esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Comparison of the Models
### Excitation of EGWs  
As a starting point, the accuracy of the SAHA in the case of an anisotropic waveguide is illustrated. Figure 10 demonstrates total displacement amplitudes calculated for \([0^{\circ},90^{\circ}]_{\rm 2s}\) composite using the SAHA with different number \(N_{\rm e}\) of elements discretizing the PWTs along \(x_{1}\)-axis and the conventional FEM (COMSOL Multiphysics 6.0; COMSOL AB, Stockholm, Sweden). In the latter, perfectly matched layers are employed to simulate an infinite waveguide, and 2,083,295 degrees of freedom (DoF) in total have been used with quintic polynomial approximation. Such a big amount of DoF is conditioned by the fact that each sublayer was discretized at least with three FE along its thickness, and very fine mesh in the contact areas between the PWTs and the waveguide was employed. Here and further on for the numerical results presented, PWTs of length \(a=10\) mm and thickness \(d=0.2\) mm with mechanical and piezoelectric properties listed in Table 3 have been used (both for the actuator and sensor). One can see that only \(N_{\rm e}=10\) of elements are enough in the SAHA to achieve a good accuracy.  
In Table 5, the computational times required to obtain the results from Figure 10 employing a multi-layered model are presented. For comparison, analogous values when the waveguide is simulated with the SLWH model are also provided (the corresponding COMSOL model consists of 518,745 DoF). The calculations have been performed using a custom work station equipped with Intel Xeon CPU E5-2660 v4 and 240 GB of RAM. Only a single CPU-core is employed by the SAHA program code (\(N_{\rm e}=10\) elements), while all 14 physical CPU-cores are utilized by COMSOL. Table 5 illustrates the fact that the employment of the single layer model sufficiently reduces the computational costs.  
The SAHA allows for investigating the elastic wave energy transfer from the source of motion into the elastic structure. The wave energy transferred into the laminated structure due to the action of the PWT \(\hat{V}^{\rm(a)}\) is calculated in terms of the vertical component \(e_{2}\) of the time-averaged power density vector (see Ref. [37]):  
\[E_{0}=\int\limits_{S_{0}^{\rm(a)}}e_{2}(x_{1},H)\mathrm{d}x_{1}. \tag{17}\]  
Figure 10: Displacement amplitudes \(|\mathbf{n}|(x_{1},0)\) on the surface of multi-layered waveguide \([0^{\circ},90^{\circ}]_{\rm 2s}\) with an actuator and a sensor at frequencies \(f=200\) kHz **(a)** and \(f=400\) kHz **(b)**.  
Corresponding values of \(E_{0}(f)\) induced by the piezoelectric actuator mounted on the surface of the multi-layered waveguide are shown in Figures 11 and 12 for \([0^{\circ},90^{\circ}]_{28}\) and \([90^{\circ},0^{\circ}]_{28}\), respectively. For both waveguides, the results provided by homogenized models are comparable with the data from the multilayered one up to the frequency \(f=500\) kHz. Moreover, when \(f<200\) kHz, good quantitative coincidence is also observed for the \([0^{\circ},90^{\circ}]_{28}\) laminate. At the same time, for \([90^{\circ},0^{\circ}]_{28}\) structure, \(E_{0}(f)\)
