esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
**Empirical Mode Decomposition of Ultrasound Imagingfor Gain-Independent Measurement on Tissue Echogenicity: A Feasibility Study**  
**[PERSON] 1,2, [PERSON] 2, [PERSON] 1, [PERSON] 2 and [PERSON] 3,4,5,***  
1 College of Life Science and Bioengineering, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (Z.Z.); [EMAIL_ADDRESS] (S.W.)  
2 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (W.W.); [EMAIL_ADDRESS] (K.J.)  
3 Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan  
4 Medical Imaging Research Center, Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital at Linkou, Taoyuan 33302, Taiwan  
5 Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan 33305, Taiwan  
*Correspondence: [EMAIL_ADDRESS]; Tel.: +886-3-211-8800 (ext. 3795)  
Academic Editor: [PERSON]  
Received: 11 February 2017; Accepted: 22 March 2017; Published: 25 March 2017  
**Abstract:** Empirical mode decomposition (EMD) is an adaptive method for decomposing a signal into intrinsic mode functions (IMFs).This study explored using EMD of ultrasound imaging for gain-independent measurements on tissue echogenicity. The IMF-based echogenicity ratio (IER) was proposed using the first (\(C_{1}\)) and second IMFs (\(C_{2}\)) of ultrasound radiofrequency data. Experiments on lipid phantoms were conducted to investigate the practical performance of IER. Phantoms with lipid concentrations 0%-30% (\(n=36\)) were scanned using a clinical ultrasound scanner to acquire the radiofrequency data under different gains (12-33 dB) for EMD and IER calculations. Experiments on a tissue-mimicking phantom were further performed using the same ultrasound system and data acquisition procedure to investigate the effect of ultrasound frequency on the IER at5-8 MHz.Experimental results showed that the IER measured under 33-dB gain decreased from 6.65 \(\pm\) 0.23 to 3.97 \(\pm\) 0.10 when the lipid concentrations were increased from 0% to 30%. When 12-dB gain was used, the IER decreased from 6.21 \(\pm\) 0.29 to 3.39 \(\pm\) 0.07. However, whenincreasing the frequency, the IER had a mean decreasing rate of \(-8.67\%\) per MHz, which was lower than those of the \(C_{1}\) and \(C_{2}\) intensities.The proposed IER may allow gain-independent measurement on tissue echogenicity.  
**Keywords:** empirical mode decomposition; intrinsic mode function; echogenicity
## 4 Results and Discussion
### Experiment Description
#### 4.1.3 Metrics  
\(P\) (Precision): The accuracy rate is the ratio of the number of correct tests to the number of positive tests. Equation (14) is as follows:  
\[\textit{precesion}=\frac{TP}{TP+FP} \tag{14}\]  
\(R\) (Recall): Recall is the ratio of the number of correct detections to the number of actual positive detections. Equation (15) is as follows:  
\[\textit{recall}=\frac{TP}{TP+FN} \tag{15}\]  
where \(TP\) indicates the number of actuals and detections that are positive; \(FP\) indicates the number of detections that are positive but negative; \(FN\) indicates the number of detections that are negative but positive.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Name** & **Pothole** & **Fog** & **Manhole** \\ \hline Pothole600(B1) & 600 & 0 & 0 \\ Pothole600-manhole(B2) & 450 & 0 & 150 \\ Pothole600-fog(B3) & 450 & 150 & 0 \\ Pothole600-manhole-fog(B4) & 300 & 150 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of photos of each type in B1, B2, B3, B4 of pothole600 dataset.  
Figure 9: Pothole600 datasets and augmented number of pictures in the dataset.  
\(F1\) is a measure of the accuracy of a model that takes into account both the accuracy and recall of a classification model. Equation (16) is as follows:  
\[F1=\frac{2\times\textit{precestion}\times\textit{recall}}{\textit{ precision}+\textit{recall}} \tag{16}\]  
_mAP_ (Mean Average Precision): mAP is the mean value of the average precision of the different categories. Equation (17) is as follows:  
\[mAP=\frac{\sum_{1}^{N}\int_{0}^{1}p(r)dr}{N} \tag{17}\]  
where \(N\) represents the total number of categories of detection objects, \(p\) represents the precision of each category, and \(r\) represents the recall of each category.  
