esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Results and Analysis
#### 4.2.6 Evaluation on Larger Dataset  
To further validate ARCS's effectiveness, we conducted additional experiments using the UNSW-NB15 dataset [33], containing 175,341 network security events. Results demonstrated consistent performance improvements across different data distributions, with ARCS maintaining superiority in detection rate (87.9%) and response time (24.8 s). For optimization analysis, we adopted a systematic approach inspired by [34], evaluating system performance under various resource configurations. The resource utilization optimization followed a three-step process: (1) resource parameter tuning using grid search across computational configurations; (2) performance-resource trade-off analysis under different load conditions; and (3) dynamic resource allocation based on threat severity and system load. This analysis revealed optimal operating points where ARCS achieves maximum effectiveness while maintaining efficient resource utilization. Under optimal configuration, ARCS demonstrated a 15% improvement in resource efficiency while maintaining performance metrics within 2% of peak values.  
In addition, when evaluating time efficiency on the UNSW-NB15 dataset, ARCS processes approximately 1200 events per minute under optimal conditions. The processing time scales approximately linearly with dataset size, with a batch processing of 10,000 events requiring an average of 8.3 min. While these results demonstrate ARCS's capability to handle large-scale historical data analysis, it is important to note that the current architecture is not designed for real-time stream processing, which would require fundamental architectural changes to achieve sub-second response times. The performance-resource trade-off analysis reveals that ARCS maintains acceptable processing speeds of up to datasets of approximately 200,000 events, beyond which processing time increases more significantly. This suggests that, for extremely large-scale applications, a distributed processing architecture would be necessary--a potential direction for future work.
## 3 Numerical Models
### The VOF Model  
[PERSON] and [PERSON] (2010) were the ones who first put forth the VOF model to solve the flow of two or more immiscible fluids. The tracking of the gas-liquid interface in this model is carried out by solving the continuity equation. The gas phase and the liquid phase share a momentum equation ([PERSON] and [PERSON], 2010). The governing equations of the VOF model are listed here:  
The continuity equation ([PERSON] and [PERSON], 2010):  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\mathbf{u})=0 \tag{1}\]  
where \(\rho\) and \(t\) are the density of the fluids and the time, respectively, and \(\mathbf{u}\) stands for the velocity vector.  
The momentum equation ([PERSON] and [PERSON], 2010):  
\[\frac{\partial(\rho\mathbf{u})}{\partial t}+\
abla\cdot(\rho\mathbf{u}\mathbf{ u})=-\
abla P+\
abla\mu\Big{(}\
abla\mathbf{u}+\
abla\mathbf{u}^{T}\Big{)}+\mathbf{F} \tag{2}\]  
where \(\mu\), \(P\) and \(\mathbf{F}\) are the viscosity of the fluids, the pressure, and the surface tension force of the fluids, respectively.  
The continuity equation for one phase (which generally refers to the liquid phase) can be solved to determine the location of the interface between the gas phase and the liquid phase ([PERSON] and [PERSON], 2010). The following equation is displayed:  
\[\frac{\partial\alpha_{i}}{\partial t}+\mathbf{u}\cdot\
abla\alpha_{i}=0 \tag{3}\]  
Figure 5: Slug flow-induced vibration experimental system across the jumper.  
The continuous surface force (CSF) model is used to obtain the surface tension between the gas and liquid. The momentum equation of Equation (2) includes the surface tension as a source term:  
\[F=\sigma_{ij}\left[\frac{\rho k_{i}\
abla\alpha_{i}}{\left(\rho_{i}+\rho_{j} \right)/_{2}}\right] \tag{4}\]  
where \(\sigma\) represents the surface tension coefficient, and \(k_{i}\) represents the surface curvature.
