esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthesis and Biological Studies of New 2-Benzoxazolinone Derivatives as Antibacterial Agents
## 3 Results and Discussion
### Chemistry
Hydrazone-type compounds **8-23** were obtained in \(E\) geometrical form [56]. As for rotamers, they are formed by the restricted rotation of the amide CO-NH group. The \(Z\) conformer is a predominating one [42; 50]. From the NMR spectra, hydrazones **8-23** exist as mixtures of _E_/_Z_ rotamers in DMSO-\(d_{6}\) solutions. In all \({}^{1}\)H NMR spectra for **8-23**, resonances for the N=CH and CO-NH group protons were observed in double sets in the ranges of 7.80-8.57 and 11.41-11.93 ppm, respectively, and the signal intensity ratio was calculated to be 0.65:0.35, with the exception of the bulky molecules **18** and **19**, with the ratio of 0.60:0.40. In the \({}^{13}\)C NMR spectra, resonance lines for carbon atoms of the N=CH and NHC=O fragments ranged at intervals, as expected. The remaining signals in the \({}^{1}\)H NMRand \({}^{13}\)C NMR spectra of the aliphatic and aromatic fragments were found at the expected chemical shift values. The split signals for the N=CH and NH fragments confirmed the successful preparation of the hydrazones and the correct condensation reaction. Benzoxazolinones with attached azole structures in the molecules are documented as considerable compounds that are characterized by a number of pharmaceutical properties, namely, anticonvulsant, antihyperglycemic, antitubercular, analgesic, antimicrobial, and anticancer properties [4]. Based on it, we opted to synthesize several benzoxazolin-2-one-azole-based compounds (Scheme 2) to evaluate the antibacterial properties and structure-activity relationship. As a result of the heterocyclic of 3-(2-oxobenzzd[d]oxazol-3(2\(H\))-yl)propanehydrazide (**3**) with diketones, the compounds with a combination of bicyclic benzoxazoline with pyrazole (comp. **24**) and pyrrole (comp. **25**) rings in the structure were formed. Upon the condensation of acid hydrazide **3** with pentane-2,4-dione in refluxing propan-2-ol containing a catalytic amount of hydrochloric acid, the corresponding derivative **24** was prepared in a 52.5% yield. Compound **25** was obtained accordingly, applying hexane-2,5-dione instead of the pentane analog and using a catalytic amount of acetic acid. The pyrrole derivative was prepared in a 78.6% yield. The characteristic signals of 3,5-dimethylpyrazole and 2,5-dimethylpyrrole were clearly visible in the NMR spectra of the compounds. Furthermore, all spectral and microanalysis data were in good agreement with the predicted structures. The chemical modification of hydrazide **3** was attempted in an aqueous itaconic acid solution to obtain compound **26**, which was resynthesized according to the procedure described in [46].
# Mobility-Multihoming Duality
## 2 Combined Mobility and Multihoming
### Mobile IPv6 (MIPv6)  
MIPv6 is the IPv6 variant of Mobile IP (MIP) [12] (the successor of Mobile IPv4 [13]). Basic MIPv6 support for mobility is via a solution that is part host and part network. It requires a mobility management server (with a proxy/forwarding function), the Home Agent (HA), to track a Mobile Node (MN)'s topological location, as well as forward packets to and from the Correspondent Node (CN). Meanwhile, the MN needs a user-land daemon to establish a tunnel to the mobility management server. Unlike MIPv4, a MIPv6 MN does not require a Foreign Agent (FA) (another proxy) to be present in the network it moves to. However, the requirement for the HA, and therefore the presence of a home network (HN), still remains, along with the use of two IP addresses to refer to the MN (as explained below).  
MIPv6 overloads the IPv6 address space semantics, using two IPv6 addresses for the MN: a Home Address (HoA) and a Care-of Address (CoA). The CoA is, effectively, a locator, as it indicates the current topological location of the MN. The HoA is, effectively, an identifier, as it is used to refer to the host globally. Packets are delivered to the CoA initially via a proxy--through the MN's HA. After a Route Optimisation (RO) handshake between the MN and CN, the value of the MN's current CoA is made known to the CN, and direct communication can take place.  
