esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Acquisition Module  
#### 2.3.1 Image Acquisition Device  
Figure 5 shows the theoretical data for the crescent-shaped bright area generated by the model eye under infrared illumination at two eccentric positions. Adjusting the eccentricity of the light source can modify the range of refractive error that this steep change region covers [10].  
Figure 4: Overview of this study. We use the collection platform to establish the model-eye dataset and the real-eye dataset. The CFGN is pretrained using model-eye data, and then the model is tuned using real-human-eye data.  
The original eccentric photorefraction method used white light as the light source, leading to significant image errors. The infrared eccentric photorefraction method improves on traditional photorefraction instruments by using infrared LEDs as the light source. To obtain better pupil responses under light stimulation, this method employs an array of multiple infrared LEDs. As shown in Figure 6, simultaneous illumination from multiple LEDs increases light intensity. Since near-infrared light causes minimal eye stimulation, subjects typically do not perceive the measurement process, making infrared light more suitable for eccentric photorefraction.  
As shown in Figure 5, different eccentric light sources have varying measurement ranges and sensitive regions. Therefore, we capture infrared images of a model eye from 1 to 23 eccentric light source positions (a total of 22 eccentric light modes) to obtain the same-ring light source position characteristics and different-ring light source temporal characteristics of the model eyes.  
In Figure 7, the basic mode image serves as a reference image, containing pupil information without eccentric light illumination. To ensure sufficient information while reducing training costs, we selected six grayscale images captured under five different lighting modes and basic modes to form a dataset with six channels of information.  
Figure 5: Theoretical and empirical data on a model eye. The width of this crescent-shaped bright area is not a linear function of the diopter. Once a threshold refractive error is reached, the crescent width increases sharply with the diopter and eventually approaches an asymptote. Beyond this threshold of 2D, the crescent area shows little measurable change.  
Figure 6: (**a**) The top view of the device. (**b**) The front view of the device.
# FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs
## 5 Performance Efficiency of FPGA-QNN
Nevertheless, it outperforms almost all schemes in BRAM utilization, yielding a competitive LUT utilization performance with a small amount of power consumption. To prove the superior performance of the FPGA-QNN, the results of a recent study proposed in [36] are provided. The outputs indicate that FPGA-QNN achieves better results in terms of resource utilization and GOPs/W. To summarize the overall trend, the results comfortably underline the applicability of FPGA-QNN in practical cases, particularly in an embedded device with constrained resources. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Work** & **Model** & **BRAM** & **LUTs** & **GOPs** & **W** & **GOPs/W** \\ \hline FP-BNN [24] & MLP & 2210 & 182 K & 5904 & 26.2 & 225.3 \\ Zhao [37] & VGG & 86 & 25 K & 207 & 4.7 & 44 \\ FINN [23] & LFC & 396 & 82 K & 9086 & 22.6 & 402 \\ FINN-R [38] & MLP-4 & 220 & 25 K & 974 & 2.5 & 390 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparisons between state-of-the-art approaches. Figure 18: Xilinx Vivado power estimation tool and actual power measurement based on quantization levels.
## 5 Experimental Analysis of a Highly Dynamical Scene
### Experimental Scheme  
The NS300 multimode satellite signal simulator generates a highly dynamical GPS RF signal, and the RF signal is received by the GN0204 satellite signal receiving device, converted into an intermediate frequency signal and stored. Then we use the SINS/GPS ultra-tight coupling softwareto receive the intermediate frequency signal processed by the system platform so that it is convenient for further testing of the proposed coupling structure and the adaptive performance of the algorithm for highly dynamical conditions. In the experiment, the SINS information is generated by the digital emulator, and the information of SINS and GPS is generated according to the pre-established standard track, so as to ensure the information equivalence between them at the same time. The structure of the scheme is shown in Figure 2.  
The signal tracking experiment is compared based on the generated data with five algorithm combinations: The first method is the designed ultra-tightly coupled structure based on the double loops, and the proposed robust adaptive CKF algorithm is used to estimate and track parameters, which is abbreviated as DLB-RACKF. The structures of the second method and third method are the same as the first method. The difference is that the algorithms used in the filtering algorithms are CKF and UKF, respectively, similarly, the corresponding methods are abbreviated to DLB-CKF and DLB-UKF. To prove the superiority of the proposed algorithm, we have further completed the comparison between similar algorithms. The fourth and fifth algorithms are respectively selected from two typical improved CKFs mentioned in the introduction, namely the STCKF algorithm proposed by [19] and the AHCKF algorithm proposed by [26]. Similarly, the corresponding methods are abbreviated to DLB-STCKF and DLB-AHCKF.
