Low & Medium & Low & Medium \\ \hline \multirow{4}{*}{K-Anonymity} & Reduces risk of identification, simple concept & Does not protect against attribute disclosure, selection of k & Medium & Medium & Low & Medium \\ \hline \multirow{4}{*}{L-Diversity} & Protects against homogeneity and background knowledge & Complex to achieve with high l values & High & Low & Medium & High \\  & & & & high l values & & & \\  & & attacks & & & & & \\ \hline \multirow{4}{*}{T-Closeness} & Better protection against attribute disclosure & More complex and computationally intensive & High & Low & High & High \\ \hline \multirow{4}{*}{Cryptographic Techniques} & Strong protection, widely accepted, and mathematically rigorous & Computationally intensive and requires key management & High & Low & High & High \\ \hline \multirow{4}{*}{Multidimensional Sensitivity-Based Encryption} & Nuanced privacy protection considering multiple factors & Complex to implement and requires detailed sensitivity analysis & High & Low & High & High \\ \hline \multirow{4}{*}{Differential Privacy} & Provids strong privacy guarantees and resistant to many types of attacks & Can reduce data utility and requires careful calibration of noise & High & Medium & High & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of privacy preservation techniques.  
Sometimes, it is possible to re-identify anonymized data by data linkage attacks, especially when combined with other datasets. Data masking techniques are used in data anonymization, such as randomization, which replaces identifiable data with random values, and pseudonymization, which substitutes identifiable information with pseudonyms or tokens that can be reversed only with a specific key or method. Techniques for anonymization must change to keep up with improvements in ways of re-identifying data.
## 3 Results and Discussion
### Effect of Tea Extracts on Oxidative Status of \(\omega\)-3 FA During Digestion  
The peroxide value (PV) of \(\omega\)-3 FAs was measured in order to determine whether the various tea extracts prevent the oxidation of \(\omega\)-3 FA during digestion. It was found that the PV was higher in \(\omega\)-3 FAs alone than when co-digested with various tea extracts. The values were 4.36 \(\pm\) 0.36, 3.73 \(\pm\) 0.47, 3.67 \(\pm\) 0.56, 3.37 \(\pm\) 0.87, 3.07 \(\pm\) 0.80, and \(1.03\pm 0.25\) met\(\,\)O\({}_{2}\)/kg oil for \(\omega\)-3 FAs, GTE35, GTE65, NFGT100, NFGT30, and AdiphenonTM, respectively (\(p<0.05\)), as illustrated in Figure 1. The co-treatment with AdiphenonTM showed the lowest PV, indicating a significant difference compared to \(\omega\)-3 FAs alone (\(p<0.05\)). The previous studies found that antioxidants, such as green tea extracts, enhanced the stability of \(\omega\)-3 FAs by neutralizing free radicals, which accelerate lipid oxidation [13; 14]. It was also revealed that supplementation with green tea extracts inhibited the oxidation of DHA by approximately 55% compared to DHA-rich oil without any added antioxidants [14]. The current study further confirmed that radical scavenging, measured using the DPPH assay and then expressed as Trolox equivalent antioxidant activity (TEAC, \(\mu\)mol TE/g sample), was significantly higher in \(\omega\)-3 FAs combined with various tea extracts compared to \(\omega\)-3 FAs alone (\(p<0.05\)). The values obtained were 161.71 \(\pm\) 11.73, 1036.61 \(\pm\) 40.58, 1123.58 \(\pm\) 4.68, 984.41 \(\pm\) 47.18, 712.64 \(\pm\) 14.98, 712.64 \(\pm\) 14.98, and 1251.96 \(\pm\) 26.03 \(\mu\)mol TE/g sample for \(\omega\)-3 FAs, GTE35, GTE65, NFGT100, NFGT30, and AdiphenonTM, respectively (\(p<0.05\)), as shown in Figure 2. The results from this study suggest that the antioxidant properties of various tea extracts inhibit the oxidation of \(\omega\)-3 FAs, thereby enhancing their digestive stability.
## 3 Results and Discussions
The models that are discussed in this paper can be used for phase behavior modelling with a single machine learning run once tuned and trained once at the beginning of the simulation, instead of the traditional iteration-based approach for reservoir simulations. Moreover, CO2 storage project design and prediction need simulations that cover decades and centuries from case studies with fast and reliable modelling, where traditional full field simulations can be challenging due to their high computational intensity. Thus, machine learning-based hybrid models can be a possible solution; however, one needs to ensure the underlying physics is honored during implementation. We have studied a well-defined model with known fluid characterizations based on limited pure components. However, more complex systems such as real crude hydrocarbon systems should also be tested in future studies, including the typically unknown plus fractions. Another limitation of this study is that the dataset is based on a deterministic set of input values that do not have uncertainties that are typical in experimental data processing. Thus, the methods should also be tested and tuned with real fluid experimental data that include experimental uncertainty and error bars in future studies.
