esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Simulation Results and Analysis of S-U-S
#### 3.2.2 Field Performance Verification Test  
According to the above field performance test design, the bionic shovel S-U-S-_I7h_3.5 and the O-P-S were tested separately. The curves of the test results are shown in Figure 17 (the negative value indicates that the direction of the force was the opposite of direction of movement).  
As shown in Figure 17, the time ranging from 7 s to 20 s can be regarded as the steady working time of the shovel. Hence, the average values of the testing results were analyzed at 7-20 s, as shown in Figure 18.  
Figure 18 shows that the average traction resistance of O-P-S was 5153.86 N and the average traction resistance of S-U-S-_I7h_3.5 was 4422.42 N. It was calculated that the drag reduction rate of S-U-S-_I7h_3.5 was 14.19% compared to the O-P-S. Hence, the S-U-S-_I7h_3.5 had a better drag reduction performance than the O-P-S.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Drag Force**} & \multicolumn{2}{c}{**Type of Shovel**} & \multirow{2}{*}{**Drag Reduction Rate vs. O-P-S**} \\ \cline{2-2} \cline{4-4}  & **O-P-S** & & **S-U-S-I7h3.5** \\ \hline Average value & 1117.78 & 868.95 & 22.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the average value of the soil bin test.  
Figure 17: Curves of the results for field performance verification test.
## 3 Materials and Methods
### Recycled Aggregates  
According to the geometrical, physical, and chemical requirements imposed by the technical standard UNI EN 12620, the inert fraction composed of fine aggregates (d \(\leq\) 5 mm) and coarse aggregates (d \(\geq\) 5 mm) was subjected to different experimental tests (Table 1). The main goal was to evaluate the feasibility of using recycled aggregates for new concrete production. At the same time, to evaluate the possible release of contaminants into the surrounding environment, the recycled aggregates were subjected to the leaching test according to the UNI EN 12457-2 standard (d\({}_{\rm max}\)\(\leq\) 4 mm; liquid to solid ratio = 10 L/kg; contact time 24 h; demineralized water). The eluate obtained was then chemically characterized in order to assess compliance with the limits imposed by Ministerial Decree 186/2006 for the recovery and reuse of non-hazardous waste.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experimental Test** & **Method** & **Type of Aggregate** \\ \hline Particle size distribution & UNI EN 933-1,2 & Fine and coarse \\ Sand equivalent & UNI EN 933-8 & Fine \\ Methylene blue & UNI EN 933-9 & Fine \\ Particle density and water absorption & UNI EN 1097-6 & Fine and coarse \\ Alkali-silica reactivity & UNI EN 8520-22 & Fine and coarse \\ Water-soluble chloride content & UNI EN 1744-1 & Fine \\ Acid-soluble sulphates & UNI EN 1744-1 & Fine \\ Total sulfur content & UNI EN 1744-1 & Fine \\ Petrographic description & UNI EN 932-3 & Fine and coarse \\ Light organic impurities & UNI EN 1744-1 & Fine \\ Radioactivity & PRD 100 instrument & Fine and coarse \\ Leaching test & UNI EN 12457-2 & Fine and coarse \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental tests carried out on fine and coarse recycled aggregates.  
Figure 3: Proposed returned concrete treatment process. Section dedicated to the recovery of fine and coarse aggregates (blue zone) and section dedicated to the treatment of washing water to obtain microfiltered water and cement mud (red zone).
# Use of Social Media Data in Disaster Management: A Survey
## 4 Data Sources for Social Media Data Analysis
### Sensors
#### 4.1.2 Human Sensors  
Human sensors utilise people to observe and measure real-world phenomena and generate different types of observation data, including social media data. The emergence of social networks and mobile applications has enabled people to report about observed events. These activities are considered as human sensing [16] and can be a significant data source for effective urban risk analytics. The data sources include RSS feeds, social media, Instagram, Twitter, Facebook, SMS, and online news. Similarly, crowdsourcing is a process that encourages people to give their contributions with regard to certain tasks in a specific context. This process is widely used in disaster management applications, where people can report a disaster event that they observed. For example, in 2010, people used Ushahidi, a web-based and mobile crowdsourcing application, to report about the earthquakes in Haiti [19].
