esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Developing Design Method for Constant-Amplitude Fatigue  
Based on the obtained S-N curve, the design method for the fatigue of M60 high-strength bolts under constant amplitude could be developed using the nominal stress amplitude [\(\Delta\sigma\)] as the design parameter. The checking formula is given by:  
\[\Delta\sigma\leqslant[\Delta\sigma] \tag{6}\]  
\[[\Delta\sigma]=\left(\frac{C}{N}\right)^{1/\beta} \tag{7}\]  
where \(\Delta\sigma\) is the nominal stress amplitude of the high-strength bolt connection: \(\Delta\sigma=\sigma_{\text{max}}-\sigma_{\text{min}};\sigma_{\text{max}}\) indicates the maximum stress on the high-strength bolt connection; \(\sigma_{\text{min}}\) represents the minimum stress on the high-strength bolt connection; [\(\Delta\sigma\)] denotes the corresponding allowable stress amplitude; \(C\) and \(\beta\) are constants: \(C\) = 1.3326 \(\times\) 10\({}^{6}\) and \(\beta\) = 2.4876; \(N\) is the loading cycle number, i.e., fatigue life.  
Taking an \(N\) of 2 \(\times\) 10\({}^{6}\) as the reference, Equation (7) calculates the allowable stress amplitude of the M60 high-strength bolt as [\(\Delta\sigma\)]2\({}_{\times 10}\)6 = 63.198 MPa.  
Table 4 and Figure 10 compare the experimental results with the literature values for M20 high-strength bolts [11], M30 high-strength bolts, and M39 high-strength bolts [12] to assess the M60 high-strength bolt's fatigue properties. The fatigue strength of M60 high-strength bolts did increase as the diameter of the bolt grew.  
Figure 9: The M60 high-strength bolt’s failure modes under fatigue loading. (**a**) the fatigue fracture of all specimens; (**b**) The first fatigue failure form of specimens (**c**) The second fatigue failure form of specimens.
## 3 Proposed Control Methods
### Power Matching
At the same time, the error calculated by the loss function may become very large, making the optimization process unstable and difficult to converge. This paper proposes an input enhancement method to address these issues. During the training process, this method demonstrated both faster training speeds and more stable model performance. This method replaces zero input data with known target output data after minor scaling, thereby avoiding the numerous problems mentioned above. There is an input data vector \(x\) containing many zero-value elements and a target output data vector \(y\). The aim is to replace the zero values in \(x\) with scaled-down values. Let the scaling value be \(\alpha\), where \(0<\alpha\ll y\) (meaning it is a small constant much less than target data). Then, this method can be expressed as:  
\[{x_{i}}^{\prime}=\left\{ \begin{array}{ll}x_{i},&\text{if }x_{i}\
eq 0\\ \alpha,&\text{if }x_{i}=0\end{array} \right. \tag{17}\]  
where, \(x_{i}^{\prime}\) is the \(i\)-th element of the updated input data vector \(x^{\prime}\). If the original input data \(x_{i}\) is not 0, it remains unchanged; if it is 0, it is replaced by the target output data \(y_{i}\) multiplied by a small scaling coefficient \(\alpha\). It should be noted here that if the value of \(\alpha\) is too large, it will affect training accuracy. Setting a small, non-zero value only serves to avoid issues such as anomalies in weight updates to some extent. In our work, the choice of \(\alpha\) was primarily guided by empirical observations and iterative experimentation through extensive trial-and-error. The complete data processing flow is illustrated in the Figure 5. After these steps are completed, frequency regulation can be performed. As shown in Figure 5, the data processing workflow involves several key steps. The process begins by using each DG's output power as the power reference value input for the droop controller and collecting input-output datasets from frequency deviation elimination scenarios in secondary control. Data enhancement processing is then applied to address zero-value inputs in the training dataset. After the construction of an SNN model and completion of network training, dynamic timing regularization is performed to align data points more precisely, which improves prediction accuracy. This approach enhances both frequency elimination and frequency deviation correction. Finally, the entire data processing workflow is completed.
