esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Experimental Results
#### 4.2.1 Behavior Recognition Using Video Data  
For the video data, FasterRCNN, YOLOv3, and YOLOv4 were used to check the dog detection rate. To confirm the detection rate, an experiment was conducted using a pre-trained model with the COCO dataset in three dog detection methods. Figure 5 displays one of the detected results for \"sitting\". YOLOv4 detected the dog's size better than the other methods. Figure 6 shows the detection distribution according to the amount of data. YOLOv3 had the largest amount of data with a 100% detection rate and the largest amount of data with a 0% detection rate. On average, FasterRCNN's detection rate was 68.62%, YOLOv3's was 67.13%, and YOLOv4 had the highest detection rate at 72.01%. Figure 7 shows the detection distribution by behavior. Overall, the recognition rate was high when the dog performed B2 behavior, and the detection rate was the lowest when the dog performed B7 behavior.  
Figure 5: Result of dog detection for “sitting”: (**a**) FasterRCNN; (**b**) YOLOv3; (**c**) YOLOv4.  
Figure 6: Distribution of detection by method according to the amount of data.  
Table 5 shows the performance evaluation results for behavior recognition with the object detection method using only video data. On average, the accuracy was 86.7%, and among the methods, YOLOv4 had the highest accuracy (89.2%). Figure 8 shows the fusion matrix of behavior recognition by behavior. In general, B1-B4, which did not involve movement, had a high recognition rate. In contrast, in the case of B7, since the B7 data of FasterRCNN were too small compared to that of YOLOv3 and YOLOv4, it was judged that YOLOv3 and YOLOv4 were relatively well aware of running, whereas FasterRCNN was not.
# Situational Awareness Classification Based on EEG Signals and Spiking Neural Network
## 1 Introduction
The results showed an average accuracy of about 80% for classification of the four emotion states (arousal, valence, dominance, and liking). [PERSON] et al. [30] suggested using the DWT to remove noise from the EEG signals while retaining the ECG signal morphology effectively. [PERSON] et al. [31] presented a deep learning-based EEG decoding method to read mental states. They proposed a 1D CNN with different filter lengths to capture different frequency band information, demonstrating 96% accuracy. Numerous studies have been conducted to classify cognitive load using various methods, including EEG [32; 33], ECG [34], and gaze detection [35; 36]. Moreover, there is a growing demand for executing cognitive load estimation with edge devices across diverse fields, including healthcare [37; 38] and automotive industries [39]. The ability to classify physiological signals using machine learning tools with low-hardware resources and low power allows its integration, especially in wearable sensors [39; 40]. A configurable parametric model of a digital spike continuous-time neuron (SCTN) was introduced in [41]. Efficiency of usage of the SCTN for sound signal processing was presented in [42]. In this paper, we adapt the basic architecture of the published SCTN and propose using an SCTN-based bio-inspired network for classifying situational awareness based on EEG signals. First, we introduce the usage of SCTN-based resonators for feature extraction in the frequency domain. A supervised spike timing-dependent plasticity (STDP) learning algorithm is used for training the network and adjusting the weights to detect the frequencies of EEG sub-bands. Then, we propose a method for integrating the extracted features via spatial mapping, leveraging the interrelationships between the various EEG channels using a two-dimensional structure of spiking neurons. Finally, an SCTN-based three-layer network is used for mental state classification. Our main contributions are listed below:  
1. We propose a novel approach for mental states and situation awareness classification based on a biologically inspired spiking neural network using EEG signals. 2. A new SCTN-based 2D neural network is introduced for efficient feature mapping, aiming to achieve a spatial representation of each EEG sub-band. 3. We utilize the SCTN-based resonator for feature extraction in the frequency domain and demonstrate high correlation with the known FFT features.
# Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
## 2 Proposed Frequency-Based Haze and Rain Removal Network (FHRR-Net) Framework  
This section introduces methods for analyzing images degraded by rain and fog in detail. We present frequency-based haze and rain removal network (FHRR-Net). Architecture of the network is depicted in Figure 2. Overall framework includes four basic elements:  
1. Analysis of haze and rain models to create a synthesized training dataset.
2. Image decomposition using a guided filter.
3. Architecture of encoder-decoder networks.
4. Image restoration using image degradation model.  
In the first section, we illustrate the characteristic of the haze and rain model. Second, we demonstrate a image decomposition method using trainable guided filter. Finally, a deep convolutional encoder-decoder model is introduced which restores hazy and rainy images from degradation model.
