esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatiotemporal Modeling of the Smart City Residents' Activity with Multi-Agent Systems
## 4 Model City
### Terra Incognita (Inspecified Space)  
Research question: Will the district be built in a \"closed\" way (gated communities) or (because of the increased level of social (geo)participation and trust) in an \"open\" way? It is of particular interest to the residents from three marked districts, and young, relatively wealthy, and well-educated people.  
Figure 6 shows the comparison between the number of strong leaders and the level of mutual social trust in a model city through a simulated time of 10 years, with fan-idol and fan-anti-idol relations inactive. In the absence of strong leaders, the slope of mutual trust decreases over time and the increase in 10 years is 1.12%. For 20 strong positive leaders, trust increases by 5.44% within the simulation, but the slope is changing; during the manifestation, the slope slightly decreases. It can be explained by the concentration of all the positive leaders in one district of the city. However, in the case of 20 strong negative leaders, the authors observe a slight increase in trust in the beginning, but on the 2000-ish day of simulation (around year 5.5), it begins to drop. Ultimately, after 10 years, trust increases by 1.12%, which is the result of accumulating negative opinions during protests. Similar dependencies, with accuracy to value, occur for other attractors. Finally, the existence of both 20 positive and 20 negative  
Figure 5: Comparison of the fan-idol and fan-anti-idol relations on the level of mutual social trust in a model city through a simulated time of 10 years in the presence of 20 strong positive leaders.  
strong leaders results in a different outcome than the non-existence of strong leaders. On the contrary, one can note that the positive leaders have more impact on mutual trust than the negative ones. For this case, the mutual trust after 10 years of simulation increases by 2.94%.  
In Figures 7 and 8, one can see that the changes in the level of trust differ significantly in individual districts of the model city. These changes also have different intensity over time. This process depends not only on the number of strong leaders, but also on the characteristics of residents of particular districts and the level of their involvement in the problem of this attractor. Positive leaders influence a slight increase in trust in Bedroom, while negative leaders considerably reduce the level of trust of the residents of this district. For those who are not very interested in the spatial development of the new district (residents of Old Town and GreenLand), the level of trust decreases both in the presence of positive and negative leaders, although with varying intensity.  
Figure 6: Comparison of the number of strong leaders and the level of mutual social trust in a model city through a simulated time of 10 years, with fan-idol and fan-anti-idol relations inactive.  
Figure 7: Changes in the level of mutual social trust in a model city through a simulated time of 10 years (20 negative leaders in the “Terra incognita” attractor); iteration 0, 1200, 2400, 3650.
# Influence of Blade Wrap Angle on the Hydrodynamic Radial Force of Single Blade Centrifugal Pump
## 2 Pump Model and Simulation Method  
### Pump Model  
The pump model in this study is a single blade centrifugal pump which is the same as the author's early paper [22]. The main design parameters are Qd = 20 m\({}^{3}\)/h, \(H\) = 11 m, rotational speed \(n\) = 2940 r/min, and the main geometry parameters are shown as Table 1. The impeller was 3D designed by Bladegen based on the main parameters, and the design process of single blade centrifugal impeller by Bladegen is shown in Figure 1. The blade wrap angle is an important parameter of a single blade centrifugal pump. Properly increasing the blade wrap angle can enhance the restriction of the impeller to the fluid, but an excessively large blade wrap angle will result in increased friction loss. In the current research, impellers with five different wrap angles are investigated, the values of the wrap angles are 300\({}^{\circ}\), 330\({}^{\circ}\), 360\({}^{\circ}\), 390\({}^{\circ}\), and 420\({}^{\circ}\). Other parameters for the impellers are kept consistent, which is shown in Table 1.
## 2 Numerical Model
### Formulations  
#### 2.1.1 Static  
Static electric and magnetic problems take the form of a Poisson equation:  
\[\
abla\cdot(k\
abla\phi)=\chi. \tag{1}\]  
For an electrostatic problem \(\phi=\varphi\), \(k=\epsilon_{r}\), and \(\chi=-\frac{\rho}{\epsilon_{0}}\); \(\phi\) is the scalar electric potential, \(\epsilon_{0}\) and \(\epsilon_{r}\) are the dielectric constant of vacuum and the relative dielectric constant; \(\rho\) is the electric charge density. For a DC conduction problem--where the conduction current density vector field \(\vec{J}\) is solenoidal--one has again \(\phi=\varphi\), and \(k=-\sigma\), obtaining \(-\
abla\cdot(\sigma\
abla\varphi)=0\), where \(\sigma\) is the electrical conductivity of the given medium. In this case one obtains the \(x\) and \(y\) components of the current density from \(\vec{J}=-\sigma\
abla\varphi\).  