GFLOPs (Giga Floating-point Operations Per Second): the number of floating-point operations per second can be used to measure the complexity of a model. Smaller GFLOPs indicate that the model requires less computation and runs faster.  
Parameter: parameter refers to the total number of parameters to be trained in model training. It is used to measure the size of the model. The unit is usually M. M refers to millions and is the unit of count.
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 3 The Proposed Method
### Vicinity Region-based Communications and Query Processing  
In this subsection, we describe how each moving object and the server communicate each other to cooperatively process range monitoring queries. The query processing consists of server-side tasks and object-side tasks.
## 3 Results and Discussion
### Analysis Outputs
#### 3.2.3. Furnace Operation Indicators  
The adiabatic flame temperature decreases from 2026 to 1839 \({}^{\circ}\)C, while the standard deviation decreases from 326 to 279 \({}^{\circ}\)C (see Figure 10). The higher moisture content, the lower LHV, and the higher amount of flue gas from biomass combustion lead to this temperature decrease. Compared to the previous parameters, the adiabatic flame temperature exhibits a much larger uncertainty for both coal and biomass but slightly lower for biomass. This parameter is mainly impacted by the uncertainty on the fuel composition;  
Figure 8: Steam pressure to high-pressure steam turbine. Coal: mean = 235 bar, standard deviation = 14 bar. Biomass: mean = 185 bar, standard deviation = 8 bar.  
Figure 9: Steam flow to high-pressure steam turbine. Coal: mean = 542 kg/s, standard deviation = 53 kg/s. Biomass: mean = 410 kg/s, standard deviation = 22 kg/s.  
The effective radiation temperature decreases from 1655 to 1489 \({}^{\circ}\)C, while the uncertainty decreases from 200 to 166 \({}^{\circ}\)C (see Figure 12). The effective radiation temperature is a heat-weighted average between the adiabatic flame temperature (see Paragraph 'Adiabatic Flame Temperature' in Section 3.2.3) and the FEGT (see Paragraph 'Furnace Exit Gas Temperature (FEGT)' in Section 3.2.3) [37]. This evolution can therefore be explained by the evolution of the two previous output parameters. The probability distribution of the adiabatic flame temperature, however, dominates and leads to a skewed distribution with a significant standard deviation for this parameter as well. As this parameter gives an image of the radiative heat transfer in the furnace, such a large uncertainty is concerning in terms of operation and maintenance of the boiler. The risk of tube failure because of excessive heat transfer is real in such large boilers. These results tend to show that the accurate monitoring of the combustion process in the furnace is key to ensuring a smooth operation of the power plant.
## 4 Method for UAV Electromagnetic Interference Suppression
### Anti-Electromagnetic Interference Method for UAV Hardware Equipment
#### 4.2.1 Shield  
The shielding technology of electromagnetic waves mainly uses insulating materials to block electromagnetic wave interference [69]. In actual use, shielding technology can  
Figure 5: UAV data link electromagnetic interference adaptive process.  
only slightly reduce electromagnetic waves through absorption and reflection, but cannot effectively block them.  
In traditional electromagnetic shielding, components made of conductive materials or ferromagnetic materials are commonly used to shield and isolate the protected objects to control the induction and radiation of electric fields, magnetic fields and electromagnetic waves from one area to another. However, due to the lightweight requirements of drones, conventional shielding materials (copper, iron, aluminum and other metals) are basically not used to make drone shells.  
Currently, UAV shell parts generally use engineering plastics or carbon fiber composite materials. Engineering plastic does not have much protection against electromagnetic interference. However, some researchers have applied a metal layer [70] or other electromagnetic wave shielding coatings [71] on the engineering plastic shell to turn the processed engineering plastic shell into a Faraday cover to achieve a certain anti-electromagnetic interference effect. This approach is simple to operate and design, and the production cost is generally low, but there are corresponding shortcomings. For example, coating scratches may cause shielding failure. During thermal cycling, the coating may decompose due to heat and cause other coating adhesion problems. It may be necessary to add additional protective surfaces to avoid coating oxidation. Therefore, some researchers have turned their attention to developing carbon fiber composite materials with electromagnetic shielding properties [72; 73; 74; 75].  