# Numerical Analysis of Fluid Stream Division to Supply Multiple Hydraulic Receivers
## 2 Working Principle of a Valve Block  
The research object in the form of a grinder valve block is shown in Figure 1.  
Figure 1: Scheme of the analyzed valve block: 1—connection plate; 2—flow control valve; 3—electromagnetic control valve; 4—relief valve; P, T—connection ports.  
The valve block includes a connection plate (1) that supplies hydraulic oil to multiple parallel-connected flow control valves (2). There are also 4-way, 2-position electromagnetic control valves (3) mounted on the plate, which switch on or off the flow of the working medium to individual hydraulic motors and relief valves (4) that make it easier to stop the engine movement after cutting off the supply of working fluid.  
A simplified hydraulic system circuit diagram is shown in Figure 2. A single pump (1) supplies the hydraulic motors (5) to which the grinding disks are attached. The fluid flow is controlled by a block containing flow control valves (2) and electromagnetic valves (3). The grinding wheels rotate at a high speed of more than 5000 rpm. After cutting off the supply flow to the hydraulic motor, it is necessary to enter the braking phase because the high inertia of the grinding disk can keep it rotating for a long time. Therefore, relief valves (4) inhibit motor movement after the flow has been cut off since the resistance moment created by maintaining relatively high pressure in the return line stops rotation effectively. Separate motors may have different load values but must operate approximately at a similar rotational speed. Obtaining the appropriate speed value is ensured by flow control valves, which enable the supply of a proper fluid stream and protect against an excessive increase or decrease in revolutions due to sudden load changes.  
The main aim of this article is to investigate whether the applied flow control valves can maintain the required speed and compensate for the effects of unfavorable phenomena related to the differentiation of loads imposed on individual motors.
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 3 Atomic Clocks and Time Scale Metrics
### Atomic Clock Output Signal Model  
The ideal output of an atomic clock is expressed as  
\[u(t)=U_{0}\sin(2\pi v_{0}t), \tag{5}\]where \(U_{0}\) is the amplitude, and \(v_{0}\) is the atomic clock frequency. This can be rewritten as  
\[u(t)=U_{0}\sin(2\pi v_{0}h_{0}(t)), \tag{6}\]  
with \(h_{0}(t)\) representing the ideal clock reading. In practical applications, random disturbances affect the amplitude and phase, resulting in an output signal approximation [22]:  
\[u(t)=[U_{0}+\varepsilon(t)]\sin(2\pi v_{0}h_{0}(t)+\varphi(t)), \tag{7}\]  
where \(\varepsilon(t)\) is the amplitude disturbance, and \(\varphi(t)\) is the phase disturbance. This can be further expressed as  
\[u(t)=[U_{0}+\varepsilon(t)]\sin(2\pi v_{0}h(t)), \tag{8}\]  
where \(h(t)=h_{0}(t)+x(t)\), and \(x(t)\) is the time deviation from the ideal reading.  
For practical high-performance atomic clocks, amplitude perturbation is usually negligible, but phase perturbation is not. This phase disturbance introduces a frequency deviation:  
\[v(t)=v_{0}+\frac{1}{2\pi}\frac{d\varphi}{dt}, \tag{9}\]  
leading to the definition of frequency stability \(y(t)\):  
\[y(t)=\frac{v(t)-v_{0}}{v_{0}}=\frac{1}{2\pi v_{0}}\frac{d\varphi(t)}{dt}= \frac{dx(t)}{dt}. \tag{10}\]  
In practice, \(x(t)\), \(y(t)\), and the clock reading \(h(t)\) are not directly observed. The observed quantity is the clock offset \(x_{ij}(t)\) between two clocks \(i\) and \(j\):  
\[x_{ij}(t)=h_{i}(t)-h_{j}(t). \tag{11}\]  
This can be expressed as  
\[x_{ij}(t)=x_{i}(t)-x_{j}(t). \tag{12}\]  
To assess the performance of atomic clocks, the typical method involves measuring the clock offset between different atomic clocks, representing the difference in the time deviation of their output signals.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 3 Research Methodology
### Preprocessing
#### 3.2.1 Dataset Imbalance Handling Mechanism  
In this study, the number of legitimate datasets is significantly larger than the malicious dataset, which indicates a class imbalance. Dealing with class imbalance is important because models trained on imbalanced datasets are biased towards the majority class, leading to suboptimal performance in detecting the minority class (i.e., the malicious URLs). There are different data imbalance handling mechanisms, including over-sampling minority samples, under-sampling majority samples, and class weight balancing (assigning a higher weight for minority classes) [20]. In our case, we have used an under-sampling of the legitimate URL samples, which have higher values, and balanced these with the malicious samples, which are minor samples in our dataset.