# An Improved Reference Paper Collection System Using Web Scraping with Three Enhancements
## 9 Evaluation and Analysis
### Limitations
#### 9.5.1 API Rate Limits and Format Discrepancies  
Several APIs limit the number of requests that can be made in a given time, especially when processing large volumes of data. _Semantic Scholar API_ returns up to 1000 papers per request, if more paper matches are found, the API returns a continuation token to continue fetching up to a maximum of 10,000,000 papers [44]. _DOAJ API_ imposes a rate limit of two requests per second but allows bursts of up to five requests to be queued to maintain that average and a page size limit of 100 articles per page [45]. _PubMed API_ limits 3-10 requests per second with a maximum of 10,000 articles that can be fetched in a single request [46]. The proposed system restricts data retrieval to a maximum of 100 articles per query per API to ensure compliance with rate limits and maintain stable access.  
Several APIs return data in different formats, requiring data standardization and integration. For example, _Semantic Scholar_ and _DOAJ_ APIs return results in _JSON_ format, while _PubMed_ uses _XML_, requiring different data parsing and transformation techniques. In terms of data quality, _Semantic Scholar_ provides fairly complete metadata, but we observed some incomplete field values such as discipline, journal volume, article pages, citations, and PDF URLs. In _DOAJ_, incomplete field values include the number of citations and article pages, although PDF URLs are generally complete due to the high number of _open-access_ articles. _PubMed_ offers well-organized metadata managed by NCBI, particularly detailed metadata retrieved from _PubMed Central_, including open-access PDF URLs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Case(s)**} & **Semantic Scholar** & **DOAJ** & **PubMed** \\ \cline{2-4}  & **Percentage (\%)** & **Percentage (\%)** & **Percentage (\%)** \\ \hline Case 1 & 84.62\% & 7.69\% & 7.69\% \\ Case 2 & 16\% & 42\% & 42\% \\ Case 3 & 21.21\% & 4.55\% & 74.24\% \\ Case 4 & 25\% & 54\% & 21\% \\ Case 5 & 13\% & 38\% & 49\% \\ \hline Average & **32\%** & **29\%** & **39\%** \\ \hline \hline \end{tabular}
\end{table}
Table 10: Percentage of Web APIs in providing accessible PDFs.
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 6 Evaluation
### Scenarios  
Initially, we employed 26 Tmote Sky nodes (1 sink, 19 fixed, and 6 mobiles nodes), as is presented in the topology of Figure 4.  
In this scenario, there are 9 source nodes (nodes 12-20, white), 10 relay nodes (nodes 2-11, light grey), and 6 mobile nodes (nodes 21-26, dark grey). The mobile nodes are initially placed near the sink in a sleep mode until the moment that is required by the network, as described in [6]. It is clear that based on the topology, two nodes (3 and 8) become congested due to their placement in the network. Their location is critical as these nodes are receivers from many paths and, at the same time, are the nodes that provide the path towards the sink.  
\begin{table}
\begin{tabular}{l l} \hline Simulator/OS & COOIJA/Contiki 3.0 \\ Protocol & Contiki Multihop/Rime \\ MAC & ContikiMAC/CSMA \\ Simulation Time & 15 min \\ Emulated Mote & Tmote sky \\ Number of Nodes (Sink/Fixed/Mobile) & 1/19/6 \\ Transmission Range (m) & 25 \\ Max Data Rate (kbps) & 250 \\ Queue Length (Pkts) & 8 \\ Packet Size (Bytes) & 48 \\ Initial Source Rate (Pkts/s) & 25 \\ Rate Increase & 50 pkts/s every 1 min \\ Mobile Node Speed & 0.65 m/s \\ \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 4: Initial Topology.  
To evaluate our algorithms, we used congestion as the case study. For this reason, we employed DAIPaS [31], a resource control congestion control algorithm. DAIPaS employs a dynamic way to control topology without adding any extra load to the network. Initially, during the topology control phase, it builds a spanning tree from sink to source, sorting the nodes in accordance with their level (distance in hops from the sink). Thus, every node that is going to transmit data searches in its neighbor table and finds the most appropriate node, the one with the lowest level (closer to the sink), and transmits its data through this node.  