# A High-Fidelity Energy Efficient Path Planner for Unmanned Airships
## 7 Conclusions  
In this paper, a wavefront expansion trajectory planner incorporating a comprehensive multi-objective function for unmanned airships is presented with the purpose of quantifying the reductions in energy consumption achievable when using spatial WVFs, high resolution grid and temporal vehicle velocities in the planning process. The planner was subjected to large, highly realistic, 3D environments and simulated over a variety of flight conditions. The inclusion of spatial WVF data significantly improved the trajectory feasibility (convergence rate) and reduced the energy consumption by 53-76% when compared to other planners. The results have also illustrated that the trajectory planner could operate in semi-real time on a less powerful on-board computer as the average processing times were a small fraction of the flight times.  
Although the energy objective function encompasses many of the factors contributing to the energy consumption, there is still room for improvement. The aerodynamic drag on the vehicle is not constant for all flight conditions as was assumed in this model. Furthermore, various degrees of stabilization are often required to maintain a UAV on a given trajectory, which are not accounted for. Errors due to this stabilization effort will increase when aggressive arrival times are set. Applying a trajectory following controller with the complete non-linear dynamic model and a temporal WVF would help validate the realism of the generated trajectories and would provide a more accurate prediction of the travel time and energy consumed.  
This research was partially funded by Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canada Foundation for Innovation (CFI).  
This article is an outcome of the Ph.D. research of [PERSON]. He conceived of, designed and performed the experiments. He also wrote most of the paper. [PERSON] and [PERSON] co-supervised and co-directed the research. They also helped analyse the results and contributed in writing and editing the article.  
The authors declare no conflict of interest. The funding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.  
The following abbreviations are used in this manuscript:
## 2 Materials and Methods  
The highly pure Fe, Ni, and Mn powders (Sigma Aldrich, St. Louis, MO, USA; purity \(\geq\) 99.6%), as raw materials, were used for the synthesis of polycrystalline Fe\({}_{65}\)T\({}_{35}\) (\(T\) = Ni and Mn) alloy. Both of these alloys were synthesized by the planetary ball milling technique. In this process, the stoichiometric ratio of all raw materials was mixed and ground in 250 mL ball mill container under Toluene medium for 24 h. Milling speed was 600 rpm, and the powder-to-ball (diameter 5 mm) ratio of 3:1 was maintained for each composition. As-milled powder has been used for the further investigation. The structural characterization and phase analysis of the alloys were carried out through X-ray diffraction (XRD); they were recorded with a PANalytical (Malvern, UK) X-pert Pro-diffractometer containing a Cu-K\(\alpha\) X-ray radiation (\(\lambda\) = 1.54 \({}^{\circ}\)A) source. The microstructure and elemental composition of the samples were acquired from the high-resolution field emission scanning electron microscope provided by Jeol (HR-FE-SEM; JSM-7900F/15 kV). The magnetic properties of the as-milled samples were carried out from the vibrating sample magnetometer option of the cryogen free versa lab physical properties measurement system (PPMS) (Quantum Design, San Diego, CA, USA). The magnetocaloric properties in terms of isothermal magnetic entropy change are derived from the isothermal magnetization data, calculation, and protocol adopted from [11; 42].
## 4 Discussion  
The experimental campaign involved the calibration and the combination of various in situ non-destructive investigation techniques through laboratory tests on stone samples. A variant of the Sonreb method was tested in the specific case study of the stone walls of the church of St. Agostino in Amatrice, damaged by the 2016 Central Italy earthquake. More specifically, within the Sonreb protocol, the sclerometric test was replaced with the impact test, which highlighted the accuracy of the ultrasonic test to evaluate the elastic modulus of the stone. In this way, it was possible to obtain useful correlations to acquire reliable values of the stones' compression strengths and elastic modulus only by means of in situ non-invasive techniques. The results showed the values with an expected dispersion. The authors evaluated this dispersion as physiological in the case of stone materials, and this was confirmed in other research studies that are present in the scientific literature [36; 37; 38; 39] including ongoing experimental studies that demonstrate the anisotropic behavior of the stones relative to the different layers. Furthermore, in this specific case, it is also necessary to consider the presence of microcracks inside the stones, generated by the fall during the seismic actions.  
