esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Hyperparameter Tuning for Synthetic Data Validation  
To optimize the methodology, specific hyperparameters must be fine-tuned for each experimental setup. The following hyperparameters are central to achieving effective, valid data generation:  
* Density threshold for cluster area definition: cluster validation involves establishing a radius around each centroid within which synthetic samples are validated. The radius is adjusted until the sample density within it falls below a pre-set threshold and the percentage of points in the cluster within this radius meets an established threshold. The percentage threshold depends on the aim of the methodology; in this case, the interest is to define a cluster area that comprises a wide majority of the samples, and therefore the threshold is set to 90%. Synthetic samples falling within the validated  
Figure 3: Cardiovascular data (Ind-CardioDB) visualizations using different dimensionality reduction techniques.  
cluster areas are then assigned to the corresponding cluster; those outside any cluster radius are not validated and thus discarded.  
Since the density threshold determines the effective cluster area for synthetic data validation, it should be adjusted based on the reference data sample size and the shapes observed in the UMAP-reduced space. This adjustment ensures that the synthetic samples conform well to the reference dataset's inherent structure.
* \(\epsilon\) for reliability score assignment: the reliability score quantifies the validity of each synthetic sample based on feature disparity. The feature disparity measure is the absolute difference between the synthetic value of a given feature and the mean value of the same feature among the nearest neighbors. However, depending on the attribute, the score is controlled by the hyperparameter \(\epsilon\), as shown in Table 1.  
The hyperparameter \(\epsilon\) is determined by evaluating the mean feature disparity within the reference dataset. For each reference sample, the average feature value among the ten nearest neighbors is computed. The rounded version of the mean feature disparity is assigned to the feature \(\epsilon\).
As the 802.11 technology continues to evolve towards higher PHY data transmission rates, data transmission times are reduced, thus making microsleep even more challenging. In order to determine how the aforementioned system parameters can influence the feasibility of microsleep, this paper evaluates the performance of a TXOP PSM-based mechanism using burst transmissions. More specifically, this mechanism allows an STA to send one or various data frames to another STA in a single channel access attempt, while a third one can microsleep during the data exchange. An analytical model to calculate the throughput and energy efficiency of the TXOP PSM-based mechanism under network saturation is presented in this paper. By means of the proposed model and computer-based simulations, the performance of the TXOP PSM-based mechanism is evaluated and compared to those of other power save mechanisms. Results show that the performance of the TXOP PSM-based mechanism strongly depends on the system parameters, since depending on the selected system parameters microsleep may not be feasible. When microsleep operation is feasible, the TXOP PSM-based mechanism can improve the energy efficiency of PSM by up to 424% under high traffic loads. This paper represents an extension of our previous work published in [20], in which a preliminary analysis of the energy efficiency of the TXOP PSM-based mechanism was provided. The differences between this paper and [20] are detailed in Section 2. The remainder of this paper is structured as follows. Section 2 provides a review of related work and a summary of the contributions of the paper. A brief overview of the TXOP PSM-based mechanism is then presented in Section 3. Section 4 includes the derivation of the proposed analytical model for the throughput and energy efficiency of the TXOP PSM-based mechanism under network saturation. In Section 5, a comprehensive performance evaluation of the TXOP PSM-mechanism is provided. Finally, Section 6 draws conclusions and outlines future work.
# Evaluation of Viscoelastic and Rotational Friction Dampers for Coupled Shear Wall System
## 3 Shake Table Test of Six-Story Coupled Wall System with Damper
### Measurement Scheme  
The experimental setup was designed to monitor the structural response of the six-story frame specimen under various loading conditions, as illustrated in Figure 16. Accelerometers (A0 to A18) were positioned across multiple stories to capture acceleration data, enabling a detailed analysis of dynamic responses at each level. Laser displacement sensors (D0 to D6) were employed to measure inter-story displacements. High-precision small laser displacement sensors (VE01 to VE04) were utilized to obtain detailed displacement measurements for the VE damper, while gyro sensors (R01, R02) were incorporated to detect rotational movements. Additionally, bolt gauges (F01, F02) were installed to determine the clamping force applied, and wire sensors (W01, W02) were used to record circumferential displacements of the RF damper.
