esa-sceva/satcom-chunk-collection huggingface dataset:
# Cutting Force Estimation Using Milling Spindle Vibration-Based Machine Learning
## 3 Cutting Force Estimation Using LSTM-Based Time-Series Machine Learning
### LSTM-Based Time-Series Machine Learning  
The primary capability of LSTM in effectively handling time-series data originates from its memory cell (\(C_{t}\)). Figure 9 shows a block diagram of LSTM. As shown in Equations (3) and (4), the memory cell retains only the essential information from both the new input (\(\widetilde{C}_{t}\))and the past state (\(C_{t-1}\)), preventing gradient vanishing problems commonly encountered in recurrent neural networks (RNN):  
\[\widetilde{C}_{t}=tanh(W_{\text{C}}\cdot[h_{t-1},x_{t}]+b_{\text{C}}) \tag{3}\]  
\[C_{t}=f_{t}\cdot C_{t-1}+i_{t}\cdot\widetilde{C}_{t} \tag{4}\]Additionally, as illustrated in Figure 7, LSTM incorporates three gates (input gate, forget gate, and output gate) to regulate the flow of information. The input gate (\(i_{t}\)) in Equation (5) controls how much new information is stored in the memory cell, while the forget gate (\(f_{t}\)) in Equation (6) determines how much past information is retained. The output gate (\(o_{t}\)) in Equation (7) regulates the amount of processed information that is passed to the next time step:  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{5}\]  
\[f_{t}=\sigma\left(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\right) \tag{6}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{7}\]  
where \(\sigma\) and \(tanh\) represent the sigmoid and hyperbolic tangent activation functions, respectively. The parameters \(W\) and \(b\) denote trainable weights and biases updated iteratively during training.  
Finally, the hidden state (\(h_{t}\)), representing the LSTM's output at each time step, is derived by combining the output gate with the updated memory cell, as shown in Equation (8):  
\[h_{t}=o_{t}\cdot tanh(C_{t}) \tag{8}\]  
The combination of the memory cell and gating mechanisms allows LSTM to effectively capture long-term dependencies and temporal patterns in sequential data. This capability makes it particularly well-suited for tasks involving sequential information, such as cutting force estimation in this study.
## 2 Materials and Methods
### Response Surface Methodology  
Response surface methodology (RSM) is a collection of statistical and mathematical techniques used to model and optimize the relationship between multiple input variables (factors) and a response (output). It is often used in process optimization, design of experiments (DoE), and predictive modeling. RSM usually involves the design of experiments (e.g., central composite design, Box-Behnken design) and the fitting of a regression model (usually a linear or quadratic model). The main objective of this method is to analyze the response surface to find optimal conditions.  
Since surface roughness after laser cutting exhibits nonlinear behavior, it is necessary to develop nonlinear prediction models to achieve the best correlation between the predicted and experimental results. The RSM was used to represent the relationship between the input parameters and the output parameters, as shown in Equation (1).  
\[Y=f(v,\ P_{w},\ P)+error \tag{1}\]  
In this study, experimental errors are evaluated through residuals, and the response \(Y\) is modeled using a quadratic polynomial multiple regression (Equation (2)).  
\[Y=c_{0}+\sum_{i=1}^{3}c_{i}x_{i}+\sum_{i=1}^{3}c_{ii}x_{i}^{2}+\sum_{i<j}^{3} c_{ij}x_{i}x_{j} \tag{2}\]  
Here, \(c_{0}\) is the constant term, while \(c_{i}\), \(c_{ii}\), and \(c_{ij}\) represent the coefficients of the linear, quadratic, and interaction terms, respectively. Using cutting speed (\(v\)), laser power (\(P_{w}\)),  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Code Factors**} & \multicolumn{3}{c}{**Input Factors**} & \multicolumn{3}{c}{**Outputs**} \\ \hline
**No.** & \(\mathbf{x_{0}}\) & \(\mathbf{x_{1}}\) & \(\mathbf{x_{2}}\) & \(\mathbf{x_{3}}\) & \(\mathbf{\mathit{v}}\) & \(\mathbf{\mathit{P_{w}}}\) & \(\mathbf{\mathit{P}}\  
and assist gas pressure (\(P\)) as the three input parameters, the surface roughness response \(Y\) can be expressed by Equation (3).  
