esa-sceva/satcom-chunk-collection huggingface dataset:
# The Debris Flow Risk Prediction Model Based on PCA-Elman
## 2 Materials and Methods
### Adasyn  
The ADASYN (Adaptive Synthetic Sampling) algorithm is an oversampling technique designed to address imbalanced datasets. It evaluates the classification difficulty of each minority class sample to determine the number of synthetic samples needed, and interpolates between the minority class samples and their k-nearest neighbors to generate new synthetic samples, thereby increasing the number of samples in the minority class and enhancing the classification performance in imbalanced datasets. Employing ADASYN can help improve model performance on imbalanced datasets and enhance the learning effectiveness for minority classes [17]. Consequently, this study employs the ADASYN algorithm via the MATLAB (R2023a) software platform to augment the 37 debris flow sample datasets from Yunnan Province, aiming to address the issue of data imbalance in debris flow samples and to explore the impact of data imbalance on model predictive accuracy, attempting to enhance the model's generalization capabilities. The principle is as follows:  
\[d=\frac{m_{s}}{m_{l}} \tag{4}\]  
\[G=(m_{l}-m_{s})\times\beta \tag{5}\]  
\[r_{i}=\frac{\Delta_{i}}{K} \tag{6}\]  
\[\hat{r}_{i}=\frac{r_{i}}{\sum\limits_{i=1}^{m}r_{i}} \tag{7}\]  
\[g_{i}=\hat{r}_{i}\times G \tag{8}\]  
In this context, \(d\) represents the imbalance degree of the dataset (\(d\in(0,1]\)), where \(m_{s}\) and \(m_{l}\) denote the number of samples in the minority and majority classes, respectively. \(G\) is the number of minority class samples that need to be synthesized. \(\beta\) is a parameter controlling the number of synthesized minority class samples (\(\beta\in(0,1]\)). When \(\beta=1\), the number of majority class samples equals that of the minority class samples after synthesis. \(\Delta_{i}\) is the number of majority class samples among the nearest neighbors, and \(r_{i}\) is the ratio of \(\Delta_{i}\) to the \(K\) nearest neighbors for each minority class sample. \(\hat{r}_{i}\) is the weight of the minority class sample, and \(g_{i}\) is the number of synthesized samples required for each minority class sample.
## 5 Numerical Results
### Single Force Identification Simulation

Table 1: Force identification results error of four-degree-of-freedom system. Figure 12: Identification result of impact load at SNR 30 dB: (**a**) Tikhonov method; (**b**) the proposed method. Figure 13: Identification result of impact load at SNR 20 dB: (**a**) Tikhonov method; (**b**) the proposed method. the noise is low, while the error in identifying the peak load level for higher noise, which is expected. This is also observed for other types of load where the augmented method shows a significant reduction in spurious oscillations compared to the standard method.
# A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
## 3 Results and Discussion
### Live Diatom Detection
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Statistics** & **Sensitivity** & **Specificity** & **Precision** \\ \hline VJ & _mean_ & 0.645 & 0.754 & 0.259 \\  & _std. dev_ & 0.107 & 0.159 & 0.120 \\ \hline SCIRD & _mean_ & 0.334 & 0.934 & 0.451 \\  & _std. dev_ & 0.179 & 0.056 & 0.254 \\ \hline YOLO & _mean_ & 0.846 & 0.962 & 0.727 \\  & _std. dev_ & 0.063 & 0.026 & 0.107 \\ \hline SegNet & _mean_ & 0.960 & 0.692 & 0.628 \\  & _std. dev_ & 0.038 & 0.061 & 0.051 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the results for detection with several methods (the best values are highlighted in boldface): Viola–Jones (VJ), scale and curvature invariant ridge detector (SCIRD), YOLO, and SegNet.
