esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Architecture
### Recovery Analysis
#### 2.2.2 SEU at QB  
When an SEU causes the node QB to flip from '0' to '1', both N5 and N1 are activated, while P8 remains in the off state. For the S0 node, even though P8 is off, the pull-up path P2 is also kept off, resulting in S0 entering a high impedance (high-Z) state, thereby preserving its initial value. Furthermore, each pull-down path for nodes Q and S1 consists of two transistors. In this scenario, transistors N3 and P7 remain off, while the pull-up transistors P3 and P1 remain activated, allowing both Q and S1 to retain their initial state of '1'. Consequently, QB is restored to its initial state through the actions of N6 and N4.
# Image Mosaicing Applied on UAVs Survey
###### Abstract  
The use of UAV (unmanned aerial vehicle) technology has allowed for advances in the area of robotics in control processes and application development. Such is the case of image processing, in which, by the use of aerial photographs taken by these aircrafts, it is possible to perform surveillance and monitoring tasks. As an example, we can mention the use of aerial photographs for the generation of panoramic images through the process of stitching images without losing image resolution. Some applications are photogrammetry and mapping, where the main problems to be solved are image alignment and ghosting images, for which different stitching techniques can be applied. These methodologies can be categorized into direct methods or feature-based methods. This paper aims to show an overview of the most frequently applied mosaicing techniques in UAVs by providing an introduction to those interested in developing in this area. For this purpose, a summary of the most applied techniques and their applications is given, showing the trend of the research field and the contribution of different countries over time.  
blending methods; feature matching; filtering algorithms; homography; image mosaicing; image registration; image stitching; panoramic image; review; UAV images +
Footnote †: journal: Applied
# Aging-Friendly Design Research: Knowledge Graph Construction for Elderly Advantage Applications
## 2 Theoretical Overview  
### Aging-Friendly Design  
Aging-friendly design refers to a design method that follows the design concept of being \"elderly-oriented\" [15], aiming to create products, environments, or services that meet the needs of the elderly by taking into account their physiological, psychological, and social characteristics in a comprehensive manner. Foreign research on aging-friendly design began early and has facilitated the formation of corresponding systems and standards, such as the United States' Universal Housing Design and Japan's Residential Design Guidelines for a Longevity Society [16].
## 3 Materials and Methods
### Classification Algorithm: XGB  
One of the state-of-the-art algorithm-based classification networks is XGB which is more efficient compared to the traditional gradient boosting decision tree. This gradient-enhanced decision tree improves computational speed through parallel computing, performance, and scalability. We have used XGB in the dataset because of the ease of finding the optimal solution through the second-order Taylor expression [6]. The classification performance of the XGB model mainly depends on quite a few hyperparameters. Hyperparameters are parameters containing certain values to determine the learning process as well as evaluate the model parameters. Before training the model, hyperparameters are set, and then, we obtain the model parameters that were learned. Those several hyperparameters are mostly important for optimization in the case of our rehabilitation dataset [14]. In the case of the TRSP dataset, learning_rate, gamma, colsample_bytes, max_depth, min_child_weight, subsample, and alpha are the seven necessary hyperparameters which influence the training process and model architecture. The learning rate generally controls the model's learning speed. More precisely, it governs the pace at which an algorithm updates the values of a parameter estimation and controls the step size for a model to achieve the lowest loss function. Gamma is used to make an additional part of the decision tree [15]. Colsample_bytes determines the percentage of column or feature numbers to build each tree. The maximum depth of the tree is represented through max_depth which also prevents overfitting so that trees cannot grow so deep. It mainly measures the number of nodes defining the longest path from the root node to the most distant leaf node. Min_child_weight should be smaller as it is an extremely imbalanced class problem. Subsample reduces overfitting and it occurs once in the case of every iteration. Last but not least, alpha controls L1 regularization of the leaf weights. It also demonstrates the depth of each tree with a round of boosting. Table 3 represents the hyperparameters and their optimal values. Both the epoch and the pop size are 50 and the upper and lower bounds are set to a certain value. However, these seven hyperparameters have to be optimized to achieve proper results and performance [16,17].  
We calculated the precision as well as recall curves using the equations below, and the curves are compared in the Section 4. In the case of various thresholds, precision and recall refers to the rate of false positive as well as false negative classes.  
\[Precision=\frac{T\_pos}{T\_pos+F\_pos} \tag{6}\]  
\[Recall=\frac{T\_pos}{T\_pos+F\_neg} \tag{7}\]  
where \(T\_pos\) is the true positive value, \(T\_neg\) is the true negative value, \(F\_pos\) is the false positive value, and \(F\_neg\) is the false negative value.
