esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 RIS-Assisted Secret Key Extraction Method
### Performance Optimization  
Three critical evaluation metrics must be considered while optimizing the key-extraction performance, namely the bit generation rate (BGR), the bit mismatch rate (BMR), and the secret bit generation rate (SBGR). The BGR is a measure of the efficiency of this process and typically represents the number of generated bits per channel sample, expressed as:  
\[BGR=\frac{\text{Total extracted bits}}{\text{Channel sample}}. \tag{7}\]  
A high valuation of the BGR indicates a more efficient extraction process and a higher rate of secret bit generation, resulting in improved security and faster key establishment for the communication system. On the other hand, the BMR represents the number of mismatched bits extracted from each channel sample, expressed as:  
\[BMR=\frac{\text{No. of mismatched bits}}{\text{Channel sample}}. \tag{8}\]  
We define the SBGR as the number of matched bits, which is represented as \(SBGR=BGR-BMR\). Hence, the SBGR considers both the BGR and the BMR in the process  
Figure 2: System modeling.  
of secret key extraction. For negligible channel-phase decorrelation \((\xi-\xi^{\prime})\approx 0\), the phase distribution of the equalized signal \(\angle e(t)\) in (4) is normally distributed with means \(\{T^{\prime}_{n_{i}}(\theta_{i})=n_{i}\theta_{i},T^{\prime}_{m_{i}}(\theta_{i})= m_{i}\theta_{i}\}\) and variance \(2\sigma^{2}\) for [[PERSON], [PERSON]], respectively [5]. Thus, its cumulative distribution function (CDF) is approximated as:  
\[\begin{split}\phi(x)&=\frac{1}{2}\Bigg{[}1+\text{ erf}\Bigg{(}\frac{x-T^{\prime}_{n_{i}(m_{i})}(\theta_{i})}{2\sigma}\Bigg{)} \Bigg{]},\\ \text{erf}(z)&=\frac{2}{\sqrt{\pi}}\int_{0}^{z}e^{ -t^{2}}dt\end{split} \tag{9}\]  
where \(\text{erf}(z)\) is the error function. Thus, the probability of error \(P_{e}\) is the probability of the estimated \(\angle e(t)\) in (4) to be out of the interval \(\left[T^{\prime}_{n_{i}(m_{i})}+\frac{\pi}{2^{\prime}},T^{\prime}_{n_{i}(m_{i })}-\frac{\pi}{2^{\prime}}\right)\), which can be represented by:  
\[P_{e}=2\rho\Big{(}T^{\prime}_{n_{i}(m_{i})}(\theta_{i})-\frac{\pi}{2^{\prime}} \Big{)}. \tag{10}\]  
Accordingly, the communicating terminals can agree on the optimum quantization order \(r\in\{1,2,3\}\) for an acceptable \(P_{e}\leq a_{1}\) as:  
\[x=\arg\max_{x^{\prime}}\text{erf}\Bigg{(}\frac{x^{\prime}-T^{\prime}_{n_{i}(m _{i})}(\theta_{i})}{2\sigma}\Bigg{)}\leq a_{1}-1. \tag{11}\]  
Based on \(x\), \(r\) is optimized as:  
\[r=\arg\max_{r^{\prime}}2^{r^{\prime}}\leq\frac{\pi}{x}\quad\text{ for }r^{\prime}=1,2,3. \tag{12}\]
## 3 Background: Models
### Word Embedding
#### 3.1.1 Word-Based Representations (Non-Contextualized Embeddings)  
Context-independent methods produce the same embedding vector for each word in the vocabulary, irrespective of its context. Examples of the most common context-independent pre-trained embeddings include:  
* A 300-dimensional vector was generated by **Word2 Vec.** using a large corpus of news articles with 300 million tokens.
* Pre-trained **GloVe** used 27 billion tokens in a huge corpus of tweets, resulting in a 200-dimensional vector.
## 5 Testing and Evaluation
### Subjective User Evaluation  
#### 5.1.1 Gesture Usability  
The gestures and underlying metaphor were selected in a pre-development user study, including a survey resulting from both subjective and objective measurements. Two gesture sets were considered: the palm-based gesture, where the drone mimics the orientation of the user's palm, and a finger-based gesture set with no common underlying metaphor, where three different commands (pitch, yaw, and throttle) were mapped to distinct gestures, differentiated by which fingers are extended. The study aimed to define a finger-based gesture set and then measure user preference and performance in the two gesture sets: the  
Figure 9: Diagrams of different streaming methods.  
mutually exclusive, rigidly defined finger-based gestures, and the freer and combinable palm-based gestures.  
