esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Security Analysis and Simulation Verification
### Formal Simulation Verification of Protocol Security  
#### 4.3.1 Formal Modeling of the Protocol Flow  
In this section, the ProVerif tool is used to model the protocol formally. Before using the ProVerif tool for security simulation verification, it is necessary to use the modeling language provided by the ProVerif tool to model the execution process of the proposed protocol fully. For space reasons, only the formal modeling process of protocol entities A and B in the authentication-key negotiation phase is described here.  
Definitions of the relevant terms in ProVerif can be found in [31]. The formal modeling algorithm for the authentication-key negotiation phase of protocol entity A is shown in Algorithm 1.  
```
Input: Null Output: Process \(pEa\) let pEa= event startAuthB; new ra:Random; let Na = computeNx(ra,ida,P) in let hab = Minus(ya,xa) in let ExaKey = exor(Na,hab) in new TSeed:ab:istring; let TSa = generate_Timeline(TSseeda) in let Ha = threeElementsHash(ida,TSa,Na) in let eIDa = symc(ida,hab) in out (c,(eIDa,ExaBay,Ha,TSa)); in (c,(xeIDb:bitstring,xEbxEby:bitstring,xHb:bitstring,xTSb:TimeLine)); let xNb = dxor(xEbxEby,hab) in let xidb = symdec(xeIDb,hab) in let xxHb = threeElementsHash(xidb,xTSb,xNb) in if (xxHb = xHb) then let SKAB = computeKeySK(ra,ida,xNb) in let SSKAB = oneElementHash(SKAB) in event endAuthA.
```  
**Algorithm 1** Formal modeling algorithm for the authentication-key-agreement phase of entity A.  
Algorithm 1 shows the modeling process of the authentication key-agreement phase of protocol entity A, which is described by the Horn clause provided by the ProVerif tool. The execution steps of the algorithm correspond to the detailed execution steps of the protocol described in Section 4.3. \"\(eventstartAuthB\)\" represents the **event** \"Protocol entity B starts authenticating protocol entity A\"; \"\(eventendAuthB\)\" represents the **event** \"Protocol entity A successfully completes the authentication of protocol entity B\".  
The algorithm for the authentication key-agreement phase of protocol entity B is shown in Algorithm 2.  
Algorithm 2 shows the modeling process for negotiating the authentication key of protocol entity B, which is described by the Horn clause provided by the ProVerif tool. The execution steps of Algorithm 2 correspond to the detailed execution steps of the protocol described in Section 4.3. \"\(eventstartAuthA\)\" represents the event \"Protocol entity A starts authenticating protocol entity B\"; \(eventendAuthA\) represents the event \"Protocol entity B successfully completes authentication of protocol entity A\".  
After completing the above steps, edit the statement \"\(process(!pEa|!pEb)\)\" to start the main process. The ProVerif tool automatically simulates the simultaneous execution of the protocol by executing the aforementioned main process.
## 4 Coordinate System Conversion Algorithm-based Depth Estimation
### Transformation Matrix between Camera and World Coordinate Systems  
If the internal factors and distortion coefficients are obtained through camera calibration, the transformation matrix between the camera and world coordinate systems can be obtained. The transformation matrix is composed of a rotation matrix and a movement vector and is represented as \(R=R_{2}(\theta)R_{y}(\varphi)_{x}R(\psi)\) when it is rotated by \(\psi\), \(\varphi\), \(\theta\) angles around the x, y, and z axes, respectively, in three dimensions. In the case of a movement vector, it expresses the movement of the center position along the x, y, and z axes. That is, it can be viewed as an offset between the origins of two different coordinate systems. When the transformation matrix is known, a point \(P_{w}\) in the world coordinate system can be converted into a point \(P_{c}\) in the camera coordinate system through Equation (3).  
\[P_{c}=R*P_{w}+\mathrm{T} \tag{3}\]  
Table 4 presents the conversion matrix between the camera and world coordinate systems obtained using the solvePnP function of the OpenCV library.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Transformation Matrix**} & \multicolumn{2}{c}{**Factor Value**} \\ \hline R & \(\begin{bmatrix}r_{1}&r_{2}&r_{3}\\ r_{4}&r_{5}&r_{6}\\ r_{7}&r_{8}&r_{9}\end{bmatrix}=\left[\begin{array}{ccc}0.9999&0.0050&-0.01 25\\ -0.0123&-0.0416&-0.9990\\ -0.0055&0.9991&-0.0416\end{array}\right]\) \\ T & & \(\begin{bmatrix}t_{x}\\ t_{y}\\ t_{z}\end{bmatrix}=\left[\begin{array}{ccc}-5573.60\\ 1180.75\\ 5685.04\end{array}\right]\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Transformation matrix between camera and world coordinate systems.  
