esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparative Analysis
### Coherence Bandwidth  
The measurement results in Table 2 indicate that the coherence bandwidths at the two frequency bands are similar. For simplicity, our analysis begins with the two-ray model. The antenna patterns are assumed to be similar for the different frequencies.  
For the LOS path, we can express the electric far field at a point \(u\) in space as [29]  
\[E(f,t)=\frac{\alpha_{s}(\theta,\psi,f)cos[2\pi f(t-\frac{d}{\varepsilon})]}{d}, \tag{8}\]  
where \(\alpha_{s}(\theta,\psi,f)\) is the radiation pattern of the TX antenna at frequency \(f\) in the direction \((\theta,\psi)\), \(d\) is the distance from the TX to the point \(u\), and the constant \(c\) is the speed of light.  
Because the phase difference between the two rays determines whether the two waves add constructively or destructively, we focus on the phase of the signal. Then, Equation (8) is simplified as  
\[E(f,t)=Acos(2\pi ft+\theta), \tag{9}\]  
where \(A\) equals \(\frac{\alpha_{s}(\theta,\psi,f)}{d}\), and \(\theta\) equals \(-2\pi f\frac{d}{\varepsilon}\).  
For the reflected path, the electric far field at the point \(u\) is expressed as  
\[E_{r}(f,t)=A_{r}cos[2\pi f(t+\delta t)+\theta], \tag{10}\]  
where \(A_{r}\) is the amplitude of the reflected signal, and \(\delta t\) is determined by the distance difference of the two rays. The phase difference between the two rays is  
\[\phi=2\pi f\delta t. \tag{11}\]  
For another frequency \(f^{\prime}\), the phase difference is  
Figure 7: (color online) Path loss versus link distance.  
\[\phi^{\prime}=2\pi f^{\prime}\delta t. \tag{12}\]  
The phase difference between the two rays, \(\phi\), determines the constructive or destructive interference pattern. Then, the difference of \(\phi\) for two frequencies, i.e., \(|\phi^{\prime}-\phi|\), should be insignificant within the coherence bandwidth. The difference of \(\phi\) is  
\[|\phi^{\prime}-\phi|=|2\pi\delta t(f^{\prime}-f)|. \tag{13}\]  
Equation (13) indicates that the coherence bandwidth is related to the relative frequency (\(|f^{\prime}-f|\)), not related to the absolute frequency, under the assumption of having similar antenna patterns over the frequency band. Therefore, the coherence bandwidths at 14.6 GHz and 3.6 GHz should be similar. The measurement results verify the above analysis.
# Degradation Characteristics and Reliability Assessment of 1310 nm VCSEL for Microwave Photonic Link
## 3 Discussions  
### Liv Characterization  
The accelerated life tests are performed to investigate the VCSELs degradation with electrical stress, and a typical sample is selected for analysis under each set of stresses. Variations of power and voltage with current at different electric stress are shown in Figure 2, indicating the light-current-voltage (L-I-V) curves of VCSELs with accelerated aging current at 6 mA, 8 mA and 10 mA, respectively. The black line represents the LIV curves before accelerated aging, and the red line represents the LIV curves after aging. As can be seen, the maximum output power of the device is 0.82 mW and 0.78 mW before and after aging at 6 mA, and the power is reduced by 5% of the initial value. At an electric stress of 8 mA, the maximum output power of the device is 0.68 mW and 0.64 mW before and after aging, and the power is reduced by 6% of the initial value. At an electric stress of 10 mA, the maximum output power of the device is 0.79 mW and 0.67 mW before and after aging, and the power is reduced by 15% of the initial value. The power saturation currents and V-I curves are basically unchanged, and the error for all measured values is less than 0.01 mW.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Temperature**(\({}^{\circ}\)**C**) & **Aging Current (mA)** & **Number of Samples** \\ \hline
35 & 6 & 3 \\
35 & 8 & 3 \\
35 & 10 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test conditions.  