The main drawbacks with MIPv6 are the following:  
* The network into which the HA moves, the Foreign Network (FN), must support MIPv6. The control plane messages required for operation of MIPv6 must be able to ingress and egress the FN, e.g., the FN firewall needs to be specifically configured.
* While the MN can move to potentially any network, additional infrastructure at the HN introduces deployment and OAM overhead.
* In addition, if the MN or CN cannot enable RO, sub-optimal 'triangular routing' will be used, with traffic tunnelled from the MN to the HA and then from the HA to the CN. This adds unnecessary traffic load to the HN in terms of both ingress and egress, potentially adding latency and becoming a throughput bottleneck between the MN and CN. Additionally, the more MNs an HN has to support, the more the HN and the HA suffer from the increasing load for tunnelling traffic.
* Overall, the HA might become a performance bottleneck, a SPoF, and a point of attack for disruption to the MIPv6 service.
* MIPv6 does not support multihoming.
## 3 Results and Analysis
### Feature Extraction  
To address the computational intensity and information redundancy associated with full-band models, the study employed SPA and CARS algorithms for sensitive band selection. As summarized in Table 3 and illustrated in Figure 4, a noticeable difference emerged between the two algorithms in terms of sensitive bands. The CARS algorithm frequently selected consecutive wavelength information, while the SPA algorithm followed a more dispersed pattern. Although the locations of the characteristic wavelengths extracted by these algorithms differed, they consistently contained three spectral regions, 400-756 nm, 900-1250 nm, and 1900-2500 nm. Under the SPA, all three transformed spectra obtained sensitive wavelengths both in blue and red regions. In contrast, the CARS algorithm only identified the sensitive position of the red band through MSC transformation and pinpointed the sensitive location at 422 nm in the blue band through SG processing. Both algorithms successfully captured reflectance peaks as sensitive bands ranging from 900 to 1250 nm. Moreover, the SPA also identified spectral bands near 800 nm, 1400 nm, and 1800 nm. In summary, both screening methods effectively reduced the dimensionality of spectral data and trimmed down the number of full-band feature parameters, compressing the original 2101 spectra into 8-20 significant spectral features.  
Figure 3: The response of kiwifruit surface spectral reflectance to different sweetness levels. Reflectance with (**a**) SG processing, (**b**) SNV processing, and (**c**) MSC processing.
# A New Understanding and Modelling of TSP and BP Indices Compared to Safety IMO Ship Requirements
###### Abstract  
Due to the lack of information about the concept of Tons of Steering Pull (TSP) of many escort tugs, and the lack of research works relating the TSP demanded by a tethered vessel with respect to the TSP provided by tugs, the present paper shows an original study with mathematical models on how to solve these problems. What is more, an important percentage of the towing sector always employs Bolard Pull (BP), which is considered the only parameter capable of defining performance, so this paper aims to relate BP with TSP. The present research was carried out based on more than 25 escort tugs of different towing companies. Furthermore, a real case study of different tank vessels was used for modelling purposes of tethered vessels' TSP. Finally, once the proposed models were obtained, they were compared with International Maritime Organization (IMO) guidelines. The results showed charts with the main independent variables of tugs and vessels in order to be as useful and practical as possible to the shipping industry, mainly to ship owners and tug operators, from a safety point of view.  
tug: pollard pull; tons of steering pull; escort towing; ship-handling towing; mathematical model
# Simulation of Two-Phase Flow of Shotcrete in a Bent Pipe Based on a CFD-DEM Coupling Model
## 5 Conclusions  
In this paper, the flow characteristics of wet-shotcrete materials in the horizontal hose were simulated, and a CFD-DEM model suitable for simulating the flow characteristics of wet-shotcrete materials was developed to study the flow of wet-shotcrete materials in the horizontal hose. In addition, the accuracy of the numerical simulation results was verified through the on-site pressure drop experimental test. The main conclusions are as follows:  
Figure 14: Variation in particle energy loss concerning time.  
Figure 13: Number of particle–pipe wall contact times in the pipe in 0.5 s.  