## 2 Literature Background
### Background Articles on CORS Accuracy
The massive amount of data available from GNSS CORS has allowed an accurate and thorough evaluation of the influence of effects correction, specifically the troposphere and ionosphere ones. For instance, [PERSON] et al. proposed a new algorithm for troposphere tomography using a truncation factor model (TFM) to solve the sectional slant of water vapour within the tomographic area [95]. On the other hand, [PERSON] et al. using data from three different CORS, analysed the large-scale travelling ionospheric disturbances (LSTIDs) during a geomagnetic storm using Total Electron Content (TEC) [96]. Meanwhile, [PERSON] et al. estimated the inter-frequency biases of satellites and receivers and the total electron content (TEC) of the ionosphere among 1000 available CORS using an algorithm developed at the Jet Propulsion Laboratory (JPL), based on the Global Ionospheric Mapping (GIM) software [97]. [PERSON] et al. evaluated the influence of the correction accuracy on-the-fly (OTF) ambiguity resolution (AR) in long-range RTK GPS on instantaneous (single-epoch) ARvand; the results proposed that the correction accuracy deteriorates to the point (during a strong ionospheric storm) that OTF AR takes much longer to fix integers [98]. [PERSON] et al. discussed comparisons of ionosphere database analyses post-processed for the Wide Area Augmentation System (WAAS) with Jet Propulsion Laboratory (JPL) data from the CORS database [99]. [PERSON] et al. also proposed a specific algorithm that uses the geometric conditions between satellites and tracking receivers to determine the differential code bias (DCB) of receivers using CORS stations. This method can be used to estimate the DCBs of receivers in a regional network, as long as one of the DCBs of the receivers is known and therefore does not require a traditional ionosphere model [100]. In 2011, [PERSON] et al. and, more recently, [PERSON] et al. (2021) studied the slant TEC (STEC) observations of local CORS: in the first paper, the Equatorial ionospheric zonal drift was analysed; in the latter, using a newly defined Modified PPP (MPFP) model, the degradation of PPP performance and the temporal variations of the receiver code biases were estimated; also, their negative impacts on PPP parameters were mitigated, such as ionospheric delays, ambiguity parameters, or receiver clock offsets [101; 102]. Last, [PERSON] et al. using the neural network algorithm, determined a newly modelled Zenith tropospheric delay (ZTD) for high-precision positioning by CORS [103], while [PERSON] et al., studying ZTD with the effects that a hurricane had on spatial and temporal behaviour, showed that the ZTD time series exhibited a rapid increase of more than 10 cm in a few hours when the study area was inundated by the hurricane [104].
# Modeling of Satellite-to-Underwater Integrated FSO-PON System Using NOMA-VLC
## 3 Numerical Analysis
### Impact of Channels  
To calculate the performance of the proposed system using distinct OCDMA codes, gain \((G)\) and noise figure \((NF)\) together play an important role. Thus, \(G\) and \(NF\) are described as [50; 51]:  
\[G=10 log_{10}\bigg{(}\frac{P_{o}}{P_{i}}\bigg{)}\text{dB}=P_{o}(dB)-P_{i}( \text{dB}) \tag{40}\]  
and  
\[NF=10 log_{10}\bigg{(}\frac{SNR_{i}}{SNR_{o}}\bigg{)}\text{dB}=SNR_{i}(\text{ dB})-SNR_{o}(\text{dB}) \tag{41}\]  
where \(SNR_{i}\) means input SNR respectively.  
Table 9 tabulates the comparisons of integrated FSO-PON using NOMA-VLC system with seven codes in terms of \(G\), \(NF\), \(P_{i}\), output power (\(P_{o}\)), input OSNR (OSNR\({}_{i}\)) and output OSNR (OSNR\({}_{o}\)) for various DN wavelengths over 10 km FSO, 10 km fiber and 10 m VLC transmission range for 300 devices in the satellite-to-underwater network.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Transmission Channel** & **Wavelength (nm)** & **G** & **NF** & **P\({}_{1}\)** & **OSNR\({}_{i}\)** & **P\({}_{o}\)** & **OSNR\({}_{o}\)** \\  & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** & **(dB)** \\ \hline \multirow{4}{*}{Hybrid TWDM-WDM-OCDMA PON} & 1596 (DN) & \(-2.5\) & \(2.5\) & \(7\) & \(110\) & \(5.5\) & \(107\) \\  & 1527.2 (UP) & \(-2\) & \(2\) & \(7\) & \(110\) & \(5.5\) & \(108\) \\ \cline{1-1}  & 1575 (DN) & \(-3\) & \(2.5\) & \(7.5\) & \(110\) & \(5\) & \(107\) \\ \cline{1-1}  & 1260 (UP) & \(-3.5\) & \(2\) & \(7.5\) & \(108\) & \(6\) & \(107\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparisons of proposed work utilizing distinct DN and UP channels.  
