esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridging the Gap: A Survey and Classification of Research-Informed Ethical Hacking Tools
## 5 Classification
### Limitations Surrounding the Classification of Tools  
The four classification systems identified in this study were chosen due to their overall topic coverage and relevance to computing topics and concepts. When combined, the classifications can give a precise idea of what a tool can do and the cybersecurity field it falls under.  
Although these four classifications are fit for purpose when considered within the scope and the goal of this survey and its goals, there are some challenges and potential limitations in classifying tools in this manner. The first issue is an inconsistency within the specificity of the tools.  
For example, within _Mitre_[10], despite having hundreds of individual attack techniques and vectors, such as _Enterprise: Privilege Escalation: Access Token Manipulation: SID-History Injection_ and _Enterprise: Defence Evasion: Hijack Execution Flow: Path Interception by PATH Environment Variable_, which are very specific vectors down to the operating system architecture within individual target systems, Mitre lumps the entire concept of compromising a web application under _Enterprise: Initial Access: Exploit Public-Facing Application_. There are other means of gaining specificity within this classification system, such as the _Reconnaissance: Vulnerability Scanning_ field. All together, these give a more specific view of the tool. However, both fields do not offer the specificity of the _SID-History Injection_ or _Path Interception by PATH Environment Variable_ fields.  
Another issue relates to tools that can be used for activities potentially unintended by the application designer. Deciding whether to include the ACM CSS _Security and Privacy: Network Security: Denial-of-Service Attacks_ field when considering a web application fuzzer that could potentially crash the target web application (and making similar decisions throughout the classification of the many tools within this survey) posed a significant challenge, as opinions on whether to include the field may vary between researchers.  
Another issue was found when trying to discern the exact scope of any given tool. There were many instances when, in the paper, a tool would present itself as having one function, for example, being capable of completing a specific task within the abstract and majority of the discussion in the associated paper, only to reveal that the tool itself is a _proof-of-concept_, with more limited capabilities than initially assumed.  
To fully understand each tool's potential, an in-depth evaluation involving running the tools and testing their capabilities would be necessary. However, this is beyond the scope of this survey. Future work could focus on specific subject clusters to provide an in-depth comparison of the tools.
# Affordable Thin Lens Using Single Polarized Disparate Filter Arrays for Beyond 5G toward 6G
## 4 Conclusions  
This paper proposes a novel design approach to obtain higher values of capacitance and inductance with a limited fabricable gap and width. The proposed approach is validated by designing a high-gain thin lens at a high frequency of 60 GHz. To increase the tunable range of a phase shift up to 274\({}^{\circ}\), a new class of disparate filter arrays that employ both low-pass and band-pass unit cells are proposed for single-polarized unit cells. A 60 GHz single-polarized thin lens applicable to a wireless backhaul system is designed, simulated, and fabricated for demonstrating the proposed design approach. The gain of a 4 \(\times\) 4 patch antenna array is improved by approximately 33 dBi by using the proposed lens. This suggests the feasibility of applying the proposed antenna configuration to various 5G platforms, such as wireless backhaul systems and repeaters.  
The presented work was carried out as a collaboration by all authors. [PERSON] performed the methodology, validation, and wrote the original draft. [PERSON] performed the conceptualization, review, and editing. [PERSON] supervised the study and participated in the conceptualization, methodology, and validation.  
This work was supported in part by Creative-Pioneering Researchers Program through Seoul National University (SNU), and in part by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00056, Development of 3D high accuracy motion recognition SoC for automobile applications based on 140 GHz electromagnetic waves).  
The authors declare no conflict of interest.  
Figure 19: Simulated and measured radiation patterns of 4 \(\times\) 4 antenna array in the presence of the lens.  
Figure 18: Simulated and measured radiation patterns of 4 \(\times\) 4 antenna array in the absence of the lens.
