esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Considering the effects of light intensity scintillation and pointing errors, the closed expressions of the PDF and the cumulative distribution function (CDF) for the satellite-to-HAP link and the HAP-to-terrestrial link are derived, respectively. These derivations are further used to obtain the closed expression for the BER of the RIS-SHTLC system. The effects of CBPSK, CBPSK, NBFSK, and DBPSK on the RIS-SHTLC system performance are simulated and analyzed under weak turbulence. At the same time, the relationship between the average SNR and BER of the RIS-SHTLC system under different RIS elements is simulated and analyzed, and compared with the traditional SHTLC system. Furthermore, the influence of the zenith angle, receiving aperture and divergence angle on the performance of the system is studied. Finally, Monte Carlo simulations are used to verify the correctness of the analytical results. The rest of this paper is organized as follows: In Section 2, the system model and channel model are presented. In Section 3, the BER of the RIS-SHTLC system is analyzed. The performance of the RIS-SHTLC system is simulated and analyzed and, compared with the SHTLC system in Section 4. The paper is concluded in Section 5.
# Optimization of Storage Medium for Hematological Reference Samples in External Quality Assessment
## 2 Materials and Methods
### Optimization of Components in Storage Medium  
The concentrations of blood cells in 1000 mL storing buffer were determined at specific values in the normal range of human blood samples, which were 4.12 \(\times\) 10\({}^{12}\) cells/L, 6.3 \(\times\) 10\({}^{9}\) cells/L, and 195 \(\times\) 10\({}^{9}\) cells/L for RBCs, pseudo-leucocytes, and pseudo-platelets, respectively. The components in the storage media were set at specific concentration ranges, and the concentrations of blood cells were set at 3.5-4.5 \(\times\) 10\({}^{12}\) cells/L for RBCs, 4.5-8.5 \(\times\) 10\({}^{9}\) cells/L for pseudo-leucocytes, and 150-250 \(\times\) 10\({}^{9}\) cells/L for pseudo-platelets. The storing duration in this test was set at 3 months, at a storing temperature of 2-8 \({}^{\circ}\)C.  
The optimal values of three main factors chosen from the previous test were evaluated using a scale of 5 levels (\(-\alpha\), \(-\)1, 0, \(+\)1, \(+\alpha\)) with CCD for 20 experiments, following the formula \(2n+2n+6\) (\(n\) was the number of factors used in RSM-CCD). The RSM-CCD matrix with eight different experiments was conducted using three factors at both high and low levels. Six experiments were then conducted for \(\alpha\) hand and another six for the center.  
The chosen response functions were RBCs (Y1, A \(\times\) 10\({}^{9}\) cells/L), pseudo-leucocytes (Y2, B \(\times\) 10\({}^{9}\) cells/L), and pseudo-platelets (Y3, C \(\times\) 10\({}^{9}\) cells/L), where A, B, and C were the cell concentrations after 3 months of storage at 2-8 \({}^{\circ}\)C. The functions were expressed as a quadratic equation: Y = b\({}_{0}\) + b\({}_{1}\)x\({}_{1}\) + b\({}_{2}\)x\({}_{2}\) + b\({}_{3}\)x\({}_{3}\) + b\({}_{11}\)x\({}_{1}^{2}\) + b\({}_{22}\)x\({}_{2}^{2}\) + b\({}_{33}\)x\({}_{3}^{2}\) + b\({}_{12}\)x\({}_{1}\)x\({}_{2}\) + b\({}_{23}\)x\({}_{2}\)x\({}_{3}\) + b\({}_{13}\)x\({}_{1}\)x\({}_{3}\), where b\({}_{1}\), b\({}_{2}\), and b\({}_{3}\) were linear coefficients, b\({}_{12}\), b\({}_{23}\), b\({}_{13}\) were interactive term coefficients, b\({}_{11}\), b\({}_{22}\), b\({}_{33}\) were quadratic term coefficients. The optimal values of the factors were determined as values where maximum cell concentrations were maintained [10].
