esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Discussion
### Single Dynamic Impact Test
#### 4.1.2 Dynamic Stress and Dynamic Increase Factor  
As can be seen from the stress-strain curves in Figure 4, the greater the strain rate of the fiber-reinforced UHPC, the greater the peak stress. In the D-SF6 curve shown in Figure 4, the peak stress was 46 MPa when the strain rate was 45.19 s\({}^{-1}\). When the strain rate increased to 76.15 s\({}^{-1}\), the peak stress also increased to 84.8 MPa. With the further increase of the strain rate from 109.94s\({}^{-1}\) to 144.67 s\({}^{-1}\) to 191.56 s\({}^{-1}\), the stress peak also increased to 128.8 MPa, 172.7 MPa, and 192.5 MPa. This is the strain rate effect, which may be caused by the Stefan effect caused by the free water in the concrete material [5].  
Modern architecture may be under various loads; it is simple to foresee that its static load by its structural static mechanics performance was not comprehensive. The accurate calculation and simulation of the structure may bear the dynamic impact load, which can be more scientific. To analyze the dynamic effects of UHPC, the dynamic increase factor (DIF) was introduced. The DIF refers to the ratio of the dynamic compressive strength to the quasi-static compressive strength, which can be calculated according to Equation (4).  
\[DIF=P_{\text{dcs}}/P_{\text{scs}} \tag{4}\]  
where \(P_{\text{dcs}}\) and \(P_{\text{scs}}\) represent the dynamic compressive strength and static compressive strength, respectively. It was used to characterize changes in strength due to changes in the load strain rate. The effects of different steel fiber contents and fiber orientation on the strain rate effect are summarized in Figure 5.  
In the impact compression test, the strain rate and DIF of the specimens with different fiber contents showed a positive linear correlation, and gradually increased with the increase of the strain rate. In Figure 5, compared with U-SF6, the D-SF6 curve showed a stronger sensitivity to the dynamic enhancement factor. It showed that in multiple working conditions, with 6% content as the first, fiber-reinforced (fiber-oriented) UHPC can obtain greater strength enhancement under the condition of a higher strain rate.
## 2 Data Preparation
### Internal Defects Classification from Collected B-Scan Images
#### 2.2.1 Distinction of Defects Based on Colors and Morphological Characteristics  
The three boundary lines in the B-scan images divide the rail cross-section into three parts: rail head, rail web, and rail base, as shown in Figure 3a. The corresponding area in the ultrasonic B-scan image is shown in Figure 3b. When the reflectors appear in different parts, the channel displays different colors and morphological characteristics, which represent the category and extent of the defects: When reflectors are located in the rail head or rail fillet areas, channels of reflectors are shown as monochrome or multi-color. In this area, the reflectors are likely to belong to two situations: a weld joint which is a normal structure, and the other is rail head nuclear injury, which is a defect. The key to distinguishing these two situations is the channel color of the straight 70\({}^{\circ}\) probe; when the color appears, the reflector is nuclear injury; when reflectors are located in the rail web areas, channels show a monochrome or combination of red, green and blue. In this area, the reflectors will likely be normal bolt holes or the cracks around the hole. If there are cracks, the channels of the upper cracks and lower cracks of the hole are regularly shown as green and blue, respectively, and the horizontal crack is shown as red. When reflectors in the rail base area, channels show a monochrome or combination of red, green, and blue. If there are rail base transverse cracks, the channels of the horizontal crack of the rail base are blue and green, and the longitudinal crack are red. Through this way, the rail internal defects have been coarsely classified. The classification of rail defects and the representatives of B-scan images of each category are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Probe Angel** & **Channel Name** &
\begin{tabular}{c} **Channel Color** \\ **(Customized)** \\ \end{tabular} & **Detection Area** \\ \hline
70\({}^{\circ}\) & ABCDEF & dark pink, light green, brown, \\  & & cyan, black, light pink \\
37\({}^{\circ}\) & GH & green, blue \\
0\({}^{\circ}\) & I & red \\ \hline \hline \end{tabular}
\end{table}
Table 2: The properties of different channels.  
