esa-sceva/satcom-chunk-collection huggingface dataset:
Although there have been notable advancements in detecting fire spots using deep learning techniques, identifying fire spot features in remote sensing images continues to be a challenge. The main issue is that individual fire spots often have unclear characteristics in the images, resulting in a low accuracy for models trained only on these features. However,during fire incidents, the center of the fire spot shows a clear contrast with its surroundings. Thus, it is essential to compare the characteristics of fire spots with environmental features to enhance detection accuracy. The Swin Transformer offers several advantages over traditional CNNs when it comes to detecting fire points. While CNNs predominantly depend on fixed local receptive fields, which may inadequately capture cross-scale information when analyzing high-resolution images or intricate scenes, the Swin Transformer utilizes a sliding window approach coupled with a hierarchical architecture. This design facilitates the effective extraction of image features across various scales and the capture of long-range dependencies via its self-attention mechanism. Consequently, the Swin Transformer demonstrates enhanced capabilities in identifying fire points within complex backgrounds, particularly in environments such as forests and grasslands. Additionally, its hierarchical structure allows for the precise detection of subtle variations in fire points, thereby improving both detection accuracy and robustness. Additionally, fire spots experience a unique progression over time, from their initial creation to their subsequent growth. BiLSTM excels at handling time-series data and, in contrast to LSTM which functions in one direction, it analyzes sequences in both the forward and backward directions at the same time. This feature allows BiLSTM to capture a greater range of characteristics and significantly reduce information loss, especially in tasks that require understanding dependencies. Taking the aforementioned factors into account, we suggest a novel fire point detection network that integrates the Swin Transformer architecture with the BiLSTM network, with an emphasis on detailed features associated with wildfires. The main goals of this research are as follows: (1) to assess how well the combination of the Swin Transformer and BiLSTM performs in detecting fire points; (2) to compare its effectiveness against current baseline methods; and (3) to perform ablation studies to determine the impact of each component in the network.
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 5 Analysis of PCEB-MS  
The aim of this section is to analyze and explain the involvement of parameters \(T\), \(\omega_{1}\), \(\omega_{2}\), and \(R_{\text{max}}\) in the PCEB-MS protocol.  
Firstly, the value of \(T\) directly influences the number of candidate cluster heads. This value cannot be extremely large or small; if the value is too large, the candidate cluster head number will be too many, thus increasing the election expenses, reduce the network lifetime, if the value is too small, cannot reflect the advantages of clustering. Therefore, determining a reasonable value for \(T\) is very important in the optimization of network energy consumption; in this paper, the value of \(T\) is set at 0.4.  
The values of \(\omega_{1}\) and \(\omega_{2}\) reflect the influence of the residual energy of the sensor node, and of the distance between the sensor node and the sink, on the competition radius of the cluster. Reasonable values optimize the competitive radius and facilitate the energy balance, and the influence on the competitive radius is proportional to the value of \(\omega_{1}\) and \(\omega_{2}\). Through the simulation experiment, the values of \(\omega_{1}\) and \(\omega_{2}\) are confirmed to be 0.6 and 0.2.  
The value of \(R_{\text{max}}\) directly influences the unequal competition radius, \(R_{\text{ran}}\). Clustering occurs after the communication radius of all the sink nodes is adjusted, and therefore, \(R_{\text{max}}\) is not larger than \(R_{Si}\). Experiments in some of the large-scale coalmines show that the maximum transmission radius of \(R_{\text{max}}\) is 75 m [33]; therefore, \(R_{\text{max}}\) is 75 when \(R_{si}\) is more than 75 m in the calculation process. In addition, the value of \(R_{\text{max}}\) is \(R_{si}\) when \(R_{si}\) is less than 75 m.
# Design and Control of a Battery Charger/Discharger Based on the Flyback Topology
## 3 Sliding-Mode Controller Design
### Transversality Condition  
The transversality condition proves the presence of the control signal \(u\) into the derivative of the switching function \(\Psi\); therefore, this condition evaluates the capability of the SMC to modify the system behavior. The mathematical formalization of this procedure is given in (21).  
\[\frac{d}{du}\left(\frac{d\Psi}{dt}\right)\
eq 0 \tag{21}\]  
This analysis is based on the switching function derivative, which is calculated as given in (22).  