# GenAI-Assisted Database Deployment for Heterogeneous Indigenous-Native Ethnographic Research Data
## 3 Comprehensive Review of Database Type for ERD
### Vector Databases  
Vector databases, a novel type of database system, harness machine learning technologies (like the transformers in natural language processing, image processing and audio analysis) to transform diverse and complex data types (such as images, videos, text and audio files) into vector embedding. This approach preserves the original data features and enables similarity searches based on the characteristics of vectors, using various distance-calculation methods like Cosine, Manhattan, Euclidean and Chebyshev distances. There are numerous benefits to be gained from using vector databases, including:  
1. Enhanced Search Techniques: vector databases hold information in vectors, a series of numbers that represent specific features and these vectors are created using machine-learning principles. Vector databases enable researchers to find nearby results in vector distance. As shown in Figure 4, this allows users to perform searches by finding the closest results in vector distance, thereby facilitating more nuanced and precise search capabilities compared to traditional keyword-based searches.
2. Diverse Data Type Compatibility: Vector databases are capable of effectively processing diverse data types. They have access to a variety of formats including text, audio, image and video. When carrying out possible functions between equally different data types, they can also handle unstructured and structured data. Through machine learning techniques, vector databases would use various set-functioning values to undertake embedding and save the dataset features for accessibility. This also provides vector databases with better efficiency and management.  
Vector databases excel in situations where traditional database systems are not available. [PERSON] et al. [20] explored vector databases and vector embedding techniques, and presented the evolution, architecture, advantages and challenges of vector databases in their study. They also discussed the challenges of vector databases that include selecting distance metrics, dimensionality, data integrity, cost, etc. These databases are especially useful for managing complex and intricate data types. For instance, they can conduct searches on the basis of form or style similarity in image or audio identification systems, which is not feasible using conventional keyword-based techniques. Additionally, they are well suited for use in AI and data analytics for implementing mathematical models in machine learning, and for naturally storing and querying complex data.
# Semi-Active Vibration Control Based on a Smart Exciter with an Optimized Electrical Shunt Circuit
## 3 Numerical Analysis of Vibration Reduction Potential  
### Simulation Framework  
#### 3.1.1 Modeling Structural Vibrations Using the Finite Element Method  
In order to analyze the vibration reduction potential of a SE attached to a structure, numerical investigations have been performed. A flat panel has been used to represent a lightweight structure. To generate a state space model (SSM), a plate structure has been analyzed using the finite element method implemented in COMSOL 5.5. The panel has a size of 350 \(\times\) 480 \(\times\) 11.2 mm, and all edges are simply supported. In this model, three different coupling positions (P1, P2, P3--compare Figure 3) for the SE have been selected, which also function as the input and output positions for the SSM. This model has then been exported to MATLAB/Simulink. The panel model is a 3D Shell model. It has been used for time-domain simulations. The panel material has been characterized by a density of 137.14 kg/m\({}^{3}\), a Young's modulus of 650 MPa and a Poisson's ratio of 0.35. In addition, stiffness proportional Rayleigh damping, theory and new developments have been reported in [22], and have been selected to model the damping effect of the panel material.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \(M_{e}\)(g) & \(R_{e}\)(\(\Omega\)) & \(L_{e}\)(mH) & \(Bl\)(N/A) & \(D_{e}\)(kg/s) & \(K_{e}\)(N/mm) & \(R_{2}\)(\(\Omega\)) & \(L_{2}\)(mH) \\ \hline
15.000 & 7.540 & 0.141 & 5.016 & 5.648 & 15.540 & 8.000 & 0.300 \\ \hline \end{tabular}
\end{table}
Table 1: Values of variables used in the simplified model [21].  
Figure 2: Simplified model of SE.  
This model parameter has been set to \(\beta\) = 0.0000032. Prescribed Displacement/Rotation boundary conditions (simply supported) have been applied.  