## 5 Case Study: Smart Conservation of Cultural Heritage
### Reasoning in a Smart Cultural Heritage Space
#### 5.2.3 Rules Module  
The rules module performs the core of reasoning processing. It requests sensory data from the uncertainty module, retrieves profiled data from the KB and performs reasoning in real-time. The module is continuously executed and transforms low-level contextual data into high-level contextual information. It is not application specific, thus it can be exploited by any application that is active inside the smart cultural space. The rules follow _if-then_ logic, but also take into consideration confidence and fuzzy logic, dealing with probabilities rather than certainties, tagging all high-level contextual information  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline
**ID** & **Activity** & **Thing** & **Location** & **Time** & **Asserted Context** \\ \hline
2F1 & Temperature measurement & Sensor: 456 FA & Room 6 & Visit hour Monday & - \\
2F2 & Turn on A/C & A/C: 6B & Room 6 & (exact timestamp) & Observation: 2F1 Rule: 17 AF \\  & &... & & & \\
2 FC & Visitor Proximity & Visitor Device: 0122 & Room 4 & Rush hour Monday & - \\
2 FD & Staff Notification & Staff Device: 0023 & Room 3 & (exact timestamp) & Observation: 2 FC Profiled data on staff Scensed data on staff location Rule: 15 ED \\ \hline \hline \end{tabular}
\end{table}
Table 5: Examples of observation tuples.  
with a probability score. Specific rules include the triggering of an action when sensory measurements surpass predetermined values, such as the rule shown in Figure 9.
# Network Slicing for Beyond 5G Systems: An Overview of the Smart Port Use Case
## 4 Machine Learning and AI for Network Slicing Beyond 5G Systems
### Supervised Learning for Network Slicing  
Predefined labeled data are used by supervised learning algorithms to train the system model in order to forecast possible outcomes. It can be described by following function:  
\[g=h(X) \tag{1}\]  
where \(X\) represents the predefined input variable and \(g\) represents the outcome. Hence for a new input data, the algorithm predicts the output by learning from the model. It can be further categorized into two techniques namely, regression and classification. Regression algorithms predicts and forecasts the real output values based on the input statistical elements by using the training data. Classification algorithms classify or distinguish the output based on discontinuous values such as categories. Supervised learning mechanism can be used in order to provide data integrity, localization, security, and tracking of objects [24; 25]. Naive Bayes algorithm can be used for the localization [26]. By determining the possibilities, Bayesian algorithm can manage the missing or incomplete data sets, which is quite useful for localization.
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 4 Self-Drilling Anchor Grouting Slope Simulation Analysis
### Analysis of the Supporting Performance of Self-Drilling Anchor Bolts  
#### 4.3.1 Orthogonal Experiment on the Impact of Different Support Parameters on Slope Stability  
To investigate the influence of anchor rod installation angle, length, and spacing on the stability of slopes when using self-drilling anchor rods for slope reinforcement and to determine the optimal anchor rod support parameters, an orthogonal experiment was designed with three factors and five levels. Additionally, the number of anchor rods per meter of slope was used as the criterion for optimal selection. Numerical simulation analysis was performed using FLAC3D software to solve the slope stability factor. In the simulation, the modified pile element established in Chapter 3 of this study was used for self-drilling anchor rods. The results are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Number** & **Bolt Length (m)** & **Anchor Angle(\({}^{\circ}\))** & \begin{tabular}{c} **Anchor Rod** \\ **Spacing (m)** \\ \end{tabular} & \
\begin{tabular}{c} **Quantity of** \\ **Anchor Rods** \\ **(m/per m of Slope)** \\ \end{tabular} &
\begin{tabular}{c} **Safety Factor** \\ **Safety Factor** \\ \end{tabular} \\ \hline
1 & 6 & 25 & 1 & 102 & 1.3 \\
2 & 6 & 30 & 1.5 & 68 & 1.305 \\
3 & 6 & 35 & 2 & 51 & 1.294 \\
4 & 6 & 40 & 2.5 & 40.8 & 1.287 \\
5
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 2 Common Methods for Detecting Underwater Cracks in the Surface of Concrete Dams  
### Manual Visual Inspection  
Manual visual inspection is the traditional method to detect cracks in dams. The cracks in the underwater concrete structure of the dam were mainly observed by professional technicians through visual inspection in the past [32]. The underwater cracks on the surface of concrete dams were mainly detected by building a cofferdam or emptying the reservoir water due to the backwardness of diving equipment in the early days [33; 34; 35]. However, emptying the reservoir is a waste of water resources, and the sudden change of engineering conditions easily causes engineering problems [36]. Additionally, most reservoir dams do not have emptying conditions as the reservoir capacity increases. With the development of diving technology, inspectors can use diving equipment to dive deep into the water and carry underwater detection equipment for artificial visual inspection of underwater dam cracks. This method, which does not require too high technical requirements, only inspects the underwater concrete by touch and observation. Therefore, it is widely used in the early underwater crack detection of dams. However, special underwater conditions (such as poor visibility, complex dam environment, strong current, and small operation area) make this method inefficient, time-consuming, and a serious threat to the safety of divers [37,38]. In addition, the results of underwater crack detection heavily depend on the subjective experience of the technician, and this method usually limits the operation of the hydropower station and affects its normal production [32].
