esa-sceva/satcom-chunk-collection huggingface dataset:
# Phonocardiography Signals Compression with Deep Convolutional Autoencoder for Telecare Applications
## 6 Conclusions  
In this paper, we have proposed a convolutional autoencoder's architecture for the compression of 1D PCG signals. When the input segment length of 6000 samples, the achievable CR is 32 under the constraint that the corresponding PRD is less than 5%. The proposed method is more robust than the DCT compression method when considered the impact of the link quality. As the BER rose from \(10^{-4}\) to \(10^{-3}\), the DCT method increased the PRD by about 7.6%, whereas our proposed method increased the PRD by only about 0.3%. We also considered the fix-point issue. When comparing the case of floating-point representation, we observed that the evaluation results have shown that the resulting PRD was slightly increased by 0.2% as the word-length of 10 bits with Q3.7 format. Our future work is jointly considered the denoising and data compression tasks for the PCG signals while using the deep convolutional autoencoder.  
Conceptualization, Y.-R.C. and K.-C.H.; Data curation, K.-C.H.; Formal analysis, Y.-R.C. and K.-C.H.; Funding acquisition, Y.-R.C.; Investigation, Y.-R.C. and K.-C.H.; Methodology, [PERSON]-R.C. and [PERSON]-[PERSON]; Project administration, Y.-R.C.; Resources, Y.-R.C. and H.-W.T.; Software, [PERSON]-C.H.; Supervision, Y.-R.C. and H.-W.T.; Validation, Y.-R.C. and H.-W.T.; Visualization, Y.-R.C. and [PERSON]-[PERSON]; Writing--original draft, Y.-R.C. and [PERSON]-[PERSON]; Writing--review & editing, [PERSON]-R.C., [PERSON]-C.H. and H.-W.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology (MOST), Taiwan, under the Grant MOST 108-2221-E-197-010.  
The authors declare no conflict of interest.
# Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models
###### Abstract  
With the development of 3D scanning technology, a huge volume of point cloud data has been collected at a lower cost. The huge data set is the main burden during the data processing of point clouds, so point cloud simplification is critical. The main aim of point cloud simplification is to reduce data volume while preserving the data features. Therefore, this paper provides a new method for point cloud simplification, named FPPS (feature-preserved point cloud simplification). In FPPS, point cloud simplification entropy is defined, which quantifies features hidden in point clouds. According to simplification entropy, the key points including the majority of the geometric features are selected. Then, based on the natural quadric shape, we introduce a point cloud matching model (PCMM), by which the simplification rules are set. Additionally, the similarity between PCMM and the neighbors of the key points is measured by the shape operator. This represents the criteria for the adaptive simplification parameters in FPPS. Finally, the experiment verifies the feasibility of FPPS and compares FPPS with other four-point cloud simplification algorithms. The results show that FPPS is superior to other simplification algorithms. In addition, FPPS can partially recognize noise.  
point cloud simplification; natural quadric shape models; simplification entropy; shape operator; point cloud matching model +
Footnote †: journal: Applied Sciences
# Inferring Urban Social Networks from Publicly Available Data
## Appendix A Population Synthesis: Territory and Households
We, therefore, count the number of unassigned people in two different sets of simulations: (i) we consider a single tile and vary the number of individuals in the tile (see Figure 2a, with the range for the population size taken from the city of Florence); or (ii) we consider the whole city and vary the side of the tiles used to define the grid (see Figure 2b, again for the case of Florence). Figure 2a shows that the number of people left out of all households is negligible, provided that the number of individuals in each tile is large enough to guarantee that all roles are sufficiently represented. Accordingly, Figure 2b shows that the number of unassigned people in the city drops very fast as soon as the tiles are large enough to contain, on average, a sufficient number of people; this number approaches zero as the tile size grows, i.e., as the population of each tile approaches the total population \(N\). The quality of the heuristics decreases when the noise volume \(\omega\) grows, thus highlighting the importance of internal consistency in the data. However, the plots show that the number of unassigned people stays safely below 1% of