esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Output Voltage and Feasibility
[100] found that a BP5-GFRP composite with 5 wt% BaTiO3 (d33 \(\approx-33.9\) pC/N) generated up to 2 uW under 1 g sinusoidal vibration; BP5-CFRP provided 1.5 uW. Over 5,000 vibration cycles, these setups remained stable. In real-world tests, BP5-CFRP yielded 33.69 uW on an aircraft and 3.44 uW in a vehicle, suitable for structural health monitoring. [PERSON] et al. [67] introduced a PTNG reaching 200 V output, suggesting enough power for small devices or IoT sensors. This modular design could scale to larger surfaces (e.g., mosque floors) and stay efficient under many cycles. Finally, for smart mats, [PERSON] et al. [97] highlight the importance of a textile that is home-friendly--washable, durable, and able to match interior decor--while [PERSON] et al. [26] emphasize foldability, critical for venues such as religious sites that require frequent cleaning. Figure 9 compares the output voltage and circuit type across these studies, indicating that [PERSON] et al. [3] achieved the highest voltage with an open-circuit configuration. Regarding the impact of high weight on the output voltage, Figure 10 compares the reviewed studies. All studies presented in the figure reported the output voltage when weight and height were low. The work of [PERSON] et al. [26] reported the highest voltage when the weight was low and high. Figure 10: Voltage output across different weight conditions ([PERSON] et al. [3], [PERSON] et al. [5], [PERSON] et al. [16], [PERSON] et al. [18], [PERSON] et al. [27]). Figure 9: Comparison Heatmap: Output Voltage Across Studies ([PERSON] et al. [3], [PERSON] et al. [4], [PERSON] et al. [5], [PERSON] et al. [6], [PERSON] et al. [16], [PERSON] et al. [17], [PERSON] et al. [18], [PERSON] et al. [26]). Figure 11 presents the time of continuous foot traffic and its relationship with output voltage. On average, if there are 20 s of constant foot traffic, the voltage output records its highest amount. Noticeably, the work of [PERSON] et al. [18] shows a consistent increase in voltage while foot traffic is continuous. Finally, Figure 12 presents the design capability proposed in each study reviewed regarding portability, durability, washability, modularity, and continuous use suitability. It is noticeable that the work of [PERSON] et al. [26] was the only design that met all those criteria. Figure 11: Voltage Output over time under continuous foot traffic ([PERSON] et al. [5], [PERSON] et al. [18], [PERSON] et al. [26]). Figure 12: The capability of the design proposed in each study is reviewed in terms of portability, durability, washability, modularity, and continuous use. 1–5 is rating score ([PERSON] et al [3], [PERSON] et al. [5], [PERSON] et al. [16], [PERSON] et al. [18], [PERSON] et al. [26]).
## 5 Data Processing and Definition of the EAR Index
### Definition of the Performance Coefficients
#### 5.2.1 TDC Coefficient  
At a structural level, the occurrence of top-down cracking (TDC) is an increasingly common problem on highway networks characterized by draining mixtures and in correspondence with particularly strong superstructures. This phenomenon was fully investigated in Phase 1 of the HiPER project: a project launched by the Autostrade per I'Italia group in 2020 aimed at optimizing the management of motorway pavement maintenance processes [28] during which a model and a methodology for calculating and quantifying the TDC based on traffic volume, age of the asphalt and the indirect tensile strength of the material were identified. The parameters used in the model are actually available following pre-qualification, making this methodology particularly well suited for the purpose.  
