esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Scalability of UWB Indoor Localization Solutions for High User Densities
## 4 Localization Schemes and MAC Considerations  
This paragraph discusses general aspects of two ToF-based localization techniques that most often are used in current UWB localization research, namely TDoA and TWR. For each positioning approach, the benefits and drawbacks are outlined.
###### Abstract  
When an unexpected obstacle occupies some of the lanes on a multi-lane highway, connected vehicles (CVs) may be able to avoid it cooperatively. For example, a CV that detects the obstacle first can immediately notify the following vehicles of the obstacle by using vehicle-to-vehicle (V2V) communication. In turn, the following vehicles can take action to avoid the obstacle smoothly using wide range behind the obstacle without sacrificing safety and ride comfort. In this study, we propose a method to realize safe, smooth, and fair wide-range cooperative lane changing, reacting to a sudden obstacle on the road. The proposed method is based on the authors' previous work, which utilizes multi-hop communication to share the obstacle position and controls the inter-vehicular distance of vehicles away from the obstacle to assist in a smooth lane changing operation, while existing lane-changing methods for CVs focus on microscopic operation around the obstacle. Though the previous work treats only a two-lane road, the proposed method is extended to work on a three- or more lane road assuming only one lane is blocked. In the proposed scheme, each vehicle approaching the obstacle selects a lane to change to in accordance with the obstacle's location and the vehicle density in each lane estimated from the beacon messages broadcast by each CV, thereby improving traffic fairness among all lanes without degrading safety or ride comfort. We confirmed the effectiveness of the proposed scheme on realizing fairness among lanes, safety, ride comfort, and traffic throughput through comprehensive simulations of a two-lane road and a three-lane road with various traffic scenarios.  
os +
Footnote †: This paper is an extended version of our paper published in VTC2022-Spring.
# Vectorized Numerical Algorithms to Solve Internal Problems of Computational Fluid Dynamics
## 9 Numerical Scheme
### Wave Convection  
Equation of wave convection is a simple model of convective vortex transport. When studying wave processes, the case when the initial data are a sinusoidal (or cosine) distribution is interesting. The linear transport equation transports such a wave without distortion, while difference schemes that implement this equation can distort the wave, changing its amplitude and shape. The wave convection equation (scalar transport equation) is written as [26; 27]  
\[\frac{\partial u}{\partial t}+a\frac{\partial u}{\partial x}=0, \tag{18}\]  
where \(a=\text{const}\). The initial condition includes Gaussian wave, square wave, triangular wave, or semi-elliptical wave. The boundary condition is  
\[u(x,0)=u_{0}(x).\]  
The general solution of transport equation for \(a=\text{const}\) is  
\[u=u_{0}(x-at).\]  
To validate the reconstruction step of the numerical algorithm, the initial condition for Equation (18) is specified in the form  
\[u(x,0)=\exp\Bigl{(}-x^{2}/16\Bigr{)}.\]  
The exact solution is  
\[\widehat{u}(x,t)=\exp\Bigl{[}-(x-t)^{2}/16\Bigr{]}.\]  
The numerical error is estimated as  
\[\delta=\frac{1}{N}\left[\sum_{i=1}^{N}(u_{i}-\widehat{u}_{i})^{2}\right]^{1/2},\]  
where \(N\) is the number of mesh nodes.  
To discretize time derivatives, the three-step Runge-Kutta method is used, and UDS-3 scheme (upwind difference scheme) and STVD-4 and STVD-6 schemes (symmetric TVD scheme) are used to calculate fluxes.  
The results of numerical calculations (solid line) are shown in Figure 9 in comparison with the exact solution (symbols \(\blacksquare\)). The results demonstrate that upwind scheme is too dissipative. It underestimates the maximum value of the unknown and leads to oscillations of the solution. Two numerical schemes of TVD type of second and third orders provide similar results. Increasing the order of numerical scheme makes it possible to achieve a higher accuracy with a smaller number of nodes but requires an increase in number of operations on each time layer (Figure 10).
