esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Overview  
Extensive research has been conducted to determine an efficient data collection and transmission scheme in healthcare over the last two decades. The developed monitoring systems implemented various types of patches for healthcare signal collection. The systems are developed not only for patients with chronic diseases, but also for those who are in critical conditions. Patients' health conditions that should be monitored in real-time have many types. For example, wearable body sensor networks for blood pressure monitoring were proposed in [32]. A wearable wireless ECG monitoring system was developed in [33], particularly focusing on low power and cost effectiveness. Healthcare systems that monitor diabetic patients were developed in [34; 35] by using a smartphone. In the literature [36], a remote monitoring system was proposed to supervise patients developing Alzheimer's disease by tracking their movement patterns and locations. The same task was implemented in [37] using ZigBee. A wearable monitoring system was also developed to monitor sleep quality by investigating the respiration rate of patients [38]. The types of wearable technologies and monitoring systems for patients with Parkinson's disease are surveyed in [39]. Patch devices are embedded in patients' bodies. Thus, it must be ensured that a patient's natural movements are not troubled by the integration of the device. In addition, the healthcare signals must be transmitted at low power with no or minimal errors. A wide range of low-power devices are used by researchers to transmit healthcare information into another processing unit. Bluetooth was the most widely used owing to its wide availability, robustness to obstacles, and simple protocol structure [16; 17]. Bluetooth has now been replaced by BLE, a recent development with low power consumption and a moderate communication range. Meanwhile, additional RF-based technologies are being developed and utilized in remote health monitoring. For example, a survey was conducted focusing on 6 LowPAN-based wireless monitoring [40]. Mobility management has had the priority in the literature. A discussion on mobility support using 6 LoWPAN is also provided in the literature [41; 42]. ZigBee- and ANT-based remote monitoring systems have also been reported [11; 33; 43]. The collected signals can be processed using a smartphone or personal computer (PC). Table 2 presents a summary of the existing health monitoring systems. It is worth noting here that the aforementioned systems can suffer from a considerable amount of BER due to interference generated from neighboring devices [31; 44]. In addition, the smartphone-based management systems are not very influential in cases where the monitoring person resides in another
## 2 Samples Descriptions and Test Setup
### Test Setup and Instruments Descriptions  
In order to study the influence of the number of freeze-thaw cycles and the minimum freeze-thaw temperature on the initial damage of sandstone samples, as well as the law of crack propagation during the loading process under the influence of freeze-thaw action, a series of experimental steps were implemented:  
1. The saturated sandstone samples were placed in a temperature- and humidity-controlled (THC) container; the temperature was set to \(-20\)\({}^{\circ}\)C; and 0, 5, 10, 15, 20, 25, and 30 freeze-thaw cycles were performed. In addition, the minimum freeze-thaw temperature was set to \(-5\), \(-10\), \(-15\), \(-20\), \(-25\), or \(-30\)\({}^{\circ}\)C (Table 1), and the number of freeze-thaw cycles was 20 to study the influence of the minimum freeze-thaw temperature on the rock properties. The freeze-thaw cycle process is shown in Figure 3. The P-wave velocity was measured every five freeze-thaw cycles for each sandstone sample.  
Figure 1: Coarse sandstone samples.  
Figure 2: Distribution diagram of the sandstone porosity.  
2. The electronic universal testing machine that was used for the uniaxial compression experiments was a CMT5105 produced by MTS Corporation, with a maximum test force of 100 kN and a control accuracy within 0.01 kN. A loading rate of 0.02%/min under axial strain control was continually applied until the sandstone samples experienced failure in the uniaxial compression tests, where the axial strain and the lateral strain were measured by strain gauges.
3. A DS5-8B AE instrument with eight channels was used for the acquisition of AE signals during the rock-loading process. When the AE threshold was set as 40 dB, almost no interference signal appeared, which indicated that 40 dB was an appropriate threshold for the acquisition of rock fracture signals. Therefore, the AE threshold of these experiments was set to 40 dB. The AE signals obtained from the AE transducer were amplified by 40 dB to enhance the signal. Each data set that was used for signal processing contained 4096 data points with a sampling frequency of 500 kHz. The peak definition time (PDT), hit definition time (HDT), and hit locking time (HLT) were set to 50, 100, and 500 us, respectively. The test instrument is shown in Figure 4.  
Figure 4: Electronic universal testing machine and AE instrument.  
Figure 3: Temperature curve of one freeze–thaw cycle.
## 4 Results and Discussion
### Comparison with Other Works
Also, among the 64 channels, only five channels (Pz, FCz, FC1, FC2, FPz) are most frequently selected by MHPSO. Since the presented work has only two channels, it seems that it is not necessary to apply this powerful channel selection method to our current BCI system. Nevertheless, the MHPSO method in [39] can also be introduced into our BCI system to speed up the parameter optimization. Currently, the parameters of Kernel FDA and SVM are optimized using the grid-searching and cross validation procedures, which is time consuming and the obtained parameter values are not necessarily optimum. In the future, the MHPSO method should be used in our system to automatically find the optimal values of the parameters.
