esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Pre-Processing
### Variational Auto-Encoder Based on Gaussian Mixture Model
#### 3.2.3 Improved Variational Auto-Encoder  
In this paper, the measurement error data set is used as the input of the VAE algorithm. The measurement error corresponding to each working range follows a single Gaussian distribution. Therefore, the Gaussian mixture model is used to describe the overall distribution of all measurement error data. The output of Decoder are the parameters of the Gaussian mixture model of measurement error data.  
The latent variable \(z\) is assumed to have a normal Gaussian distribution in the traditional VAE algorithm. The Variational auto-encoder based on Gaussian mixture model (VAE-GMM) algorithm assumes that the hidden variable \(z\) is the Gaussian mixture distribution. Namely: \(p_{\theta}\left(z\right)=\sum_{i=1}^{N}\alpha_{i}N\left(0,1\right)\). Variational posterior distribution \(q_{\phi}\left(z\mid w\right)=\sum_{i=1}^{N}\pi_{i}N\left(\mu_{zi},\sigma_{zi}^ {2}\right)\). Since the coefficients of the GMM cannot be trained and updated as trainable parameters by back propagation algorithm, the coefficients of the GMM are super-parameters, that is, \(\alpha_{i}=\pi_{i}=\frac{1}{N}\). Then:  
\[\begin{split}& D_{KL}\left(q_{\phi}\left(z\mid w\right)\parallel p _{\phi}\left(z\right)\right)\\ &=\sum_{i=1}^{N}\pi_{i}\left(log\frac{\pi_{i}}{\alpha_{i}}+D_{KL} \left(N\left(\mu_{zi},\sigma_{zi}^{2}\right)\parallel N(0,1)\right)\right)\\ &=\sum_{i=1}^{N}\left(\frac{1}{N}\cdot\left(1+log\left(\sigma_{zi }^{2}\right)-\mu_{zi}^{2}-\sigma_{zi}^{2}\right)\right)\\ &-ln\left(q\left(w\mid z\right)\right)=\sum_{i=1}^{N}\pi_{i} \left(\frac{\left(w-\varepsilon_{i}\right)^{2}}{2\sigma_{wi}^{2}}+\frac{1}{2} ln2\pi+\frac{1}{2}Inv_{wi}^{2}\right)\end{split} \tag{13}\]  
Due to the true distribution of measurement error data \(p_{data(w)}\) is unknown, we use distribution \(p_{model(w)}\) to approximate it.  
\[p_{model}\left(w\right)=\sum_{i=1}^{N}\pi_{i}N\left(w\mid\varepsilon_{i}, \sigma_{wi}^{2}\right) \tag{15}\]  
where \(\varepsilon_{i}\) and \(\sigma_{wi}^{2}\) are the mean and variance of the \(i^{th}\) Gaussian distribution of the Gaussian mixture model optimized by Decoder respectively, that is, \(w_{i}=\varepsilon_{i}+\varepsilon_{i}\), and \(\varepsilon_{i}\sim N\left(0,\sigma_{wi}^{2}\right)\) where \(\varepsilon_{i}\) is the system error, \(\sigma_{wi}^{2}\) is the variance of random error. The coefficient of \(i^{th}\) Gaussian distribution is \(P(p(dis_{i})=1)=\pi_{i}\). It can be understood that when the working range of acoustic sensor is \(dis_{i}\), the measurement error follows a single Gaussian distribution, that is \(N\left(w\mid\varepsilon_{i},\sigma_{wi}^{2}\right)\), namely the system error is \(\varepsilon_{i}\), the variance of random error is \(\sigma_{wi}^{2}\). The structure of VAE-GMM is shown in Figure 13. The Support Vector Regression (SVR) algorithm is used to divide the overall distribution into multiple Gaussian distributions and its corresponding working range, and fit the relationship between them.