The study, conducted between December 2019 and February 2020, included two parts. The first part aimed to define the finger-based gesture set. Based on a questionnaire completed by 29 participants, we selected those finger-based gestures voted as both comfortable and intuitively appropriate and mapped them to the 3 drone control commands.  
The second part employed a drone flight simulator developed in Unity and coupled with a gesture acquisition peripheral and gesture interpretation programming. It aimed at measuring the ease of learning, usability, and comfort of each control mode, as well as the performance in terms of mission completion time and collision avoidance. An outdoor and an indoor scene were prepared for testing. Participants, including 27 men and 12 women with varying degrees of familiarity with drone control, were asked to control a virtual drone in the simulator and pass through specific waypoints while avoiding collision with walls. Each participant tried both modes (palm- and finger-based) and both types of scenes (indoor and outdoor). Both objective and subjective results were obtained: the former by measuring the timings and collision from the simulator itself and the latter via a questionnaire.  
An overview of the results can be seen in Figure 10. The results showed an overall preference for palm-based control. This included both faster timings and less collisions, and a subjective evaluation of it being easier to learn and more comfortable to use. In contrast, finger-based control was generally slower, but could be more precise, and hence useful in very tight environments (e.g., indoors). For more information and further results, the interested reader is referred to the full user study publication [32].
# A Signaling-Free Underwater Code Division Multiple Access Scheme
## 2 An Overview of the Characteristics of the Underwater Channel and System Model
### System Model  
We considered a data gathering CDMA network with a star topology, where the sink is located at the center and sensor nodes in charge of data-sensing and reporting are uniformly distributed over an ideal circle-shaped coverage. It is assumed that each sensor node acquires the distance to the sink and the unique orthogonal code for CDMA communication through the network initialization stage, in which measurement of round-trip time and orthogonal code allocation take place. Based on [PERSON]'s underwater channel model, sensor nodes are able to control transmission power with the knowledge of the distance and the frequency, in such a way that regardless of their locations, the received power at the sink is equal to a specific level.
## 3 Proposed Methods
### Improve the Small Object Detection Layer  
In the YOLOv8n network structure, three detection scales are designed to capture targets of different sizes. The feature extraction process progresses from shallow to deep layers. Shallow features are rich in specific geometric details of the targets due to their high resolution, while deep feature maps possess larger receptive fields and abundant semantic information. The network model performs 8\(\times\), 16\(\times\), and 32\(\times\) downsampling on the input image of size 640 \(\times\) 640, utilized for detecting small, medium, and large objects, respectively. When detecting electric bike riders and their helmets on complex traffic roads, the helmets, which are small targets [14], require detection. If the original three detection heads are still used for detection, it may lead to insufficient utilization of shallow feature information, resulting in poor recognition ability for small targets [15] and the loss of detection accuracy, coupled with frequent instances of overlooked detections and incorrect positives.  
If the original three detection heads in the YOLOv8n network structure are still used for the detection task, the recognition accuracy for small targets will be excessively low. Therefore, a dedicated detection layer for small targets is incorporated into the original network structure. This layer performs 4\(\times\) downsampling on the input image, significantly enhancing the extraction of shallow feature information and thereby improving the detection capabilities for small targets [16]. For small targets, this method has achieved a further improvement in accuracy. Additionally, in detection tasks where most targets are small [17], the original detection layer designed for large targets only increases the model's parameter count and size. Therefore, this paper refines the detection process by pruning the layer originally designed for detecting large targets. The principal objective is to refine the detection criteria for small targets. The findings from the experimental studies, as depicted in Table 1, demonstrate that the effectiveness has been adequately proven. Although the increase in detection accuracy is most pronounced after adding the small target layer, the model's parameter count only decreases slightly. After refining the small target layer, despite a slight decrease in detection accuracy, the model's parameter count is reduced by 33%, achieving a lightweight improvement while enhancing accuracy.
# NewApproach to Predict the Motion Characteristics of Single Bubbles in Still Water
## Materials and Methods
### Data Extraction  
The motion of single bubbles is closely related to several factors, such as frequency of separation, physical properties of the liquid, size of the bubble, and shape of the bubble. The dynamic and kinematic characteristics of single bubbles in the still water were studied by changing the bubble detachment frequency and the orifice diameter in the specially designed tank.  
