esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Although the ASPWM scheme always incurs noticeable dc-offset current in the transformer, resulting in increasing transformer core loss [20], the dc-offset current can be easily eliminated by connecting a dc-balance capacitor in series with the primary winding. Aiming to developing a dimmable LED driver with the features of a high power factor and ZVS, this manuscript proposes a single-stage circuit based on integrating a boost-type PFC converter and a half-bridge converter. The developed LED driver integrates the advantages of soft switching and PFC techniques, leading to high efficiency and a high power factor. Besides, it can achieve wide dimming range. The circuit operation is analyzed, and the mathematical equations for designing component parameters are generated. Finally, a 115-W prototype LED driver circuit is designed and implemented to validate the feasibility of the proposed circuit.
## 5 Conclusions  
In OFDMA-based D2D systems, it is necessary to consider the interference from other sub-channels because of in-band emission. In this paper, we have discussed the performance degradation resulting from the interference from other sub-channels for OFDMA-based D2D group-casting systems. We considered three different scenarios of relaying, and performed analyses and simulations for the outage probabilities according to the relay positions. Although there is some approximation in the analysis, the difference between the results by the analysis and by the simulation is not significant, and the optimal relay location that minimizes the outage probability can be readily obtained using the analysis.  
The analytical and simulation results show that the optimal location of a relay is determined by the source position as well as the system scenario. Especially when the source is near or at the edge of the group, the optimal location of a relay in a group is not the center of the group. For some scenarios, the relay needs to be near the source for reliable transmission from the source to the relay. For other scenarios, the relay can be placed away from the source to take care of receivers far from the source.  
In this paper, we focused on the outage resulting from the interference from other sub-channels, and other performance-degradation factors were not considered, including collisions between intra-group devices, insufficient signal power from fading, and co-channel interference from other devices on the same sub-channel. More rigorous theoretical analyses and accurate simulations need to be performed with more practical assumptions. In the future, we will also consider multiple relays for retransmission to further improve the performance.  
Conceptualization, M.R.; Formal analysis, M.R.; Simulation, E.G., M.R; Writing-original draft, E.G., M.R.; Writing-review & editing, M.R.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2016R1A2B1008953).  
The authors declare no conflict of interest.
# End-to-End Delay Model for Train Messaging over Public Land Mobile Networks
## 3 Analysis of Experimental Data
If we even include all the events leading to loss of message sequence (such as the presence of coverage holes and tunnels without radio facilities inside, etc. ), the loss probability may raise up to 7% as indicated in [19]. However, the positions of tunnels and coverage holes are known in advance so that connection failure can be predicted and properly taken into account for train management operations. Figure 5: Examples of saw-tooth like behavior for the message generation rate of 5 msg/s.
## 6 HVDC Converters  
The HVDC systems serve as a DC link between AC systems, where the connection needs to convert from AC to DC and vice versa. This process is done using converters, such as Line-Commutated Converters (LCC), Current-Source Converters (CSC), or Voltage-Source Converters (VSC).  
Thanks to the development of new electronic devices such as GTO and IGBT in the 1990s, the use of VSC technology became fundamental in HVDC, mainly because of VSC's capability of providing secure commutation in an AC or DC system with the help of Pulse  
Figure 4: Power loss comparison between AC and DC lines.  
Width Modulation (PWM). In that way, a VSC operating as an inverter can provide a sinusoidal output to the AC system [45].  
Even though LCC systems are a mature technology that has been used for over 50 years in HVDC [49], Self-Commutated Converters have more advantages for mitigating issues related to low-inertia generation [30], which is increasingly being added to grids.  
Of these Self-Commutated Converters--VSC and CSC--VSC is a more flexible technology since it allows for controlling active and reactive power independently [45]. Therefore, more advanced HVDC systems tend to use VSC-based technologies.
# Computational Study of Inertial Flows in Helical Microchannels
## 2 Theoretical Background
### Description of the Numerical Model  
The liquid in our model is calculated using the lattice-Boltzmann (LB) method [18]. This method uses fictitious particles that propagate and collide over a fixed three-dimensional discrete lattice. For most simulations, we used a grid of 1 \(\upmu\)m. The particle density function \(n_{i}(\vec{x},t)\) is defined for each lattice point \(\vec{x}\), discrete velocity vector \(\vec{e}_{i}\) and time \(t\). We use the D3Q19 version of the LBM (three dimensions with 19 discrete directions \(\vec{e}_{i}\) along the edges and diagonals of the lattice). The governing equations are  
\[\underbrace{n_{i}(\vec{x}+\vec{e}_{i}\Delta t,t+\Delta t)=n_{i}(\vec{x},t)}_{ propagation}-\underbrace{\Delta_{i}(\mathbf{n}(\vec{x},t))}_{collision} \tag{12}\]  
where \(\Delta t\) is the time step, and \(\Delta_{i}\) denotes the collision operator that accounts for the difference between pre- and post-collision states and satisfies the constraints of mass and momentum conservation. This collision operator also includes \(\vec{F}\)--the external forces exerted on the fluid that come from expression (14).  
