esa-sceva/satcom-chunk-collection huggingface dataset:
# Examining the Effects of Induced Plant Defenses on _Spodoptera frugiperda_ Performance
###### Abstract  
Knowing the duration insect pests are in the environment is vital for growers to determine management schemes and apply treatments. Unfortunately, experiments to determine long-term insect performance across plant cultivars are infrequently conducted. With that in mind, we report here the performance of _Spodoptera frugiperda_, the fall armyworm, on jasmonic acid (JA) induced/non-induced soybean cultivars. JA induction increases plant defensive compounds and can be considered an equivalent to a plant's response to herbivory. _S. frugiperda_ is a global pest, with infestations in soybeans becoming an emerging problem, making information on this pest's performance on soybeans warranted. Thus, we reared larvae on two different soybean cultivars with contrasting defensive strategies when induced with JA and measured 7-day survival, development time to pupation, and pupal mass. Plant cultivar and JA induction were both important causes of mortality. Although plant cultivars varied in their amounts of constitutive/inducible defenses, this did not cause an interactive effect between plant cultivar and induction. Insect development to pupation was also extended when fed on induced plants regardless of cultivar, while pupal mass was not affected. Overall, induced plant defenses lowered larval survival and extended development time which would alleviate pest pressure and extend the period growers have for detecting infestations.  
Givcine max; Bayesian modeling; jasmonic acid; fall armyworm; slow-growth high-mortality hypothesis; plant-herbiore interactions
## 5 Results and Discussion  
### Preliminary Investigation  
In this section, a preliminary investigation is presented, focusing on the influence of significant parameters, such as the \(R\), \(L\) values or the graphene and fibre volume contents, \(V_{GPL}\), \(V_{F}\), on vibration reduction that can be achieved by the shunted piezoelectric composite beam.  
Two parametric simulations are conducted, the first adopting a range of values for the \(R\) and \(L\) parameters, while the graphene and fibre reinforcement \(V_{GPL}\), \(V_{F}\), as well as the fibre angles \(\theta\) are constant. In the second parametric simulations, constant \(R,L\) and fibre angle values \(\theta\) are considered, and the graphene-fibre reinforcements \(V_{GPL}\), \(V_{F}\) vary. In both simulations, the output is the vibration reduction index (\(PI\)).  
In the first group of parametric simulations, graphene volume content \(V_{GPL}\) is equal to 1%, fibre volume content \(V_{F}\) is 30% and stacking sequence \(\theta\) is 45\({}^{\circ}\) per layer, for all layers. According to Figure 3, optimal \(R,L\) parameters leading to maximum vibration suppression are derived for \(R=\) 10,000 \(Ohm\), \(L=39\)\(H\).  
In the second group of parametric simulations, \(R\) and \(L\) are constant and equal to \(R=\) 10,000 \(Ohm\), \(L=39\)\(H\), respectively, and the fibre angle \(\theta\) is 45\({}^{\circ}\) for all layers. The influence of varying graphene and fibre reinforcement \(V_{GPL}\), \(V_{F}\) on the vibration suppression is shown in the graph of Figure 4. To better visualise this graph, labels with graphene content \((X)\), fibre content \((Y)\), and vibration attenuation \((Z)\) values are added to chosen points of the graph. According to Figure 4 the optimal vibration attenuation is obtained for graphene volume content \(V_{GPL}\) equal to 0.012(1.2%). For increasing graphene volume content above this value, no improvement in the vibration response is derived.  
Figure 3: Vibration suppression for varying \(R,L\) parameters.