The evaluation of the TDC coefficient (simply indicated as \(TDC\)) follows the procedure developed by [PERSON] et al. [29], which starts from the evaluation of the _traffic factor_\(n\) according to (7), that expresses the passages of \(N_{\text{sa}}\) standard axles of 120 kN (i.e., ESAL\({}_{120}\)) scaled by a factor equal to \(10^{8}\).  
\[n=N_{\text{sa}}/10^{8} \tag{7}\]  
The second and third steps consider the evaluation of the _aging factor_\(a\) of the pavement according to (8) and the parameter \(\beta\) linked to the indirect tensile strength value (\(ITS\)) of the wearing course of draining mixtures according to (9). \(\tau_{y}\) is the age of the pavement expressed in years.  
\[a=1.008-0.071\cdot\tau_{y} \tag{8}\]  
\[\beta=0.716-0.220\cdot ITS \tag{9}\]  
Finally, the \(TDC\), expressed in millimeters, can be evaluated for different passages \(N_{\text{sa}}\) as:  
\[TDC(N_{\text{sa}})=TDC_{max}\cdot e^{-\left(\frac{\beta}{\beta}\right)\beta} \tag{10}\]  
In the evaluation of the \(EAR_{\text{c}}\) index, the corresponding multiplication factor \(I_{1}\) is evaluated by setting the age of the asphalt mixture equal to 10 years and a traffic volume equal to 80 MESAL\({}_{120}\) as:  
\[I_{1}=\frac{1}{\frac{TDC_{\text{rt}}}{TDC(N_{\text{sa}})}} \tag{11}\]The value of \(TDC_{\text{ref}}=85\) mm is used to normalize the \(I_{1}\) index. It was obtained by applying the model described in (11) by setting a value \(\tau_{y}\) equal to 10 years (which is a typical value for pavement resurfacing) and annual traffic equal to 80 MESAL\({}_{120}\). The value of 85 mm is the average value obtained by varying \(ITS\) between 0.4 and 0.9 (which are the limit values imposed by the specifications [30, 31, 32, 33, 34]) and is therefore taken as the reference value.  
Hence, the coefficient \(I_{1}\) owns a value higher than 1 if the characteristics of the asphalt mixture lead to a top-down cracking type lesion deeper than 85 mm after 10 years. It is worth recalling that this coefficient applies to the open-graded mixtures but it is set equal to one for all other kinds of mixtures.
## 2 Methods and Data
### Data Sources and Solving Strategies  
In this paper, the dual-frequency GPS observation files of Swarm (Swarm-A/B/C) and GRACE-FO satellites (GRACE-C and GRACE-D) during the day of year (DOY) 152-165 (from 1 June to 14 June) in 2021 were adopted, which were provided by the ESA and theGFZ. In the processing, the GPS precise orbit products with a 15 min sampling rate and the precise GPS clock offset products with a 30 s sampling rate were provided by IGS, and the Earth Orientation Parameter (EOP) data were provided by the Center for Orbit Determination in Europe (CODE).  
The RDPODs of Swarm and GRACE-FO satellites were simultaneously carried out with KPOD, while the result of RDPOD was used as a reference to control the quality of the KPOD result. We used Bernese GNSS Software to process the data, replacing an explicit modeling of non-gravitational forces by dedicated empirical orbit parametrizations [27], as the main purpose is KPOD in this paper. The force models, data sets and processing strategies of POD are summarized in Table 1.
# Optimal Design of Droplet Ejection for PZT Printhead Based on Surrogate Model
## 2 Description of the PZT Printhead  
Figure 1 is a single ejection unit of the PZT printhead used in this study. Each ejection unit has the same structure and working principle, and the nozzle can be controlled for independent ejection. The ejection units are arranged in a straight line and are driven by piezoelectric ceramic sheets. Each printing unit has an independent driving mode and a switching mode so that each nozzle will not affect the other in the process of working. Each individual nozzle unit contains an ink reservoir, an ink chamber that absorbs ink from an ink box, a nozzle, a PZT film and a pair of positive and negative electrodes that control the movement of the PZT membrane. When the excitation pulse voltage with a certain edge slope (as shown in Figure 2) is loaded to the PZT membrane, the excited membrane will bend and deform to squeeze the ink chamber; thereby, the volume of the ink chamber will decrease, and the ink will be ejected from the nozzle. After experiencing a pulse voltage signal, the excitation pulse voltage will be removed with a certain edge slope, and the PZT membrane will return to its original state. Negative pressure will be generated in the chamber to pull the neck of the droplet off, forming an independent droplet.  
Figure 1: PZT printhead ejection unit.  
