esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Analysis and Parameter Determination
### Numerical Analysis  
In this section, numerical analysis is conducted to analyze the influence of each parameter and determine the value of the dilation angle and the inclination angle of the failure zone boundary.  
The toe capacity of a pile \(R_{t}\) in sand can be defined in the following equation:  
\[R_{t}=r_{t}A_{t}=N_{t}\sigma_{v}^{\prime}A_{t}, \tag{16}\]  
where \(r_{t}\) is the unit toe resistance, \(A_{t}\) is the cross-sectional area of the pile, and \(N_{t}\) is the bearing capacity factor. The bearing capacity factor \(N_{t}\) is back-calculated from the above equation and used to analyze the relationship between the computed ultimate toe capacity, the friction angle, the inclination angle, and the dilation angle. Figure 13 gives the relationship between the bearing capacity factor \(N_{t}\) and the friction angle under different inclination angles when the dilation angle \(v=0.1\phi\). According to Equation (14), the inclination angle should be larger than the friction angle; hence, each \(N_{t}\)-\(\phi\) curve in Figure 13 ends when the friction angle is equal to the inclination angle.  
As shown in Figure 13, the capacity factor \(N_{t}\) increases with friction angle and different inclination angles make for a different change rate in the bearing capacity factor. When the inclination angle is small, the bearing capacity factor increases faster with the increase in the friction angle, yielding a much higher computed ultimate toe capacity. This article suggests that the inclination angle of the failure zone boundary can be determined by Equation (17) when the influence of the friction angle on the inclination angle is taken into account:  
\[\alpha_{SA1}=36.13+4.259\times 10^{-7}\phi^{4.425}, \tag{17}\]  
when the friction angle is smaller than \(30^{\circ}\), an inclination angle equal to \(37.5^{\circ}\) will not cause the predicted ultimate toe capacity to deviate too much.  
Figure 12: The influence of friction angle on the computed toe capacity: \(\alpha_{SA1}=37.5^{\circ}\), \(v=0.1\phi\).  
Figure 14 gives the relationship between the bearing capacity factor \(N_{t}\) and the friction angle under different dilation angles when the inclination angle \(a_{SA1}=46^{\circ}\). It is shown in Figure 14 that the computed toe capacity increases with dilation angle. When the friction angle is small, the difference caused by the dilation angle is not significant, but as the friction angle increases, the difference begins to increase. Reasonable prediction of the ultimate toe capacity requires an appropriate dilation angle value. For the case of ultimate toe capacity, the soil around the pile tip is actually in a high shear state, corresponding to very small volume change and dilation angle. Following [PERSON] et al. [12], the dilation angle is taken as a small value equal to \(0.1\phi\) in the subsequent analysis.
# Erlang-U: Blocking Probability of UAV-Assisted Cellular Systems
## 2 Related Works  
In this section, we present the most relevant and previously published works on the use of UAVs in cellular networks and wireless communication systems to improve the system performance.  
In [1], the authors study the use of UAVs for data sensing and then transmit them to a base station. This work explores the trade-off between keeping a certain _freshness_ in the data and the communication tasks. However, the UAVs are not used to improve the performance of the cellular system; instead, the cellular system is used to transmit data from the UAVs.  
In [12], the authors propose methods to recharge IoT nodes, and present a model for the energy consumption of the drones used to directly recharge nodes using different schemes. In [2] the authors also propose an energy consumption model focused on charging multiple UAVs according to a particular schedule. Also, in [3; 14], the authors concentrate on charging drones; but, in this case, they propose using drones to charge other drones while in flight, in order to maintain a constant coverage of drones in a cellular system.  
In [11], the authors study the use of UAVs to assist cellular systems with additional channels, in this case also using satellites for a backhaul connectivity. However, they do not consider the average dwelling times in the region of interest. Conversely, we focus on the time that the UAV remains in the system, since these are the times that the blocking probability is reduced.
# Image Retrieval Method Based on Image Feature Fusion and Discrete Cosine Transform
## 3 Materials and Methods
### Deep Feature Fusion-Based Method  
#### 3.2.1 Place-Centric Networks  
How to assign a semantic category to an image based on the visual content of the image (for example, classroom, street, etc.) is the goal of image scene classification, and it is also the basis for problems such as image retrieval, image content analysis, and target recognition. We can combine scene semantics with object content and use multimodal information to enhance image retrieval performance. The research has studied that using place features has good performance for scene classification [53].  
