esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Basis  
### Equivalent Source Method (ESM)  
Considering an arbitrarily-shaped complex structure submerged in homogenous fluid medium, the pressure \(p\) and the particle velocity \(v\) at any field point \(\mathbf{r}\) could be represented as the superposition of sound field generated by a series of point sources distributed on a virtual surface inside the structure [8]:  
\[p(\mathbf{r})=\mathrm{i}\rho_{0}\varOmega_{0}\sum_{i=1}^{N}q(\mathbf{r}_{i})g(\mathbf{r}_{ i},\mathbf{r}) \tag{1}\]  
where \(\rho_{0}\) is the density of the medium; \(\mathrm{j}=\sqrt{-1}\), \(\varOmega_{0}\) is the angular frequency; \(N\) is the number of equivalent sources; and \(q(\mathbf{r}_{i})\) is the strength of the \(i\)th equivalent source, which is located at the position of \(\mathbf{r}_{i}\). [PERSON] et al. indicated that the form of a single equivalent source could be various, such as monopole, dipole, or the combination of monopole and dipole [11]. However, using monopole as the form of equivalent source has an obvious advantage because the transfer function could be obtained easily even in a non-free field. \(g(\mathbf{r}_{i},\mathbf{r})\) is the transfer function ([PERSON]'s function), given by the following:  
\[g(\mathbf{r}_{i},\mathbf{r})=\frac{\exp(-jk_{0}|\mathbf{r}_{i}-\mathbf{r}|)}{4\pi|\mathbf{r}_{i}- \mathbf{r}|} \tag{2}\]  
where \(k_{0}\) is the wavenumber. If the structure is submerged in the water with finite depth, the reflection sounds from the water-air boundary should not be ignored. Thus, the expression of \(g(\mathbf{r}_{i},\mathbf{r})\) could be rewritten as follows:  
\[g^{\prime}(\mathbf{r}_{i},\mathbf{r})=\frac{\exp(-jk_{0}|\mathbf{r}_{i}-\mathbf{r}|)}{4\pi|\bm {r}_{i}-\mathbf{r}|}+\eta\frac{\exp(-jk_{0}|\mathbf{r}_{i}^{\prime}-\mathbf{r}|)}{4\pi|\bm {r}_{i}^{\prime}-\mathbf{r}|}\]  
tion of overdetermined equations. Thus, the approximate solution of the unknown source strength vector is often solved with minimum \(\ell 2\)-_norm_ via the minimization problem [34]:  
\[\arg_{Q}\text{min}\|\mathbf{Q}\|_{2}\text{ subject to }\mathbf{v}=\mathbf{D}\mathbf{Q} \tag{7}\]  
When the interference of noise could not be ignored, the Tikhonov regularization is usually chosen to avoid the ill-posed inverse problem, which is caused by the poorly conditioned dipole matrix [35]. Thus, Equation (7) could be rewritten as follows:  
\[\arg_{Q}\text{min}\|\mathbf{v}-\mathbf{D}\mathbf{Q}\|_{2}+\Lambda\|\mathbf{Q}\|_{2}^{2} \tag{8}\]  
where \(\Lambda\) is the regularization parameter. It is essential to acquire accurate equivalent source strengths for sound prediction with high efficiency. Particularly, the reflection sounds could make a significant contribution to the structural vibration in the non-free-field, which makes the solution of equivalent source strengths computationally costly [36]. In the work [1], [PERSON] proposed a method to separate the normal velocity on the surface of the structure, which could make a contribution to the radiation sound field. With the method, the 'useful' strength of equivalent sources could be calculated with the normal derivative of free Green's function, which simplifies the computation.
## 4 Conclusions  
This study investigated the impact of the salt type of GLDA chelating agent on oil recovery. Several measurements were conducted including coreflooding, interfacial tension, contact angle, and dynamic adsorption. The following conclusions can be drawn based on this work:  
* Incremental oil recovery between 19% and 32% of the OOIP can be achieved, based on the salt type and rock lithology.
