esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Optimization Algorithm Used in the Identification Process
### Description of the HS-JAYA Algorithm
#### 3.2.4 Step 4: Convergence Check  
Standard deviations on identified values of material/structural properties and error cost functional values of the candidate solutions stored in [HM] decrease as optimization progresses and HS-JAYA approaches the solution of the inverse problem. HS-JAYA normalizes standard deviations with respect to the average solution \(\mathbf{X}_{\text{aver}}=\left(\sum_{k=1}^{N_{\text{TOP}}}\mathbf{X}_{\text{k}} \right)/N_{\text{POP}}\) (component for jth variable is \(x_{\text{aver,j}}=\left(\sum_{k=1}^{N_{\text{TOP}}}x_{\text{j}}^{\text{k}} \right)/N_{\text{POP}}\)) and the average cost functional \(\Omega_{\text{aver}}=\left(\sum_{k=1}^{N_{\text{TOP}}}\Omega(\mathbf{X}_{\text{k}} )\right)/N_{\text{POP}}\). The termination criterion is:\[\text{Max}\left\{\begin{array}{l}\frac{\text{STD}\{\|\mathbf{X_{1}}\!-\!\mathbf{X_{ \text{aver}}}\|,\|\mathbf{X_{2}}\!-\!\mathbf{X_{\text{aver}}}\|,...,\|\mathbf{X_{\text{NPOP}}} \!-\!\mathbf{X_{\text{aver}}}\|\}}{|\|\mathbf{X_{\text{aver}}}\|};\\ \frac{\text{STD}\{\Omega_{1},\Omega_{2},...,\Omega_{\text{NPOP}}\}}{\Omega_{ \text{aver}}}\end{array}\right\}\leq\varepsilon_{\text{CONV}} \tag{30}\]  
where \(\varepsilon_{\text{CONV}}\) is the selected convergence limit--usually between \(10^{-15}\) (below the double precision limit) and \(10^{-4}\) (to save computation time). Equation (30) must be verified over the last three optimization iterations. Steps 2 through 4 are repeated until the HS-JAYA algorithm converges to the global optimum.
# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 6 Conclusions  
Nowadays, 5G is a leading sophisticated technology for cellular connectivity. This superfast generation brings flexible, reliable, and unsurpassed control for smartphones, machines, and vehicles. It represents a new path to improving lifestyle through low latency, fast access, and instant communication. However, at high frequency, 5G networks introduce several challenges; i.e., distortion and multi-path propagation, air-interfaces such as crosstalk, reflection, fading, EVM, cell-interference, jamming, etc. This paper discussed these challenges, their existing solutions, and the required machine learning techniques. To augment the spectral efficiency of the channel, multiple novel technologies were introduced. Benefits of CRKG techniques, the IPL model, the heuristic algorithm, PCA, and CASC schemes were discussed to reduce crosstalk issues in the physical layer. We also explained PHY-SI and the several potentials of PHY-SI to avoid unauthorized access and protect the channel from an eavesdropper. This study also drew the reader's attention to challenges related to eMBB, uRLLC, and mMTC use cases and 5G supporting technologies that leverage AI, machine, and deep-learning tools.  
Conceptualization, J.T. and A.K.; methodology, J.T. and A.H.; validation, J.T. and A.H.; formal analysis, J.T., R.A. and A.H.; investigation, J.T. and A.H.; resources, A.K. and A.H.; data curation, J.T. and R.A.; writing--original draft preparation, J.T.; writing--review and editing, J.T., A.H. and A.K.; visualization, J.T. and R.A.; supervision, A.H. and A.K.; project administration, A.H. and A.K.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Research Foundation of Korea (NRF) grant funded by Korean government (MSIT) (IITP-2021-0-01816) and Strengthening R&D Capability Program of Sejong University.  
The authors declare that they have no conflict of interest regarding the present study.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & Fifth-generation \\ AIPN & All IP network \\ AI & Artificial intelligence \\ BBU & Base band unit \\ BS & Base stations \\ CN & Core network \\ CoMP & Coordinated multipoint \\ CRKG & Channel reciprocity-based key generation \\ CSI & Channel state information \\ DDoS & Distributed denial of service \\ DPC & Dirty paper coding \\ E-HARQ & Early hybrid automatic repeat request \\ \end{tabular}
# Model Test on Grouting Properties of Alluvial Filler Soil
## 8 Grouting Model Test Results and Discussion
### Discussion on Slurry Diffusion
#### 8.3.2 Relationship between Grouting Time and Diffusion Radius  
In order to obtain the relationship between slurry diffusion radius and grouting time, the Geo-radar test was carried out every five minutes during initial and second grouting.  