# Quantifying the Occurrence of Multi-Hazards Due to Climate Change
## 2 Materials and Methods  
### Area of the Study and Model Datasets  
The study area included the country of Greece. The country presents several climatic variations, always in the Mediterranean climate frame, due to the influence of its vivid geomorphologic complexity (interplay of mountainous regions and plains, extended coastline, and numerous islands) on the different atmospheric-pressure dependencies from the Atlantic, central Mediterranean area, Eurasia and North Africa. This enhances the need for higher-resolution climate modeling to resolve the topography features more effectively.  
In the present work, climate data of EC-Earth (1.125\({}^{\circ}\) horizontal resolution originally) downscaled by the WRF-ARW version 3.6.1 model to 5 \(\times\) 5 km\({}^{2}\) were employed at a temporal resolution of 6-h. The hindcast climate simulations have been extensively evaluated in our previous works, whereby exhaustive quantitative validation of the highly resolved fields of temperature, precipitation, wind speed and solar radiation were performed for our observations [45, 47, 48, 49, 50]. The WRF-ARW modeling domain covering Greece comprises a grid of 185 \(\times\) 185 cells in the horizontal and 40 levels in the vertical that are arranged according to terrain, following the hydro-static-pressure vertical coordinates (up to \(\sim\)50 mbars). A more detailed description of the WRF model setup and physical parameterization schemes can be found in [47].  
For the simulations of future years under the influence of climate change, the two IPCC greenhouse-gas-emission scenarios, RCP4.5 and RCP8.5, were selected as they constitute the most commonly used scenarios by impact-assessment modelers. In particular, RCP4.5 represents an increase in the radiative forcing of the atmosphere of 4.5 W/m\({}^{2}\) relative to the pre-industrial era with a profile of greenhouse-gas emissions increasing until the mid-century (\(\sim\)2050) and stabilizing thereafter until the end of the century (2100). On the other hand, RCP8.5 is considered to be the most extreme scenario with greenhouse-gas emissions increasing sharply until the end of the century, implying at its end a radiative forcing of 8.5 W/m\({}^{2}\) relative to the pre-industrial era. The future time periods in the RCP4.5 and RCP8.5 simulations were selected in order to study the projected climate-change effects on hazard dynamics, both in the middle and near the end of the 21 st century. For the present analysis, the model-downscaled data that were used and the corresponding 25-year-period slots are presented in Table 1.
# Grassland Wildfires in the Southern Great Plains: Monitoring Ecological Impacts and Recovery
## 4 Discussion
### Use of Remotely Sensed Information for Monitoring and Assessment of Wildfire Recovery  
Information about the recovery of the grasslands can support a wide range of efforts in post-fire recovery and mitigation. One critical need following a grassland fire is the provision of feed for livestock, since pastures for grazing cattle is the dominant use of grassland in this region. To ensure long-term recovery of the grassland, deferral or careful grazing management is needed post-fire ([PERSON] et al., 2019; [PERSON] et al., 2019), but there are few quantitative guidelines on how to balance short-term needs for forage with longer term sustainability of the grassland ecosystem. The GPP estimates the biomass productivity, providing needed information to guide stocking rates and the duration of differences in burned and unburned portions of a ranch. In the Northern Great Plains, [PERSON] et al. (2019) found that defoliation following a spring fire increased grassland productivity for 2 years following a wildfire and had minimal impact on community composition. They concluded that a post-fire rest from grazing may not be necessary to maintain productivity and species composition in a mixed-grass prairie. [PERSON] et al. (2019) also found that prescribed burning increased crude protein levels during the first year after burning in a semi-arid steppe rangeland. In an evaluation of fire-grazing interactions in two northern Great Plains pastures, [PERSON] et al. (2019) found that cattle showed significant preference, use, and grazing utilization in recently burned patches that declined as time since fire increased. Cattle selection was driven by significantly increased crude protein in recently burned areas. They concluded that mixed-grass pra prairie of the Northern Great Plains are resilient to the fire-grazing interaction and that rest from grazing following fire is not required to sustain the ecological system. The results of this study in the Southern Great Plains (Figure 5) indicate that vegetation regrowth was very rapid and productivity in burned areas was very high, indicating that utilization of forage may be possible during the summer and fall following a wildfire. [PERSON] et al. (2019) proposed recoupling fire and grazing for landscape management on agricultural grasslands which would have the benefit of sustained productivity and reduced wildfire risk.
