esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Permanent magnet synchronous machines (PMSMs) are widely used in electric vehicles due to their high power density, high efficiency, etc. Core losses account for a significant component of the total loss in PMSMs. Therefore, it is necessary to carefully consider it when designing PMSMs according to actual scientific research project applications. This paper extracts the characteristic operating points of the PMSMs under different operating conditions at different speeds. Then a harmonic analysis of air-gap flux density, phase current, core loss was completed, and detailed comparative analysis was performed. A novel method for comprehensively analyzing the stator core loss of PMSMs for vehicles is proposed, which reveals the law of the core loss of the PMSM under Maximum-Torque-Per-Ampere (MTPA) and Space Vector Pulse Width Modulation (SVPWM). The method was verified by a prototype experiment where the actual core loss of PMSMs was measured to verify the correctness of the method. This research provides a reference for accurately predicting core loss during the forward design of PMSMs and completing core loss evaluation for existing PMSMs.  
**Keywords:** permanent magnet synchronous machines; core loss; flux density; Maximum-Torque-Per-Ampere; Space Vector Pulse Width Modulation; harmonic analysis
# Optical Amplifiers for Access and Passive Optical Networks: A Tutorial
## 1 Introduction
Even Ethernet PONs are serious candidates for asynchronous transfer mode (ATM)-based PONs, and it is clear that optical amplifiers should be part of such activities, especially in countries such as Canada, the USA, Norway and Sweden, where the distances to be overcome are certainly longer than those proposed years ago in PON standards (this situation is very similar to that for Ethernet then and now). Fortunately for PONs, optical amplifiers have come a long way. Today, optical amplifiers with small form factors are widely available, and high-quality EDFAs can be bought even with form factors known from the pluggable transceiver market. This means that optical amplifiers are no longer expensive parts of optical systems and that the power consumption is very promising. Of course, no active elements can be compared to totally passive optical elements such as splitters, but optical amplifiers and Ethernet switches have lower power consumption than OLTs and ONUs (of course, OLTs and ONUs are increasingly better in every generation). Optical amplifiers are perhaps the great unknown for people working in the PON environment, as many of us who work in these areas know, and therefore, it is very desirable to provide such papers as our attempt to bridge long-haul high-speed networks with PONs. The rest of this paper is organized as follows. Section 2 provides details about the 3R reamplification, reshaping, and retiming methods for signal reconstruction after optical fiber transmission. Section 3 introduces optical amplifiers for telecommunications networks and their usage in passive optical networks along with principle details. Section 4 concludes this paper.
## 2 Frequency Regulation Model of DFIG
### Rotor Speed Protection  
In the process of DFIG participating in FR, especially in the low WS region, the excessively large rotor speed drop may cause the DFIG to be unstable or stalled [12]. Therefore, the rotor speed protection is usually introduced to make the DFIG directly exit FR when the rotor speed reaches the lower limit (0.7 p.u.). However, the protection method, with fixed limit value, does not consider the influence of the external WS condition, and DFIG directly exiting FR is also likely to cause a secondary deviation of the system frequency.  
Therefore, a rotor speed protection block is added before the DFIG controller in this paper, as shown in Figure 6, where \(v_{1}=6.7\)m/s and \(v_{2}=9.1\)m/s. The principle of the protection block is that the FR task of DFIG is gradually reduced with the decrease in WS in low WS region so that the DFIG gradually exits FR with the decrease in rotor speed. The input signal of protection trigger is the system FR task, which ensures the synchronous action of rotor speed protection and DFIG's participation in  
Figure 5: FR (frequency regulation) control principle block diagram of DFIG (double-fed induction generator).  
FR. The function of saturation block is set to make the output equal to zero when the input is negative, so the FR task of DFIG will become zero when the WS is below 6.7 m/s, which means that DFIG is totally exiting FR. In this way, the stability of the wind turbine during the FR process is maintained, and the system frequency is prevented from secondary deviation.  
In conclusion, based on the de-loading strategy for FR capacity, considering rotor speed control, the secondary FR model for DFIG to participate in both system frequency up-regulation and down-regulation in whole WS range is established through the combination of rotor speed and pitch angle control.
