esa-sceva/satcom-chunk-collection huggingface dataset:
# A New BAT and PageRank Algorithm for Propagation Probability in Social Networks
## 4 Simulation Experiment
### Results and Discussion
#### 4.2.1 Neighbors and Propagation Probability of Node \(i\)  
From the results of \(R_{1}\) and \(R_{2}\) in Table 14, if the adjacent nodes of two different nodes are the same, the probability of propagating to \(N_{page}\) nodes is the same; that is, \(Pr(i,N_{page})=Pr(j,N_{page})\) if \(V(i)=V(j)\). For example, in Figure 7, the adjacent nodes of node 1 and node 4 are node 5 and node 3, that is \(V(1)=V(4)\). At this time, the probability of propagating to \(N_{page}\) = 1, 2, \(\ldots\), 8 is all the same that are 1.0000, 0.9079, 0.9007, 0.8878, 0.8536, 0.7997, 0.6704 and 0.3942, respectively. However, in \(R_{3}\), because the personalized vector changes the assigned weight of node 1, the PageRank value of node 1 is increased resulting in different propagation probabilities even if the adjacent nodes of node 1 and node 4 are the same. Therefore, it can be judged that if the adjacent connected nodes are the same and the PageRank values of the nodes are the same, the probability of the final propagation to \(N_{page}\) nodes is the same although the propagation paths are different. And the propagation probability is different if the PageRank values are different due to the intervention of the personalized vector.  
The total number of adjacent nodes also affects the propagation probability. It can be seen from Table 14 that when the number of adjacent nodes is propagated to a smaller number of \(N_{page}\), the node with more total adjacent nodes has a higher propagation probability; on the contrary, when it propagates to a larger number of \(N_{page}\), the node with fewer total adjacent nodes has a higher propagation probability. For example, the total number of adjacent nodes of node 3 and node 5 are the first and the second, respectively, which are the nodes with the highest total number of adjacent nodes in Figure 7. Whether in Case 1, Case 2 or Case 3, when \(N_{page}\) = 0, 1, \(\ldots\), 4, node 3 and node 5 have higher propagation probabilities than other nodes--especially node 3, which has the largest number of adjacent nodes. However, when \(N_{page}\) = 8, node 3 has the lowest propagation probability compared to other nodes. On the contrary, the total number of adjacent nodes of node 0, node 1, node 4 and node 6 is 2, which is the lowest total number of adjacent nodes in Figure 7. When \(N_{page}\) = 0, 1, \(\ldots\),7, node 0 has the lowest propagation probability compared to other nodes. However, when \(N_{page}\) =8, the propagation probability of node 0 compared to other nodes: \(R_{1}\) = 0.4056 and \(R_{3}\) = 0.4214 ranks first, and \(R_{2}\) = 0.3947 ranks second. Hence, the node with a larger total number of adjacent nodes can be selected as the source node when a small range must be propagated. However, in the final stage of propagation, nodes with fewer adjacent nodes have a higher probability of propagating to more pages.
# Context-Aware Trust and Reputation Routing Protocol for Opportunistic IoT Networks
## 1 Introduction
### Main Contribution  
This paper presents a novel routing protocol (referred to as Context-Aware Trust and Reputation Routing (CATR)) protocol for OpploT networks, which utilizes the popular beta distribution and node's context information to compute the trust and reputation of a node. This value is then compared with a specific predefined threshold to determine whether that node is a suitable forwarder or not for the message. In addition, an effective security model based on trust rating and reputation is also proposed, which systematically identifies and isolates those nodes with malicious intent, preventing them from participating in the data forwarding process. The main contributions are as follows.  
* This article addresses the network routing issues in OpploT scenarios, proposing a solution to ensure the stability of the system.
* We propose a Context-Aware Trust and Reputation Routing (_CATR_) protocol and verify its performance using the real CRAWDAD dataset Cambridge/haggle (29 May 2009).
* We have evaluated our proposed CATR routing protocol for OpploT using an Opportunistic Network Environment (ONE) simulator [5] and compared its performance against that of the Epidemic [6], BTRES [7], and PPHB+ [8] protocols.  
The rest of this paper is organized as follows. Section 2 discusses some representative routing protocols for OpploT networks. Section 3 describes our proposed CATR routing model for OpploT networks. Section 4 describes the simulation experiments conducted to evaluate the proposed CATR protocol and the results obtained. Finally, Section 5 concludes this paper.
