esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancement of Yaw Moment Control for Drivers with Excessive Steering in Emergency Lane Changes
## 4 Yaw Moment Control Design
### Yaw Moment Limit and Wheel Torque Distribution
After the stabilizing yaw moment \(M_{z}\) is calculated, the four wheels are assigned with control torques as  
\[T_{1} =-sign(M_{z})r_{e}f_{z1}k|s_{L}|,\ T_{2}=sign(M_{z})r_{e}f_{z2}k|s_{L}| \tag{40a}\] \[T_{3} =-sign(M_{z})r_{e}f_{z3}k|s_{L}|,\ T_{4}=sign(M_{z})r_{e}f_{z4}k|s_{L}| \tag{40b}\]  
In accordance with (38) and (40), the longitudinal friction forces of the four wheels become  
\[f_{x1} =-sign(M_{z})f_{z1}k|s_{L}|,\ f_{x2}=sign(M_{z})f_{z2}k|s_{L}| \tag{41a}\] \[f_{x3} =-sign(M_{z})f_{z3}k|s_{L}|,\ f_{x4}=sign(M_{z})f_{z4}k|s_{L}| \tag{41b}\]  
and therefore the yaw moment (2) develops into  
\[M_{z} =-f_{x1}l_{d}+f_{x2}l_{d}-f_{x3}l_{d}+f_{x4}l_{d}=sign(M_{z})l_{d}mggk|s_{L}| \tag{42}\]  
By using \(M_{z}=sign(M_{z})|M_{z}|\), the equation above can be rearranged as  
\[f_{z1} =\frac{1}{2}\frac{l_{r}}{l_{f}+l_{r}}mg-\frac{1}{2}\frac{h}{l_{f}+l_{r}}ma _{x}-\frac{h}{2l_{d}}\frac{k_{f\phi}}{k_{f\phi}+k_{r\phi}}ma_{y} \tag{39a}\]0  
In view of (43), the yaw moment magnitude \(|M_{z}|\) reaches its maximum allowable amount \(\widetilde{M}_{z,allow}\) when the longitudinal slip magnitude \(|s_{L}|\) also achieves its allowable limit \(\widetilde{s}_{L,allow}\). Thus, the constraint on the yaw moment (43) can be considered as  
\[f_{z1} =\frac{1}{2}\frac{l_{r}}{l_{f}+l_{r}}mg-\frac{1}{2}\frac{h}{l_{f}+l_{r}}ma _{x}-\frac{h}{2l_{d}}\frac{k_{f\phi}}{k_{f\phi}+k_{r\phi}}ma_{y} \tag{39a}\]1  
To calculate \(\widetilde{s}_{L,allow}\) in (44), we adopt the concept of the friction circle restriction [27; 28; 29]:  
\[f_{z1} =\frac{1}{2}\frac{l_{r}}{l_{f}+l_{r}}mg-\frac{1}{2}\frac{h}{l_{f}+l_{r}}ma _{x}-\frac{h}{2l_{d}}\frac{k_{f\phi}}{k_{f\phi}+k_{r\phi}}ma_{y} \tag{39a}\]2  
where \(\widetilde{s}_{Res,allow}\) represents the allowable combined wheel slip magnitude \(\|\mathbf{S}\|\) without saturating the tire/road friction and \(\widetilde{s}_{S,allow}\) is the allowable lateral wheel slip. The selection of \(\widetilde{s}_{Res,allow}\) depends on the road conditions; for example, \(\widetilde{s}_{Res,allow}\approx 0.1\) can be used for the dry asphalt road condition and \(\widetilde{s}_{Res,allow}\approx 0.08\) for the wet asphalt road condition. We estimate the allowable lateral slip \(\widetilde{s}_{S,allow}\) in (45) in accordance with the constraint on the steady-state lateral slip (19)-(20), and its selection is proposed with  
\[f_{z1} =\frac{1}{2}\frac{l_{r}}{l_{f}+l_{r}}mg-\frac{1}{2}\frac{h}{l_{f}+l_{r}}ma _{x}-\frac{h}{2l_{d}}\frac{k_{f\phi}}{k_{f\phi}+k_{r\phi}}ma_{y} \tag{39a}\]3  
By calculating (45) with (46) and \(\widetilde{s}_{Res,allow}\) specified by the road condition, the maximum allowable longitudinal slip \(\widetilde{s}_{L,allow}\) and therefore the yaw moment constraint \(\widetilde{M}_{z,allow}\) in (44) are established.
