esa-sceva/satcom-chunk-collection huggingface dataset:
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 6 Performance Evaluation
### Cooperative Data Relaying with UAVs  
#### 6.2.1 Methodology and Setup  
We assess the performance of the proposed TSP and CETSP algorithms with cooperation, namely the TSP-COPE and CETSP-COPE and compare it to the performance of the (_selfish_) TSP and CETSP where no cooperation among nodes holds.  
Given a set of points and the corresponding distance between such points as an input, the TSP algorithm generates the shortest path that visits all nodes at least once. Similarly, the CETSP generates the shortest path that visits points that are _close enough_ to the original ones. For the sake of clarity, below are summarized all the four path planning algorithms.  
* TSP: finds the optimal route that visits each node in the network; there is no cooperation among the nodes to relay data among each other.
* CETSP: determines the minimum number of stops from which the drone can still communicate with all nodes without having to stop at each of them, and further constructs the shortest path that visits all such stops.
* TSP-COPE: similar to TSP, where node cooperation is supported; the optimal route is calculated based on the \(n_{3}\) tier nodes.
* CETSP-COPE: similar to CETSP, where node cooperation is supported; the optimal route is calculated based on the anchor points obtained from the \(n_{3}\) tier nodes.  
The following assesses and compares the performance of the two proposed TSP-COPE and CETSP-COPE algorithms (see Section 5) with the selfish TSP and CETSP algorithms. All the four schemes have been implemented as additional modules to the ONE simulator ([[https://akaranen.github.io/the-one/](https://akaranen.github.io/the-one/)]([https://akaranen.github.io/the-one/](https://akaranen.github.io/the-one/))). The considered scenarios consist of various network densities, where the nodes are randomly situated in an urban area. A drone flies over the disaster area with a speed of 10 m/s. We assume that 2 s is a sufficient time to exchange data with a node, especially given the fact that the data, generally in disaster scenarios, consists of light-weight messages useful for rescue operations and  
Figure 9: Difference between the highest and the lowest energy values in the network.  
assistance. However, we consider a _time-guard_ of a minimum of 5 s for a drone to hover above a node. Moreover, the hovering time of a drone extends in proportion to the cardinality of the cluster that the node is CH of. For instance, a drone visiting a CH point of a cluster of three members would stop for a time of max (5 s, 6 s). We impose such time-guards to take into account for possible unsuccessful transmissions or collisions.
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
## 4 Application and Validation  
### Experimental Analysis of Faulty Gear  
In order to verify the effectiveness of the proposed method with faulty gear, the collected vibration signal of faulty gear is analyzed. The experimental device is shown in Figure 2. The whole device is driven by a 550 W (220 V, 50 Hz) AC motor that drives the shaft system with couplings. There are two rolling bearings on the shaft system. The shaft section between two bearing seats is equipped with  
Figure 1: The summarized procedure of the proposed method.  
a belt wheel, and the belt drives the active gear shaft of the gear box. One acceleration transducer is installed vertically on the shell near the shaft. The sampling frequency \(f_{s}=8192\) Hz and the sampling point number is 8192. The motor speed is 850 rpm, that is, the rotation frequency \(f_{r}=14.2\) Hz. The number of gear teeth \(Z=20\). According to \(f_{c}=Z\times f_{r}\), the gear mesh frequency \(f_{c}=283.3\) Hz.  
In this experimental analysis, the collected vibration signal of a broken tooth fault is selected, and the number of the broken tooth \(Z_{b}=1\). From experience, when the gear is affected by a broken tooth fault, the rotation frequency \(f_{r}\) and its frequency multiplication will be the main features in the frequency domain, and the frequency \(f_{z}=Z_{b}\times f_{c}\) will also exist. The time-domain waveforms and amplitude spectra obtained by fast Fourier transform (FFT) [23] of a healthy signal and faulty signal are shown in Figure 3.  
Figure 3: The time-domain waveforms and amplitude spectra of a healthy signal and faulty signal. (**a**) The time-domain waveform of a healthy signal. (**b**) The amplitude spectra of a healthy signal. (**c**) The time-domain waveform of a faulty signal. (**d**) The amplitude spectra of a faulty signal.  
Figure 2: The test rig of faulty gear.  
