esa-sceva/satcom-chunk-collection huggingface dataset:
# Dental Hydrogels with Essential Oils with Potential Activity in Periodontitis
## Materials and Methods
### Cytotoxicity Assay  
Human mesenchymal stem cells (D1 MSCs) generated from the dental papilla of human wisdom teeth germs were used to evaluate the hydrogels' cytotoxicity (D1 MSCs). The mixed technique was used to obtain D1 MSCs (tissue explants and enzymatic treatment). Dental pulp was removed, diced (1-2 mm\({}^{2}\)), and then subjected to a 30-min enzymatic treatment using a 0.1% IV collagenase and trypsin-EDTA solution from Sigma-Aldrich (Sigma-Aldrich, St. Louis, MO, USA).  
The remaining tissue explants and the cell solution were put into culture plates that had already been coated with 4% porcine skin gelatin (Sigma-Aldrich, St. Louis, MO, USA). The propagation medium was composed of 10% fetal calf serum (FCS) (Gibco Life Technologies, Paisley, UK), 100 mg/mL streptomycin (Gibco Life Technologies, Paisley, UK), 100 U/mL penicillin (Gibco Life Technologies, Paisley, UK), and 50 mg/mL gentamicin (Gibco Life Technologies, Paisley, UK) (Gibco Life Technologies, Paisley, UK). The evaluation was carried out daily while the plates were incubated at 37 \({}^{\circ}\)C in a microenvironment enhanced with 5% CO\({}_{2}\) and 90% humidity. After 72 h, the culture media was replaced. The passages were carried out at a confluence of 60-70% after the explants were removed after 5 days. A concentration of 5 \(\times\) 10\({}^{3}\) cells/plate was used for subculturing. After 6 passages, the immunophenotypic (CD 44, CD79a, HLA-DR, CD105, CD29, CD34/45, CD49f, CD14, and CD90) and functional (trilinear differentiation, clonal capacity, migration capacity, and adhesion) characterization were assessed.  
The cytotoxic potential of the essential oils supplemented hydrogels was assessed through MTT assay (4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide). In order to obtain cell suspensions, the D1 MSCs cultures were treated with 0.25% trypsin-EDTA, and after centrifugation (1500 rpm for 5 min), 1 \(\times\) 10\({}^{4}\) cells/well were seeded on 96 wells plates in 200 \(\mu\)L complete culture medium. After 24 h, the hydrogels were added at the predetermined concentrations (4 concentrations: 2.4, 4.7, 7, and 9% (\(v\)/\(v\)) of gel). The concentration of 100% oil extracted by the cold press method implies the quantity of 0.01 \(\mu\)L, 0.02 \(\mu\)L, 0.03 \(\mu\)L and 0.04 \(\mu\)L of pure oil for each type of tested composite.  
As a control, cells kept in the typical propagation medium were employed. Three duplicates of each experimental condition were carried out. After 24 h, the degree of cell proliferation was evaluated. The culture media was taken out, 100 L of MTT solution (1 mg/mL) was added, and the mixture was incubated at 37 \({}^{\circ}\)C for three hours. The formazan was dissolved in DMSO (dimethyl sulfoxide; Honeywell, Fluka, Seelze, Germany) solution, and the chromogenic reaction was assessed at 450 nm using a BioTek Synergy 2 microplate reader (Winooski, VT, USA). By reporting the optical density in comparison to the control, the results were calculated.
## 4 Experiments
### Ablation Studies
#### 4.3.3 Ablation study on AFEM  
We conducted ablation experiments on both the VisDrone and DOTA datasets to evaluate the impact of the AFEM on enhancing the features of small objects. The experimental results are presented in Table 5.  
As demonstrated in the quantitative ablation experiments presented in Table 5, the AFEM exhibits improvements in the mAP of small-object detection. By adaptively fusing features from different object pipelines, the AFEM effectively preserves more features of small-object pipelines, resulting in a more significant improvement in the detection performance of small objects. To visually observe the impact of the AFEM in feature extraction, we compare the feature maps with and without AFEM in Figure 12. The visualization results clearly show that the addition of the AFEM enriches the feature details within the small-object pipeline. This confirms that the module effectively enhances the feature maps for small objects.  