To achieve the desired results, the input force setting in COMSOL has been selected as face loads. Hence, three circles have been created around P1, P2 and P3 (compare Figure 3) to define the face load inputs. A mesh consisting of triangular elements has been used to discretize the panel. The resulting finite element model consists of 81 triangle elements with 1044 DOFs. The outputs of the SSM are displacements. To obtain these results, the centers of these three circles are defined as output positions. The outputs are defined by using the function Boundary Point Probe with the expression shell.disp. After successful mesh generation and calculation, the matrices of the SSM can be exported to MATLAB/Simulink via the function mphstate. The full MATLAB Code is shown in A. AA, BB, CC and DD are the state (system) matrix, input matrix, output matrix and feedthrough matrix, respectively.
# Utilizing Duplicate Announcements for BGP Anomaly Detection
## 3 Proposed Methodology
### Data Collection  
We have downloaded the raw data from the BGP update messages. The BGP update message consists of network prefix addresses and network layer reachability information. The collected raw data were taken from RIPE and ROUTE Views collection sites [2]. The downloaded data were processed to extract various features, and the dataset was labeled. The dataset is analyzed for various event periods to determine the occurrence of cyber anomalies. In various time intervals, anomalies such as Moscow blackout, Slammer, and WannaCrypt occurred in varied instances, and their data points were taken as regular and anomalous for one minute of the routing record. The occurrence period for slammer anomaly was observed for five days. The occurrence period was eight days for the WannaCrypt anomaly, while the occurrence period for the Moscow blackout anomaly was five days. The dataset that is being collected is analyzed under different anomaly conditions, as presented in Table 1.  
Figure 2: Proposed classification process.  
The downloaded update messages from the RIPE and ROUTE Views collection sites are undergoing processing and have been inserted into the Python script. Based on the volume and AS-Path, a list of 37 features was extracted. The data points obtained from the dataset are granular routing records representing one minute of the original routing record, which is collected and analyzed. The list of extracted features is categorized in terms of feature definitions based on their type and the category to which they belong, i.e., either volume or AS-Path.
###### Abstract  
This paper introduces a Markov-process-based model to analyze the performance of hybrid Free Space Optics and Radio Frequency communication systems. The proposed model captures the dynamic behavior of the link by representing the transitions between various operational states under changing environmental conditions. State probabilities are derived analytically, providing insight into the system's reliability and efficiency within diverse scenarios. These probabilities can be employed to define and evaluate various performance metrics. This study examines the loss function associated with the suboptimal utilization of Free Space Optics when Radio Frequency is required and vice versa within hybrid Free Space Optics and Radio Frequency communication systems. A problem statement for optimizing system performance is proposed, and the corresponding solution is obtained. The results facilitate the identification of an optimal trade-off between the cost of switching configurations and communication efficiency. The findings provide a quantitative framework for designing adaptive hybrid communication systems that improve reliability and reduce communication disruptions.  
hybrid FSO/RF communication; Gilbert-Elliott channel; continuous-time Markov chains; Internet of Things; steady-state probabilities; performance analysis Article
# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 1 Introduction
The developed models all show a better performance in predicting shear strength and outperform traditional machine learning methods [19]. [PERSON] et al. used the XGBoost model to predict the load-carrying capacity of FRP-reinforced concrete columns. They also analyzed the model for interpretability using SHAP architecture. The results of the study showed that the proposed prediction model has a better performance [20]. [PERSON] et al. assessed the seismic vulnerability of buildings in Kavrepalanchok, Nepal, using the XGBoost model. The developed model has a high accuracy [21]. No research has been conducted to establish a prediction model between the mechanical performance index of pavement systems and the structural design parameters using machine learning algorithms. This study aims to fill this gap by developing an XGBoost-based mechanical performance prediction model for steel bridge deck pavement systems. This study focuses on the following aspects: (1) establishing the dataset by conducting numerical simulation tests at the most unfavorable load locations using a finite element model; (2) establishing the XGBoost model and optimizing its parameters; (3) comparing the XGBoost model with traditional machine learning models to evaluate its accuracy; and (4) using the SHAP framework to perform explanatory analysis. The technical route is shown in Figure 1.