Figure 2: \(Cont.\)
# Force-Based Self-Organizing MANET/FANET with a UAV Swarm
## 4 A Novel Self-Organizing Approach to Drone-Based MANETs
### Drone Movement
#### 4.1.1 Moving Towards Clients  
We model the individual \(x\)- or \(y\)-axis attraction exerted by any client \(j\in\{1,\dots,n\}\) on its node as a virtual force that decreases as the square of the Euclidean distance between them. This effectively means that closer clients have a stronger impact than those that are further away.  
Mathematical Modeling of the Attraction of Affiliated Clients  
\[f^{\prime}_{x_{i}} =\sum_{j=1}^{n}\frac{dx_{j,i}}{r_{j,i}^{3}} \tag{5}\] \[f^{\prime}_{y_{i}} =\sum_{j=1}^{n}\frac{dy_{j,i}}{r_{j,i}^{3}}\]
## Performance Improvement in Geographic Routing for Vehicular _Ad Hoc_ Networks  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 2**  
Wireless Communications and Networking Research Lab, School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India; E-Mails: [EMAIL_ADDRESS] (S.K.); [EMAIL_ADDRESS] (D.K.L.)  
Faculty of Computing, Universiti Teknologi Malausia (UTM), Skudai Johor 81310, Malaysia; E-Mails: [EMAIL_ADDRESS] (A.H.A.); [EMAIL_ADDRESS] (A.N.H.)  
Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +91-901-331-1302.  
External Editor: [PERSON]  
Received: 24 September 2014; in revised form: 19 November 2014 / Accepted: 20 November 2014 / Published: 25 November 2014  
Geographic routing is one of the most investigated themes by researchers for reliable and efficient dissemination of information in Vehicular _Ad Hoc_ Networks (VANETs). Recently, different Geographic Distance Routing (GEDIR) protocols have been suggested in the literature. These protocols focus on reducing the forwarding region towards destination to select the Next Hop Vehicles (NHV). Most of these protocols suffer from the problem of elevated one-hop link disconnection, high end-to-end delay and low throughput even at normal vehicle speed in high vehicle density environment. This paper proposes a Geographic Distance Routing protocol based on Segment vehicle, Link quality and Degree of connectivity (SLD-GEDIR). The protocol selects a reliable NHV using the criteria segment vehicles, one-hop link quality and degree of connectivity. The proposed protocol has been simulated in NS-2 and its performance has been compared with the state-of-the-art protocols: P-GEDIR, J-GEDIR and V-GEDIR. The empirical results clearly reveal that SLD-GEDIR has lower link disconnection and end-to-end delay, and higher throughput as compared to the state-of-the-art protocols. It should be noted that the performance of the proposed protocol is preserved irrespective of vehicle density and speed.  
keywords: geographic distance routing; vehicular _ad hoc_ networks; traffic environment; segment vehicle; link quality; degree of connectivity +
Footnote †: journal: Journal of the Royal Society Interface
## 2 Material and Methods
### Adsorption Equilibrium  
Once the equilibrium time was determined (\(t_{e}\) = 240 min), adsorption equilibrium experiments were conducted in order to determine the adsorption isotherms. These experiments were carried out at different initial paracetamol concentrations (15 mg L\({}^{-1}\)\(\leq\)\(Ci\)\(\leq\) 150 mg L\({}^{-1}\)). The solution volume was 50 mL and the adsorbent mass (\(m_{ads}\)) was 0.05 g in the case of _Synechocystis_ sp. and 0.005 g of activated carbon. All the experiments were run in triplicate in parallel with the corresponding triplicate controls (50 mL of paracetamol solution at the experimental \(C_{i}\) but in the absence of adsorbent) and blanks (50 mL of distilled water together with the corresponding mass of adsorbent). For each adsorption experiment, the amount of paracetamol adsorbed in the equilibrium (\(q_{e}\)) was determined as a function of the equilibrium concentration (\(C_{e}\)), according to the mass balance in Equation (4).  
