esa-sceva/satcom-chunk-collection huggingface dataset:
# Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm
## 1 Introduction
In conclusion, GSA has been successfully applied in many global optimization problems, such as, multi-objective optimization of synthesis gas production [28], the forecasting of turbine heat rate [29], dynamic constrained optimization with offspring repair [30], fuzzy control system [31], grey nonlinear constrained programming problem [32], reactive power dispatch of power systems [33], minimum ratio traveling salesman problem [34], parameter identification of AVR system [35], strategic bidding [36], _etc._ In this paper, the research on the function optimization problem is solved based on the gravitation search algorithm (GSA). Then the parameter performance comparison and analysis are carried out through the simulation experiments in order to verify its superiority. The paper is organized as follows. In Section 2, the gravitational search algorithm is introduced. The simulation experiments and results analysis are introduced in details in Section 3. Finally, the conclusion illustrates the last part.
# Durability of Textile Reinforced Concrete: Existing Knowledge and Current Gaps
## 4 Conclusions  
This paper aimed to provide a review of the existing literature on the durability of textile-reinforced concrete and mortars (referred to as TRC, TRM or FRCM in the literature) under aggressive environmental conditions. Overall, the following observations are made:  
* The existing data on the durability of TRC composites, although growing, remains limited. Most previous studies have focused on chemical attacks, freeze-thaw and high-temperature effects. The role of the synergistic action of mechanical and environmental loads remains very poorly addressed. Considering the variety of existing textile and matrix types, the durability of TRCs is expected to vary significantly, hence the need for more comprehensive investigations in this field.
* Due to the lack of standard durability test methods, different test procedures or environmental conditions are often followed by different researchers. As a result, some of the existing data are not comparable. Additionally, the reported data often lack sufficient information on the procedures followed especially the curing conditions and period (especially important when lime-based mortars are used) or the preparation procedure followed after environmental exposure and before performing the post-ageing tests (e.g., were the tests performed immediately after exposure or after a while. If latter was followed, how this was controlled).
* In most of the previous tests, tensile tests are used as a macro mechanical characterization method to evaluate the changes in performance after exposure to environmental conditions. Micro-mechanical test data (e.g., pull-out test results) are still very scarce but needed for better understanding the underlying deterioration mechanisms. In tensile tests data, sometimes different test setups are used making the results incomparable. Additionally, the results from tensile tests do not have good repeatability and are not representative of the actual field behaviour in many cases (this also depends on the gripping mechanism used). To consider these factors, flexural tests are suggested to be considered for future studies. Flexural tests are simple to perform and have better repeatability. The round-robin tests planned in the framework of the RILEM TC 20-IMC is expected to address a significant part of these concerns.  
Conceptualization, methodology, literature review, analysis of data, writing, revision and editing, M.A., B.G. and A.D.; supervision, B.G.; project administration, B.G.; funding acquisition, B.G. All authors have read and agreed to the published version of the manuscript.  
The support to the third author through grant SFRH/BD/131282/2017 is acknowledged.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Theories and Methods  
### sEMG Preprocessing  
sEMG gives a nonlinear signal, so the sampled signal needs to be divided by a short time window to maintain the stability of the signal data. A segment length should be large enough to avoid degradation of the classification performance because the deviation and variance of the feature values increase as the segment length decreases. On the other hand, the segment length should be small enough to meet the application of sEMG control, and the control response time must be less than 300 ms [14]. Therefore, in our study, the time window of the signal was divided into 250 ms. The sEMG signal carries low frequency (near DC) and high frequency interference signals. First, the signal collected from the surface electrode was processed by a high pass filter with a 1 Hz cutoff frequency. Second, the high frequency interference signal was filtered out by a Butterworth low pass filter with a 600 Hz cutoff frequency. Finally, the output signal was processed by a notch filter with a power frequency of 50 Hz as the center frequency to further improve the signal-to-noise ratio. The sEMG signal was processed offline in Matlab software (The MathWorks, version 8.3.0.532, 64-bit, 2014).