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 2 Circuit Structure and Operation Analysis
### Description of Operating Range
#### 2.3.1 Stage I [\(t_{1}<t\leq t_{2}\)]  
In this stage, Figure 6a shows that \(Q_{1}\), \(Q_{2}\), \(Q_{3}\), and \(Q_{4}\) are all cut off. At the secondary side, \(D_{BS}\) and \(D_{BB}\) conduct, while \(D_{B6}\) and \(D_{B7}\) remain cut off. At the primary side, the resonant current \(i_{L,t1}\) passes through \(D_{BI}\) and \(D_{B4}\) to keep the resonant current flow and prevent the devices from voltage spike  
Figure 4: Power propagation of the forward charging mode.  
Figure 5: Important theoretic waveforms operating in Region II.  
damage. In addition, the circuit is ready for the ZVS of \(Q_{1}\) and \(Q_{4}\). At \(t=t_{2}\), as gate signals \(v_{GSI}\) and \(v_{GS4}\) turn from low level to high, the next stage follows.
## Materials and Methods
### Total Hg and MeHg Determination  
Total mercury (THg) extraction from samples (fish tissues and feed, \(n\) = 3), as well as methylmercury (MeHg), was performed according to the methodologies previously described [25; 26; 43]. Both THg and MeHg concentrations were determined by atomic absorption spectrometry, using an automatic Hg analyzer (Leco apparatus AMA 254, LECO, St. Joseph, MI, USA). The detection limit of the methodology was 0.005 mg\(\cdot\)kg\({}^{-1}\). Results were obtained in mg\(\cdot\)kg\({}^{-1}\) DW and subsequently converted to wet weight basis according to sample moisture contents. To avoid contamination, all laboratory material was cleaned beforehand with nitric acid (20% _v/v_ for 24 h) and then rinsed with ultrapure water. All analyses were performed in triplicate and using reagents of pro analysis grade or higher. Certified reference materials (DORM-4 and TORT-4, National Research Council of Canada, Canada) were used to check accuracy. Results obtained (_n_ = 6) in the present study for THg (DORM-4: 0.410 \(\pm\) 0.055 mg\(\cdot\)kg\({}^{-1}\); TORT-4: 0.27 \(\pm\) 0.06 mg\(\cdot\)kg\({}^{-1}\)) and for MeHg (0.152 \(\pm\) 0.013 mg\(\cdot\)kg\({}^{-1}\)) were within the range of certified values.
## 4 Conclusions  
This study systematically explored how Cu\({}^{2+}\) influences the flotation separation of chalcopyrite and molybdenite with STG as depressant, and elucidated the interaction mechanism between Cu\({}^{2+}\), STG, and molybdenite surfaces. Based on the experimental results and in-depth analysis, the following key conclusions can be drawn:  
1. Flotation experiments showed that at pH 8.0, neither Cu\({}^{2+}\) nor STG alone significantly affected molybdenite flotation. However, the combination of STG with Cu\({}^{2+}\) strongly depressed molybdenite flotation, degenerating the separation efficiency.
2. Contact angle, zeta potential, UV-Vis, and DFT calculation revealed that Cu\({}^{2+}\) facilitated STG attachment onto molybdenite, enhancing its surface hydrophilicity and thereby diminishing its floatability.
3. XPS analysis revealed that Cu\({}^{2+}\) predominantly adsorbed onto the molybdenite surface as Cu(I), providing reactive sites for the -SH or -COO\({}^{-}\) group in STG. The formation of a stable molybdenite-Cu(I)-STG complex effectively suppressed molybdenite flotation.  
Figure 14: Hydrated structure of molybdenite (001) surface **(a)**; STG adsorption on hydrated molybdenite (001) surface (**b**); CuOH\({}^{+}\) adsorption on hydrated molybdenite (001) surface (**c**); STG adsorption on CuOH\({}^{+}\) modified molybdenite (001) surface (**d**).  
