esa-sceva/satcom-chunk-collection huggingface dataset:
# Displays Based on Dynamic Phase-Only Holography
## 3 Spatial Light Modulators (SLMs)
The analogue address known as the Dynamic Random Access Memory (DRAM) is used for driving the nematic LC to a number of grey levels. The digital address known as Static Random Access Memory (SRAM) applies digital bit array sequences to the LCOS. In the SRAM device, analogue modulation depth is achieved by temporal multiplexing. If the response time of the LC is slow relative to the period of the sequence, then the LC gives an average response, which is the required analogue RMS value. For example, a thick LC layer will provide the required average and give higher phase modulation depth than 2\(\pi\). However, additional problems arise in the effective resolution of the device and the frame speed. Due to the growth of the pico-projector and near-eye markets, there has been a push towards high resolution LCOS micro-displays. Developing a phase-only spatial light modulator based upon these existing micro-display backplanes enables the incumbent liquid crystal assembly facilities to be used without significant upfront capital investment. In small pixel LCOS devices, the NLC layer must be designed to minimize the fringing field. Otherwise, when high resolution patterns are used, the available phase retardation from a pixel falls short of what is intended. The effect on the performance of the device is that the resolution is severely reduced at high spatial frequencies. The design of the NLC layer to minimize the effect of the fringing field is considerably assisted by using a thin NLC layer [16]. The frame speed also benefits from a thin NLC layer. A doubling of the cell thickness causes a four times increase in the liquid crystal rise and fall times [17]. For phase only modulation, it is important to delay the illumination of the SLM until the director of the liquid crystal is stable. Therefore, a slower liquid crystal response time directly impacts the available illumination time and reduces the display brightness. The reduced liquid crystal response times may be achieved either by using high birefringence liquid crystals and, thereby, enabling the cell gap to be reduced or by operating the spatial light modulator at more than 45 \({}^{\circ}\)C, which lowers the viscosity. The latter option is not available when SRAM backplane devices are used because the reduced viscosity gives an enhanced phase ripple and consequent noise in the replay field. In practice, all of these techniques are utilized at least partially to achieve an acceptable level of performance from the PSLM. The finished device is equipped with a ceramic backplane for low thermal resistance (see Figure 5). The limited space bandwidth product (SBWP) of current SLMs (1-10 megapixels) does not allow overly complex, off-axis holograms. Therefore, current implementations are restricted to 2D displays. Figure 4: Electrically Controlled Birefringence Mode.
# Study on the Vibration Variation of Rock Slope Based on Numerical Simulation and Fitting Analysis
## 6 Conclusions  
In this paper, two prediction formulas of blasting vibration were modified by multiple linear regression analysis of the measured blasting vibration data. Four working conditions were set for numerical simulation. The blasting vibration response law of slopes with different elevations and the blasting vibration change law of slopes with the same elevation were obtained, and the following conclusions were drawn.  
The blasting vibration prediction formula derived in the present study can be well applied to the prediction and control of the blasting vibration at the slope. By comparing Equations (2) with (3), we can infer that the vertical vibration speed was more significantly affected than the horizontal vibration velocity and that the blasting vibration amplification effect was more significant than the attenuation effect. It is worth noting that Equation (1)  
Figure 14: Blast velocity attenuation rate of feature points B\({}_{1}\)–B\({}_{6}\).  
is not applicable to blast vibration prediction in flat areas. For blast vibration prediction in flat areas, [PERSON]'s formula should be used.  
By analyzing the numerical simulation results, we obtained three conclusions. First, when the horizontal distance (\(D\)) is constant and the height difference (\(H\)) becomes larger, the blast vibration in the vertical and horizontal directions shows an overall attenuation trend. The vertical vibration velocity shows a slight amplification at the position near the top of the slope. Secondly, the change of the blasting vibration speed on the slope surface shows the same regularity. Thirdly, the internal vibration velocity of the slope as a whole shows a decreasing trend from the center to the far side. The above three conclusions can be fully explained by the whipping effect and slope effect, and they verify that the whipping effect and slope effect do affect the change of blast vibration inside the slope.  
