esa-sceva/satcom-chunk-collection huggingface dataset:
# Minimizing the In-Cloud Bandwidth for On-Demand Reactive and Proactive Streaming Applications
## 2 Related Work
### Previous Work
In a recent work, the authors proposed reactive broadcasting protocols named slotted stream tapping (SST) [11; 23]. This protocol performs like proactive protocols at a high workload and like reactive protocols at a low workload. It is based on the same ST Broadcasting scheme. The novelty of SST is to consider a slotted time axis where all clients arriving within one slot obtain their CS (or FTS) at the beginning of the next slot. In Table 1, we summarize the principles and the performances (the consumed bandwidth) of the representative related works. Through, this table we can make the following observations:  
* In reactive protocols: Dyadic is outperforming Unicast, ST and SST techniques in terms of bandwidth consumption. But, it is very hard to implement as reported in [8]. This raises the following question: Could we assure a trade off between the bandwidth consumption and the implementation level of difficulty in the reactive category? * In proactive protocols: modern HTTP streaming protocols are based on (1) dividing the video into equally-sized segments and (2) sending them to the client as fast as segments are ready. SB is the only protocol compliant with these two properties (of modern streaming protocols). Unfortunately, its segments to channels scheduling is not optimal in terms of the consumed bandwidth. This raises the following question: How could this scheduling problem be stated and solved? To answer the aforementioned two questions, we propose in this paper new streaming techniques, namely, SASST as a reactive technique for unpopular video streaming and NOPP as a proactive optimal technique for popular video streaming. Both proposed techniques minimize the server and the internal cloud (or CDN) streaming bandwidth. Since only one quality (the highest) is considered by SASST and NOPP, we suppose that any video adaptation (transcoding) mechanism is done by the streaming nodes on the fly.
## 3 Results  
By using the method of folding the ground and patch plane, it was possible to reduce the size of the antenna to a size of 99 by 150 mm. Figure 5 shows the S\({}_{11}\) parameters of the proposed antenna configuration; despite the size reduction, the two-resonance mode was still present.  
We performed 50 ohms matching of the two channels to apply for the MRI application and RF heating application. Separate matching circuits base on LC networks were applied to the antennas. Figure 5b,c show S11 parameter of the proposed antenna was obtained below \(-60\) dB each mode.  
The resonant frequencies have a current distribution such that a uniform \(\mid\)B\({}_{1}\mid\) field can be produced, the magnitude and phase of the current density J for the 300 MHz and 1.06 GHz is show in Figure 6, a uniform current density can be seen at the middle patch plane, thus the reason for selecting a non-uniform meander pattern.  
Figure 5: The S\({}_{11}\) parameters of the proposed patch antenna, (**a**) with two main resonance frequencies of 300 MHz and 1.06 GHz. performed 50 ohm matched (**b**) at 300 MHz and (**c**) at 1.06 GHz each of two proposed patch antennas.  
Figure 6: The current density distribution J of the proposed patch antenna. Showing in the top of the figure the 300 MHz and bottom the 1.06 GHz, for (**a**) the vector field of the current density, (**b**) the x component of current density, \(\rm J_{x}\) and (**c**) the phase of \(\rm J_{x}\).
# A New Image Oversampling Method Based on Influence Functions and Weights
## 3 The Proposed IFW-SMOTE
### Key Ideas of the IFW-SMOTE Program  
This subsection briefly summarizes and outlines our scheme's design principles and critical ideas. Our scheme is based on the SMOTE algorithm, which focuses on the feature space of the samples. Since new samples are generated by linear interpolation using nonlinear SMOTE, intermediate values can be reasonably constructed for numerical features.  
However, an image's pixel information is unsuitable for simple linear interpolation. An image's complexity and texture information are challenging to retain by a linear method such as SMOTE. In image processing, the generated synthetic samples need to have visual consistency, and the samples generated by SMOTE may lack visually reasonable features, affecting the learning effect of the model. Aiming at these problems, we design the IFW-SMOTE scheme based on sample weights and influence functions.  
Our scheme is divided into three parts: (1) Initial image sample selection introduces the influence function into the pre-trained model Resnet34 network, calculating how the change in sample weights changes the model parameters, combining the sample weights, and filtering the noise images and low-quality images. (2) Sample synthesis improves the Resnet34 network by removing the global pooling and fully connected layers so that the network can completely extract image features, perform feature spreading on the features, perform SMOTE interpolation on the spread features to derive the synthetic image features and perform feature reshaping, and input the synthetic image features into the pre-trained DCGAN network. The pre-trained generator maps these features into the high-dimensional image space to produce a realistic image; this way, the local structure and features of the image can still be maintained when generating new features. (3) And with image filtering, the synthetic image is fed into the pre-trained model again to calculate the importance of the samples to the model parameters and discard the noisy samples.