The curve of diffusion radius with time can be obtained by comparing and analyzing the adjacent geological scanning image. The relation between grouting time and the diffusion radius is shown in Figure 20.  
Figure 20: Relation between grouting time and diffusion radius.  
It can be seen from Figure 20 that the slope of the curve at the start stage of grouting is very small, which indicates that the diffusion radius increases rapidly with the increase in grouting time. With the increase in grouting time, the increasing rate of grouting radius is faster in the early stage of grouting and slower in the later period, which indicates that grouting is more difficult and that the limit value of grouting radius exists with the increase in slurry diffusion radius. One possible reason might be that the pore water hinders the seepage flow of the slurry.
## 1 Introduction
It serves to prolong the postharvest lifespan and mitigate the deterioration of quality [13]. Moreover, it presents an environmentally friendly substitute to synthetic packaging materials [10; 14]. Edible coatings are composed of food-grade ingredients that are safe for human consumption, and are formed into thin, semi-solid films [15]. These are specifically engineered to possess a range of functional characteristics that prevent the decay of fruits during storage and distribution. These characteristics include safeguarding against physical damage, regulating the movement of vapor and fluids, and creating barriers against UV (ultraviolet) and visible light [16]. In addition, they may be modified to include preservatives (such as antimicrobials or antioxidants) that extend the shelf life (\"active packaging\") or sensors that detect changes in food quality, freshness, or safety (\"smart packaging\") [15; 17]. Food surfaces may be coated with a homogeneous layer of the required thickness through the use of methods such as panning, brushing, spraying, or dipping [18]. For edible films and coatings, it is important to use substances that are considered safe for consumption. These substances should be considered \"generally recognized as safe\" (GRAS) by the Food and Drug Administration (FDA) in the United States or by equivalent regulations in other jurisdictions [15; 18]. In recent years, there has been a significant effort to produce effective and economically viable edible coatings as an alternative to petroleum-based coatings in order to address related issues [15; 19]. Specifically, there has been an increase in consumer apprehension over the adverse ecological effects of non-biodegradable single-use plastics [16]. Natural edible coatings may address these issues and provide further advantages, such as improved health via nutritional fortification [15; 20]. Hence, the advancement of edible coatings that might improve the quality and prolong the shelf life of red fruits may lead to increased consumption, thereby resulting in positive health outcomes. The objectives of this review were to (i) analyze the health benefits of red fruits, (ii) evaluate the postharvest management and technological uses of red fruits, (iii) examine the existing literature on the potential applications of innovative edible coatings for red fruits, focusing on their ability to maintain the quality of bioactive compounds, prevent degradation, and reduce microbial contamination, and (iv) present scientific evidence concerning the effects of various sustainable and healthy edible coatings on the quality of bioactive compounds, sensory acceptability, antimicrobial properties, and shelf life of red fruits, with the aim of minimizing quality deterioration.
## 4 Proposed Algorithm
### Main Framework of the Proposed Algorithm
For MMF14_a and SYM_PART simple, the ZS-MMBSO_2 performs better than ZS-MMBSO, but the difference is small. Overall, the novel individual generation strategy can help the proposed algorithm locate the equivalent PSs in the decision space. #### 5.3.3 Impact of the Number of Clusters

The number of clusters may impact the performance of the BSO. If there are too many clusters, the population diversity will be improved. Conversely, if the number of clusters is too small, the population may converge quickly, and the equivalent solutions will be difficult to preserve. To validate the influence of different \(K\) values on the performance of the ZS-MMBSO, \(K\) is set to be 10, 15, 20, 25, and 30, respectively. In addition, ZS-MMBSO with different \(K\) values is independently run 20 times on 22 functions. The best results are marked in bold. And [PERSON]'s test Friedman (1986) is employed to analyze the experimental results. In Table 5, it is observed that for different \(K\) values, the ZS-MMBSO can only achieve the best results on a few problems. Namely, the differences for different \(K\) values are small. This means that users can easily set the \(K\) value. The Friedman rankings with different \(K\) values are represented in Figure 3. From Figure 3, if the number of clusters is too large or small, the performance of the proposed algorithm will be degraded. When \(K=20\), the ZS-MMBSO can balance diversity and convergence and its overall performance is the best. Hence, the \(K\) value of ZS-MMBSO is set to 20.
