esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Isabela**} \\ \cline{2-7}
**Land Cover** & \multicolumn{2}{c}{**Agricultural Area**} & \multicolumn{2}{c}{**Buffer Zone**} & \multicolumn{2}{c}{**Total**} \\ \cline{2-7}  & **Ha** & \%** & **Ha** & \%** & **Ha** & \%** \\ \hline Bare Ground & 0.72 & 0.01 & 356.17 & 4.73 & 356.89 & 2.80 \\ Built Environment & 24.83 & 0.48 & 2.71 & 0.04 & 27.54 & 0.22 \\ _Cedrela_—Cedar & 471.34 & 9.05 & 185.60 & 2.46 & 656.94 & 5.16 \\ _Cinchona_—Quinine & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 \\ _Coffea_—Coffee & 113.41 & 2.18 & 24.58 & 0.33 & 137.99 & 1.08 \\ Cultivated Grass & 512.09 & 9.83 & 52.16 & 0.69 & 564.26 & 4.43 \\ Deciduous Forest & 35.33 & 0.68 & 337.23 & 4.48 & 372.56 & 2.92 \\ Evergreen Forest and Shrubland & 120.36 & 2.31 & 124.55 & 1.65 & 244.91 & 1.92 \\ Evergreen Seasonal Forest & 344.71 & 6.62 & 244.00 & 32.42 & 2785.61 & 21.87 \\ Freshwater & 0.71 & 0.01 & 4.64 & 0.06 & 5.35 & 0.04 \\ Humid Tallgrass & 157.60 & 3.02 & 652.98 & 8.67 & 810.58 & 6.36 \\ Mixed Forest & 599.48 & 11.51 & 1282.43 & 17.03 & 1881.91 & 14.77 \\ _Pennisetum_—Elephant Grass & 30.71 & 0.59 & 401.17 & 5.33 & 431.88 & 3.39 \\ Permanent Crops & **741.13** & **14.22** & 57.14 & 0.76 & 798.27 & 6.27 \\ Pioneer & 21.03 & 0.40 & 19.84 & 0.26 & 40.87 & 0.32 \\ _Psidium_—Guava & 1304.19 & 25.03 & 1543.43 & 20.50 & 2847.61 & 27.35 \\ _Rubus_—BlackBerry & 5.95 & 0.11 & 0.00 & 0.00 & 5.95 & 0.05 \\ Silvopasture & 504.38 & 9.68 & 13.85 & 0.18 & 518.23 & 4.07 \\ _Syzygium_—Ponnarosa & 59.67 & 1.15 & 19.36 & 0.26 & 79.03 & 0.62 \\ Transitory Crops & 162.68 & 3.12 & 10.59 & 0.14 & 173.27 & 1.36 \\ \hline Total & 5210.30 & 100.00 & 7529.33 & 100.00 & 12,739.64 & 100.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Land cover surface area and percentages of agricultural area and buffer zone occupied per category for Isabela island. *  
Figure A2: Distribution of invasive vegetation in and around the agricultural zones of the inhabited islands of the Galapagos. Figure A3: Distribution of pastures in and around the agricultural zones of the inhabited islands of the Galapagos. Figure A4. Distribution of food crops in and around the agricultural zones of the inhabited islands of the Galapagos.
# Jamming Detection and Suppression Technique for OTFS Systems in an AWGN Channel
## 2 System Model  
### OTFS System  
The diagram of the OTFS system is illustrated in Figure 1. At the transmitter end, the modulated information symbols \(\mathbf{X}[m,n]\) are placed in the delay-Doppler domain grid \(\Gamma=\left\{\left(\frac{m}{M\Delta f},\frac{n}{NT}\right),m=0,\ldots,M-1,n=0, \ldots,N-1\right\}\), where \(M\), \(N\), \(\Delta f\), and \(T\) denote the delay dimension, Doppler dimension, subcarrier spacing, and time sampling interval, respectively.  