In our previous research [54], we studied recognizing the scene type of a picture by using a neural network to train the images in the scene category. The place datasets we used are mainly taken from the Places database [53]. Some existing categories have been merged based on this data set, and some new types have been added. The final number of categories is 434. Considering the performance, parameters, and computing complexity, we used the pre-trained GoogleNet and ResNet\(-\)50 support packages from the MATLAB Neural Network Toolbox [55] to train the Place dataset. We took about 540 images (500 normal images and 40 cartoon images) from each category for the training set. We used 60 images (50 normal images and 10 cartoon images) for the validation set with 100 images (80 normal images and 20 cartoon images) for the test set. The training dataset is much smaller than those in Places365, and the training iteration is also not large enough. The accuracy values of classification are lower than those in the Places365 dataset.
# Research on Intelligent Optimization of Wellbore Trajectory in Complex Formation
## 2 Related Work  
### Mathematical Description of Optimization Problems  
When solving the optimization problem in the actual scene, the most important thing is to transform the specific problem into a mathematical form; that is, to establish a mathematical model. In general, the optimization problem can be expressed by the following mathematical model:  
\[\left\{\begin{array}{c}\min f(x)\\ \text{s.t.}\left\{\begin{array}{c}h_{j}(x)=0,\;\text{j}=1,2,\cdots,\text{p}, \text{p}<\text{n}\\ g_{i}(x)\geq 0,\;\text{i}=1,2,\cdots,\text{m}\end{array}\right.\end{array}\right. \tag{1}\]  
It can be seen from Equation (1) that \(f(x)\) represents the objective function of \(x\), \(h_{j}(x)\) represents the equality constraint condition, and \(g_{i}(x)\) represents the inequality constraint condition, where \(h_{j}(x)\) and \(g_{i}(x)\) may be linear or nonlinear.  
In order to establish an effective optimization model, it is necessary to carry out detailed research and analysis on specific engineering problems, and clarify the key points of the problem and the relationship between them. In this process, we should not only use mathematical theory, but also combine the knowledge of other fields. When constructingthe optimization model, there are three core elements that are essential: first, the objective function; that is, the goal we want to optimize; the second is the design variables, which can be adjusted factors; the third is the constraint condition, which refers to the constraints that must be complied with in the optimization process.  
(1) Objective function  
The objective function is composed of multiple design variables, which are used to evaluate the quality of different design schemes so as to help us achieve optimal design. The specific form of the objective function can be expressed as:  
\[f(X)=\{f_{1}(X),f_{2}(X),\cdots,f_{m}(X)\}^{T} \tag{2}\]  
(2) Design variables  
Design variables are usually those parameters that have a greater impact on the objective function. In mathematical expression, we can use the vector \(X\in R^{n}\) to represent these design variables, where BBB represents the total number of design variables, which often reflects the complexity of the optimization problem. Usually, we choose independent parameters as design variables to ensure that each variable can independently affect the final optimization results.  
(3) Constraint conditions  
When dealing with practical problems, we often encounter various constraints. These conditions require us to follow certain rules when setting design variables to ensure that the solution conforms to the actual situation. These rules are also called constraints. They are usually divided into two types: equality constraints and inequality constraints.  
Inequality constraints: \(g_{i}(x)\geq 0,\;i=1,2,\cdots,m\)  
Equality constraints: \(h_{j}(x)=0,\;j=1,2,\cdots,p,p<n\)
# Using Virtual Soundwalk Approach for Assessing Sound Art Soundscape Interventions in Public Spaces
## 4 Discussion
### Potential Application of the Research Results in Urban Design  
Noise sources in an urban environment range from road and railway to airborne traffic [88; 89; 90] and this study also speaks about the masking potential sound art can have. The virtual soundwalk was used in this study as a post hoc evaluation tool, but it goes without saying that consideration of a future acoustic environment in the early stage of planning and design process is highly recommended and a similar tool can be applied [39; 91]. However, the approach demonstrated might be useful as it is not always feasible to conduct the assessment before the intervention takes place due to conditions in situ, time frame of the research, or planning and design process.