\[q_{e}=\frac{(C_{i}-C_{e})}{m_{ads}}\times V \tag{4}\]  
Aiming to describe the equilibrium isotherms, the fittings of experimental results to Langmuir [22] and Freundlich [23] isotherm models were determined. In the equilibrium, the Langmuir isotherm assumes that the maximum adsorption capacity by an adsorbent corresponds to the saturation of the adsorption sites surface when covered by a monolayer of adsorbate molecules, which do not interact with each other. This model expresses the amount of adsorbed adsorbate depending on the maximum adsorption capacity of the adsorbent material (\(Q_{m}\)), and the Langmuir constant, related to the adsorption energy (\(K_{L}\))  
\[q_{e}=\frac{Q_{m}K_{L}C_{e}}{1+K_{L}C_{e}} \tag{5}\]  
On the other hand, [PERSON] proposed an empiric model for the case of heterogeneous surface energies, corresponding to an exponential distribution of the adsorption heats. This model is defined by the Freundlich constant (\(K_{f}\)) and the constant related to the intensity of the adsorption process (\(N\)).  
\[q_{e}=K_{f}C_{e}^{\frac{1}{N}} \tag{6}\]  
The fittings of the equilibrium experimental results to Langmuir and Freundlich isotherms were obtained by non-lineal regression using GraphPad Prism.
## 2 Experimental
### Characterization Techniques  
Methylene blue (MB) adsorption was used to quantify the CEC and the total SSA of the zeolites. A MB solution was prepared by dissolving 1 g of MB in 200 mL of distilled water. In parallel, 50 mg of the oven-dried zeolite was dispersed in 10 mL of distilled water. Then, consecutive volumes of 0.5 mL of the MB solution were added to the zeolite dispersion. After addition of each 0.5-mL aliquot of the MB solution, the mixture was homogenized by magnetic stirring for 1 min. Then, a drop was removed from the dispersion and placed onto Fisher brand filter paper, until a permanent blue halo of unadsorbed MB appeared on the filter paper. The SSA was determined from the MB amount required to reach the end-point, according to the equation  
\[\text{CEC}=\frac{[\text{MB}]\times V}{W}\times 100 \tag{1}\]  
where CEC is the cation exchange capacity (mEq\(\cdot\)100 g\({}^{-1}\)), [MB] is the MB solution concentration (mEq\(\cdot\)L\({}^{-1}\)), \(V\) (L) is the volume of MB solution used in the analysis, and \(W\) (g) is the mass of zeolite used in the experiment.  
The SSA was calculated according to the Hang and Brindley method; Equation (2) was applied [22]  
\[\text{SSA}=F_{MB}\times\text{CEC} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} Molecule & & & \\  & MB  
where SSA is the specific surface area (m\({}^{2}\cdot\)g\({}^{-1}\)), and \(F_{MB}\) is the normalized value of the MB molecule surface area in m\({}^{2}\).  
Powder X-ray diffraction (PXRD) patterns of non-oriented powder samples were recorded in a Siemens D-500 diffractometer (Siemens Espana, Madrid, Spain) with Ni-filtered Cu K\(\alpha\) radiation, working at 40 kV and 30 mA, at a scanning speed of 2\({}^{\circ}\)/min. Element chemical analyses were carried out by inductively coupled plasma-atomic emission spectrometry (ICP-AES) at Activation Laboratories Ltd. (Ancaster, ON, Canada). Infrared (FT-IR) spectra were recorded in the 350-4000 cm\({}^{-1}\) range in a Spectrum-One Spectrometer (Perkin-Elmer, Waltham, MA, USA). The KBr pellet technique was employed: about 1 mg of the sample and 300 mg of KBr were used to prepare the pellets. Thermogravimetric (TG) and differential thermal (DTA) analyses were conducted in a SDT Q600  Simultaneous DTA-TGA-DSC equipment (Thermal Analysis TA, New Castle, PA, USA), from 25 to 900 \({}^{\circ}\)C, under nitrogen atmosphere, at a heating rate of 20 \({}^{\circ}\)C/min. The BET specific surface areas were determined from the corresponding nitrogen adsorption data at \(-\)196 \({}^{\circ}\)C, measured in an ASAP 2020 analyzer (Micrometrics, Norcross, GA, USA); 0.2 g of the sample was used. The samples were previously degassed at 200 \({}^{\circ}\)C for 24 h at a pressure lower than 50 \(\upmu\)mHg. Scanning electron microscopy (SEM) photographs of the materials were obtained in a digital scanning microscope DSM 960 from (Zeiss, Carl Zeiss Iberia--Division Microscopy, Madrid, Spain); the samples had been coated with a thin gold layer by evaporation.