# Single-Pixel Imaging Based on Enhanced Multi-Network Prior
## 2 Single-Pixel Imaging System  
The single-pixel imaging system is shown in Figure 1, which consists of an illumination LED, a collimating pipe, a digital micromirror device (DMD), an FPGA control circuit board, a PMT, and a computer. The pipe contains frosted glass, a reticle, a convex lens, an attenuator, and an aperture. The light emitted by the LED is uniform after passing through frosted glass and illuminates the reticle placed on the focal plane of the convex lens. The light emitted from each point on the reticle becomes parallel light after passing through the convex lens and forms a weak single-photon light source after passing through the attenuator. The imaging object is a projected pattern etched on glass. When illuminated by the system's light source, the imaging object is projected onto the digital micromirror device (DMD) through the lens. The DMD (TI 07 XGA DMD USA) is composed of 1024 \(\times\) 786 individual micromirrors, each of which is independently controllable to achieve light modulation. The DMD loads the measurement matrix to facilitate the modulation of spatial light. Each micromirror has a size of 13.68 \(\upmu\)m \(\times\) 13.68 \(\upmu\)m and has two reflection states, +12\({}^{\circ}\) and \(-\)12\({}^{\circ}\), which are \"1\" and \"0\", respectively. Micromirrors in state \"1\" reflect the optical signal into the collection optical path, while the micromirrors in state \"0\" reflect the light out of the optical path. The two-dimensional measurement matrix loaded on the DMD controls the state of each micromirror to achieve the modulation of the input light. The modulated optical signal is received by a PMT and operated in photon counting mode. In this photon counting mode, the PMT (Hamamatsu Photonics H10682-110 PMT Japan) acts as a point detector, allowing the simultaneous collection of light intensity values from multiple micromirrors on the DMD in a single acquisition and then outputs discrete pulses to the FPGA. A specifically designed FPGA control and counting circuit helps to load the binary measurement matrix into the DMD controller and calculates the single-photon pulse output of the PMT for each measurement.
# Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection
## 4 Experiments
### Ablation Study
#### 4.4.3 Parallel Connections in MFANet  
We also empirically analyzed the effect of parallel multi-scale connection in network architecture and conducted experiments to study two variants of the network architecture. (1) The parallel connection of the encoder backbone is maintained, but the decoder adopts a direct series connection mode, as shown in Figure 9. Specifically, starting from the lowest scale feature, it is first mapped to the higher scale through DAFU. After that, it is added to the original feature in this scale, and then up-sampled to the higher scale through DAFU again. In this way, each scale feature from the encoder is directly up-sampled layer by layer through DAFU and superimposed together, and the feature with the raw resolution is finally output. In addition, there is no fusion bottleneck in the process. (2) The whole backbone adopts parallel connection, and multi-scale features are fully fused and exchanged in the entire process. This is the strategy we applied. The above networks were both trained from scratch. The results on the CULane testset given in Table 7 show that multi-scale parallel connection is helpful and gains a better performance at the cost of negligible computational efficiency degradation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Upsampling** & **Fusion Method** & **F1-Score (\%)** \\ \hline \hline Bilinear interpolation & - & 72.6 \\ TransConv & - & 74.3 \\ \hline DAFU & DAF & 74.8 \\ DAFU & AFF & **75.2** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison results on CULane dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Connection Method** & **F1-Score (\%)** & **Runtime (ms)** \\ \hline Series connection & 72.3 & 25 \\ Parallel connection & **75.2** & 28 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison results on CULane dataset.  
Figure 9: The decoder with the series connection.
## 3 Methodology
### Thematic Analysis  
The thematic analysis involved two complementary techniques: bigram word clouds and a term co-occurrence network. The extracted bigrams were pairs of consecutive words from the patent titles that captured meaningful technical phrases. These were visualized as word clouds, with bigram frequency determining font size, allowing quick identification of dominant themes across categories. The workflow then generated a term co-occurrence network using the high-scoring TF-IDF terms to reveal clusters of interrelated concepts. Together, these methods enhanced topic clarity and uncovered thematic relationships across the patent corpus.
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 4 Outcomes and Discussion
### Machine Learning Models with FastText  
Table 3 demonstrates the outcomes of ML models trained with FastText embeddings, which extend Word2 Vec by incorporating subword-level information. The results indicate that FastText embeddings significantly improve performance across all ML models, as they effectively handle misspellings and phonetic variations in Roman Urdu. Gradient Boosting Machines (GBMs) emerge as the best-performing ML model, showing higher accuracy and recall compared to TF-IDF and Word2 Vec-based models.  
