esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Artificial Intelligence Approach
### Supervised ML
#### 3.1.2 Linear Discriminant Analysis (LDA)  
LDA is a machine learning methodology in a supervised manner to classify data or perform dimensionality reduction. It has provided the idea of projecting data in lower-dimensional space by finding a linear combination of features, which maximizes the separation between different classes. This approach implicitly assumes that data belong to a normal distribution, with classes sharing a common covariance matrix, and under those conditions, it works very effectively [108; 109].  
The studies have applied this technique to classify tea varieties from different geographical origins using NIR spectra, which resulted in a 100% discrimination of five varieties, namely Argentinean and Brazilian green teas and Sri Lankan black tea [110]. The application of LDA in the detection and classification of adulterants in minced beef and pork has also been explored by HSI. The method successfully discriminated the pure from the adulterated, whilst identifying the adulterants themselves [111]. Another work, that of [PERSON] et al. 2021, uses LDA in conjunction with the Hyperspectral Microscopic Imaging (HMI) technique for the classification of foodborne pathogens, resulting in a classification accuracy of 92.9% [112]. The potential of LDA in terms of classification will lead to the study of the classification of different foods by this method in future studies.  
Despite its advantages, LDA also faces some drawbacks in terms of requiring certain distributional assumptions and being constrained with regard to linear transformations; hence, nonlinear or advanced models may perform better in many cases [112]. In this case, it is necessary to completely understand the characteristics of the products and the reason behind using LDA.
# Ensuring Stable Operation of Wind Farms Connected to Distribution Networks
## 3 Materials and Methods  
Transients in the distribution network to which the wind farm is connected were calculated using the PowerFactory 2021 software (Version 15). The PowerFactory software from DIgSILENT GmbH (Gomaringen, Germany) is designed for computer-aided design of power systems, trunk networks, distribution networks, and power supply networks of industrial enterprises. The PowerFactory software allows performing all simulation functions and calculations in a single software environment, including calculations of steady-state conditions, electromagnetic and electromechanical transients, static and dynamic stability, and short circuit currents. It also enables the coordination of relay protection settings, harmonic and modal analysis, and other calculations. The PowerFactory software has a large library of verified models of generators and electrical equipment from various manufacturers, including wind turbines and FACTS devices. This software is used by students and researchers, as well as by system operators and power grid companies around the world, to calculate the power flows of electric power systems and plan their expansion [44; 45; 46]. The simulation was carried out in a calculation model of a real power system, which was verified by the regional system operator. The computational model of the power system contains 920 nodes, 53 generators, 225 transmission lines with a voltage of 35 kV and more, as well as 227 power transformers and autotransformers. The computational model of the wind farm includes a collection network; verified models of wind turbines with an inverter and ACS; a common wind farm controller; wind turbine controllers; and protection of wind turbine inverters, including the LVRT capability. In the computational model, the collection network of the wind farm is represented by the technical characteristics (\(R\), \(X\), \(B\)) of the cable transmission lines that are used to connect the wind turbines to each other and that are connected to the 35 kV switchgear of step-up power transformers and the wind farm auxiliary load [47]. The list of standard disturbances in 110-220 kV distribution networks is given in Table 1[48]. Figure 4 shows the schemes for the connection of wind farms with a total capacity of 41.64 MW (12 wind turbines with a capacity of 3.47 MW each) to a 110 kV distribution network. The correctness of the choice of parameters for the setting of the LVRT characteristic of wind turbines was assessed by comparing the results of calculation of \(U_{\text{res}}\) at the output of the wind turbine inverters with the settings of the LVRT characteristic. The calculations factored in the total short-circuit clearance time, including the opening time of the high-voltage circuit breakers, and the operating time of the relay protection devices (main protection; second and third stages of backup protection) and automatic controls (automatic reclosing and breaker failure protection) [49; 50].
