esa-sceva/satcom-chunk-collection huggingface dataset:
# Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
## 4 Proposed Method
### Cluster-Aware Loss  
In many downstream tasks, only a few target nodes are labeled, making it difficult to optimize the representations of other types of nodes directly through supervised constraints. Additionally, frequent information passing between different types of nodes in HGNNs can lead to feature over-mixing, causing the loss of nodes' original representation distribution. To address these challenges, we propose a simple and fundamental constraint goal: we aim to maintain the original feature distribution of each node category by ensuring that the node features of different categories have clustering properties in the representation space, rather than being mixed together. Building upon this goal, we propose the following loss design scheme, where \(B_{\phi(i)}\) represents the center of the learnable node type anchor corresponding to each node type \(\phi(i)\), and \(Norm_{2}\) denotes the \(l_{2}\) normalization operation.  
\[L_{agg}=\frac{1}{|v|}\sum_{i\in V,j\in A,j\
eq\phi(i)}exp(min[(X_{i}-B_{\phi( i)})^{2}-(X_{i}-B_{j})^{2},0]) \tag{16}\]  
\[L_{push}=\frac{1}{|A|^{2}-|A|}\sum_{i,j\in A,j\
eq j}exp(Norm_{2}(B_{i})\cdot Norm _{2}(B_{j})) \tag{17}\]  
The idea of \(L_{agg}\) is that the distance from each node to its own class's center anchor should be less than the distance to other classes' center anchors, thus providing an inter-class clustering effect. To prevent the problem of representation collapsing, which means that the representation space of the nodes within the same class collapse into too small a space, we introduce a stop mechanism to let the optimization end when the distance difference \((X_{i}-B_{\phi(i)})^{2}-(X_{i}-B_{j})^{2}\) is larger than zero. Meanwhile, \(L_{push}\) aims to extend the distance between different class center anchors, thus making it easier to separate the representation distribution of each class. It also enlarges the range of space that each class of node representation can occupy, which is helpful to relieve the representation collapsing problem. In order to minimize \(L_{agg}\) and maximize \(L_{push}\) at the same time, we develop the final \(L_{cluster}\), similar to the formula of InfoNCE loss [31]:  
\[L_{cluster}=-log\frac{L_{agg}}{L_{agg}+L_{push}} \tag{18}\]  
The optimization target of our model HGPN is the combination of downstream task loss and \(L_{cluster}\). Take the supervised node classification task as an example. The final loss \(L\) can be computed as the weighted sum of cross-entropy loss and \(L_{cluster}\):  
\[L=L_{sup}+\lambda_{L}L_{cluster}=-\sum_{i=1}^{N}\sum_{c=1}^{C}y_{i,c}\cdot log (p_{i,c})+\lambda_{L}L_{cluster} \tag{19}\]  
where \(\lambda_{L}\) is a weighted coefficient to balance these two parts of loss.
## 2 Materials and Methods
### Sampling and Handling of Material  
At the three sites surveyed (Figure 1b), two surveys were carried out in 2022 (June and October). About 100 honeybees were collected in nylon bags placed at the entrance of the hives. Simultaneously, collection of the main product of the hive, i.e., the honey, was also carried out (about 5 g taken per sample). Sampling in both June and October at each site provided a better understanding of the contamination levels throughout the year. Contaminant levels are influenced by meteorological phenomena and the seasonal blooms of different plant species from spring to autumn [19]. All samples collected were refrigerated on site (2-4 \({}^{\circ}\)C) and then transported to the laboratory for storage in a freezer at a temperature of approximately \(-20\)\({}^{\circ}\)C.  
From each sample, 45 honeybees were selected and washed in ultra-pure water in 3 plastic tubes (15 specimens per tube), each containing 15 mL of ultra-pure H\({}_{2}\)O, to eliminate any pollen that might be in the pollen baskets of the hind legs, as well as any other particles or parasites on the body surface. After shaking each plastic tube for 1 min, we waited 20 min; then, with plastic tweezers, we took the honeybees out of each plastic tube and placed them on three Petri dishes until completely dry.  