Using an inverse symplectic finite Fourier transform (ISFFT), the symbols \(\mathbf{X}[m,n]\) are mapped to samples \(\mathbf{X}_{\mathrm{TF}}[l,k]\) in the time-frequency domain as  
\[\mathbf{X}_{\mathrm{TF}}[l,k]=\frac{1}{\sqrt{NM}}\sum_{n=0}^{N-1}\sum_{m=0}^{M- 1}\mathbf{X}[m,n]e^{2\pi\left(\frac{\pi}{N}-\frac{m!}{M}\right)}, \tag{1}\]  
where \(l=0,1,\ldots,M-1\) and \(k=0,1,\ldots,N-1\). The ISFFT refers to 2D transformation, where each column of \(\mathbf{X}[m,n]\) undergoes an \(M\)-point FFT, followed by an \(N\)-point IFFT on each row. Next, a Heisenberg transform is employed to convert the time-frequency signal \(\mathbf{X}_{\mathrm{TF}}[l,k]\) to a continuous time-domain signal as  
\[s(t)=\sum_{k=0}^{N-1}\sum_{l=0}^{M-1}\mathbf{X}_{tf}[l,k]g_{tx}(t-kT)e^{2\pi t \Delta f(t-kT)}, \tag{2}\]  
where \(g_{tx}\) is the transmit shaping filter. Considering an AWGN channel, the received signal can be expressed as \(r(t)=s(t)+w(t)\), where \(w(t)\sim\mathcal{CN}(0,\sigma^{2})\) denotes the noise component.  
At the receiver end, the received signal \(r(t)\) is first match-filtered using a receive shaping filter \(g_{rx}\) as follows:  
\[Y(f,t)=\int r\big{(}t^{\prime}\big{)}g_{rx}^{*}(t^{\prime}-t)e^{-2\pi f(t^{ \prime}-t)}dt^{\prime}. \tag{3}\]  
Sampling on \(Y(f,t)\) forms the time-frequency domain received samples matrix as  
\[\mathbf{Y}_{\mathrm{TF}}[l,k]=Y(f,t)|_{f=l\Delta f,t=kT}, \tag{4}\]  
for \(l=0,\ldots,M-1\) and \(k=0,\ldots,N-1\). These two steps are collectively known as the Wigner transform, which samples the received signal and organizes it into a matrix. Finally, a symplectic fast Fourier transform (SFFT) is applied on \(\mathbf{Y}_{\mathrm{TF}}[l,k]\) to recover the original modulated information symbols as  
\[\mathbf{Y}[m,n]=\frac{1}{\sqrt{NM}}\sum_{k=0}^{N-1}\sum_{l=0}^{M-1}\mathbf{Y} _{tf}[l,k]e^{-2\pi\left(\frac{\pi}{N}-\frac{m!}{M}\right)}. \tag{5}\]
## 4 The Experiments
### Feature Importance Analysis  
The complement to the conducted analysis is the evaluation of the attribute importance. As mentioned earlier, attribute importance can be assessed using tree-based algorithms, including Mean Decrease in Impurity (MDI). This measure is generated with results obtained from the training data, which can lead to overestimation of the importance of individual attributes. Therefore, in this work, the permutation feature importance measure was used to assess attribute importance [28; 29]. This measure is model-independent, allowing it to evaluate tree-based models, as well as kNN and neural networks. This method measures the contribution of each feature to the statistical performance of the fitted model by randomly shuffling the values of a single feature and observing the impact on the model's prediction quality. By breaking the association between the feature and the labels, we determine how much the model relies on that particular feature while keeping the distribution of the feature unchanged. Moreover, this method can also be applied to test data, thus avoiding the issue of overestimating feature importance due to model overfitting, as it often appears for the MDI feature importance estimator.
###### Abstract  
Recently, STDenseNet (SpatioTemporal Densely connected convolutional Network) showed remarkable performance in predicting network traffic by leveraging the inductive bias of convolution layers. However, it is known that such convolution layers can only barely capture long-term spatial and temporal dependencies. To solve this problem, we propose Attention-DenseNet (ADNet), which effectively incorporates an attention module into STDenseNet to learn representations for long-term spatio-temporal patterns. Specifically, we explored the optimal positions and the types of attention modules in combination with STDenseNet. Our key findings are as follows: i) attention modules are very effective when positioned between the last dense module and the final feature fusion module, meaning that the attention module plays a key role in aggregating low-level local features with long-term dependency. Hence, the final feature fusion module can easily exploit both global and local information; ii) the best attention module is different depending on the spatio-temporal characteristics of the dataset. To verify the effectiveness of the proposed ADNet, we performed experiments on the Telecom Italia dataset, a well-known benchmark dataset for network traffic prediction. The experimental results show that, compared to STDenseNet, our ADNet improved RMSE performance by 3.72%, 2.84%, and 5.87% in call service (Call), short message service (SMS), and Internet access (Internet) sub-datasets, respectively.  
spatiotemporal data prediction; DenseNet; attention mechanism +
Footnote †: journal: Medical Image Analysis
## 3 HJ-1B images simulation
### View geometries and atmospheric conditions  
The scan angle of HJ-1B may reach a maximum of 29\({}^{\circ}\) from the nadir, and the solar zenith angles were assumed to change from 0\({}^{\circ}\) to 60\({}^{\circ}\). The latter is important in MIR channel data simulation because solar zenith angle can affect the performance of the algorithm. The satellite altitude was fixed to 650 km. Six standard atmospheric conditions, which include tropical, temperate, mid-latitude and sub-Arctic climates, together with 23 km visibilities, were selected in the MODTRAN 4 based simulation. A brief description of MODTRAN 4 is given in the next section.
