esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proposed Method
#### 2.4.2 Decoder  
The decoder blocks in the DL network use transposed convolution for up-sampling the feature maps, effectively increasing their size. However, spatial resolution is preserved, and output feature map quality is improved through fully connected connections between the encoder's and the decoder's output feature maps. Each decoding step involves up-sampling the previous layer's output using the Rr-Unit, which halves the feature map count and doubles the size. The final decoder layer restores the feature map size to the original input image size. Batch normalisation (BN) is employed during up-sampling for stability and convergence acceleration during training, and the BN output is then passed to the Attention Gating Modules (AGMs) [44; 48]. Additionally, a zero-shot linear classifier is incorporated with AGMs to further enhance the model's classification capabilities [49].  
The network utilises AGMs to enhance the down-sampling output and combine it with the equivalent features from the decoder, which prioritises high-quality features and helps to preserve spatial resolution, ultimately enhancing the quality of the feature maps. In addition, a zero-shot linear classifier is incorporated to enhance classification performance further. The combination of AGMs and the linear classifier improves feature representation and classification accuracy, making the approach well-suited for multi-class classification tasks. First, the attention values are calculated for each individual pixel of all the input features \(\omega_{p}^{i}\). Then, the gating vector \(\delta_{p}\) is applied to each pixel to capture pixel-wise information for fine-grained details and local contextual information, resulting in accurate and discriminative classification \(ST_{m+1}\) outcomes. The additive equation is as follows:  
\[\beta_{p}^{i}=S_{max}\ \Big{(}ST_{m+1}\ \Big{(}\gamma_{2}\ \Big{(}\varphi^{T}\ \Big{(}\gamma_{1}\ \Big{(}W_{\omega^{T}_{p}}+W_{\delta^{T}}\times\delta_{p}+B_{\delta}\Big{)} \Big{)}+B_{\varphi}\Big{)}\Big{)}\Big{)}, \tag{4}\]  
where the activation functions ReLU (\(\gamma_{1}\)) and sigmoid (\(\gamma_{2}\)) are utilised in the Attention Gating Modules (AGMs), along with linear transformation weights (\(W_{\omega}\)) and (\(W_{\delta}\)) and biases (\(B_{\delta}\)) and (\(B_{\varphi}\)). The introduction of \(ST_{m+1}\) enables a tailored transformation of attention values for multi-class classification, thereby improving the model's accuracy and performance in handling multi-class tasks. Prior to applying the activation functions \(\gamma_{1}\) and \(\gamma_{2}\), attention values are transformed using the transpose of variable \(\varphi\), identified as \(\varphi^{T}\). The final prediction output is generated using a SoftMax activation function.
# Real-Time Intensity Domain Characterization of Fibre Lasers Using Spatio-Temporal Dynamics
## 2 Experimental Section
### Principle
The value of the ACF increases over an average level at the periodic intervals corresponding to the round trip time of the features, as the overlap between the functions are maximized. Particularly, the ACF would peak at a mean round trip time interval \(\tau_{RT}\) (Figure 1b). It can be shown that the ACF can be mathematically expressed as  
\[K(\tau)=I_{0}^{2}\left[\int_{-\infty}^{\infty}S(t^{\prime})S(t^{\prime}+t)dt ^{\prime}\right]\otimes\sum_{T=-\infty}^{\infty}\delta(t-\tau_{RT}T). \tag{3}\]  
That is, the ACF periodically peaks at intervals of the round trip time \(\tau_{RT}\), where each peak is convolved with the autocorrelation of the peak shape function \(S(t)\). Thus, the round trip time for a coherent feature of interest can be determined by simply noting the separation between the peaks generated by it in the ACF. It is desirable to perform the autocorrelation over a large number of round trips, as this would average out any uncorrelated noise and help reveal weak periodicities. The value of \(\tau_{RT}\) can now be used to select windows of intensity dynamics occuring at round trip intervals (Figure 1c), arriving at a two dimensional representation of intensity evolution \(I(t,T)\), where \(T\) now takes the notion of an evolution co-ordinate. Figure 1c then essentially presents the intensity evolution of the pulse, as observed in its rest frame, whilst it makes round trip circulations within the cavity. Figure 1: Principle of spatio-temporal dynamics—**(a)** Intensity dynamics \(I(t)\) (simulated) depicting evolution over multiple round trips; **(b)** The autocorrelation \(K(\tau)\) of the intensity dynamics \(I(t)\). The periodic interval between the peaks gives the round trip time \(\tau_{RT}\); **(c)** The spatio-temporal dynamics, describing evolution of the pulse features. Horizontal co-ordinate—temporal, vertical co-ordinate—spatial.