The cells are taken into account as an immersed objects with fully 3D discretization using tetrahedrons that cover the whole inner space inside the sphere. At the edges of the small tetrahedrons, fairly rigid springs are set so that the object undergoes almost no deformation during the flow. The springs are modelled using stretching forces \(F_{s}\) that tend to preserve a relaxed length of the edge \(l_{AB}\) between vertices \(A,B\). The stretching force acting on vertex \(A\) is defined as  
\[F_{s}(A)=k_{s}\Delta l_{AB}, \tag{13}\]  
with the opposite force for vertex \(B\). With \(k_{s}\) high enough, one obtains rigid spheres as cells. Detailed descriptions of the underlying models are available in [19; 20; 21]. The validation and verification of the computational models has been provided in [22; 23; 24]. For all simulations, we used tetrahedral meshes with edges of sizes approximately 0.4 \(\upmu\)m.  
Both fluid and cell discretizations are coupled by dissipative force coupling enforcing the no-slip condition such that the force exerted by the fluid on one mesh point is proportional to the difference of the velocity \(\vec{v}\) of the mesh point and the fluid velocity \(\vec{u}\) at the same position,  
\[\vec{F}=\gamma(\vec{u}-\vec{v}). \tag{14}\]  
where \(\gamma\) is a frictional coupling parameter calibrated in [25].  
Elastic forces \(F_{s}\) together with fluid force \(F\) compose a total force \(F_{tot}\) acting on cell mesh points that causes motion according to [PERSON]'s equation:  
\[m\frac{d^{2}\vec{x}(t)}{dt^{2}}=\vec{F}_{tot}, \tag{15}\]  
where \(m\) is the mass of the mesh points.  
The calculations of the fluid are performed on a GPU [26]. The calculation of immersed cells is performed on a CPU. Due to low number of cells, this does not cause performance issues.
# Applying Deep Learning to Medical Imaging: A Review
## 2 Deep Learning Techniques in Medical Imaging
### Generative Adversarial Networks (GANs)  
#### 2.3.1 Basic Concepts  
Generative adversarial networks (GANs) has been regarded as a class of DL models designed for generating realistic samples from complex data distributions [95]. GANs consist of two neural networks, a generator and a discriminator, which are trained simultaneously in a minimax game [96]. The generator learns to produce samples that resemble the training data, while the discriminator learns to differentiate between real and generated samples. The training process continues until the generator produces samples that the discriminator cannot reliably distinguish from real data [97].
## Appendix B Explanation of Layer Shapes for CP Convolutional Layer  
Here, we provide a detailed explanation of the shapes of CP convolutional layers: the replacement layers resulting from the CP decomposition of a convolutional layer's weight tensor.  
Let us denote the weight tensor of convolutional layer as \(W\in\mathbb{R}^{T\times S\times K\times K}\), where \(T\) and \(S\) are the number of output and input channels, respectively, and \(K\times K\) is the kernel size of the convolution.  
The CP decomposition with a rank \(R\) approximates \(W\) as follows:  
\[W(t,s,i,j)=\sum_{r=1}^{R}w_{1}(t,r)\cdot w_{2}(r,s)\cdot w_{3}(i,j,r),\]  
where \(w_{1}\in\mathbb{R}^{T\times R}\), \(w_{2}\in\mathbb{R}^{R\times S}\) and \(w_{3}\in\mathbb{R}^{K\times K\times R}\).  