* Flooding carbonate rocks with the calcium-based GLDA chelating agent yielded the highest oil recovery (32% of OOIP), followed by that with the potassium-based GLDA chelating agent, and the sodium-based GLDA chelating agent yielded the lowest oil recovery.
* The adsorption behavior of the calcium-based GLDA on the rock surface was the highest, which was indicated by the contact angle and dynamic adsorption experiments.
* This study will help in maximizing the oil recovery from carbonate and sandstone reservoirs by suggesting the most suitable salt type of chelating agents.  
Figure 10: Adsorption profiles for different types of GLDA at a temperature of 100 \({}^{\circ}\)C.  
**Author Contributions:** Conceptualization, M.M. and S.P.; methodology, M.M.; validation, M.M., A.H. and S.P.; formal analysis, M.M., A.H. and S.P.; investigation, M.M., A.H. and S.P.; resources, M.M.; data curation, M.M.; writing--original draft preparation, A.H.; writing--review and editing, M.M. and S.P.; visualization, M.M.; supervision, M.M. and S.P.; project administration, M.M. and S.P.; funding acquisition, M.M. and S.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** The College of Petroleum and Geoscience at King Fahd University of Petroleum & Minerals is acknowledged for the support and permission to publish this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Materials and Methods
### Data Collection  
Since a morphological and image database of these ten commercial bamboo species is not available from any other source, a morphological and image database was created by collecting samples of these bamboo species from two different locations: the Bamboo Garden Amravati and the fly ash disposal area of Koradi Thermal Power Plant, Nagpur, Maharashtra, India. Most of the bamboo species in the study area are commercially used in many countries, and the selected species were _Bambusa_ (_bambos_, _tulda_, _valgaris_, _vamin_), _Dendrocalamus_ (_giganteus_, _membranecus_), _Gigantocchio atroviolacea_, _Melocanna bacifera_, _Susa fortune_, and _Thyrsostachys oliveri_. The images were collected by a camera in the natural environment; each species had 200 samples, so a total of 2000 samples were used to classify the species and determine the variations. Regarding the physical attributes of all species, we collected morphological data with the help of questionnaires, and we call this DS1, whereas the capture image dataset is called IDCBS10. These questionnaires included the following questions related to bamboo and its physical attributes, which were later used for classification purposes.  
* How do you differentiate the commercial and non-commercial bamboo species?
* What are the attributes you focus on for species identification?
* Out of many attributes, which one do you choose for species identification?
* What is the approximate range for each attribute?
* What is the approximate range for height, diameter, internode length, wall thickness, and flowering cycles for the individual species?
* What is the approximate range for leaves (length and width) and sheath (long and broad)?
* What is the color and texture variation that is used for species differentiation?
## 3 Results
### DEAP Model
#### 3.1.4 Classification  
Although nine different ML models were compared, only the results from the models with the highest accuracy (and lowest fitting time) for each signal or grouping of signals are shown in Table 3. These models were trained using 60 s of DEAP data and used a PCA for feature identification. There was a slight improvement when using multiple signals as compared to the independent signals of PPG, GSR, and RESP (23% accuracy as compared to 21%). Overall, the SVM-rbf had the best accuracy along with the lowest fitting times, while the Gradient Boosting and SVM-poly models had the worst fitting times and inferior accuracies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Physiological Sensors** & **Features** & **PCA** \\ \hline PPG & 108 & 21 \\ GSR & 108 & 24 \\ RESP & 84 & 14 \\ TOTAL (individually) & 300 & 59 \\ ALL & 300 & 54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PCA number of features extracted.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Score** & & **Pleasure** & **Arousal** & **Dominance** & **PAD** \\ \hline \multirow{3}{*}{PPG} & Model & SVM-rbf & SVM-linear & SVM-rbf & \\ \cline{2-5}  & Accuracy & 21\% & 21\% & 23\% & 21\% \\ \hline \multirow{3}{*}{GSR} & Model & SVM-rbf & SVM-rbf & SVM-rbf & \\ \cline{2-5}  & Accuracy & 20\% & 22\% & 20\% & 21\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative accuracy of ML models based on each signal or combination of signals.
