esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 6 Bromelain Studies
### Cancer  
In vitro and in vivo studies have demonstrated the anti-cancer effects of bromelain; it quite likely switches-off the NF-kB gene signal, thus inhibiting Cox-2 expression, activating several caspases and promoting apoptosis [109; 110]. In addition, bromelain exhibits anti-metastatic properties, which it exerts via the upregulation of p53 and the activation of the autophagy mechanism [67]. The potential effects of this supplement, in terms of tumor-size reduction and/or regression and improvements in chemotherapeutic-agent efficiency, have been highlighted in many studies carried out on several cell lines, including A-375 melanoma, P-388 leukemia, ADC-755 breast cancer, sarcoma (S-37), A431 epidermoid carcinoma and Lewis lung cancer [111; 112; 113]. Although there is a lack of RCTs to investigate the effects of bromelain on tumor progression, bromelain-related decreases in the side effects caused by conventional treatments have been evaluated by a number of research groups.  
In a clinical investigation, 494 patients with breast cancer that were undergoing adjuvant hormone therapy and suffering from severe arthralgia as well as severe mucosal dryness were treated with a complementary adjuvant, which included bromelain in addition to papain, sodium selenite and Lens culinaris lectin. Two-thirds of patients showed an improvement in both arthralgia and mucosal dryness after four weeks, with the conventional treatments causing limited side effects (\(p<0.001\)) [114]. Similar results have been obtained by the group of [PERSON] and colleagues [115]. Bromelain also stimulates the deficient monocytic cytotoxicity of mammary tumor patients [116] and promotes the apoptosis process [117].  
Finally, bromelain supplementation, along with \(\alpha\)-lipoic acid, Boswellia serrata and methylsulfonylmethane, has been shown to decrease chemotherapy-induced peripheral neuropathy in patients treated with neurotoxic chemotherapy [118].
## 2 The Lattice Boltzmann Method  
The LBM can be seen as a special discrete format for solving continuous Boltzmann equations [16; 17]. In 1954, [PERSON], [PERSON], and [PERSON] proposed the famous Bhatnagar-Gross-Krook (BGK) collision model [18], which replaced the nonlinear term in the original Boltzmann equation with a simple collision operator, and it guaranteed some conditions, such as mass momentum and energy conservation. According to the BGK collision model, the lattice Boltzmann equation with the discretization of velocity and time can be obtained as the evolution equation of the particle:  
\[f_{\alpha}(x+e_{\alpha}\delta_{t},t+\delta_{t})-f_{\alpha}(x,t)=-\frac{1}{\tau }\Big{[}f_{\alpha}(x,t)-f_{\alpha}^{eq}(x,t)\Big{]}+\delta_{t}F_{\alpha}(x,t), \tag{1}\]  
where \(\tau\) is the relaxation time and \(f_{\alpha}^{eq}\) is the equilibrium distribution function. According to the DnQm series model proposed by [PERSON] et al. [19] (where \(n\) and \(m\) are respectively represented as the spatial dimension and the discrete velocity number), the equilibrium distribution function is unified and expressed as  
\[f_{\alpha}^{eq}=\rho\omega_{\alpha}[1+\frac{e_{\alpha}\cdot u}{c_{s}^{2}}+ \frac{(e_{\alpha}\cdot u)^{2}}{2c_{s}^{4}}-\frac{u^{2}}{2c_{s}^{2}}], \tag{2}\]where \(c_{s}=\sqrt{RT}\) is the lattice sound velocity and the value is \(\frac{1}{\sqrt{3}}\), \(u\) is the velocity, \(\omega_{a}\) is the weight coefficient, and \(e_{a}\) is the direction vector. In this paper, the D2Q9 model is used (Figure 1), and the parameters are selected as follows:  