## 1 Introduction
SIF emission is in the range of 640-850 nm and has two peaks: one in the red spectral region (640-700 nm) and the other in the near infrared region (700-850 nm). Recent satellite-based studies [30; 31] showed that SIF emission has good performance in assessing the impacts of water stress on photosynthetic activities. The field-based study [32] also confirmed that SIF emission estimated \(T\) with better accuracy than processed-based approaches such as the P-M and the Priestley-Taylor equation [33]. Considering good performance of SIF in predicting both GPP and \(T\), one would expect SIF may have a potential to estimate WUE, WUE\({}_{i}\), IWUE, and uWUE (hereinafter referred to as WUEs) when ET is mainly determined by \(T\). By using continuous measurements of SIF emission, carbon exchange, and latent heat in a temperate deciduous forest, we aim to evaluate the ability of SIF emission in tracking WUEs under different environmental conditions. We implemented this study in the following aspects: (1) analyzing correlations between SIF emission and WUEs at the hourly and daily time steps by using the linear and nonlinear regression analysis, (2) exploring whether multi-bands SIF have more powerful capacity to estimate WUEs, and (3) investigating how SIF-WUEs correlations respond to variations in the different environmental conditions including VPD, solar radiation, air temperature, leaf area index (LAI), and observation time.
# Studying Semi-TCP and Its Application in Marine Internet
## 2 Overview of Related Algorithms
### Introduction to the Basic Idea of the Algorithm  
The RTS/CTS-based Semi-TCP algorithm is also divided into intra-node and inter-node congestion control [16; 17]. The principle of intra-node congestion control is not described here. The following is a detailed introduction of the congestion control between nodes.  
According to the RTS/CTS protocol, when node A wants to send data to node B, after obtaining a transmission opportunity, it needs to send an RTS frame to node B before sending the data, and node B responds to node A with a CTS frame after receiving the RTS frame. With the RTS/CTS-based Semi-TCP  
Figure 1: The example of hidden terminals.  
congestion control algorithm, the idle bits in the RTS and CTS frames are used to carry congestion information to indicate whether they are in a congested state [18; 19]. Thus, two control frames, RTS-with-Congestion-status (RTSC) frame and Clear-To-Send-with-Congestion status (CTSC) frame are introduced, and a new type of frame, named nCTS (negative-CTS), has been added. The function of the three frames are analyzed as below:  
1. RTSC: The sending node A sends data to node B. If A is in congestion, RTSC will be sent; otherwise, RTS will be sent.
2. nCTS: After receiving the RTS frame, the receiving node B judges its own congestion status. If it is congested, it returns an nCTS frame and refuses to receive data; otherwise, it returns a CTS frame. If node A receives an nCTS, it cannot send packets to node B.
3. CTSC: The introduction of CTSC frames is mainly to solve the deadlock situation. A deadlock means that two congestion nodes, say A and B, need to send data to each other to relieve their own congestion, but they are in a state of mutually rejecting each other. If both node A and node B are in a congested status, node A and B send head-data in cache to each other. The sending node A will send the RTSC frame first, and node B will reply an nCTS to reject the data. Due to node B's rejection, nodes A and B will be in a dead-end state until they exceed the upper limit of retransmission to drop the packet. Therefore, when node B receives the RTSC, if it is determined that a deadlock may occur, the CTSC frames are returned to each other to help relieve the congestion.  
Using the above three new frames, when a node in the network is congested, it can rapidly transmit congestion information to the upstream nodes. Each upstream node performs hop-by-hop congestion control, and ultimately transmits congestion information to the source node, reducing the number of data transmissions from the source, resulting in better network performance.
## 6 Result and Discussion
### The Influence of Fuzzy Entropy on Prediction Effect  
Since this paper introduces the feature of fuzzy entropy in the prediction process, it is necessary for us to explore whether fuzzy entropy has a positive effect in the prediction of inherently disordered proteins.  
To ensure that fuzzy entropy plays an active role in the prediction of inherently disordered proteins, we compared three learning algorithms with and without the feature of fuzzy entropy, and the results of the comparison are shown in Tables 8-10.  
Figure 5: Prediction performance comparison based on test set R80 (Sens, Spec).  
Figure 6: Prediction performance comparison based on test set R80 (Sw, MCC).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sens** & **Spec** & **Sw** & **MCC** \\ \hline Fuzzy entropy included & 0.8461 & 0.7023 & 0.5484 & 0.4396 \\ Fuzzy entropy not included & 0.8263 & 0.6902 & 0.5165 & 0.4218 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Influence of fuzzy entropy on LDA-FE prediction results.  