## 2 Materials and Methods
### Data Acquisition
#### 2.1.2 Land Cover Dataset  
To study the phenological changes above 30\({}^{\circ}\)N from 1982 to 2015, we used the MCD12C1 dataset; however this dataset is available only from the year 2000, so we analysed the dataset from 2000 to 2015. Its classification standard is established by the International Geosphere-Biosphere Program (IGBP) and is divided into 17 land types with a spatial resolution of 5600 m ([[https://lpdaac.usgs.gov/products/mcd12c1v006/](https://lpdaac.usgs.gov/products/mcd12c1v006/)]([https://lpdaac.usgs.gov/products/mcd12c1v006/](https://lpdaac.usgs.gov/products/mcd12c1v006/)), accessed on 7 October 2022). Initially in our study, we merged the 17 land types into 11 land types (Evergreen needleleaf forests, Evergreen broadleaf forests, Deciduous broadleaf forests, Mixed forests, Open shrulands, Woody savannas, Savannas, Grasslands, Permanent wetlands, Croplands, and Changed areas and others) and only selected pixels that had the same class between 2000 and 2015 (Figure 2). Later in our research, we selected the following eight main vegetation types for analysis: Evergreen needleleaf forests (ENF), Deciduous broadleaf forests (DBF), Mixed forests (MF), Open shrulands (OS), Woody savannas (WS), Savannas (S), Grasslands (G), and Croplands (C).  
Figure 2: Distribution of land types above 30\({}^{\circ}\)N from 2000 to 2015. The black dots represent the distribution of Flux2015 stations.
## 4 Theoretical Simulation and Experimental Verification  
### Theoretical Simulation  
By subtracting the output signals U(\(t\)), B(\(t\)) with the metal plate from the output signals U_air(\(t\))_, B_air(\(t\))_ without the metal plate, the differential signals U_ref(\(t\))_ and B_ref(\(t\))_ can be obtained. As seen in Figures 4 and 5, the characteristics of ferromagnetic metals are distinctly different from those of non-ferromagnetic metals. For ferromagnetic metals, U_ref(\(t\))_ and B_ref(\(t\))_ are on the same side as U(\(t\)) and U_air(\(t\))_, and B(\(t\)) and B_air(\(t\))_, respectively, whereas for non-ferromagnetic metals, they are on opposite sides. This is because under the action of the single probe, ferromagnetic metals are magnetized in the region and cause the output signal U(\(t\)) and B(\(t\)) to rise faster and with a higher amplitude than the signal from the non-metal plate. On the other hand, non-ferromagnetic metals, due to the eddy current effect, hinder the rise of the output signal U(\(t\)) and B(\(t\)), producing the opposite result. The typical characteristics of the output signals can thus distinguish ferromagnetic and non-ferromagnetic metals.
# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 2 Materials And Methods
### Tests Procedure and Evaluation Criteria  
The tests and evaluation of the different developed tracker proposals were carried out considering several evaluation criteria. First, the tests were performed using the ALOV video dataset (described in Section 2.6.1). Secondly, a quantitative evaluation is carried out using the F-score criterion for each proposal and analyzed using a survival curve (detailed in Section 2.6.2). Subsequently, the average time per frame criterion is obtained for each proposal and analyzed using a survival curve (detailed in Section 2.6.3). Besides, the time complexity is derived for each proposal and contrasted with the obtained results. Finally, performance in terms of accuracy and processing time for each proposal is analyzed through a proposed criterion, called Score-Time Efficiency (detailed in Section 2.6.4).
## 2 Materials and Methods
### Uncombined Dual- and Triple-Frequency Multi-GNSS Decoupled Clock Model
#### 2.1.2 Uncombined Dual- and Triple-Frequency DCM
The term arises from the fact that the other state terms cannot absorb the additional receiver bias that comes with the third pseudorange measurement, as opposed to the first and second frequency receiver pseudorange biases, which are absorbed by the receiver pseudorange clock and ionospheric delay. Ignoring this state term leads to it appearing in the pseudorange residuals, and potentially affecting the estimation of other states. * Equation (2) also contains two state terms which are specific to the DCM, referred to here as the receiver L2 phase bias \(\delta t_{12}\), and the receiver L3 phase bias \(\delta t_{13}\). The two state terms are defined as: \(\delta t_{12}=\lambda_{2}(\delta N_{2}^{ref}+B_{r,2})-\lambda_{1}(\delta N_{1} ^{ref}+B_{r,1})-\frac{f_{1}^{2}+f_{2}^{2}}{f_{1}^{2}-f_{2}^{2}}(b_{r,1}-b_{r,2})\) and \(\delta t_{13}=\frac{f_{2}^{2}-f_{2}^{2}}{f_{1}^{2}-f_{2}^{2}}(b_{r,1}-b_{r,2} )-\lambda_{1}(\delta N_{1}^{ref}+B_{r,1})+\lambda_{3}(\delta N_{3}^{ref}+B_{r,3})\). Both state terms contain receiver biases, as well as the reference satellite's ambiguities. In the decoupled clock model, assumptions regarding the behaviour of the biases are not made. Therefore, any state term containing receiver biases should be treated as a clock term and be estimated as a white noise process. That is the case for the receiver pseudorange clock \(\overline{dt}_{r}\), receiver carrier-phase clock \(\overline{dt}_{r}\), the interfrequency bias \(IFB_{r}\), the receiver L2 phase bias \(\delta t_{12}\), and the receiver L3 phase bias \(\delta t_{13}\). The ionospheric delays are estimated as white noise processes as well.