# LSTM Accelerator for Convolutional Object Identification
## 4 Experiments and Results
### Evaluation
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **Accuracy (Percentage)** & **Loss (Cross-Entropy)** & **Time (s)** \\ \hline \multicolumn{4}{c}{**Epoch = 1**} \\ \hline C1 + C2 & 21 & 2.29 & 211 \\ C1 + C2 + B\_N & 40 & 1.85 & 201 \\ C1 + C2 + B\_N + LSTM & 31 & 1.81 & 122 \\ C1 + C2 + C3 & 14 & 2.27 & 257 \\ C1 + C2 + C3 + B\_N & 38 & 1.91 & 225 \\ C1 + C2 + C3 + B\_N + LSTM & 36 & 1.85 & 132 \\ \hline \multicolumn{4}{c}{**Epoch = 5**} \\ \hline C1 + C2 & 25 & 2.28 & 1037 \\ C1 + C2 + B\_N & 52 & 1.54 & 1051 \\ C1 + C2 + B\_N + LSTM & 43 & 1.53 & 582 \\ C1 + C2 + C3 & 31 & 2.29 & 1291 \\ C1 + C2 + C3 + B\_N & 53 & 1.37 & 1311 \\ C1 + C2 + C3 + B\_N + LSTM & 52 & 1.36 & 652 \\ \hline \multicolumn{4}{c}{**Epoch = 10**} \\ \hline C1 + C2 & 25 & 2.28 & 2035 \\ C1 + C2 + B\_N & 59 & 1.64 & 2152 \\ C1 + C2 + B\_N + LSTM & 53 & 1.44 & 1205 \\ C1 + C2 + C3 & 50 & 2.18 & 2415 \\ C1 + C2 + C3 + B\_N & 57 & 1.9 & 2407 \\ C1 + C2 + C3 + B\_N + LSTM & 60 & 1.24 & 1335 \\ \hline \multicolumn{4}{c}{**Epoch = 20**} \\ \hline C1 + C2 & 27 & 2.27 & 4107 \\ C1 + C2 + B\_N & 64 & 1.65 & 4318 \\ C1 + C2 + B\_N + LSTM & 63 & 1.4 & 2594 \\ C1 + C2 + C3 & 55 & 2.15 & 5003 \\ C1 + C2 + C3 + B\_N & 66 & 1.81 & 4995 \\ C1 + C2 + C3 + B\_N + LSTM & 63 & 1.22 & 2809 \\ \hline \multicolumn{4}{c}{**Epoch = 40**} \\ \hline C1 + C2 & 30 & 2.19 & 8265 \\ C1 + C2 + B\_N & 69 & 1.53 & 8719 \\ C1 + C2 + B\_N + LSTM & 67 & 1.38 & 5187 \\ C1 + C2 + C3 & 59 & 2.11 & 10,089 \\ C1 + C2 + C3 + B\_N & 72 & 1.51 & 10,105 \\ C1 + C2 + C3 + B\_N + LSTM & 70 & 1.19 & 5817 \\ \hline \multicolumn{4}{c}{**Epoch = 50**} \\ \hline C1 + C2 & 33 & 2.19 & 10,320 \\ C1 + C2 + B\_N & 72 & 1.5 & 10,952 \\ C1 + C2 + B\_N + LSTM & 70 & 1.33 & 6404 \\ C1 + C2 + C3 & 65 & 2.09 & 12,631 \\ C1 + C2 + C3 + B\_N & 76 & 1.38 & 12,708 \\ C1 + C2 + C3 + B\_N + LSTM & 73 & 1.07 & 7158 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy, loss and time for different numbers of epochs (20% sample) for the CIFAR10 dataset. Figure 2: Accuracy (percentage) for different numbers of epochs for CIFAR10 and MNIST. Figure 3: Time (s) for different numbers of epochs for CIFAR10 and MNIST.
# Enhancing Detection of Arabic Social Spam Using Data Augmentation and Machine Learning
## 4 Experiments
### Experiment 3: Augmented Dataset  
In our dataset, 5 k tweets were randomly selected for the labeling process, leading to 420 spam tweets and 4580 non-spam tweets. Using the data augmentation process in Section 3.2, the number of spam tweets were doubled four times, resulting in 1648 spam tweets.  
