esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Variability in OC Content and DHA
The obtained results (Figure 7) depict a small increase in dehydrogenases activity in autumn in comparison to other seasons, however this pattern was observed in research plots Nos. 2-3 and Nos. 5-7. The mean values for spring, summer and autumn from the entire research period range 0.013-0.029, 0.008-0.030 and 0.016-0.056 umol TPF-g d.m.\({}^{-1}\).20 h\({}^{-1}\), respectively. Figure 6: The distribution of data of dehydrogenases activity in A horizon. Figure 7: The average seasonal dehydrogenases activity. Despite the lack of seasonal differences in DHA, visible variability was observed among analyzed years. The highest value of DHA, independently of a research plot, was noted in 2012 (range 0.030-0.106 and mean 0.069 \(\upmu\)mol TPF-g d-m\({}^{-1}\).20 h\({}^{-1}\)) (Figure 8). Next year DHA significantly decreased and ranged from 0.010 to 0.050 \(\upmu\)mol TPF-g d-m\({}^{-1}\).20 h\({}^{-1}\) (mean 0.022). This reduction was observed till the end of the study, 2016 (range 0.001-0.002 and mean 0.001 \(\upmu\)mol TPF-g d-m\({}^{-1}\).20 h\({}^{-1}\)). Furthermore, change-point analysis was used to depict possible abrupt changes in OC content in the studied horizons and dehydrogenases activity. Change-point analysis is one of the statistical approaches that have been widely adopted by researchers to detect change points in a data stream [34]. Change-point is a point that shows where a shift in the data pattern occurs. It is also defined as a point at which the parameters (mean, variance, and trend, etc.) of an underlying distribution or the parameters of a model used to describe time series abruptly changes [35]. Conducted analyses for OC content showed that in the case of the O horizon abrupt changes in data stream occurred in research plots No. 1 and Nos. 4-7 (Figure 9). For plots No. 1, No. 4 and No. 5 such a change appeared only once after 7 th, 6 th and 7 th sampling time, respectively. For plots No. 5 and No. 6 two change points were found, both after 5 th and 10 th sampling time. However, in the A horizon such a change-point was found only for research plot No. 7, where twice during measurement a period shift in the data pattern was observed. The change was noticed after 7 th and 11 th sampling time (beginning of 2014 and end of 2015). More consistence results were obtained for dehydrogenases activity--in all research plots a sharp change occurred after 6 th sampling time (2014 spring) (Figure 10). Figure 8: Annual average dehydrogenases activity in A horizons. Figure 9: _Cont_. Figure 10: _Cont._  
Figure 9: Change—point analysis for OC content in the O horizon on research plots: (**a**) No. 1, (**b**) No. 4, (**c**) No. 5, (**d**) No. 6, (**e**) No. 7.
# Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning--A Review
## 3 Machine Learning Techniques in Cybersecurity
### Unsupervised Learning
Furthermore, well-known dimensionality reduction techniques such as linear discriminant analysis (LDA), principal component analysis (PCA) and Pearson correlation, as well as positive matrix factorization, can be used to handle such problems [67]. Another example is association rule mining, in which machine learning-based policy rules can learn to avert cyber incidents. The rules and logic of an expert system are normally manually documented and implemented by a knowledge engineer working with a domain expert [126, 30, 121]. In contrast, association rule learning identifies the rules or associations among a set of security aspects or variables in a dataset [127]. Different statistical analyses are performed to quantify the strength of associations [102]. In the domain of machine learning and data mining, various association rule mining methods have been presented, such as tree-based [128], logic-based [129], frequent pattern-based [130, 131, 132], etc. Moreover, a priori [130], a priori-TID and a priori-Hybrid [130], AIS [127], FP-Tree [128], RARM [133] and Eclat [134] have been used extensively for association rule learning. These algorithms are able to resolve such difficulties by creating a set of cybersecurity policy rules. The authors in [104] applied sequential pattern mining on the DARPA 1999 and 2000 data to reduce the redundancy of alerts and minimize false positives. Using the a priori algorithm, they discovered attack patterns and created a pattern visualization for the users with a support threshold of 40%. They were able to detect 93% of the attacks in twenty seconds. The study reported a real-time scenario with 84% attack detection, with the main variation coming from the support threshold. In [105], DBSCAN was used as a clustering method to group normal versus anomalous network packets in the KDD'99 dataset. The dataset was preprocessed and features were selected using correlation analysis. In this study, the performance was shown at 98% for attack or no-attack detection. The authors in [106] took a different data mining approach to create an intrusion detection system called ADMIT. This system does not rely on labeled data; rather, it builds user profiles incrementally using a dynamic clustering technique. It uses sequences, which are a list of tokens collected from users' data, to build a profile. These sequences are classified as normal or anomalous based on using a modified form of K-means clustering. They set a value of 20 for the maximum sequence length, which resulted in 80% performance accuracy with a 15% false positive rate. A unique algorithm based on the signature a priori algorithm was used to find new signatures of attacks from existing attack signatures, proposed in [107]. Here, the authors compared their algorithm processing time to that of the a priori and found that their algorithm was much faster. A summary of unsupervised approaches is also shown in Table 3.