## 2 Related Work
### Calibration Methods
#### 2.4.1 The Math behind openCV's Calibration  
The notation complies with the camera model introduced in _OpenCV Toolbox_[41] and builds on the openCV toolbox. The intrinsic camera model has 9 degrees of freedom: two focal lengths \(f_{x},f_{y}\); two principal point coordinates \(c_{x},c_{y}\); three radial distortion parameters \(k_{1},k_{2},k_{3}\); and two tangential distortion coefficients \(p_{1},p_{2}\), which comprise the distortion parameters \(d=(k_{1},k_{2},p_{1},p_{2},k_{3})\). This vector \(d\) encodes the influence of the camera lens. The most evident this kind of a distortion is such that straight edges of real objects are depicted as bows in images. It is particularly visible when wide angle lens are used. These non-linear parameters are part of the geometric calibration, but will not be discussed further here, cf. [41]  
The basic calibration transformation is presented as  
\[s\underbrace{\begin{bmatrix}x\\ y\\ w\end{bmatrix}}_{p}=\underbrace{\begin{bmatrix}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{bmatrix}}_{A}\underbrace{\begin{bmatrix}r_{11}&r_{12}&r_{13}&t_{x} \\ r_{21}&r_{22}&r_{23}&t_{y}\\ r_{31}&r_{32}&r_{33}&t_{z}\end{bmatrix}}_{[R|t]}\begin{bmatrix}X_{w}\\ Y_{w}\\ Z_{w}\\ 1\end{bmatrix} \tag{1}\]  
where \(A\) is the intrinsic matrix encoding 4 of the 9 intrinsic parameters, and \(R\) and \(t\) are rotation and translation matrix, respectively. There are 6 degrees of freedom for extrinsic camera parameters \([R|t]\), which comprise position and rotation of the camera in a global coordinate system. NB, this matrix has 12 entries, but only 6 d.o.f., as the parameters \(r_{ij}\) denote a rotation. \(s\) is a scaling factor. This form can be abbreviated as  
\[sp=A[R|t]P_{w} \tag{2}\]  
It transforms every uncalibrated point \(P_{w}\) to new coordinates \(p\) in a calibrated image with reduced distortions. The modification of this equation for the purpose of calibrating EBCA is presented in Section 4.1. In practice, one takes a pattern--openCV works with different types, but the most common is a checkerboard pattern that needs to be fully visible to the camera and presents it under different angles to the camera. openCV routines are used to extract the points of the pattern (say, the corners of the checkerboard). Furthermore, the user needs to input the dimensions of the checkerboard array (e.g., 6 by 8) and the distances between corners in some unit (e.g., mm) to the calibration algorithms, which outputs matrix \(A\) and for each image the calibration pattern the rotation \(R\) and translation \(t\). This information (together with the computed distortion parameters \(d\)) allow for undistorting the images acquired by this camera into rectified views.
# Identification of Factors Influencing Fluoride Content in Tea Infusions: A Systematic Review
## 3 Results
### Main Study Outcomes
#### 3.3.3 Effect of Brewing Time on Fluoride Release  
There is strong agreement across studies that fluoride release increases with longer brewing times [19; 31; 32; 51; 52; 54; 56; 61; 63; 64; 68]. [PERSON] et al. [61] demonstrated that after approximately 20 min, fluoride concentrations tend to reach a plateau--a finding corroborated by [PERSON] et al. [68] and [PERSON] et al. [56]. This supports the view that extended infusion times enhance fluoride extraction, though the marginal gain diminishes over time.  
Figure 4: Variability in fluoride levels among tea samples from different regions reported in selected studies ([PERSON] [49], [PERSON] et al. [56], [PERSON] et al. [54]).
## 2 Emergency Representation Model
### Geographical Event Analysis
#### 2.3.2 Short-Scale Adjustment of the Parzen Width  
The previous subsection has described the use of Parzen windows to estimate the density function of events, for which we have to decide the value of the represented bandwidth. At a global observation scale, for example, the whole city, the method is robust for empirical methods of determining the bandwidth, as the samples contain a large number of events. For this reason, a heuristic criterion for bandwidth of about 0.01 can been used for exploration from a city representation scale. However, we would like to be able to work at different observation scales, in order to scrutinize further details in different regions of interest. For this purpose, we use two elements. First, a system of geographical representation yields the required flexibility in the support or our framework. Using Google Maps, we represented the emergencies on an adequate scale that allowed us to identify relevant details, such as the street names and sectors. Second, the option to work at different scales of observation depends on how the bandwidth is set, particularly if there are multimodalities. This last element is further developed next. [PERSON] et al. (2017) proposed a purely data-driven algorithm to determine the bandwidth of Parzen windows, which avoids the use of any arbitrary reference rule and is particularly suitable for auto adjusting the bandwidth at different scales in our system. This method introduces an adaptive kernel density estimation (KDE) method based on the smoothing properties of the linear diffusion process. This method is known as _Parzen kernel density deviation via diffusion_ (Parzen-KDD, for short). The use of Parzen-KDD allows for a better representation of Parzen windows in narrow scales and provides this with accuracy when locating small sets of events, as illustrated in Figure 4d. We consider a KDE, given by  
Figure 4: Overview of emergency events in the city of Quito during 6 December, as represented by their Parzen estimation for (**a**) police; (**b**) health; and (**c**) transit services, using a global width for all of these; (**d**) representation of event density using Parzen kernel density deviation via diffusion (KDD) for police on a short scale over Mariscal town. \[\hat{f}(\vec{r},\sigma)=\frac{1}{N}\sum_{i=1}^{N}\phi(\vec{r},\vec{r}_{i};\sigma) \tag{13}\]  
where \(\phi(\vec{r},\vec{r}_{i};\sigma)=\frac{1}{\sqrt{2\pi\sigma}}e^{-(\vec{r}-\vec{ r}_{i})^{2}/(2\sigma^{2})}\) is a Gaussian kernel with center location \(\vec{r}_{i}\) and scale \(\sigma\), also known as the bandwidth. In order to calculate the bandwidth in higher dimensions, it is necessary to know the asymptotically optimal squared bandwidth (\(\sigma^{*}\)), which is given by  
\[\sigma^{*}=(2\pi N(\psi_{0,2}+\psi_{2,0}+\psi_{1,1}))^{-1/6} \tag{14}\]  
where \(\psi\) is a twice continuously differentiable function \(\psi(1)=0\) and \(\psi^{\prime\prime}(x)>0\) for all \(x\in\mathbb{R}\). In order to estimate all \(\psi_{i,j}\) for which \(i+j=k\) and \(0\leq k\leq 2\), the method uses established formulas for \(k\geq 3\).