Conceptualization, B.Y.; literature search, B.Y.; investigation, B.Y.; experiment, B.Y.; methodology, B.Y.; data analysis, B.Y.; validation, B.Y., Y.L. and S.D.; formal analysis, Y.L.; resources, M.L.; Q.M. and T.L.; writing--original draft preparation, B.Y.; writing--review and editing, M.L.; figures, B.Y.; supervision, M.L.; project administration, Y.L.; funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
Funded by the National Natural Science Foundation of China (Nos. U2006213, U1806230), the Fundamental Research Funds for the Central Universities (201962011), and the Open Foundation of Key Laboratory of Marine Environment and Ecology, Ministry of Education (MGQNLM-KF201804).  
Not applicable.  
The authors would like to thank [PERSON] and [PERSON] for their help during the experiment.  
The authors declare no conflict of interest.
## 5 Conclusions  
In this study, we presented a phenology-based classification method to map multi-year maize and sunflower in Hetao Irrigation District from 2009 to 2015. The main feature of this study is that we used NDVI time series based on HJ-1A/1B 30 m optical imagery as the identification of vegetation parameters, and developed annual maize and sunflower map products with acceptable accuracy. The main conclusions of this study are as follows:  
1. The reconstructed NDVI time series based on HJ-1A/1 BCCD images could represent the phenological characteristics of maize and sunflower in the study area, and the phenological characteristics of these two crops had significant differences in the NDVI increasing period. The crop identification ellipse normalized with mean grassland NDVI_inf as the NDVI characteristic and FGP as the phenological metric were proved to be the optimal identification ellipse. In future studies, other vegetation indexes can also be used as classification factors for comparative analysis.
2. The multi-year spatial distribution of maize and sunflower in the study area could be effectively identified with the Kappa value of consistency test of 0.62. The sunflower classifier performed better than maize.
3. The planting areas of maize and sunflower were increasing during the study years. Maize was mainly distributed in Hangjinhouqi and Linhe, while sunflower mainly in Wuyuan, and the planting sites of sunflower were gradually expanded from Wuyuan to the northern part of Hangjinhouqi and Linhe, and these results were in agreement with the local economic policy.  
This research was partially supported by National Natural Science Foundation of China (grant number: 51479090). We are especially grateful to the research group of Professor [PERSON] of China Agricultural University for providing the data of the verification points in 2014 and 2015. We are also grateful to the real and all the reviewers for their insightful comments and constructive suggestions.  
[PERSON] outlined the research topic, assisted with manuscript writing, and coordinated the revision activities. [PERSON] performed data collection and processing, data analysis, model building, the interpretation of results, manuscript writing, and coordinated the revision activities.  
The authors declare no conflicts of interest.
## 5 Result Analysis
### Effect of the Groove-Weir Ratio on Sealing Performance  
According to Figure 7, when \(\lambda_{c}\) = 48, the average vapor phase volume fraction and leakage rate both increase with an increase in the groove-weir ratio, whereas the opening force decreases with an increase in the groove-weir ratio. When the groove-weir ratio is 0.1, a larger weir area between the two periodic spiral grooves exists, and the groove area is smaller. Therefore, the low-pressure area is formed when the liquid film flow decreases. Moreover, the phase transition area decreases, thus resulting in a lower average vapor phase volume fraction and a higher opening force. As the groove-weir ratio increases, the area occupied by the weir area in the two periods gradually decreases, whereas the groove area gradually increases. Therefore, both the low-pressure area of the liquid film and the phase transition area increase. The average vapor phase volume fraction increases and the opening force decreases. When the groove-weir ratio is 0.9, the weir area between two periodic spiral grooves is the smallest, the groove area and the low-pressure area are the largest, and the degree of phase transition is the highest. The average vapor phase volume fraction increases to the maximum value and the opening force decreases to the minimum value. With an increase in the groove-weir ratio, the exit area of the spiral groove continues to increase. Moreover, the leakage reduction effect continues to decrease, thus resulting in a continuous increase in the leakage rate.  