\[\omega_{i}=\left\{\begin{array}{ccccc}4/9,\;\epsilon_{i}^{2}=0&\\ 1/9,\;\epsilon_{i}^{2}=c^{2}&\\ 1/36,\;\epsilon_{i}^{2}=2c^{2}&\\ \end{array}\right.\] \[e=c(\begin{array}{ccccc}0&1&0&-1&0&1&-1&-1&1\\ 0&0&1&0&-1&1&-1&-1\end{array})\]  
where \(c=\delta x/\delta t,\delta x,\delta t\) are the grid step size and time step, respectively. Velocity and density can be obtained from the following formula:  
\[\rho=\sum_{a}f_{a}^{eq},\rho u=\sum_{a}e_{a}f_{a}^{eq}.\]
# Exploratory Study of a Green Function Based Solver for Nonlinear Partial Differential Equations
## 2 Mathematical Foundations of the Method
### Closed Expressions for the Green Function
We can cast this new form of the Green function for a general, quasi linear operator as:  
\[\mathcal{G}\big{(}\vec{x},t\,|\,\vec{x}^{\prime},t^{\prime}\big{)}=\big{\{} \mathrm{Exp}\big{[}\big{(}t-t^{\prime}\big{)}\mathcal{L}_{\vec{x},t}\big{]}+ \Psi_{\mathcal{L}}\big{\}}\delta\big{(}\vec{x}-\vec{x}^{\prime}\big{)},\,\,\, \mathcal{O}(\Psi_{\mathcal{L}})=(t-t^{\prime})^{2}, \tag{20}\]  
where \(\Psi_{\mathcal{L}}\) is this term that groups the aforementioned corrections or variations over the exponential term. It is important to point out that, thanks to the properties of the Dirac delta as a function, the dependencies on \(\vec{x}\) of the terms inside the operator \(\mathcal{L}_{\vec{x},t}\) and \(\Psi_{\mathcal{L}}\) can be either on \(\vec{x}\) or \(\vec{x}^{\prime}\). The next step will be to expand the Dirac delta appearing in expression (20) in the basis of Eigenfunctions, \(\varphi_{p}(x)\), and Eigenvalues, \(\lambda_{p}\), that correspond to the space at which we are looking for a solution, i.e., at the domain and quasilinear operator we are studying. This operation can be performed as long as the transport coefficient does not depend either on time or space, and while it might looks as a limitation, we will show how to implement these effects as corrections in the likes of the \(\Psi_{\mathcal{L}}\) term. Therefore, we can leave the expression of the Green function in a general form for closed an open domains as,  
* Closed Domain \[\mathcal{G}\left(\vec{x},t\mid\vec{x}^{\prime},t^{\prime}\right)=\sum_{p=- \infty}^{\infty}\frac{\varphi_{p}(\vec{x})\varphi_{p}(\vec{x}^{\prime})}{ \left|\varphi_{p}\right|^{2}}\text{Exp}\big{[}\lambda_{p}\big{(}t-t^{\prime} \big{)}\big{]}.\] (21)
* Open Domain \[\mathcal{G}\left(\vec{x},t\mid\vec{x}^{\prime},t^{\prime}\right) =\frac{1}{(2\pi)^{3}}\int_{\mathbb{R}^{3}}\text{Exp}\big{[} \mathbf{i}\vec{\omega}\cdot\big{(}\vec{x}-\vec{x}^{\prime}\big{)}+\lambda( \vec{\omega})\big{(}t-t^{\prime}\big{)}\big{]}d\Omega,\] (22) \[\lambda(\vec{\omega}) =\vec{\bar{a}}_{\alpha_{2}}\cdot(\mathbf{i}\vec{\omega})^{\alpha _{2}},\] where, \(\mathbf{i}\) is the imaginary unit, \(\vec{\omega}\) is the three dimensional frequency vector corresponding to the spatial \(\vec{x}\) and the expression for \(\lambda\) is written in an oversimplified notation to avoid the structure of an \(\alpha_{2}\)-th-order tensor. Recall that, we are using the frequency vector \(\vec{\omega}\) as the open domain Eigenfunctions for an operator of any \(\alpha_{2}\)-th-order collapse towards the Fourier kernel, \(\text{Exp}(\mathbf{i}\vec{\omega})\) as the Dirac delta can be expanded as its Fourier transform. Once we have the general expression for both the Closed domain and Open domain Green functions together with their short-time approximation as a Taylor series, we proceed to obtain the specific expressions of the Green functions for the operators of interest: the advection and the diffusion ones.
