esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Our results indicate that this selective irradiation therapy can affect the viability of heterogeneous H1299 lung cancer cells in vitro. MMW irradiation is reported to interact with cell compartments such as membranes, cytoskeletons, chromosomes, and nuclei [30; 31; 32; 33]. Biological membranes are suggested to be the main target of MMW irradiation within 1-80 GHz [34; 35]. 50 GHz MMW exposure at 10 mW/cm\({}^{2}\) for 2 min resulted in a transient membrane permeability change in immortalized epithelial H1299 cells [36]. We report accelerated cellular mortality and senescence of H1299 cells observed over a period of 7-14 days after W-band (75-105 GHz) MMW exposure at 0.2 mW/cm\({}^{2}\). The 2 min dose induced a slightly increased mortality effect as compared to controls, the 4 min dose gave enhanced mortality and the 10 min exposure resulted in complete mortality of irradiated cell populations. Molecular resonance with MMW induce reversible externalization of phosphatidylserine molecules in human keratinocyte and murine melanoma cells exposed in vitro likely affecting the functional state of phospholipids in bio-membranes without detectable membrane damage [37]. Thus, energy dose and frequency determine the extent of biophysical modulation occurring in cells in response MMW exposure. Further, the observations suggest that duration of exposure can be appropriately tuned to elicit specific resonances of structures with corresponding dimensions within the cellular environment. As an application of Frohlich resonance this results in a cascade of events causing the observed changes to activate targeted response. As cancer cells present biochemical differences from normal cells; their resonant energies for eliciting biophysical responses are expected to be different. The biochemical properties arising from oncogenic heterogeneity [38] account for the altered physical properties of cancer cells [39]. This allows for distinct electromagnetic interactions [40] and responses in cancer cells as compared to normal cells. The challenge involved finding sharp resonances for biological objects like cells containing a large variety of different macromolecules making it a difficult task. Even if such resonances are found in vitro they may change in vivo. The advantages of using the sweeping regime for MMW exposure as described here and in our previous report [19] overcomes this problem by excluding the necessity for fine tuning the irradiation setup operating in vitro. The quasi-optic processes in this range of frequencies can produce either constructive or destructive interference conditions. Sweeping through the W-band (75-105 GHz) of frequencies produces both cases of interference but allows for the constructive points to contribute to the effects observed and reported here.
## 3 Results and Discussion
### Microstructure Analysis  
Figure 3 presents the results of geopolymers' chemical composition tests performed by EDS analysis. The structures of the tested geopolymers can be categorised as poly(isalate-siloxo) and poly(isalate-disiloxo) based on the Si to Al ratio [49]. The increase in the Si to Al ratio contributes to the creation of a three-dimensional polymer matrix called poly(isalate-siloxo) and poly(isalate-disiloxo) [49]. The EDS analysis shows that the Si to Al ratio was 1.77, 1.95, 2.35, and 2.64 when WG content was 0%, 10%, 20%, and 30%, respectively. According to [PERSON] et al. [50], the Si to Al ratio suitable for mechanical properties development should be within the range of 3.5 to 4.0. The molar ratio SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) reported in [46] was between 2.5 and 5.0. According to [51], the molar ratio SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) most favourable for the strength development in the tested geopolymers was between 3.4 and 3.8. In the present investigation, the determined Si to Al ratios were significantly lower than those obtained by the cited authors. However, as stated in [49; 50; 51], the optimum mechanical performance for WG content below 30% has been achieved.  
Figure 3: EDS analysis of geopolymers containing various amounts of waste glass.  
Figure 2: The density of geopolymers depending on the percentage of the waste glass addition.  
An SEM analysis of the hardened geopolymer pastes was conducted to study the microstructures of the geopolymers containing various amounts of WG. The micrographs of geopolymer specimens containing various amounts of WG are presented in Figure 4. There are many spherical particles--also known as unreacted FA balls (Figure 4a). Unreacted WG particles with smooth surfaces and non-regular, sharp edges are present in all specimens containing WG (Figure 4b-d). The number of unreacted FA particles diminishes with growing WG content, resulting from more reactive SiO2 presence.
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 3 Experiment
### Experimental Results
#### 3.3.1 Comparison Performance  
In this part, the raw data were trained and tested with a few neural network methods [38]. For testing the comparison methods, the best method was the Medium Neural Network, whose accuracy was 99.0%. The poorest one was the Narrow Neural Network, whose accuracy was 98.6%. For the proposed method, the accuracy achieved was 99.8%. The accuracy data are shown in Figure 6.  