Table 5 and Figure 6 show the obtained results for the augmented dataset. These findings showed that experimenting with data augmentation achieved the maximum macro F1 score value of 89% for the LinearSVC classifier. While the LR classifier Macro F1 is 87%, and the lowest classifier is NB, with Macro F1 of 85%. In addition, the LinearSVC surpasses the other tested classifiers with 96% of the AUC score. While LR's AUC score is 95%, and the lowest classifier is NB, with an AUC score of 85%. In terms of accuracy, the LinearSVC surpasses the other tested classifiers with 92.1% of correctly classified instances. While LR's Accuracy is 91.3%, and the lowest classifier is NB, with an accuracy of 88.5%. The accuracy range was between 92.1% and 88.5%.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{6}{c}{**Non-Augmented Dataset-All Features**} \\ \cline{2-9}  & **Class** & **Precision** & **Recall** & **F1** & **Macro-F1** & **Accuracy** & **AUC** \\ \hline \multirow{2}{*}{NB} & 0 & 0.52 & 0.16 & 0.25 & \multirow{2}{*}{0.54} & \multirow{2}{*}{0.74} & \multirow{2}{*}{0.63} \\  & 1 & 0.76 & 0.95 & & & & \\ \hline \multirow{2}{*}{LR} & 0 & 0.24 & 0.59 & 0.33 & \multirow{2}{*}{0.65} & \multirow{2}{*}{0.922} & \multirow{2}{*}{0.79} \\  & 1 & 0.98 & 0.93 & & & & \\ \hline \multirow{2}{*}{SVC} & 0 & 0.16 & 0.65 & 0.26 & \multirow{2}{*}{0.61} & \multirow{2}{*}{0.923} & \multirow{2}{*}{0.77} \\  & 1 & 0.99 & 0.93 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Non-augmented dataset with all features—Experiment 2 confusion matrix.  
Figure 5: Plot of the non-augmented dataset with all feature results (Experiment 2).
## 3 Results
### Shorter Prediction Length  
A paired \(t\)-test was used to compare the results of both methods for three different channels (O1, O2, and Oz) at two time points (64 ms and 128 ms). Figure 4 depicts the mean values of [PERSON]'s \(Z\) value (PLVrz) for all the subjects. Part A shows the results for the AR model, and part B reflects the PLVrz values of the LMS-based AR model. The PLVrz decays slowly, indicating that the prediction performance decreases with time. At 128 ms, there is a significant difference between the two methods where the AR performs better than the LMS-based AR model. For approximately 700 trials per subject across 21 subjects, PLVrz \(>\) 2.9957 is considered statistically significant with respect to the \(p<0.05\) significance level.  
Figure 4: [PERSON]’s PLV for the AR model and the LMS-based AR model, ns represents not significant \(p\)-value while Asterisk (*) indicates significant \(p\)-value. (**A**) Mean of [PERSON]’s Z value (PLVrz) at the current time point (64 ms); (**B**) Mean PLVrz at the 128 ms. Error bars indicate the standard deviation of the mean.  
Figure 3: Diagram of calculation method of phase-locking value (PLV) [42] between origanl and predicted data segments. Intertrial variability of phase differences between the original and predicted data segments from the same electrodes was calculated. PLV was calculated for three different electrodes (O1, O2, and Oz) and at three different time points (0, 128, 256 ms), respectively.
# Cross-Modal Manifold Propagation for Image Recommendation
## 4 Experimental Analysis
### Experimental Comparison  
To verify effectiveness, the recommendation performance of the proposed CMP was further compared to that of NBI [14], CF [40], CB [11], CBG [41], CRC [42], SVM [43], hybrid [21], PMR, and MMR [23]. Figure 8 presents the _precision_ and _MAP_ of CMP as compared with that of state-of-the-art methods. The proposed CMP consistently achieved better performance than that of its competitors in terms of _precision_ and _MAP_. Performance demonstrates that CMP learnt better user-image correlations by incorporating user interest and semantic visual distributions in recommendation. The proposed CMP recommendation method also took greater advantage in cases of recommending the front images. Such performance greatly benefits from the semantic visual propagation of users' records to  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **UMP** & **VMP** & **CMP** \\ \hline _MRR_ & 25.6\% & 32.0\% & **45.1\%** \\ \hline \hline \end{tabular} Bold number represents best performance by _MRR_.  
\end{table}
Table 1: Recommendation performance of CMP as compared with that of UMP and VMP by _MRR_.  