# Deep Convolutional Neural Network Model for Automated Diagnosis of Schizophrenia Using EEG Signals
## 3 Deep Learning  
Since EEG signals are nonlinear in nature, nonlinear feature extraction techniques are often employed to differentiate between EEG signals of normal versus SZ patients [25]. Machine learning is prevalently used for pattern recognition. However, this state-of-the-art technique exhibits some impediments. It works well for simple recognition tasks [26], but in realistic settings where the features studied display substantial variability, larger training datasets are needed in order to recognize them [27]. Additionally, a model with a sizeable learning capacity enables higher level features to be studied through learning of data from large datasets as compared to the traditional machine learning techniques. Moreover, conventional techniques require features to be extracted manually. In deep learning, both the feature extraction and classification processes are conducted automatically [28; 29] unlike the traditional machine learning techniques. Amongst others, CNN is the most prevalent type of deep learning network that has been exploited by researchers to identify abnormal EEG signals [30] and to study these signals to diagnose disorders such as depression [31], seizure [32], attention deficit hyperactivity disorder [33] and autism [34]. In this study, an eleven-layer deep CNN model has been implemented to discern between normal and SZ classes for non-subject based testing and subject based testing, respectively. Figures 2 and 3 illustrate the models used for non-subject based testing and subject based testing, respectively.  
Figure 1: Illustration of normal (**left**) and Schizophrenia (SZ) (**right**) Electroencephalograms (EEG) recordings. (X axis: seconds, Y axis: channels).
# A Data Transmission Method with Spectral Switches via Electroabsorption
## 2 Theory
Specifically, the heavy hole excitonic absorption effectively vanishes, and its strength is transferred into the single remaining light hole peak at higher photon energy. Figure 3 shows absorption spectra \(\alpha(\lambda)\) of the quantum-well waveguide for s-polarization mode as a function of the electric field applied perpendicular to the layers for (i) 1.6 \(\times\) 10\({}^{4}\) V/cm and (ii) 10\({}^{5}\) V/cm. The data marked with circles in the figure were adapted from [PERSON] [20] and are interpolated by cubic splines to give the curves. The light hole peaks are seen clearly and when the applied field is increased, the peak is decreased and broadened. Note that this method utilizes the waveguide mode to transmit the light signal, and the modes depend on the real part and imaginary part (related to the absorption) of the complex refractive index. Consequently, the change of the absorption spectra caused by the applied voltage may have some effects on the mode to different extents. However, because the length of the wave guide is short and the absorption spectra \(\alpha(\lambda)\) are not changed greatly (as seen in Figure 3), those effects may not be significant. Figure 2: (**a**) The waveguide device configuration used in spectrum peak shift control. (**b**) The waveguide schematic structure.
## 2 Perfluorocarbon (PFC) Filling  
### Methodology  
The instillation of lung using PFC based liquids was introduced early in pneumology and neonatal care. Herein, their specific physical-chemical properties are used as an inert volatile liquid, denser than water with low surface tension as well as high oxygen/carbon dioxide solubility. Instilled hydrophobic and biocompatible PFC is not resorbed by perfusion or tissue and remains in the alveolar airspace. Under continued ventilation, PFCs transports oxygen and carbon dioxide, and therefore supports maintaining the gas exchange.  
Pulmonary PFC administration is clinically established for the management of acute lung distress syndrome (ARDS) of preterm infants [9] as an effective technique improving the gas exchange with less barotrauma in comparison to gas ventilation.  
Recent work showed optimization and standardization strategies to control exogenously the pulmonary gas exchange and temperature [10]. The so called liquid lung ventilation (LLV) is performed partially on an isolated lung wing (PLV) or can be applied to both lungs, which is called total lung ventilation (TLV). Such lung ventilation manages the entire oxygenation and gas exchange, requiring exogenously PFC processing and ventilation control (Figure 1). Despite its use for treatment of ARDS, the PFC filled lung can serve as a heat exchanger. This can lead to applications in oncology as intrapulmonary hyperthermia [11] or as hypothermia for the management of acute traumatic conditions [12].  
The use of diagnostic imaging in PFC filled lung was hypothesized for Magnet Resonance MRI [13] or Computer Tomography (CT) [14] imaging. The applicability of pulmonary PFC administration for ultrasound imaging could be assumed due to the gas solubility, absorbing alveolar air and the safety of PFC administration even as an intravenous contrast agent [15].  
Figure 1: Schematic structure of a perfluorocarbon (PFC) liquid lung ventilator, requiring gas exchange of O\({}_{2}\) and CO\({}_{2}\) using condenser, ventilation of liquids to lung by separated pups for inspiration and expiration, and temperature control. Adapted with permission from [PERSON] et al. [10].