For proposed method, the network output was used to classify tasks (using 'CrossEntropLoss'). The loss curve of changes with training progress shows that the training converged, as is shown in Figure 7.
# Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost
## 2 Theoretical Background  
### Improved Complete Ensemble Empirical Mode Decomposition with the Adaptive Noise (ICEEMDAN) Model  
ICEEMDAN adds a special white noise \(E_{k}(w^{(i)})\) that is the \(k\)-th IMF component of Gaussian white noise after EMD decomposition [34; 37]. Then, by obtaining a unique residual, IMF is defined as the difference between the existing residual signal and its local mean. The results show the residual noise in IMF is greatly reduced, and the problem of illusive components and shortage of modal aliasing in the early stage of decomposition is also solved [38].  
Let \(E_{k}(\cdot)\) denote the operator of the kth IMF component obtained by EMD, and \(M(\cdot)\) denote the operator for calculating the local mean of the signal. The relationship between the first IMF value \(\tilde{c}_{1}\) and the residual \(r_{1}\) are as  
\[\tilde{c}_{1}=\frac{1}{N}\sum\limits_{i=1}^{N}E_{1}(x)=x-r_{1} \tag{1}\]  
where \(r_{1}=\frac{1}{N}\sum\limits_{i=1}^{N}M(x^{(i)})\), \(x\) is the original signal, and \(N\) is the number of data points of the \(x\). The specific decomposition process of ICEEMDAN can be described as follows.  
* Adding white noise \(E_{1}(w^{(i)})\) to the original signal \(x\), \(x^{(i)}\) is obtained as \[x^{(i)}=x+\beta_{0}E_{1}(w^{(i)})\] (2) where \(w^{(i)}\) represents the \(i\)-th white noise to be added.
* EMD is used to calculate the local mean of \(x^{(i)}\), and the first residual \(r_{1}\) was obtained by taking the average of them; then, the first IMF value can be calculated by \(\tilde{c}_{1}=x-r_{1}\).
* The second mode component value(IMF2) can be calculated by \(\tilde{c}_{2}=r_{1}-r_{2}\), where \(r_{2}=\frac{1}{N}\sum\limits_{i=1}^{N}M(r_{1}+\beta_{1}E(w^{(i)}))\).
* Similarly, calculate the \(k\)-th IMF value according to \(\tilde{c}_{k}=r_{k-1}-r_{k}\), where \(r_{k}=\frac{1}{N}\sum\limits_{i=1}^{N}M(r_{k-1}+\beta_{k-1}E_{k}(w^{(i)}))\).  
Until the obtained residual component can be further decomposed by EMD, ICEEMDAN can be used to decompose IMF accurately, which lays a foundation for improving the accuracy of state recognition.
###### Abstract
In addition, even small load disturbances around the static equilibrium point of the QZS designs can significantly affect their response. This paper proposes a Vibration Control System (VCS) that can be used as a supplement to conventional BI techniques and combines the beneficial characteristics of the aforementioned control systems, TMDs and QZSs, without their respective drawbacks. More specifically, the internal mechanism of an extended version of KDamper [28; 29] is implemented in parallel to a BI. Compared to the TMD, the KDamper has been shown to present extraordinary damping/absorption properties, with relatively small (even minimal) values of the additional mass [30; 31]. Although the KDamper incorporates a NS element, it is designed to be statically and dynamically stable, as the nominal system frequency is a freely selectable parameter, overcoming the fundamental limitation of QZS configurations. The VCS is implemented in the bases of new or existing structures for seismic protection, as a supplement to BI approaches. The design of the VCS is realistic as it accounts for the inherent nonlinear behavior of NS, and accounts for different soil types and SSI effects. The essential features and novel aspects of the current paper are:  
1. The VCS is implemented as a supplement to BI and provides a novel passive retrofitting option for building structures;
2. The optimal VCS parameters are selected by a constrained optimization procedure that accounts for geometrical and manufacturing limitations;
3. Different set of optimized VCS parameters are obtained with respect to the underlying soil, resulting in a soil-dependent design procedure;
4. The effect of the soil-structure-interaction (SSI) is taken into consideration with the use of nonlinear spring stiffnesses with respect to the considered soil type;