the total population even in the presence of (reasonable) fluctuations in the input data. Next, we focus on the distribution of households by type and by number of children. In Figure 14(a), we plot the number of households of each type obtained for the city of Florence with our heuristics, on both clean and noisy data, compared with an estimate based on ISTAT data. In the plot, \"ISTAT data\" refers to the weighted average of the conditional probability of being the head of a specific household type given the age, computed through the law of total probability from ISTAT data. For instance, the number of households of type \"1 Parent w/Children\" is computed as the weighted average of the role (single-parent, parent). This approach allows to gain an estimate based on ISTAT data that takes into account the specific age distribution of Florence. The figure shows that our results do match the expected distribution almost perfectly and that the heuristics is very robust with respect to noise. In Figure 14(b), we instead show the distribution of the number of children for the types of household composed of parents and children. In this case, \"ISTAT data\" is obtained from the national aggregate distribution of the number of children per family; to the best of our knowledge, equivalent data are unfortunately unavailable for the province of Florence. The two distributions might significantly differ in light of the demographic differences between the residents of the city and the entirety of the Italian population. This difference may explain, at least in part, the divergence between the ISTAT data and the results of our simulations. In any case, the results look reasonable and are, again, stable with respect to perturbations in the input data.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 4 Experimental Verification
### Antenna Prototype  
As illustrated in Figure 9, the prototype consists of a 6 \(\times\) 6 array of chip sub-arrays, with each sub-array comprising 4 \(\times\) 5 unit cells. To protect the internal switches from electrostatic breakdown, electrostatic discharge (ESD) protection modules are integrated into the bonding pads located on the lower side of each sub-array. The sub-arrays measure 1.45 mm \(\times\) 1.45 mm in size, with an element period of 0.26 mm. The spacing between adjacent chip sub-arrays is set to one element period, resulting in an overall phased electromagnetic surface antenna size of 10 mm \(\times\) 10 mm.  
Figure 8: Simulated amplitude of the RRA element.  
Figure 7: Simulated current distribution of the RRA element at 290 GHz (**a**) ON-state and (**b**) OFF-state.  
The control methodology for the array adopts a column-wise addressing scheme. Specifically, the gate voltages for the switches in the 30 columns of elements are routed through bonding jumpers to the bonding pads on the upper section of the PCB, enabling external control. Meanwhile, the Direct Current (DC) ground lines for all switches are uniformly connected via bonding jumpers to the lower bonding pads on the PCB.
## 2 Materials and Methods
### Methods
#### 2.2.1 Calculation of Net Primary Production (NPP)  
The Monteith equation [46] was used to estimate _NPP_ based on satellite images. According to Equation (1), annual _NPP_ is obtained from gross primary productivity (_GPP_).  
Figure 1: Geographical location of Iran and its ecosystems. Ref: developed by USGS 2020 ([[https://rmgsc.cr.usgs.gov/outgoing/ecosystems/](https://rmgsc.cr.usgs.gov/outgoing/ecosystems/)]([https://rmgsc.cr.usgs.gov/outgoing/ecosystems/](https://rmgsc.cr.usgs.gov/outgoing/ecosystems/)), accessed on 26 October 2022).  
Figure 2: Flowchart of methodology.  
To this end, daily gross _NPP_ was first calculated and then the respiration rate required for maintenance and growth of the plant organs was subtracted, yielding NPP [47; 48; 49].  
\[NPP=\sum_{1}^{365}GPP-R_{m}-R_{g} \tag{1}\]  
where _GPP_ is the gross primary productivity rate, _Rm_ is the residual plant respiration, and _Rg_ is the respiration rate required for plant growth. \(R_{m}-R_{g}\) was calculated based on leaf area index (LAI) product, MODIS sensor (MODIS 15), and climate data [48]. MODI7A3 HGF V6 product provides information about annual NPP at 500 m pixel resolution from the given year, which was retrieved from [[https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)]([https://lpdaac.usgs.gov](https://lpdaac.usgs.gov)), accessed on 18 October 2022, maintained by the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC) at the USGS Earth Resources Observation and Science (EROS) Center.
