esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Mobile Sink Routing in Wireless Sensor Networks Using Bipartite Graphs
###### Abstract  
Wireless sensor networks (W.S.N.s) are a critical research area with numerous practical applications. W.S.N.s are utilized in real-life scenarios, including environmental monitoring, healthcare, industrial automation, smart homes, and agriculture. As W.S.N.s advance and become more sophisticated, they offer limitless opportunities for innovative solutions in various fields. However, due to their unattended nature, it is essential to develop strategies to improve their performance without draining the battery power of the sensor nodes, which is their most valuable resource. This paper proposes a novel sink mobility model based on constructing a bipartite graph from a deployed wireless sensor network. The proposed model uses bipartite graph properties to derive a controlled mobility model for the mobile sink. As a result, stationary nodes will be visited and planned to reduce routing overhead and enhance the network's performance. Using the bipartite graph's properties, the mobile sink node can visit stationary sensor nodes in an optimal way to collect data and transmit it to the base station. We evaluated the proposed approach through simulations using the NS-2 simulator to investigate the performance of wireless sensor networks when adopting this mobility model. Our results show that using the proposed approach can significantly enhance the performance of wireless sensor networks while conserving the energy of the sensor nodes.  
bipartite graph; mobility model; mobile sink; path planning; wireless sensor networks (W.S.N.s) +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Mobility Management Scheme with Mobility Prediction in Wireless Communication Networks
## 2 Registration Scheme with Flexible TAL Forming
If the UE moves to one of the neighboring cells that is not in current TAL, it will register its location and a new TAL is set up so that entered cell is cell 1 of Figure 1.  
To analyze the performance by adopting a semi-Markov process model, we assume the random walk model when the UE moves to another cell except for movements of the UE that have been registered in the current cell. Note that in the ring-based TAL scheme [22], which is previously proposed in LTE network, the TAL is allocated only when the future direction cannot be predicted. A central-policy is then adopted in the scheme. For example, the UE registers its new TAL such that the central cell (cell 3 in urban area or cells 2 and 3 in the rural area) is included in the new TAL.
## 1 Introduction
However, when it came to assessing the quality of the oral productions, we only achieved approximately 78.5% accuracy using the same training feature set and classifier. The evaluation focused mainly on evaluating prosodic quality, covering factors such as intonation, accent, and phrasing. The assessment of these aspects was conducted at two levels: correct or incorrect. In this paper, we delve into an analysis of the training data used in our previous studies to gain insights into the reasons behind this discrepancy in classification performance. Our findings not only shed light on the factors contributing to these performance differences but also provide valuable indications for exploring alternative approaches in future research endeavors. In this paper, we undertake a systematic analysis of the prosodic features present in the utterances of the corpus to identify the most informative features and their corresponding values for predicting the quality of oral productions. To achieve this, we pose the following research questions:  
* RQ1: Is it possible to select prosodic features by integrating information about prosodic quality and differences between types of speakers? * RQ2: Can specific issues of a user be identified using the extracted features? To answer these questions, we introduce a novel feature selection procedure that integrates information derived from the distinctions between correctly and incorrectly pronounced DS utterances, based on evaluations performed by human experts, and the differences between the utterances produced by DS speakers and those of typical speakers (further elaborated in Section 2.3). As a result, the significance of the selected features becomes more straightforward to convey when explaining potential limitations of the specific speakers under examination (as presented in Section 3 and discussed in Section 4). The paper is organized as follows. The Section 2 provides detailed information about the compiled corpus and the manual evaluation of the utterances. Additionally, it presents the procedure used for the individual analysis of various prosodic features. The Section 3 presents the selected prosodic features and their effectiveness in modeling the quality of utterances, considering human scores as the basis for evaluation. Finally, the Section 4 encompasses an examination of limitations encountered during the study, proposes potential avenues for future research, and draws overall conclusions.