FastText embeddings provide an advantage in low-resource languages by improving the representation of rare words and spelling variations. SVM and Naive Bayes still performed slightly worse compared to GBM and RF, indicating that while statistical models struggle with dense embeddings, ensemble learning methods can utilize them effectively. To detect hate speech in Roman Urdu, FastText embeddings are highly suitable as shown in the results, particularly for ML-based classification.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Recall** & **Precision** & **Accuracy** & **F-Score** \\ \hline LR & 80.3 & 91.0 & 87.1 & 88.9 \\ SVM & 74.0 & 76.4 & 89.5 & 94.0 \\ RF & 94.7 & 84.7 & 93.5 & 73.8 \\ NB & 72.9 & 79.5 & 85.9 & 90.6 \\ KNN & 74.0 & 71.3 & 81.7 & 86.3 \\ GBM & 84.3 & 87.5 & 81.6 & 88.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of ML Models with TF-IDF.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Recall** & **Precision** & **Accuracy** & **F-Score** \\ \hline SVM & 81.7 & 89.9 & 80.2 & 81.9 \\ RF & 72.8 & 78.0 & 90.9 & 73.6 \\ LR & 87.5 & 87.2 & 93.5 & 81.8 \\ NB & 80.1 & 82.7 & 79.2 & 81.1 \\ KNN & 74.5 & 89.3 & 83.9 & 89.5 \\ GBM & 86.8 & 72.7 & 76.1 & 83.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of ML Models with Word2 Vec.
# Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition
## 2 Related Works
### Image Processing  
The use of neural networks for image processing--in particular, with convolutional neural networks--is a widely accepted alternative to other classic image processing techniques. A drawback is that they require extensive datasets to train them adequately. This, in turn, requires the use of other techniques to prepare the necessary datasets.  
Our proposal addresses this process with an alternative setup based on chroma key techniques and the HSV color model.  
The chroma key technique [28] is applied by identifying a specific color in the image and replacing the area it occupies with another image.  
Similar approaches based on chroma key separation are applied on semantic segmentation [29] for object detection [30; 31] and the detection of the hands of drivers [32].  
The HSV color model is useful to make selections in an image based on specific colors [33]. The colors are described in this model with three parameters: hue (H), saturation (S), and value (V). The hue parameter represents the color spectrum. Saturation determines the purity of the color, and value represents its brightness. This model allows comparisons of different colors with the use of simple techniques focused on the parameter H, which is especially useful for fast processing with chroma techniques.  
In our proposal, the input images are segmented using chroma key techniques in the HSV color space. The segmented output is then used to generate fictitious images through the superposition with the images of an indoor target scenario. This method allows for creating an augmented dataset that will be used to train the neural networks.
## 4 Rheological Properties
### Frequency Sweep Test  
Figure 6 shows the storage modulus and loss factor or tan \(\delta\) of the MRE samples as a function of frequency. The storage and loss modulus for isotropic and anisotropic increased with the increasing frequency at the 0 and 0.8 T magnetic field. It is a typical phenomenon for viscoelastic material where frequency will cause an increase, since the deformation of the particle chain had been unsuccessful to sustain with the shear force in the matrix, thereby causing the storage modulus to increase with increasing frequency [24].  
Figure 6: Storage modulus (**a**) and loss factor (**b**) of MRE under different magnetic flux densities as a function of frequency.  
The storage modulus at an on-state condition was observed to be higher as compared to the off-state condition. Applying the magnetic field strengthened the interaction between the cobalt particles in the matrix. In addition, the magnetic field also led to stronger formation chains structures and will improve the storage modulus. The chain-like structure in the anisotropic MRE also causes the storage modulus of anisotropic MRE to be higher as compared to isotropic MRE. This is due to the easy magnetization axes between particles and led to a decrease of inter-particle distance.  
Loss factor during the on-state condition for the isotropic and anisotropic samples had shown a higher value than during the off-state condition. A higher value of loss factor had higher heat dissipation, leading to stronger cobalt particle interaction. The lower value at the off-state condition for both the isotropic and anisotropic samples due to the smaller friction indirectly led to less energy dissipation [25]. On the other hand, a higher value of loss factor for the anisotropic sample indicates that the chain-like structure had turned out to be more complex and had reinforced the interaction force between the particle and the matrix [15]. Besides particle bonding, the interaction between the particle and matrix also influenced the value of the loss factor.