The 2-D convolution operation of the CP convolutional layer can then be expressed as follows:  
\[P(n,t,x,y)=\] \[=\sum_{s=1}^{S}\sum_{i=1}^{K}\sum_{j=1}^{K}\sum_{r=1}^{K}w_{1}(t,r )w_{2}(r,s)\] \[\quad\times w_{3}(i,j,r)Q(n,s,i+x-1,j+y-1)\] \[=\sum_{i=1}^{K}\sum_{j=1}^{K}\sum_{r=1}^{R}w_{1}(t,r)w_{3}(i,j,r)\] \[\quad\times\bigg{(}\sum_{s=1}^{S}w_{2}(r,s)Q(n,s,i+x-1,j+y-1) \bigg{)}\] \[=\sum_{i=1}^{K}\sum_{j=1}^{K}\sum_{r=1}^{R}w_{1}(t,r)w_{3}(i,j,r)\] \[\quad\times\hat{Q}(n,r,i+x-1,j+y-1)\] \[=\sum_{r=1}^{R}w_{1}(t,r)\Big{(}\sum_{i=1}^{K}\sum_{j=1}^{K}w_{3} (i,j,r)\] \[\quad\times\hat{Q}(n,r,i+x-1,j+y-1)\Big{)}\] \[=\sum_{r=1}^{R}w_{1}(t,r)H(n,r,x,y)\]This operation can be compactly represented as  
\[P=\text{conv}(w_{1},out,T,R,1,1)\]  
where  
\[out=\text{conv}(w_{3},\text{conv}(w_{2},Q,R,S,1,1),R,R,K,R).\]  
This shows that convolutional layer can be replaced with a sequence of three convolutional layers:  
1. With \(S\) and \(R\) input and output channels and a \(1\times 1\) kernel size.
2. With \(R\) and \(R\) input and output channels, a \(1\times 1\) kernel size, and group size \(R\).
3. With \(R\) and \(T\) input and output channels and a \(1\times 1\) kernel size.
## 4 Performance Evaluation  
To demonstrate the effectiveness of the proposed method, three different recording environments were considered: anechoic, convolutive, and real reverberant. Figure 7 illustrates the configuration for the room impulse response (RIR) filter design. The dimensions of the room were \(4.5\,\mathrm{m}\times 7.5\,\mathrm{m}\times 2.5\,\mathrm{m}\), and a stereo microphone array with 3-cm spacing was located in the room at the coordinates denoted in the figure. To simulate the convolutive environment, a RIR filter was designed based on the image method [27], and the response of the left channel is shown in Figure 8. As shown in the figure, \(RT_{60}\) of this RIR was measured as 230 ms.  
The performance of the proposed method was evaluated in terms of three different measurements. First, the directivity pattern of the proposed method was compared with that of a conventional method  
Figure 8: Simulated room impulse response of the left channel based on the image method, where \(RT_{60}\) was measured as 230 ms.  
Figure 7: Experimental setup for simulating the room impulse response to simulate a reverberant environment.  
based on a DS beamformer [13] in the three recording environments. Second, the accuracy of direction estimates for the stereo audio processed by the proposed method was compared with that processed by the conventional method by measuring the perceived directions of listeners depending on the horizontal directions of sources. Third, a DMOS assessment [20] was carried out to evaluate the audio quality degradation after the proposed method had been applied.
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 4 Experimental Results
### Computational Complexity Analysis  
To thoroughly address scalability and efficiency, we analyze the computational complexity of the proposed system's core components. This analysis confirms the architecture's suitability for large-scale deployments in remote wildfire-prone areas.
## 2 Background and Theory
### Feature Extraction Techniques
#### 2.2.2 Petrosian  
The Petrosian fractal dimension (PFD) [52] quantifies the degree of irregularity or self-similarity in a signal by counting the sign changes in its first derivative. This measure is formally described by Equation (7):  
\[FD_{Petrosian}=\frac{\log_{10}(n)}{\log_{10}(n)+\log_{10}\Bigl{(}\frac{n}{n+0A \cdot N_{5}}\Bigr{)}} \tag{7}\]  
In this context, \(\log_{10}\) denotes the logarithm with base 10, \(n\) represents the total number of data points in the time series, and \(N_{5}\) corresponds to the count of sign changes in the derivative. This parameter provides significant insights into both the frequency characteristics and waveform complexity of the analyzed signal.
## 3 Results
### Classifier Training Process
#### 3.1.1 Rc Hyperparameters Selection  
The effectiveness of the RC for feature extraction hinges on a set of crucial hyperparameters, which govern the internal dynamics and representation capabilities of the RC:  
* The number of RC's internal nodes N, which controls the RC capacity.
* The spectral radius \(\rho\), which designs the scaling of the internal connections within the reservoir.
* The connection percentage \(\beta\), which determines the sparsity of connections within the RC's nodes.
* The leakage percentage \(l\), which introduces a decay factor to the RC's internal activations over time.
* The input scaling \(\omega\), which scales the input data.  