1. The pipeline exhibits a significant pressure loss and pressure difference at the inside and outside walls of the bent pipe. The pressure gradient force leads to a secondary flow, which limits the hitting velocity of the solid phase toward the outside wall of the bent pipe. In addition, the central part of the outer surface of the bent pipe experiences more wear.
2. The bending angles have a great effect on the particle velocity. The particle loss decreases first and then increases with increasing bend angle. Owing to the inertia and inelastic collisions among the particles and between the particles and pipe wall, the concrete material particles accumulate in the outside area of the bent pipe section in the pneumatic conveying process, which may block the pipe. Nevertheless, the turbulence and secondary flows cause the aggregated particle rope to disperse gradually. The particle velocity decreases by more than 73%, and the particle is accelerated behind the 30\({}^{\circ}\) bent pipe section owing to the turbulence and secondary flows. When the bend angle is 90\({}^{\circ}\), the turbulence and secondary flows have less effect on the particle movement behind the bent pipe, and the particle loss is minimal.
3. With increasing bend angle, the number of particles-pipe wall contact times and the normal contact force firstly increase and then decrease; both reach a maximum at a bend angle of 45\({}^{\circ}\). The wear impact on the 45\({}^{\circ}\) bent pipe is stronger than those on the other bent pipes, and the main wear position diffuses radially toward the outside wall of the bent pipe with increasing bend angle. Furthermore, the particle energy loss in the bent pipe is approximately 30 times that in the straight pipe.  
Investigation, H.M.; Methodology, G.M.; Resources, Z.S.; Validation, H.M.; Visualization, G.M.; Writing--original draft, G.M. and Z.S.; Writing--review & editing, Z.S. All authors have read and agreed to the published version of the manuscript.  
This study was funded by projects such as Major scientific and technological innovation projects of Shandong Province (Grant No. 2019 SDZY0203).  
Not applicable.  
The authors declare that they have no conflict of interest.
# Hard-Decision Coded Modulation for High-Throughput Short-Reach Optical Interconnect
## 4 Numerical Analysis for an Optical Transmission System
### Single-Span Fiber System Setup
#### 4.1.2 Post-FEC BER Results  
In order to evaluate the reach increase for the proposed CM for single-span transmission, we implemented SCCs for the central WDM channel (number 6) at the optimum launch power, which is obtained from Figure 7. Figure 8 shows the post-FEC BER performance for 256 QAM and 256-GS modulation formats at two different line rates \(R=233/252\approx 0.925\) and \(R=95/114\approx 0.833\). For 256-GS and SCC with standard decoding at 666 Gbit/s and 600 Gbit/s, we observe a shaping gain of \(>\)2 km compared to 256 QAM. The transmission reach extension of 256-GS and SCC with SABM decoding decoding is measured to be 4 km (3.7%).  
Figure 8: Post-FEC BER as a function of the transmission distance for different modulation formats and FEC codes over single-span transmission link.  
Figure 7: AIRs as function of the transmitted power for different modulation formats over single-span 110 km and 120 km SSMF.
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 3 Materials and Methods  
The process of the method proposed in this paper is shown in Figure 1. The method in this paper is two-stage, including the detection and tracking stage. In the detection stage, the detection boxes are generated by the object detection algorithm [1; 2; 3], which is input into the tracking stage as the detection result. The tracking stage predicts the position of the target in the frame according to the tracking result of \(t-1\) frames, and uses the IOU value of detection results and predicted position to perform NMS to obtain the predicted boxes. The prediction boxes are divided into two categories according to the relationship between the confidence and the confidence threshold, including high-scoring boxes and low-scoring boxes. Match the high-scoring boxes first, and then match the unsuccessfully matched trajectories with the low-scoring boxes to obtain the final tracking result.
# No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction
## 3 Approach
### Quality Score Prediction  
In the second step of training, the central attention peripheral inhibition module combines the brightness information of the image to weigh the multi-scale contour features learned in the first stage. The central attention peripheral inhibition module adopts a double Gaussian difference model, which is composed of two parts: the center position of the image has strong attention and the edge position is weakened, which simulates the different attention and different residence times of humans in different areas of the image. The distribution is:  
\[S(x,y)=k_{c}\frac{1}{2\pi\sigma_{c}}\exp\left[-\frac{(x^{2}+y^{2})}{2\sigma_{c }{}^{2}}\right]-k_{p}\frac{1}{2\pi\sigma_{p}{}^{2}}\exp\left[-\frac{(x^{2}+y^ {2})}{2\sigma_{p}{}^{2}}\right] \tag{6}\]  
where \(k_{c}\) is the central attention enhancement coefficient, \(k_{p}\) is the peripheral inhibition coefficient.  