## 2 Network Model
### Non-Maximum Suppression Improvement  
In vehicle detection tasks, NMS is commonly used to remove redundant detection frames by calculating the IOU ratio between each detection frame and its corresponding prediction frame. However, in road traffic videos, dense targets and large overlapping areas can make it difficult to accurately reject prediction frames, resulting in failed target detection. The DIOU metric considers both the overlapping area and the distance between the center points of the prediction and target frames based on the IOU metric. Thus, introducing DIOU as the NMS criterion can address the aforementioned issues and improve the overall detection performance in such scenarios.  
\[DIOU=IOU-\frac{D^{2}\big{(}m,m_{gt}\big{)}}{d} \tag{7}\]  
where \(D^{2}(\bullet)\) is the calculated Euclidean distance, \(m\) is the center point of the prediction frame, \(m_{gt}\) is the center point of the detection frame, and \(d\) is the diagonal distance between the prediction frame and the smallest outer rectangle of the detection frame. The DIOU-NMS algorithm flow description is shown in Algorithm 1.  
```
Input:\(B=\{\texttt{b}_{1},\texttt{b}_{2},\ldots,\texttt{b}_{\texttt{n}}\},\texttt{S}=\{ \texttt{s}_{1},\texttt{s}_{2},\ldots,\texttt{s}_{\texttt{n}}\},\texttt{N}_{ \texttt{t}}\) is the DIoU-NMS threshold begin \(D\leftarrow\{\}\)  While\(B\
eq empty\)do \(\texttt{m}\leftarrow\text{argmax}S\) \(M\leftarrow\text{b}_{\texttt{m}}\) \(D\gets D\cup M;B\gets B-M\) for\(\texttt{b}_{\texttt{i}}\) in \(B\)do  if \(\text{diou}(M,\texttt{b}_{\texttt{i}})\geq\texttt{N}_{\texttt{t}}\)then \(B\gets B-b_{\texttt{i}};S\gets S-s_{\texttt{i}}\)  end if  end for return\(D,S\)
```  
**Algorithm 1** DIoU-NMS  
Figure 7: YOLOv5_H network structure.
## 3 Simulation Experiment
### Analysis of Denoising Effects under Different \(f_{1}\)  
Power-line harmonics pose a serious problem in MRS signal denoising when \(f_{1}\) is close to or even completely coincident with a power-line harmonic frequency. At present, most denoising algorithms cannot easily achieve effective separation of signals and noise. One of the most important aspects in investigating the universality of an algorithm is that the algorithm should be able to solve the harmonic removal of frequency deviation within the range of a few hertz. On the basis of the simulation data in Section 3.1, two adjacent power-line harmonics, 2300 Hz and 2350 Hz, both near \(f_{l}\), were added, and the amplitude was randomly generated within [150 \(\sim\) 180] nV. The \(f_{l}\) value of the MRS signal was set at 2306 Hz to 2301 Hz, the difference between the nearest harmonic was within [1\(\sim\)6] Hz, and random noise with amplitude 150 nV was added to form the noisy MRS signal. For each group, the algorithm was executed 20 times under condition \(f_{l}\). The denoising results for different \(f_{l}\) values are shown in Table 2.  
Table 2 shows that when the frequency difference was within 3 Hz, the range of uncertainty of the fitted \(E_{0}\) and \(T_{2}^{*}\) was large. However, as the frequency difference increased, the uncertainty of the fitting result decreased, and the reconstruction accuracy was improved. Figure 3 shows the time-domain and frequency-domain diagrams before and after noise cancellation when \(f_{l}\) was 2305 Hz, 2303 Hz, and 2301 Hz. Figure 3b,d,f shows that the noise was effectively filtered out. Even the power-line harmonic of 2300 Hz, which was the nearest to \(f_{l}\), could be accurately reconstructed and removed. After denoising by this algorithm, SNR was improved remarkably and the lifting range was 24.46 dB\(\sim\)33.79 dB, illustrating the universality of the MP algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(T_{2}^{*}\)/ ms & \(\overline{SNR}\) / dB & \(\Delta\)SNR / dB & \(E_{0}\) / nV & \(T_{2}^{*}\) / ms \\ \hline
50 &
## 3 Materials and Methods
### Data Collection  
We used the data collected from the accelerometer positioned at the right ankle of 12 subjects from our previous experiment [10] for this study, since the right ankle was determined to be the most suitable location to identify irregular walking surfaces. To collect the acceleration data, we conducted the experiment at The Peter Kiewit Institute of the University of Nebraska at Omaha. We selected a segment of a well-paved, smooth and leveled walking route in that area and denoted the starting and ending points. Then, four irregular walking surface segments were set up within that path: a segment covered in grass, an object-obstructed segment, an uneven segment and a segment covered with debris. These segments represented irregular sidewalk walking surfaces that are highly probable in less walkable neighborhoods in the real-world. The good and irregular walking segments are shown in Figure 2.  
