esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Approach
### Feature Extraction for SER  
A vast amount of information reflecting emotional characteristics is present in the speech signal. One of the key issues within SER research is the choice of which features should be used.  
Previously, traditional feature extraction methods, such as prosodic features, were used for SER [30, 31], including the variance, intensity, spoken word rate, and pitch. However, some traditional features are shared across different emotions, as discussed by [PERSON] et al. [30]. For example, as observed in Table 11.2 of [PERSON] et al., angry and happy utterances have similar trends in F0 and speaking rate, compared to neutral speech.  
Manually extracted traditional features may work well with traditional classification methods in machine learning, where a set of features or attributes describes each instance in a dataset [32]. In contrast, however, deep learning can itself determine which features to focus on to recognize verbal emotions. Finding some sets of feature vectors or properties that can give a compact representation of the input audio signal has therefore become the main aim of feature extraction methods. The spectrum extraction methods convert the input sound waveform to some discrete shape or feature vector. Normally, the speech signal is not static but when looking at a short period of time, it acts as a static signal. This short, detached snap is called a frame. The acoustic model extracts features from the frames [33, 34]. Feature extraction deals with obtaining useful information for reference by removing irrelevant information. These extracted feature vectors are fed into deep learning models. In short, spectrum extraction methods can convert audio signals into vectors that deep learning models can handle. The model can then be trained to learn the features of each emotion and hence classify it. Overall, this is one reason why deep learning models can perform better than machine learning models.  
After reviewing many works on SER, it is clear that Mel-Frequency Cepstral Coefficients (MFCCs) are widely used in audio classification and speech emotion recognition [35]. An MFCC is a coefficient that expresses the short-term power spectrum of a sound. It uses a series of steps to imitate the human cochlea, thereby converting audio signals. The Mel scale is significant because it approximates the human perception of sound instead of being a linear scale [36]. In our previous work [10], we compared MFCCs to alternatives and found them to be the best. This is the reason we chose MFCC features for the present study.
## 4 Simulation Tests
### Effectiveness of the Proposed Allocation Strategy  
To further investigate UPCE and the dynamic allocation method in the proposed control scheme, three allocation strategies are presented to demonstrate the performance of frequency regulation. For the sake of clarity, the allocation strategies for comparison in Case 2 are numbered as control strategy 3 and control strategy 4.  
1. Control strategy 3: The piecewise allocation method proposed in [4] is utilized based on ACE and the distribution factor is fixed in the same control area.
2. Control strategy 4: The hybrid allocation method proposed in [28] is introduced. The distribution factor of generators is fixed as the traditional allocation strategy. The frequency control signal of AC system contains two parts: 1) the distribution factor of individual ACs in the control strategy 2; 2) the out-of-limit over maximum capacities of generators.
3. The proposed coordinated control strategy  
The system frequency responses under control strategy 3, control strategy 4 and the proposed control strategy are shown in Figure 9. As Figure 9 illustrates, the frequency nadir and the frequency recovery are inferior to the last two control strategies. That is mainly attributed to the fact that control strategy 3 depends on the absolute value of ACE instead of UPCE.  
Figure 8: The energy states of all individual ACs.  
Figure 7: The power states of all individual ACs.  
The total power output of regulation generators and aggregated ACs under three control strategies are respectively shown in Figures 10 and 11. The power output of generators reaches the maximum of available capacity of generators during the time interval [811s-817s], which is prominently longer than the last two control strategies. The sharp increase in AC systems for frequency deviation is obviously hysteretic. In comparison with the proposed control scheme, control strategy 4 has a larger frequency nadir and slower frequency recover speed. The reason behind this is that individual ACs play an important role only under the condition that the allocation power exceeds the limits of regulation generators. In the proposed control scheme embedded with an UPCE and associated allocation factors, the maximum of frequency deviation is within the reasonable range and the frequency fluctuation is mitigated within seconds.  
Figure 10: The power output of regulation generators under different allocation strategies.  
Figure 9: The frequency response under different allocation strategies.