# The Effects of Aerodynamic Interference on the Aerodynamic Characteristics of a Twin-Box Grider
## 3 Results and Discussions  
### Undisturbed Surface Pressure Distribution and Aerodynamic Forces  
To analyze the effects of the aerodynamic interference methods mentioned above, the aerodynamic characteristics of the bare twin-box girder were experimentally investigated, and the important aerodynamic parameters (including the surface pressure distribution and the aerodynamic forces) were obtained. Figure 8 gives the distribution of the mean pressure coefficient on the surface of the twin-box girder at different Reynolds numbers. At Re = 6.13 \(\times\) 10\({}^{3}\), the profile of the surface pressure indicates that the pressure of the upstream girder reaches a peak near the windward corner A, because the incoming flow passes through the windward slope on the upper surface and the incoming flow is affected by the forward pressure gradient, which enhances the flow speed and promotes the continuous increase in suction. After the windward corner, the flow velocity gradually decreases, and the negative pressure also decreases. It should be noted that a short-term pressure plateau is formed after the windward corner on the upper surface of the upstream girder at low Reynolds numbers (e.g., Re = 6.13 \(\times\) 10\({}^{3}\)), but this pressure plateau will disappear at high Reynolds numbers. The pressure plateau is formed by the separation bubble, because the internal and external fluids of the separation bubble are relatively stable; thus, the pressure in the separation bubble is almost identical. Moreover, the endpoint of the pressure platform could be regarded as the reattachment point. Figure 8 also shows that the amplitude of the surface pressure increases with the Reynolds number, and the pressure on the lower surface of downstream girder has a common tendency to decrease first and then increase at different Reynolds numbers.  
Figure 9 shows the mean and fluctuating aerodynamic force coefficients of the twin-box girder at various Reynolds numbers. The aerodynamic force coefficients show a significant change in the Reynolds number range adopted in this study. With the increase in the Reynolds number, the time-averaged drag force coefficient and the fluctuating drag force coefficient both decrease, and the rate of decrease first increases and then decreases; moreover, the maximum rate of decrease is achieved at Re = 9.21 \(\times\) 10\({}^{3}\), while the time-averaged lift force coefficient significantly increases first and then flattens with the increase in the Reynolds number. With the Re rising, the time-averaged moment force coefficient  
Figure 8: The mean surface pressure distribution of the twin-box girder at different Re: (**a**) the pressure surface distribution of the upper surface, and (**b**) the pressure surface distribution of the lower surface.  
Figure 7: Locations of the Cobra Probe.  
first increases rapidly, and then decreases. The fluctuating lift force coefficient and the fluctuating moment force coefficient show a similar tendency to the fluctuating drag coefficient with increasing Re.
## 3 Results and Discussions
### Antioxidant SOD, CAT, and Lipid Peroxide (MDA)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters Groups** & **Zn ug dL\({}^{-1}\)** & **Cu ug dL\({}^{-1}\)** & **Mn ug dL\({}^{-1}\)** \\ \hline \multicolumn{4}{c}{**Liver**} \\
**Control\(-\)** & 13.68 \(\pm\) 2.31  & 111.5 \(\pm\) 10.03  & 0.29 \(\pm\) 0.03  \\
**Control\(+\)** & 31.75 \(\pm\) 2.04  & 169 \(\pm\) 6.55  & 1.08 \(\pm\) 0.07  \\
**G3-1** & 40.94 \(\pm\) 3.94  & 250.6 \(\pm\) 11.01  \\
**G3-2** & 29.07 \(\pm\) 2.03  & 153.66 \(\pm\) 3.21  & 0.95 \(\pm\) 0.04  \\
**G3-3** & 23.72 \(\pm\) 1.48  & 135 \(\pm\) 5.00  & 0.69 \(\pm\) 0.19  \\
**G3-4** & 26.03 \(\pm\) 1.57  & 157.33 \(\pm\) 2.51  & 1.004 \(\pm\) 0.09  \\
**G3-5** & 20.08 \(\pm\) 1.19  & 132.9 \(\pm\) 8.5  & 0.63 \(\pm\) 0.1  \\
**Brain** & & & \\
**Control\(-\)** & 10.29 \(\pm\) 0.93  & 77.5 \(\pm\) 3.9  & 0.13 \(\pm\) 0.004  \\
**Control\(+\)** & 33.62 \(\pm\) 0.94  & 117.65 \(\pm\) 6.05  & 0.26 \(\pm\) 0.006  \\
**G3-1** & 42.58 \(\pm\) 5.54  & 135 \(\pm\) 9  & 0.35 \(\pm\) 0.04  \\
**G3-2** & 20.03 \(\pm\) 1.6  & 99.09 \(\pm\) 0.98  & 0.23 \(\pm\) 0.007  \\
**G3-3** & 14.45 \(\pm\) 1.05  & 79.65 \(\pm\) 4.5  & 0.19 \(\pm\) 0.021  \\
**G3-4** & 22.84 \(\pm\) 2.1  & 91.65 \(\pm\) 3.47  & 0.22 \(\pm\) 0.009  \\
**G3-5** & 15.52 \(\pm\) 0.88  & 88.66 \(\pm\) 5.1  & 0.19 \(\pm\) 0.10  \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Effect of wheat and barley grass on changes in Cu, Mn, and Zn in rat serum and liver tissues exposed to those toxic metals (mean \(\pm\) SD).