## 3 Results  
In total, 389 articles were included in primary search, with eight of them meeting the criteria. All the articles used a cohort study design, as shown in Figure 2. From the initial pool of articles, 123 were excluded because they did not meet the required criteria. Following title screening, 227 of the 266 articles were excluded because they were reviews, irrelevant studies, or used treatment modalities other than chemoradiotherapy (chemotherapy, radiotherapy, or surgical resection). The abstracts of the remaining 39 articles were reviewed, and 24 were excluded for reasons such as lack of relevance or lack of focus on the recurrence of OSCC. Following a review of the full texts, seven of the remaining 15 articles were eliminated. The main reasons for their exclusion were a failure to focus on the prognostic significance of the biomarkers and the inclusion of irrelevant biomarkers. Following the final screening, eight articles were chosen. Since there were not enough publications on the molecular expressions of p53, cyclin D1, and EGFR in recurrent OSCC after chemoradiation, other molecular markers were included. The tables below show the main outcomes and general aspects of the included studies (Tables 1-5). \begin{table}
\begin{tabular}{c c c c} \hline
**Authors** & **Objectives** & **Methodology** & **Statistical Analysis** & **Limitations** \\ \hline \multirow{6}{*}{[PERSON] et al., 2001 [7]} & \multicolumn{3}{c}{A cohort of 59 patients who were exposed to CRT but were not recruited in the trial, as well as 80 patients from the experimental CRT arm of the randomized trial, were taken into account.} & \multirow{2}{*}{Lack of uniformity in HIC techniques in determining the cut off point for the markers, Mary other markers need to be employed to have a validated conclusion.} \\  & & & \\ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author** & **Biomaker Expression** & \begin{tabular}{c} **Biomaker Expression and** \\ **Clinographic Variables** \\ \end{tabular} & **Pathologic-Response** & **Treatment Response** & **Tanum Recurrence** & **Quality of Response** \\ \hline [PERSON] et al., 2015[20] & 27.5% low & \begin{tabular}{c} \\ 72.5% high \\ \end{tabular} & = & = & \begin{tabular}{c} As expression level \\ increases, response \\ decreases. \\ \end{tabular} & = & \begin{tabular}{c} Significant and inverse \\ correlation between EGFR \\ expression and QOL. \\ \end{tabular} \\ \hline [PERSON] et al., 2015[21] & 92.8\% & \begin{tabular}{c} EGFR did not exhibit \\ significant association. \\ \end{tabular} & = & \begin{tabular}{c} Significant association was \\ observed between strong \\ positive expression of EGFR \\ and partial response. \\ \end{tabular} & = & = \\ \hline [PERSON] et al., 2015[22] & \
\begin{tabular}{c} Out of 89 patients 13, 15 \& \\ 31 patients exhibited low, \\ moderate, and high \\ \end{tabular} & = & = &
\begin{tabular}{c} \\ - \\ - \\ \end{tabular} & = & = & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prognostic significance of EGFR.
# Effects of Polypropylene Fiber on the Liquefaction Resistance of Saturated Sand in Ring Shear Tests
###### Abstract  
This study focused on investigating the effects of polypropylene fiber on the liquefaction resistance of saturated sand. We performed a battery of tests with a state-of-the-art ring shear apparatus on fiber-reinforced saturated sand, considering the influences of fiber content and sand density. Two different shearing methods named shear-torque-controlled (STC) and cyclic-torque-controlled (CTC) were considered for carrying out the tests. An energy approach was chosen to evaluate the results, and the fiber reinforcement mechanisms were analyzed. Our test results showed that in STC tests, the shear strength and shearing time of saturated sand increased proportionally to an increase of fiber content and sand density. The cycles required for liquefaction in CTC tests also increase with an increase in sand density and fiber content. The presence of fibers clearly increases the shear energy required for liquefaction. The shear energy increases with an increase in sand density and fiber content. Greater total shear energy is required in specimens with a higher density or larger fiber content. Fiber reinforcement in sand has acted as a spatial network in interlocking soil grains, thereby resulting in the necessity of more energy for overcoming the resistance during the shearing process. After performing the shearing test, the unreinforced specimen with loose structure collapsed totally, and the one with a dense structure collapsed partially, while fiber reinforcement specimens still maintained structural stability.  
saturated sand; fiber reinforcement; liquefaction resistance; energy approach; cyclic ring shear test +
Footnote †: journal: Applied Physics Letters
_Appl. Sci._**2015**, \(5\), 1272-1283; doi:10.3390/app5041272  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**Chitosan as a Natural Polymer for Heterogeneous Catalysts Support: A Short Review on Its Applications**  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Environmental Science and Engineering, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Taichung City 40704, Taiwan; E-Mails: [EMAIL_ADDRESS] (B.-Y.C.); [EMAIL_ADDRESS] (W.D.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +886-4-2359-0121 (ext. 33620); Fax: +886-4-2359-4276.  