## 2 Materials and Methods
### Aerial Imagery and Processing  
The aerial image used for this research was obtained from Google Earth Pro (ver. 7.3.2.5776; Google, Mountain View, CA, USA), which uses a composite between satellite and aerial images. The only available image closer to the harvest was for 27 May 2016. However, it is expected from the biological point of view that the LAI will be similar within this month and that the trends between plants should also be maintained. The image was saved as a maximum high-resolution (\(4800\times 2679\) pixels) Joint Photographic Expert Group (JPEG) file. The aerial image was processed to recognize every single plant and extract information from each one using the methodology proposed and described in previous studies [24; 25] through customized codes written in Matlab(r) (Mathworks Inc., Natick, MA, USA). In general, the automated procedure consists of the following steps:  
1. Identification of the area designated by the plant (distance between plants and between rows). This was done by cropping the area for blocks and automatically sub-cropping each tree area for further analysis according to the distance between plants and the distance between rows [24; 25] (Figure 3A).
2. Automatic Individual segmentation of tree biomass material using the CIELab (specifically the \"a\" band) color code to generate a binary image of sub-images analyzed per tree (Figure 3B).
3. Analysis of binary images per area assigned per plant using algorithms described previously (Equations (1)-(6)). Each sub-image is treated as an individual upward-looking image obtained by VitiCanopy, using the sky as a background. A subsequent subdivision of \(5\times 5\) was used for each tree image for gap analysis, as described by [17; 21] (Figure 3C). From the aerial image,it was assumed to be a downward-looking image at Nadir (0\({}^{\circ}\) angle) using the inter-row as a background [17; 21].
4. The obtained canopy architecture data were compiled in a matrix form to preserve plant positions and for easier handling for mapping purposes.
5. If canopies were of low cover (\(f_{\mathrm{c}}>0.1\)), they were considered as missing trees [24].
6. Tree GPS positions were extracted from the GPS coordinates of the corners of blocks for data mapping purposes.
## 6 Stress Analysis of the Bare Tower Subjected to Solar Radiation Variation  
### Temperature Field Heat Conduction Theory  
The heat exchange of the main concrete tower is mainly energy transfer, which is manifested as the temperature change of the inner and outer walls of the tower column. There are three forms of energy transfer: heat conduction, convection heat transfer, and heat radiation, which have different characteristics.  
1. Heat conduction refers to the heat transfer process between the same substance under the action of different temperatures. According to Fourier law, the formula of heat conduction can be expressed by Equation (6) [17]: \[Q=-\lambda\frac{\varphi T}{\varphi X}\] (6) On the type of \(Q\) for heat flow density (W/m\({}^{2}\)); \(\lambda\) for the coefficient of thermal conductivity (W/m\({}^{2}\) \({}^{\circ}\)C); \(\frac{\varphi T}{\varphi X}\) for heat transfer in the direction of the temperature gradient.
2. Convection heat transfer refers to the energy transfer caused by the temperature difference between the solid surface and the fluid, which can be generally divided into natural convection and forced convection. During the construction and operation of the concrete main tower, a convective heat transfer process is usually generated. That is, energy exchange occurs when the fluid contacts with the tower wall. When the fluid temperature is high, there is also heat conduction effect on the column, and heat transfer efficiency will be reduced under the influence of wind speed. Therefore, the convective heat transfer process is usually described by [PERSON]'s cooling equation, as expressed by Equation (7) [13]: \[Q=K(T_{\alpha}-T_{\beta})\] (7) On the type of \(T_{\alpha}\) for pillar surface temperature (\({}^{\circ}\)C); \(T_{\beta}\) for pillar surrounding fluid temperature (\({}^{\circ}\)C); \(K\) for convective heat transfer coefficient (W/m\({}^{2}\) \({}^{\circ}\)C).
