esa-sceva/satcom-chunk-collection huggingface dataset:
# COVID-19 Detection Model with Acoustic Features from Cough Sound and Its Application
## 5 COVID-19 Detecting Application  
We developed an application using the proposed model so that it could be used to diagnose COVID-19 in many people. An application for Android, which is a mobile operating system based on open-source software produced and released by Google, was produced. Figure 5 shows the execution process of the developed application.  
When the application is executed and recorded, the recording is transmitted to the server using Transmission Control Protocol (TCP)/Internet protocol (IP) socket communication. The user's voice is recorded as a binary pulse-code modulation (PCM) file using the Android AudioRecord API [32]. In order for the user to play and listen to the recorded file in the application, the AudioTrack API [33] provided by Android was used. The recording format is designated as a sampling rate of 48 kHz, stereo channels, PCM 16 bit. The main screen of application, the screen during recording, and the screen when the recorded voice is transmitted to the server and processed are all shown in Figure 6.  
After receiving the data, the server converts it to a mono-channel WAV file, which is the same format used by the database data used in this study. Then, the cough segment is extracted through the preprocessing process described in Section 3.1. The extracted cough segments are input into the trained model to measure a COVID-19 diagnosis prediction value, and the result is transmitted to the application. There are three types of results: positive, negative, and retry. A retry occurs when a cough is not detected during preprocessing. The application shows the diagnosis result screen based on the results transmitted from the server. Figure 7 shows a screen displaying diagnostic results from the application.  
Figure 5: Execution process of the application.  
Figure 6: Application screen 1: (**a**) the main screen; (**b**) the screen during recording; (**c**) the screen during processing.
# Networked Microgrids: A Review on Configuration, Operation, and Control Strategies
## 3 Networked Microgrids' Control
### Communication
#### 3.1.3 Challenges  
While the progress of the communication network contributes to improving the reliability and scalability of NMGs to a certain extent, they are vulnerable to communication limitations such as bandwidth constraints, time delays, traffic congestion, and packet losses, which can significantly impact overall system responses [100]. To address these challenges and ensure the performance of NMGs, various methods, including predictive controllers [103; 104; 105], lead-lag compensation controller [106], and adaptive controllers [107; 108], are proposed. However, a thorough assessment is necessary to minimize their potential adverse effects on NMGs as much as possible.
# Sound Activity Monitor Circuit for Low Power Consumption of Always-On Microphone Applications
## 2 Proposed Approach and Detailed Circuit  
Figure 2 shows a top-view configuration of the circuit that will be covered in this paper. The top circuits include SF, SAM, and CLK_GEN. The SAM block consists of an open-loop amplifier (OA), a second amplifier (AMP2), a comparator (CMP), and a decision logic circuit (LOGIC). The CLK_GEN block generates the clock used by the LOGIC block. Initially, when there is no external sound input, the _WUP_ signal is logically low (_LOW_) and the SF operates in low power mode. In low power mode, the source follower is not turned off completely and is operated with a low bias current of 100 nA level. The SF block consists of a stack type of PMOS acting as a source follower and NMOS supplying current to the source follower. The current flowing through the source follower can be controlled by adjusting the gate bias of the NMOS. When an external sound starts to be input, the SAM recognizes it and the _WUP_ signal becomes logically high (_HIGH_), so the SF starts to detect the sound in normal power mode. In normal power mode, the source follower operates with a normal bias current of 7 \(\mu\)A level.  
The output of SF is amplified by OA and AMP2, CMP generates a 1-bit digital signal that responds to sound above a certain level, and LOGIC outputs a _WUP_ signal with a low or high logic value depending on the situation.  
The architecture of Figure 2 incorporates the four proposed approaches. The first approach concerns where the SAM's input terminals should be connected. The second approach relates to whether the SAM should have an accurate amplification gain. The third approach concerns whether it is possible to remove the conventional envelope detector that was in use. The fourth approach relates to how to realize the falling edge of the _WUP_ signal when it sounds and then disappears.
