esa-sceva/satcom-chunk-collection huggingface dataset:
# Speech Emotion Recognition Based on Two-Stream Deep Learning Model Using Korean Audio Information
## 2 Materials and Methods
### Feature Extraction Methods of Speech Data
#### 2.1.2 Two-Dimensional Spectrogram Feature Extraction  
To train the 2D CNN model with speech data of the 1D time domain, we must convert the speech data into 2D spectrogram images by converting them from the time domain into the frequency domain. In this study, we extracted Bark spectrogram, ERB spectrogram, and log-mel spectrogram features, which are 2D images, based on the time-frequency conversion, and used them as inputs to the CNN transfer learning model.  
The Bark spectrogram is based on the Bark frequency scale, which distinguishes sound characteristics. The Bark scale was first proposed by [PERSON] as a subjective measurement of loudness [15]. The Bark spectrogram can be obtained by designing an auditory filter bank using the Bark frequency scale and then applying it to the spectrogram through a short-time Fourier transform. Figure 1 visualizes the Bark spectrogram for eight emotions in which the features are demonstrated.  
The ERB spectrogram is based on the ERB scale, which is used in psychoacoustics. The ERB scale provides an approximation for the filter bandwidth in human hearing [16], and if an ERB auditory filter is designed based on it and is applied to the STFT spectrogram, an ERB spectrogram image can be obtained. Figure 2 presents a visualization of ERB spectrograms for eight emotions and shows the characteristics of the ERB spectrograms.  
The log-mel spectrogram is based on the mel scale, which was created to be most similar to the human auditory structure. The auditory filter bank was created based on the mel scale and applied to the STFT spectrogram to obtain the mel spectrogram, and log conversion is then applied to obtain the log-mel spectrogram. Figure 3 visualizes the log-mel spectrogram for eight emotions in which the features are shown.  
Figure 1: Visualization of Bark spectrograms for eight emotions.  
Figure 2: Visualization of ERB spectrograms for eight emotions.
## 4 Simulation Analysis
### Effect of Different Parameters on Velocity Estimation  
The selected QPSK signal, where the carrier frequency \(f_{c}\) is 100 MHz and the symbol rate is 1000 baud, is used for simulation experiments. The actual speed of the QPSK signal is set to 100 m/s, moving radially relative to the receiving end.  
Figure 6 shows a vertical FFT of the received QPSK signal after segment combination without phase balancing. It can be seen from the simulation diagram that the accurate speed of the moving target cannot be directly obtained due to the influence of the modulation phase.  
In Figure 7, the number of symbols is selected as 700, the signal-to-noise ratio is 10 dB, and the duration of a single symbol is 0.001 s. The speed corresponding to the peak point is 98.57 m/s. Considering the actual speed measurement accuracy, Equation (17) can be obtained as 2.14 m/s, which is in line with the error range of the parameter setting value, verifying that this method can effectively estimate the speed of the moving communication radiation source.  
In Figure 8, the horizontal axis represents the number of symbols, and the vertical axis represents the error value of the speed. It can be seen from Figure 8 that the actual speed measurement accuracy is inversely proportional to the number of symbols. When the noise and speed are constant, the speed measurement accuracy decreases with the increase in the number of symbols and finally gradually approaches a minimum error. At this time, increasing the number of symbols cannot further reduce the error.  
Figure 5: Signal arrangement simulation diagram.  
Figure 6: Vertical FFT simulation of the unbalanced initial phase.  
It can be seen from the four curves representing different symbol durations in Figure 9 that, under the same signal-to-noise ratio and number of symbols, the duration of a single symbol is also inversely proportional to the measurement accuracy. When the duration of a single symbol becomes longer, the measurement speed error gradually decreases.  
If the signal amplitude is small and the noise is large, the peak of the FFT transform spectrum may be submerged or covered by the noise, and the noise component may produce a false signal peak on the spectrum, resulting in an erroneous estimation of the speed. However, from the comparison between Figures 7 and 10, it is not difficult to see that this method can still accurately estimate the motion speed under the condition of a signal-to-noise ratio of \(-10\) dB. This proves that this method can also estimate the speed well under the condition of a low signal-to-noise ratio.  
