esa-sceva/satcom-chunk-collection huggingface dataset:
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
## 7 Results
### Real Data Results  
The models have been tested with data from two real radars. The maximum range of both radars is bigger than 150 NM and the separation between both radars is 52 NM. This configuration gives enough common coverage and avoids observability problems due to the sensor separation. Radar measurements have been transformed to local Cartesian coordinates and projected to a common stereographic plane. The center of the stereographic projection (point (0, 0) in Figure 12) is far away from the radars in order to evaluate if the models work with the rotation, translation and scaling produced by the stereographic projection.  
The real scenario has 227 aircraft distributed in the airspace in different flight phases. As it can be seen in Figure 16 tracks are not uniformly distributed in the airspace. The major part of the measures is concentrated in the bottom-left corner of the common radar coverage. As the algorithm minimizes the mean square error the measurement of this corner will be better aligned than the measurements from other parts of the scenario, as described in Section 6.3.  
As the biases values are not known, the performance of the model will be evaluated estimating the RMS value of the deviation in measured position of the same track at the same time from two different radars (73). The evaluation will compare the deviation of the positions for all the measures in the common coverage. The RMS error of the measurement noise of the radar is 160.95 m for the first radar and 88.92 m for the second radar. The RMS value of the difference of uncorrected measured positions is 532.45 m.
# Effect of Inense Ash on the Engineering Properties of Cement-Based Composite Material
## 3 Results and Discussion
### Heat Conduction of Inense Ash Paste  
Thermal conductivity is measured by dissipating and absorbing heat on a single probe to determine a unit area's temperature change over time; a low thermal conductivity coefficient corresponds to low thermal conductivity. As Figure 5 indicates, the thermal conductivity coefficient decreased as the incense ash replacement ratio increased. This occurred because incense ash has a lower specific weight than cement. As the replacement ratio increased, the voids in the cement grew in size and number, and the increased convective heat transfer and radiant heat transfer through the air in the voids caused the thermal conductivity coefficient to drop [38]. Incense ash is noncrystalline and contains various substances. In crystallites, because atoms have fixed positions, heat cannot be transferred directly through them but instead through waves created by the vibration of crystal lattices. Being noncrystalline, incense ash does not create waves through crystal lattices and is prone to phonon scattering. Because incense ash has a large specific surface area, it contains many closed pores that can impede heat wave transfer, which results in a low thermal conductivity coefficient. The thermal conductivity coefficients of the specimens increased with curing time. At an early stage when the pozzolanic reaction has yet to start, cement mortar with incense ash contains a considerable number of pores, causing its thermal conductivity coefficient to drop as pore water evaporates. With the passage of time, the progressive hydration produces C-S-H gel that gradually fills the pores, causing the thermal conductivity coefficient to steadily rise. However, when the water/binder ratio rises, the increased water content reduces the cement mortar's cementing ability. As a consequence, an excess of pores are formed and the cohesion of the cement mortar is compromised, which causes the thermal conductivity coefficient to drop.  
Figure 4: TGA of incense ash paste (replacement of cement 0-30%; 120 days).  
Figure 5: Thermal conductivity developments of incense ash paste.
# Design of an Energy Harvester Based on a Rubber Bearing Floating Slab Track
###### Abstract  
It is known that a large amount of vibration mechanical energy will be generated during train operation. If the mechanical energy can be obtained from the track structure, it can greatly optimize the energy configuration of the metro. Currently, most sensors are limited to disk or cymbal structures and are not used in the track bed; different from existing structures, this paper designs a spherical energy harvester based on a rubber bearing floating slab track, wherein the size range of the spherical energy harvesting structure was approximately determined based on the geometric spatial relationships of the actual track bed internal structure. Compared to the traditional disk and cymbal energy harvesters, the mechanical and electrical properties of the spherical energy harvesting structure was studied by a numerical simulation method, and the optimal size of the spherical energy was determined by calculation. The main conclusions are as follows: (1) Compared with the traditional disk harvester and cymbal harvester, the spherical harvester had better mechanical and electrical properties. (2) By calculating the output power of energy harvesters under load matching, we found that the output power of the spherical harvester was two orders of magnitude higher than that of the disk harvester and 53% higher than that of the cymbal harvester. (3) The optimum size of the spherical harvester was when the thickness of piezoelectric layer was 2 mm, the radius was 16 mm, the axial ratio of the spherical shell was 0.4, and the thickness of the spherical shell was 4 mm.  