## 2 Methods and Materials
### Correction for Lens Distortion
#### 2.4.5 Spatial and Temporal Calculations  
Aiming at obtaining the source function \(\hat{q}\) from the heat Equation (10), one needs to proceed with numerical derivatives of the temperature with respect to time and coordinates. The value of the temperature rate \(\frac{\partial T}{\partial t}\) can be calculated based on the motion correction. The input images can be either raw images with or without background subtraction, or the temperature field extracted from the regression analysis. The temperature distribution obtained after regression is smoother, as the regression exerts the effect similar to low-pass filtering. In the particular tests exemplified in the present work, the regression data were used to calculate both the temporal and spatial derivatives of the first and second order, respectively. Ideally, when no heat dissipation occurs, and the temperature field reaches equilibrium, the second derivative should be zero. It is, therefore, possible and reasonable to subtract the Laplacian term obtained for the reference image taken after the test has been paused, i.e., when no heat dissipation occurred, from all images in the recording session. This step allows to remove remaining occasional systematic noise, if it exists and is seen in the Laplacian term.  
Figure 12: Comparison of the raw image obtained during cyclic FCG testing (**a**), and the results of background subtraction of the reference image (**b**) using rigid body (**c**,**e**), and IRT/DIC-based image subtraction (**d**,**f**).
## 4 Discussion
Another study published by [PERSON] and his colleagues described how an expander anchored to the first permanent molars caused increased upper intermolar distance and the angulation of the upper maxillary molars compared to an expander anchored to the second primary molars, even though the latter resulted in an increased upper intercanine distance [48]. In addition, dental and skeletal outcomes may also be influenced by the activation protocol adopted. In the present pilot study, two activations per day were performed, but other protocols are described in the literature [49; 50; 51]. [PERSON] and his colleagues demonstrated how the activation protocol can affect the upper dental arch; according to their comparative study, a faster activation protocol could increase the midpalatal suture's anterior disjunction and promote major expansion in the molar area [52]. Furthermore, a faster activation protocol also resulted in an increased overjet, especially in subjects with lower skeletal maturation [53]. In the literature, other authors followed the same protocol as this study in which the timing of post-RME CBCT is not standardized. Most studies executed CBCT 6 months after RME treatment [31; 54; 55], but the timing varies between 3 and 12 months after treatment [33; 56; 57]. The present study has some limitations: RMEs' effects on the upper airways were not evaluated, and it would have been interesting to compare the results of the 3D analysis with those of a 2D analysis to evaluate the differences as far as the accuracy of the two methods is concerned. Furthermore, only two software programs were involved in the present study, and it is possible that different digital X ray machines could provide different results. In addition, further studies should evaluate the interface with optical impressions. The digital cephalometric analysis of CBCT scans may become part of a completely digital workflow in the future; currently, orthodontics relies more and more frequently on computer-aided design and manufacturing (CAD-CAM) processes. Firstly, this process involves intra-oral scanners, which allow the acquisition of digital data. Subsequently, digital tridimensional design is performed and then the study models/appliances are produced using one of the currently available 3D printing techniques [58]. Digital workflow in orthodontics provides many advantages: it overcomes conventional impressions and gypsum casting and decreases laboratory working time [59]. The 3D cephalometric analysis provided by modern software may enable the avoidance of traditional cephalometry by offering all of the information required for an accurate diagnosis and appropriate treatment planning, both for orthodontics and orthognathic surgery [60]. Future studies will assess and compare the reliability of emerging software, as technology constantly develops, in order to perform an accurate tridimensional cephalometric analysis evaluating sagittal, transversal and vertical discrepancies and the outcomes of orthodontic treatment.
###### Abstract  
The paper attempts to estimate the profitability of combining wood and CFRP into a composite (BSH-CFRP), intended to be used in load-bearing beams, including mechanical performance and prices of constituent materials. Prices of glue laminated timber (BSH) and CFRP tapes over the years were provided by ABIES Poland Ltd. and S&P Poland Ltd. companies. Referring to the uncertainty of wood prices on the market, two levels of BSH prices were adopted. A prediction of the beams' mechanical behaviour was made based on the analytical model prepared by the author. The calculated members varied in width, height and length and included twelve types of CFRP tapes (different thickness, width and modulus of elasticity) glued between wooden lamellas. The total cost of each BSH-CFRP beam was compared to the total cost of the corresponding BSH beam in GL24h class, which led to calculating a cost multiplier. Consecutively, the multiplier was referred to the standard class improvement of BSH according to the bending stiffness and load-bearing capacity. A cloud of points got from many analyses resulted in obtaining exponential approximating functions. The averaged results led to general conclusions that at the assumed price level, improving the BSH standard class by using CFRP tapes was associated with a 1.86 BSH cost in the case of expensive wood or 2.81 in the case of cheap wood. Improving class by two was connected with the 3.55 BSH cost for expensive wood, and 6.79 for cheap wood. At present, the profits from composing wood with CFRP appear to be not very high in terms of their price. However, they can radically increase, especially when wood cutting limits are imposed on manufacturing companies, which significantly reduce the available timber for construction use.  