Figure 10: Original image (left) and distortion corrected image (right).
# A Binaural MFCC-CNN Sound Quality Model of High-Speed Train
## 4 Results and Discussions  
### Subjective Evaluation Results and Data Check  
The Kendall concordance coefficient was employed to check the consistency of the standard of a single subject across all participants. The Kendall concordance coefficient ranges from 0 (no agreement) to 1 (complete agreement). Suppose that there are total \(n\) sound samples and \(m\) subjects, where sample \(i\) is given the rank \(r_{i,j}\) by subject number \(j\). Then, the total rank \(R_{i}\) given to object \(i\) is:  
\[R_{i}=\sum_{j=1}^{m}r_{i,j}\,. \tag{6}\]  
Figure 11: Binaural MFCC-CNN sound quality model.  
The mean value \(\overline{R}\) of these total ranks is:  
\[\overline{R}=\frac{1}{n}\sum_{i=1}^{n}R_{i}. \tag{7}\]  
The Kendall concordance coefficient \(W\) is calculated as follows:  
\[W=\frac{12\sum_{i=1}^{n}\left(R_{i}-\overline{R}\right)^{2}}{m^{2}(n^{3}-n)}. \tag{8}\]  
The Kendall concordance coefficient of each subject is shown in Figure 12.  
The maximum coefficient was 0.94 (subject number 14), and the minimum coefficient was 0.43 (subject number 22). The criterion for screening the results of the subjective evaluation was 0.6. Thus, the subjective responses from three subjects were eliminated. The Kendall concordance coefficient across all subjects was 0.86. This meant that a similar standard was applied to the evaluation of the 23 subjects.  
Generally, subjects' evaluation of the same sample follows the law of normal distribution. The Lilliefors test was conducted to check whether the response data belong to the family of normal distributions. Figure 13a shows the subjects' responses' distribution of noise of measuring point 11. The Lilliefors test of each measuring point was conducted in MATLAB 2020a, and the results were shown in Figure 13b. The returned value of h = 0 indicates that the Lilliefors test does not reject the null hypothesis at the 1% significance level, so the subjective results of all the measuring points followed the law of normal distribution.  
The mean values and standard deviation of subjective responses of each noise sample are shown in Figure 14. The highest evaluation appears at point number 22 (windshield area in carriage 5), and the lowest evaluation appears at point number 8 (carriage 3). The standard deviation is relatively small which shows that the subjective evaluations were clustered closely, and subjects followed the same standard.  
Figure 12: Kendall concordance coefficient of each subject.
# Community Detection Framework Using Deep Learning in Social Media Analysis
## 4 Experiments and Analysis
### Model Results  
To demonstrate the effectiveness of our unsupervised community detection scheme, we use some benchmark models for comparative analysis, including K-means, GCN and ClusterNet. To demonstrate that communicating content information can improve the performance of community detection, we also choose pure ClusterNet and our framework for comparison.  
**K-means.** In this model, we utilized the vectorization technique explained in Section 3.3 to vectorize the nodes. We then employed the K-means algorithm to cluster the nodes and detect communities. To determine the appropriate value of \(k\), we utilized the elbow approach, which resulted in a value of four for this experiment.  
**GCN+K-means.** To detect communities in our experiment, we utilized a traditional GCN network which was trained by learning the network topology and node vectors. The K-means algorithm was used in a similar manner to cluster the nodes and detect the community. In this experiment, we set the value of \(k\) to four.  
**ClusterNet.** In this experiment, we used ClusterNet for both node vectorization and community detection. We set the number of communities to four to ensure a credible and reasonable comparison result. Additionally, we set the parameters to their suggested values in the corresponding research.  
