esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Temperature Chemoselective Hydrogenation of Aldehydes over a Magnetic Pd Catalyst
## 3 Results and Discussion  
### Effect of the Reaction Solvents on the Hydrogenation of Benzaldehyde  
Generally speaking, the reaction solvent playeda key role in the chemical reaction, as different solvents had different properties such as the polarity, dielectric constant, and steric hindrance [27]. Therefore, the hydrogenation of benzaldehyde was initially carried out in various solvents catalyzed by \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd to study to the solvent effect. As listed in Table 1, it was noted the conversion of benzaldehyde over the \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd catalyst was greatly affected by the reaction solvents, while the selectivity was all above 90%. The lowest benzaldehyde conversion was observed in hexane with the weakest polarity (Table 1, Entry 1). The conversion of benzaldehyde increased from 34.2% in tetrahydrofuran (THF) to 56.9% in _iso_-propanol, further greatly increasing to 95.6% in ethanol (Table 1, Entries 3-4). The possible reason should be that the \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd catalyst can well dispersed in polar solvents instead of nonpolar solvents, as the surface of the \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)@HAP-Pd catalyst was a layer inorganic HAP, which was hydrophilic. Hence, the higher polarity of the solvents led to the higher conversion of benzaldehyde. Interestingly, the best result was attained in water with the highest benzaldehyde conversion of 98.7% (Table 1, Entry 5), although benzaldehyde was less soluble in water than in ethanol. We presumed that a stronger hydrogen bonding between water and benzaldehyde should benefit the hydrogenation reactions [28; 29]. It was noted that benzyl alcohol was attained in an almost quantitative yield at room temperature and atmospheric H\({}_{2}\) pressureafter reacted for 4 h (Table 1, Entry 6), which was capable with the Pd-BNP (Table 1, Entry 7) [17].
## 2 Proposed Method
### Data Cleaning  
The condition monitoring systems of large FTUs usually have a huge scale and complex structures. The state-monitoring data and operating condition parameters are usually distributed and monitored by several different monitoring modules, and collected into the computer monitoring system of the hydropower station through various data communication protocols and long-distance communication cables, which are prone to communication packet loss or short-term failure. Moreover, FTUs work in a humid, high-electromagnetic-interference and drastic-vibration environment. These factors lead to an apparent anomaly or missing values in the on-site raw data. Traditional signal denoising methods are mainly based on signal decomposition and reconstruction. Frequent changes in operating conditions will affect the effectiveness of these methods. Meanwhile, random missing values make these methods challenging to apply to engineering practice.  
The DBSCAN algorithm is a kind of unsupervised clustering method. As an effective density-clustering method, DBSCAN can adaptively identify clusters with irregular shapes and automatically mark sample points with low density as noise. The DBSCAN is adopted  
Figure 1: The framework of the proposed method.  
to adaptively recognize the anomaly values in the raw data set. The schematic diagram of the DBSCAN algorithm is illustrated in Figure 2.  
For each sample \(p\) in the data set \(\Psi\), the region for which the Euclidean distance from the sample point \(p\) is less than \(\varepsilon\) is defined as the \(\varepsilon\)-neighborhood of \(p\). Its element set is expressed as:  
\[N_{\varepsilon}(p)=\{q\in\Psi|\rho(p,q)\leq\varepsilon\} \tag{1}\]  
where \(\rho(p,q)\) represents the Euclidean distance between samples \(p\) and \(q\).  
If sample \(q\) is in the \(\varepsilon\)-neighborhood of sample \(p\), \(p\) and \(q\) are called directly density-reachable to each other. If sample \(r\) is also directly density-reachable to \(q\), but \(r\) is not directly density-reachable to \(p\), \(r\) and \(p\) are called density-reachable to each other. If the element number of \(N_{\varepsilon}(p)\) is greater than the minimum density threshold \(Z\), sample \(p\) is defined as a core point. If sample \(r\) is located in the \(\varepsilon\)-neighborhood of a particular core point, but \(r\) is not a core point, then \(r\) is defined as a border point.  
The samples which are directly density-reachable or density-reachable to the core point construct a cluster, as illustrated by blue circles in Figure 2. The samples that do not belong to any clusters are noise points, marked as red points in Figure 2. The recognized noise points in the raw data set are dropped out.
## 4 Experiment Setup
### Statistical Analysis  
To statistically evaluate and compare the performance differences between the proposed SpikeCAN model and benchmark methods, we conducted an independent two-sample two-tailed \(t\)-test. Differences were considered statistically significant if the resulting \(p\)-value was below the conventional threshold of 0.05 (\(p<0.05\)).