# Analysis on Response of a Single Pile Subjected to Tension Load Considering Excavation Effects
## 3 Load-Transfer Model Considering Excavation Effects
### Nonlinear Shear Displacement of the Soil Outside the Pile-Soil Interface  
The soil displacement outside the pile-soil interface is caused by shear stress between pile and soil. During excavation unloading and pile loading stage, the soil outside the pile-soil interface remains elastic. As recommended by [PERSON] [28], the elastic shear-induced soil displacement can be calculated by:  
\[\omega_{st}=\frac{\tau_{sp}r_{0}}{G_{s}}\int_{r_{0}}^{r_{m}}\frac{1}{r}dr= \frac{\tau_{sp}r_{0}}{G_{s}}\ln(\frac{r_{m}}{r_{0}}) \tag{7}\]  
where \(r_{0}\) is the pile radius, \(G_{s}\) is the shear modulus of surrounding soil. \(r_{m}\) is the limiting radius where the shear strain induced by the pile can be negligible. For a pile in layered soils, the limiting radius \(r_{m}\) can be determined as follows:  
\[r_{m}=2.5\frac{\sum\limits_{i=1}^{n}G_{si}h_{i}}{G_{sm}}\left(1-\frac{\sum \limits_{i=1}^{n}v_{si}h_{si}}{L_{p}}\right) \tag{8}\]  
where \(G_{si}\) and \(v_{si}\) are the shear modulus and Poisson's ratio of \(i\)th soil layer, respectively. \(G_{sm}\) is the maximum shear modulus of soils. \(h_{si}\) is the thickness of \(i\)th soil layer. \(L_{p}\) is the pile length.
## 3 Results
### Physiological Assessment of Mental Workload
#### 3.6.4 Electrodermal Activity  
Electrodermal activity (EDA), also known as galvanic skin response, is a gold-standard bioindicator that is frequently employed to assess mental workload. The electrical potential created on the skin's surface due to sweat gland activity is measured by the EDA sensor [24]. The use of EDA to gather psychophysiological data is non-invasive, reliable, affordable, and simple [65; 66]. Unlike other target organs of the human body, which are connected to the sympathetic and parasympathetic nervous systems, the skin and sweat glands are exclusively innervated by the sympathetic nervous system. This makes EDA an ideal indicator of sympathetic activation and, therefore, a good indicator of when a response is triggered due to mental workload [67].  
Two components in the high-resolution EDA signal were found [24], i.e., the tonic (skin conductance level, SCL) and phasic response (skin conductance response, SCR), and these derived metrics were used to quantify cognitive states and stressful periods [68]. Skin conductance shows an overall rising tendency over time, especially in humid environments, referred to as the tonic skin response [69]. The tonic phase, which establishes the baseline skin conductance, evolves gradually and exhibits minor fluctuations in the range of 10 to 100 s. Depending on a subject's level of hydration, the condition of their skin, or their autonomic regulation, the signal's rise and fall fluctuates continuously within that subject. Additionally, among subjects, this response can differ greatly [70]. The palms and soles, which are areas where sweat glands are most active, are the optimal places to collect such signals [71], although it has been demonstrated that the shoulder is an excellent alternative spot to capture this signal [24].  
Physiological indicators in EDA studies primarily centered around SCR, as it provides a reliable indicator of the body's answer to mental workload. Its capacity to record variations in sympathetic nervous system activity in response to workload fluctuations offers insightful data on the person's level of mental exertion while performing a task [72; 73]. In the study conducted by [4], EDA was found to be sensitive to task complexity and robot support, with higher SCR and SCL values observed in more complex tasks and in manual mode. Moreover, according to ref. [43], EDA, measured through SCL and SCR, also increased with assembly complexity. In the collaborative setting, EDA values were generally lower, suggesting a reduction in mental workload when the robot was present.  
Regarding the limitations of EDA, signal fluctuation varies continuously within the same individual due to factors such as hydration, dry skin, and autonomic regulation. Additionally, this response can differ significantly between individuals [70], as sweat gland activity is influenced by both internal and external factors. Internal factors include age, gender, and ethnicity, while external factors encompass temperature, humidity, time of day, season, and medication use [74].
# Ginger: Panacea or Consumer's Hype?
## 2 Pharmacology of Ginger
### Biological Activity
#### 2.3.6 Equivocal Activities  
To fully comply with its traditional reputation of panacea, ginger should be able to provide cure against practically all ailments. Nevertheless, it lies far from it and a few studies are already demonstrating the equivocal activities on conditions such as IBS, fatty liver and obesity.  
A pilot study on irritable bowel syndrome (IBS) patients who were randomly consuming placebo or ginger powder at daily doses of 1 g or 2 g for 28 days, the ginger groups (\(n=15\)) did not show better effects than the placebo one [78].  