**Our framework.** Node2 Vec is used to calculate the node features. The learning rate, embedding dimension, and training iterations are set to 0.01, 100, 1000, respectively.  
We calculate the standard community detection evaluation metric, modularity, to measure the performance. Figure 3 lists the modularity curves of the ClusterNet baseline and our framework. The modularity is recorded every 100 iterations. The abscissa of Figure 3 is iteration, and the ordinate is modularity. As can be seen, the modularity reaches its highest value at 1000 iterations. Our community detection framework achieves the highest modularity value so far. The performance improvement can be attributed to several factors. First, our framework utilizes Node2 Vec for feature extraction, which effectively captures the structural relationships in the graph, leading to better community representation. Additionally, hyperparameters like learning rate, embedding dimension, and training iterations are optimized to enhance the model's ability to learn meaningful embeddings. In contrast, K-means relies on simpler distance metrics that may not capture the complex relationships in the data, and ClusterNet, while more advanced, may not leverage the same level of tailored feature extraction as your framework. This combination of advanced feature learning and parameter tuning contributes to our framework's modularity value.  
Table 2 shows the evaluation results, measured by modularity, of our framework and other benchmark models. Our framework almost outperforms state-of-the-art baselines.
## 1 Introduction
Previous studies mainly focused on the result comparison of the spatial coupling between the LULC pattern and LST distribution between different time points, which cannot provide detailed description of the influencing process of LULC change on the UHI effect [15; 27; 28; 29]. Monitoring the spatiotemporal impact of LULC change on LST variation helps to understand the dynamic response process of the UHI effect to LULC change [14; 15]. It is of great significance for urban planners but less discussed. Therefore, the objective of this study is to investigate the dynamic response of UHI variation to LULC change over space and time. Specifically, it aims to (1) monitor class-to-class transitions between different LULC types from 1987 to 2016, (2) monitor the spatiotemporal variation of thermal characteristics based on level-to-level transitions between different LST levels, (3) spatially correlate the LULC pattern with LST distribution, (4) dynamically couple LULC change with UHI variation and (5) discuss the contribution of LULC transition to thermal characteristic variation.
## 5 Optimization
### Unit Commitment Problem
In their work, the authors propose a mathematical model for power consumption in a Smart House, and present an algorithm, based on mixed-integer linear programming, to minimize the cost of the daily forecasted energy consumption. Their model schedules an operation time window for each device in the house (dishwasher, air conditioners, boiler, _etc._) and then forecasts a duration for each usage. Based upon these data, and also on various parameters of tariffs and efficiency, the algorithm solves the objective function, which, according to the authors, can provide 18.7% saving on the energy bill.
# A Finite State Machine-Based Improved Cryptographic Technique
###### Abstract
Reverse Data Hiding (RDH) [5] provides information on how to inject data into digital media without any quality degradation. After data has been extracted from digital media, the whole storage device may be retrieved. The owner of the picture has little reason to trust the cloud service providers, since they will almost certainly encrypt the shot before uploading it. The picture restoration and analysis processes must be carefully separated for usage in medical and defense applications. The original photo should be encrypted using the owner's public key. The data hider uses a data-hiding key to include the information into the encrypted signal. After receiving the information, it may be decoded from the image. If the sender and receiver both have the data-hiding key, the recipient may decode the sent signal without knowing what is in the encrypted data. Therefore, even if the re-receiver has the cypher [6], they cannot decode the data in the image without also obtaining the corresponding cypher. His cover photo might be obtained via public decoding of the encrypted signal he was allegedly sent. The use of the Modified Cryptographic Turbo Code Finite State Machine (MCTC-FSM) is expanding into new domains, such as privacy protection [7], encrypted image authentication, and authorship verification. One major drawback of current methods is  
Figure 1: Basic Data Hiding Technique. that the encoded information can only be accessed either before or after the image has been decoded. Thus, the MCTC-FSM method has been validated as a practical choice for both secret extraction and image recovery. Most TC methods may be an improvement over what has come before, but none of them can ensure complete success when it comes to extracting data or restoring images. Due to their prohibitive computational cost, slow embedding rate, and inadequate security [8], these approaches are not well-suited for usage in real-time applications. The proposed work includes a ground-breaking MCTC-FSM technique with robust security, complete cover and secret reversibility, and a large embedding capacity at a reasonable cost. In this research, we provide a unique MCTC-FSM method based on two-level embedding. The cypher text may be incorporated into the cypher image using this method, even if the cover and secret are unknown. Only those in possession of the corresponding decryption keys will be able to read the encrypted information. The suggested methodology improves the quality of both embedding and encryption, with correlations tending towards zero and histogram values becoming uniform. The final image should have an SSIM of 1, a BER of 0, and a PSNR of infinity.
