esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion
### Energy Consumption  
Every sensor node in the data transmission environment in the WSN is equipped with rechargeable batteries that consume the least amount of energy, making battery recharging difficult. The cluster and duty cycle scheduling mechanisms start the data transfer. The data transmission process is completed without interruption, and data are transmitted in the quickest way possible while consuming the least amount of energy. The transmission nodes' energy consumption is minimized as a result of this condition is given in Table 2.  
In Figure 2, energy consumption during data transmission for a different number of nodes is illustrated. The energy consumption of the proposed approach is minimal than existing approaches, namely DWMA, CBR-RPL, WEMER and PEGASIS.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline No. of nodes & 200 \\ Simulation area & \(1000\times 1000\) m\({}^{2}\) \\ Routing protocol & RPL \\ Initial energy & 100 Joule \\ Packet size & 300 bits \\ Simulation time & 65 ms \\ \hline \end{tabular}
\end{table}
Table 1: Simulation Setup.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No of Nodes** & **DWMA** & **CBR-RPL** & **WEMER** & **GG-Conv_Clus-FSO** \\ \hline
0 & 0 & 0 & 0 & 0 \\
20 & 5.12 & 3.47 & 2.49 & 1.08 \\
40 & 12.17 & 9.22 & 4.13 & 3.42 \\
60 & 25.46 & 13.79 & 5.43 & 4.79 \\
80 & 31.45 & 19.47 & 8.24 & 6.33 \\
100 & 43.78 & 29.55 & 11.85 & 7.04 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of energy consumption.
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
## 4 Experimental Evaluation
### Diagnosis of a Bearing with an Outer Race Fault
#### 4.2.1 Experimental Study #1  
The collected vibration signal and the rotating speed are shown in Figure 21a,b. The frequency spectrum of the vibration signal is shown in Figure 21c, in which spectrum smearing could be observed due to the variable rotating speeds. In addition, the TFR of the signal is blurry and lacks detail due to background noise interference, which may come from other coupled machine components and the working environment, making it more difficult to identify the fault type in Figure 21d.  
Figure 22 shows the signal analysis results for the outer race fault case when a is equal to 4/3 Hz/s. The FK is paved in Figure 22a, in which the optimal frequency band is (2916.67, 3333.33) Hz. Figure 22b shows the WPTK, in which the maximum kurtosis is calculated at the 4 th decomposition level, and its corresponding optimal frequency band is (3125, 3750) Hz. The Protrugram is shown in Figure 22c and the center frequency is 638.3 Hz. Figure 22d gives the FOSCK, in which the maximum OSCK is calculated at the 3.5 th level, and the optimal frequency band is (3333.33, 4166.67) Hz. Different  
Figure 20: The envelope order spectra of the signal obtained by different methods: (**a**) FK, (**b**) WPTK, (**c**) Protrugram and (**d**) FOSCK.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acceleration (Hz/s)** & **Experimental Study \#1** & **Experimental Study \#2** & **Experimental Study \#3** \\ \hline a & 4/3 & 3/2 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of each experiment.  
band-pass filters are used to filter out the corresponding frequency band signals, and their envelopes are calculated using the Hilbert transform. Then, each envelope of these filtered signals is resampled into the angular domain by using COT, and the envelope order spectrum analysis results are shown in Figure 23a-d, respectively. It is clear that all the filtered signals contain fault components, which also verifies that the fault impulse has broadband characteristics. In Figure 23, the FCO of the bearing outer race fault and its triple octaves are very clear. Therefore, all the methods mentioned above can effectively detect the bearing outer race fault, as in the case of acceleration a = 4/3 Hz/s.  
Figure 21: The signal measured from an outer race fault bearing: (**a**) time-domain signal, (**b**) the shaft rotational frequency from 20 Hz to 25 Hz, (**c**) the frequency spectrum of (**a,d**) TFR by using STFT.  
Figure 22: The optimal frequency band obtained by different methods: (**a**) FK, (**b**) WPTK, (**c**) Protrugram and (**d**) FOSCK.