## 5 Performance Comparison and Validation
### Results and Discussion  
In order to verify the effectiveness of the adaptive routing protocol proposed in this paper, we first verify the effect of extending the network life cycle of each protocol, and then simulate environmental changes and different application scenarios by changing some system parameters. Several aspects, such as extending the comprehensive efficiency index, are compared with the three routing protocols mentioned above, which verifies the superiority of DQAAR.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Representation** & **Value** \\ \hline \(E_{elce}\) & Energy consumed to transmit a unit of _bit_ data & 50 nJ/bit \\ \(\varepsilon_{fs}\) & Power amplifier normal loss & 10 pl/bit/m\({}^{2}\) \\ \(\varepsilon_{amp}\) & Power amplifier for multipath attenuation & 0.0013 pj/bit/m\({}^{2}\) \\ \(E_{int}\) & Initial energy of nodes & 0.5 \\ \(d_{0}\) & Distance threshold & 87 m \\ \(D_{l}\) & Lifetime requirements & \([0.2,0.5,0.8]\) \\ \(D_{t}\) & Real-time requirements & \([0.2,0.5,0.8]\) \\ \(r_{DA}^{\max}\) & Maximum aggregation reward & 1 \\ \(\alpha,\gamma\) & Learning rate and discount factor & 0.8,0.9 \\ \(\eta\) & Magnification factor of lifetime & 9 \\ \(\xi\) & Magnification factor of real time & 0.8 \\ \(R\) & Network range & 200 m \\ \(Data\) & Network single packet size & 200 bit \\ \hline \hline \end{tabular}
\end{table}
Table 5: Aggregation model parameters.
# A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability
## 3 Datasets used in State-of-the-Art Techniques
### Techniques in the State of the Art  
Recently, datasets recorded in the wild have gained attention for allowing evaluations in uncontrollable scenarios, such as laboratory environments. The AfeW benchmark [51] was one of the first public databases used for this evaluation scenario and was employed as the main dataset for evaluation at the emotion recognition in the wild (EmotiW) [61] challenges. Subsequently, datasets such as EMOTIC [16; 53] and CAER [17] have been published to explore the participation of context in emotion recognition deeply. They are the datasets most commonly used today by the state-of-the-art techniques for evaluation.  
In Table 3, we survey techniques from May 2019 to January 2023 and compare the participation of EMOTIC and CAER on the evaluation pipeline for these techniques. Althoughthere are other promising datasets, especially BoLD [55], that could be used for emotion recognition, given that most of the techniques focus only on context, it is justified that EMOTIC and CAER still dominate the evaluation focus in the state-of-the-art techniques. iMiGUE [54] and GroupWalk [56] are also not very common as benchmarks, mainly due to their specificity. AfeW and AfeW-VA are also more common in approaches for FER. In these cases, they are listed here for also containing visible context information.
## 6 Discussion  
The protection system used in this paper is a master-slave database type. Therefore, it requires high-speed communication and synchronization [13; 14; 15]. Every parameter is constantly tracked and measured by slave controllers. The measured values are sent to the master controller, which is the main processor of the protection system. So, first, the master controller processes the data, and, if a fault occurs, the slave controller will isolate the faulted segment by means of the IGBTs. By comparison of the simulation and experiment, it can be concluded that there is a reasonable difference between fault detection time in the simulation and experimental implementation. Fault detection time based on the proposed SDRE in the simulation section is 30 ms and 96 ms for experimental implementation based  
Figure 21: Voltage of resistive load and the current of freewheeling in no protection, differential, and SDRE observer modes.  
Figure 22: Voltage of IGBT breakers with snubber attendance in differential, and SDRE observer modes.  
on the proposed SDRE method. Almost 66 ms difference in fault detection between the simulation and experimental implementation is noted due to the ACS 712-30 and ADS1115 sampling frequency and computation time in the microcontroller.  
In this work, the PV module can have a level of uncertainty. The proposed model is based on state variables and the uncertainty in PV will lead to changes in the state variables, such as \(\Delta x_{2}\) and \(\Delta x_{3}\) which is considered in the proposed method (Equation (9)). As a result, with the occurrence of uncertainty, the values of the state variables (\(\Delta x_{2}\) and \(\Delta x_{3}\)) will be changed and updated, and as a result, the proposed SDRE method can have the appropriate control and protection performance in this state. So, one of the merits of the proposed method is robustness against uncertainty.