# A Fast and Simple Method for Absolute Orientation Estimation Using a Single Vanishing Point
## 3 Experiments and Results
### Synthetic Data
#### 3.1.3 Noise Sensitivity  
Noise might come from the 2D point/line in the image and 3D point/line in the world frame. If the noise of the 3D point/line exists, it could be responding in the image reprojection through the camera imaging model, and thus we can say that the noise of the 2D point/line could contain both the extraction noise in the image and the noise of the 3D point/line. For simplifying the source of noise, we only add zero-mean Gaussian noise to the 2D point/line, and the noise deviation level varies from 0 to 2 pixels in this section. Here, 50,000 independent trials are performed at each level of noise with unchangeable deviation. Then, the mean errors at each level of noise are reported in Figure 5.  
From Figure 5, it can be seen that when the noise increases, all the errors for all of the three methods increase. In detail, the total rotation error of our proposed method is the lowest and has the best performance for noise sensitivity, while the RPnP solver has second best performance and the P3P solver has the worst performance. In terms of rotation error around the \(X\)-axis, our proposed method performs only slightly better than the RPnP solver, and both perform much better than the P3P solver. In addition, when the noise increases, the performance superiority becomes more obvious. In terms of rotation error around the \(Z\)-axis, the RPnP solver has the best performance, while our proposed method has the second best and the P3P solver has the worst. Although our proposed method does not have the best performance in terms of rotation error around the \(Z\)-axis, it performs the best in the total error and still shows excellent noise sensitivity.  
Figure 4: The performance of numerical stability for our proposed method (red), P3P solver (black), and RPnP solver (blue). (**a**) Distribution of the total rotation error; (**b**) distribution of the rotation error around the \(X\)-axis; (**c**) distribution of the rotation error around the \(Z\)-axis.  
Figure 5: The performance of noise sensitivity for our proposed method (red), P3P solver (black), and RPnP solver (blue). (**a**) The total rotation error; (**b**) the rotation error around the \(X\)-axis; (**c**) the rotation error around the \(Z\)-axis.
## 4 Experiments and Results
### Experiment 1--Effect of Genetic Operators on the Variety of Individuals in the Population  
The genetic operators have a significant impact on the performance of the proposed IMFEOA algorithm. It directly determines the diversity of individuals in the population. As described in Section 3.2.1, individuals have two forms of coding. In this experiment, Two-Point Crossover, Uniform Crossover, and the method of sampling RBM probability model (Prob) applicable to binary vectors are chosen, as well as the Simulated Binary Crossover (SBX) and Gaussian Mutation (Gauss) methods suitable to float vector.  
During this experiment, we randomly select the user with ID A9Q28 YTLYRE07 in the music dataset, and the first sliding window data are selected after processing according to the method in Figure 3. Common recommended settings used for all genetic operators. Combining the three crossover operators and two mutation methods, the effect of the experimental genetic operators on the variety of individuals in the population is shown in Figure 4. It is clear that only the SBX and Gaussian Mutation operators are capable of maintaining the diversity of the population.  