# Two-Degrees-of-Freedom (2 DOF) Magnetic Spring-Based Electromagnetic Energy Harvester
## 2 Architecture of the Two-Degrees-of-Freedom (2 DOF) Electromagnetic Generator/Energy Harvester  
The basic architecture of the electromagnetic generator or energy harvester contains four-ring permanent magnets (axially magnetized), a circular shaft, and winding coils. The 2 DOF energy harvester is designed so that every function's performance (such as magnetic restoring forces and induced voltage) should not affect or influence each magnet's magnetic field, particularly when the floating magnets move. Minimizing the harmful cogging force (caused by the magnetic attraction between the stator and the permanent magnets mounted on the translator, leading to undesirable vibrations, noise, and a ripple in the thrust force) generated from the magnets and coil movement is required. Different nonmagnetic materials were used to avoid magnetic field interference. The polarity of the magnets is arranged so that the levitating magnet experiences a repulsive force because of the fixed magnets. A few multilayer coils are attached around the outer surface of the two floating magnets. The height and diameter of the shaft are 550 mm and 12 mm, respectively. Figure 1 presents the test rig setup with winding coils.  
Figure 2 presents the schematic of the 2 DOF system. The magnetic poles are oriented (NS-SN-NS-SN) to repel each other. The height and width of the test rig are 550 mm and 300 mm, respectively. The equilibrium height of the 2 DOF oscillator is 303 mm; therefore, the fourth magnet is attached and locked to the vertical shaft 303 mm away from the first magnet. Two winding coils were added to the test rig, and both are connected to the data acquisition system to capture the induced voltages. Winding coil 1 was placed outside the first floating magnet, whereas winding coil number 2 was placed outside the second floating magnet. In Figure 1, sensor 1 is positioned on top of the bottom fixed magnet to measure  
Figure 1: Test rig of the proposed 2 DOF system.  
the displacement of the first floating magnet, and sensor 2 is positioned underneath the top fixed magnet to measure the displacement of the second floating magnet. Reflective sensors (GP2Y0A41 SK0F, Sharp) were used to measure the displacement of the floating magnets. The GP2Y0A41 SK0F is a distance-measuring sensor, consisting of a PSD (position-sensitive detector), an IR-LED (infrared-emitting diode), and a signal processing circuit. The sensor's output signal is voltage, and the relationship between this sensor's output voltage and the inverse of the calculated distance is nearly linear over the sensor's usable range. The sensor's output voltage is converted to an estimated distance by constructing a best-fit line that relates the inverse of the output voltage (V) to distance (mm).
# Biometric Vibration Signal Detection Devices for Swallowing Activity Monitoring
## 3 Results and Discussion
### Experimental Results
The location of the device attachment played a crucial role in acquiring biometric vibration signals accurately, with optimal positions identified near the neck, closely associated with both the swallowing and respiratory cycles. Furthermore, the detection algorithm, employing an adaptive threshold-based peak detection approach, demonstrated robust performance across various activities, ensuring accurate identification of swallowing events. The comparison between results obtained using one accelerometer versus two accelerometers highlighted the superiority of using two accelerometers, showcasing improved precision and sensitivity in classifying swallowing events. However, there were limitations in the experimental scenarios utilized within this study. The focus was primarily on detecting swallowing and the respiratory phase during physical activities, consequently restricting food intake to the use of a cup. Notably, the study excluded the participation of elderly subjects and did not assess their swallowing ability through non-instrumental methods, which may restrict the generalizability of the findings across all potential clinical scenarios. This selection bias highlights an area for further research to extend the applicability of the technological solutions developed in this study to a broader range of dysphagia patients and conditions. Overall, this study presents a comprehensive framework for reliable monitoring and detection of swallowing activities, even in dynamic environments, laying the groundwork for enhanced dysphagia diagnosis and therapeutic interventions. This research was supported by the 2023 scientific promotion program funded by Jeju National University. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Northwestern University Institutional Review Board, Chicago, IL, USA (STU00212981 for UMA device validation studies). The data presented in this study are available on request from the corresponding author due to concerns for the privacy of the subjects. The author declares no conflicts of interest.
# MT-VTON: Multilevel Transformation-Based Virtual Try-On for Enhancing Realism of Clothing
## 4 Approach
### Cloth Segmentation Warping Process  
Given the cloth segmentation map \(M^{c_{s}}\) for the clothing item \(c_{s}\), this procedure calculates the warped cloth segmentation map, which is aligned to the person's pose. The cloth segmentation warping network is composed of three primary components: (1) the segmentation map encoder, (2) the deformable convolutional layer [29; 30], and (3) the segmentation decoder. Both the encoder and the decoder are traditional convolutional networks, which consist of strided convolutional layers, normalization layers, and activation functions. The key component in this model is the deformable convolutional layer, which warps the segmentation feature to align with the target pose. We employed Deformable Convolution v2 [30] for this layer. The loss function used to train these modules can be represented as follows:  
\[\mathcal{L}_{\text{segwarp}}=\text{CE}(\hat{M}^{c_{s}}_{p},M^{c_{s}}_{p})+ \mathbb{E}_{c_{s}}\Big{[}(1-D_{\text{segwarp}}(\hat{M}^{c_{s}}_{p}))^{2}\Big{]}, \tag{5}\]  
where \(\hat{M}^{c_{s}}_{p}\) is the estimated warped cloth segmentation map aligned to the person's pose and \(D_{\text{segwarp}}\) denotes a discriminator for adversarial training.