## 1 Introduction  
_Helicoverpa armigera_ (Hubner) (Lepidoptera: Nocutiidae) is one of the most harmful agricultural pests [1, 2]. It is a cosmopolitan species with presence in Europe, Asia, Africa, and Oceania [3], and it is known as a polyphagous moth feeding on a plethora of important crops such as cotton, tomato, sorghum, chickpea, and others [4]. Eggs are laid on fruits and flowers and hatched larvae feed on plant tissue, causing significant damage [5, 6]. Another parameter that ranks _H. armigera_ among the most serious crop pests is its extraordinary resistance to synthetic insecticides [7, 8, 9, 10].  
Entomopathogenic microorganisms such us fungi, bacteria, viruses, and protozoa are among the most promising alternatives to chemical insect control because of their high efficacy and compatibility with other IPM methods [11, 12, 13, 14]. Entomopathogenic fungi (EPF) are natural components of the ecosystem with low mammalian toxicity [15, 16]. Additionally, they may grow on cadavers reintroducing more inoculum into the system. Due to this, long term residual persistence, a major disadvantage of conventional insecticides, is desirable for EPF [17].  
The focus of research concerning _Fusarium_ genus is reasonably limited to the phytopathogenic species such as _Fusarium graminearum_ Schwabe (anamorph) (Hypocreales: Nectriaceae) and _F. oxysporum_ [PERSON] (Hypocreales: Nectriaceae) as they cause diseases of great economic importance [18, 19]. Among entomopathogenic fungi, _Fusarium_ can parasitize species of many major insect orders such as Coleoptera, Lepidoptera, Hemiptera, and others [20,21]. It has been well documented that they can be effective against various classes of insect pests [20, 21, 22, 23]. There are species of _Fusarium_ that can synthesize secondary metabolites with insecticidal action (e.g., Beauvericin), which helps the fungus control its target pest [19,24]. Beauvericin is a cyclic hexadepsipeptide and a common EF metabolite with insect toxicity [25, 26, 27].  
Although the potential effects of the entomopathogenic _Fusarium_ species have not been thoroughly studied yet, the fact that _Fusarium_ isolates can cause high insect mortality is undoubftul [28]. Additionally, the advantages of EPF on host specificity and safety towards plants, since the ability to parasitize requires specific adaptations to the host, has been well described in the literature. [21, 29, 30, 31]. The aim of the investigation was to determine the pathogenicity of several _Fusarium_ species, isolated from soil on _H. armigera_ larvae under field conditions. We also discuss the key issues regarding the use of _Fusarium_ species as biocontrol agents against crop pests.
# Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network
## 4 Discussion
### Experiments and Comparative Analysis  
#### 4.3.1 Ablation Experiments  
In order to verify the effectiveness of the illumination maps in dual-stream and C-RPN, the ablation experiments with various frameworks are evaluated on synthetic dataset. The 'GE stream', 'IIC stream' and 'image stream' are, respectively, single stream for Mask R-CNN network with the input of GE, IIC and image. The 'Dual-stream (GE + Image)' and 'Dual-stream (IIC + Image)' are double streams with MFPN. The 'Ours (GE)' and 'Ours (IIC)' are proposed framework with the input of GE and IIC. The comparison results are shown in Table 2, where the bold entities denote the greatest performance. The AP score of dual-stream network with the input of 'IIC + Image' is almost 8.1%, 7.7% and 2.6% higher than the single-stream with the input of GE, IIC and image, which indicates that the illumination inconsistent feature and the image feature are complementary. In addition, our proposed method with 'IIC' is 3.6% higher than dual-stream with 'IIC + image', which indicates that  
Figure 4: Visualization training loss in (**a**) IIC and (**b**) GE.  
the application of MFPN fuses the low-level and high-level features to provide sufficient tampering features. In addition, the adaptive anchor size by applied C-RPN is conducive to return appropriate bounding boxes for improving splicing forgery location.