Figure 8: Schematic diagram of the signal segmentation principle.  
Figure 7: Vertical FFT simulation diagram of the velocity \(100\) m/s after the initial equilibrium phase.  
Figure 9: Relationship between actual speed measurement accuracy and symbol period.
## 2 Antenna System Configuration
### Antenna System Detailed Settings and Radiation Characteristics Analysis  
In this study, we assumed that the beams employed for establishing an aviation communication network are formed on a tile base. Based on the antenna system configuration presented in this paper, a total of 10 tiles were arranged. Therefore, single-tile reference beam steering must operate within a range from +18\({}^{\circ}\) to -18\({}^{\circ}\) based on the broad side of each tile. In a planar, linear, uniformly spaced array antenna, the distance between radiating elements is set so that only a single grating lobe exists in the visible region due to beam steering. The  
Figure 1: Proposed antenna system configuration.  
distance between the radiating elements based on the maximum beam-steering angle required to satisfy these conditions is calculated through Equation (1), as follows:  
\[d\leq\frac{(N-1)}{N}\frac{\lambda}{1+\sin\theta_{m}} \tag{1}\]  
where \(d\) is the distance between radiating elements, \(N\) is the number of radiating element arrays, \(\lambda\) is the wavelength, and \(\theta_{m}\) is the maximum beam-steering angle.  
For the azimuthal reference, eight elements are arranged and the maximum beam-steering angle is 18\({}^{\circ}\). Meanwhile, for the elevation reference, four elements are arranged, and the maximum beam steering angle is 0\({}^{\circ}\). Accordingly, the distance between each radiating element is set to 0.66\(\lambda\) and 0.75\(\lambda\) for azimuth and elevation, respectively.  
Figure 2 shows the radiation characteristics based on a single tile according to the above conditions. Here, the single radiating element is assumed to be a microstrip patch, and the radiation characteristics of the single radiating element are applied.  
As a result of the analysis, for the case in which there is no beam steering (\(\phi=0^{\circ}\)), the half-power beamwidth (HPBW) based on the azimuth and elevation were 9.6\({}^{\circ}\) and 17.4\({}^{\circ}\), respectively. Moreover, the gain was calculated to be about 21.01 dBi. When maximum beam steering (\(\phi=18^{\circ}\)) was performed based on a single tile, the HPBW based on the azimuth and elevation were 10.2\({}^{\circ}\) and 17.4\({}^{\circ}\), respectively, and the gain was about 20.14 dBi. As a result, the required gain of 20 dBi can be satisfied according to the antenna system configuration presented in this paper. In addition, the gain deviation due to single-tile-based beam steering for the azimuth-based one is less than 1 dBi.
## 6 Performance Comparison
### Comparison of Electromagnetic Performances  
Figure 17 presents the electromagnetic performance of the motor under no-load conditions. The magnetic flux density distribution and flux line distribution are calculated. As shown in Figure 17a, the magnetic flux density of both schemes is mainly concentrated at the magnetic bridge and stiffeners. The no-load line back-EMF of the initial design is 345 V, while that of the optimized motor is 366.5 V.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Params** & **Initial** & **Optimized** \\ \hline Bridge thickness (mm) & 2 & 1.7 \\ Rib width (mm) & 2 & 2.1 \\ Stiffener thickness (mm) & 2 & 2.4 \\ PM thickness (mm) & 6 & 6.6 \\ PM width (mm) & 50 & 52.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Initial and optimized rotor parameters.  
Figure 16: Relationship between multi-objective performances.(**a**) Relationship between back-EMF, efficiency and rotor stress; (**b**) Relationship between rotor core loss, efficiency and rotor stress; (**c**) Relationship between power factor, efficiency and rotor stress; (**d**) Relationship between output torque, efficiency and rotor stress.  