\[\frac{d\Psi}{dt}=\frac{K_{v}}{C_{dc}}\cdot\left[\frac{i_{m}\cdot(1-u)}{n}-i_{ dc}\right]+K_{i}\cdot\left[\frac{v_{b}\cdot u}{L_{m}}-\frac{v_{dc}\cdot(1-u)}{n \cdot L_{q}}\right]-\frac{di_{dc}}{dt} \tag{22}\]  
Then, replacing Equations (22), (15), and (16) into (21) leads to the expression given in (23).  
\[\frac{d}{du}\left(\frac{d\Psi}{dt}\right)=-\frac{K_{v}\cdot i_{dc}}{\mathrm{C} _{dc}\cdot n}\cdot\left(M\cdot\frac{L_{m}}{L_{q}}+n\right)+K_{i}\cdot\left( \frac{v_{b}}{L_{m}}+\frac{v_{dc}}{n\cdot L_{q}}\right) \tag{23}\]  
The following subsection will explain that the sign of the transversality value defines the reachability conditions, thus the sign of (23) must be the same for any operating condition. The values of \(K_{v}\), \(C_{dc}\), \(n\), \(M\), \(L_{m}\), \(L_{q}\), \(v_{b}\), and \(v_{dc}\) are positive; the values of \(K_{v}\) and \(K_{i}\) are also positive as will be demonstrated in Section 3.4, but the bus current \(i_{dc}\) could be positive, negative, or zero. Evaluating expression (23) for \(i_{dc}=0\) leads to a positive value; hence, the proposed SMC must have positive transversality. Therefore, expression (23) must be positive, which leads to the following design equation for the SMC parameters:  
\[-\frac{K_{v}\cdot i_{dc}}{\mathrm{C}_{dc}\cdot n}\cdot\left(M\cdot\frac{L_{ m}}{L_{q}}+n\right)+K_{i}\cdot\left(\frac{v_{b}}{L_{m}}+\frac{v_{dc}}{n \cdot L_{q}}\right)>0 \tag{24}\]  
In conclusion, the SMC fulfills the transversality condition when expression (24) is satisfied, thus such an expression must be considered in the calculation of the parameters.
## 2 Methodologies of Developing Fragility Curves
### Maximum Likelihood Estimation (MLE) Method  
The method of MLE [38] provides the median (\(\mu\)) and dispersion (\(\beta\)) so that the fitted fragility function is more consistent with the probability data points obtained through Equation (2).  
The likelihood function can be calculated as:  
\[\text{Likelihood}= \prod_{j=1}^{m}\left(\begin{array}{c}n_{j}\\ z_{j}\end{array}\right)\Phi\left(\frac{\ln\left(\text{IM}_{j}\right)-\mu}{ \beta}\right)^{z_{j}}\left(1-\Phi\left(\frac{\ln\left(\text{IM}_{j}\right)-\mu }{\beta}\right)\right)^{n_{j}-z_{j}} \tag{5}\]  
where m is the number of IM levels, z\({}_{j}\) is the number of ground motions that exceed a performance level out of n\({}_{j}\) seismic excitations, and \(\Pi\) denotes a product over all the considered IM levels.  
Through maximizing the likelihood function the median (\(\mu\)) and dispersion (\(\beta\)), are defined as:  
\[\left\{\mu,\beta\right\}=\max_{\mu,\sigma}\sum_{j=1}^{m}\left\{\ln\left( \begin{array}{c}n_{j}\\ z_{j}\end{array}\right)+z_{j}\ln\Phi\left(\frac{\ln\left(\text{IM}_{j}\right)- \mu}{\beta}\right)+\left(n_{j}-z_{j}\right)\ln\left(1-\Phi\left(\frac{\ln \left(\text{IM}_{j}\right)-\mu}{\beta}\right)\right)\right\} \tag{6}\]  
Figure 2: Set of IDAs curves and corresponding values of IM at the level of capacity \(\hat{C}\).  
[PERSON] [38] introduced the method of maximum likelihood estimation to develop fragility curves when incremental analysis was performed up to some level of IM for some ground motions.