## 3 Results
### Investigation of Ligand-Receptor Interaction Using Molecular Docking
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**1. Catalase (1 DGH)** & & & \\ \hline
**Ligands** & **PubChem CID** & **Binding Energy (kcal/mol)** & **Interacting Amino Acids** \\ \hline
2,3-Furandione & 13586830 & \(-\)5.1 & Phe334, Arg365, Arg72, Arg112 \\ \hline Curzerene & 5316217 & \(-\)7.7 & Phe334, His75, Gly147, Ash148, Ala133, Arg354, Val146, Tyr358 \\ \hline delta-Elemene & 12309449 & \(-\)6.9 & Val146, His75, Tyr358 \\ \hline Furanoeudesma-1,3-Dine & 643237 & \(-\)9.0 & His75, Ala133, Val146, Val74, Tyr358, Arg72, Arg112, Phe334, Val73, Thr361, Ser114, Gly131, Phe132, Gly147, Asn148, Arg354 \\ \hline Positive Control: & & & Gly216, Arg354, Asp65, Tyr358, His75, Gly147, Ser114, Ala133, Arg72, Ile332, Arg112, Arg365, Phe161, Val146, Arg148, His218, Phe153, Leu299 \\ \hline
**2. Superoxide dismutase (5 YTU)** & & \\ \hline
2,3-Furandione & 13586830 & \(-\)3.4 & Asn65, His63 \\ \hline Curzerene & 5316217 & \(-\)2.5 & Leu42, Asn86, Thr88 \\ \hline delta-Elemene & 12309449 & \(-\)4.2 & Leu144, Glu121, His43, Thr39, Val14, Pro13, Gly12 \\ \hline Furanoeudesma-1,3-Dine & 643237 & \(-\)5.0 & Asp11, Cys57, Leu144, Pro13, Val14, Gly12, Arg143, Gly10 \\ \hline Positive Control: & & & Arg143, Gly10 \\ \hline Levisoprenaline & 443372 & \(-\)5.7 & His63, Lys136, Lys70, Arg69, Asn65, Pro62 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Specifications of docking investigations; the ligands, interacting functional residues (amino acids), and corresponding binding energy. Figure 12: _Cont._Figure 12: Cont. Figure 12: The docking investigations highlighting the interactions of antioxidants including Catalase (1 DGH) and Superoxide dismutase (SYTU) to bioactive components of myrrh resin extract with ligands (1) 2,3—Furandione (**A,I**); (2) Curzerene (**C,K**); (3) delta\(-\)Elemene (**E,M**); and (4) Furanoeudesma\(-\)1,3–Diene (**G,O**), respectively. With every protein—ligand interaction, Ramachandran plots for corresponding interactions have been presented (**B,D,F,H,J,L,N,P**). The positive controls are shown in (**C1,C2**). The study found that myrrh acts by directly causing the inactivation of free viral particles and interfering with the virion envelope structures, which are crucial for the virus to enter the host cells [36]. So, we studied the molecular interaction of SARS-CoV-2 spike protein S (6 VXX) with active constituents of myrrh resin extract, 2,3-Furandione, Curzerene, delta-Elemene, and Furanoeudesma-1,3-Diene, to find the inhibitory action of these against the SARS-CoV-2 virus. The main functional residues found in interactions between two active constituents (Curzerene and Furanoeudesma-1,3-Diene) are Leu1024, Arg1039, Phe1042, and Thr1027, while the other two active constituents (2,3-Furandione and delta-Elemene) do not have common functional residues (Table 3) (Figure 13).
# Manipulation Planning for Large Objects through Pivoting, Tumbling, and Regrasping
## 6 Simulation and Experiments
### Experiment 1: Pivoting Gait  
In the first experiment, we perform the simulated pivoting gait in a real scenario. The inputs are the initial and target poses of the piano. The system automatically plans the grasp, pivoting motion, and regrasping motion. Figure 16 depicts the experimental process. The robot first grasps the piano (see Figure 16a) and pivots it around the right (from the robot view) rotational vertex (see Figure 16b). Further, the robot changes the rotational vertex to the left (see Figure 16c) and pivots the piano (see Figure 16d,e). In Figure 16e, both the arms are close to the limit of the robot workspace; hence, the robot cannot further move the piano forward. Pivoting gaits generated by the graph MPC [3] faces such limitations and the object cannot be placed in the target location. To solve this problem, regrasping is planned in this work and the robot changes the grasp poses to grasp the rear part of the object (see Figure 16f,g) and successfully pivots the piano to the target pose (see Figure 16h-j. Table 2 compares the performances of two methods where only the proposed planner can generate motions to move the object to the target location.  
