esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Pure Random Search  
Pure random search [21] (PRS) can be considered as the simplest and most obvious random search method, also known as \"blind search\" [22]. The method, first defined by [PERSON] [23], simply searches for candidate solutions at random within the search space by sampling repeatedly from the feasible region \(\Omega\), typically according to a uniform sampling distribution. The method in its simplest form maintains and generates a single point at each iteration and it does not adapt the current sampling strategy to information that has been previously gathered during the search. It can be proven that PRS converges on the global optimum with probability one [22], yet the convergence speed is very slow. In addition, the expected number of function evaluations for PRS to find the optimal solution within a given value (\(\epsilon\)) increases exponentially with the dimensionality (\(D\)) of the problem, which makes it useless in problems with high dimensionality. The method has major difficulties navigating towards an optimum solution, especially in search spaces with a high number of dimensions. Improvements of the basic method have been suggested [24] together with more sophisticated versions of it, such as pure adaptive search [21], where each iteration \"adapts\" to the current level set by restricting its search domain to improving points, or localized random search [21].  
The single-point PRS algorithm can be described as follows:  
* Initialize \(\mathbf{x}\) with a random position in the search space (feasible region \(\Omega\)), i.e., \(lb_{i}\leq x_{i}\leq ub_{i}\) for every \(i\in\{1,\,\ldots,\,D\}\)Until a termination criterion is met (e.g., number of iterations performed, or adequate fitness reached), repeat the following: \(\circ\) Sample a new position \(\boldsymbol{y}\) randomly in the search space, using uniform distribution \(\circ\) If \(f(\boldsymbol{y})<f(\boldsymbol{x})\) then move to the new position by setting \(\boldsymbol{x}=\boldsymbol{y}\) At the end, \(\boldsymbol{x}\) holds the best-found position and \(f(\boldsymbol{x})\) is the best objective function value found.
## 4 Conclusions  
In summary, we developed and validated an UPLC-MS/MS assay for the quantitation of Hu7691 in dog plasma, which was characterised by good selectivity, high separation efficiency, and fast operation. This bioanalytical method was validated by examining its linearity, precision, and stability. The LOQ ranged from 5 to 1000 ng/mL. We then successfully employed this method to study the pharmacokinetics of Hu7691 in beagles. The results showed that Hu7691 had a high bioavailability in dogs and that the \(AUC\) and \(C_{\max}\) of Hu7691 in dog plasma increased with the increasing dose. The research achievement indicated that Hu7691 had high gastrointestinal absorption and stable pharmacokinetic characteristics and was suitable for oral administration. This study is the first to investigate the pharmacokinetics of Hu7691 in beagles, the findings of this research are essential for further development of Hu7691 and provide a valuable reference for the clinical application of this compound.  
Conceptualisation, D.Z.; methodology and software, X.D. and M.Z.; validation, Y.H. and L.P.; writing--original draft preparation, L.P.; writing--review and editing, D.Z.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This study was approved by the Institutional Animal Care and Use Committee of the Center for Drug Safety Evaluation and Research of Zhejiang University (IACUC-19-010). Not available.  