When congestion occurs, each node searches in its neighbor table and finds the most appropriate node based on parameters, such as level and energy. The process iterates until there are no available paths from the source to the sink. In this case, the DAIPaS algorithm stalls. This is exactly the point where our proposed algorithms start to run.  
In the next subsections, we present the topologies after the execution of the Node Placement Algorithms--NPA (cf. Section 4) and the Dynamic MobileCC+ (Energy Node Placement Algorithm--eNPA) (cf. Section 5).
## 3 DCPSO with Cooperation
### Standard Particle Swarm Optimization (PSO)  
Particle swarm optimization (PSO) is a population-based heuristic method. It was first proposed by [PERSON] and [PERSON] in 1995, who were inspired by the social behavior of bird flocks. The PSO algorithm is widely used in optimal scheduling [37; 38], path-planning [11; 23], big data digging [39], and so on. PSO owns several evolutionary characteristics similar to genetic algorithms (GA), such as initialization with a population of random solutions and updating based on previous generations. However, PSO has fewer setting parameters, simpler implementation, and a faster convergence rate compared with GA.  
A swarm of particles are randomly initialized in the PSO algorithm. Each particle has three properties, which are position, velocity, and fitness. Assuming a swarm of n particles in a \(D\)-dimensional optimization problem, the velocity and position of particle \(i\) are denoted as \(V_{i}=(v_{i1},\ldots,v_{iD})\) and \(X_{i}=(x_{i1},\ldots,x_{iD})\), respectively. The fitness of particle \(i\) can be solved by the fitness function. The best solution of particle \(i\) found so far is represented as \(p_{\text{best}i}=(p_{i1},\ldots,p_{iD})\), generally known as the personal best location and the best position found by all particles so far is denoted as \(g_{\text{best}}=(g_{1},\ldots,g_{D})\), known as the global best location. In each iteration, each particle's velocity and position are updated using the following equation:  
\[v_{id}(t+1)=\omega(t)v_{id}(t)+c_{1}r_{1}(p_{id}(t)-x_{id}(t))+c_{2}r_{2}(g_{d }(t)-x_{id}(t)) \tag{8}\]  
\[x_{id}(t+1)=x_{id}(t)+v_{id}(t+1) \tag{9}\]  
where \(i=1,2,\ldots,n\), \(d=1,2,\ldots,D\), \(t=1,2,\ldots,T\), \(T\) is the maximum generation number, \(\omega\) is the inertia weigh, \(c_{1}\) and \(c_{2}\) are acceleration coefficients which are typically set to \(c_{1}=c_{2}=2\), and \(r_{1}\) and \(r_{2}\) are randomly generated within \([0,1]\), which are used to keep the diversity of the swarm. From Equation (8) we can see that the velocity updating term consists of three parts: \(\omega(t)v_{id}(t)\), which is the inherited velocity from previous generation, reflecting the particle's memory property; \(c_{1}r_{1}(p_{id}(t)-x_{id}(t))\), which is the cognitive learning velocity from its history, attracting the particle to itshistorical best position; and \(c_{2}r_{2}(g_{d}(t)-x_{id}(t))\), which is the social learning velocity from the swarm history, attracting the particle to the historical best position of the swarm. Generally, for fear of a blind search, the velocity and position of each particle are limited to \([-V_{\max},V_{\max}]\) and \([X_{\min},X_{\max}]\).