The fault features of the collected signal cannot be found in Figure 3. The performance of feature extraction of a broken tooth fault with wavelets, EMD, VMD, EEMD and the proposed method are compared in the following section.
# IoT-Based Intelligent Modeling of Smart Home Environment for Fire Prevention and Safety
## 6 Results
### Sensor Behavior  
We simulated two different scenarios to compare the sensor's behavior and overcome the problem discussed in this paper. First, we simulated a single-sensor scenario by deploying a temperature sensor for each portion. Then, we used a multi-sensor as in our proposed work. After performing both simulations, two types of data were generated, i.e., (i) data generated during the single-sensor environment simulation; (ii) data generated in the multi-sensor environment simulation. Then, we examined the sensor's behavior for these datasets, as shown in Figures 10 and 11, respectively. For the first scenario (uni-sensor), we assumed that the fire started from the kitchen, we made kitchen sensor in-active and then checked the behavior of the other sensors. Figure 10 shows the sensors' behavior in the unifunctional, single sensor simulation. WE found that the kitchen sensor was in-ctive throughout the fire. When the fire and heat spread out, the other sensors deployed in TV lounge, living room, and bedroom started detecting a temperature increase after 8, 14, and 18 min respectively.  
When we simulated and analyzed our multi-sensor system, we found that it detected the event in quite a short time as compared to the single sensor. As the fire started from the kitchen, we analyzed the sensor's behavior only in the kitchen.  
The graph in Figure 11 shows that, when the fire started, the sensor that we deployed in the kitchen started sensing the environment immediately. When the temperature, gas, and smoke values exceed the threshold values, the sensor started sending fire alerts to the sink. The simulation results  
Figure 9: Indoor scenario of the simulation using FDS.  
proposed approach and compared it with the 50% and 100% duty cycle, and the results are shown in Figure 12. Duty cycles of 100% and 50% indicate the time when the radio interface remains in the active state. An energy-efficient communication stack has a duty cycle less than 1%. The sensors used in each room had a pair of AA batteries (3000 mAh). The total energy consumption of the sensors during 12 h was calculated (for both 50% and 100% duty cycles), and the results are presented in Figure 12. The energy consumption of the ZigBee sensor was within the limit. A pair of three sensors were deployed in each portion, e.g., for the kitchen, we used temperature, smoke, and gas sensors, as in the other rooms. The energy consumption of these sensors was calculated in our proposed approach. Figure 13 shows the results for energy consumption
# Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation
## 5 Simulation Results
### Performance Evaluation
#### 5.2.2 Reduction of CQI Feedback Bits  
Through this experiment, the system throughput of the conventional CQI feedback method and the time-coherence-based CQI feedback method were compared. The simulation set-up is listed in Table 3.  
Table 4 shows the system throughput according to the existing 4-bit wideband CQI feedback report in various SNR environments, and Table 5 shows the difference in system throughput between the existing method and the proposed method. In addition, all the system throughputs and the difference for each SNR are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**SNR (dB)** & \(\mathbf{-10}\) & \(\mathbf{-7}\) & \(\mathbf{-4}\) & \(\mathbf{-1}\) & \(\mathbf{2}\) & \(\mathbf{5}\) & \(\mathbf{8}\) \\ \hline
**Throughput** & 0.0146321 & 0.0560517 & 0.1404964 & 0.2860831 & 0.4981283 & 0.7843385 & 1.1504057 \\ \hline
**SNR (dB)** & **11** & **14** & **17** & **20** & **23** & **26** & **29** \\ \hline
**Throughput** & 1.5927229 & 2.1139200 & 2.6759019 & 3.1811068 & 3.5524640 & 3.7814499 & 3.9047303 \\ \hline \hline \end{tabular}
\end{table}