The tabulated values in Table 9 reveal that the MNZCC code performs best in the proposed design at a 10 Gbps data rate over 10 km fiber and 10 m VLC range.
# Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network
## 3 Methods
### Mbcfm  
To effectively carry out the CMFM identification from multiple types of genomic data, we propose a new method called MBCFM (detecting Composite Function Modules based on Maximal Biclique enumeration) to implement the solution. We first employ LASSO regression model to reconstruct miRNA-mRNA interaction network by integrating mRNA/miRNA expression profiles and target sit information. Next, we identify CMFMs based on the enumerated maximal bicliques. We here treat each maximal biclique as a CMFM core. The details are described as Algorithm 1.  
```
Input: target site information matrix \(C\), miRNA express profile matrix \(X\), mRNA express profile matrix \(Z\) Output: CMFM core set TDATA
1. # reconstruct miRNA-mRNA interaction matrix
2. \(W\) = lasso(\(C\), \(X\), \(Z\))
3. # use maximal biclique enumeration to get cores
4. TDATA = mica(\(W\))
5. # return CMFM core set
6. return {TDATA}
```  
**Algorithm 1** Identifying CMFM Cores  
Finally, we build up CMFMs by expanding these CMFM cores. The expanding progress contains three main stages:  
1. We first add mRNA-mRNA interactions into the CMFM cores and get rid of the cores whose mRNAs have no mRNA-mRNA interaction;
2. We add neighbor mRNAs as well as corresponding interactions into the CMFM cores to maximize miRNA-mRNA regulatory module density values and mRNA-mRNA interaction density values of these modules;
3. Under the condition that the regulation density value of a CMFM is not lower than the threshold value \(\theta\) (0.5 by default), miRNAs are added into the module to further expand it.  
The details are described as Algorithm 2.  
Figure 2: Three types of maximal miRNA-mRNA bicliques. In the miRNA regulation network, there are many types of maximal miRNA-mRNA bicliques. Their \(R\_density\) values are all 1.  
```
0: miRNA-mRNA interaction matrix \(W\), CMFM core set \(TDATA\), MRNA-mRNA matrix \(PPIN\), density threshold \(\theta\)
0: MC # the set of CMFM module
1. add \(PPIN\) interaction data to \(TDATA\)
2. \(MD=\{\)the cores which have no mRNA-mRNA interaction\(\}\)
3. #get the \(R\_pair\) modules \(CMS\)
4. \(CMS=TDATA-MD\)
5. \(MC=CMS\)
6. # add mRNAs into CMFMs
7. for each \(mc\in MC\) do
8. \(GMR=\) the neighbor mRNAs of node \(mc\) in \(PPIN\);
9. for each \(mrna\in GMR\)
10. \(mnew=mc+\{mrna\}\);
11. \(r=\) R-density(\(mc\))
12. \(i=\) I-density(\(mc\))
13. if (R-density(\(mnew\)) + I-density(\(mnew\)) \(>r+i\))
14. \(mc=mnew\);
15. \(\text{update}(MC\), \(mc)\)
16. endif
17. endfor
18. endfor
19. # add miRNAs into CMFM cores
20. for each \(mc\in MC\) do
21. \(GMIR=\) the neighbor miRNAs in \(W\) of nodes in \(\text{mc}\) ;
22. for each \(mirna\in GMIR\)
23. \(mnew=mc+\{min\}\);
24. if (R-density(\(mnew\)) \(>\)\(\theta\))
25. \(mc=mnew\);
26. update (\(MC\), \(mc\))
27. endif
28. endfor
29. endfor
30. return MC
```  
**Algorithm 2** Expanding Cores to CMFMs
# A Method for Adapting Stereo Matching Algorithms to Real Environments
## 2 Related Work
### Bundle Adjustment  
Bundle adjustment (BA) is a method commonly used in multiple-view geometry; it aims to reconstruct the 3D structure of objects visible in images [22]. Typically, bundle adjustment is performed on a set of images taken from a variety of different points of view in a scene. The purpose of bundle adjustment is to estimate the camera poses from which the images were taken. This method is used in algorithms for multiple-view stereo setups and structures from motion [9]. Bundle adjustment has a wide range of applications; e.g., [PERSON] et al. used it for satellites [23; 24].  