By comparison, the values of SW and MCC improved for all three models we constructed after adding fuzzy entropy as a feature. In BP-FE, the most effective of the three algorithms, the addition of the fuzzy entropy feature improved the value of MCC by 9.09% and the accuracy of prediction from 84% to 88%. The MCC values for LDA-FE and SVM-FE also improved by 4.22% and 3.92% respectively. This shows that fuzzy entropy shows a positive effect in the prediction of inherently disordered proteins.  
In order to compare the different models more intuitively, we conducted significance tests for the three models we used. We make the assumption that the two algorithms used as a comparison have the same performance. With this assumption we perform a significance test; their \(p\)-values against each other are shown in Table 11:  
By calculating the \(p\)-value, we find that the \(p\)-value between LDA-FE and SVM-FE, BP-FE is much less than 0.05, so we can assume that the performance of LDA-FE is inferior to the latter two; while the \(p\)-value between SVM-FE, BP-FE is 0.1679, which is much greater than 0.05; therefore, according to SW as well as the MCC values, SVM-FE, BP-FE are equally good.
## 3 Results Analysis
### Installation Construction Stage
#### 3.8.2 Summary  
It is concluded from extensive reading of the literature that the research and application of assembled M&E systems can significantly improve the efficiency of installation and construction. The assembled M&E system can pursue higher quality and cost-effectiveness by improving new methods and materials to realize cost reduction and efficiency. Moreover, the safety of workers is always a priority, following the concept of being people-oriented. In conclusion, the research and application of assembled M&E systems in the installation and construction stages have significantly contributed to the architectural and engineering fields regarding risk reduction, efficiency, cost savings, flexibility, and customizability. Its ability to drive the industry in a more efficient and sustainable direction.  
After the construction team has installed all the prefabricated modules into the building with precision and accuracy, the MEP system has been perfected, and all the components and units have been integrated to form a coherent whole. This leads to the next stage of operation and maintenance, where more emphasis is placed on the process of monitoring and managing the MEP system.
# Boost Your Brainpower: 24 Daily Sleep Hacks for Active Lifestyles
## 3 Pre-Sleep Phase
### Melatonin, Herbs, and Tryptophan  
Melatonin is known as the \"sleep hormone\" and has various applications, including in the treatment of jet lag and some sleep disorders [75]. A meta-analysis by [PERSON] et al. (2014) found that melatonin slightly improves sleep quality, but the evidence is of low quality [76],and the effectiveness of taking exogenous melatonin in shift workers is still a subject of debate [77].  
Herbs such as Valerian and Chamomile are often used as sleep aids, but scientific research has not yet provided solid evidence of their effectiveness [78]. The systematic review by [PERSON] et al. (2020) found limited evidence supporting Valerian's efficacy for sleep improvement [78]. Similarly, [PERSON] et al. (2019) conducted a meta-analysis on Chamomile's effects on sleep quality, showing modest benefits [79].  
Other plants like passionflower, lavender, lemon balm, magnolia bark, and ashwagandha have shown potential, but results are mixed [80,81]. Herbal teas, however, have shown positive signs in improving sleep quality [82].  
Tryptophan is an essential amino acid that acts as a precursor to serotonin, a neurotransmitter that regulates mood, sleep, and appetite [83]. A deficiency in tryptophan can lead to low levels of serotonin, causing conditions such as depression, anxiety, and insomnia [84]. Tryptophan is involved in the production of serotonin and melatonin, which regulate sleep-wake cycles [85]. [PERSON] et al. (2022) conducted a meta-analysis demonstrating that tryptophan supplementation can improve sleep quality, particularly sleep latency and sleep efficiency [86].  
To improve sleep quality, it is advisable to follow a diet rich in tryptophan. Foods such as tuna, salmon, chicken, beans, pumpkin seeds, almonds, bananas, yogurt, milk, and cheese are good sources of tryptophan [87]. The absorption of tryptophan is facilitated when consumed with complex carbohydrates [87].
## 4 Results
### Findings per Technical Debt Type
#### 4.3.8 Test Debt  
Definition: Accumulation of inadequate or missing tests, poor validation pipelines, and inability to assess robustness or fairness of submitted models.  
Problem: Testing focuses narrowly on metric-based evaluation, neglecting edge cases, fairness, and generalization properties.  
Example: A fairness-sensitive competition lacks bias checks in test data, allowing discriminatory models to top the leaderboard undetected.  
Stakeholder: Both, Organizer and Participant  
Severity Score: 4--Undermines reliability, trust, and real-world applicability.