# GaN-on-Si: Monolithically Integrated All-GaN Drivers for High-Voltage DC-DC Power Conversion
###### Abstract  
This paper presents a novel integrated half-bridge driver architecture using GaN-on-Si process for high-speed and high-voltage DC-DC converters. The entire circuit includes only enhancement mode (E-mode) and depletion-mode (D-mode) GaN transistors. The high-side driver circuit adopts the E-stacked E/D-mode (EED) architecture, which can directly drive the gate of the high-side transistor with a low-voltage signal without using an additional level shifter, which simplifies the design and reduces propagation delay. In addition, the low-side power transistor is driven by stacking two D/E-mode devices. This architecture separates the high-side pulse from the low-side drive signal to prevent false triggering of the low-side driver. The designed fully integrated GaN driver can output a high-voltage pulse wave with an operating frequency greater than 1 MHz when the input voltage is greater than 200 V. The rise and fall times of the high-voltage pulse wave operating at a peak voltage of 200 V are 54.4 ns and 57.6 ns, respectively. The experimental results show that the circuit can effectively drive the half-bridge circuit and be applied to a buck converter. The designed buck converter can deliver up to 20.5 W of output power, and the maximum efficiency achieves 90.7%.  
GaN technology; high-side driver; DC-DC conversion; integrated power converter +
Footnote †: journal: Applied  
15 April 2022  
Accepted: 13 May 2022  
Published: 19 May 2022
###### Abstract  
In this work, a new plasma reactor operating with a butane/propane (C\({}_{4}\)H\({}_{10}\)/C\({}_{3}\)H\({}_{8}\)) gaseous mixture, designed for hydrogen molecule production, was experimentally studied. This reactor is based on a rotating electrode, biased by an AC high voltage. The plasma discharge was investigated for different AC voltages, rotational frequencies, and gas flow rates. A discharge in the filamentary mode was produced as proved by the electrical characterization. Gas Chromatography (GC) was applied to study the LPG remediation. The maximum conversion factors of 70% and 60% were found for the C\({}_{3}\)H\({}_{8}\) and C\({}_{4}\)H\({}_{10}\), respectively, with an H\({}_{2}\) selectivity of 98%. Hydrogen atomic lines from the Balmer series and various molecular bands were detected by optical emission spectroscopy (OES). The stark broadening of the H\({}_{\alpha}\) Balmer line was used for the determination of the electron density. The spectra simulation of the C\({}_{2}\) band was permitted to obtain the gas temperature while the first five lines of hydrogen atoms were used to calculate the electron temperature. A non-equilibrium plasma with two very different temperatures for electrons and heavy particles was found. The spectroscopic study allowed us to explain the experimental results of the LPG conversion and its dependence on the plasma conditions, resulting in optimizing the H\({}_{2}\) formation.  
rotating plasma reactor; LPG decomposition; hydrogen production; optical emission spectroscopy +
Footnote †: journal: Physics Letters B
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed entirely using the open-source R statistical software (v. 4.4.1; R Core Team 2024, Vienna, Austria) for Windows [46]. All data are presented as mean \(\pm\) standard deviation (SD). First, a descriptive analysis of force and power outputs across all load configurations was conducted, including the calculation of the eccentric/concentric (E/C) ratio, a parameter commonly used to assess the presence of eccentric overload [26; 33; 47]. Agreement between the criterion measures (MuscleLab) and the practical measures (HandyGym) of force and power was evaluated using a dedicated R code based on [PERSON] work for the analysis of validity [48]. This analysis included the calculation of mean bias, typical error of the estimate (TEE), and Pearson correlation coefficient, all with 90% confidence limits. The mean bias and TEE were standardized using the SD. The standardized mean bias was classified as trivial (\(\leq\)0.19), small (0.2-0.59), moderate (0.6-1.19), or large (1.2-1.99) [48]. The standardized TEE was classified as trivial (<0.1), small (0.1-0.29), moderate (0.3-0.59), or large (>0.59) [48]. The magnitude of the correlation was rated as trivial (<0.1), small (0.1-0.29), moderate (0.3-0.49), large (0.5-0.69), very large (0.7-0.89), or nearly perfect (0.9-0.99) [48]. Additionally, Bland-Altman plots were used to graphically complement the analysis of differences between the two systems [49]. Finally, the statistical power for the Pearson correlation test was calculated using the G*Power program (version 3.1.9.7, Heinrich Heine University Dusseldorf, Dusseldorf, Germany). Post hoc statistical power for an alpha error of 0.05 was 0.977 for Pearson's correlation coefficients. Additional details on the interpretation of the statistical methods used in this study can be found in the following reference [50].