In the context of this paper, it is significant that bundle adjustment does not operate on predefined patterns, contrary to pattern-based stereo calibration. Bundle adjustment is based on identifying keypoints. Keypoints are located using descriptors such as the scale-invariant feature transform (SIFT) or speeded-up robust features (SURFs) [25]. Keypoints are matched with each other in order to identify the same parts of objects in different images from the processed set of images. During the process of matching keypoints, some errors can appear. Therefore, having many images of the same part of a reconstructed object is crucial.  
The second feature of bundle adjustment, which affects the research presented in this paper, is that BA is intended for use on images not taken by cameras adjacent to each other, such as the stereo camera setup. This issue is discussed in papers describing the problem of image selection for the process of 3D reconstruction [26]. It is not possible to determine the exact poses of cameras when using BA. Any inaccuracies are crucial in the case of stereo camera calibrations where the cameras are close to each other. For this reason, pattern-based calibration is used with this equipment.  
Another difference from pattern-based camera calibration is that BA is an iterative process in which multiple camera poses are estimated. BA minimizes errors in pose estimation. Each pose corresponds to every processed image. Similarly, a series of images taken from different points of view are used in the case of pattern-based stereo camera calibration, but the purpose of calibration is different. Camera poses with respect to pattern locations in 3D space fall outside the scope of the calibration process in pattern-based stereo camera calibration. The aim is to estimate the mutual location of cameras in stereo camera setups and their intrinsic parameters.  
In general, bundle adjustment is not typically used simultaneously with pattern-based calibration. However, a prominent paper by [PERSON] et al. described the application of bundle adjustment to images obtained from a stereo camera after using pattern-based calibration [27]. They used bundle adjustment to reduce distortions in the y-axis. They also supported their calculations with structured lighting. Other authors also used descriptors to calibrate camera arrays [28].
# Subtitling 3D VR Content with Limited 6 DoF: Presentation Modes and Guiding Methods
## 3 Materials and Methods: Subtitling Solutions and Stimuli
### Guiding Methods  
Regardless of the presentation mode being provided/enabled, users have the freedom to explore the 3D VR environment. During exploration and navigation, it may be the case that the active speaker is outside the current user's FoV. Although spatial audio may support users in perceiving the location of the speaker, deaf and hard-of-hearing users cannot hear it, or the audio cannot be heard in noisy/public environments.  
Therefore, appropriate visual guiding methods are additionally needed to help the users in intuitively finding the speaker or main action in the 3D VR environment when audio cues are lacking or cannot be accessed.  
The study in [9] reported on a series of iterative user tests comparing visual guiding methods for VR360 video: side text, arrows, and a radar. It was shown that the use of arrows was preferred by users, and was perceived as the simplest, yet intuitive and effective, method to support finding the target speaker in the 360\({}^{\circ}\) environment.  
The implementation for VR360 video in [9] relied on only considering the position of the speaker in the horizontal direction (latitude angle, from \(-180^{\circ}\) to \(+180^{\circ}\)). It was found that considering the vertical direction (longitude angle, from \(-90^{\circ}\) to \(+90^{\circ}\)) was not necessary because identifying the speaker in the vertical axis is highly intuitive, and it is not common to find speakers near the poles of the sphere. Therefore, the arrows simply pointed to the right or left, depending on the relative position of the speaker compared to the current user's FoV. This was found to be sufficiently effective, and the simplest, less invasive, and most natural solution. However, this approach cannot be adopted in 3D VR content, because the users can freely explore and navigate the environment (i.e., 6 DoF). Accordingly, a 3D arrow, which is able to point in all directions, was implemented in this work as the visual guiding method (see Figure 1, bottom right image). The arrow only points to the target active speaker, if any, by comparing the current user's position (and associated FoV) to that of the speaker. The arrow is made visible each time a speaker is active, and thus subtitle frames need to be shown. Alternatively, this may by presented only when the speaker is out of the FoV and/or at a distance greater than a threshold.  
Figure 1: Adopted and Proposed 3D VR Subtitling Solutions.  
However, this optional feature was not enabled in the conducted tests. The position of the arrow can also be customized; however, according to the results in [9], users preferred it to be positioned close to the subtitles for easier identification and better integration.