Digital image processing was used to extract the data such as bubble velocity, drag force coefficient, and bubble shape from the images captured during the experiments. The shape categorisation of the bubbles is basically based on the aspect ratio, circularity of the bubble and visualization of the bubble. The aspect ratio is the ratio of the major axis to the minor axis. If such a ratio ranges from 0.9 to 1, the bubble is considered as a spherical shape. Overall, the smaller the aspect ratio, the flatter the bubbles. On the other hand, circularity is a parameter used to describe the complexity of an object, it is a ratio of the square of perimeter to the area. Combining aspect ratio and circularity, together with bubble visualization, the bubbles were divided into three groups in this study. Figure 2 illustrates the three main categories of the bubble shape, which are spherical-cap shape, elliptical shape, and irregular shape.  
\begin{table}
\begin{tabular}{c c} \hline
**Properties** & **Value** \\ \hline Density of air (kg/m\({}^{3}\)) & 1.225 \\ \hline Density of water (kg/m\({}^{3}\)) & 988.2 \\ \hline Viscosity of air (Pa.s) & \(1.789\times 10^{-5}\) \\ \hline Viscosity of water (kg/m\({}^{3}\)) & 0.001 \\ \hline Surface tension coefficient (N/m) & 0.074 \\ \hline \end{tabular}
\end{table}
Table 1: Properties of tested samples.  
Figure 2: Three main categories of bubble shape: (**a**) spherical-cap shape, (**b**) elliptical shape, and (**c**) irregular shape.
# A Quadrilateral Geometry Classification Method and Device for Femtocell Positioning Networks
## 2 Femtocell Positioning Network Model
The accuracy of range-based positioning location (PL) systems depends on the geometric relationship between the locations of the FeNBs and the location of the MUE, since errors arising from channel or hardware perturbations in the TDOA estimates are transformed into geographic position errors in the hyperbolic solution [10]. The positioning accuracy of different geometric FeNB configurations can be evaluated using the GDOP parameter, which is defined as the ratio of the root mean square (RMS) position error to the RMS ranging error. The GDOP for an unbiased estimator and a 3D hyperbolic system are given by [10]:  
\[\text{GDOP}=\frac{1}{\sqrt{N}}\sum_{n=1}^{N}\frac{\sqrt{\left(x-\hat{x}_{n} \right)^{2}+\left(y-\hat{y}_{n}\right)^{2}+\left(z-\hat{z}_{n}\right)^{2}}}{ \sigma_{e_{i}}} \tag{6}\]  
where \(N\) is the number of measurements; \(\sigma_{e_{i}}\) is the standard deviation of the measurement error \(e_{r_{i,0}}\); and \((x,y,z)\) and \((\hat{x}_{n},\hat{y}_{n},\hat{z}_{n})\) are the true location and the \(n\)th estimated location of the MUE, respectively. To calculate the GDOP, it is necessary to know the positions of the FeNBs around the moving MUE. A symbol table is shown in Table 1, which is used to keep track of the symbols and notations used in the paper. \begin{table}
\begin{tabular}{c c} \hline \(e_{r_{i,0}}\) & Measurement error \\ \(\sigma_{e_{i}}\) & Standard deviation of the measurement error \\ \(w_{i}[n]\) & Discrete white Gaussian noise \\ \(r_{i,0}\) & The differential distance between the MUE and the master FeNB, and the MUE and FeNB\({}_{i}\) \\ \(\hat{\tau}_{i,0}\) & Analog time delay between FeNB\({}_{i}\) and FeNB\({}_{0}\) \\ \(f_{s}\) & Sampling frequency of the analog-to-digital converter \\ \(\mathbf{W}_{nm}^{(s)}(q)\) & Weight \\ \(\mu_{0}\) & Learning rate \\ \(\mathrm{c}\) & Light velocity \\ \(\mathrm{G}\) & The variance of the linear optimum unbiased estimator divided by the variance of measurement noise \\ \(\mathrm{\varepsilon}\) & Measurement error \\ \(\mathrm{R}\) & Sphere radius \\ \(\mathrm{T}\) & Transpose of the matrix \\ \(\mathrm{N}_{\mathrm{c}}\) & Total number of correct classifications \\ \(\mathrm{P}\) & Total number of 4 FeNBs within the sphere \\ \hline \end{tabular}
\end{table} 
Table 1: Symbol table.