Once dry, 15 honeybees from each sample were analysed as whole bodies (body, B). The remaining 30 honeybees from each sample were dissected by cutting with ceramic scissors at the level of the petiole, separating the abdominal segments starting from the second. This type of sample was called the \"gaster\" (G). The remaining part of the body, including the head, thorax, and the first abdominal segment was called \"body without the gaster\" (B-G) [41]. The purpose of sectioning the honeybee bodies was to identify whether the relationship between the bioaccumulation in the gaster (where most of the intestines and the excretory apparatus of the Malpighian tubules are located) and in the body without the gaster was distinctive and specific for the different elements [41].  
The honey samples were not subjected to a washing procedure.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### Particular Types of Accelerometers
#### 1.4.4 Surface Micromachined Capacitive Accelerometer (MEMS)  
MEMS stands for microelectromechanical system, identifying any sensor manufactured using microelectronic fabrication techniques (Figure 2). These are silicon-based mechanical sensing structures that are microscopic in size. MEMS sensors can measure physical parameters such as acceleration when coupled with microelectronic circuits [18; 19; 20].  
The inertial mass is moving, and the corresponding capacitances C1 and C2 are modified accordingly. In Figure 3, there are some examples of boards featuring MEMS sensors.  
Three-axis accelerometers are commonly utilized across various sectors, including aerospace, robotics, automotive, and medical applications. These devices are essential for accurately measuring the inclination angle of objects, which is crucial in several contexts. Examples include inertial measurement systems in spacecrafts, inclination assessments in vehicles and ships, balance attitude detection in robotic systems, and limb posture monitoring in healthcare settings. Employing MEMS (microelectromechanical system) technology, three-axis accelerometers are characterized by their compact size, lightweightdesign, and affordability. Importantly, their use does not interfere with the mechanical operations of the objects being measured [21].
# Traffic Signal Time Optimization Based on Deep Q-Network
## 5 Conclusions  
This paper proposes a DQN-based TSC system that can learn optimal policies through a direct interaction with the environment. The smart TSC distributes the order of green signals according to the situation or sets the time of green signals in a fixed signal order. The proposed TSC system sets the time in a fixed signal order by considering the standard deviation of the waiting time and the capacity as parameters. To understand the situation at the intersection, we defined the state by considering the traffic loads at an intersection, the lane assigned the green light, and the lane used to exit. The aim of the proposed TSC system is to set the green signal duration time to maximize the capacity at intersections and minimize the standard deviation of the waiting time between lanes. Experimental results showed that the proposed technique has more capacity and better TSC performance than those of TFP-CTS and QT-CDQN. Accordingly, it showed effective improvement in TSC performance by controlling the duration time of the green signal according to the situation. The advantage of adjusting the time duration of the signal is that it is possible to increase the efficiency of the signal by reducing the wasted time of the green signal. In addition, because the time duration is set, it can be applied to different types of intersection regardless of the structure. However, this study has limitations in that it requires prior work to find the optimal order of signal directions. Because the traffic flow is different for each intersection, a study on the order of signal directions that can maximize the performance of traffic lights for each intersection is needed. Therefore, to apply the study results to an actual environment, we need to identify the traffic flow at the intersection and find an optimized sequence of signal directions that can maximize the performance of the TSC, as well as to find the optimal time length of the green light in each traffic direction.  
In the future, we will explore a TSC system that can consider the pedestrians. In addition, we are planning to study TSC in terms of urban traffic control, such as grouping intersections with geographical characteristics through federated learning to form clusters.  
Conceptualization, H.J. and Y.L.; Methodology, H.J.; Software, H.J.; Writing - Review & Editing, H.J. and Y.L.; Supervision. Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by stage 4 BK21 project in Sookmyung Women's Univ of the National Research Foundation of Korea Grant. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No.2021R1F1A1047113).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.  
Figure 12: Performance results: (**a**) average queue length and (**b**) its standard deviation.
