esa-sceva/satcom-chunk-collection huggingface dataset:
# Multifunctional Integrated Underwater Sound Absorption Materials: A Review
## 1 Introduction  
In recent decades, advancements in underwater vehicle technology have led to significant improvements in their overall performance. As a result, there is an increasing research interest regarding underwater sound absorption materials (USAM). This is because sound waves can travel long distances (up to ten thousand meters in the ocean), whereas electromagnetic waves quickly decay when propagating through the water. There are two primary applications for USAMs to prevent detection by sonar systems: (i) active control noise reduction materials, which reduce the noise radiated from underwater vehicles, with a sound absorption frequency range from 50 Hz to 10 kHz; and (ii) passive control noise reduction materials, which reduce the acoustic target strength of underwater vehicles, with a sound absorption frequency range from 1 kHz to 30 kHz [1]. Passive control noise reduction materials have received more attention than active control noise reduction materials due to their high performance-to-cost ratio, ease of fabrication, and reliable service. Normally, USAMs are placed on the surface of vehicles, particularly those that travel in the depths of the ocean [2; 3; 4]. As a result, the materials required for acoustic absorption in water differ from those used for air. Consequently, research on sound-absorbing materials with high hydrostatic pressure resistance, corrosion resistance, and other important properties facesincreasingly serious challenges. To date, there have been only a few reviews on the topic of USAMs applied in underwater vehicles [1; 5; 6].  
USAMs typically fall into one of four categories: polymer-based [7], metal-based [8], ceramic-based [9], or acoustic metamaterials [10], as shown in Figure 1. However, to the best of our knowledge, no relevant review paper exists on multifunctional sound-absorbing materials for underwater sound absorption, particularly those with high hydrostatic pressure resistance and corrosion resistance. Given the difficulties involved in preparing metamaterials and materials with singular performance, this review aims to fill this gap in the literature and serve as a beneficial reference for the study of USAMs. This review focuses on the following topics and their relevant research advances:  
* The mechanism of acoustic absorption in underwater environments and the various factors that impact the performance of USAMs;
* The corrosion mechanism of USAMs, as well as crucial factors and methods for achieving corrosion resistance;
* The hydrostatic pressure resistance mechanism of USAMs, and key factors and methods to enhance the hydrostatic pressure resistance.  
Figure 1: _Cont._  
Lastly, we present an overview of the recent research progress in the field of USAMs integrated with hydrostatic pressure resistance, corrosion resistance, existing challenges of multifunctional integrated underwater sound-absorbing materials, and proposed solutions.
## 2 Analysis of Prototype Machines
### Analysis of Electromagnetic Performance  
The analysis of the prototype machine is performed by a finite element model (FEM), which is shown in Figure 3a. The mesh arrangement is shown in Figure 3b. The materials in each part of the IPMSM are assigned according to the corresponding entries in Table 1. One section mesh is applied to the solid parts of the IPMSM to better analyze the high-frequency electromagnetic field while the \"band region\", which covers the whole rotating area including rotor and air gap, has an in-section mesh to meet the basic requirements of finite element analysis. Due to the need for a special consideration of high-frequency eddy current losses, additional fine mesh is applied to the permanent magnets. The total number of elements eventually reaches 7214.  
The simulation operating point was selected at the rated speed of 6000 rpm. Under no load, the phase EMF and line EMF waveforms are shown in Figure 4. For the defined loading condition, a 300 A RMS sinusoidal phase current with a 16\({}^{\circ}\) current advance angle was implemented as the representative operational scenario. The balanced three-phase excitation with frequency-synchronized 120\({}^{\circ}\) phase spacing was applied, resulting in an output torque as depicted in Figure 5a, and the line EMF waveform is shown in Figure 5b. The output power reached approximately 100 kW, and the system efficiency was calculated as 98.05%. (This value is high due to the fact that this finite element analysis only considers iron  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Rated power & 100 kW \\ Rated speed & 6000 rpm \\ Max speed & 12,000 rpm \\ Material of stator core & DW315 Silicon steel sheet \\ Material of rotor core & DW315 Silicon steel sheet \\ Material of conductor wire & Copper \\ Material of PMs & NdFeB N35 \\ Outer diameter of stator & 250 mm \\ Inner diameter of stator & 153 mm \\ Axial length & 150 mm \\ Slot tooth width & 5 mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of prototype IPMSM.  