# A Doppler Frequency-Offset Estimation Method Based on the Beam Pointing of LEO Satellites
## 2 Model Establishment
### Satellite-Ground Doppler Frequency Offset  
The Doppler frequency offset between satellite \(S\) and ground terminal \(M\) arises from their relative velocity. It can be expressed as follows:\[F_{d}=F_{c}\frac{|V_{SM}|}{c} \tag{1}\]  
As shown in Figure 1, \(V_{SM}\) is the relative speed between satellite \(S\) and ground terminal \(M\), \(c\) is the speed of light, \(F_{c}\) is the working frequency of the communication system, and \(F_{d}\) is the Doppler frequency offset. Figure 1 is the satellite-ground Doppler frequency-offset model.  
In Figure 1, \(V_{S}\) is the motion speed of the satellite, and \(\alpha\) is the angle between the direction of the satellite's motion and the connection between the two sides of satellite-ground communication; we obtain the following:  
\[|V_{SM}|=|V_{S}|\cos\alpha \tag{2}\]  
Thus, Equation (1) yields \(F_{d}\) as follows:  
\[F_{d}=F_{c}\frac{|V_{S}|\cos\alpha}{c} \tag{3}\]  
In this model, \(F_{c}\) and \(c\) are constants, and the satellite speed \(V_{S}\) can be calculated according to the orbital altitude of the satellite.  
However, the angle \(\alpha\) is related to both the magnitude and direction of the satellite speed \(V_{S}\), and it changes in real time, so the calculation process of \(\cos\alpha\) is complex. Although Equation (3) is difficult to calculate, it provides an excellent approach for studying the Doppler frequency shift. We can use the projection of the satellite velocity onto the line connecting the satellite and the ground terminal (SM) to calculate the satellite-to-ground relative velocity, thus completing the estimation of the satellite-to-ground Doppler frequency shift. Based on the satellite velocity-based satellite-to-ground Doppler frequency-shift model, this paper establishes a simple Doppler frequency-shift estimation model based on satellite beam pointing. The model uses the beam-pointing information of the satellite's antenna to complete the velocity projection of the satellite's velocity onto the satellite-to-ground direction, which can simply estimate the satellite-to-ground Doppler frequency shift.
## 2 ACE-FHBOC Multiplexing
According to (9) and (12), the phase dimension expression can be expressed as a look-up table (LUT) from power allocation \(p\) and \(f_{h}^{i}\) to the modulation phase. Figure 1 shows waveforms of the real part \({\rm Re}[s_{\rm A}(t)]\) and the imaginary part \({\rm Im}[s_{\rm A}(t)]\) for an example signal ACE-FHBOC(10:1:1,1,1,3,3), which illustrates how the subcarriers  
change with the hopping of the frequency-hopping pattern. Figure 2 shows the modulation constellation for the example signal ACE-FHBOC(10:1:1,1,1,1,3,3); the phase points are distributed on a circle, indicating that the signal has a constant envelope.
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 3 Background
### System Model
#### 3.1.2 Delay Propagation Model  
Underwater delay propagation considers temperature, the depth of water and salinity of water, and it is given as follows [1]:  
\[\begin{split}\
u&=1448.96+4.591\tau-5.304\times 10^{- 2}\tau^{2}+2.374\times 10^{-2}\tau^{3}\\ &+1.340(\delta-35)+1.63\times 10^{-1}d+1.675\times 10^{-7}d^{2}\\ &-1.025\times 10^{-2}\tau(\delta-35)-7.139\times 10^{-13}\tau d ^{3}.\end{split} \tag{4}\]  
Here, \(\
u\) represents the propagation speed of the acoustic signal, which is measured in ms\({}^{-1}\), \(\tau\) represents the temperature, \(\delta\) shows the salinity and \(d\) denotes the depth of water. The acoustic propagation speed is directly proportional to the temperature, salinity and depth of the water. Equation (4) is effective when it fulfills the conditions as: \(0\leq\tau\leq 30\), \(30\leq\delta\leq 40\) and \(0\leq d\leq 8000\).