\begin{table}
\begin{tabular}{c c} \hline \hline DRBM-Social [13] & It uses a model based dual-RBM and social knowledge to describe the user’s preference information \\ \hline RBM [42] & It uses an RBM-based model to describe the user’s preference information \\ \hline MPUSMs based on RBM & They use RBM as the underlying model for building MPUSMs \\ \hline SC-DRBMIEDA [13] & It is an IEA that uses the same DRBM-Social as the proposed algorithm, but it does not implement knowledge transfer between preferences. \\ \hline DRBM-Social-IGA & It is an IEA using a general genetic algorithm to optimize DRBM-Social. \\ \hline RBM-IGA & It is an IEA using a general genetic algorithm to optimize RBM. \\ \hline MPUSMs-IMFEOA based on RBM & It differs from the algorithm proposed in this paper in that it replaces the underlying surrogate model for constructing MPUSMs \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison models and algorithms used in this paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(\alpha\) & 0.3 \\ \hline \(\beta\) & 0.2 \\ \hline \(K\) & 3 \\ \hline \(\varepsilon\) & 3 \\ \hline \(U_{sum}\) & 200 \\ \hline \(rmp\) & 0.3 \\ \hline \(pop\) & 50 \\ \hline \(G\) & 100 \\ \hline \(Q\) & 100 \\ \hline \(N\) & 10 \\ \hline \(W\) & 50 \\ \hline \hline Number of visible units of RBM & \# of attributes \\ \hline Number of hidden units of RBM & 1.2 * \# of attributes \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameters of the proposed algorithm.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 6 Experiments Simulation and Analysis
### Algorithm Testing and Analysis

Table 3: Comparison of different algorithms. The proposed PAGWO algorithm demonstrates superior convergence accuracy compared to conventional algorithms in single-peak functions \(f_{1}\)-\(f_{6}\) while maintaining a low standard deviation, indicating its excellent fundamental optimization capability and rapid convergence speed. Notably, in function \(f_{5}\) with introduced random noise, PAGWO exhibits robust performance, further verifying its capability to handle interference environments. For multimodal functions, PAGWO not only successfully attains the global optimum of 0 in both \(f_{7}\) and \(f_{9}\) but also outperforms other algorithms on \(f_{8}\), \(f_{10}\), and \(f_{11}\), thereby confirming its robust global search capability and effective mechanism for escaping local optima. In low-dimensional fixed functions, PAGWO outperforms baseline methods in \(f_{12}\), and the average solution quality in \(f_{13}\) also reaches the theoretical optimal value, demonstrating its  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Function** & **Metric** & **GA** & **SCA** & **PSO** & **GWO** & **PAGWO** \\ \hline \multirow{4}{*}{\(f_{3}\)} & Mean & \(3.76\times 10^{4}\) & \(2.24\times 10^{3}\) & \(4.46\times 10^{2}\) & \(1.12\times 10^{-20}\) & \(2.1  
consistent superiority in high-dimensional scenarios. In the combined functions \(f_{14}\) and \(f_{15}\), the average performance of PAGWO surpasses that of the other algorithms, demonstrating that it is more robust when tackling complex, multimodal optimization problems. These experimental results collectively validate PAGWO's state-of-the-art performance across diverse benchmarks. The algorithmic improvements can be specifically attributed to four key strategies: enhanced initial exploration through piecewise chaotic mapping, balanced global-local search dynamics via a nonlinear convergence factor, accelerated convergence through optimized position updating, and robustness improvement via EPD for local optima avoidance.
## 2 Technological Advances in Point-of-Care Testing (POCT)
### POCT for Cancer Detection  
Cancer remains a leading cause of premature mortality, and workplace health programs represent an untapped opportunity for systematic early-detection initiatives. Several POCT-based screening tools have demonstrated utility in this context.  
POCT for cancer is emerging as a transformative tool in oncology, offering rapid, decentralized detection of cancer biomarkers that support early diagnosis, treatment monitoring and disease recurrence surveillance. These systems commonly utilize lateral flow assays, electrochemical biosensors or microfluidic platforms to detect tumor markers such as prostate-specific antigen (PSA), carcinoembryonic antigen (CEA) and cancer antigen 125 (CA-125) in blood, saliva or urine with high sensitivity and minimal sample preparation [28; 29] (Figure 2).  
Prostate-specific antigen (PSA) tests, adapted into lateral flow or immunohistochemical-graphic formats, allow rapid, semi-quantitative assessment of prostate cancer risk within approximately 15 min [30; 31]. Similarly, fecal immunochemical tests (FITs) for colorectal cancer screening can be conducted conveniently onsite, offering non-invasive detection of occult blood in stool samples with high specificity [32].  