Keywords:rail transit; sensor; floating slab track; energy harvester; piezoelectric layer dimension +
Footnote †: journal: Applied sciences
# Wheat Yield Estimation Study Using Hyperspectral Vegetation Indices
## 2 Materials and Methods
### Study Area and Data
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Argument** \\ \hline Wavelength Range & 350–2500 nm \\ Spectral Resolution & 3 nm@700 nm \\  & 6 nm@1400/2100 nm \\ Sampling Interval & 1.4 nm@350–1000 nm \\  & 2 nm@1001–2500 nm \\ Scanning Time & 100 ms \\ Number of Channels & 2151 \\ Maximum radiation & VNIR 2 times the sun, SWIR 10 times the sun \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance metrics for ASD FieldSpec4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Vegotation Index** & **Calculation Formula** \\ \hline Normalized Difference Infrared Index (NDII) & \(R_{819}-R_{1649}/R_{819}+R_{1649}\) \\ Normalized Difference Vegetation Index-1 (NDVI-1) & \(R_{800}-R_{670}/R_{800}+R_{670}\) \\ Normalized Difference Vegetation Index-2 (NDVI-2) & \(R_{810}-R_{860}/R_{810}+R_{800}\) \\ Modified Simple Ratio Index-1 (mSRI-1) & \(R_{750}-R_{445}/R_{705}-R_{445}\) \\ Modified Simple Ratio Index-2 (mSRI-2) & \(R_{750}-R_{445}/R_{750}+R_{445}\) \\ Enhanced Vegetation Index-2 (EVI-2) & \(2.5\times(R_{824}-R_{651})/(1+R_{824}+2.4\times R_{651})\) \\ Difference Vegetation Index-1 (DVI-1) & \(R_{824}-R_{651}\) \\ Difference Vegetation Index-2 (DVI-2) & \(R_{800}-R_{670}\) \\ Difference Vegetation Index-3 (DVI-3) & \(R_{810}-R_{680}\) \\ New Vegetation Index (NVI) & \(R_{810}/R_{560}\) \\ Atmospherically Resistant Vegetation Index (ARVI) & \(R_{872}-(R_{661}-(R_{488}-R_{661}))/R_{872}+(R_{661}-(R_{488}-R_{661}))\) \\ Green Atmospherically Resistant Vegetation Index (GARI) & \(R_{892}-(R_{599}-(R_{488}-R_{661}))/R_{872}+(R_{599}-(R_{488}-R_{661}))\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Vegetation indices selected in this study.
## 3 Results
### Experimental Results  
On the basis of optimal experimental parameters, we respectively trained and tested the model without heartbeat-attention mechanism (Experiment 1) and with heartbeat-attention mechanism (Experiment 2) in the same conditions. The performance evaluation was calculated on the testing set containing 134 samples, and the average results were obtained after 20 independent repetitions.  
As shown in Tables 2 and 3, the accuracy of Experiment 2 is about 2% higher than Experiment 1, and the better accuracy can reach 94.77%. In addition, it can be seen from the confusion matrix that many healthy samples (HC) are wrongly identified as sick (MI) in Experiment 1, and its specificity only reaches 85.00%. The results of Experiment 2 clearly show that the specificity is significantly improved, reaching 90.48%. Simultaneously, the sensitivity in Experiment 2 is also maintained at a high level, reaching 95.58%.  
The model with heartbeat-attention mechanism achieves better performance when the classifier and experimental condition are identical. From the methods section above, the principle of the heartbeat-attention mechanism is to make the model pay more attention to the key heartbeats  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{5}{c}{**Predicted**} \\ \cline{2-7}  & & **HC** & **MI** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline \multirow{2}{*}{**Real**} & **HC** & 17 & 3 & \multirow{2}{*}{92.54\%} & \multirow{2}{*}{93.86\%} & \multirow{2}{*}{85.00\%} \\  & **MI** & 7 & 107 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of Bi-LSTM without the heartbeat-attention mechanism (Experiment 1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{5}{c}{**Predicted**} \\ \cline{2-7}  & & **HC** & **MI** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline \multirow{2}{*}{**Real**} & **HC** & 19 & 2 & \multirow{2}{*}{94.77\%} & \multirow{2}{*}{95.58\%} & \multirow{2}{*}{90.48\%} \\  & **MI** & 5 & 108 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance of Bi-LSTM with the heartbeat-attention mechanism (Experiment 2).  