Similarly, in non-alcoholic fatty liver disease (NAFLD), no significant reduction of liver fibrosis, the main pathophysiolog alteration of the disease, was observed between the ginger group and the placebo group (both with \(n=22\)); patients in the test group were taking 2 g of ginger powder per day for 3 months [79].  
Ginger also failed to demonstrate any slimming or appetite reduction action. A daily dose of 2 g of ginger powder for three months caused no changes in body mass, body fat and total energy and macronutrients intake in a group of 40 women in regard to the control (40 women taking placebo) [80; 81].
## 2 Fundamental Assumptions and Formulations
### Integral Governing Equations  
Assuming the top surface of the FGM plate is subjected to a distributed transverse force \(\varrho(x,y)\), the integral governing equations of the FGM plate can be derived based on  
Figure 2: Chebyshev polynomials. (**a**) Chebyshev polynomials of the first kind; (**b**) Chebyshev polynomials of the second kind.  
the d'Alembert principle and the principle of virtual work [40]. These equations can be written as  
\[\int_{V}\delta\varepsilon^{\mathsf{T}}\mathbf{\sigma}\mathrm{d}v=\int_{\Omega}\left[ \sum_{i=1}^{M}\delta w_{i-1}\kappa_{i}^{\prime}\left(\frac{h}{2}\right)\right] \varrho(x,y)\mathrm{d}a \tag{11}\]  
where \(V\) and \(\Omega\) represent the volume and the midplane of the FGM plate, respectively. Combined with Equations (4)-(8), Equation (11) can be rearranged as  
\[\int_{\Omega}\delta\tilde{\varepsilon}^{\mathsf{T}}\widetilde{\mathbf{D}}\tilde{ \mathbf{\varepsilon}}\mathrm{d}a=\int_{\Omega}\tilde{\mathbf{\omega}}^{\mathsf{T}} \mathbf{\vartheta}\varrho\mathrm{d}a \tag{12}\]  
where \(\widetilde{\mathbf{D}}\) is the modulus matrix, \(\mathbf{\vartheta}\) is a load indicator vector, and  
\[\begin{split}&\widetilde{\mathbf{u}}=\left\{\begin{array}{l}\mathbf{u}_{0} \\ \vdots\\ \mathbf{u}_{N}\end{array}\right\},\quad\tilde{\mathbf{\varepsilon}}=\left\{\begin{array}[ ]{l}\varepsilon_{0}\\ \vdots\\ \varepsilon_{N}\end{array}\right\}\,\quad\tilde{\mathbf{D}}=\left[\begin{array}{ l}\widetilde{\mathbf{D}}_{00}&\cdots&\widetilde{\mathbf{D}}_{0N}\\ \vdots&\ddots&\vdots\\ \widetilde{\mathbf{D}}_{N0}&\ldots&\widetilde{\mathbf{D}}_{NN}\end{array}\right], \quad\tilde{\mathbf{D}}_{ji}=\left(\widetilde{\mathbf{D}}_{ij}\right)^{\mathsf{T}}, \\ &\left(\widetilde{\mathbf{D}}_{00},\widetilde{\mathbf{D}}_{0j},\widetilde{\mathbf{D}}_{ij }\right)=\int_{-\frac{h}{2}}^{\frac{h}{2}}(\mathbf{D},\mathbf{D}\mathbf{B}_{j}(z),\mathbf{B}_ {i}(z)\mathbf{D}\mathbf{B}_{j}(z))\mathrm{d}z,\quad 1\leq i,j\leq N\\ &\left(\mathbf{\vartheta}\right)_{s}=\left\{\begin{array}{l}\kappa_{i}^{\prime} (h/2),\ \ s=3(i+1),\ i\in[1,M]\\ 0,\ \ \text{otherwise}\end{array}\right.\end{split} \tag{13}\]
## 4 Experiment
### Experimental Results Analysis
#### 4.5.2 Ablation Experiment  
This study designed ablation models and conducted experiments on the DuIE dataset to analyze the contributions of pinyin, part-of-speech features, and the progressive contrastive learning fusion network to model performance.  