# FD-LTDA-MAC: Full-Duplex Unsynchronised Scheduling in Linear Underwater Acoustic Chain Networks
## 3 Simulation Scenarios  
### Linear UWA Chain Full-Duplex Network  
The full-duplex based underwater acoustic network scenarios with line topology studied here are representative of the subsea asset (pipeline) monitoring scenario depicted in Figure 1. A pipeline is deployed at a depth of 480 m and then connected through a riser to the platform. The network is made up of multiple transmitting sensor nodes and a sink node arranged in line multi-hop fashion, such that each node connects to a node one hop closer to the sink node and to a node one hop further down the chain. The sink node sends REQ packets to the transmitting nodes. The transmitting nodes propagate the REQ packets down the chain to the last node. The transmitting sensor nodes either send their own packet up the chain or forward packet(s) up the chain after receiving them from a node further down the chain upon receiving an REQ packet.  
The nodes in the chain network topology are able to operate in full-duplex fashion. Figure 4 depicts full-duplex communication in a underwater chain network, where the relay nodes are able to transmit and receive simultaneously in time and frequency. This allows the nodes to send and receive REQ or data packets in-band thereby potentially improving spectrum reuse.  
The scenarios are categorised as small, medium and large scale in accordance with the pipeline length. In each of the scenarios, the maximum sea depth is considered to be 500 m. Different scenarios are described in Table 1. The scenarios range from very small networks with few nodes to long pipelines comprising many nodes. For all the scenarios, sensor nodes are spread across the length of the pipeline at equidistant points of 1 km/2 km based on the acoustic modem range. An acoustic modem range of 1 km is considered to be a reliable range and 2 km approaching the range limit. The nano-modem [25] assumed in this work has the advantage of low cost, which makes it feasible to consider deploying a large number of monitoring devices. Another benefit for considering short range acoustic communication is the provision of regular monitoring points for the detection of problems such as leaks and movement of pipelines.
# Wood Defect Detection Based on Depth Extreme Learning Machine
## 3 Experimentation
### Empirical Method and Result  
The specific experimental process is shown in Figure 7. First, we preprocessed 5000 original images via NSST and randomly selected 4000 images for training. Second, for each pixel in each image, the neighborhood subgraph was taken as the input of CNN, and a total of 40,000,000 samples were obtained as the experimental training set to train the network model. The remaining 10,000,000 samples were used as test images to evaluate the algorithm. The features extracted from the test samples were input into the ELM network classifier, the number of hidden layer nodes of the extreme learning machine was set to 100, then the accuracy and stability of the feature extraction method was statistically analyzed. We found that when the number of iterations exceeds 3500, the loss function is around 0.2 and the convergence performance is acceptable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Excitation** & \multicolumn{2}{c}{**Classification Accuracy Rate (\%)**} & \multicolumn{2}{c}{**Number of Hidden Node**} \\ \cline{2-5}
**Function** & **GA-ELM** & **ELM** & **GA-ELM** & **ELM** \\ \hline Sigmoid & 95.93 & 93.85 & 70 & 90 \\ Hardlim & 93.98 & 91.37 & 90 & 170 \\ Radbas & 95.37 & 93.36 & 110 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy of GA-ELM and ELM under different excitation functions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Type** \\ \hline Server & Intel(R)Xeon(R)CPU E5-4603v2 \\ Quantity of physical CPUs & 2 \\ Main frequency of CPUs & 2.20 GHz \\ Memory & 16 GB \\ Experimental platform & Anaconda 3.5, \\ Microsoft Visual Studio 2017 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameters.  