## 2 Overview of PAL-XFEL
### Beamlines
The focusing optics of the NCI involve a microfocusing Kirkpatrick-Baez (K-B) mirror system [23], which gives a typical focus size of less than 5 \(\upmu\)m. Each experimental technique of the NCI can be performed in a vacuum or helium atmosphere using a dedicated chamber, and all chambers have pump laser capability for the time-resolved experiments. The coherent X-ray imaging (CXI) endstation utilizes a single-shot-based fixed target raster scanning for the sample delivery [22]. This technique can be used to investigate ultrafast phenomena on nanometer and picosecond scales. It has recently been reported that nanoscale characterization, such as morphology and strain distribution, is possible at a resolution of 20 nm using our equipment [24]. In addition, a multiplexing technique for CXI experiments is currently in preparation, which enables the simultaneous measurement of SAXS, WAXS, and XES from a single XFEL pulse. The SFX endstation provides sample injectors developed for particle solution or carrier matrix delivery [21], as well as 1D or 2D fixed-target sample delivery systems, so that fresh samples can be delivered for every XFEL pulse [25; 26]. In recent SFX experiments conducted at the PAL-XFEL, the frequency of use of fixed-target scans has increased, which allows the solving of a protein structure with only a few tens of microliters of microcrystals. There are mainly two endstations in the soft X-ray scattering and spectroscopy (SSS) beamline. The resonant soft X-ray scattering (RSXS) endstation [27] was newly launched in 2020. The technique can take the advantages of both spectroscopy and scattering experiments in the soft X-ray range of the PAL-XFEL. The sample is mounted on a six-axis cryostat manipulator, and an avalanche photodiode detects the signal in a \(\pi\)-polarization configura  
Figure 1: Schematic of PAL-XFEL beamlines. XSS: X-ray scattering and spectroscopy; NCI: nanocrystallography and coherent imaging; SSS: soft X-ray scattering and spectroscopy; FXS: femtosecond X-ray scattering; FXL: femtosecond X-ray liquidography; CXI: coherent X-ray imaging/scattering/spectroscopy; SFX: serial femtosecond crystallography; XES/XAS: X-ray emission/absorption spectroscopy; RSXS: resonant soft X-ray scattering. tion. We plan to introduce a controllable magnetic field for extended sample environment and detection capability in a \(\sigma\)-polarization plane. The X-ray emission/absorption spectroscopy (XES/XAS) endstation [28; 29] aims to make photon-in/photon-out spectroscopies possible for all sample phases (solid, liquid, and gas). In addition to bulk and thin-film samples, it is currently possible to use gaseous samples from a pulsed electrospray.
## 2 The Mathematical Modeling of OPF Objective Function
### Objective Function
#### 2.3.2 Active Power Loss (APL)  
Transmission lines always experience power loss because of wire resistance. As a result, active power loss is a significant variable to consider. The mathematical model for power loss is provided below in Equation (22):  
\[F_{2}(x,y)=\sum_{j=1}^{NTL}\sum_{i=1}^{NL}g_{ij}\Big{(}V_{i}^{2}+V_{j}^{2}-2V_{ i}V_{j}cos\theta_{ij}\Big{)} \tag{22}\]  
where \(g_{ij}\) is the conductance value of the transmission line between buses \(i\) and \(j\). \(V_{i}\) and \(\theta_{i}\) are the voltage magnitude and angle of bus \(i\), while \(V_{j}\) and \(\theta_{j}\) are the voltage magnitude and angle of bus \(j\).
## 3 Hydro-Mechanical Coupled Model for Fractured Rock Mass
### Governing Equations
The software ABAQUS adopts the direct coupling method based on [PERSON]'s consolidation theory, and the virtual work principle and effective stress principle are used to discretize the finite element on the basis of the stress-strain relationship and flow law. The coupling mathematical model is established [73] via:  
\[\left[ \begin{array}{cc}\overline{K}&C\\ \mathrm{E}&G\end{array} \right]\!\frac{d}{dt}\left\{ \begin{array}{cc}\overline {u}\\ \overline{P}_{w}\end{array} \right\}+\left[ \begin{array}{cc}0&0\\ 0&\mathrm{F}\end{array} \right]\left\{ \begin{array}{cc}\overline{u}\\ \overline{P}_{w}\end{array} \right\}=\left\{ \begin{array}{c}\frac{d\overline {i}}{d\overline{i}}\\ \mathrm{f}\end{array} \right\} \tag{17}\]where \(\overline{\upu}\) is the nodal displacement of the element, \(\overline{\text{p}}_{\text{w}}\) is the nodal pore pressure of the element, \(\overline{\text{K}}\) is the stiffness matrix, C is the nodal force corresponding to the pore pressure of the node, E is the volume change corresponding to node displacement, G is the water storage matrix, F is the seepage matrix, \(\overline{t}\) is the nodal load, and \(\overline{t}\) is the nodal flow. The mechanism of a hydro-mechanical coupled system is shown in Figure 8a. In the actual process of fluid flow, on the one hand, due to the change of water pressure in the fracture, the system will cause a change in the effective stress on the fracture surface (Equation (15)). On the other hand, the change in effective stress will lead to a change in the fracture aperture (Equation (7)), and the permeability also change (Equation (13)); these changes will have an effect on the flow and distribution of water pressure. The solution process is based on ABAQUS (Equation (17)). During the solution of ABAQUS, CPE6 element is used for the rock matrix, and CPE6 MP element is used for the fracture as shown in Figure 8b. A subroutine is used to obtain the normal stress and flow velocity for each fracture element and to calculate the ultimate permeability coefficient \(\text{K}_{\text{F}}\). In order to solve the coupled mathematical model, ABAQUS also needs to set corresponding definite solution conditions, such as the stress boundary, displacement boundary condition, and initial head boundary.