## 3 Prediction Based on BP Neural Network
### Latin Hypercube Sampling  
Latin hypercube sampling (LHS) is a constrained method of approximate random sampling in obeying a multivariate parametric distribution. In this paper, we will adopt the  
Figure 5: (**a**) Loading path curve. (**b**) Finite element forming results.  
non-normal distribution method of correlated random variables for the multi-dimensional sampling of forming internal pressure, axial feeds, and friction coefficients, respectively. [PERSON] et al. [11] proposed the Nataf conversion method, the spatial transformation of non-normal distributions to independent standard normal distributions based on the marginal distributions of the variables and the correlation coefficients between the variables. The operation of converting the original space to relevant standard orthotropic space is given by the following: entering a set of correlated random variables \(X\ =\ (x_{1},x_{2},\ldots,x_{n})\), if the probability density function \(f_{i}(x_{i})\) and the cumulative density function \(F_{i}(x_{i})\) of the associated random variable \(x_{i}(i\ =\ 1,2,\ldots,n)\) are known, by the principle of equal probability transformation [12], it is obtained that  
\[\left\{\begin{matrix}\int_{-\infty}^{x_{i}}f_{i}(x_{i})dx_{i}\ =\ F_{i}(x_{i})\ =\ \phi(y_{i})\\ y_{i}\ =\ \phi^{-1}[F_{i}(x_{i})]\end{matrix}\right. \tag{2}\]  
where \(y_{i}\) is a variable in a set of standard normal correlated random variables \(Y\ =\ (y_{1},y_{2},\cdots,y_{n})\): \(\phi(\cdot)\) and \(\phi^{-1}(\cdot)\) are the standard normal cumulative density distribution function and the inverse cumulative density distribution function.  
The number of samples \(n\ =\ 70\) was taken according to the experimental requirements after using the numerical method. The 3D distribution of the samples is shown in Figure 6. Details of some of the sample data extracted can be found in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**Process Parameters**} \\ \cline{2-4}  & **Internal Pressure (MPa)** & **Axial Feeds (mm)** & **Friction Coefficient** \\ \hline
1 & 50 & 16.31 & 0.07 \\
2 & 7.31 & 10.56 & 0.1 \\
3 & 39.62 & 15.49 & 0.03 \\
4 & 5 & 7.69 & 0.15 \\
5 & 39.62 & 8.1 & 0.025 \\
6 & 14.23 & 9.74 & 0.05 \\
7 & 10.77 & 4.82 & 0.05 \\
8 & 36.15 & 14.67 & 0.1 \\
9 &
# Towards AI-Generated Essay Classification Using Numerical Text Representation
## 3 Natural Language Processing
### Large Language Models
These mechanisms enable the model to focus on different parts of the text or word sequences, which are then combined to create more precise representations. There are many ways to implement this mechanism [29]:  
* Self-Attention: This mechanism is used to combine all positions of a given sequence while preserving their complexity. It is essential for learning long-term dependencies between input data. * Cross-Attention: In this process, the information representing the input data (often called keys and values) is also used as a query within the component that interprets and processes this information. This allows the model to focus on the most relevant parts of the input data. * Full Attention: This type of mechanism refers to the calculation of self-attention using the naive method, which assumes that each element of the sequence is directly related to the other elements. This approach is termed naive because, in practice, it can be very computationally expensive. * Sparse Attention: This is an approximation method for the attention mechanism that reduces the number of relationships between individual elements in the sequence. This technique is used, for example, in GPT to reduce computational complexity. Attention mechanisms do not consider the order of words in input sequences, so it is essential to use techniques known as positional encoding. There are two main types of positional encoding: absolute and relative positional encoding. Absolute positional encoding involves simply assigning unique identifiers to each position in the sequence before passing them to the attention mechanism [29]. The second method, relative positional encoding, aims to convey information about the mutual dependencies of tokens appearing at different positions in the sequence. One example of this technique is the Alibi method, which reduces the influence of the distance between two tokens when calculating attention. The greater the distance, the lesser the influence. This causes the model to focus on the most recent tokens, ignoring earlier ones in the sequence [32]. Another well-known method of relative positional encoding is rotary positional embedding (RoPE), which operates by rotating the representation of the query and key proportionally to their absolute positions in the input sequence. The greater the distance between the tokens, the less their influence on each other in this technique. In other words, rotary positional embedding allows for considering the positions of tokens, but their significance diminishes with distance [33]. The use of attention mechanisms, along with appropriate positional encoding, enhances the ability of models to capture complex relationships and dependencies within the text, leading to more accurate and contextually aware language representations. This is crucial for tasks that require a deep understanding of context, such as machine translation, text summarization, and question answering.