# Circularly Polarized Broadband Printed Antenna for Wireless Applications
## 1 Introduction
But the antenna achieves impedance bandwidth of 116.6% (2.37-9 GHz), and AR bandwidth of 58.7% (3.22-5.9 GHz) with 0.71.3\(\times\) 0.71.3\(\times\) 0.02\(\lambda\) at low frequency [25]. A moon shaped [26], a wide slot antenna [27], and a chifire-shaped antenna [28] with 3-dB AR bandwidths of 40%, 49%, and 41.6% were offered, respectively. An inverted-L grounded strip with two asymmetric T-shaped feed lines was stated to attain 60% impedance and AR bandwidth [29]. Furthermore, the two Vivaldi antenna components were vertically and horizontally placed to diminish the antenna size and also to understand broadband CP operation [30] which is a comparatively complex design. Recently, an antipodal Y strip with a square slot antenna [31], cross-shaped printed antenna with extended ground plane [32], parasitic strips with a partial ground plane [33], CPW-fed open loop [34] and compact wideband directional CP antenna [35] have been presented to realize wide impedance and AR bandwidth. Nevertheless, above the investigations of different printed antennas, none of these antennas AR bandwidth exceeds over 70%. In this article, a compact broadband sickle-shaped printed CP antenna excited by a straightforward tapered microstrip feed line is offered. The achieved overlapping 3 dB AR bandwidth is 73.33%, which is much larger compared to the bandwidth accomplished previously with comparable dimensions and style techniques. The fabricated antenna is easy in design structure, broad impedance (\(-\)10 dB) and 3 dB AR bandwidth and does apply for integration in a variety of multiband wireless applications. The realized antenna was first designed, simulated and optimized in Ansoft 3D electromagnetic high-frequency simulator (HFSS) and then validated by fabrication and measurement. Finally, we are scaling the dimension to check the impedance and CP property of the realized antenna.
# Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems
## 5 Design of Executor Model
### Determining Optimal Parameters for Adaptation  
Due to the Gaussian distribution, any change of the weight values is possible. However, the selection of the appropriate standard deviation (\(sd\)) may improve the results of the neural network adaptation.  
To select the best standard deviation value, we performed experiments for subsequent values of this parameter (\(sd=0.05,\;0.02,\;0.01,\;0.005,\;0.002,\;0.001\)). For each \(sd\) value, the average response times of requests in the system were examined. The changes in the request handling times were not too large. Therefore, the diagram (Figure 7) narrowed the view only to measurements for high system load. Another modification of the algorithm was concerned with the fact that for each request, during learning, several attempts are made to change the weights with different values of standard deviations (\(sd=0.005,\;0.002,\;0.001,\;0.0005,\;0.0002,\;0.0001,\;0.00005\)). The last method (tentatively named \(NN1\)) turned out to be slightly more effective for the highest workloads of the system. This method was used in subsequent studies.  
Figure 7: Charts of the average request service time for different values of the standard deviation used for the modification of weights.