Three hyperparameters are considered in the scanning process, namely the spectral radius \(\rho\), and the internal nodes N. Experiments were conducted for the two spectral regimes of the RC (i.e., LSR (\(\rho=0.1\)) and HSR (\(\rho=0.98\))). Figures 8 and 9 illustrate the results obtained while selecting the conventional RC internal state number N when the Ridge and MLP readouts are considered. Here, 'dim' is fixed to 24 features. The remaining hyperparameters are empirically selected. The connection percentage is selected to be equal to \(\beta=0.98\), the leakage percentage is \(l=0.98\), input scaling is \(\omega=0.1\), and noise level is \(\xi=0.001\).  
The highest score was obtained when N = 15 in the MLP readout case, whereas N was 55 in the Ridge readout case. However, some classifiers achieve better results for different dim hyperparameter values.
## 3 Results
### Scale-Up of the EO Treatment in Airtight Glass Container  
The results obtained in Petri dishes with MEA medium were used for the scale-up of the EO treatment in a glass airtight container (GAC) applying an EO vaporization method at a slower rate (see Section 2.3). Two different volumes were tested: 420 \(\upmu\)L were selected to obtain in the GAC vapor phase the same EO concentration as that of the Petri dishes (10 \(\upmu\)L EO), and 210 \(\upmu\)L aimed to verify the effect of the EO volume. The obtained results were reported in Figure 5.  
Figure 4: Thyme EO effect on _A. alternata_ colonies at different exposure times.  
Figure 3: (**A**) Mean colony diameter of _A. alternata;_ the time of disc removal is reported on the corresponding line. (**B**) Mycelial growth rate (ns, \(p=0.16\)).  
In the first 3 days of incubation, the mean diameter of the colonies, treated with the two different EO volumes, were comparable (33 \(\pm\) 1.2 mm) and lower than that of the control (50 \(\pm\) 0.8 mm). After 14 days, the mean diameter reached a constant value of about 42 \(\pm\) 0.8 mm for 420 \(\upmu\)EO and 47 \(\pm\) 1 mm for 210 \(\upmu\)EO, and a total growth inhibition was observed until the 42 nd day (six week) of incubation. The corresponding inhibition percentage was equal to 45% and 52%, respectively.
###### Abstract
SAM-Road [30] introduced a lightweight graph neural network built on a Transformer architecture, integrating Non-Maximum Suppression (NMS) to extract road network vertices. This design significantly enhances the speed of road extraction tasks while maintaining high accuracy. The analysis of datasets reveals significant class imbalance in road data, emphasizing the importance of global context for road recognition. Obviously, it is precisely why Transformers have been widely applied and deeply explored in road extraction. While they excel at capturing global context, the unique shape and distribution of roads also require local detail features to enhance extraction accuracy by reducing misclassification and improving topological connectivity. This paper proposes a stepped parallel encoder architecture that combines a CNN Encoder Module (CEM) and a Transformer Encoder Module (TEM). By leveraging CNN's inductive bias for spatial locality and translation invariance, alongside the Transformer's global feature learning capability, the design effectively integrates local details and global context, enhancing feature representation and model generalization. Feature fusion between the CEM and TEM facilitates knowledge sharing, allowing the attention mechanism to flexibly focus on key features. To address the linear characteristics of roads, the decoder integrates a Linear Convolution Module (LCM), which improves the capture of linear features while significantly reducing parameters and computational complexity. Our contributions are summarized as follows:  
* Novel Stepped Parallel Architecture: We propose a stepped parallel encoder architecture that integrates the CNN Encoder Module (CEM) and Transformer Encoder Module (TEM), enabling the fusion of local and global information, compensating for the limitations of conventional CNNs. * Enhanced Encoder Module Design: We redesign the CNN and Transformer encoder modules from classic models to focus on local feature capture and global context collection, respectively. This design maintains their feature-capturing strengths while enabling integration into a parallel architecture. * Innovative Linear Decoder Module: We specifically design the Linear Convolution Module (LCM) in the decoder for linear shape characteristics and large-span distribution features of roads, which improves the edge integrity while reducing computational complexity.
# REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs
## 5 Discussion
### Security Analysis
#### REISCH Scheme with AVISPA  
In this subsection, we explain the REISCH scheme in AVISPA. REISCH includes four roles, namely localServer (\(LS\))), sensori (\(SN_{i}\)), clusterHeadi (\(CH_{i}\)), and centralServer (\(CS\)), as well as supporting roles, namely session and environment. Moreover, there are three sections to complete communication properly and securely: transition, composition, and goal specification. The transition section is used in the essential roles to keep a correct communication sequence. The composition section is used in the supporting roles to connect essential roles in specific sessions. The goal specification section includes security goals such as secrecy and authentication. Secrecy means known secrets only for specific entities while authentication depends on witness (freshness claim) and request (validation) processes to perform strong authentication. In addition, our scheme uses parameters such as RCV (receiving process), SND (sending process), _inv (private key), dy (communication channel by Dolev-Yao model), and \(intruder\_knowledge\) (known information for an intruder). We assume that the intruder uses the public key (\(k_{i}\)) and knows public keys for REISCH entities (\(kSNpu\), \(kCHpu\), and \(kLS\)). Figure 9 shows the REISCH framework in AVISPA. Figures 10-13 show the REISCH roles in AVISPA.  