Figure 7: _Precision_ and _MAP_ of top-N recommendation for proposed cross-modal collaborative manifold propagation (CMP) as compared with single-modal user interest manifold propagation (UMP) and visual manifold propagation (VMP).  
involve semantic correlation in user-image inference. The proposed CMP performed better than CF and NBI, which illustrates the effectiveness of visual semantics in cross-modal collaborative learning. CMP outperforms MMR in terms of _precision_ and _MAP_, which demonstrates the collaborative ability of user interest based manifold propagation in capturing global interest trend for recommendation. Lack of investigating users' interests, CB recommends images similar to users' records, limiting its performance.  
Table 2 provides a global evaluation of the proposed CMP recommendation by _MRR_ compared with other methods. It shows the proposed CMP achieved better _MRR_ than the others did, and implied the correct recommendation taking front order in the list. Regarding _MRR_, CMP achieved 45.0%, a 13.0% improvement on the second 32% of MMR. This also verifies the effectiveness of collaborative learning over user interest and visual semantics in CMP. CMP thus effectively utilizes semantic the visual distribution of images and global user-image correspondence by collaborative manifold propagation, which successfully recommends high-quality and interest-aware images for users.
# Hypoplastic Modeling of Soil-Structure Contact Surface Considering Initial Anisotropy and Roughness
## 2. Fundamental of the Hypoplastic Constitutive Model for the Soil-Structure Contact Surface
### Contact Surface Thickness  
Within the constitutive model of the contact surface outlined in this document, it is essential to specify the shear zone thickness to determine the relationship between strain and displacement. In this context, we utilize the relationship formula suggested by [PERSON] [23] and [PERSON] [24]. The strain at the contact surface is computed on the basis of the predefined shear zone thickness:  
\[\tan\gamma_{i}=\frac{u_{i}}{d_{s}} \tag{47}\]  
where \(u_{i}\) is displacement at the contact surface, and \(d_{s}\) is the assumed contact surface thickness. The geometric relationship is shown in Figure 5. The value of contact surface thickness depends on factors such as density, average particle size, and contact surface roughness. According to the research of [PERSON] [23], it can be approximated as \(5\sim 10d_{50}\).
## 6 Opportunities and Future Research Directions
### Regulation  
The first opportunity and future work are more so action points for regulators rather than engineers. The big drawback of these networks is the hostile interference present in the unlicensed bands. Although LoRaWANs are envisioned with gateways at ground level, different proposals have suggested putting gateways in the air or even launching them into space [114; 115; 116]. Although it might be technically achievable, these networks will see many more devices due to the line of sight and see more collisions than there are gateways on the ground. For these scenarios and the LoRaWANs on the ground, it is strongly recommended to free up the spectrum for these networks, because there is a real need for these powerful networks as a good representative for LPWANs. The dedicated spectrum can also allow relaxed constraints for gateways such that gateways can transmit more downlink messageswith a higher power, making the acknowledgements more reliable. Alternatively, with the spectrum freed up for these networks, improved spectrum management of unlicensed or shared bands is also an interesting route. With (private) spectrum management, different LPWANs, including SigFox and LoRa, can be rolled out in different or overlapping regions, allowing for other configurations and potentially sharing a spectrum. A third party could oversee the rules at several locations and verify if everybody is playing according to the rules.
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 3 Methodology  
### Overview of the Proposed RS-DETR Algorithm  
In this study, we propose a novel RS-DETR model to improve the performance of remote sensing image object detection. Based on the RT-DETR model, the proposedmethod makes three effective improvements, including integrating CGA [36] into the attention-driven feature interaction (AIFI) module to improve interaction between features, proposing an EBiFPN to enhance multi-scale feature fusion, and designing a new bounding box regression loss function, Focaler-GloU, to improve the detection accuracy of small and overlapping objects. The architecture of RS-DETR is shown in Figure 1.
# Direct-Write Spray Coating of a Full-Duplex Antenna for E-Textile Applications
## Materials and Methods
### Characterization of the Antenna  
A buckling test was conducted with a commercial tensile tester (Instron, model 5566) to compare the flexibility of different substrates. The sample (8 cm\(\times\) 8 cm) was gripped between two claps, where the lower clamp was fixed while the upper clamp moved at 50 mm/min. The tested distance between the two clamps was 70 mm.  