# Rotavirus A Genome Segments Show Distinct Segregation and Codon Usage Patterns
## 1 Introduction  
The high mutation rates and large population sizes of RNA viruses allow them to rapidly explore adaptive landscapes, expand host-ranges, and adapt to new environments. Segmented RNA viruses also may undergo'reassortment' whereby viruses swap entire genome segments during coinfection [1]. Reassortment may allow rapid evolution of specific viral traits such as, for example, the acquisition of novel spike glycoproteins during the emergence of H1N1 influenza A in 2009 [2]. Similarly, reassortment among segmented dsRNA rotavirus may have significant implications for human health [3], but it is challenging to determine the prevalence of rotavirus reassortment in nature. Our motivation here is to elucidate apparent restrictions (or lack thereof) to RVA genetic exchange in nature by comparing the relative linkage between each of the RVA segments as shown by phylogeny. In addition, we parse the evolutionary constraints that may contribute to the distinct phylogenies of each segment.  
The rotavirus genome consists of 11 segments of double-stranded RNA, each possessing a single open reading frame, except for segment 11, which contains two genes. Rotaviruses are classified based on the antigenicity of the VP6 protein into groups A through I [4]. The consensus is that viruses from different groups cannot reassort withone another [5], though some rare cross-group reassortment events appear to have occurred [6]. While mammalian RVA strains routinely reassort with other mammalian RVA strains, reassortment between avian and mammalian RVA strains does not seem to occur outside of laboratories [7]. Nevertheless, there have been cases of mammalian strains transmitting between birds [8] (Figure 1), avian strains infecting mammals [9] (Figure 1) and causing encephalitis [10], evidence of an avian RVA isolate with a mammalian VP4 gene [11], and some in vitro reassortment assays [7,12], most recently confirming that mono-reassortments with avian segments 3 and 4 can be recovered using the SA11 reverse genetics system [13]. Reassortment between RVA strains from different mammalian species is also less common; however, it is not clear whether this lack of reassortment is due to biological incompatibilities as opposed to genetic incompatibilities. Genotypes resulting from interhost-species reassortment, while rare, have not only occurred but have fixed in populations [14, 15, 16, 17, 18, 19, 20, 21, 22], indicating that reassortment between even distantly related strains is potentially a significant driver of rotavirus evolution [23, 24, 25, 26].
## 3 The key Technology of the Multi-DOF Automatic Image Acquisition System
### The Calculation of Camera Position
#### 3.1.3 Comparison of the Two Methods  
To find the optimal solution between these two methods, the calculation results of the two methods were compared. The method of obtaining \(\Delta x\), \(\Delta y\), \(\Delta z\), \(\alpha\), and \(\beta\) values was the same as the method in Section 3.1.2. The values of \(\Delta z\), \(\alpha\), and \(\beta\) were calculated by introducing \(\Delta x\) and \(\Delta y\) into Equations (2) and (3), respectively, and the calculated values were compared with the actual values.  
It can be seen from Figure 7 that the errors of \(\Delta z\), \(\alpha\), and \(\beta\) of method 2 were less than those of method 1, indicating that method 2 is more suitable for the calculation of camera pose. Therefore, the system uses the result of data fitting (Equation (3)) to calculate the camera pose.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Degrees** & \(\Delta z\) **(mm)** & \(\alpha\) **(\({}^{\circ}\))** & \(\beta\) **(\({}^{\circ}\))** \\ \hline
1 & 15.9265 & 1.36364 & 0.355234 \\
2 & 2.0463 & 1.231859 & 0.372423 \\
3 & 1.7221 & 0.280749 & 0.189076 \\
4 & 1.5537 & 0.257831 & 0.177617 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Polynomial fitting error.  
Figure 7: Error comparison of the two methods: **(a)** Error comparison of \(\Delta z\); **(b)** error comparison of \(\alpha\); **(c)** error comparison of \(\beta\); method 1 is the camera position calculating method by mathematical modeling, method 2 is the camera position calculating method by data fitting.
# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 5 Discussion  
Modern societies depend on efficient road infrastructure, enabling safe and cost-effective transportation. Traditional manual road inspection is time-consuming and dangerous, resulting in repair delays. The detection of road pavement damage is crucial to sustaining a functional transportation network. Limited research has been conducted on the effectiveness of the RDD 2022 dataset, which encompasses road damage from six nations, including Japan, Norway, the United States, the Czech Republic, and China. Automated methods, such as deep learning models, have emerged as valuable instruments for identifying road pavement damage types with high accuracy. These developments contribute to a safer and more sustainable transportation system by mitigating risks, decreasing maintenance costs, and enhancing overall road management.  