## 2 Materials and Methods
### Data Sources  
The Moderate Resolution Imaging Spectroradiometer (MODIS) products ([[https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)]([https://modis.gsfc.nasa.gov/](https://modis.gsfc.nasa.gov/)), accessed on 21 November 2022) analyzed in this study include GPP, ET, surface albedo, and land use/land cover data. WUE was calculated from the ratio of GPP to ET, where GPP and ET were obtained from MOD17A2H and MOD16A2 data for 2001-2021 with a temporal and spatial resolution of 8 days and 500 m. MOD17A2H data were produced by the Numerical Terra Dynamic Simulation Group, and a detailed description of the data algorithm can be found in previous studies [34]. The MOD16A2 algorithm was developed and improved based on the Penman-Monteith equation proposed by [PERSON] et al. [35]. These products have been updated through a series of modifications and are now widely used in water-carbon cycle studies.  
Surface albedo data was obtained from MCD43A3 with 500 m spatial resolution and a daily temporal resolution. This data was used for the calculation of net radiation. Land use/land cover data was derived from the MCD12Q1 at a yearly temporal resolution with 500 m spatial resolution. For the land use/cover map of China, we used the International Geosphere-Biosphere Project (IGBP) global vegetation classification dataset version in MCD12Q1, which defines 17 land use/land cover types including eleven natural vegetation, three non-vegetated land and three anthropogenic altered classes. The IGBP vegetation classification was reclassified into five vegetation types (Figure 1) according to the vegetation distribution characteristics across China, which was used to analyze the changes of GPP and WUE for China's multiple vegetation types in the four arid-humid zones.  
The meteorological data underlying this study include specific humidity, water vapor pressure, downward long/short wave radiation, temperature, precipitation, wind speed and potential evapotranspiration. Of these, long/short wave radiation was used for the calculation of net radiation combined with surface albedo data [7]. Relative humidity can be calculated based on specific humidity, water vapor pressure and temperature [36]. Details of the calculation are given in Equations (A1) and (A2) in Appendix A. This dataset was obtained from daily 0.25\({}^{\circ}\) Global Land Data Assimilation System (GLDAS) version 2.1 ([[https://ldas.gsfc.nasa.gov/gldas/](https://ldas.gsfc.nasa.gov/gldas/)]([https://ldas.gsfc.nasa.gov/gldas/](https://ldas.gsfc.nasa.gov/gldas/)), accessed on 21 November 2022) for 2001-2021, which was resampled to 500 m resolution using bilinear interpolation [37] to match the spatial resolution of the MODIS product.
## 4 Proposed Method
### Hardware Monitoring Module
#### 4.4.4 Bitmap Search  
If the jump instruction of the currently executed BB is an indirect-jump-type instruction, the CFI checking method will no longer be applicable to this type of situation due to the lack of a CFI\({}_{\text{label}}\) during offline analysis. To solve this problem, the HMM will determine whether the jump instruction is an indirect jump instruction (I.jr) or an indirect function call instruction (Ljalr) according to the opcode, and the HMM will extract the jump target address of these instructions. If it is an indirect jump instruction, the HMM will find the point corresponding to the target address in the BB\({}_{\text{Address}}\) Bitmap. Only when the value of the point is 1 is the target address judged to be legal. If it is an indirect function call instruction, the HMM will retrieve the point corresponding to the target address in theFE\({}_{\text{Address}}\) Bitmap. Compared with the searching algorithm proposed in [12], the bitmap-based search method proposed in this paper requires only one clock cycle to determine whether the target address of the indirect jump instruction is legal.
# Review of Studies on User Research Based on EEG and Eye Tracking
## 4 Application Scope
### Industrial Products
#### 4.1.1 Human-Computer Interaction  
The research in human-computer interaction mainly focuses on improving the functional elements of a product to enhance the user experience. Brainwave signals and visual attention are used as feedback information to study the effects of color, speech, and other prompt features on information processing. This information is used to optimize interaction design, reduce cognitive load, and minimize usage errors. [PERSON] et al. [47] conducted research on human-computer interactions in helicopter cockpits, and constructed a cognitive load evaluation model based on brainwave signals and other indicators. [PERSON] et al. [48] studied the human-computer interaction method of intelligent wheelchairs, using brainwave alpha/beta waves to control the wheelchair's movements by identifying the alpha waves of brainwave signals when the user's eyes are closed and relaxed. The experimental results demonstrate that this has an effective correction effect on the closed-loop control system involving human-computer interaction. [PERSON] et al. [49] established a mapping relationship model between user perception and preference for a children's rehabilitation pedal exerciser, using the eye-tracking method based on a BP neural network. In the study conducted by [PERSON] et al. [50] pertaining to brain-computer interfaces (BCIs) applied in EEG hybrid systems, which incorporated a total of eight electrodes for the acquisition of EEG signals. These electrodes were strategically positioned at the Fz, Cz, P7, P3, Pz, P4, P8, and Oz locations. Notably, the ground and reference electrodes were shared with the electrooculography (EOG) electrodes, which were placed on the forehead and earlobe regions. During user interactions with the BCI, the visual stimuli presented through the flashing icon were found to elicit specific event-related potentials (ERPs), namely P300,  
Figure 8: The application scope of EEG and ET in user research.  