**Author Contributions**: Conceptualization, F.J.; Methodology, F.J.; Software, Y.L.; Validation, H.T.; Formal analysis, Y.L.; Investigation, F.J., S.H. and J.Q.; Data curation, S.H. and J.Q.; Writing--original draft, F.J.; Writing--review & editing, S.H., Y.L. and H.T.; Supervision, H.T.; Project administration, H.T.; Funding acquisition, F.J. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research was funded by the [National Key R&D Program of China] grant number [2022 YFC2904502, 2022 YFC2904501].  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
## 1 Introduction
### Objective Function
\begin{array}{rl}\text{F}=&\sum\limits_{t\in T}\sum\limits_{i\in\Phi}\sum \limits_{k=1}^{8760}(Cf_{i,t}+Cv_{i,t})P_{i,t,k}\Delta T\cdot Fin_{t}+\sum \limits_{i\in\Phi}Cg_{i,t}\Big{(}R_{i,t}+\sum\limits_{t\in T}\Big{(}\Delta R_ {i,t}-\Delta R_{i,t}^{dw}\Big{)}Fin_{t}\Big{)}\\ &+\sum\limits_{t\in T}\sum\limits_{k=1}^{8760}\left(\sum\limits_{i=1}^{N1}Cv_{ i,t}^{stor}\Big{|}P_{i,t,k}^{stor}\Big{|}\Delta T+\sum\limits_{i=1}^{N2}\sum \limits_{j=1}^{3}Cv_{i,j,t}^{CCUS}P_{i,j,t,k}^{CCUS}\Delta T+\sum\limits_{i=1} ^{N3}\sum\limits_{j=1}^{2}Cv_{i,j,t}^{P2H}P_{i,j,t,k}^{P2H}\Delta T\right)Fin_{t }\\ &+\sum\limits_{i=1}^{N1}Cg_{i,t}^{stor}\Big{(}R_{i}^{stor}+\sum\limits_{t\in T }\Delta R_{i,t}^{stor}\Big{)}Fin_{t}\\ &+\sum\limits_{i=1}^{N2}\sum\limits_{j=1}^{3}Cg_{i,t}^{CCUS}\Big{(}R_{i,j}^{ CCUS}+\sum\limits_{t\in T}\Delta R_{i,j,t}^{CCUS}\Big{)}Fin_{t}\\ &+\sum\limits_{i=1}^{N3}\sum\limits_{j=1}^{3}Cg_{i,t}^{P2H}\Big{(}R_{i,j}^{ P2H}+\sum\limits_{t\in T}\Delta R_{i,j,t}^{P2H}\Big{)}Fin_{t}\\ \end{array}  \tag{7}\]  
\(Cf_{i,t}\), \(Cv_{i,t}\) represent the fuel cost coefficient and variable operation and maintenance cost coefficient per unit of electricity generation for power source \(i\) in the \(\mathbf{t}-\mathbf{th}\) planning year, respectively; \(P_{i,t,k}\) represents the power generation of power source \(i\) at time \(\mathbf{k}\) in the \(\mathbf{t}-\mathbf{th}\) planning year. \(\Delta T\) represents the time step length, \(Cg_{i,t}\) represents the fixed operation and maintenance cost coefficient for power source \(i\) in the \(\mathbf{t}-\mathbf{th}\) planning year, \(R_{i,t}\) represents the initial installed capacity of power source i in the \(\mathbf{t}-\mathbf{th}\) planning year, and \(\Delta R_{i,t}^{dw}\) represents the retired installed capacity of power source i in the \(\mathbf{t}-\mathbf{th}\) planning year. \(Cv_{i,t}^{stor}\), \(G_{S,t}^{stor}\), \(P_{i,t,k}^{stor}\), \(\Delta R_{i,t}^{stor}\) represent the variable operation and maintenance cost coefficient, fixed operation and maintenance cost coefficient, charging and discharging power at moment \(k\), and newly added installed capacity of new energy storage \(i\) in the \(\mathbf{t}-\mathbf{th}\) planning year. \(Cv_{i,j,t}^{CCUS}\), \(Cg_{i,j,t}^{CCUS}\), \(P_{i,j,t,k}^{CCUS}\), \(\Delta R_{i,j,t}^{CCUS}\) represent the variable operation and maintenance cost coefficient, charging and discharging power at moment \(k\), and newly added installed capacity of the \(\mathbf{j}-\mathbf{th}\) type of hydrogen energy technology station \(\mathbf{i}\) in the \(\mathbf{t}-\mathbf{th}\) planning year. \(R_{i}^{stor}\), \(R_{i,j}^{CCUS}\), and \(R_{i,j}^{P2H}\) represent the initial installed capacity of new energy storage \(i\), the \(\mathbf{j}-\mathbf{th}\) type of CCUS technology application station \(\mathbf{i}\), and the \(\mathbf{j}-\mathbf{th}\) type of hydrogen energy technology station \(\mathbf{i}\) at the initial level year, respectively.