## 3 Methods
### Inference Using DNN Model  
After comparing the prediction accuracy of the trained DNN models, that with the highest coefficient of determination and lowest MAPE for the test data was selected as the optimal model. To verify the optimal DNN model, the model was used to predict the ore production and crusher utilization occurring during truck haulage work over the course of 10 days, starting on 1 November 2018. Moreover, the values predicted by the DNN model were compared to those observed at the actual mining site. The ore production at the actual mining site was calculated based on the number of times that the trucks hauled ore and the truck loading capacity. The crusher utilization was calculated by Equation (10), where UR is the crusher utilization (%), NH is the number of times that ore is hauled, AWT is the average working time of trucks at crushing area (min), and DWT is the daily work time (min). AWT was set to 0.59 min for all work days.  
\[\text{UR}=\ \frac{\text{NH}\ \times\ \text{AWT}\ \times\ 100}{\text{DWT}}\ ( \%) \tag{10}\]  
The DNN model input nodes were set to be the same as the operating schedule for the truck haulage work performed over the 10 days. Table 5 displays the DNN model input node values that  
Figure 5: Flowchart illustrating procedures for DNN model training algorithm.  
were set to predict the ore production and crusher utilization. The number of trucks dispatched on all days was set to 1, the truck dispatch interval was set to 1 min, the truck loading capacity was set to 30 tons, the total truck haulage work days was set to 1, and the first truck dispatch time was set to 0 min.  
The root mean square error (RMSE) between the observed and calculated values was calculated to determine the error for the ore production and crusher utilization predicted by the optimized DNN model (see Equation (11)).  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y_{i}}\right)^{2}}{n}} \tag{11}\]
## 3 Materials and Methods
### Image-Query-Based Video Searching  
The related approaches have been described in Section 2.1. In this section, we study the method for video searching and fingerprint detection. Generally, the fingerprint should follow the main properties below:  
1. Robustness. It should have invariability to common video distortions.
2. Discriminability. The features of different video contents should be distinctively different.
3. Compactness. The feature size should be large enough to retain the robustness.
4. Complexity. The computing complexity should be simple enough.  
The local features are the first choice when generating video fingerprints since they can be used directly and can also be quantized by applying a quantization method. Here, we employ 12 different local features for image matching. Some of them are in VLfeat [58] and OpenCV. An image pair comprises the original image and its corresponding distorted image. The local descriptors are listed in Table 8 [59; 60; 61; 62; 63; 64; 65; 66; 67].  
The image matching results are shown in Figure 7. The numbers that are marked in the images are the ID of the descriptors. In the experiments, the image pairs are resized to 256 \(\times\) 256 pixels and the threshold of the match is set as 2.0.  