## 4 Discussion
### TMDs Prevalence and Etiology
#### 4.1.3 The Role of Occlusion  
Malocclusions, particularly untreated Class II and III cases, have been associated with increased TMDs prevalence [51]. Orthodontic treatment, while previously thought to contribute to TMDs risk, has been shown to stabilize TMJ function and mitigate symptoms in severe malocclusion cases [52]. However, contemporary evidence suggests that occlusal factors act more as risk modifiers than primary etiological agents [53].
# Advances in Organic and Organic-Inorganic Hybrid Polymeric Supports for Catalytic Applications
## 2 Organocatalysed Non-Asymmetric Reactions
### C-C Bond Forming Reactions
Moreover, **39** showed higher catalytic activity and reusability in the benzoin condensation reaction compared to the polymerized pre-catalysts obtained from 1-vinylimidazolium [32; 33]. The pre-catalyst **39** was recovered and reused without loss of performance over seven cycles. The material **39** showed high catalytic activity even in the tandem formation of \(\gamma\)-butryrolactone from benzaldehyde and methyl acrylate [31]. In 2015 [PERSON] and [PERSON] reported the first highly efficient and fully recyclable heterogeneous catalyst systems for self-coupling of furaldehydes, particularly furfural and 5-hydroxymethylfurfural [34]. At first, three pre-catalyst structures **41**, **43a**, **43b** were prepared by grafting of the respective triethoxysilpropyl functionalized azolium salts **44**, **46a**, **46b** onto the silica surface (Scheme 18). The catalytic performances of **41**, **43a**, **43b** were investigated in self-coupling reactions of furfural and 5-hydroxymethylfurfural into their respective furoins. **Scheme 18. Preparation of silica-supported ionic-liquids 41**, **43a-b** and their application as catalysts in the synthesis of furoins. Benzimidazololium pre-catalyst **43b**, carrying a long-chain (C12) substituent, showed the best catalytic activity, producing furoins with high yields (94.3%-96.6%). According to the authors, the higher activity of **43b** could be attributed to presence of sterically demanding substituents, that facilitated the shift of the Wanzlick equilibrium (equilibrium between the carbene and its corresponding dimer) toward the formation of free carbenes [35]. The long-chain alkyl group placed in the proximity of the carbene center could also provide a hydrophobic protection pocket for the catalyst to minimize its decomposition or poisoning caused by adventitious protic or oxidative sources. After this preliminary study, the Merrifield's peptide resin, an organic support, was utilized to synthesize the grafted salt pre-catalyst **44** (Scheme 19) [34]. 1-Dodecylbenzimidazole, prepared from reaction between benzimidazole and dodecyl bromide, was grafted onto the Merrifield's peptide resin through alkylation of the 1-dodecylbenzimidazole with the benzyl chloride functional group contained in the resin. In presence of DBU or KO\({}^{\prime}\)Bu, the pre-catalyst **44** showed excellent catalytic activity in the self-coupling reaction of furfural (96.8% yield with DBU and 96.5% with KO\({}^{\prime}\)Bu) and in the self-coupling reaction of 5-hydroxymethylfurfural (94.7% with DBU and 93.3% with KO\({}^{\prime}\)Bu). Both catalytic systems **43b** and **44** showed excellent recyclability in furfural self-coupling without loss of the catalytic activity after ten cycles. This result was obtained by adopting a catalyst recycling procedure that involves activation of the pre-catalyst with a base to generate the NHC catalyst and recycle of the catalyst by quenching the reaction with HCl to convert the catalyst back to the pre-catalyst.
# Caching Transient Contents in Vehicular Named Data Networking: A Performance Analysis
## 4 V-NDN: Design Concepts and Caching Strategies  
### V-Ndn  
Originally designed in [21], V-NDN extends the NDN model to accommodate the distinctive and challenging features of VANETs, namely ad hoc intermittent connectivity and mobility, to fit the spatio-temporal validity of contents. A reference V-NDN architecture is shown in Figure 2. As with the vanilla NDN implementation, each V-NDN node maintains CS, PIT and FIB tables but, to take full advantage from the broadcast nature of the channel and maximize the possibility of content sharing, major modifications are introduced in the forwarding and caching process.  
**Forwarding.** Due to the high dynamicity of vehicular topologies, V-NDN does not implement a proactive routing protocol to build the FIBs. Instead, it assumes that Interest and Data packets are always broadcasted over the wireless medium, and it designs a reactive distance-based forwarding scheme that limits packet collision and redundancy and speeds up the data retrieval. More specifically, when sending an Interest, each node \(S\) includes its Global Positioning System (GPS) coordinates. Each receiving node \(R\) calculates its distance from \(S\) and sets a random _Defer Time_ that is inversely proportional to such distance. The smaller the distance the larger the time a node waits before transmitting; therefore, the farthest node from the sender has higher transmission priority. This speeds up the Interest dissemination. If, during the _Defer Time_, \(R\) overhears the same packet broadcasted by another node in the same area, then it can cancel its own transmission. Re-broadcasted Interests act also as an implicit acknowledgment for the sender \(S\). In case no rebroadcasting is overheard, then \(S\) will retransmit the packet up to 7 times before giving up. To avoid the unrestrained dissemination of Interests, a maximum hop count limit is set to 5 [21].  