## 3 Results  
### Computational Analysis of S. mutans Glucansucrase Binding Potential of Mangiferin  
Docking analysis of _S. mutans_ glucansucrase and mangiferin revealed binding energy of \(-7.35\) and 10 hydrogen interactions (Table 2). It was noticed that a hydrogen bond between ASPS'593/OD1 of glucansucrase and C7-OH of mangiferin with a bond length of 2.2 A. Two hydrogen bonds were noticed between ASP'588/OD2 of glucansucrase and C1-OH and C6'-OH group of mangiferin respectively with bond length of 2.0 A. Similarly, two hydrogen bonds were noticed between ASP'909/OD1 of glucansucrase and C2'-OH and C3'-OH group of mangiferin with bond length of 2.1 A and 2.5 A respectively. Amino acid, GLN'592/1 HE2 of glucansucrase and C2'-O group of mangiferin revealed a hydrogen interaction with a bond length of 2.4 A. While both the amino acids HIS'587/HE2 and ARG'475/1 HH2 of glucansucrase interacted with C6'-O group of mangiferin with a hydrogen bond length of 2.4 A and 2.0 A (Figure 1A,B; Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**SL.No**} & \multirow{2}{*}{**Ligand**} & \multirow{2}{*}{**Binding**} & \multirow{2}{*}{**Ligand**} & \multirow{2}{*}{**Intermole**} & \multirow{2}{*}{**Legend**} & \multirow{2}{*}{**Legend Atoms (Ring)**} & \multicolumn{2}{c}{**Docked Amino**} \\  & & & & & & & **Acid Residue (Bond Length)** \\ \hline \multirow{8}{*}{1.} & \multirow{8}{*}{Mangiferin} & \multirow{8}{*}{-7.35} & \multirow{8}{*}{-0.25} & \multirow{8}{*}{-8.66} & C7-OH (B) & ASP’593/OD1 (2.2 Å) \\  & & & & & C1-OH (A) & ASP’588/OD2 (2.0 Å) \\  & & & & & C2’-O & GLN’592/1 HE2 (2.4 Å) \\  & & & & & C2’-OH & ASP’909/OD1 (2.5 Å) \\  & & & & & C3’-OH & ASP’909/OD1 (2.1 Å) \\  & & & & & C3’-O & ASN’862/1 HD2 (2.5 Å) \\  & & & & & C4’-O & HIS’587/HE2 (2.0 Å) \\  & & & & & C6’-O & HIS’587/HE2 (2.4 Å) \\  & & & & & C6’-O & ARG’475/1 HH2 (2.0 Å) \\  & & & & & C6’-OH & ASP’588/OD2 (2.0 Å) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hydrogen bond interaction of mangiferin and amino acid residues of _S. mutans_ glucansucrase.
# Predictive Analysis of Vehicular Lane Changes: An Integrated LSTM Approach
## 1 Introduction
Addressing these limitations, our study introduces a nuanced two-stage model that synergizes data situation classification with vehicle lane change prediction models, with each tailored for specific scenarios. In our work, we harness data from the Next Generation Simulation (NGSIM) program [13], a renowned vehicle trajectory dataset from I-80 in Emeryville, CA. Our trajectory prediction model builds upon a three-phase prediction process:  
1. Lane change labeling and prediction: Vehicles' lane-changing maneuvers in upcoming timesteps are labeled. We then employ various regression models to both train the lane change model and forecast the lane-changing behavior. During this phase, each timestep of the vehicle movement trajectory is classified for lane-changing maneuvers. For example, a zero denotes a lane-keeping maneuver, while a one signifies a lane-changing maneuver. Several algorithms, including regression methods, SVMs, and a multilayer perceptron, will be utilized for this classification. A continuous series of zeros or ones will then depict the maneuvers for that journey. 2. Trajectory prediction based on maneuvers: Each categorized maneuver segment undergoes a distinct machine learning trajectory prediction model application. This entails the lane-keeping prediction model processing data exclusive to lane keeping, while the lane-changing prediction model exclusively processes lane-changing data. Our model of choice for this task is the long short-term memory (LSTM) algorithm, a subset of recurrent neural networks. We theorize that employing maneuver-specific models could enhance the overall predictive accuracy. 3. Integration model: The models developed in steps 1 and 2 are integrated into a new model. The maneuver classification model takes precedence to predict the vehicle's driving behavior (either a lane change or lane keeping). Depending on the predicted maneuver for each timestep, an appropriate trajectory prediction model is applied. This method facilitates a holistic prediction of the vehicle trajectory. The key contributions of this research are as follows:  
1. An empirical analysis is conducted on the performance of various classification models using the NGSIM dataset. The results reveal that logistic regression and LinearSVC outperformed other models in terms of classification efficacy. 2. Regarding vehicle behavior prediction, segmenting the data beforehand and tailoring prediction methods based on the classification outcomes renders the model more versatile. Consequently, this approach achieves a heightened prediction accuracy. A visual representation of our methodology is provided in Figure 1.  