Figure 7: _Cont._When \(\lambda_{c}\) = 0.1, with an increase in the groove-weir ratio, the average vapor phase volume fraction and the leakage rate both increase, and the opening force decreases accordingly. Compared with the case when \(\lambda_{c}\) = 48, the variation range of the average vapor phase volume fraction is much smaller, and the variation range of the opening force remains unchanged. An increase in leakage rate has gradually decreased, and the relative error has increased from 0.5% to 14%.
## 2 Existing Research  
This section describes the existing research about the mating connector comprising technique and data analysis. Figure 3 illustrates the literature review mapping for the board-to-board connector as well as related topics, including (1) position error mitigation, (2) blind search strategies, and (3) vision force-guided system. These topics are strongly related to the mating process on the Peg-Hole Connector Insertion technique. In addition, the data analysis section discusses probabilistic, machine learning, and encoding approaches. For a quick overview of each existing research approach in this application, please see Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{3}{c}{**Mating Category**} \\ \hline
**Reference** & **Category** & **Sub Category** & **Novelty on Insertion Process** \\ \hline
[2] & Technique & Position Error & Passive Compliance Control \\
[3] & Technique & Position Error & Variable Remote Control \\  & & Mitigation & \\
[4] & Technique & Position Error & Remote Center Compliance \\
[5] & Technique & Position Error & Error Recovery Strategy \\
[6] & Technique & Mitigation & The Tilt Strategy \\
[7] & Technique & Blind Search Strategies & Spiral, Probing, and Binary Search \\
[8] & Technique & Vision-Force Guide & Hybrid Force and Vision \\
[9] & Technique & Vision-Force Guide & Hybrid Force and Vision \\
[10] & Technique & Vision-Force Guide & Robotic Gripper \\
[11] & Technique & Vision-Force Guide & Robotic Gripper \\
[12] & Technique & Vision-Force Guide & Robotic Gripper \\
[13] & Data Analysis & Probabilistic Approach & Analysis on Connector States \\
[14] & Data Analysis & Logistic Regression & Analysis on Visual, Audio, and Force \\
[15] & Data Analysis & Generalization & Fuzzy Analysis for Diagnosis \\
[16] & Data Analysis & Prediction & Speed Analysis \\
[17] & Data Analysis & Machine Learning & Reinforcement Learning \\
[18] & Data Analysis & Machine Learning & Fuzzy Logic Driven Approach \\
[19] & Data Analysis & Machine Learning & Deep Q Learning Prediction \\
[20] & Data Analysis & Machine Learning & Meta-Reinforcement Learning \\
[21] & Data Analysis & Machine Learning & Generative Adversarial Learning \\
[22] & Data Analysis & Machine Learning & Learning From Human Demonstration \\
[23] & Data Analysis & Machine Learning & Assembly Motion Demonstration \\ \hline \end{tabular}
\end{table}
Table 1: Highlights of the literature review.  
Figure 3: Literature mapping review.
## 2 Materials and Methods
### Supernatant Analysis  
The concentration of EDCs in the supernatant was analysed daily for all samples using high-performance liquid chromatography (HPLC) (Agilent Technologies, Santa Clara, CA, USA) equipped with a DAD detector (Agilent Technologies, model 1260 DAD WR) and an Ascents Express C18 column (100 mm \(\times\) 4.6 mm, 5 \(\upmu\)m, Supelco, Merck, Darmstadt, Germany). The mobile phase consisted of acetonitrile (with 0.1% formic acid) and water in a ratio of 30:70 for MeP and 40:60 for the rest of the EDCs detection. The flow rate was set to 1 mL/min. The detection wavelengths were established at 254 nm for parabens and BP and 210 nm for BPA and E. The signal-to-noise method was used to determine the limit of detection (LOD) and limit of quantification (LOQ). A signal-noise (S/N) ratio of three was adopted for LOD determination, and a S/N ratio of ten was selected for LOQ [63]. The results are presented in the Supplementary Material, along with the calibration curves and the working concentration ranges (Table S1).  
