esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Skid-Steered Wheeled Vehicle States Using STUKF with Adaptive Noise Adjustment
## 3 System Modeling
### Dynamic Model
#### 3.2.1 Tire Model  
In this study, a modified brush tire model which considers combined slip, both lateral and longitudinal [46], is used to model the tire force in the vehicle dynamic model. The tire longitudinal and lateral force can be calculated as (9):  
\[\begin{array}{l}\kappa=\frac{R\omega-u_{x}}{u_{x}}\\ \alpha=\arctan\left(\frac{u_{y}}{u_{x}}\right)\\ f=\sqrt{C_{x}^{2}\left(\frac{\kappa}{1+\kappa}\right)^{2}+C_{\alpha}^{2}\left( \frac{\tan\alpha}{1+\kappa}\right)^{2}}\\ F=\left\{\begin{array}{rl}f-\frac{1}{3\mu F_{z}}f^{2}+\frac{1}{2\mu^{2}F_{z }^{2}}f^{3}&f\leq 3\mu F_{z}\\ \mu F_{z}&f>3\mu F_{z}\\ F=\left\{\begin{array}{rl}f-\frac{1}{3\mu F_{z}}f^{2}+\frac{1}{2\mu^{2}F_{ z}^{2}}f^{3}&f\leq 3\mu F_{z}\\ \mu F_{z}&f>3\mu F_{z}\\ \end{array}\right.\\ F_{x}=\frac{C_{x}\kappa}{(1+\kappa)}\frac{f}{f}\\ F_{y}=-\frac{C_{x}\tan\alpha}{(1+\kappa)}\frac{f}{f}\end{array}\right.\end{array} \tag{9}\]  
where \(\mu\) denotes the friction coefficient between road and tire; \(C_{x}\) and \(C_{\alpha}\) denote the longitudinal and lateral tire stiffness, respectively; \(\kappa\) and \(\alpha\) denote longitudinal and sideslip rate on each tire, respectively. The longitudinal and lateral velocity are calculated from (3) and (5).  
Figure 3 shows the tire longitudinal and lateral forces when the tire load is 5000 N and the road-tire adhesion coefficient is 0.8.
# Synthesis of Zeolitic Material with High Cation Exchange Capacity from Paper Sludge Ash Using EDTA
## 1 Introduction
Calcium-containing ash can be converted into zeolites, but the product has a low cation exchange capacity (CEC) [47]. In previous studies, we applied acid leaching to reduce the Ca content in calcium-containing ash to synthesize zeolitic materials with high CECs [48; 49; 50]. However, in practice, it is difficult to add an acid leaching process before the synthesis process. In this study, we applied Ca-masking with a chelating agent, ethylenediaminetetraacetic acid (EDTA), to reduce Ca interference during zeolite synthesis. EDTA is the most popular chelating agent and is used as a Ca-masking agent to decrease the hardness of water. Adding EDTA to a conventional reactor is a simple technique that can improve the synthesis of a product with a high CEC. The effect of EDTA on zeolite synthesis from PSA under an alkali reaction was determined to synthesize zeolite materials with a high CEC.
# A Wi-Fi FTM-Based Indoor Positioning Method with LOS/NLOS Identification
## 3 The Proposed Indoor Localization Method
### The Identification Model of LOS/NLOS
#### 3.2.1 The Determination of d  
In the paper, it was assumed that the ranging results could reflect the true distance to a certain extent. Therefore, we needed to evaluate the accuracy of the ranging results based on Wi-Fi RTT.  
Figure 4: Flow chart of the LOS/NLOS identification process.  
As shown in Figure 5, a tester collected the ranging results of the FTs at each grid point. Here, the grid side length was 1 m, the sampling time was 30 s, and the sampling frequency was 5 Hz. The MT and FTs were basically at the same height during sampling. The ranging accuracy is shown in Figure 6. The red and blue solid lines are the ranging errors of the LOS signals and NLOS signals, respectively. Through calculation, the average ranging error of LOS signals was 0.87 m and the variance was 0.36 m. The average ranging error of the NLOS signals was 1.16 m and the variance was 0.54m. It can be seen that RTT-based ranging results can accurately reflect the true distance between the transceivers. At the same time, the ranging accuracy and stability of the LOS signals were better than those of the NLOS signals.