## 4 Applications of PCEM
### PCEM for Label-Free Live Cell Imaging  
Label-free live cell imaging involves a sensing transducer surface, which typically generates an electrical or optical signal when cells interact with it. Biosensors that measure intrinsic cellular properties (such as dielectric permittivity) can be used to determine the number of cells in contact with the transducer, or the distribution/redistribution of focal adhesion areas. Such transducers (e.g., PC biosensors) may be prepared with different surface chemistry coatings that either mimic the _in vivo_ microenvironment within tissues or selectively capture specific cell populations through interaction with proteins expressed on their outer cellular membranes. Therefore, the PCEM-based label-free images of cell attachment can assist the study of cell-substrate interactions, including identifying, capturing, and quantifying cells expressing specific surface molecules (Figure 2C) [38; 39; 40; 41; 42; 43; 44; 45; 50].  
Recently, PCEM has been successfully demonstrated as a label-free live cell imaging approach to provide visualizations of each individual cell with subcellular details [45]. As shown in Figure 5A-C, Panc-1 cells were seeded onto a fibronectin-coated PC biosensor and allowed to incubate for 2 h before imaging. The non-uniform distribution of the PWS and the subcellular activity can be visualized clearly for each single cell. Figure 5B shows that the middle cell (No. 2) demonstrates higher PWS in regions near the boundary of lamellipodia formation (consistent with the creation of actin bundles). These darker shadings in the cell indicate regions of higher protein concentration, which may be attributed to higher modulation in the strength of cellular material attachment.  
In addition, the kinetics of dynamic interaction between cellular materials and surface coating materials can be measured quantitatively using PCEM. As shown in Figure 5D, a sequence of movie frames demonstrates murine dental stem cells (mHAT9a) gradually attaching on the PC surface. Cells were seeded at 20,000 cells per mL on a fibronectin-coated PC biosensor surface. After three minutes, initial cell attachment appears as small, round regions, which is consistent with spheroid, trypsinized cells coming out of suspension and attaching to a surface. As time progresses, both the size of the cells and intensity of the PWS induced by them increases, indicating a higher localization of cellular material at the biosensor surface, which can be expected during cell spreading. Finally, once cells are sufficiently attached, cellular processes can be observed sensing the cells' microenvironment in all directions. The outer irregular boundaries of the cells have a relatively low PWS (consistent with thin, exploratory filopodia) accompanied by a more heavily attached region slightly immediately adjacent in the cell interior (likely a result of actin bundle formation). Figure 5D illustrates distinct modulation distributions of the attachment strength for both individual cells and the whole cell group during different periods of the adhesion procedure.
# Comprehensive Evaluation of a Sparse Dataset, Assessment and Selection of Competing Models
## 4 Performance Evaluations of Models  
To evaluate the performance of models' goodness of fits, the literature review regularly checks the residuals, or deviation of observation \(\mathrm{y_{i}}\) from the corresponding predicted means. This approach has been used to ensure that after transforming the dataset, e.g., response, the model residuals follow a normal distribution. This can be achieved by checking quantile-quantile (Q-Q) plots. This is a scatterplot created by plotting two sets of quantiles against one another to check if both sets come from the same distribution.  
Various measures such as the Akaike Information Criterion (AIC) were used in this study for a comparison across various included models. The AIC was used to compare various incorporated models. This measure follows \(AIC=-2(log-likelihood)+2k\).  
Where \(k\) is the number of model parameters including the intercept, and likelihood is a measure of model fit. The benefit of this measure over others, such as log likelihood, is that this measure includes a penalty for including more predictors, preventing an over-fitted model from being highlighted to have a better fit.  
Rootograms have been designed for diagnosing and addressing issues such as over dispersion and an excess number of zeros in the data [2], being extended from [PERSON]'s work in the literature review [18]. This is an improved approach to the assessment of a count model such as Q-Q plot. There are a few important observations that should be taken into account while using this figure:  
* Based on the identified model, expected counts are shown by a red line.
* Real/observed counts are shown as bars.