## 5 Conclusions  
A dynamic micro-layer model and nano-structure model were proposed based on the MPS-MAFL method. The present method is applicable to simulate the pool boiling bubble growth behavior on heating surfaces, especially on the nano-structured surface. The main conclusions of the current study are as follows:  
1. Corresponding experiments were adopted as validations for the present method. The comparisons between the computed and experimental bubble growth rates showed great agreement. The maximum relative error was less than 5%, which reveals the robustness of the current method in simulating the growing bubble on both bared and nano-structured surfaces.
2. Micro-layers beneath bubbles were investigated. Variations of their thickness and occupied area were successfully captured. The micro-layers' max thickness was found to increase first and then decrease in all cases. The proportion of \(P_{ml}\) in the bubble growth process on the bared surface was 40.55% at \(\Delta T_{w}\) = 7.22 \({}^{\circ}\)C and 32.23% at  
Figure 18: Heat transfer rates from different parts vary with time on 100 bilayer LbL heater at \(\Delta T_{w}\) = 22.69 \({}^{\circ}\)C.  
\(\Delta T_{w}=10.15\)\({}^{\circ}\)C, which is strong evidence that the micro-layer evaporation cannot be neglected during the pooling boiling simulation.
3. The heat transfer process was investigated during bubble growth. The heat transfer contributions of the micro-layer and the wicked fluid were about 42.13% in the bubble growth on the nano-structured heater. The ratio of \(P_{wick}\) and \(P_{ml}\) was 14:11.
4. The current research was the first attempt at directly simulating the bubble growth on the nano-structure. Simulations in this study give us a quantitative awareness of the heat transfer process during the bubble growth on the nano-structured surface.  
Data curation, K.G. and S.L.; Formal analysis, K.G.; Funding acquisition, G.S.; Investigation, K.G. and S.L.; Methodology, K.G., R.C. and M.W.; Project administration, W.T. and S.Q.; Writing--original draft, K.G.; Writing--review and editing, Y.Z., S.L. and K.G. All authors have read and agreed to the published version of the manuscript.  
The present study is supported by the National Natural Science Foundation of China (No. 12105215), the China Postdoctoral Science Foundation (No. BX2021236) and the CNNC Science Fund for Talented Young Scholars.  
The author thanks [PERSON] from MIT NSE department for affording a one-year visiting opportunity.  
The authors declare no conflict of interest.
## 3 Results
### Bone Metabolic Markers  
There were significant interaction effects for group by time on serum 25-(OH) D (\(F\) = 4.451, \(p\) = 0.044, \(\eta_{\text{p}}\)\({}^{2}\) = 0.133) and OC (\(F\) = 10.514, \(p\) = 0.003, \(\eta_{\text{p}}\)\({}^{2}\) = 0.266) (Figures 1 and 2). The post-test showed that serum 25-(OH) D concentration was increased significantly following the four-months of high impact weight-bearing exercise (\(p\) < 0.01), while no significant change was found in the CON group. OC level was not changed in the HWE group, but this value decreased significantly in the CON group (\(p\) < 0.05). There were significant time effects on intact PTH (\(F\) = 4.447, \(p\) = 0.044, \(\eta_{\text{p}}\)\({}^{2}\) = 0.133), OPN (\(F\) = 5.480, \(p\) = 0.026, \(\eta_{\text{p}}\)\({}^{2}\) = 0.159), CTX (\(F\) = 8.768, \(p\) = 0.006, \(\eta_{\text{p}}\)\({}^{2}\) = 0.232), and calcium (\(F\) = 7.986, \(p\) = 0.008, \(\eta_{\text{p}}\)\({}^{2}\) = 0.216) (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \multicolumn{2}{c}{**HWE**} & \multicolumn{2}{c}{**CON**} & \multicolumn{3}{c}{\(F\)**-Value**} \\ \cline{2-9}
**Variables** & **Pre** & **Post** & **Pre** & **Post** & **(n\(\mathbf{p^{2}}\))** & **Group** & **Interaction** \\  & **(95\% CI)** & **(95\% CI)** & **(95\% CI)** & **(95\% CI)** & **Time** & **Group** & **Interaction** \\ \hline Femur BMD & 0.891 \(\pm\) 0.103 & 0.895 \(\pm\) 0.103 & 0.898 \(\pm\) 0.109 & 0.898 \(\pm\) 0.113 & 0.515 & 0.015 & 0.458 \\ (g/cm\({}^{2}\)) & (0.838–0.946) & (0.842–0.950) & (0.846–0.952) & (0.846–0.952) & (0.017) & (0.001) & (0.016) \\ Lumbar BMD & 1.036 \(\pm\) 0.158 & 1.036 \(\pm\) 0.167 & 1.009 \(\pm\) 0.111 & 1.009 \(\pm\) 0.112 & 0.003 & 0.290 & 0.009 \\ (g/cm\({}^{2}\)) & (0.956–1.122) & (0.952–1.126) & (0.957–1.062) & (0.958–1.064) & (0.000) & (0.010) & (0.000) \\ Forearm BMD & 0.563 \(\pm\) 0.036 & 0.560 \(\pm\) 0.037 & 0.579 \(\pm\) 0.041 & 0.576 \(\pm\) 0.045 & 3.413 & 1.243 & 0.048 \\ (g/cm\({}^{2}\)) & (0.544–0.584) & (0.541–0.581) & (0.560–0.598) & (0.555–0.596) & (0.105) & (0.041) & (0.002) \\ \hline \end{tabular} Values are expressed as mean \(\pm\) SD. 95% CI: 95% confidence interval, BMD: bone mineral density, HWE: high-impact weight-bearing exercise, CON: control.  