\[Y=c_{0}+c_{1}v+c_{2}P_{w}+c_{3}P+c_{11}v^{2}+c_{22}P_{w}^{2}+c_{33}P^{2}+c_{12}vP _{w}+c_{13}vP+c_{23}P_{w}P \tag{3}\]
## 3 Mathematical Formulation and Methodology
### Large-Scale Attenuation  
As the small-scale fading samples pass successfully on the KS test, we characterize the large-scale attenuation in order to determine the parameters for path loss and log-normal shadowing. The large-scale attenuation can be modeled as [38]  
\[\overline{PL}(dB)=\overline{PL}(d_{0})+10n\log\left(\frac{d}{d_{0}}\right)+X_{ \sigma}, \tag{2}\]  
where \(d\) is the distance between transmitter and receiver, \(n\) is the exponent of the path loss, \(\overline{PL}(d_{0})\) the attenuation in dB at the start point, \(d_{0}\) the start point of measurement, and \(X_{\sigma}\) is a zero-mean normal random variable with standard deviation \(\sigma\) that models the shadowing effect [39; 40]. The value of \(n\) is obtained by the linear regression method [41; 42].  
Figure 2: Equipment used in measurement campaigns. (**a**) UAV with XBee and power battery modules. (**b**) Base station for collecting measurement data.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & \begin{tabular}{c} **Energy** \\ **Consumption** \\ \end{tabular} & \begin{tabular}{c} **Data** \\ **Transmission** \\ \end{tabular} & \begin{tabular}{c} **Network** \\ **Overhead** \\ \end{tabular} & **Scalability** & **Adaptability** \\ \hline \begin{tabular}{c} Dynamic Energy-Aware \\ Routing \\ \end{tabular} & Low & Efficient & Minimal & High & High \\ \hline \begin{tabular}{c} Adaptive Clustering RL \\ \end{tabular} & Low & Optimal & Moderate & High & High \\ \hline \begin{tabular}{c} Swarm Intelligence \\ Optimization \\ \end{tabular} & Moderate & Moderate & Low & High & Moderate \\ \hline \begin{tabular}{c} Genetic Algorithm \\ Resource Alloc. \\ \end{tabular} & Low & Efficient & Minimal & High & High \\ \hline \begin{tabular}{c} Fuzzy Logic Energy \\ Management \\ \end{tabular} & Moderate & Adaptive & Low & Moderate & High \\ \hline \begin{tabular}{c} Particle Swarm \\ Optimization \\ \end{tabular} & Low & Efficient & Minimal & High & High \\ \hline \begin{tabular}{c} ML-driven Dynamic \\ Sleep Schedule \\ \end{tabular} & Low & Adaptive & Low & High & High \\ \hline \begin{tabular}{c} Ant Colony Optimization \\ Routing \\ \end{tabular} & Low & Optimal & Minimal & High & High \\ \hline \
\begin{tabular}{c} Q-Learning Data \\ Transmission \\ \end{tabular} & Low & Efficient & Low & High & High \\ \hline
\begin{tabular}{c} Adaptive Network \\ Traffic Pred. \\ \end{tabular} & Moderate & Adaptive & Low & High & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of methods for energy-efficient WSNs in e-commerce. Figure 1: Optimizing energy-efficient wireless sensor networks in e-commerce: methodological workflow. support systems. This research introduces pre-existing innovative techniques to improve the efficiency and performance of wireless sensor networks employed in online shopping platforms, including the Path Selection Algorithm, Adaptive Clustering, the Genetic Algorithm for Resource Allocation, Fuzzy Logic-Controlled Energy Management, and Deep Sleep Scheduling. After comparing these novel methods against the traditional approaches, this research establishes that the usage of AI-based techniques results in significant improvements in energy efficiency. It highlights AI's essential role in transforming wireless sensor networks for consumer electronics in e-commerce and enhancing online shopping experiences. The research is broken down as follows. In Section 2, the Materials and Methods section, we outline the methodology employed and the algorithms that are applied; Section 3 includes the results of the research conducted, comparing different methods of optimizing wireless sensor networks; Section 4 contains a detailed discussion of the implications of the research and its potential applications; finally, Section 5 contains some concluding remarks.
### Goals and Scope  
The methodology used in the current paper estimates or measures GHG emissions in all EU member states and revealed the sustainability implications of the EU's road transport policy. Vital points relevant to long-term sustainability implications are taxes for fuel, technology transfer, and fiscal and regulatory areas. Road transport GHG emissions are projected to increase rapidly, and this rise has big implications for climate change and the EU's political ambitions. For example, road transport emissions could consume the entire carbon budget allocated to the EU [8]. The practical goal is to integrate road transport policy with the EU's carbon budget and identify opportunities that could limit global warming to 1.5 \({}^{\circ}\)C or 2 \({}^{\circ}\)C.  