# A Dual Stage Low Power Converter Driving for Piezoelectric Actuator Applied in Micro Mobile Robot
## 3 Design and Control of the Drive Circuit
### Control Method of the Drive Stage  
A traditional method used to generate a sinusoidal signal from a DC voltage is adopting an LC resonant network. For a sinusoidal PWM control technology, the inductor and two capacitors in a half-bridge inverter are generally required to have a large volume [30]. However, power density is particularly important for a miniaturized system in harsh applications, which is inconsistent with the ideal design for this study.  
[PERSON] et al. considered the limitations of this method and proposed a control method for a reactive load (e.g., a piezoelectric actuator) to reduce the size of the inductor and meet the requirements for compactness [31]. In every switching cycle, the amplifier transfers the exact amount of energy  
Figure 7: Switching pulse waveforms (Q\({}_{H}\) and Q\({}_{L}\)), drain-to-source voltage (\(v_{ds}\)), inductor current (\(i_{L}\)), and output voltage (\(v_{c2}\)) of the drive stage during one switching cycle.  
that is necessary to achieve the desired output value at the load. This approach allows unnecessary switching cycles, thereby permitting higher system dynamics than those of converters using traditional (PWM or current mode) controllers.  
However, this method is current-controlled. Therefore, the controller is required to calculate the switching time of the MOSFET. In such case, the parameters of the load and the components in the circuit should be determined in advance. If the parameters of the circuit are slightly changed, then the control method will become highly complicated.  
A modified version of this method that can generate arbitrary waveform drive signals is embodied in this work through a four-phase control algorithm: Acquisition, Lookup, Charge, and Discharge.  
During Acquisition, the output voltage is sampled using a resistive divider, which is regarded as the input of the analog comparator. In the Lookup phase, the sampled result and the desired value of \(V_{o}\) are used to generate a control pulse for switches \(\mathrm{Q_{H}}\) and \(\mathrm{Q_{L}}\). The associated control architecture is shown in Figure 8.  
When the output voltage is increased, the high-side power MOSFET is constantly turned on or off, which corresponds to the a-b cycle when energy is transferred to \(\mathrm{C_{2}}\) in Figure 8. Conversely, it corresponds to the d-e cycle in Figure 6 when voltage drops. Apart from the amplitude of the drive signal, each working interval is timed by a timer overflow interruption when the frequency of the output signal requires adjustment.
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 2 PLC Transmission via Overhead MV Power Lines
### Two-Conductor Transmission Lines  
The line voltage at distance \(L\) from the injection point is given by  
\[V(L)=H(f,L)V(0), \tag{1}\]  
where  
\[H(f,L)=e^{-\gamma(f)L}=e^{-\alpha(f)L}e^{-j\beta(f)L} \tag{2}\]  
is the transfer function of the transmission line at this distance. The frequency response of a matched two-conductor power line used for PLC transmission is determined via the complex propagation constant  
\[\gamma(f)=\alpha(f)+j\beta(f). \tag{3}\]  
Among the various approaches proposed to determine \(\gamma(f)\), taking into account the topology and the electromagnetic parameters of the power grid, the current article follows [9] as it applies to the whole PLC frequency range and can easily be extended to MTL channels.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Management
#### 4.5.7 Smart Grid Vendee  
The project \"Smart Grid Vendee\", which is run by the French DSO ERDF, also addressed smart network management and aimed at the optimisation of local distribution grids and the development of the necessary tools needed for grid management and management of distributed resources. In particular, the project entails the integration of energy produced by wind parks and PV panels and therefore, management at the transmission level is required. The project includes six wind farms, 30 PV sites, 350 EV stations, whereas at the distribution side, six primary substations and some hundreds of secondary substations. Regarding smart metering solutions, \"Linky\" smart meters are chosen, with 500 smart meters being installed [104].
# A Lidar-Inertial Navigation System for UAVs in GNSS-Denied Environment with Spatial Grid Structures
## 4 Conclusions  
According to the characteristics of the lidar point cloud in the spatial grid structure, this paper proposes a simple and efficient extraction algorithm for grid features and shell features that only use logical judgment. The algorithm can quickly and accurately extract these two features in the spatial grid structure.  
Based on the features extracted from the spatial grid structure, reasonable assumptions are made in this paper. Based on the assumptions, a GNSS-denied environment laser inertial navigation system that adapts to the spatial grid structure is designed and implemented. The navigation system achieves accurate estimation of the UAV's pose.  