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 2 Nanofluid
### The Mechanisms of Enhancement in Nanofluids  
Many research works have focused on the role of fine particles in the enhancement of mass transfer in gas-liquid systems. These findings indicated that partial pressure enhancement will give rise to the gas absorption capacity, meanwhile, temperature enhancement decreases the gas solubility in the liquid phase, which is in accordance with the ideal gas law [72]. The theory that suggests enhancement in mass transfer by using nanoparticles has not been established. Several mechanisms are accepted: micro convection; mixed boundary layer; bubble coalescence prevention; and the shuttle mechanism. The bubble breaking effect, shuttle mechanism, and micro convection are discussed in this paper as shown in Figure 3[73].
## 3 Methods
### Multi-Scale Feature Extraction Using Swin Transformer
#### 3.3.3 Shifted Window Attention  
The standard self-attention in Transformers relates all tokens in the sequence to each other, which leads to a quadratic computational complexity. To improve efficiency, SwinT adopts a shifted window-based self-attention, where each token only attends to tokens within a local window.  
Formally, given an input feature map \(\mathbf{X}\in\mathbb{R}^{H\times W\times C}\), it is first partitioned into non-overlapping windows of size \(M\times M\):  
\[\mathbf{X}_{i,j}=\mathbf{X}[iM:(i+1)M,jM:(j+1)M,:] \tag{24}\]  
where \((i,j)\) indexes the windows. The self-attention is then performed within each window \(\mathbf{X}_{i,j}\) independently.  
To enable communication between windows, SwinT shifts the window partitioning in consecutive self-attention layers. For example, in the next layer, the windows are shifted by \(M/2\):  
\[\mathbf{X}_{i,j}^{\prime}=\mathbf{X}[iM+\frac{M}{2}:(i+1)M+\frac{M}{2},jM+ \frac{M}{2}:(j+1)M+\frac{M}{2},:] \tag{25}\]  
Therefore, each token can attend to different tokens from overlapping windows in different layers. This achieves a balance between efficiency and modeling power.  
The shifted window attention restricts self-attention to local regions while allowing global communication across layers. It enables SwinT to model both local and global dependencies in an efficient manner.
# Modal Properties of Photonic Crystal Cavities and Applications to Lasers
## 4 Line-Defect Cavities
### Resonant Modes: Real Space Distribution
The amplitude of the plane waves with wavevector \(\pm k_{2}\) (\(\pm k_{1}\)) is the magnitude of the spatial average \(b_{0}\) (the spatial harmonic \(b_{-1}\)). Therefore, the ratio \(|b_{-1}/b_{0}|\) represents the relative strength of one pair of plane waves as compared to the other. As the number of unit cells is varied, the resonance frequency changes and the ratio \(|b_{-1}/b_{0}|\) adjusts itself accordingly. This is emphasized by Figure 9c, which displays \(k_{2}\) (left) and \(|b_{-1}/b_{0}|\) (right) versus the number of unit cells. From Equations (13) and (17), the wavevectors \(k_{1}\) and \(k_{2}\) read \(k_{1}=\pi/a-\pi/L\) and \(k_{2}=\pi/a+\pi/L\), respectively. As \(L\) increases, both \(k_{1}\) and \(k_{2}\) move towards the band edge. Correspondingly, \(|b_{-1}|\) tends to \(|b_{0}|\) (see Figure 7d) and the fundamental mode gradually turns distributed-feedback-like (DFB-like). This attribution [47] stems from the fact that the mode distribution in a DFB laser is determined by _two_ pairs of wavevectors [23]. On the contrary, the shorter the cavity is, the more a single pair of plane waves (that with wavevectors \(\pm k_{2}\)) dominates over the other. Therefore, the fundamental mode turns Fabry-Perot-like. Indeed, in a Fabry-Perot laser the mode distribution is determined by a single pair of plane waves. Resonance condition (see Equation (13)) and electric field distribution (see Equation (18)) are consistent with previous FDTD simulations [47]. In particular, the ratio \(|b_{-1}/b_{0}|\) (see Figure 9c) is obtained in [47] by fitting. Here, we have elucidated the physical origin of this parameter, which directly stems from the spatial and frequency dependence of the Bloch modes of the underlying line-defect waveguide. As mentioned at the end of Section 4.3, the phase of the mirrors at their respective reference planes in Figure 8 can be considered to be either zero or \(\pi\) to good approximation. In this section, we have assumed the phase of the right mirror to be zero. Assuming this phase to be \(\pi\) would invert the parity of the mode with respect to the center of the cavity. In this case, the parity would be inconsistent with the FDTD simulations of [47]. We view this result as a hint that the phase of the right and left mirror, as defined by the interfaces in Figure 8, is indeed zero to good approximation.