###### Abstract  
The damaging competition between crops and parasitic weeds has a negative impact on agricultural productivity; however, the impact of disturbance on the soil's microbial community has received less attention. Hence, this study investigates the microbial composition and diversity of the maize rhizosphere infected with _Striga hermorthica_ using a shotgun sequencing approach from two maize-growing fields (Eruwa, Nigeria and Mbuzini, South Africa). The rhizosphere soil DNA was extracted from infested soil using a Nucleospin soil genomic DNA extraction kit and sequenced on an Illumina platform. The dominant phyla were Actinobacteria, Bacteroidetes, Deinococcus-Thermus, Acidobacteria, Chloroflex, Cyanobacteria, Planctomycetes, Vermucomicrobia, Chlorobi, Proteobacteria, Firmicutes, Nitrospirae, Thermotogae, Synerggistetes, Ascomycato, Euryarchaeota, and Crenarchaeota. Bacteria phyla were observed to be of higher proportion in the rhizosphere soil samples obtained from _Striga_-infested maize field in Eruwa (Es) than those recovered from Mbuzini (Ms). The alpha diversity of microbial communities indicated insignificance differences (\(p>0.05\)) between the five taxonomical groups (phylum, class, order, family, and genus), while the beta diversity produced a significant (\(p=0.01\), \(\mathrm{R}=0.52\)) difference in the microbial diversity of the infested soil. In summary, the study sheds light on the diversity and composition of the microbiome of _Striga hermorthica_-infested soil, which influences the microbial functions in the management and sustenance of plant health against parasitic weeds.  
parasitic weed; microbial distribution; shotgun sequencing; plant health; infested soil Article
# A Comparative Study of Control Methods for X3D Quadrotor Feedback Trajectory Control
## 3 X3D Quadrotor Controller Design
### LQR Control System  
The linear quadratic regulator (LQR) controller has been widely used in engineering applications such as voltage source inverters and wheeled inverted pendulum vehicles [25]. There is comparably little research on using the LQR control technique to track and stabilize the quadrotor. The LQR control system is designed to give optimum control while being cost-effective [26]. Owing to the significant uncertainties and nonlinearities in quadrotor dynamics, as well as model unreliability due to parameter fluctuations and linearity approximation, implementing the LQR control system on the quadrotor is a difficult undertaking. As a result, the quadrotor system dynamics are described by a linear state-space equation, with a quadratic cost function as the least appropriate cost function [27]. Equations (19) and (20) depicts the form of the continuous state-space model:  
\[\dot{x}_{s}(t)=Ax_{s}(t)+Bu(t) \tag{19}\]  
\[y(t)=Cx_{s}(t)+Du(t) \tag{20}\]  
To develop the continuous state-space model, the X3D's linearized state-space Equation (2  
control the X and Y positions, respectively. All six fuzzy logical controllers use the same two identical inputs as described below.  
* Error \((e)\) denotes the difference between the desired and measured signals.
* Derivative error \((de)\) is the error rate.  
Figure 6 depicts the implementation of a fuzzy system for quadrotor control. Error is stabilized between [\(-\)1, \(+\)1] and [\(-\)3, \(+\)3], whereas the error rate is stabilized between [\(-\)3, \(+\)3].  
The output with three fuzzy logic values (N, Z, and P) is used for the input variables e and de, as indicated in Table 4; N stands for negative, Z for zero, and P for positive. Table 1 explains the rules: If the error \((e)\) is negative and the rate of error \((de)\) is negative, then the output will be negative. Figure 7 depicts all of the FCs' controller inputs as well as membership functions.  
The surface view of all Fuzzy controllers is shown in Figures 8 and 9.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Error (\(e\))**} \\ \hline  & & \(FC\left(X\right)\), \(FC\left(Y\right)\), \(FC\left(\phi\right)\) & \\ \cline{2-4}  & P & Z & N \\ \cline{2-4}  & P & P & P & Z \\ \cline{2-4}  & Z & P & Z & N \\ \cline{2-4} Rate of Error \((de)\) & N & Z & N & N \\ \cline{2-4}  & & \(FC\left(Z\right)\), \(FC\left(\psi\right)\) & \\ \cline{2-4}  & P & Z & N \\ \cline{2-4}  & P & Z & Z & N \\ \cline{2-4}  & Z & Z & N & P \\ \cline{2-4}  & N & N & P & P \\ \hline \hline \end{tabular}
\end{table}
Table 4: X3D Quadrotor’s Fuzzy Rules.  