# Survey of Network Coding Based P2P File Sharing in Large Scale Networks
## 4 Network Coding Based P2P File Sharing Systems
### Multi-Generation Mixing (MGM) and Overlapping Network Coding
However, in contrast to EOC, they consider deterministic overlapping structures by arranging generations circularly and allowing a generation to overlap with its successive generations in a modular arithmetic scheme. Another improved deterministic circular structure called distributed is proposed which allows overlapping based on a distance. Additionally, in opposition to the foregoing studies which consider a random scheduling, round-robin scheduling is considered and compared with the random scheduling. Analysis shows that round-robin scheduling can accelerate the download time significantly for small generation sizes (\(\leq\)8). Simulation results show that the proposed deterministic overlap structures minimize the expected download time compared to the random annex, and the distributed structure has almost the same download time as EOC but with less network overhead and indexing complexity of EOC. Figure 9: a \(d\)-regular graph (\(d=3\)) with 5-vertices (generations). In contrast to the aforementioned schemes, [PERSON] et al. [101] propose an overlapping network coding with unequal generation sizes. Sizes vary from a minimum, which achieves the maximum information flow of the network, to a maximum that is constrained by the buffer size or by the decoding complexity, namely, Gaussian elimination can be applied computationally. Pieces per generation are selected randomly from all the original pieces with replacement such that overlapping among generation is allowed. Sequential and random scheduling policies are merged and applied. For each generation, a fixed number of pieces are transmitted sequentially such that small generations can be decoded and subsequently help large generations for earlier decodability. Next, random scheduling is applied among generations for the remaining pieces. Experimental results show that the proposed scheme achieves the minimum overhead (10%) compared to disjointed generations (60%), head-to-toe (40%), and random annex (20%) codes. However, the disjointed generations code achieves the minimum decoding complexity. The overall better overhead-complexity trade-off is achieved by the proposed scheme. The previous studies show clearly the benefit of overlapping coding over disjoint generation, in terms of downloading time and decoding rate, by mathematical analysis and simulation. However, only two studies [91,100] consider P2P networks. Table 6 summarizes the most important studies related to generations overlapping network coding.
## 5 Conclusions  
This work proposes a pronunciation prior knowledge-assisted end-to-end vowel reduction detection method. Based on the CTC/Attention joint framework, a novel main and auxiliary encoder structure is designed to introduce auxiliary information generated from the reference text for vowel reduction detection. The end-to-end model adopts a hierarchical attention mechanism to fuse pronunciation prior information and the acoustic information dynamically. Furthermore, we study a multi-stream expansion method based on the attention mechanism, which uses the correlation between different inputs to enhance the encoded vector. Experiments show that the proposed method is better than the HMM-DNN hybrid method and the general end-to-end method in vowel reduction detection and overall phoneme recognition performance. Moreover, the multi-stream expansion method can effectively improve the phoneme recognition performance when combined with a priori composed letter sequences. The mechanism by which the auxiliary encoder information works and the impact of different automatically generated auxiliary inputs on performance have been further analyzed. In order to ensure the pronunciation prior contains less interference and provides more reliable information, a model that automatically predicts the stress and generates the pronunciation prior can be added in the future. In addition, acoustic features or front-end filters for the vowel reduction phenomenon are also worth considering.  
Methodology, validation and writing--original draft, Z.L. Review and formal analysis, Z.H. Review, editing, and formal analysis, L.W. Review and supervision, P.Z. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the National Key Research and Development Program (No. 2020 YFC2004100).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{\(ih_{schwa}\)} & \multicolumn{2}{c}{\(ah_{schwa}\)} \\ \cline{2-5}  & **Pre/Rec** & **F1** & **Pre/Rec** & **F1** \\ \hline Human trans & 0.80/0.82 & 0.810 & 0.82/0.82 & 0.820 \\ G2P & 0.64/0.67 & 0.655 & 0.62/0.55 & 0.583 \\ Letter & 0.58/0.72 & 0.642 & 0.60/0.57 & 0.585 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of different auxiliary inputs.