# Fast and Reliable Burst Data Transmission for Backscatter Communications
## 3 Challenge and Motivation
### Challenge 2: Variable Harvesting Rate  
The energy-harvesting rate has a significant impact on communication goodput, since higher harvesting rate means that more energy can be used for data transfer. Figure 2 shows the trend of energy-harvesting rate for both theoretical and empirical measurements as the sleep time between transmissions. One might expect to collect more energy by increasing the charging time. However, for longer sleep durations, the energy-harvesting rate drops to zero.  
Next, we will explain this phenomenon by looking at how capacitors buffer energy.  
The charging process of the capacitor can be described the formula of voltage variation across the capacitor:  
\[V=V_{\max}(1-e^{-t_{s}/\tau}) \tag{1}\]  
where \(t_{s}\) is the sleep time, \(\tau\) is the RC circuit time constant, and \(V_{\max}\) is the maximum voltage achievable by the capacitor under the current energy-harvesting conditions. The energy-harvesting rate follows the following formula:  
\[H=C\times V_{\max}^{2}\times\tau^{-1}(1-e^{-t_{s}/\tau})e^{-t_{s}/\tau} \tag{2}\]  
When the harvesting conditions are constant (i.e., \(V_{\max}\) and \(\tau\) are fixed), \(H\) is a concave function of \(t_{s}\). When the energy-harvesting condition changes, both \(V_{\max}\) and \(\tau\) change, so the optimal operating point also changes. When the tag's capacitor stores more energy than the threshold, the energy-harvesting  
Figure 2: Energy-harvesting rate curve.  
rate drops sharply from a high level to near zero. This means that, after getting enough energy, if the tag does not perform the task immediately, the energy-harvesting rate will drop sharply. Therefore, in order to optimize goodput, it is important to adapt to current energy-harvesting conditions and to keep track of the maximum harvesting points.
## 3 Test and Verification
### The Method of Obtaining Discharge Current and Judging Corona Inception  
The corona cage test system and numerical simulation model were established to simulate the process of corona discharge from the tip defect on the surface of the wire to the cathode. The corona discharge characteristics such as corona inception voltage and pulse current amplitude measured in our experiment can be used to verify the corona characteristics obtained by the simulation model under the same conditions.  
When the applied voltage was less than the corona inception voltage, the amplitude of the discharge current was very small and no pulse appeared. When the applied voltage was greater than or equal to the corona inception voltage, the discharge current exhibited periodic and pulsed characteristics. The reason for the periodic current is that the positive ions hit the surface of cathode to produce secondary electrons, which moved to the high-electric-field-strength region and then triggered a new corona discharge with continuous voltage. The method of obtaining the discharge current in the simulation was as follows: the cathode boundary of model was set to metal contact, and a circuit terminal was placed at the boundary to introduce the calculation equation of current. Finally, the corona current \(Ic\) can be obtained by calculating the integral of current density along the closed boundary \(L\). The calculation equation is as shown in Equation (9):  
\[\int_{L}(n\cdot J_{\mathrm{i}}+n\cdot J_{\mathrm{e}}+n\cdot J_{\mathrm{d}})\ dl=I_{\mathrm{c}}, \tag{9}\]  
where \(n\) is the normal vector of the boundary, \(J_{\mathrm{i}}\) is the density of the conduction current of ions, \(J_{\mathrm{e}}\) is the density of the conduction current of electrons, and \(J_{\mathrm{d}}\) is the density of the displacement current caused by time-varying potential.  
In the corona cage test, a sampling box of leakage current was used to detect the current signal. Using a high-precision non-inductive resistor, the current signal was converted into a voltage signal, which was then transmitted through a high-frequency cable to the data acquisition card with a minimum sampling interval of 2 ns.  