Figure 6: Impact of hyper-parameters.  
by giving different weights to different heartbeats. As shown in Figure 7, we plotted a 12-lead record that was correctly predicted as myocardial infarction. The weights given to heartbeats by the heartbeat-attention mechanism in this record are represented by the green histogram. The thirteenth heartbeat is an obviously abnormal heartbeat, and it is given a larger weight. In addition, other general heartbeats are relatively evenly assigned weights. The model with heartbeat-attention mechanism can weigh different heartbeats differently for samples of myocardial infarction. Further, the difference in weights will make feature vectors more representative and result in better experimental performance, as shown in Table 3.
# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
## 3 The Proposed Coding Optimization Algorithm and Network Architecture
### Network Architecture
#### 3.2.2 Loss Function  
We adopt the commonly used Mean Absolute Error (MAE) to supervise the proposed network. The MAE can be represented as:  
\[MAE\Big{(}I_{R_{4}},I_{R_{4}}^{E}\Big{)}=\frac{\left\|I_{R_{4}}-I_{R_{4}}^{E} \right\|_{1}}{W\times H+\frac{W}{2}\times\frac{H}{2}+\frac{W}{2}\times\frac{H }{2}}\]  
\[=\frac{\left\|Y_{R_{4}}-Y_{R_{4}}^{E}\right\|_{1}+\left\|U_{R_{4}}-U_{R_{4}}^{E }\right\|_{1}+\left\|V_{R_{4}}-V_{R_{4}}^{E}\right\|_{1}}{\frac{3}{2}WH} \tag{7}\]  
\[=\frac{4}{6}MAE\Big{(}Y_{R_{4}},Y_{R_{4}}^{E}\Big{)}+\frac{1}{6}MAE\Big{(}U_{ R_{4}},U_{R_{4}}^{E}\Big{)}+\frac{1}{6}MAE\Big{(}V_{R_{4}},V_{R_{4}}^{E}\Big{)}\]  
Furthermore, the loss function can be described accordingly as:  
\[loss=4 MAE\Big{(}Y_{R_{4}},Y_{R_{4}}^{E}\Big{)}+MAE\Big{(}U_{R_{4}},U_{R_{4}}^{ E}\Big{)}+MAE\Big{(}V_{R_{4}},V_{R_{4}}^{E}\Big{)} \tag{8}\]  
where \(Y_{R_{4}}^{E}\), \(U_{R_{4}}^{E}\), and \(V_{R_{4}}^{E}\) are the components of the enhanced frame, \(Y_{R_{4}}\), \(U_{R_{4}}\), and \(V_{R_{4}}\) are those of the ground truth. Weighting in the ratio of 4:1:1 can better reflect the importance of each component.
## 2 Theoretical Foundation and Hypotheses Development
### Legal Factors
#### 2.3.1 Corruption Perception and Control  
Corruption control refers to the extent to which public power is used for private gain such that the political structure is influenced by the wealthy. Corruption perception refers to how the public perceives and trusts a nation's leadership structures. Money laundering is one of the most widespread global economic challenges owing to corruption [41]. Money laundering promotes corruption and is frequently associated with organized crime [41]. CBDCs that operate on blockchain technology permit the transparent record-keeping and elimination of intermediaries, making them an innovative anti-corruption solution for governing structures [42; 43; 44]. Furthermore, blockchain-based CBDCs enable countries, international organizations, and law enforcement agencies to work together transparently [45]. A few studies have evaluated the benefits of central bank digital currencies in dealing with money laundering and criminal activities [46; 47; 48]. If a country's public power is exploited for private advantage such that the political structure is influenced by individuals, officials may not value a transparent system. However, countries with a high level of corruption control will opt for a more robust and transparent system and are more likely to implement CBDCs. Considering this, we propose the following hypotheses:  
**H3.**_Corruption control is positively associated with the adoption of a central bank digital currency._  
**H4.**_Corruption perception is positively associated with the adoption of a central bank digital currency._
## 4 Implementation and Operations
### Maintaining Sensing Data of Virtual Sensor  
Each _VirtualSensor_ has a Map-based cache where the key is a property name and the value is the instance of a _Property_ class. The _Property_ class has a timestamp, a property name and value. If a physical sensor has a sensing ability of temperature and humidity, the corresponding virtual sensor will have two instances of the _Property_ class as shown in the upper part of Figure 4.  