## 4 Types of FL
### Vertical FL (VFL)
This collaborative procedure poses novel security issues related to data privacy and model inversion attacks, in which attackers seek to reverse-engineer sensitive data from shared model modifications. Because the entities in VFL frequently work with complimentary datasets, it is critical that no sensitive information is exposed during model aggregation. Homomorphic encryption and secure multi-party computation (SMPC) are often used to safeguard data in VFL, allowing organizations to perform calculations on encrypted data while never disclosing the raw data. The security vulnerabilities in VFL, especially surrounding data leakage during model updates, underline the necessity for advanced cryptographic methods, which are discussed in later sections. In VFL, consider a bank collaborating with an insurance company, where both parties share different attributes of the same users to create a credit risk model. If either party's data are not fully encrypted or anonymized, there is a risk of model inversion, where an attacker might infer sensitive customer details from the shared model. This emphasizes the need for homomorphic encryption or secure multi-party computation (SMPC) to ensure that the shared updates do not expose any private information.
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 3 Results
### Flooding
#### 3.1.2 Riverbed Flooded by Increased Water Flow  
A total of 175 F2 type changes were identified, and they were all located within the riverbed. As per the example in Figure 9, these changes show a decrease in the radar signal due to the increase in the water flow and rise in the water level within the river, and typically occurred in correspondence with the transition from the dry to the rainy season (e.g., between December 2016 and March 2017). While these changes have a relative relevance for the flooding hazard itself (they occur in non-urban areas, in A-class slopes, i.e., \(\beta\leq 5^{\circ}\)), and are a typical consequence of the rainfall events and water excess, they are important to mark the sections of the river course where waters rise but, depending on the height of the riverbanks, may flow out and inundate the surrounding land, or not.  
In this regard, it is worth noting the absence of change patterns along the long section of the urban canyon flowing between El Ejercito Bridge in the district of Lima, and Universitaria Bridge in the district of San Martin de Porres (see the visible sections of the dashed line in Figure 7). There, the river flows across a canyon-like morphology, and is flanked by very high and steep banks (see Figure 1f), so any water flow excess is accommodated within the channel, and inundation is very unlikely.  
Figure 8: Example of F1 “Flooded areas” changes in relation to the 16/03/2017 flood event: (**a**) Sentinel-1 RGB composite and (**b**) ratio map. The blue rectangles indicate the path of the Rimac River, and the red rectangle the urban area affected by the flood event. Google Earth imagery (© 2020 Maxar Technologies) acquired (**c**) before (22/02/2017) and (**d**) after (21/03/2017) the event. Contains modified Copernicus Sentinel-1 data.
## 4 Results and Discussion  
### Error Analysis of Model  
We evaluated our approach using the JTA dataset. We used the mean value of the position error per joint (MPJPE) to determine the performance of our network. MPJPE is the mean Euclidean distance between the ground truth and the prediction for multiple joints [40]. The lower the value, the better the performance. Table 2 presents the results of the study. Because we tend to test image-based performance, we do not use a time-electric sander. Because little thought has been given to speed-based 3D creation solutions, we compared them with alternative solutions. Alternative RGB image-based solutions do not target speed and cannot succeed in period performance. Some strategies also use 2D poses. This implies that the standard and speed of the preprocessing methodology can significantly affect the performance of the subsequent 3D estimation strategies. We tested these strategies by adding a typical 2D perspective estimation system, OpenPose, for live speed and accuracy. Open pose requires all interconnected limbs to generate pose recognition results directly for all individuals. Open pose generates two parts of output: one part calibrates the type of area in the input screen, including limb types such as elbows and necks, as well as the background; while the other part of it encodes \"connectivity\" information [41; 42].  
However, many situations in the test dataset were unsuitable for our design goals. The test dataset contained many people of different sizes, with some human bodies obstructing each other or being obscured by the foreground and background. This is significantly different from the application scenarios used in our design. Therefore, we also used test sets that were more similar to games or entertainment to test our work. The resulting image is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Input** & **MPJPE (mm)** & **Velocity** \\ \hline [PERSON] et al. [34] & Video & 65.1 & - \\ [PERSON] et al. [35] & 2D pose & 64.1 & - \\ Open Pose (high accuracy mode)/[PERSON] et al. [35] & RGB image & 71.2 & 834.0 \\ Open Pose (high speed mode)/[PERSON] et al. [35] & RGB image & 83.1 & 114.2 \\ LCR-Net [36] & RGB pose & 83.1 & - \\ This study & RGB pose & 113.2 & 47.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison between our methods and previous ones. ‘-’ indicates that this solution is not designed for real-time, so demonstrating its speed is meaningless, or its speed is much lower than real-time.  
Figure 6: 3D pose recognition.