5. A realistic mechanism is proposed for the realization of two-dimensional NS.
# Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU
## 3 Proposed Method
### Integration with Attention Mechanism  
In this model, feature aggregation is primarily achieved by incorporating video and spatial attention mechanisms. The feature aggregation module plays a pivotal role in refining the extracted features by combining these two mechanisms. These mechanisms enhance the most relevant aspects of the input and filter out redundant information. The video attention mechanism assigns a relevance score to each feature, enabling the model to focus on the most significant temporal attributes. Mathematically, this attention is structured using a scaled dot-product approach, in which the query, key, and value matrices--denoted as \(Q_{v}\), \(K_{v}\), and \(V_{v}\)--work together to compute the attention weight matrix. This process can be formulated as follows:  
\[A_{v}=\text{Softmax}\bigg{(}\frac{Q_{v}K_{v}^{T}}{\sqrt{d_{k}}}\bigg{)}V_{v} \tag{10}\]  
where \(d_{k}\) represents the dimensionality of the key features. This ensures that the video feature space is dynamically weighted based on contextual importance. Similarly, the spatial attention mechanism processes spatial feature representations to improve the model's ability to recognise relevant regions within frames. As with its video counterpart, the spatial attention computation follows the same scaled dot-product formulation:  
\[A_{s}=\text{Softmax}\bigg{(}\frac{Q_{s}K_{s}^{T}}{\sqrt{d_{k}}}\bigg{)}V_{s} \tag{11}\]  
Within the overall architecture, the feature aggregation module processes the video attention features (\(A_{v}\)) and spatial attention features (\(A_{s}\)) using separate layers. These are then combined to create a comprehensive feature vector \(F_{agg}\), which effectively capturestemporal and spatial dependencies. This consolidated feature set is then transformed to enhance contextual richness before being forwarded for further processing.  
\[F_{agg}=\text{Connet}(A_{v},A_{s}) \tag{12}\]  
The DCGAN framework refines the aggregate distribution \(F_{agg}\) to a more refined feature representation, producing \(G\) structured to align with the features of the real aggregate distribution. Meanwhile, the discriminator \(D\) continuously adapts to learn the differences between the original aggregate distribution \(F_{agg}\) and the synthesised output \(G\). This dynamic interaction enables the DCGAN module to reduce feature redundancy, enhance the contextual understanding of aggregated information, and strengthen downstream classification and decision-making processes, while also fostering a robust adversarial training mechanism.
## 2 Model for Entangled Double-Slit Experiment Using Gaussian Wave Packets  
In this section, we shall introduce a wavefunction which describes an _entangled_ double-slit experiment, based on the famous EPR state. In the original EPR paper [6], the following state is presented  
\[\psi_{EPR}\left(x_{1},x_{2}\right)=\int_{-\infty}^{\infty}dx\,\delta\left(x_{ 1}-x\right)\delta\left(x-x_{2}+x_{0}\right). \tag{1}\]  
For simplicity, we shall choose \(x_{0}=0\). However, the above state does not reside in the Hilbert space \(L^{2}\) of square integrable functions. Recalling that the delta function may be realized as a limit of Gaussian functions  
\[\lim_{\Delta\to 0}\frac{1}{\left(\pi\Delta^{2}\right)^{1/2}}e^{-\left(x-x^{ \prime}\right)^{2}/\Delta^{2}}=\delta\left(x-x^{\prime}\right), \tag{2}\]  
we substitute the delta functions in (1) with the LHS of (2), taken with finite \(\Delta\). This yields the following wavefunction  
\[\psi_{G}\left(x_{1},x_{2}\right)=\frac{a}{\pi}\int_{-\infty}^{\infty}dx\,e^{- a\left(x_{1}-x\right)^{2}}e^{-a\left(x_{2}-x\right)^{2}}, \tag{3}\]  
where \(a:=1/\Delta^{2}\). We note that (1) is reproduced in the limit \(a\rightarrow\infty\). Now, we wish to pass both particles through a double slit, i.e., we are interested only in Gaussians centralized at one of two separate pairsof slits, located at \(x_{i}=\pm h_{i}\) for \(i=1,2\) (see Figure 1 for an illustration). Thus, immediately after passing through the double slit, our initial wavefunction becomes  
\[\psi\left(x_{1},x_{2}\right)=A\left(e^{-a\left(x_{1}-h_{1}\right)^{2}}e^{-a \left(x_{2}-h_{2}\right)^{2}}+e^{-a\left(x_{1}+h_{1}\right)^{2}}e^{-a\left(x_{2 }+h_{2}\right)^{2}}\right), \tag{4}\]  
where \(A=\sqrt{\frac{a/\pi}{1+e^{-2a\left(\left(\frac{h_{1}^{2}+h_{2}^{2}}{2}\right) \right)}}}\) is a normalization factor.