## 4 Proposed Anomaly Detector
### Normal Model
As discussed in Sections 2.2 and 3.2, the two general approaches are as follows: (1) to impose differential misclassification costs on FP and FN errors and use a cost-sensitive classifier to minimize the total error cost; and (2) stratification-based approaches that over-sample or under-sample specific classes in the training dataset to minimize the number of FN or FP errors. Our proposed method follows the stratification approach and specifically is inspired by the SMOTE synthetic-example technique [20]. In SMOTE, a synthetic example is created by randomly choosing a point in feature space on the line connecting two nearest neighbors in the minority class (to ensure that the new point is interior to the minority class boundary). The empirical investigation in [20] showed that creating new synthetic examples broadened the decision regions learned by a C4.5 tree, whereas replicating existing samples caused the decision regions to focus tightly on the examples themselves. This is a promising characteristic, but creating a _useful_ synthetic example that can also reliably be considered \"normal\" is not so simple. Merely using SMOTE based on the AD training dataset would not be expected to expand the decision boundary; by definition, every synthetic example should be interior to the class boundary and should thus _not_ be a new support vector. If the support vectors do not change, then the learned boundary will not change. In addition, the context for the (_forecast_, _actual_) data pairs is not a part of the AD training set, and so there is no way to decide whether or not the synthetic data point legitimately represents a \"normal\" instance. Instead, we propose to use the normal model's forecasts to create synthetic examples. Consider a forecast for observation \(X_{n}\), generated from lags \(X_{n-1}\), \(X_{n-2}\), \(\ldots X_{n-m}\). The forecasted observation \(\hat{X_{n}}\) can then be treated as the most recent lag, and we forecast \(X_{n+1}\) based on \(\hat{X_{n}}\), \(X_{n-1}\), \(\ldots X_{n-(m-1)}\). This is in addition to the forecast we obtained based on \(X_{n}\), \(X_{n-1}\), \(\ldots\), \(X_{n-(m-1)}\). The two forecasts (\(X_{n+1}^{\hat{x}}\) and \(X_{n+1}^{\hat{x}}\), respectively) give us two (forecast, actual) pairs in place of just the one, assuming that \(X_{n+1}^{\hat{x}}\
eq X_{n+1}^{\hat{x}}\). This seems like a reasonable assumption, as we would expect the (inevitable) forecast error present in \(\hat{X_{n}}\) to lead to increased error in \(X_{n+1}^{\hat{x}}\). If we repeat this step for every forecast data point, we should ultimately double the size of the AD training dataset, and there should thus be a high likelihood that the set of support vectors computed by the OC-SVM in feature space will change.
## 3 Model Evaluation
### Comparison of Estimation Methods and Performance Indicators  
For performance evaluation, several different methods are also applied on the same data set. The methods chosen include the state-of-the-art simple mean method and the moving average method as comparison groups. The simple mean method is the easiest alternative to estimate travel times by collecting real-time data on the segments. This method is generally used by Korean Expressway Corporation to display travel time information on variable message signs. Therefore, the simple mean method is considered a state-of-the-art method for quantifying the trade-off between simplicity and estimation accuracy. The moving average is often used with time-series data to smooth out short-term fluctuations in real-time travel time estimations by creating a series of averages of different subsets of the full data set to analyze the calculation of data points. The threshold depends on the application, and the parameters of the moving average will be set accordingly. In this study, three and five orders of moving average are selected, which entails travel time patterns in short-term traffic flow.  
Figure 1: Study site and assumption of ground truth: (**a**) Gyeongbu Expressway, Giheung IC-Dongtan JC, (**b**) Ratio of traffic volume between loop detectors and ETC.  
To evaluate different estimation methods, performance criteria are specified using unbiasedness and efficiency to obtain a good estimator. The unbiasedness means that the estimated value of the estimators should be equal to the true value of the variable estimated. This study adopts both the absolute and root mean square estimation errors for measuring the unbiasedness of the different estimators. The Mean Absolute Percentage Error (MAPE) is the average absolute percentage change between the estimated and the true values. Also, the Root Mean Square Error (RMSE) is also the difference between the estimated and the true values as demonstrated as follows:  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\frac{|\text{T}_{i}^{\text{E}}-\text{T}_ {i}^{\text{G}}|}{\text{T}_{i}^{\text{G}}} \tag{15}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\text{T}_{i}^{\text{E}}- \text{T}_{i}^{\text{G}}\right)^{2}} \tag{16}\]  
where \(\text{T}_{i}^{\text{E}}\) is the estimated travel time of the ith time interval, \(\text{T}_{i}^{\text{G}}\) is the ground truth of travel time of the ith time interval, and n is the total number of the estimated travel times.  
The efficiency is a measure of the quality of an estimator and an efficient estimate of the good estimator is one that has the smallest standard error among all unbiased estimators. This study adopts the standard deviation of MAPE for measuring the efficiency of the different estimators.