# FPGA-QNN: Quantized Neural Network Hardware Acceleration on FPGAs
###### Abstract  
Recently, convolutional neural networks (CNNs) have received a massive amount of interest due to their ability to achieve high accuracy in various artificial intelligence tasks. With the development of complex CNN models, a significant drawback is their high computational burden and memory requirements. The performance of a typical CNN model can be enhanced by the improvement of hardware accelerators. Practical implementations on field-programmable gate arrays (FPGA) have the potential to reduce resource utilization while maintaining low power consumption. Nevertheless, when implementing complex CNN models on FPGAs, these may may require further computational and memory capacities, exceeding the available capacity provided by many current FPGAs. An effective solution to this issue is to use quantized neural network (QNN) models to remove the burden of full-precision weights and activations. This article proposes an accelerator design framework for FPGAs, called FPGA-QNN, with a particular value in reducing high computational burden and memory requirements when implementing CNNs. To approach this goal, FPGA-QNN exploits the basics of quantized neural network (QNN) models by converting the high burden of full-precision weights and activations into integer operations. The FPGA-QNN framework comes up with 12 accelerators based on multi-layer perceptron (MLP) and LeNet CNN models, each of which is associated with a specific combination of quantization and folding. The outputs from the performance evaluations on Xilinx PYNQ Z1 development board proved the superiority of FPGA-QNN in terms of resource utilization and energy efficiency in comparison to several recent approaches. The proposed MLP model classified the FashionMNIST dataset at a speed of 953 kFPS with 1019 GOPs while consuming 2.05 W.  
accelerator; FPGA; QNN; deep learning; FINN +
Footnote †: journal: Applied sciences  
1
# Enhanced Photocatalytic Performance of Bi2O2 CO3 Loaded Activated Carbon for Toluene Removal in Air
## 2 Materials and Methods
### Photocatalytic Activity Test
#### 2.5.2 Photocatalytic Activity of BOC/AC Composites  
The photocatalytic performances of the BOC/AC composites were investigated using a similar system to that described in Section 2.5.1. To imitate practical applications, the stainless steel canister was replaced by a 60 L Tedlar polyvinyl fluoride (PVF) pocket in a thermostatic test chamber and kept at a constant temperature (e.g., 20 \({}^{\circ}\)C). Then, 3 g BOC/AC particulates was placed on the dish and into the pocket. The incident light can pass through the PVF film to the BOC/AC surface. The structure is illustrated in Figure 1.  
To test the photocatalytic activity, the reaction reactor was first vacuumized by pump. Then, 60 L toluene gas (50.0 ppm in air) was injected as the target indoor pollutant. This required about 12 min. After that, the reaction reactor was kept in the dark for 0.5 h to reach the adsorption/desorption equilibrium. A 300 W xenon lamp (CEL-HXF300, Beijing China Education AuLight Technology Co., Ltd., Beijing, China) was used as the simulated sunlight source. The gas product in the reactor was analyzed at regular time intervals using GC. In addition, the toluene gas in the pocket could not flow completely to the automatic gas sampling valve of the GC at 0 min because the pipe diameter of the valve is relatively small and the gas delivery pump has just started. Thus, the toluene concentration is low. This means that the first point (at t = 0) is not 100% for all studies.
## 2 Materials and Methods
### Predictive Model Building
#### 2.3.3 ICC Analysis  
Intraclass Correlation Coefficient (ICC) two-way mixed-effects single-rater model approach (shortly ICC3) was used to determine if identified RFs--by using the LASSO-Cox-based pipeline described in Section 2.3.1--were stable (in terms of concordance and robustness) in both the Par-CT and Med-CT datasets, for the investigated patients. Calculation of the ICC metric was performed using the irr package. Conventionally, it is considered the agreement has been divided into four levels: poor for ICC3 < 0.5, moderate for 0.5 \(\leq\) ICC3 \(\leq\) 0.75, good for 0.75 < ICC3 < 0.90 and excellent for ICC > 0.9 [17].