# The Role of Macrophage Dynamics in Atherosclerosis Analyzed Using a Petri Net-Based Model
## 2 Materials and Methods
### Petri Nets
#### 2.2.5 Atherosclerosis  
Atherosclerosis, a chronic inflammatory disease, affects mainly large and medium-sized arteries. Risk factors contribute to endothelial damage, allowing LDL to enter and oxidize within the intima, activating chemokines and adhesion factors such as CCL2, ICAM-1, VCAM-1, and E- and P-selectin. This process recruits monocytes that become M1 macrophages (equipped with scavenger receptors SRA and CD36), which ingest oxidized LDL and transform into foam cells, initiating atherosclerotic lesion formation [3; 9; 34]. Foam cells secrete pro-inflammatory cytokines (such as IL-1, IL-2, IL-6, IL-23, and TNF) and chemokines (like CCL2, CCL5, and CXCL1), intensifying inflammation [3; 35; 34]. Over time, foam cell accumulation leads to plaque formation, which can become unstable and potentially rupture, highlighting foam cells' central role in atherosclerosis progression [33].  
Macrophage dynamics affecting atherosclerotic plaque formation, which have been taken into account in this study, are presented in Figure 2.
# A New Adaptive Spatial Filtering Method in the Wavelet Domain for Medical Images
## 3 Results and Discussions
From the research carried out in this article we propose an algorithm that responds to the first three points mentioned above. The proposed image enhancement method using spatial filtering algorithm can be successfully applied to low contrast CT (Computer Tomography) images. Figure 6 shows the results obtained for such images. As it can be seen, the proposed method provides robust results in eliminating noise from images [75], as well as improving image contrast and contour detection. Obtaining images in which the contours are best preserved is done primarily in conditions of increasing computational complexity, with a decrease in the amount of noise removed from the image taking place. Figures 7-11 show the noise elimination results obtained in the case of real CT images, using Daubechies scale filter order 2, order 4 and Haar wavelet transform [76]:  
Figure 6: (**a**) Initial, low contrast image. (**b**) Scaled image. (**c**) Enhanced image. Figure 8: Noise reduction from a CT image, using Daubechies scale filter order 2 (db2) [76]. Figure 7: Noise reduction from a Computer Tomography (CT) image. (**a**) Input noisy image. (**b**) Output image, using Daubechies scale filter order 2 (db2). (**c**) Output image, using Daubechies scale filter order 4 (db4). (**d**) Output image, using Haar wavelet [76]. Figure 9: Noise reduction from a CT image, using Daubechies scale filter order 4 (db4) [76]. As it can be seen, the proposed method and the determined operators offer a better efficiency in terms of eliminating noise and preserving the contours of an image, allowing the implementation of the proposed method on existing hardware structures.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 2 Related Works  
### Imaging Photoplethysmography  
Relevant surveys in this area of research have been proposed the last past years. They are either dedicated to the measurement of cardiorespiratory signals from non-contact technologies [21; 36] or specifically oriented towards imaging photoplethysmography (iPPG) [2; 6; 37].  
The first measurements of PPG signals from facial video streams recorded by a standard camera were by [PERSON] et al. [38] and [PERSON] et al. [39] in 2007 and 2008, respectively. The authors proposed a method that detects light intensity fluctuations on the face from a set of predefined regions of interest. This technique has been employed on monochromatic ([PERSON] et al.) and color image sequences ([PERSON] et al.). PPG signals are simply formed by averaging the intensity of pixels included in the region of interest.  
Deriving pulse rate from video recordings generally follows four basic procedures [6]: (1) video recording; (2) image processing, i.e., selection of relevant pixels of interest (e.g., face and/or skin detection), channel combination, and color space conversion; (3) signal processing (e.g., band-pass filtering based on Fourier or wavelet transform); (4) biomedical parameter extraction (e.g., pulse rate, pulse rate variability, SpO\({}_{2}\)).
## 3 Results
### Influence of Phenology on the Carbon Cycle
#### 3.3.3 Influence of Phenology on NEP  
Figure 11 displays how correlation coefficients are spread out spatially, highlighting the significance of the relationships among SOS, EOS, and LOS with NEP. Figure 11a,c,e, illustrate how the correlations among SOS, EOS, and LOS relate to NEP, while Figure 11b,d,f showcase the corresponding significance test results.  
Figure 10: Spatial distribution of correlation (**a**) and significance (**b**) of SOS and NPP, correlation (**c**) and significance (**d**) of EOS with NPP, and correlation (**e**) and significance (**f**) of LOS with NPP.  
From Figure 10a,b, the correlation coefficients between SOS and NEP were negative in 62.91% of cases. Among these, 23.17% fell within the range of \(-0.2\) to \(-0.4\), and 9.12% were less than \(-0.4\). Positive correlations accounted for 37.09%, primarily located in the northwestern part of Lin'an, as well as the junction of Tonglu and Chun'an, with only 2.65% having coefficients greater than 0.4. This pattern is similar to the effects of SOS on GPP and NPP, where SOS and NEP are predominantly negatively correlated, indicating that an advancement in SOS has a positive effect on the increase in NPP.  
