esa-sceva/satcom-chunk-collection huggingface dataset:
# Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming
## 3 Background  
To stream videos with a bit rate appropriate for the available bandwidth, a DASH server provides multiple representations of a video content encoded at different bit rates. Each representation is fragmented into small video chunks that contain several seconds of video. Based on measured available bandwidth, a DASH client selects a chunk representation, i.e., bit rate, and requests it from a DASH server; this is called adaptive bit rate selection.  
A DASH client player starts a streaming session with an initial buffering phase during which a player fills its playback buffer to the maximum level (\(B_{max}\)). When the buffer is filled with the minimum amount of video during this phase, the player starts playing the video, and continues to retrieve media chunks until the initial buffering completes. After completing the initial buffering phase, a player pauses downloading until the buffer level falls below the maximum buffer level by playback. This leads to an ON-OFF traffic pattern where the player downloads chunks for a while and then waits until a specific number of chunks is consumed [14]. If the amount of buffered chunks is less than the minimum required to play out the video, the player stops playback and fills its buffer until it has a sufficient amount of video to begin playback again, called the rebuffering phase. Note that the length of a video chunk is one of the parameters that can affect the duration of the rebuffering phase; assume a client player starts playback again once the playback buffer contains at least one video chunk. As the length of a video chunk is longer, the chunk size becomes larger, resulting in a longer rebuffering duration until one chunk download for playback completes.  
[PERSON] et al. [7] models such a behavior by a DASH client player in terms of buffer status. Suppose \(L\) is the chunk length in seconds. Let \(B_{k}\) be the buffer level measured in seconds when the client starts downloading the \(i\)th chunk with a particular encoding bit rate \(R_{k}\). Assume that \(D_{k}\) is the download time for the \(k\)th chunk. The length of the OFF period after the \(k\)th chunk download, \(\delta_{k}\), is defined as:  
\[\delta_{k} = \max(\max(B_{k}-D_{k},0)+L-B_{max},0)\,.\]  
The rebuffering duration after the \(k\)th chunk download, \(\gamma_{k}\), is:  
\[\gamma_{k} = \max(D_{k}-B_{k},0)\,.\]  
Then, we can define the next buffer level, \(B_{k+1}\), as:  
\[B_{k+1} = \max(\max(B_{k}-D_{k},0)+L-\delta_{k},0)\,.\]
## 2 Model Specification of Data Processing
### Post-Processing  
To reach the deformation of the perpendicular variation of the surface, a post-processing algorithm should be applied. After the interferogram reconstruction, this image is denoised by median filtering with a window size of 3 \(\times\) 3 and symmetric padding followed by the SCA-SWT denoising method. The experiments performed with DHSPI aim to find the deformation of the surface of objects that undergo height variation of the surface of the studied artwork. To measure this deformation, the DHSPI acquires images: the phase found in these images is of interest, hence the obtained denoised image needs to be unwrapped. In our processing procedure, the unwrapping is carried out by the CPULSI method. From the unwrapped phase, the surface deformation can finally be obtained: this step is called post-processing. The deformation is calculated for each pixel through:  
\[d(x,y)=\frac{\lambda*\varphi(x,y)}{4\pi} \tag{6}\]  
where \(d(x,y)\) is the displacement of the surface at pixel \((x,y)\), \(\lambda\) is the laser wavelength and \(\varphi(x,y)\) is the calibrated unwrapped phase at \((x,y)\). The result of each pixel calculation is a deformation map that gives a quantitative displacement of the surface. The deformation maps can be drawn in 2D using the 'contourf' MatLab function or in 3D by the'mesh' function.  
To conclude, the overall processing used during the DHSPI acquisition consists of recording five images of a surface object at an initial time and five images of the same surface at a later time. During the time difference between the two acquisitions of the  
Figure 2: Scheme of the denoised method based on Sine-Cosine Average with Stationary Wavelet Transform principle. From the left to right, following the arrows, the different steps of the denoised methods are presented in different forms as different algorithms.  
image sets, the object undergoes a possible deformation. The whole data processing aims to quantify the deformation of the surface during this time difference. Several steps are necessary to obtain this deformation. The first one, called pre-processing, is the recombination of the interferogram, also called the wrapped phase because of the arctangent reconstruction. This is followed by processing, which includes denoising the interferogram and unwrapping it. Finally, post-processing is performed to calculate the deformation map to obtain the quantitative value needed to correlate the surface deformation of the object and the environmental fluctuations over time between two DHSPI acquisitions.