Figure 6: X3D Quadrotor Control using a Fuzzy Controller.
# Experimental Demonstration of 3 \(\times\) 3 MIMO LED-to-LED Communication Using RGB Colors
## 2 Digital Signal Processing Model of the 3 \(\times\) 3 MIMO LED-to-LED System
The emitted light signals then propagate through the free space channel to the receiver LEDs. The free space channel impulse response from the \(q\)th transmitter LED to the \(p\)th receiver LED is provided as:  
\[\mathbf{h}_{p,q}=\left[ \begin{array}{cc}h_{p,q,0}&h_{p,q,1}\dots&h_{p,q,L-1} \end{array} \right] \tag{5}\]  
where \(L\) denotes the length of the channel impulse response. In indoor OWC, the gain of non-line-of-sight (NLOS) components is relatively low compared to the gain of the line-of-sight (LOS) component, and the channel frequency response is flat from DC to 3 dB channel bandwidth [21]. Thereby, the channel impulse response can be approximated as LOS DC channel gain:  
\[\mathbf{h}_{p,q}\approx h_{p,q,0}=h_{p,q}^{DC} \tag{6}\]  
The DC channel gain can be calculated as follows:  
\[h_{p,q}^{DC}=\left\{ \begin{array}{cc}\frac{A_{p}}{d_{p,q}^{2}}I\big{(}\phi_{ q}\big{)}\cos\big{(}\varphi_{p,q}\big{)},&0\leq\varphi_{p,q}\leq\varphi_{p,c}\\ 0,&\varphi_{p,q}>\varphi_{p,c}\end{array} \right. \tag{7}\]  
where \(\varphi_{p,c}\) is the field of view (FOV) of the \(p\)th received LED, and \(A_{p}\) is the \(p\)th receiver LED's collection area, as follows:  
\[A_{p}=\frac{a^{2}A_{LED,p}}{\sin\varphi_{p,c}} \tag{8}\]  
where \(a\) is the concentrator refractive index of the \(p\)th LED, and \(A_{LED,p}\) is the \(p\)th LED area. The received \(n\)th time domain signal at the \(p\)th receiver LED can be represented as:  
\[r_{p,n}=\sum_{q=1}^{N_{t}}P_{q,n}\cdot h_{p,q}^{DC}\cdot R_{p,q}+w_{p,n}=\sum _{q=1}^{N_{t}}\big{(}x_{q,n}+DC_{q}\big{)}\cdot C_{E/E,p,q}+w_{p,n} \tag{9}\]  
where \(R_{p,q}\) denotes responsivity of the \(p\)th receiver at the \(q\)th transmitter LED's wavelength, \(P_{q,n}\) is the optical power of the \(q\)th transmitter LED at the \(n\)th OFDM symbol, \(w_{p,n}\) is the mean square noise current, and \(C_{E/E,p,q}\) is the electrical current-to-electrical current (E/E) conversion gain. Finally, the \(n\)th transmitted OFDM symbol can be recovered by the zero-forcing (ZF) equalizer:  
\[\hat{\mathbf{x}}_{n}=\mathbf{H}^{-1}\mathbf{r}_{n} \tag{10}\]  
The rest of the demodulation process can be performed in the reverse order of the modulation process.
# Impact of Frost on the Morphology and Chemical Composition of _cv_. [PERSON]
## 3 Results and Discussion
### Tocopherols, Total Phenols Content, and Antioxidant Activity
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **No Frost** & **With Frost** & _p-Values_ \\ \hline \multicolumn{4}{c}{Tocopherols content (mg/kgoil)} \\ \(\alpha\)-tocopherol & 248 \(\pm\) 38 & 203 \(\pm\) 34 & 0.003 \\ \(\beta\)-tocopherol & 2.5 \(\pm\) 0.4 & 2.2 \(\pm\) 0.5 & 0.098 \\ \(\gamma\)-tocopherol & 18 \(\pm\) 2 & 17 \(\pm\) 2 & 0.199 \\ \(\Sigma\) tocopherols & 268 \(\pm\) 40 & 222 \(\pm\) 34 & 0.003 \\ \hline Total Phenols content (TPC) & 4861 \(\pm\) 657 & 1469 \(\pm\) 536 & \textless{}0.001 \\ (mg GAE/kgresBulp) & & & \\ \hline DPPH assay (inhibition\%) & 55 \(\pm\) 7 & 32 \(\pm\) 8 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Tocopherolols contents, total phenols content, and DPPH radical scavenging inhibition of olives affected by frost and not affected by frost (mean \(\pm\) standard deviation).