* X-axis represents counts.
* Y-axis represents square root of expected/observed counts.
* The first line observed in this figure is related to the height of the observation with zero value (EPDO = 0).  
In this study, the use of rootograms plots were implemented after identification of a best model distribution to make sure the finalist models follow the right distribution.
## 4 Results
### Comparison with the GPP Obtained by BESS
Conversely, BESS_P demonstrates satisfactory simulations only during the early growth period, followed by a gradual overestimation of GPP. BESS_TL outperforms the other two methods in capturing seasonal shifts in GPP. The discrepancy between the simulated GPP and EC values remains relatively close to zero. GPP is slightly underestimated during the early and late growth stages and slightly overestimated during the mid-growth stage. Overall, BESS_TL demonstrates a more balanced performance across different growth stages compared to BESS and BESS_P. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Site**} & \multicolumn{2}{c}{**BESS**} & \multicolumn{2}{c}{**BESS\_P**} & \multicolumn{2}{c}{**BESS\_TL**} \\ \cline{2-7}  & **Mean** & **Std** & **Mean** & **Std** & **Mean** & **Std** \\ \hline US-Ne1 & \(-1.56\) & \(3.35\) & \(2.39\) & \(3.30\) & \(0.12\) & \(2.29\) \\ US-Ne2 & \(-1.69\) & \(3.03\) & \(2.40\) & \(2.59\) & \(-0.02\) & \(2.03\) \\ US-Ne3 & \(-1.56\) & \(2.56\) & \(2.31\) & \(3.32\) & \(0.29\) & \(2.02\) \\ Daman & \(-0.43\) & \(2.92\) & \(0.22\) & \(3.01\) & \(-1.42\) & \(1.82\) \\ Fenzidi & \(-0.06\) & \(3.29\) & \(-0.13\) & \(3.49\) & \(0.05\) & \(2.00\) \\ \hline \end{tabular}
\end{table} 
Table 4: Mean and standard deviation of the GPP results by the three methods in each site (gC m\({}^{-2}\)d\({}^{-1}\)). Figure 8: The annual GPP estimation discrepancy of the BESS (blue lines), BESS_P (green lines) and BESS_TL (red lines) at (**a**) Daman, (**b**) Fenzidi. Figure 9: The estimation of daily GPP provided by BESS (blue lines), BESS_P (green lines) and BESS_TL (red lines) compared to observation by flux sites (black lines).
## 2 Security Requirement and S-Box Design Methods
### S-Box Properties
#### 2.1.2 Differential Uniformity (DU)  
By excluding the trivial entry case (i.e., \(a=b=0\)) from the difference distribution table, the largest value present in the table can determine the value of DU. The value of DU is defined as follows:  
\begin{table}
\begin{tabular}{c c} \hline
**Properties** & **Desired Value** \\ \hline High Nonlinearity & \(100<\mathrm{NL}\leq 120\) \\ Low Differential Uniformity & \(2\leq\mathrm{DU}\leq 6\) \\ High Algebraic Degree & \(4\leq\mathrm{AD}\leq 7\) \\ Low Linear Approximation & \(8\leq\mathrm{LA}<28\) \\ Algebraic Complexity & Complex \\ Fixed \& Opposite Fixed Points & None/Low \\ Balanced Output & Permutation \\ \hline \end{tabular}
\end{table}
Table 1: Desired Value for Cryptographically Strong S-boxes.  
\[\text{DU}(F)=\max_{a,b\in\mathbb{F}_{2^{n}},a\
eq 0}|\{x\in\mathbb{F}_{2^{n}}:F(x+a)+F(x )=b\}|\]  
A smaller value of DU is preferable [14]. In our case, we determine that the value of DU must lie in the range of \(2\leq\text{DU}\leq 6\) to resist differential cryptanalysis [2].
# Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data
## 3 Methods
### Proposed Data Quality Measures  
Motivated by the ideas in [PERSON]'s LDA, we propose two types of new data quality measures: \(M_{svp}\) (class separability), \(M_{var}\) and \(M_{var}\), (in-class variability).
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 4 Results and Discussions  
This section first discusses the results of a reliable aerial cellular communication zone based on the 4G dataset. Next, the UAV path planning optimization simulation results are presented and discussed. Finally, to validate the ability of the proposed strategy to solve 3D UAV path planning problems, the simulation results of Scenario I are compared with the results of the conducted aerial drive test.