## 3 Experiments
### Data Preprocessing  
In this study, the NSL-KDD dataset was preprocessed to improve its accuracy and robustness. First, the entire dataset was read, and duplicate rows and missing values were removed. Then, discrete feature columns were subjected to One-Hot encoding [27] to generate new binary vector feature columns, and the original feature columns were removed. After One-Hot encoding, the NSL-KDD dataset contained 121 feature columns. The Min-Max method [43] is used to scale the numerical features between [0, 1].  
\[Z_{Norm}=\frac{Z-min(x)}{max(z)-min(z)} \tag{8}\]  
where \(min(z)\) and \(max(z)\) denote the minimum and maximum values of attribute \(z\), respectively, and \(Z\) and \(Z_{Norm}\) represent the original and normalized values of the feature, respectively.
# Extending Wireless Reschargeable Sensor Network Life without Full Knowledge
## 10 Simulation Results and Analysis
### Algorithm Results
When the network size increases, three possible outcomes could occur:  
* No change in performance, seen as horizontal short lines of consistent performance in Figure 4. This is due to two possible cases: (1) the new node is part of Y and ANLPP added it to \(v\) as well. (2) the new node is a high power node that does not need to be charged and ANLPP does not visit it or ignores it despite visiting. * Decrease in performance, noticed as a sharp or staircase decaying performance in Figure 4. This is due to three possible cases: (1) the new node is in Y, but ANLPP does not reach it, or reaches it and decides not to add it to \(v\) (due to threshold); (2) the new node is a high power node that does not need to be charged, while ANLPP adds it to \(v\); (3) the new node belongs to the \(k\) nodes that may be dropped, but ANLPP adds it to \(v\). * Increase in performance, observed as a sharp or staircase increases in Figure 4. This is due to two possible cases: (1) the new node replacing a node in Y, and the replaced node was not in \(v\) but the new one is, bringing \(v\) closer to Y; (2) the new node belongs to the \(k\) nodes that may be dropped, and the addition of this node to the \(k\) nodes modifies Y, bringing it closer to \(v\). Next, we will look into the impact of network parameters on the performance of ANLPP. Figure 4: All Network Least Possible Probability (ANLPP) behaviour on four random networks with the same parameters, ANLPP tuned for maximum size. All cases starting and ending with optimal life increase for the maximum size of the network, while having different behaviours in between.
# Quad-Band Circular Polarized Antenna for GNSS, 5G and WIFI-6E Applications
## 3 Parametric Study  
To examine the sensitivity of each parameter, a systematic study has been conducted. The purpose of doing this is to find the fabrication tolerance and, more importantly, to pinpoint the effects of these parameters on AR and bandwidth. We have chosen four parameters, i.e., the length of the L-shaped radiator L1, the L-shaped branch length L4, the width of the rectangular patch S, and the width of the narrow feeding line connecting to the coaxial cable. The first three parameters correspond to the evolution process, and the last parameter is chosen because it determines the input impedance, and therefore, in most cases, it is the most sensitive parameter.  
The L-shaped patch acts as field excitation and a radiator. Letting L1 = 35.5, 36.5, and 37.5 mm, it is found that the reflection coefficients for these cases do not see significant  
Figure 3: Simulated results for Ant. 1–Ant. 4: (**a**) S11, (**b**) AR.  
Figure 2: Evolution of the proposed antenna.  
variation, as shown in Figure 4. Only a slight change takes place in the 10 GHz band. For the axial ratio, the change occurs in the 7.6 GHz band. The criterium of \(-3\) dB is not met for L1 = 37.5 mm. For the L-shaped branch, when changing the length, it is also found that the reflection coefficients do not change significantly, as shown in Figure 5. In addition, the axial ratio sees apparent change in the 4 GHz and 7.6 GHz bands. It is seen from Figure 6 that the width of the patch S does produce some effect on the reflection coefficients. However, the \(-10\) dB bandwidths for each band do not change significantly. Its effect on the axial ratio happens in all four of the bands--particularly for the 4 GHz, 6 GHz, and 7.6 GHz bands.  