## 4 Assimilation and Impact of GNSS-R Data
### Impact of GNSS-R Data
However, the influence of CYGNSS data rapidly diminished within 4 h after assimilation. Furthermore, CYGNSS data had a limited impact on precipitation forecasts. [PERSON] assimilated the CYGNSS scalar winds and vector winds utilizing a global data assimilation and forecasting system and found, in the first 60 h, a significant track forecasting improvement of 20-40 km. In the forecast of MSW, a few degradations of 2 kt would appear at some lead times, especially at the beginning of 24 h [91], but at most lead times, there was no statistically significant impact. [PERSON]'s experiments were performed using the HWRF model and a GSI- 3 DenVar system [92]. Compared with the influence of Advanced Scatterometer (ASCAT) wind, assimilating CYGNSS data into a hurricane-forecasting model could refine track and intensity forecasts at the beginning of the simulation (within 48 h) with a suitable thinning distance application. Furthermore, the inner-core symmetric structure forecasts performed better when using the CYGNSS data. Figure 15: (**a**,**d**,**g**) 6 h; (**b**,**e**,**h**) 3 h; and (**c**,**f**,**i**) 1 h DA cycling experiments for average storm forecast error. Averaged errors/deviations are colored by OSSE experiment: CNTL (conventional observation assimilated) is black/gray, CYG (CNTL plus CYGNSS speeds) is red/light red, and VAM (CNTL plus CYGNSS VAM vectors) is blue/light blue [87].
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 2 Review Protocol
### Selection Method  
This review relied on certain elements and settings to gather its related articles from the search stages to the final stage of the preparation of this paper. The first sub-element applied is the key words. Identifying areas of research through the use of suitable keywords is an important aspect for any study. For existing studies on LiFi technology, the most common keywords used for such a process includes the word LiFi. This keyword has been used in all research related to LiFi, including the work on LiFi by its founder [PERSON] and other related works [14]. Thus, this keyword is deemed sufficient and suitable to cover the areas of importance in this review. All articles selected in this study were written in English. They are either journal research articles that present primary research or review studies that are also valuable sources of knowledge that present secondary studies. All papers included in this review were published in the span of 10 years since 2011, when LiFi was first introduced to the world. The applications and domains of LiFi are very diverse and wide. Hence, including them, all in one paper will be nearly impossible. Instead, selecting certain areas of research on LiFi and presenting them significantly will still yield remarkable findings. Therefore, the last element in this section will be directed to the eligibility criteria (i.e., inclusion and exclusion), which have been applied in this review. All studies included were LiFi-related simulation studies. The rationale behind our selection  
Figure 1: Development Study selection, including search query, inclusion criteria, and exclusion criteria.  
is to present studies that researched LiFi using simulation. LiFi, as a technology, is still not much widely used around the world, and only certain countries and industries have adopted it in their communications. Therefore, presenting all related simulation studies will encourage researchers and developers to have hands-on testing and to experiment LiFi even before it is officially used in their countries. For the exclusion criteria, any OWC articles that do not discuss LiFi-based systems as part of their communication and hardware-based studies have been excluded.
# Medium Access Control Protocols for Cognitive Radio Ad Hoc Networks: A Survey
## 2 Cognitive Radio Networks
### Mobility Perturbs in CRAHN  
In CRAHN, node mobility can change the network topology, disrupt the steady state of the network and become a major issue that needs to be addressed. Moreover, as the network topology changes due to node mobility, it affects the link density and stability of the communication between a node and its neighbors. There are several parameters that can be considered for measuring the link stability such as link transmission quality or lifetime of the link [56]. Since the links with the neighbors are the basic building block of a CRAHN, the lack of link stability can hinder transactions at higher layers.  