## 4 Test Results Analysis
### Analysis of Test Results of Lignin Fiber-Time Composite-Improved Soil
#### 4.2.3 Hydraulic Conductivity Test  
In this experiment, a rigid wall permeameter was used to conduct saturated hydraulic conductivity tests on four groups of soil samples: untreated soil, 2% lime-improved soil, 4% lime-improved soil, and soil improved with an 8% fiber and 2% lime composite. Tests were conducted after 1, 7, and 14 days of curing, respectively.  
As shown in Figure 17, the hydraulic conductivity of the improved soil decreases with increasing curing days. With the increasing curing time, the chemical reactions between the lime and the soil, as well as the connections between the lignin fibers, cementing materials, and soil particles, become more stable. These cementing material products enhance the strength and stability of the soil, filling some pores and thereby reducing the hydraulic conductivity. Additionally, as shown in Figure 17, the hydraulic conductivity of the composite-improved soil is lower than that of the solely lime-improved soil at each curing age. This indicates that the addition of lignin fibers significantly reduces the hydraulic conductivity of the sample.  
Figure 16: The relationship between the internal friction angle and the curing days of the improved soils with different treatments.
## 2 Materials and Methods
### Biometric Parameters: Plant Growth and Trichome Density Analysis  
Destructive evaluations were performed on 30 June 2019, at 75 DAT (T3). The following biometric parameters were assessed: leaves number (LN); fresh leaves (FWL), root (FWR), and total (FWT) weight; dry leaves (DWL), root (DWR), and total weight (DWT). All the dry weights occurred after oven drying at 75 \({}^{\circ}\)C until a constant weight was reached. Leaf area (LA) was determined on each leaf by image analysis (NIS Elements D 4.00); Specific Leaf Area (SLA; cm\({}^{2}\) g\({}^{-1}\)) was calculated as leaf area/leaf dry mass; Leaf Weight Ratio (LWR; g g\({}^{-1}\)), calculated as DWL/DWT, and leaf area ratio (LAR; cm\({}^{2}\) g\({}^{-1}\)) as \(SLA\times LWR\). The Relative Growth Rate (RGR) index was determined according to the equation:  
\[RGR=\frac{\ln(DWF)-\ln(DW0)}{TF-T0} \tag{1}\]  
where ln is the natural logarithm, \(DW0\) is the dry mass of the plant at the beginning (\(T0\)) and \(DWF\) is the dry mass of the plant at the end (\(TF\)) of the experiment. The RGR was calculated to leaves and roots. For trichomes density, leaves samples were mounted on aluminum sample-holder stubs, sputtered with silver (Emitech K575X, Emitech Ltd., Ashford, UK), and examined using a GAIA 3 electron microscope (Tescan, Brno, Czech Republic) at 20 KV. Glandular and non-glandular trichomes density (number of trichomes per mm\({}^{2}\)) were evaluated. All the counts were performed on the central part of the leaf. The histochemical and confocal laser scanning microscopy observations of trichomes were performed on fresh young leaves of _Tanacetum balsamita_ sectioned by hand. The obtained sections were stained with Nadi reagent [25] to detect terpen. Using a TCS SP8 confocal microscope (Leica Microsystems CMS GmbH, Mannhein, Germany) leaf cross sections were observed. Laser excitation at 405 nm was used to acquire hydroxycinamate fluorescence (over the 415-480 nm emission spectral band).
## 1 Introduction
However, unlike the DA market, the probability of VPP being called in the BE market is much lower. Therefore, the optimization model in this work considers the probability of reserve activation from VPP to assess the opportunity to profit from the BE market. The salient features of this study include the following:  
1. This research presents a two-stage stochastic optimization model to determine the VPP's optimal trading strategy in the DA and BE markets. This scheduling model considers the requirements of the pre-signed BC contract, the probability of reserve activation in the BE market, and the uncertainty in RESs and demand. 2. ESS's reserve provisioning model is proposed to fully exploit ESS's capacity in the BE market without affecting the operating schemes of ESS and VPP in the DA market. 3. The impacts of the BC contract and the reserve activation probability on the VPP's optimal trading strategy are analyzed. The rest of the paper is organized as follows: Section 2 demonstrates the operating model of VPP in the DA and balancing market, as well as the assumptions used in this paper, thereby presenting the ESS's reserve provisioning model. Section 3 presents the mathematical formulation of the VPP's optimal scheduling model in the DA and BE markets. Then, the proposed model is implemented on a test VPP system in Section 4. This section also collects and analyzes the computation results. Finally, Section 5 concludes the paper.