The torque and rotor core losses for the rated load are shown in Figure 18. It can be seen from Figure 18a that the optimized average torque increases slightly from 76.2 N\(\cdot\)m to 78.3 N\(\cdot\)m. In Figure 18b, the optimized rotor core loss drops to 538 W, a reduction of 355 W compared to the initial design. The decrease in rotor core losses indicates that the temperature distribution of the optimized motor will have a significant improvement.
# Controller-Pilot Data Link Communication Security
## 5 Potential Countermeasures
### Identity-Defined Networking  
For the transport domain, security must be managed pro-actively over the system as a whole. This must also extend to include interfaces to critical supporting infrastructures such as communication networks and satellite systems. The challenge is to migrate these systems and infrastructures to a higher level of cybersecurity. [PERSON], a technology policy expert from the aviation consultancy firm Helios, says that adding security systems retroactively can be an extremely costly and technically difficult process [31]. He advises building security in all new technologies and processes from the beginning.  
The networking community has established a number of security standards and protocols, enabling the ATC authorities to adopt one or several of the existing standards for wireless technology and mobility. A representative example of generic security solutions for industrial communication networks was proposed by Tempered Networks [32].  
Tempered Networks offer an identity-defined networking solution with military grade security that 'cloaks' critical infrastructure. They provide hardened, drop-in security appliances that enable airline data center technicians to easily create private overlay networks with unlimited scale. Airlines can easily segment and effectively hide their most critical networks and control systems that are utilized for in flight and ground operations.  
The solution provides a secure overlay network among HIPswitch appliances that connects and secures an airline's in flight or ground operations network and control systems. The HIPswitch Conductor orchestration engine coordinates configuration, security policies, trust relationships, monitoring, and analytics. The solution facilitates easy administration and provisioning of private overlay networks with delegated control, centralized governance, and oversight by IT. The approach provides the possibility to cloak the critical infrastructure, segment and extend the network, preserve legacy investment, easily add or revoke contractor/employee access, and troubleshoot and optimize your network.  
The key capabilities of identity-defined networking are summarized below:  
1. **Cloaking the Critical Infrastructure.** Devices can only be seen by trusted peers.
2. **Segmenting Networks.** Smaller, more manageable networks are more robust and secure.
3. **Increased Operational Integrity and Availability.** Visibility into network traffic enables diagnostics, debugging, and performance optimization.
4. **Secure Remote Access.** Highly constrained remote access that is simple to grant and revoke.
5. **Preserve Legacy Investments.** Integrates with your existing devices and infrastructure for in-depth security.  
Implementing such a standard within the whole communication system at the scope of the air traffic control unit or broader implies global system changes and may require several years of standardization and adaptation, and significant resource consumption.
## 4 Performance
### Variation of Number of Sending Nodes
#### 4.2.2 Transmission duration  
While RAIT can guarantee the reliability of image transmission with a preemption scheme and double sliding windows, these schemes increase the duration of image transmission. To see the effect in terms of duration, we estimated the average transmission duration of images. Figure 11 shows the average transmission duration of images with variations in the number of sending nodes.  
As shown in Figure 11, the image transmission duration with RAIT took 0.6 s more than MintRoute, even with one sending node. This could be analyzed as overhead of RAIT. However, this overhead does not increase with an increase in the number of sending nodes. In the case of 20 sending nodes, the duration increased up to 2.6 s with RAIT and 1.7 s with MintRoute. This amounted to about a 40% increase in both RAIT and MintRoute, showing a ratio similar to that shown in Figure 11, while the total number of transferred packets with RAIT was 33% larger than that of MintRoute, showing a large difference in the ratios shown in Figure 9. As a consequence, with RAIT, the transmission duration of an image can increase, but this would not be greatly affected by an increase in sending nodes. We might attribute this result to the effect of the preemption scheme explained in Section 3.3. With a preemption scheme in RAIT, the competition among child sensor nodes with the same parent sensor node can be greatly reduced because the preemption scheme guarantees the exclusive possession of a parent node. Given such reduced competition, the number of collisions between packets can also be decreased, and the sensor node that exclusively possesses the parent node can send successive packets without interruption or disturbance from other nodes. As a result, each packet can be transferred more quickly in RAIT than in MintRoute and SRI.  