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 4 Measurements and Analysis
### Indoor Signal Level Measurements
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**434**} & \multicolumn{4}{c}{**868**} \\ \cline{2-9}  & \multicolumn{2}{c}{**DAY**} & \multicolumn{2}{c}{**NIGHT**} & \multicolumn{2}{c}{**DAY**} & \multicolumn{2}{c}{**NIGHT**} \\ \cline{2-9}  & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) & \(\mu\) & \(\sigma\) \\ \hline Monday 9 April & \(-72.4\) & \(4.0\) & \(-70.3\) & \(0.8\) & \(-89.5\) & \(4.1\) & \(-91.3\) & \(1.9\) \\ Tuesday 10 April & \(-70.9\) & \(2.3\) & \(-71.9\) & \(1.2\) & \(-88.9\) & \(2.8\) & \(-96.6\) & \(4.4\) \\ Wednesday 11 April & \(-71.1\) & \(2.4\) & \(-79.8\) & \(5.3\) & \(-88.7\) & \(4.0\) & \(-86.9\) & \(0.7\) \\ Thursday 12 April & \(-70.1\) & \(2.7\) & \(-69.8\) & \(0.6\) & \(-89.3\) & \(5.2\) & \(-87.0\) & \(0.1\) \\ Friday 13 April & \(-71.9\) & \(3.4\) & \(-69.5\) & \(0.6\) & \(-89.2\) & \(4.1\) & \(-90.7\) & \(0.6\) \\ Saturday 14 April & \(-69.7\) & \(0.6\) & \(-69.4\) & \(0.6\) & \(-91.3\) & \(0.6\) & \(-88.8\) & \(1.4\) \\ Sunday 15 April & \(-69.2\) & \(0.5\) & \(-69.4\) & \(0.6\) & \(-90.7\) & \(0.8\) & \(-89.2\) & \(1.6\) \\ \hline \hline \end{tabular} All values expressed in dBm.  
\end{table} 
Table 1: Statistics describing the second week of indoor power measurements gathered at RX1. (DAY: 9 a.m.–5 p.m. & NIGHT: 9 p.m.–5 a.m.). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**434**} & \multicolumn{4}{c}{**868**} \\ \cline{2-9}  & \multicolumn{2}{c}{**DAY**} & \multicolumn{2}{c}{**NIGHT**} & \multicolumn{2}{c}{**DAY**} & \multicolumn{2}{c}{**NIGHT**} \\ \cline{2-9}  & \(\bar{\mu}\) & \(\bar{\sigma}\) & \(\bar{\mu}\) & \(\bar{\sigma}\) & \(\bar{\mu}\) & \(\bar{\sigma}\) & \(\bar{\mu}\) & \(\bar{\sigma}\) \\ \hline RX1 & \(-71.11\) & \(2.998\) & \(-70.75\) & \(0.8242\) & \(-90.82\) & \(4.296\) & \(-92.52\) & \(1.086\) \\ RX2 & \(-88.84\) & \(2.745\) & \(-87.51\) & \(0.7685\) & \(-116.5\) & \(4.052\) & \(-115.4\) & \(1.349\) \\ \hline \hline \end{tabular} All values expressed in dBm.  
\end{table} 
Table 2: Average values of the descriptive statistics describing subsets of indoor power measurements. (DAY: 9 a.m.–5 p.m. & NIGHT: 9 p.m.–5 a.m.).
# OTFS-Based Handover Triggering in UAV Networks
## 5 Proposed UAV-Ground Handover Triggering Based on DD Channel Estimation  
In this section, the conventional time-varying RSS and FT channel gains-based handover decision schemes will be presented, showing their shortcomings in highly time-varying channel conditions especially those related to UAV-to-ground communications. Then, a handover-triggering strategy based on the estimated DD channel \(\hat{h}_{DD}[l,k]\) will be introduced.