# Identifying Earthquakes in Low-Cost Sensor Signals Contaminated with Vehicular Noise
## 3 Proposed Methodology  
Our work proposes the use of an amalgamated deep neural network constituent of a deep neural network trained on earthquake signals from professional sensory equipment, as well as a deep neural network trained on vehicular signals from low-cost sensors. These sensors were placed at points with vehicular activity, enabling them to record passing vehicles for model training. On the other hand, the professional sensory equipment used consisted of high-end seismographs, which are used to record seismic events. The key purpose is to convey the amalgamated deep neural network with the capability to effectively discern earthquakes in signals from low-cost sensors that are contaminated with vehicular noise, thereby avoiding false positives caused by vehicles. The proposed low-cost sensors could be used in bulk and placed in different areas so they can record an upcoming seismic event. This could benefit researchers and give them the ability to record the events from different sensors and extract valuable information. In addition, the low-cost sensors are easy to maintain or replace, given their affordability in comparison to professional equipment, and could be placed near roads for easier access to them. Finally, the model we propose could be very useful when it comes to earthquake recognition, as it has the ability to recognize the seismic event from a passing vehicle; our model is trained to discern the difference between them. This model supports our proposal of placing the low-cost sensors near roads for easier access, as passing vehicles will not affect the recognition process of the model. A bird's-eye view of the key pillars of this work is presented in Figure 1.  
The online availability of the programming code and data in the scientific research offers numerous benefits, i.e., it improves transparency, collaboration, and accountability by enabling independent verification of the findings. To this end, all data and code of this work are available online ([[https://github.com/[PERSON]/Identifying_earthquakes_in_low_cost_sensors_signals_contaminated_with_vehicular_noise](https://github.com/[PERSON]/Identifying_earthquakes_in_low_cost_sensors_signals_contaminated_with_vehicular_noise)]([https://github.com/[PERSON]/Identifying_earthquakes_in_low_cost_sensors_signals_contaminated_with_vehicular_noise](https://github.com/[PERSON]/Identifying_earthquakes_in_low_cost_sensors_signals_contaminated_with_vehicular_noise)), accessed on 13 September 2023).
## 4 Discussion  
Measurements and oscilloscopic studies show that waveforms in the plasma cycle are characterized by two periodicities. One of them results from the pulse frequency in SMPS and another one from the plasma cycle itself. About 50 pulse periods and a plasma cycle with a length of about 2.5 ms were observed on the waveforms. It was noted that in the plasma cycle, after ignition, the voltage reached a value of up to 0.5 kV. Then, the arc column moving up the electrodes lengthens and bends, reducing its conductance, and the voltage increases gradually, reaching 7 kV at the tip of the electrodes. The reactor current changes the opposite way. After ignition, SMPS effectively limits the peak values at the level of 0.22 A, and this value remained in the plasma cycle until the discharge conductance caused a current drop.  
The discharge at the pulse-power source at 20 kHz (Figure 5) from the beginning presents different features from the discharge at 50 Hz. If the process gas stream is not fed through the nozzle, the discharge does not develop and moves up the electrodes, remaining in the ignition zone, and its voltage reaches hundreds of volts, due to the non-linearity of the receiver, which is the \(\upmu\)GAD reactor's domain.  
It is crucial how the traveling discharge changes its electrical parameters. With pulsed power supply and process gas distribution, observing the plasma reactor, one gets the impression that the discharge penetrates the inter-electrode space very closely. In noble gases, discharges are even more homogeneous. A photograph taken with an exposure time of 0.01 s does not allow observing the arc sliding over the knife electrodes. Only a fast camera test demonstrates the actual sliding movement of the discharge column along the knife electrodes.