However, when reflectors are located in the rail web areas, the complex reflection might lead to a confusing distribution of channels, which cannot be easily distinguished by the above method.
## 2 Materials and Methods
### Data
#### 2.2.7 Standardized Precipitation-Evaptranspiration Index (SPEI)  
The standardized precipitation-evaptranspiration index (SPEI, [66]) is a drought index that defines the monthly difference between precipitation and potential evapotranspiration (PET). For each grid, we used the multi-year mean value and the Mann-Kendall trend to determine the wet/dry conditions. If the grid has a negative multi-year mean SPEI and a negative trend in SPEI from 1980 to 2015, it is taken as a dry grid that is becoming drier; for a grid with positive multi-year mean SPEI and a positive trend in SPEI, it is taken as a wet grid that is becoming wetter; otherwise the grid is either dry that is becoming wetter or wet that is becoming drier. In determination of the optimal drought duration that can be captured by CCDAS when soil moisture is assimilated, we calculated the correlation between the optimized GPP and SPEI for each grid where the multi-year mean SPEI and GPP anomalies were negative and repeated this 12 times for SPEI derived from different durations (1, 3, 6, 9, 12, 15, 18, 21, 24, 30, 36, 48 months). Finally, the optimal month was determined as the one with the tightest correlation to GPP during droughts.
# Versatile High-Throughput Platform for Focused Ultrasound In Vitro Application
## 4 Discussion
The results of our investigation demonstrated that the attenuation of the maximum sound pressure values when propagating through the well plate was minimal, with a reduction of \(-2.2\) dB at 200 \(\mathrm{\mu L}\) and \(-0.6\) dB at 350 \(\mathrm{\mu L}\), in comparison to the free-field measurement. Additionally, it was observed that the distribution of sound pressure within the wells was affected by variations in filling height. The recorded values ranged from 244 kPa (SD 106 kPa) for a 200 \(\mathrm{\mu L}\) filling volume to 294 kPa (SD 107 kPa) for a 350 \(\mathrm{\mu L}\) filling volume. Furthermore, it was determined that the positions of maximum sound pressure in the LIFU setup showed variability, ranging between 6 mm and 9 mm. This is attributable to the absence of focusing in the well. This means that the filling level in each well has to be taken into account when calibrating the applicator. In contrast, the HIFU setup demonstrated a relatively lower dependence on the filling volume. The recorded values ranged from 197 kPa (SD 40 kPa) for 200 \(\mathrm{\mu L}\) to 255 kPa (SD 47 kPa) for 350 \(\mathrm{\mu L}\) filling. Furthermore, due to the strong focusing in the well, the position of the maximum sound pressure showed only a slight variation of approximately 0.2 mm. It is also crucial to consider the cross-coupling of ultrasound between neighboring wells when utilizing such platforms. If this is not sufficiently considered, the investigations conducted with these platforms may be based on erroneous values of the applied sound pressure and intensity. In this study, we demonstrated that by employing the HIFU version and integrating specific separators between the ultrasonic transducers, the cross-coupling from the active well to neighboring wells could be reduced to a value of \(-18\) dB.
# Multi-Point Time-Synchronized Waveform Recording for the Analysis of Wide-Area Harmonic Propagation
## 3 Measurement Campaign  
### Description of the Test Setup  
The Power Networks Demonstration Centre (PNDC) located in Cumbernuld, Scotland is a multifunctional power engineering facility. Among its purposes is a realistic technology testing and execution of the research projects aimed to facilitate development of smart grid sector. PNDC is in possession of a large power network, with a substantial part of it having unprecedented levels of flexibility and control over the real-time operation.  
For the purpose of harmonic propagation study, a part of the test network was configured. The diagram of this power network is shown in Figure 1. All in all three MV/LV distribution substations are connected to the main MV busbar (primary switchboard) via different impedance sections representing underground MV cables. There is a possibility to switch between sections A and B when energizing Substation G. Substation D on the other hand can be connected on demand by means of two circuit breakers at both ends of impedance MI 2.  