3. Thermal radiation refers to the electromagnetic energy radiated by the object itself interacting with other objects, resulting in the process of energy transfer and heat conversion. When the temperature of the main tower is above absolute zero, it emits thermal radiation, which can be transmitted through vacuum and other media. To calculate the net heat transfer between objects in the thermal radiation process of the main tower, the Stefan-Boltzmann equation is generally adopted, as expressed by Equation (8) [13]: \[Q=\alpha\eta_{0}M_{\text{i}}F_{\text{ij}}\left(T_{\text{i}}^{4}-T_{\text{j}}^{ 4}\right)\] (8) On the type of \(M_{\text{i}}\) for pillar i through an area (m\({}^{2}\)); \(T_{\text{i}}\) for pillar i through absolute temperature (\({}^{\circ}\)C); \(T_{\text{j}}\) for pillar j through absolute temperature (\({}^{\circ}\)C); \(F_{\text{ij}}\) pillar i through to the j shape factor; \(\eta_{0}\) to take Stephen-Boltzman constant, take 5.67 \(\times\) 10\({}^{-8}\) \({}^{\circ}\)C, W/m\({}^{2}\); \(\alpha\) for pillar radiation ratio.
## 4 The Floor Impact Noise Tests of SBR Latex-Modified Mortar
### Light-Weight Floor Impact Noise  
The test results for the light-weight impact noise of floor structures using Tg 4 \({}^{\circ}\)C and Tg \(-16\)\({}^{\circ}\)C SBR latex-mixed mortar, illustrated in Figure 12, indicate that the mortar mixed with SBR latex at frequencies below 500 Hz showed a superior noise reduction performance of approximately 3-5 dB compared to general mortar. This suggests that incorporating SBR latex in the mortar enhances its ability to reduce low-frequency impact noise more effectively.  
Figure 11: Lab top and mock-up specimen size.  
Figure 10: Block diagram for floor impact noise test.  
Especially, the mortar mixed with Tg 4 \({}^{\circ}\)C SBR latex demonstrated superior noise reduction characteristics, achieving about a 2 dB improvement at the frequency bands of 125 Hz, 500 Hz, 1000 Hz, and 2000 Hz, compared to the Tg \(-\)16 \({}^{\circ}\)C SBR latex-mixed mortar. However, this improvement did not extend to the 250 Hz frequency band.
# Generalized Spoof Detection and Incremental Algorithm Recognition for Voice Spoofing
## 4 Experimental Methods
### Training Details
#### 4.2.2 Training Parameters for Spoof Algorithm Recognition  
For the domain voice spoof algorithm recognition, we used ResNet-44 for voice feature extraction. For each category, 200 samples were stored, and each training step consisted of 20 epochs. The learning rate started at 0.001 and was divided by 10 after 15 epochs. The model parameters were optimized using a SGD optimizer, and the batch size was set to 64. In performing the incremental learning experiments, we used A01-A09 as the base category. Subsequently, two new classes of spoof methods are added on top of this one at a time. The weight size in the SGD optimizer was set according to the ratio of the stored instance size to the size of the new class dataset.
# Secure Localization in the Presence of Colluders in WSNs
###### Abstract  
We address the challenge of correctly estimating the position of wireless sensor network (WSN) nodes in the presence of malicious adversaries. We consider adversarial situations during the execution of node localization under three classes of colluding adversaries. We describe a decentralized algorithm that aims at determining the position of nodes in the presence of such colluders. Colluders are assumed to either forge or manipulate the information they exchange with the other nodes of the WSN. This algorithm allows location-unknown nodes to successfully detect adversaries within their communication range. Numeric simulation is reported to validate the approach. Results show the validity of the proposal, both in terms of localization and adversary detection.  
Keywords:colluders; detection; secure localization; wireless sensor network +
Footnote †: journal: Sensors
## 3 Results
### Study Characteristics
#### 3.2.1 Geographical Location  
The included studies analyzed data from different countries/regions, namely, Brazil [20], Canada [15], Greece [21], Italy [22; 23; 24], Korea [14], Malaysia [25], Portugal [26], Spain [27; 28; 29], Thailand [30], Japan [31], Germany [32], China [33], the United Kingdom [34], and the United States [35; 36].
# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
## 4 Original and Hybrid Whale Optimization Algorithm
### Proposed Hybrid Whale Optimization Algorithm  
Taking into account that the two most important mechanisms of any swarm algorithm are exploitation (intensification) and exploration (diversification), and that the efficiency of a swarm algorithm in both terms, convergence speed and solutions quality, heavily depends on the adjusted balance between these two processes, we tried to improve the original WOA implementation by addressing the exploitation-exploration trade-off adjustments.  