Table 4: System throughput of the 4-bit wideband CQI feedback (Mbps).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulation type & SUSISO \\ Bandwidth & 1.4 MHz \\ Carrier frequency & 2.0 GHz \\ Subcarrier spacing & 15 kHz \\ Cyclic prefix & Normal CP \\ Channel estimation & Perfect \\ Channel model & VehA \\ Velocity & 30 km/h \\ Receiver & Zero forcing \\ Number of subframes & 100,000 \\ CQI feedback period time & 1 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters for system throughput of the proposed channel quality indicator (CQI) feedback scheme.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Calibration Method** & \(\mathbf{-10}\) & \(\mathbf{-7}\) & \(\mathbf{-4}\) & \(\mathbf{-1}\) & \(\mathbf{2}\) & \(\mathbf{5}\) & \(\mathbf{8}\) \\ \hline improved & 0.014636 & 0.056152 & 0.140616 & 0.286313 & 0.498090 & 0.783961 & 1.150098 \\ \hline average & 0.014707 & 0.056163 & 0.140429 & 0.285541 & 0.497030 & 0.782343 & 1.148416 \\ \hline
**Calibration Method** & \(\mathbf{11}\) & \(\mathbf{14}\) & \(\mathbf{17}\) & \(\mathbf{20}\) & \(\mathbf{23}\) & \(\mathbf{26}\) & \(\mathbf{29}\) \\ \hline improved & 1.592679 & 2.113780 & 2.675177 & 3.180670 & 3.551829 & 3.781508 & 3.904715 \\ average & 1.590840 & 2.110598 & 2.666004 & 3.173200 & 3.545650 & 3.775832 & 3.900796 \\ \hline \hline \end{tabular}
\end{table}
Table 2: System throughput comparison of the improved calibration method and the average calibration methods (Mbps). SNR—signal-to-noise ratio.
## 3 Federated Learning Solutions in the Medical Domain
### Case: The FL Post-TAVR Complication Prediction System  
In this study, an existing Post-TAVR Navigator platform is restructured into a federated learning solution, FL Post-TAVR, based on the proposed federated learning approach through lifecycle management by using FedOps.  
Figure 2 illustrates the configuration of the centralized Post-TAVR Navigator in a decentralized federated learning (FL) environment. The conventional Post-TAVR Navigator procedure is depicted on the left side of the diagram, wherein medical institutions accumulate real-world data (RWD) predicted upon post-TAVR assessments and clinician diagnoses. These data were used to predict the complications and for subsequent monitoring. During the transition to the FL post-TAVR system, this process becomes the sequence that each participating institution follows in a federated learning environment. Each institution generated a local model based on its data.  
The right side of Figure 2 shows the transition of the existing post-TAVR complication prediction system into a federated learning environment in which multiple institutions act as clients. Each hospital's post-TAVR complication prediction system became a client in the federated learning process, and these clients created local models using their data. The local models were then sent to the FL Server, where they were aggregated to create a global model. The FL Server is responsible for aggregating the local model parameters, testing the model performance, selecting clients, and managing the version control. The FL Manager monitors the health of each client to ensure that they can continue participating in the federated learning, whereas the FL Server monitors its status. Once built, the global modelsundergo performance evaluations using the test data, and the FL Server redistributes these refined global models back to the clients.
## 3 Results
### Signalling Pathway
The Trp of essential amino acids (EAAs) exerts beneficial regulatory function in mucosal growth or maintenance and the alleviation of intestinal inflammation by the 5-HT signalling pathway [148], in the recovery of colitis [149; 150], and in the function of intestinal homeostasis and anti-inflammation in the intestine [151; 152]. One of the nonessential amino acids, Gln, controls anti-inflammatory effects through the action of NF-kB signalling pathways, intestinal tight junctions, mTOR, and mitogen-activated protein kinase (MAPKs) [153; 154; 155]. One of the possible pathophysiologies of IBS is a low-grade intestinal mucosal inflammatory response, and nuclear factor Kappa (NF-kB) is a crucial transcription factor in this response. As a transcription factor linked to the inflammatory response, NF-kB has the ability to control a number of inflammatory variables [195]. TNF-\(\alpha\), IL-1\(\beta\), and IL-8 are all promoted by NF-kB activation, which results in a huge production of TNF-\(\alpha\) and an inflammatory response [196; 197]. \begin{table}
\begin{tabular}{c c c c} \hline