In this study, to verify the effectiveness of the models, a droplet ejection observation experimental platform was built, as shown in Figure 3. The experimental bench mainly includes droplet ejection, observer and drive control modules. In the droplet ejection module, droplet jetting is performed using a Fujifilm Sapphire QS256/80 AAA model printhead, which has 36 independent array prints and uses furan resin as the ejection material. The droplet observer module contains a video recorder, a flash and a display device. High-strength, well-aimed infrared light-emitting diodes are used to illuminate the trajectory of the droplet movement and to help record the video and pictures of the droplet. The aiming direction of the camera and the LED light line are needed to form a certain obtuse angle during placement so as to achieve a better shooting effect in the case of fewer hot spots. The ignition signal that drives the printhead can be transmitted to the video recorder and flash at the same time, allowing the two modules to work simultaneously to record the complete movement of the droplet. The setting parameters of the test bench are shown in Table 1.  
Figure 3: Experimental platform.  
Figure 2: Driving pulse signal.
## 3 Second-Order System Performance
### Average Fade Duration  
The AFD shows the average time that the signal envelope spends below the specified threshold level. This LCR measurement is used to design a diversity scheme for cellular systems. The AFD is expressed in units of seconds.  
The AFD can be evaluated as the ratio of the Pout and the LCR ([35], Equation (2.106)):  
\[AFD=\frac{Pout}{N_{z}(z)} \tag{41}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**LCR** & \(z=-10\)**dB** & \(z=0\)**dB** & \(z=10\)**dB** \\ \hline \(\kappa_{y}=1\), \(\mu=1\), \(L=2\) & 5 & 9 & 8 \\ \hline \(\kappa_{y}=2\), \(\mu=1\), \(L=2\) & 7 & 12 & 10 \\ \hline \(\kappa_{y}=3\), \(\mu=1\), \(L=2\) & 9 & 14 & 12 \\ \hline \(\kappa_{y}=4\), \(\mu=1\), \  
(increases from 6 to 8), while for \(z=0\) dB and \(z=10\) dB, the number of elements increases, and the series converges more slowly.  
From Table 12, one can see that the number of additions in sums in (42) becomes bigger when \(\kappa_{y}\) and \(\mu\) increase, while this number of additions is quite small when \(L\) increases.
# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
## 3 Methodology
### Active Learning Query Strategy
#### 3.2.3 Expected Error Reduction (EER)  
EER is a informative-based strategy which selects the next instance that maximally reduces the generalization \"error\" or \"loss\" in expectation [39]. It takes into account the uncertainty or informativeness of unlabeled instances and measures the potential impact of querying them on the overall error reduction of the learning model.  
The key idea behind EER is to estimate the expected reduction in error that can be achieved by labeling specific instances. It involves selecting those samples expected to have the greatest impact on improving the model's performance. This is achieved by considering the uncertainty or lack of confidence in the current predictions made by the learning model. The intuition is that by querying instances that are difficult to classify or that lie near the decision boundary, the model can obtain crucial information to refine its decision-making process.
## 5 Performance Evaluation and Results Discussion
### Results Discussion  
The results discussed here are measured after approximately 95 simulations. Figure 2 shows the network after 200 rounds of data transmission. The number of CHs, relay nodes and CCOs will increase with the increase in the scale because with the increase in the size of the network, so a large number of coordinators will be required to connect and maintain the cooperative process. When the position of the BS and other nodes (RN, CH, CCO) is fixed, then less energy is consumed as there is no need to search again and again for these nodes. If the communication radius of the nodes is increased they can cover a large area, and then these nodes can be decreased in number in each layer. Nodes are deployed randomly, so their density is high. The number of nodes required for the cooperative process can vary according to the density of the nodes and scale of the network for energy efficient IoT. Figures 3-6 show the network lifetime for different scales of the Internet of Things. These figures show the comparison of network lifetime of the network with different traditional protocols proposed for WSNs in the recent past.