## 3 Research Framework of Internet of Emergency Services
### Components and Devices of Internet of Emergency Services
#### 3.2.2 Actuators  
Actuators are devices that convert energy into motion, such as motors, solenoids, and hydraulic and pneumatic cylinders. In the context of the Internet of Emergency Services (IoES), actuators can play an important role in responding to emergencies by enabling automated actions and the remote control of physical devices. Here are some examples of actuator use in emergency management and response:  
* Automated water shutoff valves: In the event of a water pipe rupture or other water-related emergency, automated water shutoff valves can be used to quickly stop the flow of water and prevent further damage. These valves can be remotely controlled using IoES technologies, such as IoT sensors and cloud computing.
* Autonomous robots [72; 73]: Robots equipped with actuators, such as motors and grippers, can be used to perform various emergency response tasks, such as search and rescue, firefighting, and hazardous material cleanup. For example, the RoboCup Rescue competition encourages the development of autonomous robots for disaster response.
* Emergency lighting [68]: Actuators can be used to control emergency lighting systems, such as turning on exit signs and emergency lights during power outages or other emergencies.
* Automated door locks: In the event of a security breach or other emergency situation, automated door locks can be used to quickly secure a building or area. These locks can be controlled remotely using IoES technologies, such as mobile devices or cloud computing.
* Remote vehicle control [43; 58; 74; 75; 76]: Actuators can be used to remotely control vehicles, such as drones or unmanned ground vehicles, for emergency response tasks. For example, drones equipped with actuators can be used for search and rescue operations or to provide real-time situational awareness during an emergency.  
While the use of actuators in IoES can provide significant benefits for emergency management and response, there are also some challenges and limitations to consider. One major challenge is ensuring the reliability and security of the actuators themselves, as well as the communication channels used to control them. Additionally, there may be concerns about privacy and data protection when using IoES technologies to remotely control physical devices. Overall, careful planning and implementation are necessary to ensure the effective and responsible use of actuators in IoES for emergency management and response.
## 6 Refined Authenticated Key Exchange Protocol with Point Halving Optimization
### Design Objectives and Parameter Initialization  
Let the elliptic curve \(E/\mathbb{F}_{2^{m}}\) be defined over a binary field suitable for efficient halving operations. Let \(\mathcal{P}\) and \(\mathcal{Q}\) be publicly agreed-upon base points on the curve.  
Define the key material as follows:  
* \((\text{sk}_{\mathcal{A}},\text{pk}_{\mathcal{A}})\): private/public key pair of initiator \(\mathcal{A}\);
* \((\text{sk}_{\mathcal{B}},\text{pk}_{\mathcal{B}})\): private/public key pair of responder \(\mathcal{B}\);
* \(H(\cdot)\): a cryptographic hash function (e.g., SHA-256);
* \(\text{Enc}_{\text{sk}}(\cdot)\): asymmetric encryption function under private key;
* \(\text{Dec}_{\text{pk}}(\cdot)\): corresponding decryption function.  
Let \(\Phi=\mathcal{P}^{-1}+\mathcal{Q}\) be the common elliptic curve point used for scalar multiplication.
# Optimization of Coverage and Capacity Using Smart Antennae
## 4 Evaluation
### Case Measurement  
This research focuses on conducting automatic road inspections in the remote mountainous areas of Taoyuan, Taiwan. Road tests were performed using measurement equipment to record network transmission rates. The test locations are listed in Table 7 and primarily cover eight road sections in Fuixing District, Taoyuan City, with a total length of 33.2 km.  