# VEPO-S2S: A VESsel Portrait Oriented Trajectory Prediction Model Based on S2S Framework
## 6 Conclusions  
Through a study of the relevant literature, we found that vessel features and behavioral preferences have a significant impact on trajectories. Therefore, this study proposes a new trajectory prediction model, VEPO-S2S, which fully considers the trajectory information, vessel features, and behavioral preferences. VEPO-S2S consists of two parts: the Multi-level Vessel Trajectory Representation Module and the Feature Fusion and Decoding Module. The Multi-level Vessel Trajectory Representation Module obtains trajectory information (such as the longitude, latitude, course, speed, and sailing distance) along with Multi-level Vessel Characteristics, encompassing Shallow-level Attributes (vessel length, type, and draft) and Deep-level Features (Sailing Location Preference, Voyage Time Preference, and anchoring time preference). These are encoded using multiple encoders. The Feature Fusion and Decoding Module aims to select the most relevant vessel characteristics for the current prediction environment and integrate them with the trajectory information before decoding with an enhanced decoder. The experimental results demonstrate that this model outperforms other baseline models qualitatively and exhibits excellent performance on grid-based maps.
# Optimizing the Spectral Characterisation of a CMYK Primer with Embedded CMY Printer Modelling
## 5 Conclusions  
In this paper, we proposed a novel approach to optimise our formerly built spectral printer characterisation workflow, with no need of extra sampling. Our key insight is to improve the characterisation for the light tones with an embedded CMY printer modelling. By the results of experiments involving three different substrates, the defects of the CYNSN-based CMYK printer characterisation have been demonstrated and the feasibility of our proposed method has also been verified.  
The optimization used may also apply to other ink combinations as long as a CYNSN-based method and a black are adopted. In fact, there are actually several studies that divided a multi-ink printer into several sub-printers equipped with a black ink, with the aim of efficient modelling and acquiring maximum gamut [18; 27; 28; 44; 45]. Obviously, the proposed approach will further improve the performance of these models.  
In addition, it is worth mentioning that the proposed method also contributes to printer characterisation adjustment [23; 24; 28]. In this topic, several corrective models were derived in order to update the printer characterisation in response to the systematic changes in printer device behaviour. We have found that such adjustment methods for the CYNSN-based workflow also depend on the forward accuracy and suffer from the same problem in light tones. Based on this contribution, we have also developed an approach for that issue. Details of this work will be reported in another paper.  
At last, please note that just like most of the cited studies, in this work only one printer were used. Although our main concern towards the negative influence of the oversampling for dark tones could be demonstrated theoretically and has been validated by our experiments with multiple substrates, to make a robust approach, further testing with different kinds of printers should still be encouraged.  
Conceptualisation, Q.L. and Z.H.; methodology, Q.L. and Z.H.; validation, Q.L. and M.R.P.; formal analysis, Q.L.; investigation, Q.L. and Z.H.; resources, M.R.L.; data curation, Q.L.; writing--original draft preparation, Q.L.; writing--review and editing, Q.L., M.R.P. and M.R.L.; funding acquisition, Q.L.  
This research was funded by the National Natural Science Foundation of China, grant number 61505149; and the Young Talent Foundation of Wuhan City of China, grant number 2016070204010111.  
The authors declare no conflicts of interest.  
Figure 7: The ultimate spectral accuracy (**a**) and colorimetric accuracy (**b**) of BPn-CYNSN-based separation and the proposed approach.