wood-CFRP composite; BSH-CFRP; glue laminated timber; BSH; mechanical behaviour; profitability analysis; cost of the material +
Footnote †: journal: Applied Sciences
## 5 Results and Discussion
Therefore, the presence of velocity slip between the fluid and the surface accelerates the flow of the fluid, which decreases the deficiency between the velocity in the ambient flow and in the boundary layer. In addition, the consideration of velocity slip in real life applications is one means of reducing the skin friction coefficient at the surface, and can extend the lifetime of the material because it delays the corrosion process. The velocity and temperature profiles for a stretching surface (\(\varepsilon=0.5\)), shrinking surface (\(\varepsilon=-0.5\)) and static surface (\(\varepsilon=0\)) are shown in Figure 8a,b. The velocity for the stretching case is seen to be higher than other cases for both solutions. In contrast, the temperature for the stretching surface is lower than that for shrinking and static surfaces for the first and second solutions. Physically, the stretching surface increases the velocity of the fluid, decreases the energy capability, and postpones the heat from spread up. The effects of different values of \(s\) on the velocity and temperature profiles are also displayed in Figures 9 and 10 for both stretching and shrinking sheets. According to Figures 9a and 10a, the velocity profile for the suction case \(s=1\) is higher than that for the injection case \(s=-1\), as well as over the impermeable surface \(s=0\) for the first solution,  
Figure 6: (**a**) Skin friction coefficient and (**b**) Local Nusselt number for different \(\varepsilon\) with \(s\) in Copper-water nanofluid when \(A=0.1\), \(\sigma=0.1\) and \(\varphi=0.1\). Since dual solutions are obtained, a stability analysis is implemented to identify the stability of the two solutions. According to the aforementioned authors, the upper solution is considered stable, and the solution from the first solution is physically realizable. However, the second solution is considered unstable, and is not physically realizable in real life applications. The stability of each solution is determined by the value of the smallest eigenvalue \(\gamma\) regardless of whether the value is positive or negative. A positive value of \(\gamma\) indicates that there is initial decay of the disturbance in the solution, meaning that the solution with a positive value is stable. On the other hand, a negative eigenvalue represents an initial growth of disturbance, causing the solution to become unstable. Table 3 shows the smallest eigenvalues for the upper and lower solutions for different nanoparticle volume fractions, \(\varphi\), and for some values of shrinking parameter \(\varepsilon<0\) where these values are close to the critical values \(\varepsilon_{\varepsilon}\) for each case. Based on the table, the smallest eigenvalues for the upper (first) solution are positive, while negative values are obtained for the smallest eigenvalues from the lower (second) solution. Therefore, the first solution is stable, and the results obtained are physically realizable, while those of the second solution are not.
# A Method for Predicting Radiated Acoustic Field in Shallow Sea Based on Wave Superposition and Ray
## 3 Accuracy Analysis of Combined Wave Superposition Method  
The combined wave superposition method (combined WSM) is an approximation method, and the accuracy of the approximation is needed to be discussed. It approximates not only the normal mode order of the Green's function in sea, but also the process of inverting the virtual source strength. The ray theory is also an approximate acoustic propagation model in the acoustic positive problem. In the following sections, we will analyze the error caused by the local approximation in combined WSM, and some characteristics of the whole method in numerical calculation.