The speed detected by the ultrasonic technique showed a good correlation with the cylindrical compression strength of the same stones (Figure 11). Furthermore, the impact test showed a good precision to estimate the value of the static elastic modulus (Table 1). Even for stones, there was a minimal difference between the dynamic elastic modulus (\(E_{d}\), obtained from ultrasonic tests) and the static one (\(E_{s}\), obtained by compression tests or by impact tests). According to the scientific literature [38], these differences are variable due the anisotropy that characterizes the sandstone. Even in this specific case study, \(E_{d}\) was 10-15% higher than \(E_{s}\), thus validating the results found with only non-invasive techniques.  
The correlation can also be improved for wall textures different from those of Amatrice through the intensification of the number of tests. However, the results obtained so far in the Amatrice area showed a full correlation in the average values, they are applicable in situ on other similar buildings, and are useful to estimate the residual strengths of other damaged masonry structures. The correlations between non-destructive tests and laboratory tests will be calibrated for the different types of historic masonry walls. The results of this experimental research may also be useful for further specific shear tests between strengthening mortars and stone structures [40], in order to model the behavior of the mortar-stone composite system, as already carried out in other research studies [41; 42], and to validate the mechanical compatibility and durability of strengthening materials to be applied on damaged masonry structures.  
Figure 14: Impact test on stone n. 4: (**a**) time–force diagram of the 15 beats; (**b**) trend of the restitution coefficient (_e_) vs. number of beats (_n_).
# Analysis and Reduction of Nonlinear Effects in Optical Fiber Frequency Transfer
## 3 Simulation and Results  
In the simulation and calculation of the threshold power for generating Brillouin scattering and Raman scattering, we consider an optical fiber made of fused silica single mode fiber with a refractive index of 1.451 and a light wavelength of 1.5 \(\upmu\)m. The Brillouin frequency shift is estimated to be around 11 GHz.
## 3 Materials and Methods
### Laboratory Measurements  
_Porosity and Permeability Measurements:_ The porosity and permeability of the rock samples were measured using the gas expansion method, based on Boyle's law. Helium gas is allowed to pressurize and expand to determine the pore volume and permeability of the rock samples [30].  
_Micro X-ray computed tomography (MXCT) scanning:_ The rock core samples were scanned using the micro X-ray computed tomography (MXCT) scanning machine SKYSCAN 1275 (@Bruker, Billerica, MA, USA). The samples are placed in a holder, which rotates, ensuring a 360\({}^{\circ}\) coverage, and X-rays are emitted from the source. The X-rays interact with the rock sample, either being absorbed, reflected, refracted, or transmitted, depending upon the mineral composition or pore space. When encountering voids, X-rays pass through, resulting in black regions in the produced image. Quartz, prevalent in our samples, reflects X-rays, appearing as bright white areas, while other minerals manifest as varying shades of gray in the images.  
In this study, the samples are scanned at two different resolutions, a low resolution (22 \(\upmu\)m) and a high resolution (8 \(\upmu\)m) (Figure 5). Multi-resolution scanning is performed to map the heterogeneity within the rock samples by capturing detailed information about variations in properties such as porosity and permeability across different scales.
## 2 Heuristic BCI Using L-FTM
### Heuristic BCI Using L-FTM
#### 2.2.1 Fuzzy Template Matching (FTM)  
This study uses FTM [12], a method of template matching based on simplified fuzzy reasoning. Each fuzzy template was constructed with two fuzzy labels of \"high\" and \"low\" in an antecedent clause of a fuzzy rule. As shown in Figure 2, the 2\({}^{16}\) rule is constructed from 16 inputs based on the number of EEG measurement channels (8 electrodes, 2 frequencies), and 2 inputs based on the number of fuzzy labels. For instance, a template can be composed of inputs having features of different types such as \"EEG power of Oz measurement site in the \(\alpha\) wave frequency band\" and \"amplitude of the surface electromyography (EMG) signal of the lower right limb.\" This approach, which reduces the computational complexity of the heuristic BCI, can classify EEG signals quickly into several categories linked respectively to specific cognitive states.  