Figure 11: Image transmission duration with variations in number of sending nodes
## 4 Discussion  
### Comparison of Simulation Results between Scheme I and Scheme II  
To explore the response trends of vegetation with the two schemes to future climate change, we analyzed the simulation results of NEE, GPP, and ER with the two vegetation schemes in different time periods, and compared the differences between them. On the one hand, at the same time scale, the simulation results of NEE, GPP, and ER with the two schemes will increase under different scenarios from RCP2.6 to RCP8.5. It could be concluded that relatively moderate climate change has an apparent positive effect on the peatland ecosystems. Extension of growing season [67] and more plant biomass could lead to higher autotrophic respiration [73]. Considering the impact of climate change  
Figure 8: Simulation results of NEE, ER, and GPP from 2021 to 2100 in Scheme II.  
Figure 7: Simulation results of NEE, ER, and GPP from 2021 to 2100 in Scheme I.  
and plant biomass, it is worth noting that ER and GPP will increase significantly under RCP6.0 and RCP8.5. Overall, the carbon sequestration capacity of the peatland ecosystem showed a weak increasing trend and remained stable by the end of the 21 st century. On the other hand, when the climate change reaches a certain degree, it will form a negative correlation with the carbon sequestration capacity of the peatland ecosystem, which means the carbon sink capacity will be weakened. In particular, NEE shows a decreasing trend after 2053 under RCP8.5. The results show that the changes in NEE of the two vegetation schemes under the RCP8.5 in the peatland ecosystem will decrease by the end of the 21 st century relative to the values during 2050-2060. This trend is different from the emission pattern under RCP2.6 and RCP6.0. Both the simulation results of the carbon dynamics of pan-Arctic peatlands show that the carbon sink capacity of the peatlands will be weakened from the middle of the 21 st century under RCP8.5 [48]. Finally, the overall trends in the simulation results with the two vegetation schemes are similar, but the specific values show that Scheme II is greater than Scheme I. The main reason is that with Scheme II, the number of shrubs will increase annually, and the photosynthesis, respiration, and carbon sequestration capacities of shrubs are higher than those of herbaceous plants. This shows that the change in vegetation type will lead to the change in carbon budget. In addition, there were significant differences in NEE, ER, and GPP for the two schemes. Peatlands associated with graminoid-dominated vegetation experience greater carbon losses from decomposition and are more sensitive to temperature than shrub-dominated soils.
## 3 Proposed Methodology
### Proposed Architecture Using BERT and Neural Networks
#### 3.5.2 BERT + Neural Network  
In this approach, we have integrated a neural network with a BERT encoder as specified in Algorithm 6. The model begins with an input layer that expects text inputs of variable length, allowing flexibility for different text sequences. It then incorporates a pre-trained text preprocessing layer, which handles essential initial text processing steps like tokenization. Next, a pre-trained encoder layer known as a BERT encoder is utilized. This encoder processes the preprocessed text inputs and generates contextualized word embeddings or representations, thus capturing the intricate meaning and context of the text.  
```
1. Load the pre-trained BERT model
2. Create Neural Network Model (a) Add dense layer (activation function = ReLU) (b) Add dropout layer (rate = 0.2) (c) Add dense layer (activation function = softmax)
3. Loop for \(n\) epochs: (a) BERT Encoder  i.  Tokenize data into sequences of subwords tokens using BERT tokenizer  ii.  Add special Tokens (e.g., [CLS],[SEP]) (b) Encoded data  i.  Pass the tokenized sequence through BERT to obtained contextualized word embeddings.  ii.  Extract the final hidden state representation for each sequence from BERTs output. (c) Train the model  i.  compile the model with categorical cross-entropy loss function and Adam optimizer.  ii.  fitted the model by feeding the BERT embeddings as input (d) Evaluate the model  i.  Assessed the model performance using metrics like precision, recall and, F1 score.