The signal measurement results for this mountainous road area are shown in Figure 10, with the majority of signal transmission rates falling within the range of 5 Mbps to 20 Mbps, which ensures that IoT devices on road inspection vehicles can successfully transmit relevant road measurement data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Line** & **Road Name** & **Length (km)** & **Starting Point** & **Ending Point** \\ \hline
1 & Tao 113 & 8.1 & 0 + 000 & 8 k + 100 \\
2 & Tao 116 & 10 & 0 + 000 & 10 k + 000 \\
3 & Tao 112 & 4.3 & 0 + 000 & 4 k + 300 \\
4 & Tao 114 & 3.0 & 0 + 000 & 3 k + 000 \\
5 & Tao 115 & 3.2 & 0 + 000 & 3 k + 200 \\
6 & Tao 117 & 1.5 & 0 + 000 & 1 k + 500 \\
7 & Tao 118 & 2.1 & 0 + 000 & 2 k + 100 \\
8 & Tao 119 & 1.0 & 0 + 000 & 1 k + 000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Road inspection sections in remote mountainous areas.  
Figure 10: Network signal measurement results of the mountain roads.
## 3 Architecture  
This section will elaborate on the proposed on-path computing resource scheduling architecture based on ICN, which is named IPCRSA. Section 3.1 presents the overall design of the architecture, including the topology structure and relationships among various entities. Section 3.2 provides an explanation of the design of the computing resource request packet format within this architecture, while Section 3.3 introduces the typical process of computing resource scheduling in IPCRSA.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **Publication** & **Distributed** & **Network** & **Results Achieved** & **Advantages** & **Disadvantages** \\  & **Year** & **Centralized** & & **Type** & & \\ \hline \multirow{3}{*}{CABA [8]} & \multirow{3}{*}{2021} & \multirow{3}{*}{Distributed} & \multirow{3}{*}{TCP/IP} & A computing-aware & \multirow{3}{*}{High utility in relation to bandwidth allocation} & \multirow{3}{*}{Neglect user preferences, lack optimization} \\  & & & & & \\  & & & & & \\ \hline \multirow{3}{*}{CARP [13]} & \multirow{3}{*}{2022} & \multirow{3}{*}{Distributed} & \multirow{3}{*}{TCP/IP} & A computing-aware & \multirow{3}{*}{Capacity and end-to-end average} & \multirow{3}{*}{Overlook resource competition issues, lack of flexibility} \\  & & & & & & \\  & & & & & \\ \hline \multirow{3}{*}{PSS [33]} & \multirow{3}{*}{2022} & \multirow{3}{*}{Centralized} & \multirow{3}{*}{TCP/IP (SRv6)} & A vital implementation method for integrated scheduling of computing and network resources & Increased flexibility of network programmability & Scalability and reliability limitations \\  & & & & & \\  & & & & and network resources & \\ \hline \multirow{3}{*}{K8\&K3s-Based [34]} & \multirow{3}{*}{2020} & \multirow{3}{*}{Centralized} & \multirow{3}{*}{TCP/IP} & A lightweight, multi-cluster hierarchical edge resource scheduling solution & \multirow{3}{*}{Wide applicability limitations} \\  & & & & & \\ \hline \multirow{3}{*}{SDN-Based [35]} & \multirow{3}{*}{2021} & \multirow{3}{*}{Centralized} & \multirow{3}{*}{TCP/IP} & A computing power scheduling technology based on programmable network technology & \multirow{3}{*}{Scheduling in underlay network limitations} \\  & & & & & \\ \hline \multirow{3}{*}{Named-Based [4]} & \multirow{3}{*}{2021} & \multirow{3}{*}{/} & \multirow{3}{*}{ICN} & A scheme for constructing a named Computing Network based on ICN & More efficient & No specific mechanism designs \\ \hline \multirow{3}{*}{CRDTs-Based [14]} & \multirow{3}{*}{2019} & \multirow{3}{*}{Distributed} & \multirow{3}{*}{ICN} & A distributed resource scheduling architecture for a Computing Network based on ICN & Advantages in simplicity, performance, and fault recovery capability & Pose significant deployment challenges \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related works on computing resource scheduling.