Figure 7 shows that the noise, contrast changes, cropping, rotation, and brightness changes transforms have less of an effect than horizontal flip transition. The BRISK, FREAK, MSER, and LATCH features under the flip and projection transitions show bad performance. In total, SIFT, SURF, DAISY, and KAZE show invariant characteristics to those transformations. Considering the computing time and storage, many researchers choose SURF as their first choice and some people choose DAISY and KAZE, but most people have chosen SIFT due to its invariant properties. Therefore, in this paper, we use the SIFT features to represent the fingerprint of the video shots and query images.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID-Features** & **Feature Detector** & **Feature Descriptor** & **ID-Features** & **Feature Detector** & **Feature Descriptor** \\ \hline
1-SIFT & VL\_SIFT & VL\_SIFT & 7-DAISY [62] & OPENCV\_SURF & OPENCV\_DAISY \\
2-SURF & OPENCV\_SURF & OPENCV\_SURF & 8-LATCH [63] & OPENCV\_BRISK & OPENCV\_LATCH \\
3-BRISK [59] & OPENCV\_BRISK & OPENCV\_BRISK & 9-KAZE [64] & OPENCV\_KAZE & OPENCV\_KAZE \\
4-FREAK [60] & OPENCV\_BRISK & OPENCV\_FREAK & 10-ASIFT [65] & VL\_SIFT & VL\_SIFT \\
5-MSER [61] & OPENCV\_MSER & OPENCV\_SURF & 11-BR [66] & VL\_SIFT & VL\_SIFT \\
6-ORB & OPENCV\_ORB & OPENCV\_ORB & 12-GMS [67] & OPENCV\_ORB & OPENCV\_ORB \\ \hline \hline \end{tabular}
\end{table}
Table 8: The list of local features.  
Figure 6: The results of the shot boundary verification.
# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 2 Types of Fault in the Li-Ion Battery System
### External Battery Faults
#### 2.2.2 Cooling System Fault  
The cooling system helps manage the thermal aspect of the battery. It transfers the heat away from the battery pack and ensures that the battery remains in the optimal temperature range. Cooling system faults occur when the cooling motor or fan fails to operate due to outdated fan wiring, faulty temperature sensor, or a broken fuse [2; 38]. The temperature sensor and cooling system fault cannot be separated from each other as they both depend on a temperature range [34]. A cooling system fault is one of the more severe faults as it leads to a direct failure of the battery due to overheating, and ultimately thermal runaway. Therefore, it is important to diagnose it as early as possible.
## 4 Experimental Analysis
### Accident Liability Assessment
#### 4.5.1 Key Frame Extraction Evaluation  
A real traffic accident video from Sohu News' official reports was used for analysis. This publicly accessible video depicts a collision between two vehicles on the road,  
Figure 10: SVM classifier performance: precision–recall curve with AP = 0.88.  
Figure 9: Hyperparameter optimization via grid-search. The heatmap displays the grid-search results for optimizing the performance of our model. Each cell represents the accuracy score achieved with different combinations of the hyperparameters \(C\) and gamma, aiding in the selection of the optimal settings.  
capturing several key moments before, during, and after the accident [31]. We extracted frames 80 to 90 from this video to test the effectiveness of our algorithm in extracting key frames. These 20 frames contained the main details of the accident. Figure 11 illustrates the sequence of these original frames and the results after clustering by example.  
To analyze the video frames effectively, we utilized the color histogram features of each frame as inputs for the K-means clustering algorithm. To facilitate a clear visualization of these clustering results, we applied PCA (principal component analysis) to reduce the high-dimensional color histogram features down to two dimensions. This reduction allowed us to construct a more interpretable clustering distribution map. Figure 12 illustrates the distribution of video frames within the feature space, where different colors correspond to frames that belong to different clusters.  
As specified in Definition Formula (21), the number of clusters was set to eight. Consequently, eight key frames were identified and extracted, representing distinct stages or events within the video. These key frames are displayed in Figure 13.  
Figure 11: Key frame extraction using K-means clustering from the video sequence.  
Figure 12: PCA-reduced clustering distribution of video frames.  
Figure 13: Key frames extracted from the K-means cluster analysis.
# DACSR: Decoupled-Aggregated End-to-End Calibrated Sequential Recommendation
## 6 Results and Analysis
### RQ2: Parameter Influence
#### 6.2.2 Temperature Factor \(\tau\)  
In this section, we analyze the influence of parameter \(\tau\), which controls the sharpness of the distribution in the calibration loss function. We tune \(\tau\) in \(\{0.25,0.5,0.75,1.0,1.25,1.5,1.75,2.0\}\), and show the performances in Figure 4. Red lines represent the performance of \(MRR@20\) and correspond to the right axis, and blue lines stand for the \(C_{KL}@20\) performance which follows the left axis.  