Figure 9: REISCH’s framework.  
role sensori (SHi,CHi,LS:agent, KSNpu,KCHpu,KLSpu:public_key, ECDSA:hash_func, SNpeeud,SHsl,Data:text, SND,RCV:channel (dy)) played_by SNi def= local State:nat, SNrni,SigLsFi,SigSnEI,SNparity:text, MAXv:message, SigShN,SigSnT1,SigSnT2,SigSnT3,SigSnT4,SigLSi:text, Dif,SNts,SkNet,Slotp,Date,Time,SS,SNp,LSotpi:text  
cont scc1,sec2,sec3,auth3:protocol_id init State := 0 transition % SNi receives (LSotpi) from LS 1.State=0 /*RCV (SNi:LSotpi'.SNrni') =|> State':=1 /*SigSnEI':=mev () /*SigLSi':=(ECDSA (SNpeeud))_inv (KSNpu) /*SigLsFi':=xor (LSotpi',SigLSi) /*SigSN':=(ECDSA (Data.SNpeeud))_inv (KLSpu) /*SNGparity':=SigSN/*SigSnT1':=SNparity' /*SigSnT2':=xor (SigSnT1',SigSnEI') /*Nts':=new ()/*SNGotp':=new () /*SNGutp':=new () /*SNGutp':=0 /*SigSnT3':=xor (Sip,SigSnT2')/*SigSnT4':=xor (SigLSi,SigSnEI') /*secret ((SigSN),SigLeSi',SigSnEI'),sec1,{SNi,LS}) /*secret ({SNpeeud,SNsl},sec2,{SNi,LS}) /*secret ({MAXv,SNts'},sec3,{SNi,CHi}) /*SNN sends (Slm) to CHi /*SMD (CHi.SigSnT3':SigSnT4':SNrni'.Dif.SNtat':SS.Data) /*witness (SNi,CHi,auth3,{SNtat',Skrni',Dif}) end role ``` rolelocalServer(LS,CHi,SNi,CS:agent,KLSpu_KClpu_KNSpu_KCSpu:public_key,ECBSA:hash_func,SHpseudi,SNi1:text,SND,KCV:channel (djy))played_byLSdef= locclcl State:mat, Shmi_S,S
# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
## 4 Experimental Verifications of the Methodology
### Detection of Double Damages  
For the detection of multiple damages, the experiment is performed on an aluminum alloy 6061 beam specimen featuring two open cracks. The two cracks are introduced into  
Figure 23: The damage localization results of single damage: (**a**) based on the 1 st mode shape, (**b**) based on the 2 nd mode shape, (**c**) based on the 3 rd mode shape.  
the specimen via wire-electrode cutting, with the length of 3 mm, 4 mm and the depth of 4 mm, 5 mm. The specimen's dimensions and the cracks' location are displayed in Figure 24a, and an image of the employed specimen is provided in Figure 24b.  
For this experiment, we similarly select 1000 pixels along the beam's length as virtual sensors to capture high-spatial-resolution mode shapes. The results of the MS-LIE are presented in Figure 25a,c,e, where the Gaussian multi-scale space is established by the scale range of 2-10 with the step 0.1. Enlarged heatmaps corresponding to the larger scales are displayed in Figure 25b,d,f. The behavior of the damage representation in the multiple damage scenario closely resembles that in the single damage case. With the aid of the proposed data fusion algorithm, the advantage of high localization sensitivity at small scales and the advantage of strong noise robustness at large scales are fully integrated. The final damage localization results using the first, second, and third mode shapes are presented in Figure 26a-c, showcasing the proposed method's ability to clearly reveal multiple damages and achieve precise localization.  
Figure 24: The specimen  
Figure 26: _Cont_.  
Figure 25: The MS-LIE results of double damages: (**a**,**c**,**e**) the MS-LIE heat map obtained from the 1 st, 2 nd and 3 rd mode shapes, (**b**,**d**,**f**) the corresponding enlarged heatmaps at larger scales.