It is necessary to estimate or measure downstream GHG emissions to fully understand the mechanics of road transport sustainability. Road transport sustainability pivots on the input and output of energy and emissions throughout the life cycle of vehicles. To accomplish the transition to low carbon or zero carbon transport, it is important to highlight the significance of downstream GHG emissions. The transition to low or zero carbon would also deliver sustainable development goals in urban infrastructure within the EU. Transition to sustainable transport requires a clear and accurate understanding of the complete life-cycle emissions that take place in the road transport sector.  
This paper looks at the energy input from diesel and gasoline, the emissions output of EU road transport and the new sustainability requirements. The sustainability approach identifies the prevailing political and domestic economic opportunities and the future trajectories in EU road transport. Future road transport policy goals within and outside the EU will eventually integrate road transport sustainability with the TCRE and carbon budget requirements. This approach will be unique to the EU.  
The EU has already announced major sustainability and decarbonisation initiatives in road transport policy, e.g., the Green Deal [9] and the fleet-wide CO\({}_{2}\) emission targets for 2020 and 2024 announced by the European Commission [10]. These targets are 95 gCO\({}_{2}\)/km for cars and 147 gCO\({}_{2}\)/km for vans.
## 2 Related Works
### Cooperative jamming for Enhanced Security  
Cooperative jamming is another critical strategy to bolster PLS. [PERSON] et al. formulated a social tie-based cooperative jamming game among multiple jammers for physical layer security, proving the existence of a pure Nash equilibrium, developing an algorithm to achieve the minimum secrecy outage probability, and validating the theoretical findings with numerical results [24]. [PERSON] et al. proposed a secure downlink transmission scheme for IoT networks using cooperative jamming and artificial noise-aided secrecy beam-forming to minimize secrecy outage probability and enhance both security and power efficiency against multiple passive eavesdroppers [18]. [PERSON] et al. addressed secure wireless communications over an untrusted full-duplex relay, deriving optimal power allocation between confidential and jamming signals to maximize the secrecy rate and providing a detailed analysis of secrecy outage probability and average secrecy rate, highlighting the negative impact of transmit-power-dependent self-interference on secrecy performance [25]. [PERSON] et al. introduced a joint security approach combining physical layer security and noisy ciphertext, demonstrating that this method can achieve secrecy rates beyond the traditional PhySec capacity and providing optimal power allocation solutions for relay networks with cooperative jamming [26]. [PERSON] et al. proposed a cooperative jamming-aided secure communication scheme for wireless-powered sensor networks that addressed issues of disguised eavesdroppers, imperfect channel estimation, and distance-related power limitations, significantly improving secrecy rates through a two-level optimization algorithm [27].
# Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication
###### Abstract  
Low frequency communication, taking advantage of the features of low frequency electromagnetic signals in near field, is widely used in through-the-earth (TTE) wireless applications. However, the low frequency non-Gaussian noise severely limits the communication performance. In this paper, an adaptive noise cancellation algorithm based on time delay estimation (ANC-TDE) and maximum correntropy criterion (MCC) is proposed. The explicit time delay estimation algorithm based on MCC (MCC-ETDE) is used to estimate the time-varying delay and the noise correlationship between the primary input and reference inputs. With a reference noise selected and time delay compensated, the non-Gaussian noise is canceled by adaptive filter based on MCC. The proposed algorithm is implemented on field programmable gate array (FPGA) and the performance is evaluated by simulation and experiment. As shown in the results, the ANC-TDE algorithm can reduce the complexity of ANC filter and adaptively compensate the varied time delay between the primary input and reference input. With the time delay compensated, the ANC-TDE algorithm has better performance in non-Gaussian noise environment and is more suitable for real-time systems.  
low frequency communication; non-Gaussian noise; adaptive noise cancellation; time delay estimation; maximum correntropy criterion (MCC); field programmable gate array (FPGA) +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
In this study, we applied TG 318 as the method of dispersion of nanomaterials to our ecotoxicity test, and the following conclusions were confirmed:  
ZnO was fairly dispersed in DI water without aggregation regardless of the presence of NOM, whereas excellent stability was only observed with NOM in the ecological toxicity media. For the ecological toxicity test, the addition of NOM in the test medium of algae, water fleas, and fish resulted in less aggregation and confirmed the effect of dispersion.  