## 3 Computational Experiments
### First Experimental Campaign: Small Instances  
Small instances have been optimally solved by the mathematical model presented in [12], thus we are able to compare the results obtained by the proposed \(CH_{-}IPA\) algorithm with the optimal solutions. Thanks to the following results, it is possible to understand the behaviour of the proposed algorithm, in particular the effectiveness of \(IPA\), and to compare \(Grid\) and \(Rand\) instances. Table 1 shows the number of optimal solutions obtained, respectively, using \(CHa\) and \(CH_{-}IPA\). Looking at Table 1, we can observe that \(IPA\) always improves the solutions obtained by \(CHa\). This improvement is more evident for instances with costs \([-10,10]\), while the number of optimal solutions remains almost the same for \([-1K,1K]\) instances. The two sets of instances (\(Grid\) and \(Rand\)) have the same behavior. All instances can be solved in less than one millisecond. Actually, in Table 2 are reported the absolute objective function values obtained at the end of \(CHa\) and \(CH\_1 Pa\) for instances with randomly generated costs in \([-10,10]\), \([-100,100]\) and \([-1K,1K]\). Note that, the optimal value for all instances is zero (as shown in [12]). From Table 2, it is evident that it is always possible to improve the objective function values using \(IPA\) after \(CHa\). In particular, for the largest instances (i.e., last row of each group), both Grid and Rand, with costs in \([-10,10]\), \(IPA\) is able to provide optimal solutions. On average, for grid instances, the improvements are 92.3%, 92.2% and 87.8%, respectively, for instances with costs \([-10,10]\), \([-100,100]\), and \([-1K,1K]\). The improvements are about 70% for instances of type Rand_100, and about 82% for Rand_100. In all cases, the improvement is lower when costs are in \([-1K,1K]\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{4}{c}{\(CHa\)} & \multicolumn{4}{c}{\(CH\_1 Pa\)} \\ \cline{2-7}  & \([-10,10]\) & \([-100,100]\) & \([-1K,1K]\) & \([-10,10]\) & \([-100,100]\) & \([-1K,1K]\) \\ \hline Grid\_100\_10 & 2 & 0 & 0 & 2 & 1 & 0 \\ Grid\_225\_15 & 0 & 0 & 0 & 3 & 1 & 0 \\ Grid\_400\_20 & 1 & 0 & 0 & 5 & 0 & 0 \\ \hline
**\# OPT** & **3** & **0** & **0** & **10** & **2** & **0** \\ \hline Rand\_100\_02 & 0 & 0 & 0 & 1 & 0 & 0 \\ Rand\_100\_03 & 0 & 1 & 1 & 3 & 1 & 1 \\ Rand\_100\_04 & 1 & 1 & 0 & 4 & 1 & 0 \\ Rand\_100\_05 & 2 & 0 & 0 & 3 & 1 & 0 \\ Rand\_100\_10 & 3 & 1 & 0 & 5 & 3 & 0 \\ Rand\_100\_20 & 5 & 0 & 0 & 5 & 2 & 0 \\ \hline
**\# OPT** & **11** & **3** & **1** & **21** & **8** & **1** \\ \hline Rand\_200\_02 & 2 & 0 & 0 & 3 & 1 & 0 \\ Rand\_200\_03 & 2 & 0 & 0 & 4 & 1 & 0 \\ Rand\_200\_04 & 0 & 0 & 0 & 5 & 0 & 0 \\ Rand\_200\_05 & 2 & 0 & 0 & 4 & 0 & 0 \\ Rand\_200\_10 & 4 & 2 & 0 & 5 & 3 & 0 \\ Rand\_200\_20 & 5 & 1 & 0 & 5 & 2 & 1 \\ \hline
**\# OPT** & **15** & **3** & **0** & **26** & **7** & **1** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of optimal solutions identified—instances with random costs.