###### Abstract  
Rainfall has always been a concern for wireless communications systems. As 5G technology relies on high-frequency bands, it is fundamental to model and simulate the interaction of such radio waves with rainfall, as the deployment of large-scale infrastructure for 5G is highly expensive. This research presents a reformulation of the Maxwell equations for a bi-dimensional space in a transverse electric propagation mode, for a linear, inhomogeneous, and isotropic propagation medium with its magnetic and electric properties dependent on time. This reformulation was solved using the Finite Differences in Time Domain (FDTD) method with the Convolutional Perfectly Matched Layer (CPML) boundary condition. Two main frequency propagation scenarios were studied: 5 GHz (corresponding to Wi-Fi in the 802.11n standard as well as to the lowest bands of 5G) and 25 GHz (corresponding to 5G), within a 10 m \(\times\) 3 m rectangular domain in air and with rain. The rainfall was simulated using a parallel Ziggurat algorithm. According to the findings, while 5 GHz waves experience scattering processes, 25 GHz waves experience substantial dispersion and attenuation throughout the domain in low- to moderate-intensity rain.  
Finite Differences in Time Domain (FDTD); Convolutional Perfectly Matched Layer (CPML); fifth generation (5G); Ziggurat algorithm; meteorological phenomenon; dispersion; attenuation; high frequencies; electromagnetic wave propagation; rainfall; absorption 2020 August 2023  
19 September 2023  
1
# Multi-Modality Cardiovascular Imaging Assessment in Fabry Disease
###### Abstract  
Fabry disease (FD) is a rare X-linked lysosomal storage disorder manifesting as progressive multi-organ accumulation of sphingolipids due to deficiency in the enzyme \(\alpha\)-Galactosidase A. Sphingolipid accumulation can take place in all cardiac cell types which manifests as left ventricular hypertrophy, microvascular ischaemia, conduction abnormalities, arrhythmia, heart failure, and valvular disease. The use of advanced cardiovascular imaging techniques have enabled clinicians to stage and prognosticate the disease and guide therapy. Transthoracic echocardiography (TTE) and cardiac magnetic resonance imaging (CMR) in conjunction are the hallmark imaging modalities to allow for this assessment. Traditionally, the assessment of cardiac involvement in FD was based on the assessment of maximal wall thickness (MWT) and the development of left ventricular hypertrophy (LVH). It is now understood that sphingolipid accumulation takes place before the development of LVH. Advances in techniques within TTE and CMR, particularly that of strain assessment and T1/T2 mapping, have meant that Fabry cardiomyopathy (FCM) can be diagnosed earlier in the disease process. This potentially provides a window for initiation of enzyme replacement therapy (ERT) at a stage where it is likely to have the most beneficial effect in reducing the high mortality associated with FCM. This review outlines the advances in multimodality imaging in staging and prognosticating FCM, as well as the applications of cardiac imaging in assessing symptoms and complications of FCM.  
Fabry disease; cardiomyopathy; transthoracic echocardiography; cardiac magnetic resonance imaging; artificial intelligence +
Footnote †: journal: Medical Imaging
# Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems
## 6 Evaluations
### Summary of the Experimental Results  
Table 1 shows the average execution time per iteration when deep learning is performed. As shown in the table, when layer dropping is applied, the computation time of worker and parameter server increases. We performed compression on a layer basis to minimize overhead due to communication data compression. Therefore, the worker's computation time increased by only about 5% in a 56 Gbit network environment. In the case of the parameter server, the computation time increased by up to about 23% in a 56 Gbit network environment. However, in a distributed deep learning system, the computational time of the parameter server took up very little. In our experiment, the computation time of the parameter server only increased by about 0.01 s. When layer dropping was applied, the computation time slightly increased, but the communication time decreased significantly. When layer dropping was applied, communication time was reduced by about 95% in a 1 Gbit Ethernet environment. As a result, the overall execution time was reduced by about 82%. In addition, even in a 56 Gbit Ethernet environment, which is a high bandwidth network, when layer dropping was applied, communication time was reduced by about 81% and total execution time by about 70%. Therefore, when the communication optimization scheme proposed in this paper is applied, communication can be efficiently performed not only in a low bandwidth network environment, but also in a high bandwidth network environment.