# Influence of Cracking on Oxygen Transport in UHPFRC Using Stainless Steel Sensors
## 3 Results and Discussion
### Discussion: Oxygen Availability Relation with Porosity and Permeability
The reduction of the air permeability and, therefore, oxygen entrance, of UHPFRC concrete types, even after suffering several aggressive levels of stress, will certainly improve the protection of the reinforcement against corrosion. Subsequently, the amount of oxygen that will be able to reach the reinforcement surface will be smaller and the kinetics of the REDOX reaction will be reduced, since this gas is the main oxidizing agent of the rebar corrosion reaction. This demonstrates that a situation with high number of cracks of small width is more beneficial in terms of durability than a situation with a single crack of higher crack width, even if their global stress level and total crack width are comparable. In other words, having 10 cracks of 0.01 mm is better in terms of durability than having one crack of 0.10 mm. These results support that using these concretes in construction with a design fitted for their specific behavior will produce high durability structures with an extended service life, even after suffering conditions producing cracks.
## 2 Antenna Structure
### Circular Patch Design  
The proposed antenna has a circular patch that is fully covered by the ground plane, so the initial design is a conventional circular patch antenna. To design a circular patch antenna at the frequency 3.7 GHz, the radius of the patch (a) should be calculated as in (5) [24], which is shown below:  
\[\text{a}=F\{1+\frac{2h}{\pi F\epsilon_{r}}[\ln(\frac{\pi F}{2h})+1.7726]\}^{-0.5} \tag{5}\]  
where \(f_{r}\) represents the resonant frequency, \(h\) denotes the height of the substrate, and \(F\) is given by the following equation:  
\[F=\frac{8.791\times 10^{9}}{f_{r}\sqrt{\epsilon_{r}}} \tag{6}\]  
As a result of these calculations, the radius is found to be equal to 10.98 mm, but it is reduced to 9.4 mm due to the reasons mentioned in the next paragraph.  
After estimating the radius of the conventional patch, the antenna is fed using the proximity-coupled feeding technique to improve the impedance bandwidth of the antenna. Since the hybrid coupler is the feeder of the patch antenna, the antenna is fed by two orthogonal signals with almost equal amplitudes. This results in circularly polarized radiation  
Figure 3: The simulation results of the 90\({}^{\circ}\) hybrid coupler: (**a**) CST model, (**b**) magnitude of the S-parameters and (**c**) phase angles of \(S_{31}\) and \(S_{41}\).  
at the antenna center frequency (as will be explained in the next section). Subsequently, the ARBW is improved by etching four slits on the circular patch. These slits lead to an increase in the current path along the perimeter of the patch, so the radius of the circular patch is reduced to 9.4 mm to compensate for this increment.
## 3 Methodology
### Reliability Matching Function  
The need to introduce the reliability matching function \(s_{g}(p_{g})\) is due to the fact that the quality of the UAV is not a linear function of the reliability \(p_{g}\), which is implied when using the criterion in the form of (1). So, for example, if the reliability of the UAV is below some threshold \(p_{g}^{*}\), then this UAV is not competitive and should not be considered at all when compared with other options.  
This fact should be reflected in the following way: the reliability matching function with a value of \(p_{g}\) below a certain threshold \(p_{g}\leq p_{g}^{*}\) is equal to zero, i.e., \(s_{g}\Big{(}p_{g}\leq p_{g}^{*}\Big{)}=0\). It is also obvious that for the value \(p_{g}=1\), it is natural to take \(s_{g}\big{(}p_{g}=1\big{)}=1\), i.e., the UAV fully complies with the requirements for reliability.  
We formulated the requirements that the reliability matching function \(s_{g}\big{(}p_{g}\big{)}\) must satisfy:  
* The function \(s_{g}\big{(}p_{g}\big{)}\) is defined on the interval \([0,1]\) and its range of values also belongs to the interval \([0,1]\);
* If the reliability of the UAV \(p_{g}\) satisfies the inequality \(p_{g}<p_{g}^{*}\), then the reliability matching function is \(s_{g}\Big{(}p_{g}\leq p_{g}^{*}\Big{)}\approx 0\);
* The function \(s_{g}\big{(}p_{g}\big{)}\) must be non-decreasing in the variable \(p_{g}\) and for the value \(p_{g}=1\) it is natural to take \(s_{g}\big{(}p_{g}=1\big{)}=1\);  
Figure 1: Probability matching functions according to (**a**) dependency (5) and (**b**) dependency (6).  