# Robust Controller Decorated by Nonlinear S Function and Its Application to Water Tank
## 2 Mathematical Model
### Application of Robust Control in the Level Control of Water Tank
#### 2.2.2 Controller Design Based on the Closed-Loop-Gain Algorithm  
According to Formula (7), the closed-loop gain algorithm was used in the controller design (as Figure 4). If \(1/T_{1}=1\) rad/s, all parameters of robust controller can be obtained as \(k_{p}=\frac{1}{0.8},\ k_{i}=0,\ k_{d}=\frac{2}{0.8}\). This study emphasizes on the application of the nonlinear S function \(\frac{1-e^{-2u}}{1+e^{-2u}}\) in modifying the output of the controller mathematically and experimentally. The new PD controller, improved by the S function, which is proved by its result in improving the controller performance.
## 4 Results and Analysis
### Analysis of Influencing Factors
#### 4.2.3 Effect of OSD  
As the \(OSD\) increased, the RWT decreased, but the range of variation was less than 1 \({}^{\circ}\)C, as shown in Figure 9a.  
The \(OSD\) values of 44 and 28 cm in the numerical simulation were compared and the flow field distribution was shown in Figure 9b. It was concluded that the change of \(OSD\) had little effect on the flow field upstream of the curtain, and the change mainly affected the outflow position of curtain-downstream water. Thus, all else equal, as the \(OSD\) increased, the RWT decreased. But the  
Figure 10: Analysis of different values of released flow quantity (\(RQ\)): (**a**) Comparison of calculated and tested data of different \(RQ\); (**b**) distribution of velocity at different values of \(RQ\) (the red line represents the TCC).  
change was not that obvious. It can be explained at this point that the released water mainly gathered by the nearby layer of the outlet, so RWT mainly depended on the thermal stratification of the forebay. As previously seen in Figure 5, the vertical temperature was very close when the depth from the water surface was between 2 and 40 cm. Thus, during the \(OSD\) value range set in this study, the change of \(OSD\) had little effect on RWT.
## 3 Proposed CE Method
### Performance Analysis
Therefore, it was expected from the results that the proposed method with the optimal weights would provide a more accurate channel estimation with fewer iterations compared with the conventional SBL method. It is worth noting that the proposed LR in (21) can be applied easily to other SBL-based algorithms [7; 11; 12] and can improve the convergence rate and estimation performance simultaneously. Figure 2: The MSE performance of the proposed F-SBL algorithm according to different values of weights \(w_{1}\) and \(w_{2}\) for SNR = 12 dB.
## 3 Results
### Analysis of Agarodetrol Content  
A total of 72 samples were obtained via continuous sampling of three batches of agarwood at 2-month intervals. The chromatogram of agarodetrol was measured using HPLC (Figure 3), and its content was calculated via the chromatograph's integrated data tool (Table 6). The results showed that after 2 months of induction, agarodetrol content in all experimental groups exceeded that in cold-drilled controls. CJYB-1 (fungal-insect co-induction) yielded the highest content (0.046%), significantly differing from other groups (\(p<0.05\)). At 4 months, agarodetrol decreased markedly in most groups, with only seven samples surpassing controls. ZCX-5 (fungal treatment) showed peak content (0.274%), which was significantly higher than others (\(p<0.05\)). By 6 months, eight samples exceeded control levels, among which CJYB-1 (co-induction) again showed maximal content (0.054%, \(p<0.05\)). These results indicate that co-induction treatments consistently outperformed \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**Day 2**} & \multicolumn{2}{c}{**Day 4**} & \multicolumn{2}{c}{**Days 7-14**} \\ \cline{3-6}  & & **Survival** & **Laval Gallery** & **Survival** & **Laval Gallery** & **Survival** & **Laval Gallery** \\ \hline
1 & _Odontottermes formosanus_ & Y & N & N & N & N \\
2 & _Scleroderma guani_ & Y & N & N & N & N \\
3 & _Tomicus yumanensis_ & Y & N & Y & N & N \\
4 & _Nadezhdiella cantori_ & Y & Y & Y & Y & Y \\ \hline \hline \end{tabular} Note. Y—yes; N—no.  