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 3 Materials and Methods
### Representation and Construction of Tourist Attraction Multidimensional Association Network
 \tag{12}\]  
where \(I\) and \(J\) represent the collections of association itemsets containing attractions \(i\) and \(j\). For instance, (Hankou Riverside, Yellow Crane Tower, Yangtze River Bridge) constitutes a single association itemset. \(S_{l}\) is the support degree of itemset \(I\), reflecting its frequency of occurrence across all reviews. \(N\) is the number of all association itemsets, and \(Count(I)\) is the number of itemsets containing attraction \(i\). The thematic-sentiment relationship encapsulates the similarity in emotional responses evoked by the core characteristics of an attraction, constituting an implicit semantic association. This association is quantified by constructing a sentiment-weighted thematic feature vector (TSV), which integrates topic distribution vectors with sentiment weight vectors, and assessing thematic-sentiment similarity (TS) via the JS divergence [43], reflecting the degree of alignment in affective experiences across attractions:  
\[ \begin{split} TS_{ij}=ReLU\left(1-\frac{JS(TSV_{i}||TSV_{j})}{ \theta_{ts}}\right),\\ TSV_{i}=\frac{dist_{i}\odot weight_{i}}{\left\|dist_{i}\odot weight _{i}\right\|_{1}},\end{split}  \tag{13}\]  
where \(TSV_{i}\) is the sentiment-weighted modified thematic-sentiment feature vector; \(dist_{i}\) is the Thematic Distribution Vector (TDV) of attraction \(i\). \(weight_{i}\) is the Sentiment Weight Vector (SWV) of attraction \(i\). \(\theta_{ts}\) is the thematic-sentiment distance threshold. A multidimensional association quantification model is developed by integrating SP, RC, and TS, effectively transcending the constraints of traditional single-dimension approaches. The static spatial relationships are complemented by dynamic semantic associations derived from tourists' cognitive and affective behaviors, enabling joint computation of spatial and semantic linkages among attractions and improving the model's adaptability to behavioral dynamics. Additionally, an explicit weighting strategy is employed to construct the MAN-SRT framework, which lays the structural groundwork for subsequent travel route recommendation systems.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## 3 Results and Discussion
### The Impact of Eclipsing Satellites on GNSS PPP
#### 3.3.4 The Effect of the Satellite Geometry Structure
One of the reason for this slight reduction is that only when eclipsing satellites appear in the stage of the convergence may influence the convergence time.
## 3 Experimental Results
### Correlation between \(\gamma_{\rm r}\) and \(t\), \(\sigma_{\rm c}\)  
In this study, the relationship between \(\gamma_{\rm r}\) and the treatment time or confining stress waves examined. Figure 9a illustrates the correlation between the reference strain and the treatment time. The reference shear strain can be described as a power function of processing time:  
\[\gamma_{\rm r}=1/(a+b\cdot t^{\rm c}) \tag{9}\]  
where \(t\) is the treatment time, and \(a\), \(b\), and \(c\) are the fitting parameters. The fitting parameters \(a\) and \(b\) decrease from 3503.8 to 2396.9 and 383.9 to 106.2, respectively, and \(c\) increases from 0.74 to 0.92 as the confining stress increases from 25 kPa to 100 kPa.  
Figure 9b indicates the correlation between the reference shear strain and the confining stress. The \(\gamma_{\rm r}\) increases as \(\sigma_{\rm c}\) rises, and the difference in \(\gamma_{\rm r}\) between various treatment times grows with the increase in \(\sigma_{\rm c}\). This also demonstrates that the confining stress still significantly influences the shear modulus attenuation curve of the sand sample with a lower cementation level. In addition, as shown in Figure 9b, the difference of \(\gamma_{\rm r}\) under various confining stresses decreases gradually as the treatment time rises, indicating that the effect of \(\sigma_{\rm c}\) on the attenuation of the shear modulus drops during the treatment process. The relationship between \(\gamma_{\rm r}\) and \(\sigma_{\rm c}\) is linear and can be described by Equation (10), and the fitting _R-square_ for all cases is greater than 0.99:  
\[\gamma_{\rm r}=\delta\cdot\sigma_{\rm c}+\phi \tag{10}\]  
where \(\delta\) and \(\phi\) are the fitting parameters. Both \(\delta\) and \(\phi\) drop gradually as the treatment time increases, resulting in a decrease in the slope and intercept of \(\gamma_{\rm r}\)-\(\sigma_{\rm c}\).  