The samples from the LOM experiments were centrifuged at 3500 rpm for 20 min. The supernatant was separated from the microalga cell pellets and filtered using a 0.45 \(\upmu\)m nylon membrane. The samples were concentrated with solid-phase extraction (SPE), using 500 mg Extrabond C18 cartridges (Scharlau, Barcelona, Spain), and were sequentially preconditioned with methanol and water. The analytes were finally eluted with 2 mL of methanol, and 10 \(\upmu\)L of the eluent was injected for the HPLC analysis previously mentioned. The concentrations of ammonium and phosphates were determined with 100,683 and 100,798 Spectroquant commercial kits, respectively (Merck, Darmstadt, Germany).  
Supernatants from L, O, and LO experiments were filtered through 0.45 \(\upmu\)m nylon membrane, and subsequently, EDCs were extracted and analysed by HPLC, as described for LOM experiments.
## 4 Experiments  
To verify the effectiveness of our architecture as a universal backbone, we conduct experiments on three representative visual tasks (i.e., image classification, object detection, and semantic segmentation). Specifically, the image classification is done on the Imagenet1K [52] dataset, the object detection is conducted on the COCO2017 [53] dataset, while the semantic segmentation is finished on the ADE20K [54] dataset. Finally, we perform comprehensive ablation experiments to show the important design of our backbone.
## 3 Materials and Methods
### Research Method
#### 3.2.3 Keyword Centrality Analysis  
To uncover the underlying knowledge structure within GAI research and its security implications, a keyword centrality analysis was performed using the document-term matrix created earlier. Centrality is a key metric in network analysis that indicates the importance of nodes--represented here by keywords--within a research network. Keywords with high centrality play a central role in shaping discourse and highlight essential areas of focus. This study utilized three commonly used centrality measures in the text network analysis: degree centrality, closeness centrality, and betweenness centrality [37].  
* Degree centrality measures the number of direct connections that a keyword has within the network, indicating the frequency with which it co-occurs with other keywords. Keywords with a high degree of centrality often co-occur with other significant terms, underscoring their prominence in the research network [38].
* Closeness centrality (also known as proximity centrality) assesses how close a keyword is to all other keywords in the network. It reflects a keyword's ability to connect disparate concepts within the broader research landscape, thereby demonstrating its accessibility and importance [38].
* Betweenness centrality evaluates the extent to which a keyword acts as a bridge between different clusters of keywords. Keywords with high betweenness centrality facilitate the flow of information across disconnected groups, maintaining the coherence of the research network [38].  
This analysis aimed to identify the most influential keywords in GAI and security, offering valuable insights into the core concepts and their interconnections. Understanding these central keywords provides a better perspective on the evolving themes in the GAI security landscape and highlights areas for future research focus.
## 2 The Simplified Frequency-Domain Feedback Active Noise Control Algorithm  
### Estimated the Total Delay  
The secondary path is composed of a sensor, a controller, a primary loudspeaker, a secondary loudspeaker, and a related circuit, as well as the acoustic path between the secondary loudspeaker and the error sensor [6]. Thus, the secondary path impulse response is always composed of electrical delay and acoustic delay components, while the acoustic delay component is composed of a direct acoustic delay and a series of reflected acoustic delays, among which the influence of the reflected acoustic delay is small. However, the electrical delay introduced by the sensor, controller, and related peripheral circuit is negligible and can be disregarded [6]; only the input signal delay from the input to the output end of the controller is considered. As such, the total delay \(\tau\) of secondary path can be approximately expressed as the summation of electrical delay \(\tau_{1}\) and direct acoustic delay \(\tau_{2}\), which is denoted as the delay from the secondary loudspeaker to the error sensor [32]  
\[\tau=\tau_{1}+\tau_{2} \tag{1}\]  
In Formula (1), \(\tau_{1}=Q/f_{s}\), where \(f_{s}\) denotes the sampling frequency. \(Q\) denotes the delay number from the controller input end to the output end of the reference signal. In addition, \(\tau_{2}=d/c\), where \(d\) is the distance between the secondary loudspeaker and the error sensor, and \(c\) represents the sound velocity, which is generally taken as 340 m/s in air.  