Figure 12: (**a**) represents the input image (from the VisDrone and DOTA datasets), (**b**) represents the feature map without the AFEM, and (**c**) represents the feature map with the AFEM.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **AFEM** & **mAP** & **AP50** & **AP\({}_{\mathbf{S}}\)** & **AP\({}_{\mathbf{M}}\)** & **AP\({}_{\mathbf{L}}\)** \\ \hline \multirow{2}{*}{**VisDrone**} & **w/o** & 35.24 & 60.92 & 28.33 & 44.52 & 63.12 \\  & **w/** & **36.86** & **63.22** & **31.57** & **44.55** & **63.10** \\ \hline \multirow{2}{*}{**DOTA**} & **w/o** & 63.85 & 85.47 & 50.12 & 77.23 & 91.31 \\  & **w/** & **64.76** & **86.31** & **52.66** & **77.22** & **91.33** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation comparison with and without the AFEM on the VisDrone and DOTA datasets.
## 5 Lattice Boltzmann Methods (LBMs)
### Multiple Relaxation Time (MRT) Collision Model  
The MRT-LB model is the most general formulation within the theoretical framework of the LB equation and kinetic theory. It is generally turning out to be appropriate for flow problems with and without heat transfer in porous or non-porous media for which the numerical scheme stability has to be significantly raised compared to the BGK model when moments relax at different rates. Simply put, the MRT-LBM using multiple relaxation parameters can achieve more stable and accurate simulations by adjusting these parameters, making it an approach known to be more accurate than its earlier SRT version. However, a comprehensive asymptotic analysis is necessary for an optimal selection of all relaxation parameters involved [147,148]. That being said, the MRT collision operator for any problem (_fluid_, _thermal_, etc.) without a forcing term can be expressed as:  
\[\underbrace{\Omega(f)=-(M^{-1}S_{f})(m-m^{\alpha})}_{Dynamic\ field},\ \ \underbrace{\Omega(g)=-(N^{-1}S_{g})(n-n^{\alpha q})}_{Thermal\ field} \tag{10}\]  
where \(M(N)\) and \(S_{f}(S_{g})\) stands for transformation and diagonal relaxation matrices, respectively. \(m\) (or \(n\)) and \(m^{\alpha q}\) (or \(n^{\alpha q}\)) are the velocity moment of \(f(g)\) and their equilibria \(f^{\alpha q}(g^{\alpha q})\). Note that the relaxation times of the diagonal matrices (\(S_{f}\), \(S_{g}\)) have a significant influence on the collision phase.  
In two-dimensional (2D) problems, and without loss of generality, the D2Q9 discrete velocity set (_with nine velocities_) is commonly deemed, and thereby, quantities involved in the collision operator can be described as follows [153,154] to cite a few: the relaxation matrix \(S_{f}(S_{g})\) in moment space is a diagonal matrix given by \(diag(\tau_{i})\) where \(\tau_{i}\) represents the relaxation rates. The passage from one space (_discrete velocities_) to another (moments) is achieved by the matrices \(M(N)\) via the following relations, e.g., [154]:  
\[m=M\cdot f;\ m^{\alpha q}=M\cdot f^{\alpha q};\ n=N\cdot g;\ n^{\alpha q}=N \cdot g^{\alpha q} \tag{11}\]  
Finally, the macroscopic fluid variables (_density_, _velocity_, _scalar_ (e.g., _temperature_)), are updated from the moments of the distribution functions according to the relationships (9) up-cited.
## 3 Conclusions  
In conclusion, the present study comprehensively explores the potential of spilanthol isolated from _A. paniculata_ in attenuating NDDs through a combination of in silico and in vitro studies. Our research indicates that spilanthol demonstrates favorable pharmacokinetic properties and drug-like characteristics based on in silico ADMET prediction. Network pharmacology analysis reveals a complex interaction between spilanthol and key target proteins in the TLR4 signaling pathway involved in NDDs. Additionally, molecular docking and dynamics simulations demonstrate robust binding affinities between spilanthol and key proteins in the TLR4 pathway. Experimental validation using LPS-induced BV-2 microglial cells demonstrates the ability of spilanthol to reduce the expression of proinflammatory cytokines and mediators including NO, IL-6, and TNF-\(\alpha\) without causing significant cytotoxicity. However, comprehensive investigations are necessary to assess the efficacy of spilanthol in in vivo models. Additionally, extensive toxicological studies are imperative to pave the way for its clinical application.