As shown in the lower part of Figure 4, _VirtualSensor_'s cache can be dynamically extended when a reported data packet contains new kinds of properties even after deployment. For example, if a  
Figure 3: A Linux machine and sensors for the prototype implementation of TinyONet.  
physical sensor which reports residual energy is newly spread on the existing sensing field, this sensor may report its residual energy periodically. After receiving the first report packet, the corresponding _VirtualSensor_ will add a new property holding'residual energy' which other existing _VirtualSensors_' caches do not have. With this approach, _VirtualSensor_ can dynamically adopt a new kind of property without the system modification or the system halt. This paper is limited in that the cache entry deletion was not implemented. We did not include cache entry deletion for two reasons. First, we anticipated that a physical sensor rarely removes the existing properties provided by its hardware after deployment. Second, we concluded that the aging of cache entries could be enough to complement the cache entry deletion. However, the function of the cache entry deletion may be needed for general purpose, therefore it would be added in the future study.
## 5 Conclusions  
In this paper, according to the real helicopter cabin noise, based on the sound absorption structure of microperforated panels, porous materials and acoustic metamaterials are introduced. Simulation and experimental studies of noise suppression in helicopter cabins based on the composite acoustic structures of the microperforated panels were conducted to verify their feasibility and effectiveness. The main conclusions are as follows:  
1. The proposed MPP composite sound absorption structures can suppress the noise from the main gearbox in the target frequency band to a satisfactory degree.
2. Based on the constructed finite element model of the helicopter main gear/body acoustic vibration coupling, the in-cabin noise reduction characteristics of the composite microperforated plate acoustic structures were obtained via simulation analysis. The simulation results show that the Combined MPP and MPP+Porous structure significantly improve the acoustic bandwidth and acoustic performance and can effectively reduce the cabin noise level in the range of 500-2000 Hz, with an attenuation amplitude of up to 35 dB. The MPP+PAM achieves low-frequency noise control in the range of 350-450 Hz and has a certain noise control effect in the middle- and high-frequency ranges.
3. Based on the constructed model helicopter test platform, the cabin sound field conditions of composite acoustic structures equipped with microperforated panels were tested under real main gear vibration excitation. The cabin sound fields were compared with that of the original wall panel to verify the cabin noise reduction characteristics of the composite MPP acoustic structures. The test results show that the Combined MPP and MPP+Porous structure realize an overall cabin SPL attenuation of 8-10 dB, on average, in a wide frequency range of 500-2000 Hz, with an amplitude  
Figure 23: Average SPL frequency response curve in the cabin equipped with different MPP composite sound absorption structures (compared with the original wall plate). (**a**) Combined MPP. (**b**) MPP+Porous. (**c**) MPP+PAM.  
of more than 20 dB. The MPP+PAM achieves low-frequency sound absorption in the frequency range of 300-450 Hz; the sound absorption effect reaches 45%, and it also has good noise reduction effects in the middle- and high-frequency bands.  
Conceptualization, Y.L. and C.L. ([PERSON]); Methodology, C.L. ([PERSON]); Software, C.L. ([PERSON]); Formal analysis, Y.W.; Writing--original draft, C.L. ([PERSON]); Project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
# Promising Novel Biomarkers in Cardiovascular Diseases
## 1 Introduction
### sst2  
Soluble suppression of tumorigenicity 2 (sST2) is a cardiac biomarker, which has its main application in HF and acute coronary syndrome (ACS). In these disease entities, it is known to have a prognostic value, while being unaffected by possible confounders such as renal failure, age, sex or anaemia. There are two known isoforms of ST2, namely, the membrane bound ST2L and the soluble form sST2. A known ligand to ST2 is interleukin-33 (IL-33). By binding to the ST2L receptor, IL-33 can mediate cardioprotective effects in response to an increase in cardiac stress or injury. While the ST2L-IL-33 interaction is considered as cardioprotective and beneficial, sST2 acts as a decoy receptor for IL-33, attenuating its beneficial effects. Thus, elevated levels of sST2 are associated with higher amounts of cardiac remodelling and cardiac injury. In HF with reduced ejection fraction, sST2 was identified as a potential monitoring parameter with prognostic impact on mortality and long-term survival. In patients with myocardial infarction, sST2 was shown to be significantly elevated, and provided good prognostic value in the acute setting as well as for long-term survival.  