Figure 2: Three-dimensional schematic of the motor.
## 4 Numerical Evaluations
### Numerical Evaluation Model  
In the numerical evaluation, we consider radio propagation in free space. The received power is calculated using the Frith transfer formula in Equation (1), where \(P_{r}\) is the received power, \(\lambda\) is wavelength, \(P_{t}\) is the transmitted power, \(G_{r}\) is the receive antenna gain, and \(G_{t}\) is the transmit antenna gain [61].  
\[P_{r}\left[\mathrm{dB}\right]=20\mathrm{log}\left(\frac{\lambda}{4\pi d} \right)+G_{t}+G_{r}+P_{t}-L_{ox} \tag{1}\]  
This paper considers the usage of a 60 GHz mmWave band, which is particularly affected by atmospheric attenuation [62]. Therefore, atmospheric attenuation \(L_{ox}\) is considered in the above model. For the antenna gain, the antenna model in Equation (2) is employed, where \(\theta_{3 dB}\) is the half beamwidth [63].  
\[G(\theta)=\left\{\begin{array}{ll}10^{G_{0}-3.012(\frac{2\theta}{3\lambda \theta})^{2}}&(\theta\leq\frac{\theta_{ml}}{2}|)\\ 10^{-0.41\mathrm{ln}\theta_{3 dB}-10.6}&(other)\end{array}\right. \tag{2}\]  
In addition, \  
An example of this antenna gain model is shown in Figure 13. When considering ground reflection, the two-ray model in Equation (5) is adopted [64].  
\[P_{r}=10\text{log}\Bigg{(}P_{t}\Big{|}\sqrt{G_{t}(0)G_{r}(0)}\frac{\lambda}{4\pi r _{d}}+\sqrt{G_{t}(\theta_{1})G_{r}(\theta_{2})}+\frac{\lambda}{4\pi r_{d_{r}}} \Gamma e^{-j\frac{2\pi}{\lambda}(r_{d}-r_{d_{r}})}\Big{|}^{2}\Bigg{)}-L_{ox} \tag{5}\]  
The reflection coefficient \(\Gamma\) is shown in Equation (6) [65].  
\[\Gamma=\frac{n^{2}cos\varphi-\sqrt{n^{2}-\sin^{2}\varphi}}{n^{2}cos\varphi+ \sqrt{n^{2}-\sin^{2}\varphi}} \tag{6}\]  
where \(\varphi\) is the angle of incidence of the ground-reflected wave with respect to the ground, as shown in Figure 14. Also, \(n\) is dependent on the ground material and is shown in Equation (7) [66].  
\[n=\varepsilon_{r}-j\frac{\sigma}{2\pi f\varepsilon_{0}} \tag{7}\]  
where \(\varepsilon_{r}\) is dielectric constant, \(\varepsilon_{0}\) is dielectric constant of vacuum, \(f\) is radio frequency, \(\sigma\) is conductivity. The Signal to Noise Ratio (SNR) [65] is computed using Equation (8). \(P_{n}\) is the thermal noise power provided by Equation (9).  
\[\text{SNR}=\frac{P_{r}}{P_{n}} \tag{8}\]  
\[P_{n}\left[dB\right]=10\log_{10}\bigl{(}k_{q}KB\bigr{)}+\text{NF} \tag{9}\]  
where B is bandwidth, \(k_{q}\) is Boltzmann's constant, \(K\) is temperature in Kelvin, and NF is Noise figure. The throughput is calculated using [PERSON]'s famous capacity equation, where \(C_{0}\) is the maximum performance value defined by the used hardware [67].  
\[C=B\text{log}_{2}(1+\text{SNR}) \tag{10}\]  
\[T=\min(C_{0},C) \tag{11}\]  
The actual throughput in the measurements will be degraded due to many factors and the above theoretical throughput would only indicate the upper bound.  
Figure 13: Antenna gain model.
# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 2 Materials and Methods
### Method
#### 2.3.2 GF-7 Image Parcel Extraction
As shown in Figure 5, a larger value for Point results in more sampling points, leading to a finer segmentation outcome. When dealing with remote sensing images, higher resolution implies a more detailed representation of features and greater internal complexity. Additionally, the size of each parcel to be segmented may vary. Therefore, it is crucial to select an appropriate number of sampling points. For the aforementioned reasons, this study aimed to achieve optimal segmentation results while ensuring computational efficiency. To accomplish this, we considered the internal edge features and the area of the parcels to be segmented when adaptively computing the Point parameter. The formula used for this computation is as follows:  
\[P_{i}=30+f_{i}\cdot g_{i} \tag{1}\]  
\[f_{i}=\left\{ \begin{array}{c}\log_{1,5}e_{i}\ \ \ (0<e_{i}<10)\\ -15\ \ \ (e_{i}\leq 0\ or\ e_{i}\geq 10)\end{array} \right. \tag{2}\]  
\[g_{i}=a_{i}/a_{mean} \tag{3}\]  
where \(P_{i}\) represents the number of points, \(e_{i}\) represents the edge ratio parameters, \(a_{i}\) represents the area of the current parcel, and \(a_{mean}\) represents the average area of all parcels. Formula (1) is used to calculate the Point parameter of a parcel, consisting of a base value plus the product of \(f_{i}\) and \(g_{i}\). Here, \(f_{i}\) is a piecewise function that, when \(e_{i}\) is between 0 and 10, scales \(f_{i}\) non-linearly to a smooth range through a logarithmic transformation. In other cases, \(f_{i}\) is assigned a fixed penalty value. This design addresses situations where parcels have highly uneven internal gray levels, causing \(e_{i}\) to fall outside a reasonable range. The penalty mechanism helps identify and handle invalid or anomalous values, ensuring that only reasonable \(e_{i}\) values contribute positively to the Point calculation. \(g_{i}\) represents the ratio of the area of the current parcel to the average parcel area. Combining this measure with \(f_{i}\) allows for adjusting the final Point parameter based on the parcel's area, leading to more accurate segmentation results.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 6 Results and Discussions
### Performance Comparison between Federated Learning and Machine Learning
#### 6.1.3 Computation Time  
The computation time for the different approaches was assessed for model training times only. They do not include the data pre-processing time or communication time. All experiments were performed on a CPU-based machine with 8 GB of RAM.  
* _Machine Learning:_ For machine learning, the training took 100 epochs to converge. The entire training phase took 62 s to be completed.
* _Federated Learning:_ For federated learning, all devices performed the training of model parameters in parallel. During the training phase, one communication round took 5 s on average. The federated averaging algorithm running on the server took 0.016 s. Hence, one communication round took a total of 5.016 s for computation. Hence, three communication rounds took 15.048 s for computation.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **TN** & **FP** & **FN** & **TP** \\ \hline Machine learning & 404 & 7 & 10 & 579 \\ \hline Federated learning & 407 & 4 & 16 & 573 \\ \hline Federated learning with PCA & 407 & 4 & 20 & 569 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance of models trained with different methods.  
Figure 10: Traffic volume for machine learning (100 epochs), federated learning (3 communication rounds and 100 epochs), and federated learning with PCA (20 components, 3 communication rounds, and 100 epochs) approaches.  
The results, plotted in Figure 11, indicate that federated learning takes less computational time in comparison to machine learning as it trains models in a decentralized way. Federated learning also provides the benefit of requiring a substantially lower amount of data to be communicated while practically providing the same quality of performance.
# Optimal Power Allocation with Hybrid Relaying Based on the Channel Condition
## 3 Optimal Power Allocation Schemes for Hybrid Relay Networks
### The DF and AF Relay Networks  
In this subsection, we propose the optimal power allocation scheme for the three-hop DF and AF relay network.  
The optimization problem to maximize the achievable rate under a sum relay power constraint can be written as  
\[\max_{P_{da,1},P_{da,2}}R_{da}(P_{da,1},P_{da,2}),\ \ \ s.t.\ \ P_{da,1}+P_{da,2}=P. \tag{26}\]We define the ratio of \(P_{da,1}\) to \(P\) as \(\alpha_{da}\). By using \(\alpha_{da}\), the optimization problem is rewritten as  
\[\max_{\alpha_{da}}\ \gamma_{da}(\alpha_{da}),\ \ \ s.t.\ \ 0<\alpha_{da}<1, \tag{27}\]  
where \(\gamma_{da}(\alpha_{da})\) is given as  
\[\gamma_{da}(\alpha_{da})=\frac{\min(P_{s}|h_{1}|^{2},\alpha_{da}P|h_{2}|^{2})P (1-\alpha_{da})|h_{3}|^{2}}{\min(P_{s}|h_{1}|^{2},\alpha_{da}P|h_{2}|^{2})+P(1- \alpha_{da})|h_{3}|^{2}+1}. \tag{28}\]  
To determine \(\min(P_{s}|h_{1}|^{2},\alpha_{da}P|h_{2}|^{2})\) in Equation (28), we take into account two cases as follows.