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Purpose and Applications of Image-Based Structural Health Monitoring
#### 4.1.4 To Promote Development, and Create 3D Models
Since the reasons for deflection (changes in influence lines) are dictated by the moving vehicles, a threshold may be set following the design capacity of the bridge and see if vehicles moving it are overloaded. Most of the countries prohibit vehicles from overloading. An additional camera may be setup at the exit point of the bridge to capture more pictures since plate numbers may be missing on the front part of the vehicle. A newly created, completely contactless SHM system framework, established on regular photographic camera and CV techniques, is launched by [10] for obtaining displacements and ambiances of structures. These are dangerous responses the for performance-based design and assessment of structures from their work; the current vision-based displacement measurement methods are enhanced by eliminating the bodily target attachment to provide contactless and real-world monitoring. This is achieved using imaging key points as virtual targets. Pixel-based displacements of a monitored structural site may be computed using an improved detection and match key-points approach, in which erroneous matches are recognized and dismissed outright. To convert pixel-based displacements to engineering units due to the lack of calibration standards on physical targets, a helpful camera calibration technique was developed here [10]. Furthermore, [10] devised a study method for assessing the accuracy of vision-based deformation measurements, which provides users with the most critical measurement information. The proposed framework, a conventional sensor network, and a data collection system, are employed and proved in a real-life venue during football matches for structural assessment. Utilizing information gathered by sensors like accelerometers and linear variable differential transformers, the outcomes of the unique technique have been successfully tested. The suggested method does not need sensor and target connection, therefore typical activities such as sensor setup, wiring, and the preservation of conventional information and data-collection systems, are not required. For real-world structures in particular, this benefit offers a cheap and practical structural analysis.
# Research on TBM Cutterhead Crack Damage and Fatigue Reliability
## 3 Fatigue Reliability Analysis of the Cutterhead Crack  
### The Fatigue Reliability Evaluation Model of Cutterhead Crack  
In fracture mechanics, the Paris formula given as Equation (1) is widely used in the calculation of crack growth life:  
\[\frac{da}{dN}=C(\Delta K)^{m}, \tag{2}\]  
where \(C\) and \(m\) are constants that are determined by the material and \(\Delta K\) is the stress intensity factor, which can be defined as shown in Equation (3):  
\[\Delta K=Y(a)S\sqrt{\pi a}. \tag{3}\]  
The fatigue life expression can be obtained by integrating Equations (2) and (3):  
\[T_{f}=\frac{1}{CS^{m}}\int_{a_{0}}^{a_{\mathrm{c}}}\frac{1}{\left[Y(a)\right] ^{m}\left(a\pi\right)^{m/2}}da, \tag{4}\]  
where \(a_{0}\) is the initial crack depth and \(a_{\mathrm{c}}\) is the critical crack depth.  
Figure 5: Equivalent SIF for different position angles and parameters in the fore-end of the crack: (**a**) \(a_{1}/c_{1}=0.5\), (**b**) \(a_{1}/c_{1}=0.625\), (**c**) \(a_{1}/c_{1}=0.75\), and (**d**) \(a_{1}/c_{1}=0.875\).  
Figure 6: Distribution of the SIF with different shape ratios: (**a**) \(K_{\mathrm{I}}\), (**b**) \(K_{\mathrm{II}}\), and (**c**) \(K_{\mathrm{III}}\).  
In the calculation of the fatigue life, the stress and geometric correction coefficients are random; therefore Equation (4) can be written as:  
\[T_{f}=\frac{1}{CB^{m}\Omega}\int_{a_{0}}^{a_{c}}\frac{1}{B_{Y}m^{m}Y^{m}(a)(a\pi) ^{m/2}}da, \tag{5}\]  
where \(B\) is used to describe the uncertain parameters in the stress calculation and \(B_{Y}\) is the deviation factor of the stress intensity factor calculation, which is used to adjust \(Y(a)\):  
\[Y(a)=\frac{1.12}{q}, \tag{6}\]  
\[q=\int_{0}^{\pi/2}\sqrt{1-\left[1-\left(\frac{a}{b}\right)^{2}\right]\sin^{2} \theta}d\theta, \tag{7}\]  
where \(a/b\) is the crack shape ratio.  