**Caching.** Unlike other wireless terminals, such as smartphones or sensors, vehicles do not have strict energy or memory constraints. Therefore, V-NDN nodes can, in principle, cache all Data overheard over the wireless channel, even if they do not have a matching PIT entry. We call this strategy  
Figure 2: Vehicular communications and V-NDN reference architecture.  
Cache Everything in the Air (CEA). By implementing CEA, vehicles can serve as data mules between disconnected areas and enable opportunistic delivery services. In practice, however, this strategy may lead to inefficient performance due to the high cache redundancy (which is even higher than CEE) and it is not convenient in areas with a high density of vehicles [22]. This is why other caching strategies were proposed in the literature, as discussed in the following section.
# Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method +
## 2 Fundamental Theory  
### Principal Components Analysis for Multi-Channel Information Fusion  
Based on the phase space embedding theory proposed by [PERSON] [20], the time series is constructed as a series of spatial trajectories. Use the same embedding dimension to construct a phase space for each channel, and then join the phase spaces of different channels to construct a multivariate phase space. Compared with single-channel analysis, the phase space constructed by multiple embedding theory can provide more comprehensive fault information for fault identification.  
PCA is a statistical method of reducing the dimensions of high-dimensional variables into a few principal components. These few principal components can represent most of the information of the original data, which can usually be expressed as linear combinations, each principal component is independent of each other to avoid overlapping information. PCA is one of the important dimensionality reduction methods, which is widely used in signal processing and data visualization. PCA can be briefly summarized as the following steps:  
1. Form the original data into an \(n\)-by-\(m\) data Hankel matrix \(\mathbf{X}\);
2. Each row of matrix \(\mathbf{X}\) is subtracted from its corresponding mean value, that is, zero mean value;
3. Calculate the covariance matrix \(\mathbf{C}=\frac{1}{m}\mathbf{X}\mathbf{X}^{\mathrm{T}}\);
4. Calculate the eigenvalues and corresponding eigenvectors of the covariance matrix;
5. Arrange the eigenvectors into a matrix according to the size of the corresponding eigenvalues from large to small, and take the first \(k\) (determined by the cumulative contribution rate of the principal components) columns to form a matrix \(\mathbf{P}\);
6. \(\mathbf{Y}=\mathbf{P}^{\mathrm{T}}\mathbf{X}\) is the principal component matrix of the original data \(\mathbf{X}\) after dimensionality reduction.  
After PCA, calculate the cumulative contribution rate of the principal components. The cumulative contribution rate is calculated by dividing the corresponding first \(k\) eigenvalues matrix by the sum of all eigenvalues of the covariance. When it reaches more than 95% according to the confidence level in probability statistics (90%, 99%, etc., can be selected according to actual needs), the selected principal components can be considered effective.
## 3 Discussion  
The updated mechanism by [PERSON] et al. [36] shows the importance of efficient thermochemistry prediction, and this can be achieved through high-level quantum calculation and other advanced computational methods. The formation of Ammonia (NH\({}_{3}\)) from N\({}_{2}\)H\({}_{4}\) and NH\({}_{2}\), as shown in Figure 3, shows a similar trend from lower temperatures rising steadily to higher temperatures. However, the rate constants, as estimated by the two compared studies, show a difference of various orders of magnitude. The H-atomabstraction reactions of N\({}_{2}\)H\({}_{4}\) with H and NH\({}_{2}\), as shown in Figures 3 and 4, show a steady rise in the rate constant as temperature increases. However, two different rate constants estimation show quite a difference with an order of magnitude difference. This confirms the need for more theoretical and experimental studies to develop a validated mechanism that can accurately predict the behavior of hydrazine-based fuels under different temperature and pressure conditions.  
Regarding oxidation mechanisms, the literature presents different theories regarding converting UDMH to nitroso dimethylamine (NDMA). Evaluating and comparing these theories is crucial to determine the most plausible oxidation mechanism for UDMH. Further research is needed to investigate multiple reaction pathways under the simultaneous action of various oxidants to accurately determine the main pathways for NDMA formation from UDMH in the atmosphere.  
Second, regarding detecting UDMH and its transformation products, a range of techniques has been employed. However, there is a need for critical evaluation and comparison of these methods. By thoroughly assessing each detection technique's sensitivity, selectivity, and applicability, researchers and practitioners can choose the most suitable method for their specific requirements. This would significantly enhance the accuracy and reliability of UDMH detection and contribute to effective monitoring and management of soil and air contamination. Future studies should focus on conducting comparative analyses to determine the strengths and limitations of different detection methods, enabling the selection of the most appropriate approach.  