Figure 4: Mean plasma concentration-time curves of Hu7691 in beagles following (**a**) oral administration and (**b**) intravenous administration.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dose (mg/kg)** & \(C_{\max}\) **(ng/mL)** & \(T_{1/2}\) **(h)** & \(\mathbf{MRT_{0+}}\) **(h)** & \(AUC_{0+}\) **(ng\(\cdot\)h/mL)** & \(AUC_{0-\infty}\) **(ng\(\cdot\)h/mL)** & \(F\) **(\%)** \\ \hline
5 (p.o.) & 248.2 \(\pm\) 49.3 & 34.3 \(\pm\) 20.4 & 28.8 \(\pm\) 7.8 & 7850 \(\pm\) 2473 & 10389 \(\pm\) 5049 & 86.7 \\
10 (p.o.) & 302.5 \(\pm\) 94.0 & 25.0 \(\pm\) 4.9 & 26.7 \(\pm\) 2.6 & 9189 \(\pm\) 3955 & 9785 \(\pm\) 4142 & 50.8 \\
20 (p.o.) & 415.9 \(\pm\) 152.0 & 37.2 \(\pm\) 18.3 & 34.6 \(\pm\) 6.9 & 18289 \(\pm\) 4099 & 22174 \(\pm\) 4229 & 50.5 \\
10 (i.v.) & 456.1 \(\pm\) 90.8 & 29.4 \(\pm\) 13.9 & 25.3 \(\pm\) 4.2 & 18099 \(\pm\) 6980 & 23428 \(\pm\) 12786 & – \\ \hline \end{tabular}
\end{table}
Table 4: Pharmacokine parameters of Hu7691 in dogs.  
**Data Availability Statement:** The data that support the findings of this research are available on request from the corresponding author and are not publicly available due to confidentiality reasons.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 LPBF Sensing Techniques
### Optical Techniques
#### 3.1.4 Techniques Using Other Sources  
**Optical emission spectroscopy (OES)** uses an excitation mechanism like an electric spark or arc source to produce a high-voltage pulse between the tip of a counter-electrode and the sample surface. This high voltage pulse ionizes the atmospheric gap, causing it to become conductive with low impedance and generating a stable current and plasma. When electrical discharge interacts with an atom, electrons in the atom's outer shells are ejected, creating a vacancy. Higher-orbit electrons will drop down to fill the vacancy, emitting excess energy as the electron moves between the two energy levels. Electron transport causes element-specific light, known as optical emission. The characteristic light can be  
Figure 23: Example of CLSM images provided by [91] showing the high surface roughness of a Ti6A14V LPBF-printed surface on the **(a)** XY-plane and **(b)** XZ-plane. CLSM can measure the height of surfaces, pinpointing potential surface defects [124]. Figure 24: Comparison of **(a)** CAD model and **(b)** DSLR imagery of an SS316L LPBF-printed part provided by [125], highlighting locations of intentionally designed defects. processed to generate a spectrum showing the light intensity peaks compared to their wavelengths. These peak wavelengths can then be used to identify the element. The advantages of OES include very high accuracy and precision. The disadvantage of OES is the possibility of spectral interference affecting chemical composition percentages. Table 5 displays OES data showing the chemical composition of various steel-based LPBF-printed cubic samples [128]. **Atom probe tomography (APT)** device provides 3D compositional mapping of materials by holding the specimen in front of a counter electrode located a set distance away and connecting it to a direct current high-voltage power supply. The pulsing high voltage causes field evaporation, followed by a measurable flight time of each ion. The time of flight is measured using a single-particle delay-line detector that converts the impact of a single ion into hundreds of thousands of electrons. The electrons are collected, and the impact position is calculated using the processed electronic signals detected at each end of each delay line. The advantages of APT are its atomic resolution capability and ability to identify chemical compositions in 3D space. The disadvantages of APT are that it is destructive to the sample, is chemically sensitive compared to other technologies, and only analyzes tiny sample sizes. Figure 25 shows APT analysis for an LPBF 2205 duplex stainless-steel-printed part, showing a 3D map of the different elements in the sample [56]. Table 5 below summarizes all-optical techniques that use other sources.