The exact same formulation is retained for planar magnetostatic problems, where the current density and the magnetic vector potential \(A\) (MVP) have a preferential direction, e.g., \(z\). In this case, referring to (1), \(\phi=-A_{z}\), \(k=1/\mu_{r}\) and \(\chi=\mu_{0}J_{z}\), where \(A_{z}\) is the \(z\) (out of the page) component of the MVP field \(\vec{A}\); \(\mu_{0}\) and \(\mu_{r}\) are the magnetic permeability of vacuum and the relative magnetic permeability of the given medium, respectively; \(J_{z}\) is the \(z\) component of the current density.
## 5 Simulation Results
### Grid Networks  
We next examine the performance of the proposed scheme using grid topologies as shown in Figure 7(b). In the grid networks, 36, 64, 100, and 144 nodes are deployed in rows and columns, respectively. The distance from neighbor nodes in the right, left, upper and lower directions is 20 m. As shown in 7(b), half of the border nodes are selected as senders, and the corresponding node on the opposite boarder is the target receiver. A half of the sources transmit a CBR traffic of 1,000-byte frames to the border node on the opposite side, while the rest of sources transmit 700-byte frames.  
of the IEEE 802.11 MAC are 85.23%, 91.41%, 87.12%, and 84.36%, respectively. The proposed MAC protocol achieves considerable improvements in both throughput and delay for the grid networks with a regular topology structure.
## 4 Theoretical Background and Research Model
* Biometric data collected for one purpose may be used for an unintended or unauthorized purpose in the future [63]. * Biometrics can be used for purposes other than those explicitly stated, or biometrics can be abused to generate extra information [63]. * Individuals can be identified or tracked by biometrics. Since biometric data is considered unique, it can be used to track and locate persons when they try to get access to certain facilities or as their biometric attributes are collected by a surveillance system [63]. * Biometrics can be stored and/or transmitted incorrectly so they are outside the control of the user. External attacks against biometrics would be possible as a result of this [64]. * The potential threat to users' privacy posed by the misuse of biometric information is a topic of debate and frequently prevents the widespread adoption of biometric systems [63]. * Biometrics cannot be protected via conventional encryption due to their fuzzy nature. As a consequence, they are vulnerable to a variety of threats [62]. It is worth noting that determining the \"true\" risk of privacy violation requires taking into account a variety of factors including the factors that affect users' intention to use BBCA technologies. Given the above debate, we focus on the privacy issues of individual smartphone users, the risks they view as reasonable, and their effect on the intention to use BBCA as a self-protective behavior.
## 4 ETSI M2M Architecture
In an M2M ecosystem, [PERSON], a user, connects his smartphone, acting as an M2M Gateway, to collect information from  
Figure 1: European Telecommunications Standards Institute (ETSI) Machine-to-Machine (M2M) high level system overview. sensors, M2M devices, over Bluetooth (the M2M Area Network technology) using an M2M Application. The M2M Gateway sends the data using 3G to a Network SCL (NSCL), which main function is to manage the data. In this case, the NSCL stores the data for backup storage purposes and sends the content to a medical Network Application. The M2M Management functions are all the functions required to manage M2M Applications and M2M SCs in the Network and Applications Domain, and the Network Management functions are all the functions required to manage the access, core, and transport networks [48]. The management functions include performance management, configuration management, fault management and software and firmware upgrading management. M2M Application life cycle management includes installing, removing, and upgrading applications in an M2M Device or Gateway. M2M service management includes configuration management for the M2M SCs in the M2M Device or Gateway. M2M Area Network management includes configuration management for the M2M Area Networks. M2M Device management includes the configuration management of the M2M Device or Gateway. In ETSI M2M, the M2M service platform is a horizontal middleware, thus applications share common environments, infrastructure, and network elements. To ensure interoperable M2M networks and services, M2M should be agnostic of technologies.
# Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data
## 4 Results and Discussion
### Interpretation of Results
The first segment maintains an average speed range of 83-93 kph, juxtaposed with a second segment registering a speed of 60 kph. The former segment attains Level of Service A, yet it faces the influence of lane-changing vehicles decelerating into the right-hand turn lane. Concurrently, vehicles operating at standard speeds along the mainline further affect this segment. In contrast, the second segment reflects Level of Service C, contending with the deceleration of vehicles converging towards the toll plaza ahead, alongside a blend of both sluggish and rapid-moving vehicles. 4. The final classification designates the speed range of 91-99 kph as UF. This range is subject to fluctuations attributable to frontal classifications, culminating in a mixture of slow and swift vehicles traversing the lanes. Hence, UF characterizes short road segments spanning less than 100 m, wherein pronounced fluctuations in vehicle speeds ensue, potentially culminating in side or rear-end collisions. As exemplified in Figure 10, instance 1 highlights scenarios where segment driving speeds remain relatively low, yet substantial disparities emerge between front and rear vehicles due to hindrances preceding the lead vehicle or the downstream impact \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Count** & **Length (m)** & **CHGRID** & **Average Speed (km/h)** & **Flow** \\ \hline
1 & 599.52 & 21231111300 & 83.12 & UF \\
2 & 599.52 & 21231111301 & 86.60 & UF \\
3 & 599.60 & 21320000 & 107.62 & SF \\
4 & 472.33 & 212301 & 85.19 & SF \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
8 & 4900.36 & 212310103133 & 100.81 & SF \\
9 & 468.00 & 212301 & 85.19 & SF \\
10 & 4900.36 & 212310103130 & 103.25 & SF \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
27 & 39.16 & 21231011 & 99.78 & SF \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
320 & 599.52 & 212311112 & 82.50 & SF \\
321 & 599.52 & 21320000 & 107.62 & SF \\ Minimum & 39.16 & & 4.00 & \\ Maximum & 5450.80 & & 112.61 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average speed and traffic flow on westbound segment of Expressway No. 50.  
of diminished speeds. Additionally, example 4 underscores that even within a relatively seamless segment marked by elevated overall speeds, noteworthy discrepancies in speed distribution between left and right lanes may arise. These differentials can be attributed to abrupt lane shifts, deceleration events related to entering and exiting vehicles, or the influence of junctions and classification zones. Such nuances elude comprehension through conventional average segment speed analyses, underscoring the criticality of delving deeper into speed variations to provide insights into hazardous segment attributes. This distinctive facet distinguishes the findings of this study from the existing body of research, spotlighting the paramount importance of driving safety considerations.
# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
###### Abstract
Real-time monitoring and analysis of the ECG has proven to be an effective strategy to detect the main abnormalities in the frame of professional sport practice [24]. There are challenges to implementing this idea effectively. The simultaneous monitoring of many athletes could overflow the computing capabilities of the wearables and mobile devices of the in-situ medical staff. This obstacle could be overcome by the deployment of a centralized system that performs a deeper analysis and takes advantage of big data methods on historical aggregated information, although some bottlenecks and delays may result from the communications among the wearable sensors and the centralized expert system. In this work, a distributed sensing and monitoring framework for Internet of Things' environments has been proposed to overcome the previous problems. The **aim of this proposal** is to optimize the use of biomedical sensors and computing resources for being able to provide advanced applications to the user. The main contribution of this paper is the design of a distributed computational framework to use the available computing capabilities of the smart devices for sharing the processing of advanced health monitoring applications. The target audience of this work covers an interdisciplinary set of professionals including computer scientists, doctors, coaches, directors of sports centers, among others. The remaining part of the paper is organized as follows: Section 2 summarizes related literature on the use of wearable devices; Section 3 describes the proposed framework; Section 4 presents the case study of this approach in order to verify the feasibility of our proposal; Finally, Section 5 describes the conclusions and future lines of this research.