Figure 16: Experiment 1: The robot manipulates the object by pivoting in (**a–d**) and regrasping the object in (**e–g**). In (**e**), without regrasping, the robot cannot further move the object forward because the robot arms are close to the kinematic limit. However, after regrasping, the robot can move the object forward for two pivoting steps, see (**h–j**). The yellow dots indicate the object vertices that contact the table.  
Figure 15: Manipulation of the piano by the robot for moving it forward by pivoting. The support vertices are marked as black dots. The initial configurations are shown in (**a**). The robot manipulates the object by pivoting in (**b–d**). When the joint configurations are close to the limits (**d**), regrasping is performed in (**d–f**). After regrasping, the robot moves the object to the goal pose, see (**f–h**).  
In the experiment, after moving the piano a few steps, the robot cannot further move the object with the initial grasp poses because of the robot workspace. To continue moving the object, the robot finds a new grasp configuration, regrasps the piano, and manipulates it to the target location. Compared with the pivoting gaits generated by the graph MPC [3], the regrasping motion enables further motion of the object by as much as 12.5 cm along the \(x\)-axis.
# Low Current Ripple Parameter-Free MPCC of Grid-Connected Inverters for PV Systems
## 3 Control Algorithm
### Optimization Strategy for Voltage Vector Selection  
To improve the stable state performance of the control system, VVVs are applied to expand the finite control set so that more choices of candidate VVs can be evaluated in the cost function. Specifically, the eight voltage vectors in Figure 3 divide the control space into six sectors, and thus, the VVVs can be synthesized by three real VVs at most. When one sampling period is factitiously divided into three parts, three types of VVVs will be generated in Sector I, as presented in Figure 4. The first type is synthesized by an active VV and a zero VV, like V\({}_{8}\). The second type is synthesized by two active VVs, like V\({}_{27}\). The last type is synthesized by two active VVs and a zero VV, like V\({}_{15}\). Via extending the vector synthesis of Sector I to other sectors, a new VVs distribution can be obtained, where the added 30 VVVs can be found in Table 1. The whole voltage space is further divided into twelve right-angled triangle areas.  
First, on the principle of cost function minimization, the optimal vector from small VVVs is selected to locate the hypotenuse of the right triangle.  
Then, as shown the example in Figure 6, if the hypotenuse is in the direction of V\({}_{9}\), the medium VVVs, V\({}_{15}\) and V\({}_{17}\), which are located on the corresponding right-angle sides, should be put into cost function to confirm a correct right-angled triangle.  
Finally, all VVs located in this triangle are viewed as the candidate VVs, as V\({}_{0}\), V\({}_{2}\), V\({}_{7}\), V\({}_{9}\), V\({}_{15}\), V\({}_{16}\), and V\({}_{27}\). To sum up, only 7 candidate VVs should be considered in one sampling period rather than 38 VVs, and thus, the computation burden can be significantly reduced.
# Effect of Impact and Bearing Parameters on Bird Strike with Aero-Engine Fan Blades
## 2 Bird and Fan Modeling
### Model Validation  
The model was verified by comparing the numerical results with [PERSON]'s experimental data [29], which used a compressed air cannon device to shoot a bird body with a mass of 1 kg to a rigid target plate with a speed of 253 m/s. In the LS-DYNA, a mallard and a hemispherical-ended cylinder bird model were impacted onto a rigid target with an initial velocity of 253 m/s. A shell unit was positioned at the center of impact, representing a sensor to monitor the pressure variations, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(C\) & 1480 m/s \\ \(S_{1}\) & 1.92 \\ \(S_{2}\) & 0 \\ \(S_{3}\) & 0 \\ \(\gamma_{0}\) & 0.1 \\ \(a\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in Gruneisen EOS for the mallard bird model [11].  
Figure 2: Comparison of the pressure at the center of the plate for the mallard and hemispherical-ended cylinder bird models and [PERSON]’s experiment.  