\begin{table}
\begin{tabular}{r c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Confining Stress, \(\sigma_{\rm c}\)**} \\ \cline{2-10} Treatment Time, \(t\) (b) & \multicolumn{3}{c}{**25 kPa**} & \multicolumn{3}{c}{**50 kPa**} & \multicolumn{3}{c}{**100 kPa**} \\ \cline{2-10}  & \(\zeta\) & \(\gamma_{\rm r}\)/\(\gamma_{\rm r}\) & \(\mathbb{R}^{2}\) & \(\zeta\) & \(\gamma_{\rm r}\)/\(\gamma_{\rm r}\) & \(\mathbb{R}^{2}\) & \(\zeta\) & \(\gamma_{\rm r}\)/\(\gamma_{\rm r}\) & \(\mathbb{R}^{2}\) \\ \hline
6 & 0.93 & 0.01989 & 0.997 & 0.93 & 0.02389 & 0.997 & 0.88 & 0.03362 & 0.991 \\
12 & 0.95 & 0.01844 & 0.997 & 0.92 & 0.02186 & 0.996 & 0.84 & 0.02990 & 0.984 \\
18 & 0.94 & 0.01423 & 0.996 & 0.90 & 0.01893 & 0.996 & 0.83 & 0.02590 & 0.998 \\
24 & 0.97 & 0.01264 & 0.992 & 0.92 &  
It should be noted the empirical relationships presented in this study are only applicable to MICP-treated calcareous sand. The applicability of these empirical formulas to pure calcareous sand needs further study and verification.
# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 4 Proposed Trust Model  
This section presents the trust model for trust estimation in MASs, where agents' behaviors might differ over time. The proposed method relies on direct trust learned from direct experience and indirect reputation by integrating all indirect experience provided by third-party agents (witness). We study the confidence of witnesses and then combine both direct trust and indirect reputation. In more detail, the direct satisfaction statement based on interactive feedback is first illustrated, mainly emphasizing that recent interactions are more prominent by assigning higher weight. In this way, the proposed method is fitting for dynamic trust estimation. The uncertainty that stems from randomness, incomplete information, and inefficient interactions is then addressed, and the Dempster-Shafer theory of evidence is employed to represent direct trust. We then studied the confidence of individual evidence to determine indirect reputation. Subsequently, direct trust and indirect reputation are combined, and we reach the resource provider's trustworthiness. The agent with the highest trust value is selected to raise interactions. The credibility of witnesses is updated by the interactive feedback and the average evaluation after the interaction. Finally, credits are reassigned to witnesses, with which they are able to purchase information for trustee evaluations. In this way, information providers are motivated to present accurate information. For simplicity, the detailed processes are shown in Figure 1, and some related parameters are listed in Table 2.  
\begin{table}
\begin{tabular}{c l} \hline \hline \(c_{i}\) & The resource customer agent \(c_{i}\) \\ \hline \(s_{i}\) & The resource supplier agent \(s_{i}\) \\ \hline \(w_{k}\) & The information provider (witness) \(w_{k}\) \\ \hline \(Scur_{ij}(t)\) & The evaluation of the \(t\)th interaction \\ \hline \(S_{ij}(t)\) & The satisfaction degree before the \(t\)th interaction \\ \hline \(\mu_{ij}(t)\) & The relationship between evaluation and current satisfaction \\ \hline \(fq_{ij}(R)\) & The uncertainty factor \\ \hline \hline \end{tabular}
\end{table}
Table 2: Explanation of main variables.  
Figure 1: Trust estimation processes.