# Condition-Based Monitoring and Maintenance: State of the Art Review
## 2 Literature Review
### Remaining Useful Life  
Remaining useful life (RUL) is used both in theory and applications. Engineers use it mostly when they have to decide whether to perform maintenance or to delay it due to production requirements [90]. Most often, RUL is used for later life of equipment in wear-out period. Condition assessment is used commonly to predict remaining useful life (RUL) of the equipment or systems. A prognostic approach is presented to estimate the remaining useful life of gas turbine engines before their next major overhaul based on historical health information [91; 92]. A combined regression including both linear and quadratic models is proposed to predict the remaining useful life. Two-stage prognostic model is used for the life of a piece of production equipment, with the first stage as the normal working stage and the second stage as the failure delay period [93; 94; 95; 96]. With the help of condition monitoring, the equipment hidden defects may be detected for maintenance planning purposes; the prediction of the second stage; and, more importantly, the residual life. Artificial neural network-based prediction methodology has been developed for remaining useful life of rotating machinery [97]. Tian et al. developed a neural network approach for remaining useful life prediction, utilizing both failure and suspension histories of age and condition-monitoring data from the equipment [98]. In another methodology, Technical Condition Index (TCI) is used for remaining useful life of natural gas export compressors [99]. There are greater challenges to select or develop appropriate prediction algorithm since it is on a case-by-case basis and environment-related.
## 4 Grid Synchronization for the Secondary Control Unit
### Weighted Least Square Estimation (WLSE)  
The synchronization technique of WLSE was introduced back in 2000 [173] and is gaining more attention for its robustness against disturbances that may exist in the power grid. This method is able to estimate separately the positive and negative sequences of frequency and phase angle without significant delay and with covariance resetting, especially under sudden voltage sag or unbalanced conditions. However, to estimate more accurate harmonic phasors, especially under the deviations of power and frequency, a phasor estimator that is realized via the merger of the harmonics components is applied in [174]. It offers a better and more efficient update for the unit reference signal matrix, along with frequency tracking. Thus, the phasor can be maintained as a constant when the power grid frequency deviates from the nominal value. Several works have tried to improve the performance of the sparse algorithm when suffering from the presence of transients on the tracked phase. An extension of the least-square-style WLS is proposed in [175], wherein it allows the measurement of the instantaneous frequency value of real signals for single- or three-phase systems. This technique is applied to the decoupling methods, adaptive filtering of input signals, and frequency estimation. It provides more robustness of system frequency because the estimated frequency is computed using the provided information. A much simpler structure that can be implemented recursively is proposed in [132]. This synchronizer technique compares the sampled grid voltages to complement a straight line to emulate the estimation of the grid phase angle. Hence a frequency filter is added to expand the synchronizer performance, thus making it immune when operating in a polluted grid.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Underwater Ocean
[300] reported that six days of field survey is equal to 20 s of airborne acquisition time. Furthermore, the consistent archived and frequent observations of spaceborne RS systems allow for identifying underwater changes over time in a cost-efficient manner. Both airborne and spaceborne RS systems have been of interest in underwater studies since 1975 and the interest has grown on an annual basis [301]. Table 6 summarizes the advantages and disadvantages of the most important RS systems for underwater studies. The application and details of each of these systems are also discussed in the two following subsections of bathymetric and AV/CR mapping.
# Focused and TSOM Images Two-Input Deep-Learning Method for Through-Focus Scanning Measuring
## 2 Materials and Methods
### TSOM Imaging System  
The experimental images are acquired by TSOM system imaging system shown in Figure 2. The system is divided into two parts: the illumination system (the red-light path in Figure 2a) and the imaging system (the blue-light path in Figure 2a). In the illumination system, LED with wavelength of 520 nm is used as the light source. The light emitted by LED passes through the objective lens (OL1), an aperture, a lens (L1), polarizer, the field diaphragm (FD), lens (L2) and the splitting prism, and then converging on the conjugate rear focal plane of the objective lens (OL2). The light passed through the objective lens (OL2) irradiates on the nanostructured target to achieve Kohler illumination. The imaging system consists of an objective lens (OL2), a splitter prism and a lens (L3), which help in imaging the target on a CCD camera. While through-focus scanning, the objective lens (OL2) is controlled to scan along the optical axis by the piezoelectric objective locator (PZ) and the piezoelectric position mechanism. Background images and target images are captured every 200 nm during the scanning process. After the background images are subtracted from the target images, a series of images of the target without background noise are obtained, consisting of a focused image and lots of defocused images.  