Mobility in CRAHN can occur in two ways, namely spectrum mobility and user mobility, where both can affect the link stability [42; 57; 58]. In spectrum mobility, CR users have to vacate the frequency in use to avoid causing interference with PUs. User mobility refers to changes in the physical locations of nodes, CR user or PUs [59], which result in a changing network topology [60]. Since a network consists of mobile nodes, as well as static nodes, maintaining a connection between nodes would be a challenging task. Moreover, since nodes change physical locations, spectrum handoffs may also follow. Thus, for designing a mobility management scheme for CRAHN, both types of mobilities must be carefully considered. A self-organizing and self-correcting CRAHN needs to mitigate the effects caused by mobility.
# Comparative Analysis of Local Differential Privacy Schemes in Healthcare Datasets
###### Abstract  
In the rapidly evolving landscape of healthcare technology, the critical need for robust privacy safeguards is undeniable. Local Differential Privacy (LDP) offers a potential solution to address privacy concerns in data-rich industries. However, challenges such as the curse of dimensionality arise when dealing with multidimensional data. This is particularly pronounced in \(k\)-way joint probability estimation, where higher values of \(k\) lead to decreased accuracy. To overcome these challenges, we propose the integration of Bayesian Ridge Regression (BRR), known for its effectiveness in handling multicollinearity. Our approach demonstrates robustness, manifesting a noteworthy reduction in average variant distance when compared to baseline algorithms such as LOPUB and LOCOP. Additionally, we leverage the R-squared metric to highlight BRR's advantages, illustrating its performance relative to LASSO, as LOPUB and LOCOP are based on it. This paper addresses a relevant concern related to datasets exhibiting high correlation between attributes, potentially allowing the extraction of information from one attribute to another. We convincingly show the superior performance of BRR over LOPUB and LOCOP across 15 datasets with varying average correlation attributes. Healthcare takes center stage in this collection of datasets. Moreover, the datasets explore diverse fields such as finance, travel, and social science. In summary, our proposed approach consistently outperforms the LOPUB and LOCOP algorithms, particularly when operating under smaller privacy budgets and with datasets characterized by lower average correlation attributes. This signifies the efficacy of Bayesian Ridge Regression in enhancing privacy safeguards in healthcare technology.  
Keywords: healthcare data; attribute correlation; LASSO regression; Gaussian copula; Bayesian ridge regression; local differential privacy +
Footnote †: journal: Medical Informatics
## 1 Introduction
Existing multi-topology construction methods fail to optimize and balance the transmission capacity to all destination nodes while considering switch flow entry constraints. This shortfall prevents them from providing efficient switching paths for mobile data flows. This highlights the need for a new method that specifically targets these issues. In this paper, we focus on multi-topology construction to provide efficient paths with high-bandwidth, low packet loss rates, and low data transmission delays for mobile data flow scheduling. The main contributions of this paper are as follows:  
1. We propose a multi-topology deployment scheme based on software-defined networking (SDN) and analyze the constraints and requirements involved in efficient multi-topology construction for scheduling mobile data flows within SDN. 2. We model the multi-topology construction problem and divide it into two subproblems, for which we propose two algorithms. The first algorithm introduces a novel path-ranking method based on contributions to overall transmission capacity. The second algorithm constructs a multi-topology to maximize and balance transmission capacity between node pairs, considering the path rankings and switch flow entry constraints. 3. We implement the proposed multi-topology construction method on Mininet [13] and compare its performance with the AMPLE [6] method, the k-shortest path method, and the baseline single-path switching method. The results show that our approach reduces the average packet delay, decreases the average packet loss rate, and improves throughput, regardless of the mobile device's new location. The structure of this paper is as follows. Section 2 reviews related work on multi-topology construction. Section 3 provides details on the deployment of multi-topology routing in SDN. Section 4 models the problem and introduces our multi-topology construction method for scheduling mobile data flows in SDN. Section 5 presents experimental results and a comparative analysis against three other methods. Finally, Section 6 concludes the paper and outlines directions for future work.