###### Abstract  
Pipeline inspection gauges (PIGs) are widely used for nondestructive testing of oil and natural gas pipelines, while above ground markers (AGMs) can locate and track the PIG through a variety of methods, including magnetic flux leakage signals, acoustic signals, and extremely low-frequency (ELF) magnetic signals. Traditional AGMs have the disadvantages of low positioning accuracy and only one-dimensional tracking capability. In this paper, a newly-designed PIG tracking system based on the ELF magnetic field is proposed by assembling dual receivers. Moreover, this paper develops a magnetic field sign-integration algorithm to achieve high-precision and four-dimensional (4-D) tracking of PIG. The simulation and experiment results demonstrate that the tracking system has the capability of 4-D tracking. In comparison with the previously published work, the designed tracking system improves the positioning accuracy and orientation tracking accuracy by more than 50%. The dual receivers tracking system also has the characteristic of high-robustness. Even in the state of lateral offset or tilt, it can still achieve accurate tracking of PIG. The realization of PIG's high-precision 4-D tracking can improve the accuracy of defect location. Moreover, it can also provide the latest pipeline network layout and facilitate pipeline maintenance and pipeline surveying applications.  
ab _et al._ Article
## 3 Comparison of the Models
Figure 14: Lateral dynamics simulation results (V10-S1-C1). (**a**) Lateral displacement; (**b**) yaw angle; (**c**) differential wheel rotational speed; (**d**) \(y-\psi\) phase diagram. Under the above conditions, the simulation results of the G2-IRW model are verified close to the SIMPACK model, demonstrating that the G2-IRW model is more accurate than the classical IRW model. The significant effects of the gravitational restoring forces and gyroscopic moment analyzed in Sections 2.2 and 2.3, respectively, are verified by simulation results. The steady-states analysis in Section 2.5 and the detailed steady convergent process described by Equations (45)-(47) are also verified by simulation results. Figure 7:0 Lateral dynamics simulation results (V10-S10-C10). (**a**) Lateral displacement; (**b**) yaw angle; (**c**) differential wheel rotational speed; (**d**) \(y-\psi\) phase diagram. Figure 7:1 Lateral dynamics simulation results (V10-S6-C6). (**a**) Lateral displacement; (**b**) yaw angle; (**c**) differential wheel rotational speed; (**d**) \(y-\psi\) phase diagram. Furthermore, the closeness between the G2-IRW model and the SIMPACK model indicates another accurate description of the IRW's dynamics for future study. Under consideration of cost, complexity, and real-time performance, only the rotational speeds of the wheels and the yaw angle of the IRW can be accurately measured. However, for better stability control and curve negotiation control of the IRW, the lateral displacement and the lateral velocity are key state variables that should be measured in real-time. Owing to the programmability and rapid iterative computability of the differential equations, Equations (28)-(31) is a better option for state observer design. Although irregularities input from the rail track and uncertain nonlinearity influences of the model coefficients exist in practice, according to the study on finite-time continuous extended state observers [22], the observer can be precisely designed and obtain excellent performance. After verifying the G2-IRW model through comparison in simulations, the dynamic characteristics described by Equations (28)-(31) and the steady convergent process described by Equations (45)-(47) will be demonstrated and analyzed in the next section.
# A Two-Echelon Electric Vehicle Routing Problem with Time Windows and Battery Swapping Stations
## 4 Algorithms for the 2E-EVRPTW-BSS
### Solution Representation and Encoding  
A full solution is composed of two parts; the first part includes a sequence of satellite nodes that are visited by ICVs that start and end at the depot for the first echelon, while the second part contains a sequence of customer nodes (including BBSs) which start from and end at a given satellite node for the second echelon.  