## 1 Introduction
The spatial and temporal coverage of satellite soil moisture allows for data assimilation for crop yield estimation at a regional scale. In the field of crop model applications, several previous studies have used soil moisture as a state variable in crop model data assimilation systems for crop yield estimations. [PERSON] and [PERSON] utilized the Ensemble Kalman filter (EnKF) method to assimilate soil moisture, derived from a microwave sensor, into the WOFOST (World Food Studies) crop model and the conclusion indicated that the assimilation of soil moisture into the crop growth model has a beneficial effect on the overall performance of the yield forecasting system [4]. [PERSON] et al. jointly assimilated the leafarea index (LAI) and soil moisture into the CERES-Maize model with the EnKF algorithm and the results showed that yield estimation was improved substantially [25]. [PERSON] et al. assimilated SMOS (Soil Moisture and Ocean Salinity) soil moisture into the DSSAT (Decision Support System for Agrotechnology Transfer) model to investigate the effects of agricultural drought on crop yield using the EnKF technique. They found that the assimilated crop yield was improved during the growing season [26]. However, the soil moisture data used in previous studies were mostly retrieved from SAR data only or with a coarse spatial resolution (e.g., SMOS soil moisture data at 25 km). Thus, we would like to assess the ability of assimilating soil moisture at 10 m scale, retrieved from SAR and optical data together, into the crop model to improve crop yield estimation. The objectives of this study are twofold, as follows: (1) To evaluate the performance of combining SAR and optical remotely sensed data in the water cloud model for soil moisture inversion; and (2) to assess the accuracy of winter wheat yield estimation by assimilating the retrieved soil moisture into the WOFOST model with the EnKF method. Based on multi-source observations (optical and SAR remotely sensed data, in situ data, and meteorological data), we developed an assimilation framework for time series soil moisture images into the WOFOST model to improve its accuracy for winter wheat yield estimation at a regional scale. The study area and four major datasets are presented in Section 2 and this section also introduces the water cloud model, the WOFOST model, and the EnKF assimilation algorithm in detail. The results Section 3 is divided into three parts, with (1) the water cloud model soil moisture retrieval results, (2) the simulation of soil moisture with the WOFOST model and (3) the assimilation of soil moisture into the WOFOST model with the EnKF method. Uncertainties and perspectives of this study are described in Section 4 and the conclusion is given in Section 5.
# Novel Fault Injection Attack without Artificial Trigger
## 2 Background  
### Electromagnetic Fault Injection Attack  
Laser fault injection attacks essentially require performing decapsulation of a target chip, as it is based on the property that semiconductors are sensitive to light. As a hardware countermeasure against laser fault injection attacks, establishing a decapsulation prevention shield has been proposed [18]. Electromagnetic fault injection attacks do not require the decapsulation because a fault caused by electromagnetism can affect semiconductors through integrated circuit packages. Therefore, electromagnetic fault injection attacks can circumvent the hardware countermeasures against laser fault injection attacks. Moreover, electromagnetic fault injection attacks can have similar effects on chips as the ones based on the other fault sources. For example, they can cause logic timing violation and change the current state of a transistor to induce temporary malfunctions on a device, similarly to the clock glitch injection attack.  
Figure 1 represents the main principle of electromagnetic fault injection attacks. To generate electromagnetic induction, electromagnetic fault injection attacks cause an instantaneous current to flow through an electromagnetic probe tip implemented by winding ferrite with a coil. As a result, an electromagnetic field is instantaneously generated around the probe tip. Due to the generated electromagnetic field, an eddy current is induced in the chip and affects the inner transistor. Consequently, a malfunction occurs in a circuit. Falsification of the memory data, omission of instructions, or omission of function calls can be generated as a result of this malfunction.