**Amino Acids** & **Signaling Pathways** & **Functions** & **References** \\ \hline Glycine & NF-\(\kappa\)B & \begin{tabular}{c} Strengthen the intestinal mucosal \\ barrier and reduce oxidative stress \\ and TNF-a, IL-1, and IL6 levels \\ \end{tabular} & [156; 157; 158; 159] \\ \hline Alanine & Unclear & \begin{tabular}{c} Enhance intestinal defense and \\ protection function \\ \end{tabular} & [147; 160] \\ \hline Glutamine & NF-\(\kappa\)B, mTORAPK/ERK & \begin{tabular}{c} Enhance the intestinal barrier, \\ reduce proinflammatory cytokines, \\ and have anti-inflammatory effect \\ \end{tabular} & [161; 162; 163; 164; 165] \\ \hline Glutamate & Unclear & \begin{tabular}{c} Strengthen the intestinal mucosal \\ barrier, alleviates heat \\ stress-induced impairment of \\ intestinal morphology, and reduce \\ oxidative stress and TNF-a, and \\ IL-1 levels \\ \end{tabular} & [166; 167; 168; 169] \\ \hline Cysteine & NF-\(\kappa\)B, Nrf2 mTOR & \begin{tabular}{c} Enhance intestinal barrier function, \\ tight junctions, and homeostasis \\ while lowering oxidative stress and \\ decreasing TNF-\(\alpha\), IL-1\(\beta\), IL-6, \\ and IL-8 \\ \end{tabular} & [170; 171; 172; 173; 174; 175; 176] \\ \hline Proline & Unclear & \begin{tabular}{c} Increase levels of superoxide \\ dismutase, tight junction proteins \\ \end{tabular} & [177; 178; 179] \\ \hline Aspartate and asparagine & NF-\(\kappa\)B and MAPK & \begin{tabular}{c} Enhance intestinal barrier function \\ and lower the levels of \\ proinflammatory cytokines \\ \end{tabular} & [180; 181; 182; 183; 184; 185; 186] \\ \hline Tyrosine & Calcium-sensing receptors & \
\begin{tabular}{c} Improve intestinal health and \\ immune response \\ \end{tabular} & [147; 187; 188; 189; 190] \\ \hline Serine & Unclear &
\begin{tabular}{c} Increase colonic protection, \\ mucosal healing and gut microbiota \\ \end{tabular} & [191; 192; 193; 194] \\ \hline \end{tabular}
\end{table} 
Table 1: Functions of non-essential amino acids in intestinal inflammation on signalling pathways.
# Pulsed Thermography Dataset for Training Deep Learning Models
## 4 Experiments with Deep Models for Defect Segmentation  
### U-Net  
Introduced in [7], U-Net is a popular and effective neural network approach for biomedical image segmentation. It is composed of two key components: the encoding stage and the decoding stage. The encoding stage involves several consecutive blocks of convolutional neural network operations, each followed by a max pooling operation. The decoding stage is comprised of an equal number of transposed convolution blocks to upsample the output back to the same dimensions as the input data. Importantly, U-Net also includes skip connections, which facilitate the transfer of high-resolution information from the encoding stage to the decoding stage by performing copy and concatenation of feature maps. In this work, we employ a lightweight version of U-Net, with a single convolutional layer used as an operation block, as shown in Figure 6.
## 3 Our Approach  
### Overview of the Ultimate Compression Method  
Our ultimate compression method addresses the challenge of deploying deep neural networks on resource-constrained edge devices by combining tensor decomposition techniques with binary neural networks. The method consists of the following key steps:  
1. Training a floating-point deep neural network model;
2. Applying tensor decomposition to reduce the model's complexity;
3. Binarizing the decomposed model using the XNOR-Net approach;
4. Fine tuning the resulting model to recover accuracy.  
This approach leverages the strengths of both tensor decomposition, which reduces the number of parameters, and binary neural networks, which minimize the bit width of each parameter. The combination of these techniques allows for significant compression ratios while maintaining model performance.  
A crucial aspect of our method is the novel rank selection mechanism for tensor decomposition, which considers the sensitivity of individual layers to compression. This ensures that critical layers maintain their representational power while less important layers are more aggressively compressed.  
Figure 3 illustrates the work flow of our ultimate compression method. The process begins with the training of a floating-point model, followed by the application of tensor decomposition. We explore the following three decomposition techniques: CP, Tucker, and Tensor Train. The rank for decomposition is selected using one of the following three methods: random selection, our novel sensitivity-based approach, or VBMF. After decomposition, the model is binarized using the XNOR-net approach and fine-tuned to recover accuracy. Finally, the compressed model is evaluated for both performance and efficiency.  