Our framework for object detection using deep learning, YOLOv5, has produced significant outcomes. Accuracy and generalization were substantially improved by incorporating techniques such as the Efficient Channel Attention module, label smoothing, the K-means\({}^{++}\) algorithm, Focal Loss, and an additional prediction layer. Despite containing a different 1.1 million parameters, our model showed an extraordinary 1.9% improvement in mAP and 1.29% improvement in F1-Score over Yolov5s. In addition, it demonstrated a 0.11% improvement in mAP and a 0.05% improvement in F1 score over YOLOv8s while containing 3 million fewer parameters. These results underscore the effectiveness of our model in advanced road pavement damage detection, contributing to the creation of safer and more efficient transportation networks. Figure 12 shows the comparison of performance between the YOLOv5s-baseline and our improved YOLOv5s.  
In real-world scenarios, it is essential to observe that our model is primarily trained to detect four common types of pavement damage found in six countries. If the detection setup encounters a damage type that lies outside the scope of our model's training data, it may not be recognized as damage. Future work includes crafting a substantial dataset for our country. While object detection is standard, instance segmentation can be valuable in locating damage precisely. We plan to develop an instance segmentation model with a robust instance-segmentation-based dataset to accurately find road damage in front-view images, as this task has yet to be carried out due to its labor-intensive nature. Moreover, we would like to develop a model for indoor road environments that includes areas such as indoor car parking areas, tunnels, and underground facilities.  
Figure 11: Precision–recall curve of our proposed improved YOLOv5s.
## 3 Results and Discussion
### Organic and Elemental Carbon

Table 3: Average values of organic carbon (OC) and elemental carbon (EC) within classroom C3 and on the GRS for the entire experimental period. Furthermore, Figure 4 illustrates that concentrations of EC on the GRS were higher than in classroom C3 for all experimental days. In addition, Table 4 shows that the average value of the I/O ratio was 0.3. These results indicate that EC concentrations were mostly linked to ambient sources such as exhaust emissions from vehicles or other combustion processes. More specifically, amid the cold period, mean concentration of EC on the GRS were found to be 2.92 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\), and the respective values amid the warm period were 2.60 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\). On the contrary, in classroom C3, EC demonstrated very low concentrations, ranging from 0.20 to 1.95 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\) for the cold period and from 0.14 to 0.83 \(\mathrm{\SIUnitSymbolMicro m^{-3}}\) for the warm. It should also be mentioned that [PERSON]'s rho correlation coefficient between the outdoor OC and EC was 0.77 (\(p<0.01\)), and, thus, OC and EC fractions may have originated from multiple emission sources and not only from a primary one, as [PERSON] et al. [70] and [PERSON] et al. [71] reported. Figure 4: Indoor and outdoor EC concentrations for all sampling days. Figure 3: Indoor and outdoor OC concentrations for all sampling days. Moreover, the calculation of the OC/EC ratio is commonly used in order to investigate the presence of secondary organic aerosols (SOA), as according to [PERSON] et al. (2017) and [PERSON] et al. (2018), when this ratio overcomes the value of 2, it corresponds to SOA formation and traffic emissions. Table 4 depicts such a behavior both indoors (34.6) and outdoors (6.4). Additionally, as [PERSON] et al. (2018) report, for the case of fine particles (PM\({}_{2.5}\)), higher values of OC/EC within the indoor environment compared to the outdoors demonstrates multiple possible indoor sources of OC concentrations. This is in accordance with the results of the present study (concerning PM\({}_{10}\)), as the indoor OC/EC ratio was found to be almost 5.4 times higher than the respective outdoor value. Finally, the same table presents I/O and OC/EC ratios for the examined building, compared to other similar experimental campaigns. The higher ratio of indoor OC/EC may be due to a large variety of organic materials within the classroom C3.
## 3 Methodology
### Evaluation Metrics  
In this study, evaluation metrics such as accuracy, precision, recall, average precision (AP), mean average precision (mAP), and F1 score are used to compare the experimental results. For a single category, let \(N\) represent the total number of samples, \(T\) the number of correctly predicted samples, \(TP\) the number of true positive predictions, \(TN\) the number of true negative sample predictions, \(FP\) the number of false positive predictions, and \(FN\) the number of false negative sample predictions. AP is the area under the precision-recall curve, while mAP is the average of all AP values.
# A Novel Charging Station on Overhead Power Lines for Autonomous Unmanned Drones
## 2 Drone Charging Station Design and Development
### Power Electronics  
The designed drone charging station is with three power stages for the energy conversion and management process includes (Figure 4):  
* An electromagnetic induction energy harvester;
* A rectifier and overvoltage protection;
* A DC/DC converter with an MPPT charge controller.  