## 5 Results and Discussion
### Numerical Results
#### 5.1.2 Numerical Results of Temperature Field  
The plots of temperature in Figure 7a-i (Tables 3-5) indicate that the PTFHS exhibited the lowest \(T_{\text{sp}}\), followed by MFLHS and TTFHS. As the geometries were complex, the edge lines of the solid parts in all temperature field plots were hidden for easily visualizing the plot with the altered color range. Each of the heat sinks under higher Reynolds numbers exhibited higher heat transfer performance than under a lower Reynolds number.
## 4 Experiments and Results
### Results Analysis
#### 4.3.4 Energy Consumption  
In this study, a trust method is proposed for Low-Power and Lossy Networks (LLNs), which are found to be 30% more energy efficient than the PCC-RPL, as illustrated in Figure 13. The average energy consumption (J) used by our mechanism is 0.042, while the PCC-RPL consumed 0.057.  
Figure 11: Packet Loss Ratio at the Node Level.  
Figure 12: Average Packet Loss Ratio at the Network Level.
## 4 Evaluation and Benchmarking
### LoRaWAN Connectivity Evaluation  
The data packets collected from the first sub-layer of the edge-based processing layer (EPL-A) were pre-processed and forwarded to the Tergo nodes (EPL-B) for detailed stream-based analysis. Recall from Section 3.2 that the Max nodes support basic data processing functionality and can only be used for elementary tasks. The pre-processed data were forwarded to the nearest Tergo nodes using the LoRaWAN. It is now important to assess the connectivity of networking technology. In this second experiment, we wished to measure signal quality and the resulting number of re-transmissions over a given period of time, as well as the achieved packet delivery rate. Communication was carried out over a private LoRaWAN network that operated on the publicly accessible, license-free 868 MHz radio frequency band and was easily affected by nearby transmission and increased background noise [101, 102]. This noise originated from any electronic equipment with wireless connectivity or RF transmitters (e.g., garage door openers) and could be especially harmful to the Internet of Things devices. Figures 10 and 11 showcase the effects of this background noise on our LoRaWAN installation. The first figure shows the re-transmissions required to upload the collected data over a weekend from the Max node to the LoRa server. When we compare these data to the ones from Figure 11, we see how the transmissions became more difficult, resulting in many more re-transmissions. We also see how on a bank holiday, which was on Thursday, the number of re-transmissions was again low, similar to the conditions of the weekend.  
Figure 9: wM-Bus RSSI variability during a day.  
Table 2 presents the absolute numbers for the received and expected LoRaWAN packets from Mox nodes in the four locations presented above. We can see that the number of received packets was much higher than the ones observed for the wM-Bus network. These results also agreed with the ones presented in [23], with LoRaWAN appearing to be a much more reliable communication protocol than wM-Bus, even at larger distances. Unfortunately, the power requirements of LoRaWAN were much higher than those of wM-Bus and could limit the overall lifetime of a LoRaWAN-enabled smart water meter. Furthermore, we need to note that during our evaluation, we had no LoRaWAN-enabled smart meter that we could place inside the cast iron-covered inspection pits to evaluate its behaviour in a straight comparison.  
Figure 11: LoRaWAN re-transmissions during weekdays and a bank holiday (Thursday).  
Figure 10: LoRaWAN re-transmissions during the weekend.
###### Abstract  
In this paper, we propose a hybrid genetic algorithm (HGA) that embeds the tabu search mechanism into the genetic algorithm (GA) for multiple-input multiple-output (MIMO) detection. We modified the _selection_ and _crossover_ operation to maintain the diverse and wide exploration areas, which is an advantage of the GA, and the _mutation_ operation to perform a local search for a specific region. In the _mutation_ process, the 'tabu' concept is also employed to prevent the repeated search of the same area. In addition, a layered detection process is applied simultaneously with the proposed algorithm, which not only improves the bit error rate performance but also reduces the computational complexity. We apply the layered HGA (LHGA) to the MIMO system with very high modulation order such as 64-quadrature amplitude modulation (QAM), 256-QAM, and 1024-QAM. Simulation results show that the LHGA outperforms conventional detection approaches. Especially, in the 1024-QAM MIMO system, the LHGA has less than 10% of computational complexity but a 6 dB signal-to-noise ratio (SNR) gain compared to the conventional GA-based MIMO detection scheme.  
MIMO detection; high-order QAM; genetic algorithm; tabu search; metaheuristic 3