```  
**Output:** Classification results (hate, offensive, neither hate nor offensive)  
**Algorithm 6** Hate Speech Classification Using BERT Encoder and Neural Network.  
The encoder's output is passed through a dense layer with 512 units and ReLU activation, enabling non-linear transformations and facilitating the model's learning of complex patterns in the data. To prevent overfitting, a dropout layer is incorporated. During the training, this layer randomly drops a fraction (0.2) of the connections within the dense layer, encouraging the model to generalize better and avoid reliance on specific connections. Finally, the model includes an output layer with softmax activation, producing class probabilities for three target classes. The softmax function ensures that the predicted probabilities sum up to 1, representing the model's confidence in each class. Additionally, an optional output bias initializer can be provided to address class imbalance issues. By leveraging pre-trained encoders, dense layers (with ReLU activation), and dropout regularization, this classifier model excels in processing text inputs and conducting accurate hate speech classification. This approach harnesses the power of contextual embeddings, non-linear transformations, and regularization techniques to effectively comprehend and classify text/tweets.
## 4 Case Study
### Control Group Design and Results
#### 4.2.1 Mass Model Size  
As described in Section 4.1, two types of model size were made; the augmented results are shown in Figure 4. The bigger model displays more clearly where there is dense detailed texture, such as the contents behind the window, glass door area, window frame texture, and sill. During the projection calibration process, the six reference points of the bigger model could be more precisely matched, which led to higher calibration accuracy. In addition, since the micro adjustment of one reference point would also slightly change the monolithic angle and distortion of the projected model in order to be consistent with the original geometric shape of the design model, there was less change and distortion in the bigger model and it was easier to operate.
## 4 Experiment
### Generalization Test  
Finally, the generalization framework proposed in Section 3.4 was tested. We manually constructed new filter products based on the trained product by making alterations to the screw positions. Specifically, we linearly scaled each screw of the old cavity filter by multiplying with a set of factors \([0.5,1.0,1.5,0.25]\) to simulate a new one. Then, the mapping model was learned as the learning phase described in Section 3.4. It is important to reduce the number of collected data pairs because frequently adjusting the screws on a new filter product should be avoided. We collected 50, 100, and 1000 data examples, respectively, on the new filter product for training and comparison. Afterwards, the trained mapping models _Mapping 50, Mapping 100,_ and _Mapping 1000_ were applied directly in the transferring phase. At each tuning step, the action is predicted by the RL policy network and then mapped to the corresponding value in the new filter. Then, the mapped action is executed on the new filter product.  
For each of the three models--i.e., _Mapping 50, Mapping 100,_ and _Mapping 1000_--100 episodes with a maximum of 50 tuning steps in each episode have been tested. Figure 12 provides some test results. In Figure 12 (left), the number of tuning steps in each episode for each mapping model has been shown. Figure 12 (right) quantitatively compares the three mapping models. With _Mapping 50_ model, only in 65 episodes among 100 could the new constructed filter be successfully tuned, while with _Mapping 1000_ model, 82 episodes are target-reached. It is evident that with more training data, the new product could be more accurately transferred to the old trained model. In practice, to find a good trade-off between data size and performance should be carefully treated.  
It should also be noted that, in this work, we simulate the new filter product by simply rescaling the screws of the old. However, constructing other kinds of differences should also be possible, such as by shifting or adding noises to screws of the old one. In practice, the differences between individuals could be more nonlinear and complicated, which is also another important topic to be studied in the future.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 5 Anthropometric and Morphological Approaches for HRTF Personalisation
### Regression  
Regression methods aim to generate an HRTF directly from anthropometric measures using a trained regression model. Given that anthropometric features can be represented as sparse measurements, two-dimensional images, three-dimensional meshes, or a combination of these, it is worthwhile categorising existing studies in the literature based on the type of input data used.