We recruited twelve healthy participants, comprising eight males and four females, for this study. A tri-axial accelerometer was affixed at the right ankle on each subject, as shown in Figure 3. It was affixed to the outer side of the shoe of each participant. We measured the linear acceleration along the X, Y and Z axes of the subject's body with Mbient sensors (MetaMotionR, Mbient Lab, San Francisco, CA, USA) that were configured to capture data at a rate of 100 Hz. The subjects were directed to walk at their usual pace along the path, starting from the initial point, proceeding towards the endpoint and then returning to the starting point along the reverse path. Video recordings were taken throughout the experiment for each subject to help facilitate data labeling.  
Figure 1: Overview of the proposed framework.  
Figure 4 displays the raw acceleration measurements captured with the right-ankle sensor of a subject. The figure visualizes an individual's gait for five seconds, which is equivalent to 500 data points. The raw acceleration data points were categorized by cross-referencing with the experiment videos.  
Figure 3: Placement of sensor at the right ankle of a subject.  
Figure 2: Good and irregular walking surfaces; (**a**) well-paved (good), even; (**b**) grassed surface (irregular); (**c**) surface obstructed with objects (irregular); (**d**) uneven (irregular); and (**e**) surface covered with debris (irregular).
## 2 Traditional Calibration Method of Intradyne Coherent Receiver Using Single-Polarized Signal
There are six traces: traces A and B are the spectral responses of X polarization and Y polarization; traces C to F are the time domain signals for four tributary channels. The frequency difference can be clearly identified by the locations of peaks in traces A and B. The peak-to-peak swing of traces C to F reduces when the frequency difference is swept from 2.5 GHz to 22.5 GHz, demonstrating the impact of bandwidth on the output of ICR. The difference in the time domain signal within the same polarization (XI vs. XQ, or YI vs. YQ) is caused by the IQ imbalance between the I tributary and Q tributary. Most importantly, the XY imbalance, defined as (XI + XQ)/(YI + YQ), remains very small when the frequency is swept from 2.5 GHz to 22.5 GHz. This shows that the active stabilization maintains an ideal SoP. Thus, both polarizations are injected with an equal amount of power. To further manifest the necessity of the active stabilization of the SoP, we measure the response of ADC when the active stabilization of the SoP is not engaged. In addition, we demonstrate that ICR calibration can be performed within the coherent transceiver. Instead of using an OMA to analyze the signal, we measure the electrical output of the ICR integrated within a pluggable CFP2 form-factor pluggable analog coherent optics module (CFP2-ACO), as specified by Optical Internetworking Forum (OIF) [23]. Here, the pluggable module is a class-2 CFP2-ACO with a linear RF amplifier, which can support a 200 Gb/s (gigabit per second) data rate using the DP-16 QAM modulation format at a baud rate of approximately 30 GBd. The detailed characteristics of this CFP2-ACO module can be found in [24]. The outputs of ICR are sampled by DSP ASIC in the receiver side and stored in the internal memory. We download the waveforms from the memory and perform post-processing. We measure the output of the ICR four times without using active SoP stabilization. Next, we plot the output of the I tributary along the X axis and plot the output of the Q tributary along the Y axis, forming the typical Lissajous curves. As shown in Figure 5, the Lissajous curves change dramatically when the SoP of  
Figure 3: Setup to characterize ICR using single-polarization heterodyne method. The polarization controller and polarimeter are integrated into the Keysight N7788 component analyzer. the input signal drifts around. An active stabilization of the SoP is indeed needed for the accurate calibration of ICR using the traditional method. Figure 4: Experimental results with active stabilization on. (**a**) The sweeping frequency is 2.5 GHz. (**b**) The sweeping frequency is 22.5 GHz.