Because the optic nerve has different sensitivity to images of different brightness, it is necessary to add brightness information of the image while considering the attention of  
Figure 4: Examples of scale space and contour features of a image in the LIVE database. (**a**)–(**e**) are an image scale space of (**a**). (**f**)–(**i**) are contour features of different scales of (**a**).  
Figure 3: Examples of scale space and contour features of a image in the TID2013 database. (**a**)–(**e**) are an image scale space of (**a**). (**f**)–(**i**) are contour features of different scales of (**a**).  
different areas of the image. We normalized the overall image brightness and increased the quality score weight of image blocks with strong brightness.  
The MSIQA model learns to predict image quality score. The loss function is defined as:  
\[L2=\frac{1}{N}\sum_{i=1}^{N}\left(S(I_{i})-h^{\prime}_{\theta}(I_{i})\right) \tag{7}\]  
where \(h^{\prime}_{\theta}(I_{i})\) is the image quality score of the image \(I_{i}\) predicted by the model, \(\theta\) is the parameters of the CNN network, \(S\) is the ground truth subjective score of the input image \(I_{i}\).
# BDS Satellite Clock Prediction Considering Periodic Variations
## 1 Introduction
The time-varying performance of GPS satellites based on the clock products of the International GNSS Service (IGS) is characterized, and the results show that the amplitudes of periodic terms vary with the eclipse seasons, and there is a relationship between the periodic noise and orbit dynamics [29]. The Galileo satellite clock offsets have a similar performance. A detailed time-varying analysis of the first two significant periodic terms is carried out for Galileo satellites based on the European Space Agency (ESA) clock products, and confirms that amplitude variations of the first periodic term affected by eclipse seasons, and the amplitude sinusoidal variations of second periodic term related to the J2 relativistic effect [30]. Studies show that the BDS periodic terms have some distinction between the GPS and Galileo, but similar variations of the periodic variations have been found in the BDS satellite clock offsets [31]. Considering that the FFT cannot characterize the periodic variations of the clock offsets in time domain [32], the periodic terms obtained by FFT may have certain deviations. In this case, the accuracy of the SAM will be affected, and it is necessary to quantify the periodic terms more accurately. In this paper, we investigate the periodic variations of BDS satellites clock offsets based on the clock products from WHU and GFZ. To obtain more periodic variation details, the STFT is used, and the results are compared with FFT. The time-varying amplitude and frequency of the periodic terms in BDS satellite clock offsets are detected through STFT. Awareness of periodic variations can be used to improve the clock prediction model, we proposed a time frequency analysis model (TFAM), and the fitting performance as well as prediction performance are evaluated. The following section is arranged as follows. First, the periodic variations of the BDS satellite clock offsets are characterized and analyzed, and the TFAM considering periodic variations is proposed in Section 2. Then, the fitting and prediction performance of the proposed TFAM are evaluated and compared with that of the SAM in Section 3. The discussions are given in Section 4. Finally, the conclusions are summarized in Section 5.
# Study of a Small Robot for Mine Hole Detection
## 1 Introduction
Firstly, the functional analysis and structural design of the robot were carried out according to the engineering requirements, the key components were mechanically modeled, and their specific structural parameters were calculated. Secondly, a three-dimensional model was constructed, and the design of the hardware circuit and control program was carried out. Finally, the robot was machined with 3D printing, and a practical test was conducted to test the basic parameters and crawling ability of the robot. In this paper, the design of the mine hole-inspection small robot included a cable power supply, a minimum body diameter of 65 mm, and a variable diameter function able to adapt to 65 mm to 100 mm pipe. The gear drive form was used in order to ensure low power consumption and, at the same time, to provide sufficient traction to meet the need for dragging the cable and ensure the smooth operation of the robot. The waterproof and explosion-proof design can adapt to the unique environment of coal mine holes containing water and gas. Compared with manual propulsion, the mine hole robot can better adapt to small-diameter mine holes. The speed is stable and adjustable, with a long moving distance. It is safe and reliable and can significantly improve the effect of mine hole detection and promote the intelligence and safety of the production of the coal mining industry.