The performance of the assembled antenna was tested with Agilent E5071C VNA. The antenna was connected to a VNA using two coaxial cables with SMA connectors to measure the reflection coefficient and mutual coupling. The radiation test was conducted in an anechoic chamber, as shown in Supplementary Figure S1. More information about the radiation test can be found in Supplementary Figure S2.
# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 2 Signal Model and Potential Error Sources  
The signal performance obstructed in the challenged environment results in limited satellite availability, continuity and poor signal quality. This section discusses the potential error sources and signal model under a challenged environment.
## 3 The Proposed Scheme
### Cycle Maintenance  
Each cell head on the cyclic chain plays an important role in transmitting data. The data transmission path is actually maintained by each cell head. As long as all cell heads remain active, the data transmission path will work normally. When the residual energy of a cell head is below a threshold \(E_{head}\)  
Figure 3: Scheme for data gathering in each round.  
it will broadcast a request to reelect a new cell head. All ordinary nodes with the residual energy above \(E_{head}\) will take part in the competition utilizing the cell head election procedure described above.  
It is possible for a cell head to transmit data to a void cell, where all nodes are dead. A void cell is detected by a timeout. When a cell head never receives acknowledgement _Ack_cycle_ from the uplink cell head for a certain time, it broadcasts a _Uplink_poll_ message containing the IDs [\(X_{m}\), \(Y_{m}\)] and [\(X_{d}\), \(Y_{d}\)] of the uplink cell and itself. The cell head with downlink the same as [\(X_{m}\), \(Y_{m}\)] will set its downlink to be [\(X_{d}\), \(Y_{d}\)] and reply a _Uplink_reply_ message containing its own ID [\(X_{u}\), \(Y_{u}\)]. After receiving _Uplink_reply_, the cell head with ID [\(X_{d}\), \(Y_{d}\)] will set its uplink to be [\(X_{u}\), \(Y_{u}\)] and transmit the aggregated data to the cell head of the new uplink cell. For example, consider the cell [1, 2] is a void cell, where all nodes are dead, as shown in Figure 4. When the cell head of cell [1, 1] never receives acknowledgement _Ack_cycle_ from the uplink cell [1, 2] for a certain time, it broadcasts a _Uplink_poll_ message with the IDs [1, 2] and [1, 1] of the uplink cell and itself. Owing to the cell head of cell [2, 2] with downlink the same as [1, 2], it will set the downlink to be [1, 1] and reply a _Uplink_reply_ message containing its own ID [2, 2]. After receiving _Uplink_reply_, the cell head with ID [1, 1] will set its uplink to be [2, 2] and transmit the aggregated data to the cell head of the new uplink cell [2, 2].
## 6 Discussion and Future Considerations
### Traffic Prioritization  
Proposed trajectory optimization and scheduling algorithms should consider node prioritization, especially in applications with node heterogeneity, where some nodes hold more critical or larger-sized packets. For instance, mechanisms must be put in place to factor in the priority of sensor node traffic for dynamic trajectory scheduling. In other words, the UAV might need to determine its trajectory to serve higher priority nodes dynamically. Related work in this area includes [84], whereby sensor nodes can be divided into frames and assigned different priorities for transmission to manage or reduce the packet loss rate. Such prioritization can be considered in other different architectural setups.