Figure 8a shows the relationship between the training loss value and the number of iterations. Although the training loss value fluctuates a little during the iteration process, it shows a downward trend as a whole. When the iteration is completed, the training loss value was around 0.2. Figure 8b shows a graph of accuracy. When the number of iterations was 1500, the accuracy of the proposed algorithm reached 90%. Accuracy continued to increase as iteration quantity increased until reaching a maximum of about 98%.  
Figure 8: _Cont._  
Figure 7: Flowchart of wood defect feature extraction and classification process.  
Figure 9 shows our final recognition effect on the test set. We surround the identified wood defects with different colored rectangular borders
# SiamCAM: A Real-Time Siamese Network for Object Tracking with Compensating Attention Mechanism
###### Abstract  
The Siamese-based object tracking algorithm regards tracking as a similarity matching problem. It determines the object location according to the response value of the object template to the search template. When there is similar object interference in complex scenes, it is easy to cause tracking drift. We propose a real-time Siamese network object tracking algorithm combined with a compensating attention mechanism to solve this problem. Firstly, the attention mechanism is introduced in the feature extraction module of the template branch and search branch of the Siamese network to improve the feature representation of the network to the object. The attention mechanism of the search branch enhances the feature representation of both the target and the similar backgrounds simultaneously. Therefore, based on the above two-branch attention, we propose a compensated attention model, which introduces the attention selected by the template branch into the search branch, and improves the discriminative ability of the search branch to the object by using the feature attention weighting of the template branch to the object. Experimental results on three popular benchmarks, including OTB2015, VOT2018, and LaSOT, show that the accuracy and robustness of the algorithm in this paper are adequate. It improved occlusion cases, similar object interference, and high-speed motion. The processing speed on GPU reaches 47 fps, which can achieve real-time object tracking.  
object tracking; Siamese network; attention +
Footnote †: journal:
# Fair and Transparent Student Admission Prediction Using Machine Learning Models
## 3 Materials and Methods
### Law School Dataset Description  
The Law School Dataset [37] was compiled from a 1991 survey conducted by the Law School Admission Council (LSAC) across 163 U.S. law schools. This dataset includes a variety of candidate information relevant to admissions decisions, such as undergraduate GPA, LSAT scores, demographic details, and socioeconomic backgrounds. The prediction tasks focus on determining whether a candidate would pass the bar exam or forecasting a student's first-year grade average. In this research, we will focus only on predicting whether a candidate will pass the bar exam. For our research purposes, we will use that dataset as curated by [PERSON] [38]. This dataset is characterized by 12 features:  
* 3 categorical (fulltime, fam_inc, tier);
* 3 binary (Gender, Race (White, Non-white), Pass_Bar);
* 6 numerical (decile1b, decile3, lsat, uppa, zfygpa, zgpa).  
For the Law School Dataset, the calculated imbalance ratio is 9.18, indicating a significant disparity between majority and minority outcomes. The gender ratio (men to women) is computed at 1.29, while the race ratio (White to Non-white) is 14.56, highlighting pronounced racial imbalances.  
These ratios underscore the unique characteristics of the dataset, making it particularly well suited for research on fairness in educational settings, particularly in the context of university admissions. The imbalance ratio reflects the uneven distribution of the target variable, which can influence classification performance and necessitate careful model evaluation to avoid biased outcomes. Meanwhile, the gender and race ratios emphasize the importance of addressing demographic disparities to ensure equitable decision-making.  
This dataset provides a valuable testbed for developing and evaluating algorithms and methodologies aimed at reducing bias, promoting fairness, and ensuring that admissions decisions are transparent and justifiable. It supports investigations into critical questions, such as the impact of systemic inequalities, the role of demographic features in predictive modeling, and the potential trade-offs between accuracy and equity in admissions processes.