# Cryobiotechnology of Plants: A Hot Topic Not Only for Gene Banks
## 2 Cryopreservation of Plants
### Plant Cryopreservation Methods
#### 2.1.3 Encapsulation-Dehydration  
This method was first reported by [PERSON] and [PERSON] (1990) [26] and combines the encapsulation of plant samples with alginate beads and physical dehydration carried out with silica gel or in the air flow in a laminar flow cabinet [1]. Applying this method, plant material is precultured with 0.3-0.6 M sucrose medium for one to three days and then incubated in a liquid medium supplemented with sucrose and sodium alginate. Finally, this mixture is released drop by drop into liquid medium containing calcium chloride. Alginate beads with explants encapsulated inside them are formed during this process. The bead formation is followed by culturing in highly concentrated sucrose solution to achieve the osmoprotection, subsequent physical dehydration to a water content of 20%-30%, and direct immersion in liquid nitrogen (Figure 2) [26]. The encapsulation-dehydration method is relatively simple; however, it requires more time-consuming handling of encapsulated samples. The main advantage of this method is elimination of the need for other cryoprotectants such as DMSO and ethylene glycol that could be toxic for plants and could cause genetic changes after regrowth [22].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Plant Species** &
\begin{tabular}{c} **Main** \\ **Advantage** \\ \end{tabular} & **Main Disadvantage** & **Survival Rate** & **References** \\ \hline slow freezing & _Asparagus_ & high survival & need of a & 100\% & [PERSON] et al. 1983 [15] \\ simple one-step & _Solanum_ & no special & & & & Grout and \\ freezing & _goniocalyx_ & equipment & low survival rate & 20\% & [PERSON] 1978 \\  & _Citrus sinensis_ & & not suitable for all & & [16] \\ vitrification & _Osb._ (nuellar cells) & relatively easy & plant species (without & & [PERSON] et al. 1990 [21] \\  & & to carry out & additional & & \\  & & & osmoprotection) & & \\ encapsulation- & _Wasabia japonica_ & better & & & \\ vitrification & _Wasabia japonica_ & protection of & more laborious method & 95\% & [PERSON] et al. 1995 [23] \\  & & & shoot tips & & \\ encapsulation- & _Solanum phureja_ & no need of toxic & time-consuming & & Fabre and \\ dehydration & _Solanum phureja_ & cryoprotectants & method & & [PERSON] 1990 [26] \\ cryo-plate & _Tancactum_ & easy method to & not suitable for all & & \\ methods & _cinerarifolium_ & carry out with & plant species (toxic & 77\% & [PERSON] et al. 2011 [27] \\  & & more shoot tips & cryoprotectants) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of various cryopreservation methods.
# Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things
## 2 Related Works
The identified constraints within the distributed cloud environment, specifically the lack of consideration for certain parameters, will be addressed by refining the IES method \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Methods** & **Advantages** & **Disadvantages** \\ \hline
[7] & PSO \& FOA & Improves the convergence speed & High computation time in large-scale \\ \hline
[22] & Skyline + GA & Low computation time; high scalability & Lack of compatibility \\ \hline
[23] & GA + FOA & Low computation time & Restriction is in the distributed cloud environment \\ \hline
[24] & PSO + GA & High-speed convergence & High time complexity \\ \hline
[25] & PSO + FOA & Choose the right service & More resources needed \\ \hline
[6] & ABC + DE & High-speed convergence & Restrictions in multi-objective service selection \\ \hline
[5] & ABC + GA & Low computation time & Without considering the QoS parameter \\ \hline
[26] & ES + WOA & High-speed convergence & Restriction is in the distributed cloud environment. \\ \hline
[27] & CSA & High-speed convergence & Lack of fault tolerance \\ \hline This paper & IES & Reduced computation time in large-scale environments; balances functional and non-functional attributes & Testing needed for scalability in different cloud environments \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary and Comparative Analysis of Proposed IES with Existing Methods. ology to prioritize and enhance QoS attributes--non-functional attributes of WS--ensuring a more responsive and user-centric service selection process. In summary, our review of the existing literature reveals a concentrated effort on specific aspects of IoT service composition. The IES model presented in this study extends these efforts by providing a more balanced approach, focusing on both computational efficiency and service reliability, thus contributing novel insights to the field.