# Rainfall Similarity Search Based on Deep Learning by Using Precipitation Images
## 5 Conclusions  
This paper proposes a rainfall similarity research method based on deep learning by using precipitation images. Firstly, the regional precipitation, precipitation distribution, and precipitation center are extracted from the precipitation images, and the similarity measurement for each feature is calculated separately.  
Additionally, an ensemble weighting method of normalized depreciation cumulative gain-improved particle swarm optimization (NDCG-IPSO) is proposed to weigh and fuse the three extracted features as the similarity measure of the precipitation image. Finally, the comparing experiment of our method with CH, PCA, BORDA, and NDCG-PSO on the daily precipitation images in the Jialing River Basin illustrates that the methods proposed in this paper can better characterize the spatiotemporal characteristics of the precipitation image and discover similar rainfall processes, which will provide a new idea for hydrological forecasting.  
Although some achievements have been made, many problems must still be solved. One problem is that it only considers the daily precipitation images similarity searching. However, a rainfall process may be composed of multiple single-day precipitation images.  
Hence, how to conduct the rainfall process similarity search based on the similarity measurement method of NDCG-PSO and thus build a rainfall-flood similarity pattern repository to provide guidance and technical support for hydrological forecasting and water resource utilization is the direction of our future work.  
Conceptualization, Y.Y.; Methodology, Y.Y., X.H., Y.Z. and D.W.; Software, X.H.; Validation, Y.Z.; Formal analysis, X.H.; Writing--original draft, X.H.; Writing--review & editing, Y.Y. and D.W.; Visualization, X.H.; Supervision, Y.Y., Y.Z. and D.W.; Project administration, Y.Y., Y.Z. and D.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (No. 2021 YFB3900605 and 2018 YFC1508100).  
Not applicable.  
The authors would like to thank the Bureau of Hydrology, Changjiang water Resources Commission, especially [PERSON] and [PERSON], for their great help in data provision, algorithm design, and model training of this paper.  
The authors declare no conflict of interest.  
Figure 8: Comparison of experimental results between PSO algorithm and IPSO algorithm.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 2 Related Work
### Delay Tolerant Networking (DTN) Routing Protocols
#### 2.1.2 Epidemic  
[PERSON] and [PERSON] [14] proposed the opportunistic routing protocol called Epidemic, which is a flooding-based forwarding protocol. The objectives of this routing are to maximize the delivery of the messages, and minimize the delivery latency in message delivery.  
In the Epidemic protocol, nodes send copies of messages to all nodes that they encounter with no knowledge of the network. The encountered nodes save the messages in their buffer using the store-carry-forward method until meeting another node or destination. The list of messages, which is stored in the node's buffer, is called the summary vector. Whenever two nodes encounter each other they exchange their summary vectors and compare them to identify new messages that should be downloaded from the encountered node.  
The Epidemic algorithm guarantees high delivery rates and the destination can receive all messages from nodes if the buffer capacity of nodes is sufficiently large [9]. A common approach in Epidemic is to set a Time-To-Live (TTL) on the expiry time for each message, so nodes only keep message copies until the TTL has expired [4]. In fact, all routing algorithms can benefit from dropping very old packets by using a TTL timer. Although Epidemic has high delivery rates, flooding is likely to cause a high overhead due to the number of copied messages in the network, which can congest the network. Also, by spreading large numbers of messages over the network, this type of algorithm will consume a significant amount of resources such as power, bandwidth and buffer space.
## 3 Results and Discussion
### Analysis of Aroma Index of Black Tea in Different Seasons  
To evaluate the differences in the aroma quality of Rucheng Baimao black tea in different seasons, this study calculated two flavor indices. [PERSON]'s flavor index (OFI) is the ratio of the sum of the contents of volatile components with sweet and floral aromas (Group II) to the sum of the contents of volatile components with green/grassy notes (Group I). The larger this ratio is, the higher the aroma quality [56]. The Wickremasinghe-Yamanishi ratio is the ratio of the sum of the contents of volatile components with a retention time shorter than that of linalool to the sum of the contents of volatile components with a retention time longer than that of linalool. The smaller this ratio is, the better the aroma quality [44]. In order to calculate the OFI index, the aroma characteristic descriptions of 140 volatile components were retrieved from the relevant literature and database websites and grouped, as shown in Table S1.  