## 3 Smart and Secure HIE Systems: SSHES
### Blockchain and Integration into HIE Systems
#### 3.3.1 Public Blockchains in HIE Systems  
Public blockchain is a type of blockchain network where the data and transactions are publicly available for verification and validation by participants in the network. Public blockchains operate on a trustless principle, eliminating the need for a central authority or intermediary to verify transactions. Instead, they employ consensus mechanisms like proof-of-work or proof-of-stake to uphold the network's integrity and security. Over the past decade, numerous public blockchains have emerged, including Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC), Cardano (ADA), Stellar (XLM), NEO (NEO), EOS (EOS), Tezos (XTZ), Tron (TRX), etc. Integrating public blockchain technology into an HIE system can bring numerous benefits such as enhanced security, interoperability, transparency, and data integrity [21; 28]. However, such integration process involves intricate steps necessitating careful planning, collaboration, and compliance with regulatory mandates. Thus, when implemented successfully, it can revolutionize health care data management by providing enhanced security, interoperability, transparency, and data integrity.
## 3 Modified Hybrid Integration Processing
### Modified Hybrid Integration of OFDM IRCS Frequency Signal  
In this section, the modified hybrid integration method is introduced in two steps, named intra-frame integration and multi-frame integration. The total integration time \(T\) is divided into \(N_{c}\) subfragments without the constraint of a single pulse. Each subfragment contains multiple frames of OFDM symbols, and the duration of each subfragment is \(T_{c}=T/N_{c}\)where \(N_{c}=S\). For convenience, we refer to the integration within each subfragment as intra-frame integration and that across subfragments as multi-frame integration.  
A critical step in implementing multi-frame processing is determining the subfragment duration \(T_{c}\). The maximum allowable subfragment duration, considering velocity effects, is crucial to prevent RW within a single subfragment:  
\[T_{c}\leq T_{ARU}=\frac{c}{2v_{\text{max}}f_{s}}, \tag{20}\]  
where \(v_{\text{max}}\) denotes the maximum radial speed of the detected target and \(f_{s}\) is the sampling frequency.
# Malicious JavaScript Detection Based on Bidirectional LSTM Model
## 2 Related Work
### Static Analysis of Malicious JavaScript  
Static analysis aims to analyze the static behavior features of JavaScript programs. It usually transforms the code into some suitable intermediate representation (e.g., token sequence, AST), and extracts features from them. These features are modeled to determine whether the sample is malware.  
[PERSON] et al. [19] extracted features from the JavaScript code and URL associated with the HTML content of the web page, and then used static analysis techniques to analyze these features.  
Similarly, Zozzle [20] also extracted features from the AST. Zozzle is a static JavaScript malware detection system deployed in most commercial browsers. It uses Bayesian classification of hierarchical features of the AST of JavaScript to identify syntax elements of highly predictive malware. Experiment results show that Zozzle can effectively detect JavaScript malware through static code analysis.  
Some researchers prefer to extract features manually. For example, [PERSON] et al. [21] manually selected 65 malicious JavaScript features through statistical analysis of a large number of samples, and then test the effectiveness of these features. However, manual feature selection relies on expertise and extensive experience. This means the method cannot detect new malware variant.  
In addition, the authors in [22; 23; 24] use lexical analysis to extract code features, and propose detection tools for malicious pdf and SQL injection. Some other methods [25; 26; 27; 28] extract syntax information from AST to detect malicious JavaScript.  
Compared with dynamic analysis, static analysis requires less resources and has fast detection speed. Through reasonable design, static analysis can also achieve high performance.
