esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feasibility Study of the Use of PZT Actuators for Active Control to Enrich Engine Sound
## 2 Active Sound Enrichment Using PZT Actuators  
### Sound Radiation by a PZT-Plate Actuator  
Figure 1a,b show the sound radiation of the combination of a rectangular PZT actuator and a rectangular finite plate propagating to the ear location. It is assumed that the four edges of the plate are fully clamped, and the sizes of the PZT actuator and plate in the \(x\) and \(y\) directions are given with \(l_{x}\times l_{y}\) and \(a\times b\), respectively.  
When the PZT actuator is operated by an input voltage, a bending moment is induced by the piezo actuator [1]. The bending moment can make the plate vibrate and radiate structure-borne sound. In this case, it is known that the governing equation of the flexural vibration of an arbitrary location \((x,y)\) of the finite plate when a _centralized_ bending moment \(M(x,y,t)\) by a PZT actuator is applied at continuous time \(t\) can be written as [1; 17]:  
\[D\bigg{(}\frac{\partial^{4}}{\partial x^{4}}+2\frac{\partial^{4}}{\partial x ^{2}\partial y^{2}}+\frac{\partial^{4}}{\partial y^{4}}\bigg{)}w(x,y,t)+\rho h \frac{\partial^{2}w(x,y,t)}{\partial t^{2}}=M(x,y,t), \tag{1}\]  
where \(w(x,y,t)\) is the flexural displacement of the plate and \(D=Eh^{3}/12\big{(}1-\
u^{2}\big{)}\) is the flexural rigidity of the plate, assuming that it is much greater than that of the piezoactuator,in which, \(h\), \(v\), \(\rho\) and \(E\) are the thickness, Poisson's ratio, density, and Young's modulus of the plate, respectively. The magnitude of the bending moment \(M\) can be assumed to be proportional to the input voltage \(V\) and given as [1; 17]:  
\[M=\alpha V, \tag{2}\]  
where \(\alpha\) is the coefficient determined from the combined mechanical and electrical properties of the PZT actuator and plate [1].  
The bending moment generated at the edges of the piezoactuator can be assumed to be concentrated, as plotted in Figure 1a,b, and excites the plate to vibrate in out-of-plane flexural modes. These flexural vibrations \(w(x,y,t)\) of the plate radiate sound, and this is a vibro-acoustic phenomenon. Sound radiation in this manner is crucially affected by the properties of the PZT actuator and plate [1]. The specifications and dimensions of a proper PZT actuator for adequately generating supplementing engine sound should be carefully selected. The impulse response of a well-designed and well-located piezo-plate actuator can satisfy the requirement for a robustly stable ASE control, which will be discussed in-depth.
# Design and Implementation of a Two-Wheeled Self-Balancing Car Using a Fuzzy Kalman Filter
## 2 The Two-Wheeded Self-Balancing Car System
### The PD Control for the Two-Wheed Self-Balancing Car  
Upon initialization of the self-balancing car, the first step involves state detection to determine whether the current orientation matches the target inclination angle. If not, the motor is adjusted to accelerate accordingly, aiming to bring the vehicle to an upright position through angle control. Concurrently, the velocity sensor provides feedback on motor status to fine-tune input values, thereby establishing a complete negative feedback system. Upon the acquisition of angle information by the sensors, the angular velocity undergoes PD control within the upright loop to regulate the vehicle's posture. Increasing the proportional coefficient enhances the vehicle's responsiveness, while the differential coefficient influences the stability of the self-balancing car when it is not inclined. Once the proportional and differential coefficients are established, adjustments to the motion posture of the two-wheeled self-balancing car can be made based on speed information provided by the encoder feedback. The expression for its PD control within the upright loop can be simplified as follows:  
\[\begin{array}{ll}M(k)&=k_{p}e(k)+k_{d}(e(k)-e(k-1))\\ &=k_{p}[\overline{\theta}(k)-\theta(k)]+k_{d}(0-\theta(k))\end{array}. \tag{6}\]  
where \(\overline{\theta}(k)\) denotes the desired equilibrium angle, typically set to zero, and \(M(k)\) signifies the PWM value. \(k_{p}\) and \(k_{d}\), respectively, correspond to the proportional and derivative coefficients of the PD control algorithm.  