# Validation of Wired and Wireless Interconnected Body Sensor Networks
## 1 Introduction
Another key contribution of the paper is to perform series of experiments to observe the impact of body movements over the topology and power utilized to transfer the data in wireless DigiAID. Power efficiency is the most important factor for WBSN because of the fact that the sensor nodes are  
Figure 1: Generic body sensor network scenario. not only smaller in size but also have limited battery life [10; 11]. Different techniques for reducing the power levels have been discussed in References [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23] for WBSN. The goal is to keep a low radio power level for transmission and reception which is not only safe for human body, efficient for network protocols and also save battery life. In this paper, to ensure efficient power consumption, a dynamic power control algorithm is proposed which depends upon Received Signal Strength Indicator (RSSI) values. Main characteristics of proposed strategy is to observe the effect of different radio power level at regular body movements for communication reliability. A dynamic power control mechanism is considered, where three different transmit radio levels are taken into account. This adaptive strategy works depending on the average Packet Reception Ratio (PRR). Beacon packet fields are used to trigger the transmitter for a change in radio level to ensure the reliable transfer of data. Once the data is gathered by BS it will be transmitted onwards to a server or cloud. Generally, the amount of data transmitted by BSN/WBSNs is enormous. The collected data at cloud can be used for further analysis and predictions about health routines and the spread of diseases [24; 25]. Recently, deep architectures [26; 27], machine learning [28; 29; 30], signal processing [31] and data fusion [32; 33] are utilized on the gathered data to understand the activities performed by the subjects. The rest of the paper is organized as follows. Section 2 describes the wired and wireless BSN design. Section 3 discusses about the proposed energy efficient data transfer mechanism for wireless BSN design. Experimental setup is explained for wired and wireless systems in Section 4. The performance evaluation is carried in Section 5. Finally, Section 6 concludes and provides future direction.
## 4 Materials and Methods
### Material Characterization  
The thermal decomposition curve was obtained using a thermogravimetric (TG) analyzer (Manufacturer: METTLER TOLEDO, Greifensee, Switzerland, Model: DSC 3+). For this analysis, PHZ samples were weighed (mg) and subjected to thermal analysis in the temperature range of 30-800 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min. The thermal analysis was conducted under a nitrogen (N\({}_{2}\)) atmosphere with a flow rate of 100 mL/min. Fourier-transform infrared (FTIR) spectra were recorded using an FTIR spectrometer (Manufacturer: Thermo, Waltham, MA, USA, Model: Nicolet iS5) operating in attenuated total reflectance (ATR) mode. Infrared measurements were performed by mixing a small amount of potassium bromide (KBr) to absorb polymer peaks in the wavenumber range of 4000-400 cm\({}^{-1}\), with a resolution of 4 cm\({}^{-1}\). Phase analysis of the materials was conducted using an X-ray diffractometer (Manufacturer: Dandong Haoyuan, Dandong, China, Model: DX-2700, Cu-K\(\alpha\), 40 kV \(\times\) 30 mA). The scanning range was set from 5\({}^{\circ}\) to 80\({}^{\circ}\) (20), with a scanning rate of 0.5\({}^{\circ}\) and a step size of 0.1\({}^{\circ}\) min\({}^{-1}\). The overall morphology of the composite materials was observed using a scanning electron microscope (SEM) (Manufacturer: Hitachi, Tokyo, Japan, Model: HITACHI SU8010). The cross-section of the materials was obtained via liquid nitrogen freeze-fracture to examine the internal pore distribution and the embedding state of ZIF-8 particles.
## 3 Results and Analysis
### Influence of Pressure Fluctuations and Flow Changes on Dynamic Trajectories
For large GVO conditions, after the onset of BFVSs on the hub side in turbine mode, the total pressure \(P_{\text{GN}}\) increases progressively until the operating points enter into reverse pump mode in the forward direction (Figures 4a and 5a). The total pressure \(P_{\text{GN}}\) then drops sharply with the location of BFVSs turning from hub side to mid-span. When the operating points move out from the turbine-braking mode to the turbine mode in the backward direction, the total pressure \(P_{\text{GN}}\) decreases with BFVSs turning location. Although the changes of total pressure \(P_{\text{GN}}\) of GVO at \(6^{\circ}\) are different from those of GVOs at larger values, the local change trends around the turbine braking region are similar (Figure 6a). Therefore, it can be concluded that the local pressure rise caused by the blockage of BFVSs in front of the guide-vane is not the main reason for un-damped runaway oscillations.