The setup offers a choice between two distinct power sources: the network can be energized by means of 11 kV public grid directly connected to Site Services Switchboard or alternatively via a motor-generator (MG) group providing stable 50 Hz output containing only fundamental frequency component. By engaging this power source, it is possible to provide a non-distorted voltage at Primary Switchboard. At this fashion, we could make sure that fluctuating MV background voltage harmonics do not enter the setup after MG-set and minimum deviations of fundamental frequency could be secured.  
Though, as it was explained above, while background voltage harmonics are not to enter the test network from the upstream MV grid when MG-set is employed, another source of voltage distortion was present in this network. The distortion originated from the series of pole-mounted MV/LV transformers connected to the MV busbar of Substation G. These transformers produced magnetizing current while being energized via impedance section A. Alternatively, when bypassing connection via impedance section B, the primary windings of the pole-mounted transformers become disconnected from the rest of the network. The impact of this harmonic distortion will be mentioned later in corresponding test cases.  
Several specific loads were configured and used for this study:  
1. 3-phase 10 kVA solar inverter SMA Sunny Tripower
2. EV charger with Nissan Leaf car capable of operating at 1-phase AC 6 kW
3. Triphase in current source mode with programmed industrial harmonic profile (30 A fundamental current)
4. Banks of linear loads operating at unity power factor  
The control of the most of the devices can be performed through SCADA-software; however, in some cases, manual switching actions and subsequent check procedures were required.
# Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising
## 6 Conclusions  
In this paper, a convolved feature vector based adaptive fuzzy filter is proposed which converts a large size processing window to multiple small size sub-windows for better noise estimation and removal. A simple mechanism is introduced to select optimal number of sub-windows that will ultimately reduce the computational cost of the noise detector as well as remove un-necessary information due to multiple windows. The selected windows add more information of neighbors for better noise estimation and removal. Furthermore, fuzzy rules and fuzzy membership functions play a vital to avoid crisp decision in noise detection and noise removal phases.  
The proposed filter is tested on a large datasets including standard as well as medical data set from MedPix. We have found satisfactory results in term of noise detection as  
Figure 11: Shows edge preservation index (EPI) for Lena de-noised image when corrupted with 30% RVIN.  
Figure 10: Shows image restoration results on medical image dataset. 1 st column are the original images from MedPix database, 2 nd column are the noisy images with 40% RVIN, third column shows de-noised results using AFIDM filter, fourth column shows de-noised results using QSAF and fifth column shows restoration results of proposed filter.  
well noise removal. Our method uses limited number of iterations, maximum of four, to achieve the best PSNR value against different types of images which ultimately reduces the algorithm running as well as hardware utilization cost. Our noise detector has less number of MD and FD which adds more power to noise detector and in results better noise estimation and detail preservation in noise removal phase.  
In subsequent work, we will discuss and analyze different fuzzy membership functions and weighted parameters in noise detection as well as noise removal phases.  
Conceptualization, M.H.; methodology, M.H. and A.H.; formal analysis, E.R. and S.M.M.; data curation, M.A. and S.F.J.; writing--original draft preparation, M.H.; funding acquisition, M.A.; resources, B.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by University of the West of Scotland, UK.  
Not applicable.  