The precision of angle data acquisition significantly impacts the efficacy of control algorithms. However, relying solely on accelerometers and gyroscopes for angle determination presents certain limitations. Accelerometers provide relatively accurate long-term readings but exhibit notable fluctuations, potentially due to high-frequency noise contamination. Conversely, gyroscopes offer short-term accuracy but are susceptible to integration errors, suggesting the presence of low-frequency noise. To mitigate the adverse effects of noise on data accuracy, angle measurements obtained from accelerometers and gyroscopes can undergo filtering and smoothing procedures, thereby enhancing measurement precision.
# Novel Drug Combinations in Lung Cancer: New Potential Synergies Between 5-FU and Repurposed Drugs
## 3 Results
### The Effect of Sildenafil as a Single Agent and in Various Combinations with 5-FU  
In this study, A549 cells were treated with increasing concentrations of Sildenafil (1 \(\upmu\)M to 100 \(\upmu\)M), either alone or in combination with 5-FU at various concentrations. According  
\begin{table}
\begin{tabular}{c c} \hline
**Drug** & **IC\({}_{50}\) (\(\upmu\)M)** \\ \hline
5-FU & 5.03 \\ Sildenafil & 32.59 \\ Tezosentan & \textgreater{}100 \\ Levosimendan & 16.14 \\ \hline \end{tabular}
\end{table}
Table 1: IC\({}_{50}\) values of 5-FU, Sildenafil, Tezosentan, and Levosimendan in A549 cells.  
Figure 1: Viability of A549 cells following treatment with 5\(-\)FU. The cells were exposed to different concentrations of 5\(-\)FU (0.1\(-\)100 \(\upmu\)M) and after 48 h, the MTT assay was performed to assess cell viability. (**A**) Cell viability and (**B**) dose–response curve. Data are presented as percentages relative to the control group and represent the mean \(\pm\) SD. Each experiment was performed independently three times (\(n=3\)); **** statistically significant vs. control at \(p<0.0001\).  
to Table 1, the IC\({}_{50}\) of 5-FU was tested in combination with increasing concentrations of the vasodilator (0.1, 1, 10, 25, 50, and 100 \(\upmu\)M). After 48 h, cell viability was measured using the MTT assay, as described previously, and the results were recorded. Additionally, morphological evaluations were performed to observe any visible changes in the cells.  
Sildenafil alone showed no significant anticancer activity at any of the tested concentration, as shown by the MTT assay (Figure 2). However, combining 5-FU with Sildenafil at 0.1 \(\upmu\)M, 10 \(\upmu\)M, 25 \(\upmu\)M, and 100 \(\upmu\)M resulted in a notable reduction in cell viability compared to Sildenafil alone, with up to a 41% decrease observed. Furthermore, the morphological analysis confirmed that only 5-FU was active in this cell line, as Sildenafil alone had no impact on the appearance of the cells (Figure 3). Morphological changes were only evident when 5-FU was combined with Sildenafil, suggesting that these changes were caused by the antineoplastic activity of 5-FU (Figure 3).  
Figure 2: Cell viability of A549 cells following treatment with Sildenafil alone and in combination with 5-FU. Data are presented as percentages relative to the control group and represent the mean \(\pm\) SD. Each experiment was performed independently three times (\(n=3\)); * statistically significant vs. drug alone at \(p<0.05\); ** statistically significant vs. drug alone at \(p<0.01\); *** statistically significant vs. control at \(p<0.0001\). The green, red, black and gray bars are used solely to differentiate between data groups and do not represent specific experimental variables.  
Figure 3: Morphological assessment of A549 cells treated with 5-FU, Sildenafil alone, and their combination. Cells were exposed to the vehicle (0.1% DMSO) as a control. The results are representative of three separate experiments. Scale bar: 50 \(\mu\)m.