# Optimizing Lattice Basis Reduction Algorithm on ARM V8 Processors
## 4 Experimental Results and Performance Analysis
### Correctness Verification
#### 4.2.4 Hadamard Ratio  
The Hadamard Ratio (HR) is an important metric used to measure the quality of lattice transformation in lattice specification algorithms. It is defined as follows:  
\[\mathbf{HR}=\left(\frac{\mathbf{det}(\mathbf{B})}{\mathbf{\Sigma}_{i=1}^{n}\left\|\mathbf{b}_{i} \right\|}\right)^{\mathbf{1}/n} \tag{11}\]  
where \(\mathbf{B}\) is the lattice base matrix, \(\mathbf{det}(\mathbf{B})\) is the determinant of the lattice base matrix \(\mathbf{B}\), and \(\left\|\mathbf{b}_{i}\right\|\) is the Euclidean norm of the lattice base vector \(\mathbf{b}_{i}\). After validation, the HR for the LLL_FP and the optimized LLL_FP algorithms coincided for lattices of order 10, 20,..., 50.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & CPU & Phytium FT-2000+ \\  & Arch & Aarch64 \\  & SIMD & 128 bits \\  & L1 cache & 2 MiB \\  & L2 cache & 256 MiB \\ \hline Software environment & Compiler & GCC9.3.0 \\  & NTL & 11.5.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Environment configuration.
## 5 Results and Analyses  
### Main Results  
Table 2 presents the experimental results of our proposed CLEAR model on the CodRED dataset, showing comprehensive comparisons against six state-of-the-art baseline models. CLEAR consistently outperforms all baselines across all evaluation metrics on both the development and test sets.  
With BERT as the backbone, CLEAR achieves impressive results on the test set, with an F1 score of 63.71% and an AUC of 64.39%, surpassing the next-best model (REIC) by 2.44 and 1.80 percentage points, respectively. On the development set, CLEAR attains an F1 score of 65.78% and an AUC of 66.39%, with notable precision metrics of 79.67% for P@500 and 64.76% for P@1000.  
The performance gains are even more pronounced when using RoBERTa as the backbone encoder. CLEAR reaches 68.42% F1 and 68.78% AUC on the test set, improving upon REIC (the strongest baseline with RoBERTa) by 3.40 and 2.90 percentage points, respectively. Similarly, on the development set, CLEAR achieves 67.29% F1 and 69.02% AUC, with impressive precision values of 82.21% for P@500 and 66.69% for P@1000.  
These results demonstrate that CLEAR's architecture effectively captures cross-document relational information, with consistent performance improvements across different encoder backbones. The substantial gains in precision metrics (P@500 and P@1000) particularly highlight CLEAR's effectiveness in identifying and prioritizing the most semantically relevant evidence paths. To further validate the statistical significance of these improvements, we conducted paired \(t\)-tests as detailed in Table 3.
# An Optimized Graphene-Based Surface Plasmon Resonance Biosensor for Detecting SARS-CoV-2
## 3 Results and Discussions
### Optimization of Thiol-Tethered ssDNA Thickness  
Now, we examine the impact of ssDNA layer thicknesses from 3.2 to 50 nm. Figure 6 and Table S7 summarize the results, while Figure S4 highlights the sensitivity enhancement trend. Particularly, Figure 6a shows the SPR reflectance curves as the ssDNA thickness increases. With thicker ssDNA layers, the SPR dip shifts toward higher angles and broadens. The baseline configuration, ssDNA\({}_{\text{3.2 nm\_base}}\) (ssDNA in water), maintains a sharp dip, but with increasing thickness, excessive broadening is observed, especially at 30 nm and 50 nm. This broadening negatively affects the sensor's precision, as it reduces the sharpness of the resonance dip.  
Figure 6b and Table S7 evidence the effect of increasing ssDNA thickness on attenuation. Attenuation rises sharply with layer thickness, reaching 96.93% at 50 nm. At 5 nm, attenuation is relatively low (10.34%), providing a strong SPR response with manageable energy loss. This low attenuation at 5 nm suggests that it balances sensitivity with signal clarity, making it preferable over thicker layers that result in substantial reflectance loss.  
The FWHM values in Figure 6c and Table S7 show an increasing trend with ssDNA thickness. Starting from 4.99\({}^{\circ}\) at 3.2 nm, FWHM rises to 5.20\({}^{\circ}\) at 5 nm, remaining within an acceptable range for maintaining a sharp resonance. Beyond 5 nm, FWHM increases considerably, reaching 9.29\({}^{\circ}\) at 20 nm and an excessive 53.86\({}^{\circ}\) at 50 nm. The 5 nm thickness offers a balance, allowing for a well-defined SPR dip essential for accurate and reliable biosensing.  