It can be seen that the pressure variations of the mallard model are closer to that of [PERSON]'s experiment as compared to the bird model with traditional geometry. The peak pressure recorded from the mallard model was 172 MPa, while it was 218 MPa for the hemispherical-ended cylinder model. The initial peak pressure of the experiment was 99 MPa, and it was used as a reference to calculate the error for the two other models. It was found out that the discrepancy between the peak pressure of experimental test and the hemispherical-ended model was 120%, while the error for the mallard model was 73%, which showed a great improvement. Comparing the noted values with the pressure measured by the experiment, the mallard model has improved the accuracy of the impact pressure acquisition. Therefore, the mallard model can better reflect the real bird body impact process.
# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 3 Methods  
### Network Architecture for Dehazing  
This chapter details a progressive lightweight dehazing model system optimized for a static scene captured by a fixed camera environments. The system encompasses a comprehensive process from pretraining on large-scale datasets to scene-specific fine-tuning and efficient model pruning.  
Our proposed approach for developing an efficient, scene-specific dehazing model for a static scene is illustrated in Figure 1. The process consists of several key stages, each contributing to the optimization of the model for a particular viewpoint while maintaining computational efficiency.  
The first stage involves pretraining the dehazing model on a massive dataset containing a wide variety of hazy scenes. This initial training allows the model to learn general haze removal techniques applicable to diverse environments. The pretrained model serves as a robust foundation for subsequent optimization steps.  
Following pretraining, the model undergoes localized fine-tuning tailored to the specific viewpoint of a fixed camera. This crucial step allows the model to adapt to the unique characteristics of the particular scene, such as recurring architectural elements, landscape features, or traffic patterns. By specializing in a single viewpoint, the model can significantly enhance its dehazing performance for that specific scene.  
After fine-tuning, we conduct a channel importance analysis to quantitatively assess the contribution of each channel to the dehazing task. This analysis provides insights into which parts of the network are most critical for maintaining high performance in the specific scene.  
Based on the results of the channel importance analysis, we implement an importance-based channel pruning step. This process strategically removes less essential channels from the network, reducing the model's size and computational requirements while striving to minimize any degradation in dehazing quality.  
The pruned network then undergoes a final round of fine-tuning, resulting in a lightweight model optimized for the specific fixed camera environment. This model balances high-quality dehazing performance with the efficiency required for real-time processing.  
In the deployment phase, the fine-tuned, pruned network processes hazy images from the fixed camera in real-time, producing clear, dehazed output images. This approach enables efficient, high-quality dehazing tailored to specific monitoring scenarios, making it particularly suitable for applications such as traffic management or outdoor surveillance systems.  
This approach is particularly valuable in scenarios such as traffic monitoring, surveillance systems, and environmental monitoring, where fixed cameras are commonly employed and clear visibility is crucial. The progressive nature of the model development ensures that it can adapt to specific deployment conditions while maintaining the generalization benefits of large-scale pretraining.
# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
## 4 Results and Analysis
### Result from TL Methods
#### 4.2.2 Results from TL Method Based on 2D-CNN  
The results from the 2D-CNN-based TL solution are plotted in Figure 13, and key metrics are summarised in Table 4. Two source domains, the fuel system and ImageNet, were used in this solution to pre-train the 2D-CNN, and the results following the TL process are plotted with the non-TL baseline. From Figure 13, for both ECS and APU, the 2D-CNN-based TL result from both source domains outperformed the non-TL baseline, demonstrating positive transfer. The average accuracy of ECS diagnosis when 30 or fewer cases existed in the target training set was 83.38% using ImageNet as the source domain, and the accuracy was 81.96% when using the fuel system as the source domain, corresponding to a 6.90% and 5.48% improvement, respectively. As for the APU, the average accuracies when there were 50 or fewer cases in the target training set were 61.96%, with ImageNet being the source domain and 58.95% with the fuel system as the source domain, resulting in a 5.04% and 2.03% improvement, respectively. It should be pointed out that the same 2D-CNN had been applied to the datasets in a non-TL setting, which resulted in extremely poor accuracy, so it is safe to rule out the concept that the improvement was brought about by the difference between the 1D-CNN and 2D-CNN. The results from non-TL 2D-CNN are not included as they do not have a significant contribution to the overall discussion.  