Without adding special parts inside the drone, use appropriate insulating materials to fill all the gaps in the drone to prevent electromagnetic waves from entering. These filling materials include wire mesh pads, conductive cloth pads, soft metals, etc. [76]. Adding a shielding layer near the electronic components can also reduce electromagnetic interference. For example, use tin foil for the whole package, and use shielded twisted pair in the signal circuit [17].  
You can also reduce the impact of electromagnetic interference by optimizing the design of the drone. For example, keep the key components of the drone, the flight control system, and the measurement system away from the interface and the gap, so as to weaken the electromagnetic waves in these parts. Separate the instrument cord from the power cord as much as possible. If it must pass, cross it at 90\({}^{\circ}\)[77].
## 6 Important Applications
### Continuous Porous Polymers as Reactive Supports  
A high chemical reactivity and a large capacity of accessible functional groups are the requirements for the application of supports in solid phase chemistry. Within this field, continuous poly (CMS-co-DVB) was used as acylating resin to transform amines into amides [134]. To obtain these resins, the radical initiator 4,4'-azobis(4-cyanovaleric acid) was attached to the surface of the resin. Subsequently, grafting reactions were performed with acetoxystyrene or CMS. Acylation reactions were carried out by passing the reactant mixture through the resin in different solvents. It should be noted that these polymers could be easily regenerated and reused.  
[PERSON] _et al._[135] developed composite materials consisting of inorganic carrier materials with monolithic polymers incorporated in their structures which were used as supports to carry out different chemical reactions in a flow-through mode. Therefore, carrier materials (with pores in the micrometer up to millimeter range) were immersed in the polymerization mixture used for the synthesis of the macroporous monoliths, and the polymerization reactions were started by radical polymerization. Thus, by introduction of the inorganic support, a second monolithic material was created. The reaction conditions were chosen in such a way that pores with diameters in the micrometer range were formed. Thus, the final composites were highly porous materials, allowing good penetration of liquids at  
Figure 9: Schematic drawing of the preparation of an affinity monolith produced by _in situ_ polymerization [131]. Reprinted with permission from the American Chemical Society.  
low-pressure drop. The large number of monolithic materials synthesized with different polymer functionalities was used in a wide variety of chemical reactions such as selective reductions, Suzuki cross-coupling reactions, nucleophilic substitutions, and catalytic transfer hydrogations, obtaining very high yields in all cases.  
[PERSON] _et al._ synthesized \"high internal phase emulsion polymer\" (polyHIPE) monoliths to be used as polymer-support for Suzuki cross-coupling reactions (Figure 10) [136]. PolyHIPEs containing high loadings of chlorophyll groups were efficiently prepared by the direct copolymerization of 4-vinylbenzyl chloride and DVB monomers. The supports were used in batch and flow-through modes and a high yield of a pure biaryl product was obtained using the polyHIPE support in cubic form and an electron-rich boronic acid.
## 6 Results and Discussion: Back-Emf
### DE: Comparison of Slot/Pole Combinations for PMSMs and MPMSMs  
In Table 14, the fundamental component of back-emf in the presence of the maximum DE magnitude (1.00 mm) is presented for both the modular and monolithic machines considering different slot/pole combinations. As in the case of the 24-slot, 22-pole machine, no significant effect is observed in the back-emf fundamental and third-order harmonics caused by eccentricity.  