## 2 Experimental Setup
### Sensor Setting  
Figures 2 and 3 show a sketch and a photograph of the mechanical setup inside the temperature-controlled box respectively. This concentrates on a good acoustic coupling between the PZT sensor and the probe and an equally distributed air flow throughout the experiment.  
Figure 1: A sketch of an acoustic burst with some key indicators that are used for parameter-based analysis: peak-to-peak amplitude (AMPL), duration (D), rise time (RT), acoustic energy (E) and the trigger threshold (THD).  
The sample holder for the water/ice and the PZT sensor were both positioned on a 1 mm thick aluminum plate that serves as a wave-guide for the AE signals. To guarantee a good coupling between the PZT and the plate, some vacuum grease (Dow Corning, High vacuum grease) is used as couplant. As the sample holder has no bottom, the water/ice is directly touching the aluminum plate and no additional couplant is needed. The hollow sample holder, thus sealed with vacuum grease, allows the initially liquid water directly touching the plate. The PZT sensor is placed close to the sample (ca. 2 cm) and acoustically coupled to the plate with some vacuum grease.  
Small foam pieces are placed under the aluminum plate to damp mechanical and acoustic vibrations from the outer housing. The aluminum plate is providing a good acoustic coupling of the ice and the PZT sensor and reproduces realistic conditions as in a real application (e.g., in an aircraft fuel tank), where signals will also be transmitted via sheet-structures comparable to the aluminum plate. Although later applications monitor melting ice systems that that are a factor of 10-100 larger in size, a down-scaled experimental setup was designed to ensure an equal and thorough controlled temperature during the measurements with in-house equipment.  
As described in Section 2.2, for an optimal performance of the temperature control system, a good air circulation inside the box is required. The drawback of the internal ventilator is its vibration that interferes with the AE measurement and is therefore reliably damped with a piece of foam between the ventilator and the outer housing of the box.  
Figure 3: A photo of the sensor setup. The specimen and the PZT sensor are positioned on an aluminum plate which acts as a wave-guide. The plastic cylinder holds the water (for demonstration purposes colored in blue) and has no bottom so that the water is directly touching the aluminum plate.  
Figure 2: A sketch of the experimental setup inside the temperature-controlled box. An 1 mm thick aluminum plate is holding the sample of water (**a**) as well as the PZT sensor (**b**) and therefore couples the two acoustically. The internal van (**c**) ensures sufficient air movement and a uniform temperature inside the box.
## 5 Simulation Debugging
### Simulated Waveforms for Optical Axis Pointing Calculation  
The algorithm is used for waveform simulation on the FPGA platform. According to the VESA standard, the pixel clock of \(1920\times 1080@60 Hz\) is 148.5 MHz. Testbench uses a 100 MHz simulation clock in the waveform simulation in order to facilitate the simulation calculations.  
The simulation of the optical axis pointing calculation waveform is shown in Figure 13. Pos_qin_en is the enable signal for the input quaternions \(q_{1}\), \(q_{2}\), \(q_{3}\), and \(q_{4}\) from the upper computer. The signals named in the figure beginning with mult are multiplication operations of quaternions; the signals named beginning with sub and sum are addition and subtraction operations; and register x\({}_{0}\), register y\({}_{0}\), and register z\({}_{0}\) are the three elements of the optical axis pointing to the space vector. Waveform simulation results show that 10 multipliers complete the computation at 605 ns, 10 adders or subtractors complete the operation at 615 ns, and three elements of the optical axis pointing to the space vector are obtained at 625 ns, which is in accordance with the computation of Equation (6). The parallel computation of the FPGA here consumes 40 ns, which would consume 330 ns if a fully serial computation method were used.  
Figure 12: (**a**) MATLAB-simulated star map; (**b**) star point markings on the star map.  
Figure 13: Simulation of the optical axis pointing calculation waveform.