###### Abstract  
One technique that is widely used in various fields, including nonlinear target tracking, is the extended Kalman filter (EKF). The well-known minimum mean square error (MMSE) criterion, which performs magnificently under the assumption of Gaussian noise, is the optimization criterion that is frequently employed in EKF. Further, if the noises are loud (or heavy-tailed), its performance can drastically suffer. To overcome the problem, this paper suggests a new technique for maximum correntropy EKF with nonlinear regression (MCCEKF-NR) by using the maximum correntropy criterion (MCC) instead of the MMSE criterion to calculate the effectiveness and vitality. The preliminary estimates of the state and covariance matrix in MCKF are provided via the state mean vector and covariance matrix propagation equations, just like in the conventional Kalman filter. In addition, a newly designed fixed-point technique is used to update the posterior estimates of each filter in a regression model. To show the practicality of the proposed strategy, we propose an effective implementation for positioning enhancement in GPS navigation and radar measurement systems.  
extended Kalman filter; maximum correntropy criterion (MCC); fixed-point iteration; nonlinear regression +
Footnote †: journal: Applied  
1
Footnote 1: Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 On-Site Operation Experiments and Results
### The Schematic of On-Site Operation for SEMs  
All the target SEMs were installed in an uncontrolled room exposed to the natural environment, and the reference meter was installed in a temperature-controlled room at around 23 \({}^{\circ}\)C. A power source was employed to imitate various loadings and was connected to the target meters and the reference meter. Electricity energy was measured by both target meters and the reference meter; then the measurement error was computed in the error processor and was saved to the database every four hours. Temperature and humidity were also monitored, and the environmental data were also saved to the database. The diagram of on-site operation of SEMs is presented in Figure 1.
# Comparative Study of Indoor Propagation Model Below and Above 6 GHz for 5G Wireless Networks
## 6 Results and Discussion  
As 5G is expected to deliver high amount of data rate, which is useful to design an IoT based smart city network. The use of the mm-wave frequency band in 5G network brings various scattering, fading and penetration losses issues, especially in indoor environment. These issues can be mitigated easily if we can predict the propagation channel, where the signal is propagating. Path loss is one of the important elements to define any channel behavior used for wireless communication. It can depend on many transmission factors, for example, transmitter power, antenna type and its gain, channel structure and its reflection, refraction and diffraction effect. The purpose of path loss modeling is to evaluate the magnitude of attenuation experienced by broadcasting radio signals over a distance, which is useful for designing for 5G communications systems [14]. This section provides the results and analysis for path loss, diffraction, frequency drop, power delay profile and time dispersion parameters. To make an appropriate comparative study for the two measured frequencies, 3.5 GHz (below 6 GHz) and 28 GHz (above 6 GHz), the results of the parameters for both bands are explored in the same figure in each analysis.
## 1 Introduction  
The problem addressed in this article is the modeling of a classification system that encompasses human factors with the circumstances of an aviation incident or accident. In this way, a predictive system could be built, expected to proactively help with increasing safety standards within the industry.  
Air transportation has developed into a crucial method of long-distance travel, making widely known contributions to economic and social development on a global scale. Technological and management systems in air travel benefit from a close relationship between aviation manufacturers and regulators, aimed at safety improvement, leading to one of the asset transportation methods [1; 2; 3].  
However, with the sharp decrease of the accident rate, not only has air traffic considerably increased, but also the absolute number of accidents fails to decrease [4; 5], and market demands are such that professionals are required to work through large stretches of the day and/or night [1; 6]. The majority of aviation accidents have been caused by human error (see, e.g., [7], where a detailed analysis of several aircraft accidents is performed), and studies within the industry helped prove a link with the causation of human error in aviation and the added work effort requirements [1; 4; 6]. Hence the interest of this paper's approach, which aims to answer the following question: are the current human factorspolicies and considerations, first studied in the 1970s and introduced into the aviation industry from the late 1980s on, still relevant at present? If their relevance is no longer the same as before, there could be new threats, or other factors not yet considered, even though the aviation ecosystem is expected to respond quickly to new challenges identified in this area.  
The paper is organized as follows. Section 2 presents a brief state-of-the-art of the relation between human errors and safety in aviation. Section 3 describes the algorithms used to implement this paper's models, together with the criteria commonly used to evaluate the performance of classification models. Section 4 details the Aviation Safety Network (ASN) database, and how its data were expanded and pre-processed regarding human factors. Section 5 shows the results of models applied, their validity, and the optimization of the algorithms. Finally, the Section 6 addresses how the achieved model can be used in practice and how the results and findings of this paper can be built on.