## 3 Model Design
### Cooperation Probability and Energy Decomposition
#### 3.3.2 Residual Energy Ratio  
We consider the residual energy of the current moment of the mobile node. When the remaining energy of the mobile node is greater, the node is more willing to cooperate to forward the data.  
\[E_{i}=\frac{E_{r}}{E_{d}} \tag{18}\]  
where \(E_{r}\) represents the energy remaining in the device and \(E_{d}\) is the energy required for the packet to be transmitted.
# Augmented Reality Assisted Assembly Training Oriented Dynamic Gesture Recognition and Prediction
## 4 Experiments and Discussion
### Result of Operation Recognition and Prediction  
In the ARAAT system, the action sequence is important for the evaluation of the standard and achievement of trainees' operations. Thus, recognition and prediction of the operation is as necessary as action recognition.  
To validate the reliability of the proposed algorithm, 30 participants took part in performing real-time ARAAT tasks. Half of the participants were beginners in assembly operations and the other half had basic assembly operation knowledge but little practical experience. Participants completed assembly training tasks from an application on HoloLens written in C#. The demonstration scenario is shown in Figure 18. Participants could assemble workpieces in the tasks in an arbitrary order depending on their own needs. During assembly, participants chose whether to use their bare hands or various virtual tools, such as a wrench or screwdriver, to complete the tasks, as illustrated in Figure 18. The tasks were mainly completed by 6 actions: \"Pointing\", \"Moving\", \"Grasping\", \"Releasing\", \"Scaling\", and \"Rotating\", and 3 operations: \"Inserting\", \"Fastering\", and \"Equipping\".  
Figure 17: Effect of image resolution on accuracy.  
Figure 18: The experimental scenario of augmented reality assisted assembly training. (**a**) Experimental scenario, (**b**) screwing operation, (**c**) inserting operation, (**d**) fastening operation.  
To evaluate the sequence recognition accuracy and prediction efficiency, we developed two measures: the sequence accuracy ( \(A_{sequence}\)) and the degree of early recognition (DER), defined respectively as the following:  
\[A_{sequence}=\frac{\sum_{i=1}^{N}\left(\sum_{j=1}^{M}E\left(\widetilde{A_{i,j}} =A_{i,j}\right)\right)}{N\times M} \tag{22}\]  
\[DER=\frac{N_{re}}{N_{total}} \tag{23}\]  
where \(\widetilde{A_{i,j}}\) is the ground truth of \(A_{i,j}\) and \(E\left(\widetilde{A_{i,j}}=A_{i,j}\right)\) means if \(\widetilde{A_{i,j}}=A_{i,j}\) is true, the output is 1 while 0 otherwise. \(N_{re}\) is the number of frames of the recognized operation and \(N_{total}\) is the total number of frames of the whole operation. A lower DER means that the operations are recognized early and a higher DER means that the operations are recognized late. The experiment results shown in Table 7 indicate that the proposed algorithm can predict the operations up to 40% of the time with a high recognition rate of 93.5%. These results mean that the prediction can give the machine enough time to make a reaction and provide the trainees with a smooth and friendly human-machine interaction in ARAAT.
# Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
## 4 Improved DQN Algorithm
### Algorithm Performance Analysis  
#### 4.3.1 Time Complexity Analysis of the HDQN Algorithm  
To discuss the time complexity of the DQN algorithm, it is necessary to first analyze the time complexity of its core DNN. This is because the DQN algorithm approximates the maximum cumulative reward through DNNs, so analyzing the time complexity of DNNs is crucial. The time complexity of DNNs is influenced by multiple key factors, including the number of layers (i.e., depth), the number of neurons contained in each layer, and the size of the convolution kernel. This paper assumes that the total number of layers in a DNN is \(\mathbb{L}\), the edge length of the feature map output by the convolution kernel of the \(l\)-th layer is \(S_{l}\), the size of the convolution kernel is \(M_{l}\), the number of input channels is \(C_{l-1}\), and the number of output channels is \(C_{l}\). The time complexity of the DNN can be obtained as \(O(\sum\limits_{l\in L}S_{l}^{2}\times M_{l}^{2}\times C_{l-1}\times C_{l})\).  
As shown in the system model of Figure 2, there are \(N\) sensor nodes in the HWSNs. Assuming there are \(N_{CH}\) clusters and \(A_{total}\) actions in the DQN algorithm, the time complexity of the HDQN information transmission energy efficiency optimization algorithm is \(O(\sum\limits_{l\in L}{(S_{l}^{2}\times M_{l}^{2}\times C_{l-1}\times C_{l}) \times A_{total}\times N\times N_{CH}})\). It can be seen that the computational complexity of this algorithm has advantages compared to other exponentially increasing information transmission energy efficiency optimization algorithms.