# Degree-Constrained Steiner Problem in Graphs with Capacity Constraints
## 2 Previous Work
An improvement can be found in [19]. An approximation algorithm for some degree-bounded directed network design problems, such as the minimum cost connected sub-graph problem and upper bounds on in-degrees and out-degrees of the vertices, is proposed in [20]. In [21], two degree-constrained sub-graph problems (the maximum d-degree-bounded connected sub-graph and the minimum sub-graph of minimum degree \(\geq d\)) and their approximations are discussed. The network design problems with degree constraints are also analyzed in [22]. After the minimum-cost degree-constrained two-node-connected sub-graph problem and the problem of the arborescence with at least \(k\) terminals with minimum maximum out-degree, the terminal Steiner tree problem (where a degree-bound one must be respected for a set of nodes) is analyzed. The problem description is in [23], and a \(2\rho\)-approximation algorithm for the terminal Steiner problem can be found in [24]. A special case of the degree-constrained Steiner tree, the binary Steiner tree, is analyzed in [25]. An exact solution and an approximation ratio are presented (in these trees, which are important in computational biology, the maximal degree bound is 3 for all nodes). Instead of individual degree bounds, a new norm of the node degree vector is proposed in [26]. This norm tries to find a compromise between the maximum degree and the number of edges in the sub-graph and keeps degrees small. A direct application of the degree-constrained Steiner arborescence problem in a symbol graph to the symbolic regression can be found in [27] (the symbolic regression is a task for discovering a symbolic expression that fits a given data set from the space of mathematical expressions). In the case of degree bounds that correspond to the _capacities_ of the nodes, a new degree-constrained Steiner problem can be formulated. The objective is to cover a given node set with a minimum-cost, connected structure that satisfies the imposed capacity-like degree constraints. Assuming that a node can be used several times and the capacity of its occurrences is the same, good solutions can be obtained. The solutions are hierarchies [28] presented hereafter. They can be considered as \"non-elementary trees\" in \(G\). Recently, a study on the minimum spanning hierarchies in graphs was published [29]. The minimum spanning hierarchies are particular cases of the minimum Steiner hierarchies, where the node set to be covered is the node set in the graph. The Steiner problem with capacity constraints can be solved even if trees satisfying the constraints do not exist. The model is useful especially for the optical multicast routing, where the light can cross a switch several times. Optimal (minimum-cost) solutions correspond to light hierarchies [5].
## 1 Introduction  
### Research Background and Topic  
Emotion recognition using AI has garnered significant attention in recent years, particularly in areas such as fashion, where understanding consumer sentiment can drive more personalized and effective marketing strategies. The demand for emotional analysis and image-based recommendation systems in the fashion industry has been rapidly growing. Nonetheless, the emotion analysis was accepted in the studies on consumers' emotions about fashion brands traditionally [1, 2]. Small fashion brands, in particular, face difficulties in utilizing digital content effectively due to limited resources. To address these challenges, AI-based technologies have emerged as a solution. Analyzing and classifying fashion images, especially in terms of their emotional emotions, is a highly complex task that heavily relies on the performance of advanced deep learning models. [PERSON] et al. suggest that sentiment analysis refers to predicting the emotion and attitude of an individual person underlined in the context and content. Commonly, the sentiment analysis research tends to focus on not only the text contents but also the emotional scores of images [3].  
Previous studies on image analysis have been adopted successfully. Convolutional neural networks (CNNs) and newer studies also introduce that vision transformers (ViT) and residual networks (ResNet) are widely used architectures for image analysis. CNNs have proven effective in learning features from fashion images and performing well in classification tasks. ViTs, on the other hand, excel in processing broader image information efficiently, utilizing resources optimally. ResNet, developed to solve the vanishing gradient problem in deeper networks, performs exceptionally well in analyzing complex images due to its deep learning capabilities.  
This study aims to propose an AI model that automatically analyzes the emotional emotions of fashion images (e.g., sexy, classic, calm) and compares the performance of CNN, ViT, and ResNet to determine the most suitable model. This study will assist fashion companies in improving marketing strategies through emotional analysis and providing content tailored to consumer preferences.