# A Recursive Least-Squares with a Time-Varying Regularization Parameter
## 3 Regularization Parameter Selection  
The optimal regularization parameter can be set to minimize the MSE error of the estimate as follows [23, 24, 25]:  
\[\gamma_{\text{opt}}=\operatorname*{arg\,min}_{\gamma}\text{MSE}, \tag{6}\]  
where \(\text{MSE}:=\mathbb{E}[\|\mathbf{x}-\hat{\mathbf{x}}_{\text{RLS}}\|_{2}^{2}]\). Practically, \(\gamma_{\text{opt}}\) does not have a closed-form solution because the actual vector \(\mathbf{x}\) is unknown. Some methods are proposed in the literature that assume a specific structure of the noise vector \(\mathbf{z}\) and/or data vector \(\mathbf{x}\). For example, if the noise vector \(\mathbf{z}\) is zero-mean with i.i.d. elements, and \(\mathbf{x}\) is of zero-mean and independent components, the optimal regularization parameter can be approximated as follows [23]:  
\[\gamma_{\text{opt}}\approx\frac{n\sigma_{\mathbf{z}}^{2}}{\text{tr}[\mathbf{ R}_{\mathbf{x}}]}, \tag{7}\]  
where \(\sigma_{\mathbf{z}}^{2}\) is the variance of \(\mathbf{z}\) and \(\mathbf{R}_{\mathbf{x}\mathbf{x}}\triangleq\mathbb{E}[\mathbf{x}\mathbf{x}^{ T}]\) is the covariance matrix of \(\mathbf{x}\). From Equation (7), we observe that optimal tuning of the regularization parameter should take into account any variation in the noise of signal second-order statistics. In RLS, computing a new regularization parameter at each iteration will increase the computational complexity.  
In the following section, we present a method for recursively updating regularization parameter in a computationally efficient manner.
## 3 Traumatic Corneal Injuries
### Celestial Dust Hazards  
Lunar and Martian dust particles present unique challenges to ocular health due to their sharp, abrasive characteristics, high adhesiveness, and reactive surfaces. Unlike terrestrial dust, which undergoes weathering processes, celestial dust and larger particles remain jagged and highly reactive because of their formation in low-gravity and vacuum conditions. These abrasive properties make them particularly harmful when they encounter human tissue, including the eyes [46; 47].  
During the Apollo missions, astronauts frequently reported ocular discomfort and irritation caused by lunar dust introduced into spacecraft after EVAs on the lunar surface. Efforts to brush or vacuum dust off spacesuit prior to cabin entry were largely ineffective, leading to widespread contamination within the cabin. This resulted in acute symptoms, such as watery eyes and erythema, which could impair vision and performance during critical mission tasks [48; 49]. These experiences underscored the limitations of current protective measures and the need for improved protocols.  
The small size of lunar dust particles, often less than 20 um, increases the risk of inhalation and ocular exposure. This microscopic dust as well as larger particles not only cause mechanical abrasions due to their sharp edges but can also generate reactive oxygen species (ROS) when interacting with human tissue, contributing to oxidative stress and delayed epithelial healing. Prolonged exposure during long-duration missions could exacerbate these effects, leading to chronic damage to the cornea and other ocular structures [21; 46; 47].  
To mitigate these risks, advanced protective measures are essential. High-efficiency particulate air (HEPA) filtration systems within spacecraft can significantly reduce airborne dust, while shielded goggles and improved spacesuit designs aim to minimize dust transfer into habitats [48; 49]. Additionally, innovations such as bioengineered tear substitutes with anti-inflammatory properties and sustained-release formulations offer promising therapeutic options to manage irritation and enhance recovery following dust-related injuries [34].  
Future planetary exploration missions must prioritize understanding the long-term impacts of celestial dust and larger particulate matter on ocular health. Research into its mechanical and chemical effects, coupled with the development of robust protective and therapeutic strategies, is critical to ensuring astronaut safety and the success of extended space missions.