In general, any swarm algorithm may be enhanced by using minor and/or major improvement strategies. Minor improvements include modifications of some component(s) of the search equation(s), as well as tweaking behavior of the algorithm's control parameters (in many cases, researches introduce dynamic parameter behavior). Major improvements usually refer to hybridization with some other metaheuristics or heuristics. Hybrid algorithms combine the best features of two or more approaches, by replacing the weakness of one approach with advantages (strengths) of some other approach. By examining available literature sources, it can be seen that hybrid algorithms can be very efficient in tackling different types of problems [85; 86; 87]. Moreover, hybrids between different categories of artificial intelligence (AI) algorithms also exist. For example, artificial neural networks (ANNs) can be efficiently combined with swarm intelligence [27].  
Based on our previous research with hybrid swarm intelligence algorithms [4; 31; 40; 48; 59], we devised and implemented a hybridized WOA approach that overcomes deficiencies of the original version. Modifications of the basic WOA can be summarized as follows:  
* first, we adapted the exploration mechanism from the ABC metaheuristics;
* second, we introduced an additional dynamic control parameter that controls a new exploration mechanism, and
* third, our proposed approach incorporates the search equation of the firefly algorithm (FA).  
Taking into account all listed modifications, we named the proposed approach WOA ABC exploration firefly search (WOA-AEFS). In the following subsections, each of the listed modifications will be explained and the pseudo-code will be provided.  
By the end of this section, the following notation will be used: each candidate solution \(i\) from the population is represented as a vector \(\vec{X}_{i}\), where each solution is comprised of \(M\) decision variables \(\vec{X}_{i}=x_{i}^{1},x_{i}^{2},...x_{i}^{M}\). The value of each decision variable \(j\) of the \(i\)-th solution (\(x_{i}^{j}\)) is within the scope \([lb_{j},ub_{j}]\), where \(lb_{j}\) and \(ub_{j}\) denote the lower and upper bound of the search space in \(j\)-th dimension, respectively. The notation \(fit(\vec{X}_{i})\) represents the fitness of the \(i\)-th solution, while the objective function value of the same solution is denoted as \(f(\vec{X}_{i})\).
## 1 Introduction
### Research Gap and Study Objective  
Although link chain mechanics has been investigated for more than a century, there is still no holistic framework that correlates link geometry, material class, and manufacturing route with performance indicators such as ultimate load, fatigue life, and environmental footprint. Design practice therefore remains largely heuristic and metal-centric.  
Accordingly, this review tackles the following research problem: How can lightweight, recyclable, and electrically non-conductive materials be systematically evaluated and dimensioned so that load-bearing composite chains match or exceed the reliability of conventional steels?  
We address three guiding questions:  
* Which combinations of polymer matrices, reinforcements, and functional additives achieve the minimum working load limit demanded by civil engineering chain classes?
* How do link-scale geometric parameters modify stress localization across different material categories?
* Which knowledge gaps currently prevent codification of design rules for non-metallic chains?  
These questions are pursued within the LIDER 14/0270/2023 project \"Composite non-conductive chain with an anchoring system,\" whose long-term goal is to deliver load capacity equivalent to a steel chain but with a weight reduction of approximately 40%. This also includes chains featuring ergonomic luminescent links, inherent antistatic and flame-retardant behavior, and zero brittle failure under mining duty cycles.