## 5 Common Systems
### Timing and Synchronization
#### 5.1.4 Optical Master Oscillator Synchronization  
For optimized overall phase noise performance, including the lower acoustic frequency regime, the OMO is synchronized to a master oscillator (MO, SMA 100A from Rohde & Schwarz GmbH) at 2.9988 GHz, which itself is locked to a 10 MHz Rubidium frequency standard (SRS FS725). The synchronization is implemented with a phase-locked loop (PLL), for which the master oscillator serves as reference input and the photo-detected filtered OMO signal coupled out from the harmonic extraction unit output is used as the comparator signal. Both are fed into a phase detector and the resulting error signal is sent to an amplifying piezo driver unit via a PID controller. The amplifier output drives a mirror mounted on a piezoelectric stage in order to keep the frequency output of the laser cavity extremely stable. Thus the integrated absolute jitter from 10 Hz to 1 MHz is as low as 14 fs rms. In the near term it is foreseen to develop an all-digital PLL with similar performance.  
Figure 23: Schematic overview of the reference timing generation and distribution system in its current state. Only active elements are shown. OMO: Optical Master Oscillator, MO: Master Oscillator, PLL: Phase-locked loop, POL Tx/Rc: Pulsed Optical Link Transmitter/Receiver, BAM: Bunch Arrival Monitor, LAM: Laser Arrival Monitor, VOA: Variable Optical Attenuator (with power stabilization feedback), ULNHEU: Ultra-Low Noise Harmonic Extraction Unit, A: Amplifier, SA: Stabilized Amplifier, ETM: Event Timing Master, subdist: coaxial sub-distribution, BPM: Beam Position Monitor, DiagMonitors: various Diagnostic Monitors, Doub: Stabilized Frequency Doubler, LLRF: Low-Level RF, Quad: Stabilized Frequency Quadrupler, GL1: Gun Laser 1, GL2: Gun Laser 2, EPL1,2: Experiment Laser 1,2. Details are described in the text.  
In addition to the piezo-electrical fine tuning, the OMO has a coarse tuning range of 6.4 kHz. Depending on the environmental conditions it can be necessary to bring the laser frequency into the fine tuning range by changing the frequency via the coarse tuning. This is done by regulating the temperature of the laser's base plate. This synchronization procedure is automated as well as the survey of all relevant signals such that the OMO can be kept synchronized for months. If necessary, the synchronization program corrects the laser temperature in order to keep the piezo-driving voltage in its range between 0 V and 150 V. Since the automated search for synchronization between the OMO and the MO via the laser temperature could be very time-consuming, an additional phase-frequency detector unit was implemented. This allows determining the sign of the phase difference between both signals and therefore simplifies the search.
## 1 Introduction
Following the traditional object tracking literature, the methods are subdivided into three groups, according to whether tracking is achieved by detection, model evaluation, or filtering, with limited space given to DL approaches. Well-known commercial and open-source cell tracking tools are summarized, and typical challenges are highlighted. [PERSON] et al. [22] present a comparison of 21 cell-tracking algorithms participating in three editions of the Cell Tracking Challenge (CTC), an initiative promoting the development and objective evaluation of cell segmentation and tracking algorithms. The compared methods are summarized based on common principles, features, and methodologies, as well as pre- and post-processing strategies. They are evaluated for both the segmentation and tracking tasks (see Section 5), and their overall average performance is used to compile the final ranking. Started in 2013, this challenge is still ongoing, and since 2019, it has been articulated into two different challenges, the Cell Tracking Benchmark (CTB) and the Cell Segmentation Benchmark (CSB), sharing the same dataset (see Section 4). Although the described surveys provide extremely useful insights on specific tasks or specific datasets, the landscape of the scientific research on the subject still appears fragmented. The aim of our review is to provide a broad and up-to-date view of AI methods for the analysis of label-free images and videos acquired by traditional LI microscopy techniques and all the ingredients needed to afford it. Thus, it covers the most recent methods, especially for cellular segmentation, event detection, and tracking over time-lapse videos, available datasets, software, and evaluation metrics. The review is organized as follows. In Section 2, we introduce the considered microscopy analysis tasks and provide brief descriptions of the reviewed literature methodsfor each of them. Sections 3 and 4 provide brief descriptions and links to the publicly available software and data. Section 5 introduces the most frequently used metrics to evaluate the AI algorithms for the considered tasks. Section 6 summarizes the open problems, providing hints on possible future research directions.