## 3 Life Cycle Assessment Methodology
### Life Cycle Impact Assessment  
The life cycle inventory results are used for the following phase as the life cycle impact assessment, where the magnitude of environmental burden is evaluated through four different steps: classification, characterization, normalization, and weighting. According to ISO 14040, classification and characterization are obligatory while the last two steps are discretionary. In the classification step, life cycle inventory results (in particular, elementary flows) are combined and organized into impact categories. For example, carbon dioxide, nitrogen dioxide, methane, chlorofluorocarbons, hydrochlorofluorocarbons, and methyl bromide are combined in the global warming impact category. In the following step, in the characterization stage, a characterization factor is used to translate the life cycle inventory results into comparable impact indicators, so that a comparison can be carried out. For one of them, global warming, the characterization factor is the global warming potential while the impact indicator is evaluated in kgCO2 eq. The most used characterization methods convert inventory flows into impact category indicators at the midpoint level because they evaluate the potential impact of the analyzed  
Figure 5: Suggested methodologies for co-products in the life cycle assessment analysis: (**a**) system expansion with the environmental impact of u kg of CO2 for x kg of A and y kg of B; (**b**) avoided burden with the environmental impact of (u\(-\)) kg of CO2 for x kg of A; (**c**) allocation with the environmental impact of u kg of CO2 for y kg of B. [Modified after [PERSON] [62]]. system differently from the conversion at the endpoint level that measures the damages to human health and the ecosystem. In particular, midpoint indicators are intermediate measures of environmental impact that reflect the changes in the natural environment caused by emissions or resource use. On the other hand, endpoint indicators are final measures of environmental impact that reflect the damage or benefit to human health, natural resources, or biodiversity caused by emissions or resource use. Midpoint indicators are often easier to calculate and understand than endpoint indicators, as they are closer to the source of the impact and less affected by uncertainties and assumptions. However, midpoint indicators may not capture the full consequences of environmental changes for human well-being or ecosystem services, and they may not reflect the relative importance or severity of different impact categories. LCIA can convert the inventory results into what is referred to as the 'areas of protection' of the LCIA. These areas of protection represent the entities that society wants to protect by using the LCA. Different characterization methods are used for analysis at the midpoint and endpoint, as reported in Tables 1-4 [65].
# Numerical Simulation of Piles in a Liquefied Slope Using a Modified Soil-Pile Interface Model
###### Abstract  
The liquefaction of soil surrounding a pile significantly affects the dynamic interaction between the soil and the pile. In particular, liquefaction of the sloping ground can induce permanent deformation and a bending moment on the pile due to the lateral displacement of the liquefied soil in the downslope direction. However, numerical analysis studies on piles installed in a liquefiable slope have been very limited and have not properly simulated the behavior of the pile. Therefore, a modified soil-pile interface model was proposed, which linearly decreases the interface friction angle with the increase in the excess pore pressure ratio. The proposed model was validated by comparing it with the centrifuge test results of [PERSON] et al. (2023). Simulation results on the slope crest settlement and the pile-bending moment showed good agreement with the centrifuge test results. A parametric study was conducted by applying the validated model to analyze the effect of slope inclinations and the amplitude of input motions on the slope displacement and the pile moment. The simulation results showed that the slope inclinations affected the area of the sliding mass, causing a larger pile-bending moment with a larger inclination. When the amplitude of the input motion was sufficiently large to trigger the failure of the liquefied slope, the slope displacement and the pile-bending moment did not increase any further.  
earthquake; liquefaction; slope; soil-pile interface; centrifuge test; numerical simulation
# Speaker Diarization: A Review of Objectives and Methods
## 3 Analysis of Speech Signals
### Time Windows  
Speech is non-stationary, i.e., changes with time, as speakers utter a dynamic sequence of phonemes and words. For analysis, speech is regularly segmented into successive short sections; within each section, one assumes local stationarity [17]. Typical smooth (lowpass)windows will yield a representation that is an average of local events. For rapid transient speech sounds, such as plosives that have acoustic events as brief as 1 ms, even brief windows often smear out phonetic effects.  
Durations of speech vary greatly in informational purpose, e.g., sometimes, spectral changes owing to significant VT movements are rapid, while, other times, speakers articulate long vowels with little VT (and, consequently, spectral) movement. As a suitable and simple compromise, most speech processing calculates measures over brief time windows, updated every 10 ms. Such acoustic features are usually determined over a range of 20-30 ms, and frames are moved every 10 ms. Rates of 100 frames/s are found for many applications of speech; this rate is a compromise to minimize computation while also modelling VT coarticulation well.
# Artificial Intelligence, Sensors and Vital Health Signs: A Review
## 4 Internet of Things (IoT) for Healthcare Delivery
### Diabetes treatment  
Recently, an increasing number of smart sensor solutions have been used in diabetes treatment. Many wearable and mobile monitoring devices such as blood glucose monitors, insulin pens, insulin pumps, and closed-loop artificial pancreas systems are capable of wireless transmission, with smartphones or tablet devices providing simple analytic services without cloud support, as reported in [64]. Additional contributions in this area focused more on model interpretation to predict the onset of the disease. For instance, [65] proposes a decision support system for diabetes prediction based on ML techniques and compares handcrafted machine learning with deep learning approaches. Finally, a deep learning model to predict diabetes, stress types, and hypertension attacks from wearable smart devices is discussed in [66], where the authors implemented a FOG-based deep learning model (DeepFog) that collects data from patients and classifies their wellness state using a robust multi-dimensional data deep neural network model.