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 4 Simulation Results
### Introduction to Baseline Algorithms and Proposed Approach  
1. Baseline 1 (hierarchical FL in [4]): In this baseline, after each round of cloud aggregation and before local updates, five edge servers randomly select 10 clients in their coverage to participate in the training process. The client selection takes into account the possibility of client relocation during the training. Once the clients complete their local updates, the edge servers receive the uploaded model parameters if the clients remain in their coverage. The edge servers proceed to collect the parameters from all the clients. If the waiting time exceeds a threshold, the edge server performs a round of edge aggregation and uploads the aggregated model parameters to the cloud server. Upon receiving the parameters from the edge servers, the cloud server performs a round of cloud aggregation.
2. Baseline 2 (greedy optimization): In this baseline, 10 clients are randomly selected from the clients in the coverage of each edge server for training. The selection process takes into account the potential client relocations during the training process. Once the local training is completed, each selected client is connected with its closest edge server and uploads the model parameters. After a specific time period, each edge server performs edge aggregation on the uploaded model parameters. The parameters that are not uploaded on time are buffered and used in the next edge aggregation based on the uploading delay, which is adjusted according to the outdated function. Finally, the cloud server receives the model parameters from all the edge servers simultaneously, along with the information about the number of participating clients in the cloud aggregation.
3. Proposed asynchronous hierarchical FL: In our approach, after each round of cloud aggregation and before local updates, we first determine the number of clients that are associated with each edge server. Then, the client scheduling and bandwidth allocation are optimized simultaneously based on training delay and data quality. In addition, client mobility is also considered and is modeled as a Markov process. After finishing model uploading, the edge server releases the idle bandwidth resource. If a client fails to upload its model parameters to its edge server within a given time interval, the edge server is forced to release the corresponding bandwidth. Each edge server performs edge aggregation on the uploaded model parameters in a predetermined interval and uploads them to the cloud server. The cloud server receives all the model parameters uploaded by the edge servers, along with information about the number of clients participating in the cloud aggregation.
## 2 Preliminary Theory and Related Work
### Singular Value Decomposition (SVD)
Similarly, according to the linear combination of \(v_{1},\ v_{2}\) and \(v_{3}\) then the 3D flow field on the time-lag embedded space can be approximately reconstructed for observing the dynamical characteristics of the system. Additionally, since the transformations between Koopman modes should be linear, and Dynamic Mode Decomposition (DMD) is commonly used to identify linear systems, we next consider applying DMD to the V matrix to search for linear mappings between its columns.
# Multimodal Classification of Anxiety Based on Physiological Signals
## 3 Materials and Methods
### Raw Signals Filtering  
After extracting the signals from the baseline condition, all signals were shortened to 19 min to ensure the same length for all participants. Thereafter, the signals were filtered to remove the noise and isolate frequencies of interest, improving the overall signal quality by removing different noises and artifacts. Besides the powerline interference (around 50 Hz), different noises affect the ECG, EMG, and EDA signals.  
In an ECG, there are mainly two types of noises present. High-frequency noises, including electromyogram noise and additive white Gaussian noise; low-frequency noises, including baseline wandering due to movement and respiration (normally the content of the baseline wander is below 0.5 Hz) [27].  
EDA signals show expected skin conductance morphology at a low amplitude. Hence, it is mainly affected by the noise caused by electrical interference, which can manifest in the data as a small amount of fuzz on top of the signal [28].  
Regarding EMG signals, common low-frequency artifacts occur when the signal cables move due to the subject's motion or incorrectly attached electrodes. In addition, other muscles near the testing site can generate an EMG signal, and crosstalk can be recorded. EMG recordings close to the subject's heart may detect the subject's pulse as a regular beat underlying the EMG signal [27].  
The physiological signals were filtered after power spectral density (PSD) analysis, supporting which frequencies contained the most information. Thus, different orders of Butterworth bandpass filters, with cutoff frequencies defined accordingly with the frequency components of interest for the various signals, were used to process the signals. Although it is an infinite impulse response filter, the Butterworth filter was chosen due to the insurance of a frequency response that is as flat as possible in the passband. Moreover, the nonlinear phase distortion was eliminated by processing all the signals in both the forward and reverse directions. After that, the order or filter was decided based on the mean absolute error and mean squared error computed from the raw and filtered signals.  
Figure 2: Excerpt of ECG, EDA, and EMG (**left, middle**, and **right**, respectively) of two participants with anxiety levels “High” and “Low and moderate” (orange and blue, respectively) from the WESAD dataset.  
Regarding the ECG signal, the baseline wander, although not significant, was present in all participants. Thus, a 4 th-order Butterworth bandpass filter was applied between 0.5-40 Hz. The EDA signal was filtered using a 4 th-order Butterworth 5 Hz lowpass filter. As for the EMG signal, the powerline interference was removed in the 50 Hz range with a Notch filter, and a 4 th-order Butterworth bandpass filter was applied between 20-200 Hz.