In the FT-IR analysis of NOM-coated ZnO, alcohol and phenyl radical bonds were confirmed in the range of 3500-3200 cm\({}^{-1}\). In addition, the surface charge indicated by the zeta potential was changed from positive to negative owing to the presence of NOM; therefore, the absolute value was increased. As a result, the analysis of particle sizes showed that the particles were small and well-dispersed in the ecotoxicity test medium.  
During the exposure with or without NOM, the ionic concentrations in each ecotoxicity test medium hardly changed to a value below 2 mg/L, implying that ionization would have had no effect on the toxicological variations.  
It is regarded that NOM has a dispersion effect on nanomaterials and is an appropriate dispersant because no toxic effects are seen on algae, water fleas, and fish. However, further investigation is required to establish a standard test method for the evaluation of toxicities of nanomaterials.  
Figure 9: _Oryza latipes_ mortality by ZnO NPs with/without NOM.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/18/6431/s1](http://www.mdpi.com/2076-3417/10/18/6431/s1)]([http://www.mdpi.com/2076-3417/10/18/6431/s1](http://www.mdpi.com/2076-3417/10/18/6431/s1)), Figure S1: Comparison of algae growth inhibition by ZnO depending on the NOM, Table S1: Zeta potential and pH changes of test medium with ZnO with/without NOM.  
**Author Contributions:** Conceptualization, writing--original draft preparation, B.-c.L.; methodology, G.H. ([PERSON]) and H.L.; formal analysis, G.H. ([PERSON]) and H.L.; investigation, D.-Y.S.; writing--original draft preparation, B.L.; writing--review and editing, P.K. ([PERSON]), G.H. ([PERSON]) and P.K. ([PERSON]); project administration, B.-c.L. and G.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This study was supported by grants from the National Institute of Environmental Research (NIER), funded by the Ministry of Environment of the Republic of Korea (NIER-RP2019-01-01-218).  
**Conflicts of Interest:** The authors declare that there are no potential conflict of interest with respect to the research, authorship, and/or publication of this article.
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 3 Results
### Moisture Dependence of the Dielectric Properties of Samples  
To investigate the influence pattern of the moisture content on the dielectric properties of samples, the dielectric constant \(\varepsilon^{\prime}\) and the loss factor \(\varepsilon^{\prime\prime}\) of the straw compost samples with moisture content (5-35%) at various temperatures (25-65 \({}^{\circ}\)C) were measured at the selected measurement frequency (10 kHz) under the condition that the sample density was level II. The results are displayed in Figure 5.  
At five temperatures, \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) of the samples exhibited a monotonic increasing trend with the increase in the moisture content. At a constant temperature, both \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) of the compost samples increased with the increase in the moisture content. When the moisture content was 25-35%, \(\varepsilon^{\prime}\) and \(\varepsilon^{\prime\prime}\) curves increased sharply. Since water is a polar molecule  
Figure 5: Moisture dependence of dielectric properties at various temperatures: (**a**) dielectric constant, (**b**) loss factor.  
with a complex permittivity of 81 and a strong electric dipole moment, it is the primary factor influencing the dielectric properties of the samples [35].
## 3 Methodology
### Dataset Collection and Preparation  
The Kvasir-Seg [30] dataset was utilized for segmentation, and the Kvasir-V2 [31] and Hyper-Kvasir [32] datasets were utilized for classification. Our dataset contains four diseases, i.e., ulcers, polyps, esophagitis, and ulcerative colitis, as well as a normal class, with 1000 instances for each malady, other than the ulcer malady, which has only 854 instances. As a result, our dataset consists of 4854 images divided into five classes: ulcerative colitis, polyps, ulcers, esophagitis, and normal. For segmentation, the Kvasir-Seg dataset is used, which contains the 1000 images of the polyp class with their ground truths.  
Initially, the segmentation results were collected based on Kvasir-Seg dataset, and then this method was applied to all other diseased images, and classification was performed.  
Figure 1: Proposed methodology of localization and classification of GI tract disorders.