The magnetic energy converted into electromotive force by the energy harvester has a sinusoidal AC component with a typical frequency of 50/60 Hz. The amplitude varies with the magnetic field and the load impedance. A rectifier circuit converts the AC voltage to DC, and the overvoltage protection prevents damage to the power electronics due to overvoltage. Two low-side MOSFETs short-circuit the harvester coil when the battery is unable to drain the collected energy, and TVS diodes prevent voltage transients.  
A buck-boost DC/DC converter provides a compact solution for multicell and multichemistry drone battery charging applications. The buck-boost charger operates over a wide input and output voltage range and offers high efficiency. An MPPT charge controller is used to maximize the amount of current flowing from the energy harvester to the battery. The maximum power varies mainly with the current on the overhead line; then, the MPPT algorithm in the charge controller is used to extract the maximum available power from the energy harvester under certain conditions.  
Figure 3: Magnetic field simulation with energy harvesting for battery charging via a 200 A power line.
# Replay Speech Detection Based on Dual-Input Hierarchical Fusion Network
## 4 Results  
### The System Performance of Different Input Methods  
In this section, we evaluate the effectiveness of the proposed replay speech detection system based on a dual-input hierarchical fusion approach. In order to verify the effectiveness of the proposed method, ResNet50 and HFN were used as classification networks in this paper. At the same time, different input methods were employed, including the single-input, overlay-input, and dual-input methods. Furthermore, CQT was chosen as the acoustic feature for the model input. As shown in Table 2, when applying the original speech signal and time-reversed speech signal as inputs to the ResNet50 and HFNmodels, the min t-DCF is better than for other input methods. Furthermore, our proposed dual-input hierarchical fusion network exhibits significantly better performance than the ResNet50 model. The experimental results show that the best system has an EER of 28.51% in the test set of the PA track. Compared to the best system that employs the ResNet50 network, the proposed system achieves a relative reduction in EER of 25.3%.
# A High-Precision Active Vibration Isolation Control System: Experimental Study
## 3 Controller Design
### Computation of Actuating Signals and Measurement Outputs  
The proposed 6-DoF AVIS is operated by four vertical actuators and four horizontal actuators, defined as \(\mathbf{U}_{\mathbf{a}}=\big{[}F_{vx1}\ F_{vx2}\ F_{vy1}\ F_{vy2}\ F_{vz1}\ F_{vz2}\ F_{vz3}\ F_{vz4}\big{]}^{T}\), as mentioned before. However, the designed control input in Equation (12) is \(\mathbf{U}_{\text{p}}=\big{[}F_{vx}\ F_{vy}\ F_{vz}\ M_{\theta x}\ M_{\theta y}\ M_{ \theta z}\big{]}^{T}\), containing a total of six forces and torques in the corresponding six DoF. Therefore, the actual control forces for the eight actuators \(\mathbf{U}_{\mathbf{a}}\) can be derived from \(\mathbf{U}_{\text{p}}\) by the following equations:  
\[\mathbf{U}_{\mathbf{a}}=\mathbf{B}_{\mathbf{a}}^{+}\mathbf{U}_{\text{p}} \tag{13}\] \[\mathbf{B}_{\mathbf{a}}^{+}=\mathbf{B}_{\mathbf{a}}^{T}\big{(}\mathbf{B}_{\mathbf{a}}\mathbf{ B}_{\mathbf{a}}^{T}\big{)}^{-1}\]  
where \(\mathbf{B}_{\mathbf{a}}\in\mathbf{R}^{6\times 8}\) is the allocation matrix of the eight actuators and \(\mathbf{B}_{\mathbf{a}}^{+}\) is the right inverse of it. Similarly, the eight geophones attached to the top platform give the measured output vector \(\mathbf{Y}_{\mathbf{m}}=\big{[}\hat{x}_{1t}\ \ \ \hat{x}_{2t}\ \ \hat{y}_{1t}\ \ \hat{y}_{2t}\ \ \hat{z}_{1t}\ \ \hat{z}_{2t}\ \ \hat{z}_{3t}\ \ \hat{z}_{4t}\big{]}^{T}\). With \(\mathbf{C}_{\mathbf{m}}\in\mathbf{R}^{8\times 6}\) being the distribution matrix of these sensors, the output vector \(\mathbf{Y}\) corresponding to 6-DoF motion can be obtained from \(\mathbf{Y}_{\mathbf{m}}\) as follows:  
\[\mathbf{Y}=\mathbf{C}_{\mathbf{m}}^{+}\mathbf{Y}_{\mathbf{m}}, \tag{14}\] \[\mathbf{C}_{\mathbf{m}}^{+}=\big{(}\mathbf{C}_{\mathbf{m}}^{T}\mathbf{C}_{\mathbf{m}}\big{)} ^{-1}\mathbf{C}_{\mathbf{m}}^{T}\]  
\(\mathbf{B}_{\mathbf{a}}\) and \(\mathbf{C}_{\mathbf{m}}\) are easily derived from the system configuration demonstrated in Figure 1.