# Deep Learning Methods in Image Matting: A Survey
## 4 Deep Learning Methods
### Trimap-Free Methods
#### 4.2.3 Attention Method  
Attention mechanisms have demonstrated significant effectiveness and power as a tool in the field of computer vision, which can help models better focus on key information in images. In the image matting domain, many CNN models have adopted attention mechanisms to obtain more precise alpha mattes. We will now introduce the relevant research works of other researchers in detail.  
[PERSON] et al. [20] proposed an attention module that projects the entirety of the feature map's channels onto a singular attention map. This map is adaptively learned from the training data and guides the up-sampling and down-sampling processes of the matting network for better boundary detail enhancement. [PERSON] et al. [19] proposed a hierarchical attention-based matting network that utilizes spatial attention to capture image textures and appearance clues; the method also uses channel attention to extract semantic features, similarly to some dual decoder networks [35; 36]. [PERSON] et al. [29] classified foreground types in images and employed SE attention [63] to learn the semantic features of different foreground types, enabling the attention to guide the matting network to improve detail in the transition regions of different foreground types.
###### Abstract  
Thermoacoustic technologies rely on a direct power conversion between acoustic and thermal energies using well known thermoacoustic effects. The presence of the acoustic field leads to oscillatory heat transfer and fluid flow processes within the components of thermoacoustic devices, notably heat exchangers. This paper outlines a two-dimensional ANSYS FLUENT CFD (computational fluid dynamics) model of flow across a pair of hot and cold heat exchangers that aims to explain the physics of phenomena observed in earlier experimental work. Firstly, the governing equations, boundary conditions and preliminary model validation are explained in detail. The numerical results show that the velocity profiles within heat exchanger plates become distorted in the presence of temperature gradients, which indicates interesting changes in the flow structure. The fluid temperature profiles from the computational model have a similar trend with the experimental results, but with differences in magnitude particularly noticeable in the hot region. Possible reasons for the differences are discussed. Accordingly, the space averaged wall heat flux is discussed for different phases and locations across both the cold and hot heat exchangers. In addition, the effects of gravity and device orientation on the flow and heat transfer are also presented. Viscous dissipation was found to be the highest when the device was set at a horizontal position; its magnitude increases with the increase of temperature differentials. These indicate that possible losses of energy may depend on the device orientation and applied temperature field.  
parallel-plate heat exchanger; standing wave; oscillatory flow; thermoacoustics 25 January 2017; 7, 417; doi:10.3390/app7040417
# Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models
## 3 Results  
In this section, we present the parameters that we obtained for three different estimated solutions described in the previous section (i.e., _cats_d_, _cats_, and _tiasd_) along with further analysis of the results. The properties of these three models are summarized in Table 2. Then, we can compare the effect of draconitic adjustment by comparing _cats_d_ and _cats_, and we can compare the modelisation of interannual variations and stochastic parts by comparing _cats_d_ and _tiasd_.
## 1 Introduction
### Review of State-of-the-Art Detection Methods
#### 1.2.6 Impulse Response and Waveform Design for Enhanced Peak Detection  
Accurate peak detection in FMCW radar systems is fundamentally governed by the characteristics of the system's impulse response, most notably, the peak-to-sidelobe ratio (PSR) observed in the frequency domain. A high PSR ensures that true target reflections manifest as distinct spectral peaks, effectively standing out from sidelobe clutter and noise, thereby improving detection accuracy and robustness. One effective strategy to enhance the PSR is through advanced waveform design, which directly shapes the spectral characteristics of the transmitted signal and its autocorrelation function. For instance, orthogonal nonlinear frequency-modulated (ONLFM) waveforms that achieve concurrent sidelobe suppression and range ambiguity mitigation in satellite SAR imaging are introduced in [11]. Although developed for SAR applications, the underlying principles of improved spectral compactness and sidelobe control are equally beneficial in FMCW radar contexts, particularly where precise and unambiguous target localization is required under low-SNR or multipath conditions.  
Furthermore, modern automotive radar environments introduce the challenge of mutual interference among coexisting LFMCW systems. This interference can mask true peaks and degrade range estimation. An adaptive approach to mitigate such interference through intelligent waveform and scheduling strategies, thereby improving target separability and reducing false alarms is presented in [12]. These developments underscore the interplay between waveform design, interference management, and peak detection accuracy in practical radar deployments.