Figure 6d and Table S7 demonstrate the sensitivity enhancement for each ssDNA thickness, while Figure S4 shows a linear trend in sensitivity up to 20 nm. Sensitivity peaks at 6.84% for a 20 nm thickness. However, this comes at the cost of high attenuation (80.48%) and broad FWHM (9.29\({}^{\circ}\)). For practical purposes, the 5 nm thickness provides a reasonable sensitivity enhancement (2.27%) without the detrimental increase in attenuation and FWHM associated with thicker ssDNA layers. After 20 nm, sensitivity enhancement declines sharply, making thicker layers less effective. Therefore, the 5 nm ssDNA layer offers an optimal trade-off, preserving sensitivity while maintaining low attenuation and a sharp resonance.
## 3 Data Acquisition and Analysis  
### Data Acquisition  
With the support of the China Geological Survey project, one magnetotelluric sounding profile was set up in the study area. The location of survey line is shown in Figure 1b. The magnetotelluric sounding profile started from the Wulungu depression in the south and reached the northwest of the Dulate arc in the north, with a total length of 105 km. There were 100 magnetotelluric sounding points, with an average distance of 1 km. The measuring instrument was an MTU-5A. We measured three mutually orthogonal magnetic field components (Hx, Hy, and Hz) and two mutually orthogonal electric field components (Ex and Ey). The subscripts x and y indicate the north-south direction and the east-west direction, respectively. The acquisition frequency band of the instrument was 0.003 s-3000 s. To reduce the influence of random noise on data quality, the observation time was more than 14 h.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strata** & **Rock Types** & **Resistivity (\(\Omega\) -m)** \\ \hline Quaternary-Perman & \begin{tabular}{c} Mudstone \\ Sandstone \\ \end{tabular} & 560 \\ \hline Carboniferous & \begin{tabular}{c} Mudstone \\ Sandstone \\ Tuff \\ Pyroclastic rock \\ \end{tabular} & 580 \\ \hline Devonian to Ordovician & \
\begin{tabular}{c} Chisst \\ Gneiss \\ \end{tabular} & 3300 \\ \hline Magmatic rock &
\begin{tabular}{c} Diabase \\ Basalt \\ Granite \\ \end{tabular} & 3350 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rock physical property parameters in study area.
# Tube-Based Robust Model Predictive Control for Autonomous Vehicle with Complex Road Scenarios
## 4 Simulation Result and Analysis  
### Co-Simulation Process  
We conducted an in-depth study on the path-tracking performance of robust model predictive control (Tube-RMPC) under various typical vehicle conditions, focusing on model parameter uncertainty in road adhesion coefficients.  
To comprehensively evaluate the performance of the Tube-RMPC controller, the experiments simulated various real-world driving conditions by varying two key parameters. The first is vehicle speed, set at 10 m/s, 20 m/s, and 30 m/s to represent urban driving,highway driving, and high-speed maneuvers, respectively. The second is the road adhesion coefficient, set at 0.4, 0.6, and 0.8, corresponding to wet/icy roads, mixed conditions, and dry asphalt.  
These test conditions provide a rigorous evaluation of the controller's robustness at varying speeds and road friction levels. Based on this foundation, two representative path scenarios were selected for simulation testing: a double lane change test requiring a high vehicle dynamic response and a complex S-curve test involving continuous steering. These scenarios encompass various typical driving conditions and effectively reflect the performance advantages and potential issues of the Tube-RMPC algorithm in dynamic path tracking. Moreover, the predefined reference trajectories, though not exact solutions of the nonlinear dynamics, are approximately feasible and serve as practical benchmarks for robustness evaluation under modeling errors and disturbances and provide detailed simulation and theoretical support for autonomous system design.  