Academic Editor: [PERSON]  
_Received: 5 October 2015 / Accepted: 10 November 2015 / Published: 17 November 2015_  
**Abstract:** Chitosan, a bio-based polymer which has similar characteristics to those of cellulose, exhibits cationic behavior in acidic solutions and strong affinity for metals ions. Thus, it has received increased attention for the preparation of heterogeneous catalysts. Recent studies demonstrated that chitosan-based catalysts had high sorption capacities, chelating activities, stability and versatility, which could be potentially applied as green reactants in various scientific and engineering applications. This study intends to review the recent development of chitosan-based catalysts, particularly in the aspects of the main mechanisms for preparing the materials and their applications in environmental green chemistry. Studies on the preparation of catalyst nanoparticles/nanospheres supported on chitosan were also reviewed.  
**Keywords:** chitosan; green chemistry; catalyst; polymer
# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
###### Abstract
In our case, we consider the need of relying in a VANET routing protocol for efficient transmission of the packets, and the reduction of the payload size of the data packets compared to the case of gathering all the sampled data. For this purpose, a delay function controls the time at which nodes start sending their sampled data. This function is designed in such a way that the transmission of the information is started by the nodes farther from the FC, and intermediate nodes take advantage of the received packets to append their sampled data as the data packet is forwarded. Finally, thanks to the use of CS technology, the new data is combined with the previously stored information without practically increasing the overall size of the packet. The remainder of this paper is organized as follows. Section 2 provides an overview of the operation of the CS technology. A review of the main related work is presented in Section 3. In Section 4, a thorough view of our proposal is described, detailing every phase of the harvesting protocol. The evaluation of our proposal by means of simulations is presented in Section 5. Finally, Section 6 concludes the paper.
## 2 Problem Formulation
### Sparse DOA Estimation Model  
In the sparse representation of DOA estimation, the overcomplete representation of the two-dimensional array manifold matrix \(A\) can be represented as:  
\[\overline{A}=[a(\theta_{1},\varphi_{1}),a(\theta_{2},\varphi_{2}),\cdots a( \theta_{k},\varphi_{k})\cdots,a(\theta_{P},\varphi_{P})] \tag{12}\]  
where \(\overline{A}\) is the overcomplete representation of \(A.a(\theta_{k},\varphi_{k})\) is the steering vector, \(k=1,2,\cdots,P\), \(P\geq N_{s}.\{(\theta_{1},\varphi_{1}),(\theta_{2},\varphi_{2}),\cdots,( \theta_{P},\varphi_{P})\}\) is the set of all potential source positions. In the sparse DOA estimation, \(\overline{A}\) is known and independent of the actual source positions.  
The source position is represented as a \(P\times 1\) vector, in which only \(N_{s}\) elements are nonzero, so the source is sparse in space. The number of nonzero elements in \(P\times 1\) vector corresponds to the angle position of the actual sound source. The array model of sparse representation can be expressed as:  
\[y(t)=\overline{A}s(t)+n(t) \tag{13}\]  
In summary, the sparse DOA estimation problem is to find out the position index of all nonzero elements in the \(P\times 1\) vector under the given observation vector \(y(t)\) and overcomplete array manifold matrix \(\overline{A}\), which is also the goal of this paper.  
From Equations (12) and (13), it can be found that the count of nonzero items is an ideal measure of sparsity, denoted by \(\|s\|_{0}\), which is called \(\ell_{0}\)-norm. However, this is a difficult combinatorial optimization problem, therefore, the \(\ell_{1}\)-norm is often adopted to replace the \(\ell_{0}\)-norm, thus the problem can be transformed into a convex optimization problem. In this case, the global optimal solution can be solved through linear programming, namely, sparse regularization. The solution can be expressed as:  
\[\overset{\wedge}{s}(\mu_{1})=\underset{S}{\text{argmin}}\big{\|}y-\overline {A}s\big{\|}_{2}^{2}+\mu_{1}\|s\|_{1} \tag{14}\]  
Equation (14) is a traditional sparse DOA estimation model named LASSO. When there are only a small number of sound sources, the model can significantly improve the resolution of sparse DOA estimation. However, when the sound source is a spatially extended source with obvious grouping and block sparse characteristics, the sparsity of a single numerical solution domain cannot show the block sparse characteristics well and cannot effectively represent the sparse of sources. Therefore, the total variation is introduced and a 2D total variation sparse DOA estimation method is proposed.