Furthermore, the environmental risks associated with UDMH necessitate careful evaluation and the development of effective mitigation strategies. The literature highlights the importance of conducting risk assessments and implementing measures to minimize the impact of UDMH on human health and ecosystems. However, there is a lack of critical analysis and discussion in this area. To address this gap, it is essential to thoroughly examine the environmental risks and evaluate the effectiveness of current mitigation measures. By doing so, researchers and policymakers can make informed decisions to ensure the safe and responsible application of UDMH fuel. Additionally, future research should focus on identifying novel mitigation strategies and exploring their feasibility and effectiveness.
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 2 Learning-Based Approaches
### Holistic Approaches  
Unlike keypoint-based approaches, holistic approaches are an end-to-end architecture that can be faster than keypoint-based approaches. [PERSON] et al. [36] proposed PoseNet, which firstly applies the CNN architecture to 6D pose estimation, and found that it was able to adapt well to the environment. [PERSON] et al. [37] proposed SSD, which was the first method to associate bounding box priors with the feature maps of different spatial resolutions in the network that was able to detect objects in images using a single deep neural network. This method improved accuracy and retained a low time cost. [PERSON] et al. [38] proposed SSD-6D, which extended the SSD method to 6D pose estimation and allowed for easy training and handling of symmetries. [PERSON] et al. [39] proposed the deep-6 DPose network, the detection and segmentation in which leverage the Region Proposal Network (RPN) [40] based on Mask R-CNN [41]. In pose estimation, it decouples the parameters into translation and rotation so that the rotation can be regressed via a Lie algebra representation. However, because the network uses the ROIs from RPN as inputs and predicts the 6D pose of the object in ROIs, the network was not able to work well when measuring the 6D pose of small or symmetrical objects. To overcome this problem, [PERSON] et al. [42] proposed a new network PoseCNN. This method calculated the translation vector by ensuring the center of the objects in the image and estimating the distance between the center and the camera. Then it calculated the rotation matrix by regressing to a quaternion representation. Additionally, it especially employed a novel loss function for symmetric objects. The method was able to handle occlusion and symmetric objects in cluttered scenes with RGB or RGB-D images as input.
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 5 Result and Performance Analysis
### Probability Analysis  
The probability of BFD and TNFD communications with respect to PGR is shown in Figure 8, and the probability of BFD and TNFD communications with respect to the total number of nodes is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Packet length & 1500 bytes \\ \hline Data rate & 54 Mbps \\ \hline Control frame (RTS, CTS-AI, etc.) rate & 12 Mbps \\ \hline RTS & 20 bytes \\ \hline CTS-AI & 14.125 bytes \\ \hline ACK & 14 bytes \\ \hline RREQ & 21 bytes \\ \hline RREP & 17 bytes \\ \hline DIFS time & 28 μs \\ \hline SIFS time & 10 μs \\ \hline Time slot & 9 μs \\ \hline PLCP preamble duration & 16 μs \\ \hline PLCP header duration & 4 μs \\ \hline \hline \end{tabular}
\end{table}
Table 2: Declaration of variables.  
Figure 8: Probability for TNFD and FD communications vs. PGR.  
Figure 9: Probability for TNFD communications vs. total number of nodes.  
Figure 8 shows the probability analysis for TNFD communications with respect to PGR by each node (packets/s). In this simulation, the number of nodes is 50. The PGR by each node is varied from 25 packets/s to 70 packets/s. According to the figure, the probability for TNFD communications increases significantly with the increase in PGR. However, the probability for BFD communications grows slowly with the increase in PGR. Therefore, the overall probability for FD communications (\(P_{TNFD}+P_{BFD}\)) has an increasing trend.  
The results of the probability analysis of different type FD communications with respect to the total number of nodes are depicted in Figure 9, where total number of nodes is varied from 5 to 60 nodes. Here, the PGRs are considered as 70 packets/s. According to Figure 9, the probability for TNFD communications is growing significantly, as the total number of nodes increases. This is because, if the total number of nodes increases, the possibility of having more nodes to send data after initiating a transmission by a PT also increases.  
On the other hand, the probability for BFD communications is increasing slowly, as the number of nodes increases, because most of the cases of BFD communications takes place when MS initiates the transmission, which is discussed earlier in Section 3.2.2. Therefore, the increase in the number of nodes does not have a high impact on BFD communications, but has a great impact on TNFD communications.  
From Figures 8 and 9, it is significant that if the ad hoc network becomes saturated from unsaturated condition, the probability of BFD and TNFD communications increases rapidly, and thus the overall throughput increases.