\end{table}
Table 3: Changes of bone mineral density (BMD) between pre- and post-tests in middle-aged premenopausal women.  
Figure 1: Serum 25-(OH) D before and after the four-month exercise program. For serum 25-(OH) D, statistical analyses revealed an increase between pre- and post-tests in the HWE group. HWE: high-impact weight-bearing exercise, CON: control. Significant difference between pre- and post-tests, **\(p<0.01\). Significant interaction effect, #\(p<0.05\).**  
Figure 2: Osteocalcin before and after the four-month exercise program. For osteoclastcin, statistical analyses revealed a decrease between pre- and post-tests in the CON group. HWE: high-impact weight-bearing exercise, CON: control. Significant difference between pre- and post-tests, *\(p<0.05\). Significant interaction effect, #\(p<0.01\).
# CaAIS: Cellular Automata-Based Artificial Immune System for Dynamic Environments
## 6 Experimental Study
### Experiments
#### 6.3.4 Comparison of the CaAIS with Peer Immune Algorithms  
In this experiment, the performance of the CaAIS is compared with several algorithms, including the simple artificial immune system (SAIS) [39], artificial iterated immune algorithm (AIIA) [34], B-cell algorithm (BCA) [40], clonal selection algorithm (CLONALG) [41], artificial immune network (opt-aiNet) [67], learning automata-based immune algorithm (LAIA) [46], and the cellular PSO based on clonal selection algorithm (CPSOC) [45]. A statistical test is also applied to validate the significance of the results. The statistical test results of comparing algorithms by one-tailed \(t\)-test with 28 degrees of freedom at a 0.05 level of significance are reported in Table 2. Table 2 consists of two main columns for 5 peaks and 50 peaks as different environments. For each environment, the offline error and standard errors are given along with the results of the statistical significance test. The \(t\)-test result regarding the CaAIS with each alternative algorithm is shown as \"+\", \"\(-\)\", and \"\(-\)\" when the CaAIS is significantly better than, significantly worse than, and statistically equivalent to the alternative algorithm, respectively. According to Table 2, the results of the proposed method are statistically equivalent to those of BCA. They show better results than other general relativity algorithms. This is due to the cellular structure and immune properties that provide an adaptive balance between local and global search in changing environments.  
Figure 8: Comparison of different re-randomization for the CaAIS from 10 to 100%.
# Causal Inference-Based Self-Supervised Cross-Domain Fundus Image Segmentation
## 2 Related Works  
Currently, deep learning models have been widely applied in the medical field [17, 18, 19, 20], which has led to the development of numerous automatic segmentation networks for the OD and OC. [PERSON] et al. [21] designed an end-to-end multi-label deep network (M-Net) that jointly segments the OD and OC through a multi-scale U-Net architecture with side-output layers, while incorporating a polar transformation to enhance spatial constraints and balance the data, thereby improving the accuracy of the cup-to-disc ratio for glaucoma screening.  