\end{table} 
Table 4: Survival and colonization capacity of wood-boring insects in _A. sinensis_ under controlled conditions. fungal-only or insect-only methods in enhancing agarotetrol accumulation, accelerating agarwood formation in _A. sinensis_. We propose that the 'high (February) \(\rightarrow\) low (April) \(\rightarrow\) high (June)' fluctuation pattern reflects three alternating physiological phases, short-term defense (stress response), medium-term repair (tissue adjustment), and long-term homeostasis (metabolite accumulation), rather than linear accumulation. This suggests that co-induction rapidly stresses _A. sinensis_, potentiating secondary metabolism and enhancing agarwood production. Furthermore, ZCX-5, CX-3, YB-1, and ZCX-1 maintained relatively high agarotetrol across multiple timepoints, identifying them as potential dominant induction strains, although fungal viability and infectivity declined over time. Figure 2: (**A**) (YB-1), (**B**) (YB3-1), (**C**) (HLH), (**D**) (YML); (**A1–D1**) are two-month total ion chromatograms. (**A2–D2**) are fourth-month total ion chromatograms.
## 3 Materials and Methods
### Instrumentation
#### 3.5.4 Thermal Desorption-Gas Chromatography/Mass Spectrometry (TD-GC/MS)  
TD-GC/MS analyses were used to ascertain the presence of the corrosion inhibitor in coatings over time. Sampling for TD-GC/MS analyses was carried out on unaged coatings and after 72 h of thermal aging at 80 \({}^{\circ}\)C.  
The analyses were performed with a micro-furnace Multi-Shot Pyroizer EGA/Py-3030D (Frontier Lab, Koriyama, Japan) coupled to a GC/MS system. Samples were placed into a stainless-steel cup and inserted into the micro-furnace. The desorption temperature was set at 250 \({}^{\circ}\)C and kept constant for 1 min. The interface temperature of the pyrolizer was 280 \({}^{\circ}\)C and the temperature of the GC injector was kept at 280 \({}^{\circ}\)C. The GC was equipped with a methylphenyl-polysiloxane cross-linked 5% phenyl methyl silicone (30 m, 0.25 mm i.d., 0.25 \(\upmu\)m film thickness) capillary column. The carrier gas was helium (1.0  mL/min) and the split ratio was one-twentieth of the total flow. The following temperature program was used for the gas chromatographic separation: isotherm of 2 min at 50 \({}^{\circ}\)C, ramp of 10 \({}^{\circ}\)C/min up to 300 \({}^{\circ}\)C, and isotherm at 300 \({}^{\circ}\)C for 10 min. An Agilent 8860 Gas Chromatograph and a 5977B Mass Selective Detector (Agilent Technologies, Santa Clara, CA, USA) were used. Mass spectra were recorded under electron impact at 70 eV, with a scan range of 45-800 \(m/z\). All instruments were controlled by Agilent Mass Hunter Workstation (ver. 10.1.49) software. The mass spectra assignment was done by mass library searches (NIST2008), by comparison with the literature data and the interpretation of fragmentation paths of mass spectra.
# Design and Investigation of Capsule Networks for Sentence Classification
## 3 Proposed Model
### Preprocessing  
In order to replace each word in a given sentence by its vector representation, the input sentence has to be broken down into words. This is achieved through a process called tokenization. We used a simple string cleaning or stripping technique similar to the one used in [1] as a tokenizer. After the tokenization process, the result is passed to data augmentation, which is followed by word embedding. For both data augmentation and word embedding purposes, we relied on the publicly available pre-trained Word2 vec model by [PERSON] and colleagues [22]. This Word2 vec model with an embedding dimension of 300 was pre-trained on a huge Google News dataset of around 100 billion words for a vocabulary of three million words and phrases.  
For different datasets and different sentences within a given dataset, the sentence length varies widely. Since the network architecture requires an input with fixed dimensions, we apply zero padding to the sentences so that every sentence in a given dataset has the same length.