## 3 Results and Discussion
### Tomato Fruit Color
\begin{table}
\begin{tabular}{l l l l l} \hline \multicolumn{4}{c}{**Storage Treatment**} & \multicolumn{3}{c}{**Tomato Varieties**} \\ \hline
**Abbreviation** & **Day-\({}^{\circ}\)C** & **Ghandowra-F1** & **Forester-F1** & **Feisty-Red** \\ \hline ST-0 & (0–22; Control) & 1.72 \({}^{\text{h},*}\) & 1.70 \({}^{\text{h}}\) & 1.05 \(\lx@math@degree\) \\ ST-1 & (4–12) & 1.72 \({}^{\text{h}}\) & 1.70 \({}^{\text{h}}\) & 1.14 \({}^{\text{f}}\) \\ ST-2 & (4–22) & 1.89 \(\lx@math@degree\) & 1.88 \({}^{\text{g}}\) & 1.15 \({}^{\text{f}}\) \\ ST-3 & (8–12) & 2.05 \({}^{\text{f}}\) & 2.03 \({}^{\text{f}}\) & 1.25 \({}^{\text{e}}\) \\ ST-4 & (8–22) & 2.25 \({}^{\text{e}}\) & 2.23 \({}^{\text{e}}\) & 1.37 \({}^{\text{d}}\) \\ ST-5 & (12–12) & 2.46 \({}^{\text{c}}\) & 2.44 \({}^{\text{c}}\) & 1.50 \({}^{\text{bc}}\) \\ ST-6 & (12–22) & 2.73 \({}^{\text{a}}\) & 2.70 \({}^{\text{a}}\) & 1.66 \({}^{\text{a}}\) \\ ST-7 & (16–12) & 2.50 \({}^{\text{a}}\) & 2.48 \({}^{\text{b}}\) & 1.52 \({}^{\text{b}}\) \\ ST-8 & (20–12) & 2.43 \({}^{\text{d}}\) & 2.41 \({}^{\text{d}}\) & 1.48 \({}^{\text{e}}\) \\ ST-9 & (24–12) & 2.70 \({}^{\text{a}}\) & 2.67 \({}^{\text{a}}\) & 1.63 \({}^{\text{a}}\) \\ LSD\({}_{0,05}\) & & 0.03 & 0.03 & 0.05 \\ Pr \(>\) F & & 0.0001 & 0.0001 & 0.0001 \\ CV & & 2.09 & 2.26 & 5.65 \\ SE & & 0.02 & 0.02 & 0.03 \\ \hline \end{tabular} \({}^{\text{*}}\)Means with different letters indicate statistically significant differences at the 0.05 significance level (LSD\({}_{0,05}\)).  
\end{table} 
Table 4: The effect of storage conditions on the color difference values for the three tomato varieties. Figure 3: Effect of the interaction between salinity levels and storage conditions on color values of tomato fruits for all varieties: (**A**) Ghandowra\(-\)F1; (**B**) Forester\(-\)F1; (**C**) Feisty\(-\)Red. Columns with different letters showed significant differences (LSD\({}_{0.05}\)).
# Similarity Analysis for Time Series-Based 2D Temperature Measurement of Engine Exhaust Gas in TDLAT
## 4 Statistical-Based Mean Difference Test and Similarity Analysis
### [Step 2] Similarity Analysis
#### 4.2.1 Two-Sample \(t\)-Test  
Two-sample \(t\)-test is a widely used hypothesis test used to compare whether the averages are equal. The assumptions made while doing a Two-sample \(t\)-test include: (i) the data are continuous, (ii) the data follows normal probability distribution, (iii) the variances of the two populations are equal, (iv) two-sample is independent, and (v) both samples are simple random samples from their respective populations. The Two-sample \(t\)-test is based on the test statistic shown below:  
\[T_{tw}=\frac{\overline{T}_{Thc}-\overline{T}_{RecS}}{\sqrt{T_{S^{2}}}}\sqrt{ \frac{n_{T^{\prime}Rg}}{n_{T^{\prime}}+n_{Rg}}}, \tag{11}\]  
\[T_{S^{2}}=\frac{\sum_{i=1}^{n_{T}}\!\!\left(T_{Thc(i)}-\overline{T}_{Thc}s \right)^{2}+\sum_{i=1}^{n_{Rg}}\!\!\left(\overline{T}_{Rc(i,j)}-\overline{T}_ {RecS}\right)^{2}}{n_{T^{\prime}}+n_{Rg}-2}, \tag{12}\]  
where, \(T_{S^{2}}\) denotes the variance of \(T_{Thc(i)}\) and \(T_{Rec(i,j)}\). \(T_{Thc(i)}\) denotes the TC temperature at \(i\)th time (s), \(i=1.0\), \(1.1\), \(1.2\), \(\cdots\), \(9.9\), \(\overline{T}_{Thc}s\) denotes the mean of all \(T_{Thc(i)}\) in the \(g\)th group, \(\mathrm{g}=1,2\), \(\cdots\), \(9\), \(\overline{T}_{Rec}s\) denotes the mean of all \(\overline{T}_{Rec(i,j)}\) in the \(g\)th group. \(\mathrm{n}_{Tg}\) denotes the number of all \(T_{Thc(i)}\) in the \(g\)th group. \(\mathrm{n}_{Rec}s\) denotes the number of all \(T_{Rec(i,j)}\) in the \(g\)th group.  