Figure 1: Block diagram of heuristic brain–computer interface (BCI) system. Electroencephalogram (EEG).  
Each membership function in a rule (template) is constructed by calculating the power of each channel (microV2) and finding maximum and minimum values. The domain of membership function was ascertained from the maximum and minimum values of the power. The shape of the membership function is a symmetrical triangle, where the peak turns to 1 located at the middle of the domain and the bottom turns to zero [13; 14].  
The EEG power was input to the membership function of the defined fuzzy label of the \(i\)th input of the \(i\)th fuzzy rule. The multiplied outputs of all membership functions of the \(i\)th fuzzy rule, \(G_{j}\), are obtained as the compatibility degree of the \(i\)th fuzzy rule, \(\mu_{i}\), which is expressed as Equation (1):  
\[\mu_{i}=\Pi_{j=0}^{n}G_{j} \tag{1}\]  
Output value \(Z\) was calculated as the weighted average of the consequent values of all rules. Here, \(Z_{i}\) is the consequent value of the \(i\)th rule, which is described as Equation (2):  
\[Z=\frac{\Sigma_{i=1}^{n}(\mu\cdot Z_{i})}{\Sigma_{i=1}^{n}\mu_{i}} \tag{2}\]  
The output value corresponds to the relative compatibility degree during motor imagery.
# Wideband-Narrowband Switchable Tapered Slot Antenna for Breast Cancer Diagnosis and Treatment
## 2 Design of the Proposed Frequency Switchable Tapered Slot Antenna
### Frequency Switchable Tapered Slot Antenna with Meander Line Resonator  
To design the frequency switchable antenna, a meander line resonator was printed on the back layer of the wideband antenna, as shown in Figure 7a. The middle point of the symmetric meander line resonator was placed at a point corresponding to the middle of the flared slot. When the current flows alongside the flared slot, an E-field is formed between the two plates of the slot, and EM energy is transferred to the meander line resonator. The combined equivalent circuit model of the TSA and meander line resonator is shown in Figure 8.  
The flared slot of the TSA has various frequency responses that depend on position D of the meander line resonator. The higher band operation is related to the narrow end of the flared slot, whereas the lower band operation is related to the wide end of the flared slot. Therefore, the proposed antenna achieved optimal frequency switchable characteristics by placing a meander line resonator at the optimal point to compensate for the reactance component. The maximum reactance compensation is related to the impedance matching characteristics of the TSA at 2.45 GHz, which is the resonant frequency of the meander line resonator. Changes in the reflection coefficient for different positions of the meander line resonator in the narrowband mode are presented in Figure 7b. It can be observed that for D = 33 mm, the image impedance of the meander transmission line \(Z_{TL}\) is matched to the characteristic impedance of the slotline \(Z_{SLOT}\). As a result, the maximum EM energy transfer between the flared slot and the meander line resonator occurred, and  
Figure 8: The equivalent circuit model of the meander line resonator with TSA.  
Figure 7: (**a**) The integrated position of the meander line resonator for narrowband operation and (**b**) comparison of the simulated reflection coefficients in terms of position D.  
the conventional traveling-wave mode changed to the resonance mode in the meander line resonator.
## 3 Test Results and Analysis
### Effect of Biopolymer on Compressive Strength
#### 3.1.1 Unconfined Compressive Strength
From 7 to 14 days, strength continues to increase but at a slower rate, suggesting that the reaction between LBG and soil particles has reached equilibrium. As GG content increases, the compressive strength of GRS generally shows an upward trend at the same curing time. On day 0 of curing, variations in GG content have minimal impact on compressive strength. However, as curing time increases, the cementing effect of GG becomes more prominent, leading to progressively greater improvements in compressive  
Figure 7: UCS of biopolymer-treated soil at different contents: (**a**) XG; (**b**) LBG; and (**c**) GG. strength. This indicates that the reaction between GG and soil particles requires a certain amount of time to fully develop, thus enhancing the overall stability of the soil.
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
## 3 Results
### Feasibility of Wireless Power Delivery to the Receiving End when a Tap Water Was Used  
The objective of this test was to test for the feasibility of wireless power transfer if the conductivity of medium 2 is low. In this test, tap water was used as medium 2.  