# Identify High-Impact Bug Reports by Combining the Data Reduction and Imbalanced Learning Strategies
## 5 Experimental Results
### RQ1:What Are the Better Classification Results for the Reduced Datasets and the Unreduced Datasets?
To answer this question, we first use several different imbalanced processing strategies, i.e., RUS, ROS, SMOTE and CMA, to balance the preprocessed datasets after performing feature selection and instance selection and record the experimental results as the GA(FS+IS)_imbalance. After we balance the original dataset, we simultaneously perform feature selection and instance selection and record the results as imbalance_GA(FS+IS). Second, we perform feature selection on the original datasets and then use the different imbalanced processing strategies, RUS, ROS, SMOTE and CMA, to balance the datasets. The experimental results are recorded as the GA(FS)_imbalance. We perform the feature selection after the raw datasets are balanced by the different imbalanced processing strategies, RUS, ROS, SMOTE and CMA, and the experimental results are recorded as imbalance_GA(FS). Third, we perform instance selection on the original datasets and then use the different imbalanced processing strategies, RUS, ROS, SMOTE and CMA, to balance the datasets. The experimental results are recorded as the GA(IS)_imbalance. We perform the feature selection after the raw datasets are balanced by the different imbalanced processing strategies, RUS, ROS, SMOTE and CMA, and the experimental results are recorded as imbalance_GA(IS). Fourth, we perform feature selection on the original datasets first, then perform instance selection, and then use the different imbalanced processing strategies, RUS, ROS, SMOTE and CMA, for balance processing; the experimental results are recorded as the GA(FS_IS)_imbalance. We first balance the original dataset with the RUS, ROS, SMOTE and CMA method, then perform feature selection and instance selection; we record the experimental results as imbalance_GA(FS_IS). Finally, we perform instance selection on the original datasets first and then perform feature selection; subsequently, we use the different imbalanced processing strategies, RUS, ROS, SMOTE and CMA, for balance processing, and the experimental results are recorded as the GA(IS_FS)_imbalance. We first balance the original dataset with the RUS, ROS, SMOTE and CMA methods then perform instance selection and feature selection; the experimental results are subsequently recorded as imbalance_GA(IS_FS). We use the five evaluation metrics mentioned above (accuracy, precision, recall, F-measure and AUC) to make comparisons and only retain the experimental results of NBM as a classifier. Tables 9-13 shows the performance of the processing sequence for balance processing and reduction denoising to identify high-impact bug reports for Mozilla, GCC and Eclipse.
## 3 Materials and Methods
### Analyzing the Dataset
#### 3.5.3 Data Scaling and Normalization  
Scaling and normalization boost data preprocessing, which helps transform the dataset's features into a standard scale without distorting differences in the range or magnitude of the features. Standard Scaler, a data preprocessing module from the scikit-learn library, was utilized in this study to provide transparency in the mean and unit variance between the features. This also enables the improvement of the algorithm's performance by ensuring that the input features are on a similar scale and possibly have Gaussian distribution. Scaling also helps reduce the computational burden of training the algorithm, and normalization can make it easier to compare features measured on different scales. It is a technique for scaling the features of a dataset so they have zero mean and unit variance [34]. The formula for z-score normalization is as follows:  
\[z_{i}=\frac{xi-\mu}{\sigma} \tag{3}\]  
where \(x\) represents the original data point, \(\mu\) (mu) is the mean of the sample of the data points, \(\sigma\) (sigma) is the standard deviation, and \(z\) is the standardized data point. This formula subtracts the mean of the feature from each value and divides the resulting values by the standard deviation of the feature. This helps to enable the components to be normalized at the same level of scale, transform features with skewed distributions, reduce the influence of outliers on the features, and make a larger dynamic range of distance measurement stable, which reduces the computational burden while training the model.