## 5 Proposed System
### Secure Broker Selection  
Our cluster broker architecture uses a secured link between the broker and the nodes in a cluster. Inside a cluster, the broker acts as a Certification Authority (CA), which provides a secured communication link within the intra-cluster by using symmetric encryption and a key-exchange protocol to prevent intrusion, based on Algorithm 2. The stenography method offers a secure connection from one cluster to another (intra-cluster) for communication. Each broker sends its address and security methods over a hidden channel, by maintaining a neighbor node and routing table based on Algorithm 3. The security methods travel through a hello message to another cluster; the broker will provide secure communication for the cluster-to-cluster architecture.  
```
1. Input: Select Prime Number \"p\", an element g which is the prime root of \"p\", and consider two nodes A and B
2. For any \"n\" node in the cluster
3. \"A\" select random integer X\({}_{\text{A}}\). < p
4. Compute
5. \(\text{Y}_{\text{A}}=\text{g}^{\text{X}_{\text{A}}}\) (mod p)
6. Set
7. Private Key\({}_{\text{A}}=\text{X}_{\text{A}}\)
8. \"B\" select random integer X\({}_{\text{B}}\). < p
9. Compute
10. \(\text{Y}_{\text{B}}=\text{g}^{\text{X}_{\text{B}}}\) (mod p)
11. Set
12. Private Key\({}_{\text{B}}=\text{X}_{\text{B}}\)
13. Broadcast \"Y\"
14. \"A\" compute
15. \(\text{K}=(\text{Y}_{\text{B}})^{\text{X}_{\text{A}}}\) (mod p)
16. \"B\" compute
17. \(\text{K}=(\text{Y}_{\text{A}})^{\text{X}_{\text{B}}}\) (mod p)
18. End for
```  
**Algorithm 2** Intra-Cluster Security  
```
1. For any \"n\" node
2. If (broker-node + \(W_{n}\)) exceeds then //W\({}_{n}\) is the Net Weight of a particular node
3. Listen (Address, Channel)
4. If (Listen + \(W_{n}\)) exceeds then
5. Update (Routing Table)
6. End if
7. End if
8. If (Listen) then
9. Stegosmg (Listen) //Compute steganography for listen hello message
10. Find (Channel, Address)
11. End if
12. If (Update)
13. Update Routing table
14. End if
15. End for
```  
**Algorithm 3** Inter-Cluster Security
## 3 Model Problems and Numerical Results
### Numerical Results for Model Problem 1  
#### 3.2.1 Rates  
Figure 15 shows the value of \(\mathbb{E}\left[P_{\ell}\right]\), \(\mathbb{E}\left[\Delta P_{\ell}\right]\), \(\mathbb{V}\left[P_{\ell}\right]\) and \(\mathbb{V}\left[\Delta P_{\ell}\right]\) as a function of \(\ell\) for a user requested tolerance on the RMSE of \(2\times 10^{-5}\) for a moderate and a high stochastic dimension. Note that \(\mathbb{E}\left[P_{\ell}\right]\) and \(\mathbb{V}\left[P_{\ell}\right]\) remain constant across the levels, while \(\mathbb{E}\left[\Delta P_{\ell}\right]\) and \(\mathbb{V}\left[\Delta P_{\ell}\right]\) decrease for increasing \(\ell\). However, we observe for P-ML(Q)MC for the moderate stochastic dimension, a slight increase of \(\mathbb{E}\left[\Delta P_{L}\right]\) with respect to \(\mathbb{E}\left[\Delta P_{L-1}\right]\). However, this does not impact the results seeing that the overall descend of \(\mathbb{E}\left[\Delta P_{\ell}\right]\) is guaranteed, i.e., \(\mathbb{E}\left[\Delta P_{0}\right]>\mathbb{E}\left[\Delta P_{L}\right]\). The rates \(\alpha\) and \(\beta\) are included in the figures for h-ML(Q)MC and p-ML(Q)MC. These rates respectively represent the slopes of \(\mathbb{E}\left[\Delta P_{\ell}\right]\) and \(\mathbb{V}\left[\Delta P_{\ell}\right]\). Figure 16 shows the cost of one solve per level, expressed as the runtime in seconds on a \(\log_{2}\) scale. It is logical that per increasing level, the cost increases. This is because the stiffness matrix of the problem becomes larger due to a higher number of finite elements used to discretize the problem for h-ML(Q)MC. For p-ML(Q)MC this is due to a higher number of nodes. We observe that the cost per increasing level for p-ML(Q)MC grows slower than for h-ML(Q)MC. Combining this with the fact that the values of \(\mathbb{V}\left[\Delta P_{\ell}\right]\) for p-ML(Q)MC are smaller than for h-ML(Q)MC, i.e., less samples are to be taken, we can expect that p-ML(Q)MC will outperform h-ML(Q)MC.  