###### Abstract  
The objective of this study is to present a method for developing fragility curves for soil liquefaction that align with seismic hazards using Monte Carlo simulation. This approach can incorporate all uncertainties and variabilities in the input parameters. The seismic parameters, including earthquake magnitude (\(M\)) and associated peak ground acceleration (PGA), are jointly considered for the liquefaction assessment. The liquefaction potential and the resulting damages obtained by this method are more realistic. A case study is conducted using data from a sand-boil site in Yuanlin, Changhua County, where liquefaction occurred during the 1999 Chi-Chi earthquake in Taiwan. The findings indicate that the liquefaction potential index, \(I_{L}\), the post-liquefaction settlement, \(S_{t}\), and the liquefaction probability index, \(P_{W}\), are all appropriate parameters for assessing liquefaction damages. The fragility curves for soil liquefaction developed through this method can support the performance-based earthquake engineering (PBEE) approach, provide guidance for liquefaction evaluation to the Taiwan Earthquake Loss Estimation System (TELES), and serve as a foundation for scenario simulation and an earthquake early warning system for liquefaction damages.  
soil liquefaction; fragility curve; hazard consistent; Monte Carlo simulation 2024202414948  
data from the sand-boil site in Yuanlin City of Changhua County, which was affected by the 1999 Chi-Chi earthquake in Taiwan. The results can not only match the development trends of PBEE, but also provide guidance for the simulation of liquefaction damage scenarios in the Taiwan Earthquake Loss Estimation System (TELES) [10; 14] to enhance the management of seismic risks and damage from soil liquefaction.
# Non-Invasive and Accurate Blood Glucose Detection Based on an Equivalent Bioimpedance Spectrum
## 8 A Non-Invasive Blood Glucose Detection Experiment  
Impedance spectra of the tested persons are acquired using the proposed hardware platform. Then, the non-invasive blood glucose detection model is used for blood glucose estimation so as to evaluate its accuracy and generalization ability. The experiment consists of two parts: a personal blood glucose test and a multi-person blood glucose test.  
In this section, the designed impedance acquisition platform is used to obtain the bioimpedance spectrum of the tested person, and the non-invasive blood glucose detection model is used to calculate the blood glucose estimation to judge the accuracy and generalization ability of the model. Specific experiments include single-person blood glucose experiments and multi-person blood glucose experiments. The single-person blood glucose experiment focuses on testing the blood glucose fluctuation process, aiming to describe the trend of single-person blood glucose changes; the multi-person blood glucose experiment focuses on the generalization of the blood glucose model and identifies \"high blood glucose samples\" from the impedance spectrum data of different testers. The present study tested the developed non-invasive measurement method to obtain blood glucose-related properties. The connection between the measurement system and the human body is shown in Figure 16.  
Figure 14: Q–Q chart of 20 samples of standard blood glucose grouping.  
Figure 15: Q–Q chart of 20 samples of hyperglycemia grouping.
# Performance Analysis of a Keyword-Based Trust Management System for Fog Computing
## 4 Results and Discussions
### Optimizing the Network
#### 4.6.2 Packet Delivery Ratio (PDR)  
During transmission, a certain number of packets are sent out and a certain number successfully received. In computing a packet reception ratio, the relationship between data received by the top layer of receiving nodes and data transmitted by the top layer of sender nodes is described. For data packet loss modeling in our approach, we employ the well-known Gilbert-Elliott model, which has been around for a long time and has been widely used by the industry. Calculating data packet loss necessitates contrasting the connections between a good and a bad node. After that, the model's parameters are calibrated to match the data. The chance of transitioning from a good state into an undesirable one and the likelihood of migrating from an undesirable condition into a desirable state are estimated as a consequence of the outcomes of experiments conducted in a fog computing environment. The packet delivery ratio (\(PDR\)) is calculated using the formula:  
\[PDR=\frac{DR}{DR}+DL\]  
Figure 10 shows the relationship between latency and packet delivery ratio (\(PDR\)). The \(x\)-axis represents the latency values, while the \(y\)-axis shows the PDR values for each corresponding latency value. Each data point represents a specific measurement taken during the simulation. The plot shows that there is a trade-off between latency and PDR. As the latency values increase, the PDR values tend to decrease, indicating a higher probability of packet loss or failure.  
Conversely, as the latency values decrease, the PDR values increase, indicating that packets are more likely to be delivered successfully. Figure 10 visually represents the relationship between two important performance metrics, highlighting their trade-offs. It can be a helpful tool for understanding the performance characteristics of a system and identifying areas where improvements or optimizations may be needed. Figure 11 shows the computational trust cycle. The \(x\)-axis represents the different stages of the cycle, while the \(y\)-axis shows the level of trust associated with each stage. Each data point represents a specific measurement taken during the simulation. The plot shows several stages in the trust computation cycle, including trust calculation, update, and decision. The level of trust associated with each stage varies, with some stages having higher levels of trust than others. Figure 11 visually represents the computational trust cycle, highlighting the stages of building and maintaining trust in a system. It can be useful for understanding a system's trust dynamics and identifying areas where improvements or optimizations may be needed.