## 2 Research Questions and Hypothesis
### Hypothesis  
An STPC is a type of semiconductor-based equipment. Power semiconductors are known to generate harmonics due to their switching function, which enable the device to turn ON and OFF. Harmonics can distort the waveform and interfere with the normal operation of the loads. Therefore, utility companies such as KEPCO regulate the harmonic level to ensure that the harmonic distortion rate in the PDS remains below 5%. Accordingly, STPCs must comply with this standard [14].  
The switching function of power semiconductors is implemented through a closed-loop control mechanism that continuously analyzes the output signal and adjusts the switching period. As a result, voltage fluctuations can occur depending on the number of connected loads and the amount of electricity consumed. These variations are detected  
\begin{table}
\begin{tabular}{c c} \hline
**PICOT** & **Research Questions** \\ \hline \multirow{4}{*}{Population} & \(\backslash\)1 A substantial financial investment is required for constructing power facilities to supply a three-phase voltage (challenge). \\  & \(\backslash\)2 Target population: Phase conversion equipment, PDS, fault analysis (target population). \\ \hline Intervention & Unbalanced loads, nonlinear loads, and motor load. \\ \hline \multirow{2}{*}{Comparison/Control} & Maintaining the standard voltage range, generating three-phase voltage (expected outcome). \\ \hline \multirow{4}{*}{Timeframe} & \(\backslash\)1 Review of research. \\  & \(\backslash\)2 Analysis of PDS challenges. \\  & \(\backslash\)3 Assertive hypothesis. \\  & \(\backslash\)4 Derivation of results. \\ \hline \end{tabular}
\end{table}
Table 2: Research questions.  
in the output signal, and the switching period is modified. Further details on the control method are provided in Section 3.2 (Topology).  
Basically, a PTS is based on a balanced three-phase voltage, and the system analysis is performed with concentrated loads, such as a single load. However, a PDS is an unbalanced system that accommodates both single-phase and three-phase loads, and the load types are very diverse. Therefore, a more comprehensive system analysis is required.
# Intelligent Reflecting Surface-Assisted Wireless Communication Using RNNs: Comprehensive Insights
## 5 Proposed Model Architecture  
We consider different RNN approaches for this study to estimate the channel performance for IRS-based communication. Two different approaches are considered for the experiment. First, the RNN techniques, namely LSTM, BiLSTM, and GRU, are tested with a single layer and double layer. Figure 7 shows the model architecture for a single RNN approach. To make a fair comparison, we keep the overall architecture similar for each technique. Each layer starts with a sequence input layer followed by an RNN layer and a dropout layer. Another RNN layer and dropout layer are added for the double layer. After that, layer normalization is applied and a fully connected layer is added. Finally, softmax and classification layers are used to classify the input data. LSTM, BiLSTM, and GRU single and double-layer architectures are shown in Figure 7a-c. In the next step, a hybrid approach is applied to see the performance of each combination. We consider combining two approaches, i.e., LSTM-BiLSTM, LSTM-GRU, and BiLSTM-GRU, and vice-versa. The model architectures for the LSTM and BiLSTM layers are shown in Figure 8a. The positions of the LSTM and BiLSTM layers are interchanged to observe the model accuracy effect. Similarly, the LSTM and GRU layer-based model architectures are shown in Figure 8b. The LSTM and GRU layers are switched for model accuracy observations. Lastly, BiLSTM and GRU model architectures are presented in Figure 8c. Here, the positions of the BiLSTM layer and the GRU layer are changed for model accuracy improvements. The other parameter for model training is presented in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Minibatch size & 200 \\ Maximum number of episodes & 25 \\ Learning rate & 0.001 \\ Hidden units & 200 \\ Number of classes & 16 \\ Dropout rate & 0.02 \\ Optimizer & ADAM \\ Gradient threshold & 1 \\ Validation frequency & 50 \\ Shuffle & every episode \\ Iteration per episode & 2560 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Machine learning parameters.  
Figure 7: (a) LSTM model for the single layer and double layer, (b) BiLSTM model for the single layer and double layer, (c) GRU model for the single layer and double layer.