## 2 Materials and Methods
### Measurements
#### 2.3.2 Physical Parameters During the Competitive Match  
Players' movements during the competitive match were recorded using GPS-accelerometer units (Wimu ProTM, RealTrack Systems, Almeria, Spain) at a frequency of 10 Hz. These GPS-accelerometer units have shown a good level of accuracy in the assessment of running patterns in official sports competitions [39; 40]. Following the guidelines provided by the manufacturer, all instruments were placed within a vest and activated 15 min before data collection. This allowed for satellite signal acquisition and the synchronisation of the GPS clock with the atomic clock of the satellite. Additionally, a synchronised heart rate band was placed on the player's chest to monitor heart rate during the match. The selected parameters analysed were distance covered, high-speed running distance covered at different speed thresholds (18.0-20.9 km/h, 21.0-23.9 km/h and \(\geq\)24.0 km/h), explosive distance covered with accelerations over 1.12 m/s\({}^{2}\), total number of accelerations/decelerations, distance covered with acceleration/deceleration over 3 m/s\({}^{2}\) and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Points** & **Measurements and Parameters (Units)** & **Tools** \\ \hline \hline  & Strength measurements: & \\  & \(\bullet\) & IPF (N) and its H:Q ratio & \\  & \(\bullet\) & RFD\({}_{\text{max}}\) (N/s) and its H:Q ratio & Load cell \\  & \(\bullet\) & RFD\({}_{\text{150}}\) (N/s) and its H:Q ratio & \\  & \(\bullet\) & RFD\({}_{\text{250}}\) (N/s) and its H:Q ratio & \\  & & Match-derived parameters & \\  & \(\bullet\) & Distance covered (m) & \\ Baseline & \(\bullet\) & Explosive distance covered over & \\ Post-match & 1.12 m/s\({}^{2}\) (m) & \\
48 h post-match & \(\bullet\) & Distance covered at different speed & \\  & & thresholds [18.0–20.9 km/h; & GPS– \\  & 21.0–23.9 km/h and \(\geq\)24.0 km/h] (m) & accelerometer \\  & \(\bullet\) & Total number of & device and \\  & & accelerations/decelerations (n) & heart-rate band \\  & \(\bullet\) & Distance covered with & \\  & & accelerations/decelerations over & \\  & \(\pm\)3 m/s\({}^{2}\) (m) & \\  & \(\bullet\) & Mean heart rate (bpm) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of time points, measurements, parameters and tools employed.  
mean heart rate [41,42]. A summary of match-derived parameters is shown in Table 1. After each match, data obtained by the GPS-accelerometer units were downloaded using the customised software package SPROTM v1.0.0 and analysed afterwards to obtain the above-mentioned parameters.
# A Survey of Blockchain Applications for Management in Agriculture and Livestock Internet of Things
## 6 Conclusions  
This paper explored how blockchain and the Internet of Things (IoT) can address key challenges in agriculture and livestock, including data security, transparency, and reliability. We began by outlining the principles of both technologies, underscoring their complementary nature in real-world farming contexts. By examining issues such as unclear product origins and vulnerable IoT devices, we highlighted the urgent need for secure, transparent solutions.  
We identified four main areas of blockchain application--data management, supply chain traceability, smart contracts, and security--showing how distributed ledgers can validate sensor data, automate operations, and build trust among stakeholders. While these advancements hold promise, they also face obstacles like regulatory constraints, resource limitations, and network scalability. Future work should focus on designing domain-specific consensus mechanisms, balancing centralized and decentralized frameworks, and integrating AI-driven analytics. Tackling these challenges will integrate blockchain's core advantages--immutability, trust, and decentralized security--more deeply into agricultural IoT, ultimately enhancing efficiency, resilience, and sustainability in food systems.  
Conceptualization, Y.Y. and C.W.; methodology, M.L. and C.Z.; formal analysis, M.L. and C.W.; investigation, M.L. and C.Z.; writing--original draft preparation, Y.Y.; writing--review and editing, C.Z. and Y.L.; supervision, M.L. and C.W.; project administration, Y.L. and C.W.; funding acquisition, C.W. and M.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China under Grant No. 62062031, in part by the Inner Mongolia Science and Technology Key Project No. 2021 GG0218, in part by JST ASPIRE Grant Number JPMJAP2325, and in part by ROIS NII Open Collaborative Research under Grant 24S0601.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
No potential conflicts of interest were reported by the authors.
## 2 Principles and Methods
### Modal Parameters Identification of the Optomechanical Coupling System  
To identify the modal parameters of the spatial filter Optomechanical coupling system, the vibration test was carried out in the experimental hall. Beijing East Institute vibration test analyzer (DASP) was used. The sensor is a high-sensitivity piezoelectric acceleration sensor (model is 393B05) from PCB company of USA, with sensitivity KCH = 10 V/g. The measurement frequency range is 0.5-450 Hz. The sampling frequency is 200 Hz. It can be seen from Figure 2 that the frequency of the vibration response signal in the Y direction of the spatial filter support truss of the PW laser device is mainly within 20 Hz, and the frequency of the vibration response signal in X and Z directions is about 3.18 Hz, indicating that the energy of the response signal in each direction of the support truss is mainly concentrated near these frequencies. The modal parameters of the structure in all directions can be identified according to the actual working conditions.  