Figure 14: The vertical displacements, indicated with arrows, of the bottom nodes as the QoI for model problem 1.  
Figure 13: Memoryless transformation used to generate the gamma random field.  
We can now evaluate in which regime, according to Theorem 1, h-ML(Q)MC and p-ML(Q)MC operate. We find for the moderate stochastic dimension that \(\beta>\gamma\) for both h- and p-ML(Q)MC. We thus expect the h-MLMC and p-MLMC cost to be proportional to \(\epsilon^{-2}\). For the high stochastic dimension, we find that \(\beta>\gamma\) for p-MLMC and \(\beta<\gamma\) for h-MLMC. We respectively expect a cost proportional to \(\epsilon^{-2}\) and \(\epsilon^{-2.05}\). The cost for p-MLQMC and h-MLQMC cannot be evaluated from this figure, because no rate \(\delta\) is available. The rate \(\delta\) appears as an exponent in the integration error bound for QMC, see [28], and is not shown here.  
Figure 16: Times for one solve per level for model problem 1.  
Figure 15: Variances and expected values for model problem 1.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 2 Related Work
The \"Emirati speech database\" [30] is the second database, in which 30 Emirati speakers (15 males and 15 females) were used as respondents and subjected to neutral, shouted, slow, loud, soft and fast talking conditions. Results based on different classifiers and compensators as reported in our previous results, Refs. [31; 32; 33] reported speaker identification performance under shouted/stressful talking conditions using the Emirati accent dataset. Their reported speaker identification performance in shouted/stressful talking conditions was 58.6%, 61.1%, 65.0%, 68%, 74.6%, 75%, 78.4%, 81.7%, 78.7%, 83.4%, and 85.8% based, respectively, on \"First-Order Hidden Markov Models (HMM1s), Second-Order Hidden Markov Models (HMM2s), Third-Order Hidden Markov Models (HMM3s), Second-Order Circular Hidden Markov Models (CHMM2s), First-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM1s), Suprasegmental Hidden Markov Models (SPRMMs), Second-Order Left-to-Right Suprasegmental Hidden Markov Models (LTRSPHMM3s), First-Order Circular Suprasegmental Hidden Markov Models (CSPHMM1s), Second-Order Circular Suprasegmental Hidden Markov Models (CSPHMM2s), and third-order circular suprasegmental Hidden Markov models (CSPHMM3s)\". Table 1 shows more comparison with previous studies. The main contributions of this study are as follows:  
* To the best of our knowledge, this is the first work that uses and evaluates an RBFNN-CNN model for speaker identification under stressful/emotional conditions. * We conduct extensive comparisons between traditional classifiers and deep learning models to predict the model that yields the highest performance. * We showed that the proposed RBFNN-CNN model outperforms other models.
## 5 Simulation Study
### Network Life Analysis  
Network life is a very important metric for DTMSNs, because the sensor nodes are generally energy-constrained. We assume the network ends when half of the sensor nodes exhaust their energy. The initial energy reserve of each sensor node is 10 J. The simulation results are presented in Table 5.  
We can see that the LABEL scheme enjoys the longest network lifetime, since sensors receive and transmit messages to destinations, or to nodes belonging to the same group as the destinations only. The flooding protocol has the shortest network lifetime. This is because too many message copies are generated and these copies consume much sensor energy. We also see that ARP achieves a longer network lifetime than flooding, which is mainly attributed to both its data delivery scheme and the effectiveness of the data delivery scheme. Though the network lifetime of ARP is shorter than LABEL, the data delivery ratio of ARP is much higher than that in LABEL scheme, demonstrating that the proposed ARP scheme can better deal with the tradeoff between the data delivery ratio and the delivery overhead than LABEL.