When the stress characteristics obey the Weibull distribution, the probability distribution function [36] is as shown below:  
\[f_{s}(S)=\frac{\xi\ln N_{L}}{S_{L}}S^{\xi-1}\cdot\exp(-\frac{S^{\xi}}{S_{L}^{ \xi}}\ln N_{L})\qquad(0\leq S\leq+\infty), \tag{8}\]  
where \(\xi\) is the shape coefficient, which is approximately taken as 1; \(S_{L}\) is the maximum stress range, which can be determined according to stress characteristics; \(N_{L}\) is the number of loading cycles, assumed to be 500,000 times. When the load spectrum is continuous, the stress parameters [36] can be expressed by Equation (9):  
\[\Omega=f_{L}\frac{S_{L}^{m}}{\left(\ln N_{L}\right)^{m/\xi}}\Gamma[\frac{m}{ \xi}+1], \tag{9}\]  
where \(\Gamma\) is the gamma function; \(f_{L}\) is the average stress frequency, which is determined by the stress characteristics, and \(T_{\text{D}}\) is the design life of the cutterhead. When the fatigue life of the cutterhead is less than the design life, this will lead to fatigue failure; thus, the limit state function can be determined as follows:  
\[Z=\frac{1}{CB^{m}\Omega}\int_{a_{0}}^{a_{c}}\frac{1}{B_{Y}m^{m}Y^{m}(a)(a\pi) ^{m/2}}da-T_{D}. \tag{10}\]
# Coexistence in Wireless Networks: Challenges and Opportunities
## 5 Interference and Mitigation Techniques
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Proposed Technique/Algorithm** & **Advantages** & **Limitations** & **References** \\ \hline Spread spectrum & Distributes signals across a wider frequency band to minimize interference impact & May require additional bandwidth and resources & [84] \\ \hline Beamforming & Creates a focused signal in a specific direction using multiple antennas & Requires advanced hardware and spatial processing capabilities & [85] \\ \hline Frequency hopping & Mitigates interference by rapidly switching frequencies & Susceptible to frequency hopping jammers & [100] \\ \hline Interference mitigation via relaying & A relay channel with correlation is used & Requires a large number of antennas, assumes optimal quantization noise covariance, may not be practical. & [101] \\ \hline BICM OFDM system & Minimize interference using a bit-interleaved coded system & Effective in the absence of narrowband interference & [103] \\ \hline Spectrum sharing & Explore spectrum sharing techniques for multiple systems & May require complex interference management schemes & [104] \\ \hline Hybrid techniques & Combine Maximum Likelihood Estimation and self-cancellation & Results are dependent on modulation and data rates & [105] \\ \hline Null steering MU-MIMO SDMA & Employs null steering MU-MIMO SDMA for interference mitigation & Requires knowledge of victim FSS ES direction angles & [106] \\ \hline Highly selective filter for suppressing interference of 5G signals at C-band satellite receiver & Filters are used to provide less loss & Parameters are not considered & [109] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 9: Bar graph with interference reduction.