In response to the requirements of UAVs for navigation in GNSS-denied environments, we conducted experiments with UAVs equipped with navigation systems in real application scenarios to verify the system's real-time accuracy with respect to positioning and mapping. By comparing the existing navigation system based on lidar, it was found that the proposed navigation system possesses significant advantages in terms of positioning accuracy, real-time performance, and mapping quality.  
Conceptualization, Z.Q. and J.L.; data curation, Z.Q., J.L. and Z.S.; formal analysis, Z.Q.; investigation, Z.Q. and Y.Y.; methodology, Z.Q., Y.Y. and J.L.; project administration, Z.Q., D.L. and Y.Y.; resources, D.L. and Y.Y.; software, Z.Q., J.L., Z.S. and Z.Z.; supervision, D.L. and Y.Y.; validation, Z.Q., J.L., Z.S. and Z.Z.; visualization, Z.Q., J.L. and Z.S.; writing--original draft, Z.Q. and J.L.; writing--review and editing, Z.Q. and Z.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
## 4 Electric Field Modeling  
Modeling the electric field transition of HVDC systems from the initial (capacitive) to the final (resistive) distribution requires the solution of the EQS limit of [PERSON]'s equations, which, in terms of the scalar electric potential \(\varphi\), reads as follows:  
\[\
abla\cdot\left[\varepsilon\
abla\left(\frac{\partial\varphi}{\partial t} \right)\right]+\
abla\cdot\left(\sigma\
abla\varphi\right)=0. \tag{5}\]  
The solution of Equation (5) requires the specification of \(\varepsilon\) and \(\sigma\) for each domain (gas or solid). The solid insulator, usually realized with cast epoxy resin or Alumina-filled epoxy resin [26], is characterized by nonlinear conductivity function on temperature \(T\) and electric field strength \(E\):  
\[\sigma_{I}(T,E)=\sigma_{I,0}\exp(\alpha_{I}E+\beta_{I}T), \tag{6}\]  
where \(\alpha_{I},\beta_{I}\) are two parameters. The functional dependence, even for FGMs, is usually extracted from experimental campaigns [27]. The conductivity of gas, in the simpler case, is usually kept constant; however, it may depend on different parameters, such as the temperature, electric field, pressure, and relative humidity percentage, i.e., \(\sigma_{\text{G}}=\sigma_{\text{G}}(T,E,P,\%RH)\). This functional dependence is difficult to argue a priori for general geometries, insulating gases, and operational conditions, even if some attempts are made to extrapolate it from experimental results. For example, in [28], an analytical expression for \(\sigma_{\text{G}}\) of pure SF\({}_{6}\) has been obtained by fitting experimental data, resulting in the following functional form:  
\[\sigma_{G}(E,P,T)=\sigma_{\text{G},0}\left[\alpha+\beta\left(\gamma+\frac{E}{ E_{c_{1}}}\right)^{\xi}\right]\left(\tau+\epsilon\frac{E}{E_{c_{2}}}\right)^{- \iota}\exp(\xi P)\exp(\
u T) \tag{7}\]  
where \(\sigma_{\text{G},0}\) is a base-level conductivity and \(\alpha\), \(\beta\), \(\gamma\), \(\zeta\), \(\tau\), \(\varepsilon\), \(\iota\), \(\xi\), and \(\
u\) are parameters to be determined by fitting experimental data. In this expression, while the exponential  
Figure 2: Overview of DTT Tokamak hall with highlighted parts.  
dependence on gas pressure and temperature is quite trivial, the dependence on the electric field norm exhibits two different trends, embodied by the two field-dependent terms: \(E_{c_{1}}\), \(E_{c2}\). The former captures the gas conductivity behavior below a first threshold electric field norm value defined by \(E_{c_{1}}\), while the latter term describes the effect on these quantities at higher electric field regions. The electric field magnitude at which this second trend becomes important in the description of the gas is embodied by \(E_{c_{2}}\). It is important to notice that the choice of both \(E_{c_{1}}\) and \(E_{c_{2}}\) is not trivial and affects the extrapolation accuracy and quality of all the other parameters in (7).