On the two datasets, a relatively lower value of \(\tau\) can achieve a better performance of calibration and lower accuracy in general. For example, on the MI-1m dataset, the \(C_{KL}@20\) performances are 0.6466 and 0.6915 when \(\tau\) equals to 0.25 and 1.0 respectively. This isbecause a lower value of \(\tau\) amplifies the scores of top items, and other items are ignored because their scores are normalized to 0. The lower value of \(\tau\) increases the calibration performance, but decreases the recommendation accuracy, as shown by the blue lines in Figure 4. In contrast, a higher value of \(\tau\) (\(\tau>1\)) causes a negative impact on calibration, because scores of all items are normalized to close values (i.e., \(1/\mid I\mid\) for all items). Therefore, no useful information for calibration can be propagated to the model.  
Figure 3: Performance comparison when \(\lambda\) changes.
# Influence of Welding Parameters on the Weld Pool Dimensions and Shape in a TIG Configuration
## 2 Experimental Procedure  
### Experimental Setup  
The experiments were performed with an ESAB TIG torch BTE250M (Figure 1) and an MIG U5000i welding generator operating in current regulation mode and in direct polarity.  
Figure 1: Tungsten inert gas (TIG) welding device.  
The electric arc is initiated between a 1.5% lanthanated tungsten cathode and a piece of AISI 304L stainless steel (anode). The cathode diameter is 3.2 mm and the shielding gas is injected around it with a volume flow rate of 10 L/min. The anode is a cylinder of 50 mm diameter and 10 mm height placed on a copper support (no water cooling). This support has no effect on the evolution of the weld pool depth [19]. The sulphur content of the anode has been evaluated to 300 ppm [19].  
The torch is used in a static mode (spot TIG) in vertical position without inclination. Effects of the following parameters are studied: welding current, arc length, nature of the shielding gas, cathode tip angle. The experiments were conducted by keeping all welding parameters constant except the one investigated (Figure 2a).  
Figure 2: (a) Illustration of the experimental setup detail of the torch elements and welding parameters; (b) Illustration of the experimental setup synchronization system.  
A high-speed camera (Photron Fastcam SA5, San Diego, CA, USA) is used to observe the weld pool changes during the interaction. This camera is able to reach 1 million frames per second (fps) and 7500 fps with full resolution. Its CMOS sensor contains 1024 \(\times\) 1024 pixels of 20 \(\upmu\)m. The exposure time is set to 50 \(\upmu\)s and 10,000 fps are recorded. A 105 mm focal length lens (Sigma Macro, Ronkonkoma, NY, USA) is mounted on the camera. The camera is placed 25 cm away from the weld pool with an inclination angle of 30\({}^{\circ}\) providing a resolution of 21 \(\upmu\)m in the horizontal direction of the image and 100 \(\upmu\)m in the vertical one.  
The applied power is evaluated with voltage and current intensity measurements. The time reference (t = 0) fits with the arc ignition. Recordings start through a photo diode which collects the flash emitted by the arc at ignition via an optical fibre (Figure 2b). Devices are plugged to a pulse generator which allows synchronizing the acquisitions of the voltage, the current intensity and the high speed camera.
## 4 SDR Implementation of FM-ACSK System Prototype
### FM-ACSK Receiver  
The communication system's receiver part consists of receiving SDR, low-pass filter (LPF), FM and ACSK demodulators as seen in Figure 3. Additionally, there are symbol timing synchronization and decision-making (DM) units. Before any demodulation is carried out, LPF is used to limit received noise when SDR's baseband rate is larger than the useful bandwidth. Downsampling, similar to upsampling in the transmitter, is used to decrease and align SDR's sample rate with that of ACSK demodulator, which is working at a lower sample rate. A Hamming decoder is added after DM unit to decode and correct an error if it is possible.  
Figure 7: Implementation of chaotic generator in the ACSK modulator.  