## Materials and Methods
### Experimental Program  
#### Isothermal Calorimetry  
The isothermal calorimetry technique was used to study the reaction heat and hydration. A TAM-Air isothermal conduction calorimeter (TA Instruments, New Castle, DE, USA) was used to measure the heat release rate and cumulative heat of the adhesives. Calibration was done at 20 \({}^{\circ}\)C, before measurements for a period of 72 h. All binders were mixed by a mortar mixer and transferred to the calorimeter. In addition, [PERSON] et al. [22] experimentally found that the curve obtained by the first-order derivative of the heat release rate curve showed a good correlation with the setting time. Therefore, the samples' setting time was obtained by the first derivative of the heat release rate curve. This method can accurately assess the hydration process while accurately determining the setting time.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **MgO** & **SO\({}_{3}\)** & **ZnO** & **K\({}_{2}\)O** & **P\({}_{2}\)O\({}_{3}\)** & **Loss** \\ \hline Cement (\%) & 21.95 & 6.07 & 2.45 & 61.38 & 2.80 & 2.54 & 0.11 & 1.58 & 0.10 & 0.46 \\ Nano-silica (\%) & 99.80 & 0.05 & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of cement and nano-silica.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{3}{c}{**Binder/\%**} & \multirow{2}{*}{**w/b**} & \multirow{2}{*}{**Superplasticizer/\%**} \\ \cline{2-2} \cline{6-6}  & **Cement** & & & **Nano-Silica** & \\ \hline nS0 & 100 & 0 & 0.2 & 0.9 \\ nS2 & 98 & 2 & 0.2 & 2.4 \\ nS4 & 96 & 4 & 0.2 & 3.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mix proportion design of ultra-high strength concrete (UHSC).  
The final setting time (start time of the autogenous shrinkage test) is related to the time at which the concrete evolves at the maximum rate during the acceleration period [23].
# Application and Assessment of Cooperative Localization in Three-Dimensional Vehicle Networks
###### Abstract  
The trajectory planning and control of multi-agent systems requires accurate localization, which may not be possible when GPS signals and fixed features required for SLAM are not available. Cooperative Localization (CL) in multi-agent systems offers a short-term solution that may significantly improve vehicle pose estimation. CL algorithms have been mainly developed and assessed for planar mobile robot networks due to complexities and singularities in three-dimensional (3D) motion. In this paper, we develop the required singularity-free equations and apply and assess an EKF-based CL for 3D vehicle networks. We assess the performance of CL with respect to the number of simultaneous and redundant measurements. We further assess CL performance with only relative position measurements available. Finally, experiments are performed to validate the proposed algorithms. We further investigate the effect of absolute position measurements in CL  
data sharing [12]. In an early application of CL, an EKF algorithm [13] was developed that distributes vehicle state information while keeping track of the cross-covariance terms. These terms are created by relative measurements of each 2D vehicle's pose. However, this method requires a completely synchronous and interconnected communication network. A decoupled CL algorithm is proposed [14] that eliminates the need for complete and constant between vehicles. Vehicles are assumed to be equipped with the capability of obtaining their own pose (via equations of motion and onboard sensors), measuring the poses of nearby vehicles relative to their own reference frame, and processing this information and communicating with other vehicles. CL allows a group of vehicles to cooperatively approximate an individual vehicle's pose by sharing the relative and absolute pose measurements that each vehicle has obtained.
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 4 Our Proposal
### Improving the Ant Clustering Algorithm for Sensor Node Clustering
#### 4.2.2 Main Clustering
The pattern of the pick-up function increases as the value of K\({}_{1}\) increases, but the dropping probability decreases as the value of K\({}_{2}\) increases. We chose the most relevant K\({}_{1}\) and K\({}_{2}\) during our experiment to achieve the best outcomes. In our contribution, we assigned the pheromone concept to the density function, as named in our paper, and the function of similarity is illustrated in Equation (11). This function defines the similarity between a sensor and its neighbours. Hence, a high similarity value signifies a high pheromone value in the context of ant behaviour. The quantity of pheromomes (similarity between a sensor and its neighbours) controls how an ant moves. In more detail, the updating pheromone mechanism is defined by computing the pick-up and dropping probabilities concerning removing and inserting a sensor from a cluster, as illustrated in Equations (13) and (14). The calculation of the pick-up and dropping probability functions is based on the similarities between a sensor and its neighbouring sensors. If the pick-up probability value of a sensor in a given field is high, the similarity function (pheromone) between the remaining sensors in this field becomes higher when this sensor is removed. Similarly, when the dropping probability value of a sensor in a new field is higher, the similarity function in this new field (cluster) increases after it is added, which means that the pheromone concentration increases, which indicates convergence to the solution (cluster).