# Adaptive Feature Extraction of Motor Imagery EEG with Optimal Wavelet Packets and SE-Isomap
## 4 Experimental Research
### Comparison of the Computational Cost with Multi-Feature Extraction Methods  
The advantage of the SE-isomap algorithm is that both the low-dimensional representation of the data and the explicit mapping relationship of the data from the high-dimensional observation space to the low-dimensional representation space can be obtained at the same time, which facilitates the feature extraction of the new samples and improves the classification efficiency of the test sample. This is beneficial to the online realization of the recognition algorithm. Therefore, the training time and test time for multi-feature extraction methods, including PCA, MDS, etc., are recorded, respectively. The classification accuracies and their standard deviations are shown in Table 4. The experimental environment is the Windows 10 64-bit operating system; the CPU is Intel (R) Core (TM) i3 M370; the memory is 4 G; the software is MATLAB R2014a; and KNN is selected as the classifier in each experiment uniformly.  
From Table 4, it can be seen that the SE-isomap method takes the longest time in the training procedure when WPD has not been implemented; however, the test time is the shortest. This is mainly resulting from the explicit mapping matrix. At the same time, the classification accuracy of SE-isomap is also the highest. As combined with WPD, the time consumption of WPD + SE-isomap in the training procedure is still the longest; however, the test time consumes slightly increases, and the classification accuracy increases from 88.5-92.7%. In addition, the classification accuracy of WPD + SE-isomap is the highest, and its standard deviation is nearly the lowest among all feature extraction methods. Hence, the proposed method is superior to other methods in terms of computational efficiency, classification accuracy and stability. It is verified that the explicit mapping matrix resulting from this method is helpful to reduce the computation cost, and extracted features are more complete and robust than that with other methods, showing the rationality of the combination of SE-isomap and WPD.
# Cross-Project Defect Prediction Method Based on Manifold Feature Transformation
## 1 Introduction
Additionally, the original feature space has feature distortions, which results in the data distribution between the transformed source project and target project not being effectively reduced. Therefore, the effect of feature transformation and migration will be restricted. These problems are also very common in the field of image recognition [22]. Related researchers [23; 24] proposed a method to measure the distribution of different domains in the manifold space and reduced the drift between domains, which effectively improves the accuracy of image classification. Inspired by this, we introduce the method of manifold feature transformation and propose a cross-project defect prediction method based on manifold feature transformation (i.e., MFTCPDP). Specifically, first, the original feature space was transformed into manifold space, and then the data distribution difference between the source project and the target project in the manifold space was reduced. Finally, the classifier was constructed by using the source project data and labels in the manifold space to predict the defects of the target project. In order to demonstrate the effectiveness of the MFTCPDP method, we conducted experiments on the Relink datasets and the AEEEM datasets to conduct experimental research on the overall performance of the method. The experimental results show that the prediction performance of the MFTCPDP method is better than the benchmark method and has certain advantages, compared with several CPDP methods. The rest of the paper is organized as follows: Section 2 briefly introduces the existing methods in the field of cross-project defect prediction. Section 3 introduces the cross-project defect prediction method based on manifold feature transformation proposed in this paper in detail. Section 4 conducts empirical research on the methods proposed in this paper, including evaluation objects, evaluation indicators, experimental settings, and experimental results and analysis. Section 5 summarizes the full text and presents the next steps.
# Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA
## 3 Results
### SOS and EOS Determination
#### 3.1.2 SOS and EOS Validation through Analysis of Fluctuations  
The median SOS date for the salt marshes of southeast and southwest Louisiana as derived from TIMESAT was observed at \(\sim\)60 Julian days (Figures 4a and 5a). However, in the years 2000, 2010, and 2013, the salt marshes of southeast LA showed a late SOS. A late SOS was also observed in southwest LA in 2010. The median EOS for the LA salt marshes was observed at \(\sim\)322 Julian days. The salt marshes of both southeast and southwest LA experienced an early end to the growing season in the years 2005 and 2008.  
Figure 4: **(a)** SOS and **(b)** EOS dates for the salt marshes of southeast LA. The dotted line represents median SOS and EOS dates. Late SOS and early EOS dates are highlighted in red along with environmental events (such as dieback, late spring onset, BP Oil Spill, Hurricane Isaac for the SOS, and Hurricanes Katrina and Gustav for the EOS) possibly influencing such events.  
Figure 3: Residual plots matching the Julian dates derived from the derivative analysis (x-axis) and TIMESAT thresholds (y-axis) for (**a–d**) start of season (SOS) and (**e–h**) end of season (EOS), from \(\sim\)400 randomly selected pixels, across LA salt marshes. TIMESAT thresholds (5%, 10%, 15%, 20%) have been specified for individual residual plots.