The baseline model is CasRel. The ablation models include the following:  
1. [label=(0)]
2. CasRel+PY: Add pinyin features based on CasRel and fuse them through vector splicing;
3. CasRel+PY+POS: Add both pinyin and part-of-speech features based on CasRel and fuse them through vector splicing;
4. CasRel+PY+POS+PCL (CJE-PCHF): Based on pinyin and part-of-speech features, combine them with progressive contrastive learning interactive fusion network.  
The experimental results, as presented in Table 5, demonstrate that, in comparison to CasRel, models augmented with pinyin, part-of-speech features, and their combination exhibit notable improvements in precision, recall, and the F1 score. Specifically, CasRel+PY's F1 score were increased by 2.9 percentage points over CasRel, CasRel+POS improved by 3.3 percentage points, and CasRel+PY+POS saw a rise of 4.9 percentage points. This suggests that the addition of pinyin and part-of-speech features significantly enhances the model's representational capacity, improves contextual modeling, and thereby boosts entity relation extraction performance. Furthermore, CJE-PCHF achieved an F1 score 0.5 percentage points higher than CasRel+PY+POS, indicating that the progressive contrastive learning fusion network optimizes the alignment of heterogeneous features, enhances the quality of feature integration, and further elevates the model's overall performance. In conclusion, the ablation experiments affirm that the proposed modules contribute positively to the model's performance. The introduction of pinyin and part-of-speech features enriches the model's representational abilities, addressing the issue of insufficient prior information consideration in joint extraction tasks. The progressive contrastive learning fusion network further enhances the fusion quality of heterogeneous information, leading to improved overall performance in entity relation extraction tasks.
## 4 Results and Discussion
### Lamb Wave and Bearing Estimation
#### 4.4.2 Relationship between the Lamb Wave and the Second Peak  
When a Lamb wave is excited, it takes away energy from the incident beam in certain frequency bands, which induces a resonance structure in reflectivity, and the dips in the reflection coefficient diagram correspond to some Lamb modes of the ice plate [27]. Using the full wave integration model OASES, let the longitudinal wave velocity \(c_{\text{P}}\) of the ice layer be 3600 m/s, the shear wave velocity \(c_{\text{s}}\) be 1800 m/s, the density \(\rho\) be 0.9 g/cm\({}^{3}\), the longitudinal wave attenuation coefficient \(\alpha_{\text{P}}\) be 0.216 dB/\(\lambda\) (\(\lambda\) is wavelength), the shear wave attenuation coefficient \(\alpha_{\text{s}}\) be 0.648 dB/\(\lambda\)[31], the density of seawater be 1000 kg/m\({}^{3}\), the sound velocity of seawater be 1480 m/s and the frequency band be 10-1000 Hz. The reflection coefficient is calculated for the water/ice interface with 2 m smooth sea ice, as shown in Figure 9a. Using Snell's Law, the incident angle can be changed to phase velocity, and the reflection coefficient can be shown by the phase velocity and frequency. The Lamb modes of the ice plate can be calculated [27]. The reflection coefficient and the Lamb modes are shown in Figure 9b.  
In Figure 9a, four obvious dips with low reflection coefficients are marked. The dips in the reflection coefficient diagram correspond to some modes of the Lamb waves of the ice plate [27]. It can be seen in Figure 9b that the vibration frequency of the Lamb waves is connected with the incident angle (phase velocity) of the acoustic wave and the thickness of the ice layer.  
When the ice thickness is 1, 2 and 4 m, the incident angles are  
no modes of the Lamb waves of the ice plate were excited at this site. As we can see from Figure 8a, at site S3, there is no bimodal structure but a single peak structure, which is consistent with the fact that the Lamb waves were not excited in the ice layer; thus, the second peak cannot be generated.