In order to obtain the inception voltage of corona discharge in the simulation and experiment, it was necessary to identify the moment of corona occurrence. The specific judgment method uses the characteristics of discharge current, which were as follows in our study: the applied voltage increased following a gradient of 0.5 kV each time and the simulation result and actual sampling waveform of discharge current were obtained; when the current showed pulse characteristics and the pulse amplitude was greater than 1.5 \(\mu\)A, it was considered that the corona began and the applied voltage at that moment was considered the corona inception voltage.
# A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector
## 5 Conclusions  
The parabolic reflector, a physical amplification, was proposed. It focuses the target speech considerably from noisy speech and provides a heterogeneous channel. The channel captures relatively clean data to estimate masks. The source-to-array distance is so long so that it is difficult to collect effective signals. The physical amplification model reduces the system complexity and provides favorable data. Moreover, by utilizing complementary information between the parabolic reflector and the microphone array, the proposed signal fusion system achieves better performance for noise and speech interference mixed conditions, especially in low SNR environments. In the future, we will extend the current work in several ways, such as upgrading a neural beamformer module to track more phase information from microphone arrays. Our most important future work is to acquire RIR by measuring and utilizing the framework in far-field multi-talker microphone array speech enhancement.  
Conceptualization, J.S. and Y.G.; methodology, J.S.; software, J.S.; validation, Y.G., and T.Z.; formal analysis, C.J.; investigation, T.Z.; resources, T.Z.; data curation, J.S.; writing--original draft preparation, J.S.; writing--review and editing, Y.G., T.Z.; visualization, J.S.; supervision, T.Z.; project administration, J.S.; funding acquisition, J.S., B.D. and Y.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Zhejiang Lab's International Talent Fund for Young Professionals.  
The authors want to express their acknowledgements to the Zhejiang Lab's International Talent Fund for Young Professionals.  
The authors declare no conflict of interest and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# A PINN-Based Nonlinear PMSM Electromagnetic Model Using Differential Inductance Theory
## 3 Nonlinear Electromagnetic Modeling of PMSM
### The Optimal Linear Approximation Model (OLAM)  
OLAM regards the harmonics in the periodic excitation current as small-signal components \(\Delta\mathbf{i}_{dq0}\), and the flux linkage increment \(\Delta\mathbf{\psi}_{dq0\_AM}\) can be obtained by using the tangent-plane function and the differential inductance can be calculated in the approximate linear region by using the differential inductance in the light of the characteristics of the linear electromagnetic structure [25]. Figure 2 shows the locally linearized flux linkage surface. The differential magnetic energy \(\Delta W_{dq0\_AM}\) can be calculated in the approximate linear region, and then the localized linearized flux linkage and magnetic energy model containing the differential part can be built at each non-FEA working operating point. According to the flux linkage Equation (25) and magnetic energy Equation (26) the \(\Psi^{\prime}_{dq0\_AM}\) and \(W^{\prime}_{dq0\_AM}\) in the model can be computed by the following equation [14]:  
\[\left\{\begin{array}{l}\Psi^{{}^{\prime}}_{dq0\_AM}=\Psi^{\text{FEA}}_{dq0\_ AM}+\mathbf{L}^{\text{FEA}}_{dq0}\Delta\mathbf{i}_{dq0}\\ W^{\prime}_{dq0\_AM}=W^{\text{FEA}}_{dq0\_AM}+\mathbf{i}^{{}^{\prime}T}_{dq0} \Delta\mathbf{i}_{dq0}+\frac{1}{2}\Delta\mathbf{i}^{T}_{dq0}\Delta\mathbf{i} _{dq0}\end{array}\right. \tag{34}\]  
where \(\Delta\mathbf{i}_{dq0}=\mathbf{i}^{{}^{\prime}}_{dq0}-\mathbf{i}^{\text{FEA}}_ {dq0}\), the superscript FEA stands for the value of each physical quantity taking the value at the FEA operating point near the USE operating point. When \(\Delta\mathbf{i}_{dq0}\to 0\), the model becomes an exact representation of the nonlinear mathematical model, which not only includes spatially non-sinusoidal distributions of the inductance and the flux linkage, but at the same time embodies the saturation characteristics of the magnetic circuit in terms of the differential inductance. However, when the current excitation deviates from the known operating point, \(\Delta\mathbf{i}_{dq0}\) increases, the local linearization is no longer valid and the model error increases. In essence, the traditional interpolation method realizes function fitting by selecting the basis function, and the differential properties of the resulting fitted function are only related to the basis function, so it is difficult to realize the fitting of complex field features. Limited by the FEA points, the above method is difficult to realize the approximation of higher-order derivatives and partial derivatives, and it is not possible to further increase the constraints to improve the numerical computation accuracy, which also brings difficulties for the subsequent adoption of the optimization method of gradient descent.  