## 2 Literature Review
Moreover, a traveler can wait a long time at their activity, compared to a public stop station, which leads to a longer acceptable waiting time for AVs, which includes the access/egress time, and a slightly higher waiting time than that for public transport [24]. Thus, travelers do not need to walk a certain distance to reach AVs, while in the case of using public transport, they need to walk to the nearest public transport stop [24]. In conclusion, the minimum acceptable waiting time equals the sum of the waiting time at the public transport stop and the walking distance to get there. A study conducted by [PERSON] et al. (2016) mentioned that the acceptable waiting time affected the required fleet size of AVs to serve all of the demand, and it decreased the fleet size by 90% [25]. [PERSON] et al. (2018) used 10 min as an acceptable waiting time for AVs, 60 s as the waiting time for public transport stop, and 8 passengers as the capacity of AVs, when they studied the impact of AVs on demand-responsive transport (DRT) [26]. A simulation was conducted, and it was considered that the first rider should not be exposed to a waiting time of more than 10 min, based on the number of passengers along the assigned path [27]. [PERSON] et al. (2015) said that one shared autonomous vehicle (SAV) with a capacity of 4 passengers could replace 9.3 conventional private cars in the urban area of Austin city. The authors divided the city into certain blocks and assigned a certain number of AVs to each block. Those assigned SAVs can give priority to the demand on their blocks, and in the case of a surplus, they can go and serve the demand in the vicinity blocks. Using this strategy, they minimized the waiting time and the traveled distance, which was also the finding in [28]. [PERSON] et al. produced a strategy that allowed for a lower waiting time than that of a random distribution of SAVs on the road network of a city, without giving any priority to a specific zone or block [28]. The study of [PERSON] and [PERSON] (2019) showed that one SAV can replace up to 8 conventional private vehicles, with an acceptable average waiting time of 7 to 10 min [12]. The impacts of both AVs and the P&R system on the mobility of travelers have not been well covered in the literature. However, different tools can be used to study the impact of these two elements on mobility, such as the MATSim open source tool. This paper used MATSim in studying the travel time, traveled distance, and required supply of AVs to serve a certain demand. It is worth mentioning that MATSim uses an activity-based model, where daily activity plans are simulated, and utility is maximized. Relevant literature was used to find related parameters to be used in this particular study, such as an acceptable waiting time, parking time, location of parking facilities, and passenger drop off and pick up time.
# Multibody Dynamics Simulation and Vibration Test for High Clearance Orchard Sprayer
## 3 Results
### Transient Dynamics Analysis of Sprayer Frame
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Points**} & \multirow{2}{*}{**Peak Points**} & \multicolumn{2}{c}{**X**} & \multicolumn{2}{c}{**Y**} & \multicolumn{2}{c}{**Z**} \\ \cline{2-9}  & & **Frequency** & **Amplitude** & **Frequency** & **Amplitude** & **Frequency** & **Amplitude** \\ \hline \multirow{3}{*}{3} & 1 & 81.06 & 7.91 & 81.06 & 3.28 & 161.13 & 1.27 \\  & 2 & 161.13 & 1.06 & 0.88 & 0.88 & 81.06 & 1.27 \\  & 3 & 121.09 & 0.90 & 0.41 & 0.40 & 363.28 & 0.68 \\ \hline \multirow{3}{*}{4} & 1 & 80.08 & 7.13 & 80.08 & 1.11 & 161.13 & 1.34 \\  & 2 & 161.13 & 2.45 & 161.13 & 0.70 & 80.08 & 1.15 \\  & 3 & 118.16 & 0.70 & 40.04 & 0.26 & 181.64 & 0.59 \\ \hline \multirow{3}{*}{5} & 1 & 81.06 & 6.01 & 81.06 & 0.97 & 161.13 & 0.74 \\  & 2 & 161.13 & 2.17 & 161.13 & 0.55 & 81.06 & 0.69 \\ \cline{1-1}  & 3 & 242.18 & 0.56 & 40.04 & 0.29 & 40.04 & 0.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 15: Time and frequency domain diagram of measurement point 1 in X, Y and Z directions. (**a**) is time-domain diagram in the X direction; (**b**) is the frequency domain graph in the X direction; (**c**) is time-domain diagram in the Y direction; (**d**) is frequency-domain graph in the Y direction; (**e**) is time-domain diagram in the Z direction; (**f**) is frequency-domain graph in the Z direction. By analyzing the time domain and frequency spectrum diagrams of point 1, we can see that the amplitude of the time domain curve changes less, and the curve distribution area is more concentrated, which means that the frame has no obvious defects and the overall frame stiffness is more suitable. The frequency spectrum diagram reveals that the frequencies around 30 Hz and 80 Hz are the main frequency components of frame vibration, which are in good agreement with the idle excitation frequency and rated RPM excitation frequency of the engine. It means that the vibration of the sprayer frame is mainly caused by the excitation of the engine, the vibration amplitude is small, and there is no obvious resonance phenomenon. It is shown that the engine excitation has a small effect on the frame, and its excitation frequency does not overlap with the intrinsic frequency of the frame, which does not cause a significant vibration response and verifies the correctness of the finite element model analysis.