In female employees, high-risk human papillomavirus (HPV) infections--a precursor to cervical cancer--can be identified using molecular POCT platforms such as GeneXpert HPV, which provide results within an hour. Emerging technologies targeting circulating tumor DNA (ctDNA) through minimally invasive liquid biopsies, while still largely investigational, promise future expansion of workplace-based cancer screening modalities [33].  
Despite these advances, it is crucial to recognize that POCT-based cancer screening must adhere to rigorous clinical validation protocols to ensure sensitivity, specificity and appropriate post-test counseling pathways.  
Table 1 summarizes the currently used POCT for chronic disease screening.  
Figure 2: Graphical demonstration of various POCT approaches for major cancer types.  
POCT for cancer faces several key challenges that limit its widespread clinical adoption. These include the low concentration and limited specificity of cancer biomarkers, the biological heterogeneity of tumors, and the need for multiplex detection to improve diagnostic accuracy. Technical issues such as device calibration, quality control and standardization across settings also pose hurdles. Clinically, there is limited trust from healthcare providers, especially for screening or standalone diagnosis, while regulatory barriers and data privacy concerns further complicate POCT deployment. Finally, infrastructure constraints--such as power, connectivity and cost--can hinder implementation, particularly in low-resource environments.
## 2 Prediction of Tire Pattern Noise Based on System Identification
### System Identification Based on Adaptive Filter Theory  
Simple data scanning for estimating pattern noise, which is the conventional method, predicts the pattern noise only using pattern shape and has limitations because it does not consider the transfer function and precise mechanism. To improve this method, image processing was applied to predict tire pattern noise by considering the effect of groove depth on noise. In this study, the adaptive filter theory was also applied to calculate the transfer function between the pattern groove image data and the measured data. Figure 2 shows the procedure used for the application of the adaptive filter to predict actual pattern noise in the time domain. First, the images of the tire pattern were processed to predict the input force exciting the tire. Second, an optimal adaptive filter was designed to identify the transfer function between the input force and noise output. To design the optimal filter, [PERSON]'s adaptive filter was applied [17]. The output data of the optimal filter, \(f\), should be the same as the measured noise to minimize the error. The transfer function of the optimal filter \(f\) becomes that of the transfer path between the excitation force and the measured noise, as shown in Figure 2. In this study, the tire pattern noise was measured in a semi-anechoic chamber, as shown in Figure 2. A test tire was installed on a roller which was controlled by a dynamometer. Pattern noise was measured at 1 m from the center of the tire. Three positions were selected for this test. All data presented in this study were measured at 1 m from the tire center in the front of the tire. Four test tires were used, and their input forces were scanned using image processing.  
To predict the noise radiated from the new tire pattern, the transfer function or impulse response function of the optimal filter waveform can be used. The noise from the new tire pattern is predicted by convoluting the excitation force obtained throughout the image processing for the new tire pattern and the impulse response of the optimal filter.  
Figure 2: Prediction of transfer function based on system identification using an optimal adaptive filter.
## 1 Introduction
The first relationship that was investigated is between the water level and several independent variables (called the traditional relationship hereafter), including daily rainfall, daily evaporation, minimum daily temperature, maximum daily temperature, daily relative humidity during the daytime, daily relative humidity at nighttime, and daily average wind speed. Second, the temporal relationship between daily water levels was investigated. Rainfall was considered the primary factor influencing water balance at the spatial and temporal scales [38]. NNs were selected to model both relationships to maintain a common platform for comparing the two relationships in this study. A simple feed forward neural network (FFNN) was chosen to model the traditional relationship and at the same time, the long short-term memory neural network was used for temporal relationship modeling. Moreover, the authors need to find the simplest NN architectures that require minimum computational processing power and memory requirements with substantial prediction accuracy to run the model on an average computer [39]. It will enable the easy and economical use of the prediction model, especially in remote installations in a country like Sri Lanka. Primarily, the temporal relationship was never investigated for water level prediction in the past on a comparative basis. The study further investigates the relative accuracy of the relationships, advantages vs disadvantages, and limitations in the two types of predictions. It also examines the available data volume's impact on prediction accuracy. Moreover, the information extraction patterns of each NN will be discussed. The selected NN architectures are the low-load high, performing NNs selected via detailed model comparison. This is the first-ever application of deep machine-learning techniques for predicting wetland water levels in Sri Lanka. The remainder of the paper is organized as follows: Section 2 briefly discusses two neural networks considered for water level prediction. Section 3 describes the proposed approach, data sets, and the related techniques used for comparison. Section 4 presents the results with a comprehensive discussion, and Section 5 concludes this work.