## 2 Materials and Methods
### Climate Data Description
#### 2.3.1 Present and Future Average Climate (TRY2020 and TRY2050-RCP4.5)  
The present-day reference-year dataset TRY2020 describes weather conditions that are as close as possible to the 30-year typical climatological conditions at the Helsinki-Vantaa weather station in southern Finland. The selection of TRY2020 was based on a standard method [33] with some modifications [32; 34]. In TRY2020, the average monthly mean temperature ranges between \(-4.5\)\({}^{\circ}\)C in February and \(17.4\)\({}^{\circ}\)C in July, and average monthly mean global horizontal irradiance between 6 W/m\({}^{2}\) in December and \(235\) W/m\({}^{2}\) in July.  
By combining the weather observations selected for TRY2020 with multi-model mean climate change projections for our study region [35; 36], a reference year weather dataset representing future climate in the middle of this century was constructed with the aid of a so-called delta-change method [34]. In the current study, we assume that the future evolution of greenhouse gas concentrations in the atmosphere will follow an intermediate pathway called Representative Concentration Pathway 4.5 (RCP4.5) [37]. Accordingly, the dataset describing average future climate is denoted by TRY2050. Under the RCP4.5 scenario, the projected increase in the monthly mean temperatures for 2050, relative to TRY2020, range from 1.5 to 2.3 degrees. Meanwhile, due to changes in cloudiness and aerosol particle concentrations, the monthly mean global horizontal irradiance is projected to slightly decrease during November-March and to increase during April-October (by 1-5%).  
It is worth noting that TRY2020 and TRY2050 refer to typical climatic conditions, in terms of outdoor air temperature and global horizontal irradiance, humidity, and wind speed, during the 30-year periods of 1989-2018 (midpoint 2004) and 2035-2064 (midpoint 2050), respectively. Therefore, differences between TRY2050 and TRY2020 should be interpreted as projected changes in almost five decades rather than in three decades. Regarding the future evolution of greenhouse gas concentrations in the atmosphere, which is naturally unknown, by the mid-century, the selected RCP4.5 scenario does not considerably deviate from other available plausible alternatives [35; 37]. Therefore, the impact of the choice of RCP4.5 on the results is relatively small. On the other hand, there are other sources of uncertainty in TRY2050, arising from internal climate variability and differences among the various climate models (for further discussion, see [35]).
## 3 Results and Discussion  
### Analysis of Isolates  
These strains, which were obtained from the initial screening, were identified by the comparison results of the 16S rRNA gene. They belong to Phylum _Bacilita_, Class _Bacililia_, Order _Bacillus_, Family _Bacilidaee_, and genus _Bacillus_. _Bacillus_ can control pests and diseases [47] and plays a key role in crop production [48]. It can be used as an edible probiotic for humans [49], while its spores are often used as feed additives due to their resistance to heat, drying, and acids [50]. Moreover, _Bacillus_ isolated by [PERSON] et al. [51] was also found to be effective in reducing total nitrogen (TN), chemical oxygen demand (COD), etc., in landscape water in their study. [PERSON] et al. [52] also discussed that _Bacillus_ sp. can produce degrading enzymes and thus it can be used in the bioremediation of aquaculture-polluted water. These studies suggest that the genus _Bacillus_ can be effective in degrading certain pollutants in sewage.  
The turbidity of sewage samples decreased significantly after the twelve strains were incubated into sewage samples separately for a period of time, with the effect of strain Q3-6 being more pronounced than that of the remaining eleven. Strain Q3-6 was selected for further study.
# EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid
## 3 Hybrid Artificial Intelligence
### The Impact of EV User Charging Behavior on the Grid  
Unregulated charging of electric vehicles will likely pose great challenges to a city's power supply system. In contrast, the controlled charging behavior of an EV can actually enhance the stability of the smart grid and achieve peak shaving and valley filling effects [27]. The user's charging behavior mainly affects the stability of the grid by the following factors: (1) The time to start charging and time to end charging: If all users choose to start charging their electric car after work and end charging in the morning, this activity would accentuate the peaks and valleys of the grid. (2) The amount of charging required: Users that need more charge will absorb more power from the grid. (3) Charging scene of the electric car: If an electric car is charged during travel, it has a higher charging priority and is not very predictable. However, if a user is charging at home at night, the charging priority is lower, and the charging behavior of the user can be reasonably scheduled to improve the stability of the smart grid.
# Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement
## 3 Proposed Method  
A picture contains hundreds of LED chips, so that segmentation is performed first, and then detection. As shown in Figure 3, the surface defect detection method was carried out by the following steps:  
* Pre-estimate all the coordinates of possible chips in the entire picture;
* Rotation correction is performed to ensure the accuracy of subsequent precise location and reduce misjudgments;  
Figure 2: Schematic diagram of image acquisition system: (**a**) color image acquisition system; (**b**) coaxial image acquisition system.  
Developing modified NCC algorithm to locate chips precisely to eliminate the influence of brightness changes;
* Element identification is applied by dividing the LED chip into multiple areas according to its composition (such as electrode area, light-emitting area, etc.).
# Non-Real-Time Wireless System for Lightning Effect Measurements
###### Abstract  
This article presents the results of experimental tests of a measuring system dedicated to the study of lightning phenomena. A wireless non-real-time communication arrangement was used as a prototype to protect the system by overvoltage and electromagnetic noise generated by high-current pulses. All data were collected after analog-to-digital conversion in the RAM of the measuring probe and then transmitted to the recorder after the surge current disappeared. The current generator creates electromagnetic disturbances resulting from its work and those arising from the impulse generated at the output. The wireless measuring system ensures safe operation and avoids measurement disturbances by resigning from the physical connection of the probe and the recorder. The proposed solution enables simultaneous (synchronous) measurement at many points, regardless of the location (for convenient change of the measurement site without cables or optical fibers). Long battery life allows measurements in the laboratory or on the test site without a power source. High accuracy of the measured signal value was obtained thanks to the 16 bit resolution, and the device parameters can be remotely modified. The wireless connection guarantees the safety of people and equipment throughout the laboratory.  
high voltage; lightning strikes; generators; radio communication +
Footnote †: journal: Applied  
1
###### Abstract  
Substrate Integrated Circuits (SICs) represent a significant advancement in microwave communication systems due to their high efficiency, performance, and integration capabilities. Empty Substrate-Integrated Waveguides (ESIWs) are a type of SIC that offers benefits such as cost-effectiveness while maintaining high performance. This paper presents the design and implementation of the first floating patch antenna integrated into an ESIW, fed by a metallic rod. The proposed antenna is designed to operate in the X-band (8-12 GHz), with a resonance peak at 10 GHz. The patch antenna is square, which provides interesting radiation characteristics. It is excited by a metallic rod that connects the patch to the ESIW line, resulting in excellent performance in terms of measured radiation efficiency (over 90%) and \(-10\) dB impedance bandwidth (approximately 20%). The prototype demonstrates minimal differences between the simulated and manufactured versions. These results highlight the potential of ESIW-fed floating patch antennas for advanced satellite communication systems. This will enable the integration of complete communication systems within ESIWs and facilitate the straightforward development of 2D element arrays.  
ESIW--empty substrate integrated waveguide; SIC--substrate integrated circuit; floating patch; antenna; dielectricless; rod; efficiency; bandwidth Article  
* 2025
# Benchmarking Anomaly Detection Methods for Extacardiac Findings in Cardiac MRI
## 4 Results and Discussion
### General Challenges and Limitations  
The benchmarked methods exhibit several limitations, as evidenced by the results. One key challenge is their difficulty in modeling and recognizing less representative normal structures within the dataset. A specific example is sample C.I, which contains a pericardial effusion--an abnormality not labeled in this study, as it falls within the cardiac domain (and therefore not an ECF). Although samples with pericardial effusion are included in the normal training set, they are underrepresented. As the methods predominantly learn the average appearance of normal images, this underrepresentation can lead to the misclassification of images with pericardial effusion.  
Furthermore, all methods treat individual slices as independent samples, failing to account for the spatial continuity of the anatomical structures they capture. While this limitation is expected, given that they were originally designed for 2D image-based tasks, the lack of 3D awareness likely contributed to their underperformance on this dataset. An example of this limitation is seen in sample A.II, which captures the transition between the liver and the right lung. Without integrating 3D positional information, the models may incorrectly identify this transition as an abnormality in the right lung. As shown in Figure 4, most image reconstruction methods removed the liver entirely, except for H-TAE-S and DAE, which apply minimal modifications to all images. Curiously, other types of methods do not exhibit this behavior. This example highlights the importance of incorporating 3D spatial awareness into future approaches to improve performance. This could involve using both anterior and posterior adjacent slices as input or conditioning the model with additional information about the relative spatial location of the input slice in the body.