# Upscaling Gross Primary Production in Corn-Soybean Rotation Systems in the Midwest
## 2 Material and Methods
### Data Preparation, and Statistical Analysis  
The variation of corn and soybean GPP among EC stations was quantified by calculating the 95% confidence intervals (CI) using the bootstrap percentile method with two thousand replications. Bootstrapping was run with the boot package in R [41]. Relationships between NEP, GPP, RE and climate parameters were evaluated with Pearson correlation and the regression coefficient (r) was calculated. The ACPF land use maps were validated with the 2016 county-wide corn and soybean production area [1] using linear regression analysis (p \(<\) 0.05) and cross-checked with the MODIS land cover type product (MCD12Q1) from 2016 [38; 42]. The median EC-footprint and county-wide fPAR, GPP\({}_{\text{calc}}\), and GPP\({}_{\text{MODIS}}\) were calculated for further analysis. The footprint fPAR was validated against field-measured NDVI with linear regression analysis (p \(<\) 0.05). GPP\({}_{\text{calc}}\) was validated against EC-derived GPP using a linear regression analysis (p \(<\) 0.05) and was also compared to the GPP\({}_{\text{MODIS}}\) product [5; 38]. The median county-wide GPP\({}_{\text{calc}}\) was interpolated with an inverse weighted time average procedure to produce a daily dataset, and grain yield was calculated [7; 12] as:  
\[Yield_{GPP}=\ \frac{GPP_{\text{calc}}*\frac{HI}{(1+RS)}*\frac{1}{(1-M)}}{OC} \tag{6}\]  
where: Yield\({}_{\text{GPP}}\) = calculated yield data (Mg dry weight ha\({}^{-1}\)), GPP\({}_{\text{calc}}\) = seasonal sum of daily median county GPP\({}_{\text{calc}}\) (g C m\({}^{-2}\)), HI = Harvest index, RS = root-shoot ratio, M = % moisture content, OC = % grain carbon content.  
The HI was set to 0.50 and 0.57 [43; 44], the RS was 0.18 and 0.15 [45], M was 15.5% and 13%, and OC was 44.7% and 54% [46], for corn and soybean, respectively. The Yield\({}_{\text{GPP}}\) was compared to the 2016 county yield data [1]. All figures were created with the R programming language [41].
# An Optimal Scheduling Model for Connected Automated Vehicles at an Unsignalized Intersection
## 5 Conclusions and Future Work  
In this paper, a CAV timing optimization model for intersections is developed, and a rolling optimization algorithm is designed to improve the efficiency of the algorithm solution. Through numerical simulation experiments, the following conclusions are drawn.  
(1) Compared to the FCFS control method, the algorithm and solver proposed in this paper can significantly reduce the average vehicle delay. Under different traffic demand scenarios, the proposed algorithm can obtain solutions that are not significantly different from those obtained by the solver.  
(2) Compared to the solver, the proposed model and algorithm can significantly reduce the solution time while ensuring the optimization effect of the model to the greatest extent.  
Figure 3: Sensitivity analysis diagram of safe headway. (**a**) Average delay; (**b**) Delay reduction percentage.  
(3) The average computation time of the proposed algorithm shows a trend of first decreasing and then increasing with the optimized time window length. This indicates that in this scenario, an optimal time window exists for computation time, and its value is 20 s.  
(4) The average vehicle delay at the intersection gradually decreases as the safe headway distance decreases.  
The proposed model and algorithm can be applied to relatively simple scenarios, such as automated guided vehicles (AGV) in logistics warehouses or freight trucks in cargo ports. However, the intersection considered is relatively simple and does not consider turning vehicles at the intersection. Therefore, the next step will be to investigate the timing optimization model and efficient algorithms for more complex intersections, such as multi-turn intersections [39; 40]. Additionally, considering that vehicles passing through multiple intersections can simultaneously optimize their time to pass through these intersections, the vehicle timing optimization model and algorithm for multiple intersections or intersection groups [41; 42] will also be one of the future research directions.  