This paper is structured as follows. Section 2.1 gives an introduction to the NGSIM data, detailing their preprocessing and chosen features. Section 2.2 has a deep dive into our algorithmic approach and subsequent predictions. Section 3 shows detailed results from both the lane change and trajectory prediction models. Section 4 contains insightful analysis of our results, culminating in a discussion on future research directions.
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 3 Related Work-Coordination Analysis
### Coordinated VNE
#### 3.2.2 One Stage Coordination  
For the realization of a single stage VNE solution, the simultaneous mapping of virtual links and virtual nodes is presupposed. One good example for this variant is the approach that incorporates the the Sub-graph Isomorphism Detection (SID) algorithm as suggested by [PERSON] and [PERSON] in [PERSON] (2018). An SID-based VNE solution was developed, incorporating node and link mapping during the same phase. The authors' main goal was to optimize the revenue to cost ratio by efficiently managing the underlying substrate resources.  
The devised algorithm searches for isomorphic subgraphs of VNs in the managed SN that, at the same time, satisfy the demands of the processed VNR. Furthermore, the authors proposed a parameter called the Search Length Limit (SLL), which thresholds the algorithm's search space and, consequently, the length of the formed SPs. The presence of the SLL helps in preventing execution times of unacceptable proportions; however, depending on its restrictiveness, the number of rejected VNRs may be increased substantially. Two variations of the proposed algorithm are derived from their study. The SimpleVNMFlib, whose SLL is statically set to a value of 10 hops, and the AdvancedVNMFlib with an SLL value ranging between 1 and 10 hops.  
It is advocated that these algorithms achieve better and faster mappings than the two stage heuristic approach they compare it with and was developed by the authors of [PERSON] et al. (2017). Their results are allegedly more compelling especially regarding large SNs with high resource consumption, which may be harder to map.
# Advances in the Monitoring of Algal Blooms by Remote Sensing: A Bibliometric Analysis
## 2 Materials and Methods
### Data Analysis  
We read all the literature individually to build a database according to the following criteria for further analysis. The database included the following information: title, publication year, author country, journal, research area, study duration, water type (ocean, coastal, inland), satellite, bloom type (phytoplankton in general or specific such us cyanobacteria, dinoflagellate, diatom, etc., at species level when known), studied variable, algorithm (generic or empiric for a specific area or type of bloom), and citation count (until December 2019). Information on the article country was only selected based on the first author. Also, information was gathered about monitoring programs if the study was done in the context of one. Finally, we searched if climate change was a topic of study of either oceanographic phenomenon dependent on climate.  
The following aspects were analysed intensively: (1) core journals, (2) geographic distribution of author country, (3) geographic distribution of studied area, (4) citation counts, (5) satellite use evolution, and (6) study period.  
To evaluate academic influence, we calculated a citation index (C.I.) based on citations per publication according to next equation:  
\[C.I.\ =\ \frac{\text{total\ citations\ in\ December\ 2019}}{\left(2020-\text{publication\ year}\right)}. \tag{1}\]  
C.I. equals to total citations in December 2019 for publication of 2019.  
Publications were classified as described in Table 1:
## 3 Experiments
### Comparative Experiments among Gesture Recognition Algorithms  
To confirm the effectiveness of our gesture recognition algorithm, we compared our method with five other gesture recognition algorithms, namely methods based on: (1) the FPN [44]; (2) RefineDet [45]; (3) Faster R-CNN [31]; (4) convolutional pose machines [46] and Resnet101 [29]; and (5) YOLOv3 [27]. We collected four image groups under different conditions: i) simple indoor background; ii) complex indoor background; iii) simple outdoor background; and iv) complex outdoor background. We prepared two dataset for evaluation as: NYU hand pose dataset [47] containing 52,360 training samples and 22,440 test samples from 11 users; our own dataset containng 5272 training samples (2000 positive and 3272 negative) and 870 test samples from 12 users. All of the methods were trained and tested with the same samples.  