In the vicinity of the points \(p_{g}=p_{g}^{*}\) and \(p_{g}=1\), the change in \(s_{g}(p_{g})\) should be smooth, i.e., have no jumps or breaks. As in the case of the probability matching function, the choice of the \(s_{g}(p_{g})\) function was carried out heuristically, and as a result of which, we settled on the following option:  
\[s_{g}(p_{g})=\exp\biggl{\{}-\biggl{|}\frac{p_{g}-1}{\delta}\biggr{|}^{n}\biggr{\}}. \tag{7}\]  
This function has two degrees of freedom: the exponent \(n\) allows changing the form of the function, and the denominator \(\delta\) defines the width of the branch. Function (6), whereby the form of which is shown in Figure 2, satisfies all of the above requirements for the reliability matching function. In this case, the values of the coefficients \(n\) and \(\delta\) are determined by the method of expert assessments.
## 2 Beam Bending Vibration and FE Models
### 2D FE Model of Healthy and Damaged Beam  
Equation (1) is discretized using 2-nodes beam elements with 2 degrees of freedom (DOFs) per node, a vertical translation \(v\) and a bending rotation \(\theta_{z}\). As can be seen in Figure 1, the beam is divided into N elements of equal size of and the model has N + 1 nodes. The damage is assumed to be placed within the beam elements of the FE model. Consider the damage at location \(x_{l}\) within node \(i\) and \(i+1\) of beam. It will be assumed that stiffness reduction falls within one or several elements.  
Natural frequencies of healthy and damaged 2D beam are obtained from solving the eigenvalue formulation using MATLAB _eig ()_ function and their expression (in Hz) is \(f_{i}=\frac{\omega_{i}}{2\pi}\).  
In this 2D FE model, the damage is simulated by reducing the stiffness of selected element. Here, damage severity is represented by the reduction coefficient \(\alpha_{i}\), which is the ratio of the stiffness reduction to the initial stiffness. The stiffness matrix of a damaged beam is defined as a sum of elemental matrices multiplied by coefficient by the following equation:  
\[[K_{d}]=\sum_{i=1}^{N}(1-\alpha_{i})[k_{e}], \tag{6}\]  
where \([K_{d}]\) is the global stiffness matrix for damaged beam, \([k_{e}]\) is elemental stiffness matrix and \(\alpha_{i}\) is a bending stiffness reduction coefficient, which varies from 0 to 1 for the damaged structure. A value of \(\alpha_{i}=0\) indicates healthy element.  
According to a convergence study, the beam is divided into 100 elements of equal size (10 mm). The 2D FE model has a total number of 202 DOFs.  
Figure 1: 2D FE model of a cantilever beam and cross-section area.
## 4 Future Research Trends for ML-Based IRS-Assisted Wireless Communication
### Optimal Placement of IRS  
The position of the reflecting surface is fixed and cannot be changed after implementation. Thus, it is necessary to have sufficient information for the performance of IRS in the  
Figure 10: IRS-aided wireless communication applications.  
Figure 9: FL-based IRS-enhanced communication system.  
environment before practical deployment. To obtain a maximum performance, the placement of the IRS device should be based on the operating performance. This is applied for both the indoor and outdoor placement of IRS. ML models can be employed to estimate the particular cases with optimized results [106]. As each of the installation cases may be different, new models can be effective to estimate the performance.
## 3 The Optimized Kinematic Modeling Method
### Limitations of Kinematic Modeling
#### 3.2.1 Limitations of Kinematic Modeling of the Manipulator  
In this article, the initial parameters and installation positions of these motors are similar to the typical UR5 robot. To simplify the control of the UR arm, researchers have developed a classical kinematic modeling result based on the Standard Denavit-Hartenberg(SDH) method [41]. Therefore, the kinematic modeling of the manipulator subsystem based on the classical method is shown in Figure 2a. In the classical method, motor 2, link 2 and motor 3 are regarded as a complete connecting link. With the comparison between the actual physical model and the mathematical model, we found that (1) the huge difference between the position of the second joint and the location of the actual motor cannot be ignored, and it can also be seen in the third joint; (2) the position of the connecting link between the second joint and the third joint acquired by the classical method is unable to match the actual position.  
Comparing kinematic modeling results, we can find that the mathematical model based on the classical kinematic modeling method by only establishing the local coordinate frame at each joint cannot accurately describe the actual physical model. Due to the great difference between the mathematical model and the physical model, the collision between one component and another component or the external environment cannot be accurately judged. Furthermore, the establishment of each coordinate frame needs to be complicated according to the specified requirement, which is not suitable for quickly establishing a kinematic model for the UR5 robot. With in-depth research on the SDH method, we can establish seven local coordinate frames for the UR series robot based on the driving joints, but more coordinate frames were required to describe the basic structure of the manipulator. Therefore, the inability to establish some coordinate frames on the structure of some components is the primary factor that leads to the inaccuracy of kinematic modeling.