Two different types of DM units were tested. The first type is a classic strobe detector (SD) that, at the optimal time instant, samples waveform at the output of ACSK demodulator. The second type--energy detector (ED)--calculates the mean value of the whole symbol interval to make a decision.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 5 Experimental Results and Discussion
### Resistance to Statistical Attacks
#### 5.5.4 NIST SP800-22 Tests of Encrypted Bit Stream  
NIST tests SP800-22 are used to detect the randomness of the sequence [43]. This test works on binary data by converting it in the form of \(\pm 1\) and calculate the absolute sum. The test statistic is obtained as follows by Equation (21).  
\[S_{obs}=\frac{|S_{n}|}{\sqrt{n}} \tag{21}\]  
The value of \(P\) determine the randomness of a sequence. If the value of \(P\) is less than 0.01 the sequence is considered to be non-random otherwise random. The test results on the encrypted bit stream are shown in Table 8.  
\[P-value=erfc\bigg{(}\frac{S_{obs}}{\sqrt{2}}\bigg{)} \tag{22}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Image (Encrypted)** & **Information Entropy** \\ \hline Lena & 7.99642 \\ Baboon & 7.99645 \\ Pepper & 7.98953 \\ Airplane & 7.99231 \\ House & 7.99483 \\ Lake & 7.99681 \\ \hline \end{tabular}
\end{table}
Table 6: Global information entropy of encrypted images.  
\begin{table}
\begin{tabular}{c c c} \hline
**Image (Encrypted)** & **Information Entropy** & **Result** \\ \hline Lena & 7.90642 & Pass \\ Baboon & 7.90382 & Pass \\ Pepper & 7.91124 & Pass \\ Airplane & 7.90235 & Pass \\ House & 7.90321 & Pass \\ Lake & 7.91156 & Pass \\ Mean \(\pm\) Std. & 7.90643 \(\pm\) 0.0040 & \\ \hline \end{tabular}
\end{table}
Table 7: Local information entropy of encrypted images (\(k\) = 30, \(T_{B}\) = 1936, \(\alpha\) = 0.05).
## 3 System Model
### Communication Model
#### 3.4.1 Ground-to-UAV Communication  
Similar to the existing works [30], the channel between IoTD \(k\) and UAV \(m\) follows a Rician fading model, capturing both line-of-sight (LoS) and non-line-of-sight (NLoS) components. The channel coefficient is expressed as  
\[h_{k,m}(t)=\sqrt{\beta_{0}d_{k,m}^{-\alpha}(t)}\cdot\hat{h}_{k,m}(t), \tag{6}\]  
where \(\beta_{0}\) represents the reference channel gain at unit distance, and \(\alpha\) is the path-loss exponent (typically \(\alpha=2\) for UAV communications). Then, the \(\hat{h}_{k,m}(t)\) denotes the small-scale fading component:  
\[\hat{h}_{k,m}(t)=\sqrt{\frac{\kappa}{\kappa+1}}\hat{h}_{k,m}+\sqrt{\frac{1}{ \kappa+1}}\hat{h}_{k,m}, \tag{7}\]where \(\kappa\) is the Rician factor, \(|\bar{h}_{k,m}|=1\) represents the LoS component, and \(\bar{h}_{k,m}\sim\mathcal{CN}(0,1)\) denotes the NLoS component.  
When the MLLM tasks are offloaded to the UAVs, the uplink transmission rate \(R_{k,m}^{up}\) from the IoTD \(k\) to the UAV \(m\) is expressed as  
\[R_{k,m}^{up}(t)=B_{m}\log_{2}\left(1+\frac{h_{k,m}(t)P_{k}(t)}{\sum_{j\in \mathcal{K}\setminus\{k\}}h_{j,m}(t)P_{j}(t)+N_{0}}\right), \tag{8}\]  
where \(P_{k}\) denotes the uplink transmit power of the IoTD \(k\), \(B_{m}\) signifies the channel bandwidth of the UAV \(m\), and \(N_{0}\) signifies the power spectral density of the AWGN. Correspondingly, the downlink transmission rate \(R_{k,m}^{dl}\) is given as  
\[R_{k,m}^{dl}(t)=B_{m}\log_{2}\left(1+\frac{h_{k,m}(t)P_{m}(t)}{\sum_{j\in \mathcal{M}\setminus\{m\}}h_{k,j}(t)P_{m}(t)+N_{0}}\right). \tag{9}\]