However, due to domain shift phenomena that may degrade model performance, researchers have begun to incorporate domain adaptation methods to alleviate the interference caused by distribution discrepancies across different domains. For instance, [PERSON] et al. [10] designed a patch-based output space adversarial learning (pOSAL) framework, which addresses the domain adaptation problem by combining morphology-aware segmentation loss with patch-level adversarial learning. [PERSON] et al. [22] designed the CFEA network that simultaneously applies adversarial losses at multiple levels in both the encoder and decoder to extract domain-invariant features; it further incorporates a self-ensembling mechanism by building a teacher network based on historical predictions to guide the training of the student network, thereby achieving temporal smoothing. [PERSON] et al. [12] designed an IOSUDA framework that, in the input space, employs image translation to decompose images into shared content features and domain-specific style features, and in the output space, utilizes adversarial learning to enforce consistency in segmentation results. [PERSON] et al. [14] proposed a domain adaptation network named MeFDA, which enhances prediction confidence in the target domain via both direct and adversarial entropy minimization and improves semantic consistency between the source and target domains by exchanging low-frequency information through Fourier transform. [PERSON] et al. [23] designed a self-ensembling-based model that jointly extracts mask and boundary information,enforcing their consistency with a mask-boundary segmentation loss; simultaneously, they adopted an output-level adversarial domain adaptation technique to align the prediction results of the source and target domains.  
Recently, some studies have begun to explore the use of causal inference methods to address the challenges posed by domain shift. [PERSON] et al. [24] designed the CIADA method, which constructs a causal graph for the source and target domain data using a constraint-based PC algorithm and performs feature classification and mapping based on causal influences; subsequently, graph structure and temporal sequence features are extracted via a two-dimensional processing approach, and finally, an adversarial domain adaptation and fine-tuning strategy is employed to build the detection model.
# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
## 1 Introduction
### Visible Boundary Detection and Extraction for Cadastral Mapping
The mean-shift segmentation was applied to a full extent of satellite images. Accordingly, some of the automatic object extraction methods were applied also using UAV images. The study from [PERSON] et al. [26] outlines the potential of the Global Probability of Boundary (gPb) contour detection method for an automatic boundary delineation based on UAV imagery. gPb is open-source and available as pre-compiled Matlab package. The method was found to be applicable only for processing images of fewer than 1000 x 1000 pixels due to the demanding computing process [26]. The contour map or detected objects were in raster format and required vectorization. Furthermore, [PERSON] et al. [37] discuss the interactive method of visible boundary extractions. The interactive method combines the gPb contour detection, simple linear iterative clustering (SLIC) super pixels and random forest classifier, which allow a semi-automatic approach for the delineation of visible boundaries. The interactive method was tested on visible road outlines based on UAV datasets. The results show that the approach is much more efficient than manual boundary delineation, and all road boundaries were delineated comprehensively. All the case studies reviewed, both automatic boundary extractions from HRSI and UAV images, have been tested in rural areas since it is argued that most of the cadastral boundaries are visible in such areas [26]. However, not all computer vision automatic feature extraction methods suitable for visible cadastral boundary delineation have already been tested. Another tool that is also referred to as the'state-of-the-art' for automatic detection and extraction of features from images is the ENVI feature extraction (FX) module [39; 40]. ENVI FX is an object-based module for detecting and extracting multiple object outlines from high-resolution multispectral or panchromatic digital images. The extraction is based on spectral (brightness and color), texture, and spatial characteristics [41]. To the best of the authors' knowledge, there have been no previous publications, nor evidence, that the ENVI FX module has been applied for detecting and extracting visible cadastral boundaries on UAV images. The justification for using this method is based on [PERSON] et al. [22], in which general workflow and feature extraction methods appropriate for cadastral mapping are provided. The main aim of this study is not to compare automatic feature extraction methods already used for cadastral mapping. Instead, the study focuses on the potential of a feature extraction method which has not been tested yet in cadastral applications. The study can be seen as an important contribution to land administration discussions focusing on cadastral mapping, as there have been a limited number of studies for automatic visible cadastral boundary delineation from imageries acquired using high-resolution optical sensors.