Another useful metric to assess the performance of methods in the small sample scenarios in the target is also recorded in Table 4, which is the minimum number of cases each method requires to reach and stay above a certain level of predictive accuracy. This can assess how many cases are required by each method to produce an accuracy that is comparable to obtaining a large training set, i.e., the minimum number of cases needed in the training set to avoid the small sample problem. The smaller this number is, the more economical and convenient it is to apply the method in a similar real-world scenario. Using 95% as the accuracy in ECS and 75% as the accuracy in APU, the minimum number of cases required by each method is shown in Table 4. For the ECS, the 2D-CNN-based TL solution using ImageNet reduced the minimum number of cases to 15 compared to  
Figure 13: Results from TL solution based on 2D-CNN for (**a**) the ECS case; (**b**) the APU case.  
the 27 cases required by the non-TL method, which is a considerably smaller training set, hence justifying its superior capacity in real-world small sample challenges. The same trend is observed for the APU. The 2D-CNN-based TL using the fuel system as the source domain reduced the number of cases required in the ECS but not in the APU. In general, the 2D-CNN TL method is effective in increasing the accuracy obtained and reducing the minimum number of cases required to avoid the small sample problem.
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 2 Experimental Procedure
### Moisture Monitoring  
AE signals were captured using a Micro-SHM system with a frequency range of 1 kHz to 1 MHz and Nano-30 AE sensors (125 kHz-750 kHz) (Physical Acoustics, West Windsor Township, NJ, United States) on two channels. The facesheet elastic modulus, constituent density, and core and skin thickness influence the AE wave velocity in FRPSSs. Moisture ingress was monitored using a regular pencil break (Hsu-Nielsen) test on Type A (GSP) and Type B (GSV/GSH) samples to assess core thickness and configuration effects. AE accuracy was verified with conventional weighing methods, offering a non-destructive, practical approach for in situ structural health monitoring of marine vessels  
\[v\ =\ \frac{D}{t_{2}-t_{1}}, \tag{2}\]  
where \(t_{2}\) and \(t_{1}\) are the acoustic feature readings of sensors 1 and 2, respectively, and \(D\) distance between them as shown in Figure 2.
# Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
## 2 AI for Propagation Modeling
### Machine Learning for Propagation Modeling  
Modeling the path loss to estimate the RSS is essentially a regression problem; although AI is also used for scenario identification, such as LoS and non-LoS (NLoS), this is not within the scope of this research, and it is further studied in [21]. Therefore, researchers have experimented with several regression-based models, the most popular being the multilayer perceptrons (MLP), which is a type of artificial neural network (ANN) [22]. For instance, [PERSON] et al. [23] proves that a multilayer perceptron with three hidden layers can approximate an arbitrary continuous function.  
Authors in [24; 25] trained an MLP using empirical measurements and environmental features such as the position and gain of the transmitter (Tx) and receiver (Rx) antennas, maximum transmission power, angle of arrival (AoA), and departure (AoD) as the input features of their models. These features are extracted using deterministic approaches applied before the regression training. Authors of [10] deployed image processing to extract environmental features, such as base station altitude, AoA and AoD, clutter, number of buildings on the LoS path, NLoS to LoS ratio, street width, and street direction from GoogleMaps to train an MLP.  
However, [PERSON] et al. [26] tried to address two of the shortfalls of using MLPs, namely, (a) collecting a large number of data samples that are required to train the MLP and (b) overfitting the MLP and losing generalization of predictions. This was performed using a hybrid model for LoRaWAN that combined COST231 in conjunction with an MLP, leading to a smaller number of neurons in the hidden layers. In this design, the MLP only had to complement the COST231 model rather than learning the propagation from scratch. This also reduced the number of required data samples and prevented overfitting. Using this approach, authors were able to create a predictive path-loss model that accepted LoRa's SF as one of the input features, and therefore, the SF could have been modeled to have a direct impact on the estimation.  
To simplify the data collection campaigns, authors in [27] suggested an adaptive neuro-fuzzy model that allows embedding an expert's knowledge into the system with the ability to learn and adjust its parameters. This achieved similar results to MLPs by utilizing only 30% of the data; this 70% reduction directly translates into a less exhaustive data collection campaign and saves significant time and cost. Authors in [28] also recommended the use of relevance vector machine (RVM) compared to other AI techniques when the volume of training samples is limited and noted a higher estimation accuracy for k-nearest neighbor (KNN) and support vector machine (SVM) algorithms compared to RVM.