W.B., Writing--original draft, Writing--review and editing. C.F., Writing--original draft, Methodology, Visualization, Writing--review and editing. B.Z., Methodology, Formal analysis, Validation, Writing--review and editing. G.L., Resources, Software, Writing--review and editing. Z.Y., Conceptualization, Funding acquisition, Project administration, Supervision, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Intelligent Policing Key Laboratory of Sichuan Province, No. ZNJW2024 KFQN001).  
No new data were created or analyzed in this study.  
The authors declare no conflicts of interest.
# Inductive Thermography as Non-Destructive Testing for Railway Rails
## 6 Measurements of Head Checks from Rails in Service
### Characterization of the Rail Pieces  
With the methods discussed in the previous sections, the average inclination angle distance as well as the average head check length were determined by the novel image processing algorithm. Since the panorama images are rectified, the calculated angles and lengths are not distorted any more by the camera optic and by the curvature of the rail. Table 1 summarizes the results for the 3 presented rail pieces. It is of note that \(\alpha\) is negative for RP02 and for RP03, as the head checks have an orientation of NW-SE, and on the other hand RP04 has a positive angle due to the NE-SW orientation.
## 3 Results and Discussion  
### Evaluation of Physicochemical Properties of cosmetic Macroemulsions Tested  
#### 3.1.1 pH  
The pH values were measured on day 0 and on day 30 for the macroemulsions studied stored at room temperature for 30 days. It is assumed that the pH of the macroemulsions should be slightly acidic (pH of 5.5) or neutral (pH of 6-7.7) [19] so as not to disturb the functioning of the hydrolipid skin coat. Changes in the pH during a cosmetic's storage may indicate the growing instability of a given cosmetic formulation caused by the compromised quality of the raw products used or the earlier contamination of the formulation in the process of production [20]. The changes in the pH observed for 30 days were insignificant. For macroemulsion 1 (face cream), the pH decreased by about 2%, from 6.52 \(\pm\) 0.02 (day 0) to 6.38 \(\pm\) 0.01 (day 30), while for macroemulsion 2 (serum for the body), the value of the pH increased from 5.27 \(\pm\) 0.02 (day 0) to 5.42 \(\pm\) 0.01 (day 30). The pH values of the macroemulsions studied were concluded to be correct, and the small changes in the pH over a period of 30 days confirmed the high quality of the raw products, including the _Solidago virgaurea_ extract, and the effectiveness of the technological process preventing the contamination of the products.
## 3 Results and Discussions
### Quantifying Safety Benefits via Connected Vehicles
However, this assumption only applies to collisions that occurred on surface conditions our model was trained on--snowy and city conditions. After filtering the data and removing duplicate reports, a total of 336 collisions remained. Among these collisions, random sampling without replacement was implemented to select 76.9% of the dataset (294 collisions); these represent the hypothetical collision events that could have been avoided due to our model. In the case of scenario one, the process stopped here as all 294 collisions were considered avoidable as a result of rerouting. In comparison, for scenario two, an additional sampling procedure was performed to select 18% of the 229 collisions to account for the 18% CRF. Table 2 identifies the potential number of collisions mitigated in each scenario using the proposed framework. The total savings for the four days was estimated to be CAD 7,796,420 (CAD 1,949,105 per day) and CAD 1,141,726 (CAD 285,431 per day) for scenarios one and two, respectively. These benefits were larger than those calculated in Table 1, which was expected since this was a city-wide evaluation. While the findings presented herein offer valuable insights into the potential safety benefits of utilizing connected vehicles (CVs) for real-time hazardous road surface conditions monitoring, it is important to acknowledge that several assumptions have been made in generating these results. These assumptions include the availability of city-wide friction information, the model's ability to maintain its performance when data variation increases, the rerouting process successfully resolving the incident without causing issues elsewhere, and the driver responding to the safety message by slowing down. As a result of these assumptions, it may introduce certain limitations to the generalizability of our conclusions. Nonetheless, the proposed framework serves as a pioneering effort toward quantifying the safety benefits of this advanced technology. It provides a structured pathway for future research and practical applications, thereby underscoring the transformative potential of CVs in enhancing road safety, especially during challenging weather conditions.