This paper focuses on the \(x\) direction modal parameters, which have a great influence on the beam pointing. It can be seen from Figure 2 that the \(x\) direction modal frequency is concentrated around 3.18 Hz (i.e., 20 rad/s). Refer to the actual working conditions to determine the modal parameters of the optical mechanical structure, as shown in Table 1.  
According to the response amplitude frequency and phase frequency curve of Figure 3 Optomechanical structure, the natural frequency of the uncontrolled spatial filter is 20 rad/s and the transmission rate is 15.7. The parameters are used as the reference index for the subsequent research.  
Figure 1: Misalignment principle diagram of spatial filter and its support truss coupled Optomechanical system: (**a**) Integrated support ANSYS torsion pendulum posture; (**b**) split support torsion pendulum posture.
## 2 Methods  
### Modeling of the Active Suspension System  
The active suspension model considered in this paper is the linear quarter model shown in Figure 1. It consists of two masses supported by two dampers and two springs. The first one is the sprung mass \(m_{s}\), which represents the vehicle body and is supported by the suspension, which is modeled by the damper \(b_{s}\) and \(k_{s}\). The wheel mass and the tire contact with the road are represented by the unsprung mass \(m_{us}\), the damper \(b_{us}\), and the stiffness \(k_{us}\), respectively. The nominal values considered in this study are shown in Table 1, and the mathematical model of the active suspension is represented by the following state space:  
\[\dot{\mathbf{x}}=\mathbf{A}\mathbf{x}+\mathbf{B}\mathbf{u}+\mathbf{d} \tag{1}\]  
\[\mathbf{y}=\mathbf{Cx}+\mathbf{D}\mathbf{u} \tag{2}\]  
\[\mathbf{A}=\begin{bmatrix}0&1&0&0\\ -\frac{k_{x}}{m_{s}}&-\frac{b_{x}}{m_{s}}&\frac{k_{x}}{m_{s}}&\frac{b_{x}}{m_{ s}}\\ 0&0&1\\ \frac{k_{x}}{m_{us}}&\frac{b_{x}}{m_{us}}&-\frac{k_{x}+k_{us}}{m_{us}}&-\frac{ b_{x}+b_{us}}{m_{us}}\\ -\frac{1}{m_{us}}\end{bmatrix}\mathbf{B}=\begin{bmatrix}0\\ \frac{1}{m_{s}}\\ 0\\ -\frac{1}{m_{us}}\end{bmatrix}\mathbf{d}=\begin{bmatrix}0\\ 0\\ 0\\ \frac{b_{us}}{m_{us}}\hat{z}_{r}+\frac{k_{us}}{m_{us}}z_{r}\end{bmatrix}\mathbf{x}= \begin{bmatrix}x_{s}\\ \hat{x}_{s}\\ x_{us}\\ \hat{x}_{us}\end{bmatrix}\]  
\[\mathbf{y}=\begin{bmatrix}x_{s}-x_{us}\\ \hat{x}_{s}\end{bmatrix}u=F_{c}\]  
\[\mathbf{C}=\begin{bmatrix}1&0&-1&0\\ -\frac{k_{x}}{m_{s}}&-\frac{b_{x}}{m_{s}}&\frac{k_{x}}{m_{s}}&\frac{b_{x}}{m_ {s}}\\ \end{bmatrix}\mathbf{D}=\begin{bmatrix}0\\ \frac{1}{m_{s}}\end{bmatrix}\]  
The states are defined as: the displacement of the sprung mass \(x_{s}\), the vehicle body velocity \(\dot{x}_{s}\), the displacement of the unsprung mass \(x_{us}\), and the vertical velocity of the wheel \(\dot{x}_{us}\). Other states can be obtained such as the suspension travel \(x_{s}-x_{us}\) and the wheel relative deflection with respect to the road profile \(x_{us}-z_{r}\). \(z_{r}\) and \(\dot{z}_{r}\) represent the changes of the road profile and the rate of its change.  
Figure 1: Quarter model.  
The actuator is positioned between the sprung and unsprung mass and generates a counter force to improve the system performance by reducing the acceleration and the suspension travel. The open loop response of the system can be obtained by setting \(F_{c}=0\), which corresponds to the performance of the passive suspension system. We assume the working of the actuator, also referred to as the action space later, to be between \(-60\) N and \(60\) N.