As mentioned before, a full solution to 2E-EVRPTW-BSS is denoted as the set \(S=\{S_{1},S_{2}\}\), where \(S_{1}=\{r^{1}_{1},r^{2}_{2},...,r^{1}_{K_{1}}\}\) is the set of routes for the first echelon and \(S_{2}=\{r^{2}_{1},r^{2}_{2},...,r^{2}_{K_{2}}\}\) is the set of routes for the second echelon. For each route in \(S_{1}\), \(r^{1}_{k}=\left(0,s^{1}_{1},s^{k}_{2},...,0\right),k=1,2,...,K_{1}\) represents a sequence of satellites traveled by the ICV \(k\) which starts and ends at the depot \(0\). Similarly, for each route in \(S_{2}\), \(r^{2}_{k}=\left(s_{k},r^{k}_{1},r^{k}_{2},...,s_{k}\right),k=1,2,...,K_{2}\) means a sequence of customers (including BSSs) traveled by the EV \(k\) which starts and ends at the satellite \(s_{k}\). Let \(S(r^{1}_{k})\) denote the set of satellites in route \(r^{1}_{k}\). In route \(r^{2}_{k}\), \(C(r^{2}_{k})\) and \(B(r^{2}_{k})\) are denoted as the sets of customers and BSSs, respectively. An example is depicted in Figure 2. The full solution \(S=(S_{1},S_{2})\) for 2E-EVRPTW-BSS includes two parts, with \(S_{1}=\{(0,1,3,0)\), (0,2,0), (0,1,2,0)\(\}\) and \(S_{2}=\{(1,5,16,6,7,1)\), (1,8,9,10,1), (2,11,12,18,13,2), (3,14,19,15,3)\(\}\).  
In our study, the optimization for the routes of the two echelons are interrelated. Specifically, the routes of the first echelon are generated based on that of the second echelon. Hence, the solution can be encoded with the routes of only the second echelon, i.e., the encoded solution is composed of only \(S_{2}=\{r^{2}_{1},r^{2}_{2},...,r^{2}_{K_{2}}\}\). The full solution will be generated through a decoding process.
## 3 Calculation Mesh and Boundary Condition Setting  
### Steady-State Mesh and Boundary Condition Setting  
Figure 4 shows the surface mesh of the missile, inlet, and far field. The mesh form adopts unstructured polyhedral mesh. The far-field calculation domain of the conventional submerged inlet configuration and the distributed submerged inlet configuration are identical. Both are cuboids with length, width, and height of 40 m, 10 m, and 10 m respectively. The two configurations of the inlet and the body model body surface mesh size are the same. The minimum size of the body surface mesh is 1 mm and the maximum size is 5 mm; the minimum size of the inlet surface mesh is 1 mm, the maximum size is 3 mm.  
The Spalart-Allmaras turbulence model is mainly used in the numerical simulation in this paper. The S-A turbulence model is a single equation model, which can directly solve the modified turbulent viscosity. It is often used in the flow around the aviation field and is characterized by high accuracy and small amount of calculation. In order to capture the complex flow on the surface of the missile and inlet as well as possible, enhanced wall treatment (EWT) is required, which requires that the near-wall mesh is very dense, and y+ is close to 1. However, considering that too dense near-wall mesh will lead to low mesh quality, the boundary layer mesh shall be properly adjusted without reducing the calculation accuracy. The final number of boundary layers is 10, the first layer of mesh height is 0.01 mm, and the growth rate is 1.2. In order to make the calculation results more accurate and reliable, an encryption area is set around the missile body, the maximum size of the encryption area is not more than 30 mm, and an encryption area is also set near the inlet, the maximum size of the encryption area is not more than 3 mm, as shown in Figure 5. The number of volume mesh of the conventional submerged inlet configuration is about 0.9 million, of which the boundary layer prismatic mesh accounts for 0.4 million and the polyhedral mesh away from the wall accounts for 0.5 million. The number of the distributed inlet configuration volume mesh is about 1.2 million, of which the boundary layer prismatic mesh accounts for 0.5 million and the polyhedral mesh away from the wall accounts for 0.7 million.  
Figure 4: Surface mesh of projectile body, inlet, and far-field. (**(a)** Surface mesh of missile body in the conventional inlet, **(b)** Surface mesh of missile body in the distributed inlet, **(c)** Surface mesh of the conventional inlet, **(d)** Surface mesh of the distributed inlet, **(e)** Surface mesh of far-field).  
Figure 5 shows the setting of steady-state boundary conditions. The pressure far-field boundary is used for the far-field, and the freestream parameters are given according to the atmospheric conditions at 5 km altitude, \(M_{0}\) = 0.7; The outlet of the inlet adopts the pressure outlet boundary condition, and the pressure is set to the corresponding pressure when \(M_{e}\) = 0.4; the body and inlet adopt non-slip adiabatic wall boundary.