Figure 4: Effect of L1 on antenna performance: (**a**) S11, (**b**) AR.  
Figure 5: Effect of L4 on antenna performance: (**a**) S11, (**b**) AR.  
The last parameter, wf, is the width of the feed. It is clearly seen from Figure 7 that the change in the width affects the reflection coefficients considerably. In other words, the impedance matching varies significantly with the change in wf. For the axial ratio, the change is much greater for the higher frequency bands than the lower frequency bands. It appears that the 4 GHz and 7.6 GHz bands are more sensitive to changes in the parameters.  
It is seen, from the parametric study, that the most sensitive parameter on the impedance bandwidth is the width of the feedline wf. The width of the patch S also produces some effect on the impedance bandwidth. Other parameters have very limited effects on the impedance bandwidth. In comparison, the width of the patch S produces a considerable effect on the AR bandwidth. While the width of the feedline wf produces some effect on the AR bandwidth, other parameters have fewer effects on the AR bandwidth.
# Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem
## 6 Conclusions and Future Work  
In this work, the potential of the heuristic sampling strategies to deal with the multi-class imbalance problem on DL-MLP neural network in big data scenarios was studied. Big data, multi-class imbalanced datasets obtained from hyperspectral remote sensing images were used for experimentation. Results presented in this work exhibit that ROS and SMOTE are very competitive strategies to deal with this problem, but ROS shows a better performance than SMOTE.  
Hybrid methods such as SMOTE+TL, SMOTE+ENN, SMOTE+OSS and SMOTE+CNN, although presenting evidence of being effective techniques for facing the class imbalance problem,do not improve the SMOTE's performance. Cleaning and sampling reduction strategies (TL, ENN, OSS and CNN) do not show results with respect to a DL-MLP neural network on big data scenarios.  
RUS gives evidence of that with a few samples, favorable results can be obtained, but they are not better (or competitive) than the results of ROS and SMOTE. However, RUS results strengthen the hypothesis that with a small subset, the DL-MLP neural network can be successfully trained.  
In this study it was found that cleaning strategies should be applied on ANN output instead of input feature space, in order to obtain the best classification results. Thus, SMOTE+ENN* is proposed, which consists of training the neural network, applying ENN on the neural network's output and removing those samples (from the training dataset) related to neural network outputs suspicious to be outliers or noise; i.e., those outputs identified by ENN. Results presented in this work evidence a trend to obtain better results when a neural network's output is edited than when input feature space is preprocessed.  
Typically, most of the research reported in literature has been focused on the two-class imbalance problem by using classical classifiers. Thus, the study of the relevance and capabilities of heuristic sampling methods to deal with the big data multi-class imbalance problem in deep learning context is especially interesting.  
Future work should be addressed toward deep learning, in the study of reduction and cleaning strategies to improve the DL-MLP neural network's performance in big data scenarios.  
[PERSON] and [PERSON] formulated and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work has been partially supported under grants of project 5106.19-P from TecNM and 5046/2020 CIC from UAEMex.  
The authors declare no conflict of interests.
## 3 Results
### Antioxidative Potential  
Statistical analysis of the antioxidant potential of DPPH showed highly significant differences between the pumpkin dried at 40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C and 80 \({}^{\circ}\)C (Table 3). It was observed that the ability to inhibit the DPPH radical decreased with increasing temperature. The differences, although statistically significant, were very small.  