The calculation results of the flavor indices are shown in Figure 3. The OFI index of spring black tea was significantly higher than that of summer and autumn black tea, while there was no significant difference between summer and autumn (Figure 3A). Regarding the Wickremasinghe-Yamanishi ratio, the ratio of summer black tea was significantly higher than that of spring and autumn black tea, with spring black tea having the lowest ratio (Figure 3B). Considering the two flavor indices together, the aroma quality of spring black tea was the best, followed by autumn black tea, and summer black tea was the worst, which was consistent with the results of sensory evaluation (Table 1). [PERSON] et al. believed that the aroma quality of tea produced in spring was the best, followed by autumn tea, and summer tea was the worst, which was consistent with the results of this study [57]. [PERSON] et al.'s research showed that the aroma quality of autumn black tea was the best, followed by summer black tea, and spring black tea was the worst, which was contrary to the results of this study [58]. This might be related to differences in raw material varieties or processing techniques.  
Figure 2: Statistics of the top 10 volatile components in Rucheng Baimao black tea in different seasons. (**A**) The top 10 volatile components in terms of content in black tea in spring; (**B**) the top 10 volatile components in terms of content in black tea in summer; (**C**) the top 10 volatile components in terms of content in black tea in autumn.
## 5 Performance Evaluation
### Simulation Results
#### 5.3.4 CPU and Memory Utilization  
Figures 13 and 14 show the CPU utilization and memory utilization of these three schemes, respectively. The CPU and memory utilization refer to the amount of CPU and memory resources consumed by the entire network simulation program when it is fully operational. When the system is not running the simulated network environment, the CPU utilization is about 4.5% and the memory utilization is about 12%. As seen from these two figures, there is only a slight increase in the CPU utilization and memory utilization of the three schemes as the number of flows increases. Benefiting from the resource management and scheduling mechanisms of the operating system, as well as the high performance of modern computer systems, fluctuations of CPU and memory utilization exist while within  
Figure 11: Numbers of backup flow rules required for three schemes with bar graph.  
Figure 12: Numbers of backup flow rules required for three schemes with curve graph.  
a very small range. In addition, it can be observed that there is no significant difference in CPU usage and memory usage among these three schemes. In conclusion, when compared with the other two schemes, the FAPR scheme does not cause additional hardware resource costs, which is applicable in practice.
## 3 Example Analysis  
### Data Sources  
The public data provided by Mississippi State University were used to assist in the performance verification of the proposed network attack detection method [34]; the specific dataset we used is \"the multi-class of dataset 1: Power System Datasets\". The physical grid topology is a two-machine three-bus transmission system, and the specific topology is shown in Figure 7. The physical grid contains the devices as follows: P1 and P2 are two three-phase generators, and R1-R4 are four pieces of electronic equipment, respectively, which control on and off circuit breaker BR1-BR4 separately.  
The data include five types of scenarios, which are described as follows:  
1. The power system operates or maintains normally, and there is no network attack or failure state.
2. Fault: a small current ground fault occurs in the power system.
3. False data injection attack: the attacker tampered with the amount of data and bypassed the residual detection mechanism, which caused the dispatcher to lose normal control of the power system.
4. Remote tripping command injection attack: the attacker tampered with the control signal of the circuit breaker, so that the circuit breaker could not normally turn on and off.
5. Attack on fragile device attack: the attacker tampered with the relay settings so that the relay cannot be disconnected when the power system fails.  
The data have a total of 128 features, including data collected by 4 PMUs and snort logs. Each PMU measures 29 features, and the remaining 12 features are log information of the control panel. Table 1 describes the names and the physical meanings of the data features. Since the data acquisition device is four PMUs, each PMU measures the same index name, so only the data features collected by the first PMU are listed. The \"#\" in  
Figure 7: The topology of a three-bus two-line transmission system.  
\"R#-\" represents the index of the PMU code, for example, \"R1-PM1\" represents the voltage amplitude measured by PMU R1.