## 4 Experimental Validations
### Inverter
#### 4.2.2 Achieve Complete Decoupling  
This example adopts the optimization model (13) with the same four groups of values related to \([\delta]\) and \([\gamma]\) as in the previous example, i.e., \([\delta]=[\gamma]=0.1\), \([\delta]=[\gamma]=0.01\), \([\delta]=[\gamma]=0.001\) and \([\delta]=[\gamma]=0.0001\). The four different design results are plotted in Figure 15, and we can see that the four optimized topologies are obviously different.  
Figure 12: Design domain and boundary conditions of the inverter.  
Figure 13: Design result of the inverter (**a**) using Equation (17) and the distribution of the local strain energy density (**b**).  
Tables 5 and 6 list the values of concerned displacements and coupling indices of the design results shown in Figure 15. The desired displacements \(u_{O1}\) and \(u_{O2}\) are all negative, and it means the displacement inverter is successfully designed by using the proposed method. The smaller the values of \([\delta]\) and \([\gamma]\) are, the smaller the values of undesired displacements are. From the data in Table 6, one can learn that both the output and input coupling indices decrease greatly when \([\delta]\) and \([\gamma]\) become small, which are all strictly restricted within the permissible coupling indices \([\delta]\) and \([\gamma]\).  
Figure 14: Design result of the inverter (**a**) using Equation (19) and the distribution of the local strain energy density (**b**).  
Figure 15: Inverters obtained with different \([\delta]\) and \([\gamma]\): (**a**) 0.1, (**b**) 0.01, (**c**) 0.001, (**d**) 0.0001.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \([\delta]=[\gamma]\) & \(u_{O1}\) & \(\widetilde{u}_{O2,1}\) & \(\widetilde{u}_{I2,1}\) & \(u_{O2}\) & \(\widetilde{u}_{O1,2}\) & \(\widetilde{u}_{I2,2}\) \\ \hline Figure 15a & \(0.1\) & \(-15.7907\) & \(-4.1756\) & \(4.9923\) & \(-24.1248\) & \(4.5064\) & \(4.9923\) \\ Figure 15b & \(0.01\) & \(-14.3172\) & \(-0.4670\) & \(-0.4670\) & \(-13.5221\) & \(1.0904\) & \(-0.4670\) \\ Figure 15c & \(0.001\) & \(-12.9474\) & \(-0.4063\) & \(-0.4063\) & \(-12.8545\) & \(0.4063\) & \(-0.4063\) \\ Figure 15d & \(0.0001\) & \(-17.5345\) & \(0.1752\) & \(0.1752\) & \(-17.8203\) & \(0.1781\) & \(0.1752\) \\ \hline \end{tabular}
\end{table}
Table 5: Desired and undesired displacements of the four design results shown in Figure 15.
## 3 Flexible Demand Resources (FDRs) and Demands Response Program (DRPs)
### Time-Ahead Dynamic Pricing (TADP) Demand Response Program
#### 3.3.1 Time-Ahead Dynamic Pricing Model  
The electricity price for the next hour (\(E_{TADP}^{pr}(t+1)\)) is determined based on the difference between forecasted total generation output power from renewable energy sources (PV and WT) and the load demand \(\hat{L}_{D}(t+1)\) using the following equation:  
\[E_{TADP}^{pr}(t+1)=E_{std}^{pr}(t+1)\left(1+\frac{\hat{L}_{D}(t+1)-(\hat{WT}_ {p}(t+1)+\hat{PV}_{p}(t+1))}{\hat{L}_{D}(t+1)}\right) \tag{12}\]  
where \(\hat{PV}_{p}(t+1)\) and \(\hat{WT}_{p}(t+1)\) represent the forecasted generation output power from the PV and WT, respectively. \(E_{std}^{pr}(t+1)\) is the initial (standard) Kenyan electricity price initially present for hour \(t+1\) before TADP DRP implementation. \(E_{TADP}^{pr}(t+1)\) is the next hour electricity price after the implementation of TADP DRP.