## 2 Spatiotemporal Grid Failures and Emergency Scenarios During Typhoons
### Abnormal States of Typical Grid-Source Equipment During Typhoons  
#### 2.1.1 Abnormal States of Wind Farm During Typhoons  
Usually, once a typhoon enters the 24 h warning zone, meteorological departments continuously update the typhoon's real-time information at 1 h resolutions. For the convenience of analysis, the prediction period of typhoons is set to 24 h, and the sampling time period is set to 1 h. Suppose that the distance between wind farm \(i\) at time period \(t\) is \(r_{i,t}\). The maximal wind speed at time period \(t\) is denoted by \(v_{\max,t}\). The radius of maximal wind speed is denoted by \(R_{\max,t}\). The perceived wind speed of wind farm \(i\) can be written by [24]:  
\[v_{i,j}=\left\{\begin{array}{l}\frac{r_{i,t}}{R_{\max,t}}v_{\max,t}r_{i,t} \in[0,R_{\max,t})\\ \frac{R_{\max,t}}{r_{i,t}}v_{\max,t}r_{i,t}\in[R_{\max,t},\infty)\end{array}\right. \tag{1}\]  
where \(r_{i,t}\) is defined by:  
\[r_{i,t}=\sqrt{\left(x_{i}-x_{0,t}\right)^{2}+\left(y_{i}-y_{0,t}\right)^{2}} \tag{2}\]  
where \(\left(x_{i},y_{i}\right)\) represents the fixed coordinate of wind farm \(i\); \(\left(x_{0,t},y_{0,t}\right)\) represents the coordinate of the typhoon center at time period \(t\). By substituting the perceived wind speed \(v_{i,t}\) into the equivalent wind-turbine power curve function, the abnormal wind power can be expressed by [25]:  
\[P_{i,t}^{\mathrm{w}}=\left\{\begin{array}{l}\frac{1}{2}\rho v_{i,t}^{3}sc_{ \mathrm{p}}(\lambda,\beta),v_{\mathrm{in}}\leq v_{i,t}<v_{\mathrm{rate}}\\ p_{\mathrm{rate}}^{\mathrm{w}},\hskip 56.905512 ptiv_{\mathrm{rate}}\leq v_{i,t}<v_{ \mathrm{out}}\\ 0,\hskip 56.905512 ptiv_{i,t}\geq v_{\mathrm{out}}\end{array}\right. \tag{3}\]  
where \(\rho\) represents the air density; \(s\) represents the wind-affected area of the rotor blade; \(c_{\mathrm{p}}\) represents the power coefficient; \(\lambda\) and \(\beta\) represent the the tip-speed ratio and pitch angle of the equivalent wind turbine. \(v_{\mathrm{in}}\), \(v_{\mathrm{rate}}\), and \(v_{\mathrm{out}}\) represent the cut-in, rated, and cut-out velocity. \(P_{\mathrm{rate}}^{\mathrm{w}}\) represents the rated wind power. Equations (1)-(3) show that the abnormal wind power \(P_{i,t}^{\mathrm{w}}\) changes with the movement of the typhoon's position \(\left(x_{0,t},y_{0,t}\right)\). When the perceived wind speed \(v_{i,t}\) is beyond the cut-out speed \(v_{\mathrm{out}}\), wind farm \(i\) will be disconnected from the grid. The wind power \(P_{i,t}^{\mathrm{w}}\) will fluctuate if the perceived speed \(v_{i,t}\) changes drastically with the movement of typhoons. Drastic fluctuations in wind power output can affect the safe operation of the power grid.
# Extended Smoothing Methods for Sparse Test Data Based on Zero-Padding
## 5 Twice Zero-Padding of Two-Dimensional Discrete Data  
### The First Zero-Padding of 16 \(\times\) 8 Two-Dimensional Discrete Data  
Much smoother results can be obtained through multiple zero-padding. Smoothing results of two-dimensional discrete data after one and two zero-paddings are given to illustrate this characteristic.  
Figure 6 shows the known two-dimensional discrete data, which is randomly generated. Similarly, we assume that the sampling theorem is satisfied. The data numbers in \(x\) and \(y\) direction data are 16 and 8, respectively, and the spatial interval is 0.1 m. Test data values at four specific locations (0.1, 0.7), (0.3, 0.2), (0.8, 0), and (1.5, 0.6) are marked in Figure 6 for comparison with the smoothed results.  