## 2 Materials and Methods
### PXRF Measurements  
A portable X-ray fluorescence analyzer (pXRF) was used to identify and measure PTEs at: (i) profile scale, in situ, at field conditions, on soil profiles, from the surface to a variable depth ranging from 70 and 185 cm; and (ii) on samples collected at 2 depths (0-15 and 15-40 cm) by hand drilling, following the regular grid designed for the systematic investigations carried out by \(\gamma\)-rays spectrometer and penetrometer surveys, with an additional 39 points (for a total of 120 points). Measurements on hand drilling samples were performed after drying and sieving (<2 mm) soils in lab. Scanning was performed with a Delta Professional (Olympus, DPO-4000) using an 8 mm\({}^{2}\) window. The instrument featured a Ta X-ray tube operating at 15-40 kV with integrated large area silicon drift detector (165 eV). Innov-X software was used in Soil mode, the modality dedicated to trace element quantification, consisting of three beams operating sequentially, with acquisition times of 30 s per beam. Twenty elements (As, Ca, Cd, Cr, Cu, Fe, K, Mn, Nb, Ni, Pb, Rb, Sn, Sr, Th, Ti, U, V, Zn and Zr) were measured, on smooth uniform surfaces completely in contact with the instrument to minimize surface effects. Descriptive statistics of data measured with sampling on the regular grid, as well as data concerning quality checks on certified materials, are reported in [PERSON] et al. [22].
## 5 Titanium Dioxide as a Photocatalyst for Water Remediation Applications
### Single Atom Doped TiO\({}_{2}\) Composites
In the study, a 300 W solar lamp was used with an emission spectrum similar to natural light and mainline radiation at 365 nm. The reaction solution was constantly stirred with airflow. The optimal concentration of photocatalyst of 1 g \(\times\) dm\({}^{-3}\) was chosen as the optimal concentration for photochemical treatment of bacteria based on the authors' previous research. The tests were repeated three times in order to ensure reproducibility. The achieved margin of error was 0.1%. Reaction time was set at 5 h. The samples were incubated outdoors for 24, 48, and 72 h to check for bacteria regrowth. The turbidity of the samples was reduced to half of the initial value. The following various light intensities, 30, 60, and 120 W \(\times\) m\({}^{-2}\) were also studied. The highest CFU \(\times\) cm\({}^{-3}\) reduction was achieved for 120 W \(\times\) m\({}^{-2}\) light intensity. In tests without the photocatalyst, no complete removal was achieved even after 5 h of irradiation. In the presence of a 2 wt% Pt-TiO\({}_{2}\) and at 120 W \(\times\) m\({}^{-2}\) light intensity, all bacteria (_E. coli_, total coliforms, and other _Enterobacteriaceae_) were eliminated after 3 h of irradiation and no bacterial regrowth was detected even after 72 h. In tests, where light with the intensity of 120 W \(\times\) m\({}^{-2}\) was used for the given irradiation time, it was possible to completely remove _E. coli_ species and most of the other accompanying bacteria. Addition of the photocatalyst allowed us to remove a higher number of remaining bacterial contamination in the sample. It is worth noting that in the case of irradiation with such a high light intensity, temperature increase (up to 57 \({}^{\circ}\)C) within the reactor also contributed toward the total bactericidal effect. The authors performed a recycling test of the photocatalyst and found that there was no loss of photoactivity of the photocatalyst after three cycles of the reaction [122].
# Experimental Study of Scour Hole Depth around Bridge Pile Using Efficient Cross-Section
###### Abstract  
Extensive numerical and analytical studies exist that focus on the scours around bridge piers with different cross-sections. However, studies addressing the scour around bridge piles are rather limited and not conclusive. Therefore, the most efficient cross-section reducing the local scour around a pile group is experimentally examined herein. Accordingly, comparative scouring of three different shapes, namely, circular, rectangular and lenticular, is investigated by installing a cohesion-less bedding material around scaled bridge piles immersed in clear water. It is also shown that the available pier scour predictive models grossly underestimate the depth of pile scour holes. For example, the pier predictive models underestimate the pile scours hole by more than 50% in the case of square and circular cross-sections. It also transpires from the experimental investigation that the scour hole depth around the square shape pile is significant, around the circular pile is moderate and around the lenticular pile is minimal.  
pile group; local scour; efficient cross-section; experimentation Article