The DC conductivity of the tap water was about 60 uS. A surface power was successfully received by more than one live-wire detectors at the receiving end; however, each of the neon bulbs was very dim.  
The measured maximum peak-to-peak voltages across the 1 ohm resistor at the transmitting end and the receiving end were respectively 4.31 mV and 0.83 mV. Hence, the captured currents at the transmitting end and the receiving end were respectively 4.31 mA and 0.83 mA, as opposed to 2.04 A and 1.62 A obtained from the previous experiment.
## 5 Engineering Application of Smart Active Control
### Active Control Process and Data Analysis of the Tower  
The GNSS displacement automatic monitoring system dynamically calculates the observation data to obtain the single period coordinates of each monitoring point, and intuitively obtain the instantaneous changes of the off-balance along the bridge of the tower top in different lifting states. Due to the space limitation, only partial results are selected. The construction group division and monitoring lifting sections are shown in Table 6 below. To eliminate the impact of high frequency noise in the monitoring results, the single period positioning results of the measuring points are also filtered [36]. The segments at both sides of the bridge are the same, thereby, the south and north banks can be used as the control group. Figure 17 below shows the monitoring results of GNSS in each construction group.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Construction Group 1** & **Unregulated Segment (South Bank)** & **Construction Group 3** & **Regulated Segment (North Bank)** \\ \hline CS1 & Section 1 \# of Matan Hongshui He Bridge & TK1 & Section 1 \# of Matan Hongshui He Bridge \\ CS2 & Section 5 \# of Matan Hongshui He Bridge & TK2 & Section 5 \# of Matan Hongshui He Bridge \\ CS3 & Section 6 \# of Matan Hongshui He Bridge & TK3 & Section 6 \# of Matan Hongshui He Bridge \\ CS4 & Section 1 \# of Pingnan Third Bridge & TK4 & Section 1 \# of Pingnan Third Bridge \\ CS5 & Section 5 \# of Pingnan Third Bridge & TK5 & Section 5 \# of Pingnan Third Bridge \\ \hline \hline \end{tabular}
\end{table}
Table 6: The experiment construction group division.  
Figure 16: The schematic diagram of active control. (**a**) Jack tension initial state; (**b**) Jack tension state.  
Figure 17: The measured results of tower top deviation under normal working conditions and active control of different lifting sections.  
Through an active control technology, the finite element results and measured results are within 2 cm, and the displacement of the tower top fluctuates within the range of 8-14 cm in the normal lifting process of the arch ribs. When the maximum value that occurred in Section 6 locates upstream of the right of the south bank, the horizontal deviation of the tower top (the direction toward the river is positive) reaches 14.0 cm, which affect the safety of the tower itself greatly. The mean value of the maximum horizontal deviation of the tower is effectively controlled within 2 cm through active control. The elevation change of arch rib affected by deviation is only 1 mm, greatly reducing the deviation value. The control is quite effective and the experiment objective of smart active control of the horizontal displacement of the tower is basically achieved. Furthermore, the filtered curve includes the displacement of the entire tower from the lifting of the arch ribs to the lifting point in the lifting process, further proving the reliability and accuracy of the GNSS and the good effect of the smart tower deviation control system.
# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
## 2 System Model
### Uplink Data Transmission  
In the uplink data transmission phase, the users transmit the data signals to the APs. Since each user is served by a subset of APs \(A_{u}\), the data is detected using the serving APs and optimal receive combining schemes:  
\[\widehat{s}_{ua}=v_{ua}^{H}G_{ua}y_{a}^{Ull} \tag{5}\]  
where \(y_{a}^{Ull}~{}=~{}\sum_{u=1}^{U}h_{ua}s_{u}~{}+~{}n_{a}\) is the signal received at the \(a\)th AP, \(s_{u}\) is the signal transmitted by node \(u\), and \(n_{a}\) is the receiver noise. \(G_{ua}\) in the uplink is given as  
\[G_{ua}v_{ua}=\left\{\begin{array}{cc}v_{ua}&a\in A_{u}\\ 0_{N}&a\
otin A_{u}\end{array}\right\} \tag{6}\]  
and \(G_{u}=diag(G_{u1},.....G_{uA})\)