As illustrated in Figure 11c,d, the correlation coefficients between EOS and NEP in Hangzhou forests were negative for 24.43%, with 5.69% ranging from \(-0.2\) to \(-0.4\). Positive correlations dominate, accounting for 75.57%, and the distribution range was wide. Of these, 29.4% fell between 0.2 and 0.4, while 14.65% exceeded 0.4. This is similar to the  
Figure 11: Spatial distribution of correlation (**a**) and significance (**b**) of SOS and NEP, correlation (**c**) and significance (**d**) of EOS with NEP, and correlation (**e**) and significance (**f**) of LOS with NEP.  
effect of EOS on GPP and NPP. EOS and NPP are positively correlated in most forests in Hangzhou, indicating that a delayed EOS has a positive impact on the increase in NPP.  
Figure 11e,f show that 23.18% of Hangzhou forests had a negative correlation coefficient between LOS and NEP. These negative correlations were primarily spread in the west of Lin'an and the northeast of Chun'an, with 16.86% falling between \(-\)0.2 and 0. Positive correlations, accounting for 76.82%, were widely distributed. Among these, 30.17% had coefficients between 0.2 and 0.4, and 16.58% is greater than 0.4. This is similar to the effect of LOS on GPP and NPP. LOS is positively correlated with NPP in most forests in Hangzhou, indicating that the lengthening of the growing season has a positive impact on NEP.
# Predictive Quantization and Symbolic Dynamics
## 2 Information Dynamics
### Adding Simplicity Requirement of the Latent Representation  
As mentioned above, our formulation of the time series modeling goal is to find a meaningful latent \(Z\) that best approximates the next observation \(Y\). Since minimizing KL could be trivially satisfied by taking \(Z=X\), in order to avoid such a trivial solution, we add a constraint on \(Z\) by requiring it to be the most compact or simplest latent \"explanation\" derived from the observation \(X\). In information theoretical terms, we can write the criteria as a minimization of \(I(X,Z)\). In terms of coding, we look for the least amount of bits of information about \(X\) to be contained in \(Z\). We also need a third constraint to prevent \(I(X,Z)\) from going to zero. This is achieved by adding a fidelity requirement or bound on distortion between \(X\) and \(Z\), denoted as \(D(X,Z)\). It is important to note that this distortion \(D\) is not the same as a KL divergence; rather, it is some physically motivated distortion, such as mean square error (MSE) or some other fidelity measure between the \"compressed\" representation of \(X\), as expressed by \(Z\), and the original data \(X\). For the moment, we will ignore this fidelity constraint and only consider the first competing relations between maximizing \(I(Z,Y)\) and minimizing \(I(Z,X)\). Combining the two goals, we arrive at the target function for our learning system  
\[\max_{P(Z|X)}\{I(Z,Y)-\lambda I(X,Z)\} \tag{6}\]  
This formulation bears a close resemblance to the idea of the information bottleneck (IB) [5]. The formulation of the IB is to say that a goal of a learning system is to find the most compact representation \(Z\) of \(X\) that still provides most information about a different variable \(Y\). Accordingly, a predictive IB looks at a combination of factors when predicting the next observation \(Y\) from \(Z\), which can be solved separately from the encoding of \(X\) by \(Z\). In the following, we will use the symbolic dynamics of the latent representation to solve the predictive IB criteria using VAE representation learned from \(X\).  
To summarize what we have discussed so far, we presented the following competing criteria for our time series model, combining three factors \(I(X,Z)\), \(I(Y,Z)\), and \(D(X,Z)\)  
* Finding a compact representation \(Z\) of \(X\) from which \(X\) can be recovered with minimal distortion \(D(X,Z)\) (i.e., reconstruction qualify);* Finding a representation \(Z\) of past data \(X\) that is most informative about the next sample \(Y\) (i.e., time information).