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 2 Methodology and Materials
### Experimental Setup
#### 2.4.1 Transmitting End
The nonlinear mixing effects of the spark gap enormously amplify the harmonics as revealed in Equations (21)-(25). \(V_{0}\) depends on the antenna height, while \(V_{1}\) is the magnitude of the input voltage feeding the spark gap. Based on the results of the above analysis, a high voltage input to the spark gap together with a highly suspended monopole antenna tends to have a positive effect on the transmission efficiency.
# Efficient Knowledge Distillation for Brain Tumor Segmentation
## 4 Experiments
### Implementation Details  
To verify the effectiveness of our distillation method, we employed networks with good performances as teacher networks, such as UNet and AttUNet. We used networks with less effective performance as student networks, such as DeepResUNet and UNet++. We implemented all the segmentation networks in our experiments with PyTorch and ran them on a single NVIDIA GeForce RTX 2060 GPU (6 GB). The Adam algorithm was used to train the networks with a weight decay of 0.0001. The batch size was 8 and the initial learning rate was 0.0003. On the BraTs dataset, we first normalized each sequence of the brain MR images by z score, cropped the redundant background information around the brain images, then sliced them into 2D data to fit the 2D network. In addition, to mitigate the category imbalance problem, the parts of the slices that did not contain lesions were discarded, and, finally, because it was multimodal, the slices of each modality were combined into a training case with four channels. A total of 285 BraTs2018 cases were training sets. As BraTs only published a training set and did not have test set, the test set was part of the training set that BraTs2019 had more of than BraTs2018, including 49 HGGs and 1 LGG. There were 50 test sets in total. During the training, we used the \(160\times 160\) images as training samples, and we trained all the networks to reach the fusion of 20 epochs of training.
## 4 Experiments and Results
### Results Analysis
#### 4.4.3 Evaluation of Model Training Performance  
In order to evaluate the training convergence performance of the classification model based on Triplet-EfficientNet, we trained five models to do a comparative study. These models are respectively EfficientNet-B7, VGG16, GoogleNet, AlexNet [31], and Triplet  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Number of Images** & **Final Accuracy in the Training Set** & **Top-1 Accuracy in** \\  & **in the Rock Dataset** & **(Epoch = 60)** & **the Test Set** \\ \hline Triplet-EfficientNet & 315 & 61.2\% & 70.8\% \\ + Original dataset & & & \\ \hline Triplet-EfficientNet & 6949 & 92.6\% & 93.2\% \\ + Augmented dataset & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The results of the ablation experiments on the data augmentation methods.  
Figure 13: Performance comparison of model with transfer learning versus model without transfer learning. (**a**) Accuracy; (**b**) Loss.  
EfficientNet. The training strategies and hyperparameters used in the training of each model were consistent.  
The changes in accuracy and loss value in the training process are shown in Figure 13(a),b, respectively. As can be seen from Figure 13(a), the average accuracy in the training set of the five models in 60 epochs from high to low is as follows: Triplet-EfficientNet, EfficientNet-B7, VGG16, GoogleNet, and AlexNet. As shown in Figure 13(b), the rank of loss values is reversed. The following Table 3 shows the concrete accuracy and loss values of each model. As can be seen from the table, the classification model based on Triplet-EfficientNet proposed in this study shows higher accuracy and lower loss value in the training set compared with EfficientNet-B7 and other models. The results show that the compound model scaling method based on EfficientNet obtains better network structure and more reasonable parameter configuration by NAS technology, and the introduction of a triplet attention mechanism further improves the overall accuracy and training convergence performance of the model.