In Table 15, the maximum unbalance between phases in the presence of the maximum DE magnitude (1.00 mm) is presented for both the modular and monolithic machines considering different slot/pole combinations. It can be noted that the number of phase groups evenly distributed in the stator circumference is a strong indicator of the magnitude of back-emf unbalance for a given slot/pole combination. No significant differences were found by comparing the results of modular and monolithic machines.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \(Q_{s}\)/2\(p\)ECC & & & & & & & & & & & & \\ \hline \multirow{8}{*}{185 12P} & \multicolumn{8}{c}{Modular [V]} & \multicolumn{8}{c}{Monolithic [V]} & \multicolumn{8}{c}{Modular [V]} & \multicolumn{8}{c}{Monolithic [V]} \\  & HO & A & B & C & A & B & C & A & B & C & A & B & C \\  & 1 st & 247.5 & 247.5 & 247.5 & 263.7 & 263.8 & 263.7 & 247.6 & 247.7 & 247.6 & 263.7 & 263.9 & 263.7 \\  & 3 rd & 21.9 & 21.9 & 21.9 & 24.6 & 24.0 & 24.6 & 22.0 & 22.0 & 22.0 & 24.8 & 24.4 & 24.9 \\  & 1 st & 281.0 & 281.1 & 281.0 & 275.4 & 275.5 & 275.4 & 281.7 & 281.7 & 281.6 & 276.2 & 276.2 & 276.2 \\  & 3 rd & 31.6 & 31.6 & 31.6 & 35.9 & 36.0 & 36.0 & 33.3 & 33.3 & 33.3 & 37.8 & 37.8 & 37.8 \\  & 1 st & 276.7 & 276.7 & 276.6 & 275.9 & 275.9 & 275.8 & 277.5 & 277.5 & 276.7 & 276.6 & 276.6 \\  & 3 rd & 37.9 & 37.9 & 37.9 & 37.8 & 37.8 & 37.9 & 40.5 & 40.6 & 40.6 & 39.7 & 39.7 & 39.7 \\  & 1 st & 283.4 & 283.4 & 283.3 & 283.2 & 283.2 & 283.2 & 284.1 & 284.1 & 283.9 & 283.9 & 283.9 \\  & 3 rd & 28.7 & 28.7 & 28.7 & 29.0 & 28.9 & 29.0 & 30.7 & 30.7 & 30.7 & 30.8 & 30.8 & 30.9 \\  & 1 st & 300.9 & 300.9 & 300.9 & 292.5 & 292.5 & 292.5 & 303.4 & 303.4 & 303.4 & 294.8 & 294.8 \\  & 3 rd & 9.5 & 9.5 & 9.7 & 18.9 & 18.9 & 19.1 & 9.6 & 9.6 & 9.8 & 19.8 & 19.8 & 19.8 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Fundamental component and third-order harmonic of back-emf for different PMSM and MPMSM slot/pole combinations in the presence of DE.
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
## 3 Method and Algorithm
### Federated Network Algorithm
#### 3.2.2 Computing Server  
The computing server is an intermediate platform in the learning process. The main functions are receiving the gradient information from multiple learning clients, performing calculations on the gradients, integrating the information learned by multiple models, and transmitting the result to each learning client separately.
# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 3 Cases for Investigation
### Selected Jamming Incidents  
In [8], a GPS L1 jamming incident was evaluated, and it was shown that this had little impact on a single-station carrier phase-based PPK solution. This paper further investigates the consequences of jamming on carrier phase-based positioning solutions.  
This is done by analyzing two cases of high impact, long term jamming registered by TAPAS. These cases represent one-in-a-year occurrences, making them some of the most  
Figure 3: \(\mathrm{C/N_{0}}\) values for GPS L1 observations during the last jamming incident registered at TA03 on 18 January 2019.  
Figure 2: Sum-of-squares (SoS) values for TA03 on 18 January 2019. The red line marks the SoS detection threshold.  
significant incidents detected by TAPAS to this day. Therefore, they should be seen as representatives of real-world worst-case scenarios.  
The third, and final, case is a revisit of the case in [8], which is a less significant case. It is included mostly in order to compare code and carrier phase positioning during a jamming incident.
## 3 System Model  
An SISO-NOMA-based power-domain multiplexing system model is considered mathematically, consisting of a BS with users (\(U_{1}\), \(U_{2}\),..., \(U_{N}\)). Here, we are considering a downlink SISO-NOMA communication scenario. At the transmitter side, the users' messages are multiplexed using superposition coding, whereas, at the receiver end, the received users' messages are demultiplexed (decoded) to retrieve the original message and perform Successive Interference Cancellation (SIC) to remove interference from other users' messages.