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 4 Location and Installation of the Measuring Devices
### Considerations of the WMO
#### 4.1.2 Concerning the Vertical Scales  
According to the WMO, heat exchanges occur in the urban canopy layer (UCL), and its height is approximately equivalent to that of the mean height of the main rough elements (buildings and trees), (zH). The roughness sublayer, or blending height (zr), can be as low as 1.5 zH for densely built sites, but greater than 4 zH in low density areas. An instrument placed below zr may register microclimate anomalies; however, above that, it sees a blended, spatially averaged signal that is representative of the local scale. Based on the above, the source area (or footprint) is calculated as \"100(zr \(-\) zd)\", where \"zd\" is the zero-plane displacement length, expressed as follows: 0.5 zH, 0.6 zH, and 0.7 zH for the roughness classes 5 (scattered obstacles (buildings) at a relative distance of 8 to 12 obstacle heights for low solid objects), 6 (area moderately covered by low building at relative separations of 3 to 7 obstacle heights and no high trees), and 7 (densely built-up area without much building height variation), respectively (Davenport classification [94]).  
On the other hand, measurements at heights of 3 to 5 m above ground level are considered equally accurate to those collected at standard heights for non-urban areas (1.25 to 2 m.) and, in addition, more convenient for avoiding damage and proximity to vehicles.
## 1 Introduction
studied the effect of SA and fuel injection strategy on the combustion and emission characteristics of a direct-injection engine operating with dimethyl ether (DME). Through the study, they found that the in-cylinder peak pressure in the single-injection strategy using narrow-spray-angle injection (60 and 70 degrees) with advanced fuel injection timing was increased compared to that in the wide-spray-angle injection (156 degrees) with later injection timing [24]. In direct-injection CI diesel engines, the target injection point in the engine's combustion chamber is very important because it causes wetting of the combustion chamber walls, which causes the formation of unburnt hydrocarbons (UHC). Additionally, the consumption of O\({}_{2}\) in the combustion process also greatly depends on the targeting point [25; 26; 27]. Furthermore, rapid combustion in fuel-rich regions inside engine combustion chambers causes the deterioration of the combustion efficiency, knocking phenomenon, and high levels of NOx emissions from the engine. Therefore, many researchers have tried to determine the optimal SA [28; 29; 30; 31]. [PERSON] et al. reported in [31] that using narrow-angle injection with a 70-degree SA led to higher soot formation due to the deposition of the fuel film on the piston wall. It, however, lowers NOx emissions because of the leaner fuel-air mixture near the piston surface. [PERSON] et al. used the computational fluid dynamic (CFD) method coupled with a chemical kinetic model to investigate the emission characteristics of an NG-diesel DF engine at various SA [7]. They concluded that NOx emissions increased by 93% when the fuel injector SA increased from 60 to 140 degrees but decreased by 15% when the SA was further increased to 160 degrees. [PERSON] and [PERSON] performed a CFD analysis in a diesel engine operating with various SAs in order to reduce soot and minimize wall impingement [32]. They found that at the SA of 100 degrees, soot was reduced by 41% compared to that in the case of the SA of 70 degrees. From the above literature review, it is very important to note that although many studies have been carried out on NG-diesel DF engines to investigate the effect of SA on the combustion and emission characteristics of the engine, studies on the effect of SA and IP on NG-diesel ME-GI DF marine engines are very rare. The authors have reviewed a wide variety of literature but still have not found any studies similar to the one in this report. In this study, the effect of SA and IP on the combustion process and emission characteristics of an NG-diesel ME-GI DF marine engine was investigated by using the CFD method coupled with a reduced chemical kinetic model. The combustion and emission formation occurring inside the engine cylinder were modeled by using the ANSYS Fluent code. The ultimate target of this study was to specify the optimal SA and IP for the engine. The CFD models were validated by the experimental results reported in the engine's shop test technical data. The study also successfully assigned the optimal SA and IP for the engine to achieve certain emission reductions.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### Scattering Parameters (S-Parameters)
#### 6.2.2 Integral Measures over Frequency  
In many wideband applications, one may be interested in the integrated performance of a scattering parameter over a specified bandwidth \(\Delta f\). For example, the average transmitted power coefficient \(\overline{T}_{ij}\) from port \(j\) to port \(i\) over a bandwidth \(\Delta f=f_{2}-f_{1}\) can be expressed as  
\[\overline{T}_{ij}\ =\ \frac{1}{\Delta f}\ \int_{f_{1}}^{f_{2}}\bigl{|}S_{ij}(f) \bigr{|}^{2}\,df. \tag{46}\]  
Such integral measures provide insight into the average behaviour of the SPDT switch or other multi-port networks across the band of interest.