Figure 1: Relationship between flux linkage, magnetic energy and electrical angle in the GLM. The shaded area represents the variation in magnetic energy induced by changes in electrical angle.
# Classroom Expression Recognition Based on Deep Learning
## 3 Theoretical Framework of Facial Expression Recognition
### Multi-Attention Fusion Network for Expression Recognition
#### 3.4.2 MAF-ER Algorithm Structure  
The network model comprises four primary components: the feature extraction network, the channel attention mechanism, the spatial attention mechanism, and the self-attention mechanism. The feature extraction network is responsible for extracting facial features, while the combination of channel and spatial attention mechanisms allows the network to focus on salient facial features. The self-attention mechanism helps suppress uncertain samples caused by factors such as lighting, occlusion, and variations in body posture, thereby enhancing the model's expressive capability.  
The MAF-ER algorithm process can be summarized in the following steps:  
**Feature Extraction**: Facial images are processed through a ResNet-34 network that integrates channel and spatial attention mechanisms for feature extraction. This network effectively abstracts important information from the images, providing valuable facial expression features.  
**Self-Attention Mechanism**: This mechanism consists of three steps to further optimize the feature extraction results:  
* Weighting Process: The extracted expression features are assigned weights to emphasize key characteristics, enhancing the model's expressive power.
* Regularization Process: Features are sorted based on their weights, categorizing them into high and low importance groups. This enables the model to focus on crucial features while reducing noise interference, thereby improving robustness and generalization capabilities.
* Relabeling Process: Uncertain samples in the low importance group are relabeled to identify more definitive labels, further improving the model's recognition accuracy and stability.  
Figure 9: Feature extraction network based on ResNet-34.  
By employing these steps, the MAF-ER algorithm can effectively address expression recognition tasks under complex conditions such as varying lighting and occlusion, thereby enhancing the network's accuracy, robustness, and reliability. As illustrated in Figure 10, the facial image is first input into the ResNet-34 network, which integrates channel and spatial attention mechanisms for the efficient extraction of key information. Subsequently, the self-attention mechanism optimizes the feature processing through the weighting, regularization, and relabeling processes. During the weighting process, emphasis is placed on reinforcing key features; in the regularization process, feature weights are sorted to differentiate importance; and in the relabeling process, uncertain samples are reassessed to further improve recognition accuracy and the stability of the model.
# Optimized Dynamic Deployment of UAVs in Maritime Networks with Route Prediction
## 2 Related Work
These models are used for cases without and with reference trajectories, respectively, effectively reducing prediction errors. Ref. [32] proposes a ship trajectory-prediction algorithm called Deep Bidirectional Information Empowerment, which utilizes an integrated network and attention mechanism. It combines the strengths of bidirectional long short-term memory and bidirectional gated recurrent unit networks, optimizing the weights of both network units through the attention mechanism to enhance prediction accuracy and efficiency. Considering the multi-density distribution characteristics of trajectory data, a multi-density adaptive trajectory-clustering algorithm is proposed in ref. [33], which determines the input parameters adaptively and introduces a trajectory direction identification mechanism to make it perform better in dealing with complex trajectory-clustering problems. To address the heterogeneity of vessel motion patterns, ref. [34] proposes a generalized vessel trajectory-prediction method based on clustering. This method uses historical AIS data to cluster route patterns for each vessel type, considering spatial and heading attributes as well as environmental factors, thereby improving the accuracy and computational efficiency of trajectory prediction. To overcome the reliance on historical position data while ignoring key factors like speed and heading in voyage trajectory prediction, ref. [35] proposes a novel ship trajectory-prediction model based on a sequence-to-sequence structure. This model integrally considers multifaceted ship information and improves prediction accuracy through a multi-semantic encoder and a type-oriented decoder. User ships are mobile when operating at sea, and route prediction for mobile users can effectively anticipate their future locations. This aids in the efficient deployment of communication nodes and enables dynamic coverage for less computationally demanding or non-urgent tasks. The lack of existing research in this area will affect the effectiveness of dynamic coverage to a certain extent. Therefore, the research in this paper focuses on studying a model that can realize the prediction of ship trajectories, and at the same time can improve the coverage of communication as well as the UAV flight trajectory, reduce the communication delay, and improve the quality of service for users.