Data Availability Statement: Data will be provided on request.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Surface Resistivity Measurements--Recognition of Subsoil and Hydrotechnical Structures
The top of undisturbed Pliocene deposits is parallel to the terrain slope, and then declines rapidly (between 40 and 50 m of the profile) towards the Vistula River. The second study shows the geoelectrical cross-section of the subsoil in the vicinity of water intake in one of the southern districts of Warsaw. The graph shows changes in electrical resistivity in the area to the depth of approximately 15 m below ground level (Figure 17). Analysis of the cross-section shows that non-cohesive soils with electrical resistivity of up to 200 \(\Omega\)m occur at the depth of approximately 6 m. However, in the middle of the cross-section, the base of non-cohesive formations occurs at the depth of approximately 4.5 m, and in the areas planted with trees and shrubs it was determined at the depth of approximately 6 m based on changes of electrical resistivity. In this area, the layer of soils showing higher electrical resistivity reaches slightly deeper. This may be due to the lower moisture content of the soils in the rhizosphere, and thus the determined position of the base of sand in this area may be erroneous. The position of the top of clay deposits in this area must be validated by boreholes or probing. Soils characterized by resistivity of several to several dozen \(\Omega\)m can be observed below the non-cohesive formations. Figure 16: Geoelectrical cross-section in the Vistula slope area compared with cone resistance q\({}_{\rm c}\) from CPTU and material index from DMT tests. The cross-section also shows the cone resistance values (q\({}_{\rm c}\)) obtained during RCPT tests and the material index values (change in I\({}_{\rm D}\) value below 0.6 indicating transition into silty soil) obtained from DMT tests. Both the cone resistance and the material index show the transition in the soil type in the tested area at the depth of 4.0 \(\div\) 4.2 m below ground level. Detailed analysis of RCPT tests (Figure 10) shows a boundary between sands and clays in this area at the depth of approximately 4 m below ground level. The electrical resistivity for sands is between 100 \(\Omega\)m to several hundred \(\Omega\)m, and for clays between 4 \(\Omega\)m and 12 \(\Omega\)m.  
The spatial data on changes in electrical resistivity from geophysical surveys and RCPT and DMT tests allowed for determining the top surface of clay deposits at the entire length of the profile. In a similar case, geophysical investigations, seismic surveys and geotechnical soundings were used to identify the top of cohesive soils in the area of an earth dam [50]. In the latter case, the value of 200 \(\Omega\)m was found as a boundary between cohesive and non-cohesive formations. In addition, in geophysical studies, anomalies were identified and zones of soil loosening, which may have been caused by suffusion processes were distinguished.
# Mechanism to Estimate Effective Spectrum Availability Inside Smart Buildings
## 3 System Model
### C2 Model  
Recalling the definition established in Equation (2), we have an apartment that meets C2 when  
\[I_{SS-PS}=P_{SS}-L_{SS-PS}<P_{TH} \tag{10}\]  
where \(L_{SS-PS}\) corresponds to the loss of propagation using the MWM [25] between the SR-CRWS and the PS receiver located on the rooftop of the building. \(P_{SS}\) is the power emitted from the SR-CRWS. The MWM is represented by  
\[L_{MWM}=L_{fs}+L_{C}+\sum_{i=1}^{I}k_{Wi}L_{Wi}+k_{f}^{\frac{k_{f}^{2}+2}{[k _{f}^{2}+1]}}L_{f} \tag{11}\]  
Figure 3: The five-ray model used to calculate the indoor power level. It defines two direct rays (S1 and S5) and three reflected rays (S2, S3, and S4).  
The MWM model considers a free space loss \(L_{fs}\) between the PS receiver of the building and the SS, while the \(L_{C}\) constant is a parameter that is used to compensate losses, which are usually negligible; \(L_{Wi}\) represents the losses in each internal wall, the number of walls of \(i\) type that cross the inner path is \(k_{Wi}\), \(L_{f}\) represents the slab loss, and \(k_{f}\) represents the number of slabs that are traversed and is complemented by an empirical factor \(b\). Therefore, Condition 2 is satisfied if  
\[I_{SS-PS}=P_{SS}-L_{MWM}<P_{TH}. \tag{12}\]
# Efficient On-Off Keying Underwater Acoustic Communication for Seafloor Observation Networks
### Contributions  
In this paper, we propose an efficient OOK UAC scheme for SONs. By addressing the aforementioned challenges, several contributions are made as follows:  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Application**} & **Modulation** & **Distance** & **Bandwidth** & **Bit Rate** &
\begin{tabular}{c} **Number of Received** \\ **Hydrophones** \\ \end{tabular} \\ \hline WHOI Experiment (2010) [2] & QPSK & 2 km & 22–27 kHz & 5300 bit/s & 4 \\ WHOI Experiment (2018) [5] & QPSK & 30 km & 3–4 kHz & 350 bit/s & 4 \\ The DART system (2013) [6] & MFSK & \(\leqslant\)7 km & 9–14 kHz & 600 bit/s & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiments or applications of the cableless SON.  