## 5 Hydrogen Only Combustion with Direct Injection
### Glow-Plug-Assisted Ignition  
A glow plug is a device featuring an electrically heated surface protruding into the engine combustion chamber. It is a common equipment in diesel engines to assist engine cold start by increasing local charge temperature. When used in hydrogen DI ICE as conceptualized in Figure 2, the glow plugs need to operate continuously to ensure hydrogen ignition in every engine cycle. The required glow-plug's surface temperature in the range from 1200 to 1400 K was reported [67; 68; 69]. In 1979, [PERSON] et al. [67] reported that glow plug is a more reliable ignition source for hydrogen ignition--a short and stable delay between the start of injection (SOI) and ignition of 10\({}^{\circ}\)-13\({}^{\circ}\) CA in a cooperative fuel research engine at 1240 rpm and compression ratio of 18 was reported. This ignition performance was compared to a multi-strike spark-plug ignition system (2.5 kHz strike rate), where a fluctuating delay of 0-25\({}^{\circ}\) CA between the injection and the initial pressure rise was observed. [PERSON] et al. [67] attributed this fluctuation to a smaller surface area of the spark gap relative to the glow plug. A previous study [70] has shown that the glow-plug ignition method suffers from \(\sim\)10% increased specific fuel consumption relative to diesel operation--for instance, at 5 bar IMEP and 1200 rpm the ITE decreased from approximately 47% to 42%. Although NOx emissions in this combustion mode were lower than that in diesel operation, it was still significant, especially at high load (i.e., \(>\)500 ppm) [70]. Nevertheless, the above findings were from early research conducted decades ago. The durability of the glow plug due to the high surface temperature is questionable when it comes to commercial application and thus this technology is rarely used in recent engine development [71].
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 4 Problem Define and Experimental Design
### Experimental Design
#### 4.2.3 Experimental Method Design
Classification was conducted in a multiclass manner. Multiclass classification was applied to categorize packets into benign (0) or seven types of attacks. The labels assigned to these attacks are as follows: ARP_MitM (1), Active_Wiretap (2), Fuzzing (3), OS_Scan (4), SSDP_Flood (5), SSL_Renegotiation (6), and SYN_DoS (7). The model described in this paper was evaluated based on its ability to appropriately classify GTP-U packets. Hence, among the various available indicators, accuracy, precision, F1-score, and recall were used. In addition, the AUC and ROC curves were obtained through macro-averaging once the predicted values were one-hot encoded for each class using a one-vs-one method. The evaluation index of multiclass classification was calculated by converting the predicted result into a binary classification. Comparison of Performance  
Finally, this study compared the evaluation metric results of the base model using all 55 features and the model using the features selected through each feature selection method. Furthermore, by increasing the size of the dataset, we check the effect of the feature selection process on the reduction of time complexity.
## 3 Results
### Influence of Tissue Thickness Underneath the Measured Surface on Measured Spectrum
#### 3.3.1 Spectral Comparison before Wavelength Reduction  
Figure 7 shows the average and standard deviation of the selected spectra in both datasets. The standard deviations around the averaged diffuse reflectance spectra (Figure 7a-c) are large. This is caused by the oblique illumination of our measurement setup and the uneven surface of the tissue. This causes differences in the illumination of the tissue and scatter, nonspecific to the optical properties of the tissue (explained in Section 2.3.1). The spectra obtained with the VIS and NIR cameras are not connected because both cameras have their own suspension system and light sources, causing a slightly different illumination angle. Using SNV normalization as a pre-processing step, the standard deviation around the averaged spectra (Figure 7d-f) is reduced. Since SNV was applied to the spectrum obtained with the VIS and NIR camera individually, the SNV normalized spectra are not connected. In the remainder of this paper, we will only use the SNV normalized spectra for further analysis.  
For all tissue types, large differences are observed between the slices data and the lumpectomy data. The largest differences are present in the VIS wavelength range and in between 1000 and 1150 nm in the NIR wavelength range. These differences might be related to the fact that the optical penetration depth in this wavelength region exceeds the thickness of the tissue slices. In one location (Figure 7a, 79% IC), the effects of cutting by cauterization were clearly visible in the H & E section. In comparison with the two other malignant locations, this correlated with a decrease in the mean spectrum between 600 and 650 nm.  
Based on the large spectral differences between the slices and lumpectomy data, we expect that classification algorithms developed on the slices dataset may exhibit a lower performance on the lumpectomy dataset.