The digital controller based on an AD21469 digital signal evaluation board (Analog Devices, Wilmington, MA, USA) was used in the test, and its sampling frequency was 48,000 Hz. The sampling interval is about 2.08 \(\times\) 10\({}^{-5}\) s between two adjacent sampling data. With the aim of improving the computational efficiency, the down sampling technical is adopted. After 20 instances of down sampling, the sampling frequency was 2400 Hz. If the sampling rate and the upper cutoff frequency of primary noise do not satisfy the Nyquist sampling theorem, it will induce signal distortion, induce the loss of high-frequency details, and reduce the system character. If it satisfies the Nyquist sampling theorem, these questions can be avoided. As the sampling rate is 2400 Hz, the upper cutoff frequency of primary noise is no more than 1200 Hz to satisfy the Nyquist sampling theorem.  
Using white noise as the input signal, the electrical delay \(\tau_{1}\) was tested, and 15 sampling points were measured by shorting the input and the output ends of the controller. Thus, we determined that the electrical delay \(\tau_{1}\) was about 6.25 ms.
# Detection of Decision-Making Manipulation in the Pairwise Comparison Method
## 5 Detecting Manipulation Using Machine Learning  
As mentioned in Section 3.5, the primary focus of the presented work is on supervised learning methods. One of the most important tasks when building such models is to choose the most appropriate algorithm for the data's characteristics.  
We can consider PC matrices as mappings in the form \(f:A\times A\to\mathbb{R}\), where the domain of the function f is the Cartesian product of alternatives (or indexes of alternatives), and the values are in the set of real numbers. In this sense, they are similar to raster images understood as mapping a set of points \((x,y)\in R^{2}\) into color space, e.g., the images of handwritten digits in the MNIST dataset [79]. They can be seeded as 2-D images, representing the comparison values, e.g., as shades of gray.  
However, most ML algorithms transform the input object into a vector. This may result in the algorithm's inability to \"see\" certain phenomena in the data. For instance, if the naive manipulation maximizes the values in a single matrix column, visually forming a vertical line, this fact may not be apparent after the data have been flattened, as presented in Figure 4.  
Therefore, exploring ML algorithms that can detect 2-D relationships between values seems reasonable. One of the most prominent classes in this category is convolutional neural networks (CNNs) [76]. Compared to fully connected neural networks, these significantly reduce the number of weights (parameters) by utilizing convolution kernels (also called filters).  
Figure 3: Pairwise comparison matrices for the “advanced” algorithm (\(\alpha=3.3\)) are presented as a heat map. Lighter colors indicate larger values, whereas darker colors indicate smaller ones.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 9 Discussion
### Do Fairness Models Fulfill Their Goals?  
Fairness-aware models in principle make more equitable predictions by imposing constraints or modifying learning objectives. In practice, however, their performance varies greatly by context. Certain models fulfill fairness constraints but at the cost of lowered accuracy or generalizability, particularly on imbalanced or complex data. For example, ad versarial debiasing or reweighting strategies may improve fairness measures at the expense of inducing training instability or undesirable side effects. As our benchmarking shows, how well such models achieve their fairness goals typically depends on dataset attributes such as label skewness, feature correlation with sensitive features, and levels of noise. This disconnect between theoretical guarantees of fairness and real-world performance underscores the need for judicious, context-aware evaluation.