Figure 1: Measurement process of the two-input deep-learning TSOM method.
## 2 Power Converter Principle
### Designs of Output Capacitor and Inductor  
Because the SBBC was the topology of the power converter, which can implement the buck or boost mode, the output capacitor and inductor should be computed to address the practical circuit application. The calculating formulas and explanations are in the following sections.
## 3 Background Theory
### Lightweight Cryptography Final Candidate
#### 3.4.1 Gift-Cofb  
The GIFT-COFB algorithm is a lightweight cryptography algorithm designed based on the GIFT block cipher algorithm with COMBined FeedBack (COFB) mode. The GIFT algorithm is an algorithm of the Substitution Permutation Network (SPN) type, which has two variants, namely GIFT 64-128 and GIFT-128-128. The two variants have differences in the length of the message block that is processed and the number of rounds. GIFT 64-128 can process 64-bit message blocks with a 128-bit key with 28 block iterations. Meanwhile, GIFT-128-128 can process 128-bit message blocks with 128-bit keys with 40 block iterations. In the GIFT-COFB algorithm, the variant used is GIFT-128-128 [20].  
The COFB mode in the lightweight cryptography algorithm was designed by [PERSON] et al. with the aim of providing an authenticated encryption scheme. The COFB algorithm is an improvement algorithm of iCOFB to make it more efficient [38].
## 3 Results
### MLP, 1D-CNN, and R\({}^{2}\) Score  
Figure 4 shows the R\({}^{2}\) score for predicting speech audiometry scores from PTA data using the MLP and 1D-CNN models. First, evaluating the MLP model's performance resulted in an R\({}^{2}\) score of 88.79%, demonstrating that the model effectively learned the non-linear relationship between PTA data and speech audiometry scores. On the other hand, the 1D-CNN model recorded an R\({}^{2}\) score of 88.35%, showing a similar level of performance to the MLP model. Although both models achieved high R\({}^{2}\) scores, the MLP model exhibited slightly better performance.  
The MLP has a higher R\({}^{2}\) score than the 1D-CNN because the MLP can more effectively learn the non-linear relationships between various features of the input data. The MLP fully connects all input variables, capturing more complex interactions. In contrast, while the 1D-CNN excels at learning sequential patterns, its performance may be lower with non-sequential data.
## 2 Regulatory Framework and Guidelines
### Waste Management  
Fundamental objectives to achieve a sustainable development, which are set at international level, are the efficient use of natural resources and the 3 Rs approach, i.e., reduce, reuse, and recycle, for waste minimization and management [19; 20]. A critical step for waste management regards the definition of the concept of Extended Producer Responsibility (EPR) [21]. The focus is shifted from the phase in which waste already exists to the entire product lifecycle to the responsibility of manufacturers in terms of waste generation and to the need for the sustainable use of resources and waste minimization [22].  
In the European framework, attention to both the design phase supporting the EPR approach and the conscious choices of materials, i.e., recyclable, recycled, durable, non-hazardous, and local materials and products, and the concept of waste as a resource, is stressed [23; 24]. The definition of selective demolition is also proposed, highlighting the need to integrate it in standard design procedures [23]. Furthermore, in 2018, the EU Commission released guidelines for the definition of waste audits: they have a central role to ensure the proper identification and separation of demolition materials, thus facilitating the application of selective demolition and reusing and recycling practices [5]. Waste audits exploit their major potential if performed during the design phase since they enable a detailed estimation of the costs of the selective demolition, separation, recycling, recovery, and landfilling of materials. Consequently, a correct estimation of costs can be encompassed in the total budget for the call for tenders, supporting a successful accomplishment of the process [25].  
Concerning the Italian regulatory framework, Legislative Decree 50/2016 introduced the requirement for Public Clients to purchase products and services that are compliant with the national document called the Minimum Environmental Criteria (CAM--Criteri Ambientali Minimi) [18], which requires the reuse, recovery, or recycling rate of about 70% by weight of non-hazardous waste in cases such as renovation, maintenance, and demolition. Methodological hints and principles for demolition and material removal and requirements to conduct a pre-demolition audit were also defined in 2017 by a specific Ministerial Decree [26].  
Concerning CDW management implementation, the presented research aims to:  
* define a waste audit method for the preliminary design phase;
* integrate CDW management in the entire construction process with increased interest on the design phase, on sustainable resource use, and on waste minimization.