## 4 Results
### Statistical Significance Tests on the Results  
Normal distribution tests were performed on the PSNR and SSIM values of all test images using a one-sample Kolmogorov--Smirnov test (KS test) [80]. Comparing the empirical cumulative distribution function (CDF) of measured data with the predicted CDF of a normal model with normal-distribution-fitted parameters, the KS test rejected the null hypothesis at the 0.05 significance level, which indicated that the PSNR and SSIM values were not normally distributed. Since the PSNR and SSIM did not follow a normal distribution, a Wilcoxon signed-ranks test was used to test the significance of the improvement in PSNR and SSIM values for the proposed AB-U-Net reconstruction [81]. As shown in Figure 7, the AB-U-Net method showed a statistically improvement in reconstruction performance, with PSNR and SSIM significantly higher than those from TR, U-Net and Attention U-Net reconstructions (\(p<0.05\)). In addition, the U-Net and attention U-Net also provided significant improvements in reconstruction performance than the TR method (\(p<0.05\)). However, without a statistically significant improvement in the SSIM value (\(p>0.05\)), the attention U-Net did not outperform the traditional U-Net for PA bone imaging.
# MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
## 2 Theoretical Background
### Conditional Generative Adversarial Networks  
Generative Adversarial Networks (GANs) are deep learning models [16]. They consist of two parts: the generator and the discriminator. The core idea of the GAN is to improve the quality of generated data through the competition between the generator and the discriminator. The generator, \(G\), generates simulated data from the input random noise. These simulated data are fed, along with real data, into the discriminator, \(D\). The task of \(D\) is to identify the authenticity of the data and to optimize both \(G\) and \(D\) cyclically based on the identification results. The parameters of \(G\) remain unchanged while optimizing \(D\). After many rounds of cyclic optimization, \(G\) and \(D\) can reach their respective optimal states. The expression of the GAN is as follows:  
\[\min_{G}\max_{D}V(D,G)=E_{x\sim P_{data}(x)}[\log D(x)]+E_{z\sim p_{z}(z)}[ \log(1-D(G(z)))] \tag{13}\]  
where \(E_{x\sim P_{data}(x)}[logD(x)]\) denotes that for a real sample from the data distribution \(p_{data}\) of the true samples from the data distribution \(x\), the discriminator \(D\) expected a logarithmic probability of correctly identifying the true sample. \(E_{z\sim p_{z}(z)}[log(1-D(G(z)))]\) denotes the expected logarithmic probability for when the generator \(G\) generates a false sample through noise, the discriminator correctly identifies the true sample. When \(z\) generates a false sample, the discriminator \(D\) has the expected logarithmic probability of identifying the false sample as a true sample.  
The Conditional Generative Adversarial Network (CGAN) introduces conditional information \(C\) during the training process of the generator and discriminator [17]. This enables the model to generate data with specific features. The expression of the CGAN is as follows:  
\[\min_{G}\max_{D}V(D,G)=E_{x\sim P_{data}(x)}[\log D(x|c)]+E_{z\sim p_{z}(z)}[ \log(1-D(G(z|c)))] \tag{14}\]  
where \(G(z|c)\) is the data sample generated by noise \(z\) under the
## 3 Peak-Shaving Costs and Economic Dispatch Model  
### Modeling Peak-Shaving Costs  
The peak-shaving costs mainly include four sub-items: The coal consumption cost, wear-and-tear cost, combustion-supporting cost, and environmental cost. Detailed descriptions of these four parts are given below.
# Calculation Method of Rotational Constraint Stiffness for a New Tower-Pier Connected System
## 6 Discussion  
The proposed model in this study is a fast calculation method for the rotational constraint stiffness, which greatly reduces the calculation cost and saves time. Although there are some errors in the calculation results, the precision has been greatly improved compared with the traditional structure analysis method that does not consider the work of this paper, and the errors are completely acceptable in engineering. For a transverse symmetric bridge, the rotation behavior of the UHS in the horizontal plane can be ignored when the cable force error satisfies the construction accuracy. Therefore, this study mainly focuses on the transverse asymmetric cable-stayed bridges. In the implementation of the case in this paper, it was found that the measured displacements in the transverse and longitudinal directions were almost consistent with the theoretical displacements calculated according to the ideal hinge support, while the rotation angles of the bridge towers in the horizontal plane are quite different from it, so the rotational behavior of the UHS in the horizontal plane is considered as being important in this study. More practical case studies should be performed to further explore the practicability of the proposed method and to comprehensively evaluate the mechanical properties of UHS in the future.