* An accumulator (ACC) is introduced before a conventional memoryless OOK constellation mapping, leading to an OOK modulation with memory. It enables on the receiver a turbo iteration between the soft ACC-OOK demapper and the soft decoder and reduces the decoding error floor;
* An iterative threshold estimation (ITE) algorithm is proposed to seek an appropriate decision threshold for soft demapping and thus improve the iterative decoding performance;
* An irregular recursive convolutional code (IrCC) is designed by matching the extrinsic information transfer (EXIT) curve of the soft ACC-OOK demapper, so as to lower the SNR threshold of the turbo iteration;
* The proposed noncoherent UAC scheme is verified by both simulations and undersea experiments. It achieves a gain of 1.8 dB compared with a conventional scheme, and is only 3 dB to the channel capacity. In that sea trial at a communication distance of 2500 m, reliable transmission is achieved at a data rate of 1400 bit/s with a frequency band 6-10 kHz, i.e., the spectral efficiency is about 0.35 bit/s/Hz.
## 2 Materials and Methods
### In-Vitro Release Kinetics  
To investigate the in vitro release pattern of DOX@MSN, DOX@MSN-PLH and DOX@MSN-PLH-TAM, a dialysis diffusion technique was employed. To mimic systemic circulation where pH is 7.4, and the tumour environment where pH is \(\approx\) 5; PBS buffer solutions at each of the respective pHs were employed as releasing media. Then, 1 mg of each sample was dispersed with buffer solution (1 mL) and placed in cellulose membrane dialysis bag, which was then placed in 10 mL of respective release media at 37 \(\pm\) 0.5\({}^{\circ}\)C, with 100 rpm paddle rotation. At 2, 4, 6, 24, 28 and 72 h, aliquots of sample (1 mL) were withdrawn and replenished with the same volume of fresh media. Pure DOX in buffers was used as positive controls, while the suspensions of native MSNs in buffers were used as negative control.
## 4 The Algorithm: Space of Convergence Vectors (SCV)
### The Proposed Algorithm
#### 4.2.1 Space of Representation for Particle Position  
Because the position of a particle represents a possible solution to the problem under consideration, the SCV algorithm uses the representation space of a particle as defined by the package of GIVs (and implicitly by the design principles assigned to the GIVs) to guide engineers for solution formulation. For the maximization of the utility function \(T\) from (4),at each iteration of the SCV algorithm, to the level of each performance indicator \(i\), \(i=1\),..., \(n\), are undertaken design interventions in the direction imposed by the associated GIV. Considering the performance indicator \(i\), \(i=1\),..., \(n\), with \(h_{i}\) associated GIVs (\(h_{i}\) is 0, 1, 2, 3 or 4), these GIVs can be ordered according to their ranks \(b(1)\),..., \(b(h_{i})\), starting with the one having the highest rank and ending with the one having the lowest rank. Having, for example, the performance indicator \(i=3\) in the set of \(n\) performance indicators (\(n\geq i\)), to which are associated \(h_{i}=4\) generic inventive vectors (e.g., vectors #10, #18, #26, #32 in the list of 40 GIVs of TRIZ-MC), and having the ranks \(b(1)=0.21\) for the GIV #10, \(b(2)=0.32\) for the GIV #18, \(b(3)=0.09\) for the GIV #26, and \(b(4)=0.14\) for the GIV #32, the four vectors can be ordered as [#18, #10, #32, #26]. To use this information more conveniently in a software application, to each vector a number is further assigned: #18 \(\equiv\) 1, #10 \(\equiv\) 2, #32 \(\equiv\) 3, #26 \(\equiv\) 4. Thus, for a facile use in software applications, the representation space of a particle can be symbolized through numbers assigned to the performance indicators and numbers assigned to their associated GIVs, ordered according to their ranks. For the example above this looks like: 3[1], 3[2], 3[3], 3[4].