Furthermore, to thoroughly evaluate the control algorithm's performance, this paper compares the results of Tube-RMPC with those of MPC and PID algorithms under identical experimental settings. To ensure a fair comparison, all three controllers--Tube-RMPC, MPC, and PID--were tuned using the same performance criterion: minimizing trajectory tracking error under identical simulation conditions. As noted in the literature, PID and MPC are widely used in vehicle trajectory tracking and serve as effective baselines for evaluating the proposed Tube-RMPC approach.
## 2 Description of Problem
### Mission Evaluation Function  
In the section concerning two\(-\)dimensional goal allocation, an optimization algorithm is implemented to refine task requirements, and the objective function is \(G_{1}\). \(G_{1}\) is formulated as outlined below:  
\[G_{1}=\min:w_{1}*N+w_{2}*L_{1}+w_{3}*L_{2}+w_{4}*\sigma_{1} \tag{1}\] \[\left\{\begin{array}{l}L_{1}=\frac{1}{N}\sum L_{i}\\ L_{2}=\max:\{L_{i}\}\\ \sigma_{1}=\sqrt{\frac{1}{N}\sum(L_{i}-L_{1})^{2}}\end{array}\right.\]  
where \(N\) denotes the number of drones, \(L_{i}\) signifies the path length of each unit within the cluster, with \(i=1,2,\ldots,N\); \(\sigma_{1}\) represents the standard deviation of the path length for each unit in the cluster, and \(w_{1}\), \(w_{2}\), \(w_{3}\), and \(w_{4}\) are weighting coefficients adaptable to various task requirements.  
During the 3D track optimization phase, an optimization algorithm is used. Based on the multi\(-\)objective distribution of the swarm and the two\(-\)dimensional optimized trajectory obtained earlier, the task time is calculated, and optimization analysis is conducted on three indicators. The form of the optimization objective function is \(G_{2}\). \(G_{2}\) is represented below:  
\[G_{2}=\min:\ w_{5}*T_{1}+w_{6}*T_{2} \tag{2}\] \[\left\{\begin{array}{l}T_{1}=\frac{1}{N}\sum T_{i}\\ T_{2}=\max:\{T_{i}\}\end{array}\right.\]  
Ti represents the time allocated for the search task for each unit within the cluster, with \(w_{5}\) and \(w_{6}\) acting as the weighting coefficients. The parameters under optimization include the UAV's climbing and descending angles \(\alpha\), the discernible distance of the photoelectric load \(L_{gd}\), and the observable angle of the photoelectric load \(\theta_{gd}\).
## 5 Simulation and Analysis
### Anti-Attack Ability Brought by NVP
#### 5.1.2 Repetitive Attacks  
The repetitive attacks on an ACD node are simulated to describe the anti-attack ability of a single node for a long time. According to the above simulation, we choose the data with \(N=8\) due to the relatively small \"error\" probability. Figure 8 shows the reduction of attack success probabilities to describe the difference between ACD nodes and other standard nodes without ACD (\(BL_{i}\)) in terms of their defensive ability. The reduction of repetitive attack success probability for \(N=j\) can be represented as  
\[reduction_{j}=e^{-Ts*\overline{p_{A}}_{j}}-e^{-Ts*p_{Aj}}, \tag{34}\]  
where \(\overline{p_{A}}_{j}\) indicates the average \(p_{A}\) of \(BL_{j}\) in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \((N,M)\) & \(BL_{3}\) & (3,2) & - & (3,3) & \(BL_{4}\) & (4,3) & - & (4,4) \\ \hline \(p_{W}\) & 0.6483 & 0.7164 & - & 0.2716 & 0.5838 & 0.4467 & - & 0.1147 \\ \hline \(p_{A}\) & 0.2517 & 0.1574 & - & 0.0156 & 0.3162 & 0.0951 & - & 0.0096 \\ \hline \((N,M)\) & \(BL_{5}\) & (5,3) & (5,4) & (5,5) & \(BL_{6}\) & (6,4) & (6,5) & (6,6) \\ \hline \(p_{W}\) & 0.524 & 0.5458 & 0.2130 & 0.0362 & 0.5508 & 0.4410 & 0.1573 & 0.0248 \\ \hline \(p_{A}\) & 0.376 & 0.2727 & 0.0649 & 0.0063 & 0.3492 & 0.1096 & 0.0190 & 0.0014 \\ \hline \((N,M)\) & \(BL_{7}\) & (7,4) & (7,5) & (7,7) & \(BL_{8}\) & (8,5) & (8,6) & (8,8) \\ \hline \(p_{W}\) & 0.5586 & 0.6282 & 0.3306 & 0.0157 & 0.6006 & 0.5960 & 0.3151 & 0.0163 \\ \hline \(p_{A}\) & 0.3414 & 0.1822 & 0.0473 & \(4.48*10^{-4}\) & 0.2994 & 0.0559 & 0.0106 & \(5.59*10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 7: Probabilities of a node being compromised and working.  