To describe the performance of all compared methods, the recall precision curve (RPC) was applied for the evaluation, in which the recall rate and precision are calculated as follows:  
\[RecallRate=\frac{\sum(CR)}{\sum(GT)} \tag{7}\]  
\[Precision=\frac{\sum(CR)}{\sum(TRR)} \tag{8}\]  
Here, \"\(\sum(CR)\)\" means the number of correct recognition results; \"\(\sum(GT)\)\" refers to the number of ground truth, and \"\(\sum(TRR)\)\" corresponds to the total number of recognition results.  
The RPC analysis results of the different methods are presented in Figures 20 and 21, and Tables 5 and 6. The performance of our gesture recognition algorithm was superior to those of the compared methods using our dataset, and quite similar to those of RefineDet [45] and YOLOv3 [27] using the NYU dataset. The main reason for the difference between the results of our dataset and the public dataset is that the background of our dataset was more complicated than that of the public dataset, in which hand-like objects in the background could easily be recognized as hands.  
Figure 20: The RPC analysis results based on NYU hand gestures dataset [47].
## 3 Proposed Method  
Under severe weather conditions, the visibility of images captured by drones is significantly reduced, seriously affecting the accuracy of object detection and posing technical challenges to the risk identification needs of power construction sites. To address this issue,this section details the adverse weather conditions in datasets of power scenes captured by drones and proposes the IDP-YOLOV9 object detection algorithm specifically designed for such conditions. The algorithm aims to reveal more latent information within images by eliminating the interference of weather factors.  
The entire network framework consists of the Three-Weather Removal Algorithms (TRA) module, an Adaptive Image Enhancement (DLIE) module, and the Improved YOLOV9 detection module. The TRA module initially estimates atmospheric scattering models and parameters related to rain and snow to obtain preliminary dehazed and derained images. Subsequently, the model rescales images to dimensions of 256 \(\times\) 256 before feeding them into the DLIE module, which optimizes parameters to enhance the quality of dehazed, derained, and desnowed images. These weakly supervised enhanced images are then employed for object detection. Finally, the DLIE module processes the enhanced images as inputs for the YOLOV9 detector, leading to improved detection accuracy under adverse weather conditions.
## 3 Results and Discussion
### Cellular Antioxidant Activity
No significant differences in ROS production were observed between the hydrolysates and their corresponding simulated GI digests (\(p>0.05\)). In contrast, ref. [49] reported that SGID soybean protein hydrolysate prepared with gastric proteases enhanced ROS inhibitory activity compared to the undigested soybean protein hydrolysate in H\({}_{2}\)O\({}_{2}\) (1 mM, 6 h)-stimulated Caco-2 cells. The soybean protein fraction obtained post-SGID had a higher DH and a higher content of short chain peptides than the pre-SGID fraction, which may have been responsible for its superior ROS reducing ability, possibly through enhanced cellular absorption. Subsequent isolation and characterisation of antioxidant peptides revealed each peptide sequence contained at least one of the following amino acid residues: Pro, Asp, Leu, Val, Arg, and His. The presence of hydrophobic amino acids is associated with high antioxidant activity through a radical scavenging mechanism. Interestingly, SGID BWSPHs, BW-SPH-A-GI, and BW-SPH-E-GI contained the highest levels of hydrophobic Met (1.01% and 1.03% \(w/w\), respectively) and Phe (1.63% and 1.52% \(w/w\), respectively) [26]. Arithmetical ranking of BWSPHs and SGID BWSPHs deemed BW-SPH-B, -C-GI and D-GI to be the lowest ranking of the hydrolysates tested for overall antioxidant potential, whereas BW-SPH-A and -A-GI were the top rankers (Supplementary Table S3). Results obtained for SGID BWSPHs demonstrate that BW-SPH-A and BW-SPH-E may have potential applications as antioxidant agents in a functional food offering.