## 4 Experiment Results and Discussion
### Dataset Preparation
#### 4.1.1 UCSD Dataset  
The UCSD benchmark dataset [33] consists of two different videos streams namely ped1 and ped2 with different viewpoints. The whole dataset consists of 16,000 and 4800 frames with a TIFF image format, respectively, and all images were converted to the JPEG image format. The UCSD dataset comprises sparse, occluded and low-resolution image data.
## 6 Experimental Evaluation
### Scenarios
#### 6.3.1 Scenario 1: Federated Workload Profiling on Memory Usage  
1. **Best performing ML models with respect to memory usage.** Initially, the effects of memory usage before and after FWP resource optimization techniques were examined using five different ML algorithms as shown in Figure 4.  
After implementing the FWP resource optimization algorithms, it was found through experimentation that memory consumption dropped significantly. Additionally, it can be deduced that Random Forest Tree, followed by GBDT and Decision Tree, were the best-performing ML models. The memory usage of LightGBM was the highest of all the models evaluated. In conclusion, the experiments revealed that Random Forest Tree was the top-performing ML model in terms of memory consumption, and thus the Random Forest model was employed for the rest of the experiments.
2. **Memory usage prior to and after FWP at different edge nodes.** FWP optimization strategies were implemented across six diverse nodes as seen in Figure 5, revealing a significant drop in the memory requirements in comparison with conventional approaches. Although all the nodes benefited from FWP, Nodes 5, 6, and 2 saw the greatest reductions in the amount of memory they used, respectively.  
Figure 4: ML algorithms memory usage comparison.
## 4 Discussion and Conclusions
### Conclusions  
In order to meet the increased demand and improve service quality, additional interventions are required in the healthcare sector and new organizational and physical models need to be explored. Findings from this study help to identify and focus on the characteristics of two different healthcare infrastructures devoted to emergency care, while providing a detailed account of the challenges faced by EDs in various contexts. The literature review and the case studies analysis provided an overview of the emergency healthcare system, which intuits the Emergency Department's components from triage systems to operational models. Lessening difficulties, such as congestion and access blockage, while effectively rejuvenating the concept of care for all in the built environment are all supported by the available evidence. With the success of the US-based Free Standing Emergency Departments, this research paper shed light on the FSED model providing early data and key findings on the understanding of the functional and spatial definition in comparison to the traditional Hospital Based ED model. The collection of detailed data from both typologies is relevant for practical and academic implications to further deepen the topic and support decision makers in developing strategies to face Emergency Department issues.  
This explorative study sheds light on the specific features of FSED in comparison to ED and positions itself as a starting point for future studies on wider samples and a possible attempt at the adaptation of the model in other contexts, so they may be adjusted to meet the specific triage protocols, where the movement patterns and flow of areas for different facility users are addressed.
## 4 Experiments
### Preliminary Experiments  
In the next experimental set-up, we aim at measuring the accuracy achieved by the system when training with the different pitch and speed modified sets and testing with originally stressed utterances for the first set of speakers, Set 1. The results achieved in these experiments should reflect which modification imitates best the original stressed samples. We kept the test set fixed for these experiments, a 30% of the samples of original stressed speech. Additionally, the same 30% in every synthetic super-stressed set was removed to achieve a more accurate comparison between experiments and guarantee that the test samples were never present in the training set even if they had been modified by our augmenting procedure. In Figures 5 and 6 we present the results obtained,  
Figure 4: Schematic of Original and Modified Datasets. On the left side, we represent in a block basis the original dataset, composed by neutral and stressed samples. In this case, we used the 30% of the examples of the the stressed collection as the Test set for later experiments. On the right, we represent a diagram of one of the synthetically stressed sets, where the 30% of data used before as Test was removed to obtain more reliable results. There are several synthetic datsets, one per modification applied.  
we enumerate the data used for the training step on the \(X\) axis, the \(Y\) axis represents the accuracy achieved and each colour bar indicates the modified set used for training.  
In Figure 5 we can observe that the modifications that obtain the highest accuracies are _Pitch_ +3% and _Pitch_ -3%. When it comes to Figure 6, although the speed results are very similar, the alteration that in general works worse is _Speed_ -20%. As for the training sets used, the SSS set works better than the SS in both cases.