# A Hidden Fingerprint Device on an Opaque Display Panel
## 1 Introduction  
There are several biometric recognition systems that have been developed for applications in mobile phones, such as iris, facial and fingerprint recognition [1, 2]. Among them, fingerprint identification is very important method because of its permanence, uniqueness and reproducibility. It can be achieved by ultrasonic [3], capacitive and optical methods [4]. Optical fingerprint recognition (OFR) is the most widely used in access control systems because of its high image resolution and anti-spoofing capabilities [4, 5, 6, 7]. However, OFR cannot be used on mobile phones with opaque type displays (such as liquid crystal displays, LCDs) due to the following three main reasons. First, the device is hard to integrate into a mobile phone due to its bulkiness [1]. For example, optical coherence tomography (OCT) OFR technology [8] can obtain good quality 3D fingerprint images and information, but complex and large optical architectures make it impossible to integrate into mobile phones. Secondly, the backlight module of the LCD panel is opaque, so OFR has difficulty penetrating the LCD panel directly. So far, optical fingerprints can only be used on mobile phones equipped with OLED displays. A wide angle, 3-plastic aspherical lens (total thickness \(<\)4 mm) is placed under the OLED display directly, and then the fingerprint on the screen is taken through this camera. Since the fingerprint image quality is affected by screen obscuration, the fingerprint image must be enhanced by special image processing. Thus, an LCD display phone can only use capacitive fingerprint unlocking solutions for now.  
The last issue is the appearance requirements for a mobile phone with a high screen ratio. Although there are some optical methods to overcome opaque display panels, they cannot meet the requirements of high screen ratios. Due to the reasons above, capacitive fingerprint methods are only available on opaque display phones [9, 10].  
In this paper, we have developed a method to overcome these challenges. The integration of a waveguide into the cover glass of the mobile phone can solve the problem of it not directly being able to penetrate the opaque display, and the concept of FTIR is applied to the cover glass to enhance the contrast of the fingerprint image. The concept of combining cover glass and FTIR has been applied to the design of touch panels [11]. Cameras are used to capture light scattered by the ridge of the finger,and image processing techniques are used to detect the position of finger pressing. However, we use the unscattered light in the cover glass to form the image of finger ridges and valleys. Furthermore, using the concept of optical projection magnification, the light beam diameter can reach the minimum diameter when entering and exiting the cover glass. This optical design concept can be applied used to meet the need for a high screen ratio.
# Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence
## 5 Experiments  
We carried out the sentence-to-video retrieval task on the benchmark dataset to evaluate the proposed method. The task is retrieving the video associated with the query sentence from the test videos. We calculated similarities over the test videos with the query sentence, and then we picked up videos according to the similarities in descending order.  
We reported the experimental results using rank-based performance metrics, i.e., Recall@_k_ and Median rank. The Recall@_k_ calculates the percentage of the correct video in top-\(k\) retrieved results. In this paper, we set \(k=1,5,10\). Median rank calculates the median of the ground-truth results in the ranking. For Recall@_k_, the bigger value indicates better performance. When Median Rank is a lower value, retrieved results are closer to the ground-truth items. Therefore, a lower median rank means better retrieval performance.  
Following the convention in sentence-to-video retrieval, we used the Microsoft Research Video to Text dataset (MSR-VTT) [54], which is a large-scale video benchmark for video understanding. The MSR-VTT contains 10,000 video clips from YouTube with 41.2 h in total. The dataset provides videos in 20 categories, e.g., music, people, and sport. Each video is associated with 20 different description sentences. The dataset consists of 6513 videos for training, 497 videos for validation, and 2990 videos for testing.  
We evaluated four variants of the proposed method: single-space, sequential or I3D dual-space, and triple-space models. Firstly, the single-space model represents the proposed framework composed of the global visual and textual embedding networks. These two networks form a single embedding space. Then, we measured the final similarity in the single embedding space. Secondly, the sequential dual-space model (dual-S) is the proposed framework using two embedding spaces learned by the global and sequential visual networks, and textual embedding networks. We measured the final similarity by merging two similarities, \(\mathrm{s_{g}}\) and \(\mathrm{s_{s}}\), as described in Equation (11). Thirdly, the I3D dual-space model (dual-I) has global visual and I3D embedding networks. Lastly, the triple-space model added the I3D and textual embedding networks into the dual-space model. We produced the final similarity by merging \(\mathrm{s_{g}}\), \(\mathrm{s_{s}}\), and \(\mathrm{s_{e}}\) with the similarity aggregation.