Additionally, levels of sST2 were reported to be associated with an increased risk of type 2 diabetes compared to non-diabetic controls [7; 8]. Furthermore, levels of sST2 where shown to increase in the presence of left ventricular diastolic dysfunction in diabetic patients [8]. High circulating levels of sST2 also correlate with an increase in fasting plasma glucose [9].  
In this regard, another study could show that sST2 levels were predominantly linked to markers associated with diabetes such as insulin resistance or ectopic fat, thus speculating about a potential involvement in inflammatory processes involved in the development of type 2 diabetes [10]. Similarly, [PERSON] et al. reported elevated sST2 levels in obese patients, indicating a lower amount of IL-33-mediated beneficial effects in these individuals [11]. Of note, a correlation of sST2 and carotid intima media thickness was reported in a former study [12].  
Accordingly, high circulating levels of sST2 were correlated with increased CVD mortality in patients with type 2 diabetes [9]. [PERSON] et al. observed a prognostic impact of sST2 in diabetic patients suffering from ACS [13]. In a study by [PERSON] et al., sST2 was shown to be independently associated with both all-cause and cardiovascular mortality in diabetes mellitus [14].  
The observed correlations between sST2 and diabetes mellitus are assumed to be mainly mediated through its ligand IL-33. IL-33 is involved in the regulation of white adipose tissue (WAT) predominantly mediated via type 2 innate lymphoid cells (ILC2), thereby also increasing caloric expenditure [15]. WAT homeostasis is believed to be disturbed as a consequence of an increase in sST2 and a corresponding decrease of IL-33, promoting a pro-inflammatory state, obesity and thus insulin resistance (IR) and diabetes mellitus.
## 3 Results and Discussion
### Shield Tunneling Parameters  
A large number of engineering practices show that the shield tunneling parameters are one of the crucial factors affecting the surrounding ground deformation. According to the geological report produced by the joint application of drilling exploration and microtremor advanced exploration technology, the shield driving parameters were adjusted in time. In the process of shield tunneling, the main tunneling parameters of the shield machine, such as total thrust, cutterhead torque and rotation speed, tunneling speed, cut mud water pressure, grouting pressure, and grouting volume, are recorded in real-time. Figure 7 shows the main tunneling parameters of the monitoring section. The grouting hole at the upper right of the segment is selected for the record of grouting parameters. The chamber earth pressure is the average value of each chamber. The geological report results show that there is mainly moderately weathered argillaceous siltstone and moderately weathered tufaceous gray sandstone within the monitoring range. These have high permeability and significant losses due to dissipation after grouting. Therefore, the grouting pressure is mainly about 0.35 MPa, and the grouting volume of the upper right grouting hole is about 6 m\({}^{3}\). The torque can also reflect the difficulty of rock cutting. According to the geological survey results of microtremor exploration, there is relatively weak soil near Ring 118, therefore the torque, tunneling speed, and top pressure of the slurry chamber are also appropriately reduced. The variation trend of torque is consistent with that of total thrust, which is consistent with the conclusion of [PERSON] [26]. At about Ring 122, the soil quality is enhanced, and the corresponding total thrust, grouting pressure, and other parameters are increased. When slurry balanced shield tunneling is adopted, it is difficult to adjust the  
Figure 6: Comparison between microtremor exploration results and drilling geological results: (a) ZK082; (b) ZK085; (c) ZK086; (d) ZK093.  
front support pressure in blocks, while the grouting pressure and jack thrust can be flexibly adjusted at different locations of the shield tunnel section. This feature should be brought into full play, and the grouting pressure and jack thrust should be increased at the side of the shield tunnel's abnormal pressure load to avoid the shield tunnel's uneven settlement or axis deviation.  
Figure 7: Main tunneling parameters of the monitoring section: (**a**) grouting pressure; (**b**) synchronous grouting volume; (**c**) speed of shield cutterhead; (**d**) torque of shield cutterhead; (**e**) gross thrust; (**f**) tunneling speed; (**g**) top pressure of slurry chamber.