DFT is used to transform the original two-dimensional discrete data from the spatial domain to the wavenumber domain. Figure 7 shows the angular spectrum in the wavenumber domain after DFT.  
To expand the sequence, the original angular spectrum is zero-padded, and the value of the angular spectrum at a particular location is modified. The angular spectrum after one zero-padding is the angular spectrum after smoothing once, as shown in Figure 8.  
By comparing Figures 7 and 8, it can be concluded that the variation of the angular spectrum of each region conforms to the smoothing theory shown in Figures 2 and 3 in Section 3.2. For reasons of space, the angular spectrum values at each position are not listed here.  
Figure 6: Original two-dimensional discrete data.  
IDFT is applied to the angular spectrum after one zero-padding to produce two-dimensional discrete data after smoothing shown in Figure 9.  
By comparing Figures 6 and 9, it can be seen that the data density increases after smoothing. In Figure 9, the values at four specific locations (0.1, 0.7), (0.3, 0.2), (0.8, 0), and (1.5, 0.6) are also unchanged. The results verify the characteristics of increasing data density and constant values.  
Figure 8: Generated angular spectrum after one zero-padding.  
Figure 7: The original angular spectrum.
# XOR Multiplexing Technique for Nanocomputers
## 2 Error Distributions in the XOR Multiplexing System
### Error Distributions in the XOR Multiplexing Unit and the NAND Multiplexing Unit
Namely, the NAND multiplexing unit outputs more errors than the XOR multiplexing unit. We can obtain the same result for modest \(N\) (\(N=100\)). Obviously, with the same redundancy \(N\), same gate error probability \(\varepsilon\) and same input error level, the XOR multiplexing unit has a smaller mean value of error outputs, which means the XOR multiplexing unit produces fewer faulty outputs than the NAND multiplexing unit. In other words, the XOR multiplexing unit is more reliable than [PERSON]'s NAND multiplexing unit.
## 5 Conclusions and Future Work
### Future Work  
While the paper does not specifically provide \"measurements\" of quality of the OSM data we believe that this work could provide a platform for future studies on OSM data quality which would consider the lineage or evolutionary history of the OSM data as part of quality assessments. A survey of contributors to OSM, particularly large scale contributors, is required to gain a better understanding of the rationale behind some of the tagging behaviour we have observed in this paper. We have used the threshold of \(15\) versions as the qualifying criteria for \"heavily edited\" objects in OSM. As part of ongoing work we are investigating the effects of revising this threshold downwards whilst attempting to understand the factors that are related to new versions of objects being created in OSM. Relations in OSM are one of the code data elements. They consist of one or more tags and an ordered list of one or more nodes and/or ways as members. They are used to define logical or geographical relationships between elements. We decided against investigating relations in this research as we wanted to maintain focus on the editing of ways as a first step towards understanding the characteristics of heavily edited objects in OSM. Just under \(30\%\) of the \(15,640\) ways we analysed in this research were marked explicitly as members of relations. As part of future work we intend to carry out an analysis of the characteristics of relations in OSM. In this paper we considered an edit as a composite record of edits to an object's geometry and tags. We shall be investigating the types of edits recorded: edits to geometry and then edits to tagging. We believe this will help us understand the editing behaviour of contributors--do some contributors contribute geometry but never perform tagging or do some contributors only correct or update tagging? Finally, an analysis of a larger number of \"heavily edited\" objects is required to validate our findings here to show the existence of other characteristics (spatial autocorrelation and spatial interaction). This will be the subject of our immediate future work.
# A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery
## 3 Results
### Evaluation of Sharpening Quality
#### 3.1.3 Processing Time Comparison  
The processing times for the real Sentinel-2 images of Reykjavik from Section 3.1.2 are shown in Table 2; each method was run 10 times, and the mean and standard deviation of the processing times are shown.  
While the proposed method's per-image processing time is longer, this represents the total time needed to achieve optimal results, including training. In contrast, the comparison methods require extensive hyperparameter tuning for each image type--a time-consuming process not reflected in their reported execution times. Our method automatically adapts to each image through its training process, eliminating the need for manual parameter adjustment while achieving superior results. This makes it more practical for processing pipelines where consistent performance across varied imagery is prioritized over raw execution speed.