## 2 Materials and Methods
### Input Representation  
#### Static Images Inputs  
In traditional SNNs, static images, e.g., RGB images captured by frame cameras are typically used as inputs. These images are duplicated and utilized as input frames for each time step.
## 5 Applications of PCMs in Buildings
### PCM Panels and Wallboards
#### 5.1.4 Windows and Glazed Applications  
Modern building designs contain large, glazed areas such as windows, glazed facade, and, sometimes, glazed roof, as they allow natural room lighting and vision, although glazing materials have very poor thermal performance which leads to increased energy consumption of the building. This indicates the necessity of expanding the PCM technologies for thermal energy storage to glazed materials also. Applications of PCM technology in glazed materials has been investigated. Changyu Liu et al. [82] studied the optical and thermal performance of a PCM-filled glazed unit in comparison with an air-filled glazed unit. The thermal performance of the PCM filled unit was highly improved, although the optical properties were degraded as the transparency was very low, especially when the PCM was at the liquid phase. An important factor to this matter is the thickness of the PCM in the glazed unit. Increased thickness improves the thermal performance but decreases the transparency. In this study it was found that the thickness should not be more than 16 mm in order to satisfy both thermal and optical demands. Corresponding study was held in reference [111; 112; 113].
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
## 1 Introduction
The utilization of MSBs is still unsatisfactory in these two methods and embedding rate still has improvement room. In this paper, an RDHEI method based on a multi-MSB embedding strategy is proposed. Integrating prediction technology with pixel correlation, the multiply MSBs can be exploited to improve embedding rate. The proposed scheme has the following highlights:  
* By using a multi-MSB embedding strategy, the secret bits can be embedded in the encrypted images without any pixel oversaturation in the plaintext domain. * More importantly, by using multi-MSB embedding strategy, secrets bits can be directly extracted from encrypted domain from the multi-MSB of pixels without any error. The reconstructed image with very high visual quality can be obtained only in the case that the encryption key is obtained. * Compared with the other state-of-the-art methods [38; 39], the proposed method can achieve a significantly higher maximum embedding rate. The remainder of this paper is organized as follows. The details of the proposed method are introduced in Section 2. Section 3 describes how to choose the optimal parameters. Experimental results and performance comparison are presented in Section 4. Finally, the proposed method is concluded in Section 5.
# Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool
## 4 Experiments
### SAABC-CS for Practical Engineering Problems
#### 4.4.1 Parameter Estimation for Frequency-Modulated (FM) Sound Waves  
Frequency-modulated (FM) sound wave synthesis plays an important role in various modern music systems. To estimate the parameter of a FM synthesizer and to optimize the results, we solved a six-dimensional optimization problem, where we tried to optimize the vector \(X=\{a_{1},\omega_{1},a_{2},\omega_{2},a_{3},\omega_{3}\}\) given in Equation (19). The goal of this optimization problem was to generate a sound (19) similar to the target sound (20). This problem highly complex and multi-modal, having strong epistasis, with a minimum value \(f(\vec{X_{sol}})=0\). The expressions for the estimated sound and the target sound waves are given as  
\[y(t)=a_{1}\cdot\sin\left(\omega_{1}\cdot t\cdot\theta+a_{2}\cdot\sin\left( \omega_{2}\cdot t\cdot\theta+a_{3}\cdot\sin\left(\omega_{3}\cdot t\cdot\theta \right)\right)\right) \tag{19}\]  
\[y_{0}(t)=1.0\cdot\sin\left(5.0\cdot t\cdot\theta-1.5\cdot\sin\left(4.8\cdot t \cdot\theta+2.0\cdot\sin\left(4.9\cdot t\cdot\theta\right)\right)\right) \tag{20}\]  
Figure 7: Illustration of Average Ranking.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **D = 30** & **D = 50** & **D = 100** \\ \hline ABC & 2.93 & 3.25 & 3.64 \\ ABCNG & 2.68 & 2.89 & 2.75 \\ KFABC & 5.20 & 5.32 & 5.00 \\ SABC-GB & 3.18 & 3.00 & 3.03 \\ MGABC & 3.50 & 3.42 & 3.57 \\ SAABC-CS & **2.17** & **2.07** & **1.92** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average Ranking.  
where \(\theta=(2\cdot\pi)/100\) and the parameters are defined in the range \([-6.4,6.35]\). The fitness function is the summation of the square errors between the estimated wave (19) and the target wave (20), as follows:  
\[minf(\vec{X})=\sum_{t=0}^{100}(y(t)-y_{0}(t))^{2} \tag{21}\]