Figure 8 shows the reductions over time. There is an optimal defensive time for each \((N,M)\) pair, where the ACD provides the most effective defense. The optimal \(T\) value is related to the \(OE_{j}\) set and the threshold \(M\). Given the \(OE_{j}\) and \(M\), the optimal defensive duration \(T_{oj}\) is obtained as Equation (34).  
\[T_{oj}=\frac{ln(\overline{p_{Aj}}/p_{Aj})}{r*(\overline{p_{Aj}}-p_{Aj})}. \tag{35}\]  
Results in Figure 8 help network designers find an appropriate value for the migration period \(T\) based on the optimal defensive duration. A small \(T\) indicates a high defensive cost, so based on designers' standpoint long migration periods implicate both lower defensive cost and acceptable security.
## 3 D2 HCP (Distributed Dynamic Host Configuration Protocol)
### Message Format  
All the sent messages are packed in the protocol with the format shown in Figure 2.  
These messages will be encapsulated in turn with the headers corresponding to the MAC or TCP/IP, depending on the type of message to be included in the MESSAGE field.
# Visible Light Communication: A System Perspective--Overview and Challenges
## 3 Applications
### Smart Cities and Smart Homes  
Smart cities are envisioned to provide seamless connectivity between people, government, infrastructure, economy and environment [13; 47]. Most of the functional entities of a smart city are already available around us. However, the reliable, sustainable and high data rate wireless connectivity  
Figure 1: An intelligent transport system using visible light communication (VLC).  
is the bottleneck to connect all the enablers. The already available infrastructure of lightning (street lights, parking lights, billboards) can be utilised to provide high-speed, low energy and sustainable network connectivity for some applications (e.g., utility services) in smart cities whereas freeing up the precious RF spectrum for other mobile applications. Street lights or other lighting sources could be used as a hotspot to provide extremely high data rates to the user.  
A three-layer VLC based communication architecture is proposed to integrate different technologies in smart cities' applications seamlessly [48]. Layer one uses VLC to allow user access and a sense of events. Layer two provides communication between different LEDs and sub gateways. The last layer provides communication between different sub gateways and the service gateway using optical communication. Based on this architecture several applications (such as intelligent communication, event surveillance, and object tracking) have been demonstrated. In [49], authors have investigated optimisation algorithms to provide consistent received optical power and signal to noise ratio (SNR) across a single receiver plane in smart homes.  
In [50] a hardware design and location identification protocol are demonstrated for a VLC-based indoor positioning system for smart supermarkets. The hardware design considers the illumination flickering, brightness, signal synchronisation and noise in the indoor environment. Various practical user conditions have been used to optimise the hardware and software implementation in terms of location accuracy and bit error performance. It is concluded from the experimental work that location ID detectability is 95% for a distance of 0.7 s and under the illumination in diameter of 2 m.