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 4 Experiment and Analysis
### Ablation Studies and Analysis
#### 4.5.3 The Impact of Different Dilation Rates in the ADF Module  
To investigate how various dilation rates in the ADF module affect 3D object detection performance, we conducted ablation experiments on the validation set of the KITTI dataset, focusing on the Car category. Specifically, we set the dilation rates to standard (no dilated convolution), 2, 8, 16, or 4 (Ours), all under the same network architecture and training hyperparameters, and evaluated their \(AP_{\text{3D}}\) (Average Precision in 3D) metrics. As shown in Figure 7, different dilation rate configurations yielded varying results across the Easy, Moderate, and Hard difficulty levels. Notably, a dilation rate of 4 achieves the highest detection accuracy, reaching 24.72%, 18.63%, and 15.31% on Easy, Moderate, and Hard, respectively--outperforming the other settings. These findings suggest that using a suitable dilation rate helps capture more effective multi-scale features, thereby significantly enhancing the accuracy of 3D object detection. Hence, we chose a dilation rate of 4 for all subsequent experiments to achieve optimal performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Backbone**} & \multicolumn{3}{c}{**AP3D@IoU = 0.7**} & \multicolumn{3}{c}{**APBEV@IoU = 0.7**} \\ \cline{2-7}  & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** \\ \hline DLA-102 & 24.40 & 18.55 & 15.30 & 34.02 & 25.11 & 21.24 \\ DLA-102x2 & 24.59 & 18.56 & 15.25 & 34.01 & 25.13 & 21.32 \\ DLA-60 & 22.85 & 17.37 & 14.33 & 32.14 & 23.64 & 19.92 \\ DenseNet & 24.24 & 18.04 & 15.14 & 33.01 & 24.69 & 21.48 \\ ResNet & 24.09 & 17.98 & 15.15 & 32.97 & 24.39 & 21.33 \\ Ours & 24.72 & 18.63 & 15.31 & 34.11 & 25.18 & 21.90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different backbones on KITTI validation set for Car category; the table presents the performance (\(AP_{\text{3D}}\) and \(AP_{\text{BEV}}\) @ IoU = 0.7) using various backbones, including our proposed AuxDepthNet.
# A Review: Image Processing Techniques' Roles towards Energy-Efficient and Secure IoT
## 4 Literature Review
### IoT Security Applications Based on Image Processing
To secure IoT image classification against plaintext attacks, the research in [11] has proposed a system that is almost indistinguishable. An IoT device is not necessary to continually connect with the cloud-based image classification system. Using DNNs, a technique for the safe evaluation of linear functions was developed, such as divide-and-conquer and a set of unified ideal protocols. The lattice-based homomorphic method contributes to keeping contents hidden. Pre-trained deep convolutional neural network model of Visual Geometry Group (VGG-16) is used to extract deep information from an image.
# Student's t Kernel-Based Maximum Correntropy Criterion Extended Kalman Filter for GPS Navigation
## 2 Review on Extended Kalman Filter with Maximum Correntropy Criterion  
The probability density function of the Student's t-distribution [23] can be written as follows:  
\[S(\mu,\Sigma,v)= \frac{\Gamma(\frac{v+d}{2})}{\Gamma(\frac{v}{2})v^{\frac{d}{2}} \pi^{\frac{d}{2}}|\Sigma|^{\frac{1}{2}}}(1+\frac{1}{v}(y-\mu)^{T}\Sigma^{-1}( y-\mu))^{-\frac{v+d}{2}} \tag{1}\]  
This formula describes the probability density of the random variable \(t\) given the degrees of freedom \(v\). As \(v\) approaches infinity, the usual normal distribution is approached by the Student's t-distribution.  
\[S_{v,\sigma}(e)=\kappa_{v,\sigma}(X,Y)=(1+\frac{||X-Y||^{2}}{v\sigma^{2}})^{ -(\frac{v+2}{2})}(e=X-Y) \tag{2}\]  
In practical scenarios, it is challenging to obtain the joint probability density function of random variables. Thus, as indicated by the equation, the sample mean estimate is utilized below [24]:  
\[\hat{V}_{v,\sigma}(X,Y)=\frac{1}{N}\sum_{i=1}^{N}S_{v,\sigma}(e_{i})=\frac{1} {N}\sum_{i=1}^{N}\kappa_{v,\sigma}(x_{i}-y_{i}) \tag{3}\]Substituting the Gaussian kernel function in the MCC with the Student's t kernel function provides stronger robustness against outliers, enhancing stability and performance.