For the Two-sample \(t\) test, the hypothesis is as follows  
* Null hypothesis: \(\mathrm{H}_{0}\): \(=\overline{T}_{Thc}s-\overline{T}_{Rec}s=\delta_{0}\) (the difference between the population means (\(\overline{T}_{Thc}s-\overline{T}_{Rec}s\)) is equal to the hypothesized difference (\(\delta_{0}=0\))).
* Alternative hypothesis: \(\mathrm{H}_{1}\): \(=\overline{T}_{Thc}s-\overline{T}_{Rec}s\
eq\delta_{0}\) (the difference between the population means (\(\overline{T}_{Thc}s-\overline{T}_{Rec}s\)) is not equal to the hypothesized difference (\(\delta_{0}\
eq 0\))).  
Here, the null hypothesis is rejected when the \(p\)-value is less than or equal to 0.05, i.e., when there is no difference between the mean of the two groups. In this study, statistical software version 18 of Minitab was used for the Two-sample \(t\) test.
# Hierarchical Modelling for CO\({}_{2}\) Variation Prediction for HVAC System Operation
## 4 Method: Hierarchical Model for CO\({}_{2}\) Variation Predictions (HMCOVP)
### Ensemble Learner  
The dataset is split into training, validation, and testing sets. Models of each individual feature are developed using the training set. Since each individual learner outputs a probability prediction of each class, the probability of predicting decreasing change on the validation set is retained. This method is adopted to avoid overfitting the individual learners on the validation set. The individual learner's classification task predicts the direction of future CO\({}_{2}\) variations, instead of the variation itself. Therefore, the learner's output probability values are combined with other features, describing the historical e2s values of each environmental feature, as inputs to an ensemble model to predict CO\({}_{2}\) variations. This method is referred to as a Stacked Generalization ensemble. These features are obtained from the validation set. The set of outputs of all the individual features is denoted by \(O_{t}\) such that \(o_{it}\in O\) represents the output of environmental feature \(i\) at the current time \(t\). The set of historical features is defined as \(F_{t}\) such that \(f_{it}\) represents the historical environmental e2s values. The ensemble learner is denoted by \(\Psi\). The predictors of the ensemble learner are represented by \(L=O\cup F\) and the output is \(p_{f}\), such that \(p\) represents the variation of CO\({}_{2}\) concentrations in \(f=t+w\), whereby \(w\) represents the future time window. The notation of the ensemble learner is as follows:  
\[\Psi(L,p_{f}) \tag{6}\]  
The exploratory data analysis has shown a varying correlation between CO\({}_{2}\) values and the other environmental sensors. Because correlation values do not fully reflect the relationship between different environmental features, all the features were included in the Individual Learner step. This step yielded learners that are skillful in modelling the relationship between each environmental feature and the CO\({}_{2}\) variation direction. However, since all of the features contribute to some extent to this variation, there is a need to reflect the relative importance of each model. Assigning weights based on the classification results is a method to convey the contribution of each model to the regression task. The environmental features' predictions can be replaced by averaging the outcomes of each feature. However, this method does not explore the potential interactions existing between the input variables and undermines the classification of weak individual learners. Therefore, in this paper, different ensemble methods, representing supervised ML models, are explored and evaluated.