## 2 Analytical Modelling  
### Modal Response for a Cantilever Beam  
We considered a Bernoulli-Euler beam theory for structure in fixed free-boundary conditions with a uniform cross-section, as shown in Figure 1. The transverse vibration force \(F(x,t)\) was applied at the fixed end with an accelerometer mass at the other end. Let \(L\) be the length, \(\Delta L\) is the axial expansion of the length due to thermal expansion. Therefore, \(L_{e}\) is the effective length, \(B\) is the width, and \(h\) is the thickness of the beam. The beam is considered in one-dimensional longitudinal expansion only because the dimensions B and h are relatively small compared to the beam's length. The inertia force in the beam element can be expressed using the free-body diagram as:  
\[P=(\rho Adx+m)\frac{\partial^{2}u}{\partial t^{2}}(x,t) \tag{1}\]  
where \(u\) represents the transverse displacement, \(\rho\) is the mass density of the beam, \(A\) is the cross-sectional area, and \(m\) is accelerometer mass. The shear force equation for the beam element can be written as:  
\[(S(x,t)+dS(x,t))-S(x,t)+F(x,t)dx=(\rho Adx+m)\frac{\partial^{2}u}{\partial t^{2}}( x,t) \tag{2}\]  
where \(dS=\frac{\partial S}{\partial x}dx\), Similarly, the moment equation for beam can be written as:  
\[(M(x,t)+dM(x,t))-M(x,t)+(S(x,t)+dS(x,t))dx+F(x,t)dx\frac{dx}{2}=0 \tag{3}\]  
where \(dM=\frac{\partial M}{\partial x}dx\). The shear force and moment equations reduce to Equations (4) and (5), respectively:  
\[\frac{\partial S}{\partial x}(x,t)+F(x,t)=(\rho A+m)\frac{\partial^{2}u}{ \partial t^{2}}(x,t) \tag{4}\]  
\[-\frac{\partial M}{\partial x}(x,t)=S(x,t) \tag{5}\]  
The force equation can be written in term of displacement and transverse force as:  
\[-\frac{\partial^{2}M}{\partial x^{2}}(x,t)+F(x,t)=(\rho A+m)\frac{\partial^{2 }u}{\partial t^{2}}(x,t) \tag{6}\]  
The relation between the bending moment and transverse displacement is expressed using the Euler-Bernoulli beam theory as:  
\[M(x,t)=EI(x)\frac{\partial^{2}u}{\partial x^{2}}(x,t) \tag{7}\]  
where \(E\) is the elastic modulus and \(I\) is the moment of inertia of the beam. When the beam has a uniform cross-section, the equation of the vibrational beam can be transformed as:  
Figure 1: (**a**) Cantilever beam; (**b**) free body diagram for beam element.
# Research on Large Divergence Angle Laser Ranging System +
## 4 Experimental Results and Discussion
### Ranging Capability Test  
To verify the ranging capability of the laser rangefinder, especially under conditions where the target is not precisely aimed at, field verification testing was conducted to  
Figure 14: Laser energy before and after the pinhole: (**a**) laser energy before the pinhole E1. (**b**) Laser energy behind the pinhole E2.  
Figure 12: Laser beam divergence angle test diagram.  
Figure 13: Pinhole plate in the test.  
In Figures 16 and 17, the abscissa (\(x\)-axis) denotes the number of ranging cycles, while the ordinate (\(y\)-axis) represents the measured distance. Each circular marker in the plot corresponds to a discrete ranging measurement. At the 1011 m range, 3000 discrete data points were acquired, while 2250 measurements were recorded at the 709 m distance. For the 1011 m range testing, all measured data points fell within the interval of 1010.5 to 1011.6 m, demonstrating absolute errors ranging from \(-0.5\) to +0.6 m relative to the ground truth distance. For the 709 m range testing, all measured data points fell within the range of 708.5 to 709.5 m, demonstrating absolute errors strictly bounded by \(\pm 0.5\) m relative to ground truth.  
Given distinct application scenarios and objectives, the technical approach adopted in this study differs significantly from conventional long-range inter-satellite detection methods. Traditional long-distance laser ranging typically employs interferometric techniques, as exemplified by the LISA mission, where a laser beam divergence of 1.43 \(\upmu\)rad and  
Figure 17: Test data for 709 m with 0.6\({}^{\circ}\) aiming deviation.  
Figure 18: Aiming deviation 0.6\({}^{\circ}\) test image: (**a**) aim with a rightward deviation of 0.6\({}^{\circ}\). (**b**) Fully aim. (**c**) Aim with a leftward deviation of 0.6\({}^{\circ}\).  
sophisticated Acquisition, Tracking, and Pointing (ATP) systems are required to achieve \(\upmu\)rad-level alignment precision for successful inter-satellite ranging [12].  
In contrast, this work utilizes a large-divergence-angle laser ranging technique that achieves stable, high-precision measurements even with a pointing deviation of 0.6\({}^{\circ}\) (10.47 mrad). This approach dramatically reduces the required tracking and alignment precision--a breakthrough with no comparable published research to date. The proposed method offers a novel solution for close-range, low-cost, and miniaturized inter-satellite laser ranging applications.