## 3 Implemented Strategy
### Loss Sensitivity Indices (\(Lsi\))  
Two \(LSIs\) are used to efficiently shortlist the candidate load buses for capacitor installation. \(LSIs\) rank the load buses according to active power loss sensitivity for voltage and the reactive power demand at the receiving end. In Figure 1, two buses are shown, which are linked by line \(k\), taken from a distribution system. The sending bus is \(p\) with voltage of \(V_{p}\) at the voltage angle of \(\delta_{p}\). The receiving end bus is \(q\) with voltage of \(V_{q}\) at the voltage angle of \(\delta_{q}\). \(P_{p}\) and \(Q_{p}\) are active and reactive power, respectively, flowing from bus \(p\) to bus \(q\) through the \(k^{th}\) line with line impedance \(R_{k}+jX_{k}\). \(P_{p}\) and \(Q_{p}\) are provided by (8) and (9), respectively. The current through the line \(k\) is \(i_{k}\).  
\[P_{p} =P_{Lq}+P_{lossk} \tag{8}\] \[Q_{p} =Q_{Lq}+Q_{lossk} \tag{9}\]  
\[P_{lossk} ={i_{k}}^{2}R_{k}=\bigg{[}\frac{\big{(}P_{Lq}^{2}+Q_{Lq}^{2}\big{)}}{ \big{|}V_{q}\big{|}^{2}}\bigg{]}R_{k} \tag{10}\] \[Q_{lossk} ={i_{k}}^{2}X_{k}=\bigg{[}\frac{\big{(}P_{Lq}^{2}+Q_{Lq}^{2}\big{)} }{\big{|}V_{q}\big{|}^{2}}\bigg{]}X_{k} \tag{11}\]  
The loss sensitivity is calculated with respect to the bus voltage and the reactive power load, both at the receiving end bus. Therefore, \(LSI_{1}\) and \(LSI_{2}\) are defined as the partial derivative of \(P_{lossk}\) with respect to \(V_{q}\) and \(Q_{Lq}\), respectively, as given by (12) and (13).  
\[LSI_{1}=\frac{\delta P_{lossk}}{\delta V_{q}}=-2R_{k}\frac{P_{Lq}^{2}+Q_{Lq}^{ 2}}{V_{q}^{3}} \tag{12}\]  
\[LSI_{2}=\frac{\delta P_{lossk}}{\delta Q_{Lq}}=2R_{k}\frac{Q_{Lq}}{V_{q}^{2}} \tag{13}\]  
It is trivial to deduce that \(LSI_{1}\) are negative values whereas \(LSI_{2}\) are positive values. The more negative the value of \(LSI_{1}\) for a bus, the higher the impact of voltage variation of the bus on power loss. Such a bus will have a higher chance of selection as an optimal location because minor variations in bus voltage can have high impacts on loss reduction. Contrary to \(LSI_{1}\), for \(LSI_{2}\) the higher positive values translate to higher chances of selection as a candidate bus due to the fact that power loss has positive sensitivity, with respect to reactive power demand at receiving end bus. The buses are ranked in ascending order according to their \(LSI_{1}\), and in descending order according to their \(LSI_{2}\). The top 50% of buses are shortlisted for the input to the PSO algorithm in order to logically reduce the search space; hence, reducing the simulation burden and time. A detailed calculation of \(LSI_{1}\)s is given in [32].
# Multiple Object Tracking Using Re-Identification Model with Attention Module
## 3 Proposed Method  
This section describes our proposed MOT algorithm. Figure 3 shows the overall flow of the proposed MOT algorithm. Note that the basic structure is similar to the algorithmic flow of SORT [8] and DeepSORT [9]. Given an input image acquired from a video or real-time camera, objects are located by the detector. Here, we employed YOLOv3 [20] as the object detector. Then, objects are separately extracted from the frame and are subjected to the proposed association stage, seeking to associate them with the objects in the previous frame or assigning a new ID for unmatched objects.  
Figure 2: Example of tracking using the IOU.