# Traffic Offloading in Unlicensed Spectrum for 5G Cellular Network: A Two-Layer Game Approach
## 1 Introduction
The optimization problem is a fraction programming problem which means it is a NP-hard problem to be solved. Through analyses, the function is proved to be a quasi-concave function problem and it can be transferred to the subtractive form to deal. * In addition, to increase the throughput of both LAA and WiFi, a CAGT mechanism by using LBT-based CSAT and traffic offloading is devised. The CAGT mechanism is a two-layer game. In the first layer of the game, a coalition game is built to make a load balance among WiFi APs, and then, in the second layer of the game, a second-price sealed-bid auction between LAA and WiFi heterogeneous systems is used to optimize the throughput of the whole network. The remainder of the paper is organized as follows. Section 2 describes the system model. The optimization problem about optimal station numbers is also presented in this section. Section 3 shows the basic and the modified LBT-based CSAT structure. The proposed two-layer CAGT is described in Sections 4 and 5. Simulation results are presented in Section 6, and the paper is finally concluded in Section 7.
# Region-of-Interest Based Coding Scheme for Live Videos
## 4 Results
The visual fidelity of the ROI is consistently improved, while ensuring that the overall frame's visual quality remains satisfactory and acceptable. As evidenced in Tables 2 and 3, by employing \(K_{roi}\) = 4 to analyze the variations in bit rate and performance metrics across different datasets and resolutions. Compared to the ROI constant QPoffset method, our CNN-based ROI rate control exhibits marginally superior PSNR and SSIM quality improvements within the ROI regions while incurring smaller PSNR and SSIM quality degradation within those same ROIs. Most critically, this CNN-based approach achieves quality gains that would typically require an average 41% bitrate consumption when using ROI constant QPoffset, but does so with only a 2.8% bitrate overhead. It is evident that our method can effectively manage bitrate growth while maintaining stable metric returns for ROI regions. The controlled loss within ROU areas is evenly distributed throughout each frame. Figure 6: \(Cont.\)  
As evidenced in Table 4, in a study involving 100 participants rating video quality on a 1-to-5 Likert scale--with 1 representing the lowest perceived quality and 5 the highest--the use of ROI constant QPoffset strategy as a baseline scenario yields an average enhancement of 2.5% in the mean subjective score (MOS). The methodology presented herein attains a superior uplift of 9.1% in the mean subjective scores through the application of R-\(\lambda\). The results underscore the efficacy of this scheme in delivering enhanced ROI quality and consistency. By optimizing the QP (Quantization Parameter) in the ROI area to maintain a lower value in the proposed solution, we achieve superior perceived quality. Furthermore, the stability of QP ensures a consistent level of quality within ROI. Notably, this solution can be successfully implemented even under tight bandwidth constraints in live stream encoding scenarios. Most importantly, the integration of a CNN-based ROI rate control model sets our proposed scheme apart, enabling it to perform superior rate control on the majority of test sequences. This approach thus represents an innovative and effective means of managing bitrate allocation for improved visual experience, especially in the regions of interest. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Video Sets** & **\(\Delta\)BitRate (kbps)** & **\(\Delta\)ROI-PSNR (dB)** & **\(\Delta\)ROI-SSIM** & **\(\Delta\)ROI-PSNR (dB)** & **AROU-SSIM** \\ \hline Class B & 47.5\% & 1.57 & 0.0471 & \(-\)0.96 & \(-\)0.004 \\ Class C & 42.9\% & 2.47 & 0.0108 & \(-\)0.59 & \(-\)0.009 \\ Class D & 35.1\% & 2.26 & 0.0192 & \(-\)0.81 & \(-\)0.012 \\ Class E & 38.0\% & 2.49 & 0.0508 & \(-\)0.54 & \(-\)0.006 \\ Class F & 43.9\% & 2.07 & 0.0377 & \(-\)0.52 & \(-\)0.011 \\ Average & 41.5\% & 2.17 & 0.0331 & \(-\)0.68 & \(-\)0.008 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: ClassB–ClassF ABitRate and Ametrics, ROI constant QPoffset vs. R-\(\lambda\). Figure 6: BasketballDrive_1920 x 1080@50 fps 1.5 Mbps live stream.