## Abbreviations  
Nomenclature used in the study:  
\begin{tabular}{l l l l} Bi\({}_{\text{im}}\) & Biot number & Q\({}_{\text{w}}\) & Energy required for extraction of moisture (kj) \\ C\({}_{\text{m}}\) & Specific heat of sample (kj/kg \({}^{\circ}\)C) & Q\({}_{\text{m}}\) & Energy required for heating of sample (kJ) \\ DE & Drying efficiency (\%) & R & Constant of gases (kJ/mol k) \\ D\({}_{\text{eff}}\) & Effective diffusivity (m\({}^{2}\)/s) & S & Drying rate constant (1/s) \\ D\({}_{\text{i}}\) & Dincer number & SEC & Specific energy consumption (kWh/kg) \\ E\({}_{\text{c}}\) & Activation energy of convective mass transfer (kJ/mol) & SMER & Specific moisture extraction ratio (kg/kWh) \\ E\({}_{\text{d}}\) & Activation energy of diffusion (kJ/mol) & T & Drying air temperature (\({}^{\circ}\)C) \\ E\({}_{\text{c total}}\) & Total energy consumption (kWh) & T\({}_{\text{m1}}\) & Initial temperature of sample (\({}^{\circ}\)C) \\ G & Lag factor & T\({}_{\text{m2}}\) & Final temperature of sample (\({}^{\circ}\)C) \\ h\({}_{\text{fg}}\) & Latent heat of sample (kJ/kg) & t & Drying time (h) \\ h\({}_{\text{m}}\) & Convective mass transfer (m/s) & u & Velocity of drying air (m/s) \\ k & Function of effective diffusivity & w\({}_{\text{n}}\) & The uncertainties in the independent variables \\ L & Thickness of product (m) & W\({}_{\text{R}}\) & The total uncertainty (\%) \\ M & Moisture content (\%) & m\({}_{\text{p}}\) & Moisture content of sample (\%) \\ MR & Moisture ratio & x & Characteristic dimension (m) \\ M\({}_{0}\) & Initial moisture content (\%) & z\({}_{\text{n}}\) & Independent variables \\ M\({}_{\text{e}}\) & Equilibrium moisture content (\%) & \(\mu_{1}\) & Function of Biot number \\ m\({}_{\text{i}}\) & Initial weight of sample (g) & n\({}_{\text{e}}\) & Energy efficiency (\%) \\ m\({}_{\text{f}}\) & Final weight of sample (g) & & \\ \end{tabular}
###### Abstract  
Previous in vitro studies the have suggested anti-inflammatory properties of caffeine. Coffee is also rich in phenolic acids with several potential health benefits (chlorogenic acids), mainly 5-caffeoylquinic acid (5-CQA). The aim of the present study was to determine the effect of caffeine and 5-CQA, using concentrations comparable to typical human consumption, on LPS-induced cytokine production in whole blood in vitro cultures. Whole blood samples (n = 10) were stimulated for 24 h with LPS in the presence of caffeine or 5-CQA. The concentrations of various cytokines, malondialdehydedehyde (MDA) and adenosine-3\({}^{\prime}\),5\({}^{\prime}\)-cyclic monophosphate (cAMP) were determined in the culture media. Significant effects of caffeine were found only for the stimulated production of the culture media. The effect of caffeine and (p = 0.014) and cAMP (\(p\) = 0.001). However, only the highest caffeine concentration (50 uM) induced lower TNF-\(\alpha\) (\(p\) = 0.016) and higher cAMP (\(p\) = 0.006) levels in comparison to the control. The 5-CQA only influenced the stimulated production of IL-6 (\(p\) = 0.002), with lower values for the highest 5-CQA concentrations (2 uM: \(p\) = 0.012; 20 uM: \(p\) < 0.001). Neither caffeine (\(p\) = 0.227) nor 5-CQA (\(p\) = 0.259) influenced MDA concentrations. In conclusion, both caffeine and 5-CQA exerted limited in vitro anti-inflammatory effects. For caffeine, these effects were attained with high physiological concentrations. However, for 5-CQA, supraphysiological concentrations were needed.  