Table 4 shows the fatty acid composition of pumpkin dried at different temperatures (40 \({}^{\circ}\)C, 60 \({}^{\circ}\)C and 80 \({}^{\circ}\)C), listing the percentage of different fatty acid types, including saturated, monounsaturated, polyunsaturated, and individual fatty acids. The analyses showed significantly fewer monounsaturated fatty acids (MUFAs) in the dried material obtained at 80 \({}^{\circ}\)C. No differences were found for the sum of saturated and polyunsaturated acids (PUFAs), but they were noticeable for some individual fatty acids from this group: C20:4, C20:3, C20:2 and C18:3. Of the saturated fatty acids, only 24:0 was significantly affected by temperature. The highest value of this acid was observed for samples dried at the lowest temperature; drying at 60 \({}^{\circ}\)C and 80 \({}^{\circ}\)C reduced its content. The results indicate that raising the temperature to 80 \({}^{\circ}\)C keeps more of the most valuable fatty acids from the omega-3 group (C18:3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **40 \({}^{\circ}\)C** & **60 \({}^{\circ}\)C** & **80 \({}^{\circ}\)C** & _p-Value_ & **SE** \\ \hline Dry matter (\%) & 90.36 A & 92.27 B & 91.16 A & 0.0003 & 0.2718 \\ Ash (g/kg DM) & 8.37 A & 10.53 B & 7.90 A & 0.0000 & 0.1232 \\ NDF (g/kg DM) & 24.50 A & 27.12 B & 23.83 A & 0.0028 & 0.6191 \\ ADF (g/kg DM) & 26.75 A & 27.29 A & 21.60 B & 0.0000 & 0.5511 \\ ADL (g/kg DM) & 5.68 A & 7.80 B & 5.48 A & 0.0000 & 0.2806 \\ Protein (g/kg DM) & 19.15 A & 21.88 B & 16.50 C & 0.0000 & 0.2767 \\ Fat (g/kg DM) & 13.01 A & 13.02 A & 11.10 B & 0.0000 & 0.2603 \\ Fiber (g/kg DM) & 18.04 A & 19.86 B & 14.49 C & 0.0000 & 0.346 \\ \hline \end{tabular}
\end{table}
Table 1: Proximate content of _Cucurbita maxima_ pumpkin (var. Ambar) dried at different temperatures. Letters A, B, and C mean the values are statistically different for \(p\) < 0.05.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Individual Carotenoids** & **40 \({}^{\circ}\)C** & **60 \({}^{\circ}\)C** & **80 \({}^{\circ}\)C** & _p-Value_ & **SE** \\ \hline \(\beta\)-carotene & 33.92 A & 31.27 A & 27.20 B & 0.0005 & 1.012 \\ a-carotene & 38.56 & 38.54 & 33.62 & 0.0901 & 0.0577 \\ violaxanthin & 3.72 A & 3.16 A & 1.29 B & 0.0001 & 0.2591 \\ zeaxanthin & 0.27 A & 0.25 A & 0.18 B & 0.0394 & 0.0231 \\ lutein & 8.74 A & 8.14 A & 6.58 B & 0.0470 & 0.5279 \\ \hline \end{tabular}
\end{table}
Table 2: Content (mg/100 g d.m.) of carotenoids in pumpkin _C. maxima_ (var. Ambar) dried at different temperatures. Letters A and B mean the values are statistically different for \(p\) < 0.05.
## 4 Analysis of Optimization Calculation and Results
### Optimizing the Calculation Parameters
#### 4.1.2 Determination of Algorithm Parameters  
When using the improved NSGA-III to solve the model, the population number is taken as 200, the iteration time is taken as 1200, and the crossover probability is chosen as 0.9. The population mutation probability is taken as 0.1. The cross-operator distribution index and arithmetic operator distribution index are both taken as 20.
## 4 Asphalt Binder Test Results Analysis  
### Binder PG and Viscosity  
Table 3 shows the test results of binder PG and viscosity at different aging conditions. As shown, the modification of base asphalt was affected more for the high temperature PG compared to the low temperature PG: most modified binders experienced a similarly low PG with base binder, whereas the high temperature PG either increased or decreased to various extents depending on the additive utilized. The Evothem 3G was the only additive that decreased the high temperature PG  
of base asphalt and the reason for this needs further study. Additionally, it is interesting to note that the SBS modification increased both high- and low-temperature PGs, which could be attributed to the crosslinked elastomer network structure of the SBS [15].  
In addition, \(\Delta\)_Tc_, which quantifies the difference in continuous low temperature PG for stiffness and relaxation (\(m\)-value) properties, was also calculated. Results indicate that all the binders exhibited positive \(\Delta\)_Tc_ values (ranged from 0.2 \({}^{\circ}\)C to 2.2 \({}^{\circ}\)C), except for the modified rubber asphalts (ranged from \(-0.1\)\({}^{\circ}\)C to \(-1.5\)\({}^{\circ}\)C), indicating that most asphalts were stiffness controlled. It was pointed out that the absolute values of \(\Delta\)_Tc_ smaller than 2.5 \({}^{\circ}\)C indicated a low potential of thermal cracking, whereas the threshold value of 2.5 \({}^{\circ}\)C was proposed based on a comparison between \(\Delta\)_Tc_ results and field thermal crack amount [16].  