VPP, and N170. By carefully analyzing the temporal intervals that potentially encompassed the evoked ERPs, the appropriate output commands of the ERP signals were determined. These studies can provide valuable insights for the design and evaluation of the product interaction experience.
## 1 Introduction
Our primary contributions are summarized as follows:  
* First, we design a query architecture for sensitive location data based on the TEE, ensuring that no information of either customer, apart from the intersection, is disclosed,  
Figure 1: Architecture of optimal location query in TEE-enabled cloud environment. and that the intersection results are not exposed to the cloud service provider. Meanwhile, the TEE leverages its features to guarantee the confidentiality and integrity of both data and code within its enclave. * Second, we propose a revocable and privacy-preserving optimal location query scheme based on the above architecture (ROLQ-TEE), which not only enables the data users to obtain an optimal location of the new facility securely but also realizes the revocability of the query permission of the data users. * Third, we introduce a practical scenario where the data users (i.e., banks) are required to perform any of the three optimal location query operations (namely RNN Query (RNNQ), Average Distance Query (AVGQ), and Maximum Distance Query (MAXQ)) we designed on the cloud server (i.e., cloud service provider) according to their own needs within a specified time; otherwise, the data user's query permission will be revoked once the specified time has passed. * Finally, we conduct a security analysis and performance evaluation of the optimal location query protocols. Subsequently, we demonstrate the security of the ROLQ-TEE scheme, illustrating that ROLQ-TEE scheme effectively preserves the privacy of both the location data owner and the requesting user. Finally, we theoretically analyze the performance and provide the experimental results. The remainder of this paper is structured as follows: Section 2 discusses related works on optimal location query and privacy-preserving location-based query. Section 3 provides a brief background on bilinear pairings, discrete logarithm problems, collision-resistant hash function, nearest neighbor (NN) and RNN queries, and TEE. Section 4 details the system architecture, the threat model, and the construction of our proposed ROLQ-TEE scheme. Section 5 presents the security analysis of the ROLQ-TEE scheme. Section 6 evaluates the performance of ROLQ-TEE in terms of complexity analysis, memory usage, and efficiency. Finally, we conclude the paper in Section 7.
# Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method +
## 6 Conclusions  
* A method called SVD-CIM is proposed to extract the weak signal features, which includes using CIM to select the SCs from the SVD and estimating CCES for the reconstructed signal.
* CIM is a similarity measure based on frequency components.
* Compared with the frequency spectrum of the reconstructed signals, CCES can express more useful information for the cyclostationary signals.
* Compare with the method of difference spectrum selection, the CIM selection has a better performance on weak signal features.
* For two-channel information, two-SVD-CIM is proposed, and the two-channel information fusion can enhance the fault characteristics.  
Conceptualization, J.L. and S.L.; methodology, S.G.; validation, S.G., H.M.; formal analysis, S.G.; investigation, Y.W.; resources, G.T.; writing--original draft preparation, S.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2018 YFB2003300), the research was supported by National Natural Science Foundation of China (51975276), and Stability Support project of Sichuan Gas Turbine Research Institute of AECC (WDZC-2020-4-7).  
CWRU fault dataset at [[http://www.csegroups.case.edu/bearingdata](http://www.csegroups.case.edu/bearingdata)]([http://www.csegroups.case.edu/bearingdata](http://www.csegroups.case.edu/bearingdata)) center/pages/12k-driveend-bearing-fault-data (accessed on 27 November 2021).  
The authors would like to thank CWRU for providing the free bearing data download, and the anonymous reviewers for their valuable comments and suggestions.  
The authors declare no conflict of interest.