## 2 Methodology
### In Situ Chlorophylll a Concentration Data  
The in situ Chl-\(a\) concentration data belongs to the database collected from theIME-COCAL region (Figure 1) ranging from January 1998 to April 2016 (Table 1). To obtain the in situ Chl-\(a\), water was collected from the network of stations in 5 L Niskin bottles coupled in a rosette at varying depths: 1 m, 10 m, 20 m, 50 m, 100 m, 150 m, and 200 m. Two liters of water at each depth were filtered through Whatman GF/F filters at a pressure below 150 mm Hg. The filters were then placed in Histoprep tissue capsules (Fisherbrand, Fisher Scientific, Pittsburg, PA, USA) and frozen in liquid nitrogen, pending analysis. Chlorophylll  
Figure 1: General grid of IMECOCAL stations where in situ samples were collected. The line number increases from 100 toward the south on line 137. Numbers above transects indicate station numbers, which increase with distance from the coast. The blue arrows indicate the main directions of the California Current System in the region.  
extraction was performed using acetone at 90% and at 4 \({}^{\circ}\)C in darkness for 24 h as recommended by Ref. [20]. Chl-\(a\) concentration (mg m\({}^{-3}\)) was determined by the fluorometric method [21; 22], with a Turner Designs A10 fluorimeter for the 1998 to 2005 samples and a Turner Designs trilogy fluorimeter for 2005 to 2016. Both fluorimeters were calibrated with pure Chl-\(a\) (Sigma-Aldrich, Darmstadt, Germany). A more comprehensive description of the methodology can be found in Ref. [23]. For the purpose of this study, the mean in situ Chl-\(a\) concentration between the surface and 10 m depth data was used.
## 2 Background and Related Work
### Related Work
#### 2.3.5 SAST-DAST and SAST-IAST Hybrid Prototypes  
The goal of the proposed approach in [62] is to improve penetration testing of web applications by focusing on two areas where the current techniques are limited: identifying the input vectors of a web application and detecting the outcome of an attempted attack. This approach leverages two recently developed analysis techniques. The first is a static analysis technique for identifying potential input vectors, and the second is a dynamic analysis to automate response analysis. The empirical evaluation included nine web applications, and the results show that the solution test the targeted web applications more thoroughly and discover more vulnerabilities than other two tools (Wapiti and Sqlmap), with an acceptable analysis time.  
There are several recent works that combine SAST tools with IAST tools to runtime monitor attacks with the information of static analysis. The work of [PERSON] et al. [63] presented a novel data flow analysis approach for detecting data leaks in Java apps, which combines static and interactive analysis in order to perform reliable monitoring while introducing a low overhead in target apps. It provides an implementation as the JADAL tool, which is publicly available. The analysis shows that JADAL can correctly manage use cases that cannot be handled by static analysis, e.g., in presence of polymorphism. Additionally, it has a low overhead since it requires monitoring only a minimal set of points.  
Another study presents a new approach to protect Java Enterprise Edition web applications against injection attacks as XSS [64]. The paper first describes a novel approach to taint analysis for Java EE. It explains how to combine this method with static analysis, based on the Joana IFC framework. The resulting hybrid analysis will boost scalability and precision, while guaranteeing protection against XSS.  
Another paper [65] proposes a method where firstly vulnerabilities are detected through static code analysis. Then, code to verify these detected vulnerabilities is inserted into the target program's source code. Next, by actually executing the program with the verification code, it is analyzed whether the detected vulnerabilities can be used in a real attack. So, the proposed method comprises of vulnerability detection stage through static code analysis, instrumentation stage to link static code analysis and dynamic analysis, dynamic analysis stage of injecting an error and monitoring the impact on a program.
## 3 Scheme Design
### Attribute Encryption of Address Information
#### 3.4.1 Initialization  
Generate the pairing-related public parameters \((e,g,G_{1},G_{T},Z_{r})\), as shown in Table 2. Then, choose the parameter \(\lambda\) to determine a bilinear pair corresponding to it. Chooserandom numbers \(\alpha\in Z_{r}\) and \(\beta\in Z_{r}\), and compute \(Y=e(g,g)^{\alpha}\), \(g^{\beta}\). Therefore, the master key(\(MK\)) is \(g^{\alpha}\) and the public key is \(pk\), as shown in Equation (8).  
\[pk=(Y,g^{\beta}) \tag{8}\]  
Choose a random number \(t\) and compute \(D=g^{\alpha}g^{t\beta}\), \(D_{0}=g^{t}\). For attribute \(i\) in the user list, compute \(D_{i}=H(i)^{t}\). Then, generate the user's private key \(sk\), as shown in Equation (9).  
\[sk=[D,D_{0},(D_{i})] \tag{9}\]  
Each attribute authority manages one or more classes of attributes, and the attributes managed by different attribute authorities have intersecting parts.