# Fast Format-Aware Fuzzing for Structured Input Applications
## 3 Methodology
### Indirect Dependency Recognizer
#### 3.3.1 Non-Deps Exclusion  
As indicated above, flipping a _non-deps_ field has no effect on changing the value of all the \(cmp\) instruction operands. By utilizing \(skip\_cmp\), if we flip one fragment and none of the \(cmp\) operands change their values, we consider this fragment to be a _non-deps_ one.  
Algorithm 2 demonstrates how it recognize a _non-deps_ fragment in only one target execution. Taking initial input \(T\), the offset range of the unvalidated field \(range\), and the set of uncontrollable \(cmp\) instructions \(skip\_cmp[]\), we flip the fragment specified by \(range\), then launch it and run the target to capture all the runtime log for the \(cmp\) instruction operands in \(cmp\_map[]\). After that, it iterates over the values of the \(cmp\) operands, and once a changed value is found, we restore \(T\) and output \(false\) because it still contains fields that have dependencies on the target. Otherwise, we restore \(T\) and output \(true\).  
As described above, we recognize whether the fragment specified is _non-deps_ with low overhead and reduce the scale of workload in _Deps detection_.  
Figure 8: The overall design of _Indirect Dependency Recognizer_.  
```
Input: initial input: \(T\), offset range of suspensive field: \(range\), uncontrollable cmp: \(skip\_cmp[]\) Output: If the fragment specified by \(range\) is \(non\)-\(deps\)
1\([start,end]\gets range\)
2\(isNon\_Deps\gets true\)
3\(flip(T,[start,end])\)
4\(run\_target(T)\)
5 for\(cmp(id,v0,v1)\in(cmp\_map[]-skip\_cmp[])\)do
6 ifcmp.v0 or cmp.v1 changedthen
7\(isNon\_Deps\gets false\)
8\(break\)
9 end if
10
11 end for
12\(flip(T,[start,end])\)
13 return isNonDeps
```  
**Algorithm 2**Non-deps field recognition algorithm.
###### Abstract  
In this article, a new reversible data hiding method using most significant bits and center shifting technique in dual images is proposed. The proposed reversible data hiding method aims to securely hide high-capacity secret data. Instead of directly embedding the secret data, the method calculates new values with \((n+1)\) bits secret data and the \(n\) most significant bits of the cover image pixel values. Thus, it is impossible to extract secret data without obtaining the original cover image. Also, the center shifting process is performed to minimize the mean square error. After this process, the secret data to be hidden in the dual images are in the range \(\left[-3\times 2^{(n-1)}+1\right.\), \(3\times 2^{(n-1)}-1\)\(\left.\right]\). The pixel values of stego images are obtained by using the secret data in this range value and cover image pixel values. As a result of experimental studies, when the payload is 2.5 bits per pixel (bpp), the peak signal-to-noise ratio value (PSNR), which expresses the visual quality, is above 34 (dB). In addition, the proposed method has proven secure against RS (regular and singular) analysis attacks.  
Keywords:data security; reversible data hiding; dual-image steganography; center folding strategy; center shifting strategy; most significant bits +
Footnote †: journal: Applied Physics Letters