## 2 The Notion of Metadata  
The most widely used definition of metadata is \"data about data\". They provide the description of the what, where, when, who and how about data [18]. A comprehensive metadata example is that of a photograph, in which the metadata describe when and where the photograph was taken, who the photographer was, what is in the photograph, what the camera features are or what post-processes have been done. The metadata are generally used to describe and structure the principal aspect of data with the aim of sharing, reusing and understanding heterogeneous data sets and allowing the information searching and retrieval [18,19]. In the scope of WSN, metadata have been defined as descriptive data used to depict the WSN, including the environment, the nodes and their status, sensing data, and the WSN as a whole system [16]. The use of metadata in WSNs has been mainly related with the execution of routing protocols and in-network data aggregation processes [20-22].  
Currently, the metadata need to be become an _explicit_ part of WSN in order to preserve the knowledge of the WSN's status over time (Table 1). On the one hand, they must describe dynamically the changes of the network status and report them back to other components and systems. For example, if a node changes its location or is damaged, the system must be able to broadcast a message containing metadata elements in order to inform other sensor networks and users about these changes. On the other hand, metadata must be automatically generated and updated, since real-time sensor data require real-time metadata as well. For example, if a node fails, the network must automatically (i.e. without human intervention) reconfigure new routes to send data. In the same way if a node changes its location, the sensing data (and their metadata) must reflect the new location.
## 3 Results and Discussion
### Simulation Analysis
#### 3.1.2 Motor Rotation Response Simulation Analysis  
Given the rotational angle as \(\theta\)r = 20 \(\times\) sin(t) rad/s, the load torque changes from 0 N\(\cdot\)m to 4 N\(\cdot\)m at 5 s. Figure 8 shows the simulation curves of the rotational angle response for each control method, with an enlarged view demonstrating the motor's rotational angle response around 5 s. It can be observed from Figure 8 that after adding the load disturbance, the VSMC method exhibits the response that is closest to the reference \(\theta\)r curve compared to the PI and SMC methods.  
Figure 8: Rotation response simulation curves for each control method.  
Figure 7: Simulation curve of step position response speed for each control method.  
Figure 9 displays the simulation curves of position response errors for each control method. The initial response error of PI control is approximately 0.18 rad, with a tracking error range from approximately \(-\)0.09 to 0.09 rad; the initial response error of SMC is 0.15 rad, with a response error range from approximately \(-\)0.075 to 0.075 rad. The initial response error of VSMC is 0.1 rad, with a tracking error range from approximately \(-\)0.05 to 0.05 rad. These results show that VSMC reduces both the initial response error and the error range compared to the PI and SMC methods.  
Figure 10 shows the disturbance simulation curve, where the observer can rapidly estimate the change in disturbance. From the enlarged view, it can be observed that the response time of the observer to the load change at 5 s is 0.0085 s.  
Given the rotational angle as \(\theta\)r = 20\(\times\)sinr rad, as shown in Figure 11, the load torque changes from 0.5 N\(\cdot\)m to 1 N\(\cdot\)m at 10 s. The motor's position response, shown in Figure 12, indicates that the initial tracking error of PI control is approximately 0.08 rad, with a response error range from approximately \(-\)0.08 to 0.08 rad. The initial response error of SMC is 0.075 rad, with a response error range from approximately \(-\)0.08 to 0.08 rad. The initial response error of VSMC is 0.07 rad, with a response error range from approximately \(-\)0.07 to 0.07 rad. Adopting the VSMC strategy yields a more accurate rotational response precision and stronger disturbance rejection capability. Figure 13 depicts the angle response curve, indicating that after the load increases, the speed curve of the control strategy proposed in this paper is smoother. Figure 14 illustrates the timely estimation of load changes using the variable-parameter sliding mode control strategy based on the recursive least squares observer when the load changes. Comparing Figures 7-9, it can be seen that the experimental results are consistent with the simulation results, which proves the feasibility of the proposed MTPA-ESO control strategy.  
Figure 10: Disturbance simulation curve.  
Figure 9: Rotation response error simulation curves for each control method.