Compared to the base binder, the addition of Sasobit and Evotherm reduced viscosity, whereas the addition of SBS and rubber increased viscosity. It is also seen that the viscosity increased after TFOT aging.
## 3 Experiments and Result Analysis
### Analysis of Experimental Results  
The purpose of this experiment was to develop and evaluate AUV trajectory prediction models to improve AUV trajectory prediction accuracy. In this experiment, the same dataset was used to compare and analyze different prediction models for AUV trajectory prediction, where the dataset attributes included time, trajectory position data (longitude, latitude, and depth), and physical attitude information (pitch, yaw, and roll), and the dataset was divided into a training set and a test set in a ratio of 8:2 when experimenting. In this experiment, the models' assessment metrics were the RMSE, MSE, and MAE, which were used to compare the prediction performance of the models and measure the difference between the predicted and real trajectories. In this study, the AWOA-DBiLSTM model proposed in this paper was compared with other selected models. Furthermore, in order to test the generalization of this model, AUV trajectories from other sea areas were put into the model proposed in this paper in the same dataset and the same experimental environment for experiments. This experiment visualized the difference between the predicted trajectories and the real trajectories and compared the performance of different models through the experimental results.
# Wind-Induced Response Control of High-Rise Buildings Using Inerter-Based Vibration Absorbers
## 4 Wind-Induced Response Mitigation Using Inerter-Based Vibration Absorbers
### Optimization of the Parameters of the TMD/TID Scheme Applied to the Benchmark Building
#### 4.1.2 Optimization of Parameters of TID
The peak of the original structure (in dark cyan) is much higher than that of three other vibration absorber equipped structures, i.e., TMD-equipped structure (in blue), TMDI-equipped structure (in orange) and the TID-equipped structure (in red). Overall, Figure 11 indicates that TMD, TMDI and TID mitigate the displacement response corresponding to the first vibration mode, and the mitigation effects of the TID are better than that of TMD and TMDI whose physical mass ratios are the same as that of TID. Around the second and third natural frequencies, the transfer function of the original structure overlaps with that of structure with TMD, while the transfer function of the structure with TMDI is slightly lower, and the TID achieves the best mitigation effect. For higher natural frequencies, transfer functions of the four cases are almost identical. In Figure 12, the highest peak of the acceleration transfer function is observed around the third natural frequency. All three vibration absorbers efficiently suppress (in a comparable manner) wind-induced acceleration response corresponding to the first natural frequency with the optimal frequency ratio between 0.99 and 1.15. These graphs demonstrate the advantages of the inerter-based vibration absorbers in achieving a considerable wind-induced vibration mitigation in comparison to the TMD by employing the same physical mass ratio.
## 2 Methodology
### PaDBN Model  
#### 2.3.1 Patch to Feature Vector Conversion  
The image informative representation is a feature that facilitates robust image segmentation. Deep learning, a subfield of machine learning, is used in the identification of meaningful features of the image. DBNs recently show impressive performance in various image analysis tasks [48; 49; 50]. In this study, DBN is used to convert the overlapped patches into feature vectors. To train and test our model, the image patches are converted into feature vectors. The proposed PaDBN model is used to extract the abstract features from CT images. The DBNs [31] have learning ability to extract a deep hierarchical representation of data images. Here, DBN is used as feature extractor because it can generate robust features that lead to the improvement in classification performance [48; 49; 50; 51]. Preprocessing, i.e., cropping, normalization, smoothing, and balancing, is used to achieve the best representative features of raw images. This procedure is followed by utilizing the unsupervised modeling for extraction of deep structural features. The two hidden layers are used to extract the features from the vertebrae. The input visible layer and first hidden layer construct RBM1, and the first and second hidden layers form the RBM2 structure. The previous layer in RBM is used as a visible layer for the subsequent RBM. Considering CPU limitations and computational cost, the CT images after preprocessing are divided into 32 \(\times\) 32 overlapped patches to prevent any distortions. Thus,  
Figure 4: Positive and negative samples of the CT image.  
each individual patch is presented by a row vector with 1024 dimensions. A total of 1024 dimension features from each individual patch are used in training the model. The model starts with 1024 units in the visible layer that shows high efficiency in feature selection and then finds the suitable number for these hyperparameters.