caffee; chlorogenic acids; cytokines; LPS challenge; human blood cell culture Article
# Study on the Vibration Effects of Cyclic Blasting on Bridge Structures under Construction
## 3 Numerical Simulation and Analysis of Blasting Vibration
### Blasting Vibration Load  
The blasting load acts on the tunnel wall and its support system. The dynamic load in FLAC3D is used to simulate thestress propagation due to blasting, and the time-history curve of the explosion load is simplified to a triangular load to simulate the equivalent blasting load. The formula for calculating the equivalent blast load is as follows [27]:  
\[P_{D}=\frac{D^{2}\rho_{0}}{2(\gamma+1)} \tag{1}\]  
where \(P_{D}\) represents the average detonation pressure of the explosive, \(D\) represents the detonation speed of the explosive, rho is explosive density, and \(\gamma\) is the isentropic coefficient of explosive, which is 1.3. When the blast hole is filled with the decoupled charge, the blasting gas pressure \(P\) is  
\[P=A\rho^{v_{0}} \tag{2}\]  
where \(P\) is the pressure of the explosive gas in a certain state, \(A\) is a constant, \(\rho\) is the density of explosive gas in a certain state, and \(v_{0}\) is the isentropic index of the explosive gas. When the uncoupled charge coefficient is small, we have \(P\geq P_{k}\) and \(v_{0}=\gamma=3\). Additionally, when \(P<P_{k}\), and \(v_{0}=v=4\), \(v_{0}=v=1.4\), \(P_{k}\) is the critical pressure of blasting, and the initial average pressure resulting from the blast holes is as follows:  
\[P_{0}=\frac{1}{8}\rho_{0}D^{2}k_{d}^{-6}\eta \tag{3}\]  
If the charge uncoupling coefficient is large, the expansion of the explosive gas can only pass through the \(P\geq P_{k}\) stage, and Formula (4) can be obtained.  
\[P_{0}=P_{k}^{\frac{\gamma-v}{\gamma}}(\frac{\varrho_{e}D^{2}}{2(1+\lambda)})^{ \frac{v}{\lambda}}k_{d}^{-6}\eta \tag{4}\]  
\(p(t)\) is usually considered as an exponential time-delay function, which is generally a decreasing exponential function, and the calculation can be handled according to the triangle approximation. The load-time relationship is as follows:  
\[P(t)=P_{0}\frac{t}{t_{r}}(0\leq t\leq t_{r}) \tag{5}\]  
\[P(t)=P_{0}\frac{t_{r}+t_{b}-t}{t_{r}}(t_{r}\leq t\leq t_{r}+t_{b}) \tag{6}\]  
According to the treatment method of explosive parameters and related literature, the increase time of explosion load is set to 100 \(\upmu\)s, and the duration of blasting seismic wave is  
Figure 5: The arrangement of vibration monitoring points.  
set to 0.6 s. The triangular shock wave load is shown in Figure 6, which is the peak value of blasting load. \(t_{r}\) is the rise time.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 2 System Model
### uRLLC Traffic  
The uRLLC UEs are with delay-sensitive machine-type traffic and require high transmission reliability. Therefore, a UE \(l\) associates with a base station and superposes with an eMBB UE \(e\) who satisfy its required QoS constraint \(T_{l}^{max}\) & \(R_{l}^{min}\).  
Figure 3: An illustration of eMBB and uRLLC users down-link PD-NOMA HetNet scheme.  
2.5.1. uRLLC Data Rate Depending on Finite Block-Length Coding  
The uRLLC's bursts of small payload sizes of \(\mathcal{Z}_{l}(t)\) bytes arrive at the network according to a Poisson Point Process (PPP) with an arrival rate of \(\lambda_{l}\) [payload/s] [50]. Due to the small packet size of uRLLC traffic, [PERSON]'s data rate formulation cannot be used directly. Consequently, the DL data rate of uRLLC UE in (5) is edited based on [50] i.e.,  
\[r_{bl}^{mm}= \omega_{b}^{mm}\log_{2}(1+\Gamma_{bl}^{mm})-\sqrt{\frac{\mathcal{ X}_{bl}^{mm}}{C_{bl}}}\ Q^{-1}(\varepsilon), \tag{9}\]  
where \(C_{bl}\) is the block-length code, and \(Q^{-1}(\varepsilon)\) is the inverse of the complementary Gaussian cumulative distribution \(Q\)-function with the probability of decoding error \(\varepsilon\) and \(\mathcal{X}_{bl}^{mm}\) is the channel dispersion which is represented by  
\[\mathcal{X}_{bl}^{mm}=1-\frac{1}{1+\left(1+\Gamma_{bl}^{mm}\right)}, \tag{10}\]