# Analysis of Small-Loop Electromagnetic Signals to Detect Subsurface Anomaly Zones
## 2 Background of Electromagnetic Exploration
### Penetration Depth of the Electromagnetic Wave  
EM waves can penetrate isotropic nonconductive objects to infinity without decreasing [4]. If the object is a good conductor, however, the penetration depth of the EM wave will decrease. Therefore, EM exploration techniques are extremely useful in cases where the surveyed object is a good conductor [14]. The penetration depth of an EM wave can be calculated using Equation (1) [13].  
\[\delta=\sqrt{\frac{2}{w\mu\sigma}}\approx 500\sqrt{\frac{\rho}{f}} \tag{1}\]  
where  
\(\delta\) = penetration depth (m);  
\(w\) = circular frequency (rad/sec);  
\(\mu\) = permeability (Hz/m);  
\(\sigma\) = EC (S/m);  
\(\rho\) = ER (ohm\(\cdot\)m);  
\(f\) = used frequency (Hz).  
The penetration depth, \(\delta\), increases as the used frequency, \(f\), which is set by the equipment, decreases. This is expressed by Equation (1) for the case where the ER of the object is constant. It implies that the properties of an object located deep underground can be obtained using a low frequency and vice versa when using a higher frequency. Therefore, equipment with an appropriate frequency range for the depth to be surveyed should be employed. In addition, the penetration depth,  
Figure 1: Conceptual diagram of the FDEM (frequency domain electromagnetic) survey.  
\(\delta\), can be varied by decreasing the EM wave according to the permeability, \(\mu\). The penetration depth, \(\delta\), can also be varied according to the circular frequency, \(w\), which can be used in inverse operations after noise elimination.  
The penetration depth is also affected by the geometric composition of the equipment's transmitter and receiver. When using FDEM exploration equipment, surveys can be performed for different depths by varying the composition of the number and spacing of the transmitter and receiver [11]. For instance, the CMD Mini Explorer used in this study is capable of surveying three different depths to a maximum depth of 1.56 m by using one transmitter and three receivers spaced 0.32, 0.71, and 1.18 m from the transmitter [15].  
It should be noted that the ER of the ground is low when a large amount of water is present [16]. Therefore, a lower ER should be expected at deeper survey depths where the water content is generally higher than at shallower depths.
## 3 Formulation of Optimization Problem
### Insolation Forecasting Method  
A NN is widely employed as one of the forecasted methods for insolation [31; 32; 33]. In this paper, an NN is used for the insolation forecasting method. Parameters entered into the NN toolbox in MATLAB(r) are depicted in Table 3. Input factors such as a data of atmospheric, temperature, humidity and hours of sunlight for the previous 24 h are entered into the NN. It can then forecast insolation for the proceeding 24 h. Furthermore, re-forecast and re-plan are conducted in the optimization for the scheduling of controllable loads. The data fed into the NN is updated every 3 h. The overview of re-forecast and updating data is shown in Figure 3. Learning (in which optimize synaptic weight is optimized) is carried out for 365 days. After learning of the NN, the weights are applied for another year in order to confirm the availability of the optimized synaptic weight. A probability density of the forecast error of insolation for the year is depicted in Figure 4. In addition to this, a probability density for the case of updating data is depicted in same figure. It is observed that the forecast error can be reduced by updating the data such as atmospheric and temperature which are used to forecast.  
Figure 4: Forecast error for insolation with neural network (NN).  
Figure 3: Overview and illustration of re-plan and re-forecast.  
\begin{table}
\begin{tabular}{l c} \hline \hline Number of learning & 30,000 \\ \hline Termination condition of error & 1.0\(\times 10^{-4}\) \\ \hline Learning rate & 0.1 \\ \hline Algorithm of updating weight & Steepest descent method \\ \hline Input/output function & Sigmoid function \\ \hline Input factor & Atmosphere, Temperature, Humidity, Hours of sunlight \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter entered into neural network toolbox in MATLAB®.  
The whole flow chart of optimization including the forecasting with the NN and consideration of the uncertainties is depicted in Figure 5. At first, PV output is obtained in forecasting the insolation with NN. Next, the forecasted error based on Figure 6 is added into the forecasted PV output. In this paper, scenario-based approach is applied for the uncertainties. A number of scenarios are derived by adding forecast error which is expressed as shown in Figure 6. In process step 3\(\sim\)step 6, the neighborhood solutions are tried to operate for all scenarios and evaluated by the objective function Equation (4) until the global iterations are satisfied. In this paper, re-forecast and re-plan are considered. Thus, the schedule once planned is renewed every 3 h by using the latest data in the optimization process with the TS algorithm as shown in step 7. Hence, the re-forecast and re-plan are executed for 8 times in a day.  
Figure 5: Flow chart in optimization.  
Figure 6: Forecast error probability distribution for photovoltaic (PV) output power.