This systematic approach allows for a comprehensive exploration of different compression methods while maintaining an efficient computational solution, but it does not consider the relative importance of different layers in the model's overall performance. Because of its random nature, this method could achieve suboptimal compression in the ions, so we detail each component of our approach, beginning with the employed tensor decomposition techniques.  
We use three different tensor decomposition methods, namely CP, Tucker, and Tensor Train, on both convolutional and fully connected layers of the models.  
Figure 3: Flow chart of ultimate compression method: tensor decomposition followed by binary neural networks (BNNs).
# Analysis of the Energy Efficiency of a Pneumatic Booster Regulator with Energy Recovery
## 5 Results and Discussion
### Energy Efficiency of VBA-R
#### 5.3.2 Effect of Diameter Ratio  
Figure 2 gives the structure of the VBA-R, which we have claimed helps energy recovery. However, when we considered this again, we found that the pressure in the drive chamber had simultaneously done negative work. With an increase of the recovery chamber diameter, the volume increased, which lead the recovery pressure to decrease. Therefore, it should be confirmed carefully whether the force on the piston in the recovery chamber is increasing or decreasing. This also affected the energy efficiency of the VBA-R, so we changed the diameters to study the influence of the diameter ratio (the ratio of the recovery chamber diameter to the drive chamber diameter, \(D_{2}/D_{1}\)) in order to properly select the design parameters.  
The change of boost ratio with \(\mathrm{D_{2}}/\mathrm{D_{1}}\) is shown in Figure 15, when all other parameters are constant. It is clear that the boost ratio increases first and then decreases, and the boost ratio reaches its greatest value when the diameter ratio is nearly 1.3. The variation of energy efficiency is depicted in Figure 16, in which the energy efficiency decreases with increasing supply pressure. The energy efficiency reaches its largest value for different supply pressures when the diameter ratio is closest to 1.3.  
Figure 14: Energy efficiency of VBA-R.  
Figure 15: Relationship between boost ratio and diameter ratio.
# A Compact Dual-Band Microstrip Patch Antenna for C- and X- and Ku-Band Applications +
## 1 Introduction
Subsequent sections will provide detailed insights into the antenna design, performance evaluation, and prospects of this innovative solution [8]. Overall, the research highlights the efficacy of circular microstrip patch antennas in fulfilling the demands of high-power applications across the C-, X-, and Ku-bands. The suggested antenna design holds the potential for implementation in diverse wireless communication systems, such as satellite communication, point-to-point communication, and 5G cellular networks. Some of the latest designs are also discussed in [9, 10]. In this paper, we first provide an introduction and an overview of related research, which discussed the C- and Ku-bands, that was carried out recently. Then, we describe the design of the antenna in Section 2, which includes how we can improve and make changes in the antenna. After that, we discuss the results in Section 3 of a parametric analysis that is presented in the paper. The comparison of the different results is also discussed in this section. Finally, we conclude the paper by summarizing the key findings and offering insights into future research directions.
## 4 Design of the Soil and Pile Models
### Soil Model
#### 4.1.1 Soil-Modelling Criteria  
As stated earlier, the density of the soil model was considered to be equal to the density of the prototype soil. Several other parameters such as the nonlinear stress-strain response and the modulus degradation as well as the damping curves were not explicitly modelled from the prototype case. Instead, the technique of implied prototypes was employed to determine whether the properties of the scale model for these parameters were reasonable. The undrained shear strength and shear modulus or shear wave velocity were the key parameters in the scale soil modelling. If the elastic response is required for the free-field soil and the soil-pile system, then the soil shear modulus should be modelled appropriately. On the other hand, the undrained shear strength should be modelled appropriately when the inelastic response of the soil-pile system is required. If the full nonlinear response of the dynamic system is desired, then both criteria should be considered simultaneously, as in the current study. These parameters have distinct scaling factors according to the scaling results provided in Table 2. The static and dynamic soil behaviours are affected greatly by the plasticity index (_PI_), which is an additional soil-modelling parameter that is not reflected in Table 1. Therefore, the use of a soil model with an analogous _PI_ to the prototype is essential. As _PI_ is a dimensionless parameter, a 1:1 scaling ratio between the model and the prototype was employed.