## 4 System and Threat Model
### Threat Model  
In this paper, we posit that every participant, including all users, the fog node, and the server, operates under a semi-honest assumption. They adhere to the protocol's stipulations accurately, yet they harbor curiosity regarding the privacy of their peers and will endeavor to deduce others' confidential data from the local models that are submitted by other users. Beyond this semi-honest threat scenario, we also contemplate a more severe one where, at any juncture, the server might fall into the clutches of malevolent actors. In such a case, the server could stray from the protocol's original intent, deliver erroneous computations, and endeavor to deceive users into accepting these flawed global parameters. It is noteworthy that our proposed scheme permits collusion between any two entities within the aggregation server, a user, and a fog node to uncover a specific user's private information. However, it should be acknowledged that when the server and fog node are in collusion, it becomes quite feasible to fabricate aggregation outcomes and verifications, and our scheme does not allow the simultaneous collusion of all three parties.
## 2 Methodology
### Decomposing Method of the Trend Yield and Meteorological Yield of Each Subregion  
The change in crop yield caused by an increase in productivity is called a trend yield, the fluctuation in crop yield caused by climatological factors is called a meteorological yield, and the change in crop yield caused by other factors is considered a random error. Therefore, the yield of crop any given year may be affected by trend yield, meteorological yield, or random error, and it can be calculated using the following equation:  
\[y=y_{t}+y_{w}+\varepsilon, \tag{1}\]  
where \(y\) is the actual yield (kg/ha), \(y_{t}\) is the trend yield (kg/ha), \(y_{w}\) is the meteorological yield (kg/ha), and \(\varepsilon\) is the random error. Because \(\varepsilon\) has little influence on \(y\), it is generally neglected.  
The trend yield and meteorological yield were decomposed by using the crop yield data over the years of the representative counties in each subregion. An accurate understanding of the impact of meteorological conditions on crop yield must be based on the precise acquisition of meteorological yield. Therefore, it is necessary to select a suitable method that accurately separates the trend yield and meteorological yield from the actual yield. Logistic equations are widely used because they describe bounded growth and capture the long-term trend of time series growth more accurately than other methods [20; 21; 22]. Therefore, in this study, the logistic equation was selected to fit the trend yield, which is expressed as follows:  
\[y_{t}=\frac{K}{1+e^{b-rt}}, \tag{2}\]  
where \(K\), \(b\), and \(r\) are the parameters of the equation. \(K\) is the carrying capacity, \(r\) is the instantaneous growth rate, and \(b\) is the integral constant. Thus, the meteorological yield \(y_{w}\) can be calculated using Equations (1) and (2) (i.e., \(y_{w}=y-y_{t}\)).
# Research on Advancing Radio Wave Source Localization Technology Through UAV Path Optimization
## 4 Results
### Circular Trajectory Placement with a Radius of 100 m
#### 4.1.1 Using Only RSSI  
The results of source localization using only RSSI are shown in Figures 15-17.  
As shown in Figure 16, the spatial distribution of localization errors reveals multiple regions with significantly large estimation errors, especially in the southern part of the campus. This is likely caused by the presence of non-line-of-sight (NLoS) conditions due to buildings and other obstructions, which reduce the reliability of signal strength as a spatial feature. RSSI is known to be sensitive to multipath fading and shadowing, making it less effective in complex outdoor environments. Figure 17 shows that the cumulative distribution of errors converges slowly, with many sources exceeding 40 m of error. This indicates the presence of numerous outliers.  
These findings are consistent with prior research by [PERSON] [10], who also reported decreased accuracy of RSSI-based fingerprinting under similar conditions. This result highlights the need to incorporate angular information such as AOA or EOA to improve robustness in such environments.  
Figure 14: Overview of the sequential estimation process. (1) Global estimation is first performed using all 144 transmitters (Tx) to identify regions with high estimation error. (2) Transmission sources located in high-error areas, typically those affected by NLoS conditions (e.g., the southern part of the campus), are extracted. (3) Local estimation is then carried out by re-learning the NLoS-prone region, focusing on the extracted sources to improve overall accuracy. This iterative process enables region-specific refinement of the localization model.  
Figure 16: Heatmap of localization errors (in meters) when using RSSI as the sole feature. Warmer colors indicate higher localization errors.  
Figure 17: Cumulative distribution function (CDF) of localization errors using RSSI-only localization. The plot highlights the large error tail characteristic of RSSI-based estimation.  
Figure 15: Optimized UAV placements for RSSI-based localization. The figure includes both the horizontal positions and corresponding altitudes of UAVs, selected to minimize the 90 th percentile of localization error across all targets.